diff --git a/examples/Builtin Extensions/Index.ipynb b/examples/Builtin Extensions/Index.ipynb
index 606b801..7d50ce2 100644
--- a/examples/Builtin Extensions/Index.ipynb
+++ b/examples/Builtin Extensions/Index.ipynb
@@ -1,65 +1,57 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"../images/ipython_logo.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Back to the main [Index](../Index.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Builtin Extensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython extensions allow custom magic commands to be shipped as standalone libraries. IPython includes a few extensions that define magic commands for working with code in other languages.\n",
+    "\n",
+    "<div class=\"alert\" style=\"margin: 10px\">\n",
+    "We are in the process of moving these builtin extensions to their parent projects (Cython, oct2py and rpy2). Once this happens this documentation will move as well.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tutorials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* [Cython Magics](Cython Magics.ipynb): magics for compiling and running Cython code in a cell\n",
+    "* [Octave Magic](Octave Magic.ipynb): magics for running Octave code in a cell\n",
+    "* [R Magics](R Magics.ipynb): magics for running R code in a cell"
+   ]
+  }
+ ],
  "metadata": {
-  "name": "",
   "signature": "sha256:a89ddd606f68e27067a5e2301a4d19a3c149131f4f07be4483a65979e26bebe0"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<img src=\"../images/ipython_logo.png\">"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Back to the main [Index](../Index.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Builtin Extensions"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython extensions allow custom magic commands to be shipped as standalone libraries. IPython includes a few extensions that define magic commands for working with code in other languages.\n",
-      "\n",
-      "<div class=\"alert\" style=\"margin: 10px\">\n",
-      "We are in the process of moving these builtin extensions to their parent projects (Cython, oct2py and rpy2). Once this happens this documentation will move as well.\n",
-      "</div>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Tutorials"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "* [Cython Magics](Cython Magics.ipynb): magics for compiling and running Cython code in a cell\n",
-      "* [Octave Magic](Octave Magic.ipynb): magics for running Octave code in a cell\n",
-      "* [R Magics](R Magics.ipynb): magics for running R code in a cell"
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Builtin Extensions/Octave Magic.ipynb b/examples/Builtin Extensions/Octave Magic.ipynb
index ff642e5..576c541 100644
--- a/examples/Builtin Extensions/Octave Magic.ipynb
+++ b/examples/Builtin Extensions/Octave Magic.ipynb
@@ -1,371 +1,1956 @@
 {
- "metadata": {
-  "name": "Octave Magic"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Using Octave Inside IPython"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Installation"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The `octavemagic` extension provides the ability to interact with Octave.  It depends on the `oct2py` package,\n",
-      "which may be installed using `easy_install`.\n",
-      "\n",
-      "To enable the extension, load it as follows:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext octavemagic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Overview"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Loading the extension enables three magic functions: `%octave`, `%octave_push`, and `%octave_pull`.\n",
-      "\n",
-      "The first is for executing one or more lines of Octave, while the latter allow moving variables between the Octave and Python workspace.\n",
-      "Here you see an example of how to execute a single line of Octave, and how to transfer the generated value back to Python:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x = %octave [1 2; 3 4];\n",
-      "x"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 19,
-       "text": [
-        "array([[ 1.,  2.],\n",
-        "       [ 3.,  4.]])"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = [1, 2, 3]\n",
-      "\n",
-      "%octave_push a\n",
-      "%octave a = a * 2;\n",
-      "%octave_pull a\n",
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 20,
-       "text": [
-        "array([[2, 4, 6]])"
-       ]
-      }
-     ],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When using the cell magic, `%%octave` (note the double `%`), multiple lines of Octave can be executed together.  Unlike\n",
-      "with the single cell magic, no value is returned, so we use the `-i` and `-o` flags to specify input and output variables."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%octave -i x -o y\n",
-      "y = x + 3;"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "y"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 22,
-       "text": [
-        "array([[ 4.,  5.],\n",
-        "       [ 6.,  7.]])"
-       ]
-      }
-     ],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Plotting"
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using Octave Inside IPython"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `octavemagic` extension provides the ability to interact with Octave.  It depends on the `oct2py` package,\n",
+    "which may be installed using `easy_install`.\n",
+    "\n",
+    "To enable the extension, load it as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext octavemagic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Loading the extension enables three magic functions: `%octave`, `%octave_push`, and `%octave_pull`.\n",
+    "\n",
+    "The first is for executing one or more lines of Octave, while the latter allow moving variables between the Octave and Python workspace.\n",
+    "Here you see an example of how to execute a single line of Octave, and how to transfer the generated value back to Python:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "array([[ 1.,  2.],\n",
+       "       [ 3.,  4.]])"
+      ]
+     },
+     "execution_count": 19,
      "metadata": {},
-     "source": [
-      "Plot output is automatically captured and displayed, and using the `-f` flag you may choose its format (currently, `png` and `svg` are supported)."
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = %octave [1 2; 3 4];\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%octave -f svg\n",
-      "\n",
-      "p = [12 -2.5 -8 -0.1 8];\n",
-      "x = 0:0.01:1;\n",
-      "\n",
-      "polyout(p, 'x')\n",
-      "plot(x, polyval(p, x));"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "array([[2, 4, 6]])"
+      ]
+     },
+     "execution_count": 20,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "display_data",
-       "text": [
-        "12*x^4 - 2.5*x^3 - 8*x^2 - 0.1*x^1 + 8"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "svg": [
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-        "\t\t<text><tspan>6.5</tspan>\n",
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-        "\t</a>\n",
-        "<g style=\"fill:none; color:black; stroke:currentColor; stroke-width:0.50; stroke-linecap:butt; stroke-linejoin:miter\">\n",
-        "</g>\n",
-        "</svg>"
-       ]
-      }
-     ],
-     "prompt_number": 23
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = [1, 2, 3]\n",
+    "\n",
+    "%octave_push a\n",
+    "%octave a = a * 2;\n",
+    "%octave_pull a\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When using the cell magic, `%%octave` (note the double `%`), multiple lines of Octave can be executed together.  Unlike\n",
+    "with the single cell magic, no value is returned, so we use the `-i` and `-o` flags to specify input and output variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%octave -i x -o y\n",
+    "y = x + 3;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "array([[ 4.,  5.],\n",
+       "       [ 6.,  7.]])"
+      ]
+     },
+     "execution_count": 22,
      "metadata": {},
-     "source": [
-      "The plot size is adjusted using the `-s` flag:"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot output is automatically captured and displayed, and using the `-f` flag you may choose its format (currently, `png` and `svg` are supported)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%octave -s 500,500\n",
-      "\n",
-      "# butterworth filter, order 2, cutoff pi/2 radians\n",
-      "b = [0.292893218813452  0.585786437626905  0.292893218813452];\n",
-      "a = [1  0  0.171572875253810];\n",
-      "freqz(b, a, 32);"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "12*x^4 - 2.5*x^3 - 8*x^2 - 0.1*x^1 + 8"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "display_data",
-       "png": 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2ZvTl5+jRo0WrkCu02YtuPQtKEYTRdR32HzmO02q1NE0LP0L9gjCTmZ+fX1xcjPquoJdv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\nURRt2261Wu7IsiyP+6xTlMvzvM+zO44DC9QxRiuEqrr11OfROUzMc5Y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-      }
-     ],
-     "prompt_number": 24
+     "output_type": "display_data"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%octave -s 600,200 -f png\n",
-      "\n",
-      "subplot(121);\n",
-      "[x, y] = meshgrid(0:0.1:3);\n",
-      "r = sin(x - 0.5).^2 + cos(y - 0.5).^2;\n",
-      "surf(x, y, r);\n",
-      "\n",
-      "subplot(122);\n",
-      "sombrero()"
-     ],
-     "language": "python",
+     "data": {
+      "image/svg+xml": [
+       "<svg height=\"240px\" viewBox=\"0 0 192 115\" width=\"400px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "\n",
+       "<desc>Produced by GNUPLOT 4.4 patchlevel 0 </desc>\n",
+       "\n",
+       "<defs>\n",
+       "\n",
+       "\t<circle id=\"gpDot\" r=\"0.5\" stroke-width=\"0.5\"/>\n",
+       "\t<path d=\"M-1,0 h2 M0,-1 v2\" id=\"gpPt0\" stroke=\"currentColor\" stroke-width=\"0.333\"/>\n",
+       "\t<path d=\"M-1,-1 L1,1 M1,-1 L-1,1\" id=\"gpPt1\" stroke=\"currentColor\" stroke-width=\"0.333\"/>\n",
+       "\t<path d=\"M-1,0 L1,0 M0,-1 L0,1 M-1,-1 L1,1 M-1,1 L1,-1\" id=\"gpPt2\" stroke=\"currentColor\" stroke-width=\"0.333\"/>\n",
+       "\t<rect height=\"2\" id=\"gpPt3\" stroke=\"currentColor\" stroke-width=\"0.333\" width=\"2\" x=\"-1\" y=\"-1\"/>\n",
+       "\t<rect fill=\"currentColor\" height=\"2\" id=\"gpPt4\" stroke=\"currentColor\" stroke-width=\"0.333\" width=\"2\" x=\"-1\" y=\"-1\"/>\n",
+       "\t<circle cx=\"0\" cy=\"0\" id=\"gpPt5\" r=\"1\" stroke=\"currentColor\" stroke-width=\"0.333\"/>\n",
+       "\t<use fill=\"currentColor\" id=\"gpPt6\" stroke=\"none\" xlink:href=\"#gpPt5\"/>\n",
+       "\t<path d=\"M0,-1.33 L-1.33,0.67 L1.33,0.67 z\" id=\"gpPt7\" stroke=\"currentColor\" stroke-width=\"0.333\"/>\n",
+       "\t<use fill=\"currentColor\" id=\"gpPt8\" stroke=\"none\" xlink:href=\"#gpPt7\"/>\n",
+       "\t<use id=\"gpPt9\" stroke=\"currentColor\" transform=\"rotate(180)\" xlink:href=\"#gpPt7\"/>\n",
+       "\t<use fill=\"currentColor\" id=\"gpPt10\" stroke=\"none\" xlink:href=\"#gpPt9\"/>\n",
+       "\t<use id=\"gpPt11\" stroke=\"currentColor\" transform=\"rotate(45)\" xlink:href=\"#gpPt3\"/>\n",
+       "\t<use fill=\"currentColor\" id=\"gpPt12\" stroke=\"none\" xlink:href=\"#gpPt11\"/>\n",
+       "</defs>\n",
+       "<g style=\"fill:none; color:white; stroke:currentColor; stroke-width:1.00; stroke-linecap:butt; stroke-linejoin:miter\">\n",
+       "</g>\n",
+       "<g style=\"fill:none; color:white; stroke:currentColor; stroke-width:0.50; stroke-linecap:butt; stroke-linejoin:miter\">\n",
+       "</g>\n",
+       "<g style=\"fill:none; color:black; stroke:currentColor; stroke-width:0.50; stroke-linecap:butt; stroke-linejoin:miter\">\n",
+       "\t<path d=\"M36.4,91.2 L44.8,91.2 M177.9,91.2 L169.5,91.2 \"/>\n",
+       "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,94.2)\">\n",
+       "\t\t<text><tspan>6</tspan>\n",
+       "\t\t</text>\n",
+       "\t</g>\n",
+       "\t<path d=\"M36.4,79.8 L44.8,79.8 M177.9,79.8 L169.5,79.8 \"/>\n",
+       "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,82.8)\">\n",
+       "\t\t<text><tspan>6.5</tspan>\n",
+       "\t\t</text>\n",
+       "\t</g>\n",
+       "\t<path d=\"M36.4,68.4 L44.8,68.4 M177.9,68.4 L169.5,68.4 \"/>\n",
+       "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,71.4)\">\n",
+       "\t\t<text><tspan>7</tspan>\n",
+       "\t\t</text>\n",
+       "\t</g>\n",
+       "\t<path d=\"M36.4,57.0 L44.8,57.0 M177.9,57.0 L169.5,57.0 \"/>\n",
+       "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,60.0)\">\n",
+       "\t\t<text><tspan>7.5</tspan>\n",
+       "\t\t</text>\n",
+       "\t</g>\n",
+       "\t<path d=\"M36.4,45.5 L44.8,45.5 M177.9,45.5 L169.5,45.5 \"/>\n",
+       "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,48.5)\">\n",
+       "\t\t<text><tspan>8</tspan>\n",
+       "\t\t</text>\n",
+       "\t</g>\n",
+       "\t<path d=\"M36.4,34.1 L44.8,34.1 M177.9,34.1 L169.5,34.1 \"/>\n",
+       "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,37.1)\">\n",
+       "\t\t<text><tspan>8.5</tspan>\n",
+       "\t\t</text>\n",
+       "\t</g>\n",
+       "\t<path d=\"M36.4,22.7 L44.8,22.7 M177.9,22.7 L169.5,22.7 \"/>\n",
+       "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,25.7)\">\n",
+       "\t\t<text><tspan>9</tspan>\n",
+       "\t\t</text>\n",
+       "\t</g>\n",
+       "\t<path d=\"M36.4,11.3 L44.8,11.3 M177.9,11.3 L169.5,11.3 \"/>\n",
+       "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:end\" transform=\"translate(30.8,14.3)\">\n",
+       "\t\t<text><tspan>9.5</tspan>\n",
+       "\t\t</text>\n",
+       "\t</g>\n",
+       "\t<path d=\"M36.4,91.2 L36.4,82.8 M36.4,11.3 L36.4,19.7 \"/>\n",
+       "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(36.4,106.2)\">\n",
+       "\t\t<text><tspan>0</tspan>\n",
+       "\t\t</text>\n",
+       "\t</g>\n",
+       "\t<path d=\"M64.7,91.2 L64.7,82.8 M64.7,11.3 L64.7,19.7 \"/>\n",
+       "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(64.7,106.2)\">\n",
+       "\t\t<text><tspan>0.2</tspan>\n",
+       "\t\t</text>\n",
+       "\t</g>\n",
+       "\t<path d=\"M93.0,91.2 L93.0,82.8 M93.0,11.3 L93.0,19.7 \"/>\n",
+       "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(93.0,106.2)\">\n",
+       "\t\t<text><tspan>0.4</tspan>\n",
+       "\t\t</text>\n",
+       "\t</g>\n",
+       "\t<path d=\"M121.3,91.2 L121.3,82.8 M121.3,11.3 L121.3,19.7 \"/>\n",
+       "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(121.3,106.2)\">\n",
+       "\t\t<text><tspan>0.6</tspan>\n",
+       "\t\t</text>\n",
+       "\t</g>\n",
+       "\t<path d=\"M149.6,91.2 L149.6,82.8 M149.6,11.3 L149.6,19.7 \"/>\n",
+       "\t<g style=\"stroke:none; fill:rgb(0,0,0); font-family:{}; font-size:10.00pt; text-anchor:middle\" transform=\"translate(149.6,106.2)\">\n",
+       "\t\t<text><tspan>0.8</tspan>\n",
+       "\t\t</text>\n",
+       "\t</g>\n",
+       "\t<path d=\"M177.9,91.2 L177.9,82.8 M177.9,11.3 L177.9,19.7 \"/>\n",
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+       "\t\t</text>\n",
+       "\t</g>\n",
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+       "</g>\n",
+       "\t<a xlink:title=\"Plot #1\">\n",
+       "<g style=\"fill:none; color:red; stroke:currentColor; stroke-width:0.50; stroke-linecap:butt; stroke-linejoin:miter\">\n",
+       "\t<path d=\"M36.4,45.5 L37.8,45.6 L39.2,45.7 L40.6,45.8 L42.1,45.9 L43.5,46.1 L44.9,46.3 L46.3,46.6    L47.7,46.9 L49.1,47.3 L50.6,47.6 L52.0,48.0 L53.4,48.5 L54.8,49.0 L56.2,49.5 L57.6,50.0    L59.0,50.6 L60.5,51.3 L61.9,51.9 L63.3,52.6 L64.7,53.3 L66.1,54.1 L67.5,54.9 L68.9,55.7    L70.4,56.5 L71.8,57.3 L73.2,58.2 L74.6,59.1 L76.0,60.1 L77.4,61.0 L78.9,62.0 L80.3,63.0    L81.7,64.0 L83.1,65.0 L84.5,66.0 L85.9,67.0 L87.3,68.1 L88.8,69.1 L90.2,70.2 L91.6,71.3    L93.0,72.3 L94.4,73.4 L95.8,74.4 L97.2,75.5 L98.7,76.5 L100.1,77.5 L101.5,78.5 L102.9,79.5    L104.3,80.5 L105.7,81.4 L107.2,82.4 L108.6,83.2 L110.0,84.1 L111.4,84.9 L112.8,85.7 L114.2,86.5    L115.6,87.2 L117.1,87.8 L118.5,88.4 L119.9,89.0 L121.3,89.5 L122.7,89.9 L124.1,90.3 L125.5,90.6    L127.0,90.8 L128.4,91.0 L129.8,91.0 L131.2,91.0 L132.6,90.9 L134.0,90.7 L135.5,90.4 L136.9,90.0    L138.3,89.5 L139.7,88.9 L141.1,88.2 L142.5,87.4 L143.9,86.4 L145.4,85.3 L146.8,84.1 L148.2,82.8    L149.6,81.3 L151.0,79.6 L152.4,77.8 L153.8,75.9 L155.3,73.8 L156.7,71.5 L158.1,69.0 L159.5,66.4    L160.9,63.6 L162.3,60.6 L163.8,57.4 L165.2,54.0 L166.6,50.4 L168.0,46.6 L169.4,42.6 L170.8,38.3    L172.2,33.9 L173.7,29.2 L175.1,24.2 L176.5,19.0 L177.9,13.6 \" stroke=\"rgb(  0,   0, 255)\"/>\n",
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+       "\t</a>\n",
+       "<g style=\"fill:none; color:black; stroke:currentColor; stroke-width:0.50; stroke-linecap:butt; stroke-linejoin:miter\">\n",
+       "</g>\n",
+       "</svg>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "display_data",
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o+c0vuXcdy7LFFxupVMr3KaNMwCoUiqSkpLCwsNDQ0MDAwN27dxe/TpNTDoRQ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\n"
-      }
-     ],
-     "prompt_number": 25
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%octave -f svg\n",
+    "\n",
+    "p = [12 -2.5 -8 -0.1 8];\n",
+    "x = 0:0.01:1;\n",
+    "\n",
+    "polyout(p, 'x')\n",
+    "plot(x, polyval(p, x));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The plot size is adjusted using the `-s` flag:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "image/png": [
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+       "u66Lr63r+g/uHP5nSOvwFRhjlNY554yx7cJrqT8MXs9IKeNx52EYnoZIlBgxxTiOy5yeZRljbDbH\n",
+       "oLWOJ5MfPqUANvwAgmKwshJ2uRwAAAAASUVORK5CYII=\n"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Future work"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%octave -s 500,500\n",
+    "\n",
+    "# butterworth filter, order 2, cutoff pi/2 radians\n",
+    "b = [0.292893218813452  0.585786437626905  0.292893218813452];\n",
+    "a = [1  0  0.171572875253810];\n",
+    "freqz(b, a, 32);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "image/png": [
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+       "UFBQeKhRDKGCgoKCwkONYggVFBQUFB5qFEOooKCgoPBQ8/8BpmCrIbU64yQAAAAASUVORK5CYII=\n"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "After the next release of `oct2py`, we'll add the ability to interrupt/kill the current Octave session without restarting the Python kernel."
-     ]
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "%%octave -s 600,200 -f png\n",
+    "\n",
+    "subplot(121);\n",
+    "[x, y] = meshgrid(0:0.1:3);\n",
+    "r = sin(x - 0.5).^2 + cos(y - 0.5).^2;\n",
+    "surf(x, y, r);\n",
+    "\n",
+    "subplot(122);\n",
+    "sombrero()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Future work"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After the next release of `oct2py`, we'll add the ability to interrupt/kill the current Octave session without restarting the Python kernel."
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Builtin Extensions/R Magics.ipynb b/examples/Builtin Extensions/R Magics.ipynb
index 2fd1a4a..b94c53d 100644
--- a/examples/Builtin Extensions/R Magics.ipynb
+++ b/examples/Builtin Extensions/R Magics.ipynb
@@ -1,930 +1,2196 @@
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Using R Within the IPython Notebok"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Using the `rmagic` extension, users can run R code from within the IPython Notebook. This example Notebook demonstrates this capability. "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Line magics"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython has an `rmagic` extension that contains a some magic functions for working with R via rpy2. This extension can be loaded using the `%load_ext` magic as follows:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "%load_ext rmagic "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "A typical use case one imagines is having some numpy arrays, wanting to compute some statistics of interest on these\n",
-      " arrays and return the result back to python. Let's suppose we just want to fit a simple linear model to a scatterplot."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "X = np.array([0,1,2,3,4])\n",
-      "Y = np.array([3,5,4,6,7])\n",
-      "plt.scatter(X, Y)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "<matplotlib.collections.PathCollection at 0x107efe2d0>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAkoAAAF9CAYAAAD7tEcRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH7s1AAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x108019350>"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We can accomplish this by first pushing variables to R, fitting a model and returning the results. The line magic %Rpush copies its arguments to variables of the same name in rpy2. The %R line magic evaluates the string in rpy2 and returns the results. In this case, the coefficients of a linear model."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%Rpush X Y\n",
-      "%R lm(Y~X)$coef"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "array([ 3.2,  0.9])"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We can check that this is correct fairly easily:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "Xr = X - X.mean(); Yr = Y - Y.mean()\n",
-      "slope = (Xr*Yr).sum() / (Xr**2).sum()\n",
-      "intercept = Y.mean() - X.mean() * slope\n",
-      "(intercept, slope)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "(3.2000000000000002, 0.90000000000000002)"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "It is also possible to return more than one value with %R."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%R resid(lm(Y~X)); coef(lm(X~Y))\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 5,
-       "text": [
-        "array([-2.5,  0.9])"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "One can also easily capture the results of %R into python objects. Like R, the return value of this multiline expression (multiline in the sense that it is separated by ';') is the final value, which is \n",
-      "the *coef(lm(X~Y))*. To pull other variables from R, there is one more magic."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "There are two more line magics, %Rpull and %Rget. Both are useful after some R code has been executed and there are variables\n",
-      "in the rpy2 namespace that one would like to retrieve. The main difference is that one\n",
-      " returns the value (%Rget), while the other pulls it to self.shell.user_ns (%Rpull). Imagine we've stored the results\n",
-      "of some calculation in the variable \"a\" in rpy2's namespace. By using the %R magic, we can obtain these results and\n",
-      "store them in b. We can also pull them directly to user_ns with %Rpull. They are both views on the same data."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = %R a=resid(lm(Y~X))\n",
-      "%Rpull a\n",
-      "print(a)\n",
-      "assert id(b.data) == id(a.data)\n",
-      "%R -o a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[-0.2  0.9 -1.   0.1  0.2]\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "%Rpull is equivalent to calling %R with just -o\n"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%R d=resid(lm(Y~X)); e=coef(lm(Y~X))\n",
-      "%R -o d -o e\n",
-      "%Rpull e\n",
-      "print(d)\n",
-      "print(e)\n",
-      "import numpy as np\n",
-      "np.testing.assert_almost_equal(d, a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[-0.2  0.9 -1.   0.1  0.2]\n",
-        "[ 3.2  0.9]\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "On the other hand %Rpush is equivalent to calling %R with just -i and no trailing code."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "A = np.arange(20)\n",
-      "%R -i A\n",
-      "%R mean(A)\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 8,
-       "text": [
-        "array([ 9.5])"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The magic %Rget retrieves one variable from R."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%Rget A"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 9,
-       "text": [
-        "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
-        "       17, 18, 19], dtype=int32)"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Plotting and capturing output"
-     ]
-    },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using R Within the IPython Notebok"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the `rmagic` extension, users can run R code from within the IPython Notebook. This example Notebook demonstrates this capability. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Line magics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython has an `rmagic` extension that contains a some magic functions for working with R via rpy2. This extension can be loaded using the `%load_ext` magic as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext rmagic "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A typical use case one imagines is having some numpy arrays, wanting to compute some statistics of interest on these\n",
+    " arrays and return the result back to python. Let's suppose we just want to fit a simple linear model to a scatterplot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x107efe2d0>"
+      ]
+     },
+     "execution_count": 3,
      "metadata": {},
-     "source": [
-      "R's console (i.e. its stdout() connection) is captured by ipython, as are any plots which are published as PNG files, as with `%matplotlib inline`. As a call to %R may produce a return value (see above) we must ask what happens to a magic like the one below. The R code specifies that something is published to the notebook. If anything is published to the notebook, that call to %R returns None."
-     ]
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from __future__ import print_function\n",
-      "v1 = %R plot(X,Y); print(summary(lm(Y~X))); vv=mean(X)*mean(Y)\n",
-      "print('v1 is:', v1)\n",
-      "v2 = %R mean(X)*mean(Y)\n",
-      "print('v2 is:', v2)"
-     ],
-     "language": "python",
+     "data": {
+      "image/png": [
+       "iVBORw0KGgoAAAANSUhEUgAAAkoAAAF9CAYAAAD7tEcRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
+       "AAALEgAACxIB0t1+/AAAEYFJREFUeJzt3V1slQcdx/H/OX0JAqULuK3uLYspUAqSUWi3MLoEiWQ6\n",
+       "XMLdkGwsMXMaMxIvNm+IFXGyZPECo9udGZHohReG93mxadyicbMvTqEyRzY6NidZprRYQUrrhdps\n",
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+       "H7s1AAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x108019350>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "display_data",
-       "text": [
-        "\n",
-        "Call:\n",
-        "lm(formula = Y ~ X)\n",
-        "\n",
-        "Residuals:\n",
-        "   1    2    3    4    5 \n",
-        "-0.2  0.9 -1.0  0.1  0.2 \n",
-        "\n",
-        "Coefficients:\n",
-        "            Estimate Std. Error t value Pr(>|t|)  \n",
-        "(Intercept)   3.2000     0.6164   5.191   0.0139 *\n",
-        "X             0.9000     0.2517   3.576   0.0374 *\n",
-        "---\n",
-        "Signif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1 \n",
-        "\n",
-        "Residual standard error: 0.7958 on 3 degrees of freedom\n",
-        "Multiple R-squared:  0.81,\tAdjusted R-squared: 0.7467 \n",
-        "F-statistic: 12.79 on 1 and 3 DF,  p-value: 0.03739 \n",
-        "\n"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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RokWLiIhYsWJFnH766bW6Rtu2baNt27b7Pde6dWuvKRegLl26xMknnxy33357TJgwIaqq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\nrkJggg==\n"
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "v1 is: [ 10.]\n",
-        "v2 is: [ 10.]\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "X = np.array([0,1,2,3,4])\n",
+    "Y = np.array([3,5,4,6,7])\n",
+    "plt.scatter(X, Y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can accomplish this by first pushing variables to R, fitting a model and returning the results. The line magic %Rpush copies its arguments to variables of the same name in rpy2. The %R line magic evaluates the string in rpy2 and returns the results. In this case, the coefficients of a linear model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "text/plain": [
+       "array([ 3.2,  0.9])"
+      ]
+     },
+     "execution_count": 3,
      "metadata": {},
-     "source": [
-      "What value is returned from %R?"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%Rpush X Y\n",
+    "%R lm(Y~X)$coef"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can check that this is correct fairly easily:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "(3.2000000000000002, 0.90000000000000002)"
+      ]
+     },
+     "execution_count": 4,
      "metadata": {},
-     "source": [
-      "Some calls have no particularly interesting return value, the magic %R will not return anything in this case. The return value in rpy2 is actually NULL so %R returns None."
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Xr = X - X.mean(); Yr = Y - Y.mean()\n",
+    "slope = (Xr*Yr).sum() / (Xr**2).sum()\n",
+    "intercept = Y.mean() - X.mean() * slope\n",
+    "(intercept, slope)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is also possible to return more than one value with %R."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "v = %R plot(X,Y)\n",
-      "assert v == None"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "array([-2.5,  0.9])"
+      ]
+     },
+     "execution_count": 5,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAYAAAB91L6VAAAD8GlDQ1BJQ0MgUHJvZmlsZQAAKJGN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\nrkJggg==\n"
-      }
-     ],
-     "prompt_number": 11
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%R resid(lm(Y~X)); coef(lm(X~Y))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One can also easily capture the results of %R into python objects. Like R, the return value of this multiline expression (multiline in the sense that it is separated by ';') is the final value, which is \n",
+    "the *coef(lm(X~Y))*. To pull other variables from R, there is one more magic."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are two more line magics, %Rpull and %Rget. Both are useful after some R code has been executed and there are variables\n",
+    "in the rpy2 namespace that one would like to retrieve. The main difference is that one\n",
+    " returns the value (%Rget), while the other pulls it to self.shell.user_ns (%Rpull). Imagine we've stored the results\n",
+    "of some calculation in the variable \"a\" in rpy2's namespace. By using the %R magic, we can obtain these results and\n",
+    "store them in b. We can also pull them directly to user_ns with %Rpull. They are both views on the same data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Also, if the return value of a call to %R (in line mode) has just been printed to the console, then its value is also not returned."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.2  0.9 -1.   0.1  0.2]\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "v = %R print(X)\n",
-      "assert v == None"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "display_data",
-       "text": [
-        "[1] 0 1 2 3 4\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
+    }
+   ],
+   "source": [
+    "b = %R a=resid(lm(Y~X))\n",
+    "%Rpull a\n",
+    "print(a)\n",
+    "assert id(b.data) == id(a.data)\n",
+    "%R -o a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "%Rpull is equivalent to calling %R with just -o\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "But, if the last value did not print anything to console, the value is returned:\n"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.2  0.9 -1.   0.1  0.2]\n",
+      "[ 3.2  0.9]\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "%R d=resid(lm(Y~X)); e=coef(lm(Y~X))\n",
+    "%R -o d -o e\n",
+    "%Rpull e\n",
+    "print(d)\n",
+    "print(e)\n",
+    "import numpy as np\n",
+    "np.testing.assert_almost_equal(d, a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On the other hand %Rpush is equivalent to calling %R with just -i and no trailing code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "v = %R print(summary(X)); X\n",
-      "print('v:', v)"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "array([ 9.5])"
+      ]
+     },
+     "execution_count": 8,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "display_data",
-       "text": [
-        "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
-        "      0       1       2       2       3       4 \n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "v: [0 1 2 3 4]\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A = np.arange(20)\n",
+    "%R -i A\n",
+    "%R mean(A)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The magic %Rget retrieves one variable from R."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19], dtype=int32)"
+      ]
+     },
+     "execution_count": 9,
      "metadata": {},
-     "source": [
-      "The return value can be suppressed by a trailing ';' or an -n argument.\n"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%Rget A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting and capturing output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "R's console (i.e. its stdout() connection) is captured by ipython, as are any plots which are published as PNG files, as with `%matplotlib inline`. As a call to %R may produce a return value (see above) we must ask what happens to a magic like the one below. The R code specifies that something is published to the notebook. If anything is published to the notebook, that call to %R returns None."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "%R -n X"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "\n",
+       "Call:\n",
+       "lm(formula = Y ~ X)\n",
+       "\n",
+       "Residuals:\n",
+       "   1    2    3    4    5 \n",
+       "-0.2  0.9 -1.0  0.1  0.2 \n",
+       "\n",
+       "Coefficients:\n",
+       "            Estimate Std. Error t value Pr(>|t|)  \n",
+       "(Intercept)   3.2000     0.6164   5.191   0.0139 *\n",
+       "X             0.9000     0.2517   3.576   0.0374 *\n",
+       "---\n",
+       "Signif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1 \n",
+       "\n",
+       "Residual standard error: 0.7958 on 3 degrees of freedom\n",
+       "Multiple R-squared:  0.81,\tAdjusted R-squared: 0.7467 \n",
+       "F-statistic: 12.79 on 1 and 3 DF,  p-value: 0.03739 \n",
+       "\n"
+      ]
+     },
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 14
+     "output_type": "display_data"
     },
     {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "%R X; "
-     ],
-     "language": "python",
+     "data": {
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+       "VgwYMCDuvPPOWLp0aSxbtix7HtRLAgz1RLdu3eLOO++MYcOGRUlJSUyaNCmWLFkSLVq0yJ4G9VKD\n",
+       "mpqamuwRQN25+OKL4+WXX47p06fHtddemz0H6i2vAUM907lz59i7d2+0bt06ewrUawIM9cirr74a\n",
+       "s2bNittvvz2uu+662LJlS/YkqLfcgoZ64sMPP4xu3brFzTffHMOGDYuePXtGp06d4ic/+Un2NKiX\n",
+       "BBjqieHDh8c777wTCxYsiAYNGsSaNWuie/fu8cwzz0SfPn2y50G9I8BQD7z00ktRVlYWK1asiBNP\n",
+       "PHHf8TvvvDN+9KMfxcqVK70bGuqYAANAAm/CAoAEAgwACQQYABIIMAAkEGAASCDAAJBAgAEggQAD\n",
+       "QAIBBoAEAgwACQQYABIIMAAkEGAASCDAAJBAgAEggQADQAIBBoAE/w+5mUYqDkF0XgAAAABJRU5E\n",
+       "rkJggg==\n"
+      ]
+     },
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 15
+     "output_type": "display_data"
     },
     {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Cell level magic"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "v1 is: [ 10.]\n",
+      "v2 is: [ 10.]\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "from __future__ import print_function\n",
+    "v1 = %R plot(X,Y); print(summary(lm(Y~X))); vv=mean(X)*mean(Y)\n",
+    "print('v1 is:', v1)\n",
+    "v2 = %R mean(X)*mean(Y)\n",
+    "print('v2 is:', v2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What value is returned from %R?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some calls have no particularly interesting return value, the magic %R will not return anything in this case. The return value in rpy2 is actually NULL so %R returns None."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
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+       "QAIBBoAEAgwACQQYABIIMAAkEGAASCDAAJBAgAEggQADQAIBBoAE/w+5mUYqDkF0XgAAAABJRU5E\n",
+       "rkJggg==\n"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Often, we will want to do more than a simple linear regression model. There may be several lines of R code that we want to \n",
-      "use before returning to python. This is the cell-level magic.\n",
-      "\n",
-      "\n",
-      "For the cell level magic, inputs can be passed via the -i or --inputs argument in the line. These variables are copied \n",
-      "from the shell namespace to R's namespace using rpy2.robjects.r.assign. It would be nice not to have to copy these into R: rnumpy ( http://bitbucket.org/njs/rnumpy/wiki/API ) has done some work to limit or at least make transparent the number of copies of an array. This seems like a natural thing to try to build on. Arrays can be output from R via the -o or --outputs argument in the line. All other arguments are sent to R's png function, which is the graphics device used to create the plots.\n",
-      "\n",
-      "We can redo the above calculations in one ipython cell. We might also want to add some output such as a summary\n",
-      " from R or perhaps the standard plotting diagnostics of the lm."
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "v = %R plot(X,Y)\n",
+    "assert v == None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Also, if the return value of a call to %R (in line mode) has just been printed to the console, then its value is also not returned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%R -i X,Y -o XYcoef\n",
-      "XYlm = lm(Y~X)\n",
-      "XYcoef = coef(XYlm)\n",
-      "print(summary(XYlm))\n",
-      "par(mfrow=c(2,2))\n",
-      "plot(XYlm)"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "[1] 0 1 2 3 4\n"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "display_data",
-       "text": [
-        "\n",
-        "Call:\n",
-        "lm(formula = Y ~ X)\n",
-        "\n",
-        "Residuals:\n",
-        "   1    2    3    4    5 \n",
-        "-0.2  0.9 -1.0  0.1  0.2 \n",
-        "\n",
-        "Coefficients:\n",
-        "            Estimate Std. Error t value Pr(>|t|)  \n",
-        "(Intercept)   3.2000     0.6164   5.191   0.0139 *\n",
-        "X             0.9000     0.2517   3.576   0.0374 *\n",
-        "---\n",
-        "Signif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1 \n",
-        "\n",
-        "Residual standard error: 0.7958 on 3 degrees of freedom\n",
-        "Multiple R-squared:  0.81,\tAdjusted R-squared: 0.7467 \n",
-        "F-statistic: 12.79 on 1 and 3 DF,  p-value: 0.03739 \n",
-        "\n"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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4+BAeHk6pUqXYsmULrVu31jqWEHlGXFycWdox67nbJUuWAGnXba9fv05wcDBn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\n"
-      }
-     ],
-     "prompt_number": 16
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "v = %R print(X)\n",
+    "assert v == None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But, if the last value did not print anything to console, the value is returned:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "text/plain": [
+       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
+       "      0       1       2       2       3       4 \n"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Passing data back and forth"
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Currently, data is passed through RMagics.pyconverter when going from python to R and RMagics.Rconverter when \n",
-      "going from R to python. These currently default to numpy.ndarray. Future work will involve writing better converters, most likely involving integration with http://pandas.sourceforge.net.\n",
-      "\n",
-      "Passing ndarrays into R seems to require a copy, though once an object is returned to python, this object is NOT copied, and it is possible to change its values.\n"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "v: [0 1 2 3 4]\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "seq1 = np.arange(10)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%R -i seq1 -o seq2\n",
-      "seq2 = rep(seq1, 2)\n",
-      "print(seq2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "display_data",
-       "text": [
-        " [1] 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9\n"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "seq2[::2] = 0\n",
-      "seq2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 19,
-       "text": [
-        "array([0, 1, 0, 3, 0, 5, 0, 7, 0, 9, 0, 1, 0, 3, 0, 5, 0, 7, 0, 9], dtype=int32)"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
+    }
+   ],
+   "source": [
+    "v = %R print(summary(X)); X\n",
+    "print('v:', v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The return value can be suppressed by a trailing ';' or an -n argument.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%R -n X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%R X; "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cell level magic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Often, we will want to do more than a simple linear regression model. There may be several lines of R code that we want to \n",
+    "use before returning to python. This is the cell-level magic.\n",
+    "\n",
+    "\n",
+    "For the cell level magic, inputs can be passed via the -i or --inputs argument in the line. These variables are copied \n",
+    "from the shell namespace to R's namespace using rpy2.robjects.r.assign. It would be nice not to have to copy these into R: rnumpy ( http://bitbucket.org/njs/rnumpy/wiki/API ) has done some work to limit or at least make transparent the number of copies of an array. This seems like a natural thing to try to build on. Arrays can be output from R via the -o or --outputs argument in the line. All other arguments are sent to R's png function, which is the graphics device used to create the plots.\n",
+    "\n",
+    "We can redo the above calculations in one ipython cell. We might also want to add some output such as a summary\n",
+    " from R or perhaps the standard plotting diagnostics of the lm."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%R\n",
-      "print(seq2)"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "\n",
+       "Call:\n",
+       "lm(formula = Y ~ X)\n",
+       "\n",
+       "Residuals:\n",
+       "   1    2    3    4    5 \n",
+       "-0.2  0.9 -1.0  0.1  0.2 \n",
+       "\n",
+       "Coefficients:\n",
+       "            Estimate Std. Error t value Pr(>|t|)  \n",
+       "(Intercept)   3.2000     0.6164   5.191   0.0139 *\n",
+       "X             0.9000     0.2517   3.576   0.0374 *\n",
+       "---\n",
+       "Signif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1 \n",
+       "\n",
+       "Residual standard error: 0.7958 on 3 degrees of freedom\n",
+       "Multiple R-squared:  0.81,\tAdjusted R-squared: 0.7467 \n",
+       "F-statistic: 12.79 on 1 and 3 DF,  p-value: 0.03739 \n",
+       "\n"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "display_data",
-       "text": [
-        " [1] 0 1 0 3 0 5 0 7 0 9 0 1 0 3 0 5 0 7 0 9\n"
-       ]
-      }
-     ],
-     "prompt_number": 20
+     "output_type": "display_data"
     },
     {
-     "cell_type": "markdown",
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+       "/OTJE4oWLZrp9frqf/r0KXZ2dpmqX5IKGn3Hjj7x8fEUKlRI7w9q0Jwfnj9/jo2NjXbZmjVrGDly\n",
+       "JIqi8Oabb/LJJ5/QuHHjDJUnGY68As4HVCqVNuFaWFjorNN3AGdl8I7XJd/XrddXv0y+kpS+jCRf\n",
+       "IN3BcFQqlU7yBU2S7datG0IIHBwcdJ4JkYPr5Bx5BSxJklQApaSkkJKSgqWlpalDKbBkApYkSZIk\n",
+       "E5At7JIkSZJkAjIBS5IkSZIJyAQsSZIkSSYgE7AkSZIkmYBMwJIkSZJkAjIBS5IkSZIJyAQsSZIk\n",
+       "SSYgE7AkSZIkmYBMwJIkSZJkAjIBS5IkSZIJyAQsSZIkSSbwf/WrCZmGNOguAAAAAElFTkSuQmCC\n"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Once the array data has been passed to R, modifring its contents does not modify R's copy of the data."
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%R -i X,Y -o XYcoef\n",
+    "XYlm = lm(Y~X)\n",
+    "XYcoef = coef(XYlm)\n",
+    "print(summary(XYlm))\n",
+    "par(mfrow=c(2,2))\n",
+    "plot(XYlm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Passing data back and forth"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Currently, data is passed through RMagics.pyconverter when going from python to R and RMagics.Rconverter when \n",
+    "going from R to python. These currently default to numpy.ndarray. Future work will involve writing better converters, most likely involving integration with http://pandas.sourceforge.net.\n",
+    "\n",
+    "Passing ndarrays into R seems to require a copy, though once an object is returned to python, this object is NOT copied, and it is possible to change its values.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "seq1 = np.arange(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "seq1[0] = 200\n",
-      "%R print(seq1)"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       " [1] 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9\n"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "display_data",
-       "text": [
-        " [1] 0 1 2 3 4 5 6 7 8 9\n"
-       ]
-      }
-     ],
-     "prompt_number": 21
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%R -i seq1 -o seq2\n",
+    "seq2 = rep(seq1, 2)\n",
+    "print(seq2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "array([0, 1, 0, 3, 0, 5, 0, 7, 0, 9, 0, 1, 0, 3, 0, 5, 0, 7, 0, 9], dtype=int32)"
+      ]
+     },
+     "execution_count": 19,
      "metadata": {},
-     "source": [
-      "But, if we pass data as both input and output, then the value of \"data\" in user_ns will be overwritten and the\n",
-      "new array will be a view of the data in R's copy."
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "seq2[::2] = 0\n",
+    "seq2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print(seq1)\n",
-      "%R -i seq1 -o seq1\n",
-      "print(seq1)\n",
-      "seq1[0] = 200\n",
-      "%R print(seq1)\n",
-      "seq1_view = %R seq1\n",
-      "assert(id(seq1_view.data) == id(seq1.data))"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       " [1] 0 1 0 3 0 5 0 7 0 9 0 1 0 3 0 5 0 7 0 9\n"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[200   1   2   3   4   5   6   7   8   9]\n",
-        "[200   1   2   3   4   5   6   7   8   9]\n"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "text": [
-        " [1] 200   1   2   3   4   5   6   7   8   9\n"
-       ]
-      }
-     ],
-     "prompt_number": 22
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%R\n",
+    "print(seq2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once the array data has been passed to R, modifring its contents does not modify R's copy of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "text/plain": [
+       " [1] 0 1 2 3 4 5 6 7 8 9\n"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Exception handling\n"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "seq1[0] = 200\n",
+    "%R print(seq1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But, if we pass data as both input and output, then the value of \"data\" in user_ns will be overwritten and the\n",
+    "new array will be a view of the data in R's copy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Exceptions are handled by passing back rpy2's exception and the line that triggered it."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[200   1   2   3   4   5   6   7   8   9]\n",
+      "[200   1   2   3   4   5   6   7   8   9]\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "try:\n",
-      "    %R -n nosuchvar\n",
-      "except Exception as e:\n",
-      "    print(e)\n",
-      "    pass"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "parsing and evaluating line \"nosuchvar\".\n",
-        "R error message: \"Error in eval(expr, envir, enclos) : object 'nosuchvar' not found\n",
-        "\"\n",
-        " R stdout:\"Error in eval(expr, envir, enclos) : object 'nosuchvar' not found\n",
-        "\"\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "text/plain": [
+       " [1] 200   1   2   3   4   5   6   7   8   9\n"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Structured arrays and data frames\n"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(seq1)\n",
+    "%R -i seq1 -o seq1\n",
+    "print(seq1)\n",
+    "seq1[0] = 200\n",
+    "%R print(seq1)\n",
+    "seq1_view = %R seq1\n",
+    "assert(id(seq1_view.data) == id(seq1.data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exception handling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Exceptions are handled by passing back rpy2's exception and the line that triggered it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In R, data frames play an important role as they allow array-like objects of mixed type with column names (and row names). In bumpy, the closest analogy is a structured array with named fields. In future work, it would be nice to use pandas to return full-fledged DataFrames from rpy2. In the mean time, structured arrays can be passed back and forth with the -d flag to %R, %Rpull, and %Rget"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "parsing and evaluating line \"nosuchvar\".\n",
+      "R error message: \"Error in eval(expr, envir, enclos) : object 'nosuchvar' not found\n",
+      "\"\n",
+      " R stdout:\"Error in eval(expr, envir, enclos) : object 'nosuchvar' not found\n",
+      "\"\n",
+      "\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "datapy= np.array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')],\n",
-      "          dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "%%R -i datapy -d datar\n",
-      "datar = datapy"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "datar"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 26,
-       "text": [
-        "array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')], \n",
-        "      dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])"
-       ]
-      }
-     ],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%R datar2 = datapy\n",
-      "%Rpull -d datar2\n",
-      "datar2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 27,
-       "text": [
-        "array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')], \n",
-        "      dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])"
-       ]
-      }
-     ],
-     "prompt_number": 27
-    },
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    %R -n nosuchvar\n",
+    "except Exception as e:\n",
+    "    print(e)\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Structured arrays and data frames"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In R, data frames play an important role as they allow array-like objects of mixed type with column names (and row names). In bumpy, the closest analogy is a structured array with named fields. In future work, it would be nice to use pandas to return full-fledged DataFrames from rpy2. In the mean time, structured arrays can be passed back and forth with the -d flag to %R, %Rpull, and %Rget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "datapy= np.array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')],\n",
+    "          dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%%R -i datapy -d datar\n",
+    "datar = datapy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%Rget -d datar2"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')], \n",
+       "      dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])"
+      ]
+     },
+     "execution_count": 26,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 28,
-       "text": [
-        "array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')], \n",
-        "      dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])"
-       ]
-      }
-     ],
-     "prompt_number": 28
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "datar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')], \n",
+       "      dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])"
+      ]
+     },
+     "execution_count": 27,
      "metadata": {},
-     "source": [
-      "For arrays without names, the -d argument has no effect because the R object has no colnames or names."
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%R datar2 = datapy\n",
+    "%Rpull -d datar2\n",
+    "datar2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "Z = np.arange(6)\n",
-      "%R -i Z\n",
-      "%Rget -d Z"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')], \n",
+       "      dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])"
+      ]
+     },
+     "execution_count": 28,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 29,
-       "text": [
-        "array([0, 1, 2, 3, 4, 5], dtype=int32)"
-       ]
-      }
-     ],
-     "prompt_number": 29
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%Rget -d datar2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For arrays without names, the -d argument has no effect because the R object has no colnames or names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3, 4, 5], dtype=int32)"
+      ]
+     },
+     "execution_count": 29,
      "metadata": {},
-     "source": [
-      "For mixed-type data frames in R, if the -d flag is not used, then an array of a single type is returned and\n",
-      "its value is transposed. This would be nice to fix, but it seems something that should be fixed at the rpy2 level (See: https://bitbucket.org/lgautier/rpy2/issue/44/numpyrecarray-as-dataframe)"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Z = np.arange(6)\n",
+    "%R -i Z\n",
+    "%Rget -d Z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For mixed-type data frames in R, if the -d flag is not used, then an array of a single type is returned and\n",
+    "its value is transposed. This would be nice to fix, but it seems something that should be fixed at the rpy2 level (See: https://bitbucket.org/lgautier/rpy2/issue/44/numpyrecarray-as-dataframe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%Rget datar2"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "array([['1', '2', '3'],\n",
+       "       ['2', '3', '2'],\n",
+       "       ['a', 'b', 'c']], \n",
+       "      dtype='|S1')"
+      ]
+     },
+     "execution_count": 30,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 30,
-       "text": [
-        "array([['1', '2', '3'],\n",
-        "       ['2', '3', '2'],\n",
-        "       ['a', 'b', 'c']], \n",
-        "      dtype='|S1')"
-       ]
-      }
-     ],
-     "prompt_number": 30
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "%Rget datar2"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Customization/Index.ipynb b/examples/Customization/Index.ipynb
index a2f3b55..0822cb8 100644
--- a/examples/Customization/Index.ipynb
+++ b/examples/Customization/Index.ipynb
@@ -1,111 +1,103 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:de8cb1aff3da9097ba3fc7afab4327fc589f2bb1c154feeeb5ed97c87e37486f"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<img src=\"../images/ipython_logo.png\">"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Back to the main [Index](../Index.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Customization"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython has rich APIs for customization. Many behaviors of the different IPython applications can be configured using command line arguments or configuration files. IPython's core syntax and command line features can also be customized through input filters, custom magic commands, etc."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Tutorials"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Coming soon."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Examples"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Coming soon."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Non-notebook examples"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This directory also contains examples that are regular Python (`.py`) files."
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"../images/ipython_logo.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Back to the main [Index](../Index.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Customization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython has rich APIs for customization. Many behaviors of the different IPython applications can be configured using command line arguments or configuration files. IPython's core syntax and command line features can also be customized through input filters, custom magic commands, etc."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tutorials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Coming soon."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Coming soon."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Non-notebook examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory also contains examples that are regular Python (`.py`) files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%run ../utils/list_pyfiles.ipy"
-     ],
-     "language": "python",
+     "data": {
+      "text/html": [
+       "<a href='appconfig.py' target='_blank'>appconfig.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Customization/appconfig.py"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<a href='appconfig.py' target='_blank'>appconfig.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Customization/appconfig.py"
-       ]
-      }
-     ],
-     "prompt_number": 1
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "%run ../utils/list_pyfiles.ipy"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:de8cb1aff3da9097ba3fc7afab4327fc589f2bb1c154feeeb5ed97c87e37486f"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Embedding/Index.ipynb b/examples/Embedding/Index.ipynb
index 9682176..e5ecb6a 100644
--- a/examples/Embedding/Index.ipynb
+++ b/examples/Embedding/Index.ipynb
@@ -1,188 +1,194 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:627cdf03b8de558c9344f9d1e8f0beeb2448e37e492d676e6db7b07d33251a2b"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<img src=\"../images/ipython_logo.png\">"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Back to the main [Index](../Index.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Embedding IPython Into Other Applications"
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"../images/ipython_logo.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Back to the main [Index](../Index.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Embedding IPython Into Other Applications"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The architecture of IPython is built with reusable components. These components include:\n",
+    "\n",
+    "* The configuration system for processing command line arguments and configuration files\n",
+    "* The IPython `InteractiveShell` object that provides the core interactive features across the entire code base\n",
+    "* The IPython kernel, which provides the capabilities of the `InteractiveShell` object over a ZeroMQ/JSON based message protocol to various frontends\n",
+    "* The IPython frontends (Notebook, Qt Console, Console, Terminal)\n",
+    "\n",
+    "These components can be embedded into other applications."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tutorials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Coming soon."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Coming soon."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Non-notebook examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory also contains examples that are regular Python (`.py`) files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/html": [
+       "<a href='embed_class_long.py' target='_blank'>embed_class_long.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/embed_class_long.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "The architecture of IPython is built with reusable components. These components include:\n",
-      "\n",
-      "* The configuration system for processing command line arguments and configuration files\n",
-      "* The IPython `InteractiveShell` object that provides the core interactive features across the entire code base\n",
-      "* The IPython kernel, which provides the capabilities of the `InteractiveShell` object over a ZeroMQ/JSON based message protocol to various frontends\n",
-      "* The IPython frontends (Notebook, Qt Console, Console, Terminal)\n",
-      "\n",
-      "These components can be embedded into other applications."
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "text/html": [
+       "<a href='embed_class_short.py' target='_blank'>embed_class_short.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/embed_class_short.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Tutorials"
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/html": [
+       "<a href='embed_function.py' target='_blank'>embed_function.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/embed_function.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Coming soon."
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "text/html": [
+       "<a href='inprocess_qtconsole.py' target='_blank'>inprocess_qtconsole.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/inprocess_qtconsole.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Examples"
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/html": [
+       "<a href='inprocess_terminal.py' target='_blank'>inprocess_terminal.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/inprocess_terminal.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Coming soon."
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "text/html": [
+       "<a href='internal_ipkernel.py' target='_blank'>internal_ipkernel.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/internal_ipkernel.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Non-notebook examples"
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/html": [
+       "<a href='ipkernel_qtapp.py' target='_blank'>ipkernel_qtapp.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/ipkernel_qtapp.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "This directory also contains examples that are regular Python (`.py`) files."
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%run ../utils/list_pyfiles.ipy"
-     ],
-     "language": "python",
+     "data": {
+      "text/html": [
+       "<a href='ipkernel_wxapp.py' target='_blank'>ipkernel_wxapp.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/ipkernel_wxapp.py"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<a href='embed_class_long.py' target='_blank'>embed_class_long.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/embed_class_long.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='embed_class_short.py' target='_blank'>embed_class_short.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/embed_class_short.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='embed_function.py' target='_blank'>embed_function.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/embed_function.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='inprocess_qtconsole.py' target='_blank'>inprocess_qtconsole.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/inprocess_qtconsole.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='inprocess_terminal.py' target='_blank'>inprocess_terminal.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/inprocess_terminal.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='internal_ipkernel.py' target='_blank'>internal_ipkernel.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/internal_ipkernel.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='ipkernel_qtapp.py' target='_blank'>ipkernel_qtapp.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/ipkernel_qtapp.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='ipkernel_wxapp.py' target='_blank'>ipkernel_wxapp.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Embedding/ipkernel_wxapp.py"
-       ]
-      }
-     ],
-     "prompt_number": 1
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "%run ../utils/list_pyfiles.ipy"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:627cdf03b8de558c9344f9d1e8f0beeb2448e37e492d676e6db7b07d33251a2b"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/Animations Using clear_output.ipynb b/examples/IPython Kernel/Animations Using clear_output.ipynb
index c799dfb..8b59c77 100644
--- a/examples/IPython Kernel/Animations Using clear_output.ipynb
+++ b/examples/IPython Kernel/Animations Using clear_output.ipynb
@@ -1,194 +1,1341 @@
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Simple animations Using clear_output"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Sometimes you want to clear the output area in the middle of a calculation.  This can be useful for doing simple animations. In terminals, there is the carriage-return (`'\\r'`) for overwriting a single line, but the notebook frontend can clear the whole output area, not just a single line.\n",
-      "\n",
-      "To clear output in the Notebook you can use the `clear_output()` function.  If you are clearing the output every frame of an animation, calling `clear_output()` will create noticeable flickering.  You can use `clear_output(wait=True)` to add the *clear_output* call to a queue.  When data becomes available to replace the existing output, the *clear_output* will be called immediately before the new data is added.  This avoids the flickering by not rendering the cleared output to the screen."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Simple example"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here we show our progress iterating through a list:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "import sys\n",
-      "import time"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import display, clear_output\n",
-      "for i in range(10):\n",
-      "    time.sleep(0.25)\n",
-      "    clear_output(wait=True)\n",
-      "    print(i)\n",
-      "    sys.stdout.flush()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "9\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "AsyncResult.wait_interactive"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The AsyncResult object has a special `wait_interactive()` method, which prints its progress interactively,\n",
-      "so you can watch as your parallel computation completes.\n",
-      "\n",
-      "**This example assumes you have an IPython cluster running, which you can start from the [cluster panel](/#clusters)**"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython import parallel\n",
-      "rc = parallel.Client()\n",
-      "view = rc.load_balanced_view()\n",
-      "\n",
-      "amr = view.map_async(time.sleep, [0.5]*100)\n",
-      "\n",
-      "amr.wait_interactive()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        " 100/100 tasks finished after    6 s"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "done\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple animations Using clear_output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes you want to clear the output area in the middle of a calculation.  This can be useful for doing simple animations. In terminals, there is the carriage-return (`'\\r'`) for overwriting a single line, but the notebook frontend can clear the whole output area, not just a single line.\n",
+    "\n",
+    "To clear output in the Notebook you can use the `clear_output()` function.  If you are clearing the output every frame of an animation, calling `clear_output()` will create noticeable flickering.  You can use `clear_output(wait=True)` to add the *clear_output* call to a queue.  When data becomes available to replace the existing output, the *clear_output* will be called immediately before the new data is added.  This avoids the flickering by not rendering the cleared output to the screen."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simple example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we show our progress iterating through a list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Matplotlib example"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "from IPython.display import display, clear_output\n",
+    "for i in range(10):\n",
+    "    time.sleep(0.25)\n",
+    "    clear_output(wait=True)\n",
+    "    print(i)\n",
+    "    sys.stdout.flush()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## AsyncResult.wait_interactive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The AsyncResult object has a special `wait_interactive()` method, which prints its progress interactively,\n",
+    "so you can watch as your parallel computation completes.\n",
+    "\n",
+    "**This example assumes you have an IPython cluster running, which you can start from the [cluster panel](/#clusters)**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can also use `clear_output()` to clear figures and plots."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 100/100 tasks finished after    6 s\n",
+      "done\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline\n",
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
+    }
+   ],
+   "source": [
+    "from IPython import parallel\n",
+    "rc = parallel.Client()\n",
+    "view = rc.load_balanced_view()\n",
+    "\n",
+    "amr = view.map_async(time.sleep, [0.5]*100)\n",
+    "\n",
+    "amr.wait_interactive()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matplotlib example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also use `clear_output()` to clear figures and plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from scipy.special import jn\n",
-      "x = np.linspace(0,5)\n",
-      "f, ax = plt.subplots()\n",
-      "ax.set_title(\"Bessel functions\")\n",
-      "\n",
-      "for n in range(1,10):\n",
-      "    time.sleep(1)\n",
-      "    ax.plot(x, jn(x,n))\n",
-      "    clear_output(wait=True)\n",
-      "    display(f)\n",
-      "\n",
-      "# close the figure at the end, so we don't get a duplicate\n",
-      "# of the last plot\n",
-      "plt.close()"
-     ],
-     "language": "python",
+     "data": {
+      "image/png": [
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+       "HJXQUPb29vTo0QOAurq6i58TbMP69euJj4/H19fX0qEIjWAwGJg9ezazZs1iw4YNVzzW4iNa1dXV\n",
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+       "f58ff/yRI0eOsGbNGkuHJVwDlUp18bZnz55kZ2df9liLj2glJCSwcePGi2uytm3bRv/+/S0clSC0\n",
+       "flqtlk8//ZSBAwfSq1cvS4cjXKOMjAzKysro2bMnarUaNze3v71pFazXf7+pOX36NEePHmX8+PEW\n",
+       "jEi4Frm5uRw7doxx48ZhNBpJSkrivvvuu+zxFk+0YmNjSU1N5ZVXXgHqEy+xEF4Qmt+2bdtIT0+n\n",
+       "pqaG9evXAzBkyBAGDRpk4ciEhvD392fNmjX8/vvvAMTExNClSxcLRyU0xl/rJAXb4O/vT15eHq+8\n",
+       "8gpyuZyRI0cSFBR02eNFCx5BEARBEIRmYvE1WoIgCIIgCK2VSLQEQRAEQRCaiUi0BEEQBEEQmolI\n",
+       "tARBEARBEJqJSLQEQRAEQRCaiUi0BEEQBEEQmolItARBEARBEJqJSLQEQRAEQRCaiUi0BEEQBEEQ\n",
+       "msn/AbPOyGwnPY5DAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10f01b310>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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rNVRs3Yv35NHEPHIv3hOGk5VRwcblxzEUa0iO0/b44bsarRanqFCcokIJvGEG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\nLBZz0003XfZ4vRVaDbXt7P3tHMamekQt8b/uQbP9hVSu5OOTpZQ2i1k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DdZvvY4rfXBaO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EY9OR3nkGCavfakSW27HdklOl7ZEMPE8Mb0a27+BQR3w4ajv3L44g5mPfwddnLTXRjPHE/lwI5Y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\nxJmESq2R1zdnsiG1lHnjohgd49WgFyuZQkG7h2+n7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a2jd6jZZObMiUIO7jyHSiWn9+AI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\nmsn/AbPOyGwnPY5DAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10f01b310>"
-       ]
-      }
-     ],
-     "prompt_number": 5
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "from scipy.special import jn\n",
+    "x = np.linspace(0,5)\n",
+    "f, ax = plt.subplots()\n",
+    "ax.set_title(\"Bessel functions\")\n",
+    "\n",
+    "for n in range(1,10):\n",
+    "    time.sleep(1)\n",
+    "    ax.plot(x, jn(x,n))\n",
+    "    clear_output(wait=True)\n",
+    "    display(f)\n",
+    "\n",
+    "# close the figure at the end, so we don't get a duplicate\n",
+    "# of the last plot\n",
+    "plt.close()"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/Background Jobs.ipynb b/examples/IPython Kernel/Background Jobs.ipynb
index 165f1cd..2d8b7f5 100644
--- a/examples/IPython Kernel/Background Jobs.ipynb
+++ b/examples/IPython Kernel/Background Jobs.ipynb
@@ -1,406 +1,386 @@
 {
- "metadata": {
-  "name": "BackgroundJobs"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Simple interactive bacgkround jobs with IPython\n",
-      "\n",
-      "We start by loading the `backgroundjobs` library and defining a few trivial functions to illustrate things with."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.lib import backgroundjobs as bg\n",
-      "\n",
-      "import sys\n",
-      "import time\n",
-      "\n",
-      "def sleepfunc(interval=2, *a, **kw):\n",
-      "    args = dict(interval=interval,\n",
-      "                args=a,\n",
-      "                kwargs=kw)\n",
-      "    time.sleep(interval)\n",
-      "    return args\n",
-      "\n",
-      "def diefunc(interval=2, *a, **kw):\n",
-      "    time.sleep(interval)\n",
-      "    raise Exception(\"Dead job with interval %s\" % interval)\n",
-      "\n",
-      "def printfunc(interval=1, reps=5):\n",
-      "    for n in range(reps):\n",
-      "        time.sleep(interval)\n",
-      "        print 'In the background...', n\n",
-      "        sys.stdout.flush()\n",
-      "    print 'All done!'\n",
-      "    sys.stdout.flush()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now, we can create a job manager (called simply `jobs`) and use it to submit new jobs.\n",
-      "\n",
-      "Run the cell below, it will show when the jobs start. Wait a few seconds until you see the 'all done' completion message:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "jobs = bg.BackgroundJobManager()\n",
-      "\n",
-      "# Start a few jobs, the first one will have ID # 0\n",
-      "jobs.new(sleepfunc, 4)\n",
-      "jobs.new(sleepfunc, kw={'reps':2})\n",
-      "jobs.new('printfunc(1,3)')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Starting job # 0 in a separate thread.\n",
-        "Starting job # 2 in a separate thread.\n",
-        "Starting job # 3 in a separate thread.\n"
-       ]
-      },
-      {
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "<BackgroundJob #3: printfunc(1,3)>"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "In the background... 0\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "In the background... 1\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "In the background... 2\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "All done!\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can check the status of your jobs at any time:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "jobs.status()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Completed jobs:\n",
-        "0 : <function sleepfunc at 0x314f848>\n",
-        "2 : <function sleepfunc at 0x314f848>\n",
-        "3 : printfunc(1,3)\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple interactive bacgkround jobs with IPython\n",
+    "\n",
+    "We start by loading the `backgroundjobs` library and defining a few trivial functions to illustrate things with."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.lib import backgroundjobs as bg\n",
+    "\n",
+    "import sys\n",
+    "import time\n",
+    "\n",
+    "def sleepfunc(interval=2, *a, **kw):\n",
+    "    args = dict(interval=interval,\n",
+    "                args=a,\n",
+    "                kwargs=kw)\n",
+    "    time.sleep(interval)\n",
+    "    return args\n",
+    "\n",
+    "def diefunc(interval=2, *a, **kw):\n",
+    "    time.sleep(interval)\n",
+    "    raise Exception(\"Dead job with interval %s\" % interval)\n",
+    "\n",
+    "def printfunc(interval=1, reps=5):\n",
+    "    for n in range(reps):\n",
+    "        time.sleep(interval)\n",
+    "        print 'In the background...', n\n",
+    "        sys.stdout.flush()\n",
+    "    print 'All done!'\n",
+    "    sys.stdout.flush()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can create a job manager (called simply `jobs`) and use it to submit new jobs.\n",
+    "\n",
+    "Run the cell below, it will show when the jobs start. Wait a few seconds until you see the 'all done' completion message:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "For any completed job, you can get its result easily:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting job # 0 in a separate thread.\n",
+      "Starting job # 2 in a separate thread.\n",
+      "Starting job # 3 in a separate thread.\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "jobs[0].result"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "<BackgroundJob #3: printfunc(1,3)>"
+      ]
+     },
+     "execution_count": 10,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 12,
-       "text": [
-        "{'args': (), 'interval': 4, 'kwargs': {}}"
-       ]
-      }
-     ],
-     "prompt_number": 12
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Errors and tracebacks"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "In the background... 0\n",
+      "In the background... 1\n",
+      "In the background... 2\n",
+      "All done!\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The jobs manager tries to help you with debugging:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# This makes a couple of jobs which will die.  Let's keep a reference to\n",
-      "# them for easier traceback reporting later\n",
-      "diejob1 = jobs.new(diefunc, 1)\n",
-      "diejob2 = jobs.new(diefunc, 2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Starting job # 4 in a separate thread.\n",
-        "Starting job # 5 in a separate thread.\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
+    }
+   ],
+   "source": [
+    "jobs = bg.BackgroundJobManager()\n",
+    "\n",
+    "# Start a few jobs, the first one will have ID # 0\n",
+    "jobs.new(sleepfunc, 4)\n",
+    "jobs.new(sleepfunc, kw={'reps':2})\n",
+    "jobs.new('printfunc(1,3)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can check the status of your jobs at any time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can get the traceback of any dead job.  Run the line\n",
-      "below again interactively until it prints a traceback (check the status\n",
-      "of the job):\n"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Completed jobs:\n",
+      "0 : <function sleepfunc at 0x314f848>\n",
+      "2 : <function sleepfunc at 0x314f848>\n",
+      "3 : printfunc(1,3)\n",
+      "\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "jobs.status()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For any completed job, you can get its result easily:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print \"Status of diejob1:\", diejob1.status\n",
-      "diejob1.traceback()  # jobs.traceback(4) would also work here, with the job number"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "{'args': (), 'interval': 4, 'kwargs': {}}"
+      ]
+     },
+     "execution_count": 12,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Status of diejob1: Dead (Exception), call jobs.traceback() for details\n",
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
-        "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)\n",
-        "\u001b[1;32m/home/fperez/usr/opt/virtualenv/ipython-0.13.2/lib/python2.7/site-packages/IPython/lib/backgroundjobs.pyc\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self)\u001b[0m\n",
-        "\u001b[0;32m    482\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m    483\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m--> 484\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0m\n",
-        "\u001b[1;32m<ipython-input-1-fbbbd0d2a1c3>\u001b[0m in \u001b[0;36mdiefunc\u001b[1;34m(interval, *a, **kw)\u001b[0m\n",
-        "\u001b[0;32m     13\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdiefunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m     14\u001b[0m     \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m---> 15\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Dead job with interval %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m     17\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mprintfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\n",
-        "\u001b[1;31mException\u001b[0m: Dead job with interval 1\n"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "jobs[0].result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Errors and tracebacks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The jobs manager tries to help you with debugging:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This will print all tracebacks for all dead jobs:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting job # 4 in a separate thread.\n",
+      "Starting job # 5 in a separate thread.\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "jobs.traceback()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Traceback for: <BackgroundJob #4: <function diefunc at 0x314f668>>\n",
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
-        "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)\n",
-        "\u001b[1;32m/home/fperez/usr/opt/virtualenv/ipython-0.13.2/lib/python2.7/site-packages/IPython/lib/backgroundjobs.pyc\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self)\u001b[0m\n",
-        "\u001b[0;32m    482\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m    483\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m--> 484\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0m\n",
-        "\u001b[1;32m<ipython-input-1-fbbbd0d2a1c3>\u001b[0m in \u001b[0;36mdiefunc\u001b[1;34m(interval, *a, **kw)\u001b[0m\n",
-        "\u001b[0;32m     13\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdiefunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m     14\u001b[0m     \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m---> 15\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Dead job with interval %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m     17\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mprintfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\n",
-        "\u001b[1;31mException\u001b[0m: Dead job with interval 1\n",
-        "\n",
-        "Traceback for: <BackgroundJob #5: <function diefunc at 0x314f668>>\n",
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
-        "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)\n",
-        "\u001b[1;32m/home/fperez/usr/opt/virtualenv/ipython-0.13.2/lib/python2.7/site-packages/IPython/lib/backgroundjobs.pyc\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self)\u001b[0m\n",
-        "\u001b[0;32m    482\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m    483\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m--> 484\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0m\n",
-        "\u001b[1;32m<ipython-input-1-fbbbd0d2a1c3>\u001b[0m in \u001b[0;36mdiefunc\u001b[1;34m(interval, *a, **kw)\u001b[0m\n",
-        "\u001b[0;32m     13\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdiefunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m     14\u001b[0m     \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m---> 15\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Dead job with interval %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m     17\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mprintfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\n",
-        "\u001b[1;31mException\u001b[0m: Dead job with interval 2\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
+    }
+   ],
+   "source": [
+    "# This makes a couple of jobs which will die.  Let's keep a reference to\n",
+    "# them for easier traceback reporting later\n",
+    "diejob1 = jobs.new(diefunc, 1)\n",
+    "diejob2 = jobs.new(diefunc, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can get the traceback of any dead job.  Run the line\n",
+    "below again interactively until it prints a traceback (check the status\n",
+    "of the job):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The job manager can be flushed of all completed jobs at any time:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status of diejob1: Dead (Exception), call jobs.traceback() for details\n",
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
+      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)\n",
+      "\u001b[1;32m/home/fperez/usr/opt/virtualenv/ipython-0.13.2/lib/python2.7/site-packages/IPython/lib/backgroundjobs.pyc\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self)\u001b[0m\n",
+      "\u001b[0;32m    482\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m    483\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m--> 484\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-1-fbbbd0d2a1c3>\u001b[0m in \u001b[0;36mdiefunc\u001b[1;34m(interval, *a, **kw)\u001b[0m\n",
+      "\u001b[0;32m     13\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdiefunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m     14\u001b[0m     \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m---> 15\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Dead job with interval %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m     17\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mprintfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\n",
+      "\u001b[1;31mException\u001b[0m: Dead job with interval 1\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "jobs.flush()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Flushing 3 Completed jobs.\n",
-        "Flushing 2 Dead jobs.\n"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
+    }
+   ],
+   "source": [
+    "print \"Status of diejob1:\", diejob1.status\n",
+    "diejob1.traceback()  # jobs.traceback(4) would also work here, with the job number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This will print all tracebacks for all dead jobs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "After that, the status is simply empty:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Traceback for: <BackgroundJob #4: <function diefunc at 0x314f668>>\n",
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
+      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)\n",
+      "\u001b[1;32m/home/fperez/usr/opt/virtualenv/ipython-0.13.2/lib/python2.7/site-packages/IPython/lib/backgroundjobs.pyc\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self)\u001b[0m\n",
+      "\u001b[0;32m    482\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m    483\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m--> 484\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-1-fbbbd0d2a1c3>\u001b[0m in \u001b[0;36mdiefunc\u001b[1;34m(interval, *a, **kw)\u001b[0m\n",
+      "\u001b[0;32m     13\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdiefunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m     14\u001b[0m     \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m---> 15\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Dead job with interval %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m     17\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mprintfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\n",
+      "\u001b[1;31mException\u001b[0m: Dead job with interval 1\n",
+      "\n",
+      "Traceback for: <BackgroundJob #5: <function diefunc at 0x314f668>>\n",
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
+      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)\n",
+      "\u001b[1;32m/home/fperez/usr/opt/virtualenv/ipython-0.13.2/lib/python2.7/site-packages/IPython/lib/backgroundjobs.pyc\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self)\u001b[0m\n",
+      "\u001b[0;32m    482\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m    483\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mcall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m--> 484\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-1-fbbbd0d2a1c3>\u001b[0m in \u001b[0;36mdiefunc\u001b[1;34m(interval, *a, **kw)\u001b[0m\n",
+      "\u001b[0;32m     13\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdiefunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m     14\u001b[0m     \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m---> 15\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Dead job with interval %s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0minterval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m     17\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mprintfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\n",
+      "\u001b[1;31mException\u001b[0m: Dead job with interval 2\n",
+      "\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "jobs.status()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 17
-    },
+    }
+   ],
+   "source": [
+    "jobs.traceback()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The job manager can be flushed of all completed jobs at any time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Jobs have a `.join` method that lets you wait on their thread for completion:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Flushing 3 Completed jobs.\n",
+      "Flushing 2 Dead jobs.\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "j = jobs.new(sleepfunc, 2)\n",
-      "j.join?"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Starting job # 0 in a separate thread.\n"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
+    }
+   ],
+   "source": [
+    "jobs.flush()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After that, the status is simply empty:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "jobs.status()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jobs have a `.join` method that lets you wait on their thread for completion:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Exercise\n",
-      "\n",
-      "1. Start a new job that calls `sleepfunc` with a 5-second wait\n",
-      "2. Print a short message that indicates you are waiting (note: you'll need to flush stdout to see that print output appear).\n",
-      "3. Wait on the job and then print its result."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting job # 0 in a separate thread.\n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "j = jobs.new(sleepfunc, 2)\n",
+    "j.join?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise\n",
+    "\n",
+    "1. Start a new job that calls `sleepfunc` with a 5-second wait\n",
+    "2. Print a short message that indicates you are waiting (note: you'll need to flush stdout to see that print output appear).\n",
+    "3. Wait on the job and then print its result."
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/Beyond Plain Python.ipynb b/examples/IPython Kernel/Beyond Plain Python.ipynb
index de7126c..da3e5e0 100644
--- a/examples/IPython Kernel/Beyond Plain Python.ipynb
+++ b/examples/IPython Kernel/Beyond Plain Python.ipynb
@@ -1,1613 +1,1807 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:31071a05d0ecd75ed72fe3f0de0ad447a6f85cffe382c26efa5e68db1fee54ee"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "IPython: beyond plain Python"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When executing code in IPython, all valid Python syntax works as-is, but IPython provides a number of features designed to make the interactive experience more fluid and efficient."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "First things first: running code, getting help"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In the notebook, to run a cell of code, hit `Shift-Enter`. This executes the cell and puts the cursor in the next cell below, or makes a new one if you are at the end.  Alternately, you can use:\n",
-      "    \n",
-      "- `Alt-Enter` to force the creation of a new cell unconditionally (useful when inserting new content in the middle of an existing notebook).\n",
-      "- `Control-Enter` executes the cell and keeps the cursor in the same cell, useful for quick experimentation of snippets that you don't need to keep permanently."
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# IPython: beyond plain Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When executing code in IPython, all valid Python syntax works as-is, but IPython provides a number of features designed to make the interactive experience more fluid and efficient."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## First things first: running code, getting help"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the notebook, to run a cell of code, hit `Shift-Enter`. This executes the cell and puts the cursor in the next cell below, or makes a new one if you are at the end.  Alternately, you can use:\n",
+    "    \n",
+    "- `Alt-Enter` to force the creation of a new cell unconditionally (useful when inserting new content in the middle of an existing notebook).\n",
+    "- `Control-Enter` executes the cell and keeps the cursor in the same cell, useful for quick experimentation of snippets that you don't need to keep permanently."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hi\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print \"Hi\""
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Hi\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
+    }
+   ],
+   "source": [
+    "print \"Hi\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Getting help:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Typing `object_name?` will print all sorts of details about any object, including docstrings, function definition lines (for call arguments) and constructor details for classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import collections\n",
+    "collections.namedtuple?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "collections.Counter??"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "*int*?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "An IPython quick reference card:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%quickref"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Tab completion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tab completion, especially for attributes, is a convenient way to explore the structure of any object you\u2019re dealing with. Simply type `object_name.<TAB>` to view the object\u2019s attributes. Besides Python objects and keywords, tab completion also works on file and directory names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "collections."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The interactive workflow: input, output, history"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
      },
-     "source": [
-      "Getting help:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "?"
-     ],
-     "language": "python",
+     "execution_count": 7,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2+10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "22"
+      ]
      },
-     "source": [
-      "Typing `object_name?` will print all sorts of details about any object, including docstrings, function definition lines (for call arguments) and constructor details for classes."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import collections\n",
-      "collections.namedtuple?"
-     ],
-     "language": "python",
+     "execution_count": 8,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "collections.Counter??"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "*int*?"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "_+10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "You can suppress the storage and rendering of output if you append `;` to the last cell (this comes in handy when plotting with matplotlib, for example):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "10+20;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "22"
+      ]
      },
-     "source": [
-      "An IPython quick reference card:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%quickref"
-     ],
-     "language": "python",
+     "execution_count": 10,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "The output is stored in `_N` and `Out[N]` variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
      },
-     "source": [
-      "Tab completion"
-     ]
-    },
-    {
-     "cell_type": "markdown",
+     "execution_count": 11,
      "metadata": {},
-     "source": [
-      "Tab completion, especially for attributes, is a convenient way to explore the structure of any object you\u2019re dealing with. Simply type `object_name.<TAB>` to view the object\u2019s attributes. Besides Python objects and keywords, tab completion also works on file and directory names."
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "_10 == Out[10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "And the last three have shorthands for convenience:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "last output: True\n",
+      "next one   : 22\n",
+      "and next   : 22\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "collections."
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
+    }
+   ],
+   "source": [
+    "print 'last output:', _\n",
+    "print 'next one   :', __\n",
+    "print 'and next   :', ___"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
+     "data": {
+      "text/plain": [
+       "u'_10 == Out[10]'"
+      ]
      },
-     "source": [
-      "The interactive workflow: input, output, history"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "2+10"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 7,
-       "text": [
-        "12"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "_+10"
-     ],
-     "language": "python",
+     "execution_count": 13,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 8,
-       "text": [
-        "22"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "In[11]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "u'In[11]'"
+      ]
      },
-     "source": [
-      "You can suppress the storage and rendering of output if you append `;` to the last cell (this comes in handy when plotting with matplotlib, for example):"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "10+20;"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "_"
-     ],
-     "language": "python",
+     "execution_count": 14,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "22"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "_i"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "u'In[11]'"
+      ]
      },
-     "source": [
-      "The output is stored in `_N` and `Out[N]` variables:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "_10 == Out[10]"
-     ],
-     "language": "python",
+     "execution_count": 15,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 11,
-       "text": [
-        "True"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "And the last three have shorthands for convenience:"
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "_ii"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "last input: _ii\n",
+      "next one  : _i\n",
+      "and next  : In[11]\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'last output:', _\n",
-      "print 'next one   :', __\n",
-      "print 'and next   :', ___"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "last output: True\n",
-        "next one   : 22\n",
-        "and next   : 22\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "In[11]"
-     ],
-     "language": "python",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "-"
-      }
-     },
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 13,
-       "text": [
-        "u'_10 == Out[10]'"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "_i"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 14,
-       "text": [
-        "u'In[11]'"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "_ii"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 15,
-       "text": [
-        "u'In[11]'"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'last input:', _i\n",
-      "print 'next one  :', _ii\n",
-      "print 'and next  :', _iii"
-     ],
-     "language": "python",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "subslide"
-      }
-     },
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "last input: _ii\n",
-        "next one  : _i\n",
-        "and next  : In[11]\n"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%history -n 1-5"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "   1: print \"Hi\"\n",
-        "   2: ?\n",
-        "   3:\n",
-        "import collections\n",
-        "collections.namedtuple?\n",
-        "   4: collections.Counter??\n",
-        "   5: *int*?\n"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "subslide"
-      }
-     },
-     "source": [
-      "**Exercise**\n",
-      "\n",
-      "Write the last 10 lines of history to a file named `log.py`."
+    }
+   ],
+   "source": [
+    "print 'last input:', _i\n",
+    "print 'next one  :', _ii\n",
+    "print 'and next  :', _iii"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   1: print \"Hi\"\n",
+      "   2: ?\n",
+      "   3:\n",
+      "import collections\n",
+      "collections.namedtuple?\n",
+      "   4: collections.Counter??\n",
+      "   5: *int*?\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Accessing the underlying operating system"
+    }
+   ],
+   "source": [
+    "%history -n 1-5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "**Exercise**\n",
+    "\n",
+    "Write the last 10 lines of history to a file named `log.py`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Accessing the underlying operating system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/fperez/ipython/tutorial/notebooks\r\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!pwd"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "/home/fperez/ipython/tutorial/notebooks\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "files = !ls\n",
-      "print \"My current directory's files:\"\n",
-      "print files"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "My current directory's files:\n",
-        "['BackgroundJobs.ipynb', 'Custom Display Logic.ipynb', 'Customizing IPython - Condensed.ipynb', 'Customizing IPython - Config.ipynb', 'Customizing IPython - Extensions.ipynb', 'Customizing IPython - Magics.ipynb', 'data', 'figs', 'flare.json', 'Index.ipynb', 'Interactive Widgets.ipynb', 'IPython - beyond plain Python.ipynb', 'kernel-embedding', 'Markdown Cells.ipynb', 'myscript.py', 'nbconvert_arch.png', 'NbConvert from command line.ipynb', 'NbConvert Python library.ipynb', 'Notebook and javascript extension.ipynb', 'Notebook Basics.ipynb', 'Overview of IPython.parallel.ipynb', 'parallel', 'Rich Display System.ipynb', 'Running a Secure Public Notebook.ipynb', 'Running Code.ipynb', 'Sample.ipynb', 'soln', 'Terminal usage.ipynb', 'text_analysis.py', 'Typesetting Math Using MathJax.ipynb']\n"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!echo $files"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[BackgroundJobs.ipynb, Custom Display Logic.ipynb, Customizing IPython - Condensed.ipynb, Customizing IPython - Config.ipynb, Customizing IPython - Extensions.ipynb, Customizing IPython - Magics.ipynb, data, figs, flare.json, Index.ipynb, Interactive Widgets.ipynb, IPython - beyond plain Python.ipynb, kernel-embedding, Markdown Cells.ipynb, myscript.py, nbconvert_arch.png, NbConvert from command line.ipynb, NbConvert Python library.ipynb, Notebook and javascript extension.ipynb, Notebook Basics.ipynb, Overview of IPython.parallel.ipynb, parallel, Rich Display System.ipynb, Running a Secure Public Notebook.ipynb, Running Code.ipynb, Sample.ipynb, soln, Terminal usage.ipynb, text_analysis.py, Typesetting Math Using MathJax.ipynb]\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!echo {files[0].upper()}"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "BACKGROUNDJOBS.IPYNB\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Note that all this is available even in multiline blocks:"
+    }
+   ],
+   "source": [
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My current directory's files:\n",
+      "['BackgroundJobs.ipynb', 'Custom Display Logic.ipynb', 'Customizing IPython - Condensed.ipynb', 'Customizing IPython - Config.ipynb', 'Customizing IPython - Extensions.ipynb', 'Customizing IPython - Magics.ipynb', 'data', 'figs', 'flare.json', 'Index.ipynb', 'Interactive Widgets.ipynb', 'IPython - beyond plain Python.ipynb', 'kernel-embedding', 'Markdown Cells.ipynb', 'myscript.py', 'nbconvert_arch.png', 'NbConvert from command line.ipynb', 'NbConvert Python library.ipynb', 'Notebook and javascript extension.ipynb', 'Notebook Basics.ipynb', 'Overview of IPython.parallel.ipynb', 'parallel', 'Rich Display System.ipynb', 'Running a Secure Public Notebook.ipynb', 'Running Code.ipynb', 'Sample.ipynb', 'soln', 'Terminal usage.ipynb', 'text_analysis.py', 'Typesetting Math Using MathJax.ipynb']\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import os\n",
-      "for i,f in enumerate(files):\n",
-      "    if f.endswith('ipynb'):\n",
-      "        !echo {\"%02d\" % i} - \"{os.path.splitext(f)[0]}\"\n",
-      "    else:\n",
-      "        print '--'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "00 - BackgroundJobs\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "01 - Custom Display Logic\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "02 - Customizing IPython - Condensed\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "03 - Customizing IPython - Config\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "04 - Customizing IPython - Extensions\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "05 - Customizing IPython - Magics\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "--\n",
-        "--\n",
-        "--\n",
-        "09 - Index\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "10 - Interactive Widgets\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "11 - IPython - beyond plain Python\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "--\n",
-        "13 - Markdown Cells\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "--\n",
-        "--\n",
-        "16 - NbConvert from command line\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "17 - NbConvert Python library\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "18 - Notebook and javascript extension\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "19 - Notebook Basics\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "20 - Overview of IPython.parallel\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "--\n",
-        "22 - Rich Display System\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "23 - Running a Secure Public Notebook\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "24 - Running Code\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "25 - Sample\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "--\n",
-        "27 - Terminal usage\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "--\n",
-        "29 - Typesetting Math Using MathJax\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 27
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Beyond Python: magic functions"
+    }
+   ],
+   "source": [
+    "files = !ls\n",
+    "print \"My current directory's files:\"\n",
+    "print files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[BackgroundJobs.ipynb, Custom Display Logic.ipynb, Customizing IPython - Condensed.ipynb, Customizing IPython - Config.ipynb, Customizing IPython - Extensions.ipynb, Customizing IPython - Magics.ipynb, data, figs, flare.json, Index.ipynb, Interactive Widgets.ipynb, IPython - beyond plain Python.ipynb, kernel-embedding, Markdown Cells.ipynb, myscript.py, nbconvert_arch.png, NbConvert from command line.ipynb, NbConvert Python library.ipynb, Notebook and javascript extension.ipynb, Notebook Basics.ipynb, Overview of IPython.parallel.ipynb, parallel, Rich Display System.ipynb, Running a Secure Public Notebook.ipynb, Running Code.ipynb, Sample.ipynb, soln, Terminal usage.ipynb, text_analysis.py, Typesetting Math Using MathJax.ipynb]\r\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The IPyhton 'magic' functions are a set of commands, invoked by prepending one or two `%` signs to their name, that live in a namespace separate from your normal Python variables and provide a more command-like interface.  They take flags with `--` and arguments without quotes, parentheses or commas. The motivation behind this system is two-fold:\n",
-      "    \n",
-      "- To provide an orthogonal namespace for controlling IPython itself and exposing other system-oriented functionality.\n",
-      "\n",
-      "- To expose a calling mode that requires minimal verbosity and typing while working interactively.  Thus the inspiration taken from the classic Unix shell style for commands."
+    }
+   ],
+   "source": [
+    "!echo $files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BACKGROUNDJOBS.IPYNB\r\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%magic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 28
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Line vs cell magics:"
+    }
+   ],
+   "source": [
+    "!echo {files[0].upper()}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that all this is available even in multiline blocks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "00 - BackgroundJobs\r\n",
+      "01 - Custom Display Logic\r\n",
+      "02 - Customizing IPython - Condensed\r\n",
+      "03 - Customizing IPython - Config\r\n",
+      "04 - Customizing IPython - Extensions\r\n",
+      "05 - Customizing IPython - Magics\r\n",
+      "--\n",
+      "--\n",
+      "--\n",
+      "09 - Index\r\n",
+      "10 - Interactive Widgets\r\n",
+      "11 - IPython - beyond plain Python\r\n",
+      "--\n",
+      "13 - Markdown Cells\r\n",
+      "--\n",
+      "--\n",
+      "16 - NbConvert from command line\r\n",
+      "17 - NbConvert Python library\r\n",
+      "18 - Notebook and javascript extension\r\n",
+      "19 - Notebook Basics\r\n",
+      "20 - Overview of IPython.parallel\r\n",
+      "--\n",
+      "22 - Rich Display System\r\n",
+      "23 - Running a Secure Public Notebook\r\n",
+      "24 - Running Code\r\n",
+      "25 - Sample\r\n",
+      "--\n",
+      "27 - Terminal usage\r\n",
+      "--\n",
+      "29 - Typesetting Math Using MathJax\r\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%timeit range(10)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "10000000 loops, best of 3: 190 ns per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 29
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%timeit\n",
-      "range(10)\n",
-      "range(100)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1000000 loops, best of 3: 888 ns per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 30
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Line magics can be used even inside code blocks:"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "for i,f in enumerate(files):\n",
+    "    if f.endswith('ipynb'):\n",
+    "        !echo {\"%02d\" % i} - \"{os.path.splitext(f)[0]}\"\n",
+    "    else:\n",
+    "        print '--'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Beyond Python: magic functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The IPyhton 'magic' functions are a set of commands, invoked by prepending one or two `%` signs to their name, that live in a namespace separate from your normal Python variables and provide a more command-like interface.  They take flags with `--` and arguments without quotes, parentheses or commas. The motivation behind this system is two-fold:\n",
+    "    \n",
+    "- To provide an orthogonal namespace for controlling IPython itself and exposing other system-oriented functionality.\n",
+    "\n",
+    "- To expose a calling mode that requires minimal verbosity and typing while working interactively.  Thus the inspiration taken from the classic Unix shell style for commands."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%magic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Line vs cell magics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10000000 loops, best of 3: 190 ns per loop\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for i in range(5):\n",
-      "    size = i*100\n",
-      "    print 'size:',size, \n",
-      "    %timeit range(size)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "size: 010000000 loops, best of 3: 129 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        " size: 1001000000 loops, best of 3: 649 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        " size: 2001000000 loops, best of 3: 1.09 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        " size: 3001000000 loops, best of 3: 1.74 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        " size: 400100000 loops, best of 3: 2.72 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 31
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Magics can do anything they want with their input, so it doesn't have to be valid Python:"
+    }
+   ],
+   "source": [
+    "%timeit range(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1000000 loops, best of 3: 888 ns per loop\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%bash\n",
-      "echo \"My shell is:\" $SHELL\n",
-      "echo \"My memory status is:\"\n",
-      "free"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "My shell is: /bin/bash\n",
-        "My memory status is:\n",
-        "             total       used       free     shared    buffers     cached\n",
-        "Mem:       7870888    6389328    1481560          0     662860    2505172\n",
-        "-/+ buffers/cache:    3221296    4649592\n",
-        "Swap:      3905532       4852    3900680\n"
-       ]
-      }
-     ],
-     "prompt_number": 32
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Another interesting cell magic: create any file you want locally from the notebook:"
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "range(10)\n",
+    "range(100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Line magics can be used even inside code blocks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "size: 010000000 loops, best of 3: 129 ns per loop\n",
+      " size: 1001000000 loops, best of 3: 649 ns per loop\n",
+      " size: 2001000000 loops, best of 3: 1.09 \u00b5s per loop\n",
+      " size: 3001000000 loops, best of 3: 1.74 \u00b5s per loop\n",
+      " size: 400100000 loops, best of 3: 2.72 \u00b5s per loop\n",
+      "\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%writefile test.txt\n",
-      "This is a test file!\n",
-      "It can contain anything I want...\n",
-      "\n",
+    }
+   ],
+   "source": [
+    "for i in range(5):\n",
+    "    size = i*100\n",
+    "    print 'size:',size, \n",
+    "    %timeit range(size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Magics can do anything they want with their input, so it doesn't have to be valid Python:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My shell is: /bin/bash\n",
+      "My memory status is:\n",
+      "             total       used       free     shared    buffers     cached\n",
+      "Mem:       7870888    6389328    1481560          0     662860    2505172\n",
+      "-/+ buffers/cache:    3221296    4649592\n",
+      "Swap:      3905532       4852    3900680\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "echo \"My shell is:\" $SHELL\n",
+    "echo \"My memory status is:\"\n",
+    "free"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another interesting cell magic: create any file you want locally from the notebook:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing test.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile test.txt\n",
+    "This is a test file!\n",
+    "It can contain anything I want...\n",
+    "\n",
+    "And more..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is a test file!\r\n",
+      "It can contain anything I want...\r\n",
+      "\r\n",
       "And more..."
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Writing test.txt\n"
-       ]
-      }
-     ],
-     "prompt_number": 33
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!cat test.txt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "This is a test file!\r\n",
-        "It can contain anything I want...\r\n",
-        "\r\n",
-        "And more..."
-       ]
-      }
-     ],
-     "prompt_number": 34
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's see what other magics are currently defined in the system:"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%lsmagic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "json": [
-        "{\"cell\": {\"prun\": \"ExecutionMagics\", \"file\": \"Other\", \"!\": \"OSMagics\", \"capture\": \"ExecutionMagics\", \"timeit\": \"ExecutionMagics\", \"script\": \"ScriptMagics\", \"pypy\": \"Other\", \"system\": \"OSMagics\", \"perl\": \"Other\", \"HTML\": \"Other\", \"bash\": \"Other\", \"python\": \"Other\", \"SVG\": \"Other\", \"javascript\": \"DisplayMagics\", \"writefile\": \"OSMagics\", \"ruby\": \"Other\", \"python3\": \"Other\", \"python2\": \"Other\", \"latex\": \"DisplayMagics\", \"sx\": \"OSMagics\", \"svg\": \"DisplayMagics\", \"html\": \"DisplayMagics\", \"sh\": \"Other\", \"time\": \"ExecutionMagics\", \"debug\": \"ExecutionMagics\"}, \"line\": {\"psource\": \"NamespaceMagics\", \"logstart\": \"LoggingMagics\", \"popd\": \"OSMagics\", \"loadpy\": \"CodeMagics\", \"install_ext\": \"ExtensionMagics\", \"colors\": \"BasicMagics\", \"who_ls\": \"NamespaceMagics\", \"lf\": \"Other\", \"install_profiles\": \"DeprecatedMagics\", \"clk\": \"Other\", \"ll\": \"Other\", \"pprint\": \"BasicMagics\", \"lk\": \"Other\", \"ls\": \"Other\", \"save\": \"CodeMagics\", \"tb\": \"ExecutionMagics\", \"lx\": \"Other\", \"dl\": \"Other\", \"pylab\": \"PylabMagics\", \"dd\": \"Other\", \"quickref\": \"BasicMagics\", \"dx\": \"Other\", \"d\": \"Other\", \"magic\": \"BasicMagics\", \"dhist\": \"OSMagics\", \"edit\": \"KernelMagics\", \"logstop\": \"LoggingMagics\", \"gui\": \"BasicMagics\", \"alias_magic\": \"BasicMagics\", \"debug\": \"ExecutionMagics\", \"page\": \"BasicMagics\", \"logstate\": \"LoggingMagics\", \"ed\": \"Other\", \"pushd\": \"OSMagics\", \"timeit\": \"ExecutionMagics\", \"rehashx\": \"OSMagics\", \"hist\": \"Other\", \"qtconsole\": \"KernelMagics\", \"rm\": \"Other\", \"dirs\": \"OSMagics\", \"run\": \"ExecutionMagics\", \"reset_selective\": \"NamespaceMagics\", \"rep\": \"Other\", \"pinfo2\": \"NamespaceMagics\", \"matplotlib\": \"PylabMagics\", \"automagic\": \"AutoMagics\", \"doctest_mode\": \"KernelMagics\", \"logoff\": \"LoggingMagics\", \"reload_ext\": \"ExtensionMagics\", \"pdb\": \"ExecutionMagics\", \"load\": \"CodeMagics\", \"lsmagic\": \"BasicMagics\", \"cl\": \"Other\", \"autosave\": \"KernelMagics\", \"cd\": \"OSMagics\", \"pastebin\": \"CodeMagics\", \"prun\": \"ExecutionMagics\", \"cp\": \"Other\", \"autocall\": \"AutoMagics\", \"bookmark\": \"OSMagics\", \"connect_info\": \"KernelMagics\", \"mkdir\": \"Other\", \"system\": \"OSMagics\", \"whos\": \"NamespaceMagics\", \"rmdir\": \"Other\", \"unload_ext\": \"ExtensionMagics\", \"store\": \"StoreMagics\", \"more\": \"KernelMagics\", \"pdef\": \"NamespaceMagics\", \"precision\": \"BasicMagics\", \"pinfo\": \"NamespaceMagics\", \"pwd\": \"OSMagics\", \"psearch\": \"NamespaceMagics\", \"reset\": \"NamespaceMagics\", \"recall\": \"HistoryMagics\", \"xdel\": \"NamespaceMagics\", \"xmode\": \"BasicMagics\", \"cat\": \"Other\", \"mv\": \"Other\", \"rerun\": \"HistoryMagics\", \"logon\": \"LoggingMagics\", \"history\": \"HistoryMagics\", \"pycat\": \"OSMagics\", \"unalias\": \"OSMagics\", \"install_default_config\": \"DeprecatedMagics\", \"env\": \"OSMagics\", \"load_ext\": \"ExtensionMagics\", \"config\": \"ConfigMagics\", \"killbgscripts\": \"ScriptMagics\", \"profile\": \"BasicMagics\", \"pfile\": \"NamespaceMagics\", \"less\": \"KernelMagics\", \"who\": \"NamespaceMagics\", \"notebook\": \"BasicMagics\", \"man\": \"KernelMagics\", \"sx\": \"OSMagics\", \"macro\": \"ExecutionMagics\", \"clear\": \"KernelMagics\", \"alias\": \"OSMagics\", \"time\": \"ExecutionMagics\", \"sc\": \"OSMagics\", \"ldir\": \"Other\", \"pdoc\": \"NamespaceMagics\"}}"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 35,
-       "text": [
-        "Available line magics:\n",
-        "%alias  %alias_magic  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %cl  %clear  %clk  %colors  %config  %connect_info  %cp  %d  %dd  %debug  %dhist  %dirs  %dl  %doctest_mode  %dx  %ed  %edit  %env  %gui  %hist  %history  %install_default_config  %install_ext  %install_profiles  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %popd  %pprint  %precision  %profile  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
-        "\n",
-        "Available cell magics:\n",
-        "%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%latex  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
-        "\n",
-        "Automagic is ON, % prefix IS NOT needed for line magics."
-       ]
-      }
-     ],
-     "prompt_number": 35
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
+    }
+   ],
+   "source": [
+    "!cat test.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see what other magics are currently defined in the system:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/json": {
+       "cell": {
+        "!": "OSMagics",
+        "HTML": "Other",
+        "SVG": "Other",
+        "bash": "Other",
+        "capture": "ExecutionMagics",
+        "debug": "ExecutionMagics",
+        "file": "Other",
+        "html": "DisplayMagics",
+        "javascript": "DisplayMagics",
+        "latex": "DisplayMagics",
+        "perl": "Other",
+        "prun": "ExecutionMagics",
+        "pypy": "Other",
+        "python": "Other",
+        "python2": "Other",
+        "python3": "Other",
+        "ruby": "Other",
+        "script": "ScriptMagics",
+        "sh": "Other",
+        "svg": "DisplayMagics",
+        "sx": "OSMagics",
+        "system": "OSMagics",
+        "time": "ExecutionMagics",
+        "timeit": "ExecutionMagics",
+        "writefile": "OSMagics"
+       },
+       "line": {
+        "alias": "OSMagics",
+        "alias_magic": "BasicMagics",
+        "autocall": "AutoMagics",
+        "automagic": "AutoMagics",
+        "autosave": "KernelMagics",
+        "bookmark": "OSMagics",
+        "cat": "Other",
+        "cd": "OSMagics",
+        "cl": "Other",
+        "clear": "KernelMagics",
+        "clk": "Other",
+        "colors": "BasicMagics",
+        "config": "ConfigMagics",
+        "connect_info": "KernelMagics",
+        "cp": "Other",
+        "d": "Other",
+        "dd": "Other",
+        "debug": "ExecutionMagics",
+        "dhist": "OSMagics",
+        "dirs": "OSMagics",
+        "dl": "Other",
+        "doctest_mode": "KernelMagics",
+        "dx": "Other",
+        "ed": "Other",
+        "edit": "KernelMagics",
+        "env": "OSMagics",
+        "gui": "BasicMagics",
+        "hist": "Other",
+        "history": "HistoryMagics",
+        "install_default_config": "DeprecatedMagics",
+        "install_ext": "ExtensionMagics",
+        "install_profiles": "DeprecatedMagics",
+        "killbgscripts": "ScriptMagics",
+        "ldir": "Other",
+        "less": "KernelMagics",
+        "lf": "Other",
+        "lk": "Other",
+        "ll": "Other",
+        "load": "CodeMagics",
+        "load_ext": "ExtensionMagics",
+        "loadpy": "CodeMagics",
+        "logoff": "LoggingMagics",
+        "logon": "LoggingMagics",
+        "logstart": "LoggingMagics",
+        "logstate": "LoggingMagics",
+        "logstop": "LoggingMagics",
+        "ls": "Other",
+        "lsmagic": "BasicMagics",
+        "lx": "Other",
+        "macro": "ExecutionMagics",
+        "magic": "BasicMagics",
+        "man": "KernelMagics",
+        "matplotlib": "PylabMagics",
+        "mkdir": "Other",
+        "more": "KernelMagics",
+        "mv": "Other",
+        "notebook": "BasicMagics",
+        "page": "BasicMagics",
+        "pastebin": "CodeMagics",
+        "pdb": "ExecutionMagics",
+        "pdef": "NamespaceMagics",
+        "pdoc": "NamespaceMagics",
+        "pfile": "NamespaceMagics",
+        "pinfo": "NamespaceMagics",
+        "pinfo2": "NamespaceMagics",
+        "popd": "OSMagics",
+        "pprint": "BasicMagics",
+        "precision": "BasicMagics",
+        "profile": "BasicMagics",
+        "prun": "ExecutionMagics",
+        "psearch": "NamespaceMagics",
+        "psource": "NamespaceMagics",
+        "pushd": "OSMagics",
+        "pwd": "OSMagics",
+        "pycat": "OSMagics",
+        "pylab": "PylabMagics",
+        "qtconsole": "KernelMagics",
+        "quickref": "BasicMagics",
+        "recall": "HistoryMagics",
+        "rehashx": "OSMagics",
+        "reload_ext": "ExtensionMagics",
+        "rep": "Other",
+        "rerun": "HistoryMagics",
+        "reset": "NamespaceMagics",
+        "reset_selective": "NamespaceMagics",
+        "rm": "Other",
+        "rmdir": "Other",
+        "run": "ExecutionMagics",
+        "save": "CodeMagics",
+        "sc": "OSMagics",
+        "store": "StoreMagics",
+        "sx": "OSMagics",
+        "system": "OSMagics",
+        "tb": "ExecutionMagics",
+        "time": "ExecutionMagics",
+        "timeit": "ExecutionMagics",
+        "unalias": "OSMagics",
+        "unload_ext": "ExtensionMagics",
+        "who": "NamespaceMagics",
+        "who_ls": "NamespaceMagics",
+        "whos": "NamespaceMagics",
+        "xdel": "NamespaceMagics",
+        "xmode": "BasicMagics"
+       }
+      },
+      "text/plain": [
+       "Available line magics:\n",
+       "%alias  %alias_magic  %autocall  %automagic  %autosave  %bookmark  %cat  %cd  %cl  %clear  %clk  %colors  %config  %connect_info  %cp  %d  %dd  %debug  %dhist  %dirs  %dl  %doctest_mode  %dx  %ed  %edit  %env  %gui  %hist  %history  %install_default_config  %install_ext  %install_profiles  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %popd  %pprint  %precision  %profile  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
+       "\n",
+       "Available cell magics:\n",
+       "%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%latex  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
+       "\n",
+       "Automagic is ON, % prefix IS NOT needed for line magics."
+      ]
+     },
+     "execution_count": 35,
      "metadata": {},
-     "source": [
-      "Running normal Python code: execution and errors"
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%lsmagic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running normal Python code: execution and errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Not only can you input normal Python code, you can even paste straight from a Python or IPython shell session:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "5\n",
+      "8\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Not only can you input normal Python code, you can even paste straight from a Python or IPython shell session:"
+    }
+   ],
+   "source": [
+    ">>> # Fibonacci series:\n",
+    "... # the sum of two elements defines the next\n",
+    "... a, b = 0, 1\n",
+    ">>> while b < 10:\n",
+    "...     print b\n",
+    "...     a, b = b, a+b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 5 6 7 8 9\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      ">>> # Fibonacci series:\n",
-      "... # the sum of two elements defines the next\n",
-      "... a, b = 0, 1\n",
-      ">>> while b < 10:\n",
-      "...     print b\n",
-      "...     a, b = b, a+b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1\n",
-        "1\n",
-        "2\n",
-        "3\n",
-        "5\n",
-        "8\n"
-       ]
-      }
-     ],
-     "prompt_number": 36
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "In [1]: for i in range(10):\n",
-      "   ...:     print i,\n",
-      "   ...:     "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0 1 2 3 4 5 6 7 8 9\n"
-       ]
-      }
-     ],
-     "prompt_number": 37
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And when your code produces errors, you can control how they are displayed with the `%xmode` magic:"
+    }
+   ],
+   "source": [
+    "In [1]: for i in range(10):\n",
+    "   ...:     print i,\n",
+    "   ...:     "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And when your code produces errors, you can control how they are displayed with the `%xmode` magic:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing mod.py\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%writefile mod.py\n",
-      "\n",
-      "def f(x):\n",
-      "    return 1.0/(x-1)\n",
-      "\n",
-      "def g(y):\n",
-      "    return f(y+1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Writing mod.py\n"
-       ]
-      }
-     ],
-     "prompt_number": 38
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now let's call the function `g` with an argument that would produce an error:"
+    }
+   ],
+   "source": [
+    "%%writefile mod.py\n",
+    "\n",
+    "def f(x):\n",
+    "    return 1.0/(x-1)\n",
+    "\n",
+    "def g(y):\n",
+    "    return f(y+1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's call the function `g` with an argument that would produce an error:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "float division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-39-a54c5799f57e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import mod\n",
-      "mod.g(0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "ZeroDivisionError",
-       "evalue": "float division by zero",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-        "\u001b[1;32m<ipython-input-39-a54c5799f57e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
-       ]
-      }
-     ],
-     "prompt_number": 39
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%xmode plain\n",
-      "mod.g(0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Exception reporting mode: Plain\n"
-       ]
-      },
-      {
-       "ename": "ZeroDivisionError",
-       "evalue": "float division by zero",
-       "output_type": "pyerr",
-       "traceback": [
-        "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n",
-        "  File \u001b[0;32m\"<ipython-input-40-5a5bcec1553f>\"\u001b[0m, line \u001b[0;32m2\u001b[0m, in \u001b[0;35m<module>\u001b[0m\n    mod.g(0)\n",
-        "  File \u001b[0;32m\"mod.py\"\u001b[0m, line \u001b[0;32m6\u001b[0m, in \u001b[0;35mg\u001b[0m\n    return f(y+1)\n",
-        "\u001b[1;36m  File \u001b[1;32m\"mod.py\"\u001b[1;36m, line \u001b[1;32m3\u001b[1;36m, in \u001b[1;35mf\u001b[1;36m\u001b[0m\n\u001b[1;33m    return 1.0/(x-1)\u001b[0m\n",
-        "\u001b[1;31mZeroDivisionError\u001b[0m\u001b[1;31m:\u001b[0m float division by zero\n"
-       ]
-      }
-     ],
-     "prompt_number": 40
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%xmode verbose\n",
-      "mod.g(0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Exception reporting mode: Verbose\n"
-       ]
-      },
-      {
-       "ename": "ZeroDivisionError",
-       "evalue": "float division by zero",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-        "\u001b[1;32m<ipython-input-41-81967cfaa0c3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'xmode verbose'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[1;36mglobal\u001b[0m \u001b[0;36mmod.g\u001b[0m \u001b[1;34m= <function g at 0x237fc08>\u001b[0m\n",
-        "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y=0)\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[1;36mglobal\u001b[0m \u001b[0;36mf\u001b[0m \u001b[1;34m= <function f at 0x2367c08>\u001b[0m\u001b[1;34m\n        \u001b[0m\u001b[0;36my\u001b[0m \u001b[1;34m= 0\u001b[0m\n",
-        "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x=1)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mx\u001b[0m \u001b[1;34m= 1\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
-       ]
-      }
-     ],
-     "prompt_number": 41
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The default `%xmode` is \"context\", which shows additional context but not all local variables.  Let's restore that one for the rest of our session."
+    }
+   ],
+   "source": [
+    "import mod\n",
+    "mod.g(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exception reporting mode: Plain\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%xmode context"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Exception reporting mode: Context\n"
-       ]
-      }
-     ],
-     "prompt_number": 42
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Running code in other languages with special `%%` magics"
+     "ename": "ZeroDivisionError",
+     "evalue": "float division by zero",
+     "output_type": "error",
+     "traceback": [
+      "Traceback \u001b[1;36m(most recent call last)\u001b[0m:\n",
+      "  File \u001b[0;32m\"<ipython-input-40-5a5bcec1553f>\"\u001b[0m, line \u001b[0;32m2\u001b[0m, in \u001b[0;35m<module>\u001b[0m\n    mod.g(0)\n",
+      "  File \u001b[0;32m\"mod.py\"\u001b[0m, line \u001b[0;32m6\u001b[0m, in \u001b[0;35mg\u001b[0m\n    return f(y+1)\n",
+      "\u001b[1;36m  File \u001b[1;32m\"mod.py\"\u001b[1;36m, line \u001b[1;32m3\u001b[1;36m, in \u001b[1;35mf\u001b[1;36m\u001b[0m\n\u001b[1;33m    return 1.0/(x-1)\u001b[0m\n",
+      "\u001b[1;31mZeroDivisionError\u001b[0m\u001b[1;31m:\u001b[0m float division by zero\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%perl\n",
-      "@months = (\"July\", \"August\", \"September\");\n",
-      "print $months[0];"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "July"
-       ]
-      }
-     ],
-     "prompt_number": 43
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%ruby\n",
-      "name = \"world\"\n",
-      "puts \"Hello #{name.capitalize}!\""
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Hello World!\n"
-       ]
-      }
-     ],
-     "prompt_number": 44
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Exercise"
+    }
+   ],
+   "source": [
+    "%xmode plain\n",
+    "mod.g(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exception reporting mode: Verbose\n"
      ]
     },
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Write a cell that executes in Bash and prints your current working directory as well as the date.\n",
-      "\n",
-      "Apologies to Windows users who may not have Bash available, not sure how to obtain the equivalent result with `cmd.exe` or Powershell."
+     "ename": "ZeroDivisionError",
+     "evalue": "float division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-41-81967cfaa0c3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'xmode verbose'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[1;36mglobal\u001b[0m \u001b[0;36mmod.g\u001b[0m \u001b[1;34m= <function g at 0x237fc08>\u001b[0m\n",
+      "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y=0)\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[1;36mglobal\u001b[0m \u001b[0;36mf\u001b[0m \u001b[1;34m= <function f at 0x2367c08>\u001b[0m\u001b[1;34m\n        \u001b[0m\u001b[0;36my\u001b[0m \u001b[1;34m= 0\u001b[0m\n",
+      "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x=1)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m        \u001b[0;36mx\u001b[0m \u001b[1;34m= 1\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load soln/bash-script"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Raw Input in the notebook"
+    }
+   ],
+   "source": [
+    "%xmode verbose\n",
+    "mod.g(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The default `%xmode` is \"context\", which shows additional context but not all local variables.  Let's restore that one for the rest of our session."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exception reporting mode: Context\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Since 1.0 the IPython notebook web application support `raw_input` which for example allow us to invoke the `%debug` magic in the notebook:"
+    }
+   ],
+   "source": [
+    "%xmode context"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running code in other languages with special `%%` magics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "July"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "mod.g(0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "ZeroDivisionError",
-       "evalue": "float division by zero",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-        "\u001b[1;32m<ipython-input-45-5e708f13c839>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
-       ]
-      }
-     ],
-     "prompt_number": 45
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%debug"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "> \u001b[0;32m/Users/bussonniermatthias/ipython-in-depth/notebooks/mod.py\u001b[0m(3)\u001b[0;36mf\u001b[0;34m()\u001b[0m\n",
-        "\u001b[0;32m      2 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0m\u001b[0;32m----> 3 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0m\u001b[0;32m      4 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0m\n"
-       ]
-      },
-      {
-       "name": "stdout",
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "ipdb> x\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1\n"
-       ]
-      },
-      {
-       "name": "stdout",
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "ipdb> up\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "> \u001b[0;32m/Users/bussonniermatthias/ipython-in-depth/notebooks/mod.py\u001b[0m(6)\u001b[0;36mg\u001b[0;34m()\u001b[0m\n",
-        "\u001b[0;32m      4 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0m\u001b[0;32m      5 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0m\u001b[0;32m----> 6 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0m\n"
-       ]
-      },
-      {
-       "name": "stdout",
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "ipdb> y\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n"
-       ]
-      },
-      {
-       "name": "stdout",
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "ipdb> up\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "> \u001b[0;32m<ipython-input-37-5e708f13c839>\u001b[0m(1)\u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
-        "\u001b[0;32m----> 1 \u001b[0;31m\u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0m\n"
-       ]
-      },
-      {
-       "name": "stdout",
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "ipdb> exit\n"
-       ]
-      }
-     ],
-     "prompt_number": 38
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Don't foget to exit your debugging session. Raw input can of course be use to ask for user input:"
+    }
+   ],
+   "source": [
+    "%%perl\n",
+    "@months = (\"July\", \"August\", \"September\");\n",
+    "print $months[0];"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello World!\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "enjoy = raw_input('Are you enjoying this tutorial ?')\n",
-      "print 'enjoy is :', enjoy"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "name": "stdout",
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Are you enjoying this tutorial ?Yes !\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "enjoy is : Yes !\n"
-       ]
-      }
-     ],
-     "prompt_number": 39
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Plotting in the notebook"
+    }
+   ],
+   "source": [
+    "%%ruby\n",
+    "name = \"world\"\n",
+    "puts \"Hello #{name.capitalize}!\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a cell that executes in Bash and prints your current working directory as well as the date.\n",
+    "\n",
+    "Apologies to Windows users who may not have Bash available, not sure how to obtain the equivalent result with `cmd.exe` or Powershell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%load soln/bash-script"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Raw Input in the notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since 1.0 the IPython notebook web application support `raw_input` which for example allow us to invoke the `%debug` magic in the notebook:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "float division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-45-5e708f13c839>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mg\u001b[1;34m(y)\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m/home/fperez/ipython/tutorial/notebooks/mod.py\u001b[0m in \u001b[0;36mf\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mZeroDivisionError\u001b[0m: float division by zero"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This magic configures matplotlib to render its figures inline:"
+    }
+   ],
+   "source": [
+    "mod.g(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> \u001b[0;32m/Users/bussonniermatthias/ipython-in-depth/notebooks/mod.py\u001b[0m(3)\u001b[0;36mf\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32m      2 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m----> 3 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m      4 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "ipdb> x\n",
+      "1\n",
+      "ipdb> up\n",
+      "> \u001b[0;32m/Users/bussonniermatthias/ipython-in-depth/notebooks/mod.py\u001b[0m(6)\u001b[0;36mg\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32m      4 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m      5 \u001b[0;31m\u001b[0;32mdef\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[0;32m----> 6 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "ipdb> y\n",
+      "0\n",
+      "ipdb> up\n",
+      "> \u001b[0;32m<ipython-input-37-5e708f13c839>\u001b[0m(1)\u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32m----> 1 \u001b[0;31m\u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "ipdb> exit\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 46
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 47
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x = np.linspace(0, 2*np.pi, 300)\n",
-      "y = np.sin(x**2)\n",
-      "plt.plot(x, y)\n",
-      "plt.title(\"A little chirp\")\n",
-      "fig = plt.gcf()  # let's keep the figure object around for later..."
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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9sqUF+N3vgI0bnV6JWNywaUqU0LcfbMbrQJNw8BD63r35RzdWirEAP6G3Et3I\ndPRWhN4rm6WAGHD0q1YBo0YBY8Y4vRKxuMHRi8roAdZ5I2PTFDn60JiNbnhk9KKF3kuO3tNCr2nA\nc88BP/qR0ysRjxt2x4rK6AEgJYXVYkTDy9GLEHqnM3ozjt5NGb0X8LTQb9nCcttp05xeiXjc4OhF\nRTeA+xy9Cl03AHsMj64blfvoKaP3uND/+tfAD3/ojap5NAYOZBk1j0OXReEFoT9zRk1Hbyej59Ve\nKTu6Ed1e6ZXNUoCHhf6jj4D9+4E5c5xeiRzi4tjGqfJyp1cSHpEZPUU31oS+Z0/7Qq9p5oQXcEcx\n1iubpQAPC/2TTwI//ak3NjsYReX45vx5tgHFjOszA0U31oXebkbf3MymwXbpYvwxlNHLxZNC/49/\nsJEH8+c7vRK5qFyQ1WMbUTGam4Set6NvaWHjL6zMEOLh6M3GNgD10cvGk0K/eDHw+OPslPhYIjcX\nOHzY6VWERsTUyvbIiG4CAevOuT28hV5fk5UXUR5Cb2XcMLVXysVzQl9SwrL5Bx90eiXyGTJE3Yxe\nZD4PyHH0Z88yYTQTUYSCd3RjtRALOCv0qkc3tGFKUTQN+MlPgF/+MvbcPAAMHsymWKqIaEcvQ+h5\nxDaAOEdvBV7RjVmht5PR68Vfo26bHL3HhH7NGvb2+p57nF6JM8Sy0MuIblQWeiubpQA+7ZVmd8UC\n9hx9SwvrMjN6HChl9B4S+gsXgH/9V9Y7b/WYN7fTrx/7JeDduscDcvSX4R3dOO3oZWf0Zls5ydF7\nSOifeoodE3j99U6vxDl8vsuHkKiGaKHv2RNobbV+CLQReAl9r17sxVjT7D8X4LzQW4lu7Dh6s9m5\nnYyehF4hDh5kB4v85jdOr8R5Bg9WsyArWuh9PvHxDS+h79qVxQ68djHbLcba7aO3Et3YyejNCr2d\n6IaKsYoQCAAPPcQ2R2VmOr0a51E1pxc5/kBHdHzDY/yBDs8Jlk47eivRTfful/v/zWLF0VN043L+\n8Af2g/b97zu9EjVQVehFO3pAvNDzcvQA34KsnWKsLvR2YiQrQu/zWXfaJPTmcbXQl5WxzVHLl3vz\nLFgrqCj0fj+LVFJTxd7HLdENwLcga8fRd+3KRNfOYeVWdsYC1uMbK9FNU5P5FzMSegW4eBG4/37g\nZz8Dhg93ejXqoGIx9tQpJmyiX4xlOHq758Xq8HT0djJ6wH6LpRVHD1gvyJoV+oQE9mLW2ir2Pirj\nWqF/7DGBCpV3AAAezUlEQVRg0CDg0UedXolaqFiMlRHbALEd3dgRers5vR2htxKpWLmflfjGS47e\nlYHHn/4EbN0KlJbGxqx5M2RksEhApR9SWUIfy9GN1YwesC/0VtorAXmOHrgs9Gb+/VT6HbKL6xz9\nX/8K/L//B7z1lv3hUl4kLg7IygKOHXN6JZchR98Zrzl6lTN6/V5me+lJ6B1i61bggQeAtWuBESOc\nXo26qFaQJaHvjGpCb6eXXnZ0Y0XorXT40IYpB3j3XeDOO4GVK4HCQqdXozaqFWQpuukMz+jGbjE2\nlqIbM9CGqXZs3LgRI0aMwNChQ/H000+HvOaxxx7D0KFDMXbsWOzevdvU82sa65V/8EHgnXeAKVPs\nrtj7qFaQ9YKjDwSYoNrJwtujmqN3IrqRLfQU3VjE7/dj0aJF2LhxI/bt24eVK1di//79Ha5Zv349\nDh06hLKyMrzwwgt46KGHDD//8ePArFms+LptGzl5o6gW3cjYFQswoRfl6BsbmTDxahFVZcMU4Fx7\npeyM3oyj11sxjU7IVB1bQl9aWor8/Hzk5OQgISEB9957L9auXdvhmnXr1mHevHkAgMLCQjQ0NKC2\ntjbi8544AfzbvwGjRgFjxwI7dwL5+XZWGluoJvQyo5v6en7DwtrDc/wBwC+68fuZ87QitDpORjdW\nM3orIxfM3MtLbh6wKfRVVVXIzs6+9HlWVhaqqqqiXlNZWRny+Z58Epg8GcjLA06fBj75BPjFL2Lr\ngG8exKrQJyYyB2Z3dksoeG6WAvg5+nPnmFu1M5rbbdHN+fPiHb2XNksBNvvofQab2LUgixXucZs2\nLUZ2NtsENXVqMXJyiu0sL2bJymJxSWurGm89ZQk9cDm+4d16y7MQC/AbasbjDNuePVlMagW/n+1S\nt+J+k5KAmhrzj5OR0avs6EtKSlBSUmLqMbaEPjMzExUVFZc+r6ioQFZWVsRrKisrkRlmzOS2bYvt\nLIf4moQElolXVbEOHCdpamJCwKuIGQ09vhk8mO/z8hb63r35RDd283nAXnulLrpWNi6q3F6pstAX\nFxejuLj40udPPPFE1MfYim4mTJiAsrIylJeXo6WlBatXr8asWbM6XDNr1iysWLECALBz50706dMH\n6TIqczGOKp03J04wNy9rB7OozhsRQq+So7ca3VgdaAao3V6pstBbwZajj4+Px9KlSzFt2jT4/X4s\nWLAABQUFWLZsGQBg4cKFmDFjBtavX4/8/Hz06NEDL7/8MpeFE5FRJaeXGdsA4jpvRAh9YyMrHNt5\nEXRa6K123ADWxwfLEHovbZYCOMy6mT59OqZPn97h7xYuXNjh86VLl9q9DWGSWBV6PbrhDW+h79KF\nCcm5c/aE2u5mKcBee6UdoZft6M0YAC9tlgJctDOWMIcqu2OdcPRuEHqAT3zjtKO32loJ2Oujp/ZK\nc5DQexRVHH1tLUU34eDRecOrGGvH0dvJ6FWNbsjRE65AlWJsXZ2cXbE6boluAD6dN047eieiG1l9\n9OToCeUZNAiorLR2+DJPKLoJD4/ohkdG72R0Y9bRt7ayn2mz+0Os9NGToyeUp3t3Fg1Y3QjDC690\n3Zw5w3dnLMBnDAIvR2+1j95udGPW0esCbLZTyWxGT46ecA0qFGSp6yY8qhRju3ZlbZ5WDgi3215p\nVuitjiagjJ7wLCoUZL0U3fB29LyE3m4x1uez3mJpJ7rp3p29uJiJF+0IvZnohhw94RqcLshqGtsZ\n27+/vHv27cviEJ61CU3jP70SUCe6Aazn9HaiG5/PWqRCjt48JPQexmlH39DAflm6dZN3z/h4JjwN\nDfyes7GRCRLvAXGqFGMBe0JvZ0Sy2fjGSg89QBk9Cb2HcVroZcc2OrzjGxH5PMCvj95Jobcz6wYw\n30tPjt4aJPQexulirJNCz7PzRpTQ8+qj5zEZ1InoBjDv6K300Ov3oYye8CR6Ri/ixCUjyDpCMBje\nnTcihV4lR2+lxdJOMRaQ5+i7dmU9+G1txq4nR0+4ht692Q+4qAOzo0HRTWTsRjea5o3oxmxGb0WA\nfT5zrp4cPeEqnMzpvRLdnD7Nunl4Yze6aWpixWceRWI77ZUyoxs7R/yZyenJ0ROuwsmc3imhj5Xo\nhpebB5zL6M1GN1YzeoAcPeFhnOylp+gmMnajG16FWMBedOOG9krAXIslOXrCVVB0Yx9RQt+zJxM5\nv9/a41Vw9LIzelknWpGjJ1wFRTf2ESX0cXFMtKxOjuS1WQqwJvSaZs9hA9aiGxlCT46ecBXk6O3T\n0CCmGAvYi2+cdvRNTayrq0sX6/e10kdvR+gpoyc8iVMZfUsLEyJRAhkJ3hn96dNiHD1gr/OGd0Zv\nto/ebmwDyC3GGs3o29pYnNa1q7X7qAgJvcfp14+Jrt2NOWY5cQJITWXxhGzcEt0A9jpveDp6K+2V\ndjtuAPmO3ojQ6+fFmp15rzIk9B7H53Mmp3cqtgFYHNLUxHZC8kCk0Ls5uuHl6GV13RiNbryWzwMk\n9DGBEzn98eNARobce+r4fCwy4pXTi3b0VqMbp4uxdlsrATWLsV7L5wES+pjAiZzeSaEH+MU3gQAT\nNN6HjujEuqOXGd0YzejJ0ROuxAlHX1vrrNDz6rw5e5aJmahag92M3skNUzwyetk7Y8nRE57FiYze\naUfPq/NGZMcNYL/rhhy9uXtRRk94lljL6AF+0Y3IfB6wF93wzOi7dWNthWYK2LwyetVGIJCjJ1xJ\nLGb0vKIb0UKviqO3ckC47OgmEACam62LsJn2SnL0hOvIyGBiYuaEHbuoIPS8HL3ITV92MvqzZ/kW\nic3GN7Kjm6YmIDHRer2EMnrC08TFAdnZwLFj8u7ptNDHQnRz5oz7hd5MdGOnEAtQRk/EADJz+qYm\n9iFSIKPBK7pRuRh79iy/rhvAmtDbzei7d2dxTCAQ/Vo7hVj9XuToCU8jM6fXWyud3ELOM7oRLfRW\nHL2mOS/0PDL6uDgWxxhx2naFnjJ6wvPIdPROxzaA96ObCxfY0C0exwjqOBHdAMYLsnZHIpvJ6Eno\nCVcis5deBaHn2XUjuhhrJbrh7eYBZ6IbwHhBloejN5rRU3RDuBKZ0Y0qQu8GR9+jB8uo29rMPY53\nIRZg7ZVmRhXziG4A4wVZu8VYMxk9OXrClciObtLT5dwrHN27Xz4ByQ6ihd7nY87c7Cx4VRw9D6E3\nGqnIzOjJ0ROuJDOTFUl5je6NhAqO3ufjE9+I7roBrMU3Ihy9kxm9StENOXrCtSQkAAMGAJWV4u+l\ngtADfOIb0Y4esFaQVcXR88joZQl9YqKxVk5y9ISrkZXTqyL0PDpvZAi9lRZLFRw9r4zeTDeMHaGP\ni2MzfZqbo9+HHD3hWmTl9KoIvd3opqUFuHiR3zyZcFiJbpx29IEAv35zWcVYwNiLCjl6wtXIEHpN\nY7UAp4uxgP3o5tQp9q5A9MYvq9GNk45e3z3KY06/rGKsfq9oOT05esLVyOilP3uW1QN4ZLd2sRvd\n1Nez5xCN1eiGt6M3M72Sh+jqyMroAWMtluToCVcjI6NXJbYB7Ec39fXsOURjNboR4eiNtnny6rgB\n5Aq9kXcP5OgJVyMjuqmuVkv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-       "text": [
-        "<matplotlib.figure.Figure at 0x3436950>"
-       ]
-      }
-     ],
-     "prompt_number": 48
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "The IPython kernel/client model"
+    }
+   ],
+   "source": [
+    "%debug"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Don't foget to exit your debugging session. Raw input can of course be use to ask for user input:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Are you enjoying this tutorial ?Yes !\n",
+      "enjoy is : Yes !\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%connect_info"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "{\n",
-        "  \"stdin_port\": 50023, \n",
-        "  \"ip\": \"127.0.0.1\", \n",
-        "  \"control_port\": 50024, \n",
-        "  \"hb_port\": 50025, \n",
-        "  \"signature_scheme\": \"hmac-sha256\", \n",
-        "  \"key\": \"b54b8859-d64d-48bb-814a-909f9beb3316\", \n",
-        "  \"shell_port\": 50021, \n",
-        "  \"transport\": \"tcp\", \n",
-        "  \"iopub_port\": 50022\n",
-        "}\n",
-        "\n",
-        "Paste the above JSON into a file, and connect with:\n",
-        "    $> ipython <app> --existing <file>\n",
-        "or, if you are local, you can connect with just:\n",
-        "    $> ipython <app> --existing kernel-30f00f4a-230c-4e64-bea5-0e5f6a52cb40.json \n",
-        "or even just:\n",
-        "    $> ipython <app> --existing \n",
-        "if this is the most recent IPython session you have started.\n"
-       ]
-      }
-     ],
-     "prompt_number": 43
-    },
-    {
-     "cell_type": "markdown",
+    }
+   ],
+   "source": [
+    "enjoy = raw_input('Are you enjoying this tutorial ?')\n",
+    "print 'enjoy is :', enjoy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting in the notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This magic configures matplotlib to render its figures inline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "KvSZmZmoqKi49HlFRQWyZB+jE+O0trbijjvuwLe//W3ceuutTi/HMsnJyZg5cyY+/vhjp5diiB07\n",
+       "dmDdunUYMmQIZs+ejS1btmDu3LlOL8s0A74e+9i/f3/cdtttKA0e9K8oWVlZyMrKwsSJEwEAd955\n",
+       "Z8QpvKqyYcMGfOMb30D//v0jXueo0LffUNXS0oLVq1dj1qxZTi4pptA0DQsWLMDIkSPxL//yL04v\n",
+       "xzQnT55EQ0MDAKCpqQmbNm3C+PHjHV6VMZ566ilUVFTgyJEjWLVqFa6//nqsWLHC6WWZ4sKFC2j8\n",
+       "epb1+fPn8f7777umAy0jIwPZ2dk4+PXJKps3b8YoVc6SNMHKlSsxe/bsqNdx2RlrlfYbqvx+PxYs\n",
+       "WOCqDVWzZ8/GBx98gPr6emRnZ+PJJ5/EAw884PSyDLN9+3a8+uqrl9rjAOBXv/oVvvWtbzm8MmPU\n",
+       "1NRg3rx5CAQCCAQCmDNnDm644Qanl2UJN8aYtbW1uO3rSWdtbW24//77caPIKWSc+d3vfof7778f\n",
+       "LS0tyMvLw8svv+z0kkxx/vx5bN682VBthDZMEQRBeBz3lvsJgiAIQ5DQEwRBeBwSeoIgCI9DQk8Q\n",
+       "BOFxSOgJgiA8Dgk9QRCExyGhJwiC8Dgk9ARBEB7n/wOimSfhIIMDngAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x3436950>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "We can connect automatically a Qt Console to the currently running kernel with the `%qtconsole` magic, or by typing `ipython console --existing <kernel-UUID>` in any terminal:"
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(0, 2*np.pi, 300)\n",
+    "y = np.sin(x**2)\n",
+    "plt.plot(x, y)\n",
+    "plt.title(\"A little chirp\")\n",
+    "fig = plt.gcf()  # let's keep the figure object around for later..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The IPython kernel/client model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"stdin_port\": 50023, \n",
+      "  \"ip\": \"127.0.0.1\", \n",
+      "  \"control_port\": 50024, \n",
+      "  \"hb_port\": 50025, \n",
+      "  \"signature_scheme\": \"hmac-sha256\", \n",
+      "  \"key\": \"b54b8859-d64d-48bb-814a-909f9beb3316\", \n",
+      "  \"shell_port\": 50021, \n",
+      "  \"transport\": \"tcp\", \n",
+      "  \"iopub_port\": 50022\n",
+      "}\n",
+      "\n",
+      "Paste the above JSON into a file, and connect with:\n",
+      "    $> ipython <app> --existing <file>\n",
+      "or, if you are local, you can connect with just:\n",
+      "    $> ipython <app> --existing kernel-30f00f4a-230c-4e64-bea5-0e5f6a52cb40.json \n",
+      "or even just:\n",
+      "    $> ipython <app> --existing \n",
+      "if this is the most recent IPython session you have started.\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%qtconsole"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 83
     }
    ],
-   "metadata": {}
+   "source": [
+    "%connect_info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can connect automatically a Qt Console to the currently running kernel with the `%qtconsole` magic, or by typing `ipython console --existing <kernel-UUID>` in any terminal:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%qtconsole"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:31071a05d0ecd75ed72fe3f0de0ad447a6f85cffe382c26efa5e68db1fee54ee"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/Capturing Output.ipynb b/examples/IPython Kernel/Capturing Output.ipynb
index 6365a89..2cc7970 100644
--- a/examples/IPython Kernel/Capturing Output.ipynb
+++ b/examples/IPython Kernel/Capturing Output.ipynb
@@ -1,332 +1,489 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:df6354daf203e842bc040989d149760382d8ceec769160e4efe8cde9dfcb9107"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Capturing Output With <tt>%%capture</tt>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython has a [cell magic](Cell Magics.ipynb), `%%capture`, which captures the stdout/stderr of a cell. With this magic you can discard these streams or store them in a variable."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from __future__ import print_function\n",
-      "import sys"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "By default, `%%capture` discards these streams.  This is a simple way to suppress unwanted output."
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Capturing Output With <tt>%%capture</tt>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython has a [cell magic](Cell Magics.ipynb), `%%capture`, which captures the stdout/stderr of a cell. With this magic you can discard these streams or store them in a variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, `%%capture` discards these streams.  This is a simple way to suppress unwanted output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "print('hi, stdout')\n",
+    "print('hi, stderr', file=sys.stderr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you specify a name, then stdout/stderr will be stored in an object in your namespace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%capture captured\n",
+    "print('hi, stdout')\n",
+    "print('hi, stderr', file=sys.stderr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%capture\n",
-      "print('hi, stdout')\n",
-      "print('hi, stderr', file=sys.stderr)"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "<IPython.utils.capture.CapturedIO at 0x1076c9310>"
+      ]
+     },
+     "execution_count": 12,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "captured"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calling the object writes the output to stdout/stderr as appropriate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If you specify a name, then stdout/stderr will be stored in an object in your namespace."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hi, stdout\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%capture captured\n",
-      "print('hi, stdout')\n",
-      "print('hi, stderr', file=sys.stderr)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "captured"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 12,
-       "text": [
-        "<IPython.utils.capture.CapturedIO at 0x1076c9310>"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Calling the object writes the output to stdout/stderr as appropriate."
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "hi, stderr\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "captured()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "hi, stdout\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "hi, stderr\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
+    }
+   ],
+   "source": [
+    "captured()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "captured.stdout"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "'hi, stdout\\n'"
+      ]
+     },
+     "execution_count": 14,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 14,
-       "text": [
-        "'hi, stdout\\n'"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "captured.stdout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "captured.stderr"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "'hi, stderr\\n'"
+      ]
+     },
+     "execution_count": 15,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 15,
-       "text": [
-        "'hi, stderr\\n'"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "captured.stderr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`%%capture` grabs all output types, not just stdout/stderr, so you can do plots and use IPython's display system inside `%%capture`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%capture wontshutup\n",
+    "\n",
+    "print(\"setting up X\")\n",
+    "x = np.linspace(0,5,1000)\n",
+    "print(\"step 2: constructing y-data\")\n",
+    "y = np.sin(x)\n",
+    "print(\"step 3: display info about y\")\n",
+    "plt.plot(x,y)\n",
+    "print(\"okay, I'm done now\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "`%%capture` grabs all output types, not just stdout/stderr, so you can do plots and use IPython's display system inside `%%capture`"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "setting up X\n",
+      "step 2: constructing y-data\n",
+      "step 3: display info about y\n",
+      "okay, I'm done now\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline\n",
-      "import matplotlib.pyplot as plt\n",
-      "import numpy as np"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%capture wontshutup\n",
-      "\n",
-      "print(\"setting up X\")\n",
-      "x = np.linspace(0,5,1000)\n",
-      "print(\"step 2: constructing y-data\")\n",
-      "y = np.sin(x)\n",
-      "print(\"step 3: display info about y\")\n",
-      "plt.plot(x,y)\n",
-      "print(\"okay, I'm done now\")"
-     ],
-     "language": "python",
+     "data": {
+      "image/png": [
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+       "AAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10866ae90>"
+      ]
+     },
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "wontshutup()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "setting up X\n",
-        "step 2: constructing y-data\n",
-        "step 3: display info about y\n",
-        "okay, I'm done now\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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8HliyxByYdvCgafFceaW53aphQ3OoWlFOzNy7F/7v/8xr3TpYs8b8vm1bc2TD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\nAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10866ae90>"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "wontshutup()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And you can selectively disable capturing stdout, stderr or rich display, by passing `--no-stdout`, `--no-stderr` and `--no-display`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And you can selectively disable capturing stdout, stderr or rich display, by passing `--no-stdout`, `--no-stderr` and `--no-display`"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "hello, stderr\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%capture cap --no-stderr\n",
-      "print('hi, stdout')\n",
-      "print(\"hello, stderr\", file=sys.stderr)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "hello, stderr\n"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
+    }
+   ],
+   "source": [
+    "%%capture cap --no-stderr\n",
+    "print('hi, stdout')\n",
+    "print(\"hello, stderr\", file=sys.stderr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "cap.stdout"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "'hi, stdout\\n'"
+      ]
+     },
+     "execution_count": 20,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 20,
-       "text": [
-        "'hi, stdout\\n'"
-       ]
-      }
-     ],
-     "prompt_number": 20
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cap.stdout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "cap.stderr"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "''"
+      ]
+     },
+     "execution_count": 21,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 21,
-       "text": [
-        "''"
-       ]
-      }
-     ],
-     "prompt_number": 21
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cap.stderr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "cap.outputs"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 22,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 22,
-       "text": [
-        "[]"
-       ]
-      }
-     ],
-     "prompt_number": 22
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "cap.outputs"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:df6354daf203e842bc040989d149760382d8ceec769160e4efe8cde9dfcb9107"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/Cell Magics.ipynb b/examples/IPython Kernel/Cell Magics.ipynb
index bfa6705..f7dc4a2 100644
--- a/examples/IPython Kernel/Cell Magics.ipynb
+++ b/examples/IPython Kernel/Cell Magics.ipynb
@@ -1,593 +1,699 @@
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Cell Magics in IPython"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython has a system of commands we call 'magics' that provide effectively a mini command language that is orthogonal to the syntax of Python and is extensible by the user with new commands.  Magics are meant to be typed interactively, so they use command-line conventions, such as using whitespace for separating arguments, dashes for options and other conventions typical of a command-line environment.\n",
-      "\n",
-      "Magics come in two kinds:\n",
-      "\n",
-      "* Line magics: these are commands prepended by one `%` character and whose arguments only extend to the end of the current line.\n",
-      "* Cell magics: these use *two* percent characters as a marker (`%%`), and they receive as argument *both* the current line where they are declared and the whole body of the cell.  Note that cell magics can *only* be used as the first line in a cell, and as a general principle they can't be 'stacked' (i.e. you can only use one cell magic per cell).  A few of them, because of how they operate, can be stacked, but that is something you will discover on a case by case basis.\n",
-      "\n",
-      "The `%lsmagic` magic is used to list all available magics, and it will show both line and cell magics currently defined:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%lsmagic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "json": [
-        "{\"cell\": {\"prun\": \"ExecutionMagics\", \"file\": \"Other\", \"!\": \"OSMagics\", \"capture\": \"ExecutionMagics\", \"timeit\": \"ExecutionMagics\", \"script\": \"ScriptMagics\", \"ruby\": \"Other\", \"system\": \"OSMagics\", \"perl\": \"Other\", \"HTML\": \"Other\", \"bash\": \"Other\", \"python\": \"Other\", \"SVG\": \"Other\", \"javascript\": \"DisplayMagics\", \"writefile\": \"OSMagics\", \"pypy\": \"Other\", \"python3\": \"Other\", \"latex\": \"DisplayMagics\", \"sx\": \"OSMagics\", \"svg\": \"DisplayMagics\", \"html\": \"DisplayMagics\", \"sh\": \"Other\", \"time\": \"ExecutionMagics\", \"debug\": \"ExecutionMagics\"}, \"line\": {\"psource\": \"NamespaceMagics\", \"logstart\": \"LoggingMagics\", \"popd\": \"OSMagics\", \"loadpy\": \"CodeMagics\", \"install_ext\": \"ExtensionMagics\", \"colors\": \"BasicMagics\", \"who_ls\": \"NamespaceMagics\", \"install_profiles\": \"DeprecatedMagics\", \"pprint\": \"BasicMagics\", \"save\": \"CodeMagics\", \"tb\": \"ExecutionMagics\", \"pylab\": \"PylabMagics\", \"killbgscripts\": \"ScriptMagics\", \"quickref\": \"BasicMagics\", \"magic\": \"BasicMagics\", \"dhist\": \"OSMagics\", \"edit\": \"KernelMagics\", \"logstop\": \"LoggingMagics\", \"gui\": \"BasicMagics\", \"alias_magic\": \"BasicMagics\", \"debug\": \"ExecutionMagics\", \"page\": \"BasicMagics\", \"logstate\": \"LoggingMagics\", \"ed\": \"Other\", \"pushd\": \"OSMagics\", \"timeit\": \"ExecutionMagics\", \"rehashx\": \"OSMagics\", \"hist\": \"Other\", \"qtconsole\": \"KernelMagics\", \"dirs\": \"OSMagics\", \"run\": \"ExecutionMagics\", \"reset_selective\": \"NamespaceMagics\", \"pinfo2\": \"NamespaceMagics\", \"matplotlib\": \"PylabMagics\", \"automagic\": \"AutoMagics\", \"doctest_mode\": \"KernelMagics\", \"logoff\": \"LoggingMagics\", \"reload_ext\": \"ExtensionMagics\", \"pdb\": \"ExecutionMagics\", \"load\": \"CodeMagics\", \"lsmagic\": \"BasicMagics\", \"autosave\": \"KernelMagics\", \"cd\": \"OSMagics\", \"pastebin\": \"CodeMagics\", \"prun\": \"ExecutionMagics\", \"autocall\": \"AutoMagics\", \"bookmark\": \"OSMagics\", \"connect_info\": \"KernelMagics\", \"system\": \"OSMagics\", \"whos\": \"NamespaceMagics\", \"toc\": \"TimerMagics\", \"unload_ext\": \"ExtensionMagics\", \"store\": \"StoreMagics\", \"more\": \"KernelMagics\", \"gist\": \"Other\", \"pdef\": \"NamespaceMagics\", \"precision\": \"BasicMagics\", \"pinfo\": \"NamespaceMagics\", \"pwd\": \"OSMagics\", \"psearch\": \"NamespaceMagics\", \"reset\": \"NamespaceMagics\", \"recall\": \"HistoryMagics\", \"xdel\": \"NamespaceMagics\", \"xmode\": \"BasicMagics\", \"rerun\": \"HistoryMagics\", \"logon\": \"LoggingMagics\", \"history\": \"HistoryMagics\", \"pycat\": \"OSMagics\", \"unalias\": \"OSMagics\", \"install_default_config\": \"DeprecatedMagics\", \"env\": \"OSMagics\", \"load_ext\": \"ExtensionMagics\", \"config\": \"ConfigMagics\", \"tic\": \"TimerMagics\", \"profile\": \"BasicMagics\", \"pfile\": \"NamespaceMagics\", \"less\": \"KernelMagics\", \"who\": \"NamespaceMagics\", \"notebook\": \"BasicMagics\", \"man\": \"KernelMagics\", \"sx\": \"OSMagics\", \"macro\": \"ExecutionMagics\", \"clear\": \"KernelMagics\", \"alias\": \"OSMagics\", \"time\": \"ExecutionMagics\", \"sc\": \"OSMagics\", \"rep\": \"Other\", \"pdoc\": \"NamespaceMagics\"}}"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 1,
-       "text": [
-        "Available line magics:\n",
-        "%alias  %alias_magic  %autocall  %automagic  %autosave  %bookmark  %cd  %clear  %colors  %config  %connect_info  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gist  %gui  %hist  %history  %install_default_config  %install_ext  %install_profiles  %killbgscripts  %less  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %lsmagic  %macro  %magic  %man  %matplotlib  %more  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %popd  %pprint  %precision  %profile  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %run  %save  %sc  %store  %sx  %system  %tb  %tic  %time  %timeit  %toc  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
-        "\n",
-        "Available cell magics:\n",
-        "%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%latex  %%perl  %%prun  %%pypy  %%python  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
-        "\n",
-        "Automagic is ON, % prefix IS NOT needed for line magics."
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Since in the introductory section we already covered the most frequently used line magics, we will focus here on the cell magics, which offer a great amount of power.\n",
-      "\n",
-      "Let's load matplotlib and numpy so we can use numerics/plotting at will later on."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline\n",
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "<!--====-->\n",
-      "Some simple cell magics"
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cell Magics in IPython"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython has a system of commands we call 'magics' that provide effectively a mini command language that is orthogonal to the syntax of Python and is extensible by the user with new commands.  Magics are meant to be typed interactively, so they use command-line conventions, such as using whitespace for separating arguments, dashes for options and other conventions typical of a command-line environment.\n",
+    "\n",
+    "Magics come in two kinds:\n",
+    "\n",
+    "* Line magics: these are commands prepended by one `%` character and whose arguments only extend to the end of the current line.\n",
+    "* Cell magics: these use *two* percent characters as a marker (`%%`), and they receive as argument *both* the current line where they are declared and the whole body of the cell.  Note that cell magics can *only* be used as the first line in a cell, and as a general principle they can't be 'stacked' (i.e. you can only use one cell magic per cell).  A few of them, because of how they operate, can be stacked, but that is something you will discover on a case by case basis.\n",
+    "\n",
+    "The `%lsmagic` magic is used to list all available magics, and it will show both line and cell magics currently defined:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/json": {
+       "cell": {
+        "!": "OSMagics",
+        "HTML": "Other",
+        "SVG": "Other",
+        "bash": "Other",
+        "capture": "ExecutionMagics",
+        "debug": "ExecutionMagics",
+        "file": "Other",
+        "html": "DisplayMagics",
+        "javascript": "DisplayMagics",
+        "latex": "DisplayMagics",
+        "perl": "Other",
+        "prun": "ExecutionMagics",
+        "pypy": "Other",
+        "python": "Other",
+        "python3": "Other",
+        "ruby": "Other",
+        "script": "ScriptMagics",
+        "sh": "Other",
+        "svg": "DisplayMagics",
+        "sx": "OSMagics",
+        "system": "OSMagics",
+        "time": "ExecutionMagics",
+        "timeit": "ExecutionMagics",
+        "writefile": "OSMagics"
+       },
+       "line": {
+        "alias": "OSMagics",
+        "alias_magic": "BasicMagics",
+        "autocall": "AutoMagics",
+        "automagic": "AutoMagics",
+        "autosave": "KernelMagics",
+        "bookmark": "OSMagics",
+        "cd": "OSMagics",
+        "clear": "KernelMagics",
+        "colors": "BasicMagics",
+        "config": "ConfigMagics",
+        "connect_info": "KernelMagics",
+        "debug": "ExecutionMagics",
+        "dhist": "OSMagics",
+        "dirs": "OSMagics",
+        "doctest_mode": "KernelMagics",
+        "ed": "Other",
+        "edit": "KernelMagics",
+        "env": "OSMagics",
+        "gist": "Other",
+        "gui": "BasicMagics",
+        "hist": "Other",
+        "history": "HistoryMagics",
+        "install_default_config": "DeprecatedMagics",
+        "install_ext": "ExtensionMagics",
+        "install_profiles": "DeprecatedMagics",
+        "killbgscripts": "ScriptMagics",
+        "less": "KernelMagics",
+        "load": "CodeMagics",
+        "load_ext": "ExtensionMagics",
+        "loadpy": "CodeMagics",
+        "logoff": "LoggingMagics",
+        "logon": "LoggingMagics",
+        "logstart": "LoggingMagics",
+        "logstate": "LoggingMagics",
+        "logstop": "LoggingMagics",
+        "lsmagic": "BasicMagics",
+        "macro": "ExecutionMagics",
+        "magic": "BasicMagics",
+        "man": "KernelMagics",
+        "matplotlib": "PylabMagics",
+        "more": "KernelMagics",
+        "notebook": "BasicMagics",
+        "page": "BasicMagics",
+        "pastebin": "CodeMagics",
+        "pdb": "ExecutionMagics",
+        "pdef": "NamespaceMagics",
+        "pdoc": "NamespaceMagics",
+        "pfile": "NamespaceMagics",
+        "pinfo": "NamespaceMagics",
+        "pinfo2": "NamespaceMagics",
+        "popd": "OSMagics",
+        "pprint": "BasicMagics",
+        "precision": "BasicMagics",
+        "profile": "BasicMagics",
+        "prun": "ExecutionMagics",
+        "psearch": "NamespaceMagics",
+        "psource": "NamespaceMagics",
+        "pushd": "OSMagics",
+        "pwd": "OSMagics",
+        "pycat": "OSMagics",
+        "pylab": "PylabMagics",
+        "qtconsole": "KernelMagics",
+        "quickref": "BasicMagics",
+        "recall": "HistoryMagics",
+        "rehashx": "OSMagics",
+        "reload_ext": "ExtensionMagics",
+        "rep": "Other",
+        "rerun": "HistoryMagics",
+        "reset": "NamespaceMagics",
+        "reset_selective": "NamespaceMagics",
+        "run": "ExecutionMagics",
+        "save": "CodeMagics",
+        "sc": "OSMagics",
+        "store": "StoreMagics",
+        "sx": "OSMagics",
+        "system": "OSMagics",
+        "tb": "ExecutionMagics",
+        "tic": "TimerMagics",
+        "time": "ExecutionMagics",
+        "timeit": "ExecutionMagics",
+        "toc": "TimerMagics",
+        "unalias": "OSMagics",
+        "unload_ext": "ExtensionMagics",
+        "who": "NamespaceMagics",
+        "who_ls": "NamespaceMagics",
+        "whos": "NamespaceMagics",
+        "xdel": "NamespaceMagics",
+        "xmode": "BasicMagics"
+       }
+      },
+      "text/plain": [
+       "Available line magics:\n",
+       "%alias  %alias_magic  %autocall  %automagic  %autosave  %bookmark  %cd  %clear  %colors  %config  %connect_info  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gist  %gui  %hist  %history  %install_default_config  %install_ext  %install_profiles  %killbgscripts  %less  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %lsmagic  %macro  %magic  %man  %matplotlib  %more  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %popd  %pprint  %precision  %profile  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %rep  %rerun  %reset  %reset_selective  %run  %save  %sc  %store  %sx  %system  %tb  %tic  %time  %timeit  %toc  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
+       "\n",
+       "Available cell magics:\n",
+       "%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%latex  %%perl  %%prun  %%pypy  %%python  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
+       "\n",
+       "Automagic is ON, % prefix IS NOT needed for line magics."
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%lsmagic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since in the introductory section we already covered the most frequently used line magics, we will focus here on the cell magics, which offer a great amount of power.\n",
+    "\n",
+    "Let's load matplotlib and numpy so we can use numerics/plotting at will later on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <!--====--> Some simple cell magics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Timing the execution of code; the 'timeit' magic exists both in line and cell form:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 loops, best of 3: 6.05 ms per loop\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Timing the execution of code; the 'timeit' magic exists both in line and cell form:"
+    }
+   ],
+   "source": [
+    "%timeit np.linalg.eigvals(np.random.rand(100,100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 loops, best of 3: 6.07 ms per loop\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%timeit np.linalg.eigvals(np.random.rand(100,100))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "100 loops, best of 3: 6.05 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%timeit a = np.random.rand(100, 100)\n",
-      "np.linalg.eigvals(a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "100 loops, best of 3: 6.07 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The `%%capture` magic can be used to capture the stdout/err of any block of python code, either to discard it (if it's noise to you) or to store it in a variable for later use:"
+    }
+   ],
+   "source": [
+    "%%timeit a = np.random.rand(100, 100)\n",
+    "np.linalg.eigvals(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `%%capture` magic can be used to capture the stdout/err of any block of python code, either to discard it (if it's noise to you) or to store it in a variable for later use:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%capture capt\n",
+    "from __future__ import print_function\n",
+    "import sys\n",
+    "print('Hello stdout')\n",
+    "print('and stderr', file=sys.stderr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('Hello stdout\\n', 'and stderr\\n')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "capt.stdout, capt.stderr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello stdout\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%capture capt\n",
-      "from __future__ import print_function\n",
-      "import sys\n",
-      "print('Hello stdout')\n",
-      "print('and stderr', file=sys.stderr)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "capt.stdout, capt.stderr"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 6,
-       "text": [
-        "('Hello stdout\\n', 'and stderr\\n')"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "capt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Hello stdout\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "and stderr\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The `%%writefile` magic is a very useful tool that writes the cell contents as a named file:"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "and stderr\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%writefile foo.py\n",
-      "print('Hello world')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Writing foo.py\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%run foo"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Hello world\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "<!--====-->\n",
-      "Magics for running code under other interpreters"
+    }
+   ],
+   "source": [
+    "capt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `%%writefile` magic is a very useful tool that writes the cell contents as a named file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing foo.py\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython has a `%%script` cell magic, which lets you run a cell in\n",
-      "a subprocess of any interpreter on your system, such as: bash, ruby, perl, zsh, R, etc.\n",
-      "\n",
-      "It can even be a script of your own, which expects input on stdin."
+    }
+   ],
+   "source": [
+    "%%writefile foo.py\n",
+    "print('Hello world')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello world\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To use it, simply pass a path or shell command to the program you want to run on the `%%script` line,\n",
-      "and the rest of the cell will be run by that script, and stdout/err from the subprocess are captured and displayed."
+    }
+   ],
+   "source": [
+    "%run foo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <!--====--> Magics for running code under other interpreters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython has a `%%script` cell magic, which lets you run a cell in\n",
+    "a subprocess of any interpreter on your system, such as: bash, ruby, perl, zsh, R, etc.\n",
+    "\n",
+    "It can even be a script of your own, which expects input on stdin."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use it, simply pass a path or shell command to the program you want to run on the `%%script` line,\n",
+    "and the rest of the cell will be run by that script, and stdout/err from the subprocess are captured and displayed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello from Python 2.7.2 (default, Oct 11 2012, 20:14:37) \n",
+      "[GCC 4.2.1 Compatible Apple Clang 4.0 (tags/Apple/clang-418.0.60)]\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%script python\n",
-      "import sys\n",
-      "print 'hello from Python %s' % sys.version"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "hello from Python 2.7.2 (default, Oct 11 2012, 20:14:37) \n",
-        "[GCC 4.2.1 Compatible Apple Clang 4.0 (tags/Apple/clang-418.0.60)]\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%script python3\n",
-      "import sys\n",
-      "print('hello from Python: %s' % sys.version)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "hello from Python: 3.3.1 (v3.3.1:d9893d13c628, Apr  6 2013, 11:07:11) \n",
-        "[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython also creates aliases for a few common interpreters, such as bash, ruby, perl, etc.\n",
-      "\n",
-      "These are all equivalent to `%%script <name>`"
+    }
+   ],
+   "source": [
+    "%%script python\n",
+    "import sys\n",
+    "print 'hello from Python %s' % sys.version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello from Python: 3.3.1 (v3.3.1:d9893d13c628, Apr  6 2013, 11:07:11) \n",
+      "[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%ruby\n",
-      "puts \"Hello from Ruby #{RUBY_VERSION}\""
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Hello from Ruby 1.9.3\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%bash\n",
-      "echo \"hello from $BASH\""
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "hello from /usr/local/bin/bash\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Exercise: write your own script that numbers input lines"
+    }
+   ],
+   "source": [
+    "%%script python3\n",
+    "import sys\n",
+    "print('hello from Python: %s' % sys.version)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython also creates aliases for a few common interpreters, such as bash, ruby, perl, etc.\n",
+    "\n",
+    "These are all equivalent to `%%script <name>`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello from Ruby 1.9.3\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Write a file, called `lnum.py`, such that the following cell works as shown (hint: don't forget about the executable bit!): "
+    }
+   ],
+   "source": [
+    "%%ruby\n",
+    "puts \"Hello from Ruby #{RUBY_VERSION}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello from /usr/local/bin/bash\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%script ./lnum.py\n",
-      "my first line\n",
-      "my second\n",
-      "more"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0: my first line\n",
-        "1: my second\n",
-        "2: more\n",
-        "---- END ----\n"
-       ]
-      }
-     ],
-     "prompt_number": 29
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Capturing output"
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "echo \"hello from $BASH\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise: write your own script that numbers input lines"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a file, called `lnum.py`, such that the following cell works as shown (hint: don't forget about the executable bit!): "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0: my first line\n",
+      "1: my second\n",
+      "2: more\n",
+      "---- END ----\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can also capture stdout/err from these subprocesses into Python variables, instead of letting them go directly to stdout/err"
+    }
+   ],
+   "source": [
+    "%%script ./lnum.py\n",
+    "my first line\n",
+    "my second\n",
+    "more"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Capturing output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also capture stdout/err from these subprocesses into Python variables, instead of letting them go directly to stdout/err"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hi, stdout\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%bash\n",
-      "echo \"hi, stdout\"\n",
-      "echo \"hello, stderr\" >&2\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "hi, stdout\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "hello, stderr\n"
-       ]
-      }
-     ],
-     "prompt_number": 30
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%bash --out output --err error\n",
-      "echo \"hi, stdout\"\n",
-      "echo \"hello, stderr\" >&2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 31
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print(error)\n",
-      "print(output)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "hello, stderr\n",
-        "\n",
-        "hi, stdout\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 32
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Background Scripts"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "hello, stderr\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "These scripts can be run in the background, by adding the `--bg` flag.\n",
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "echo \"hi, stdout\"\n",
+    "echo \"hello, stderr\" >&2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%bash --out output --err error\n",
+    "echo \"hi, stdout\"\n",
+    "echo \"hello, stderr\" >&2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello, stderr\n",
       "\n",
-      "When you do this, output is discarded unless you use the `--out/err`\n",
-      "flags to store output as above."
+      "hi, stdout\n",
+      "\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%ruby --bg --out ruby_lines\n",
-      "for n in 1...10\n",
-      "    sleep 1\n",
-      "    puts \"line #{n}\"\n",
-      "    STDOUT.flush\n",
-      "end"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Starting job # 0 in a separate thread.\n"
-       ]
-      }
-     ],
-     "prompt_number": 33
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When you do store output of a background thread, these are the stdout/err *pipes*,\n",
-      "rather than the text of the output."
+    }
+   ],
+   "source": [
+    "print(error)\n",
+    "print(output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Background Scripts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These scripts can be run in the background, by adding the `--bg` flag.\n",
+    "\n",
+    "When you do this, output is discarded unless you use the `--out/err`\n",
+    "flags to store output as above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting job # 0 in a separate thread.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%ruby --bg --out ruby_lines\n",
+    "for n in 1...10\n",
+    "    sleep 1\n",
+    "    puts \"line #{n}\"\n",
+    "    STDOUT.flush\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you do store output of a background thread, these are the stdout/err *pipes*,\n",
+    "rather than the text of the output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<open file '<fdopen>', mode 'rb' at 0x112cd55d0>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ruby_lines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "line 1\n",
+      "line 2\n",
+      "line 3\n",
+      "line 4\n",
+      "line 5\n",
+      "line 6\n",
+      "line 7\n",
+      "line 8\n",
+      "line 9\n",
+      "\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ruby_lines"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 34,
-       "text": [
-        "<open file '<fdopen>', mode 'rb' at 0x112cd55d0>"
-       ]
-      }
-     ],
-     "prompt_number": 34
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print(ruby_lines.read())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "line 1\n",
-        "line 2\n",
-        "line 3\n",
-        "line 4\n",
-        "line 5\n",
-        "line 6\n",
-        "line 7\n",
-        "line 8\n",
-        "line 9\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 35
     }
    ],
-   "metadata": {}
+   "source": [
+    "print(ruby_lines.read())"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/Custom Display Logic.ipynb b/examples/IPython Kernel/Custom Display Logic.ipynb
index 2e23ac4..5b32c8b 100644
--- a/examples/IPython Kernel/Custom Display Logic.ipynb
+++ b/examples/IPython Kernel/Custom Display Logic.ipynb
@@ -1,787 +1,1325 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:86c779d5798c4a68bda7e71c8ef320cb7ba9d7e3d0f1bc4b828ee65f617a5ae3"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Custom Display Logic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As described in the [Rich Output](Rich Output.ipynb) tutorial, the IPython display system can display rich representations of objects in the following formats:\n",
+    "\n",
+    "* JavaScript\n",
+    "* HTML\n",
+    "* PNG\n",
+    "* JPEG\n",
+    "* SVG\n",
+    "* LaTeX\n",
+    "* PDF\n",
+    "\n",
+    "This Notebook shows how you can add custom display logic to your own classes, so that they can be displayed using these rich representations. There are two ways of accomplishing this:\n",
+    "\n",
+    "1. Implementing special display methods such as `_repr_html_` when you define your class.\n",
+    "2. Registering a display function for a particular existing class.\n",
+    "\n",
+    "This Notebook describes and illustrates both approaches."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import the IPython display functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import (\n",
+    "    display, display_html, display_png, display_svg\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Parts of this notebook need the matplotlib inline backend:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Special display methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The main idea of the first approach is that you have to implement special display methods when you define your class, one for each representation you want to use. Here is a list of the names of the special methods and the values they must return:\n",
+    "\n",
+    "* `_repr_html_`: return raw HTML as a string\n",
+    "* `_repr_json_`: return raw JSON as a string\n",
+    "* `_repr_jpeg_`: return raw JPEG data\n",
+    "* `_repr_png_`: return raw PNG data\n",
+    "* `_repr_svg_`: return raw SVG data as a string\n",
+    "* `_repr_latex_`: return LaTeX commands in a string surrounded by \"$\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an illustration, we build a class that holds data generated by sampling a Gaussian distribution with given mean and standard deviation. Here is the definition of the `Gaussian` class, which has a custom PNG and LaTeX representation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.core.pylabtools import print_figure\n",
+    "from IPython.display import Image, SVG, Math\n",
+    "\n",
+    "class Gaussian(object):\n",
+    "    \"\"\"A simple object holding data sampled from a Gaussian distribution.\n",
+    "    \"\"\"\n",
+    "    def __init__(self, mean=0.0, std=1, size=1000):\n",
+    "        self.data = np.random.normal(mean, std, size)\n",
+    "        self.mean = mean\n",
+    "        self.std = std\n",
+    "        self.size = size\n",
+    "        # For caching plots that may be expensive to compute\n",
+    "        self._png_data = None\n",
+    "        \n",
+    "    def _figure_data(self, format):\n",
+    "        fig, ax = plt.subplots()\n",
+    "        ax.hist(self.data, bins=50)\n",
+    "        ax.set_title(self._repr_latex_())\n",
+    "        ax.set_xlim(-10.0,10.0)\n",
+    "        data = print_figure(fig, format)\n",
+    "        # We MUST close the figure, otherwise IPython's display machinery\n",
+    "        # will pick it up and send it as output, resulting in a double display\n",
+    "        plt.close(fig)\n",
+    "        return data\n",
+    "        \n",
+    "    def _repr_png_(self):\n",
+    "        if self._png_data is None:\n",
+    "            self._png_data = self._figure_data('png')\n",
+    "        return self._png_data\n",
+    "    \n",
+    "    def _repr_latex_(self):\n",
+    "        return r'$\\mathcal{N}(\\mu=%.2g, \\sigma=%.2g),\\ N=%d$' % (self.mean,\n",
+    "                                                                 self.std, self.size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create an instance of the Gaussian distribution and return it to display the default representation:"
+   ]
+  },
   {
-   "cells": [
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 1,
+     "data": {
+      "image/png": [
+       "iVBORw0KGgoAAAANSUhEUgAAAXIAAAENCAYAAAASUO4dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
+       "AAALEgAACxIB0t1+/AAAElNJREFUeJzt3X+wXGV9x/H3JpefITfhCr1JSyCIRH5UJbQgrVhXChas\n",
+       "DXE6onbaBoownRG0tVUSOg63vxQY+8tRoVOQuTIOSq3QQGtNiGyr1SK0IfxqTBNNB2zuhUJoLogk\n",
+       "kO0fz9ncvZu9956z9+yefXbfr5mdPefZs7tPbnY/++z3POcsSJIkSZIkSZIkSZIkSZIkSZIkSZJ6\n",
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+       "/ZQm21er1WkzXopGqVQijFdKNL6m62+r36ZZ+9RrZrgt7fXUx/D91hvCayd9Ps9WWjmGyRkpRwAX\n",
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+       "/C5wOnAO8EHgVGAtIchXAJuSdUldolbfHxwcKrorarNW9uLcDXwmubwNGAeWABXglIZtq9Wq9UXF\n",
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+       "l6TIGeSSFDmDXKpjbVwxMsilOtbGFSODXJIiZ5BLPWfA8lCfMcilnvMKlof6i0EuSZEzyCUpcga5\n",
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+       "mvrTeLX9Es3arKd3zlyC/GxgO7AT2Ad8Cbg4hz6pCYM8i1qddx8edp+32k/j7QYm90vAdQe11dbV\n",
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+       "nDSH55CkfrQDeF0nnmggebLlwKHAw7izU5KicxHwPcLIe13BfZEkSZIE8B7gceBV4MyG29YRDh7a\n",
+       "Cryjw/3qBSOE2UGbk8uFM26t6XggW752Ao8QXpPfLbYr0fk8MA48Wtc2BGwEtgEbgMUF9ItTgBXA\n",
+       "/UwN8tMIdfRDCHX17XiKgKyuAz5SdCciN5/w2ltOeC26b2fufkAIH2X3VmAlU4P8RuBjyfI1wPWz\n",
+       "PUg7gnQr4ZOk0cXAHYSjNHYS3kxnt+H5e52HKs6NB7K1h6/L1nwTaDxyahUwmiyPAqtne5BOjoh/\n",
+       "klAWqPEAotZcDWwBbqWgr1yR80C2/FWB+4CHgCsK7ksvGCaUW0iuh2e7Q6unsd0ILGnSfi1wT4bH\n",
+       "8aCig033t/0D4Cbgj5L1Pwb+DLi8Q/3qFb7m8vcWYBdwLOH1u5Uw0tTcVUnxmm01yC9o4T4/BOp/\n",
+       "0uW4pE1Tpf3b3kK2D00Fja/DZUz9pqjsdiXXzwB3EcpXBnnrxgmDuTFgKfD0bHdod2mlvm62Hngf\n",
+       "4eChE4GTcQ93Vkvrlt/N1B0kSuchwmtvOeG1+F7Ca1OtORJYmCwvIMxG83U5N+uBNcnyGuDuIjrx\n",
+       "bkIN8iXCJ8rX6m67lrCjaSvwS53vWvS+QJjmtYXwnztr7UxNeSBbfk4kzPx5GHgM/55Z3QH8D7CX\n",
+       "kJuXEWYA3UfB0w8lSZIkSZIkSZIkSZIkSZIkSZIktcn/A4eK9UXawRDUAAAAAElFTkSuQmCC\n"
+      ],
+      "text/latex": [
+       "$\\mathcal{N}(\\mu=2, \\sigma=1),\\ N=1000$"
+      ],
+      "text/plain": [
+       "<__main__.Gaussian at 0x106e7ae10>"
+      ]
+     },
+     "execution_count": 4,
      "metadata": {},
-     "source": [
-      "Custom Display Logic"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = Gaussian(2.0, 1.0)\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also pass the object to the `display` function to display the default representation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "image/png": [
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+       "4Angk0n7ELAR2AZsABa3q4OSpJnNFuQ/Bt4OnAG8MVk+F1hLCPIVwKZkXZJUgDSllR8l14cC84Hd\n",
+       "wCpgNGkfBVbn3zVJUhppgnweobQyDtwPPA4MJ+sk18Nt6Z0kaVZpZq3sJ5RWFgFfJ5RX6lWTS1Mj\n",
+       "IyMHlsvlMuVyOWsfpcINDg4xMbGbhQuPZs+e54rujnpMpVKhUqm0fP+s85U+DrwEfAAoA2PAUsJI\n",
+       "/ZQm21er1WkzXopGqVQijFdKNL6m62+r36ZZ+9RrZrgt7fXUx/D91hvCayd9Ps9WWjmGyRkpRwAX\n",
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+       "Z5BLuRuozTqQOsIgl3IXfrFH6hSDXJIiZ5BLUuQMckmKnEEuSZEzyCUpcga5JEXOIJekyBnkkhQ5\n",
+       "g1ySImeQS1LkDHJJipxBLkmRM8glKXIDRXdAiounqFX3cUQuZVI7Ra2nqVX3MMglKXIGuSRFziCX\n",
+       "ZjA4OGRNXF3PIJdmMDGxG+vh6nYGuSRFLk2QLwPuBx4HHgM+lLQPARuBbcAGYHE7OihJmlmaIN8H\n",
+       "/C5wOnAO8EHgVGAtIchXAJuSdUldolbfHxwcKrorarNW9uLcDXwmubwNGAeWABXglIZtq9Wq9UXF\n",
+       "K+zorBLeKvXXNGnLet2+x6hWq1P67vswLskO9tT5nLVGvhxYCTwADBNCnOR6OONjSZJykOUQ/aOA\n",
+       "vwM+DEw03DbtoW4jIyMHlsvlMuVyOVMHJanXVSoVKpVKy/dPO3Q/BLgX+Brwl0nbVqAMjAFLCTtE\n",
+       "La2op1haURHaUVopAbcCTzAZ4gDrgTXJ8hpC7VyS1GFpEv9c4F+AR5gcAqwDvgvcCRwP7AQuAZ5v\n",
+       "uK8jckXNEbmKkHVE3u5jjw1yRc0gVxHaPWtFktRlDHJJipxBLkmRM8ilJjx9rWJikEtNePpaxcQg\n",
+       "l6TIGeSSFDmDXKpjbVwxMsilOtbGFSODXJIiZ5BLPWfA8lCfMcilnvMKlof6i0EuSZEzyCUpcga5\n",
+       "JEXOIJekyBnkkhQ5g1ySImeQS1LkDHJJipxBLkmRM8ilPlM7w+Pg4FDRXVFOBorugKTOqp3hcWLC\n",
+       "87H0CkfkkhQ5g1ySImeQS1LkDHJJipxBLkmRSxPknwfGgUfr2oaAjcA2YAOwOP+uSZLSSBPktwEX\n",
+       "NrStJQT5CmBTsi4pKuEn4ZxTHr80Qf5NYHdD2ypgNFkeBVbn2SlJnVD7SbhqMrdcsWq1Rj5MKLeQ\n",
+       "XA/n0x1JUlZ57Oys4i+9SlJhWj1EfxxYAowBS4Gnp9twZGTkwHK5XKZcLrf4lJJaE2rhCxcezZ49\n",
+       "zxXdGTVRqVSoVCot3z/tyRaWA/cAb0jWbwSeBW4g7OhcTPMdntVq1cG64lEqlQhfMGe7JsU23fcY\n",
+       "1Wq1yb+RA7epO4T/o9T5nKq0cgfwbeD1wJPAZcD1wAWE6YfnJeuSpAK0+/RnjsjVlQYHh5iY2H1Q\n",
+       "ucERubpB1hG5p7FVX/JUruolHqIvSZEzyCUpcga5+kbtJ86S+mMfGujjf3tvM8jVN2p18f49fq12\n",
+       "SL56jUEuSZEzyCUpcga5el6tNq6ZDHg624g5j1w9b7I2bphPL9TPnVcfJ0fkkhQ5g1x9zpKC4mdp\n",
+       "RX3OkoLi54hckiJnkEtS5AxySYqcQS5JkTPIJSlyBrkkRc4glwBP8aqYGeQS4CleFTODXJIiZ5BL\n",
+       "UuQMckmKnEEuSZHzpFmKyq5du3jxxRc57LDDWLZsWdHdkbqCI3JFY//+/Rx//AmsXHkhy5efyNjY\n",
+       "2JTba78E5Clp5yJMwyyVDm167d+2OzkiVzSq1SqvvvoqL7ywnQULjmfv3r1Tbq/9EpCnpJ2L2jTM\n",
+       "UtNr/7bdyRG5JEVurkF+IbAV+C/gmrl3R5KU1VyCfD7wGUKYnwa8Hzg1j07pYJVKpeguRGRqnVd5\n",
+       "mvrTeLX9Es3arKd3zlyC/GxgO7AT2Ad8Cbg4hz6pCYM8i1qddx8edp+32k/j7QYm90vAdQe11dbV\n",
+       "fnMJ8p8CnqxbfyppkyR10FyC3KGOClBlcPBXePnlZ5g3z331EoQ5Ra06Bxgh1MgB1gH7gRvqttkO\n",
+       "nDSH55CkfrQDeF0nnmggebLlwKHAw7izU5KicxHwPcLIe13BfZEkSZIE8B7gceBV4MyG29YRDh7a\n",
+       "Cryjw/3qBSOE2UGbk8uFM26t6XggW752Ao8QXpPfLbYr0fk8MA48Wtc2BGwEtgEbgMUF9ItTgBXA\n",
+       "/UwN8tMIdfRDCHX17XiKgKyuAz5SdCciN5/w2ltOeC26b2fufkAIH2X3VmAlU4P8RuBjyfI1wPWz\n",
+       "PUg7gnQr4ZOk0cXAHYSjNHYS3kxnt+H5e52HKs6NB7K1h6/L1nwTaDxyahUwmiyPAqtne5BOjoh/\n",
+       "klAWqPEAotZcDWwBbqWgr1yR80C2/FWB+4CHgCsK7ksvGCaUW0iuh2e7Q6unsd0ILGnSfi1wT4bH\n",
+       "8aCig033t/0D4Cbgj5L1Pwb+DLi8Q/3qFb7m8vcWYBdwLOH1u5Uw0tTcVUnxmm01yC9o4T4/BOp/\n",
+       "0uW4pE1Tpf3b3kK2D00Fja/DZUz9pqjsdiXXzwB3EcpXBnnrxgmDuTFgKfD0bHdod2mlvm62Hngf\n",
+       "4eChE4GTcQ93Vkvrlt/N1B0kSuchwmtvOeG1+F7Ca1OtORJYmCwvIMxG83U5N+uBNcnyGuDuIjrx\n",
+       "bkIN8iXCJ8rX6m67lrCjaSvwS53vWvS+QJjmtYXwnztr7UxNeSBbfk4kzPx5GHgM/55Z3QH8D7CX\n",
+       "kJuXEWYA3UfB0w8lSZIkSZIkSZIkSZIkSZIkSZIktcn/A4eK9UXawRDUAAAAAElFTkSuQmCC\n"
+      ],
+      "text/latex": [
+       "$\\mathcal{N}(\\mu=2, \\sigma=1),\\ N=1000$"
+      ],
+      "text/plain": [
+       "<__main__.Gaussian at 0x106e7ae10>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Overview"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use `display_png` to view the PNG representation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "image/png": [
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+       "kO0fz9ncvZu9956z9+yefXbfr5mdPefZs7tPbnY/++z3POcsSJIkSZIkSZIkSZIkSZIkSZIkSZJ6\n",
+       "1olFd6DNlgJHFt0JKYt5RXdAXe8sYAtwEyHEzym2O233DPCxojshZVEqugPqGjcD/wrc3tB+E/BV\n",
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+       "+I8mjz3Xvi0Gfly3vhc4KtkuS7t6jKUVQXhzvwysB94B/AQh3B+o22Z+w31+DvjvZLmUrLc6iq5Z\n",
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+       "ZDgT+D8OroePMvVr+YPATyfLHweuBFbNoT8fItSklxC+DbwtaT+ZyaC8kTAtEEJd/1NzeL6aS2k+\n",
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+       "DHJJipxBLkmRM8glKXIGuSRFziCXpMgZ5JIUOYNckiJnkEtS5AxySYqcQS5JkTPIpdyEn2MbHBwq\n",
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+       "0uW4pE1Tpf3b3kK2D00Fja/DZUz9pqjsdiXXzwB3EcpXBnnrxgmDuTFgKfD0bHdod2mlvm62Hngf\n",
+       "4eChE4GTcQ93Vkvrlt/N1B0kSuchwmtvOeG1+F7Ca1OtORJYmCwvIMxG83U5N+uBNcnyGuDuIjrx\n",
+       "bkIN8iXCJ8rX6m67lrCjaSvwS53vWvS+QJjmtYXwnztr7UxNeSBbfk4kzPx5GHgM/55Z3QH8D7CX\n",
+       "kJuXEWYA3UfB0w8lSZIkSZIkSZIkSZIkSZIkSZIktcn/A4eK9UXawRDUAAAAAElFTkSuQmCC\n"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "As described in the [Rich Output](Rich Output.ipynb) tutorial, the IPython display system can display rich representations of objects in the following formats:\n",
-      "\n",
-      "* JavaScript\n",
-      "* HTML\n",
-      "* PNG\n",
-      "* JPEG\n",
-      "* SVG\n",
-      "* LaTeX\n",
-      "* PDF\n",
-      "\n",
-      "This Notebook shows how you can add custom display logic to your own classes, so that they can be displayed using these rich representations. There are two ways of accomplishing this:\n",
-      "\n",
-      "1. Implementing special display methods such as `_repr_html_` when you define your class.\n",
-      "2. Registering a display function for a particular existing class.\n",
-      "\n",
-      "This Notebook describes and illustrates both approaches."
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display_png(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "It is important to note a subtle different between <code>display</code> and <code>display_png</code>. The former computes <em>all</em> representations of the object, and lets the notebook UI decide which to display. The later only computes the PNG representation.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a new Gaussian with different parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "image/png": [
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+       "/ftfQXGqt1nGl3WoycpfKhFYm2++8efZb01Kc/gBSYqQ4S5JETLcJSlChrskRchwl6QIGe6SFCHD\n",
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+       "+n4ADh7czYEDHS6UpOg0O9xfAOan5ucTau9pO3O53KImH7fnHDiwuWJJ5VfMal85J1rndzNs04zj\n",
+       "tnIfHrd39tE9x428LX5nJw8+kBRgATAD2Iw3VCUpCu8Bvku4sbqmw2WRJEmSVK/3A88APwKWVvxs\n",
+       "DeGhp23A+W0uVwyGCfc2nkg+yztamt7kw3fNtQt4knA9PtLZovSk24AR4KnUsiFgI7Ad2ADM7kC5\n",
+       "qjoRWAw8SHm4LyG0zU8ntNXvwAHN6nU98JudLkQPm0a47hYQrkPvFTXuPwhhpGzOBE6hPNxvAj6V\n",
+       "TF8D3DjZDtoZotsIf3EqXQCsAw4S/trvIDwMpfpE3U2gxdIP3x1k7OE7NcZrMrvvAJXDw64A1ibT\n",
+       "a4GVk+2gG2rIx1HeXXI34WEo1edjwBbgy3TR17UeUe3hO6/BxhSBBwhP6V3Z4bLEYg6hqYbk3zmT\n",
+       "rdzsfu4bgblVll8L3FfHfnwYaryJzu11wM3A7yXzvw98DriiTeWKgddb8/0s8H3gTYRrdxuhNqrm\n",
+       "mHJUtmaH+3kZtql88OmEZJnK1Xpub6W+P6Sq7eE71ef7yb8vAXcRmr4M98aMECp4e4B5wIuTrdyp\n",
+       "Zpl0W9y9wCWEh54WAm/Du+v1mpeavpDymzCa2mOE624B4Tq8mHBdKpuZwKxk+ihCDzivycbdC6xK\n",
+       "plcBd3ewLGUuJLRrHiD85flm6mfXEm5obQN+of1F63m3E7qdbSH8D5+0LU5V+fBd8ywk9DjaDDyN\n",
+       "5zOLdcD3gFcJuflBQu+jB+jCrpCSJEmSJEmSJEmSJEmSJEmSJEmS+tD/AzUxDUJku6WfAAAAAElF\n",
+       "TkSuQmCC\n"
+      ],
+      "text/latex": [
+       "$\\mathcal{N}(\\mu=0, \\sigma=2),\\ N=2000$"
+      ],
+      "text/plain": [
+       "<__main__.Gaussian at 0x106e9ce90>"
+      ]
+     },
+     "execution_count": 7,
      "metadata": {},
-     "source": [
-      "Import the IPython display functions."
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x2 = Gaussian(0, 2, 2000)\n",
+    "x2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can then compare the two Gaussians by displaying their histograms:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import (\n",
-      "    display, display_html, display_png, display_svg\n",
-      ")"
-     ],
-     "language": "python",
+     "data": {
+      "image/png": [
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+       "kO0fz9ncvZu9956z9+yefXbfr5mdPefZs7tPbnY/++z3POcsSJIkSZIkSZIkSZIkSZIkSZIkSZJ6\n",
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+       "4EFgHXBNm57/1wij4bOBu4Avtel5Gp0B/Drw+3VtZwGnAl/oUB+m06xvq4HTgP3AD5n8/8raLqkH\n",
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+      ]
+     },
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
+     "output_type": "display_data"
     },
     {
-     "cell_type": "markdown",
+     "data": {
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+       "/ftfQXGqt1nGl3WoycpfKhFYm2++8efZb01Kc/gBSYqQ4S5JETLcJSlChrskRchwl6QIGe6SFCHD\n",
+       "XZIiZLhLUoQMd0mKkOEuRWPAQdx0iMMPSNEoH5LA4Qj6mzV3SYqQ4S5JETLcJSlChrskRchwl6Jl\n",
+       "75l+Zm8ZKVr2nuln1twlKUKGuyRFyHCXpAgZ7pIUIcNdkiJkuEtShAx3SYqQ4a66DA4OlT0YI6k7\n",
+       "+RCT6jI6upf0gzFgwEvdyJq71DccjqCfZA33+cCDwDPA08DHk+VDwEZgO7ABmN1oASU1S2k4gvAJ\n",
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+       "0ow29wXAKcDDwBxgJFk+ksxLktqs0XA/Gvg74GpgtOJnpcY9SVKbNdIVcjoh2L8K3J0sGwHmEppt\n",
+       "5gEvVttweHj40HQ+nyefzzdQDEmKT6FQoFAoZN4+ayflHKFN/WXCjdWSm5JlnyXcTJ3N+JuqxWLR\n",
+       "Cn03GhwcqtKDYjrh/nlaZT/3yeZrWacZ23jcLPvwd7F3JA8N1pzZWcP9DOCfgCcZu1rWAI8A64E3\n",
+       "A7uAi4B9Fdsa7l0qXDzdEDoe13BXpXaFeyMM9y5luPfbcQ33XlJvuPuEqiRFyHCX+lb5cAQOSRAX\n",
+       "Bw6T+lZpOIIxo6MOBBcLa+6SFCHDXZIiZLhLUoQMd0mKkOEuSREy3CUpQoZ7H/Nl11K87Ofex3zZ\n",
+       "tRQva+6SFCHDvY/YDKOpVQ5JMMPhCXqUzTJ9xGYYTa1ySILykSQdnqB3WHOXpAgZ7pIUIcM9EpXt\n",
+       "6baPSv3NNvdIjG9Pt31U6mfW3KM2YO8YNZkv+OgV1tyjVq3ng9QIX/DRK6y5S1KEDHdJipDhLkkR\n",
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+       "FouGUVqoeZd3WZzqHFXbZnzIT7VOt+7D4/bOPqqvk75+s1zftWjVfrtV8g295sy2n7ukJhuwqbAL\n",
+       "tKJZZjmwDXgOuKYF+5fU1cqbadQZzQ73acCfEwJ+CXApcFKTj9EHxj/iXfnShDGFThVSqkGhyrKp\n",
+       "r++p5/1mMJVmN8ucCuwAdiXzdwIXAM82+Tg96/77v8n992+YYq3xj3hXb/uE8MuTb0rZpOYrVFlW\n",
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+       "+n4ADh7czYEDHS6UpOg0O9xfAOan5ucTau9pO3O53KImH7fnHDiwuWJJ5VfMal85J1rndzNs04zj\n",
+       "tnIfHrd39tE9x428LX5nJw8+kBRgATAD2Iw3VCUpCu8Bvku4sbqmw2WRJEmSVK/3A88APwKWVvxs\n",
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+       "TF8D3DjZDtoZotsIf3EqXQCsAw4S/trvIDwMpfpE3U2gxdIP3x1k7OE7NcZrMrvvAJXDw64A1ibT\n",
+       "a4GVk+2gG2rIx1HeXXI34WEo1edjwBbgy3TR17UeUe3hO6/BxhSBBwhP6V3Z4bLEYg6hqYbk3zmT\n",
+       "rdzsfu4bgblVll8L3FfHfnwYaryJzu11wM3A7yXzvw98DriiTeWKgddb8/0s8H3gTYRrdxuhNqrm\n",
+       "mHJUtmaH+3kZtql88OmEZJnK1Xpub6W+P6Sq7eE71ef7yb8vAXcRmr4M98aMECp4e4B5wIuTrdyp\n",
+       "Zpl0W9y9wCWEh54WAm/Du+v1mpeavpDymzCa2mOE624B4Tq8mHBdKpuZwKxk+ihCDzivycbdC6xK\n",
+       "plcBd3ewLGUuJLRrHiD85flm6mfXEm5obQN+of1F63m3E7qdbSH8D5+0LU5V+fBd8ywk9DjaDDyN\n",
+       "5zOLdcD3gFcJuflBQu+jB+jCrpCSJEmSJEmSJEmSJEmSJEmSJEmS+tD/AzUxDUJku6WfAAAAAElF\n",
+       "TkSuQmCC\n"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Parts of this notebook need the matplotlib inline backend:"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display_png(x)\n",
+    "display_png(x2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that like `print`, you can call any of the `display` functions multiple times in a cell."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding IPython display support to existing objects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you are directly writing your own classes, you can adapt them for display in IPython by following the above approach.  But in practice, you often need to work with existing classes that you can't easily modify. We now illustrate how to add rich output capabilities to existing objects. We will use the NumPy polynomials and change their default representation to be a formatted LaTeX expression."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, consider how a NumPy polynomial object renders by default:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline\n",
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "Polynomial([ 1.,  2.,  3.], [-10.,  10.], [-1.,  1.])"
+      ]
+     },
+     "execution_count": 9,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p = np.polynomial.Polynomial([1,2,3], [-10, 10])\n",
+    "p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, define a function that pretty-prints a polynomial as a LaTeX string:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def poly_to_latex(p):\n",
+    "    terms = ['%.2g' % p.coef[0]]\n",
+    "    if len(p) > 1:\n",
+    "        term = 'x'\n",
+    "        c = p.coef[1]\n",
+    "        if c!=1:\n",
+    "            term = ('%.2g ' % c) + term\n",
+    "        terms.append(term)\n",
+    "    if len(p) > 2:\n",
+    "        for i in range(2, len(p)):\n",
+    "            term = 'x^%d' % i\n",
+    "            c = p.coef[i]\n",
+    "            if c!=1:\n",
+    "                term = ('%.2g ' % c) + term\n",
+    "            terms.append(term)\n",
+    "    px = '$P(x)=%s$' % '+'.join(terms)\n",
+    "    dom = r', $x \\in [%.2g,\\ %.2g]$' % tuple(p.domain)\n",
+    "    return px+dom"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This produces, on our polynomial ``p``, the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "text/plain": [
+       "'$P(x)=1+2 x+3 x^2$, $x \\\\in [-10,\\\\ 10]$'"
+      ]
+     },
+     "execution_count": 11,
      "metadata": {},
-     "source": [
-      "Special display methods"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "poly_to_latex(p)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can render this string using the `Latex` class:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/latex": [
+       "$P(x)=1+2 x+3 x^2$, $x \\in [-10,\\ 10]$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Latex object>"
+      ]
+     },
+     "execution_count": 12,
      "metadata": {},
-     "source": [
-      "The main idea of the first approach is that you have to implement special display methods when you define your class, one for each representation you want to use. Here is a list of the names of the special methods and the values they must return:\n",
-      "\n",
-      "* `_repr_html_`: return raw HTML as a string\n",
-      "* `_repr_json_`: return raw JSON as a string\n",
-      "* `_repr_jpeg_`: return raw JPEG data\n",
-      "* `_repr_png_`: return raw PNG data\n",
-      "* `_repr_svg_`: return raw SVG data as a string\n",
-      "* `_repr_latex_`: return LaTeX commands in a string surrounded by \"$\"."
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Latex\n",
+    "Latex(poly_to_latex(p))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, you can configure IPython to do this automatically by registering the `Polynomial` class and the `plot_to_latex` function with an IPython display formatter. Let's look at the default formatters provided by IPython:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "As an illustration, we build a class that holds data generated by sampling a Gaussian distribution with given mean and standard deviation. Here is the definition of the `Gaussian` class, which has a custom PNG and LaTeX representation."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              text/plain : PlainTextFormatter\n",
+      "              image/jpeg : JPEGFormatter\n",
+      "               text/html : HTMLFormatter\n",
+      "           image/svg+xml : SVGFormatter\n",
+      "               image/png : PNGFormatter\n",
+      "  application/javascript : JavascriptFormatter\n",
+      "           text/markdown : MarkdownFormatter\n",
+      "              text/latex : LatexFormatter\n",
+      "        application/json : JSONFormatter\n",
+      "         application/pdf : PDFFormatter\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "ip = get_ipython()\n",
+    "for mime, formatter in ip.display_formatter.formatters.items():\n",
+    "    print '%24s : %s' % (mime, formatter.__class__.__name__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `formatters` attribute is a dictionary keyed by MIME types. To define a custom LaTeX display function, you want a handle on the `text/latex` formatter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ip = get_ipython()\n",
+    "latex_f = ip.display_formatter.formatters['text/latex']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The formatter object has a couple of methods for registering custom display functions for existing types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.core.pylabtools import print_figure\n",
-      "from IPython.display import Image, SVG, Math\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on method for_type in module IPython.core.formatters:\n",
       "\n",
-      "class Gaussian(object):\n",
-      "    \"\"\"A simple object holding data sampled from a Gaussian distribution.\n",
-      "    \"\"\"\n",
-      "    def __init__(self, mean=0.0, std=1, size=1000):\n",
-      "        self.data = np.random.normal(mean, std, size)\n",
-      "        self.mean = mean\n",
-      "        self.std = std\n",
-      "        self.size = size\n",
-      "        # For caching plots that may be expensive to compute\n",
-      "        self._png_data = None\n",
-      "        \n",
-      "    def _figure_data(self, format):\n",
-      "        fig, ax = plt.subplots()\n",
-      "        ax.hist(self.data, bins=50)\n",
-      "        ax.set_title(self._repr_latex_())\n",
-      "        ax.set_xlim(-10.0,10.0)\n",
-      "        data = print_figure(fig, format)\n",
-      "        # We MUST close the figure, otherwise IPython's display machinery\n",
-      "        # will pick it up and send it as output, resulting in a double display\n",
-      "        plt.close(fig)\n",
-      "        return data\n",
+      "for_type(self, typ, func=None) method of IPython.core.formatters.LatexFormatter instance\n",
+      "    Add a format function for a given type.\n",
+      "    \n",
+      "    Parameters\n",
+      "    -----------\n",
+      "    typ : type or '__module__.__name__' string for a type\n",
+      "        The class of the object that will be formatted using `func`.\n",
+      "    func : callable\n",
+      "        A callable for computing the format data.\n",
+      "        `func` will be called with the object to be formatted,\n",
+      "        and will return the raw data in this formatter's format.\n",
+      "        Subclasses may use a different call signature for the\n",
+      "        `func` argument.\n",
       "        \n",
-      "    def _repr_png_(self):\n",
-      "        if self._png_data is None:\n",
-      "            self._png_data = self._figure_data('png')\n",
-      "        return self._png_data\n",
+      "        If `func` is None or not specified, there will be no change,\n",
+      "        only returning the current value.\n",
       "    \n",
-      "    def _repr_latex_(self):\n",
-      "        return r'$\\mathcal{N}(\\mu=%.2g, \\sigma=%.2g),\\ N=%d$' % (self.mean,\n",
-      "                                                                 self.std, self.size)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Create an instance of the Gaussian distribution and return it to display the default representation:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x = Gaussian(2.0, 1.0)\n",
-      "x"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$\\mathcal{N}(\\mu=2, \\sigma=1),\\ N=1000$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": 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-       "prompt_number": 4,
-       "text": [
-        "<__main__.Gaussian at 0x106e7ae10>"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can also pass the object to the `display` function to display the default representation:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display(x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$\\mathcal{N}(\\mu=2, \\sigma=1),\\ N=1000$"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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RqVSoVCot3z/tyRaWA/cAb0jWbwSeBW4g7OhcTPMdntVq1cG64lEqlQhfMGe7JsU23fcY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\n",
-       "text": [
-        "<__main__.Gaussian at 0x106e7ae10>"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Use `display_png` to view the PNG representation:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display_png(x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<div class=\"alert alert-success\">\n",
-      "It is important to note a subtle different between <code>display</code> and <code>display_png</code>. The former computes <em>all</em> representations of the object, and lets the notebook UI decide which to display. The later only computes the PNG representation.\n",
-      "</div>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Create a new Gaussian with different parameters:"
+      "    Returns\n",
+      "    -------\n",
+      "    oldfunc : callable\n",
+      "        The currently registered callable.\n",
+      "        If you are registering a new formatter,\n",
+      "        this will be the previous value (to enable restoring later).\n",
+      "\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x2 = Gaussian(0, 2, 2000)\n",
-      "x2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$\\mathcal{N}(\\mu=0, \\sigma=2),\\ N=2000$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": 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-       "prompt_number": 7,
-       "text": [
-        "<__main__.Gaussian at 0x106e9ce90>"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can then compare the two Gaussians by displaying their histograms:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display_png(x)\n",
-      "display_png(x2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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-       "output_type": "display_data",
-       "png": 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6upf0gzFgwEvdyJq71DccjqCfZA33+cCDwDPA08DHk+VDwEZgO7ABmN1oASU1S2k4gvAJ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\nTkSuQmCC\n"
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Note that like `print`, you can call any of the `display` functions multiple times in a cell."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Adding IPython display support to existing objects"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When you are directly writing your own classes, you can adapt them for display in IPython by following the above approach.  But in practice, you often need to work with existing classes that you can't easily modify. We now illustrate how to add rich output capabilities to existing objects. We will use the NumPy polynomials and change their default representation to be a formatted LaTeX expression."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "First, consider how a NumPy polynomial object renders by default:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "p = np.polynomial.Polynomial([1,2,3], [-10, 10])\n",
-      "p"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 9,
-       "text": [
-        "Polynomial([ 1.,  2.,  3.], [-10.,  10.], [-1.,  1.])"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Next, define a function that pretty-prints a polynomial as a LaTeX string:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def poly_to_latex(p):\n",
-      "    terms = ['%.2g' % p.coef[0]]\n",
-      "    if len(p) > 1:\n",
-      "        term = 'x'\n",
-      "        c = p.coef[1]\n",
-      "        if c!=1:\n",
-      "            term = ('%.2g ' % c) + term\n",
-      "        terms.append(term)\n",
-      "    if len(p) > 2:\n",
-      "        for i in range(2, len(p)):\n",
-      "            term = 'x^%d' % i\n",
-      "            c = p.coef[i]\n",
-      "            if c!=1:\n",
-      "                term = ('%.2g ' % c) + term\n",
-      "            terms.append(term)\n",
-      "    px = '$P(x)=%s$' % '+'.join(terms)\n",
-      "    dom = r', $x \\in [%.2g,\\ %.2g]$' % tuple(p.domain)\n",
-      "    return px+dom"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This produces, on our polynomial ``p``, the following:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "poly_to_latex(p)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 11,
-       "text": [
-        "'$P(x)=1+2 x+3 x^2$, $x \\\\in [-10,\\\\ 10]$'"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can render this string using the `Latex` class:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import Latex\n",
-      "Latex(poly_to_latex(p))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$P(x)=1+2 x+3 x^2$, $x \\in [-10,\\ 10]$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 12,
-       "text": [
-        "<IPython.core.display.Latex object>"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "However, you can configure IPython to do this automatically by registering the `Polynomial` class and the `plot_to_latex` function with an IPython display formatter. Let's look at the default formatters provided by IPython:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ip = get_ipython()\n",
-      "for mime, formatter in ip.display_formatter.formatters.items():\n",
-      "    print '%24s : %s' % (mime, formatter.__class__.__name__)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "              text/plain : PlainTextFormatter\n",
-        "              image/jpeg : JPEGFormatter\n",
-        "               text/html : HTMLFormatter\n",
-        "           image/svg+xml : SVGFormatter\n",
-        "               image/png : PNGFormatter\n",
-        "  application/javascript : JavascriptFormatter\n",
-        "           text/markdown : MarkdownFormatter\n",
-        "              text/latex : LatexFormatter\n",
-        "        application/json : JSONFormatter\n",
-        "         application/pdf : PDFFormatter\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The `formatters` attribute is a dictionary keyed by MIME types. To define a custom LaTeX display function, you want a handle on the `text/latex` formatter:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ip = get_ipython()\n",
-      "latex_f = ip.display_formatter.formatters['text/latex']"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The formatter object has a couple of methods for registering custom display functions for existing types."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "help(latex_f.for_type)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Help on method for_type in module IPython.core.formatters:\n",
-        "\n",
-        "for_type(self, typ, func=None) method of IPython.core.formatters.LatexFormatter instance\n",
-        "    Add a format function for a given type.\n",
-        "    \n",
-        "    Parameters\n",
-        "    -----------\n",
-        "    typ : type or '__module__.__name__' string for a type\n",
-        "        The class of the object that will be formatted using `func`.\n",
-        "    func : callable\n",
-        "        A callable for computing the format data.\n",
-        "        `func` will be called with the object to be formatted,\n",
-        "        and will return the raw data in this formatter's format.\n",
-        "        Subclasses may use a different call signature for the\n",
-        "        `func` argument.\n",
-        "        \n",
-        "        If `func` is None or not specified, there will be no change,\n",
-        "        only returning the current value.\n",
-        "    \n",
-        "    Returns\n",
-        "    -------\n",
-        "    oldfunc : callable\n",
-        "        The currently registered callable.\n",
-        "        If you are registering a new formatter,\n",
-        "        this will be the previous value (to enable restoring later).\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "help(latex_f.for_type_by_name)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Help on method for_type_by_name in module IPython.core.formatters:\n",
-        "\n",
-        "for_type_by_name(self, type_module, type_name, func=None) method of IPython.core.formatters.LatexFormatter instance\n",
-        "    Add a format function for a type specified by the full dotted\n",
-        "    module and name of the type, rather than the type of the object.\n",
-        "    \n",
-        "    Parameters\n",
-        "    ----------\n",
-        "    type_module : str\n",
-        "        The full dotted name of the module the type is defined in, like\n",
-        "        ``numpy``.\n",
-        "    type_name : str\n",
-        "        The name of the type (the class name), like ``dtype``\n",
-        "    func : callable\n",
-        "        A callable for computing the format data.\n",
-        "        `func` will be called with the object to be formatted,\n",
-        "        and will return the raw data in this formatter's format.\n",
-        "        Subclasses may use a different call signature for the\n",
-        "        `func` argument.\n",
-        "        \n",
-        "        If `func` is None or unspecified, there will be no change,\n",
-        "        only returning the current value.\n",
-        "    \n",
-        "    Returns\n",
-        "    -------\n",
-        "    oldfunc : callable\n",
-        "        The currently registered callable.\n",
-        "        If you are registering a new formatter,\n",
-        "        this will be the previous value (to enable restoring later).\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In this case, we will use `for_type_by_name` to register `poly_to_latex` as the display function for the `Polynomial` type:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "latex_f.for_type_by_name('numpy.polynomial.polynomial',\n",
-      "                                 'Polynomial', poly_to_latex)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Once the custom display function has been registered, all NumPy `Polynomial` instances will be represented by their LaTeX form instead:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "p"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$P(x)=1+2 x+3 x^2$, $x \\in [-10,\\ 10]$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 19,
-       "text": [
-        "Polynomial([ 1.,  2.,  3.], [-10.,  10.], [-1.,  1.])"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "p2 = np.polynomial.Polynomial([-20, 71, -15, 1])\n",
-      "p2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$P(x)=-20+71 x+-15 x^2+x^3$, $x \\in [-1,\\ 1]$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 20,
-       "text": [
-        "Polynomial([-20.,  71., -15.,   1.], [-1.,  1.], [-1.,  1.])"
-       ]
-      }
-     ],
-     "prompt_number": 20
-    },
+    }
+   ],
+   "source": [
+    "help(latex_f.for_type)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "More complex display with `_ipython_display_`"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on method for_type_by_name in module IPython.core.formatters:\n",
+      "\n",
+      "for_type_by_name(self, type_module, type_name, func=None) method of IPython.core.formatters.LatexFormatter instance\n",
+      "    Add a format function for a type specified by the full dotted\n",
+      "    module and name of the type, rather than the type of the object.\n",
+      "    \n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    type_module : str\n",
+      "        The full dotted name of the module the type is defined in, like\n",
+      "        ``numpy``.\n",
+      "    type_name : str\n",
+      "        The name of the type (the class name), like ``dtype``\n",
+      "    func : callable\n",
+      "        A callable for computing the format data.\n",
+      "        `func` will be called with the object to be formatted,\n",
+      "        and will return the raw data in this formatter's format.\n",
+      "        Subclasses may use a different call signature for the\n",
+      "        `func` argument.\n",
+      "        \n",
+      "        If `func` is None or unspecified, there will be no change,\n",
+      "        only returning the current value.\n",
+      "    \n",
+      "    Returns\n",
+      "    -------\n",
+      "    oldfunc : callable\n",
+      "        The currently registered callable.\n",
+      "        If you are registering a new formatter,\n",
+      "        this will be the previous value (to enable restoring later).\n",
+      "\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "help(latex_f.for_type_by_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this case, we will use `for_type_by_name` to register `poly_to_latex` as the display function for the `Polynomial` type:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "latex_f.for_type_by_name('numpy.polynomial.polynomial',\n",
+    "                                 'Polynomial', poly_to_latex)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once the custom display function has been registered, all NumPy `Polynomial` instances will be represented by their LaTeX form instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/latex": [
+       "$P(x)=1+2 x+3 x^2$, $x \\in [-10,\\ 10]$"
+      ],
+      "text/plain": [
+       "Polynomial([ 1.,  2.,  3.], [-10.,  10.], [-1.,  1.])"
+      ]
+     },
+     "execution_count": 19,
      "metadata": {},
-     "source": [
-      "Rich output special methods and functions can only display one object or MIME type at a time. Sometimes this is not enough if you want to display multiple objects or MIME types at once. An example of this would be to use an HTML representation to put some HTML elements in the DOM and then use a JavaScript representation to add events to those elements.\n",
-      "\n",
-      "**IPython 2.0** recognizes another display method, `_ipython_display_`, which allows your objects to take complete control of displaying themselves. If this method is defined, IPython will call it, and make no effort to display the object using the above described `_repr_*_` methods for custom display functions. It's a way for you to say \"Back off, IPython, I can display this myself.\" Most importantly, your `_ipython_display_` method can make multiple calls to the top-level `display` functions to accomplish its goals.\n",
-      "\n",
-      "Here is an object that uses `display_html` and `display_javascript` to make a plot using the [Flot](http://www.flotcharts.org/) JavaScript plotting library:"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import json\n",
-      "import uuid\n",
-      "from IPython.display import display_javascript, display_html, display\n",
-      "\n",
-      "class FlotPlot(object):\n",
-      "    def __init__(self, x, y):\n",
-      "        self.x = x\n",
-      "        self.y = y\n",
-      "        self.uuid = str(uuid.uuid4())\n",
-      "    \n",
-      "    def _ipython_display_(self):\n",
-      "        json_data = json.dumps(zip(self.x, self.y))\n",
-      "        display_html('<div id=\"{}\" style=\"height: 300px; width:80%;\"></div>'.format(self.uuid),\n",
-      "            raw=True\n",
-      "        )\n",
-      "        display_javascript(\"\"\"\n",
-      "        require([\"//cdnjs.cloudflare.com/ajax/libs/flot/0.8.2/jquery.flot.min.js\"], function() {\n",
-      "          var line = JSON.parse(\"%s\");\n",
-      "          console.log(line);\n",
-      "          $.plot(\"#%s\", [line]);\n",
-      "        });\n",
-      "        \"\"\" % (json_data, self.uuid), raw=True)\n"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$P(x)=-20+71 x+-15 x^2+x^3$, $x \\in [-1,\\ 1]$"
+      ],
+      "text/plain": [
+       "Polynomial([-20.,  71., -15.,   1.], [-1.,  1.], [-1.,  1.])"
+      ]
+     },
+     "execution_count": 20,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 21
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p2 = np.polynomial.Polynomial([-20, 71, -15, 1])\n",
+    "p2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More complex display with `_ipython_display_`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rich output special methods and functions can only display one object or MIME type at a time. Sometimes this is not enough if you want to display multiple objects or MIME types at once. An example of this would be to use an HTML representation to put some HTML elements in the DOM and then use a JavaScript representation to add events to those elements.\n",
+    "\n",
+    "**IPython 2.0** recognizes another display method, `_ipython_display_`, which allows your objects to take complete control of displaying themselves. If this method is defined, IPython will call it, and make no effort to display the object using the above described `_repr_*_` methods for custom display functions. It's a way for you to say \"Back off, IPython, I can display this myself.\" Most importantly, your `_ipython_display_` method can make multiple calls to the top-level `display` functions to accomplish its goals.\n",
+    "\n",
+    "Here is an object that uses `display_html` and `display_javascript` to make a plot using the [Flot](http://www.flotcharts.org/) JavaScript plotting library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import uuid\n",
+    "from IPython.display import display_javascript, display_html, display\n",
+    "\n",
+    "class FlotPlot(object):\n",
+    "    def __init__(self, x, y):\n",
+    "        self.x = x\n",
+    "        self.y = y\n",
+    "        self.uuid = str(uuid.uuid4())\n",
+    "    \n",
+    "    def _ipython_display_(self):\n",
+    "        json_data = json.dumps(zip(self.x, self.y))\n",
+    "        display_html('<div id=\"{}\" style=\"height: 300px; width:80%;\"></div>'.format(self.uuid),\n",
+    "            raw=True\n",
+    "        )\n",
+    "        display_javascript(\"\"\"\n",
+    "        require([\"//cdnjs.cloudflare.com/ajax/libs/flot/0.8.2/jquery.flot.min.js\"], function() {\n",
+    "          var line = JSON.parse(\"%s\");\n",
+    "          console.log(line);\n",
+    "          $.plot(\"#%s\", [line]);\n",
+    "        });\n",
+    "        \"\"\" % (json_data, self.uuid), raw=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "x = np.linspace(0,10)\n",
-      "y = np.sin(x)\n",
-      "FlotPlot(x, np.sin(x))"
-     ],
-     "language": "python",
+     "data": {
+      "text/html": [
+       "<div id=\"e75b8189-92cb-4cbb-9996-bb8ad5ff1b4e\" style=\"height: 300px; width:80%;\"></div>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div id=\"e75b8189-92cb-4cbb-9996-bb8ad5ff1b4e\" style=\"height: 300px; width:80%;\"></div>"
-       ],
-       "metadata": {},
-       "output_type": "display_data"
-      },
-      {
-       "javascript": [
-        "\n",
-        "        require([\"//cdnjs.cloudflare.com/ajax/libs/flot/0.8.2/jquery.flot.min.js\"], function() {\n",
-        "          var line = JSON.parse(\"[[0.0, 0.0], [0.20408163265306123, 0.20266793654820095], [0.40816326530612246, 0.39692414892492234], [0.61224489795918369, 0.57470604121617908], [0.81632653061224492, 0.72863478346935029], [1.0204081632653061, 0.85232156971961837], [1.2244897959183674, 0.94063278511248671], [1.4285714285714286, 0.98990307637212394], [1.6326530612244898, 0.99808748213471832], [1.8367346938775511, 0.96484630898376322], [2.0408163265306123, 0.89155923041100371], [2.2448979591836737, 0.7812680235262639], [2.4489795918367347, 0.63855032022660208], [2.6530612244897958, 0.46932961277720098], [2.8571428571428572, 0.28062939951435684], [3.0612244897959187, 0.080281674842813497], [3.2653061224489797, -0.12339813736217871], [3.4693877551020407, -0.32195631507261868], [3.6734693877551021, -0.50715170948451438], [3.8775510204081636, -0.67129779355193209], [4.0816326530612246, -0.80758169096833643], [4.2857142857142856, -0.91034694431078278], [4.4897959183673475, -0.97532828606704558], [4.6938775510204085, -0.99982866838408957], [4.8979591836734695, -0.98283120392563061], [5.1020408163265305, -0.92504137173820289], [5.3061224489795915, -0.82885773637304272], [5.5102040816326534, -0.69827239556539955], [5.7142857142857144, -0.53870528838615628], [5.9183673469387754, -0.35677924089893803], [6.1224489795918373, -0.16004508604325057], [6.3265306122448983, 0.043331733368683463], [6.5306122448979593, 0.24491007101197931], [6.7346938775510203, 0.43632342647181932], [6.9387755102040813, 0.6096271964908323], [7.1428571428571432, 0.75762841539272019], [7.3469387755102042, 0.87418429881973347], [7.5510204081632653, 0.95445719973875187], [7.7551020408163271, 0.99511539477766364], [7.9591836734693882, 0.99447136726361685], [8.1632653061224492, 0.95255184753146038], [8.3673469387755102, 0.87109670348232071], [8.5714285714285712, 0.75348672743963763], [8.7755102040816322, 0.60460331650615429], [8.979591836734695, 0.43062587038273736], [9.183673469387756, 0.23877531564403087], [9.387755102040817, 0.037014401485062368], [9.591836734693878, -0.16628279384875641], [9.795918367346939, -0.36267842882654883], [10.0, -0.54402111088936989]]\");\n",
-        "          console.log(line);\n",
-        "          $.plot(\"#e75b8189-92cb-4cbb-9996-bb8ad5ff1b4e\", [line]);\n",
-        "        });\n",
-        "        "
-       ],
-       "metadata": {},
-       "output_type": "display_data"
-      }
-     ],
-     "prompt_number": 22
+     "output_type": "display_data"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
+     "data": {
+      "application/javascript": [
+       "\n",
+       "        require([\"//cdnjs.cloudflare.com/ajax/libs/flot/0.8.2/jquery.flot.min.js\"], function() {\n",
+       "          var line = JSON.parse(\"[[0.0, 0.0], [0.20408163265306123, 0.20266793654820095], [0.40816326530612246, 0.39692414892492234], [0.61224489795918369, 0.57470604121617908], [0.81632653061224492, 0.72863478346935029], [1.0204081632653061, 0.85232156971961837], [1.2244897959183674, 0.94063278511248671], [1.4285714285714286, 0.98990307637212394], [1.6326530612244898, 0.99808748213471832], [1.8367346938775511, 0.96484630898376322], [2.0408163265306123, 0.89155923041100371], [2.2448979591836737, 0.7812680235262639], [2.4489795918367347, 0.63855032022660208], [2.6530612244897958, 0.46932961277720098], [2.8571428571428572, 0.28062939951435684], [3.0612244897959187, 0.080281674842813497], [3.2653061224489797, -0.12339813736217871], [3.4693877551020407, -0.32195631507261868], [3.6734693877551021, -0.50715170948451438], [3.8775510204081636, -0.67129779355193209], [4.0816326530612246, -0.80758169096833643], [4.2857142857142856, -0.91034694431078278], [4.4897959183673475, -0.97532828606704558], [4.6938775510204085, -0.99982866838408957], [4.8979591836734695, -0.98283120392563061], [5.1020408163265305, -0.92504137173820289], [5.3061224489795915, -0.82885773637304272], [5.5102040816326534, -0.69827239556539955], [5.7142857142857144, -0.53870528838615628], [5.9183673469387754, -0.35677924089893803], [6.1224489795918373, -0.16004508604325057], [6.3265306122448983, 0.043331733368683463], [6.5306122448979593, 0.24491007101197931], [6.7346938775510203, 0.43632342647181932], [6.9387755102040813, 0.6096271964908323], [7.1428571428571432, 0.75762841539272019], [7.3469387755102042, 0.87418429881973347], [7.5510204081632653, 0.95445719973875187], [7.7551020408163271, 0.99511539477766364], [7.9591836734693882, 0.99447136726361685], [8.1632653061224492, 0.95255184753146038], [8.3673469387755102, 0.87109670348232071], [8.5714285714285712, 0.75348672743963763], [8.7755102040816322, 0.60460331650615429], [8.979591836734695, 0.43062587038273736], [9.183673469387756, 0.23877531564403087], [9.387755102040817, 0.037014401485062368], [9.591836734693878, -0.16628279384875641], [9.795918367346939, -0.36267842882654883], [10.0, -0.54402111088936989]]\");\n",
+       "          console.log(line);\n",
+       "          $.plot(\"#e75b8189-92cb-4cbb-9996-bb8ad5ff1b4e\", [line]);\n",
+       "        });\n",
+       "        "
+      ]
+     },
      "metadata": {},
-     "outputs": []
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "import numpy as np\n",
+    "x = np.linspace(0,10)\n",
+    "y = np.sin(x)\n",
+    "FlotPlot(x, np.sin(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:86c779d5798c4a68bda7e71c8ef320cb7ba9d7e3d0f1bc4b828ee65f617a5ae3"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/Index.ipynb b/examples/IPython Kernel/Index.ipynb
index 5627e30..9418e0e 100644
--- a/examples/IPython Kernel/Index.ipynb
+++ b/examples/IPython Kernel/Index.ipynb
@@ -1,172 +1,168 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:ee769d05a7e195e4b8546ef9a866ef03e59bff2f0fcba499d168c06b516aa79a"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<img src=\"../images/ipython_logo.png\">"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Back to the main [Index](../Index.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "IPython Kernel"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython provides extensions to the Python programming language that make working interactively convenient and efficient. These extensions are implemented in the IPython Kernel and are available in all of the IPython Frontends (Notebook, Terminal, Console and Qt Console) when running this kernel."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Tutorials"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "* [Cell Magics](Cell Magics.ipynb)\n",
-      "* [Script Magics](Script Magics.ipynb)\n",
-      "* [Rich Output](Rich Output.ipynb)\n",
-      "* [Custom Display Logic](Custom Display Logic.ipynb)\n",
-      "* [Plotting in the Notebook](Plotting in the Notebook.ipynb)\n",
-      "* [Capturing Output](Capturing Output.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Examples"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "* [Background Jobs](Background Jobs.ipynb)\n",
-      "* [Trapezoid Rule](Trapezoid Rule.ipynb)\n",
-      "* [SymPy](SymPy.ipynb)\n",
-      "* [Raw Input in the Notebook](Raw Input in the Notebook.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Non-notebook examples"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This directory also contains examples that are regular Python (`.py`) files."
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"../images/ipython_logo.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Back to the main [Index](../Index.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# IPython Kernel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython provides extensions to the Python programming language that make working interactively convenient and efficient. These extensions are implemented in the IPython Kernel and are available in all of the IPython Frontends (Notebook, Terminal, Console and Qt Console) when running this kernel."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tutorials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* [Cell Magics](Cell Magics.ipynb)\n",
+    "* [Script Magics](Script Magics.ipynb)\n",
+    "* [Rich Output](Rich Output.ipynb)\n",
+    "* [Custom Display Logic](Custom Display Logic.ipynb)\n",
+    "* [Plotting in the Notebook](Plotting in the Notebook.ipynb)\n",
+    "* [Capturing Output](Capturing Output.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* [Background Jobs](Background Jobs.ipynb)\n",
+    "* [Trapezoid Rule](Trapezoid Rule.ipynb)\n",
+    "* [SymPy](SymPy.ipynb)\n",
+    "* [Raw Input in the Notebook](Raw Input in the Notebook.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Non-notebook examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory also contains examples that are regular Python (`.py`) files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%run ../utils/list_pyfiles.ipy"
-     ],
-     "language": "python",
+     "data": {
+      "text/html": [
+       "<a href='example-demo.py' target='_blank'>example-demo.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/IPython Kernel/example-demo.py"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<a href='example-demo.py' target='_blank'>example-demo.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/IPython Kernel/example-demo.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='ipython-get-history.py' target='_blank'>ipython-get-history.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/IPython Kernel/ipython-get-history.py"
-       ]
-      }
-     ],
-     "prompt_number": 1
+     "output_type": "display_data"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/html": [
+       "<a href='ipython-get-history.py' target='_blank'>ipython-get-history.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/IPython Kernel/ipython-get-history.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "There are also a set of examples that show how to integrate IPython with different GUI event loops:"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%run ../utils/list_pyfiles.ipy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are also a set of examples that show how to integrate IPython with different GUI event loops:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%run ../utils/list_subdirs.ipy"
-     ],
-     "language": "python",
+     "data": {
+      "text/html": [
+       "gui/<br>\n",
+       "&nbsp;&nbsp;<a href='gui/gui-glut.py' target='_blank'>gui-glut.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='gui/gui-gtk.py' target='_blank'>gui-gtk.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='gui/gui-gtk3.py' target='_blank'>gui-gtk3.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='gui/gui-pyglet.py' target='_blank'>gui-pyglet.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='gui/gui-qt.py' target='_blank'>gui-qt.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='gui/gui-tk.py' target='_blank'>gui-tk.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='gui/gui-wx.py' target='_blank'>gui-wx.py</a><br>"
+      ],
+      "text/plain": [
+       "gui/\n",
+       "  gui-glut.py\n",
+       "  gui-gtk.py\n",
+       "  gui-gtk3.py\n",
+       "  gui-pyglet.py\n",
+       "  gui-qt.py\n",
+       "  gui-tk.py\n",
+       "  gui-wx.py"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "gui/<br>\n",
-        "&nbsp;&nbsp;<a href='gui/gui-glut.py' target='_blank'>gui-glut.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='gui/gui-gtk.py' target='_blank'>gui-gtk.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='gui/gui-gtk3.py' target='_blank'>gui-gtk3.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='gui/gui-pyglet.py' target='_blank'>gui-pyglet.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='gui/gui-qt.py' target='_blank'>gui-qt.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='gui/gui-tk.py' target='_blank'>gui-tk.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='gui/gui-wx.py' target='_blank'>gui-wx.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "gui/\n",
-        "  gui-glut.py\n",
-        "  gui-gtk.py\n",
-        "  gui-gtk3.py\n",
-        "  gui-pyglet.py\n",
-        "  gui-qt.py\n",
-        "  gui-tk.py\n",
-        "  gui-wx.py"
-       ]
-      }
-     ],
-     "prompt_number": 2
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "%run ../utils/list_subdirs.ipy"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:ee769d05a7e195e4b8546ef9a866ef03e59bff2f0fcba499d168c06b516aa79a"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/Old Custom Display Logic.ipynb b/examples/IPython Kernel/Old Custom Display Logic.ipynb
index 051f1e4..b147242 100644
--- a/examples/IPython Kernel/Old Custom Display Logic.ipynb
+++ b/examples/IPython Kernel/Old Custom Display Logic.ipynb
@@ -1,944 +1,1483 @@
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Defining Custom Display Logic for Your Own Objects"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Overview"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In Python, objects can declare their textual representation using the `__repr__` method.  IPython expands on this idea and allows objects to declare other, richer representations including:\n",
-      "\n",
-      "* HTML\n",
-      "* JSON\n",
-      "* PNG\n",
-      "* JPEG\n",
-      "* SVG\n",
-      "* LaTeX\n",
-      "\n",
-      "This Notebook shows how you can add custom display logic to your own classes, so that they can be displayed using these rich representations. There are two ways of accomplishing this:\n",
-      "\n",
-      "1. Implementing special display methods such as `_repr_html_`.\n",
-      "2. Registering a display function for a particular type.\n",
-      "\n",
-      "In this Notebook we show how both approaches work."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Before we get started, we will import the various display functions for displaying the different formats we will create."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import display\n",
-      "from IPython.display import (\n",
-      "    display_html, display_jpeg, display_png,\n",
-      "    display_javascript, display_svg, display_latex\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Implementing special display methods"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The main idea of the first approach is that you have to implement special display methods, one for each representation you want to use. Here is a list of the names of the special methods and the values they must return:\n",
-      "\n",
-      "* `_repr_html_`: return raw HTML as a string\n",
-      "* `_repr_json_`: return raw JSON as a string\n",
-      "* `_repr_jpeg_`: return raw JPEG data\n",
-      "* `_repr_png_`: return raw PNG data\n",
-      "* `_repr_svg_`: return raw SVG data as a string\n",
-      "* `_repr_latex_`: return LaTeX commands in a string surrounded by \"$\"."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Model Citizen: pandas"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "A prominent example of a package that has IPython-aware rich representations of its objects is [pandas](http://pandas.pydata.org/).\n",
-      "\n",
-      "A pandas DataFrame has a rich HTML table representation,\n",
-      "using `_repr_html_`.\n"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import io\n",
-      "import pandas"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%writefile data.csv\n",
-      "Date,Open,High,Low,Close,Volume,Adj Close\n",
-      "2012-06-01,569.16,590.00,548.50,584.00,14077000,581.50\n",
-      "2012-05-01,584.90,596.76,522.18,577.73,18827900,575.26\n",
-      "2012-04-02,601.83,644.00,555.00,583.98,28759100,581.48\n",
-      "2012-03-01,548.17,621.45,516.22,599.55,26486000,596.99\n",
-      "2012-02-01,458.41,547.61,453.98,542.44,22001000,540.12\n",
-      "2012-01-03,409.40,458.24,409.00,456.48,12949100,454.53\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Writing data.csv\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "df = pandas.read_csv(\"data.csv\")\n",
-      "pandas.set_option('display.notebook_repr_html', False)\n",
-      "df"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "         Date    Open    High     Low   Close    Volume  Adj Close\n",
-        "0  2012-06-01  569.16  590.00  548.50  584.00  14077000     581.50\n",
-        "1  2012-05-01  584.90  596.76  522.18  577.73  18827900     575.26\n",
-        "2  2012-04-02  601.83  644.00  555.00  583.98  28759100     581.48\n",
-        "3  2012-03-01  548.17  621.45  516.22  599.55  26486000     596.99\n",
-        "4  2012-02-01  458.41  547.61  453.98  542.44  22001000     540.12\n",
-        "5  2012-01-03  409.40  458.24  409.00  456.48  12949100     454.53"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "rich HTML can be activated via `pandas.set_option`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pandas.set_option('display.notebook_repr_html', True)\n",
-      "df"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>Date</th>\n",
-        "      <th>Open</th>\n",
-        "      <th>High</th>\n",
-        "      <th>Low</th>\n",
-        "      <th>Close</th>\n",
-        "      <th>Volume</th>\n",
-        "      <th>Adj Close</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>0</th>\n",
-        "      <td> 2012-06-01</td>\n",
-        "      <td> 569.16</td>\n",
-        "      <td> 590.00</td>\n",
-        "      <td> 548.50</td>\n",
-        "      <td> 584.00</td>\n",
-        "      <td> 14077000</td>\n",
-        "      <td> 581.50</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>1</th>\n",
-        "      <td> 2012-05-01</td>\n",
-        "      <td> 584.90</td>\n",
-        "      <td> 596.76</td>\n",
-        "      <td> 522.18</td>\n",
-        "      <td> 577.73</td>\n",
-        "      <td> 18827900</td>\n",
-        "      <td> 575.26</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>2</th>\n",
-        "      <td> 2012-04-02</td>\n",
-        "      <td> 601.83</td>\n",
-        "      <td> 644.00</td>\n",
-        "      <td> 555.00</td>\n",
-        "      <td> 583.98</td>\n",
-        "      <td> 28759100</td>\n",
-        "      <td> 581.48</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>3</th>\n",
-        "      <td> 2012-03-01</td>\n",
-        "      <td> 548.17</td>\n",
-        "      <td> 621.45</td>\n",
-        "      <td> 516.22</td>\n",
-        "      <td> 599.55</td>\n",
-        "      <td> 26486000</td>\n",
-        "      <td> 596.99</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>4</th>\n",
-        "      <td> 2012-02-01</td>\n",
-        "      <td> 458.41</td>\n",
-        "      <td> 547.61</td>\n",
-        "      <td> 453.98</td>\n",
-        "      <td> 542.44</td>\n",
-        "      <td> 22001000</td>\n",
-        "      <td> 540.12</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>5</th>\n",
-        "      <td> 2012-01-03</td>\n",
-        "      <td> 409.40</td>\n",
-        "      <td> 458.24</td>\n",
-        "      <td> 409.00</td>\n",
-        "      <td> 456.48</td>\n",
-        "      <td> 12949100</td>\n",
-        "      <td> 454.53</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 5,
-       "text": [
-        "         Date    Open    High     Low   Close    Volume  Adj Close\n",
-        "0  2012-06-01  569.16  590.00  548.50  584.00  14077000     581.50\n",
-        "1  2012-05-01  584.90  596.76  522.18  577.73  18827900     575.26\n",
-        "2  2012-04-02  601.83  644.00  555.00  583.98  28759100     581.48\n",
-        "3  2012-03-01  548.17  621.45  516.22  599.55  26486000     596.99\n",
-        "4  2012-02-01  458.41  547.61  453.98  542.44  22001000     540.12\n",
-        "5  2012-01-03  409.40  458.24  409.00  456.48  12949100     454.53"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "lines = df._repr_html_().splitlines()\n",
-      "print \"\\n\".join(lines[:20])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>Date</th>\n",
-        "      <th>Open</th>\n",
-        "      <th>High</th>\n",
-        "      <th>Low</th>\n",
-        "      <th>Close</th>\n",
-        "      <th>Volume</th>\n",
-        "      <th>Adj Close</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>0</th>\n",
-        "      <td> 2012-06-01</td>\n",
-        "      <td> 569.16</td>\n",
-        "      <td> 590.00</td>\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Exercise"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Write a simple `Circle` Python class.  Don't even worry about properties such as radius, position, colors, etc. To help you out use the following representations (remember to wrap them in Python strings):\n",
-      "\n",
-      "For HTML:\n",
-      "\n",
-      "    &#x25CB;\n",
-      "\n",
-      "For SVG:\n",
-      "\n",
-      "    <svg width=\"100px\" height=\"100px\">\n",
-      "        <circle cx=\"50\" cy=\"50\" r=\"20\" stroke=\"black\" stroke-width=\"1\" fill=\"white\"/>\n",
-      "    </svg>\n",
-      "\n",
-      "For LaTeX (wrap with `$` and use a raw Python string):\n",
-      "\n",
-      "    \\bigcirc\n",
-      "\n",
-      "After you write the class, create an instance and then use `display_html`, `display_svg` and `display_latex` to display those representations.\n",
-      "\n",
-      "Tips : you can slightly tweek the representation to know from which `_repr_*_` method it came from. \n",
-      "For example in my solution the svg representation is blue, and the HTML one show \"`HTML`\" between brackets."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Solution"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is my simple `MyCircle` class:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load soln/mycircle.py"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now create an instance and use the display methods:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c = MyCircle()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display_html(c)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "&#x25CB; (<b>html</b>)"
-       ],
-       "metadata": {},
-       "output_type": "display_data"
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display_svg(c)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "svg": [
-        "<svg width=\"100px\" height=\"100px\">\n",
-        "           <circle cx=\"50\" cy=\"50\" r=\"20\" stroke=\"black\" stroke-width=\"1\" fill=\"blue\"/>\n",
-        "        </svg>"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display_latex(c)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$\\bigcirc \\LaTeX$"
-       ],
-       "metadata": {},
-       "output_type": "display_data"
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display_javascript(c)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "javascript": [
-        "alert('I am a circle!');"
-       ],
-       "metadata": {},
-       "output_type": "display_data"
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Adding IPython display support to existing objects"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When you are directly writing your own classes, you can adapt them for display in IPython by following the above example.  But in practice, we often need to work with existing code we can't modify.  We now illustrate how to add these kinds of extended display capabilities to existing objects. To continue with our example above, we will add a PNG representation to our `Circle` class using Matplotlib."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Model citizen: sympy"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[SymPy](http://sympy.org) is another model citizen that defines rich representations of its object.\n",
-      "Unlike pandas above, sympy registers display formatters via IPython's display formatter API, rather than declaring `_repr_mime_` methods."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from sympy import Rational, pi, exp, I, symbols\n",
-      "x, y, z = symbols(\"x y z\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "r = Rational(3,2)*pi + exp(I*x) / (x**2 + y)\n",
-      "r"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 17,
-       "text": [
-        "3*pi/2 + exp(I*x)/(x**2 + y)"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "SymPy provides an `init_printing` function that sets up advanced $\\LaTeX$\n",
-      "representations of its objects."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from sympy.interactive.printing import init_printing\n",
-      "init_printing()\n",
-      "r"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\frac{3}{2} \\pi + \\frac{e^{\\mathbf{\\imath} x}}{x^{2} + y}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAFAAAAAlCAYAAADV/m7fAAAABHNCSVQICAgIfAhkiAAAA9xJREFU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\nifjCwUhTPWrTpk2bFuJflVvSLV1580UAAAAASUVORK5CYII=\n",
-       "prompt_number": 18,
-       "text": [
-        "        \u2148\u22c5x \n",
-        "3\u22c5\u03c0    \u212f    \n",
-        "\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500\u2500\u2500\n",
-        " 2     2    \n",
-        "      x  + y"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To add a display method to an existing class, we must use IPython's display formatter API.  Here we show all of the available formatters:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ip = get_ipython()\n",
-      "for mime, formatter in ip.display_formatter.formatters.items():\n",
-      "    print '%24s : %s' % (mime, formatter.__class__.__name__)\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "               text/html : HTMLFormatter\n",
-        "              image/jpeg : JPEGFormatter\n",
-        "           image/svg+xml : SVGFormatter\n",
-        "               image/png : PNGFormatter\n",
-        "  application/javascript : JavascriptFormatter\n",
-        "              text/latex : LatexFormatter\n",
-        "        application/json : JSONFormatter\n",
-        "              text/plain : PlainTextFormatter\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's grab the PNG formatter:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "png_f = ip.display_formatter.formatters['image/png']"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We will use the `for_type` method to register our display function."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "png_f.for_type?"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 21
-    },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Defining Custom Display Logic for Your Own Objects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In Python, objects can declare their textual representation using the `__repr__` method.  IPython expands on this idea and allows objects to declare other, richer representations including:\n",
+    "\n",
+    "* HTML\n",
+    "* JSON\n",
+    "* PNG\n",
+    "* JPEG\n",
+    "* SVG\n",
+    "* LaTeX\n",
+    "\n",
+    "This Notebook shows how you can add custom display logic to your own classes, so that they can be displayed using these rich representations. There are two ways of accomplishing this:\n",
+    "\n",
+    "1. Implementing special display methods such as `_repr_html_`.\n",
+    "2. Registering a display function for a particular type.\n",
+    "\n",
+    "In this Notebook we show how both approaches work."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we get started, we will import the various display functions for displaying the different formats we will create."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import display\n",
+    "from IPython.display import (\n",
+    "    display_html, display_jpeg, display_png,\n",
+    "    display_javascript, display_svg, display_latex\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implementing special display methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The main idea of the first approach is that you have to implement special display methods, one for each representation you want to use. Here is a list of the names of the special methods and the values they must return:\n",
+    "\n",
+    "* `_repr_html_`: return raw HTML as a string\n",
+    "* `_repr_json_`: return raw JSON as a string\n",
+    "* `_repr_jpeg_`: return raw JPEG data\n",
+    "* `_repr_png_`: return raw PNG data\n",
+    "* `_repr_svg_`: return raw SVG data as a string\n",
+    "* `_repr_latex_`: return LaTeX commands in a string surrounded by \"$\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model Citizen: pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A prominent example of a package that has IPython-aware rich representations of its objects is [pandas](http://pandas.pydata.org/).\n",
+    "\n",
+    "A pandas DataFrame has a rich HTML table representation,\n",
+    "using `_repr_html_`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "import pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "As the docstring describes, we need to define a function the takes the object as a parameter and returns the raw PNG data."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing data.csv\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline\n",
-      "import matplotlib.pyplot as plt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class AnotherCircle(object):\n",
-      "    def __init__(self, radius=1, center=(0,0), color='r'):\n",
-      "        self.radius = radius\n",
-      "        self.center = center\n",
-      "        self.color = color\n",
-      "    \n",
-      "    def __repr__(self):\n",
-      "        return \"<%s Circle with r=%s at %s>\" % (\n",
-      "                    self.color,\n",
-      "                    self.radius,\n",
-      "                    self.center,\n",
-      "                )\n",
-      "    \n",
-      "c = AnotherCircle()\n",
-      "c"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 23,
-       "text": [
-        "<r Circle with r=1 at (0, 0)>"
-       ]
-      }
-     ],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.core.pylabtools import print_figure\n",
-      "\n",
-      "def png_circle(circle):\n",
-      "    \"\"\"Render AnotherCircle to png data using matplotlib\"\"\"\n",
-      "    fig, ax = plt.subplots()\n",
-      "    patch = plt.Circle(circle.center,\n",
-      "                       radius=circle.radius,\n",
-      "                       fc=circle.color,\n",
-      "                       )\n",
-      "    ax.add_patch(patch)\n",
-      "    plt.axis('scaled')\n",
-      "    data = print_figure(fig, 'png')\n",
-      "    # We MUST close the figure, otherwise IPython's display machinery\n",
-      "    # will pick it up and send it as output, resulting in a double display\n",
-      "    plt.close(fig)\n",
-      "    return data"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c = AnotherCircle()\n",
-      "print repr(png_circle(c)[:10])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00'\n"
-       ]
-      }
-     ],
-     "prompt_number": 27
-    },
+    }
+   ],
+   "source": [
+    "%%writefile data.csv\n",
+    "Date,Open,High,Low,Close,Volume,Adj Close\n",
+    "2012-06-01,569.16,590.00,548.50,584.00,14077000,581.50\n",
+    "2012-05-01,584.90,596.76,522.18,577.73,18827900,575.26\n",
+    "2012-04-02,601.83,644.00,555.00,583.98,28759100,581.48\n",
+    "2012-03-01,548.17,621.45,516.22,599.55,26486000,596.99\n",
+    "2012-02-01,458.41,547.61,453.98,542.44,22001000,540.12\n",
+    "2012-01-03,409.40,458.24,409.00,456.48,12949100,454.53\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "         Date    Open    High     Low   Close    Volume  Adj Close\n",
+       "0  2012-06-01  569.16  590.00  548.50  584.00  14077000     581.50\n",
+       "1  2012-05-01  584.90  596.76  522.18  577.73  18827900     575.26\n",
+       "2  2012-04-02  601.83  644.00  555.00  583.98  28759100     581.48\n",
+       "3  2012-03-01  548.17  621.45  516.22  599.55  26486000     596.99\n",
+       "4  2012-02-01  458.41  547.61  453.98  542.44  22001000     540.12\n",
+       "5  2012-01-03  409.40  458.24  409.00  456.48  12949100     454.53"
+      ]
+     },
+     "execution_count": 4,
      "metadata": {},
-     "source": [
-      "Now we register the display function for the type:"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pandas.read_csv(\"data.csv\")\n",
+    "pandas.set_option('display.notebook_repr_html', False)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "rich HTML can be activated via `pandas.set_option`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "png_f.for_type(AnotherCircle, png_circle)"
-     ],
-     "language": "python",
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Adj Close</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td> 2012-06-01</td>\n",
+       "      <td> 569.16</td>\n",
+       "      <td> 590.00</td>\n",
+       "      <td> 548.50</td>\n",
+       "      <td> 584.00</td>\n",
+       "      <td> 14077000</td>\n",
+       "      <td> 581.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td> 2012-05-01</td>\n",
+       "      <td> 584.90</td>\n",
+       "      <td> 596.76</td>\n",
+       "      <td> 522.18</td>\n",
+       "      <td> 577.73</td>\n",
+       "      <td> 18827900</td>\n",
+       "      <td> 575.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td> 2012-04-02</td>\n",
+       "      <td> 601.83</td>\n",
+       "      <td> 644.00</td>\n",
+       "      <td> 555.00</td>\n",
+       "      <td> 583.98</td>\n",
+       "      <td> 28759100</td>\n",
+       "      <td> 581.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td> 2012-03-01</td>\n",
+       "      <td> 548.17</td>\n",
+       "      <td> 621.45</td>\n",
+       "      <td> 516.22</td>\n",
+       "      <td> 599.55</td>\n",
+       "      <td> 26486000</td>\n",
+       "      <td> 596.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td> 2012-02-01</td>\n",
+       "      <td> 458.41</td>\n",
+       "      <td> 547.61</td>\n",
+       "      <td> 453.98</td>\n",
+       "      <td> 542.44</td>\n",
+       "      <td> 22001000</td>\n",
+       "      <td> 540.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td> 2012-01-03</td>\n",
+       "      <td> 409.40</td>\n",
+       "      <td> 458.24</td>\n",
+       "      <td> 409.00</td>\n",
+       "      <td> 456.48</td>\n",
+       "      <td> 12949100</td>\n",
+       "      <td> 454.53</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date    Open    High     Low   Close    Volume  Adj Close\n",
+       "0  2012-06-01  569.16  590.00  548.50  584.00  14077000     581.50\n",
+       "1  2012-05-01  584.90  596.76  522.18  577.73  18827900     575.26\n",
+       "2  2012-04-02  601.83  644.00  555.00  583.98  28759100     581.48\n",
+       "3  2012-03-01  548.17  621.45  516.22  599.55  26486000     596.99\n",
+       "4  2012-02-01  458.41  547.61  453.98  542.44  22001000     540.12\n",
+       "5  2012-01-03  409.40  458.24  409.00  456.48  12949100     454.53"
+      ]
+     },
+     "execution_count": 5,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 28
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pandas.set_option('display.notebook_repr_html', True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now all `Circle` instances have PNG representations!"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+      "<table border=\"1\" class=\"dataframe\">\n",
+      "  <thead>\n",
+      "    <tr style=\"text-align: right;\">\n",
+      "      <th></th>\n",
+      "      <th>Date</th>\n",
+      "      <th>Open</th>\n",
+      "      <th>High</th>\n",
+      "      <th>Low</th>\n",
+      "      <th>Close</th>\n",
+      "      <th>Volume</th>\n",
+      "      <th>Adj Close</th>\n",
+      "    </tr>\n",
+      "  </thead>\n",
+      "  <tbody>\n",
+      "    <tr>\n",
+      "      <th>0</th>\n",
+      "      <td> 2012-06-01</td>\n",
+      "      <td> 569.16</td>\n",
+      "      <td> 590.00</td>\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "lines = df._repr_html_().splitlines()\n",
+    "print \"\\n\".join(lines[:20])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write a simple `Circle` Python class.  Don't even worry about properties such as radius, position, colors, etc. To help you out use the following representations (remember to wrap them in Python strings):\n",
+    "\n",
+    "For HTML:\n",
+    "\n",
+    "    &#x25CB;\n",
+    "\n",
+    "For SVG:\n",
+    "\n",
+    "    <svg width=\"100px\" height=\"100px\">\n",
+    "        <circle cx=\"50\" cy=\"50\" r=\"20\" stroke=\"black\" stroke-width=\"1\" fill=\"white\"/>\n",
+    "    </svg>\n",
+    "\n",
+    "For LaTeX (wrap with `$` and use a raw Python string):\n",
+    "\n",
+    "    \\bigcirc\n",
+    "\n",
+    "After you write the class, create an instance and then use `display_html`, `display_svg` and `display_latex` to display those representations.\n",
+    "\n",
+    "Tips : you can slightly tweek the representation to know from which `_repr_*_` method it came from. \n",
+    "For example in my solution the svg representation is blue, and the HTML one show \"`HTML`\" between brackets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Solution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is my simple `MyCircle` class:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%load soln/mycircle.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now create an instance and use the display methods:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "c = MyCircle()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c2 = AnotherCircle(radius=2, center=(1,0), color='g')\n",
-      "c2"
-     ],
-     "language": "python",
+     "data": {
+      "text/html": [
+       "&#x25CB; (<b>html</b>)"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "png": 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KfPzvhv8VHYOI3Mzi/MX4Z8k/0dbeJjqKw7A4bHCy9iSe3PCkx99iR0TO8ey3z2Jvpedc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\nTkSuQmCC\n",
-       "prompt_number": 29,
-       "text": [
-        "<g Circle with r=2 at (1, 0)>"
-       ]
-      }
-     ],
-     "prompt_number": 29
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display_html(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display_png(c2)"
-     ],
-     "language": "python",
+     "data": {
+      "image/svg+xml": [
+       "<svg width=\"100px\" height=\"100px\">\n",
+       "           <circle cx=\"50\" cy=\"50\" r=\"20\" stroke=\"black\" stroke-width=\"1\" fill=\"blue\"/>\n",
+       "        </svg>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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KfPzvhv8VHYOI3Mzi/MX4Z8k/0dbeJjqKw7A4bHCy9iSe3PCkx99iR0TO8ey3z2Jvpedc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\nTkSuQmCC\n"
-      }
-     ],
-     "prompt_number": 30
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display_svg(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "text/latex": [
+       "$\\bigcirc \\LaTeX$"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "return the object"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display_latex(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# for demonstration purpose, I do the same with a circle that has no _repr_javascript method\n",
-      "class MyNoJSCircle(MyCircle):\n",
-      "    \n",
-      "    def _repr_javascript_(self):\n",
-      "        return\n",
-      "\n",
-      "cNoJS = MyNoJSCircle()"
-     ],
-     "language": "python",
+     "data": {
+      "application/javascript": [
+       "alert('I am a circle!');"
+      ]
+     },
      "metadata": {},
-     "outputs": []
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display_javascript(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding IPython display support to existing objects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you are directly writing your own classes, you can adapt them for display in IPython by following the above example.  But in practice, we often need to work with existing code we can't modify.  We now illustrate how to add these kinds of extended display capabilities to existing objects. To continue with our example above, we will add a PNG representation to our `Circle` class using Matplotlib."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model citizen: sympy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[SymPy](http://sympy.org) is another model citizen that defines rich representations of its object.\n",
+    "Unlike pandas above, sympy registers display formatters via IPython's display formatter API, rather than declaring `_repr_mime_` methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sympy import Rational, pi, exp, I, symbols\n",
+    "x, y, z = symbols(\"x y z\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "3*pi/2 + exp(I*x)/(x**2 + y)"
+      ]
+     },
+     "execution_count": 17,
      "metadata": {},
-     "source": [
-      "Of course you can now still return the object, and this will use compute all the representations, store them in the notebook and show you the appropriate one."
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r = Rational(3,2)*pi + exp(I*x) / (x**2 + y)\n",
+    "r"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SymPy provides an `init_printing` function that sets up advanced $\\LaTeX$\n",
+    "representations of its objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "cNoJS"
-     ],
-     "language": "python",
+     "data": {
+      "image/png": [
+       "iVBORw0KGgoAAAANSUhEUgAAAFAAAAAlCAYAAADV/m7fAAAABHNCSVQICAgIfAhkiAAAA9xJREFU\n",
+       "aIHt2l2IVVUUwPHfzDQ4hc1UFpaVTvoiKNoHaTCmU/lQaERR9mGUZBSkUVEQvcR9CSKIoCgoom5F\n",
+       "BX2T+RD6EBQVZI1BBoVSFA0JUkNiiX1MD+uc5szkOPfjnHvvyP3DwF4z96y1Zp+91l577Uubuuho\n",
+       "tgMNYikG8TU+wwosxs9YjTvwWy2Kj8mMV2IeZiTGythem78txwjmYitOxHs4G7vwthonbyL7cHMy\n",
+       "vga/4/g8FLcAx+FVnIVesXDeRScW1KM4uwIH8V0yHkV3PYpbjFnifzsDt2J3Il+Mg9hTq+LJcuAr\n",
+       "Ynk/VKviJtKJe3AIv2I2Hi3SWJZzcR8O4LGijBbM0yKynsCbOKkZTtyGzzGzAbaWGJ9K6mEh/hC5\n",
+       "fD1uETmvcC7AXpFkU0dGcXUDbJfRn5Oua7EjJ10VkYbwXyLnDSfyfPyJnY10Jge+FZtCSofYNAqr\n",
+       "d9PQ2YHnsBn/iEJzrditsvTiGVyBnkl0jmIVPszb2QoYEnnvLvyCY7El8akQqnkzHaLoHMKnuEpU\n",
+       "9d/jbjwldr6D+Fi8iEooo5ToOaq5CZdl5NfRlYy31KG3LL8c2HCq2f1ezIxPSJ79W1T5p+Tp1HSi\n",
+       "1vJhPT5JxgvFRE7FC+JQP5G5WCbCfyIbRTnV8oxO8TORnTg/Ga/BN3XYLqsshKfysSk/6QqsZjNJ\n",
+       "uzZfJHKvWEU9xpcQeTOVj53YJHZeeKRAX8YZrZZ7sc1Y2A6LyVudl1M1sgbviIlbjvMaYTQ7gcvF\n",
+       "IbwkJmjlJM8sxbMZeQg/qO1l5MkCXJ+M9+DMRhqfiYcz8jrRDzy9AbbL8iljZhjrX76POTnorJgl\n",
+       "ovBNm4u9Ikmua4Dtx3FqjvouxAM56quIDhHCaaJeJCbwnEY7Uid9eLDZTsBLCmxCFsgm0UnvVt2m\n",
+       "tjlPJzaKnWy63djdIC6H9olO9OIqni3l5cRaMYFEadKfl+KcGMAG0S2/EbfjLdF+q4fSJL/vElee\n",
+       "z4vTEpwsczrKlh6rxP3BVpHUL8VpdTqWJ73i2FjGB7hTtO/3i4qhCK4UTZPsYroIP6YfSMN0Pr70\n",
+       "/xZ+n5zuTHOgR1QKh0TJNWJ86VUps0X7LZuiVuCjjLxfXKj1JfJuMYEH8GQiT9c7I0QIpWfxviN9\n",
+       "sEJKR/jbdXgjI+8Sl29o/umhGi4XJ6V+sUEMiVW0oWC7c4zdG/cn8n9XHV2HeaBVGRD16SxxXbBM\n",
+       "1KmvidCqh0GRVw/HXvFNjW7xAofxcp32jjrur/Bz20St2aZCFuEnkSoG8JWxdhmmVwg3g04xefNw\n",
+       "ifjCwUhTPWrTpk2bFuJflVvSLV1580UAAAAASUVORK5CYII=\n"
+      ],
+      "text/latex": [
+       "$$\\frac{3}{2} \\pi + \\frac{e^{\\mathbf{\\imath} x}}{x^{2} + y}$$"
+      ],
+      "text/plain": [
+       "        \u2148\u22c5x \n",
+       "3\u22c5\u03c0    \u212f    \n",
+       "\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500\u2500\u2500\n",
+       " 2     2    \n",
+       "      x  + y"
+      ]
+     },
+     "execution_count": 18,
      "metadata": {},
-     "outputs": []
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sympy.interactive.printing import init_printing\n",
+    "init_printing()\n",
+    "r"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To add a display method to an existing class, we must use IPython's display formatter API.  Here we show all of the available formatters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Or just use `display(object)` if you are in a middle of a loop"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               text/html : HTMLFormatter\n",
+      "              image/jpeg : JPEGFormatter\n",
+      "           image/svg+xml : SVGFormatter\n",
+      "               image/png : PNGFormatter\n",
+      "  application/javascript : JavascriptFormatter\n",
+      "              text/latex : LatexFormatter\n",
+      "        application/json : JSONFormatter\n",
+      "              text/plain : PlainTextFormatter\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "ip = get_ipython()\n",
+    "for mime, formatter in ip.display_formatter.formatters.items():\n",
+    "    print '%24s : %s' % (mime, formatter.__class__.__name__)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's grab the PNG formatter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "png_f = ip.display_formatter.formatters['image/png']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use the `for_type` method to register our display function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "png_f.for_type?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As the docstring describes, we need to define a function the takes the object as a parameter and returns the raw PNG data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for i in range(3):\n",
-      "    display(cNoJS)"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "<r Circle with r=1 at (0, 0)>"
+      ]
+     },
+     "execution_count": 23,
      "metadata": {},
-     "outputs": []
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "class AnotherCircle(object):\n",
+    "    def __init__(self, radius=1, center=(0,0), color='r'):\n",
+    "        self.radius = radius\n",
+    "        self.center = center\n",
+    "        self.color = color\n",
+    "    \n",
+    "    def __repr__(self):\n",
+    "        return \"<%s Circle with r=%s at %s>\" % (\n",
+    "                    self.color,\n",
+    "                    self.radius,\n",
+    "                    self.center,\n",
+    "                )\n",
+    "    \n",
+    "c = AnotherCircle()\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.core.pylabtools import print_figure\n",
+    "\n",
+    "def png_circle(circle):\n",
+    "    \"\"\"Render AnotherCircle to png data using matplotlib\"\"\"\n",
+    "    fig, ax = plt.subplots()\n",
+    "    patch = plt.Circle(circle.center,\n",
+    "                       radius=circle.radius,\n",
+    "                       fc=circle.color,\n",
+    "                       )\n",
+    "    ax.add_patch(patch)\n",
+    "    plt.axis('scaled')\n",
+    "    data = print_figure(fig, 'png')\n",
+    "    # We MUST close the figure, otherwise IPython's display machinery\n",
+    "    # will pick it up and send it as output, resulting in a double display\n",
+    "    plt.close(fig)\n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Advantage of using `display()` versus `display_*()` is that all representation will be stored in the notebook document and notebook file, they are then availlable for other frontends or post-processing tool like `nbconvert`."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'\\x89PNG\\r\\n\\x1a\\n\\x00\\x00'\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's compare `display()` vs `display_html()` for our circle in the Notebook Web-app and we'll see later the difference in nbconvert."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print \"I should see a nice html circle in web-app, but\"\n",
-      "print \"nothing if the format I'm viewing the notebook in\"\n",
-      "print \"does not support html\"\n",
-      "display_html(cNoJS)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print \"Whatever the format I will see a representation\"\n",
-      "print \"of my circle\"\n",
-      "display(cNoJS)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
+    }
+   ],
+   "source": [
+    "c = AnotherCircle()\n",
+    "print repr(png_circle(c)[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we register the display function for the type:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "png_f.for_type(AnotherCircle, png_circle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now all `Circle` instances have PNG representations!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print \"Same if I return the object\"\n",
-      "cNoJS"
-     ],
-     "language": "python",
+     "data": {
+      "image/png": [
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+       "5oO+6r4YHDIYw3sPR191X+h66aD110IboEVYYBhLglzKYdc4tmzZAr1e3+n3jEYjVCrVZZ8FBwfD\n",
+       "aDSyOMhhZDIZwgLDEBYYdtnnU4ZMQUNzA0znTDA3mWE+Z0Zdcx0aWhpwpv4MSmpKUGQqQnl9Oc40\n",
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+       "Y2ds+RmVwn4FbMsqlf1q6/RFUti3tmaVwr5dtGgRDh06BJlMBj8/Pzz++ONITk7usFy396tVIr7+\n",
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+       "7dcrTV8kxX3b1VRLgDT27aFDh7BgwQJoNBrMnj27w/d7sl9dVhy2Tl9iC2dPX9JV1oceeqhb40+d\n",
+       "OhUKxfkJ6AoKCrBgwQJkZWUhMDDQYXl/ydZ8UpgGxtYMIvZjT0lhv9pKavv1atMXSWnfXi2rVPbt\n",
+       "wIEDsWTJEuTl5eHVV1/FwoULOyzT3f3qsuJ45plnHLIdrVbb4RDKbDbbdEhuq66yVlZWdmv8Cz84\n",
+       "AJCcnAyNRoPKykr079/fIVl/ydb944r9eDXdyeDq/dhTUtiv3SGl/Xph+qLOZqIApLVvr5YVkNa+\n",
+       "BYDU1FScPXsW9fX1l00P1ZP9KplrHJf65XlBk8l08Q926fQlAGyavsSRrjb+pVkBIDc3F+3t7QCA\n",
+       "wsJCWCwWp94F0lU+Ke3H7mQFXL8fr+bSn1Gp7ddfulJWQBr7tb29HUuXLkVBQQHmzJlz2S81qe1b\n",
+       "W7MC4vdtfX09cnJyLv73//777xEaGoqgoCC796tknhzfsGEDduzYgZMnTyIyMhJxcXGYPn06AEhu\n",
+       "+pKuxv9l1rfffhvFxcVQKBRQqVR47LHHkJCQICSf1PZjd7KK2I+/dKWfUSnuV1uzSmG/Xmn6ounT\n",
+       "p2PTpk2X5RW9b7uTVfS+ra+vR0ZGBsrKyuDv7w+dTocpU6YgJibG7p9ZyRQHERG5B0meqiIiIuli\n",
+       "cRARUbewOIiIqFtYHERE1C0sDiIi6hYWBxERdQuLg4iIuoXFQURE3fL/AR8SQcFGS9m7AAAAAElF\n",
+       "TkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<g Circle with r=2 at (1, 0)>"
+      ]
+     },
+     "execution_count": 29,
      "metadata": {},
-     "outputs": []
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c2 = AnotherCircle(radius=2, center=(1,0), color='g')\n",
+    "c2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print \"But not if I print it\"\n",
-      "print cNoJS"
-     ],
-     "language": "python",
+     "data": {
+      "image/png": [
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+       "+pKuxv9l1rfffhvFxcVQKBRQqVR47LHHkJCQICSf1PZjd7KK2I+/dKWfUSnuV1uzSmG/Xmn6ounT\n",
+       "p2PTpk2X5RW9b7uTVfS+ra+vR0ZGBsrKyuDv7w+dTocpU6YgJibG7p9ZyRQHERG5B0meqiIiIuli\n",
+       "cRARUbewOIiIqFtYHERE1C0sDiIi6hYWBxERdQuLg4iIuoXFQURE3fL/AR8SQcFGS9m7AAAAAElF\n",
+       "TkSuQmCC\n"
+      ]
+     },
      "metadata": {},
-     "outputs": []
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "display_png(c2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## return the object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# for demonstration purpose, I do the same with a circle that has no _repr_javascript method\n",
+    "class MyNoJSCircle(MyCircle):\n",
+    "    \n",
+    "    def _repr_javascript_(self):\n",
+    "        return\n",
+    "\n",
+    "cNoJS = MyNoJSCircle()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course you can now still return the object, and this will use compute all the representations, store them in the notebook and show you the appropriate one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "cNoJS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or just use `display(object)` if you are in a middle of a loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "for i in range(3):\n",
+    "    display(cNoJS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Advantage of using `display()` versus `display_*()` is that all representation will be stored in the notebook document and notebook file, they are then availlable for other frontends or post-processing tool like `nbconvert`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's compare `display()` vs `display_html()` for our circle in the Notebook Web-app and we'll see later the difference in nbconvert."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print \"I should see a nice html circle in web-app, but\"\n",
+    "print \"nothing if the format I'm viewing the notebook in\"\n",
+    "print \"does not support html\"\n",
+    "display_html(cNoJS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print \"Whatever the format I will see a representation\"\n",
+    "print \"of my circle\"\n",
+    "display(cNoJS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print \"Same if I return the object\"\n",
+    "cNoJS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print \"But not if I print it\"\n",
+    "print cNoJS"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/Plotting in the Notebook.ipynb b/examples/IPython Kernel/Plotting in the Notebook.ipynb
index 941f4b3..00e713b 100644
--- a/examples/IPython Kernel/Plotting in the Notebook.ipynb
+++ b/examples/IPython Kernel/Plotting in the Notebook.ipynb
@@ -1,226 +1,740 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:74dbf5caa25c937be70dfe2ab509783a01f4a2044850d7044e729300a8c3644d"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Plotting with Matplotlib"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython works with the [Matplotlib](http://matplotlib.org/) plotting library, which integrates Matplotlib with IPython's display system and event loop handling."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "matplotlib mode"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To make plots using Matplotlib, you must first enable IPython's matplotlib mode.\n",
-      "\n",
-      "To do this, run the `%matplotlib` magic command to enable plotting in the current Notebook.\n",
-      "\n",
-      "This magic takes an optional argument that specifies which Matplotlib backend should be used.  Most of the time, in the Notebook, you will want to use the `inline` backend, which will embed plots inside the Notebook:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can also use Matplotlib GUI backends in the Notebook, such as the Qt backend (`%matplotlib qt`). This will use Matplotlib's interactive Qt UI in a floating window to the side of your browser. Of course, this only works if your browser is running on the same system as the Notebook Server.  You can always call the `display` function to paste figures into the Notebook document."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Making a simple plot"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "With matplotlib enabled, plotting should just work."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib.pyplot as plt\n",
-      "import numpy as np"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x = np.linspace(0, 3*np.pi, 500)\n",
-      "plt.plot(x, np.sin(x**2))\n",
-      "plt.title('A simple chirp');"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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GRkJ8jthyKEhHewcgpK/qQBOKsOwdPxsG0BfIFbF3VHn6UeXpu9k7LIodcK+9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\nCcy6efRRKX1ZCJP+d7/7XbzyyiuYO3c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-       "text": [
-        "<matplotlib.figure.Figure at 0x1084796d0>"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "These images can be resized by dragging the handle in the lower right corner.  Double clicking will return them to their original size."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "One thing to be aware of is that by default, the `Figure` object is cleared at the end of each cell, so you will need to issue all plotting commands for a single figure in a single cell."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Loading Matplotlib demos with %load"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython's `%load` magic can be used to load any Matplotlib demo by its URL:"
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plotting with Matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython works with the [Matplotlib](http://matplotlib.org/) plotting library, which integrates Matplotlib with IPython's display system and event loop handling."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## matplotlib mode"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make plots using Matplotlib, you must first enable IPython's matplotlib mode.\n",
+    "\n",
+    "To do this, run the `%matplotlib` magic command to enable plotting in the current Notebook.\n",
+    "\n",
+    "This magic takes an optional argument that specifies which Matplotlib backend should be used.  Most of the time, in the Notebook, you will want to use the `inline` backend, which will embed plots inside the Notebook:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also use Matplotlib GUI backends in the Notebook, such as the Qt backend (`%matplotlib qt`). This will use Matplotlib's interactive Qt UI in a floating window to the side of your browser. Of course, this only works if your browser is running on the same system as the Notebook Server.  You can always call the `display` function to paste figures into the Notebook document."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Making a simple plot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With matplotlib enabled, plotting should just work."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load http://matplotlib.org/mpl_examples/showcase/integral_demo.py"
-     ],
-     "language": "python",
+     "data": {
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+       "kL6BgYHBKIIhfQMDA4NRBEP6BgYGBqMI/x+3ghdT1LKwsgAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1084796d0>"
+      ]
+     },
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(0, 3*np.pi, 500)\n",
+    "plt.plot(x, np.sin(x**2))\n",
+    "plt.title('A simple chirp');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These images can be resized by dragging the handle in the lower right corner.  Double clicking will return them to their original size."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One thing to be aware of is that by default, the `Figure` object is cleared at the end of each cell, so you will need to issue all plotting commands for a single figure in a single cell."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading Matplotlib demos with %load"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython's `%load` magic can be used to load any Matplotlib demo by its URL:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%load http://matplotlib.org/mpl_examples/showcase/integral_demo.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "\"\"\"\n",
-      "Plot demonstrating the integral as the area under a curve.\n",
-      "\n",
-      "Although this is a simple example, it demonstrates some important tweaks:\n",
-      "\n",
-      "    * A simple line plot with custom color and line width.\n",
-      "    * A shaded region created using a Polygon patch.\n",
-      "    * A text label with mathtext rendering.\n",
-      "    * figtext calls to label the x- and y-axes.\n",
-      "    * Use of axis spines to hide the top and right spines.\n",
-      "    * Custom tick placement and labels.\n",
-      "\"\"\"\n",
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "from matplotlib.patches import Polygon\n",
-      "\n",
-      "\n",
-      "def func(x):\n",
-      "    return (x - 3) * (x - 5) * (x - 7) + 85\n",
-      "\n",
-      "\n",
-      "a, b = 2, 9 # integral limits\n",
-      "x = np.linspace(0, 10)\n",
-      "y = func(x)\n",
-      "\n",
-      "fig, ax = plt.subplots()\n",
-      "plt.plot(x, y, 'r', linewidth=2)\n",
-      "plt.ylim(ymin=0)\n",
-      "\n",
-      "# Make the shaded region\n",
-      "ix = np.linspace(a, b)\n",
-      "iy = func(ix)\n",
-      "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n",
-      "poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\n",
-      "ax.add_patch(poly)\n",
-      "\n",
-      "plt.text(0.5 * (a + b), 30, r\"$\\int_a^b f(x)\\mathrm{d}x$\",\n",
-      "         horizontalalignment='center', fontsize=20)\n",
-      "\n",
-      "plt.figtext(0.9, 0.05, '$x$')\n",
-      "plt.figtext(0.1, 0.9, '$y$')\n",
-      "\n",
-      "ax.spines['right'].set_visible(False)\n",
-      "ax.spines['top'].set_visible(False)\n",
-      "ax.xaxis.set_ticks_position('bottom')\n",
-      "\n",
-      "ax.set_xticks((a, b))\n",
-      "ax.set_xticklabels(('$a$', '$b$'))\n",
-      "ax.set_yticks([])\n",
-      "\n",
-      "plt.show()\n"
-     ],
-     "language": "python",
+     "data": {
+      "image/png": [
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+       "WFKCiEg4C354b9wIS5dCfDyMHRv05oNhy5YtVpcgImXYvXu31SVUSvDDu/jx97vvhrp1g958MCi8\n",
+       "RULfnj17rC6hUoIb3tu3w7/+BbGx8MADQW1aRCSSBHdtk2nTzI/Dh0OjRkFtOpiKioq004tICMvL\n",
+       "y7O6hMoz/KRLly4GoJdeeumlVzlejz/+eIUy12YYhoGIiIQV6xemEhGRclN4i4iEIYW3iEgYUniL\n",
+       "iIQhhbeIVClFRUXcfPPNzJ8/3+pSKiU09rCMEAsXLsTtdpORkUG9evUYMWKE1SWJyK/ExcVx4YUX\n",
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+       "RKQUW7ZsoW/fvvTu3Ruv18vWrVutLqlCNObtJ0OHDmXFihXs2LGD5s2bc/ToUatLEpFSpKWl0aVL\n",
+       "FwB27NhB7TDdyUvh7ScpKSklN0D69+9vcTUicibZ2dlcd911ZGVlYbfbSUxMtLqkCtHaJiJSpezb\n",
+       "t4+PP/6Y7OxsRo0aRYMGDawuqUIU3iIiYUg3LEVEwpDCW0QkDOmGpYiIxbxeL4sWLWL//v0kJiay\n",
+       "fv16HnjgAZKTk894jnreIiIW27x5MzfddBPJycn4fD769+9Pw4YNz3qOwltExGJXX301sbGxrF27\n",
+       "lq5du9K1a1eqVat21nMU3iIiFktNTeWHH35g27ZtNGvWjC+//LLMczTmLSJisWXLllG/fn06derE\n",
+       "kiVLqFu3bpnnaJ63iEgY0rCJiEgYUniLiIQhhbeISBhSeIuIhCGFt4hIGFJ4i4iEIYW3iEgYUniL\n",
+       "iISh/weZPyRnS1m/IAAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10848fb10>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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J1tUp5VJFvnsjmM8Ha9bg/egj3B9/TNzOndiA4pUkCho3xp2SAm3b4r7yStyXXgrx8VZW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\niISh/weZPyRnS1m/IAAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10848fb10>"
-       ]
-      }
-     ],
-     "prompt_number": 5
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "\"\"\"\n",
+    "Plot demonstrating the integral as the area under a curve.\n",
+    "\n",
+    "Although this is a simple example, it demonstrates some important tweaks:\n",
+    "\n",
+    "    * A simple line plot with custom color and line width.\n",
+    "    * A shaded region created using a Polygon patch.\n",
+    "    * A text label with mathtext rendering.\n",
+    "    * figtext calls to label the x- and y-axes.\n",
+    "    * Use of axis spines to hide the top and right spines.\n",
+    "    * Custom tick placement and labels.\n",
+    "\"\"\"\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.patches import Polygon\n",
+    "\n",
+    "\n",
+    "def func(x):\n",
+    "    return (x - 3) * (x - 5) * (x - 7) + 85\n",
+    "\n",
+    "\n",
+    "a, b = 2, 9 # integral limits\n",
+    "x = np.linspace(0, 10)\n",
+    "y = func(x)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "plt.plot(x, y, 'r', linewidth=2)\n",
+    "plt.ylim(ymin=0)\n",
+    "\n",
+    "# Make the shaded region\n",
+    "ix = np.linspace(a, b)\n",
+    "iy = func(ix)\n",
+    "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n",
+    "poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\n",
+    "ax.add_patch(poly)\n",
+    "\n",
+    "plt.text(0.5 * (a + b), 30, r\"$\\int_a^b f(x)\\mathrm{d}x$\",\n",
+    "         horizontalalignment='center', fontsize=20)\n",
+    "\n",
+    "plt.figtext(0.9, 0.05, '$x$')\n",
+    "plt.figtext(0.1, 0.9, '$y$')\n",
+    "\n",
+    "ax.spines['right'].set_visible(False)\n",
+    "ax.spines['top'].set_visible(False)\n",
+    "ax.xaxis.set_ticks_position('bottom')\n",
+    "\n",
+    "ax.set_xticks((a, b))\n",
+    "ax.set_xticklabels(('$a$', '$b$'))\n",
+    "ax.set_yticks([])\n",
+    "\n",
+    "plt.show()\n"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:74dbf5caa25c937be70dfe2ab509783a01f4a2044850d7044e729300a8c3644d"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/Raw Input in the Notebook.ipynb b/examples/IPython Kernel/Raw Input in the Notebook.ipynb
index 660d2ba..f9887b2 100644
--- a/examples/IPython Kernel/Raw Input in the Notebook.ipynb
+++ b/examples/IPython Kernel/Raw Input in the Notebook.ipynb
@@ -1,191 +1,158 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:ac5c21534f3dd013c78d4d201527f3ed4dea5b6fad4116b8d23c67ba107e48c3"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Using `raw_input` and `%debug` in the Notebook"
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using `raw_input` and `%debug` in the Notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Notebook has added support for `raw_input` and `%debug`, as of 1.0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Python 3 compat\n",
+    "import sys\n",
+    "if sys.version_info[0] >= 3:\n",
+    "    raw_input = input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The Notebook has added support for `raw_input` and `%debug`, as of 1.0."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "What is your name? Sir Robin\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Python 3 compat\n",
-      "import sys\n",
-      "if sys.version_info[0] >= 3:\n",
-      "    raw_input = input"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "name = raw_input(\"What is your name? \")\n",
-      "name"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "'Sir Robin'"
+      ]
+     },
+     "execution_count": 2,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "What is your name? Sir Robin\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 2,
-       "text": [
-        "'Sir Robin'"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "name = raw_input(\"What is your name? \")\n",
+    "name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Python 2-only**: the eval input works as well (`input` is just `eval(raw_input(prompt))`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Python 2-only**: the eval input works as well (`input` is just `eval(raw_input(prompt))`)"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "How many fingers? 4\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fingers = input(\"How many fingers? \")\n",
-      "fingers, type(fingers)"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "(4, int)"
+      ]
+     },
+     "execution_count": 3,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "How many fingers? 4\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "(4, int)"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fingers = input(\"How many fingers? \")\n",
+    "fingers, type(fingers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def div(x, y):\n",
-      "    return x/y\n",
-      "\n",
-      "div(1,0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "ZeroDivisionError",
-       "evalue": "integer division or modulo by zero",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-        "\u001b[1;32m<ipython-input-4-a5097cc0c0c5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[1;32m<ipython-input-4-a5097cc0c0c5>\u001b[0m in \u001b[0;36mdiv\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
+     "ename": "ZeroDivisionError",
+     "evalue": "integer division or modulo by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-a5097cc0c0c5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m<ipython-input-4-a5097cc0c0c5>\u001b[0m in \u001b[0;36mdiv\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mZeroDivisionError\u001b[0m: integer division or modulo by zero"
+     ]
+    }
+   ],
+   "source": [
+    "def div(x, y):\n",
+    "    return x/y\n",
+    "\n",
+    "div(1,0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%debug"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "> \u001b[1;32m<ipython-input-4-a5097cc0c0c5>\u001b[0m(2)\u001b[0;36mdiv\u001b[1;34m()\u001b[0m\n",
-        "\u001b[1;32m      1 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0m\u001b[1;32m----> 2 \u001b[1;33m    \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0m\u001b[1;32m      3 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[0m\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "ipdb> x\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "ipdb> y\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "ipdb> exit\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> \u001b[1;32m<ipython-input-4-a5097cc0c0c5>\u001b[0m(2)\u001b[0;36mdiv\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32m      1 \u001b[1;33m\u001b[1;32mdef\u001b[0m \u001b[0mdiv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[1;32m----> 2 \u001b[1;33m    \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\u001b[1;32m      3 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[0m\n",
+      "ipdb> x\n",
+      "1\n",
+      "ipdb> y\n",
+      "0\n",
+      "ipdb> exit\n"
+     ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "%debug"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:ac5c21534f3dd013c78d4d201527f3ed4dea5b6fad4116b8d23c67ba107e48c3"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/Rich Output.ipynb b/examples/IPython Kernel/Rich Output.ipynb
index 83fff95..c3e8f0b 100644
--- a/examples/IPython Kernel/Rich Output.ipynb
+++ b/examples/IPython Kernel/Rich Output.ipynb
@@ -1,1368 +1,3059 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:cf83dc9e6288480ac94c44a5983b4ee421f0ade792a9fac64bc00719263386c0"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Rich Output"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In Python, objects can declare their textual representation using the `__repr__` method.  IPython expands on this idea and allows objects to declare other, rich representations including:\n",
-      "\n",
-      "* HTML\n",
-      "* JSON\n",
-      "* PNG\n",
-      "* JPEG\n",
-      "* SVG\n",
-      "* LaTeX\n",
-      "\n",
-      "A single object can declare some or all of these representations; all are handled by IPython's *display system*. This Notebook shows how you can use this display system to incorporate a broad range of content into your Notebooks."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Basic display imports"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The `display` function is a general purpose tool for displaying different representations of objects. Think of it as `print` for these rich representations."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import display"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "A few points:\n",
-      "\n",
-      "* Calling `display` on an object will send **all** possible representations to the Notebook.\n",
-      "* These representations are stored in the Notebook document.\n",
-      "* In general the Notebook will use the richest available representation.\n",
-      "\n",
-      "If you want to display a particular representation, there are specific functions for that:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import (\n",
-      "    display_pretty, display_html, display_jpeg,\n",
-      "    display_png, display_json, display_latex, display_svg\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Images"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To work with images (JPEG, PNG) use the `Image` class."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import Image"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "i = Image(filename='../images/ipython_logo.png')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Returning an `Image` object from an expression will automatically display it:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "i"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "png": 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L0amUWggcqrXO8gg2FKHG2CdW4Uem9XvBlUflu7RUaiByU3lPa92ZKN8cSav8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\nHMRRca/E5hVINNIVwI2B56z6/3ExLRI31pXNAAAAAElFTkSuQmCC\n",
-       "prompt_number": 5,
-       "text": [
-        "<IPython.core.display.Image object>"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Or you can pass an object with a rich representation to `display`:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display(i)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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-       "text": [
-        "<IPython.core.display.Image object>"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "An image can also be displayed from raw data or a URL."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "Image(url='http://python.org/images/python-logo.gif')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<img src=\"http://python.org/images/python-logo.gif\"/>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 7,
-       "text": [
-        "<IPython.core.display.Image object>"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "SVG images are also supported out of the box."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import SVG\n",
-      "SVG(filename='../images/python_logo.svg')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 8,
-       "svg": [
-        "<svg height=\"115.02pt\" id=\"svg2\" inkscape:version=\"0.43\" sodipodi:docbase=\"/home/sdeibel\" sodipodi:docname=\"logo-python-generic.svg\" sodipodi:version=\"0.32\" version=\"1.0\" width=\"388.84pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:cc=\"http://web.resource.org/cc/\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:inkscape=\"http://www.inkscape.org/namespaces/inkscape\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\" xmlns:sodipodi=\"http://inkscape.sourceforge.net/DTD/sodipodi-0.dtd\" xmlns:svg=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
-        "  <metadata id=\"metadata2193\">\n",
-        "    <rdf:RDF>\n",
-        "      <cc:Work rdf:about=\"\">\n",
-        "        <dc:format>image/svg+xml</dc:format>\n",
-        "        <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
-        "      </cc:Work>\n",
-        "    </rdf:RDF>\n",
-        "  </metadata>\n",
-        "  <sodipodi:namedview bordercolor=\"#666666\" borderopacity=\"1.0\" id=\"base\" inkscape:current-layer=\"svg2\" inkscape:cx=\"243.02499\" inkscape:cy=\"71.887497\" inkscape:pageopacity=\"0.0\" inkscape:pageshadow=\"2\" inkscape:window-height=\"543\" inkscape:window-width=\"791\" inkscape:window-x=\"0\" inkscape:window-y=\"0\" inkscape:zoom=\"1.4340089\" pagecolor=\"#ffffff\"/>\n",
-        "  <defs id=\"defs4\">\n",
-        "    <linearGradient id=\"linearGradient2795\">\n",
-        "      <stop id=\"stop2797\" offset=\"0\" style=\"stop-color:#b8b8b8;stop-opacity:0.49803922\"/>\n",
-        "      <stop id=\"stop2799\" offset=\"1\" style=\"stop-color:#7f7f7f;stop-opacity:0\"/>\n",
-        "    </linearGradient>\n",
-        "    <linearGradient id=\"linearGradient2787\">\n",
-        "      <stop id=\"stop2789\" offset=\"0\" style=\"stop-color:#7f7f7f;stop-opacity:0.5\"/>\n",
-        "      <stop id=\"stop2791\" offset=\"1\" style=\"stop-color:#7f7f7f;stop-opacity:0\"/>\n",
-        "    </linearGradient>\n",
-        "    <linearGradient id=\"linearGradient3676\">\n",
-        "      <stop id=\"stop3678\" offset=\"0\" style=\"stop-color:#b2b2b2;stop-opacity:0.5\"/>\n",
-        "      <stop id=\"stop3680\" offset=\"1\" style=\"stop-color:#b3b3b3;stop-opacity:0\"/>\n",
-        "    </linearGradient>\n",
-        "    <linearGradient id=\"linearGradient3236\">\n",
-        "      <stop id=\"stop3244\" offset=\"0\" style=\"stop-color:#f4f4f4;stop-opacity:1\"/>\n",
-        "      <stop id=\"stop3240\" offset=\"1\" style=\"stop-color:#ffffff;stop-opacity:1\"/>\n",
-        "    </linearGradient>\n",
-        "    <linearGradient id=\"linearGradient4671\">\n",
-        "      <stop id=\"stop4673\" offset=\"0\" style=\"stop-color:#ffd43b;stop-opacity:1\"/>\n",
-        "      <stop id=\"stop4675\" offset=\"1\" style=\"stop-color:#ffe873;stop-opacity:1\"/>\n",
-        "    </linearGradient>\n",
-        "    <linearGradient id=\"linearGradient4689\">\n",
-        "      <stop id=\"stop4691\" offset=\"0\" style=\"stop-color:#5a9fd4;stop-opacity:1\"/>\n",
-        "      <stop id=\"stop4693\" offset=\"1\" style=\"stop-color:#306998;stop-opacity:1\"/>\n",
-        "    </linearGradient>\n",
-        "    <linearGradient gradientTransform=\"translate(100.2702,99.61116)\" gradientUnits=\"userSpaceOnUse\" id=\"linearGradient2987\" x1=\"224.23996\" x2=\"-65.308502\" xlink:href=\"#linearGradient4671\" y1=\"144.75717\" y2=\"144.75717\"/>\n",
-        "    <linearGradient gradientTransform=\"translate(100.2702,99.61116)\" gradientUnits=\"userSpaceOnUse\" id=\"linearGradient2990\" x1=\"172.94208\" x2=\"26.670298\" xlink:href=\"#linearGradient4689\" y1=\"77.475983\" y2=\"76.313133\"/>\n",
-        "    <linearGradient gradientTransform=\"translate(100.2702,99.61116)\" gradientUnits=\"userSpaceOnUse\" id=\"linearGradient2587\" x1=\"172.94208\" x2=\"26.670298\" xlink:href=\"#linearGradient4689\" y1=\"77.475983\" y2=\"76.313133\"/>\n",
-        "    <linearGradient gradientTransform=\"translate(100.2702,99.61116)\" gradientUnits=\"userSpaceOnUse\" id=\"linearGradient2589\" x1=\"224.23996\" x2=\"-65.308502\" xlink:href=\"#linearGradient4671\" y1=\"144.75717\" y2=\"144.75717\"/>\n",
-        "    <linearGradient gradientTransform=\"translate(100.2702,99.61116)\" gradientUnits=\"userSpaceOnUse\" id=\"linearGradient2248\" x1=\"172.94208\" x2=\"26.670298\" xlink:href=\"#linearGradient4689\" y1=\"77.475983\" y2=\"76.313133\"/>\n",
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-        "  </g>\n",
-        "</svg>"
-       ],
-       "text": [
-        "<IPython.core.display.SVG object>"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Embedded vs non-embedded Images"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "By default, image data is embedded in the notebook document so that the images can be viewed offline. However it is also possible to tell the `Image` class to only store a *link* to the image. Let's see how this works using a webcam at Berkeley."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import Image\n",
-      "img_url = 'http://www.lawrencehallofscience.org/static/scienceview/scienceview.berkeley.edu/html/view/view_assets/images/newview.jpg'\n",
-      "\n",
-      "# by default Image data are embedded\n",
-      "Embed      = Image(img_url)\n",
-      "\n",
-      "# if kwarg `url` is given, the embedding is assumed to be false\n",
-      "SoftLinked = Image(url=img_url)\n",
-      "\n",
-      "# In each case, embed can be specified explicitly with the `embed` kwarg\n",
-      "# ForceEmbed = Image(url=img_url, embed=True)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is the embedded version. Note that this image was pulled from the webcam when this code cell was originally run and stored in the Notebook. Unless we rerun this cell, this is not todays image."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "Embed"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
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oCITmOR80BIMmZwnIJoKQST/pE1KAgZnAMUepRB9u9ReQBYEAfpHt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\nTLiN8bFraIISojbIUkmcGsTa0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ndIjsDJmBn6f19y1tb/AE5lNwBqDHkuhp9aEFseqFhJ\nG4BSUrgyJSDGKxolGLrY7RmtSbMljoDl1aB62u3I83y4CiEAnI7d4/iouLG8SlVq0xbpLKvLUpKZ\nWsk/TNclmje/Y3LHp43OzaaJ1czYJbuL27YsEAKAedLTCAqc+ohOYV9YNXqHX9uvT/6dc3N1efkm\nv+QcASwtt6QQVAhW5OVTPqOIUAIrOtdVJaXx+Ua3w/5m6x+J/wAXbbV9U1UdR/nHNZCEXarphpan\nUoQUNgqCQRtB7EAwJBGK+Vqunluea7CiVbsnB7/ep0X6b0/QOTwRrVV7t8e5jN1E81a+x0OpddGv\n6w/qbbC2ErQ2220t4uFCEIShI3QJwkdq0H7oLWClhtsIQlG1M+ogRJk8n9s9q9sY6EorscpS1Nsz\nWTiFLQLlSkI3eraOB9K/oX4DeM34OdI8M2Le88G3NT6m0jT3F3d2++8hblxBSkANSlKFAgAkg7lE\nRjdXSTxeG1l71/rzRvDjeSemL3MfU+seB3QSesNU6K0nStDs7m6s06OrVSm9vmLK7ti448m2ncW4\neb8talkiUmCoSn4To1/0A/01c9U9Sr06UB11i3tW303BdQUhDYh0JQFyVbyFbc4NfAzdPLo8s8sG\n5atNezr9u56EtX9nJtXPy/2fMPK6a6g6gu7rTVjSdLCm1otn7sPOkqSN3r2pBG4HtICgCScnh6td\nFOoXLdilllj8wryypsLISCQBJ+Ir6MZy1aJLhc+ZwlFVaNJhm4Cl3Ld6z5oIhKhKjPMCCI/7it2z\n1PW9Puk3drfkOJJjMjHwQRXWTjkjpktjEdUXaO1pV3q1w9FleXdo8sJcuVovV7XCODtAHuYziTWz\naoc1a9bRqd1d3aHlrdcFw6VhSzMqzmTGT3rztxjwuDvrySu29+fU9C30DoNz0x1Lrb7bgf04pYtU\nF9DbaTJ7YkwDAM9+9ed0Xod7UHW7UNll5x5LS1qcISz3U4shJlIEkxkQZ7VmGeU4yvt/pHDJUD7D\n4L614KdLa7aWPiE9d2lk5aOhy9atnLhCnyvagKTKT5YSFEgAzI9Jr9DWn4z/AAuRqLOt650hpGq6\nbbpft7XQ9DQ/b2rCA42Um4S6iCHCFLASpQBQmUia8kP05ZprP1Dttry2SfHrar6Hvj1MIYtMbWz+\ntHwTxQ6utPEvqxN3ody3ZWXW2rP3LWio1pShYOqWEJU8VjagEnBOdo7Cvn3Uuk9IdDBIttUtNW1p\nC7u1vdNcacW3bLClNoWHRCHQUkOJUk8xII59vU58k8vgxeyW18qPbuccGJQj4k/xnEe/qF2uzN5e\nXpfTbssuJvVpdWoJA8rY2RvS2Gg0kSCnGDBAHqtG0PVus9SvOjrW3urVSy2pmw0qz/MG/vGJSlxf\nqAT6VuqKgYE4TBrxSlCPxPt9OfI9GO5fCu5o9b9QdK3HU95aaBpOuvdK3Lzb7bCn/LulPoa2KcKy\nFpJ3rXk7jConvXmNG0a1vGvKvbW584lZQNiUgKMAAqMlSQIxjJOa9uGOLFiTUrPNnm5ZGkj1em+D\nv57RtQ1i5v7Rv+jOMzpy3g3dXZdJ2+WkkbkgJ9ShwCOZr3Nx+DrxSc0dvrp3w/VpOkvypi3cu0Hz\nENsecsjc5ugoClc5mBnFebqOr8CCbd2m0l5XVmsWLW9PezPZJ6L8MtK0Tq/QWbzSuqkMX/5hl5vz\nmnVuFSGk26VAkAIXtJWSZyMivn3Ufi5151lpL2lsXGmaZpzFkzYPNN7W1PNtr3ICluEqUrgc/pQM\nQK/O9BhX6nJ9X1SbjF/DXf1+Her7P1Pr5s0ulh4GLZvk+XXSXV+lTjeJ9W4QP25rXWm4hspeaSFo\nCCltREx7/Pev2ae2x+elybml6Hf6teW+mWDZeublaUNNt+pS1EwAI7zXtul9BsOlHdR1Xqdgualo\nNy2ljSFhxJdeS4N/nFO0pbCUqSSlYUFqRiJI59RKUsbWPn/ZvDH4tUuDzvVHUHUPWGrXWua/qdxe\n390ZeuLt5S1uEd1KUeYEc8AV5a5ZdUoFQAKczurtjgoR0ozkm8knJmsW1wSUGJgmvWo1np+4urlW\nhdKadY+bp7TAXfOrfS24m22PrQDI3Or3KTIOxRTtIia2m7TQjKKi1JWZ/DdvQm+pU6711c/8rpFo\nq9ZtnSsKvnmk/wDL2wIztKggHKYQlUEGK8xq+qPa31Fc6rdsJcdvLhTrqEHaNylSQB2ya8sYSWdz\n7JUv5/g6OSWJLu3Zn1waFeak+7Y9Ov6ahd2T+WZufMQ01JltJUCSeIUSfmea0NWtbZq5da063ebt\nVLU4yh/apxKJ9IUoASY5wM9q7wm9rZylp7GJrQ9ZurVWosaVdrtWzHnJYUUz/wDuAief2rE7pt1b\n7Uu2r7alzhxspGPaeaRzQm2otNrZ7mXFrk+ip0Ri96E0bSE2PT7zzup3e+8ZUr+ooRstlELSSElt\nISrZPdbo9jXBY6f1G40ZWm2muNqsTdBRtQ8SPOCY8zyx8EjdHcivFi6hwtSTe979vI9csbbWmuKM\n2s+Ft3pa7dDGr292XbdD7n+C4jy1lMlvIzHE963NP8Hrq96a1DqR3qCwZNi6hCbQhe91BCyte7bt\nSEQiQTJ3iODXmf63hWJZNL3aXZ8uiropuWm0ei0HwCubvpvqDXnta0JTmjaexfNWw1JHnXSXVIH+\nEgGVqQFyoRA2qBIIivOeKHRXTWgXydX6X1a6vNI1JandOcubRTS3Gche7+3clQghJIzzWui/VI9b\nklGPZpU1T3V39n5m8vRvDC5c/n+0eS0FzQtK15l7qOyGp2DTjiHrdp5SN42kJUFp7biDg5A+a1tW\nGltXrrOl3Lj9rO5Ci1tUYGMHjJg5/evrXK+NjxNRUa7nOQUwVElPtI5roaRqt/pFwq9sFFKwkoko\nChB5GcUnBZIOMuGMc5Y5KceUeiavFddOuq1y/srG5tLJ14XLlud926Cpe1ZbSVLdWpQAUsx7kV5d\n9hxCQoutqEwAOQJMf61zxrw/7a4Rub1/G+S3E3y0krDhSuFnByfesKXXGkKaASAqJJAkR8811Vdj\nm01yJDK3HFEA4x+qu9p9vbajpt3a6pql0w3ptsXrNlDHmJdeU4hJSoyNghSjuz+kCMyMyp/I1Bb+\n5z3LRx5KUWjQUGRtW42FevJhSp4xjgcfepUwVWq3EuulwKhxITgp5En6/FbW5KZgRcvNNflPzTyL\ndTiXFtpVAUpM7VRwSNyoPyfem9dOOurubh1x951ZcLq1SpSickk5JJ700q7JbM905prun2rzFzcO\naitTguULaCWm0AjYUKBlUjdMgRjnmtRG4ysg7T2+cUx6q+JfnYPnY5aySM5J/egqY/LiEr80qmew\nT/3roZMRASkyogVAkq9JE+9AKASZOQaYBChu4P8AlQFIUQoGQI7GttLwbO4kYIJAPNRgfngngq9q\n9J0v1M3pC37Z9lJZfZVukxKgkwJHualXySStUcBdy46+V4hXb71lUVohJbVuUJA4nPehTtdP9SL0\nXz7XUrJd5avoKS15ykFCuykkY+uMj25r0lj1x0PbOJu7vpW/uFLti24z+aSlCHthAdSQJwYUEnEg\nzIxXzc/S5cs3LHNJP0PXhz44JKcbOf154nu9X6VouipsUsMaKwu2Q64pKn3Wy6taQtcAmAsiOP4j\nw7j0r9Rj2r09J039Li0X5/d2cc2TxZuRIIPJG4cZpz6Poc16TkBMwAfkmgxu/SDHegKQ4tKhOAfv\nW1b6lcWpUu3uHG942q2kp3CeDFTbuOD2TfVV71b1LYXV+9pNmC5bsq8xAZtW9oSje5EmMSo95JrX\nvtVd1pa/JsrJLLa3HYtbZtMCSYCo3FIGBJrzSSxu+x1Xxbt7mDonqTRNA6ttuoNf07+p2Vm6l1zT\nlEo/NJGdm8fpBIyZkTia0L7qhu9vnrz8ghpLzq3NiCfTuVMZ7Dj3rH9PKWd5W9qSr92a8VLGoVvZ\n0bLWdLcQh9bCbYgJb/UVDeAJUZ9zn71i1LXNFS2hDCXXbhL5Wp0EFvZjASRMzOZ47VZQnKdQVIkX\nFR+Lk9F4f9XdL2WuPua4y8q3XauNsoBCCXtpCCVQYAVB+QIxNdO16S6zNuz1KsW2laf5JdaubpxK\nA40Xg0pbaSdzpSpRkIBIAJjFcJpYZ/3O9Jep0T1xSj2Oh0p4etdc9XW+n9QeJen6NZXylLN5dNwy\nlexRAKUq7mBP/wBpg8V7i1/DZpzltpK0+MfRzFzqV8bR1la7hJtEbUlLq1eXEGVAxwQJOa7wzPX4\najSpb/wY8PVDW5HIY/DzdO3utW7Xip0SLXSrddyi4fvltIukhYRCAtsHdJmCBgSJGa+daO2pT4dV\nBDSVBZc9IKQk8/atNtxto5tOO56G9uOpvES+1fqDTtHfet9IY/OOmzQ6GNOty4ASmCdjYW4lIJPc\ne9eLdvGbjVfJu9RUsLIDrynlAc4JJyRxXDFGKk4rdr8+51lqSTeyZ6FPWuq9KdRNdSJvtM169Wyl\nDLjjy3/I2FIRuBiSAgQDIjt2rk9XeJnWHVnVmpda3lyzY3ur3K7h4WCE2zIcUZUEtohKRmYAr044\nY5Y6lDnzJLLkUtpHoehF+LHienQ/DTpPSbnWntIdfvNOtrO3m5aDqkl07kQSkqSgyqdsYgTXpulb\nV7p7rFvRtb0N83lrdqD+k7SXQN5SWwFA+oHAkHIEg8V4Ovlpi1HyLh/u5Kb7H0TxL0LqO760stY1\nPS9UuLnXLZGrWd7cXOwuJKyC4VLCt6ZBTM/2GYzH23xQ/ET05094MWukO2yBqgUw3b9PIbUbdLiE\nICr1TgH90KO1KhkqEAEk/iuqyZOtlj6LppJSns209oXUqvZt8rbj5H6HDGOLFLLNcevLPwl1P4i9\nb69qt1qV9qDzTrzpeDbCQw0yoER5aEgBEH/pjir6fvdZGkvN2/Ub9lZ3jiTctJJO9aG3IVAgEhK1\npknAWr3Nfuel6Tp+g6dYccdlS/8AT4OXqc0565Pc4a2bhm3t7i8Le25UpKU7iFoSkj1EcZnGexr3\n3SHhnp/U1xZt2OsXdwu52AMt2pUUlW4EYnMgATzNdc3ULFjeSO6PPKo/5bH3jpnwOtPA3qMddav4\nhWWkO6P5iLW2ubZS7xy6GCwppuFJBQSFqKgQFYBJgec658Geq7vVDqmidD9RsM6ghTo8/RLlptAU\nRATKDIzzPEc18+GXqs+THKEPhknqd8Vxt6nrloxYtE38XKR5vrLwP8UOi7N+41nQdRFjaW6X3rhV\nu4bdtLiAvC/08bZE84PFfF+qLzRrDWb7T+n746rYsO+Xb3lxblpx1tMwdm47Jn9Mn619LBKORXB+\n55pJr/Lk3egNHv8Arl9noVjV7WwTf37TyC82kI80+gbnDBSkJWsxMExiYI6N6pfhvpTjlidH1c6s\nyu2C7rTkOliGwFlAcEtrSXMKGSUpUO1YnNTy/wBO09+6f55HSENEVl2O94tK1G/8P+nOqOq9aszq\nj+m6fbaPaixVbuO6a2262XUlKAhaULb2FZJUpXvBNfL9JDVncJ/rVkol62UtJdWtspKkFTbqeJ5B\nA4OO1ejxJ5blJU+y9EkjnKKhXfuz3HhNo/hn1f1Tp2hdb9SXfT9m8H/zWquHc00Q0S2doBV+sZ5n\nd25qujumeleqNfv+nLG11DUdQWoW+kJYWCHnis5UkIkthIUYEGSM81jqMeXB0Uuqcls6rvXLfPb2\n3OWOfidQsNbef8Gbr7rDWXCvwr0rT9PvLLTw2201pJvPILrQPmvtNLXG9xKZcUUSYxEVyuuurvDT\nXB0+90n0crQrlGnhnVm0LcLRuQSkONlxxwqlISo4T6ioRAk+fpsKUIzw7KXxNbd1325vlnuy5EpS\njKPGy+TOnoGmt6P+Q6h1nStI6is72xvG2rFd4q28l3yXQhxxz0DeiA6EhRCoSnJJTXg9O1PVm2b1\n7SbZaE26A48pESlClBE5E5K0j/8AlTA8fVwb3q6p+jOfUS/plHVztv71RsWfUN9dX4vr+8U87btp\nUkrgoATEDaRCgP8Apive+INjqnTtlpeqf1W1astbsP6lbMabcNvBlh6CW7gtgDecAoIBEQR2HSP6\nXrfiY18ONcb1u0vZ8nH+r0tQlzL+NzyHUnivr2tWOl6dbt2WlN6NbuWbbmnW4t1vIW4tZLqk5cV6\n9sqztSkdq8c7qt9dC3t7m9edt2QUNIUskNpUSTtHbMnHetdL0ePpYVBb+fd+7+bN5uonmlcmbGtj\npwM6eNEcvvOTbn89+Z2hBf3r/wDjgTs2bP1Z3bu0VPTWju6/rtpo7CXHF3aw2NjZWoDkqgZIABJ+\nAa7pyjBuXO5zai5VE97qvQWk+Geu3Wj+Il+GtXtPIcas7ZLN6wpC0qK/MW06dqk+iEieTO2IPgzr\nimdRdubZi0La1FKELtwoBJPIBmD/ADXnWPJkytzVRpVv9duz9TtJxxRSXKe57PovXentVvb/AE7X\nv6FpidRcQ0i6dsFrW0CsGWyFbW42gFR7EicmvSdQ+GGlazrtpp/h/wBZabrmm3Grf0awuFuotnHD\nuAbcW24rc02rcDK4CRycV5nPJ0+Rqabgqpt3d/tXqe7penXXfDBpSfbj89kfVfEHwc8Huh/CH/8A\nP9e6hT1gw8zbqLGlr/INPBpPmWynlq/WlfmTtEenAgg18Jt9C8OB0Zda9d9T6m5ro1FNs3p6dPHk\nm22EqeLxWfVuhOzaMZmr0+XJLp4zg09Vu377fY6db0kOmy+Hl2aXbf8ANzm9IaNp3Ul4u0XeixQS\n6tLz5bS0lDaCogqJHqjsJ3EpABJAr1Nj0p4ZnpPVOqNU6+Z/q9tct21vozVosuPJWFFTu5JCEIRA\n9zKh6Yq5pdVr04op+t7fseTBDBJfG2j3ut2XgH054P6rddE9eXWq9Ranc2ls9a3enJaCLbyEPOLS\nclJS+VNyFeoIkiCK+bP9K9R+ILGqde2to45p9o2i51i40/Tgi2sEFYbSVoaAS2mQABABkRW8eSeD\nGpZd5N1+cnt/pIdVNY+na4vn/wAPn1v/AMlfW2pXGnsX9oy8245bOOqCX0BUltZQoLSFAEEgg5wQ\na1XXG7l1a0Npa3KJS2nAGeBNe9b7o+LLbY6mh9OajrlvqT2nacu6GmW4urkoXtLLZcQ2FkHkb3EJ\n+qhXP1C+vm22tOccWWbRxa0MrVuDalRuAHadqZ+lRShKWnuhTjG/MvrC26RZ1l5fRN/e3OlOrUu3\nRfNpTcMomAhwp9ClAf3JwcGEztHnzHJMfQ11OUNWlauRET2+opH/AOwjPJqGgVIM+4ySakmRJ5qg\nZUO0gdu9ZEeoySDGKAzDsRgA/wAVkYcbQSl5kuCCEwqIMYJ+h7fFTkAFQrdiJ7V32tT0CxTp9zpu\nlPvXtqsLuDdOpct3oUTAbCQoCNoPrPB4nDZcmWm+D6P0TpPSfi1q2u3fV13f2nUN5cMr0jTtK08r\nbvFuPf4jRO7/AAglBCUCMkpBIgk8fxY8JdZ8LtQb0nqLQXrC6UC7/iOyS2Z2+mMcHMkYNfAf6osf\n6mv0+e2pXHzaXPt5bn0F0kF0izR3fc+eJFqglTzS1JAIACoMnvWoFJ/U6k+0V93dnhO3pOh2Woad\nf3L97bWztgym58t642KfTvSgoaSR6nPXuiR6UqOYrT1DTmGNr9qo+S6guo8xwFQTvKQDEerEwO2a\nibugGi6W9q2osafaNOLeuV+WhLaFLUVHgBKQSfoBPxU6rZKs1obP6SCZH+8U1b6S06s0lbSdqVGf\njtTaSpawlIK4zH0En+K0Q9NqDdq441dMWFlbW7jDRCUeZ5ZKUJClbnCSSpSVFWdu4nbCYFce61I3\nLza02zVuVMhoFtGxMAxuASBmBBJkkyfoaTexE7NFW5LhSsQpJgg1um0sP6bbXLN+pV0tbiX2PK2p\nbSANigqfVu9UiBEDmcVJO7ZTUSholXmOlOIEJnP+lNDzbLu4nzEpyJEhXx71KsHodK6n0/Trj82n\nSkuOJQ4lCZ9KSpBAPfgmftXS6u8X+uOtdG0jp/W9WC9O0NnyLFhphtlLSCADhtIlR2glRkk5Oc1l\nRXf87mozlGLiuHX2PMWl/dNPNvNXSw63lBJnaocYOK+mdPfin8fOmrS6s9O8UddW1cpcQ63dXBug\noOfrI80Kgk5kQZAPNHGMlTRE6Nrrr8U/i34i9GsdFdT9SG7s0ONrdK7ZhClhsKDaQtDaVhA3klJU\nQSASJAryPWfih1N1y6i71163U8La3s97FshkFlhtLbadqABhKRJiTGSZo4qSqu9huzl23V/Ultp7\nmmNa5eotXWPyqmA+rYpnfv8AL2zG3f6o4nNcg3ClLlQzwcc1mOOMNoqiynKdanwZUPDaEh4gTJkE\nma2DbAWzV2q4ltx4tH05wAZ+eavHJk+k+BvXNr0v4oaXrupXDAtrYKbK7qybu0bQ2rZLbgKDkJ5G\nK9X0j1te6h46rvenNEskPauQ20bspDNqreFuPb0pAQ2naokpEbQREE18X9QUryKb041B797/AOJH\nt6aEdpR3dpehr9ReNvVdtqD2s2ll+d0h2zf0a3d1NsPgLUk+YponCFpLhUkJ/TuBiTNfPepuuNZu\n7W3tdWDzl+yB5r9y4VOqSpA8vB7BEe/I+86H9Mwwhjn/APSW7/heS4+h36jrsjbh2PKHWruFlTxc\nUo7pX/4rPp+v2zTN61eNvrLrKk2xbc2pacJEqUCPUCjcmMfqntFfZ8OPY+bqvk7el9QdG3nTQ0fq\nOzu7a/tnlutalZp81x5tSUBLC0rWEpSkoJCkjcS4oEkAV9T/AAz/AIzeufw1v6qrRND0TXLXUWWk\noY1O3O23dbKih1BbKVT61TJM4nIBFwY445VkVxu/zb/Yzxx5oJNe9nneuPxReKPXvUWn9X6nqlva\naxprink31owG33nCrdvdOd5yBxBAgzXp7T8cH4lr3qWy6l1Dxn6jVcWTTbaUIvS00tDcEJU0kBC5\n2+rcmVZmZrpjn4F6Fz6WZiqO/wDiE/H743+O9nedParqSNI0C+bbbe0jTwpDDgTBG+SVKlQCjJIk\nDGBX5jUy9c27l2y6gLbIC0bvXwoyByQAkz7d6zkyXTr0Ol6tlwazbt+przkJe8tsglwDCZMCSPnF\ndVq51vVdMtrG71N521tXyLa3W6SG1OxvUkdp2JmPYUtHNukad/e2vmqtX/Pc8glpuXJSkAnie09q\n0XL5bqlJUpaxsCElSj6UiIA+MRSm92E2Yy8pCiguZnJ9q6vSnVuvdIa2x1B0/q95pd/bSGry0UUP\nNbklJKCCCDBIwRyaTgskXF8MsW4u0azWuao1qH9XY1C4ZvJP/MNuFK8ghR3DOQTP1rWN06khe7eA\nQfVUUVFUkVzb5N5vWm75aW9SSsMoSralmAd5SdpzIjdE+4mK7z990nbdP3uiXvSt3bdRNrKReKvy\nlLcEEtlgpyfSRzgqzwKw4yUkovb9zScWrkjySkrYdPmKgJXBz7c1s6vqjd2vZaIhlCQEko2HjI2h\nRHPzXbc57Gh5hJAiJjvW8Bb3sIZKbYpSkbVq9JhPqVuPEkcf/b4qO1uVH0m00LwptPD8vP3Fzq+v\nXLTV0683ci3Rp21bgct/LKVeaVJLSg4CByAknNfMWH/ytx51q+6wtokocbXtI9iCPiuWKUpJqR1y\nxhCtDMTt0t9YKyCSOSTJ+prGgSCpI9UxnIrscS2lLG707iBz7RXoeleodR6Z1JvV9Mc2vMEEJUNy\nVCeCk4V9K49Rhj1GKWKfElX1OuHJLDNTjyj64PELxM8ef6T4XW93os3LotbVp22trRPmKcK96n1g\nQokkFRVJEJmABXi1aNqZ0J7RtU11m20/T1PPtMo8klx4lKXIJUkq/wDiTwSYGBk189rH0uOOJRbU\napbvjb19z6GSeTrZeNN/nPc8GpNmVbLd9xKwoyViAR2iP2qmXWW3pclbZB4zmMCfrFfU+Jo+btZF\nwtdv5ai6lXmJ3gJWlQAM8xwfg5roW/V2vWlhfaZZ62/b2l+ylq6ZbcUhFwhKgpKFpGFAKAIBxImo\n4qS3R0w58mCevG6f+zjM391bFZYcKfMwrAII9qxlbhUFlRB34+a2lRxbsSLh9pRW2spJwdpOR81K\nlqW5vUSN2SSatdyGA+j08g8GP4qCI4kT3NUDWoKIGCOMdqxmT9ZqAZ2zKaZG7CUQYqgQmCmJVzVN\np9QPaZqA2VtlMZEETyDzW0vSX2G7Z54s+XeILqNjyFKACin1JBJQZScKAMQeCCVWA/KspIClECRJ\nAz9aAhSJKeOZ4xWWD2vhf1n1V0R1E11X0lbur1PRii+YfZlX5VxC07HlCCCAspEKxJAzMV6Pxt8f\nPFPxv1tvVvFPVXL/AFS1ZRbNqW0hny20lSgny0JSJlZMkTmvNLp4SyLI+V+fyzrHM4x0HyJ5wKWJ\nOZ7GszhZYA2upWSkGRJzExkduPqO9er0ORhfX5qEgKg9wTWX8yh5IYvXFICQduxE8mYiQAMnigOj\n0p1dddK6qxqFmpKPKcSpXMx3ggggwTwZqLzXdOvNUsri+s99myltDzLCvKLiE4ICiFbSQP1QcmYr\njHClm8U6PI3j8M4ilyZSTAxzwKUjPBnmDXc5lpeWgpJJUlMYIkH4qUryNqiYERQAFAA5/egKXhJO\nIoDM044pBYD6kNqWFqBV6SRIBjuYJ/c1K1wgAKAPYe1RA2GHmXWFqfWltbTcpG2fMVuAjkRgk9+O\nK11OzwlJFASXJPpEfU05hUkHPAqgz2dve6hcJtLG2dfuFztQ0gqWqBJgDJwP4rraDoGq9Ru3Fvpd\nslbtpbu3b4ccS2lLLSCtatyiBwkwJkmAJJAqxhKbpLkjkoq2ckq2KJnufSKpt2f1ZrFUU6VpavPp\n3hghAJG8g7QQPf7j96euaRqfT9w1Zas2EOPW7N2hCXUrHluoS4hXpJAJSpJIORwQDiubmlLSaptW\nFlfs24W4UFBwUpAmfv8A75rrdXX/AFHrVvp3UF5pt1a6WpBsdOJLimEpagrbaWsmYLm5QBwXDgTX\nOUYrIpSfovz6m4yelxijy6nHgnYVK2k5TNY1uAKIcJkj3zNehI5GZm0U+04+lxDaGklQLitu4j+1\nPufivQ6D4e691B03ddUWS7MWNpeNWDnm3TbbqnnELWkJQo7lDa2qSBAwCZInUU53pXG/03MzkoK2\ncxOhE2qr5dw0Wmh6h5g3J/8A4gzEwJPcitJwsokNrC54iT/nXNS1cFTszs2bLmlu6h/UmEqaeba/\nKkqDqwpKiVpERtTtAOZlSYBzHZ6XY6VuXFnqHVF2Te5IBSlRMQfYHjH7mueSWRRbgtzppW25p9Rv\n6TY6u7b6DqDl5aJgouFIKCoxmAQDH2FTYqavdMXaMaMLi+W8om63qUrYQmE7eAQUk7hn1kHtFtqC\nlLZ9xCrqrN7rLoDqHoRdnb60GEr1CyZv20M3Tbw8pwSjfsUdqsZQqFA8gV5dD79usOtKU24jhSTB\nq4skcsVJDJjeOWmRC1LcWVLUVKJkknJmur0iOnf+IrNPVtxe2+jh2LxyySlT4b77Av0k/XFanq0v\nTySCTktXBy7gNeaosLJbJ9MjnNem0Tp9m56I1rqBxq4eftrm2tmfLTLbZWVKKlnmSEkAD59q1Trb\nkxKUY88HlSkCSTmYAms1rbC8d8tBKVkehIBUVq9hHejdKyma40nUbNCy/YXDQbUgLK21AJKgSkH6\ngKI9wDHFb2gdM691jf3TGjsKu3re1uL+4K3UpIaaQXHFkqImEgmOTwJJiuc82OEHlk9kbjCUpKK5\nZo6jbWtq1a/l9QTcuvNKW82GynyF71AIk/qO0JVIx6o7GtH2A4GTXSLfcy0k9hq2pbSd4KyoggTI\nGM096SIBUDwapDNa7niphpsqV2Cck/b7Vl/puouodcasXlIbQHlqS2SEI3BIUT2BJAk9yKjaW7Kk\n3wahBBVxIxEVkIWCAoKATBE55yPpWiF214bW6DgQlRTJIUJB/eti+utOeLT1hbvW69hLqSsFO/cY\n2dwNu3nMzUre0XsZtC1i60q8auUpS4lC0rU2oYWAcj3Ej2g1V6m91U3epWts+qzt1DcrKg0FHAJ7\nSa56EpazetuOg0LdLzy9rKFEplUDkJAkn9hNda/1hi+eXY6YX7DS1bUJt7h4vhvIJJUEj+4EyEjm\nK3szG63OKpSwYEGJiPmo3Azuk/WtEBREcHb88UyVEpAWSUnFUGR64fulec+6VqhKNxOQEjaP4AFS\nmD+oYiBUSS2Qbvc1ViRA+lIgQPTOJzVBOfefehJT37jk9qAUiZBEUioxJB5zmoAjPeDRO1Ugke1U\nGVm5cbUFTj/Kuhqt7ZB8o0p59y22IG55sIXu2jcIBIgK3AGcgAwJgKQMTLi4375Bxk1ClkEJS57i\ne1QCZedbUqFwVYwoiazl91zcVLntKjNTgGNYabJ8zJg8Gp83YgGVRFUGBb5UfQCB7nk1jUTMk5NA\nSVZxNEg0QHuVxBpbgeMZ5FUGUOkkg49jWRBQZUdpAz8/aoClNrU2l1Sxt3bQNwx9ualrykLQp7cU\nTKgkwSPYTxQGbUXtKd1G6d0lm4YsVOqVbNXDgddQ3J2pWsJSFECJISAc4HFawcUTjvg1FdbgtSgD\nnkfeoU6E45+KoBTyc4gnj2FIOSnj/WgKTcraVvbO0juDBFZxeuhraXVwvB2mJ+tAIEqTOeZEH610\nWdI1JdoNSVZPi0U6Wg/sPllYElIVxMZiucpKPLKk3we26X1XUHHl9IaQrWGtG1j8uxf2zKw+44gO\nNrXtSAkElbaVBMD9KASYmtjqfwl13p3Wvy2pqacadILY/MtqWEngHaTBA5iRNOl6TLkuSr1fqcup\n63FilHFJ770vQ+m9PfhhY1boa66uuNX0uwZsWvNSl+6IeeiTCZSEkmQI+nA5+K6nqJvba30NeuuK\n0vTn3X7axecWGmVLgubRkAqDaAY59P21k6SeBrxJXf2PP0X6iuslOMI0o7X5hf8AR51V5pXRVhe6\ngh0LKmUw++CFE5SgSEhJSNxAmFHHA8xdae7Z3C7e6ZeYW3hxDjZSUd4IORNccWW1pk1q7ntaowKa\n2JWpuShPJJgnPas9qtnzkJuHXvy6SCtCCAVe8e31g12TfYhkur5P5i9a0ZDzNldKUhDTyw44Gd4U\nlKlBIkjamSAJI4ExTtNC1S7sri+tmCpm1SHHCk5SD3/kVynOGBOctrr/AEbjGWR6YnoNI8LuudX6\nSu+ubLp69Xoli4hq5vgglltSyQkKVEAkggTzFa/S3TuodWdXWnTygu5vL11LQJXKgAnJBUpIwkT6\niBjJivG/1HDKGWUH/wDnd/Lk7/0mRSgpL/Kq+ZyNc0x7StSubJYWsW7qm5IGYVE4JH7Ej5rYXrF2\nu9N3bsM2wLYRtZZCBt+Y5PzzXqi1mhGfZr9zjOLxTcXymDF3Zqu0P6u64+ULSrYSdqxMqBIyPb71\nqaiLULLjDakpWZCSOBWlGUZegbi4+pz1zuHaBOKS4IkqntHtXdHMJIGI+9e98PustH0/TXul9Y6U\n02+Rev8AmJvH13XmJVAATsadShQHaUzk5pwcc8JThUXT+X82eKvihzUHi202yhxxUNpmECeBJJx8\nk13dY6bHTF0bRet6dcXDSkpWLV4XCFBSQoFLiJSRmDmQfvHObaqlZ6YQuLk3wY7TU9Os9L1O1vrE\n3T92wlFo62sJDDgdQorIKSVDYFJgEfqntB4zV440lRQ6pBWCkkGJB5FWMd2HLZV2NNTilL3R8U0D\n/qgdgK2YKUoBUpwQZ5pSFpjk8k0BvaWm8bukGy8xDpCkAoJBIUIIxnIJB+Ca9lp2pdXeEfULF8rS\n12dwPJuHbDUbXfb3baVpdQl5lwbXWypKFbVAgwD7VwyyhN+DJ7tP/wB/Y641KP8AcS4PJX2oL1LV\nDfm0aaddd3lLSNqdxM4TwB2gYrRbcUlxLmwLiJBnNdYQUIqKfBiUtTsTCUKuWw8sNoUoblFJVA94\nGa3bM6Uzebr5DtzbBKxtZWGlFW0hBkg4CoJESQCJBMjUWk9+DLW2xl0HXrrp3WLfW9Pat1v2jnmN\npurdu4aJz+pDgKVD4INarl0+orElPmCVAGJ+1Y0LU59zWp1Rt2F8li2vEXOmovFu23k261uLBtVe\nYlZcQEkBRIC0woEQsmJAI56nQCFlIB7gd/3rVEvYtF44A4QEK3IKZKEn9vY455rBvJBJECOaqVMg\nslJnA+aYICMnvBBqgoJAMg9sZxNWomOeO896A1CJhIIwKmeCmZoBJEEkgipWe3+dAIwo0xCQRGfn\ntUApBgGq4xOBjigJKjBnIoAITPY1QUCQQZIA+1IrOVdz7GgN7T1W6ltpuSdgUAsJMGJ7fNdHrS56\nVXrzw6IRqiNICWwyNTcbXcBQQN+4thKY37ogcROalF2o4C1lZMnPzS3TyJmqQJHMCKCRwBP1NAEg\nTAH2okEYiaAajkZ4FIiPcAUAEYBTVo3IVuGPioA8wqPt/lUlR9h3igGVECOCaU4j59qAFKJTBmaY\nUkjjP1oBbfcyP9acxgCKAMR80RMgAj4igMjbpSfVBAr610p1tr3VXRll4RL1H8totvqDmq+QgAB1\n5SAkqUcDCUnJOATHz4et6OPVqFq3GSkl6ri/3OmPL4Nt8NH6Nc0Dwr8DdE8jQupNO6h6xcaDbjum\nuoeYY3oSoJaXE/pXtWqZUdwwJFfIde1rrbpq3u+sOo/DtV3aXKCyzc3yHUNtrWCElKkqGRIIg+1f\nbxxn0+LR9fc/NVHL1MsnUvTKT0x9u1e5zvDLxhFrpVtoPUToVY6ahxQCngFrbKt21JUYKpkDIxAr\nxxvelf8AitJsrq91ezeRcISk2aWVoWtshEICl7wlUGMTt7TXg6tSypOGzaau+NvL/qPrdLgXT5py\nr4XvXHuertl6a5omuXhtxp7TNtb6c26rTwDaNypSCtSbcnzHFBIC5SshKgVEHHzDVW3tMuLzQ9Yt\nLu2vrZ7y32XUlKkONkpKFA8EH9oryYY1Jp8+/wD1/jPp5pQkloW356I22D07aWmpJauHrwuFDVsV\nsBBWiSVKg7tp44M1pW1poP8ATNQuL28u2r1Ab/IMNsJW27KvX5iioFEJyISZOMc16LlVrk4xS7i0\nzQGdRbSf6oy24VwWAlRc249XG2O3M/FelY8Lrp5pSLS5eeUUhY2NomI4y4PvjtXz+r/Ul0rqcdvN\n/wDEz29P0Tzx1Rf59jNqHRd901aXdu/1KtVk2dzrCFEJWtOEyncQSCf5rT6RtOnrFN5rGs6ntUi2\nebtmm3SlZfUkJSTGdsKV/wD4mcc+aHVz6zppT6fHTlt9e7+R2ydL/T54wyztLf2OPduWiwhCCDtT\nIWlQOO0x/qa7PSQ0lt387rek22oWlulS126rtbBegYSCnPJHHtXuz+IsTp0/b+Dy4owll33XvR5S\n8068YWp99KUjcMFU8/8AqtZ59bsla1Se3Y17ItS3R5WqZrkHmPVOM9q2nFMflQEGXSrPwIrTIZNP\nct7W9Zu3bVm5aEbmXFwF4yDBBAmaz6vrdpdPI/p2k29oEqKl7JMzHpmTgR9c803ZKOfcXQfcQpLC\nGwgAEJJM/v711el9S1Ox12yutItrW4vG7hBZbuWW3WlOTCQpDgKFCeyhFNktzXc07i9t3mVtO2iR\ncBZO9KoEe0VzVLwADifvSN8hkzkSME4AqwQBidw7VogKQoyArNW2AnBJTiOOaA3tJ1NzTrtu8ZCF\nuJ3JHmJkCQRMe4mR8gGvX+Kni91t4x68nqbrbUlahfotmbUOlsJhppAQhOAOEpArGlKWo2ptRcTx\nhLq0JdZY8oJIG9Mzu+pPxNei6W1mz0lLtrqmiW961vQoOLSgONng7SR6sdjXHqMbywcYyp+aNYpK\nEk2rR0r9XTF41+Y0xkJSrstpCIj4ArzN+yyhxS0NoSREBIBj7RXPpfEiqycm8uh7w4OdvQvamQAk\nGDt5rGSgAqJg9q9qPOZFOFhpK0KEqJ7+of8AasS1FSt6zMice9UGNRIkgjtH/qqB2cJHHtNAA3KM\nFPGeIqoyPk+9AUFZKlAwDIzVOSNvBPwaA1VABWIwP3pQBM4niKgJJnk/WalQk4/2KAYAJJ7D96RS\nAMGqA9Un570o7AgzmoAIn4BoCQJMz8VQNEmM04BAknvUAwEj9OJqSmQeTPtVAtoEkyPrSAknkVAP\ndIABonbJAFUAT7mkk/28zQDVEe9P+6QZg0AjIyEkfzVZUQO8YqAnAOZM0KPYdqoDGTn4zTUZAJJ9\n8UBJnn+KYAB/6jQDSEk5hKQcxTJlfIP84oBcznmnwnB+KAfpnA4rMw6tpXmIWUke1QG6p119G8vu\nEgZ70zc3ZZLIvnS2VT5ZUds+8UTa4FWYAlQMFwcfNZGXXrZxCmnNriCCCkkGpytwei6+606h6013\n+p9SXSH7u3tbazLjaUpSUMMoabwABhCU1zL1Gl3xsmtMUA6bdP5hSkqRueklWVLIOIyNoPtUm3KW\npKjSe25AumbZr8qHm1qRj04GfnvW3rHUzWpaJpekN6Hp9qrTEupVdW7ZD11vXuBdVPqKeBAECuTx\nOcou2qd++1U/3Kp0mqObZLaceSHtSVahXpKwkwB7GDXvL7xk68a6ft+nLXrBIs2mVtbmrdKbhSFo\nU2pKnY3kFClJI3RB4rzdX+n9P1s4SzQvQ7W9b/Ln24OuDqcnTp+G+djwOo311tRajVlXTW0LxuhJ\nInadwyR+3tWqpttNo3dfmNzqnFJLcZASEwZnuSe3avZFKK2VHGUnJ3JmNLqpklXP8103lapZJVZq\n05xlzelClbSVEqEgfcGferJJ8kVrc2dK1iwXqabjqPTF39qi2Ux+Xt3BbesMlDSypKT+lQSo4lUG\nTKia13dTc/LNW42+WySUblbgjdzA+YFc3j352NuVr1Nvp7ojrHrIvPdL9N6lqyWnmmHVWlsp0Icd\n3eWglIMKVsXtHfar2NYNQ0q90G5XpGv6Xc6feMKWHG7ptTagRjbtIwcV0ck3pT3RNElHW1s+5zdS\nuW7i7cfZtmmEOqKkttfpQCcJEknHyZrVTA5BH2rUVSSZgsSYHtWdy9dXbJt4QG0ncMCSfkjJqNWD\nVJBxzPcUyROQCJrQJEfqzPb4rID2ABnPETQD9PJT+1ZUDcChKf0g1GDGklJk/sRW1bOXLDK7hDRL\nR/wlKgkZ7T2MVGlW5Ub7NypvTnbVT6UWj60ugKAUsLSCBB5GCfrj2FS/qaLm3SytO1STIJHOMVzU\nN7RrVtRouj8uG3G3VAqBMg8dql1wQFhZUo/qPxXRbmTCQojcmBP80j6pEgEZMGtIhG5Ubt0e9UAf\n05+fagARvONscTTRiSoyRQABJMmMYrJ3BJ9QOcVQUD6pE+5JNN0AkHIkfaaA1STG4EmOaxqAPtjM\n0AQTkzQR3KhUBMbTmf8AvRyNtAATAmkRHAGMGqAgnMjiKrE81ARGfVEfFUD/ANJigGlQ7/X70KKt\n0gx9KoDEQYP3oIChugigERk4HHekYAj70Aykjt25FLO47aAZBGeKZQdqVKIG7PM4+faoBFMySYoS\nU8beB7VQAETBgjvUz8896AZ28gzTJJSmST9/9+9QCEmQZnsaCdoynIqgYMcxP+VI4gH7CgKj+Pel\nG4fxQACSMEg/WqMgyTn5zUBntnVNkpKpBj71sF5tSIUo7gePbFQDeeT5CA3EiZVOfpWDznJSvMDG\naICW4VbvUTPvUtupDiXHEhSUxKVHB+MVaA3HG1gkIEq/ioCwn+2U/JxQGZx1hbiXE2yUJnKUrP7S\nanzEkSokRj4qbgxOKK1kzOf4rLZLsm71o6iw69ahaS+0y4G1qRPqCVEKCSRMEggex4qghLiQoAg7\ncwKaFNADdvKp4Ht2NAdjpLqR3pTqOw6jt9Psb5dhcouBbX1sm4t3SkztcbUNq0nuk4Nc/Vbxd/fv\nXqmWmS8tTuxlAShJJmEpGAPYVhY1r19+Dev4dBFtql/asqt7O7eZQtaXFpQ4QFKTO0kdyJVB7Sax\nXV7dXqy9dXDry8epxRUf3NbojnJrS3sYSQTgmBVYCgBkGhkyLOwoUjBj+axSZEiRFAUBklRJHfNW\n75atoaQUmM7lTJnn4xFAY4A/USfYCqyUynIE5igLwVQBjsapLpaHok5zU5BDjhW4SAn1ZikhxzCd\n52+01aB09Na0FyxvXNVurtu6DYNmhhsKStciQsk+kRPAOY+o0ApShIBUQZE80BanXHkJQSEpAgCO\nc1jUtKgAhJG0QczJ7mpVAQBSAZGRmKakzJ9oBFUCmEf5U0pO3aR8wBzigE5uMkCAPahPAgCe0+9A\nUhsqWEhJyB271mNq43JWCIVt+/tSwUlpJMJdTumIj/Wn6kr2kbiBGaoNCYJBxPekrIBJ5qAj6qEe\nwpFQ3YE5gc1QNR24VMj5qeZgYmgKkQRBP8UjAwM0AjEdwe9BBEJjB71ABwMUxgGD2jFALKiSe2c0\nEkjdH7VQONvbE/tTSszycUASZmcdo5pjbEkZHcVAKATkUEKCojPNUAZkyYNWtCEMoc89KlKUQWwM\npiMzxnP7UBBVP6gY7GkOSQqgFJB9xRuB7D7VAONxEQB7+1G6cRHzQAnOQcUsGB3nNUDJVGPsaSjB\nGTAqAAockfTFPEEcRVADnCRwf2pyFc1ANKikyJg9u9ZN6fnd8YigElRAITAqd6ikt7iQDMT3oB7p\nzMe+KkqJMK+uRQDKp7/Ak0DJigFJ+kDiqmB3+9AIYMgR3pJnlff4qgIzPeqSODH7moDduLqwVZMN\nW1m6i5SpZfcU8FJWkxsCU7QUkeqTJmRxGdEkzJBNSNrkrrsTImCmKUqnia0QqBPee80QJBjBqAo4\nIhWOKE7eDmO3vQFpCSRkQDNZ/wAu4ppa0pJCIJyCc1G6BrqCyApYOTP1rGFEJUEwKvILS4oyJkHN\nVI2FQiJ7jvQAlIIPvzMUewEQDQFsoS46lK3UNoUsArVMJBOSYkxWZZZZSkMqSsSYVGYrLbuimBTo\nUf4qSrO5IJP81ohSVyQDAB7VR4Kj2+aAW4AQiZ9/mmlUqQARM8k4NAU+wW3i0SmUkjBkGrctSw95\nS1DeImE9yKl70Wu56rpGzY1LX9PsltDy33UJIdAMRiZP1r7F4h+BGh6W5pVxoGtq1vS12LN7rr9l\nZqCdJuFAjyFKMgqgJJiBKoExNfA/UP1P+h6iMZNJOLe/ndJfNtL5n1ek6WPU4ZPvf2Pz1cNtMXB8\nolKkqI28x/3rE8mFqSckdwa/QJ7bnyns6NNQ3K2gyRS2gGYB9oqkIVMApxP7VMEQCfmoAmTECe2a\nO04wYoA7Y70/eaAWJ4kUEk8zigAFRgETAOKRmJE/M0ASQZ7xmnGIjA9qoEDAKiTPanG0BXPvQAQY\n9pOKZ7xUA0nJI47miQDPP1qgcR2g/vUqODETzxUBJnnt+9IkEgRwP3qgJkDtzQBxmD8UAwBJBJkR\nj3o7dxFQCg5E/tREHPvVATgTNOByDzmgCRIEyOaQACSSc0A/7ojkTRgH3igGJPqP2EUwYynGO9QF\nAA4EZ96kwk496AJVkgiO9ICcg1QE7hMd6ZJ/TMYoBSSZOfrVJJ3AyKAQV2MRQCJMioBxme8UytSo\nCgPYR7UApSTjtSURMnIPvQCg4NMbRwRzFUCyonnFP9IAKvjFQDUon/fan8booDIklORMdq6tjept\n2VIfbQttyA4F8wDIAPIzWJq1RU6ZpX9xbOvn8ihTbQSICsme/wC9aYByI7zWoWluHV7AkYOPrTgw\nT8e9UhSAtSkj9/c0EkEgCEg5mgGIV6QeB78UAZCu4+KAzv8A5WUG1U76kDeFgelXeCO1Ye8Hme4q\nJutyuuwylO7J54rotaewvTxcLvAHlK2tspEkxyT+4gZnPtWZycapWWKT5ZphtkFsOpWgHC1AT35A\n/avTW3TWio6dvtUc6htzqrNw1b22mJacU6+2tJJdB2lASnbBBUFSoQCJNc8uSUEtKu2vku7+RrHB\nSe7o81+YIuGilsBKI4HOayKvFO3bl2pCVle9MLlUSCPuRP7iuum3Zi6VHteiLCyeftL5/WWrdTag\nJKo8sJg5/wB+9drxE6yatrl/SNJ6kc1G3ukNrU428stIXtCVAImCfQM/NeF9Lj6jNryQT0+a9bOk\nc+TH8MHSZ8zubW5bW4paVqH6lK2+/vWskqKitRn6mvoI5cmArjCiCTzAqDIwk4FUGMZ/TMT3pz27\njioBQTB7D2FOMGJzVAEpgwaWYG2ImgHIA/VM4pAjOINAG0+5FTJETFAMDEcmmRk5/agD6k0EEHNA\nHB544igq9PzNAOCBBWfvROTUAEZBxNMCZI4FAIqAPNCjAEc8VQKARAx/NEEckUAbYHPI4pEDB+KA\nXae9MZMyDHHzQBAA5yM0AJPvQBjkYH0pkggQO1AA3dhSyokd6AocZMH5ogmDESaAXBn2zTUSSAR9\nqAk8RHaqGDEEECoAO4iBMTQqO3B7mqAAjk/+KYMDIzQAdxHGewpSf0zUBRV6uYMx9KW4EkTAP3qg\nMADsRQADJM/9qACTGIg5OaURAI5FANQwSRUzJigKnckCTApkE+4HagLRuHqPft71kXK0bZiKyCPL\nEpjPA9qsIUVgFBAOFYmrYKvLR+zuFsOgBYAVEe4BH8GsRjJV7YFSL1K0VqnTLSYUCPifioKknKvv\nVTICdpJ4IJxVqUQQQqCMAiYqAklSoUD9Ip5ncoyTFUCgbvUokkVkQ4pBS42pSVJMgg5BHsanIOkj\nVW7jT12DmnMBxAKkvhKguZmTmDgxxWM3abvy/MYSlYShG8SP0iCce/zXFY3Ft2dNd9je6vb0BF+0\nrp9ToZ/LshfmADc6EwspA4Eic5zXBUYAJMYq9O8jxR8X/LvQzKCm9HAJddQjC9u7OMVUkD0q4+f3\nruczIl25V6Q8ohYyCcRWfT7B2/e8pKkApE+owDWW1FWVK3RzTuAiPk1JEJMia0QCkcAxnipUMGBQ\nARtPYnvSk7R/NAMn2I96k5iKgGYmDS3SI981QBVJCu1HegD/ANmhO7PcfNAGeT3pmZ57UAwAEgk4\nI5qQRP8A4oCpBElJJHtSBJJzHc0AjMyO9UCRzQCVAAgUAkjPM96AUe33pyoYGCP3oAMmSeKJAEjt\njFQAQkZ78xQpIOZgVQG0Cf2pGffn2qAeOCc0R2+OaACcTjNKQO3NUDME5kn4oJHIoAJJHJxRiSZF\nAA7iZHaKMRBn96AR9lE8xRz370BRgCTJJ5zQYxt454oBTnExQBJIGYwB71AGf7cU4JO4ETzVABRm\nJBPxQImScGaAAlRkQQfb2oPfmZoBJkgiZ7UFKQSCBj5oCjGIpCSoEn+aApSiBEyO0VYWmBMY+YqA\nA7BGAB8iu7oCNGvvMs7y7NndObU276zDYVuH6j2AyZFcs7moNwW50xaXKpHNvgt2+fUXAshZG6Zk\nDAM9+1YEoAncB+/+Vbi7SMPkxQrfKc8SKrbBAIFaIUCAo7VSYzNQCFEBIM/xQAElKp9uwrIVJ4Jk\nn2FR7gpR9AVPI96SjsiRE8Y5ogU8UCCXjujmP9/FZmH0IagqBUfcc1OxbNe6Vvd3JJIGPoamC4hJ\nJ4JHNVEMcQIJEj+a2rQNH0vQRjHvVfAOm3qGmoQGXmSYwY5rDo7Nk/rDTN04pNu4uCeCBXKpRi2b\n2k0jlEKSoHb/ALipJETP0g11MEESZwfiaRIESBA+KoCQMzlVScxNQB+rBAoyTkUApIMCl+nFUDOD\nkxNAkjP2FAAEnP70xJkce9ABxiIoHET9aAeAe8RNBPz/ABQCP155pk54xEcUACSD6ee80gc+rtxQ\nABOM1WwRP71ATgcijtMzQDBmTP2oicx+xqgRMY7USSqDQADkie/NB3c/tQCAjvj4pjtn+aAIM/8A\nmjO7nj4oABzJMUFWeMUARie5p4iBQBEJAByRSJiQeKACQO5MUQTwYoAz7U/7pNAESYJzQI5yRzQB\ntMwrPfFBkGBjPb3qACBGCARzNBIOAcDNAMEkYMVRQSndJhXGaAmYgwZ+lIzJIOTQCJOAkz/pQSQe\nM0AGSZ/bNHcD7c1QMAkCBzSBV7GgNxDyS2lAJSqDuHv7V2P6TpTXTbeqXOo3I1Fx1xJtPISEBoBv\nYsL3ySSpyRtEbBk7oHDLOUKUVdv7HXFBTvU6pHKYH5h9EnaVrCVQPesC1SVTP1A/0rovI5EArCoQ\nSCowINZEAKUpIO0p+f8AWrQEXB2T3zNT+rBV9KcAoie/piM1luj6GEkzDQg/c07lNckKEGKtcBCO\nBg/61SE+wmKyFUsoA5JM5oCN3qIJJH0qt204USe2OKoEDlRUQfpWRCy04lxJgjg0BjWQCBG2c4pL\nIMgEj/WoDGTnPvSIHeDQAoQQABntFTJjnAoAEd5pEziDE1QEzxiKJJ+agGD2EZoBjOPoaoASVRQJ\niY+ZoAnGe+fmiCZqAOU8/wA0zE4igEc42kAUo/t/0qgZEYNBng47GoAOYpk9oz/vFUCx35+KUgyK\nAcAj9Rz2FBMJjigF/bBNMnABOKAQEj/OjHHvUAz6e1AMfFABHf8AiiZnAJ+aoACRnMfNBycc0Ahw\neJFA7igGJyJMjtSgwR9qAMAn3p44/mgAkxiMUgD9aAPrNE57A0BcxxipUSTQATgTimQDye1ASScm\nIn2r0nQ/h71z4lajcaR0H01fa3e2Nou9fYtEblN26FAKcI9gVJH1UPegOlrPgx4o6Fo+h9QX3Rd+\nvTepLRd7pdzahN03cMoCdywWSrbt3okKggnIrx7lneMMMXbtq8hi4Ciy4pshDu0wraeDBwY4oDCM\n55+tPMBJkie9AGD3IFAmORg/vQDI/j+aFESYPagCVRkQK2nr599tLTsEISAMVlpN2VNox2roZfQ6\nf7FbpqQsKUSTJHE1a3shB9RJ/iraWESDwoEUoC9RxBE8YoCgJx+9UFJMgnEjitu6CE27AKQFeUCO\nPc1l8g05+OKtxSFJbgj9MHHeqCSYEH44pFR+Pr80AERODnjNUkiYCccAe1UFoTuVsTmDnHtVZcEk\nGJ7CgIURiYx8VEAziY+1AQs5yaRGMf5UAQPbOMzUwQBnPeoBQRk8Hig8xHNUDMjEieKJJMCgECeJ\nzSMRxPzQDTP/AFTQTBPtQACAOSKfOePpQBnkpoJ3GEmgDMZV9qMzKU8/NAGTBH7zRI7g8RQAcGQQ\nKZgZJoBBUiTQfUNxSKACqQYiaWT9qADgdsUHdx95oAn4MUwI4NANUEc1GJ96gKkgzwKU/wD2BzVA\nDIPzQDJgVAEySSaU5xkfSqBiTzSyAaACfvRJgTmgKEkjgjFAyYHagJVj6/WqJSREZnJoBSZnntmm\nSTGBQEgmSCcRTk5gmB80B+tPwvfh30rqToxfitbdP9K+Ld0hD7Go9CHV3LHUbBjdtTcIUDCnSN21\nCkgQUlKivCf0/wCEX4pvwd+GPTbnTTHTF/4d6t0tavId0nWtGUnUyoSpbQeAUXHFE4C1pUZEgdgP\nzn4G/js0jwG6o600DSundW1zw41PVLnUenbB1xDFzpfmOlXlgStIbKVZSD+pIUIKlz9K6G8bejPx\ni/ib6f6Iuem9O0/wx6fsNQ1O26e1VphH9T1F5pTa3XGgShToXcrUgJJI2rcmSYA/Pn4jPwWeK/hH\n1epvp7py76q0TUW7jUGHtA0y5fRZMpX6m3kwstbApMKUogjMyCB+cScEgUAcnifqa/WH4POqehdf\nsOoOheqvBDoPXV9NdLax1G3quoaeXby4eYhbbbiiqCgb9uADAGaAXht0f0/+JjoTxn13TukfDzob\nVbdzphGluOKFhp+mpLlyH/LdXuLZeDQn/qVAr3ugfh66M6Od/Dl031NpfRnUd7rnU+sMa1faU43f\nW2pMpKVNNrdAHmBAMQeDIoDw34mdI1DQegb1D3R34brS3f1Fq2bf6IcWvWWQFFYMeaoJSQ3tWdv9\n0Ymvn/h10p03qP4U/Fvqu90SzuNZ0jVdAasL5xpKn7ZDryw4ltfKQoAAxzQGh+FHwpY8WPGXTdO1\nqyduuntBZd1/XUNtlxS7G2AUWtgyrzF+W1Az/iGOK/QGqeGHhjov4k/D/X9V8M2NI8PfGvR1WCNH\n1DTw2rQ9UWgMraQhYHluIuPIUFwMPK24oDU0z8LfTWjfhv6q6N6k0dhfi7cr1nqHR1BiX02Gj3TV\nu8ygn1f4sPKQkfrCgRO2vQ9G+Ffh9pvjta+Ctp4a9Ma5rHRHhW9cakzqFq0pq/6kWhl/c8pRSClP\nmNoBKhtClCRzQHP648L9Ca6a6DvvGTwW6D8P+utR670ux0/TOm7ltbOsaQtxAfU7btvPI2AmN5WS\nTAwDCr/E70qjovSuvWNE6A/DTa6LZKetbRNkVDqRhpTgbQpLYdgXCdwJ9MCCYxFAbnjB+Gvw06tv\nOl77wk0Oztupuk9P0K96r6bYYCU6hpdyG1KvmkD9ZQVLS7j9OTEJ38Drjo7wu8GdN8VPGu68MND6\nnubXxHuuiunNFvkk6XpqEtKfLzrDak7xtlAQSIhJEbpqA5P4d9a8MfHvx201t7wI6G0Fdn0vqbl9\naoQ4dKvLxCNzLymHFFLCEiAYJP6iVcR5X8UdpeaP0zo2mX3SXgHYfnL5T6Lzw6Upy7T5bZBbfUXV\nBLSvNBiMqbGcZA/NaxC8CfYnNKJJB7mAaAfqjaP2FAJIJBI/1pQM6VFlskpEqE55ituwK1s/l0H0\nrVuPHbj/ADrL4KjTgAfpP71ijcDPbFaIJSgoYSCPrUEE9xFUArI45ogSI/eoBTP/ALpSJiAaoEfa\nCKWffNAMTE4/agGQYOKAcGJ4+9LaJxmgAwPvzQZgAjPzQDMqxGaRIHpyZz9aAFRER3p4jdJ9ooBc\nRkUwJ7wKgEdnH+lEYxAHFUBxBE0QQJP70AD44PFHHGD7TQAcYokCYPbmgDPA4onOM/agCCBmiBma\nACMnM0CN2aAQJAgdzVHmJoBfAFICCSf86AZn2AmluAMnt2oBmBz370uxiaAO5BxT784HagFJjHb2\npnnNAMGJIEie9BO6gEeZTFbujaXd65q1lolgEG51G4atWQtQSnzFqCUyTwJIzQH9ZPwp9BN/g/8A\nCS/sPHTV+hOnH7y/Xep1FGqAPPoKUgMulxCNykFPpDalgheADM/kj/8AqE+OnhF40dS9MueFup2+\nqr0pm5a1G/RpSmFOlRb8pIfcSlxxIhyExtBkgndQH5GJzKcGs9je3mm3tvqWn3TltdWjqX2HmllK\n2nEKCkqSRkEEAg0B/R38HfTXi5+JDpRzxJ62/FF1+3ZWOoO6Y7pGkvG0V5iEoXKrgylQKXEGEokA\nxuB4/GX4pulul+ivHPqbpbpPpTWunLPTnm21WGrXQuXg4W0qLoc3r3IdCkuiVqP+JzwAB8nO4D1E\n4xXu/CvxO6h8IrzWNc0jRrW6HUWg33Ty1XaFhsM3CQlxaCkiVpgRkj3FAYtC8Qde6T8O+r/DJGjs\nGz65XpdxcPPoWHmxZuOONFrIBCi6QZB4ERXsOkfxFdb9E23ht09a9Mae674Z6te6npyH23g7dPXa\ngVIdSFDAxASAfrQGLxC8a+nes9E1Lp23/D30J03qd86idT01m6TeMOJdStW3e6oAq2lCgUnClcHN\nYvCjx4vvCPpfqboHVPDjp7qfSupbi0uL+z11t+EuW5UW4Da0EZXOe4FAdlz8U/UGmaVrmkeE/QGg\n+HN31K1Y21zfdMOXbF2EW7y3EhpZdKmysubVlOVJSlNcjXvxB+LPUHh+34Z9d3uo69eW2uNa7per\navc3L+qWD4QEbGnFrJ8tQztIICjIzQHrNf8AxW+NureOmifiH1HpxpjVenbFNizaJtX0WRtghxLi\nVgq3QsvOKPqwSI4FeN6Y8fOt9O8SetPERrSbTVdZ69sNV0+9aWhxSW0XuXC0lJBBQBCQZAA4oC9L\n/EF1bp/RnSnQOtaBpurK6D19vWdBvb5LovLDa4la7MKChLClIkoIkGII2pA9F4lfiGtvEBzW0a7+\nG/oaw6i6lS6teqNWt6L0POz/AI6Ap0grmSPSRNAaX/4jPFu68YNE8aendIFhrPTtjaaOWbO3eXbv\nMW7YbU08kkkhacKEiORBAI39J/FD15pPVPXV/wBTeH+h6/0/1zfnV9d6Z1aycXZofUuUvNEneysE\ngBcngckJIAzWH4s+oNH620vrrpzwf6J0nR9E0q70G20iy05bVoWbrLvnOpUHHVnJyqBKiACpRPj/\nABM8TdI666XtrXSPAnpHo4JvUv8A9U0Zi5St5IQtJZKnXFJKSVSQBMoHsaA+fs6FrjzSXWdGvltr\nAUlSbZZBSe4IGRXPVuQSlSSlaVbSk4I+tASSqJj6kVaJ3SBMdqApSyrIEe+ea6WmubEFSDK20z78\nkViStFXJoKMiACQPbFY1SFYjHvWiEniCMn+akQPegHuUMRHsak8nE+woAUFAATE/zSgxwPpQCJkf\n96BJ+wmqAn3oGCOaAODEEikeBzQDB/ftQSSDOaAWYBPNOZMnk9qAY3cRHzQBjd3+tAKSR80CIOAM\nUA/mO8GaNxzuM0ApMEgj2mgx7A9qAMQAYFAHcCaACTwKJ+aAIJiTx3pGcfWgK+hz3NLcByMGogIm\nOxANUmckCB9aoJM5gcdqczMZoAGfjE0gePpxQBJjmiRHOaAZJOAJikT+3xQDJ4JxNA3RzigF3yD8\nCnGJnigGSPYx3qYPBFAVjt2rJb3D9o+3d2ry2HmVpcbW2opWhYMhQIyCCJkUB+6uiOqPwA9d6z0f\nY9SdE9ZdWdddWPadp925qOp3z6WL+4KG1B15y4bC0JcWZUEqwJA7V+r+qvwQfhy17orVOktG8ONI\n0G4v7bybfVbW38y6tHBBQ4hayVEggSJ9QkHmgP5G6/4e6mx4ia74fdFpvOrnNHvry2Zf02xWtd21\nbqWFvpaRvUEbUFfJATkmM1xW+ndaXeadYPac9auaqpCbM3afIQ9uXsCgtcJ27sFU7RBk0B/Rvwg/\nCD+Kb8P+g6f1N4U+K+kjVrplL+t9H6shatMde7pS6gqBXtCRvCUKkRv2mvhH45+mbBxdl4j9VeGf\nVXQ3iPrmoeTq9pd3v9Q0q+aQzHn2l0ncn07Wk+XuSUpUAEQN1AfknPHev18rpfwv6m/B14RJ8TPF\na46Iat9X6gVaONdPu6p+aUbhO8ENuI8vaAkyZnd8VAfUtU8J9J63/FX4fakq4TqnSfQXhroeuu3T\n6U2jd6lgLFmhXmna0XXfLJStX6QsE4JrleL3Qeu6n49+AX4gdV03SbXU+peqdE0jqlnSbxq7tmNX\nt7tnYoONqUn/ABWAlQTuJAbg55oPzV4i5/GL1QJ48S73/wD6i6X4x4//ABPeI0cjWV//AOiaA97+\nFhy+6R8FPGDxb8PtNYvfELpxrTbfTXV2qbh7S7J9xSbm6ZbUD6toMqg7QjPpKgfovhb1f1d43eDN\np1x43JGo6p0r4g9M2/R/UV1bpburtT1+2m7sw4lI8xCG5c75OT6QABk/F541ahbXXiP0hpn4r9Wv\nnjev6crog9GhplLSng27bfnyTIQ2VnfEq2QIJmvk34QfE7QfDfSPEIa491J05/XLSwtGOt9E0lN8\nvp9aXVqKHNwO1D4hJj1Hy8QYUkD9BM6D110/rnX3jW91Zo/iX1vZeHWnax0Hq7WiIYddsHH3G3b1\nVoUgi5aQgK3HcYcAJMlFfF/B/wAbvHTxa8U/CRnxIvrrW9CseurRVpq91pbYULolO63TdpQDASd3\nlBXcEiAmAPp/THVbPSHhj4q6m9426n4Xpc8ab5j+safo69ScfJt3j+WLSFJKUq2798wC0BGa4/gx\n4s6TYdQ+OPXnVfVt14v9P2XTWlWl1e6lp5sXNSsXLlpt5vyFlRQUB51KZOSgGQDgD3nTfgR0B054\na9P9Oo1i21/w3638WdG1XRrlboP5mxdtVpTbPdwsOoLKxgn4JgfDfFf8Rf4ornr7xE8MU2Nz/Rmm\ntR0x/pdGgtv2un6W3uCXENhs7UobCVh7iIVMRQH2DxE8SE9G9BeEViPxW6/4avq8LNAuUaJYdMu6\ng3dKLKwLgvIcSEKUU7NsGA0D3r+ft7fXGo3lxqF68p+4unVvPOH9S3FKJUo/JJNAa4PIjgUDgkxP\nPNAUCZ44zW7Y3CWitLoJ3gJxUatUVOma6gOPUrGcdqg7sCDQglEEk9jgUogyT2zQADuIATANKBMC\nc0BJzzM9u9IpAMHmqAwZkcUGOIzUAbiQIA+1Efx396oBMASonOKOBH+VASBAMGaoHJlMigAJHf60\nuTGce9QDECcZPaiJiBjvVAQBA3d6lUTg/egKMJHHPxSSD7/v2oAhPef2oJ9j/FAEADA+aJJg/wCx\nQD5ImB9aBBBk0AESJB+1BGMwPjmgERn2AGaUiMjj2oB5PeB7GmJ4wI/mgFt7gTFMCcg/agEDFMf7\nkUASkgAzj/OkM8EYoABHbigz3/yoAP0x9OKYJmgDaeSMckig+5nPNQCmUgH60wAR6jVAEK3FJA9P\nxSIkzH1oC2HnrZ5FzbPOMvNKC23G1EKQoGQQRwQe4r+oXgL42dfdO/gr6t8cPETxOHU2sMsXKdMb\ndfacXp60/wDL2rTxQN3mreUFneSopUjvNAZfwCeB+j+C/Qtl4q+Iz9vY9VeIzjVnpSbtYStu1cSX\nGWEz/wDqvbC4RzCWxghQP0/8Y/4fdG/EN4enp6yuLRnrfSGX9U6d3uJS68EbA8yQSD5SyppJVwla\nmiT2IHi+jPxU9Y6R+D+38VGuhldTdR9EuHQOq9NuL5Vm/aPW/wDhquFy2tSlQWVrRCSA4syNhr+Y\nvWPil4g+INlp2mdY9Walqtlo6316db3dwp1NoHikrS2VEqCfQgAEkAJAEUB5WMbQIPc17Beo+KPV\nXSvTvh+LXV9R0XTS/e6JYNWBXt/MXHlOONlKNywp9PlzJG8bRnFAek1PxC/ER1T0zd9L6i/1HeaP\nqFjZ6fdMt6VAetdOUvyG1KQ2FFLKg5icEK3ZBrX6I1jx/wCmul1s9C6f1MjQLfULTqcqY0lT9uzd\nWyt7N4FqbUEbfL/UCAQggyARQHvuqfGD8cHW1m50T1QeudQadQ1qbunr6b2uFu3fQ6h4pSwFBCHW\n2zu/TIAPtWPWvGn8a/ih0nfdP6pedZ67oGrMoYum2ungpt5txKHEArbYn1IW2oQcpUkjBFAfMfDn\nXPGHw36u0zWvDZXUOk6/qIXb2H5K1WXL1O8oU2lspIfTvRBTChuRxIr2/iB1/wDi48Q9da1brxPW\nl7fdD3Dd2GVaIq3Z0h9sBxLjlu20lppYSAqVoBKecUB886hsPErrdnVfF3XdD1vUbS9vlOajr39O\nWLRVy4qSFvISGkqJI9Mjniu70N4j+O3gHfamvpHVNf6VWlxm21S2fs/8HzXEKW0h9h9Bb3qQlwpC\n0yUhUYmgO5qHXn4q9R62a8aru465/r1otOnN6yjTnWkNS55YtQEthpKS4vZ5QTtKlRtJOe31h4qf\njN606rsF9UnrV/WujH2tWt7NHT/5f+nu5Ldwu1aYSgKwqFrRkbsxNAcXonxx/FB4fDV/+Cdc6g03\n+u3X9f1HytKQv8w9cNl0XB3NGAttClgiElKSRgTXH638Z/HfqQ3uq9fdR6q6OstIYtHXrqxbZTqO\nnM3ClNhBDYCm0vIX6kf3JUCcEUBFqv8AEBceHumeHtnpHVz3SWqagNU0uxRpjy2bi7DSl+Zbq2Ek\n+WHFwgxAUqOTXvOoPGv8bWsdE3HRvUGq9eq0QbNMuyvRVNPL3EJSw7dBkPKKvMCdqlkq3gGZigL6\nR/EB+NrT+m9P0TovWOsVaPo9gyxaN2ugJeQxaNhTTfq8gykeStAUTktqzINfG2ek/EHrDXtRRZ9K\na7q2rbzd3zVtpzrryFOHdvWhCCUhRVOQBnFAc6/6Z6k0pD7mp9P6lZt2ymEvqftFthpTyC40FFQw\nVoBUmf1JBIkCuYMkmIE+1AOI4B/71SVEGTycn6UBlVjsD9aggiIxioCI/wA6khQMQMD3qgMpggjN\nMkfzUBJOP00jMSeP8qAWOZNNXIwM0AxkBJSZpCJn/OqBgCQf8qlWDEj6UAhxHf60yDzQD2hXEzFI\n47/tQDmBkCifczFABBgEHI/il8gUA+Tz9KDBwkx8GgECffNAOJk0AA+mIJiiYzHPYUAieJIp5z80\nAA/tQUnJoAUc4+KJxxQACCYIJFPdH9oAoBboBHvQN2IP70ASQARPz8UfqEz/AN6ADMSTmKUDMzQF\nHaP7Zn5pTGVe2KAEKAJkY5ojOCPeaAAQJE4AogJyAT8UAT8/aiMCU/OKAZ59z70twPMAmgEeZity\nx1bUtMQtqzvHW2n1NqeZBlp7YrcgOIPpWARMKBE0B9R8YvxTeMXjvomgaF4g61avN9PPOXNu7aWw\ntVPPKCQHHQghBUkJISUpTG9XvXJ6A/ED4peHviRpPijZ9T3mr6zpCFsNDWLl66bdYWkpUwuVhRbI\nUTAUIMEQQDQGv4i+OPiJ4lav1Fqms6r+Qt+qrxq+1TS9L3W1jcPtoCUOKZCiFKAEyqSTkknNeBJJ\n+tAAJAPtX3Hwx/EjpXh3ZdKXLnh8/qOu9LNW+ns3v9ZDNu7p7etjVlNljyFEPF3zGw75hSEL/wDj\nUUg0B29Z/Gb1FqzGj3KejLG21vTHmLh6+afAavnW75VypbrAbAJdBKHYMLKlqgbimtC9/E7ouqad\n1JpN/wCGQRZ390w7oTDF3ZOI0e3t7VNtbW6TdWLyyEIQglxldu4o7juSVSAPU3n427PV+o7jWdZ8\nKiqwuri/fuNJttWYTaXYuL4XSfPD1m6pTg2NpU42W1ny0qbLKt275jrXj9qOp9M6107p2k3Glq1V\nHSiWri31Jf8AyqtF01VlKRtBPnSlz9QKNgEr/VQG9qH4kdT1jxoc8WdU0BTrD+lO6M5prd6GVtWz\n9ku2uFMPobAZdUp154LDZhxwkheZ3eqvxQPar4eu+G3TfRiLLTfLtrNi41W9Rql2i1at3miC4plA\n80l9SkuoDexKUoSkATQHM0bxs6VZ0DpLS+q+gdU1i56QZbsmW2eo/wArp15aJ1A3pbuLQ2696lLU\npClBwJKQk7NyQqu113+JHpvxV0rWGuv/AAuukapratMurq80LqFVshy7sEX7TDq0XbF04pPk36UK\nSXZP5dG1SQdoA3Lz8Yetq6l0bWbDofTGbTTL67vX7d5aHbh7z7p14pbug0lxkpS9tBTMKSFx2rb6\nV/F7Z9Dp0jS+nfDRx3SdE/JizTqutC8v2/Jubq4Uv8x+XQjf5l3LR8rY35eUOblSBhe/GBfXnTrX\nT+qdCMXSrPTbHSrK9Vfbblq3t9Gd09Ta1hr/ABEKeecukpIBQpx1IKgvcON4h+PfRfir0s3011D4\nZnRP+GbJVt0k9pWoLcUw35bLSGLsPSHEhDIWVNBoFwuK8vc6tQA9P0z+MHRtA6a0Tp1/wo/ODTtP\nYsL1031ikXIb0m704LAOnqKyUXilbLtV22AnYlASo1raJ+Lyx6ZbTYaN4XtNWLT6rhlQvbVm5QTe\nW9yptJt7Jq3Q0ryFNqS2wgw5IKSDuALj8Wuh6zp3Ulv1N4UNPXfVFtpjF2uyvLIWrRsE3Tdupi1v\nLC5bZ/wblCT5e0hTJWhSN6hWvpP4mOgNM1HqLUx4S6rcPdWWdpbasm61vTr1jzLdTSm1ss3elOtI\nH+GqQ4l0+oFKklMkD5r1l4t671j0D0p4e3qnxYdJ/mQwpx8LU8lxf+FvhCSS03/hpJJhOE7RivCj\naRgc94oAHY0yYyAM/egM0wcnNQd0ge3FQEYBiIg5pROBM0AsgEGJp7DBIHH+VUC5ycfBqcQDioAj\nGJxRExOKoADvkUpAyB9JoByP0j/Og7Y5mgJMAkgSDxTIxOf2oAA5O7PfvTGMxQAD7fXJ4pmQqRmM\n5oCYJ5ED4p/QzQATk8z80t0ZE0AvemMmSTQAAe3bNAMgnigBKgE8fzQRABHNABwTJxPtSzB7jnFA\nAPB+Kf6uBj3oAwDMQPrTV9IEUAoPYc5+lI5yD2oBjIicdzSJg8UBU+6aI7+1AIK7EUSCRAAH0oAI\nA+fnigRIBEUAZGc5xTEQZ/eOKAnIzTxGPvQABJgA5wAKXAicUA9wGNoxkUDceCD8CgAj1cxGaEkZ\nIHbNQBG76UCZyZqgCSAOMcfNKPcyD7UBUD3/AIoAkxQCjnkimBjg0ABIM5iPmgJAAlQBigEOM8ew\nNMExMUAtpGfar7g7hx7UADvipJ3c4PH0oCpIEAme85FSff8AigKT8pMCiYMAwBQARndODTxyExQA\npRJzJ+lAkH9P0k0BsKVPtP1rGSI4+mKAicmTjtSClZmM0ApBJgZoJ+R9eaADH9sjOKkn2H3oAzz/\nACKCM+5796gFOBuH/qj096oDPejsZ/egDBMzwJojv2nvQAY7du9NJKf09yMmoBgj2GeaUwOaoED9\nYpmR3A++aAR5gGftRzn/ADoABInEzmaJxzNAE4j3oMbQDQBPJB78RQR3H896AJI4mKMGZ+1AInkR\njtQIA5M/HagGZOCqPrQO3/fmgGogd/ilhJyKAf8AaOakZJMiaAojEwakGAYyKACZyIo7yDQBMGCP\nvROCB/nQATAnM96Ez3M/egGQAIMe9IyciaAcnFAkyAM0AAjvVJzAzPxQAEznaTJ4mkoer3FAIZ5B\npj3BNALHCf8AOjATE59qAeCIwPcmieYPaaARMD/SkCRzQFE8T2zEUEYHbvmgAbld/wCKJzBn6gUA\nYiSQYoxBA/cGgCADE0+T8mgFyMn+apKowJz7UAbR747TSHHq/aaAZIgiSIpTjd3oBjgK4pgCP08c\n0BmcG0g81CtsZH/moCTECAZ+aQI2yeQOZFAGSIxBqfUAQP096ANpAgfvSxA2qM0AiMYogkRMVQCZ\n25BI/inCiCaAkiTimB2nPegDMgk5FBgjmD7/ADQAffdNNRniY7UAJJMjkf50E7lRmfagACYgRGKR\ngCRP7UAwcyf3+KU4iaASYMyrijA9VAEDtANEd5+9AB5xmKcnkTNALMkHmmMEBX+VAKRPx80RmcEU\nA5MiAYHxQMSDzQAeMAUGBPJjigFgjucUE5JJ5oAkkHHPJoJMynFAAiZPagGQR/MUAGO80dxM/U0A\nRPuBFMmJoBKlRmiTwR+9ABkY4miSORNAPaff5FZQNonBn+KAc7QCY+grE6QV4NQAMiDU5B71QMCO\n+TSjMTFAMEDv9fmgSO3PM0AHE96CRMY4zQBJEAAZHNMEAAxQCkT7ijA5OaAY25midsHbgUAAYgzF\nBk9oFAAkYANMeokzBoAVM7T9qCkAxjtQAAoTFGeee30oBpiMnvTQSSISCfpQH//Z\n",
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "<IPython.core.display.Image object>"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is today's image from same webcam at Berkeley, (refreshed every minutes, if you reload the notebook), visible only with an active internet connection, that should be different from the previous one. Notebooks saved with this kind of image will be smaller and always reflect the current version of the source, but the image won't display offline."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "SoftLinked"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<img src=\"http://www.lawrencehallofscience.org/static/scienceview/scienceview.berkeley.edu/html/view/view_assets/images/newview.jpg\"/>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 11,
-       "text": [
-        "<IPython.core.display.Image object>"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Of course, if you re-run this Notebook, the two images will be the same again."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "HTML"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Python objects can declare HTML representations that will be displayed in the Notebook. If you have some HTML you want to display, simply use the `HTML` class."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import HTML"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "s = \"\"\"<table>\n",
-      "<tr>\n",
-      "<th>Header 1</th>\n",
-      "<th>Header 2</th>\n",
-      "</tr>\n",
-      "<tr>\n",
-      "<td>row 1, cell 1</td>\n",
-      "<td>row 1, cell 2</td>\n",
-      "</tr>\n",
-      "<tr>\n",
-      "<td>row 2, cell 1</td>\n",
-      "<td>row 2, cell 2</td>\n",
-      "</tr>\n",
-      "</table>\"\"\""
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "h = HTML(s)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display(h)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<table>\n",
-        "<tr>\n",
-        "<th>Header 1</th>\n",
-        "<th>Header 2</th>\n",
-        "</tr>\n",
-        "<tr>\n",
-        "<td>row 1, cell 1</td>\n",
-        "<td>row 1, cell 2</td>\n",
-        "</tr>\n",
-        "<tr>\n",
-        "<td>row 2, cell 1</td>\n",
-        "<td>row 2, cell 2</td>\n",
-        "</tr>\n",
-        "</table>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.HTML object>"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can also use the `%%html` cell magic to accomplish the same thing."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%html\n",
-      "<table>\n",
-      "<tr>\n",
-      "<th>Header 1</th>\n",
-      "<th>Header 2</th>\n",
-      "</tr>\n",
-      "<tr>\n",
-      "<td>row 1, cell 1</td>\n",
-      "<td>row 1, cell 2</td>\n",
-      "</tr>\n",
-      "<tr>\n",
-      "<td>row 2, cell 1</td>\n",
-      "<td>row 2, cell 2</td>\n",
-      "</tr>\n",
-      "</table>"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<table>\n",
-        "<tr>\n",
-        "<th>Header 1</th>\n",
-        "<th>Header 2</th>\n",
-        "</tr>\n",
-        "<tr>\n",
-        "<td>row 1, cell 1</td>\n",
-        "<td>row 1, cell 2</td>\n",
-        "</tr>\n",
-        "<tr>\n",
-        "<td>row 2, cell 1</td>\n",
-        "<td>row 2, cell 2</td>\n",
-        "</tr>\n",
-        "</table>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.HTML object>"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "JavaScript"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The Notebook also enables objects to declare a JavaScript representation. At first, this may seem odd as  output is inherently visual and JavaScript is a programming language. However, this opens the door for rich output that leverages the full power of JavaScript and associated libraries such as [d3.js](http://d3js.org) for output."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import Javascript"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Pass a string of JavaScript source code to the `JavaScript` object and then display it."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "js = Javascript('alert(\"hi\")');"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display(js)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "javascript": [
-        "alert(\"hi\")"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Javascript object>"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The same thing can be accomplished using the `%%javascript` cell magic:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "\n",
-      "alert(\"hi\");"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "javascript": [
-        "\n",
-        "alert(\"hi\");"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Javascript object>"
-       ]
-      }
-     ],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is a more complicated example that loads `d3.js` from a CDN, uses the `%%html` magic to load CSS styles onto the page and then runs ones of the `d3.js` examples."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "Javascript(\n",
-      "    \"\"\"$.getScript('//cdnjs.cloudflare.com/ajax/libs/d3/3.2.2/d3.v3.min.js')\"\"\"\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "javascript": [
-        "$.getScript('//cdnjs.cloudflare.com/ajax/libs/d3/3.2.2/d3.v3.min.js')"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 21,
-       "text": [
-        "<IPython.core.display.Javascript object>"
-       ]
-      }
-     ],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%html\n",
-      "<style type=\"text/css\">\n",
-      "\n",
-      "circle {\n",
-      "  fill: rgb(31, 119, 180);\n",
-      "  fill-opacity: .25;\n",
-      "  stroke: rgb(31, 119, 180);\n",
-      "  stroke-width: 1px;\n",
-      "}\n",
-      "\n",
-      ".leaf circle {\n",
-      "  fill: #ff7f0e;\n",
-      "  fill-opacity: 1;\n",
-      "}\n",
-      "\n",
-      "text {\n",
-      "  font: 10px sans-serif;\n",
-      "}\n",
-      "\n",
-      "</style>"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<style type=\"text/css\">\n",
-        "\n",
-        "circle {\n",
-        "  fill: rgb(31, 119, 180);\n",
-        "  fill-opacity: .25;\n",
-        "  stroke: rgb(31, 119, 180);\n",
-        "  stroke-width: 1px;\n",
-        "}\n",
-        "\n",
-        ".leaf circle {\n",
-        "  fill: #ff7f0e;\n",
-        "  fill-opacity: 1;\n",
-        "}\n",
-        "\n",
-        "text {\n",
-        "  font: 10px sans-serif;\n",
-        "}\n",
-        "\n",
-        "</style>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.HTML object>"
-       ]
-      }
-     ],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "\n",
-      "// element is the jQuery element we will append to\n",
-      "var e = element.get(0);\n",
-      "    \n",
-      "var diameter = 600,\n",
-      "    format = d3.format(\",d\");\n",
-      "\n",
-      "var pack = d3.layout.pack()\n",
-      "    .size([diameter - 4, diameter - 4])\n",
-      "    .value(function(d) { return d.size; });\n",
-      "\n",
-      "var svg = d3.select(e).append(\"svg\")\n",
-      "    .attr(\"width\", diameter)\n",
-      "    .attr(\"height\", diameter)\n",
-      "  .append(\"g\")\n",
-      "    .attr(\"transform\", \"translate(2,2)\");\n",
-      "\n",
-      "d3.json(\"data/flare.json\", function(error, root) {\n",
-      "  var node = svg.datum(root).selectAll(\".node\")\n",
-      "      .data(pack.nodes)\n",
-      "    .enter().append(\"g\")\n",
-      "      .attr(\"class\", function(d) { return d.children ? \"node\" : \"leaf node\"; })\n",
-      "      .attr(\"transform\", function(d) { return \"translate(\" + d.x + \",\" + d.y + \")\"; });\n",
-      "\n",
-      "  node.append(\"title\")\n",
-      "      .text(function(d) { return d.name + (d.children ? \"\" : \": \" + format(d.size)); });\n",
-      "\n",
-      "  node.append(\"circle\")\n",
-      "      .attr(\"r\", function(d) { return d.r; });\n",
-      "\n",
-      "  node.filter(function(d) { return !d.children; }).append(\"text\")\n",
-      "      .attr(\"dy\", \".3em\")\n",
-      "      .style(\"text-anchor\", \"middle\")\n",
-      "      .text(function(d) { return d.name.substring(0, d.r / 3); });\n",
-      "});\n",
-      "\n",
-      "d3.select(self.frameElement).style(\"height\", diameter + \"px\");"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "javascript": [
-        "\n",
-        "// element is the jQuery element we will append to\n",
-        "var e = element.get(0);\n",
-        "    \n",
-        "var diameter = 600,\n",
-        "    format = d3.format(\",d\");\n",
-        "\n",
-        "var pack = d3.layout.pack()\n",
-        "    .size([diameter - 4, diameter - 4])\n",
-        "    .value(function(d) { return d.size; });\n",
-        "\n",
-        "var svg = d3.select(e).append(\"svg\")\n",
-        "    .attr(\"width\", diameter)\n",
-        "    .attr(\"height\", diameter)\n",
-        "  .append(\"g\")\n",
-        "    .attr(\"transform\", \"translate(2,2)\");\n",
-        "\n",
-        "d3.json(\"data/flare.json\", function(error, root) {\n",
-        "  var node = svg.datum(root).selectAll(\".node\")\n",
-        "      .data(pack.nodes)\n",
-        "    .enter().append(\"g\")\n",
-        "      .attr(\"class\", function(d) { return d.children ? \"node\" : \"leaf node\"; })\n",
-        "      .attr(\"transform\", function(d) { return \"translate(\" + d.x + \",\" + d.y + \")\"; });\n",
-        "\n",
-        "  node.append(\"title\")\n",
-        "      .text(function(d) { return d.name + (d.children ? \"\" : \": \" + format(d.size)); });\n",
-        "\n",
-        "  node.append(\"circle\")\n",
-        "      .attr(\"r\", function(d) { return d.r; });\n",
-        "\n",
-        "  node.filter(function(d) { return !d.children; }).append(\"text\")\n",
-        "      .attr(\"dy\", \".3em\")\n",
-        "      .style(\"text-anchor\", \"middle\")\n",
-        "      .text(function(d) { return d.name.substring(0, d.r / 3); });\n",
-        "});\n",
-        "\n",
-        "d3.select(self.frameElement).style(\"height\", diameter + \"px\");"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Javascript object>"
-       ]
-      }
-     ],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "LaTeX"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The IPython display system also has builtin support for the display of mathematical expressions typeset in LaTeX, which is rendered in the browser using [MathJax](http://mathjax.org)."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can pass raw LaTeX test as a string to the `Math` object:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import Math\n",
-      "Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 24,
-       "text": [
-        "<IPython.core.display.Math object>"
-       ]
-      }
-     ],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "With the `Latex` class, you have to include the delimiters yourself.  This allows you to use other LaTeX modes such as `eqnarray`:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import Latex\n",
-      "Latex(r\"\"\"\\begin{eqnarray}\n",
-      "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
-      "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
-      "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
-      "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
-      "\\end{eqnarray}\"\"\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "\\begin{eqnarray}\n",
-        "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
-        "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
-        "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
-        "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
-        "\\end{eqnarray}"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 25,
-       "text": [
-        "<IPython.core.display.Latex object>"
-       ]
-      }
-     ],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Or you can enter LaTeX directly with the `%%latex` cell magic:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%latex\n",
-      "\\begin{align}\n",
-      "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
-      "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
-      "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
-      "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
-      "\\end{align}"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "\\begin{align}\n",
-        "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
-        "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
-        "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
-        "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
-        "\\end{align}"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Latex object>"
-       ]
-      }
-     ],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Audio"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython makes it easy to work with sounds interactively. The `Audio` display class allows you to create an audio control that is embedded in the Notebook. The interface is analogous to the interface of the `Image` display class. All audio formats supported by the browser can be used. Note that no single format is presently supported in all browsers."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import Audio\n",
-      "Audio(url=\"http://www.nch.com.au/acm/8k16bitpcm.wav\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "\n",
-        "                <audio controls=\"controls\" >\n",
-        "                    <source src=\"http://www.nch.com.au/acm/8k16bitpcm.wav\" type=\"audio/x-wav\" />\n",
-        "                    Your browser does not support the audio element.\n",
-        "                </audio>\n",
-        "              "
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 27,
-       "text": [
-        "<IPython.lib.display.Audio object>"
-       ]
-      }
-     ],
-     "prompt_number": 27
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "A NumPy array can be auralized automatically. The `Audio` class normalizes and encodes the data and embeds the resulting audio in the Notebook.\n",
-      "\n",
-      "For instance, when two sine waves with almost the same frequency are superimposed a phenomena known as [beats](https://en.wikipedia.org/wiki/Beat_%28acoustics%29) occur. This can be auralised as follows:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "max_time = 3\n",
-      "f1 = 220.0\n",
-      "f2 = 224.0\n",
-      "rate = 8000.0\n",
-      "L = 3\n",
-      "times = np.linspace(0,L,rate*L)\n",
-      "signal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)\n",
-      "\n",
-      "Audio(data=signal, rate=rate)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "\n",
-        "                <audio controls=\"controls\" >\n",
-        "                    <source 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\" type=\"audio/wav\" />\n",
-        "                    Your browser does not support the audio element.\n",
-        "                </audio>\n",
-        "              "
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 28,
-       "text": [
-        "<IPython.lib.display.Audio object>"
-       ]
-      }
-     ],
-     "prompt_number": 28
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Video"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "More exotic objects can also be displayed, as long as their representation supports the IPython display protocol.  For example, videos hosted externally on YouTube are easy to load:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import YouTubeVideo\n",
-      "YouTubeVideo('sjfsUzECqK0')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "\n",
-        "        <iframe\n",
-        "            width=\"400\"\n",
-        "            height=300\"\n",
-        "            src=\"https://www.youtube.com/embed/sjfsUzECqK0\"\n",
-        "            frameborder=\"0\"\n",
-        "            allowfullscreen\n",
-        "        ></iframe>\n",
-        "        "
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 29,
-       "text": [
-        "<IPython.lib.display.YouTubeVideo at 0x107c1eb50>"
-       ]
-      }
-     ],
-     "prompt_number": 29
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Using the nascent video capabilities of modern browsers, you may also be able to display local\n",
-      "videos.  At the moment this doesn't work very well in all browsers, so it may or may not work for you;\n",
-      "we will continue testing this and looking for ways to make it more robust.  \n",
-      "\n",
-      "The following cell loads a local file called  `animation.m4v`, encodes the raw video as base64 for http\n",
-      "transport, and uses the HTML5 video tag to load it. On Chrome 15 it works correctly, displaying a control bar at the bottom with a play/pause button and a location slider."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import HTML\n",
-      "from base64 import b64encode\n",
-      "video = open(\"../images/animation.m4v\", \"rb\").read()\n",
-      "video_encoded = b64encode(video).decode('ascii')\n",
-      "video_tag = '<video controls alt=\"test\" src=\"data:video/x-m4v;base64,{0}\">'.format(video_encoded)\n",
-      "HTML(data=video_tag)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<video controls alt=\"test\" 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-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 30,
-       "text": [
-        "<IPython.core.display.HTML object>"
-       ]
-      }
-     ],
-     "prompt_number": 30
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "External sites"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can even embed an entire page from another site in an iframe; for example this is today's Wikipedia\n",
-      "page for mobile users:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import IFrame\n",
-      "IFrame('http://ipython.org', width='100%', height=350)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "\n",
-        "        <iframe\n",
-        "            width=\"100%\"\n",
-        "            height=350\"\n",
-        "            src=\"http://ipython.org\"\n",
-        "            frameborder=\"0\"\n",
-        "            allowfullscreen\n",
-        "        ></iframe>\n",
-        "        "
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 31,
-       "text": [
-        "<IPython.lib.display.IFrame at 0x107c1ecd0>"
-       ]
-      }
-     ],
-     "prompt_number": 31
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Links to local files"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython provides builtin display classes for generating links to local files. Create a link to a single file using the `FileLink` object:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import FileLink, FileLinks\n",
-      "FileLink('Cell Magics.ipynb')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<a href='Cell Magics.ipynb' target='_blank'>Cell Magics.ipynb</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 33,
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/IPython Kernel/Cell Magics.ipynb"
-       ]
-      }
-     ],
-     "prompt_number": 33
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Alternatively, to generate links to all of the files in a directory, use the `FileLinks` object, passing `'.'` to indicate that we want links generated for the current working directory. Note that if there were other directories under the current directory, `FileLinks` would work in a recursive manner creating links to files in all sub-directories as well."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "FileLinks('.')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "./<br>\n",
-        "&nbsp;&nbsp;<a href='./Animations Using clear_output.ipynb' target='_blank'>Animations Using clear_output.ipynb</a><br>\n",
-        "&nbsp;&nbsp;<a href='./Background Jobs.ipynb' target='_blank'>Background Jobs.ipynb</a><br>\n",
-        "&nbsp;&nbsp;<a href='./Beyond Plain Python.ipynb' target='_blank'>Beyond Plain Python.ipynb</a><br>\n",
-        "&nbsp;&nbsp;<a href='./Capturing Output.ipynb' target='_blank'>Capturing Output.ipynb</a><br>\n",
-        "&nbsp;&nbsp;<a href='./Cell Magics.ipynb' target='_blank'>Cell Magics.ipynb</a><br>\n",
-        "&nbsp;&nbsp;<a href='./Custom Display Logic.ipynb' target='_blank'>Custom Display Logic.ipynb</a><br>\n",
-        "&nbsp;&nbsp;<a href='./example-demo.py' target='_blank'>example-demo.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='./Index.ipynb' target='_blank'>Index.ipynb</a><br>\n",
-        "&nbsp;&nbsp;<a href='./ipython-completion.bash' target='_blank'>ipython-completion.bash</a><br>\n",
-        "&nbsp;&nbsp;<a href='./ipython-get-history.py' target='_blank'>ipython-get-history.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='./ipython-qtconsole.desktop' target='_blank'>ipython-qtconsole.desktop</a><br>\n",
-        "&nbsp;&nbsp;<a href='./ipython.desktop' target='_blank'>ipython.desktop</a><br>\n",
-        "&nbsp;&nbsp;<a href='./Plotting in the Notebook.ipynb' target='_blank'>Plotting in the Notebook.ipynb</a><br>\n",
-        "&nbsp;&nbsp;<a href='./Raw Input in the Notebook.ipynb' target='_blank'>Raw Input in the Notebook.ipynb</a><br>\n",
-        "&nbsp;&nbsp;<a href='./Rich Output.ipynb' target='_blank'>Rich Output.ipynb</a><br>\n",
-        "&nbsp;&nbsp;<a href='./Script Magics.ipynb' target='_blank'>Script Magics.ipynb</a><br>\n",
-        "&nbsp;&nbsp;<a href='./SymPy.ipynb' target='_blank'>SymPy.ipynb</a><br>\n",
-        "&nbsp;&nbsp;<a href='./Terminal Usage.ipynb' target='_blank'>Terminal Usage.ipynb</a><br>\n",
-        "&nbsp;&nbsp;<a href='./Third Party Rich Output.ipynb' target='_blank'>Third Party Rich Output.ipynb</a><br>\n",
-        "&nbsp;&nbsp;<a href='./Trapezoid Rule.ipynb' target='_blank'>Trapezoid Rule.ipynb</a><br>\n",
-        "&nbsp;&nbsp;<a href='./Working With External Code.ipynb' target='_blank'>Working With External Code.ipynb</a><br>\n",
-        "./data/<br>\n",
-        "&nbsp;&nbsp;<a href='./data/flare.json' target='_blank'>flare.json</a><br>\n",
-        "./gui/<br>\n",
-        "&nbsp;&nbsp;<a href='./gui/gui-glut.py' target='_blank'>gui-glut.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='./gui/gui-gtk.py' target='_blank'>gui-gtk.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='./gui/gui-gtk3.py' target='_blank'>gui-gtk3.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='./gui/gui-pyglet.py' target='_blank'>gui-pyglet.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='./gui/gui-qt.py' target='_blank'>gui-qt.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='./gui/gui-tk.py' target='_blank'>gui-tk.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='./gui/gui-wx.py' target='_blank'>gui-wx.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 34,
-       "text": [
-        "./\n",
-        "  Animations Using clear_output.ipynb\n",
-        "  Background Jobs.ipynb\n",
-        "  Beyond Plain Python.ipynb\n",
-        "  Capturing Output.ipynb\n",
-        "  Cell Magics.ipynb\n",
-        "  Custom Display Logic.ipynb\n",
-        "  example-demo.py\n",
-        "  Index.ipynb\n",
-        "  ipython-completion.bash\n",
-        "  ipython-get-history.py\n",
-        "  ipython-qtconsole.desktop\n",
-        "  ipython.desktop\n",
-        "  Plotting in the Notebook.ipynb\n",
-        "  Raw Input in the Notebook.ipynb\n",
-        "  Rich Output.ipynb\n",
-        "  Script Magics.ipynb\n",
-        "  SymPy.ipynb\n",
-        "  Terminal Usage.ipynb\n",
-        "  Third Party Rich Output.ipynb\n",
-        "  Trapezoid Rule.ipynb\n",
-        "  Working With External Code.ipynb\n",
-        "./data/\n",
-        "  flare.json\n",
-        "./gui/\n",
-        "  gui-glut.py\n",
-        "  gui-gtk.py\n",
-        "  gui-gtk3.py\n",
-        "  gui-pyglet.py\n",
-        "  gui-qt.py\n",
-        "  gui-tk.py\n",
-        "  gui-wx.py"
-       ]
-      }
-     ],
-     "prompt_number": 34
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Rich output and security"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The IPython Notebook allows arbitrary code execution in both the IPython kernel and in the browser, though HTML and JavaScript output. More importantly, because IPython has a JavaScript API for running code in the browser, HTML and JavaScript output can actually trigger code to be run in the kernel. This poses a significant security risk as it would allow IPython Notebooks to execute arbitrary code on your computers.\n",
-      "\n",
-      "To protect against these risks, the IPython Notebook has a security model that specifies how dangerous output is handled. Here is a short summary:\n",
-      "\n",
-      "* When you run code in the Notebook, all rich output is displayed.\n",
-      "* When you open a notebook, rich output is only displayed if it doesn't contain security vulberabilities,...\n",
-      "* ..or if you have trusted a notebook, all rich output will run upon opening it.\n",
-      "\n",
-      "A full description of the IPython security model can be found on [this page](http://ipython.org/ipython-doc/dev/notebook/security.html)."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Rich output and nbviewer"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Much of the power of the Notebook is that it enables users to share notebooks with each other using http://nbviewer.ipython.org, without installing IPython locally. As of IPython 2.0, notebooks rendere on nbviewer will display all output, including HTML and JavaScript. Furthermore, to provide a consistent JavaScript environment on the live Notebook and nbviewer, the following JavaScript libraries are loaded onto the nbviewer page, *before* the notebook and its output is displayed:\n",
-      "\n",
-      "* [jQuery](http://jquery.com/)\n",
-      "* [RequireJS](http://requirejs.org/)\n",
-      "\n",
-      "Libraries such as [mpld3](http://mpld3.github.io/) use these capabilities to generate interactive visualizations that work on nbviewer."
-     ]
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Rich Output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In Python, objects can declare their textual representation using the `__repr__` method.  IPython expands on this idea and allows objects to declare other, rich representations including:\n",
+    "\n",
+    "* HTML\n",
+    "* JSON\n",
+    "* PNG\n",
+    "* JPEG\n",
+    "* SVG\n",
+    "* LaTeX\n",
+    "\n",
+    "A single object can declare some or all of these representations; all are handled by IPython's *display system*. This Notebook shows how you can use this display system to incorporate a broad range of content into your Notebooks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic display imports"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `display` function is a general purpose tool for displaying different representations of objects. Think of it as `print` for these rich representations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import display"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A few points:\n",
+    "\n",
+    "* Calling `display` on an object will send **all** possible representations to the Notebook.\n",
+    "* These representations are stored in the Notebook document.\n",
+    "* In general the Notebook will use the richest available representation.\n",
+    "\n",
+    "If you want to display a particular representation, there are specific functions for that:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import (\n",
+    "    display_pretty, display_html, display_jpeg,\n",
+    "    display_png, display_json, display_latex, display_svg\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To work with images (JPEG, PNG) use the `Image` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "i = Image(filename='../images/ipython_logo.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Returning an `Image` object from an expression will automatically display it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
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+       "yzNIZEiRD00ga3ocaLv+kUh2xo8hSuVURKmIUyiXVGYCWVzKQlJD7xrJNg85b9LX8RLgF6X6SpFU\n",
+       "9Cpe28gaJgORqEEAbNffDLlvHIQoAndR8NEYilwjExD/nwuUiTQ0GAwGw7qC7fqjEUvKqsBzmhWd\n",
+       "t05gu/5pyNoifw48J9N5PForxQeeNFMMBoPBYDD0DWL/llvK1In9jt4zCoLBYDAYDH2DePo5MwrJ\n",
+       "dv0hFPwTnjBRDAaDwWAw9A3+hPgOHRPl25iK+FhsiuR4OARx0Lwf+J1REAwGg8Fg6AMEnvNklL78\n",
+       "HMRRca/E5hVINNIVwI2B56z6/3ExLRI31pXNAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "i"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or you can pass an object with a rich representation to `display`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": [
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+       "HMRRca/E5hVINNIVwI2B56z6/3ExLRI31pXNAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An image can also be displayed from raw data or a URL."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"http://python.org/images/python-logo.gif\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(url='http://python.org/images/python-logo.gif')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SVG images are also supported out of the box."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
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+       "    <path d=\"M 110.46717 132.28575 A 48.948284 8.6066771 0 1 1  12.570599,132.28575 A 48.948284 8.6066771 0 1 1  110.46717 132.28575 z\" id=\"path1894\" style=\"opacity:0.44382019;fill:url(#radialGradient1480);fill-opacity:1;fill-rule:nonzero;stroke:none;stroke-width:20;stroke-miterlimit:4;stroke-dasharray:none;stroke-opacity:1\" transform=\"matrix(0.73406,0,0,0.809524,16.24958,27.00935)\"/>\n",
+       "  </g>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.SVG object>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import SVG\n",
+    "SVG(filename='../images/python_logo.svg')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Embedded vs non-embedded Images"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, image data is embedded in the notebook document so that the images can be viewed offline. However it is also possible to tell the `Image` class to only store a *link* to the image. Let's see how this works using a webcam at Berkeley."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import Image\n",
+    "img_url = 'http://www.lawrencehallofscience.org/static/scienceview/scienceview.berkeley.edu/html/view/view_assets/images/newview.jpg'\n",
+    "\n",
+    "# by default Image data are embedded\n",
+    "Embed      = Image(img_url)\n",
+    "\n",
+    "# if kwarg `url` is given, the embedding is assumed to be false\n",
+    "SoftLinked = Image(url=img_url)\n",
+    "\n",
+    "# In each case, embed can be specified explicitly with the `embed` kwarg\n",
+    "# ForceEmbed = Image(url=img_url, embed=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is the embedded version. Note that this image was pulled from the webcam when this code cell was originally run and stored in the Notebook. Unless we rerun this cell, this is not todays image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "pRJzJ+lAkH9P0k0BsKVPtP1rGSI4+mKAicmTjtSClZmM0ApBJgZoJ+R9eaADH9sjOKkn2H3oAzz/\n",
+       "ACKCM+5796gFOBuH/qj096oDPejsZ/egDBMzwJojv2nvQAY7du9NJKf09yMmoBgj2GeaUwOaoED9\n",
+       "YpmR3A++aAR5gGftRzn/ADoABInEzmaJxzNAE4j3oMbQDQBPJB78RQR3H896AJI4mKMGZ+1AInkR\n",
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+       "vROCB/nQATAnM96Ez3M/egGQAIMe9IyciaAcnFAkyAM0AAjvVJzAzPxQAEznaTJ4mkoer3FAIZ5B\n",
+       "pj3BNALHCf8AOjATE59qAeCIwPcmieYPaaARMD/SkCRzQFE8T2zEUEYHbvmgAbld/wCKJzBn6gUA\n",
+       "YiSQYoxBA/cGgCADE0+T8mgFyMn+apKowJz7UAbR747TSHHq/aaAZIgiSIpTjd3oBjgK4pgCP08c\n",
+       "0BmcG0g81CtsZH/moCTECAZ+aQI2yeQOZFAGSIxBqfUAQP096ANpAgfvSxA2qM0AiMYogkRMVQCZ\n",
+       "25BI/inCiCaAkiTimB2nPegDMgk5FBgjmD7/ADQAffdNNRniY7UAJJMjkf50E7lRmfagACYgRGKR\n",
+       "gCRP7UAwcyf3+KU4iaASYMyrijA9VAEDtANEd5+9AB5xmKcnkTNALMkHmmMEBX+VAKRPx80RmcEU\n",
+       "A5MiAYHxQMSDzQAeMAUGBPJjigFgjucUE5JJ5oAkkHHPJoJMynFAAiZPagGQR/MUAGO80dxM/U0A\n",
+       "RPuBFMmJoBKlRmiTwR+9ABkY4miSORNAPaff5FZQNonBn+KAc7QCY+grE6QV4NQAMiDU5B71QMCO\n",
+       "+TSjMTFAMEDv9fmgSO3PM0AHE96CRMY4zQBJEAAZHNMEAAxQCkT7ijA5OaAY25midsHbgUAAYgzF\n",
+       "Bk9oFAAkYANMeokzBoAVM7T9qCkAxjtQAAoTFGeee30oBpiMnvTQSSISCfpQH//Z\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Embed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is today's image from same webcam at Berkeley, (refreshed every minutes, if you reload the notebook), visible only with an active internet connection, that should be different from the previous one. Notebooks saved with this kind of image will be smaller and always reflect the current version of the source, but the image won't display offline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"http://www.lawrencehallofscience.org/static/scienceview/scienceview.berkeley.edu/html/view/view_assets/images/newview.jpg\"/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SoftLinked"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, if you re-run this Notebook, the two images will be the same again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## HTML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python objects can declare HTML representations that will be displayed in the Notebook. If you have some HTML you want to display, simply use the `HTML` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import HTML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "s = \"\"\"<table>\n",
+    "<tr>\n",
+    "<th>Header 1</th>\n",
+    "<th>Header 2</th>\n",
+    "</tr>\n",
+    "<tr>\n",
+    "<td>row 1, cell 1</td>\n",
+    "<td>row 1, cell 2</td>\n",
+    "</tr>\n",
+    "<tr>\n",
+    "<td>row 2, cell 1</td>\n",
+    "<td>row 2, cell 2</td>\n",
+    "</tr>\n",
+    "</table>\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "h = HTML(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr>\n",
+       "<th>Header 1</th>\n",
+       "<th>Header 2</th>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td>row 1, cell 1</td>\n",
+       "<td>row 1, cell 2</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td>row 2, cell 1</td>\n",
+       "<td>row 2, cell 2</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(h)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also use the `%%html` cell magic to accomplish the same thing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<tr>\n",
+       "<th>Header 1</th>\n",
+       "<th>Header 2</th>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td>row 1, cell 1</td>\n",
+       "<td>row 1, cell 2</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "<td>row 2, cell 1</td>\n",
+       "<td>row 2, cell 2</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%html\n",
+    "<table>\n",
+    "<tr>\n",
+    "<th>Header 1</th>\n",
+    "<th>Header 2</th>\n",
+    "</tr>\n",
+    "<tr>\n",
+    "<td>row 1, cell 1</td>\n",
+    "<td>row 1, cell 2</td>\n",
+    "</tr>\n",
+    "<tr>\n",
+    "<td>row 2, cell 1</td>\n",
+    "<td>row 2, cell 2</td>\n",
+    "</tr>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## JavaScript"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Notebook also enables objects to declare a JavaScript representation. At first, this may seem odd as  output is inherently visual and JavaScript is a programming language. However, this opens the door for rich output that leverages the full power of JavaScript and associated libraries such as [d3.js](http://d3js.org) for output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import Javascript"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pass a string of JavaScript source code to the `JavaScript` object and then display it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "js = Javascript('alert(\"hi\")');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "alert(\"hi\")"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(js)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The same thing can be accomplished using the `%%javascript` cell magic:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "\n",
+       "alert(\"hi\");"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%javascript\n",
+    "\n",
+    "alert(\"hi\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is a more complicated example that loads `d3.js` from a CDN, uses the `%%html` magic to load CSS styles onto the page and then runs ones of the `d3.js` examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "$.getScript('//cdnjs.cloudflare.com/ajax/libs/d3/3.2.2/d3.v3.min.js')"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Javascript(\n",
+    "    \"\"\"$.getScript('//cdnjs.cloudflare.com/ajax/libs/d3/3.2.2/d3.v3.min.js')\"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "\n",
+       "circle {\n",
+       "  fill: rgb(31, 119, 180);\n",
+       "  fill-opacity: .25;\n",
+       "  stroke: rgb(31, 119, 180);\n",
+       "  stroke-width: 1px;\n",
+       "}\n",
+       "\n",
+       ".leaf circle {\n",
+       "  fill: #ff7f0e;\n",
+       "  fill-opacity: 1;\n",
+       "}\n",
+       "\n",
+       "text {\n",
+       "  font: 10px sans-serif;\n",
+       "}\n",
+       "\n",
+       "</style>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%html\n",
+    "<style type=\"text/css\">\n",
+    "\n",
+    "circle {\n",
+    "  fill: rgb(31, 119, 180);\n",
+    "  fill-opacity: .25;\n",
+    "  stroke: rgb(31, 119, 180);\n",
+    "  stroke-width: 1px;\n",
+    "}\n",
+    "\n",
+    ".leaf circle {\n",
+    "  fill: #ff7f0e;\n",
+    "  fill-opacity: 1;\n",
+    "}\n",
+    "\n",
+    "text {\n",
+    "  font: 10px sans-serif;\n",
+    "}\n",
+    "\n",
+    "</style>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "\n",
+       "// element is the jQuery element we will append to\n",
+       "var e = element.get(0);\n",
+       "    \n",
+       "var diameter = 600,\n",
+       "    format = d3.format(\",d\");\n",
+       "\n",
+       "var pack = d3.layout.pack()\n",
+       "    .size([diameter - 4, diameter - 4])\n",
+       "    .value(function(d) { return d.size; });\n",
+       "\n",
+       "var svg = d3.select(e).append(\"svg\")\n",
+       "    .attr(\"width\", diameter)\n",
+       "    .attr(\"height\", diameter)\n",
+       "  .append(\"g\")\n",
+       "    .attr(\"transform\", \"translate(2,2)\");\n",
+       "\n",
+       "d3.json(\"data/flare.json\", function(error, root) {\n",
+       "  var node = svg.datum(root).selectAll(\".node\")\n",
+       "      .data(pack.nodes)\n",
+       "    .enter().append(\"g\")\n",
+       "      .attr(\"class\", function(d) { return d.children ? \"node\" : \"leaf node\"; })\n",
+       "      .attr(\"transform\", function(d) { return \"translate(\" + d.x + \",\" + d.y + \")\"; });\n",
+       "\n",
+       "  node.append(\"title\")\n",
+       "      .text(function(d) { return d.name + (d.children ? \"\" : \": \" + format(d.size)); });\n",
+       "\n",
+       "  node.append(\"circle\")\n",
+       "      .attr(\"r\", function(d) { return d.r; });\n",
+       "\n",
+       "  node.filter(function(d) { return !d.children; }).append(\"text\")\n",
+       "      .attr(\"dy\", \".3em\")\n",
+       "      .style(\"text-anchor\", \"middle\")\n",
+       "      .text(function(d) { return d.name.substring(0, d.r / 3); });\n",
+       "});\n",
+       "\n",
+       "d3.select(self.frameElement).style(\"height\", diameter + \"px\");"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%javascript\n",
+    "\n",
+    "// element is the jQuery element we will append to\n",
+    "var e = element.get(0);\n",
+    "    \n",
+    "var diameter = 600,\n",
+    "    format = d3.format(\",d\");\n",
+    "\n",
+    "var pack = d3.layout.pack()\n",
+    "    .size([diameter - 4, diameter - 4])\n",
+    "    .value(function(d) { return d.size; });\n",
+    "\n",
+    "var svg = d3.select(e).append(\"svg\")\n",
+    "    .attr(\"width\", diameter)\n",
+    "    .attr(\"height\", diameter)\n",
+    "  .append(\"g\")\n",
+    "    .attr(\"transform\", \"translate(2,2)\");\n",
+    "\n",
+    "d3.json(\"data/flare.json\", function(error, root) {\n",
+    "  var node = svg.datum(root).selectAll(\".node\")\n",
+    "      .data(pack.nodes)\n",
+    "    .enter().append(\"g\")\n",
+    "      .attr(\"class\", function(d) { return d.children ? \"node\" : \"leaf node\"; })\n",
+    "      .attr(\"transform\", function(d) { return \"translate(\" + d.x + \",\" + d.y + \")\"; });\n",
+    "\n",
+    "  node.append(\"title\")\n",
+    "      .text(function(d) { return d.name + (d.children ? \"\" : \": \" + format(d.size)); });\n",
+    "\n",
+    "  node.append(\"circle\")\n",
+    "      .attr(\"r\", function(d) { return d.r; });\n",
+    "\n",
+    "  node.filter(function(d) { return !d.children; }).append(\"text\")\n",
+    "      .attr(\"dy\", \".3em\")\n",
+    "      .style(\"text-anchor\", \"middle\")\n",
+    "      .text(function(d) { return d.name.substring(0, d.r / 3); });\n",
+    "});\n",
+    "\n",
+    "d3.select(self.frameElement).style(\"height\", diameter + \"px\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LaTeX"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The IPython display system also has builtin support for the display of mathematical expressions typeset in LaTeX, which is rendered in the browser using [MathJax](http://mathjax.org)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can pass raw LaTeX test as a string to the `Math` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$$F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Math\n",
+    "Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the `Latex` class, you have to include the delimiters yourself.  This allows you to use other LaTeX modes such as `eqnarray`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "\\begin{eqnarray}\n",
+       "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
+       "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
+       "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
+       "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
+       "\\end{eqnarray}"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Latex object>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Latex\n",
+    "Latex(r\"\"\"\\begin{eqnarray}\n",
+    "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
+    "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
+    "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
+    "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
+    "\\end{eqnarray}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or you can enter LaTeX directly with the `%%latex` cell magic:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "\\begin{align}\n",
+       "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
+       "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
+       "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
+       "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
+       "\\end{align}"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Latex object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%latex\n",
+    "\\begin{align}\n",
+    "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
+    "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
+    "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
+    "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
+    "\\end{align}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Audio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython makes it easy to work with sounds interactively. The `Audio` display class allows you to create an audio control that is embedded in the Notebook. The interface is analogous to the interface of the `Image` display class. All audio formats supported by the browser can be used. Note that no single format is presently supported in all browsers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio controls=\"controls\" >\n",
+       "                    <source src=\"http://www.nch.com.au/acm/8k16bitpcm.wav\" type=\"audio/x-wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Audio\n",
+    "Audio(url=\"http://www.nch.com.au/acm/8k16bitpcm.wav\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A NumPy array can be auralized automatically. The `Audio` class normalizes and encodes the data and embeds the resulting audio in the Notebook.\n",
+    "\n",
+    "For instance, when two sine waves with almost the same frequency are superimposed a phenomena known as [beats](https://en.wikipedia.org/wiki/Beat_%28acoustics%29) occur. This can be auralised as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio controls=\"controls\" >\n",
+       "                    <source 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\" type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio object>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "max_time = 3\n",
+    "f1 = 220.0\n",
+    "f2 = 224.0\n",
+    "rate = 8000.0\n",
+    "L = 3\n",
+    "times = np.linspace(0,L,rate*L)\n",
+    "signal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)\n",
+    "\n",
+    "Audio(data=signal, rate=rate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Video"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "More exotic objects can also be displayed, as long as their representation supports the IPython display protocol.  For example, videos hosted externally on YouTube are easy to load:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"400\"\n",
+       "            height=300\"\n",
+       "            src=\"https://www.youtube.com/embed/sjfsUzECqK0\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x107c1eb50>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo('sjfsUzECqK0')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the nascent video capabilities of modern browsers, you may also be able to display local\n",
+    "videos.  At the moment this doesn't work very well in all browsers, so it may or may not work for you;\n",
+    "we will continue testing this and looking for ways to make it more robust.  \n",
+    "\n",
+    "The following cell loads a local file called  `animation.m4v`, encodes the raw video as base64 for http\n",
+    "transport, and uses the HTML5 video tag to load it. On Chrome 15 it works correctly, displaying a control bar at the bottom with a play/pause button and a location slider."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<video controls alt=\"test\" 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+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "from IPython.display import HTML\n",
+    "from base64 import b64encode\n",
+    "video = open(\"../images/animation.m4v\", \"rb\").read()\n",
+    "video_encoded = b64encode(video).decode('ascii')\n",
+    "video_tag = '<video controls alt=\"test\" src=\"data:video/x-m4v;base64,{0}\">'.format(video_encoded)\n",
+    "HTML(data=video_tag)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## External sites"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can even embed an entire page from another site in an iframe; for example this is today's Wikipedia\n",
+    "page for mobile users:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"100%\"\n",
+       "            height=350\"\n",
+       "            src=\"http://ipython.org\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.IFrame at 0x107c1ecd0>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import IFrame\n",
+    "IFrame('http://ipython.org', width='100%', height=350)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Links to local files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython provides builtin display classes for generating links to local files. Create a link to a single file using the `FileLink` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<a href='Cell Magics.ipynb' target='_blank'>Cell Magics.ipynb</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/IPython Kernel/Cell Magics.ipynb"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import FileLink, FileLinks\n",
+    "FileLink('Cell Magics.ipynb')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, to generate links to all of the files in a directory, use the `FileLinks` object, passing `'.'` to indicate that we want links generated for the current working directory. Note that if there were other directories under the current directory, `FileLinks` would work in a recursive manner creating links to files in all sub-directories as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "./<br>\n",
+       "&nbsp;&nbsp;<a href='./Animations Using clear_output.ipynb' target='_blank'>Animations Using clear_output.ipynb</a><br>\n",
+       "&nbsp;&nbsp;<a href='./Background Jobs.ipynb' target='_blank'>Background Jobs.ipynb</a><br>\n",
+       "&nbsp;&nbsp;<a href='./Beyond Plain Python.ipynb' target='_blank'>Beyond Plain Python.ipynb</a><br>\n",
+       "&nbsp;&nbsp;<a href='./Capturing Output.ipynb' target='_blank'>Capturing Output.ipynb</a><br>\n",
+       "&nbsp;&nbsp;<a href='./Cell Magics.ipynb' target='_blank'>Cell Magics.ipynb</a><br>\n",
+       "&nbsp;&nbsp;<a href='./Custom Display Logic.ipynb' target='_blank'>Custom Display Logic.ipynb</a><br>\n",
+       "&nbsp;&nbsp;<a href='./example-demo.py' target='_blank'>example-demo.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='./Index.ipynb' target='_blank'>Index.ipynb</a><br>\n",
+       "&nbsp;&nbsp;<a href='./ipython-completion.bash' target='_blank'>ipython-completion.bash</a><br>\n",
+       "&nbsp;&nbsp;<a href='./ipython-get-history.py' target='_blank'>ipython-get-history.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='./ipython-qtconsole.desktop' target='_blank'>ipython-qtconsole.desktop</a><br>\n",
+       "&nbsp;&nbsp;<a href='./ipython.desktop' target='_blank'>ipython.desktop</a><br>\n",
+       "&nbsp;&nbsp;<a href='./Plotting in the Notebook.ipynb' target='_blank'>Plotting in the Notebook.ipynb</a><br>\n",
+       "&nbsp;&nbsp;<a href='./Raw Input in the Notebook.ipynb' target='_blank'>Raw Input in the Notebook.ipynb</a><br>\n",
+       "&nbsp;&nbsp;<a href='./Rich Output.ipynb' target='_blank'>Rich Output.ipynb</a><br>\n",
+       "&nbsp;&nbsp;<a href='./Script Magics.ipynb' target='_blank'>Script Magics.ipynb</a><br>\n",
+       "&nbsp;&nbsp;<a href='./SymPy.ipynb' target='_blank'>SymPy.ipynb</a><br>\n",
+       "&nbsp;&nbsp;<a href='./Terminal Usage.ipynb' target='_blank'>Terminal Usage.ipynb</a><br>\n",
+       "&nbsp;&nbsp;<a href='./Third Party Rich Output.ipynb' target='_blank'>Third Party Rich Output.ipynb</a><br>\n",
+       "&nbsp;&nbsp;<a href='./Trapezoid Rule.ipynb' target='_blank'>Trapezoid Rule.ipynb</a><br>\n",
+       "&nbsp;&nbsp;<a href='./Working With External Code.ipynb' target='_blank'>Working With External Code.ipynb</a><br>\n",
+       "./data/<br>\n",
+       "&nbsp;&nbsp;<a href='./data/flare.json' target='_blank'>flare.json</a><br>\n",
+       "./gui/<br>\n",
+       "&nbsp;&nbsp;<a href='./gui/gui-glut.py' target='_blank'>gui-glut.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='./gui/gui-gtk.py' target='_blank'>gui-gtk.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='./gui/gui-gtk3.py' target='_blank'>gui-gtk3.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='./gui/gui-pyglet.py' target='_blank'>gui-pyglet.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='./gui/gui-qt.py' target='_blank'>gui-qt.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='./gui/gui-tk.py' target='_blank'>gui-tk.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='./gui/gui-wx.py' target='_blank'>gui-wx.py</a><br>"
+      ],
+      "text/plain": [
+       "./\n",
+       "  Animations Using clear_output.ipynb\n",
+       "  Background Jobs.ipynb\n",
+       "  Beyond Plain Python.ipynb\n",
+       "  Capturing Output.ipynb\n",
+       "  Cell Magics.ipynb\n",
+       "  Custom Display Logic.ipynb\n",
+       "  example-demo.py\n",
+       "  Index.ipynb\n",
+       "  ipython-completion.bash\n",
+       "  ipython-get-history.py\n",
+       "  ipython-qtconsole.desktop\n",
+       "  ipython.desktop\n",
+       "  Plotting in the Notebook.ipynb\n",
+       "  Raw Input in the Notebook.ipynb\n",
+       "  Rich Output.ipynb\n",
+       "  Script Magics.ipynb\n",
+       "  SymPy.ipynb\n",
+       "  Terminal Usage.ipynb\n",
+       "  Third Party Rich Output.ipynb\n",
+       "  Trapezoid Rule.ipynb\n",
+       "  Working With External Code.ipynb\n",
+       "./data/\n",
+       "  flare.json\n",
+       "./gui/\n",
+       "  gui-glut.py\n",
+       "  gui-gtk.py\n",
+       "  gui-gtk3.py\n",
+       "  gui-pyglet.py\n",
+       "  gui-qt.py\n",
+       "  gui-tk.py\n",
+       "  gui-wx.py"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "FileLinks('.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Rich output and security"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The IPython Notebook allows arbitrary code execution in both the IPython kernel and in the browser, though HTML and JavaScript output. More importantly, because IPython has a JavaScript API for running code in the browser, HTML and JavaScript output can actually trigger code to be run in the kernel. This poses a significant security risk as it would allow IPython Notebooks to execute arbitrary code on your computers.\n",
+    "\n",
+    "To protect against these risks, the IPython Notebook has a security model that specifies how dangerous output is handled. Here is a short summary:\n",
+    "\n",
+    "* When you run code in the Notebook, all rich output is displayed.\n",
+    "* When you open a notebook, rich output is only displayed if it doesn't contain security vulberabilities,...\n",
+    "* ..or if you have trusted a notebook, all rich output will run upon opening it.\n",
+    "\n",
+    "A full description of the IPython security model can be found on [this page](http://ipython.org/ipython-doc/dev/notebook/security.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Rich output and nbviewer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Much of the power of the Notebook is that it enables users to share notebooks with each other using http://nbviewer.ipython.org, without installing IPython locally. As of IPython 2.0, notebooks rendere on nbviewer will display all output, including HTML and JavaScript. Furthermore, to provide a consistent JavaScript environment on the live Notebook and nbviewer, the following JavaScript libraries are loaded onto the nbviewer page, *before* the notebook and its output is displayed:\n",
+    "\n",
+    "* [jQuery](http://jquery.com/)\n",
+    "* [RequireJS](http://requirejs.org/)\n",
+    "\n",
+    "Libraries such as [mpld3](http://mpld3.github.io/) use these capabilities to generate interactive visualizations that work on nbviewer."
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:cf83dc9e6288480ac94c44a5983b4ee421f0ade792a9fac64bc00719263386c0"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/Script Magics.ipynb b/examples/IPython Kernel/Script Magics.ipynb
index 643d557..7d362e6 100644
--- a/examples/IPython Kernel/Script Magics.ipynb
+++ b/examples/IPython Kernel/Script Magics.ipynb
@@ -1,482 +1,448 @@
 {
- "metadata": {
-  "name": "Script Magics"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Running Scripts from IPython"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython has a `%%script` cell magic, which lets you run a cell in\n",
+    "a subprocess of any interpreter on your system, such as: bash, ruby, perl, zsh, R, etc.\n",
+    "\n",
+    "It can even be a script of your own, which expects input on stdin."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic usage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use it, simply pass a path or shell command to the program you want to run on the `%%script` line,\n",
+    "and the rest of the cell will be run by that script, and stdout/err from the subprocess are captured and displayed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Running Scripts from IPython"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello from Python 2.7.1 (r271:86832, Jul 31 2011, 19:30:53) \n",
+      "[GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2335.15.00)]\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "%%script python\n",
+    "import sys\n",
+    "print 'hello from Python %s' % sys.version"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython has a `%%script` cell magic, which lets you run a cell in\n",
-      "a subprocess of any interpreter on your system, such as: bash, ruby, perl, zsh, R, etc.\n",
-      "\n",
-      "It can even be a script of your own, which expects input on stdin."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello from Python: 3.2.3 (v3.2.3:3d0686d90f55, Apr 10 2012, 11:25:50) \n",
+      "[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import sys"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Basic usage"
-     ]
-    },
+    }
+   ],
+   "source": [
+    "%%script python3\n",
+    "import sys\n",
+    "print('hello from Python: %s' % sys.version)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython also creates aliases for a few common interpreters, such as bash, ruby, perl, etc.\n",
+    "\n",
+    "These are all equivalent to `%%script <name>`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To use it, simply pass a path or shell command to the program you want to run on the `%%script` line,\n",
-      "and the rest of the cell will be run by that script, and stdout/err from the subprocess are captured and displayed."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello from Ruby 1.8.7\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%script python\n",
-      "import sys\n",
-      "print 'hello from Python %s' % sys.version"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "hello from Python 2.7.1 (r271:86832, Jul 31 2011, 19:30:53) \n",
-        "[GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2335.15.00)]\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%script python3\n",
-      "import sys\n",
-      "print('hello from Python: %s' % sys.version)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "hello from Python: 3.2.3 (v3.2.3:3d0686d90f55, Apr 10 2012, 11:25:50) \n",
-        "[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython also creates aliases for a few common interpreters, such as bash, ruby, perl, etc.\n",
-      "\n",
-      "These are all equivalent to `%%script <name>`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%ruby\n",
-      "puts \"Hello from Ruby #{RUBY_VERSION}\""
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Hello from Ruby 1.8.7\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%bash\n",
-      "echo \"hello from $BASH\""
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "hello from /usr/local/bin/bash\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
+    }
+   ],
+   "source": [
+    "%%ruby\n",
+    "puts \"Hello from Ruby #{RUBY_VERSION}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Capturing output"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello from /usr/local/bin/bash\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "echo \"hello from $BASH\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Capturing output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also capture stdout/err from these subprocesses into Python variables, instead of letting them go directly to stdout/err"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can also capture stdout/err from these subprocesses into Python variables, instead of letting them go directly to stdout/err"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hi, stdout\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%bash\n",
-      "echo \"hi, stdout\"\n",
-      "echo \"hello, stderr\" >&2\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "hi, stdout\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "hello, stderr\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%bash --out output --err error\n",
-      "echo \"hi, stdout\"\n",
-      "echo \"hello, stderr\" >&2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print(error)\n",
-      "print(output)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "hello, stderr\n",
-        "\n",
-        "hi, stdout\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Background Scripts"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "hello, stderr\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "echo \"hi, stdout\"\n",
+    "echo \"hello, stderr\" >&2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%bash --out output --err error\n",
+    "echo \"hi, stdout\"\n",
+    "echo \"hello, stderr\" >&2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "These scripts can be run in the background, by adding the `--bg` flag.\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello, stderr\n",
       "\n",
-      "When you do this, output is discarded unless you use the `--out/err`\n",
-      "flags to store output as above."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%ruby --bg --out ruby_lines\n",
-      "for n in 1...10\n",
-      "    sleep 1\n",
-      "    puts \"line #{n}\"\n",
-      "    STDOUT.flush\n",
-      "end"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Starting job # 0 in a separate thread.\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When you do store output of a background thread, these are the stdout/err *pipes*,\n",
-      "rather than the text of the output."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ruby_lines"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "<open file '<fdopen>', mode 'rb' at 0x10a4be660>"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print(ruby_lines.read())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "line 1\n",
-        "line 2\n",
-        "line 3\n",
-        "line 4\n",
-        "line 5\n",
-        "line 6\n",
-        "line 7\n",
-        "line 8\n",
-        "line 9\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Arguments to subcommand"
+      "hi, stdout\n",
+      "\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "print(error)\n",
+    "print(output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Background Scripts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These scripts can be run in the background, by adding the `--bg` flag.\n",
+    "\n",
+    "When you do this, output is discarded unless you use the `--out/err`\n",
+    "flags to store output as above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can pass arguments the subcommand as well,\n",
-      "such as  this example instructing Python to use integer division from Python 3:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting job # 0 in a separate thread.\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%script python -Qnew\n",
-      "print 1/3"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0.333333333333\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
+    }
+   ],
+   "source": [
+    "%%ruby --bg --out ruby_lines\n",
+    "for n in 1...10\n",
+    "    sleep 1\n",
+    "    puts \"line #{n}\"\n",
+    "    STDOUT.flush\n",
+    "end"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you do store output of a background thread, these are the stdout/err *pipes*,\n",
+    "rather than the text of the output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "<open file '<fdopen>', mode 'rb' at 0x10a4be660>"
+      ]
+     },
+     "execution_count": 10,
      "metadata": {},
-     "source": [
-      "You can really specify *any* program for `%%script`,\n",
-      "for instance here is a 'program' that echos the lines of stdin, with delays between each line."
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ruby_lines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%script --bg --out bashout bash -c \"while read line; do echo $line; sleep 1; done\"\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "line 1\n",
       "line 2\n",
       "line 3\n",
       "line 4\n",
-      "line 5\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Starting job # 2 in a separate thread.\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Remember, since the output of a background script is just the stdout pipe,\n",
-      "you can read it as lines become available:"
+      "line 5\n",
+      "line 6\n",
+      "line 7\n",
+      "line 8\n",
+      "line 9\n",
+      "\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "print(ruby_lines.read())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Arguments to subcommand"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can pass arguments the subcommand as well,\n",
+    "such as  this example instructing Python to use integer division from Python 3:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import time\n",
-      "tic = time.time()\n",
-      "line = True\n",
-      "while True:\n",
-      "    line = bashout.readline()\n",
-      "    if not line:\n",
-      "        break\n",
-      "    sys.stdout.write(\"%.1fs: %s\" %(time.time()-tic, line))\n",
-      "    sys.stdout.flush()\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0.0s: line 1\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1.0s: line 2\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2.0s: line 3\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "3.0s: line 4\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "4.0s: line 5\n"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.333333333333\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%script python -Qnew\n",
+    "print 1/3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can really specify *any* program for `%%script`,\n",
+    "for instance here is a 'program' that echos the lines of stdin, with delays between each line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Configuring the default ScriptMagics"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting job # 2 in a separate thread.\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "%%script --bg --out bashout bash -c \"while read line; do echo $line; sleep 1; done\"\n",
+    "line 1\n",
+    "line 2\n",
+    "line 3\n",
+    "line 4\n",
+    "line 5\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember, since the output of a background script is just the stdout pipe,\n",
+    "you can read it as lines become available:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The list of aliased script magics is configurable.\n",
-      "\n",
-      "The default is to pick from a few common interpreters, and use them if found, but you can specify your own in ipython_config.py:\n",
-      "\n",
-      "    c.ScriptMagics.scripts = ['R', 'pypy', 'myprogram']\n",
-      "\n",
-      "And if any of these programs do not apear on your default PATH, then you would also need to specify their location with:\n",
-      "\n",
-      "    c.ScriptMagics.script_paths = {'myprogram': '/opt/path/to/myprogram'}"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0s: line 1\n",
+      "1.0s: line 2\n",
+      "2.0s: line 3\n",
+      "3.0s: line 4\n",
+      "4.0s: line 5\n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "import time\n",
+    "tic = time.time()\n",
+    "line = True\n",
+    "while True:\n",
+    "    line = bashout.readline()\n",
+    "    if not line:\n",
+    "        break\n",
+    "    sys.stdout.write(\"%.1fs: %s\" %(time.time()-tic, line))\n",
+    "    sys.stdout.flush()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configuring the default ScriptMagics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The list of aliased script magics is configurable.\n",
+    "\n",
+    "The default is to pick from a few common interpreters, and use them if found, but you can specify your own in ipython_config.py:\n",
+    "\n",
+    "    c.ScriptMagics.scripts = ['R', 'pypy', 'myprogram']\n",
+    "\n",
+    "And if any of these programs do not apear on your default PATH, then you would also need to specify their location with:\n",
+    "\n",
+    "    c.ScriptMagics.script_paths = {'myprogram': '/opt/path/to/myprogram'}"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/SymPy.ipynb b/examples/IPython Kernel/SymPy.ipynb
index ab6c590..101fec7 100644
--- a/examples/IPython Kernel/SymPy.ipynb
+++ b/examples/IPython Kernel/SymPy.ipynb
@@ -1,670 +1,1655 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:a0f4cda82587c5b8e7a4502192d1210abedac9084077604eb60aea15f262a344"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# SymPy: Open Source Symbolic Mathematics\n",
-      "\n",
-      "This notebook uses the [SymPy](http://sympy.org) package to perform symbolic manipulations,\n",
-      "and combined with numpy and matplotlib, also displays numerical visualizations of symbolically\n",
-      "constructed expressions.\n",
-      "\n",
-      "We first load sympy printing extensions, as well as all of sympy:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import display\n",
-      "\n",
-      "from sympy.interactive import printing\n",
-      "printing.init_printing(use_latex='mathjax')\n",
-      "\n",
-      "from __future__ import division\n",
-      "import sympy as sym\n",
-      "from sympy import *\n",
-      "x, y, z = symbols(\"x y z\")\n",
-      "k, m, n = symbols(\"k m n\", integer=True)\n",
-      "f, g, h = map(Function, 'fgh')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<h2>Elementary operations</h2>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "Rational(3,2)*pi + exp(I*x) / (x**2 + y)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\frac{3 \\pi}{2} + \\frac{e^{i x}}{x^{2} + y}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 2,
-       "text": [
-        "        \u2148\u22c5x \n",
-        "3\u22c5\u03c0    \u212f    \n",
-        "\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500\u2500\u2500\n",
-        " 2     2    \n",
-        "      x  + y"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "exp(I*x).subs(x,pi).evalf()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$-1.0$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "-1.00000000000000"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "e = x + 2*y"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "srepr(e)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 5,
-       "text": [
-        "\"Add(Symbol('x'), Mul(Integer(2), Symbol('y')))\""
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "exp(pi * sqrt(163)).evalf(50)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$262537412640768743.99999999999925007259719818568888$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 6,
-       "text": [
-        "262537412640768743.99999999999925007259719818568888"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<h2>Algebra<h2>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "eq = ((x+y)**2 * (x+1))\n",
-      "eq"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\left(x + 1\\right) \\left(x + y\\right)^{2}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 7,
-       "text": [
-        "               2\n",
-        "(x + 1)\u22c5(x + y) "
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "expand(eq)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x^{3} + 2 x^{2} y + x^{2} + x y^{2} + 2 x y + y^{2}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 8,
-       "text": [
-        " 3      2      2      2            2\n",
-        "x  + 2\u22c5x \u22c5y + x  + x\u22c5y  + 2\u22c5x\u22c5y + y "
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SymPy: Open Source Symbolic Mathematics\n",
+    "\n",
+    "This notebook uses the [SymPy](http://sympy.org) package to perform symbolic manipulations,\n",
+    "and combined with numpy and matplotlib, also displays numerical visualizations of symbolically\n",
+    "constructed expressions.\n",
+    "\n",
+    "We first load sympy printing extensions, as well as all of sympy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import display\n",
+    "\n",
+    "from sympy.interactive import printing\n",
+    "printing.init_printing(use_latex='mathjax')\n",
+    "\n",
+    "from __future__ import division\n",
+    "import sympy as sym\n",
+    "from sympy import *\n",
+    "x, y, z = symbols(\"x y z\")\n",
+    "k, m, n = symbols(\"k m n\", integer=True)\n",
+    "f, g, h = map(Function, 'fgh')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Elementary operations</h2>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = 1/x + (x*sin(x) - 1)/x\n",
-      "a"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$\\frac{3 \\pi}{2} + \\frac{e^{i x}}{x^{2} + y}$$"
+      ],
+      "text/plain": [
+       "        \u2148\u22c5x \n",
+       "3\u22c5\u03c0    \u212f    \n",
+       "\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500\u2500\u2500\n",
+       " 2     2    \n",
+       "      x  + y"
+      ]
+     },
+     "execution_count": 2,
      "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\frac{1}{x} \\left(x \\sin{\\left (x \\right )} - 1\\right) + \\frac{1}{x}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 9,
-       "text": [
-        "x\u22c5sin(x) - 1   1\n",
-        "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\n",
-        "     x         x"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Rational(3,2)*pi + exp(I*x) / (x**2 + y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "simplify(a)"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$-1.0$$"
+      ],
+      "text/plain": [
+       "-1.00000000000000"
+      ]
+     },
+     "execution_count": 3,
      "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\sin{\\left (x \\right )}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "sin(x)"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exp(I*x).subs(x,pi).evalf()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "e = x + 2*y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "eq = Eq(x**3 + 2*x**2 + 4*x + 8, 0)\n",
-      "eq"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "\"Add(Symbol('x'), Mul(Integer(2), Symbol('y')))\""
+      ]
+     },
+     "execution_count": 5,
      "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x^{3} + 2 x^{2} + 4 x + 8 = 0$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 11,
-       "text": [
-        " 3      2              \n",
-        "x  + 2\u22c5x  + 4\u22c5x + 8 = 0"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "srepr(e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "solve(eq, x)"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$262537412640768743.99999999999925007259719818568888$$"
+      ],
+      "text/plain": [
+       "262537412640768743.99999999999925007259719818568888"
+      ]
+     },
+     "execution_count": 6,
      "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\begin{bmatrix}-2, & - 2 i, & 2 i\\end{bmatrix}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 12,
-       "text": [
-        "[-2, -2\u22c5\u2148, 2\u22c5\u2148]"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exp(pi * sqrt(163)).evalf(50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Algebra<h2>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a, b = symbols('a b')\n",
-      "Sum(6*n**2 + 2**n, (n, a, b))"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$\\left(x + 1\\right) \\left(x + y\\right)^{2}$$"
+      ],
+      "text/plain": [
+       "               2\n",
+       "(x + 1)\u22c5(x + y) "
+      ]
+     },
+     "execution_count": 7,
      "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\sum_{n=a}^{b} \\left(2^{n} + 6 n^{2}\\right)$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 13,
-       "text": [
-        "  b              \n",
-        " ___             \n",
-        " \u2572               \n",
-        "  \u2572   \u239b n      2\u239e\n",
-        "  \u2571   \u239d2  + 6\u22c5n \u23a0\n",
-        " \u2571               \n",
-        " \u203e\u203e\u203e             \n",
-        "n = a            "
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eq = ((x+y)**2 * (x+1))\n",
+    "eq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/latex": [
+       "$$x^{3} + 2 x^{2} y + x^{2} + x y^{2} + 2 x y + y^{2}$$"
+      ],
+      "text/plain": [
+       " 3      2      2      2            2\n",
+       "x  + 2\u22c5x \u22c5y + x  + x\u22c5y  + 2\u22c5x\u22c5y + y "
+      ]
+     },
+     "execution_count": 8,
      "metadata": {},
-     "source": [
-      "<h2>Calculus</h2>"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expand(eq)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "limit((sin(x)-x)/x**3, x, 0)"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$\\frac{1}{x} \\left(x \\sin{\\left (x \\right )} - 1\\right) + \\frac{1}{x}$$"
+      ],
+      "text/plain": [
+       "x\u22c5sin(x) - 1   1\n",
+       "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\n",
+       "     x         x"
+      ]
+     },
+     "execution_count": 9,
      "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$- \\frac{1}{6}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 14,
-       "text": [
-        "-1/6"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = 1/x + (x*sin(x) - 1)/x\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "(1/cos(x)).series(x, 0, 6)"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$\\sin{\\left (x \\right )}$$"
+      ],
+      "text/plain": [
+       "sin(x)"
+      ]
+     },
+     "execution_count": 10,
      "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$1 + \\frac{x^{2}}{2} + \\frac{5 x^{4}}{24} + \\mathcal{O}\\left(x^{6}\\right)$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 15,
-       "text": [
-        "     2      4        \n",
-        "    x    5\u22c5x     \u239b 6\u239e\n",
-        "1 + \u2500\u2500 + \u2500\u2500\u2500\u2500 + O\u239dx \u23a0\n",
-        "    2     24         "
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "simplify(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "diff(cos(x**2)**2 / (1+x), x)"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$x^{3} + 2 x^{2} + 4 x + 8 = 0$$"
+      ],
+      "text/plain": [
+       " 3      2              \n",
+       "x  + 2\u22c5x  + 4\u22c5x + 8 = 0"
+      ]
+     },
+     "execution_count": 11,
      "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$- \\frac{4 x \\cos{\\left (x^{2} \\right )}}{x + 1} \\sin{\\left (x^{2} \\right )} - \\frac{\\cos^{2}{\\left (x^{2} \\right )}}{\\left(x + 1\\right)^{2}}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 16,
-       "text": [
-        "         \u239b 2\u239e    \u239b 2\u239e      2\u239b 2\u239e\n",
-        "  4\u22c5x\u22c5sin\u239dx \u23a0\u22c5cos\u239dx \u23a0   cos \u239dx \u23a0\n",
-        "- \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 - \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
-        "         x + 1                 2\n",
-        "                        (x + 1) "
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eq = Eq(x**3 + 2*x**2 + 4*x + 8, 0)\n",
+    "eq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "integrate(x**2 * cos(x), (x, 0, pi/2))"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$\\begin{bmatrix}-2, & - 2 i, & 2 i\\end{bmatrix}$$"
+      ],
+      "text/plain": [
+       "[-2, -2\u22c5\u2148, 2\u22c5\u2148]"
+      ]
+     },
+     "execution_count": 12,
      "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$-2 + \\frac{\\pi^{2}}{4}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 17,
-       "text": [
-        "      2\n",
-        "     \u03c0 \n",
-        "-2 + \u2500\u2500\n",
-        "     4 "
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solve(eq, x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "eqn = Eq(Derivative(f(x),x,x) + 9*f(x), 1)\n",
-      "display(eqn)\n",
-      "dsolve(eqn, f(x))"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$\\sum_{n=a}^{b} \\left(2^{n} + 6 n^{2}\\right)$$"
+      ],
+      "text/plain": [
+       "  b              \n",
+       " ___             \n",
+       " \u2572               \n",
+       "  \u2572   \u239b n      2\u239e\n",
+       "  \u2571   \u239d2  + 6\u22c5n \u23a0\n",
+       " \u2571               \n",
+       " \u203e\u203e\u203e             \n",
+       "n = a            "
+      ]
+     },
+     "execution_count": 13,
      "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$9 f{\\left (x \\right )} + \\frac{d^{2}}{d x^{2}}  f{\\left (x \\right )} = 1$$"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "           2          \n",
-        "          d           \n",
-        "9\u22c5f(x) + \u2500\u2500\u2500(f(x)) = 1\n",
-        "           2          \n",
-        "         dx           "
-       ]
-      },
-      {
-       "latex": [
-        "$$f{\\left (x \\right )} = C_{1} \\sin{\\left (3 x \\right )} + C_{2} \\cos{\\left (3 x \\right )} + \\frac{1}{9}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 18,
-       "text": [
-        "f(x) = C\u2081\u22c5sin(3\u22c5x) + C\u2082\u22c5cos(3\u22c5x) + 1/9"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a, b = symbols('a b')\n",
+    "Sum(6*n**2 + 2**n, (n, a, b))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h2>Calculus</h2>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/latex": [
+       "$$- \\frac{1}{6}$$"
+      ],
+      "text/plain": [
+       "-1/6"
+      ]
+     },
+     "execution_count": 14,
      "metadata": {},
-     "source": [
-      "# Illustrating Taylor series\n",
-      "\n",
-      "We will define a function to compute the Taylor series expansions of a symbolically defined expression at\n",
-      "various orders and visualize all the approximations together with the original function"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "limit((sin(x)-x)/x**3, x, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline\n",
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$1 + \\frac{x^{2}}{2} + \\frac{5 x^{4}}{24} + \\mathcal{O}\\left(x^{6}\\right)$$"
+      ],
+      "text/plain": [
+       "     2      4        \n",
+       "    x    5\u22c5x     \u239b 6\u239e\n",
+       "1 + \u2500\u2500 + \u2500\u2500\u2500\u2500 + O\u239dx \u23a0\n",
+       "    2     24         "
+      ]
+     },
+     "execution_count": 15,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 19
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(1/cos(x)).series(x, 0, 6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "# You can change the default figure size to be a bit larger if you want,\n",
-      "# uncomment the next line for that:\n",
-      "#plt.rc('figure', figsize=(10, 6))"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$- \\frac{4 x \\cos{\\left (x^{2} \\right )}}{x + 1} \\sin{\\left (x^{2} \\right )} - \\frac{\\cos^{2}{\\left (x^{2} \\right )}}{\\left(x + 1\\right)^{2}}$$"
+      ],
+      "text/plain": [
+       "         \u239b 2\u239e    \u239b 2\u239e      2\u239b 2\u239e\n",
+       "  4\u22c5x\u22c5sin\u239dx \u23a0\u22c5cos\u239dx \u23a0   cos \u239dx \u23a0\n",
+       "- \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 - \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
+       "         x + 1                 2\n",
+       "                        (x + 1) "
+      ]
+     },
+     "execution_count": 16,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 20
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diff(cos(x**2)**2 / (1+x), x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "def plot_taylor_approximations(func, x0=None, orders=(2, 4), xrange=(0,1), yrange=None, npts=200):\n",
-      "    \"\"\"Plot the Taylor series approximations to a function at various orders.\n",
-      "\n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    func : a sympy function\n",
-      "    x0 : float\n",
-      "      Origin of the Taylor series expansion.  If not given, x0=xrange[0].\n",
-      "    orders : list\n",
-      "      List of integers with the orders of Taylor series to show.  Default is (2, 4).\n",
-      "    xrange : 2-tuple or array.\n",
-      "      Either an (xmin, xmax) tuple indicating the x range for the plot (default is (0, 1)),\n",
-      "      or the actual array of values to use.\n",
-      "    yrange : 2-tuple\n",
-      "      (ymin, ymax) tuple indicating the y range for the plot.  If not given,\n",
-      "      the full range of values will be automatically used. \n",
-      "    npts : int\n",
-      "      Number of points to sample the x range with.  Default is 200.\n",
-      "    \"\"\"\n",
-      "    if not callable(func):\n",
-      "        raise ValueError('func must be callable')\n",
-      "    if isinstance(xrange, (list, tuple)):\n",
-      "        x = np.linspace(float(xrange[0]), float(xrange[1]), npts)\n",
-      "    else:\n",
-      "        x = xrange\n",
-      "    if x0 is None: x0 = x[0]\n",
-      "    xs = sym.Symbol('x')\n",
-      "    # Make a numpy-callable form of the original function for plotting\n",
-      "    fx = func(xs)\n",
-      "    f = sym.lambdify(xs, fx, modules=['numpy'])\n",
-      "    # We could use latex(fx) instead of str(), but matploblib gets confused\n",
-      "    # with some of the (valid) latex constructs sympy emits.  So we play it safe.\n",
-      "    plt.plot(x, f(x), label=str(fx), lw=2)\n",
-      "    # Build the Taylor approximations, plotting as we go\n",
-      "    apps = {}\n",
-      "    for order in orders:\n",
-      "        app = fx.series(xs, x0, n=order).removeO()\n",
-      "        apps[order] = app\n",
-      "        # Must be careful here: if the approximation is a constant, we can't\n",
-      "        # blindly use lambdify as it won't do the right thing.  In that case, \n",
-      "        # evaluate the number as a float and fill the y array with that value.\n",
-      "        if isinstance(app, sym.numbers.Number):\n",
-      "            y = np.zeros_like(x)\n",
-      "            y.fill(app.evalf())\n",
-      "        else:\n",
-      "            fa = sym.lambdify(xs, app, modules=['numpy'])\n",
-      "            y = fa(x)\n",
-      "        tex = sym.latex(app).replace('$', '')\n",
-      "        plt.plot(x, y, label=r'$n=%s:\\, %s$' % (order, tex) )\n",
-      "        \n",
-      "    # Plot refinements\n",
-      "    if yrange is not None:\n",
-      "        plt.ylim(*yrange)\n",
-      "    plt.grid()\n",
-      "    plt.legend(loc='best').get_frame().set_alpha(0.8)"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$-2 + \\frac{\\pi^{2}}{4}$$"
+      ],
+      "text/plain": [
+       "      2\n",
+       "     \u03c0 \n",
+       "-2 + \u2500\u2500\n",
+       "     4 "
+      ]
+     },
+     "execution_count": 17,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 21
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "integrate(x**2 * cos(x), (x, 0, pi/2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/latex": [
+       "$$9 f{\\left (x \\right )} + \\frac{d^{2}}{d x^{2}}  f{\\left (x \\right )} = 1$$"
+      ],
+      "text/plain": [
+       "           2          \n",
+       "          d           \n",
+       "9\u22c5f(x) + \u2500\u2500\u2500(f(x)) = 1\n",
+       "           2          \n",
+       "         dx           "
+      ]
+     },
      "metadata": {},
-     "source": [
-      "With this function defined, we can now use it for any sympy function or expression"
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plot_taylor_approximations(sin, 0, [2, 4, 6], (0, 2*pi), (-2,2))"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$f{\\left (x \\right )} = C_{1} \\sin{\\left (3 x \\right )} + C_{2} \\cos{\\left (3 x \\right )} + \\frac{1}{9}$$"
+      ],
+      "text/plain": [
+       "f(x) = C\u2081\u22c5sin(3\u22c5x) + C\u2082\u22c5cos(3\u22c5x) + 1/9"
+      ]
+     },
+     "execution_count": 18,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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dlSs8c+gQW4KCitxlo+LoFflhSRy9I8xRLULcEUbZImUar85fzqVelwn7rgr/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\n1kqshsEAkyfLGrQlSsAXX8gHl9vbSxS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-       "text": [
-        "<matplotlib.figure.Figure at 0x10797be10>"
-       ]
-      }
-     ],
-     "prompt_number": 22
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eqn = Eq(Derivative(f(x),x,x) + 9*f(x), 1)\n",
+    "display(eqn)\n",
+    "dsolve(eqn, f(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Illustrating Taylor series\n",
+    "\n",
+    "We will define a function to compute the Taylor series expansions of a symbolically defined expression at\n",
+    "various orders and visualize all the approximations together with the original function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# You can change the default figure size to be a bit larger if you want,\n",
+    "# uncomment the next line for that:\n",
+    "#plt.rc('figure', figsize=(10, 6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def plot_taylor_approximations(func, x0=None, orders=(2, 4), xrange=(0,1), yrange=None, npts=200):\n",
+    "    \"\"\"Plot the Taylor series approximations to a function at various orders.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    func : a sympy function\n",
+    "    x0 : float\n",
+    "      Origin of the Taylor series expansion.  If not given, x0=xrange[0].\n",
+    "    orders : list\n",
+    "      List of integers with the orders of Taylor series to show.  Default is (2, 4).\n",
+    "    xrange : 2-tuple or array.\n",
+    "      Either an (xmin, xmax) tuple indicating the x range for the plot (default is (0, 1)),\n",
+    "      or the actual array of values to use.\n",
+    "    yrange : 2-tuple\n",
+    "      (ymin, ymax) tuple indicating the y range for the plot.  If not given,\n",
+    "      the full range of values will be automatically used. \n",
+    "    npts : int\n",
+    "      Number of points to sample the x range with.  Default is 200.\n",
+    "    \"\"\"\n",
+    "    if not callable(func):\n",
+    "        raise ValueError('func must be callable')\n",
+    "    if isinstance(xrange, (list, tuple)):\n",
+    "        x = np.linspace(float(xrange[0]), float(xrange[1]), npts)\n",
+    "    else:\n",
+    "        x = xrange\n",
+    "    if x0 is None: x0 = x[0]\n",
+    "    xs = sym.Symbol('x')\n",
+    "    # Make a numpy-callable form of the original function for plotting\n",
+    "    fx = func(xs)\n",
+    "    f = sym.lambdify(xs, fx, modules=['numpy'])\n",
+    "    # We could use latex(fx) instead of str(), but matploblib gets confused\n",
+    "    # with some of the (valid) latex constructs sympy emits.  So we play it safe.\n",
+    "    plt.plot(x, f(x), label=str(fx), lw=2)\n",
+    "    # Build the Taylor approximations, plotting as we go\n",
+    "    apps = {}\n",
+    "    for order in orders:\n",
+    "        app = fx.series(xs, x0, n=order).removeO()\n",
+    "        apps[order] = app\n",
+    "        # Must be careful here: if the approximation is a constant, we can't\n",
+    "        # blindly use lambdify as it won't do the right thing.  In that case, \n",
+    "        # evaluate the number as a float and fill the y array with that value.\n",
+    "        if isinstance(app, sym.numbers.Number):\n",
+    "            y = np.zeros_like(x)\n",
+    "            y.fill(app.evalf())\n",
+    "        else:\n",
+    "            fa = sym.lambdify(xs, app, modules=['numpy'])\n",
+    "            y = fa(x)\n",
+    "        tex = sym.latex(app).replace('$', '')\n",
+    "        plt.plot(x, y, label=r'$n=%s:\\, %s$' % (order, tex) )\n",
+    "        \n",
+    "    # Plot refinements\n",
+    "    if yrange is not None:\n",
+    "        plt.ylim(*yrange)\n",
+    "    plt.grid()\n",
+    "    plt.legend(loc='best').get_frame().set_alpha(0.8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With this function defined, we can now use it for any sympy function or expression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plot_taylor_approximations(cos, 0, [2, 4, 6], (0, 2*pi), (-2,2))"
-     ],
-     "language": "python",
+     "data": {
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+       "/TppaWkAXLt2jXXr1ukmAq169erUqVOHo0ePAtLP3aRJE41Vmc7SpUsZOHDgA9sVmuvG1ty5oSo7\n",
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+       "YmIiQ4cOJScnh5ycHIYMGcITTzyhtSyz0KMbMykpiV69egHSFfL000/TsWNHjVUZz1dffcXTTz9N\n",
+       "ZmYmXl5ezJ8/X2tJJnHt2jU2bNhg1NqI2jClUCgUDo5+l/sVCoVCYRTK0CsUCoWDowy9QqFQODjK\n",
+       "0CsUCoWDowy9QqFQODjK0CsUCoWDowy9QqFQODjK0CsUCoWD8//NvpMqig2bWwAAAABJRU5ErkJg\n",
+       "gg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10797be10>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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j5C9DlYffn/LOqBo9QLdu3ejWrVuh740cObLQ9rx584w9TcXTvbs84dm33z40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\ntYbY1r+2UgAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10984b250>"
-       ]
-      }
-     ],
-     "prompt_number": 23
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_taylor_approximations(sin, 0, [2, 4, 6], (0, 2*pi), (-2,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "image/png": [
+       "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
+       "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XlcFdX/x/HXZVMBBU3FBRRBFFAEFMUyDUtcc81KLZU0\n",
+       "s0VNsyxb3XLLFkvrZ+baombfTBSh3K65IS7gBu6iCO6KgIDAZX5/jJIo4OUuzB04z8eDRw13mHk7\n",
+       "4rnnfubMORpJkiQEQRCEcstK6QCCIAiCeYmGXhAEoZwTDb0gCEI5Jxp6QRCEck409IIgCOWcaOgF\n",
+       "QRDKOaMa+qSkJDp27EizZs1o3rw53377bZH7jRkzBi8vL/z9/YmNjTXmlIIgCEIp2Rjzw7a2tnz9\n",
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+       "tYbY1r+2UgAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10984b250>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "This shows easily how a Taylor series is useless beyond its convergence radius, illustrated by \n",
-      "a simple function that has singularities on the real axis:"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_taylor_approximations(cos, 0, [2, 4, 6], (0, 2*pi), (-2,2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This shows easily how a Taylor series is useless beyond its convergence radius, illustrated by \n",
+    "a simple function that has singularities on the real axis:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# For an expression made from elementary functions, we must first make it into\n",
-      "# a callable function, the simplest way is to use the Python lambda construct.\n",
-      "plot_taylor_approximations(lambda x: 1/cos(x), 0, [2,4,6], (0, 2*pi), (-5,5))"
-     ],
-     "language": "python",
+     "data": {
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+       "QqFQSIoycIVCoZCU/wdDeJclXk9EegAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x109a2a8d0>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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FixfZtWuXYRqOHoVz56BJE6heXf9x6enp+Pn5eU+Yl5FFf3h4uMvHqghFoVAo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\nQqFQSIoycIVCoZCU/wdDeJclXk9EegAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x109a2a8d0>"
-       ]
-      }
-     ],
-     "prompt_number": 24
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "# For an expression made from elementary functions, we must first make it into\n",
+    "# a callable function, the simplest way is to use the Python lambda construct.\n",
+    "plot_taylor_approximations(lambda x: 1/cos(x), 0, [2,4,6], (0, 2*pi), (-5,5))"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:a0f4cda82587c5b8e7a4502192d1210abedac9084077604eb60aea15f262a344"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/Terminal Usage.ipynb b/examples/IPython Kernel/Terminal Usage.ipynb
index 5f4bb3e..aa63300 100644
--- a/examples/IPython Kernel/Terminal Usage.ipynb
+++ b/examples/IPython Kernel/Terminal Usage.ipynb
@@ -1,275 +1,269 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:993106eecfd7abe1920e1dbe670c4518189c26e7b29dcc541835f7dcf6fffbb2"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "A few things that work best/only at the IPython terminal or Qt console clients"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Running code with `%run`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%writefile script.py\n",
-      "x = 10\n",
-      "y = 20\n",
-      "z = x+y\n",
-      "print 'z is:', z"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Writing script.py\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%run script"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "z is: 30\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "10"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Event loop and GUI integration"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The `%gui` magic enables the integration of GUI event loops with the interactive execution loop, allowing you to run GUI code without blocking IPython.\n",
-      "\n",
-      "Consider for example the execution of Qt-based code. Once we enable the Qt gui support:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%gui qt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We can define a simple Qt application class (simplified version from [this Qt tutorial](http://zetcode.com/tutorials/pyqt4/firstprograms)):"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import sys\n",
-      "from PyQt4 import QtGui, QtCore\n",
-      "\n",
-      "class SimpleWindow(QtGui.QWidget):\n",
-      "    def __init__(self, parent=None):\n",
-      "        QtGui.QWidget.__init__(self, parent)\n",
-      "\n",
-      "        self.setGeometry(300, 300, 200, 80)\n",
-      "        self.setWindowTitle('Hello World')\n",
-      "\n",
-      "        quit = QtGui.QPushButton('Close', self)\n",
-      "        quit.setGeometry(10, 10, 60, 35)\n",
-      "\n",
-      "        self.connect(quit, QtCore.SIGNAL('clicked()'),\n",
-      "                     self, QtCore.SLOT('close()'))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And now we can instantiate it:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "app = QtCore.QCoreApplication.instance()\n",
-      "if app is None:\n",
-      "    app = QtGui.QApplication([])\n",
-      "\n",
-      "sw = SimpleWindow()\n",
-      "sw.show()\n",
-      "\n",
-      "from IPython.lib.guisupport import start_event_loop_qt4\n",
-      "start_event_loop_qt4(app)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "But IPython still remains responsive:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "10+2"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 7,
-       "text": [
-        "12"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A few things that work best/only at the IPython terminal or Qt console clients"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running code with `%run`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The `%gui` magic can be similarly used to control Wx, Tk, glut and pyglet applications, [as can be seen in our examples](https://github.com/ipython/ipython/tree/master/examples/lib)."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing script.py\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "%%writefile script.py\n",
+    "x = 10\n",
+    "y = 20\n",
+    "z = x+y\n",
+    "print 'z is:', z"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Embedding IPython in a terminal application"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z is: 30\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%writefile simple-embed.py\n",
-      "# This shows how to use the new top-level embed function.  It is a simpler\n",
-      "# API that manages the creation of the embedded shell.\n",
-      "\n",
-      "from IPython import embed\n",
-      "\n",
-      "a = 10\n",
-      "b = 20\n",
-      "\n",
-      "embed(header='First time', banner1='')\n",
-      "\n",
-      "c = 30\n",
-      "d = 40\n",
-      "\n",
-      "embed(header='The second time')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Writing simple-embed.py\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
+    }
+   ],
+   "source": [
+    "%run script"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 3,
      "metadata": {},
-     "source": [
-      "The example in kernel-embedding shows how to embed a full kernel into an application and how to connect to this kernel from an external process."
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Event loop and GUI integration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `%gui` magic enables the integration of GUI event loops with the interactive execution loop, allowing you to run GUI code without blocking IPython.\n",
+    "\n",
+    "Consider for example the execution of Qt-based code. Once we enable the Qt gui support:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%gui qt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can define a simple Qt application class (simplified version from [this Qt tutorial](http://zetcode.com/tutorials/pyqt4/firstprograms)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "from PyQt4 import QtGui, QtCore\n",
+    "\n",
+    "class SimpleWindow(QtGui.QWidget):\n",
+    "    def __init__(self, parent=None):\n",
+    "        QtGui.QWidget.__init__(self, parent)\n",
+    "\n",
+    "        self.setGeometry(300, 300, 200, 80)\n",
+    "        self.setWindowTitle('Hello World')\n",
+    "\n",
+    "        quit = QtGui.QPushButton('Close', self)\n",
+    "        quit.setGeometry(10, 10, 60, 35)\n",
+    "\n",
+    "        self.connect(quit, QtCore.SIGNAL('clicked()'),\n",
+    "                     self, QtCore.SLOT('close()'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And now we can instantiate it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "app = QtCore.QCoreApplication.instance()\n",
+    "if app is None:\n",
+    "    app = QtGui.QApplication([])\n",
+    "\n",
+    "sw = SimpleWindow()\n",
+    "sw.show()\n",
+    "\n",
+    "from IPython.lib.guisupport import start_event_loop_qt4\n",
+    "start_event_loop_qt4(app)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But IPython still remains responsive:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 7,
      "metadata": {},
-     "source": [
-      "Logging terminal sessions and transitioning to a notebook"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10+2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `%gui` magic can be similarly used to control Wx, Tk, glut and pyglet applications, [as can be seen in our examples](https://github.com/ipython/ipython/tree/master/examples/lib)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Embedding IPython in a terminal application"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The `%logstart` magic lets you log a terminal session with various degrees of control, and the `%notebook` one will convert an interactive console session into a notebook with all input cells already created for you (but no output)."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing simple-embed.py\n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "%%writefile simple-embed.py\n",
+    "# This shows how to use the new top-level embed function.  It is a simpler\n",
+    "# API that manages the creation of the embedded shell.\n",
+    "\n",
+    "from IPython import embed\n",
+    "\n",
+    "a = 10\n",
+    "b = 20\n",
+    "\n",
+    "embed(header='First time', banner1='')\n",
+    "\n",
+    "c = 30\n",
+    "d = 40\n",
+    "\n",
+    "embed(header='The second time')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The example in kernel-embedding shows how to embed a full kernel into an application and how to connect to this kernel from an external process."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Logging terminal sessions and transitioning to a notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `%logstart` magic lets you log a terminal session with various degrees of control, and the `%notebook` one will convert an interactive console session into a notebook with all input cells already created for you (but no output)."
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:993106eecfd7abe1920e1dbe670c4518189c26e7b29dcc541835f7dcf6fffbb2"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/Third Party Rich Output.ipynb b/examples/IPython Kernel/Third Party Rich Output.ipynb
index ac66805..1e6cb00 100644
--- a/examples/IPython Kernel/Third Party Rich Output.ipynb
+++ b/examples/IPython Kernel/Third Party Rich Output.ipynb
@@ -1,523 +1,525 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:123d82ef0551f78e5dca94db6e00f1e10ae07d930467cf44709ccc6a9216776a"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Third Party Libraries With Rich Output"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "A number of third party libraries defined their own custom display logic. This gives their objcts rich output by default when used in the Notebook."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import display"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Pandas"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Pandas](http://pandas.pydata.org/) is a data analysis library for Python. Its `DataFrame` objects have an HTML table representation in the Notebook."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import pandas"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is a small amount of stock data for APPL:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%writefile data.csv\n",
-      "Date,Open,High,Low,Close,Volume,Adj Close\n",
-      "2012-06-01,569.16,590.00,548.50,584.00,14077000,581.50\n",
-      "2012-05-01,584.90,596.76,522.18,577.73,18827900,575.26\n",
-      "2012-04-02,601.83,644.00,555.00,583.98,28759100,581.48\n",
-      "2012-03-01,548.17,621.45,516.22,599.55,26486000,596.99\n",
-      "2012-02-01,458.41,547.61,453.98,542.44,22001000,540.12\n",
-      "2012-01-03,409.40,458.24,409.00,456.48,12949100,454.53"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Writing data.csv\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Read this as into a `DataFrame`:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "df = pandas.read_csv('data.csv')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And view the HTML representation:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "df"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>Date</th>\n",
-        "      <th>Open</th>\n",
-        "      <th>High</th>\n",
-        "      <th>Low</th>\n",
-        "      <th>Close</th>\n",
-        "      <th>Volume</th>\n",
-        "      <th>Adj Close</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>0</th>\n",
-        "      <td> 2012-06-01</td>\n",
-        "      <td> 569.16</td>\n",
-        "      <td> 590.00</td>\n",
-        "      <td> 548.50</td>\n",
-        "      <td> 584.00</td>\n",
-        "      <td> 14077000</td>\n",
-        "      <td> 581.50</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>1</th>\n",
-        "      <td> 2012-05-01</td>\n",
-        "      <td> 584.90</td>\n",
-        "      <td> 596.76</td>\n",
-        "      <td> 522.18</td>\n",
-        "      <td> 577.73</td>\n",
-        "      <td> 18827900</td>\n",
-        "      <td> 575.26</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>2</th>\n",
-        "      <td> 2012-04-02</td>\n",
-        "      <td> 601.83</td>\n",
-        "      <td> 644.00</td>\n",
-        "      <td> 555.00</td>\n",
-        "      <td> 583.98</td>\n",
-        "      <td> 28759100</td>\n",
-        "      <td> 581.48</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>3</th>\n",
-        "      <td> 2012-03-01</td>\n",
-        "      <td> 548.17</td>\n",
-        "      <td> 621.45</td>\n",
-        "      <td> 516.22</td>\n",
-        "      <td> 599.55</td>\n",
-        "      <td> 26486000</td>\n",
-        "      <td> 596.99</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>4</th>\n",
-        "      <td> 2012-02-01</td>\n",
-        "      <td> 458.41</td>\n",
-        "      <td> 547.61</td>\n",
-        "      <td> 453.98</td>\n",
-        "      <td> 542.44</td>\n",
-        "      <td> 22001000</td>\n",
-        "      <td> 540.12</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>5</th>\n",
-        "      <td> 2012-01-03</td>\n",
-        "      <td> 409.40</td>\n",
-        "      <td> 458.24</td>\n",
-        "      <td> 409.00</td>\n",
-        "      <td> 456.48</td>\n",
-        "      <td> 12949100</td>\n",
-        "      <td> 454.53</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "<p>6 rows \u00d7 7 columns</p>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 12,
-       "text": [
-        "         Date    Open    High     Low   Close    Volume  Adj Close\n",
-        "0  2012-06-01  569.16  590.00  548.50  584.00  14077000     581.50\n",
-        "1  2012-05-01  584.90  596.76  522.18  577.73  18827900     575.26\n",
-        "2  2012-04-02  601.83  644.00  555.00  583.98  28759100     581.48\n",
-        "3  2012-03-01  548.17  621.45  516.22  599.55  26486000     596.99\n",
-        "4  2012-02-01  458.41  547.61  453.98  542.44  22001000     540.12\n",
-        "5  2012-01-03  409.40  458.24  409.00  456.48  12949100     454.53\n",
-        "\n",
-        "[6 rows x 7 columns]"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "SymPy"
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Third Party Libraries With Rich Output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A number of third party libraries defined their own custom display logic. This gives their objcts rich output by default when used in the Notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import display"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Pandas](http://pandas.pydata.org/) is a data analysis library for Python. Its `DataFrame` objects have an HTML table representation in the Notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is a small amount of stock data for APPL:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[SymPy](http://sympy.org/) is a symbolic computing library for Python. Its equation objects have LaTeX representations that are rendered in the Notebook."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing data.csv\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from sympy.interactive.printing import init_printing\n",
-      "init_printing(use_latex='mathjax')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from __future__ import division\n",
-      "import sympy as sym\n",
-      "from sympy import *\n",
-      "x, y, z = symbols(\"x y z\")\n",
-      "k, m, n = symbols(\"k m n\", integer=True)\n",
-      "f, g, h = map(Function, 'fgh')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "Rational(3,2)*pi + exp(I*x) / (x**2 + y)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\frac{3 \\pi}{2} + \\frac{e^{i x}}{x^{2} + y}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 15,
-       "text": [
-        "        \u2148\u22c5x \n",
-        "3\u22c5\u03c0    \u212f    \n",
-        "\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500\u2500\u2500\n",
-        " 2     2    \n",
-        "      x  + y"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = 1/x + (x*sin(x) - 1)/x\n",
-      "a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$\\frac{1}{x} \\left(x \\sin{\\left (x \\right )} - 1\\right) + \\frac{1}{x}$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 16,
-       "text": [
-        "x\u22c5sin(x) - 1   1\n",
-        "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\n",
-        "     x         x"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "(1/cos(x)).series(x, 0, 6)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$1 + \\frac{x^{2}}{2} + \\frac{5 x^{4}}{24} + \\mathcal{O}\\left(x^{6}\\right)$$"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 17,
-       "text": [
-        "     2      4        \n",
-        "    x    5\u22c5x     \u239b 6\u239e\n",
-        "1 + \u2500\u2500 + \u2500\u2500\u2500\u2500 + O\u239dx \u23a0\n",
-        "    2     24         "
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
+    }
+   ],
+   "source": [
+    "%%writefile data.csv\n",
+    "Date,Open,High,Low,Close,Volume,Adj Close\n",
+    "2012-06-01,569.16,590.00,548.50,584.00,14077000,581.50\n",
+    "2012-05-01,584.90,596.76,522.18,577.73,18827900,575.26\n",
+    "2012-04-02,601.83,644.00,555.00,583.98,28759100,581.48\n",
+    "2012-03-01,548.17,621.45,516.22,599.55,26486000,596.99\n",
+    "2012-02-01,458.41,547.61,453.98,542.44,22001000,540.12\n",
+    "2012-01-03,409.40,458.24,409.00,456.48,12949100,454.53"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read this as into a `DataFrame`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df = pandas.read_csv('data.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And view the HTML representation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Open</th>\n",
+       "      <th>High</th>\n",
+       "      <th>Low</th>\n",
+       "      <th>Close</th>\n",
+       "      <th>Volume</th>\n",
+       "      <th>Adj Close</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td> 2012-06-01</td>\n",
+       "      <td> 569.16</td>\n",
+       "      <td> 590.00</td>\n",
+       "      <td> 548.50</td>\n",
+       "      <td> 584.00</td>\n",
+       "      <td> 14077000</td>\n",
+       "      <td> 581.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td> 2012-05-01</td>\n",
+       "      <td> 584.90</td>\n",
+       "      <td> 596.76</td>\n",
+       "      <td> 522.18</td>\n",
+       "      <td> 577.73</td>\n",
+       "      <td> 18827900</td>\n",
+       "      <td> 575.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td> 2012-04-02</td>\n",
+       "      <td> 601.83</td>\n",
+       "      <td> 644.00</td>\n",
+       "      <td> 555.00</td>\n",
+       "      <td> 583.98</td>\n",
+       "      <td> 28759100</td>\n",
+       "      <td> 581.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td> 2012-03-01</td>\n",
+       "      <td> 548.17</td>\n",
+       "      <td> 621.45</td>\n",
+       "      <td> 516.22</td>\n",
+       "      <td> 599.55</td>\n",
+       "      <td> 26486000</td>\n",
+       "      <td> 596.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td> 2012-02-01</td>\n",
+       "      <td> 458.41</td>\n",
+       "      <td> 547.61</td>\n",
+       "      <td> 453.98</td>\n",
+       "      <td> 542.44</td>\n",
+       "      <td> 22001000</td>\n",
+       "      <td> 540.12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td> 2012-01-03</td>\n",
+       "      <td> 409.40</td>\n",
+       "      <td> 458.24</td>\n",
+       "      <td> 409.00</td>\n",
+       "      <td> 456.48</td>\n",
+       "      <td> 12949100</td>\n",
+       "      <td> 454.53</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6 rows \u00d7 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date    Open    High     Low   Close    Volume  Adj Close\n",
+       "0  2012-06-01  569.16  590.00  548.50  584.00  14077000     581.50\n",
+       "1  2012-05-01  584.90  596.76  522.18  577.73  18827900     575.26\n",
+       "2  2012-04-02  601.83  644.00  555.00  583.98  28759100     581.48\n",
+       "3  2012-03-01  548.17  621.45  516.22  599.55  26486000     596.99\n",
+       "4  2012-02-01  458.41  547.61  453.98  542.44  22001000     540.12\n",
+       "5  2012-01-03  409.40  458.24  409.00  456.48  12949100     454.53\n",
+       "\n",
+       "[6 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 12,
      "metadata": {},
-     "source": [
-      "Vincent"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SymPy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[SymPy](http://sympy.org/) is a symbolic computing library for Python. Its equation objects have LaTeX representations that are rendered in the Notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sympy.interactive.printing import init_printing\n",
+    "init_printing(use_latex='mathjax')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import division\n",
+    "import sympy as sym\n",
+    "from sympy import *\n",
+    "x, y, z = symbols(\"x y z\")\n",
+    "k, m, n = symbols(\"k m n\", integer=True)\n",
+    "f, g, h = map(Function, 'fgh')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/latex": [
+       "$$\\frac{3 \\pi}{2} + \\frac{e^{i x}}{x^{2} + y}$$"
+      ],
+      "text/plain": [
+       "        \u2148\u22c5x \n",
+       "3\u22c5\u03c0    \u212f    \n",
+       "\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500\u2500\u2500\n",
+       " 2     2    \n",
+       "      x  + y"
+      ]
+     },
+     "execution_count": 15,
      "metadata": {},
-     "source": [
-      "[Vincent](https://vincent.readthedocs.org/en/latest/) is a visualization library that uses the [Vega](http://trifacta.github.io/vega/) visualization grammar to build [d3.js](http://d3js.org/) based visualizations in the Notebook and on http://nbviewer.ipython.org. `Visualization` objects in Vincetn have rich HTML and JavaSrcript representations."
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Rational(3,2)*pi + exp(I*x) / (x**2 + y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import vincent\n",
-      "import pandas as pd"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$\\frac{1}{x} \\left(x \\sin{\\left (x \\right )} - 1\\right) + \\frac{1}{x}$$"
+      ],
+      "text/plain": [
+       "x\u22c5sin(x) - 1   1\n",
+       "\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\n",
+       "     x         x"
+      ]
+     },
+     "execution_count": 16,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = 1/x + (x*sin(x) - 1)/x\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import pandas.io.data as web\n",
-      "import datetime\n",
-      "all_data = {}\n",
-      "date_start = datetime.datetime(2010, 1, 1)\n",
-      "date_end = datetime.datetime(2014, 1, 1)\n",
-      "for ticker in ['AAPL', 'IBM', 'YHOO', 'MSFT']:\n",
-      "    all_data[ticker] = web.DataReader(ticker, 'yahoo', date_start, date_end)\n",
-      "price = pd.DataFrame({tic: data['Adj Close']\n",
-      "                      for tic, data in all_data.items()})"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$1 + \\frac{x^{2}}{2} + \\frac{5 x^{4}}{24} + \\mathcal{O}\\left(x^{6}\\right)$$"
+      ],
+      "text/plain": [
+       "     2      4        \n",
+       "    x    5\u22c5x     \u239b 6\u239e\n",
+       "1 + \u2500\u2500 + \u2500\u2500\u2500\u2500 + O\u239dx \u23a0\n",
+       "    2     24         "
+      ]
+     },
+     "execution_count": 17,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(1/cos(x)).series(x, 0, 6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Vincent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Vincent](https://vincent.readthedocs.org/en/latest/) is a visualization library that uses the [Vega](http://trifacta.github.io/vega/) visualization grammar to build [d3.js](http://d3js.org/) based visualizations in the Notebook and on http://nbviewer.ipython.org. `Visualization` objects in Vincetn have rich HTML and JavaSrcript representations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import vincent\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pandas.io.data as web\n",
+    "import datetime\n",
+    "all_data = {}\n",
+    "date_start = datetime.datetime(2010, 1, 1)\n",
+    "date_end = datetime.datetime(2014, 1, 1)\n",
+    "for ticker in ['AAPL', 'IBM', 'YHOO', 'MSFT']:\n",
+    "    all_data[ticker] = web.DataReader(ticker, 'yahoo', date_start, date_end)\n",
+    "price = pd.DataFrame({tic: data['Adj Close']\n",
+    "                      for tic, data in all_data.items()})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "vincent.initialize_notebook()"
-     ],
-     "language": "python",
+     "data": {
+      "text/html": [
+       "\n",
+       "           <script>\n",
+       "               \n",
+       "                function vct_load_lib(url, callback){\n",
+       "                      if(typeof d3 !== 'undefined' &&\n",
+       "                         url === 'http://d3js.org/d3.v3.min.js'){\n",
+       "                        callback()\n",
+       "                      }\n",
+       "                      var s = document.createElement('script');\n",
+       "                      s.src = url;\n",
+       "                      s.async = true;\n",
+       "                      s.onreadystatechange = s.onload = callback;\n",
+       "                      s.onerror = function(){\n",
+       "                        console.warn(\"failed to load library \" + url);\n",
+       "                        };\n",
+       "                      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "                };\n",
+       "                var vincent_event = new CustomEvent(\n",
+       "                  \"vincent_libs_loaded\",\n",
+       "                  {bubbles: true, cancelable: true}\n",
+       "                );\n",
+       "                \n",
+       "               function load_all_libs(){\n",
+       "                  console.log('loading all libs')\n",
+       "                  vct_load_lib('http://d3js.org/d3.v3.min.js', function(){\n",
+       "                  vct_load_lib('http://d3js.org/d3.geo.projection.v0.min.js', function(){\n",
+       "                  vct_load_lib('http://wrobstory.github.io/d3-cloud/d3.layout.cloud.js', function(){\n",
+       "                  vct_load_lib('http://trifacta.github.com/vega/vega.js', function(){\n",
+       "                  window.dispatchEvent(vincent_event);\n",
+       "                  });\n",
+       "                  });\n",
+       "                  });\n",
+       "                  });\n",
+       "               };\n",
+       "               if(typeof define === \"function\" && define.amd){\n",
+       "                    if (window['d3'] === undefined ||\n",
+       "                        window['topojson'] === undefined){\n",
+       "                        require.config(\n",
+       "                            {paths: {\n",
+       "                              d3: 'http://d3js.org/d3.v3.min',\n",
+       "                              topojson: 'http://d3js.org/topojson.v1.min'\n",
+       "                              }\n",
+       "                            }\n",
+       "                          );\n",
+       "                        require([\"d3\"], function(d3){\n",
+       "                            console.log('Loading from require.js...')\n",
+       "                            window.d3 = d3;\n",
+       "                            require([\"topojson\"], function(topojson){\n",
+       "                                window.topojson = topojson;\n",
+       "                                load_all_libs();\n",
+       "                            });\n",
+       "                        });\n",
+       "                    };\n",
+       "               }else{\n",
+       "                    console.log('Require.js not found, loading manually...')\n",
+       "                    load_all_libs();\n",
+       "               };\n",
+       "\n",
+       "           </script>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "\n",
-        "           <script>\n",
-        "               \n",
-        "                function vct_load_lib(url, callback){\n",
-        "                      if(typeof d3 !== 'undefined' &&\n",
-        "                         url === 'http://d3js.org/d3.v3.min.js'){\n",
-        "                        callback()\n",
-        "                      }\n",
-        "                      var s = document.createElement('script');\n",
-        "                      s.src = url;\n",
-        "                      s.async = true;\n",
-        "                      s.onreadystatechange = s.onload = callback;\n",
-        "                      s.onerror = function(){\n",
-        "                        console.warn(\"failed to load library \" + url);\n",
-        "                        };\n",
-        "                      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
-        "                };\n",
-        "                var vincent_event = new CustomEvent(\n",
-        "                  \"vincent_libs_loaded\",\n",
-        "                  {bubbles: true, cancelable: true}\n",
-        "                );\n",
-        "                \n",
-        "               function load_all_libs(){\n",
-        "                  console.log('loading all libs')\n",
-        "                  vct_load_lib('http://d3js.org/d3.v3.min.js', function(){\n",
-        "                  vct_load_lib('http://d3js.org/d3.geo.projection.v0.min.js', function(){\n",
-        "                  vct_load_lib('http://wrobstory.github.io/d3-cloud/d3.layout.cloud.js', function(){\n",
-        "                  vct_load_lib('http://trifacta.github.com/vega/vega.js', function(){\n",
-        "                  window.dispatchEvent(vincent_event);\n",
-        "                  });\n",
-        "                  });\n",
-        "                  });\n",
-        "                  });\n",
-        "               };\n",
-        "               if(typeof define === \"function\" && define.amd){\n",
-        "                    if (window['d3'] === undefined ||\n",
-        "                        window['topojson'] === undefined){\n",
-        "                        require.config(\n",
-        "                            {paths: {\n",
-        "                              d3: 'http://d3js.org/d3.v3.min',\n",
-        "                              topojson: 'http://d3js.org/topojson.v1.min'\n",
-        "                              }\n",
-        "                            }\n",
-        "                          );\n",
-        "                        require([\"d3\"], function(d3){\n",
-        "                            console.log('Loading from require.js...')\n",
-        "                            window.d3 = d3;\n",
-        "                            require([\"topojson\"], function(topojson){\n",
-        "                                window.topojson = topojson;\n",
-        "                                load_all_libs();\n",
-        "                            });\n",
-        "                        });\n",
-        "                    };\n",
-        "               }else{\n",
-        "                    console.log('Require.js not found, loading manually...')\n",
-        "                    load_all_libs();\n",
-        "               };\n",
-        "\n",
-        "           </script>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.HTML object>"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vincent.initialize_notebook()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "line = vincent.Line(price[['AAPL', 'IBM', 'YHOO', 'MSFT']], width=600, height=300)\n",
-      "line.axis_titles(x='Date', y='Price')\n",
-      "line.legend(title='Ticker')\n",
-      "display(line)"
-     ],
-     "language": "python",
+     "data": {
+      "text/html": [
+       "<div id=\"vis47fdfca404f24684b44753131b44ed27\"></div>\n",
+       "<script>\n",
+       "   ( function() {\n",
+       "     var _do_plot = function() {\n",
+       "       if (typeof vg === 'undefined') {\n",
+       "         window.addEventListener('vincent_libs_loaded', _do_plot)\n",
+       "         return;\n",
+       "       }\n",
+       "       vg.parse.spec({\"axes\": [{\"scale\": \"x\", \"title\": \"Date\", \"type\": \"x\"}, {\"scale\": \"y\", \"title\": \"Price\", \"type\": \"y\"}], \"data\": [{\"name\": \"table\", \"values\": [{\"col\": \"AAPL\", \"idx\": 1262592000000, \"val\": 205.7}, {\"col\": \"IBM\", \"idx\": 1262592000000, \"val\": 122.62}, {\"col\": \"YHOO\", \"idx\": 1262592000000, \"val\": 17.1}, {\"col\": \"MSFT\", \"idx\": 1262592000000, \"val\": 27.67}, {\"col\": \"AAPL\", \"idx\": 1262678400000, \"val\": 206.05}, {\"col\": \"IBM\", \"idx\": 1262678400000, \"val\": 121.14}, {\"col\": \"YHOO\", \"idx\": 1262678400000, \"val\": 17.23}, {\"col\": \"MSFT\", \"idx\": 1262678400000, \"val\": 27.68}, {\"col\": \"AAPL\", \"idx\": 1262764800000, \"val\": 202.77}, {\"col\": \"IBM\", \"idx\": 1262764800000, \"val\": 120.35}, {\"col\": \"YHOO\", \"idx\": 1262764800000, \"val\": 17.17}, {\"col\": \"MSFT\", \"idx\": 1262764800000, \"val\": 27.51}, {\"col\": \"AAPL\", \"idx\": 1262851200000, \"val\": 202.4}, 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\"idx\": 1388131200000, \"val\": 37.01}, {\"col\": \"AAPL\", \"idx\": 1388390400000, \"val\": 551.22}, {\"col\": \"IBM\", \"idx\": 1388390400000, \"val\": 185.39}, {\"col\": \"YHOO\", \"idx\": 1388390400000, \"val\": 40.2}, {\"col\": \"MSFT\", \"idx\": 1388390400000, \"val\": 37.01}, {\"col\": \"AAPL\", \"idx\": 1388476800000, \"val\": 557.68}, {\"col\": \"IBM\", \"idx\": 1388476800000, \"val\": 186.55}, {\"col\": \"YHOO\", \"idx\": 1388476800000, \"val\": 40.44}, {\"col\": \"MSFT\", \"idx\": 1388476800000, \"val\": 37.13}]}], \"height\": 300, \"legends\": [{\"fill\": \"color\", \"offset\": 0, \"properties\": {}, \"title\": \"Ticker\"}], \"marks\": [{\"from\": {\"data\": \"table\", \"transform\": [{\"keys\": [\"data.col\"], \"type\": \"facet\"}]}, \"marks\": [{\"properties\": {\"enter\": {\"stroke\": {\"field\": \"data.col\", \"scale\": \"color\"}, \"strokeWidth\": {\"value\": 2}, \"x\": {\"field\": \"data.idx\", \"scale\": \"x\"}, \"y\": {\"field\": \"data.val\", \"scale\": \"y\"}}}, \"type\": \"line\"}], \"type\": \"group\"}], \"padding\": \"auto\", \"scales\": [{\"domain\": {\"data\": \"table\", \"field\": \"data.idx\"}, \"name\": \"x\", \"range\": \"width\", \"type\": \"time\"}, {\"domain\": {\"data\": \"table\", \"field\": \"data.val\"}, \"name\": \"y\", \"nice\": true, \"range\": \"height\"}, {\"domain\": {\"data\": \"table\", \"field\": \"data.col\"}, \"name\": \"color\", \"range\": \"category20\", \"type\": \"ordinal\"}], \"width\": 600}, function(chart) {\n",
-        "         chart({el: \"#vis47fdfca404f24684b44753131b44ed27\"}).update();\n",
-        "       });\n",
-        "     };\n",
-        "     _do_plot();\n",
-        "   })();\n",
-        "</script>\n",
-        "<style>.vega canvas {width: 100%;}</style>\n",
-        "        "
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<vincent.charts.Line at 0x10290f710>"
-       ]
-      }
-     ],
-     "prompt_number": 10
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "line = vincent.Line(price[['AAPL', 'IBM', 'YHOO', 'MSFT']], width=600, height=300)\n",
+    "line.axis_titles(x='Date', y='Price')\n",
+    "line.legend(title='Ticker')\n",
+    "display(line)"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:123d82ef0551f78e5dca94db6e00f1e10ae07d930467cf44709ccc6a9216776a"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/Trapezoid Rule.ipynb b/examples/IPython Kernel/Trapezoid Rule.ipynb
index d82e90b..e73bbb3 100644
--- a/examples/IPython Kernel/Trapezoid Rule.ipynb
+++ b/examples/IPython Kernel/Trapezoid Rule.ipynb
@@ -1,145 +1,382 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:cbf49a9ceac0258773ae0a652e9ac7c13e0d042debbf0115cdc081862b5f7308"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Basic Numerical Integration: the Trapezoid Rule"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "A simple illustration of the trapezoid rule for definite integration:\n",
-      "\n",
-      "$$\n",
-      "\\int_{a}^{b} f(x)\\, dx \\approx \\frac{1}{2} \\sum_{k=1}^{N} \\left( x_{k} - x_{k-1} \\right) \\left( f(x_{k}) + f(x_{k-1}) \\right).\n",
-      "$$\n",
-      "<br>\n",
-      "First, we define a simple function and sample it between 0 and 10 at 200 points"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline\n",
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "def f(x):\n",
-      "    return (x-3)*(x-5)*(x-7)+85\n",
-      "\n",
-      "x = np.linspace(0, 10, 200)\n",
-      "y = f(x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Choose a region to integrate over and take only a few points in that region"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "a, b = 1, 9\n",
-      "xint = x[np.logical_and(x>=a, x<=b)][::30]\n",
-      "yint = y[np.logical_and(x>=a, x<=b)][::30]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Plot both the function and the area below it in the trapezoid approximation"
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Basic Numerical Integration: the Trapezoid Rule"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A simple illustration of the trapezoid rule for definite integration:\n",
+    "\n",
+    "$$\n",
+    "\\int_{a}^{b} f(x)\\, dx \\approx \\frac{1}{2} \\sum_{k=1}^{N} \\left( x_{k} - x_{k-1} \\right) \\left( f(x_{k}) + f(x_{k-1}) \\right).\n",
+    "$$\n",
+    "<br>\n",
+    "First, we define a simple function and sample it between 0 and 10 at 200 points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def f(x):\n",
+    "    return (x-3)*(x-5)*(x-7)+85\n",
+    "\n",
+    "x = np.linspace(0, 10, 200)\n",
+    "y = f(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Choose a region to integrate over and take only a few points in that region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "a, b = 1, 9\n",
+    "xint = x[np.logical_and(x>=a, x<=b)][::30]\n",
+    "yint = y[np.logical_and(x>=a, x<=b)][::30]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot both the function and the area below it in the trapezoid approximation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.plot(x, y, lw=2)\n",
-      "plt.axis([0, 10, 0, 140])\n",
-      "plt.fill_between(xint, 0, yint, facecolor='gray', alpha=0.4)\n",
-      "plt.text(0.5 * (a + b), 30,r\"$\\int_a^b f(x)dx$\", horizontalalignment='center', fontsize=20);"
-     ],
-     "language": "python",
+     "data": {
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+       "Hk1gYCBDhgxhzZo1WpekuaqqKsLCwpg4caLWpWgqPz+fKVOm4O/vT0BAAAcPHtS6JM0sX76cwMBA\n",
+       "goKC+N3vfofRaNS6pDYze/ZsDAYDQUFBtY/l5eURERGBr68vY8eOJT8/v8nPaLNwN+cFTu2dXq/n\n",
+       "nXfeIS4ujoMHD/Lee+912u+ixurVqwkICDDbiKv2at68eYwfP574+HhOnjzZaduaKSkpvP/++xw7\n",
+       "doxTp05RVVXFxo0btS6rzcyaNYuYmJgGj0VGRhIREUFCQgJjxowhMjKyyc9os3A35wVO7Z27uzuh\n",
+       "oaEAODo64u/vT2ZmpsZVaSc9PZ3o6GieeOIJOnOXsKCggH379jF79mxAPYfl3BpjgdsBJycn9Ho9\n",
+       "JSUlVFZWUlJSgmcnmj3wjjvuwNXVtcFj27dvZ+bMmQDMnDmTrVu3NvkZbRbu5rrAqaNJSUnh+PHj\n",
+       "jBw5UutSNLNgwQJWrlyJVSdfQzE5OZmePXsya9Yshg4dypNPPklJSYnWZWmie/fuPPfcc/Tt2xcP\n",
+       "Dw9cXFz47W9/q3VZmsrJycFgMABgMBjIyclp8vVt9q+ps/+53ZiioiKmTJnC6tWrcXR01LocTezY\n",
+       "sYNevXoRFhbWqY/aQR1KfOzYMebMmcOxY8dwcHD41T+9O6rExERWrVpFSkoKmZmZFBUV8WnNNLEC\n",
+       "nU73q5naZuHu6elJWlpa7c9paWl4eXm11e4tTkVFBQ8++CCPPvookydP1roczezfv5/t27fTv39/\n",
+       "pk2bxp49e3jssce0LksTXl5eeHl5MXz4cACmTJnSaa9UPXLkCKNGjaJHjx7Y2NjwwAMPsH//fq3L\n",
+       "0pTBYCA7OxtQr9zt1atXk69vs3AfNmwY58+fJyUlhfLycjZt2sSkTjpxjKIoPP744wQEBDB//nyt\n",
+       "y9HUsmXLSEtLIzk5mY0bN3LXXXfx73//W+uyNOHu7k6fPn1ISEgAYNeuXQR20oWD/fz8OHjwIKWl\n",
+       "pSiKwq5duwgICNC6LE1NmjSJqKgoAKKion79oLBF8w00U3R0tOLr66sMHDhQWbZsWVvu2qLs27dP\n",
+       "0el0SkhIiBIaGqqEhoYqX3/9tdZlaS42NlaZOHGi1mVo6sSJE8qwYcOU4OBg5f7771fy8/O1Lkkz\n",
+       "K1asUAICApQhQ4Yojz32mFJeXq51SW3mkUceUXr37q3o9XrFy8tLWb9+vZKbm6uMGTNGGTRokBIR\n",
+       "EaFcvXq1yc+Qi5iEEKID6tzDE4QQooOScBdCiA5Iwl0IITogCXchhOiAJNyFEKIDknAXQogOSMJd\n",
+       "CCE6IAl3IYTogP4/oENi/a9Z/A8AAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x107d67b90>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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Mmer24sUNg72mz963b18JdiFEmzFpDdXKykqOHTvG3//+d4YPH878+fOva8Ho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\nCCE6IAl3IYTogP4/oENi/a9Z/A8AAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x107d67b90>"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x, y, lw=2)\n",
+    "plt.axis([0, 10, 0, 140])\n",
+    "plt.fill_between(xint, 0, yint, facecolor='gray', alpha=0.4)\n",
+    "plt.text(0.5 * (a + b), 30,r\"$\\int_a^b f(x)dx$\", horizontalalignment='center', fontsize=20);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute the integral both at high accuracy and with the trapezoid approximation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Compute the integral both at high accuracy and with the trapezoid approximation"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The integral is: 680.0 +/- 7.54951656745e-12\n",
+      "The trapezoid approximation with 6 points is: 621.286411141\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from __future__ import print_function\n",
-      "from scipy.integrate import quad, trapz\n",
-      "integral, error = quad(f, 1, 9)\n",
-      "print(\"The integral is:\", integral, \"+/-\", error)\n",
-      "print(\"The trapezoid approximation with\", len(xint), \"points is:\", trapz(yint, xint))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "The integral is: 680.0 +/- 7.54951656745e-12\n",
-        "The trapezoid approximation with 6 points is: 621.286411141\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
     }
    ],
-   "metadata": {}
+   "source": [
+    "from __future__ import print_function\n",
+    "from scipy.integrate import quad, trapz\n",
+    "integral, error = quad(f, 1, 9)\n",
+    "print(\"The integral is:\", integral, \"+/-\", error)\n",
+    "print(\"The trapezoid approximation with\", len(xint), \"points is:\", trapz(yint, xint))"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:cbf49a9ceac0258773ae0a652e9ac7c13e0d042debbf0115cdc081862b5f7308"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/IPython Kernel/Working With External Code.ipynb b/examples/IPython Kernel/Working With External Code.ipynb
index 759a530..243ca1b 100644
--- a/examples/IPython Kernel/Working With External Code.ipynb
+++ b/examples/IPython Kernel/Working With External Code.ipynb
@@ -1,184 +1,341 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:4352d4e1c693d919ce40b29ecf5a536917160df68b19a85caccedb1ea7ad06e1"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Working With External Code"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The IPython Kernel makes it easy to incorporate external code from sources such as the internet or copy/paste. "
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Pasting code into cells"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can copy and paste code from other sources directly into cells. Pasting code with `>>>` prompts works as expected:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      ">>> the_world_is_flat = 1\n",
-      ">>> if the_world_is_flat:\n",
-      "...     print(\"Be careful not to fall off!\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Be careful not to fall off!\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "The %load magic"
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Working With External Code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The IPython Kernel makes it easy to incorporate external code from sources such as the internet or copy/paste. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pasting code into cells"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can copy and paste code from other sources directly into cells. Pasting code with `>>>` prompts works as expected:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The `%load` magic lets you load code from URLs or local files:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Be careful not to fall off!\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load?"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load http://matplotlib.org/mpl_examples/showcase/integral_demo.py"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
-    },
+    }
+   ],
+   "source": [
+    ">>> the_world_is_flat = 1\n",
+    ">>> if the_world_is_flat:\n",
+    "...     print(\"Be careful not to fall off!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The %load magic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `%load` magic lets you load code from URLs or local files:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%load?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%load http://matplotlib.org/mpl_examples/showcase/integral_demo.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "\"\"\"\n",
-      "Plot demonstrating the integral as the area under a curve.\n",
-      "\n",
-      "Although this is a simple example, it demonstrates some important tweaks:\n",
-      "\n",
-      "    * A simple line plot with custom color and line width.\n",
-      "    * A shaded region created using a Polygon patch.\n",
-      "    * A text label with mathtext rendering.\n",
-      "    * figtext calls to label the x- and y-axes.\n",
-      "    * Use of axis spines to hide the top and right spines.\n",
-      "    * Custom tick placement and labels.\n",
-      "\"\"\"\n",
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "from matplotlib.patches import Polygon\n",
-      "\n",
-      "\n",
-      "def func(x):\n",
-      "    return (x - 3) * (x - 5) * (x - 7) + 85\n",
-      "\n",
-      "\n",
-      "a, b = 2, 9 # integral limits\n",
-      "x = np.linspace(0, 10)\n",
-      "y = func(x)\n",
-      "\n",
-      "fig, ax = plt.subplots()\n",
-      "plt.plot(x, y, 'r', linewidth=2)\n",
-      "plt.ylim(ymin=0)\n",
-      "\n",
-      "# Make the shaded region\n",
-      "ix = np.linspace(a, b)\n",
-      "iy = func(ix)\n",
-      "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n",
-      "poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\n",
-      "ax.add_patch(poly)\n",
-      "\n",
-      "plt.text(0.5 * (a + b), 30, r\"$\\int_a^b f(x)\\mathrm{d}x$\",\n",
-      "         horizontalalignment='center', fontsize=20)\n",
-      "\n",
-      "plt.figtext(0.9, 0.05, '$x$')\n",
-      "plt.figtext(0.1, 0.9, '$y$')\n",
-      "\n",
-      "ax.spines['right'].set_visible(False)\n",
-      "ax.spines['top'].set_visible(False)\n",
-      "ax.xaxis.set_ticks_position('bottom')\n",
-      "\n",
-      "ax.set_xticks((a, b))\n",
-      "ax.set_xticklabels(('$a$', '$b$'))\n",
-      "ax.set_yticks([])\n",
-      "\n",
-      "plt.show()\n"
-     ],
-     "language": "python",
+     "data": {
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+       "lq5du9K1a1eqVat21nMU3iIiFktNTeWHH35g27ZtNGvWjC+//LLMczTmLSJisWXLllG/fn06derE\n",
+       "kiVLqFu3bpnnaJ63iEgY0rCJiEgYUniLiIQhhbeISBhSeIuIhCGFt4hIGFJ4i4iEIYW3iEgYUniL\n",
+       "iISh/weZPyRnS1m/IAAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x108604e50>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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J1tUp5VJFvnsjmM8Ha9bg/egj3B9/TNzOndiA4pUkCho3xp2SAm3b4r7yStyXXgrx8VZW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\niISh/weZPyRnS1m/IAAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x108604e50>"
-       ]
-      }
-     ],
-     "prompt_number": 11
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "\"\"\"\n",
+    "Plot demonstrating the integral as the area under a curve.\n",
+    "\n",
+    "Although this is a simple example, it demonstrates some important tweaks:\n",
+    "\n",
+    "    * A simple line plot with custom color and line width.\n",
+    "    * A shaded region created using a Polygon patch.\n",
+    "    * A text label with mathtext rendering.\n",
+    "    * figtext calls to label the x- and y-axes.\n",
+    "    * Use of axis spines to hide the top and right spines.\n",
+    "    * Custom tick placement and labels.\n",
+    "\"\"\"\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.patches import Polygon\n",
+    "\n",
+    "\n",
+    "def func(x):\n",
+    "    return (x - 3) * (x - 5) * (x - 7) + 85\n",
+    "\n",
+    "\n",
+    "a, b = 2, 9 # integral limits\n",
+    "x = np.linspace(0, 10)\n",
+    "y = func(x)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "plt.plot(x, y, 'r', linewidth=2)\n",
+    "plt.ylim(ymin=0)\n",
+    "\n",
+    "# Make the shaded region\n",
+    "ix = np.linspace(a, b)\n",
+    "iy = func(ix)\n",
+    "verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]\n",
+    "poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')\n",
+    "ax.add_patch(poly)\n",
+    "\n",
+    "plt.text(0.5 * (a + b), 30, r\"$\\int_a^b f(x)\\mathrm{d}x$\",\n",
+    "         horizontalalignment='center', fontsize=20)\n",
+    "\n",
+    "plt.figtext(0.9, 0.05, '$x$')\n",
+    "plt.figtext(0.1, 0.9, '$y$')\n",
+    "\n",
+    "ax.spines['right'].set_visible(False)\n",
+    "ax.spines['top'].set_visible(False)\n",
+    "ax.xaxis.set_ticks_position('bottom')\n",
+    "\n",
+    "ax.set_xticks((a, b))\n",
+    "ax.set_xticklabels(('$a$', '$b$'))\n",
+    "ax.set_yticks([])\n",
+    "\n",
+    "plt.show()\n"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:4352d4e1c693d919ce40b29ecf5a536917160df68b19a85caccedb1ea7ad06e1"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Index.ipynb b/examples/Index.ipynb
index 6e02300..a8ecab9 100644
--- a/examples/Index.ipynb
+++ b/examples/Index.ipynb
@@ -1,58 +1,50 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"images/ipython_logo.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# IPython Documentation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory contains IPython's notebook-based documentation. This augments our [Sphinx-based documentation](http://ipython.org/ipython-doc/stable/index.html) with notebooks that contain interactive tutorials and examples. Over time, more of our documentation will be pulled into this format."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Topics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* [IPython Kernel](IPython Kernel/Index.ipynb): IPython's core syntax and command line features available in all frontends\n",
+    "* [Notebook](Notebook/Index.ipynb): The IPython Notebook frontend\n",
+    "* [Interactive Widgets](Interactive Widgets/Index.ipynb): Interactive JavaScript/HTML widgets and `interact`\n",
+    "* [Parallel Computing](Parallel Computing/Index.ipynb): IPython's library for interactive parallel computing\n",
+    "* [Customization](Customization/Index.ipynb): How to configure IPython and customize it with magics, extensions, etc.\n",
+    "* [Embedding](Embedding/Index.ipynb): Embedding and reusing IPython's components into other applications\n",
+    "* [Builtin Extensions](Builtin Extensions/Index.ipynb): Extensions we ship with IPython"
+   ]
+  }
+ ],
  "metadata": {
-  "name": "",
   "signature": "sha256:0b4b631419772e40e0f4893f5a0f0fe089a39e46c8862af0256164628394302d"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<img src=\"images/ipython_logo.png\">"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "IPython Documentation"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This directory contains IPython's notebook-based documentation. This augments our [Sphinx-based documentation](http://ipython.org/ipython-doc/stable/index.html) with notebooks that contain interactive tutorials and examples. Over time, more of our documentation will be pulled into this format."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Topics"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "* [IPython Kernel](IPython Kernel/Index.ipynb): IPython's core syntax and command line features available in all frontends\n",
-      "* [Notebook](Notebook/Index.ipynb): The IPython Notebook frontend\n",
-      "* [Interactive Widgets](Interactive Widgets/Index.ipynb): Interactive JavaScript/HTML widgets and `interact`\n",
-      "* [Parallel Computing](Parallel Computing/Index.ipynb): IPython's library for interactive parallel computing\n",
-      "* [Customization](Customization/Index.ipynb): How to configure IPython and customize it with magics, extensions, etc.\n",
-      "* [Embedding](Embedding/Index.ipynb): Embedding and reusing IPython's components into other applications\n",
-      "* [Builtin Extensions](Builtin Extensions/Index.ipynb): Extensions we ship with IPython"
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Beat Frequencies.ipynb b/examples/Interactive Widgets/Beat Frequencies.ipynb
index fbbc59c..2ae3228 100644
--- a/examples/Interactive Widgets/Beat Frequencies.ipynb
+++ b/examples/Interactive Widgets/Beat Frequencies.ipynb
@@ -1,182 +1,599 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:da6a3d73881e321e5ef406aea3e7d706c9dd405c94675318afde957d292f9cb9"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Exploring Beat Frequencies using the `Audio` Object"
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring Beat Frequencies using the `Audio` Object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example uses the `Audio` object and Matplotlib to explore the phenomenon of beat frequencies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.html.widgets import interactive\n",
+    "from IPython.display import Audio, display\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def beat_freq(f1=220.0, f2=224.0):\n",
+    "    max_time = 3\n",
+    "    rate = 8000\n",
+    "    times = np.linspace(0,max_time,rate*max_time)\n",
+    "    signal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)\n",
+    "    print(f1, f2, abs(f1-f2))\n",
+    "    display(Audio(data=signal, rate=rate))\n",
+    "    return signal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This example uses the `Audio` object and Matplotlib to explore the phenomenon of beat frequencies."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(220.0, 224.0, 4.0)\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline\n",
-      "import matplotlib.pyplot as plt\n",
-      "import numpy as np"
-     ],
-     "language": "python",
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio controls=\"controls\" >\n",
+       "                    <source 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\" type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio at 0x1087cb450>"
+      ]
+     },
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "v = interactive(beat_freq, f1=(200.0,300.0), f2=(200.0,300.0))\n",
+    "display(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.html.widgets import interactive\n",
-      "from IPython.display import Audio, display\n",
-      "import numpy as np"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "{u'f1': 220.0, u'f2': 224.0}"
+      ]
+     },
+     "execution_count": 13,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v.kwargs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def beat_freq(f1=220.0, f2=224.0):\n",
-      "    max_time = 3\n",
-      "    rate = 8000\n",
-      "    times = np.linspace(0,max_time,rate*max_time)\n",
-      "    signal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)\n",
-      "    print(f1, f2, abs(f1-f2))\n",
-      "    display(Audio(data=signal, rate=rate))\n",
-      "    return signal"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(255.0, 260.0, 5.0)\n"
+     ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "v = interactive(beat_freq, f1=(200.0,300.0), f2=(200.0,300.0))\n",
-      "display(v)"
-     ],
-     "language": "python",
+     "data": {
+      "text/html": [
+       "\n",
+       "                <audio controls=\"controls\" >\n",
+       "                    <source 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\" type=\"audio/wav\" />\n",
+       "                    Your browser does not support the audio element.\n",
+       "                </audio>\n",
+       "              "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.Audio at 0x1087cb690>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "(220.0, 224.0, 4.0)\n"
-       ]
-      },
-      {
-       "html": [
-        "\n",
-        "                <audio controls=\"controls\" >\n",
-        "                    <source 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\" type=\"audio/wav\" />\n",
-        "                    Your browser does not support the audio element.\n",
-        "                </audio>\n",
-        "              "
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.lib.display.Audio at 0x1087cb450>"
-       ]
-      }
-     ],
-     "prompt_number": 12
+     "output_type": "display_data"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "v.kwargs"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1087ee990>]"
+      ]
+     },
+     "execution_count": 14,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 13,
-       "text": [
-        "{u'f1': 220.0, u'f2': 224.0}"
-       ]
-      }
-     ],
-     "prompt_number": 13
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "f1, f2 = v.children\n",
-      "f1.value = 255\n",
-      "f2.value = 260\n",
-      "plt.plot(v.result[0:6000])"
-     ],
-     "language": "python",
+     "data": {
+      "image/png": [
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+       "vn37oqKiAvX19WGaTJAgQYIEEaAkzI/btGmDn//85xg+fDh27NiB4447DjU1NRg4cOCBMosWLcK7\n",
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+       "nHMOzjjjjIz7ZWVlaGhoOPD/+vXrUVZWllEuleoD4L2w3UmQIEGCFoXevXvj3XffDV1PKMvA8zxc\n",
+       "eOGFGDRoEK666iqyzOTJk/Hggw8CAFasWIHOnTujtLSUKPkeAA/HH+8B8PD44x7OOsuD5xXH3003\n",
+       "3ZSztv75Tw8TJ/p0POQQ/3jllf5x6lT/CHg47jgP557rYdu2wnm2uLy7xkafhj/+cZqerVo1p/U3\n",
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+       "BAkSJEiARBgkSJAgQQIkwiBBggQJEgD4/8gNbnBur9NZAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x106a14450>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "(255.0, 260.0, 5.0)\n"
-       ]
-      },
-      {
-       "html": [
-        "\n",
-        "                <audio controls=\"controls\" >\n",
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\" 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-       "text": [
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WmKmLJgorzNR94hrcpy1tGbFpXbYuSxfvgSvvGtyzqPyiqIWBgO2n9HQHjCoM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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x106a14450>"
-       ]
-      }
-     ],
-     "prompt_number": 14
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "f1, f2 = v.children\n",
+    "f1.value = 255\n",
+    "f2.value = 260\n",
+    "plt.plot(v.result[0:6000])"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:da6a3d73881e321e5ef406aea3e7d706c9dd405c94675318afde957d292f9cb9"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Custom Widget - Hello World.ipynb b/examples/Interactive Widgets/Custom Widget - Hello World.ipynb
index e647171..8aafc89 100644
--- a/examples/Interactive Widgets/Custom Widget - Hello World.ipynb
+++ b/examples/Interactive Widgets/Custom Widget - Hello World.ipynb
@@ -1,4 +1,772 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Index](Index.ipynb) - [Back](Widget Styling.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function # For py 2.7 compat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Building a Custom Widget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The widget framework is built **on top of the Comm framework** (short for communication).  The Comm framework is a framework that **allows you send/receive JSON messages** to/from the front-end (as seen below).\n",
+    "\n",
+    "![Widget layer](images/WidgetArch.png)\n",
+    "\n",
+    "To create a custom widget, you need to **define the widget both in the back-end and in the front-end**.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Building a Custom Widget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get started, you'll create a **simple hello world widget**.  Later you'll build on this foundation to make more complex widgets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Back-end (Python)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### DOMWidget and Widget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To define a widget, you must inherit from the **Widget or DOMWidget** base class.  If you intend for your widget to be **displayed in the IPython notebook**, you'll need to **inherit from the DOMWidget**.  The DOMWidget class itself inherits from the Widget class.  The Widget class is useful for cases in which the **Widget is not meant to be displayed directly in the notebook**, but **instead as a child of another rendering environment**.  For example, if you wanted to create a three.js widget (a popular WebGL library), you would implement the rendering window as a DOMWidget and any 3D objects or lights meant to be rendered in that window as Widgets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### _view_name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Inheriting from the DOMWidget does not tell the widget framework what front-end widget to associate with your back-end widget.  Instead, you must tell it yourself by defining a **specially named Traitlet, `_view_name`** (as seen below)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.html import widgets\n",
+    "from IPython.utils.traitlets import Unicode\n",
+    "\n",
+    "class HelloWidget(widgets.DOMWidget):\n",
+    "    _view_name = Unicode('HelloView', sync=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### sync=True traitlets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Traitlets is** an IPython library for defining **type-safe properties** on configurable objects.  For this tutorial you do not need to worry about the *configurable* piece of the traitlets machinery.   The **`sync=True` keyword argument** tells the widget framework to **handle synchronizing that value to the front-end**.  Without `sync=True`, the front-end would have no knowledge of `_view_name`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Other traitlet types"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unicode, used for _view_name, is not the only Traitlet type, there are many more some of which are listed below:  \n",
+    "\n",
+    "- Any\n",
+    "- Bool\n",
+    "- Bytes\n",
+    "- CBool\n",
+    "- CBytes\n",
+    "- CComplex\n",
+    "- CFloat\n",
+    "- CInt\n",
+    "- CLong\n",
+    "- CRegExp\n",
+    "- CUnicode\n",
+    "- CaselessStrEnum\n",
+    "- Complex\n",
+    "- Dict\n",
+    "- DottedObjectName\n",
+    "- Enum\n",
+    "- Float\n",
+    "- FunctionType\n",
+    "- Instance\n",
+    "- InstanceType\n",
+    "- Int\n",
+    "- List\n",
+    "- Long\n",
+    "- Set\n",
+    "- TCPAddress\n",
+    "- Tuple\n",
+    "- Type\n",
+    "- Unicode\n",
+    "\n",
+    "**Not all of these traitlets can be synchronized** across the network, **only the JSON-able** traits and **Widget instances** will be synchronized."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Front-end (JavaScript)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Models and views"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The IPython widget framework front-end relies heavily on [Backbone.js](http://backbonejs.org/).  **Backbone.js is an MVC (model view controller) framework**.  Widgets defined in the back-end are automatically **synchronized with generic Backbone.js models** in the front-end.  The traitlets are added to the front-end instance **automatically on first state push**.  The **`_view_name` trait** that you defined earlier is used by the widget framework to create the corresponding Backbone.js view and **link that view to the model**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Import the WidgetManager"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You first need to **import the `widget` and `manager` modules**.  You will use it later to register your view by name (the same name you used in the back-end).  To import the modules, use the `require` method of [require.js](http://requirejs.org/) (as seen below)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%javascript\n",
+    "require([\"widgets/js/widget\", \"widgets/js/manager\"], function(widget, manager){\n",
+    "    \n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Define the view"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next define your widget view class.  **Inherit from the `DOMWidgetView`** by using the `.extend` method.  Register the view class with the widget manager by calling **`.register_widget_view`**.  The **first parameter is the widget view name** (`_view_name` that you defined earlier in Python) and the **second is a handle to the class type**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%javascript\n",
+    "require([\"widgets/js/widget\", \"widgets/js/manager\"], function(widget, manager){\n",
+    "    \n",
+    "    // Define the HelloView\n",
+    "    var HelloView = widget.DOMWidgetView.extend({\n",
+    "        \n",
+    "    });\n",
+    "    \n",
+    "    // Register the HelloView with the widget manager.\n",
+    "    manager.WidgetManager.register_widget_view('HelloView', HelloView);\n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Render method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lastly, **override the base `render` method** of the view to define custom rendering logic.  A handle to the widget's default div element can be acquired via **`this.$el`**.  The `$el` property is a **[jQuery](http://jquery.com/) object handle** (which can be thought of as a supercharged version of the normal DOM element's handle)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%javascript\n",
+    "require([\"widgets/js/widget\", \"widgets/js/manager\"], function(widget, manager){\n",
+    "    \n",
+    "    var HelloView = widget.DOMWidgetView.extend({\n",
+    "        \n",
+    "        // Render the view.\n",
+    "        render: function(){ \n",
+    "            this.$el.text('Hello World!'); \n",
+    "        },\n",
+    "    });\n",
+    "    \n",
+    "    manager.WidgetManager.register_widget_view('HelloView', HelloView);\n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You should be able to display your widget just like any other widget now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "HelloWidget()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Making the widget stateful"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is not much that you can do with the above example that you can't do with the IPython display framework.  To change this, you will make the widget stateful.  Instead of displaying a static \"hello world\" message, it will **display a string set by the back-end**.  First you need to **add a traitlet in the back-end**.  Use the name of **`value` to stay consistent** with the rest of the widget framework and to **allow your widget to be used with interact**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class HelloWidget(widgets.DOMWidget):\n",
+    "    _view_name = Unicode('HelloView', sync=True)\n",
+    "    value = Unicode('Hello World!', sync=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Accessing the model from the view"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To access the model associate with a view instance, use the **`model` property** of the view.  **`get` and `set`** methods are used to interact with the Backbone model.  **`get` is trivial**, however you have to **be careful when using `set`**.  **After calling the model `set`** you need call the **view's `touch` method**.  This associates the `set` operation with a particular view so **output will be routed to the correct cell**.  The model also has a **`on` method** which allows you to listen to events triggered by the model (like value changes)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Rendering model contents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By **replacing the string literal with a call to `model.get`**, the view will now display the **value of the back-end upon display**.  However, it will not update itself to a new value when the value changes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%javascript\n",
+    "require([\"widgets/js/widget\", \"widgets/js/manager\"], function(widget, manager){\n",
+    "    \n",
+    "    var HelloView = widget.DOMWidgetView.extend({\n",
+    "        \n",
+    "        render: function(){ \n",
+    "            this.$el.text(this.model.get('value')); \n",
+    "        },\n",
+    "    });\n",
+    "    \n",
+    "    manager.WidgetManager.register_widget_view('HelloView', HelloView);\n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Dynamic updates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get the view to **update itself dynamically**, register a function to update the view's value when the model's `value` property changes.  This can be done using the **`model.on` method**.  The `on` method takes three parameters, an event name, callback handle, and callback context.   The Backbone **event named `change`** will fire whenever the model changes.  By **appending `:value`** to it, you tell Backbone to only listen to the change event of the `value` property (as seen below)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%javascript\n",
+    "require([\"widgets/js/widget\", \"widgets/js/manager\"], function(widget, manager){\n",
+    "    \n",
+    "    var HelloView = widget.DOMWidgetView.extend({\n",
+    "        \n",
+    "        render: function(){ \n",
+    "            this.value_changed();\n",
+    "            this.model.on('change:value', this.value_changed, this);\n",
+    "        },\n",
+    "        \n",
+    "        value_changed: function() {\n",
+    "            this.$el.text(this.model.get('value')); \n",
+    "        },\n",
+    "    });\n",
+    "    \n",
+    "    manager.WidgetManager.register_widget_view('HelloView', HelloView);\n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w = HelloWidget()\n",
+    "w"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.value = 'test'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Finishing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bidirectional communication"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The examples above dump the value directly into the DOM.  There is no way for you to interact with this dumped data in the front-end.  To create an example that **accepts input**, you will have to do something more than blindly dumping the contents of value into the DOM.  In this part of the tutorial, you will **use a jQuery spinner** to display and accept input in the front-end.  IPython currently lacks a spinner implementation so this widget will be unique."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Update the Python code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You will need to change the type of the **value traitlet to `Int`**.  It also makes sense to **change the name of the widget** to something more appropriate, like `SpinnerWidget`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.utils.traitlets import CInt\n",
+    "class SpinnerWidget(widgets.DOMWidget):\n",
+    "    _view_name = Unicode('SpinnerView', sync=True)\n",
+    "    value = CInt(0, sync=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Updating the Javascript code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [jQuery docs for the spinner control](https://jqueryui.com/spinner/) say to use **`.spinner` to create a spinner** in an element.  Calling **`.spinner` on `$el` will create a spinner inside `$el`**.  Make sure to **update the widget name here too** so it's the same as `_view_name` in the back-end."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%javascript\n",
+    "require([\"widgets/js/widget\", \"widgets/js/manager\"], function(widget, manager){\n",
+    "    \n",
+    "    var SpinnerView = widget.DOMWidgetView.extend({\n",
+    "        \n",
+    "        render: function(){ \n",
+    "            \n",
+    "            // jQuery code to create a spinner and append it to $el\n",
+    "            this.$input = $('<input />');\n",
+    "            this.$el.append(this.$input);\n",
+    "            this.$spinner = this.$input.spinner({\n",
+    "                change: function( event, ui ) {}\n",
+    "            });\n",
+    "            \n",
+    "            this.value_changed();\n",
+    "            this.model.on('change:value', this.value_changed, this);\n",
+    "        },\n",
+    "        \n",
+    "        value_changed: function() {\n",
+    "            \n",
+    "        },\n",
+    "    });\n",
+    "    \n",
+    "    manager.WidgetManager.register_widget_view('SpinnerView', SpinnerView);\n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Getting and setting the value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To **set the value of the spinner on update from the back-end**, you need to use **jQuery's `spinner` API**.  `spinner.spinner('value', new)` will set the value of the spinner.  Add that code to the **`value_changed` method** to make the spinner **update with the value stored in the back-end((.  Using jQuery's spinner API, you can add a function to handle the **spinner `change` event** by passing it in when constructing the spinner.  Inside the `change` event, call **`model.set`** to set the value and then **`touch`** to inform the framework that this view was the view that caused the change to the model.  **Note: The `var that = this;` is a JavaScript trick to pass the current context into closures.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%javascript\n",
+    "require([\"widgets/js/widget\", \"widgets/js/manager\"], function(widget, manager){\n",
+    "    \n",
+    "    var SpinnerView = widget.DOMWidgetView.extend({\n",
+    "        \n",
+    "        render: function(){ \n",
+    "\n",
+    "            var that = this;\n",
+    "            this.$input = $('<input />');\n",
+    "            this.$el.append(this.$input);\n",
+    "            this.$spinner = this.$input.spinner({\n",
+    "                change: function( event, ui ) {\n",
+    "                    that.handle_spin();\n",
+    "                },\n",
+    "                spin: function( event, ui ) {\n",
+    "                    that.handle_spin();\n",
+    "                }\n",
+    "            });\n",
+    "            \n",
+    "            this.value_changed();\n",
+    "            this.model.on('change:value', this.value_changed, this);\n",
+    "        },\n",
+    "        \n",
+    "        value_changed: function() {\n",
+    "            this.$spinner.spinner('value', this.model.get('value'));\n",
+    "        },\n",
+    "        \n",
+    "        handle_spin: function() {\n",
+    "            this.model.set('value', this.$spinner.spinner('value'));\n",
+    "            this.touch();\n",
+    "        },\n",
+    "    });\n",
+    "    \n",
+    "    manager.WidgetManager.register_widget_view('SpinnerView', SpinnerView);\n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w = SpinnerWidget(value=5)\n",
+    "w"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.value = 20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Trying to **use the spinner with another widget**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import display\n",
+    "w1 = SpinnerWidget(value=0)\n",
+    "w2 = widgets.IntSlider()\n",
+    "display(w1,w2)\n",
+    "\n",
+    "from IPython.utils.traitlets import link\n",
+    "mylink = link((w1, 'value'), (w2, 'value'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Index](Index.ipynb) - [Back](Widget Styling.ipynb)"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "codemirror_mode": {
@@ -9,788 +777,8 @@
    "language": "python",
    "name": "python2"
   },
-  "name": "",
   "signature": "sha256:608e0df06fe91ef1c013fd236dda33f7ae11019453f72ae446e8ce15bce82eb2"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Index](Index.ipynb) - [Back](Widget Styling.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from __future__ import print_function # For py 2.7 compat"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Building a Custom Widget"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The widget framework is built **on top of the Comm framework** (short for communication).  The Comm framework is a framework that **allows you send/receive JSON messages** to/from the front-end (as seen below).\n",
-      "\n",
-      "![Widget layer](images/WidgetArch.png)\n",
-      "\n",
-      "To create a custom widget, you need to **define the widget both in the back-end and in the front-end**.  "
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Building a Custom Widget"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To get started, you'll create a **simple hello world widget**.  Later you'll build on this foundation to make more complex widgets."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Back-end (Python)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "DOMWidget and Widget"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To define a widget, you must inherit from the **Widget or DOMWidget** base class.  If you intend for your widget to be **displayed in the IPython notebook**, you'll need to **inherit from the DOMWidget**.  The DOMWidget class itself inherits from the Widget class.  The Widget class is useful for cases in which the **Widget is not meant to be displayed directly in the notebook**, but **instead as a child of another rendering environment**.  For example, if you wanted to create a three.js widget (a popular WebGL library), you would implement the rendering window as a DOMWidget and any 3D objects or lights meant to be rendered in that window as Widgets."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "_view_name"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Inheriting from the DOMWidget does not tell the widget framework what front-end widget to associate with your back-end widget.  Instead, you must tell it yourself by defining a **specially named Traitlet, `_view_name`** (as seen below)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.html import widgets\n",
-      "from IPython.utils.traitlets import Unicode\n",
-      "\n",
-      "class HelloWidget(widgets.DOMWidget):\n",
-      "    _view_name = Unicode('HelloView', sync=True)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "sync=True traitlets"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Traitlets is** an IPython library for defining **type-safe properties** on configurable objects.  For this tutorial you do not need to worry about the *configurable* piece of the traitlets machinery.   The **`sync=True` keyword argument** tells the widget framework to **handle synchronizing that value to the front-end**.  Without `sync=True`, the front-end would have no knowledge of `_view_name`."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Other traitlet types"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Unicode, used for _view_name, is not the only Traitlet type, there are many more some of which are listed below:  \n",
-      "\n",
-      "- Any\n",
-      "- Bool\n",
-      "- Bytes\n",
-      "- CBool\n",
-      "- CBytes\n",
-      "- CComplex\n",
-      "- CFloat\n",
-      "- CInt\n",
-      "- CLong\n",
-      "- CRegExp\n",
-      "- CUnicode\n",
-      "- CaselessStrEnum\n",
-      "- Complex\n",
-      "- Dict\n",
-      "- DottedObjectName\n",
-      "- Enum\n",
-      "- Float\n",
-      "- FunctionType\n",
-      "- Instance\n",
-      "- InstanceType\n",
-      "- Int\n",
-      "- List\n",
-      "- Long\n",
-      "- Set\n",
-      "- TCPAddress\n",
-      "- Tuple\n",
-      "- Type\n",
-      "- Unicode\n",
-      "\n",
-      "**Not all of these traitlets can be synchronized** across the network, **only the JSON-able** traits and **Widget instances** will be synchronized."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Front-end (JavaScript)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Models and views"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The IPython widget framework front-end relies heavily on [Backbone.js](http://backbonejs.org/).  **Backbone.js is an MVC (model view controller) framework**.  Widgets defined in the back-end are automatically **synchronized with generic Backbone.js models** in the front-end.  The traitlets are added to the front-end instance **automatically on first state push**.  The **`_view_name` trait** that you defined earlier is used by the widget framework to create the corresponding Backbone.js view and **link that view to the model**."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Import the WidgetManager"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You first need to **import the `widget` and `manager` modules**.  You will use it later to register your view by name (the same name you used in the back-end).  To import the modules, use the `require` method of [require.js](http://requirejs.org/) (as seen below)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "require([\"widgets/js/widget\", \"widgets/js/manager\"], function(widget, manager){\n",
-      "    \n",
-      "});"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Define the view"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Next define your widget view class.  **Inherit from the `DOMWidgetView`** by using the `.extend` method.  Register the view class with the widget manager by calling **`.register_widget_view`**.  The **first parameter is the widget view name** (`_view_name` that you defined earlier in Python) and the **second is a handle to the class type**."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "require([\"widgets/js/widget\", \"widgets/js/manager\"], function(widget, manager){\n",
-      "    \n",
-      "    // Define the HelloView\n",
-      "    var HelloView = widget.DOMWidgetView.extend({\n",
-      "        \n",
-      "    });\n",
-      "    \n",
-      "    // Register the HelloView with the widget manager.\n",
-      "    manager.WidgetManager.register_widget_view('HelloView', HelloView);\n",
-      "});"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Render method"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Lastly, **override the base `render` method** of the view to define custom rendering logic.  A handle to the widget's default div element can be acquired via **`this.$el`**.  The `$el` property is a **[jQuery](http://jquery.com/) object handle** (which can be thought of as a supercharged version of the normal DOM element's handle)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "require([\"widgets/js/widget\", \"widgets/js/manager\"], function(widget, manager){\n",
-      "    \n",
-      "    var HelloView = widget.DOMWidgetView.extend({\n",
-      "        \n",
-      "        // Render the view.\n",
-      "        render: function(){ \n",
-      "            this.$el.text('Hello World!'); \n",
-      "        },\n",
-      "    });\n",
-      "    \n",
-      "    manager.WidgetManager.register_widget_view('HelloView', HelloView);\n",
-      "});"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Test"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You should be able to display your widget just like any other widget now."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "HelloWidget()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Making the widget stateful"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "There is not much that you can do with the above example that you can't do with the IPython display framework.  To change this, you will make the widget stateful.  Instead of displaying a static \"hello world\" message, it will **display a string set by the back-end**.  First you need to **add a traitlet in the back-end**.  Use the name of **`value` to stay consistent** with the rest of the widget framework and to **allow your widget to be used with interact**."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class HelloWidget(widgets.DOMWidget):\n",
-      "    _view_name = Unicode('HelloView', sync=True)\n",
-      "    value = Unicode('Hello World!', sync=True)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Accessing the model from the view"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To access the model associate with a view instance, use the **`model` property** of the view.  **`get` and `set`** methods are used to interact with the Backbone model.  **`get` is trivial**, however you have to **be careful when using `set`**.  **After calling the model `set`** you need call the **view's `touch` method**.  This associates the `set` operation with a particular view so **output will be routed to the correct cell**.  The model also has a **`on` method** which allows you to listen to events triggered by the model (like value changes)."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Rendering model contents"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "By **replacing the string literal with a call to `model.get`**, the view will now display the **value of the back-end upon display**.  However, it will not update itself to a new value when the value changes."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "require([\"widgets/js/widget\", \"widgets/js/manager\"], function(widget, manager){\n",
-      "    \n",
-      "    var HelloView = widget.DOMWidgetView.extend({\n",
-      "        \n",
-      "        render: function(){ \n",
-      "            this.$el.text(this.model.get('value')); \n",
-      "        },\n",
-      "    });\n",
-      "    \n",
-      "    manager.WidgetManager.register_widget_view('HelloView', HelloView);\n",
-      "});"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Dynamic updates"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To get the view to **update itself dynamically**, register a function to update the view's value when the model's `value` property changes.  This can be done using the **`model.on` method**.  The `on` method takes three parameters, an event name, callback handle, and callback context.   The Backbone **event named `change`** will fire whenever the model changes.  By **appending `:value`** to it, you tell Backbone to only listen to the change event of the `value` property (as seen below)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "require([\"widgets/js/widget\", \"widgets/js/manager\"], function(widget, manager){\n",
-      "    \n",
-      "    var HelloView = widget.DOMWidgetView.extend({\n",
-      "        \n",
-      "        render: function(){ \n",
-      "            this.value_changed();\n",
-      "            this.model.on('change:value', this.value_changed, this);\n",
-      "        },\n",
-      "        \n",
-      "        value_changed: function() {\n",
-      "            this.$el.text(this.model.get('value')); \n",
-      "        },\n",
-      "    });\n",
-      "    \n",
-      "    manager.WidgetManager.register_widget_view('HelloView', HelloView);\n",
-      "});"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Test"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w = HelloWidget()\n",
-      "w"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w.value = 'test'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Finishing"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Bidirectional communication"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The examples above dump the value directly into the DOM.  There is no way for you to interact with this dumped data in the front-end.  To create an example that **accepts input**, you will have to do something more than blindly dumping the contents of value into the DOM.  In this part of the tutorial, you will **use a jQuery spinner** to display and accept input in the front-end.  IPython currently lacks a spinner implementation so this widget will be unique."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Update the Python code"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You will need to change the type of the **value traitlet to `Int`**.  It also makes sense to **change the name of the widget** to something more appropriate, like `SpinnerWidget`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.utils.traitlets import CInt\n",
-      "class SpinnerWidget(widgets.DOMWidget):\n",
-      "    _view_name = Unicode('SpinnerView', sync=True)\n",
-      "    value = CInt(0, sync=True)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Updating the Javascript code"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The [jQuery docs for the spinner control](https://jqueryui.com/spinner/) say to use **`.spinner` to create a spinner** in an element.  Calling **`.spinner` on `$el` will create a spinner inside `$el`**.  Make sure to **update the widget name here too** so it's the same as `_view_name` in the back-end."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "require([\"widgets/js/widget\", \"widgets/js/manager\"], function(widget, manager){\n",
-      "    \n",
-      "    var SpinnerView = widget.DOMWidgetView.extend({\n",
-      "        \n",
-      "        render: function(){ \n",
-      "            \n",
-      "            // jQuery code to create a spinner and append it to $el\n",
-      "            this.$input = $('<input />');\n",
-      "            this.$el.append(this.$input);\n",
-      "            this.$spinner = this.$input.spinner({\n",
-      "                change: function( event, ui ) {}\n",
-      "            });\n",
-      "            \n",
-      "            this.value_changed();\n",
-      "            this.model.on('change:value', this.value_changed, this);\n",
-      "        },\n",
-      "        \n",
-      "        value_changed: function() {\n",
-      "            \n",
-      "        },\n",
-      "    });\n",
-      "    \n",
-      "    manager.WidgetManager.register_widget_view('SpinnerView', SpinnerView);\n",
-      "});"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Getting and setting the value"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To **set the value of the spinner on update from the back-end**, you need to use **jQuery's `spinner` API**.  `spinner.spinner('value', new)` will set the value of the spinner.  Add that code to the **`value_changed` method** to make the spinner **update with the value stored in the back-end((.  Using jQuery's spinner API, you can add a function to handle the **spinner `change` event** by passing it in when constructing the spinner.  Inside the `change` event, call **`model.set`** to set the value and then **`touch`** to inform the framework that this view was the view that caused the change to the model.  **Note: The `var that = this;` is a JavaScript trick to pass the current context into closures.**"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "require([\"widgets/js/widget\", \"widgets/js/manager\"], function(widget, manager){\n",
-      "    \n",
-      "    var SpinnerView = widget.DOMWidgetView.extend({\n",
-      "        \n",
-      "        render: function(){ \n",
-      "\n",
-      "            var that = this;\n",
-      "            this.$input = $('<input />');\n",
-      "            this.$el.append(this.$input);\n",
-      "            this.$spinner = this.$input.spinner({\n",
-      "                change: function( event, ui ) {\n",
-      "                    that.handle_spin();\n",
-      "                },\n",
-      "                spin: function( event, ui ) {\n",
-      "                    that.handle_spin();\n",
-      "                }\n",
-      "            });\n",
-      "            \n",
-      "            this.value_changed();\n",
-      "            this.model.on('change:value', this.value_changed, this);\n",
-      "        },\n",
-      "        \n",
-      "        value_changed: function() {\n",
-      "            this.$spinner.spinner('value', this.model.get('value'));\n",
-      "        },\n",
-      "        \n",
-      "        handle_spin: function() {\n",
-      "            this.model.set('value', this.$spinner.spinner('value'));\n",
-      "            this.touch();\n",
-      "        },\n",
-      "    });\n",
-      "    \n",
-      "    manager.WidgetManager.register_widget_view('SpinnerView', SpinnerView);\n",
-      "});"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Test"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w = SpinnerWidget(value=5)\n",
-      "w"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w.value"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w.value = 20"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Trying to **use the spinner with another widget**."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import display\n",
-      "w1 = SpinnerWidget(value=0)\n",
-      "w2 = widgets.IntSlider()\n",
-      "display(w1,w2)\n",
-      "\n",
-      "from IPython.utils.traitlets import link\n",
-      "mylink = link((w1, 'value'), (w2, 'value'))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Index](Index.ipynb) - [Back](Widget Styling.ipynb)"
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Date Picker Widget.ipynb b/examples/Interactive Widgets/Date Picker Widget.ipynb
index 18cd3ba..42f3e80 100644
--- a/examples/Interactive Widgets/Date Picker Widget.ipynb
+++ b/examples/Interactive Widgets/Date Picker Widget.ipynb
@@ -1,1049 +1,1046 @@
 {
- "metadata": {
-  "cell_tags": [
-   [
-    "<None>",
-    null
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before reading, make sure to review\n",
+    "\n",
+    "- [MVC prgramming](http://en.wikipedia.org/wiki/Model%E2%80%93view%E2%80%93controller)\n",
+    "- [Backbone.js](https://www.codeschool.com/courses/anatomy-of-backbonejs)\n",
+    "- [The widget IPEP](https://github.com/ipython/ipython/wiki/IPEP-23%3A-Backbone.js-Widgets)\n",
+    "- [The original widget PR discussion](https://github.com/ipython/ipython/pull/4374)"
    ]
-  ],
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+  },
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Before reading, make sure to review\n",
-      "\n",
-      "- [MVC prgramming](http://en.wikipedia.org/wiki/Model%E2%80%93view%E2%80%93controller)\n",
-      "- [Backbone.js](https://www.codeschool.com/courses/anatomy-of-backbonejs)\n",
-      "- [The widget IPEP](https://github.com/ipython/ipython/wiki/IPEP-23%3A-Backbone.js-Widgets)\n",
-      "- [The original widget PR discussion](https://github.com/ipython/ipython/pull/4374)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from __future__ import print_function # For py 2.7 compat\n",
-      "\n",
-      "from IPython.html import widgets # Widget definitions\n",
-      "from IPython.display import display # Used to display widgets in the notebook\n",
-      "from IPython.utils.traitlets import Unicode # Used to declare attributes of our widget"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Abstract"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This notebook implements a custom date picker widget,\n",
-      "in order to demonstrate the widget creation process.\n",
-      "\n",
-      "To create a custom widget, both Python and JavaScript code is required."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Section 1 - Basics"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Python"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When starting a project like this, it is often easiest to make a simple base implementation,\n",
-      "to verify that the underlying framework is working as expected.\n",
-      "To start, we will create an empty widget and make sure that it can be rendered.\n",
-      "The first step is to define the widget in Python."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class DateWidget(widgets.DOMWidget):\n",
-      "    _view_name = Unicode('DatePickerView', sync=True)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Our widget inherits from `widgets.DOMWidget` since it is intended that it will be displayed in the notebook directly.\n",
-      "The `_view_name` trait is special; the widget framework will read the `_view_name` trait to determine what Backbone view the widget is associated with.\n",
-      "**Using `sync=True` is very important** because it tells the widget framework that that specific traitlet should be synced between the front- and back-ends."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "JavaScript"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In the IPython notebook [require.js](http://requirejs.org/) is used to load JavaScript dependencies.\n",
-      "All IPython widget code depends on `widgets/js/widget.js`,\n",
-      "where the base widget model and base view are defined.\n",
-      "We use require.js to load this file:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "\n",
-      "require([\"widgets/js/widget\"], function(WidgetManager){\n",
-      "\n",
-      "});"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "javascript": [
-        "\n",
-        "require([\"widgets/js/widget\"], function(WidgetManager){\n",
-        "\n",
-        "});"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Javascript at 0x109491690>"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now we need to define a view that can be used to represent the model.\n",
-      "To do this, the `IPython.DOMWidgetView` is extended.\n",
-      "**A render function must be defined**.\n",
-      "The render function is used to render a widget view instance to the DOM.\n",
-      "For now, the render function renders a div that contains the text *Hello World!*\n",
-      "Lastly, the view needs to be registered with the widget manager.\n",
-      "\n",
-      "**Final JavaScript code below:**"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "\n",
-      "require([\"widgets/js/widget\"], function(WidgetManager){\n",
-      "    \n",
-      "    // Define the DatePickerView\n",
-      "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
-      "        render: function(){ this.$el.text('Hello World!'); },\n",
-      "    });\n",
-      "    \n",
-      "    // Register the DatePickerView with the widget manager.\n",
-      "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
-      "});"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "javascript": [
-        "\n",
-        "require([\"widgets/js/widget\"], function(WidgetManager){\n",
-        "    \n",
-        "    // Define the DatePickerView\n",
-        "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
-        "        render: function(){ this.$el.text('Hello World!'); },\n",
-        "    });\n",
-        "    \n",
-        "    // Register the DatePickerView with the widget manager.\n",
-        "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
-        "});"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Javascript at 0x1094917d0>"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Test"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To test what we have so far, create the widget, just like you would the builtin widgets:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "DateWidget()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Section 2 - Something useful"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Python"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In the last section we created a simple widget that displayed *Hello World!*\n",
-      "To make an actual date widget, we need to add a property that will be synced between the Python model and the JavaScript model.\n",
-      "The new attribute must be a traitlet, so the widget machinery can handle it.\n",
-      "The traitlet must be constructed with a `sync=True` keyword argument, to tell the widget machinery knows to synchronize it with the front-end.\n",
-      "Adding this to the code from the last section:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class DateWidget(widgets.DOMWidget):\n",
-      "    _view_name = Unicode('DatePickerView', sync=True)\n",
-      "    value = Unicode(sync=True)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "JavaScript"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In the JavaScript, there is no need to define counterparts to the traitlets.\n",
-      "When the JavaScript model is created for the first time,\n",
-      "it copies all of the traitlet `sync=True` attributes from the Python model.\n",
-      "We need to replace *Hello World!* with an actual HTML date picker widget."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "\n",
-      "require([\"widgets/js/widget\"], function(WidgetManager){\n",
-      "    \n",
-      "    // Define the DatePickerView\n",
-      "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
-      "        render: function(){\n",
-      "            \n",
-      "            // Create the date picker control.\n",
-      "            this.$date = $('<input />')\n",
-      "                .attr('type', 'date')\n",
-      "                .appendTo(this.$el);\n",
-      "        },\n",
-      "    });\n",
-      "    \n",
-      "    // Register the DatePickerView with the widget manager.\n",
-      "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
-      "});"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "javascript": [
-        "\n",
-        "require([\"widgets/js/widget\"], function(WidgetManager){\n",
-        "    \n",
-        "    // Define the DatePickerView\n",
-        "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
-        "        render: function(){\n",
-        "            \n",
-        "            // Create the date picker control.\n",
-        "            this.$date = $('<input />')\n",
-        "                .attr('type', 'date')\n",
-        "                .appendTo(this.$el);\n",
-        "        },\n",
-        "    });\n",
-        "    \n",
-        "    // Register the DatePickerView with the widget manager.\n",
-        "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
-        "});"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Javascript at 0x109491750>"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In order to get the HTML date picker to update itself with the value set in the back-end, we need to implement an `update()` method."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "\n",
-      "require([\"widgets/js/widget\"], function(WidgetManager){\n",
-      "    \n",
-      "    // Define the DatePickerView\n",
-      "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
-      "        render: function(){\n",
-      "            \n",
-      "            // Create the date picker control.\n",
-      "            this.$date = $('<input />')\n",
-      "                .attr('type', 'date')\n",
-      "                .appendTo(this.$el);\n",
-      "        },\n",
-      "        \n",
-      "        update: function() {\n",
-      "            \n",
-      "            // Set the value of the date control and then call base.\n",
-      "            this.$date.val(this.model.get('value')); // ISO format \"YYYY-MM-DDTHH:mm:ss.sssZ\" is required\n",
-      "            return DatePickerView.__super__.update.apply(this);\n",
-      "        },\n",
-      "    });\n",
-      "    \n",
-      "    // Register the DatePickerView with the widget manager.\n",
-      "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
-      "});"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "javascript": [
-        "\n",
-        "require([\"widgets/js/widget\"], function(WidgetManager){\n",
-        "    \n",
-        "    // Define the DatePickerView\n",
-        "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
-        "        render: function(){\n",
-        "            \n",
-        "            // Create the date picker control.\n",
-        "            this.$date = $('<input />')\n",
-        "                .attr('type', 'date')\n",
-        "                .appendTo(this.$el);\n",
-        "        },\n",
-        "        \n",
-        "        update: function() {\n",
-        "            \n",
-        "            // Set the value of the date control and then call base.\n",
-        "            this.$date.val(this.model.get('value')); // ISO format \"YYYY-MM-DDTHH:mm:ss.sssZ\" is required\n",
-        "            return DatePickerView.__super__.update.apply(this);\n",
-        "        },\n",
-        "    });\n",
-        "    \n",
-        "    // Register the DatePickerView with the widget manager.\n",
-        "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
-        "});"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Javascript at 0x109491750>"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To get the changed value from the frontend to publish itself to the backend,\n",
-      "we need to listen to the change event triggered by the HTM date control and set the value in the model.\n",
-      "After the date change event fires and the new value is set in the model,\n",
-      "it is very important that we call `this.touch()` to let the widget machinery know which view changed the model.\n",
-      "This is important because the widget machinery needs to know which cell to route the message callbacks to.\n",
-      "\n",
-      "**Final JavaScript code below:**"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "\n",
-      "\n",
-      "require([\"widgets/js/widget\"], function(WidgetManager){\n",
-      "    \n",
-      "    // Define the DatePickerView\n",
-      "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
-      "        render: function(){\n",
-      "            \n",
-      "            // Create the date picker control.\n",
-      "            this.$date = $('<input />')\n",
-      "                .attr('type', 'date')\n",
-      "                .appendTo(this.$el);\n",
-      "        },\n",
-      "        \n",
-      "        update: function() {\n",
-      "            \n",
-      "            // Set the value of the date control and then call base.\n",
-      "            this.$date.val(this.model.get('value')); // ISO format \"YYYY-MM-DDTHH:mm:ss.sssZ\" is required\n",
-      "            return DatePickerView.__super__.update.apply(this);\n",
-      "        },\n",
-      "        \n",
-      "        // Tell Backbone to listen to the change event of input controls (which the HTML date picker is)\n",
-      "        events: {\"change\": \"handle_date_change\"},\n",
-      "        \n",
-      "        // Callback for when the date is changed.\n",
-      "        handle_date_change: function(event) {\n",
-      "            this.model.set('value', this.$date.val());\n",
-      "            this.touch();\n",
-      "        },\n",
-      "    });\n",
-      "    \n",
-      "    // Register the DatePickerView with the widget manager.\n",
-      "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
-      "});"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "javascript": [
-        "\n",
-        "\n",
-        "require([\"widgets/js/widget\"], function(WidgetManager){\n",
-        "    \n",
-        "    // Define the DatePickerView\n",
-        "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
-        "        render: function(){\n",
-        "            \n",
-        "            // Create the date picker control.\n",
-        "            this.$date = $('<input />')\n",
-        "                .attr('type', 'date')\n",
-        "                .appendTo(this.$el);\n",
-        "        },\n",
-        "        \n",
-        "        update: function() {\n",
-        "            \n",
-        "            // Set the value of the date control and then call base.\n",
-        "            this.$date.val(this.model.get('value')); // ISO format \"YYYY-MM-DDTHH:mm:ss.sssZ\" is required\n",
-        "            return DatePickerView.__super__.update.apply(this);\n",
-        "        },\n",
-        "        \n",
-        "        // Tell Backbone to listen to the change event of input controls (which the HTML date picker is)\n",
-        "        events: {\"change\": \"handle_date_change\"},\n",
-        "        \n",
-        "        // Callback for when the date is changed.\n",
-        "        handle_date_change: function(event) {\n",
-        "            this.model.set('value', this.$date.val());\n",
-        "            this.touch();\n",
-        "        },\n",
-        "    });\n",
-        "    \n",
-        "    // Register the DatePickerView with the widget manager.\n",
-        "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
-        "});"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Javascript at 0x109491b10>"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Test"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To test, create the widget the same way that the other widgets are created."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_widget = DateWidget()\n",
-      "display(my_widget)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Display the widget again to make sure that both views remain in sync."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_widget"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Read the date from Python"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_widget.value"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 12,
-       "text": [
-        "u''"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Set the date from Python"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_widget.value = \"1998-12-01\" # December 1st, 1998"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Section 3 - Extra credit"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The 3rd party `dateutil` library is required to continue.  https://pypi.python.org/pypi/python-dateutil"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Import the dateutil library to parse date strings.\n",
-      "from dateutil import parser"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In the last section we created a fully working date picker widget.\n",
-      "Now we will add custom validation and support for labels.\n",
-      "So far, only the ISO date format \"YYYY-MM-DD\" is supported.\n",
-      "Now, we will add support for all of the date formats recognized by the Python dateutil library."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Python"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The standard property name used for widget labels is `description`.\n",
-      "In the code block below, `description` has been added to the Python widget."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class DateWidget(widgets.DOMWidget):\n",
-      "    _view_name = Unicode('DatePickerView', sync=True)\n",
-      "    value = Unicode(sync=True)\n",
-      "    description = Unicode(sync=True)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The traitlet machinery searches the class that the trait is defined in for methods with \"`_changed`\" suffixed onto their names.  Any method with the format \"`_X_changed`\" will be called when \"`X`\" is modified.\n",
-      "We can take advantage of this to perform validation and parsing of different date string formats.\n",
-      "Below, a method that listens to value has been added to the DateWidget."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class DateWidget(widgets.DOMWidget):\n",
-      "    _view_name = Unicode('DatePickerView', sync=True)\n",
-      "    value = Unicode(sync=True)\n",
-      "    description = Unicode(sync=True)\n",
-      "\n",
-      "    # This function automatically gets called by the traitlet machinery when\n",
-      "    # value is modified because of this function's name.\n",
-      "    def _value_changed(self, name, old_value, new_value):\n",
-      "        pass"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now the function parses the date string,\n",
-      "and only sets the value in the correct format."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class DateWidget(widgets.DOMWidget):\n",
-      "    _view_name = Unicode('DatePickerView', sync=True)\n",
-      "    value = Unicode(sync=True)\n",
-      "    description = Unicode(sync=True)\n",
-      "    \n",
-      "    # This function automatically gets called by the traitlet machinery when\n",
-      "    # value is modified because of this function's name.\n",
-      "    def _value_changed(self, name, old_value, new_value):\n",
-      "        \n",
-      "        # Parse the date time value.\n",
-      "        try:\n",
-      "            parsed_date = parser.parse(new_value)\n",
-      "            parsed_date_string = parsed_date.strftime(\"%Y-%m-%d\")\n",
-      "        except:\n",
-      "            parsed_date_string = ''\n",
-      "        \n",
-      "        # Set the parsed date string if the current date string is different.\n",
-      "        if self.value != parsed_date_string:\n",
-      "            self.value = parsed_date_string"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Finally, a `CallbackDispatcher` is added so the user can perform custom validation.\n",
-      "If any one of the callbacks registered with the dispatcher returns False,\n",
-      "the new date is not set.\n",
-      "\n",
-      "**Final Python code below:**"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class DateWidget(widgets.DOMWidget):\n",
-      "    _view_name = Unicode('DatePickerView', sync=True)\n",
-      "    value = Unicode(sync=True)\n",
-      "    description = Unicode(sync=True)\n",
-      "    \n",
-      "    def __init__(self, **kwargs):\n",
-      "        super(DateWidget, self).__init__(**kwargs)\n",
-      "        \n",
-      "        self.validate = widgets.CallbackDispatcher()\n",
-      "    \n",
-      "    # This function automatically gets called by the traitlet machinery when\n",
-      "    # value is modified because of this function's name.\n",
-      "    def _value_changed(self, name, old_value, new_value):\n",
-      "        \n",
-      "        # Parse the date time value.\n",
-      "        try:\n",
-      "            parsed_date = parser.parse(new_value)\n",
-      "            parsed_date_string = parsed_date.strftime(\"%Y-%m-%d\")\n",
-      "        except:\n",
-      "            parsed_date_string = ''\n",
-      "        \n",
-      "        # Set the parsed date string if the current date string is different.\n",
-      "        if old_value != new_value:\n",
-      "            valid = self.validate(parsed_date)\n",
-      "            if valid in (None, True):\n",
-      "                self.value = parsed_date_string\n",
-      "            else:\n",
-      "                self.value = old_value\n",
-      "                self.send_state() # The traitlet event won't fire since the value isn't changing.\n",
-      "                                  # We need to force the back-end to send the front-end the state\n",
-      "                                  # to make sure that the date control date doesn't change."
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "JavaScript"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Using the Javascript code from the last section,\n",
-      "we add a label to the date time object.\n",
-      "The label is a div with the `widget-hlabel` class applied to it.\n",
-      "`widget-hlabel` is a class provided by the widget framework that applies special styling to a div to make it look like the rest of the horizontal labels used with the built-in widgets.\n",
-      "Similar to the `widget-hlabel` class is the `widget-hbox-single` class.\n",
-      "The `widget-hbox-single` class applies special styling to widget containers that store a single line horizontal widget.\n",
-      "\n",
-      "We hide the label if the description value is blank."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "\n",
-      "require([\"widgets/js/widget\"], function(WidgetManager){\n",
-      "    \n",
-      "    // Define the DatePickerView\n",
-      "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
-      "        render: function(){\n",
-      "            this.$el.addClass('widget-hbox-single'); /* Apply this class to the widget container to make\n",
-      "                                                        it fit with the other built in widgets.*/\n",
-      "            // Create a label.\n",
-      "            this.$label = $('<div />')\n",
-      "                .addClass('widget-hlabel')\n",
-      "                .appendTo(this.$el)\n",
-      "                .hide(); // Hide the label by default.\n",
-      "            \n",
-      "            // Create the date picker control.\n",
-      "            this.$date = $('<input />')\n",
-      "                .attr('type', 'date')\n",
-      "                .appendTo(this.$el);\n",
-      "        },\n",
-      "        \n",
-      "        update: function() {\n",
-      "            \n",
-      "            // Set the value of the date control and then call base.\n",
-      "            this.$date.val(this.model.get('value')); // ISO format \"YYYY-MM-DDTHH:mm:ss.sssZ\" is required\n",
-      "            \n",
-      "            // Hide or show the label depending on the existance of a description.\n",
-      "            var description = this.model.get('description');\n",
-      "            if (description == undefined || description == '') {\n",
-      "                this.$label.hide();\n",
-      "            } else {\n",
-      "                this.$label.show();\n",
-      "                this.$label.text(description);\n",
-      "            }\n",
-      "            \n",
-      "            return DatePickerView.__super__.update.apply(this);\n",
-      "        },\n",
-      "        \n",
-      "        // Tell Backbone to listen to the change event of input controls (which the HTML date picker is)\n",
-      "        events: {\"change\": \"handle_date_change\"},\n",
-      "        \n",
-      "        // Callback for when the date is changed.\n",
-      "        handle_date_change: function(event) {\n",
-      "            this.model.set('value', this.$date.val());\n",
-      "            this.touch();\n",
-      "        },\n",
-      "    });\n",
-      "    \n",
-      "    // Register the DatePickerView with the widget manager.\n",
-      "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
-      "});"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "javascript": [
-        "\n",
-        "require([\"widgets/js/widget\"], function(WidgetManager){\n",
-        "    \n",
-        "    // Define the DatePickerView\n",
-        "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
-        "        render: function(){\n",
-        "            this.$el.addClass('widget-hbox-single'); /* Apply this class to the widget container to make\n",
-        "                                                        it fit with the other built in widgets.*/\n",
-        "            // Create a label.\n",
-        "            this.$label = $('<div />')\n",
-        "                .addClass('widget-hlabel')\n",
-        "                .appendTo(this.$el)\n",
-        "                .hide(); // Hide the label by default.\n",
-        "            \n",
-        "            // Create the date picker control.\n",
-        "            this.$date = $('<input />')\n",
-        "                .attr('type', 'date')\n",
-        "                .appendTo(this.$el);\n",
-        "        },\n",
-        "        \n",
-        "        update: function() {\n",
-        "            \n",
-        "            // Set the value of the date control and then call base.\n",
-        "            this.$date.val(this.model.get('value')); // ISO format \"YYYY-MM-DDTHH:mm:ss.sssZ\" is required\n",
-        "            \n",
-        "            // Hide or show the label depending on the existance of a description.\n",
-        "            var description = this.model.get('description');\n",
-        "            if (description == undefined || description == '') {\n",
-        "                this.$label.hide();\n",
-        "            } else {\n",
-        "                this.$label.show();\n",
-        "                this.$label.text(description);\n",
-        "            }\n",
-        "            \n",
-        "            return DatePickerView.__super__.update.apply(this);\n",
-        "        },\n",
-        "        \n",
-        "        // Tell Backbone to listen to the change event of input controls (which the HTML date picker is)\n",
-        "        events: {\"change\": \"handle_date_change\"},\n",
-        "        \n",
-        "        // Callback for when the date is changed.\n",
-        "        handle_date_change: function(event) {\n",
-        "            this.model.set('value', this.$date.val());\n",
-        "            this.touch();\n",
-        "        },\n",
-        "    });\n",
-        "    \n",
-        "    // Register the DatePickerView with the widget manager.\n",
-        "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
-        "});"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Javascript at 0x1094eef90>"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function # For py 2.7 compat\n",
+    "\n",
+    "from IPython.html import widgets # Widget definitions\n",
+    "from IPython.display import display # Used to display widgets in the notebook\n",
+    "from IPython.utils.traitlets import Unicode # Used to declare attributes of our widget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Abstract"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook implements a custom date picker widget,\n",
+    "in order to demonstrate the widget creation process.\n",
+    "\n",
+    "To create a custom widget, both Python and JavaScript code is required."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Section 1 - Basics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When starting a project like this, it is often easiest to make a simple base implementation,\n",
+    "to verify that the underlying framework is working as expected.\n",
+    "To start, we will create an empty widget and make sure that it can be rendered.\n",
+    "The first step is to define the widget in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class DateWidget(widgets.DOMWidget):\n",
+    "    _view_name = Unicode('DatePickerView', sync=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our widget inherits from `widgets.DOMWidget` since it is intended that it will be displayed in the notebook directly.\n",
+    "The `_view_name` trait is special; the widget framework will read the `_view_name` trait to determine what Backbone view the widget is associated with.\n",
+    "**Using `sync=True` is very important** because it tells the widget framework that that specific traitlet should be synced between the front- and back-ends."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## JavaScript"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the IPython notebook [require.js](http://requirejs.org/) is used to load JavaScript dependencies.\n",
+    "All IPython widget code depends on `widgets/js/widget.js`,\n",
+    "where the base widget model and base view are defined.\n",
+    "We use require.js to load this file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "application/javascript": [
+       "\n",
+       "require([\"widgets/js/widget\"], function(WidgetManager){\n",
+       "\n",
+       "});"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript at 0x109491690>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Test"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%javascript\n",
+    "\n",
+    "require([\"widgets/js/widget\"], function(WidgetManager){\n",
+    "\n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we need to define a view that can be used to represent the model.\n",
+    "To do this, the `IPython.DOMWidgetView` is extended.\n",
+    "**A render function must be defined**.\n",
+    "The render function is used to render a widget view instance to the DOM.\n",
+    "For now, the render function renders a div that contains the text *Hello World!*\n",
+    "Lastly, the view needs to be registered with the widget manager.\n",
+    "\n",
+    "**Final JavaScript code below:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "application/javascript": [
+       "\n",
+       "require([\"widgets/js/widget\"], function(WidgetManager){\n",
+       "    \n",
+       "    // Define the DatePickerView\n",
+       "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
+       "        render: function(){ this.$el.text('Hello World!'); },\n",
+       "    });\n",
+       "    \n",
+       "    // Register the DatePickerView with the widget manager.\n",
+       "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
+       "});"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript at 0x1094917d0>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "To test the drawing of the label we create the widget like normal but supply the additional description property a value."
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%javascript\n",
+    "\n",
+    "require([\"widgets/js/widget\"], function(WidgetManager){\n",
+    "    \n",
+    "    // Define the DatePickerView\n",
+    "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
+    "        render: function(){ this.$el.text('Hello World!'); },\n",
+    "    });\n",
+    "    \n",
+    "    // Register the DatePickerView with the widget manager.\n",
+    "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To test what we have so far, create the widget, just like you would the builtin widgets:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "DateWidget()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Section 2 - Something useful"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the last section we created a simple widget that displayed *Hello World!*\n",
+    "To make an actual date widget, we need to add a property that will be synced between the Python model and the JavaScript model.\n",
+    "The new attribute must be a traitlet, so the widget machinery can handle it.\n",
+    "The traitlet must be constructed with a `sync=True` keyword argument, to tell the widget machinery knows to synchronize it with the front-end.\n",
+    "Adding this to the code from the last section:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class DateWidget(widgets.DOMWidget):\n",
+    "    _view_name = Unicode('DatePickerView', sync=True)\n",
+    "    value = Unicode(sync=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## JavaScript"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the JavaScript, there is no need to define counterparts to the traitlets.\n",
+    "When the JavaScript model is created for the first time,\n",
+    "it copies all of the traitlet `sync=True` attributes from the Python model.\n",
+    "We need to replace *Hello World!* with an actual HTML date picker widget."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Add some additional widgets for aesthetic purpose\n",
-      "display(widgets.TextWidget(description=\"First:\"))\n",
-      "display(widgets.TextWidget(description=\"Last:\"))\n",
-      "\n",
-      "my_widget = DateWidget()\n",
-      "display(my_widget)\n",
-      "my_widget.description=\"DOB:\""
-     ],
-     "language": "python",
+     "data": {
+      "application/javascript": [
+       "\n",
+       "require([\"widgets/js/widget\"], function(WidgetManager){\n",
+       "    \n",
+       "    // Define the DatePickerView\n",
+       "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
+       "        render: function(){\n",
+       "            \n",
+       "            // Create the date picker control.\n",
+       "            this.$date = $('<input />')\n",
+       "                .attr('type', 'date')\n",
+       "                .appendTo(this.$el);\n",
+       "        },\n",
+       "    });\n",
+       "    \n",
+       "    // Register the DatePickerView with the widget manager.\n",
+       "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
+       "});"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript at 0x109491750>"
+      ]
+     },
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 20
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%javascript\n",
+    "\n",
+    "require([\"widgets/js/widget\"], function(WidgetManager){\n",
+    "    \n",
+    "    // Define the DatePickerView\n",
+    "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
+    "        render: function(){\n",
+    "            \n",
+    "            // Create the date picker control.\n",
+    "            this.$date = $('<input />')\n",
+    "                .attr('type', 'date')\n",
+    "                .appendTo(this.$el);\n",
+    "        },\n",
+    "    });\n",
+    "    \n",
+    "    // Register the DatePickerView with the widget manager.\n",
+    "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to get the HTML date picker to update itself with the value set in the back-end, we need to implement an `update()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "application/javascript": [
+       "\n",
+       "require([\"widgets/js/widget\"], function(WidgetManager){\n",
+       "    \n",
+       "    // Define the DatePickerView\n",
+       "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
+       "        render: function(){\n",
+       "            \n",
+       "            // Create the date picker control.\n",
+       "            this.$date = $('<input />')\n",
+       "                .attr('type', 'date')\n",
+       "                .appendTo(this.$el);\n",
+       "        },\n",
+       "        \n",
+       "        update: function() {\n",
+       "            \n",
+       "            // Set the value of the date control and then call base.\n",
+       "            this.$date.val(this.model.get('value')); // ISO format \"YYYY-MM-DDTHH:mm:ss.sssZ\" is required\n",
+       "            return DatePickerView.__super__.update.apply(this);\n",
+       "        },\n",
+       "    });\n",
+       "    \n",
+       "    // Register the DatePickerView with the widget manager.\n",
+       "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
+       "});"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript at 0x109491750>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Now we will try to create a widget that only accepts dates in the year 2014.  We render the widget without a description to verify that it can still render without a label."
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%javascript\n",
+    "\n",
+    "require([\"widgets/js/widget\"], function(WidgetManager){\n",
+    "    \n",
+    "    // Define the DatePickerView\n",
+    "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
+    "        render: function(){\n",
+    "            \n",
+    "            // Create the date picker control.\n",
+    "            this.$date = $('<input />')\n",
+    "                .attr('type', 'date')\n",
+    "                .appendTo(this.$el);\n",
+    "        },\n",
+    "        \n",
+    "        update: function() {\n",
+    "            \n",
+    "            // Set the value of the date control and then call base.\n",
+    "            this.$date.val(this.model.get('value')); // ISO format \"YYYY-MM-DDTHH:mm:ss.sssZ\" is required\n",
+    "            return DatePickerView.__super__.update.apply(this);\n",
+    "        },\n",
+    "    });\n",
+    "    \n",
+    "    // Register the DatePickerView with the widget manager.\n",
+    "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get the changed value from the frontend to publish itself to the backend,\n",
+    "we need to listen to the change event triggered by the HTM date control and set the value in the model.\n",
+    "After the date change event fires and the new value is set in the model,\n",
+    "it is very important that we call `this.touch()` to let the widget machinery know which view changed the model.\n",
+    "This is important because the widget machinery needs to know which cell to route the message callbacks to.\n",
+    "\n",
+    "**Final JavaScript code below:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_widget = DateWidget()\n",
-      "display(my_widget)\n",
-      "\n",
-      "def require_2014(date):\n",
-      "    return not date is None and date.year == 2014\n",
-      "my_widget.validate.register_callback(require_2014)"
-     ],
-     "language": "python",
+     "data": {
+      "application/javascript": [
+       "\n",
+       "\n",
+       "require([\"widgets/js/widget\"], function(WidgetManager){\n",
+       "    \n",
+       "    // Define the DatePickerView\n",
+       "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
+       "        render: function(){\n",
+       "            \n",
+       "            // Create the date picker control.\n",
+       "            this.$date = $('<input />')\n",
+       "                .attr('type', 'date')\n",
+       "                .appendTo(this.$el);\n",
+       "        },\n",
+       "        \n",
+       "        update: function() {\n",
+       "            \n",
+       "            // Set the value of the date control and then call base.\n",
+       "            this.$date.val(this.model.get('value')); // ISO format \"YYYY-MM-DDTHH:mm:ss.sssZ\" is required\n",
+       "            return DatePickerView.__super__.update.apply(this);\n",
+       "        },\n",
+       "        \n",
+       "        // Tell Backbone to listen to the change event of input controls (which the HTML date picker is)\n",
+       "        events: {\"change\": \"handle_date_change\"},\n",
+       "        \n",
+       "        // Callback for when the date is changed.\n",
+       "        handle_date_change: function(event) {\n",
+       "            this.model.set('value', this.$date.val());\n",
+       "            this.touch();\n",
+       "        },\n",
+       "    });\n",
+       "    \n",
+       "    // Register the DatePickerView with the widget manager.\n",
+       "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
+       "});"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript at 0x109491b10>"
+      ]
+     },
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 21
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%javascript\n",
+    "\n",
+    "\n",
+    "require([\"widgets/js/widget\"], function(WidgetManager){\n",
+    "    \n",
+    "    // Define the DatePickerView\n",
+    "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
+    "        render: function(){\n",
+    "            \n",
+    "            // Create the date picker control.\n",
+    "            this.$date = $('<input />')\n",
+    "                .attr('type', 'date')\n",
+    "                .appendTo(this.$el);\n",
+    "        },\n",
+    "        \n",
+    "        update: function() {\n",
+    "            \n",
+    "            // Set the value of the date control and then call base.\n",
+    "            this.$date.val(this.model.get('value')); // ISO format \"YYYY-MM-DDTHH:mm:ss.sssZ\" is required\n",
+    "            return DatePickerView.__super__.update.apply(this);\n",
+    "        },\n",
+    "        \n",
+    "        // Tell Backbone to listen to the change event of input controls (which the HTML date picker is)\n",
+    "        events: {\"change\": \"handle_date_change\"},\n",
+    "        \n",
+    "        // Callback for when the date is changed.\n",
+    "        handle_date_change: function(event) {\n",
+    "            this.model.set('value', this.$date.val());\n",
+    "            this.touch();\n",
+    "        },\n",
+    "    });\n",
+    "    \n",
+    "    // Register the DatePickerView with the widget manager.\n",
+    "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To test, create the widget the same way that the other widgets are created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "my_widget = DateWidget()\n",
+    "display(my_widget)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Display the widget again to make sure that both views remain in sync."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "my_widget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read the date from Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Try setting a valid date\n",
-      "my_widget.value = \"December 2, 2014\""
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "u''"
+      ]
+     },
+     "execution_count": 12,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 22
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_widget.value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set the date from Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "my_widget.value = \"1998-12-01\" # December 1st, 1998"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Section 3 - Extra credit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The 3rd party `dateutil` library is required to continue.  https://pypi.python.org/pypi/python-dateutil"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Import the dateutil library to parse date strings.\n",
+    "from dateutil import parser"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the last section we created a fully working date picker widget.\n",
+    "Now we will add custom validation and support for labels.\n",
+    "So far, only the ISO date format \"YYYY-MM-DD\" is supported.\n",
+    "Now, we will add support for all of the date formats recognized by the Python dateutil library."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The standard property name used for widget labels is `description`.\n",
+    "In the code block below, `description` has been added to the Python widget."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class DateWidget(widgets.DOMWidget):\n",
+    "    _view_name = Unicode('DatePickerView', sync=True)\n",
+    "    value = Unicode(sync=True)\n",
+    "    description = Unicode(sync=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The traitlet machinery searches the class that the trait is defined in for methods with \"`_changed`\" suffixed onto their names.  Any method with the format \"`_X_changed`\" will be called when \"`X`\" is modified.\n",
+    "We can take advantage of this to perform validation and parsing of different date string formats.\n",
+    "Below, a method that listens to value has been added to the DateWidget."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class DateWidget(widgets.DOMWidget):\n",
+    "    _view_name = Unicode('DatePickerView', sync=True)\n",
+    "    value = Unicode(sync=True)\n",
+    "    description = Unicode(sync=True)\n",
+    "\n",
+    "    # This function automatically gets called by the traitlet machinery when\n",
+    "    # value is modified because of this function's name.\n",
+    "    def _value_changed(self, name, old_value, new_value):\n",
+    "        pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now the function parses the date string,\n",
+    "and only sets the value in the correct format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class DateWidget(widgets.DOMWidget):\n",
+    "    _view_name = Unicode('DatePickerView', sync=True)\n",
+    "    value = Unicode(sync=True)\n",
+    "    description = Unicode(sync=True)\n",
+    "    \n",
+    "    # This function automatically gets called by the traitlet machinery when\n",
+    "    # value is modified because of this function's name.\n",
+    "    def _value_changed(self, name, old_value, new_value):\n",
+    "        \n",
+    "        # Parse the date time value.\n",
+    "        try:\n",
+    "            parsed_date = parser.parse(new_value)\n",
+    "            parsed_date_string = parsed_date.strftime(\"%Y-%m-%d\")\n",
+    "        except:\n",
+    "            parsed_date_string = ''\n",
+    "        \n",
+    "        # Set the parsed date string if the current date string is different.\n",
+    "        if self.value != parsed_date_string:\n",
+    "            self.value = parsed_date_string"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, a `CallbackDispatcher` is added so the user can perform custom validation.\n",
+    "If any one of the callbacks registered with the dispatcher returns False,\n",
+    "the new date is not set.\n",
+    "\n",
+    "**Final Python code below:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class DateWidget(widgets.DOMWidget):\n",
+    "    _view_name = Unicode('DatePickerView', sync=True)\n",
+    "    value = Unicode(sync=True)\n",
+    "    description = Unicode(sync=True)\n",
+    "    \n",
+    "    def __init__(self, **kwargs):\n",
+    "        super(DateWidget, self).__init__(**kwargs)\n",
+    "        \n",
+    "        self.validate = widgets.CallbackDispatcher()\n",
+    "    \n",
+    "    # This function automatically gets called by the traitlet machinery when\n",
+    "    # value is modified because of this function's name.\n",
+    "    def _value_changed(self, name, old_value, new_value):\n",
+    "        \n",
+    "        # Parse the date time value.\n",
+    "        try:\n",
+    "            parsed_date = parser.parse(new_value)\n",
+    "            parsed_date_string = parsed_date.strftime(\"%Y-%m-%d\")\n",
+    "        except:\n",
+    "            parsed_date_string = ''\n",
+    "        \n",
+    "        # Set the parsed date string if the current date string is different.\n",
+    "        if old_value != new_value:\n",
+    "            valid = self.validate(parsed_date)\n",
+    "            if valid in (None, True):\n",
+    "                self.value = parsed_date_string\n",
+    "            else:\n",
+    "                self.value = old_value\n",
+    "                self.send_state() # The traitlet event won't fire since the value isn't changing.\n",
+    "                                  # We need to force the back-end to send the front-end the state\n",
+    "                                  # to make sure that the date control date doesn't change."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## JavaScript"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the Javascript code from the last section,\n",
+    "we add a label to the date time object.\n",
+    "The label is a div with the `widget-hlabel` class applied to it.\n",
+    "`widget-hlabel` is a class provided by the widget framework that applies special styling to a div to make it look like the rest of the horizontal labels used with the built-in widgets.\n",
+    "Similar to the `widget-hlabel` class is the `widget-hbox-single` class.\n",
+    "The `widget-hbox-single` class applies special styling to widget containers that store a single line horizontal widget.\n",
+    "\n",
+    "We hide the label if the description value is blank."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Try setting an invalid date\n",
-      "my_widget.value = \"June 12, 1999\""
-     ],
-     "language": "python",
+     "data": {
+      "application/javascript": [
+       "\n",
+       "require([\"widgets/js/widget\"], function(WidgetManager){\n",
+       "    \n",
+       "    // Define the DatePickerView\n",
+       "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
+       "        render: function(){\n",
+       "            this.$el.addClass('widget-hbox-single'); /* Apply this class to the widget container to make\n",
+       "                                                        it fit with the other built in widgets.*/\n",
+       "            // Create a label.\n",
+       "            this.$label = $('<div />')\n",
+       "                .addClass('widget-hlabel')\n",
+       "                .appendTo(this.$el)\n",
+       "                .hide(); // Hide the label by default.\n",
+       "            \n",
+       "            // Create the date picker control.\n",
+       "            this.$date = $('<input />')\n",
+       "                .attr('type', 'date')\n",
+       "                .appendTo(this.$el);\n",
+       "        },\n",
+       "        \n",
+       "        update: function() {\n",
+       "            \n",
+       "            // Set the value of the date control and then call base.\n",
+       "            this.$date.val(this.model.get('value')); // ISO format \"YYYY-MM-DDTHH:mm:ss.sssZ\" is required\n",
+       "            \n",
+       "            // Hide or show the label depending on the existance of a description.\n",
+       "            var description = this.model.get('description');\n",
+       "            if (description == undefined || description == '') {\n",
+       "                this.$label.hide();\n",
+       "            } else {\n",
+       "                this.$label.show();\n",
+       "                this.$label.text(description);\n",
+       "            }\n",
+       "            \n",
+       "            return DatePickerView.__super__.update.apply(this);\n",
+       "        },\n",
+       "        \n",
+       "        // Tell Backbone to listen to the change event of input controls (which the HTML date picker is)\n",
+       "        events: {\"change\": \"handle_date_change\"},\n",
+       "        \n",
+       "        // Callback for when the date is changed.\n",
+       "        handle_date_change: function(event) {\n",
+       "            this.model.set('value', this.$date.val());\n",
+       "            this.touch();\n",
+       "        },\n",
+       "    });\n",
+       "    \n",
+       "    // Register the DatePickerView with the widget manager.\n",
+       "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
+       "});"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript at 0x1094eef90>"
+      ]
+     },
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 23
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%javascript\n",
+    "\n",
+    "require([\"widgets/js/widget\"], function(WidgetManager){\n",
+    "    \n",
+    "    // Define the DatePickerView\n",
+    "    var DatePickerView = IPython.DOMWidgetView.extend({\n",
+    "        render: function(){\n",
+    "            this.$el.addClass('widget-hbox-single'); /* Apply this class to the widget container to make\n",
+    "                                                        it fit with the other built in widgets.*/\n",
+    "            // Create a label.\n",
+    "            this.$label = $('<div />')\n",
+    "                .addClass('widget-hlabel')\n",
+    "                .appendTo(this.$el)\n",
+    "                .hide(); // Hide the label by default.\n",
+    "            \n",
+    "            // Create the date picker control.\n",
+    "            this.$date = $('<input />')\n",
+    "                .attr('type', 'date')\n",
+    "                .appendTo(this.$el);\n",
+    "        },\n",
+    "        \n",
+    "        update: function() {\n",
+    "            \n",
+    "            // Set the value of the date control and then call base.\n",
+    "            this.$date.val(this.model.get('value')); // ISO format \"YYYY-MM-DDTHH:mm:ss.sssZ\" is required\n",
+    "            \n",
+    "            // Hide or show the label depending on the existance of a description.\n",
+    "            var description = this.model.get('description');\n",
+    "            if (description == undefined || description == '') {\n",
+    "                this.$label.hide();\n",
+    "            } else {\n",
+    "                this.$label.show();\n",
+    "                this.$label.text(description);\n",
+    "            }\n",
+    "            \n",
+    "            return DatePickerView.__super__.update.apply(this);\n",
+    "        },\n",
+    "        \n",
+    "        // Tell Backbone to listen to the change event of input controls (which the HTML date picker is)\n",
+    "        events: {\"change\": \"handle_date_change\"},\n",
+    "        \n",
+    "        // Callback for when the date is changed.\n",
+    "        handle_date_change: function(event) {\n",
+    "            this.model.set('value', this.$date.val());\n",
+    "            this.touch();\n",
+    "        },\n",
+    "    });\n",
+    "    \n",
+    "    // Register the DatePickerView with the widget manager.\n",
+    "    WidgetManager.register_widget_view('DatePickerView', DatePickerView);\n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To test the drawing of the label we create the widget like normal but supply the additional description property a value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Add some additional widgets for aesthetic purpose\n",
+    "display(widgets.TextWidget(description=\"First:\"))\n",
+    "display(widgets.TextWidget(description=\"Last:\"))\n",
+    "\n",
+    "my_widget = DateWidget()\n",
+    "display(my_widget)\n",
+    "my_widget.description=\"DOB:\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we will try to create a widget that only accepts dates in the year 2014.  We render the widget without a description to verify that it can still render without a label."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "my_widget = DateWidget()\n",
+    "display(my_widget)\n",
+    "\n",
+    "def require_2014(date):\n",
+    "    return not date is None and date.year == 2014\n",
+    "my_widget.validate.register_callback(require_2014)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Try setting a valid date\n",
+    "my_widget.value = \"December 2, 2014\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Try setting an invalid date\n",
+    "my_widget.value = \"June 12, 1999\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_widget.value"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "u'2014-12-02'"
+      ]
+     },
+     "execution_count": 24,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 24,
-       "text": [
-        "u'2014-12-02'"
-       ]
-      }
-     ],
-     "prompt_number": 24
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "my_widget.value"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "cell_tags": [
+   [
+    "<None>",
+    null
+   ]
+  ]
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Exploring Graphs.ipynb b/examples/Interactive Widgets/Exploring Graphs.ipynb
index bd8f41a..05f7c1d 100644
--- a/examples/Interactive Widgets/Exploring Graphs.ipynb
+++ b/examples/Interactive Widgets/Exploring Graphs.ipynb
@@ -1,117 +1,838 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:61f24f38b76cb12d46c178eadc5d85d61bd0fc27f959236ea756ef8e1adc2693"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Explore Random Graphs Using NetworkX"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In this example, we build a simple UI for exploring random graphs with [NetworkX](http://networkx.github.io/)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.html.widgets import interact"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline\n",
-      "import matplotlib.pyplot as plt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import networkx as nx"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# wrap a few graph generation functions so they have the same signature\n",
-      "\n",
-      "def random_lobster(n, m, k, p):\n",
-      "    return nx.random_lobster(n, p, p / m)\n",
-      "\n",
-      "def powerlaw_cluster(n, m, k, p):\n",
-      "    return nx.powerlaw_cluster_graph(n, m, p)\n",
-      "\n",
-      "def erdos_renyi(n, m, k, p):\n",
-      "    return nx.erdos_renyi_graph(n, p)\n",
-      "\n",
-      "def newman_watts_strogatz(n, m, k, p):\n",
-      "    return nx.newman_watts_strogatz_graph(n, k, p)\n",
-      "\n",
-      "def plot_random_graph(n, m, k, p, generator):\n",
-      "    g = generator(n, m, k, p)\n",
-      "    nx.draw(g)\n",
-      "    plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 14
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Explore Random Graphs Using NetworkX"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example, we build a simple UI for exploring random graphs with [NetworkX](http://networkx.github.io/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.html.widgets import interact"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import networkx as nx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# wrap a few graph generation functions so they have the same signature\n",
+    "\n",
+    "def random_lobster(n, m, k, p):\n",
+    "    return nx.random_lobster(n, p, p / m)\n",
+    "\n",
+    "def powerlaw_cluster(n, m, k, p):\n",
+    "    return nx.powerlaw_cluster_graph(n, m, p)\n",
+    "\n",
+    "def erdos_renyi(n, m, k, p):\n",
+    "    return nx.erdos_renyi_graph(n, p)\n",
+    "\n",
+    "def newman_watts_strogatz(n, m, k, p):\n",
+    "    return nx.newman_watts_strogatz_graph(n, k, p)\n",
+    "\n",
+    "def plot_random_graph(n, m, k, p, generator):\n",
+    "    g = generator(n, m, k, p)\n",
+    "    nx.draw(g)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "interact(plot_random_graph, n=(2,30), m=(1,10), k=(1,10), p=(0.0, 1.0, 0.001),\n",
-      "        generator={'lobster': random_lobster,\n",
-      "                   'power law': powerlaw_cluster,\n",
-      "                   'Newman-Watts-Strogatz': newman_watts_strogatz,\n",
-      "                   u'Erd\u0151s-R\u00e9nyi': erdos_renyi,\n",
-      "                   });"
-     ],
-     "language": "python",
+     "data": {
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+       "ERERJ1DAioiIOIECVkRExAkUsCIiIk6ggBUREXECBayIiIgTKGBFREScQAErIsRum1wAAACiSURB\n",
+       "VCLiBApYERERJ1DAioiIOIECVkRExAkUsCIiIk6ggBUREXECBayIiIgTKGBFREScQAErIiLiBApY\n",
+       "ERERJ1DAioiIOIECVkRExAkUsCIiIk6ggBUREXECBayIiIgTKGBFREScQAErIiLiBApYERERJ1DA\n",
+       "ioiIOIECVkRExAkUsCIiIk6ggBUREXECBayIiIgTKGBFREScQAErIiLiBP8HyD8bEel+oXYAAAAA\n",
+       "SUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1076c6e10>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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cnK6urvznn3+KdE8//fQTPTw8Cn1d1mktnp6e7NKlC2vWrElVVVVqamoWaVrL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UcfkZqayo033uhQ/dIBWFnY2RuKZT/n5eXh6uqK1WrFZrNhs9mq/Ny0aVN8fX3Jycnh7MGDUFRU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\nSUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x1076c6e10>"
-       ]
-      }
-     ],
-     "prompt_number": 16
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "interact(plot_random_graph, n=(2,30), m=(1,10), k=(1,10), p=(0.0, 1.0, 0.001),\n",
+    "        generator={'lobster': random_lobster,\n",
+    "                   'power law': powerlaw_cluster,\n",
+    "                   'Newman-Watts-Strogatz': newman_watts_strogatz,\n",
+    "                   u'Erd\u0151s-R\u00e9nyi': erdos_renyi,\n",
+    "                   });"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:61f24f38b76cb12d46c178eadc5d85d61bd0fc27f959236ea756ef8e1adc2693"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Export As (nbconvert).ipynb b/examples/Interactive Widgets/Export As (nbconvert).ipynb
index cf8340b..9967b18 100644
--- a/examples/Interactive Widgets/Export As (nbconvert).ipynb
+++ b/examples/Interactive Widgets/Export As (nbconvert).ipynb
@@ -1,4 +1,157 @@
 {
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Widget related imports\n",
+    "from IPython.html import widgets\n",
+    "from IPython.display import display, clear_output, Javascript\n",
+    "from IPython.utils.traitlets import Unicode\n",
+    "\n",
+    "# nbconvert related imports\n",
+    "from IPython.nbconvert import get_export_names, export_by_name\n",
+    "from IPython.nbconvert.writers import FilesWriter\n",
+    "from IPython.nbformat import current\n",
+    "from IPython.nbconvert.utils.exceptions import ConversionException"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a text Widget without displaying it.  The widget will be used to store the notebook's name which is otherwise only available in the front-end."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "notebook_name = widgets.Text()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the current notebook's name by pushing JavaScript to the browser that sets the notebook name in a string widget."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "js = \"\"\"var model = IPython.notebook.kernel.widget_manager.get_model('{model_id}');\n",
+    "model.set('value', IPython.notebook.notebook_name);\n",
+    "model.save();\"\"\".format(model_id=notebook_name.model_id)\n",
+    "display(Javascript(data=js))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "filename = notebook_name.value\n",
+    "filename"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the widget that will allow the user to Export the current notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "exporter_names = widgets.Dropdown(values=get_export_names(), value='html')\n",
+    "export_button = widgets.Button(description=\"Export\")\n",
+    "download_link = widgets.HTML(visible=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Export the notebook when the export button is clicked."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "file_writer = FilesWriter()\n",
+    "\n",
+    "def export(name, nb):\n",
+    "    \n",
+    "    # Get a unique key for the notebook and set it in the resources object.\n",
+    "    notebook_name = name[:name.rfind('.')]\n",
+    "    resources = {}\n",
+    "    resources['unique_key'] = notebook_name\n",
+    "    resources['output_files_dir'] = '%s_files' % notebook_name\n",
+    "\n",
+    "    # Try to export\n",
+    "    try:\n",
+    "        output, resources = export_by_name(exporter_names.value, nb)\n",
+    "    except ConversionException as e:\n",
+    "        download_link.value = \"<br>Could not export notebook!\"\n",
+    "    else:\n",
+    "        write_results = file_writer.write(output, resources, notebook_name=notebook_name)\n",
+    "    \n",
+    "        download_link.value = \"<br>Results: <a href='files/{filename}'><i>\\\"{filename}\\\"</i></a>\".format(filename=write_results)\n",
+    "        download_link.visible = True\n",
+    "        \n",
+    "def handle_export(widget):\n",
+    "    with open(filename, 'r') as f:\n",
+    "        export(filename, current.read(f, 'json'))\n",
+    "export_button.on_click(handle_export)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Display the controls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "display(exporter_names, export_button, download_link)"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "codemirror_mode": {
@@ -9,160 +162,8 @@
    "language": "python",
    "name": "python2"
   },
-  "name": "",
   "signature": "sha256:dc519f61f8a484b3a8f0b05ad1891fad701324c837325b12d31cce4f31647c2f"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Widget related imports\n",
-      "from IPython.html import widgets\n",
-      "from IPython.display import display, clear_output, Javascript\n",
-      "from IPython.utils.traitlets import Unicode\n",
-      "\n",
-      "# nbconvert related imports\n",
-      "from IPython.nbconvert import get_export_names, export_by_name\n",
-      "from IPython.nbconvert.writers import FilesWriter\n",
-      "from IPython.nbformat import current\n",
-      "from IPython.nbconvert.utils.exceptions import ConversionException"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Create a text Widget without displaying it.  The widget will be used to store the notebook's name which is otherwise only available in the front-end."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "notebook_name = widgets.Text()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Get the current notebook's name by pushing JavaScript to the browser that sets the notebook name in a string widget."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "js = \"\"\"var model = IPython.notebook.kernel.widget_manager.get_model('{model_id}');\n",
-      "model.set('value', IPython.notebook.notebook_name);\n",
-      "model.save();\"\"\".format(model_id=notebook_name.model_id)\n",
-      "display(Javascript(data=js))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "filename = notebook_name.value\n",
-      "filename"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Create the widget that will allow the user to Export the current notebook."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "exporter_names = widgets.Dropdown(values=get_export_names(), value='html')\n",
-      "export_button = widgets.Button(description=\"Export\")\n",
-      "download_link = widgets.HTML(visible=False)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Export the notebook when the export button is clicked."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "file_writer = FilesWriter()\n",
-      "\n",
-      "def export(name, nb):\n",
-      "    \n",
-      "    # Get a unique key for the notebook and set it in the resources object.\n",
-      "    notebook_name = name[:name.rfind('.')]\n",
-      "    resources = {}\n",
-      "    resources['unique_key'] = notebook_name\n",
-      "    resources['output_files_dir'] = '%s_files' % notebook_name\n",
-      "\n",
-      "    # Try to export\n",
-      "    try:\n",
-      "        output, resources = export_by_name(exporter_names.value, nb)\n",
-      "    except ConversionException as e:\n",
-      "        download_link.value = \"<br>Could not export notebook!\"\n",
-      "    else:\n",
-      "        write_results = file_writer.write(output, resources, notebook_name=notebook_name)\n",
-      "    \n",
-      "        download_link.value = \"<br>Results: <a href='files/{filename}'><i>\\\"{filename}\\\"</i></a>\".format(filename=write_results)\n",
-      "        download_link.visible = True\n",
-      "        \n",
-      "def handle_export(widget):\n",
-      "    with open(filename, 'r') as f:\n",
-      "        export(filename, current.read(f, 'json'))\n",
-      "export_button.on_click(handle_export)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Display the controls."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display(exporter_names, export_button, download_link)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Factoring.ipynb b/examples/Interactive Widgets/Factoring.ipynb
index d492e3c..bf7e969 100644
--- a/examples/Interactive Widgets/Factoring.ipynb
+++ b/examples/Interactive Widgets/Factoring.ipynb
@@ -1,141 +1,139 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:5f38c57d9570e4b6f6edf98c38a7bff81cd16365baa11fd40265f1504bfc008c"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Factoring Polynomials with SymPy"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is an example that uses [SymPy](http://sympy.org/en/index.html) to factor polynomials."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.html.widgets import interact\n",
-      "from IPython.display import display"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from sympy import Symbol, Eq, factor, init_printing\n",
-      "init_printing(use_latex='mathjax')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x = Symbol('x')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def factorit(n):\n",
-      "    display(Eq(x**n-1, factor(x**n-1)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Notice how the output of the `factorit` function is properly formatted LaTeX."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "factorit(12)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x^{12} - 1 = \\left(x - 1\\right) \\left(x + 1\\right) \\left(x^{2} + 1\\right) \\left(x^{2} - x + 1\\right) \\left(x^{2} + x + 1\\right) \\left(x^{4} - x^{2} + 1\\right)$$"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        " 12                       \u239b 2    \u239e \u239b 2        \u239e \u239b 2        \u239e \u239b 4    2    \u239e\n",
-        "x   - 1 = (x - 1)\u22c5(x + 1)\u22c5\u239dx  + 1\u23a0\u22c5\u239dx  - x + 1\u23a0\u22c5\u239dx  + x + 1\u23a0\u22c5\u239dx  - x  + 1\u23a0"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Factoring Polynomials with SymPy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is an example that uses [SymPy](http://sympy.org/en/index.html) to factor polynomials."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.html.widgets import interact\n",
+    "from IPython.display import display"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sympy import Symbol, Eq, factor, init_printing\n",
+    "init_printing(use_latex='mathjax')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x = Symbol('x')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def factorit(n):\n",
+    "    display(Eq(x**n-1, factor(x**n-1)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice how the output of the `factorit` function is properly formatted LaTeX."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "interact(factorit, n=(2,40));"
-     ],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$x^{12} - 1 = \\left(x - 1\\right) \\left(x + 1\\right) \\left(x^{2} + 1\\right) \\left(x^{2} - x + 1\\right) \\left(x^{2} + x + 1\\right) \\left(x^{4} - x^{2} + 1\\right)$$"
+      ],
+      "text/plain": [
+       " 12                       \u239b 2    \u239e \u239b 2        \u239e \u239b 2        \u239e \u239b 4    2    \u239e\n",
+       "x   - 1 = (x - 1)\u22c5(x + 1)\u22c5\u239dx  + 1\u23a0\u22c5\u239dx  - x + 1\u23a0\u22c5\u239dx  + x + 1\u23a0\u22c5\u239dx  - x  + 1\u23a0"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "latex": [
-        "$$x^{21} - 1 = \\left(x - 1\\right) \\left(x^{2} + x + 1\\right) \\left(x^{6} + x^{5} + x^{4} + x^{3} + x^{2} + x + 1\\right) \\left(x^{12} - x^{11} + x^{9} - x^{8} + x^{6} - x^{4} + x^{3} - x + 1\\right)$$"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        " 21               \u239b 2        \u239e \u239b 6    5    4    3    2        \u239e \u239b 12    11    \n",
-        "x   - 1 = (x - 1)\u22c5\u239dx  + x + 1\u23a0\u22c5\u239dx  + x  + x  + x  + x  + x + 1\u23a0\u22c5\u239dx   - x   + x\n",
-        "\n",
-        "9    8    6    4    3        \u239e\n",
-        "  - x  + x  - x  + x  - x + 1\u23a0"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "factorit(12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
+     "data": {
+      "text/latex": [
+       "$$x^{21} - 1 = \\left(x - 1\\right) \\left(x^{2} + x + 1\\right) \\left(x^{6} + x^{5} + x^{4} + x^{3} + x^{2} + x + 1\\right) \\left(x^{12} - x^{11} + x^{9} - x^{8} + x^{6} - x^{4} + x^{3} - x + 1\\right)$$"
+      ],
+      "text/plain": [
+       " 21               \u239b 2        \u239e \u239b 6    5    4    3    2        \u239e \u239b 12    11    \n",
+       "x   - 1 = (x - 1)\u22c5\u239dx  + x + 1\u23a0\u22c5\u239dx  + x  + x  + x  + x  + x + 1\u23a0\u22c5\u239dx   - x   + x\n",
+       "\n",
+       "9    8    6    4    3        \u239e\n",
+       "  - x  + x  - x  + x  - x + 1\u23a0"
+      ]
+     },
      "metadata": {},
-     "outputs": []
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "interact(factorit, n=(2,40));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:5f38c57d9570e4b6f6edf98c38a7bff81cd16365baa11fd40265f1504bfc008c"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/File Upload Widget.ipynb b/examples/Interactive Widgets/File Upload Widget.ipynb
index 004da18..6ae3a71 100644
--- a/examples/Interactive Widgets/File Upload Widget.ipynb
+++ b/examples/Interactive Widgets/File Upload Widget.ipynb
@@ -1,4 +1,162 @@
 {
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import base64\n",
+    "from __future__ import print_function # py 2.7 compat.\n",
+    "from IPython.html import widgets # Widget definitions.\n",
+    "from IPython.utils.traitlets import Unicode # Traitlet needed to add synced attributes to the widget."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a custom widget that allows the user to upload file data to the notebook server.  The file data is sent via a statefull `value` attribute of the widget.  The widget has an upload failed event that fires in the front-end and is echoed to the back-end using a custom msg."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class FileWidget(widgets.DOMWidget):\n",
+    "    _view_name = Unicode('FilePickerView', sync=True)\n",
+    "    value = Unicode(sync=True)\n",
+    "    filename = Unicode(sync=True)\n",
+    "    \n",
+    "    def __init__(self, **kwargs):\n",
+    "        \"\"\"Constructor\"\"\"\n",
+    "        widgets.DOMWidget.__init__(self, **kwargs) # Call the base.\n",
+    "        \n",
+    "        # Allow the user to register error callbacks with the following signatures:\n",
+    "        #    callback()\n",
+    "        #    callback(sender)\n",
+    "        self.errors = widgets.CallbackDispatcher(accepted_nargs=[0, 1])\n",
+    "        \n",
+    "        # Listen for custom msgs\n",
+    "        self.on_msg(self._handle_custom_msg)\n",
+    "\n",
+    "    def _handle_custom_msg(self, content):\n",
+    "        \"\"\"Handle a msg from the front-end.\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        content: dict\n",
+    "            Content of the msg.\"\"\"\n",
+    "        if 'event' in content and content['event'] == 'error':\n",
+    "            self.errors()\n",
+    "            self.errors(self)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%javascript\n",
+    "\n",
+    "require([\"widgets/js/widget\", \"widgets/js/manager\"], function(widget, manager){\n",
+    "\n",
+    "    var FilePickerView = widget.DOMWidgetView.extend({\n",
+    "        render: function(){\n",
+    "            // Render the view.\n",
+    "            this.setElement($('<input />')\n",
+    "                .attr('type', 'file'));\n",
+    "        },\n",
+    "        \n",
+    "        events: {\n",
+    "            // List of events and their handlers.\n",
+    "            'change': 'handle_file_change',\n",
+    "        },\n",
+    "       \n",
+    "        handle_file_change: function(evt) { \n",
+    "            // Handle when the user has changed the file.\n",
+    "            \n",
+    "            // Retrieve the first (and only!) File from the FileList object\n",
+    "            var file = evt.target.files[0];\n",
+    "            if (file) {\n",
+    "\n",
+    "                // Read the file's textual content and set value to those contents.\n",
+    "                var that = this;\n",
+    "                var file_reader = new FileReader();\n",
+    "                file_reader.onload = function(e) {\n",
+    "                    that.model.set('value', e.target.result);\n",
+    "                    that.touch();\n",
+    "                }\n",
+    "                file_reader.readAsText(file);\n",
+    "            } else {\n",
+    "\n",
+    "                // The file couldn't be opened.  Send an error msg to the\n",
+    "                // back-end.\n",
+    "                this.send({ 'event': 'error' });\n",
+    "            }\n",
+    "\n",
+    "            // Set the filename of the file.\n",
+    "            this.model.set('filename', file.name);\n",
+    "            this.touch();\n",
+    "        },\n",
+    "    });\n",
+    "        \n",
+    "    // Register the DatePickerView with the widget manager.\n",
+    "    manager.WidgetManager.register_widget_view('FilePickerView', FilePickerView);\n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following shows how the file widget can be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "file_widget = FileWidget()\n",
+    "\n",
+    "# Register an event to echo the filename when it has been changed.\n",
+    "def file_loading():\n",
+    "    print(\"Loading %s\" % file_widget.filename)\n",
+    "file_widget.on_trait_change(file_loading, 'filename')\n",
+    "\n",
+    "# Register an event to echo the filename and contents when a file\n",
+    "# has been uploaded.\n",
+    "def file_loaded():\n",
+    "    print(\"Loaded, file contents: %s\" % file_widget.value)\n",
+    "file_widget.on_trait_change(file_loaded, 'value')\n",
+    "\n",
+    "# Register an event to print an error message when a file could not\n",
+    "# be opened.  Since the error messages are not handled through\n",
+    "# traitlets but instead handled through custom msgs, the registration\n",
+    "# of the handler is different than the two examples above.  Instead\n",
+    "# the API provided by the CallbackDispatcher must be used.\n",
+    "def file_failed():\n",
+    "    print(\"Could not load file contents of %s\" % file_widget.filename)\n",
+    "file_widget.errors.register_callback(file_failed)\n",
+    "\n",
+    "file_widget"
+   ]
+  }
+ ],
  "metadata": {
   "cell_tags": [
    [
@@ -15,168 +173,8 @@
    "language": "python",
    "name": "python2"
   },
-  "name": "",
   "signature": "sha256:25c43e4f530e049319d587a7e47734cab5b4b4f608f71aa234e821f43076afe8"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import base64\n",
-      "from __future__ import print_function # py 2.7 compat.\n",
-      "from IPython.html import widgets # Widget definitions.\n",
-      "from IPython.utils.traitlets import Unicode # Traitlet needed to add synced attributes to the widget."
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This is a custom widget that allows the user to upload file data to the notebook server.  The file data is sent via a statefull `value` attribute of the widget.  The widget has an upload failed event that fires in the front-end and is echoed to the back-end using a custom msg."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class FileWidget(widgets.DOMWidget):\n",
-      "    _view_name = Unicode('FilePickerView', sync=True)\n",
-      "    value = Unicode(sync=True)\n",
-      "    filename = Unicode(sync=True)\n",
-      "    \n",
-      "    def __init__(self, **kwargs):\n",
-      "        \"\"\"Constructor\"\"\"\n",
-      "        widgets.DOMWidget.__init__(self, **kwargs) # Call the base.\n",
-      "        \n",
-      "        # Allow the user to register error callbacks with the following signatures:\n",
-      "        #    callback()\n",
-      "        #    callback(sender)\n",
-      "        self.errors = widgets.CallbackDispatcher(accepted_nargs=[0, 1])\n",
-      "        \n",
-      "        # Listen for custom msgs\n",
-      "        self.on_msg(self._handle_custom_msg)\n",
-      "\n",
-      "    def _handle_custom_msg(self, content):\n",
-      "        \"\"\"Handle a msg from the front-end.\n",
-      "\n",
-      "        Parameters\n",
-      "        ----------\n",
-      "        content: dict\n",
-      "            Content of the msg.\"\"\"\n",
-      "        if 'event' in content and content['event'] == 'error':\n",
-      "            self.errors()\n",
-      "            self.errors(self)\n",
-      "    "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "\n",
-      "require([\"widgets/js/widget\", \"widgets/js/manager\"], function(widget, manager){\n",
-      "\n",
-      "    var FilePickerView = widget.DOMWidgetView.extend({\n",
-      "        render: function(){\n",
-      "            // Render the view.\n",
-      "            this.setElement($('<input />')\n",
-      "                .attr('type', 'file'));\n",
-      "        },\n",
-      "        \n",
-      "        events: {\n",
-      "            // List of events and their handlers.\n",
-      "            'change': 'handle_file_change',\n",
-      "        },\n",
-      "       \n",
-      "        handle_file_change: function(evt) { \n",
-      "            // Handle when the user has changed the file.\n",
-      "            \n",
-      "            // Retrieve the first (and only!) File from the FileList object\n",
-      "            var file = evt.target.files[0];\n",
-      "            if (file) {\n",
-      "\n",
-      "                // Read the file's textual content and set value to those contents.\n",
-      "                var that = this;\n",
-      "                var file_reader = new FileReader();\n",
-      "                file_reader.onload = function(e) {\n",
-      "                    that.model.set('value', e.target.result);\n",
-      "                    that.touch();\n",
-      "                }\n",
-      "                file_reader.readAsText(file);\n",
-      "            } else {\n",
-      "\n",
-      "                // The file couldn't be opened.  Send an error msg to the\n",
-      "                // back-end.\n",
-      "                this.send({ 'event': 'error' });\n",
-      "            }\n",
-      "\n",
-      "            // Set the filename of the file.\n",
-      "            this.model.set('filename', file.name);\n",
-      "            this.touch();\n",
-      "        },\n",
-      "    });\n",
-      "        \n",
-      "    // Register the DatePickerView with the widget manager.\n",
-      "    manager.WidgetManager.register_widget_view('FilePickerView', FilePickerView);\n",
-      "});"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The following shows how the file widget can be used."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "file_widget = FileWidget()\n",
-      "\n",
-      "# Register an event to echo the filename when it has been changed.\n",
-      "def file_loading():\n",
-      "    print(\"Loading %s\" % file_widget.filename)\n",
-      "file_widget.on_trait_change(file_loading, 'filename')\n",
-      "\n",
-      "# Register an event to echo the filename and contents when a file\n",
-      "# has been uploaded.\n",
-      "def file_loaded():\n",
-      "    print(\"Loaded, file contents: %s\" % file_widget.value)\n",
-      "file_widget.on_trait_change(file_loaded, 'value')\n",
-      "\n",
-      "# Register an event to print an error message when a file could not\n",
-      "# be opened.  Since the error messages are not handled through\n",
-      "# traitlets but instead handled through custom msgs, the registration\n",
-      "# of the handler is different than the two examples above.  Instead\n",
-      "# the API provided by the CallbackDispatcher must be used.\n",
-      "def file_failed():\n",
-      "    print(\"Could not load file contents of %s\" % file_widget.filename)\n",
-      "file_widget.errors.register_callback(file_failed)\n",
-      "\n",
-      "file_widget"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Image Browser.ipynb b/examples/Interactive Widgets/Image Browser.ipynb
index 8ddd776..96a8005 100644
--- a/examples/Interactive Widgets/Image Browser.ipynb
+++ b/examples/Interactive Widgets/Image Browser.ipynb
@@ -1,124 +1,305 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:4e9f4ce8fa9be2e33ffdae399daec11bf3ef63a6f0065b1d59739310ebfe8008"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Image Browser"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This example shows how to browse through a set of images with a slider."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline\n",
-      "import matplotlib.pyplot as plt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.html.widgets import interact"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from sklearn import datasets"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We will use the digits dataset from [scikit-learn](http://scikit-learn.org/stable/)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "digits = datasets.load_digits()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def browse_images(digits):\n",
-      "    n = len(digits.images)\n",
-      "    def view_image(i):\n",
-      "        plt.imshow(digits.images[i], cmap=plt.cm.gray_r, interpolation='nearest')\n",
-      "        plt.title('Training: %s' % digits.target[i])\n",
-      "        plt.show()\n",
-      "    interact(view_image, i=(0,n-1))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 14
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Image Browser"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example shows how to browse through a set of images with a slider."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.html.widgets import interact"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use the digits dataset from [scikit-learn](http://scikit-learn.org/stable/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "digits = datasets.load_digits()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def browse_images(digits):\n",
+    "    n = len(digits.images)\n",
+    "    def view_image(i):\n",
+    "        plt.imshow(digits.images[i], cmap=plt.cm.gray_r, interpolation='nearest')\n",
+    "        plt.title('Training: %s' % digits.target[i])\n",
+    "        plt.show()\n",
+    "    interact(view_image, i=(0,n-1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "browse_images(digits)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {
-        "png": {
-         "height": 390,
-         "width": 370
-        }
-       },
-       "output_type": "display_data",
-       "png": 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-       "text": [
-        "<matplotlib.figure.Figure at 0x111c03f50>"
-       ]
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+       "1j6Y5J+T3DbD3vo/nOTLcWffvfXUDCvmL2utnZzkwxkW6U5KckOGC+pdtLx3npbkAUne3Xt/X/Vk\n",
+       "Fk2U76He++WttR/PsLLwiCSHZNjn9Q1JTu+9f75wemvdvTOcN7bN1iQ/muF/yGT4GovyPbNtdfGg\n",
+       "DBfJ7MzWDNt8ivKVuyzJr2a4acjRGXZZuTnJvyT57SS/7x9hbmX+Ncnzkjwqw8W0D06yf4YL585M\n",
+       "8go7Me2d3vs/tNaOybA7yGOSPCPD6vhbk/x27/2TlfNbq1prB2a4GdvNSV5QPB0AAAAAAAAAAAAA\n",
+       "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABgbv8fiSCmJNH72uAAAAAA\n",
+       "SUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x111c03f50>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 390,
+       "width": 370
       }
-     ],
-     "prompt_number": 15
+     },
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "browse_images(digits)"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:4e9f4ce8fa9be2e33ffdae399daec11bf3ef63a6f0065b1d59739310ebfe8008"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Image Processing.ipynb b/examples/Interactive Widgets/Image Processing.ipynb
index 7a51c54..c6d4669 100644
--- a/examples/Interactive Widgets/Image Processing.ipynb
+++ b/examples/Interactive Widgets/Image Processing.ipynb
@@ -1,381 +1,26770 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:d75ab1c53fa3389eeac78ecf8e89beb52871950f296aad25776699b6d6125037"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Image Manipulation with skimage"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This example builds a simple UI for performing basic image manipulation with [scikit-image](http://scikit-image.org/)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.html.widgets import interact, interactive, fixed\n",
-      "from IPython.display import display"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import skimage\n",
-      "from skimage import data, filter, io"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "i = data.coffee()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "io.Image(i)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "png": 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J/Wah/7oW/6uKaG8LNCmiSAiSEuJbgUQB1IN0ASkVaYBQBXSjVU0RkbHQ9voWWYSLgpLW\ns4pvtQqECUKB9ZWrNPuKESEVQAiCxPXqj1s+oJS9Qdsq6Q1/rd4baKYqM3+rwo/nvlXBIt3sa3tS\ni4al9CCM3flgSkGwqmCkm+ENgNoCxEAVm0kJQZTB3gr02sVDlaTp0aGgBMAXAE/Vu6f+7z4ef+V4\nT/o4vXmNnDV2eXcVYgv41u63dMrFqthcGVmpp4/H04Vb3/qmp8vuGlWB1sdzPn337fXbd3nebdSX\n+12B3jYgi36LcVjuFfM8X+fctouEkeM9+x3805l1v+3ezMbT07G51ah5zvPMsh4VEui4bH49uqiz\norajbftUJSgza82sGevl5bzPEfOklZ1RKG/W0EaM7759FxE5QSNqO+c5CzMjCzNio+/cxWnhk5wA\nu9F4Ko/etrq/0NWBjFa+mRrr/aX98dP2sXEmz8zLx31rPWLEPc84E8Y7BpHA5ThabyGd5wtGbAXk\nGFUS962TFlU1q/fDu5QxhuaM4+JbJyrOLKvZtkZnjooxRD+umzkqKq3QygOZ0+DHpRuQM+/3GDGb\nZL3RLJVQVtU4o5Js7NbMbadF0QV2B5hVY0a3gyVONKA3yoslE20zo8eMB/sQMpqsAFVVwTxt6kRr\nbL2q7jFfs5L9nCfut6vl09YvxwaDSjWm9+Y0VkVGzKkyWM107y5i23ZGxpxVCWc/DoeVapwxY0Cb\nO3vvZouTxD0Cw8eIRDTyOPa+Yd/d2hZjnqUxMmYVq3szB4iKnDXnYGD23fbDOxtoNQXA0cwgWJZy\nDKNJkMgsNPRuvTVVm1k1g1rUBSstNDOjE952AbCtQCoJyj0zTA6RHtbc6EIs7Ac2ccGsqkxjs81T\npJkeKxmFXgVVlsKtb11tLSiVi8STmpBVaFC5jzTlaFbtuDjNBXfLFGTB9N7MDNayag5l8pxnBivz\nshlJoomrpWRZYgkKqmntxkmhmntzSA5C5kBSb3CIUiEtUVIpzUxCllPN0MxhjqqqKQUqSRVoRgFV\nmSLpbm0dAWOWiTiyJNJlNA8VERlhRlhnii4a6WuRZxGGbuZ784yqGlXhq2DUY0E3a7Rmvbd20I/I\nyCz6yKAy6wGuCiVpqyojHUa0imxrkSUda+EXlCkW2RaU4V8iqt8WRQALv9EMKqqTSlvyEUCQgUJW\nmJMoaMlySmi0ylgEZvGthpEErAQBpCWAShLCG7OaetRLrWJdpL8BqVXAgN9UMOFBv0qiLZ74Qfku\nkLfKjxGQ8IB9jy5e5dvh0WqVpQUXiwRoi5lxrNYflrYHlfm2PyD0eHUSJZk3IR7nUPaVV7ZHsV70\n7gMUQkIVFxeBMlqhWnoRf/yw/7un7VuX5hmU0UoBaJe7CTU3GptXTdzddhGmKpCXp/2bb9+1Din6\n3vauMUYrg/B8//ny4Rsex/1+5l1nDIK0Qqpg99vLpqaoL6+ft0/vr8f19curJrbWAv3LqefbdFXf\n9fHdu6tjnvky7nNYFmRJ0bZ2Odp+dBRFZ7/AVhdEdHZvBWbWy32O+wTO3rjTbHNjM8Ay93acc8yz\nDH4KWTEnztAIy1FuvKhJE35M2T3vtAZiVFkVxozD2YzorXEjPn3qHzre9eabf3mZ6Ha57My83/R6\nxkSYWswQxX2/HJfmFRnnfTSoa8wBBWV0NwoZk5nbtpWUMcfIzPrw/qk3m0rNcNGaIasKMYdv3c1I\nZJZ3b5vHRIJW2i8bpKy43+4q694MXjotvcxAyxhaegHrm7tZVCat2JrRIjIyttagRAXgrbH1TRje\n6bzMjDljtSVKX4VvFEoAU2dE2zZAVTqrgpGzzqwasSG33a0DYkRI3LfW3LOqwDmn1sa3YBCrWt8y\n55gz7gHLbb9626z1OWZyRmU3eEfvjbIRFbOUOnOKcHm/2mXr/dhlpsBdEalZaNZ6r6030qqUUyOy\ninvH1vZGgxhxd2/uHQbAVKqZWtXA3VQgnH3btoACwZxb2+ChMpRV3QnfmkMFlZtrpreWgLlVpMEg\nru04ESgkUlXwZnKYZ0xJxmbuQLm7PBFNchiEXDScO3YXDFQyc23ijU1wtbZRkXPcpqHUbXPrnb1v\nVMyoKBnN2uo5KnLOzJGcA2dMSltvQJMKXpVGpapUVAjmVWfVpLRZp7t5QaS1yAmAcBLGDQsKCzXv\nwJYFWBbUnAaZG1CLiVUOIZAFmAiYQ55ZUAIJFemLw2VJLDMrVcQ0T3MvPfpzAPVQWS6NohkBa3Is\nsNH6kbhWjjhvBEFVDfnVaMT07vBubTMIbgqSlY/GVbS+k0zWWmWzyjKKaFVLS1IP+dPqmOMrVFoF\n1/CbZuFfiDgW7soCTcoqLaZ6retrP2G+hCVGSCjI3hSgqwY9hJYPHYxAJx+9X4oLMS0cSjG5Go6/\n3h6SQDx0pPYXOhoBfMNzmQCEtt7v18Yh3t68LxTLUpFgMyupVpUlf33fLMqUD+BYgD1q3oMu/npy\n7K0j+DgkldP5AHkgTBULUHNBWIEyVZqZYOuPkDQpBZW19t989+5vLvzd9fLy+acR5YdR2gx738Y8\nO+mEWSIUxKW5MGHVre9P7Q8/fjjv91n88OmDJ855610RYww8ffqmXw6d8Tpe3XfKI+5KjCFvdXSP\n13nL8Xf/4b/f3tuf/j//2Y3VW2Z9vr88D+27/fB0fX9YR573mEMVqiprnc37huPSm1upZ9/UPMGs\nhHfHBNxbf7nf7ne9vLzuZN943frmePkyrpen7pXhX77cXoenCNVNOOc4w2cwaxy7X/dLdfNuE6wU\nWquUSsj0rXk3y5bQGaP5+ftPl2+fuhuimEK/HE/vjzzHeZu3U1nIqpx3CNuFWwvm8zkxxtkAB2rO\nCqOxmal0nsPMtn1TMsY55gTzennnzYtiwHoj3Bnj1Jy5bReaKGWWb9vWGHNU0bv1vQsRk7eX4c2O\ntslUVS7CjdScMus7qznZfQu/190FdTdaVkna2laaXinV0nCiqu9dshlZmiknC6Q9ZGj5pvCiJN8b\nvUVMoeCW6TmHxXkxu3bsXWathNZ6cyNKtCookkbKFDVRbetmmnPMBITat82wruoxNKJK1jfv3lsr\nAjPGHJETSTnovXrzbett2yhH5L0qp7LQmu2t9Y0MTekcp5KCul8bi6gZWSkZWTB79DsjpkQpiV4V\nQHNSNXNQDTXL7KAGyiHJpsG90elRveYZ551bR8KWRJCUCIc5aWWyqgJlbijSWkEQ2HYgDLROwooT\nTCMSPqfE7J3N2AixMbOAxTYJogluZGmyteYNm7fWzB0qRQEF+tqzt6JHZJaimBkxY+sbhcb0RqhX\nyVG0LgmamVKkAAmbsRtoBmqJlyOt6gRJNSENKlnlEFOYwlaqbgTDANYBRqGQC6kE0guUo+YghEJl\nqGLJKpM0MmWoCSDzFIAsSHRjMwJCLT72DZRY0cw6mG6tBLPRtqfMjbZpnjWfWWKe0JEwS2utwBQB\nZWaZke4Gb/t7GsXVihoRSpzejMa2uEqpvpKdwK+CjlX2vmpPHkDnTd2yoJfxQcJisZS/UnyPivQo\nAxRXySelh7JUigdwKz6ERQ8Ny6pSehyMFgZM/EW77quWp752BavqjVG0ZdUo1Zv6FIT7o9/7ePDX\nH/goYAJgTqS0fmettcxcJ2b9jKoUaPCqAO2rYna16LLgTrxJetbNzRaZrKWsseUyiQfIlklp7gBS\ncjrwUBYJaWYTdOj7D8e/e3/92/ftUqPO58zz3XXzjF68NM+q3VtjONn7VgyP0Zq7m6B3n/aPn55G\nZXX7uF8MGkqzyuH9uvuHvl/6eY8z4/J08B6vI0e0L+frd+/eUfP20+tPz8//5n//3/z1v/mrL//1\nP7//cLTDfvqn8+eXGDmf3vcfPx7f7JeYt3HOKN2iZpp584s31/VymFsYZEfRxpyQeXca1HqF7me8\nvMz7+ezed3Dj7Nju9/u79x9QY0actzpPjZyDldXyzHuYClD8/odPu6H6fssMeeUZcxDAyMo69t4c\nNeoec+Z8v/v3H96/22DQLMCsX9r18BgzTmQKFeeIOi1qXt73rapeR6oi4Q1UVgolIr04pzJljWwW\nc54nZqb75o0wFiCwX3ZX5MyX29l8O97tKFaGsKRTmROSLvtWtMyRmeOcx2V3otFDo7mJLSJmpllv\niGPb5VaVAwFhffjPqt7cDBVJFOBb71z+I8/IDUitTo6FGd0bSxWZlYSrYOa29cwzx5Rx9bAr1Vrf\n2uzAZgZYQc2sdW8mFotkMxSywDRRfesy3M+iNSCtH12T1DwV562IAjrZmjfbUHGfEUNVBNmIbevc\n82l7F5xCzZrnrHmfoTDzrffmkCql80yVGXDszb2ibI6RlJk7u9BUrIrMMytTzASUcGtE5kRa9aLc\nsWW9Ag0ossPLWwN6VmZExrm1BsJQy3EAprdGt4U1Z01To7upyoSMoqFtLJFuMEaaqSwIl8TM1tZG\npJnSpMBorXNtyPWQ4mdGFfqWV29mZm1HLR1OqibojaS1BWtmVpRlMsFtezKfzrYUq4D54qvo1qwK\nVbl4vN5h3gCHLxkjCwDTrWPxZ7KYVZoL2KTSveieks0TkMxrEb6wJQ4tqQoleBOAzHjUEbyxc81c\niqrKmTHdG+1NhlMK45I1LzxgMHd6d4iFnSRrZBWVrR1GhpR5gDNSLdK3JqUyI14XympmIrZGeHJr\npBFJeUxE3p1VIwQ2oGr13X6jjvlXX3ytPV9Loz18DSRYerTrCjDwV9edsMhHRxNTi/uTA5CiimYu\nTZJVWAIrwt9IVX+UklVkUUudA7ls7eyW8KS/HR4Aq1qlxchClNzwsBtIWlYhIvPNTfEwPLz5CF1I\nciHzB/wtQFAzi1VMa52lRrK0yNLlESrY0pOTVJXa2ndgOcMgFRdbsE4JRTPWAzguclgq1CJyKMBo\nD/FMVTf8+L7/t+/6Hz9410CWz/sfLp1Z91nv9z0qXVVMF9xQNSk+XS6NxcYf/vh973bPIOdBez3v\nTx8/dKvxeu4frn5prR0vzy/b0d7vrYrPr3gd83XmD7/75gr8+b/cnl/i2z98//7D8dM//+Pr65fz\nNZ6/vNzPumz69rJ99+F6dGW8zhkvqZzIMDP3vV0O2/dLGcRGsyDGnCWZLbIaGucc+vw6Xr+8vu+t\nIUlZ2vn6zLY1jnPkOOv1Pm6yAYz0GuN1yK2k+v7j9dO7y6SdVVaZUZzRvOGcTrUPBygWQKnOH7/f\nvrtuTo1blJnt/fr+CmPc/JbDmxcwR8YZ1v2b/QlVZ8Q47966GWPSVN4c7oTN84yQu1FTk7dZTNt7\nk3kRKFZp3xsTZ4Zm9X7QaXBYgp2AYsTI3vvxZFBqRtxvaO3ovQGLX2HryIqcZOuNRLn3Uqoysxps\nmiOqamzWsGgDpTfbnVM0B7xK/thXldGqO2AdRK1WDiqRrW10izpLRTczi5RYWzOZ7dYsMjPA2vvR\nzJobaUIZ1XpL7iowwzSFilvOGe2y+dGbeUzkmDPqNsKs9mNvdEw7lTPvykTB3A6n7eodzbswJWTo\nnDVTkDcLb9acFCMxz5qVW9PWOtpegZj3krmZ0wUqI1bXpTzkY54Au6PIiACnWwdKdNUJoZBl2Bxm\nLa1JkMIRvW0yX3yecpKk92YNxHKYmBvhxcqksgQDWDFX+TEzukvJ2TJycVIOdnpVpKXUFjFpFGnh\nRuqcNwC7HX2/dmt0ZHEIZ0RGytAAp5lxVIpHFM8YVK2CZb2gAWykSqfC7CGWSgCgm2OzzZuWICYl\npWVVaVIwukgQEZU1YzWJrNgP9MMaNRWlnKLnm44iH721khAEoU2LXixUEgBcdGaE0yTWoiAqac3Y\nSjFHcSvyocQAuxxFurvZxRAopxVyQoKqufMw45FDoNJoCY0UTrCVAuneO9GLg9zNbJkFc04VQBs5\nTEV6E40C0fCr1lJv3S/9K8kMyaWLeVTIWhjR1lLuq2+2Og5IGOphH1fJaKgqwivLrJHrfPn63tdu\nnySiSwlo9c/NOparng8J6FuNJpAS3w78K9WZlMvzzVlREEV7SMkBqWQgXJVSGd08CynBHmKeXyVC\nqowHB7zuMbw5Jb42TSUz8TeiVSz1q4CqWOEAjofFcQlNK4tcPADL6qEnwmpkriot9xY5r5f9Dwd+\n1/Hdzi3GptkN6NZgs8beqKyMrFlbp5FxVj/s2Jvb+P7Hj9dvWt3wOoIlS3ud5+V6wTjvr/ftqbfr\nRur+/Hy9tH5sLNzP8eVWbPrdu/3QfPlpTOQf/u3vj12M++v5UuM2b88GXTe8f7e/u/TumCPnve4j\nMi1l3L31uuztOI6gsF1SPSNm1hSrcodbY4x4OfX85WfRrhf/+HQ9X545z6kGWkPcnuvPv4yhNgJh\ngAzSDDEzk9bssh9j5smZhRnD0mDmI82892sxzGCVYH78m4/vD0Ny3k817kc7rnspXj+XUOw1Rpz3\nM6I+fHp3tFaVv3y5z3s6O5IxZglFbGamuI+BajRWZRRQYWatEw/jc/Uud0VmzKTYLpe2dxWsBadV\n5BgR59gufb80K8w5RtyPfshAd8lEozukZDk3k8GalSxSBllv3SyrlABlPmcyhxnc3axkbe+rOSDQ\nMubaVxkNNGQBbjSx0L3bBarIEMw2UF5TzOrNrXeSFTMy3G3bW9vcfQMJmjuzClt3czNHTNyfx3lX\nYz+O7kaAUTnnCMWMzdX64bTImjEzkDWdbGRvbN32vbu3ADIs8rwPjAHU2Lfe+sXhka9zcpZAHPvh\nlHnLmjXhLuZqzUupSRHWisgZImne2NhCVbasE0srnvUmrVuyNpphngaoIF8GQa9KVSxRuwmZIcjc\njRsoxFRVabVbSxVOqhK29gqW4qyEQLOlgJgxDaaUdNrqBDUHyYwZZcm2q/dqrTtQslSOGILcTXR3\nSyoi05AzSlpidBqTVeGmop6bO9BlqJpCgG7svaE3b62vJlyochV1gsVF/S57RJAJm1lAtGYdbqxm\nvbrqnplhAa+N3ZNQQXKauAR6GsAGWWTBm3IsJWXzHlVQQgU60VOmrDKTSnM2tjLSXIUMuZeBrVvW\n5r1HkvCaiTbZullnNyBrBLLYF/5ySsoCZOY0tH5Bex/zVi8vYqEq5xSA8oh7t20peZbuHr9SkW81\n8Ssj+hUL4vFY8SF/erS2Hn/BxY6u//hbP08ol5cMqMdWdLUXH26B/NWP+OaCMLPVuqtlyyNXZ29J\nWpxQ6au97zeVaclkUwQf8MsJQSn4476C6a1amQRWGSBze7uY8dUiSdpvtwLSA/KSFJL05bjw9Ziv\nD1s/YVVGCcLinetN5LM0PQ8VM1hVK5RicbNr46KqH9+1vzr43vCpt121Ve4oEyN0j9hsg+Y5BsjN\nW86aindP/d1h+1bvf/j46f3HiXgZ4xxnprbWMvn59fxwbJfrBzTdPt9s49P1al7zFjNwG8/ffvPe\ndNzv959+uvfru7/+2/fv9st8HTHPfJ0vX15ur+G7f//N09HVWn855zk0p2ZZmbW9N9f1OPa+J8z6\nPuARNWqWCsW9b1vnOMfr7fZyjg+Xy7HtNe91fyVdrVRl6beZz6/zp/u9rO/7BWY0mmU7Lbv21vZj\nvxGMVFbERKEiARaqQMwbE9Z8P+yHHz7tzc+4nwE07b67tzjzzBkwL9ye437eveHTh+u+6TzP25kk\nvNMrq6zAbuxuFTkk990c8zyrypyXyy5U5qMjsffmjTlzjNO6bbv3fSOJVo1WzDFHzdp379sWM+ac\nFDbfSaI1eDOaEVRJ6fshYwsAlTFFsvlxPGVVYRiNlufrjBGNar3TaN69d7OoLDWubKZZ2X1XzUV0\nkCyUGbw1FTODjuYGthxj1vR+aVuHap6zxN63bTffDGBm+QLGMu8dzemdtKlSc8i7HKysyDExOQNl\n2o8lDPbMGMNmjpzamnfncfRj875tMs3Iqco771GjpNBxdOuuqltozEV4cdt2ELCaESWwNQPJRfc+\n9N2lDJTBF8ZwT2AoirYMEUa2iimAdHOHZMWoUSram4sZlCYIt6aSWZWiILO+xBwRgYgomlEJg6gU\nnd4bASAj8sEHASvWao7KinIhYVmC9Z5izFQhUN1ts+7cURypE7NKEG2ZHqh6pE9ZpVRly/sNK7IE\nVTq6VQhJJtSFRjdr1hr21r0ZzUVUrkQVGB/GMBpJK4PAVoyQw2QgjQb3tRWQYCpmTHozAt5WgW/e\nAgkZNKWo1aQCokATiax6oCAjHj4Fj7WEek/Mkbn1ZtSj1xSocFjz/WLwxkvqbjmlUMEJuBcP1V15\nryo3gKxlG117m22zvrt3gade6n6jkdaUCZUqg6Px1yq4fuUJuDTNtn9Fh7417RYWW6Jb1MPNIdKI\nx2/IzbV03AuHL32O8sE76uEueKu1JizFLaT62p+sEhZdCRiZkfDFeoOCWL8R72B5RJYx/aHKhKTU\ncizS8PBUBrQcJNLSyS2Zb5W7L6/+r03a39xISqh6VOi3IyTssceu38hkFiFO40puohkenVM9ZDnk\nozGJh0ym0d7USjBVyY7uf/uu/+EJT6GLqmke0MGGgoAxpoonCotn9dKAKS9XO7qOS//4/ccP3x73\n+7i/zNvr65AuT++Ueeb8+O6bLVh5H7Mul8vlaJlxZo17gvjmw8dN/vnn15///PLhr3748eOlZpm2\nzeLL55fzRfcZT0/H5Z13VAW/nHnPmbMipNZ7t3bw3fUDaWubOqUz55wDAGSX3ram+7h/fn2pUXux\nXuL++ezkRNxVGos612uez68M4/VyhdOMTRbnDb3etYtZg3GOySqlWK55gxFrVwslbGt4/57ffHzq\n2zZnnHX4zmaXUsSsjAIbZr3EuJ/3bz69u14bs+bI26lG3w+738aYsxuv+16Vcw4Urk/vUjHOeR/x\ndBzdi6V7VQy1zfZu1Jx33e6jH3vzRviMaaZmDszzdcbA1th6n3MgXFXb3jOrkObmbiuIxORy2tZt\n735PjRtWN8UsM0tJ71ZnRsFya63tbN1VbLuzgG5mrlWumGZNuURMq2MecBj7uIdZ+eZsDcnKWWj7\n1pp3AefMGWqtjquTeCBMd6lut1vbzKw3XVExoTLj5akdG0/leImz7q9zBnvn0771baPV/VYv55z3\nzBl79+a8Hm3bzdpWpTnnclSeEZVyZ79uzWDi6xw5mJR777vRvcA5B9XMaKBoc8wzFvDyykfAh++7\nuRo8xp2objKDuQlUTRArC4bKEgvhvlYMo5ukzHhzNFGoGUml9R1ARcy0mKejRK8sN5PZ0iJIGVV4\nmIW1jIZapopZlZmZpVlOb43UHC9VHcC22UYnG6rOzEokYd68ETKgRUmZlRAmSVqXyo1a7r1sQUMm\nAaZkBRU44Ntmx9bNG8y9VMtMUKhVn0FUyhZtVkBWSuzukvlGTrfZqlcmZKgqJdNTs8rlaq2pUrSF\nYSRbvwFzVKlvu8ygpAlE42XmHSDgGad5X1uPzS4rEQlwa48uYc2SnWiAwbwh+1LBurmaq4iEtx55\n91WezGjW+p4BWbg30t/4SI85zdLbYeb3824QuTL4sBzctsJZ3ljBX8WZXzUyD/ru8cWKawDemjCr\nAWa+Mi5IOOyRhvKQdiIShgcRKnu8dD0qbCXd32hXLn/9qhSroKoKXPYXB9h8tfdI+pveZ2VpQAgU\nwbZwWdXKFyWWbgoowd2qkkJFWvPMXJ3Ov+Q8f7NHWLxwSfpVRrsYla/t1a87hocVk2vfrbfA1XWz\nt/MjSctp/+i8mkOJ4h8/7X//afu2+N6j14mqd7s9XXoOJFQFoUugByVDOrwdJhMQ3/74u63nse+f\nf5lzzF8+f972/t03H6z48vLy4cPHp619/vynj998/+EQwddZmcjIY7f37/Yq/vIvLz+/vPyf/h//\n0/j8XLfn7LZv/R///PLymkxdtv3YDTNe73ydGXIrK0M17sd22Wy/HCLlPk0BnmPMKWVs3X1zZj6/\njJf7yVEcGRH3cGLue5M8zplZ7F2u8nfXLWT7Mk8+XdrBLL0bajHPiCoUq2IEAlnDe7eCOAmY63ro\n9z9+6t0AvN7vgB+Xi3VWzXyOyiBynjUmAfvD77/rxnG/n2fe59w3EBg319TTfgAoZWb23sxsKuYY\n8z4vW++bVc1ICbhcuzuz5hkZsP39e2+AqmYYmlx1np/vQSMdszDOKak3bccxZ8yEsVmhxlmLR2+O\n5r43VFaeMJh7ZUYUXdY8z7mW+073w623vjUayqxlKw9QJpgbjIhZI0Bfrk1KpTxjyrVd9tYsIypS\niku7zswxR0iQXY7W2iMoUwHrrpwxZxXkLmbm64QmxLY3uHUTI9BmjdLudh4bt83Fuo/bOTjvWTPN\n6tqP65NfNu/tcssxU2PEHDUzrKof1reLt57xehueYUXA6b0VFp0JQq13mDEYMUr0sqhIJUCqjks3\ny249z5OaXFWqb5XJ9vicuzvBnKck6721HlVuCIFC977UJasxSyf9UCbIOTNirE7YwgbIgLq4ykSU\nVjCBmWFtpIFChjIzIyLLlLk7cL9PNW+s7mSRQOUYNQOdbGzmzUyosnMyNFTAiuywlavJKrgRQHN4\nFoHSVKZkxnS6E51qNAgVUbJEUVg5yHRzceEJASWkqqq23hrApXV4FMdigUDJR6SDbA8buDeXUMos\nlKxSgUlZo6OR9Hp4x8CA901CRMhYFZJaeydOopn5A71Yb34k8pzsNtEaK0oBB32jN/Tm3PP2GrjR\nXXxY+Vvfk2jNMk0qaEq1aA+JlOUYpNyJgjvb2xKcK1ZtdQfJBhn4a7LJb754JOmBD1xjboA7ufpt\nC/uUVnxCgZYZpC8NEoViLST0KAvmWLYef/PUMyUvwpY776v+VAStVsUqJZcO86sv0AEol/psUaZF\n5/KvLB1LSVAtoj8qUeu/khywJc9Z2dhCETRoqadScqJK/pZlWn9xTv7itpBlI+Mrq7xayMBqMUJW\nK47V+MamWlEspum/++Onv3+vj5V7FGs01OXYmhdHqgAyzqk0MpBYxELb2KCs+Ot/+4d+aZV4fn35\n+edn3/v3f/c3HLfnz8/WN28to35+/uXDt5/eXex+xnPEGfKm7z4eGvP18/nzL788/fjj7z9t+fLz\nhw/f/Xx7/vzTT5/tOeak5NROzqFMjRn3s1K1HW6OY9+fLr21VnS4hRDp9znvYwh8Oi674xzzdtZ4\nvSGU4/zy5TRzs/j4/lLK+y0GzLvtG7xt1na1D7cxx+vnT+/ahz3Mj9E87xhzUFRMzWIqVL351mCt\noWyc87tvLz/88M5p95FDCbJvhzWrrLjFohFyYJ6KHN9//0O387zH6w0ZMlqcy8QZl61rJS+4jmOv\nyoi6j/sY49g3az5HVNm2+9MTDX4/dZ4B7x/evduOSyA4Elaqs+ao4nGQxdsYlc3MLhd297MiU5kh\nmVJ9I7zBUDmaNoyBDAkGZWWEeu/W9vv9ljEq59Y2c++N1uFbs3ZkFZxEw5LGVM5zxPnwfOytlXXd\nZ6la8+O69dbGqJqSAPWX23MEieab2k53Ej5nEGZemTeUFcrZTDnvOUbOrCDbfu7XJ/O9EnmflcJW\nT94v+4beR7ZEn3Uv3fbm190vTzy6u/k9XmdwnHlGzVHmuR9H66Aj8wz5rAoAdG8GJITMMKO3zZqT\nzBhlxWQZSae8GfbO5qbMiJMlMzNzuCtz4QajOVlVq3D2bXM89vmRE9bdXZUmRObDVy2hsqQxBmpF\nDYvq5noIAlDLTFxVgNy5PGOq0FvCiEyUm7lsZtaoNHUKe6M5wR5RqgAam/fO1pvgUzYrlu7UsGGj\nW1utJjcTSDqZK96YedecqzhD3K13d6zgzKogQWOBjWRbdjlB9I7Mykg4aa3DDSQaEbVIMKlqtTsj\nSmaCvLJCAMFCqpySZa1nwNDKzSkzGHsiyZJpOajNt8gTlWyOmmxGioR547LWoZbOZA7bcAdB6zB6\np/cd3lSt70fO8zG4QFWqUpkdxmYWkUMzgKoci7yOnAC1wmNag1/aG9jzt3LzQGerCv7FTYv/lLQs\njitQxiCaSVWkFR7ueCwLA4lf7XoEYF5VK3KbJGiyB6oyIQXQPAtL5ebeo/Jr5hu5TIirWtmbI/gB\np2JdpmZLKPZwWESaeVW491Vki8Xl91zvabG1K3V4IeO3jiQBglla+6yvLVL+ejYW/NTX5AECRPmS\n6Faa+cOU8WscXQEkVmMRThQWECySn97hf/jxx+88n2r2TGSekZd9M1MD0xVjAA2Aaa6BF1trjZbz\n7N9e/vqP3+ztcs+63263zyN2/+Hv/k08fx4365drjPMc4d1+/MMPHPf7zy9/uiMM16fj+w/v7y9f\nfvn5hubXT+/++rvfvX7+07/85z993j/PuOXkuJ/jdmtm3ex+G6vzMIMV6Ydtm1+vR9s2kkOY5YLP\nnOe8Vc7N2nEcnXa7vzw/33TO2/m8X4+Qjms356U1SPe77jNaa09b90vj9eLRX2/3Bn34uL3rXn7o\nconbbZ5RCEUygYhGu152mbbDNyoT3353/O53nyS93M6SNdvUvKrO+0AmaSaf521GWm8fLx33Lz+/\n2D3OkqFIlVJ7w3HZamac93bdr9uWEWfEmASx7Z1EJUr19OQraWWOOsfZWmv7RuV5+4XtYpJ0KoJQ\nay4pojJhrS6XrXfPzAyVLOFRcWyNcFZ679a6snIOWjL9nGfJ9/1ibue4jRjjFltHVlrvfnRrzr4X\nAKNWetVqUI2RsxZLQTrhUKmjt/3Sm1TnmSMGJCRex32eIXDftbWLrWCqTDOnU7kaBNN8A/3+OsZZ\nYipTZTPTvUdvAGXm1G5srclcJTdWw35w75euOrbcey/yNuYIzVCWxURz7oc3d5hlIpJzSSMAZ4dy\nJqhwM3JZOHLWrHLWEkqkZH3D7stbMFB0kEJlgciZ7s1aX/qEqFwfXOt95fyvRBJr3WCCqjIipTQz\ne1j44zwLLDKVmzdfDGOzXoIVC7XWHDO4uaSsgIFLvUD23sIL5rFyw725B9mMbkTVvcrktm/dN3fv\nomVizsoQKszXH1YJJckgWXM3I8gkmQqitMJCjqO1RgMrlZi1lhsamrVqZsvaXFEQW0GF9rCZtm2d\nVsidDKakUmVpMatcmCcL1hCVxWJYOoh0INZ+4HTbKosGulsysgOouoEOou1NtQGS3MTmfanEnGv6\nQqnkMDDHiNacq6HFnb6RTVQafN+sm1KgK0pdQggpmMpihuJurDWnIipUZQgI297drS3/+/KsvZGB\nD8mMftP34kPH8XXGAnwVFhNRb629MvBhDF/iKKnw1fNHGiofchraWzbmo18omkOi5FaAubtyrgFM\nb1B1kahdCKCoX2Uskmw5LQBVLYb1jTVFa/ui5h8du3zkBuhhg//asJQ98mIeyHRBOKl86TlXVrg9\ngmbWidDDrgEA9OXEWZrPR07NVzRbUuPb+CfA2KqSDihN9uPH/n/+43fv47ZXtjliDFOzqkvz7nGG\nasLNfTHRZkZtW0tFRHzzt+/+6g8/nmPebqOynp/vFA/4/PNPr2PszcfEmPZ02T9+vOIc99v85SVo\n9e31EyP/+X/751vl5cO79++uR+Mvf/6H8/46T52v95hxjrGxba0EPN8QxVq5wFX9w+U4+rvrtfde\n0sg+oFNz3s7lH9q37egd0pfnl+fXn+/Pr13tu99982/+23//p3/6xxb8/Mufbr/UL6/3s6wZP3xz\nPS5HEC+BL/efDmuHRWMf9oSt5y2fX2fdT59TU1Fj674dx0Qcbdu9tmZPn56a9dsds0SsqOEj81wL\n+EaPeZ63U6lLa428zfPLLU6v63bJrMggbHf2xnkOldm+tdZnjDGYqQaQTmPlZNN137a95cSoHMl+\nOYSacY6T27ZJd4Wpqrs3kxtjDAUvm/WjkZYZCoCsyjFH9w0Fce7HkUTGAMtERN7PCaLvvYBxP2dE\nZrTmBdA6uqPvaB4wwkrhZMagqjIVXJNQ3FXITGPj9diBqIjIrChGRer1y100mG+bX7ukV0WrSLat\nATnnImXc9qoc44w5q+RspjjrFreNekWOo6EZ/LpDQfh9nIKzwdwuW3egezm9EueZc3BmFBJo24G+\n7WZesJFDlUIz2xwUKmJaFJjN09yAngVaAxdarqpoja15XxH2FbWAICsFZc0zrD9Ze1ca1LIsC4Q5\nVxhkLBNTM0ioGWOKQE0zN1ohK2sOoLIYJm0dmdHYDEIl1LKmEL13+tu+XEmxYFiefHOpqKpEFNBs\nazq2bQGgqpJgrZnJvJk51CJqZuUMU9Hdm7opJD124Y305pvBoICCcw1XKkPurZlqWdsFKlZ4mmjN\nDfSqGiSzKFEl0c3bCnRpvtLKo9ZoHONKHEtUVa4c496ORBo4KpWEwb0Af8vaLMqiwgCmaFHymJLO\nQrgZ3WzFqZlLsXKagWa0ynJQNcwJZZlTimDnwwFPBK1lhXH27QAcxUJAmRGslQGNqlJNxIiawGbG\n1rqUKtIM3WDe7CHRWAlhf9EX/K12FMBywn3tF4owkYLWNgQEvspBpYLBlm9Ja+ewYl49Fzdoi4DE\nCtrmQzlpj5q4SEwYbYVBvdn2UQFbx8uqXNku9pt4TzNLZFWa9YePn0sBtFRBthKp1y4DAEtY2aGr\n1Yd6tPP4SNqrmm7treQ94gXW664b8BYhQz42E8ZloHxLcSvSWOVc45nsjV0Od8/I1uy//+OHv7rE\nt3r94JWK1/tQesTLH777sHe+vpZmwc1gY95ZtdlujfK8XNof//oPO8fnP/2E1ues15c7gKz2zz+9\nYtue3u9V7eX+5buPTx92zpfnCNzO26f3F9p2f3756fbql8unH949dTOdmbw/f868nOc9psbIbXPr\njWj3MWfonmdrm7Ft1/3psMv1asYAA204zmkv9xNAc9vJ3W2c95eX8/XlphHv3r+/Htcfvv3UhE8f\n3/3yTz/PV3/NPGW9++VpL9/uaK/jvL2OUo28n2hq3uvELeo8Z958IKI28Wk/0Duka9+2rsvF3394\nl+AtqcqyuvSnmHkfL4IaN6Pu4zZnOPHh46HzfH3N53Ne9qfNNaruU5tvm00vGwPWsR8GYZwxM1uz\nw6VoARTUj2PvuyPyPk817v16aRrnGVQ4DRFx3ioT757WgEIb97NUx7H3bRMlsVKZFVE07rthQhXc\n2kreoWhWGYoogN48KzkrM1BoYCnRmrXufRdYdICqIFERGacilagsmsEtc4jVe+ubExlz5KwsVXG8\nxm0EiG3TvncDzHrG0JrhoBrzTsh637anyIjgHLNyOl2cCWbt99tL5t3s3X70vS9dNGPOEqyiobqb\n7819d/YxnsfU633MNINZx7552xpoUf2cZ6X2djGDZLdKChHKFjVVoXJEjm3brZAKVEWK8qPTJVZW\nlgAuWXgoQ1nlC0flM0lFYZmYaCW6ZZbWJCKUMiPmQLFQrTt9QyHKQijmyos2a0K6kYgsVRU0SfPu\n7rbyoAGQnbCIE4KZS5Wqe1RWba25qW8GdWFmQajWXfDezeEjQkJkoNIEb20Z6VEU1gSHMq0K8CpB\nCZM7Al69PZnSrUArACkwWSCW/TmrWBVGTFiGiSaW940suVwNeQIpQZXAyosGHCySroDZXjLSIgxg\nxCRNDncBJWu0RizMuEHQGNKeEaiSrf6kYHJzc8+gLL11a4aFZmmoHCca3Ty1lLkSEzOCntSAHOzu\nhr6RvQoZ94yhCEZkDYMXSrWSXE9VEebe0I2gWYHWSFvjAB8oCFgB3FJ+1Yy8oUP8Rk4pW8LI5fxb\nlvYFzuCFMKzEpkSZsHKWigbIBcghhXsDGiqMK9jCFpAmHMTDPmG1kn7WxOCCM2uVXtgjrq3eZCkZ\nITSYlSbQvrbvvgK4h6P9MfTiYfx4421DalXA13QbrrC19jbj6SHCIpCZ+M3JIVa8uBpbIdcvF9Lq\nRa8IUvy6wxBVoEsaMX/4cPk/fPfxW873lR/RtsYxrIWnxg/ff0TWLz9/ofvmnjVqQGS7XpiRLb//\n4zc//vCunu/Pc7T9mmOM11tMxNTE6+//7ncX8xGTqr/6/vt6/eWnf36FbD/6+/dP+Tz+fL9X5//w\nf/+//ukf/8u2Xa/mf/6H/zxCGBp4Oc8Q6um6mRQzIhE0bXXwie5t0/vL5p1CjUK1yyzdzvP1zEVw\ndFr3en65n7esOQ/q04/f7lu/vTyDev38y+vLl89fXj+fZ8k/PB0ZmOEvurV7vpy3cd5N/nkmXZnZ\n9u2yoVVQycBOv7zbzhrN2Kinw65P/XI9MnlnVkLN9r4BDARE0UeKFXNG39rT0T3j8z2C9vHTu61v\nL19ekfl+W0ZyjMz92PvOiBrnLNn1crzrPVXPFTnV2kYcr7e63+uu/frNt7vvr2e9nkFZTpXqNqdZ\nPV2Pl6HXlzPz1envr5coxbhB3dlK9yz13mkbxz1Uai7bR2TbvELzFhutbRsAKRSVtZT4LICd7bKZ\nb6KLxaqqdGPOM+8jVzmt7NZlLbOI1jbvm6MQceacBhc1R85Z1vI4tr05iiDjPGHy3teSkDH3re/d\nhKHMqklo27Yyz6rgiPvM1BZ2UXWskbZbEVUj5zDKyI2tm6qyIl9Hjle9nkmry+b73lvr1jiTKjXn\nWvWFilizVufWDWVuWDVgDeQQkBlKayZ3WVExAylas01qFTrnkGCPITWJgsxI8+ZUFJAxk+m9ryTI\nyjVDM8zcG91bFVPT3MwcNfFo7KCxlWZlRaIyrVn3JTxPlNNkMJjPcxgaoKwJKKd5Hc3COrs7ujKC\nuUyHpDqaCRp5TiCjosZOtr55dzPLzJFLh79m2U2tz+EKyYMZvHlrvaAtH4J1GKBkKdXMzQFXzmSr\n2iNTlqR5Z/lstkszM5gPgAS5iCrV4tNsjRKyemRzu5lHnJmD7FARMOtOWHPA3TGngD3rFuMEstHI\n7mhaenBWEdY3MbXm9LqDXm5g89byftccbX8vyLkVYKl5v3EP9wPWrfU1e1JMUi6ENCtivFJwbwhA\niDpJWNtJ+tsEq4hb+yqM/EozLoEQ8Jjth1+TZWjgW2DmWxWhg/lgPqnKgmlpVd9SYxZuW+ExxlXm\nC2Urue2tg0iZea1SqxDWcA0WbQmZuJx4TMGhh2kHZWVAJVff1RyQFdL8qzOyuKgnvPGXqId6yx7O\nm0oYRQfC6FAV23KH2pukE18N7w+Tw0L9D+qT5BoMKSbheIzLaI/EcCOxYmIEY1Y1dxXc9B9+/O7f\nXfp7nZemq+F+e6nuKF622tpuxdvzS+/uLmZF5NP1eKh4LvaHv/vra8P9ly9q+6W12+v8/Poy7roN\nbe/7/+7f/J103l5O2/1aOn/6589fXvq793/84/ev//Knz59jsr7/b/79hjn/5U9P+26Nf/7nf/5y\nRtyjrCli37d3T3ue4/U+ZyRao2MjrbFvdjk227eImerY9jPi9T7mnCrvjq1xVj0/j4q7SYTtnnV/\ntuP74/31p5/+mbIIvjxXyJG4zxrSuE93jBoj8nUgqzKQOGn9ncu7s5JprCLr5Zxm1r2eOjeHYM+R\nUhfNe18Bm6/nCRlaQ1ZGxhxbt7bVOGOedx726XifqZfXzwXbvbvz9YyMuBwf2eznz+ftpWX/+PH7\nH/m77+PTD/3TD3/93XfXTx8vH69tt35cvV/7sXL2GtzFcj8AKxWsWIWqZR2uGhgzznF7vZ33l/P5\ny/3Pz89//lN9/jz+9E+3P//LOH+xeNnncGttb1DkPcimRkNlogCWeuslJdJbd9+tk1C1NZNNJtR9\nnrcXz5Xrh35sWVz+qra11sSsqDvl5lCpAlXad2v7pXevzERmwszdhahMsuk4mhERVWLJ+y5nlFqA\nkzXDVfH+uj91bEqcKPZqmBLBzdm8LntrrVe2KJwx4vT7uN1HXI9t25qzUT5Xp9b94vtCTpExBadf\n2sV6xVScWdDWsTi/taXdejOcpFVFVhpp1rJwZpz3KOXRtqLkW2VSsebcEkhVRYira2HQrMzKYBWt\nm9PolZUquoPsqloCzih3j3GHlMUEVnQGARSKbE2CRaYiAIYKpUgU6EZ6bO500B40HThbc8jopZrj\nNCmTYeWtEW3zh6wdkaG8Q40G5/InoGZSopON+25ukDqFfIyOVUqOLKM9hO4K7RFKnfQOgS5ztbar\nCmvlRRJeiqQtGb8kGZc1oMypVjMhxbyXUCDmtAZaN6MDyOLWpkC3qFEpKaDB/o6OcjdaZpLMzOY3\n2kZ/8G20IsnWRKq3mPe+TbIXRVJ0yTXDIO9q7QgQU5X3mmdGVE6rtHI9IsGs8mTBu3vvQKNYSx19\nO9tqAr9hvl9bbsCviPBNLgVY2WJAlzQUoh5Rc+Ys5cpsy+V8eFCMD4e+sLyeywlohqVG8RXBYkag\nuMx97OTSndbq0T1Y2jVB4q2ZWQWqUFwnfIWlrhm2JnvImenEMh0CZOnRsuOD+Xy4HgspCStY6BHA\n8Wj/ldLWRfWbHcNidR8qI29vrdO3N/fYpsnA1aJIFQ1Way/ArHq/Hf/t7z/+3THfYSJyT83zvrnb\nsuoSqHx+GZXaNzlQyvdPHdXE88On/eOnd8ovX+5ubetuz5/1+hKz7Jbnu+/ef/PtNTNH5LH3PF9f\nn88z8g//5m+7qZ31/Pn18rs//vEPf/3h6j/90z++znNWXnuf44wEG3aov9+fep9j3M6KMjZjc5n2\n47J137ZexDyR3tLBRMwac6DQkJU6h93vNxA7tV/23/3tH2ro/vO/9F111preM8Y4I2JURkUqIgq8\nF2bgNFjypYI09nZpzSnU5ERWZeJZeaXvTduGdu0TiHmaHUf38sZU5jwz1jYmzzlrGtvlaN18zqgx\njr1f9k1V96iYbYz5efS7vdu/+/0Pf/+33/7V37/7/rsPv/vx6dvfXz9etstmTfQNxkWQq1Z0H1YS\nFSrwdsWvKKW3rvba+K1r7nHJfJAeyreatAZ4VWS08eWX+y+fn3/6x5f/+l+f/+kffvnP/+mW/1DP\n/9xennt7PppZt74d6yptnuVqzcVWzdywhCEZI86JVGaYyrdLwUzFrbXORlbOqElu7pgRmRhRrbOZ\neTesRb3cWDQLqCK2zbbNDZiFmtPbse3Njus48/Z6nuc5Mhz4cG2boxNRZLByopW17jZ7x+5XI+Ke\nUTmB8845z6p6evLr1pat7RxRFvv+zptcSLFiqNY4C64FwxitmXlzJt1REqr33mlCU2EtaLmmZ0Rk\nJYnmO5q7CZll2dhQRiAzRspI6yK2ylHDSmFMoxN8bMdoSxtPshIGmG9VZ87IZJRshXSYoViP56IK\nQikXF+nIcwQltsa2W6O79VBkFIzNAt4azcyjIkKRRaZR7lp+gkIpVMp6SDqWhsNzVlZB5jC3Onb0\nbhKLllnUSp+fIMHt4ewAzCyqQllgUzZ3rg5ZJNBgmZW0pooQkSB95gmCsCpD7+scQpE5Cgit2RFR\nq3OZU9YEIotutfpmNJiIDTTvHehLmFnlBaXYNihpD2VksjWCmWkOldd8xFmvrAl5Y2sru2V9/CJe\n5/1FkRWBmqpYOYCZK7BZ5o3ezRxEnNMK83abrz+1r5HTa1n/bQK19LUltq7BZaJXsZwgOrhickia\nMmEsJX/DsQpacTC2HrPoRjNIzf1h+wPenIh6szO+AcxHW87szYn3SKXFo6u3xMyPiqvfiF70F/+s\nUETBqBLfgkb56EpWJf0rtbscQQLkj2K5Qmn+lVPikTNLUjXX2Xs0QlVke4uVgZEqtaWvMaaK5I/v\n2//4u09/taOjxcurarizO3baOUYFShYxt42XS++LK9n6mDfn/Xe/+9CbPf/LT5dv3m+938/7vPP1\nhNzMtx+/ffd03WDK/Hwxn8/PX25j69dvv718OrZ/+Id/Qn93/cPfvLtu/PwPz7809HZ//lJDPwEm\n78i2teu+mfh6G+e0EaFmrXfr7L0fW9t6m5mjTN5PjApVniPO3Ttdt9vtDMQ5Dq/dmlLXzfZMv26H\nfXj+80+nbIblGHlWjYqxXPcRqXsxUimgWTXfiplFIMbN0FQ+cp5VEp8um9u8vj+u16uyKiFu5k2t\nmTIzU+llSsSc5xhPT/veMKvH7QzFpV8j+E+vfvLYv/vDt//h37/7u7/99u/++oe/+d3Tx09g43bN\nDDCprWq8TQTTIxgeDVYPwflqfrM/1MEkliRvRS2sa9DWxBE+kodUhGWmLemCQSa7tP3Dv738Fb+J\nz8i7aQKqkc8///Iv/+kff/pf/+Pn/+0/1s//5f7TPyl+3iz21pulOathM+fuGveKwICXJdC8mdua\numNt7xsaqHNKk23vvWVGsUE49iWT9qrIGQKNRliNSOS2tdZNC5JWsTkafTtkzTa0YqNstg21Q1TE\nWaUikrLmcKtLu7g1QHOe5zjnsBmVadnq6bq17maeiVsMGva97buDmkPniBVJ0b1LlkhE0nPzlhLk\nc87m1pq70UqZUMb6VFbhPiMKRjl7a6AtdMS+gpGrMlKlhxlYvapKKt2arykPyhlEAvLWC0kt2VxB\nVbPGmg+spKp5B7LBa+UE07BEDyuXCjbnjECp2sbtONy9kamlX4Gg5t2bJEbGzJyTABrpG/etlUOQ\nQjMLMhqcXZTLpuKhfXfujZetdb/ABfTSRPWqAa2BbhRLsKmKBKKKWOFAJN1dXBSBkZOkoRdGoSSr\nytUGA6yyZEUlrAlr3FIzqWeBRGulLIBhgZAn0SQHrZQgaM2s0TfSwIJcsqoJ4/IEFKKyHI3NJRnf\nnOXWxNQjjetSglPGfQG3yrOyxjhjDt1vhlpDDM16IVHIeboJaFVJUWKMGa8vY3wxkj9+vOD/5/a1\nEAJw96ro7ssrsCqaBH/UyC4+EOBXTJm/yer89UcCi4ukVbP2VndX4cq3FAgjufAZJcnEZFmpyB45\nF5/xNmtJlJOU4mvW2irbbxoWopZuR9JKdEexoIfephTLqCg+QlMNXC1St6UFpVS+usPAm35VCwL6\no6Z+fbN6+xDmMtA+0tYXxQsYXTX/u99//DdP/aNpt9AYjNyMDdq9YtQ9UXd7HbePH48Ph5NofPDQ\nxfrhuw+IebvLDrs+bUL/ly+vlMkmyadPT9vWVGyWXfH5z18S7embD9++f7rfPr++RPvud9/8/ofz\nz/9Uz3dDf51fTPj8Ly/3l8BmvdmxtX7plri/nrfMEBoOdrTj0jZcr53ht8iwYzIIe50nKs373htn\n3F7On28jIt81u+5emX3f3n26bJs16OXl/vl1dr+8fvk5hp0jImpMzHOCnIV74mQeR3v3dLmflfOe\ngarq4KW3UkTUttvTZe8d33w6Lk9HTEREdvPer8fGRNREAsg68zbmrLxs/v7pMm7zyy0qN3//B/vw\n1x/+7X/4u//L//BXf/f3/d1FGurvS7Ac1Xcenxg33F9EsFYLAHIKDUiu3NoVCviIXMdXp5FQThcK\ncKoKa8xnECS8VqyU2iNKF6a6Oa0UOdW2jTJR9C5JmCwW5NaLp6uZIl5u//IP//Wf/t//y0//y/8y\n/ut/bPFTa7et786Z464IRSrSHAXOLAHe235cwMl5R5o5uKbYwQLV5FEFKTPH7aysbfeqigSAvqn5\nRjLmHXI065ubVVpPa1m+shio7FCNcb+fKq+am3nfer/07eC+9Srdb7rf4/Ya85yJ+9P1qT/1zkt5\n3AeiylpetsvxtMM8ZsbQyMnK5gZumZgZRtjWKxVKjdmsjm1byjTGyjVE5My0kQmDQWa08LRJmDu3\n1os0KWIkBLxliTYju3k9RHmsGpUxzJZf2mUGroQQy3HGtDE18+6G3lqnV50lobWtb+5vsW7eVMqM\n84yqNEM/9vWAiBlBkmBZ91YSOCNmoNKA8s6jwTzdLgFFrlgf8THHhzDjY353MytvuGzdWxk2NZNA\neIyZWVW5LAegldoZK1tMNJDs5ta7W4dZVdDp7rVOZuWSdKkIZSkAqDlodKftRdTIyoCSKWek1sfh\nIbIS5e7WOtYSveZamHU2W4Or6FkJMZUUvIFtJ7U8nq3vbrts0eRydzbvW2e/Wmv2SPUsZxA95REz\nzuc6704zd/gGViYE1BlCuC3D/VaJl1/+fH7+5/1p3/qFP368fGVE//9WxFUc+EZikjIzlMy8aq4Z\njKpfs7B/WwUfpRSsFWlesEeAFL8yrm8AklKsZCMpAF9TiqTFoeLrET60LuZLWvNV3cq3WNTfINrA\nr8jSsYgCE0RhQn2xF5SbFwU2R4pccmTpkbL9UHuuom0P28Vi2t+0Qm94+i/e1BtUrYXJS73p//jj\nd99b7pXve+zKJnYG1kjgodXir8pPn473Jkl2IO91H/zm47Zf/LzHyNq2TeQtqszOORrx4ftPH69H\nxizyuvn88svtLh7t08drg8Xt9ue8/81f/X2NV4D5Ml/Oc76M2/2MWbcIlV2u7empd7BYGvYyZqi5\nW1HHcbm+2zZXQvfRhrdZGcAYA8q979e9x7g/v4zP99PI3324ni/P55lO++HHd+8/XuYYz/8ybqPO\nuOm0+zl99xiVgUikWMj70M38+q6/ay3yjGLMU0MKGDBzvvv07rB27Nr3dlz9eu3nRMit1dafWCq9\nqhqb28R53m631xCPp3ec/hzdP/37v/+//U///n/8H9/9+I36xXoHEiZoEzfzQyrW64ODN+hrcfta\n49Dednv1uDLXLkcG1rrusPrA9DWJVGsoybowMEyUUXpMGwNZCKADgZUjWevnP6It1paZtcTXa5cH\nII2QipXzy+uf/uP/9h//5//5p//X/7P98p82/bx3HPsxM2NW4jTrfbu4V2EY2MCsaVPVvbfOtgEe\nOTLGPDMzrKwyM6sM+3F4k8Y0GrysmftWwBgzymDm3puDCGTWzDljjsyAN16Ofhz92KztW85xP3m/\nz5fn++11ZMz376/vPj6pk/Bz1ij4ZpfD982M24icGZoQwmWEj0jRZOXscFXtE/cu31zNEoWqCThk\nyHyNisjmormhl00LRJytdd97M1ehMgjKDVURQ+Vo+7apbw1AZOT9rDI3CsGVmry4TDKDZ2TNOeZs\nre37ZoJBkS9RoHW35p5962i+BP1nTpSD2cy9weGVOWIWemu27wcaUDrPM5I5RAKdT8fORTxMiUu3\nCfROuqo0Akyam3tv/1+2/u3ZtmxL68O+dul9jDHnXLedOzNPnjx1LkXdKUBcSghLINDNIBMyOMIO\nh/8yPzgcYT84rLBCOIyF9SBDKLBsIWFEIVBRGKlMXeBwzsnce6+15pxj9N4ufuhj7pMF3vmSe+fK\nueaae4zRemvt+35fKUrKSTreixA789Szc0q6MSJBJgKz1SJoGY1EjtErKasSBZFEOHEhCqfwYPMQ\nDGSJYoSsR4ALCUutwWru7IA39y5SMzaCBJElIihgyVFIVJbYP8JKUkkKZ4KacmHmRCBLSKYTvEmd\nISzKRCmijLKzocVKOYio1ImmCSkICbuSte5dJSEcXpBO3ocHACSOoVdyawB1gialt227bK8ffiis\npdanzz7Vb9aYW6XZG8ExuowIlZ09NkYcGFa5iExiloQ7AjfqNCWcxm5kr3ZO4IQkMe+rNx5wlbEC\nHDLN4WXPQVIs7gNww5lBgyOK254lx5Yf+5MCe/H72JndvDVEpOMtJQ3KThDfJlisIDIfep8IgCHD\n8zQQ37cwqVuKBDB8kaPYg3R4Sen3qWp3xs0Y8Pr46SKZ0yOfZv3Bw3xvm5DVNCYV6YeqcLHe14sF\nxL0flnKcisKlVk24hQPHI4Py/ftLXY7zkbaNn3ufZmELJTw9PT2+KdvFSG2J+vrj91vftE53h0LN\nvvpw1uPp6eEJ18v59eJO/Xy9eruezYOQlETHUz0uGhHPF4NwWiQncySozvVwoiLcES1oJWmG3mPz\njVNOx2OFra+vz6/N3O9neTwux8PhR+fz6bB86zufM2+Xl+vX//zD1tkj1rUz1wSpi4cFiCuo09nD\nSz7WQmjX1T2DE0Q269ys8SJfvH1aSlJqPRxECbWvF21s06Es85zb6mYRezpc2/rra9t8kYfvnH7l\nT3z3j/3RT3/2i+X+SXTxATAYQCVUQgYnYPAPBN3HVBimHh2ZWBwRlGPUCRCxgBgkxCVJBy8tdw3Y\n0DL/SwV0yK3HxntEcXoygAQNkpWvBMuIhBM7gSkyyfdYaBJGxKCMJsbLDbGXPB2+eHjzxR/+w+7r\n9v787p/89u/83V//p7/xd+n1t498lbmCAbZMKYWF4Cu8mycpNAlDVBppGGJVUO8R5iAs80xknBJE\n0KzlQESR1jZsLcM6F5qWPrG6Ze+9d7iR96TweSpzoWkmltLMeqfrtV3O17ZBVe/up2kpI5qmGzqB\nKy1TmWumo2UzIxrTSEdmbr2DWBgOBkl6Z7JZi+z+NgEApjRYwJtb2CQ1xFk4PZDJRDKVIkrE4R4g\n4T0pJ8AempRTaVKYtHhvYT2ogMOBTE0bnkUIYBnm2ZszcllKqUUARG+G5EkZvTuFgyDh3NnRARKI\nVCERynTr3TZ3CnApVKaiU3YuvkVPyogkl6ku0yQTxUaZHjlojDkAVEOEIUCGM9GkMhUhGZipof8x\nB0kYc0gmCsCSULKIhMgeX0EJTw2z9M3JlCSZiNVjg0tH9N6IMPoe1hoRA96ccIVGGgcrRWQj4lKn\nyCSeMyKYkWSxUQJOzsbqhMIOVv6oYtzV9yMBL5lIwGmh7M4UZMJ1BN51RFLyoLxJUamapB6RfvHW\nrG1pffVLnU9aSDSpLJlsviEY2YOSqUqJTI1w39r1svr2mkBZ5sent2U+0udPh8z8mMD3zY6KRmge\n8dh6CBETMzHgoy4EbCBdEzmA1Hv87Td7ygTzYLOEMOX4mxjYH2CIhT76929TzbzhQzPSmTX36nwr\n2GNBgwRol6Tu7St9LEgfu7Th5Pv4fiKFaGyLB4cihdgRZVBOxzgVAyFDRBj5gkX2aTUGnuPW9BLp\nx3zdn6qNPm43sftgvnM3f7nQiWkyX9CPCz3U6VAjgl6e1+bRGonE41xViGGnY5XAZe3l4Shu66WX\nk96/eSiJl/N1tXjz9LjZ2ZPnpc6CDjsenqxv53cfLLMUnqn01vtq/HQ4FiGzACysrWhbmDfKYt7N\n/fS43J2O/bqez424DGrsQBDePxyWWROw1M66Jm0r1n5pFso6Cyu39ZrP161yv69yqAUWH85nPR4e\nHu+Pldfr+v7DBs529svVDV5JktkomAiBdXOLDEFhiYxu5sERSRGzqlIeH8pnnz5l9EY51yN7T8Lq\nSA+e9HCsZNaai0dkbKt//VLkk+9+/1/9Uz/zq3/ozXd/Zj7NySVRCJEoxLFzhG7bZBpu0dG27UJz\nAXGCiAuh5k7+JRAlcw7o3q0b/Jd/0Q5C+n2F8Ju/HVtF/PRUtcOc9ivabyArC3hQbogW6IPwMt5p\nRIArE+1TfbfkIJIEiU7bq7/7//4P//w3//5X/+C/3H7vH9/VD4ejSoneu19bZmgtSiVCXDzTGSnM\nfWu9h23BWlR8WLiYICTMjFK6tXXrbQvzLFXujjyrhrXe0Zr11T0lqR+mqS5ymEhVHNR6Xl7t5fXc\nt6hVD3dcZaKi3bBagqFLnUstYuaDZRYjf0qgvV3Mwp1VmIl2T3fl8ciGG8FYC6VGhDda26sbpKJo\nVebNtsHZLCwqzEC4I5NKoQQim4cZMbmWlMLCNTPMWjjMk0gzPSKIg1OTAiGx47tknrTUZOiI7g0f\n8Q3jQyOC8ciAY2YVKUVZib1ffW3WOzLactBpWsrpAJmcwlr0y5ZuwpjmSefiHr1FutGOyXLcVH4R\nISCRnFR1EtU5OGP45JFMAoIEsRIP97dIdzfPGEDSVCq883N7w0g3JiYuSdydvffNO2VkpkrRQgDH\nft0WRxcqXCCiyN1plpnunkyRZQxDWm9MwZzKUykTs4D36EESECHClImZx6A1WTI00LMTcSiTLkW0\nMJfIZBCXqNPC08JykMJt3WxtET16tzR4aqlSUOfKNA09UbRu0ZWVChEKkNu1rc/vvL+4e53n5f40\nL4/T4YG+9Xj4Zi+FjyVl6D8CI+bSKZUJmcxCoMzYY3dBgdjVrqMeADLs5yNgFkN9NUyHycwgkkQQ\ncs+2HYWQPs4Yf9pd0S3+9xs93wDWjJ1c5s3LODbI41Gzq/WSdl/7jRSTINKEU5LDCQpYIph0L71D\nVIw9Vuz2mbjwaCRBTBnJjBEseXvIxccPcH+fyL1ehijyFz67+3ZF9fUEqEctfCxQwVRpvdq6UvNW\nGfcLF6JMqGit2MKP98dK+f75ynP9zne/1dr28u7au82FCMTHenw4eW9ueVgogs/nCwKzqjc/v2xr\n7w9Pnwitkppk29aa5dY8B/TGTZTn07Lcn4jrh3fvbPXeXIsKU0reHeb5cDBCEBnKZrl2W1sfWD1B\nZtsuvV23vlCehMQCGZvH8nT6/Mtveduef/L82rZELFTfP6+vW4JSWaVqoXSz82apXJlZxb2nhad4\noLtNUnpcvvXJ/du3d1trBA0FUFvvBCKRWYXU0cndHB5tuuqbp5/7V7//p3/t+7/4c5gKIBhEfZIg\nkhAfsvFwGjPCHOetCDBDiEoSE09UZuiwTiETey7dKGeDnHQreMA3at7vO/79/ymBH383mrq97N1e\ncF8x73ff7yuTcESCuqe1TKO+ZmysBN5Th5GZBCFBGModpjsUTgefz+9/9x/98Nf/q/f/3X+z/fAf\nsn09CUiyJPWx+wFEiIWiR+8RESoKDYGDQMQyQEiE1dI69eYJL0UOSxVBmPfebcseCQM4l6PWQtMi\nZGlO1+62+eXFtt6Xhe6OR6mSSVuPFilVpnkusxC69yH/DLBKOSWabdi2K1GO1QQSpBAWkhHNG3Aj\npiQlknS3vrVMzRGuRO5mSGYqQlOZCAYfFi4lpvRmrfcgYhZxUSZi+GhBM9y7sQcoohQFhTf3HJNp\nF5E611oLCGlpaSMIhAQsYOIIAmWEEUSUylRFBClbnLcL1hZh63Gal3kpM2jS1GMAcDZbxQmcXJlS\nLSMMYW3fQ3vjsMAONirMqqpKMpV0SuLRioBSqZAOeleMc1NCLDOSMghMwpwDjWo9rGc21sI0JUVz\n2jqsR4YlNmapXCAhXEklWAHxNAJLBnGyMoNTCGBzeGYk9+gZOZzfYCmFRIpo2a1nEUFQgmfqWJUJ\nRJRLJUhP2NbNm4C0Sp1UZUph5Qml1+mo0zHTI7j3LWzDiHKEIhLsTKpFVNQz3Y0DkQA5MTHViLxe\nnv360tdrqfPxdJofTqRTqQf61uMB3+hmPjZSAHYeKIa/fX8O3J747g6Aguijv2TUnvE6zEQDLT1G\nnEO5ghGIzJzw20jzm08K7LZ9ulkvfl+nlbcRKjEPZpSnj3h6Hq7CsTkeQtZvPHaAAUjk/bslJdvt\noSOUADsgdFtzymCS70gYol3dSkmhVJIifTdQEgTDE5K4FU5E+lCxPU71j3zx9rPZ+fxyryp2oeD0\nZME0zwiL1luPh6fDwggzKcSA9+Cax8Oiwue+3j1+cv/m0Nb15fWMBvG4er97+8keGyWoic03bAwF\nQX3t56sll9NdKMBdw/v52lvP67ZKURExX4/1/u5xvpwvVJeIXLct3dzBy1Sq3i+qTM4lpDh4s3jt\na2992JaL0mWz86tf1/MRuBunO8Q0l6c3xwC1zYxyu/Z51vNrf3n1zYy0RuLNaWG21nFprUxaK1NQ\n2ywjMzvzZOaeYMT9ozyeHno2A4qqI5pLpEyU85QT68t5XU1A98vP/PL3/40/83N/9I/Wu0MMhdSe\nhSw5qHkM7L7iSCJQASgjiAu4ULmHEgl2t+c35uE+dNkBZMA9so+TPzIoGqIjx7Vs2INc/HZJB2Xu\n+c8AcRlM5BENDxpPcAUnIONO209a9C+W1MyPMIbxwkgDLNPOGcbRgB4UH1XcDE2eSY+YCoJyexV7\nefd7v/Pb/9X/88Ov/7/w+ruKD4O2wqwEAnnvDTRQlUxMyA4tk7Jn2GbXzbsP+T0dFpomYqqO2FYf\nGRQkqoTpwFOliagj11esrV/WtW1J4ctcjnMhSYNspj28FDo9HWqZwNEbbL1ydqKJppmFW9/a6kmA\nBzySQmRipVolMwganBSJPciabT/cRQb1bkM6kKIqKBLMw31A4YmARQ+33obaBCzJUjLWRAI1w3pY\n9/QGJipK7tGat96FaV54qqWqgABSC4tAeGQQTVRIqBB6ekZGCJdSdFJypa1T961v3KxNrEVpWkS4\nuLLIwspJKMLRG5H0gUYYaJExwLbVrXE6MbOoFK2sLK5aAkjoRzKWEBSaRQnY4wD3yKbYryRSpmJo\n4Zk9SSiigwQUPagHN3P3IGKhHGJoEmIw1TlECZLMGY08mXe8s04jU3EyT3fbzMKSlSdhVmEpA7Iz\njBDRLWlVVDAzi6iASITrMgstnWK7bmYNvZNmKTqVRSdWVqpTKRJSKbVta7eLMnOpoCIiBHY0oYow\nVrgj3AuGhZvde/StRwv3uG5SsBzupuUkh4lL9d73jvCnSstbZ4NxtwLMQrHHwnxsgpIcOTpC9jFm\nDJCAYyRVJTEUaulDrEzY/ezfjNL9OMPcZbmg2IVRcQvHDaIES44ki/Ak8MgJs3H16bBejOUfJTyD\niS29BEJ2EygRUQrgmQyO4WTZYxH3TKgBDr2RYnbfwxAGhhANy85Ph59g2s/wdNPOyP7Q2jMa7Qdv\n3/zC/fSIVmK9L4revLm7tujLxHeH5fXlA5I+fTgp+6ARIbL1tSxLqVxYqPLTt56qlA8fXiySCf31\nMi3zdLy/Xrfzdn3z9BCx9deOQuyJiLWlRzVpX375qb0+E+bn5w/bq6198xhmTQLy7dORslnwec0g\nZ1LLABFCl5MuyxEUziVZuocHNm9r626YpWTEuffr2nA1gXPbKFEnffv2vlSY4Xn1zTwz58Jt257P\nac5E1LPfLQf0fglj0tPMtUjrbm5bD2WdVDK5h6fi7jQfZgCVCrOgN2/hDBStE4tRv77SS7z5xT/z\nF375z/zJu2+9TdUU5tQkJQwa7cjFIYwgWtBY5UEYccQ0USlD1Plx6D4CV2hUPhhshXXEhojwKyEj\ng/ahelBKJiWMQYM99M2D1x4zMr4v3+pYShJo1EgIEYEcpBgJXCjQCpmzHIh1F5x+syJ+4w/2f08g\nkA3or+EdaJR9YP2Tyl7H+UDlDnZOe4adP/z2b/3GX/+vv/67/9kb+aClR+tOxlqYB5+Qk7Mo6TwB\n2a7r9dzP1zVBLOXuNBfOJDTrFhKtm0Er1UWOy4mliYevdOnt+tJfmkdELXyaq0pS8tp9dfSI46k8\n3M2y1AS7WbQIM2YKCElxt/AxbyP3hkiwiKjWypQeXXVioYjo1jxIddZCJMiAtdjaysxSUXSiZKIo\nrCRE4LBuZma3LnrggNOGEoJyhAGJebOMNKYgR4Tn9dpYcXcsS1EW9dg4ORmelElJEOZBVXDKQpKA\nEBWSFCPUju7Jm3eOiVmEkimBACukkyx1nkqpAGLguq0PdfsYx6e7e0O/QFS11EIiwizpjiHzy9Hl\noWoJykIcQknojsyM3pIJJLdnu7q7eXMbYM9CxJYZNrATGJ4DDD7AcJkTWDSkEktCUnwkKVFGjud+\nQRUB6pae3XuKW2fJSWculYSJOAnp4Xt6dUsH8c79JBHhWicuqlHYO1tG8/TWNHrRXI5H0QllSs5I\nIvdtu/r6usxLPd2TzFIKyIlAmGJERXl335RqcsDRttdtPVOCQVKhUg7Lgy4LqSYhA/TF0/EbZ8/9\nNmZmgAguLNijdxm3iQ7t48cxpBxDznGQTSGxYWYY/43AxEwft263PQzoNrfch4o+AmcjSITx0z3l\n/q6YKD++vUxwuoNyH3Wy0JjEug+BQyI42REZw4hqSAGFJI/9F93CUcYeMAaN/fYcY+bMGAAG2jvC\nny4cAdCAOsJvcRxDQhpBhKRj5Z+/rz93NxffFsk7jeyxruGOHlYrnUSbWy345DiVkqrSN/cR9iR6\nP9WX7eXp7ZvDoZrw+eVcD5WAdr4cTieqennuIOIS2v3ar6fHN5X5w0+ev/7wfPf0RpgL9bvjsXuz\nSzyvl/XFg5JSIrtKPJ4OKYZNrobuaNTKNKkWVV5mlVop+bVnytQpm1nv1zCkQUUAvrR2PV/VM9sa\nvWfE09uHzz9/JMHltb9e8xqtEPXr2jou10ZTBUGyKIdZd6csWjjuJlBSt0gnSAoJSBr6fNDDPBPY\nC90tx8jeW+sdSCxTSfLXV26f/sof/3f+wrf/4M/P93eQEqKSuudkEjBUykmAIJPJAsSxhC5c51Bi\nJtx6vlFL4FvaRtnTWkYjt2Efp7hZI3bR1k0wRaC4hb5kcgKSyEH8MgKGcYooKTkpaGcQDSLGuIVk\nyNmG4As03rUmg5OINcHQmlJZpihHVv19K8lbVdynEQQ40gItsn/IXFlq+KtkpjJoQjLSafw0gX55\n96O//4/+0f/9/7z+1t9/ul9VkVkTxgAVUlURab2t13ZdE21bDmU6zALZul8vF49kYUph5Xnhu+NR\nFNHjemmXy/X52S7bJlKOh3p3B45qmZdLX68W3O/uj/f3iyolITwjA8ngmmluCUtLSe5Ms6eR5whT\nI9LkpGAtnWXKhHdvkawiojKR0tz8CmOiKAKW4rEr7EQqp1Pehpi5999IH3lMo1dBaG89HAknZHhu\nW+8WzVIVh0VO80wU42hImaBgFiaBiBBbWKQzUZ0X1XGGloxo5pRIEpkqETGzB2V6RiAyNeZyxzMp\nz06Zo0xEUAbI0yWiRTekE9WiWYuozkyJLKuvkbsYUQtVrVVKSErCI5unAUyKaKoFqhH7Dqu5uwfG\nZ5vkQZ7kCWSYm2gBMllhDnIiZhYSMSXmKROUTjvqXIeR0mFlUuI5kGHuXJBgZZXC7DFo5EgeM27P\nLR0tgI0gzMqahSsXXqqiHrgUA4Hny/Xczh+K2XLQ+fhA02yZbbV2vtj1vYDn01wXXZY7aB1IKdXi\nCdtG1OalslikRVi/pnUupaoIZalLKYWVHQKtBPkXC+G4LXfzDAUyR0zgruoGxgNmJDOMjzUZH3eE\nBERij/ED8eBmZzLxsIbkx5x67A0XYsxDdwno/o2Yh1x4H0EOTplF8jAsSGaO1SV4uEIzkDJS/Yb0\nxr9h6h+La3LZjc97SaebZT52y18yDenNTjMdhXAcASIDTBnBoF07QRSU6aGsQzdUlD+t9EtPxzex\n3S88V67I9rptvWstYRwW4b0Ufpxxd5Ra+e7u0Nbt+dWD826eM3rvYKL5UDqCT8vpuPTtOlytleq6\nbTzV47y8/OTFOY/3c/HYul+eEyf57JPT+vLeHJXmy/p6eU2LnkFIVkUpPIuYNXN2cJA7JFlUeFlk\nORwgmsLdcLG4mnUDugc5QQnwDG+xXs/ZnL2lZZnp29/+/HjUZv2y4bzlFp3N4jW/vpxJCuqUnAsR\nWTfz7kEkTqmIWWSuGrEKz+5hSF2m432tpQYac5XC8OjmFqQsk8yXdZ6//ws/+6f/3e/88vfr8hAU\nQAHzCI4cHXsmDbU0IoM4wcwLpgNJhQJDwhRAWFon39JX9DHktExLjFHn3tzTWKUlkA2gfQI+ZuE7\npWgEoWgMrfQwHRIGZZaRIM5B2kMScTAoC0AY7zqTBs+PNCmG9nTsIsC+m0hBYGEukAo9ZplRZvBP\nV4m3AyMGVokC5JlbC7+iXYhWZh1Sb6Kyu1vJGRq5Pv+T3/on/8V/+bt/+29O6z87HbOIUjGW2tbW\nemy2UY/DPC9LCXB3u7y2q5kAhajOMk96PFQuZGtcLv31w/r1a189DkWPJ707FUDd+uVia4vMPB6n\n01GnwpFuAFKkFlXllK1tWw+LkLGFIibZ9eERlBlJKELJKckGQ0iyiKqIYMRCgEVYhtsDHukEjgxK\npbSxLynMKZyjFlnHAA4gQWQ9trVTJFFaRO9pPcy9VFIt04RCAqLmzc3hJMqqWsoce8ZfB6DTdJiV\nlRDwFmt3gpNynY4qQsIe2Z0sksIAaJ2UFcWRNVncNwIDxOmePTvCtwgQUERqZVUVLoGwns1aRiZD\nCUVIS1EREMxjC/G9t6OipKqg4mbhbntoXU+HI8LdjZOEmTOMiDycQJAJkUke4UxMwlAhUpBkDtTO\nOA8WpEU6S2FVCATVhaVMxFmoephZs+5CJKWOn6xT2nlzvwKUEELOKqVqqbwcjlSXnI/JujW8vHuf\n69eqeTrdoRwAvZ7Pfnnp64c6zWVe5kNRObAmiojqXO+Mua892hpxjZYexpyiJVkWUS3VYysyDIgM\nUqpTgn9aCDOCblgZGdJQIh2F4KeSzRyyqD1vA5m32Aenva4N2SQlgUKwpyyOJ9AIYEJmIm+5hUNx\nTiPM9qN0YKRY7BIVEXf76F4nwJOIMKJBE87Dp4dgDDz3oF7dXgohoz+QEeGH/WDy+47XuQtlMohS\niOPWH4vIjcGaFAFI4qPrdvS3TIikmJi/e5p/5a4u2SvbZ4/T8XT80Q+/6msQpM7cru4WVfzpqPdV\np5llhia9rA1cqmo0f13dg1iTNOb7u8OyrJfzfJBK6M2I5Hh/7Jfz1jpN5eF0ury+Xj6YVZmEppp3\nh/lyWbc1bMu1d+8ISk7iSg9LQcZ5czcJzqnoyJYO6ofDoUyVaTJwY2wdr9va1lamUkEB9N7Xc9s8\nore4tMwo4m/fPnz720/eW29xXv391SwcIZfz9drNeJ7mA6F9/snj+vLu9aV56ySaoJHUeJSclKZK\nvaMl68Kn+6OykLJUpfDNAz0gSTwBT9P3f+UX/vSf/fYv/hwVcZpoJGqxAtitCftsexhmhDGFzDzP\nGHkCANyRDjtnWym2dEf0JI8QIk+PfbF3y1ABnNIR48LrFBjjs9sl6jxAuIjbBSw58pbz4+U80II5\nZDr7joaEBimJHOCkEUlOMVY8RIAQyZBFgBwgDAAFyQ2yIaQTypLlPosw6+/bLOY+76UAOtyuZBt8\nBdYb5c0TBCqRyUKMOH/97jf/xn/+k7/zN5f19wpdo289wroT/DQdSiVweGBdY2tmaUJ8t5QqPC9F\nC7fm59f29Yfru5fNQ4v6J/fL6TQx82rt9ZLXtavwaSmHRSkHgIS5uNa7Mk8Et27bZmYIBxETd2Yl\n2bF07gFUKSCKDIvgIBdVKRNI4D3Cih5GGvdwGXgYi0R4DAYakqRKUVWizLBEhA8FKAJJ4Z0ies/W\nWiSZWViSkBZSHbFJKFwSuVn03sO9VKmljq2PkLJEUS5VVatTblt62xJN+LDMk1QhJfM01G7B4cya\nksQ05g6ZIGUhzXAZCpc0N4s0pQpORWopQ4xoRGHu/cpUREeMYRk0G0szL/0WGFfKLJoEzkzrnSND\nJNMT2o2abd33oMpJCzNbeJgRyDOZNTAShokYEJAWoASS00lEdA6K6O7pyqxVpEzKGkXqtKQows2a\nW0Y3Hr+UmdTB7r5uW9uadQvbJpF5prmW6TjNx09lniHlmnR+PsflVchItBTxltf1tb9eJNc61ePD\nQzksGTV8q6WQFi7Kor1F266+Xbx1UEjV5bDMx9Mwg4QbwqgUQIZpH8T0xdMxM4EQqZlBu56AiBjk\nepMNJN2M6wnQCLfNvEknx7zSKQkkYMfHJZ+PxGcmIUJgd7gDMppBogGRHofbYNaPj7WkjFtm5i4Q\nhdA+kdzrGrIPEwY8Esp7l9lz975/dLuPFeAtky0TSObhMkSCRgPBJIFROpOZaYQvJyj59mLj5x99\nM1EGIZMoA48Vv/L27udPKu0a6XeTHufJ2sWdL6/e+lV1FuAw40BEHI9vD8e5rq/rFnZ6OlHP15eX\n12taoupEcyxzfXlZQ/XxUApZOtdFp2np7bKudvd4yH69PqNX8Q5d9MA17VVYwunatui0WQ9nz+3h\ncCwa3vCyDnkaeJG7MntJJZ7mWUg6ZdK8Jjrldt2uramWqcip6Ffny/nV12sL3/h8tSJ/8k//a+9+\n5x9++ubN6/rhcrF19eulXy7mweetZSk6LaaMwNNx4XZZX8+vZ28Wx+PkbiJaGZMmj1Rn4TLr6TQF\nEZUy6RTc7BodHckpb+9//td+8d/6tz/97re4LMPNzkyEkvRRTjVOTUZQzxQ+oiwoMwRAwgN+RW/w\nF7cVZOyaMMD3tMxwIBBAdoQAlkhkZDoledg+AY0A9TESG0JSgg3tTdCN5zdwgGPQcZPaACDSPV1a\nEyEgEDmygINIE+CBmwJDKnIPJhszCqaBzq2g3SMxFM5gZmgwMR1oPmQ5oco+DiHEx+8NIBAd2c7c\nX8Ofs12lKOkB0NtGNKCMc/z3f/vv/OZ/+lfyn//GwmcWX5apMIDugei8RY9kZRxm4cJVi4ZdW5xX\ne/+8vn8xj5hA9/f14aQivAauLc7XVqo+HOoyT+u1r90z2t0y1btSplOmUaRZbusWvQR3IqlDwDKI\nk96TlbWAHEbNDQmdRMvELB4NPalSHciuXa1EycCYG7pREIiJo9TK4/kRtOvuukdYugcSjuZ9XaO3\nNTJE5mXWScgpfOQ5A57Uu5tFwkSnMbQqhSfOsnAtM+kS2bs3b5TppfDERecJxEkWTj0RoVQgyhma\n6NY92YWOUC9M8KE5NUujKCxRRJOzknh0A0cgMzgApLLICKoVJLj3aGmWARRi0SoqcwxLXyRgkhSg\nILHMvqGjZ6QQV0lhIS3hbt6jU0+vLM5IhHmCmERZxx0FThARSwUjHN03LVzrpKVoqVwr1zmkcIa1\nsO1MAJGqAlRYIkiK1qvj5WzX13ft5euZqB70UBY9zqfDzMtE9Q6q/Xptzcw37WkRsfn1+pP0Xooe\n5tN0vJ+OE+ncrXM0mu7Jw93Nr9vrul42xLlwOZ2mw8ObuhxY2OGACTHx7EQAsTLRDXVGN8v5LpXh\nBJzxscwNvehO9wGwY4jGWXiQqMYocVg9gZtKBY7kRLJFgkkxYHHktwh57M7DjNFGZlBGIyoRe3Kv\nDJb//nzZO0pKCmr7+TcTmcz+sQYTeKciJ5j45oQkIorsSrqf6AFi8nQkyUBjj/ZwbGMy93BhGh5n\nAo1UAR6b32QgCBk/+1j/6Kfzt4oimiFemzelY28zl9f16m6UUjWXisrc/PrlF5/NtfzzH70D16eH\ne1/b9bpdG3n6PJXjsYLjx+9fpsPdneb60mPmu/tj286vH94vx+l4P/fXy+vVReepiBQmMluvyewB\nawPjy1x0KqUqFPRysesVPZKUl1onTRAOU5FJAenOHRqElrGu5r2Xyo+luq8v57ZdN5jX6LFu0zz/\n6h//1Sq+neavfvT1GYLAdrGX15bM19bK4S4nTZBEHJRofW3XnlkKU50R7lV1qphkckRYL8dymGsR\ncTAXmSrQNgdZ8stl+fwP/qk/9Of//NN3v+S6uBBzpeiMCkLkjpzdKw3UQSwL1wU8gQm+YTujb2kt\nchUPzwAyYR4NhDEfIxgcQU4eOQYaGW7bPua3cDjcEz6kWDvlKse8oUXgFlFSE412zZRRSoBBK5FG\n2qhPRCkNSbrvyNEgIRixbAgqzAA3JgFLjEQVIlANAUUnDYSBJHkFNInHZR7k7M+gCiLUBdMDlYPw\nvsPYm88Kmo4UR94+i+uHiA98izYbUAt0z9PDH/iL/7Pv/slf+9Hf+29+/a/8x/fPv1Hp0i3TJZIS\nTXVi4WmmQlSYw+3DuV03f35pHy6tBZai96fpuCSIzpt1l7X1w1KOhzppWS3O5m1ryywyay2n6C1y\n5KZyemZuQJ2UoSwk1q8QUq2knOnd0pJItOhMMlJHnYcMPZNHFB6r1ILuYWHWY0S/EYoks1JkZggB\nzOFByJ6e+xtoSA1H37buUaqeDlPRJOboriRDcBAZxMyKyGltbYSzCafUWalGpLdL9hyGPaIkkiTf\n2kZaM12oCnKaBMxObNH7iGwQAhknONmtjZgqohRwqcwQ1pIGB3k0DMoCiIUYxOSAuPtmFgEbwkNJ\nrUxcEhLZohOoA5ZZA+gRW3iiEBUVInRSyUBYD3CkWCIyVnMWcVAmE2m4jwd/RDKIlQydUxFjfFoH\nEYyJ4A4LQqNkRCcKZqgmUwFzOoYnfZ5qppgvZqdtXZMZbkuLVayIkr8yi4jWosIUbtZebH3JrSv5\ndDxInUkiGUrFVeHCGdCy9Yu9XrZru15f4K5HMCn5NbO6G7zPDw9RH+GabY3YRn9HXzwtI2sBGEFH\nCMdAevLwZt6kMczyUyl3ZgL+UUmQA92Z8Bik1NtBeKgC9loLsn9RCYfx3wNIYdlVmO4AxwijYB6q\nvG8oNhGZjHROSQFnBmRQ8iDEY8SLzBElP+QTLqIDdryLFSTDhyEESQIERuHFEL2DaKhR99Ewdr/g\n6DwgOVaRXlX/6LfufvnIM21Cel3XFu1UlnvlKnG+9OcPF6n17q4q3DebjuVnvvfFdn398U+uZZol\nVzSsdp10DqYyqQiNnzwIE5ev3r2v82GZmRyrt4enO2W8fn11IlYc6qxKVJfo2+X5nLmbbYlJhetU\n0qNv/XK18zWIiZXLrMvCqnL/8HQ46LlHYw6Ta9s29627ez8cpjup1vzDtb1c14qMa+9ti/C5zG/f\n3j0+Lc+XDxv4dLx7/tH7r5/PjWR5eGrJmeV1e/n000/aT35ULK5r56Tw6ImMXpWm5aBVBRQada7L\nslgmwpUGMy8d+v68Tt/5Y7/2l//ip9/9nhxPyWW4WEGcxBl78MPe7exXycJlAR+AC7pFf+ZoEdv+\nl5aRkZQ9kwkBeMRGgQxP9HShyEiL3BhwNwq4h4dTZMCZEdHJ4ZnjIBQxnD4U2YFEjuOdAAkSTx8W\nbEIyxDgkCEwBpaFuJ8tB1BgMJqkASJJ5LDWFiZ1ZIBAmIqiIi3MICDzixsbKXVOCsg4yI5AigiRI\nIV0w36MeIXv8GO+HPCBBkXHtYV9xJqHj45hfZ4qgtGjXf/p3f/03/i//cf6z/26ujZWmpehUtFAB\nMqK163bJD2e/rO269QQfZzoeREUQuF79mn2a6lLloDWJLtbXFt25AnfHsswUiYQkIp2QFNkpwAUi\nJVkQluRj4udga9k9mGuZUkRB6b1TBkUKa6YTELXWOkU6Ar21GC5jFWFlISLLTEVNCiLprfW2cVCg\nhWdEDMy3u2spc+FJhUSBHkxw9uh9M4iCOFw228ydQdM0zTXnUgnoQI/OzoJgCSLBSBwWRYJViGSa\nK4TBaoHe3XwjElUaZl90t+4jLFcEVZRqKTo7k/VIit43cRBDIcQhVJLTLTZ3G8IuTtUCYtYJDEAy\nyO0KRBLBvYd055bJYFUqMJGh91Mi2XrfelKQIbAHzcku+qfgBIQzRQEWsNDHSWGtRZVFJx2XiBaW\nGs6eHXuArCkvxJRBrl1lgcrWsW39cn7ZLi+crqxlqkutMotKFRFRZqrubq1fXl7W8/vo16XU6fhQ\nD4dymMpyJ3rwjN57eodns/X6/P78/I78MqEe7+fT6bAcjzodjOLukzf88J2QB7Lr+d1XaM/BPNWJ\nvnxzwDc6PwB0SyAeGtkxtxnTdwJuoyCOzFEId6XMLiTANwWfAAQ3XdyotxRAZvBtxkmZRJzCo2sb\nMhuKuElpGImgkI91dZy1CPCRZ5FgZdxSD5NooF2GG5E+MkMJwhQRCVKRMXQdk1HC6OzwsbkgEAOe\nOVReQwE7PhBgH2kB+VD9137m019+1Hj94Aa3ON5NmnlU8X5ZL7Zem07T4SSFcO3x6bfuH+7K9Rzv\nnlekw9O2UEGphSsd7w/ZvLdWlgOTt/P1vLZSZnLuvaHS4+Odt/78epFJl8NcWIoZQJ706i239B7u\nVmc+LFUTzWPd8rLlujaHLbXWueisx4PePz2w0BaUXN3x8nq9tLZ5iPDDcToQX6/tq5eXdTVujrR2\nbcfH+8Ox4LXlpJl29+YwTfX6enl+6S1KV42yoMj55XIs8v030w9/7/fWK0fv5+YkpUiZZ5qrSqk8\nkdZpOc2RYclCqcjs3tZra2L3P/NL/86//7N//E/w/cI0g4SYc4c0EKcAQQgwx85wn8EzmDNeqDeP\nlR2RG2Xk7pDOhFFw5kZjBGYOsrCegUinQPgaiUyHI9PDemZGOpIyR/kLGhz4GPvp/QJDRiI4JXgM\nI4OQTDpOYMQ8JDKZw0aVREIpKQEacHxkpDATKIiFNRFCA+hWmAuxCEtwCE0kY2HBQ38dDIakOmfZ\nPyXacw+gzElJzDJTPUKPmA+DhnPbbiABdOS2ZfvAuRLT7Y85KBhKGe36/nf+1q//5l/9j5b1//P2\nkyK1ahH3aOt6OW8/+fpyXWEWIvR4nO6OAmVzfP1y8aR5kjf3R1HPlV7W9doikrTiQatIRsIzRcrt\n6ZCUUUZMONiHIUCFkzy9t2hGKVBeRLsIJ4Mz0h2+H4RomMxRe5r16NaYpZaJZppEYuTEIukmJLpe\n1t43IhW4dd+62+al0qRSay2cvE+EKAGL9HBOiUAPN7cEi2KuWqWC08M9ApkKLiyimRQew7+sVApz\nUUEpyipJYokITuKB/AdlePTebD1zJJPoAGrqkiVBJUTNkR7IIDehUCJWJJfm0VvvYQmIVBTh4Y0m\nDYKQgimS3NZmZi0290ziIVIqpCPPiCQhkbDuzb21HhRMJKxB7GCEJyXgowZMWlQLE1j3Ib2WolrA\nUphJklVTKpMEMi2EUpTBCiFKHrLZVDbUiGzra+9O5j1d2SuRlKlI0ao6WlVPN7+8vsvePTaVpRzq\ntBym+SQH1XIfmdvW1utrP1+i9ev1HNcPs8Q8H5elzMeDLqVMd9Pj3eHTL/n4fdLl+vL15Ue/Zeev\ndJlFq45Yq1tYGt3k5OO+pbzxokaUUuY4TH5DqnbzP3yDsfHTFKfb14xxJWJ/udHeDV9GisjwpDKB\nSBExiu9QdYISMUKUMKrvSI5AEufuIMwcW5yMiL2rvUnKR+0efzbiHwk5/iWCRyxAjuTFAO0DUYAQ\ne9kFIsbmJ3d1q6uU8PUPPBz+xLeevnMUbJe1pyJPJ50kqHk/N/fUQt/59DPN3ppfu/3gD3y7KL7+\nyQePVIa3lExVkiLlVOY6kbUW/fDmwEbP71/XNZQXay2deeHHN4+vH77qG+k0399Piuitva7uDkxK\nMkW2zds0yTwpeTaPc7f1mmbJUkrVOtXTUefTdLq/o2xXq02ldxsnejCfJr2rQLbXl3x3vfZrZm/X\n68aFfunXfunpVN/99o9+9H4lbZ9++xO4vXx1NRSjvCQu160QiWOKphf7sYmkHID3lsLCwnXBXFVE\np6PqXLRMJEoecxWJbK2dr9ctP/nyT//5X/1z/8bydAdZQArmHS708W8Uljf9FTOlTJkOe0+2IraA\nIJqPUC/3PTwiHDD0QIRHi2zkiOzhjSI9Ir1FCtw8DRERI/EtkpB7Q0kYJQsdOWYkRoRMHhdPwDKY\nxldTgJ1GSu7I5Rx4xWRmHRDn/Q4i4ggk93Biwu7PiBw5Ptx6NmIaFKoQA4doZa4sJUd+GJK7UPZk\ny4E93f08GskQzvD0M5HiUlDvaH7Ioe8YN2YBlYn8M6zu7R3nJfdtuoyQHlnu/sCf+zPf+7U//o//\n+n/21X/91x/yh+Std9+u8fpqr5dM5uOBHu50mjD2GtZjPt5Lkcc7VqK+5dUsLCpLncukjmDvm49G\nmxIlgSKAcGOGAxQQCpUlycxb79zMR0uSMLNgEhHJbDuSh0WUKSg8m60tvYeDuIiyyiAnKo/cO6Fg\nzx4ZntRS2EcKb66tTaXOlaqqsHsYUGDhiA4mmgicEVtbuwczT1UOh1qUKLGZozsJqbAWmbVmorlh\nP4yDKEoRrUIpZs3NMzlLMk2gJFAz6y231sisEGlhJaeg9HBi0nTfs5ZALKJIR4ZD3WkLsmAQlzJx\nWSDi3T22iK1ocTIMBK9n79jaCAV2ZcjuqFbwFDu8KLb01bedHcYCZSYlWFgiER5EICmeibSiBSOk\nnZJG/DjILChDAA6kTsMsP3yTRMN6ICSwMGvW4RQsmaLaINUaQBFEPqKewlEzqPerd6cEqyjfMUSY\niRREGZwRSbX3dX153V5emr1g26Zsx3laJo6iKQyZoMy1gEqyg+GJnlbrFCw3pvjAbwTtMR9jSTiY\nisOnNAIfSHLAqZEjY3GMPD/a+zD+b/5pOzg8CENTQ7shfRjYf0qxcTcIcxIN807uoW6jW0v3UfuG\nO3Bv2MbZlXgHuKaQUAYR2XAGDlExEN/8XvvPs2cd6ugtRUaXuafvEon//twBzxTwIDRQQonFL3/w\n6f4Pf7rcy9YvV+5Ng53SL35Bm7WSx3LSTz+7h/v5xevMT58+2Pb61Y83wmAe4Ki1tzVq0Uk56HK5\nzHfl7untenl9fndpnURLO28h+eaz01z18vwBqPOhzBMVmDu8e+veXSjh3Kz1u+OxwrLHltmsd6MM\nNfSiZVnk7ih3D4cyVQtao/Tgy+u6rr6FKZW7OatwX+P12l/Ptl2vbfPI7dO3x+/+wvc+Pd29/+c/\nPPdVtB9P99f3r9vmm9NKdjbpPLU8Sz8/zUtrW1imRY9tbeQyl5K11jLIU5RlUi3zOGXM04Js67at\nbcbnf/DP/i//V0/ffqJyABgyBHJDd3nLUwblfmJLyowwspZhkSsnJQLhGYa0W16NwTtaS/gQ4KVH\nZMAtvLlv5GJhYVtCCORhlIjIvFnQMDrI3Sw0rsD4ePJL7GCj3Pkyux6ZEjsfEMiBqE0GMsKGihk0\nUsrhQRSU7BnMHBbBQkFJe+6aJNKTQ0DWiHpqA4jKxFJVC2NOKg1P6a8qFyYn6k7jDF6T1qCKVGRj\n9bxsvD5nPXC9x3zYLbRACnAUObz1S6f2wrgOGj4xCBKRcph+6T/497f/0Z/8h/+3v/bjv/c3YD9c\nV780K4UPUzzelVKrG1p3cPJUTpWrMgVt1re1IXM5TMyswt3Ct+YguHuGaqVBtEmLIKQwu1aJFMCs\nB1KZowpnoFOGO7Ex6y7uBbHW8SgAstvFOjnnVO9ESZSVhdSVayaICuAWFkgSnaZEmjd4plZ5Wg6T\nlCEY6C1AcN8yNRDgABMYzRPJVbJOssyic3FD924eSjQxz0vhohTTapZMFD0pixQtLJUjEYa1ZbgT\nUEhFzSLMsNpYh3RlTEWLEI8+zB3i6I4iyhMrcwZchgbIh/DDQWVimaHCsgTSwq0bgzp3Is6QbmmO\n3kOoqcwsuv/VRxIjSJIkMntSc4JFZARTyaHoAaemhJuDeBjYVAtJDipPRIqwhxITwoeiPsxUUrmK\nMMnAlY+Fj4zdKQsnkBYeK/HI3zQMYEVKeg5kdlqA1d1sawRLEDJZhUVpx652265mr3Y+b5fX7fpK\nDEZXhZalLDW1SDlKOQpzGNr1MpUP1i52fQ7buFYayTNfvjncJoKaMNxAoB/lmkQ81jIRLjygfJ7M\nY/D0jUJ4O2ZmMjMRZ7iMkzvdsDV7bPc+5vyYWOTIQgL4bjcGMvgbpOx9bBkDK5Mj4DuT+GNKIG64\nl0xnjNPUcEvIeDKNCW/6N+riN6a4o8dIkBLf4gr3ZaHnGGd5QoDt81l/+ZPpS+EJeZww5zhHhLeA\nU0g+LYel+sPjwgJbnSumpV7ery9mk/B2MZ3mwh6e01JlyvWSgbh/OEjR83k9X8zXdrmsLHVZlref\nH3Btzy/XTlkWeTjceawU5D0vrZnhulF3vzvp/TKbt766GxlRRljGxagUf3y8e3w8LvPJOHoGOa89\nn1tbr2tkLdqPk1bBdrGXs5+3c79s1w+vMpc/8sf+0Fw41+v6+ny5tuhpbo5MKRvx6hz3s7t0wyQm\nLy+VhCzNuvWxJiPWnEpJYhTM8zIt81QVgM6VmKzndvV3/vhH/id/+Wd/7VdlOrLUZEliGbtAIpAF\nklCSaTjWeaR+RwdZhg31FAEejRKJRq6JBm9hW0aGB2XPvpl7mEW694iwcN9TtR1OEYPB5g5I5njI\ngJMzZZAFkwB0Ih0HJBDTDhgCwHvxoIzo4DLSWW9RSgNsiyQQ50AyMfPYeu+mIaJBsdiX0KLujUiI\nklmCkqCi+73GVMbQXkXb2z/09N2fRcfld/6h5FcZRJnMYwwixAICZNoHqjRCQSdooek+5wcSiiGB\nuM39/eLcvgKt2JcayBzSoE7pX/8Pv/f/+N/+r/G7v65TLFUOB5lqXbdLa+4BVZ5mrZXd5Xpdh1v+\nOM9SEY5+6d1774gIIrAc6gQoVCnNrZMw6kIKtvTs4khmJUZEd4MjRYoK1To130QVXCBVMiLNrmuP\nLUnLfOAyiRIyVUDEEglIhm9mmSDhsOjNYzPvK5irmggoi5u7uzmZ9YwhKYZIBaOHM0kRmqaoExOX\nrfO2Ne+QpDrzXIOLJpNFyeCIQPaiPtUDOCC1G6y5mY05lhZiiLu57eA+VarEhcY8HgGHCIhZq5Ra\nSxWmdO+9m6ebN2YiComiB+ICTg/q4W3buDeCUylIslRzdu+IpgmtqnVyWO58tDmVDWgezXpfV5hl\nIihLmahUZkJypEd4WpptpWgpRYuwVjAP8c4khQhURnrPoHF5nY5SWKCJMcchFBGtDgJnUDXDas23\nK7v1brkFYRubOKGxDKbhLIrmN86vEZFOU12maZpTqq2xbuft/OH88r6mz0I6HZn8eFzqco9JdDrw\ndADrNIksByp3EDl/+LEQk0xchP7FQkjDtiBjq0HMg3TExLvaahjkw8euPjK+kSmIb9YY7NtBAoh3\nHimB6OaIGF+/V7udWzPoDntY2w5tRY7QXGTubHfNMSIdM1bBsEUSR0SmE1EGSOmbroykHas22okc\nLK3BgRlPFhYRiTSBkACRlkEQGiPZoGQc2X/+4fC54IuD1uyVoBxT4SK6ntduicy7RR8ey92doqc1\n1EWFsPXWzKqUthmJsoAKne5PDFzWPs11OZTzc3t+uaqW6/ny4f3KVR8/u3tzmtrr+eWlX7I93C0P\nd3dpDYB7XNZmRq2Zmx2W+TCL91gtt4bI8HDiygXCenpT39w/EHHLdOXWszWct221QOZpKodC2exi\n+Xzdtpc12rpervdfvvmFn/1Wbf3ycvnqqw+bRUaQS2QYM5bjOfTweDoc9OX9h4cy2flDu1rhpKDz\n6pfNS5W7uWYRJ2Klh6djKcWsi7CWRTPXHu+u08Mv/5E/+e/9henzz3l+kAFiAeXuoJGPu+DYgTFA\nWoYFugyT9UjwC6NEYktPZEtv4S07MlrGGr1lZm9mEdENmekByohMd6eAjX0zMvalwMinGDBKIr5x\n1QW3PM7bnjo/XkIY1+aY0u+RhPspLeA04HZ7jvRHoi/vEgZg3xLulgfax2D7KY0IkeABrAdGHgsz\nKVgCdrz7nrOX7AlDqYri5MOgS9Dd8soCUmLNnRVSAY0iUu+wvIFS3LYdCbAhr9fsH0CNcyiAkGkJ\nZ1C/vv7jv/m3/vu/+n/87PA1qVHjc7t6TxEcTlVV3K2d+3ntpejhVJflRPBtbdvq1sx6+gi1KJjn\nAqEMSusDwVwLZ4h5J1GuRcDJ1LdrekKolKmUFD25EngSpQjzrXm/RKMQl1LqYWEuICZAUpKaBPfe\nzAzMHBmITLFtNW+UoiVLsoP6eslE6xgYsGFGHduzBEmlRWg6VSVB+qXFecO1rZVy0jrXSQoYYawR\nGJ2sapFCHOgRhvTgnTSSUKaIgEWQAaySyjpVFSpB5NF7z0CoMBHXOumkRWYLa5bb1tZ+QSqTci06\nSwaDpIWNgA7fLKNxSDA8YSmZCesgV5DWinEtk4KVmIO4j31A7+m0pXOksCQTq6RIICnGzj2RnZSq\nKhURnog0PTxt8L1ZssgM9iCuIsQqtahouhNl0QIBa4mJBccQStLWbT2/zx5u3a9b2lalQEERqgUU\nQSAuYy0fzmYXiq2ozoc7EEEQntvl2l5fEe1YWVV1XrQUqXOdKs8L1XkMU5mLlEKldDMkqc7JhTkR\nQV++OdCYOibv5BQahoX9Xv9p3bot0RjkSP+XGsH9fgZAI9uKgLhtBPf5Ku+Z9KM13AMigJvugGhX\nuOwvufOXcMPZjFiRZN8r6T7KzY+H8iRQytCcBpmgBIZRmhKRwbiB025KvxHklLuyAbyjIImAkYxB\nAn47xR98XN4IzRQHtoMyRS5zqQz3uJzPUvhwmI8LnabKlOu6MZUUzIepn6/W2dWPU81uvBzqVFpr\nkDg9Hhl6/nD58K4R07au7y7Xu8PT47emx6fT9V374Y9/3Fp+cqenpZAIiNtqzSwcZttynKYy+dp7\nuLlsQelOgKsWwXKYDvfLXOCkDdSDR7DPy/ViQROpqt3XaVvt5bKt1uO82dbWvP7Sv/KHn6aWL+uP\nv3pd2/b64o5QpcwpMvoy0/2JAPZeKXlt6uZuSpIercXWuwfm46yleOT0MH/6+dui5eX5QwLCiox1\nzWf+7Ff/wn/ws3/8l+nuB+Xx0+yW15+AeqYQgnIkxu01hoDIkdPTBpAhh+Q9hp/GyD1jRXSLRgaP\nlta9X90sLT16eFgA3jIAJ8eGhEcMCctOWsUQg/LteyZBiDnDQLTHPYxzaWIAGsYZbT8GjmYxHCRD\nw3mrebHbKoCksSYkpI4i+rEi3lbUySw3xBNhsB+ogH56xCQaKdcQVjCIKRFMB+IUSVYRriwTyjwE\nzpw3Lz7J8HFACgAWQQqpUn3A8oQiHxfkAqAjrle0d8wcFIBzcsY29Kov/+yf/oO/8le/+nt/fdZn\nBkqVusxV0NZ+udi2dpVYjvN0mgoV62vvva/ePTOV2IsWyCY5BWdGMunYrER0QKWQlgmqzOK9ww1I\nLqJSk531QHPRuiCpbWu/nM02kcJF5qkGCVMlJoJnZLoPG+Rwi2cgh3HQg5lURUS8Nfce7tua6T42\niMzpHZuFhRWSu0WPx1oLwPy6xnXrl+YyUlAoC1EMpgIT86IVWoqIkF/7plu2ACqrlBmcnHDrmTbk\nC0VKra5cWZik9vAtghyZKVyIe1EROYC5+3btaGuzuDIftbKKQmls5gLIoQF0jFD6QQIF1CMzN92P\n9jHkLaI1hcClRVoYAgxlICg8MpmJSUTd0zmyDTEOBEllEpl4LMoCZtbCIjijq+akRauqTJnOVFBE\nuRC5o81SSFREUZTnI9dFeGph6/q6vazZLn51769VKxQ0Ri7MyZyU8CnZI8ys9/OHwqxSiF0Pi8ph\n9UaXK9bnpYouk9ZFyky1sJYsRfVAVcdxUKWGZkKYa0YXppHhQl++OSD3IIUbipqwh8Xjm8fSwdHI\nHGh/RER8ZJNCIoNpsFWZmBCxB9EDAce+5BmHX75NWyjhgdxPncA3wwVHxxa3Ko1xE9/iKZjIs++4\nGSIwI1Ni2PDHPIoTIxZj90FkJoOdjHMidkQkNDMIQcwCDYq9KclMGpZ/TOnfO55+8T7eFiKyE5iz\nT1KKIikEwcStx+mu3t/Pc53M1utr21ZKjvun++18bhs55+NDFc8eXVUuW6uH+fHNXWxtfT4blWuz\n63lt5k+ff/b27WE+HV5er//sn75/9/XLz3xygnethZmsW7fMDBG7Py5F6vl6tY5LdzcJCZGaTHXK\n42E+Hg5Si0Vsqav1Zn5pnqFhNk1YiIXQNn93Xtt6FXfrbVrqF19++oMffO+f/Ld//+W1WWA9r+fe\nP/3yC9Ly1fniqJ9+/zu9nS8fXmnb7gu154sUFeLYzCybtSCUqWotCX344v6zb3/CsPP7y7YFscS2\nvWwiX/6RX/uf/qX7Lz7F8nk5feYIXr+GtRxiSk6nwpG7aioysyN3M8PYUJM3pCU8vcM8wjOv1MX8\nHOZmq9vm7mEJaz6EMp57z+dIhOeQCAztKcZTjGIfQVAkEY9t8zc2BUTgGxqNmOljEQSY9hOVjKwU\n5pHl+3FSkiPHGcFE2KERxMg9ARu7rtQJREOaQwKi/Tc8hDA3hsBwMgoxCghEIiAIg5PBNDYzRUTn\nLIVFCIpMoggSSYEwWMA2IEbEVahEPfHhMYv+9HQL5Aa5vo9YiRKwBHNawjHd07r+5t/467/51/4P\nb/Unp5PC2+Xc1tW7GYPnRed5Biw8E2adY02QsSiXZJFwmBsyVfZ48N47mKUULaI6gy2dIyN8E4ww\noBKFp+mAWrUU6327Xvu2UZGiEynJiA1k4URGwtwzcpR8ZERm94AxoKyQZCI348SIHmzN2hYgR1L6\n9OHy2ppVldOBTjNPZUrR1XxtLb0wqRQbY4CwyIQnSZFprjoXYYrNt7Z1s4ioWrjOVUrSILd3IJRY\nKmZRrrrDjZycEOCIAkllMLMQGUVY2bpZ9IiAklIhVU6OaJkWXISHcpCjUffWrCcSqSPwNiIzOgEy\nFJGMIlMyOdgpPGlI0hjKOjjMDuEgDmLvPboJpbBoKTpPJAKW8OhtNaPNWmSHmwrPOtUiqjJMFqCk\nUhnKiUTXAqFCU5XpTmYp9Rgsbbtury/tcsmLJ64kdQwUdST+kaTwgC6Fx9rX7fnVXp8FJocyHabT\n8fH4+HD++jWvXxcGzbNOk0yHlImGa46Za2VRMJMIWEUrpbhvoIZkQHaY4fCfJX1s8oiJPOKbpr+d\nP0zALp75RjsIJxozUOwRRjSeK5Q3j8JouPaHAoagk4g03YZjfa+pxCM9ZD8vM4+vxHAIEu/DqByh\ngEnMlJLWwZTEY67PmZ4Gpt3ZMc7m0DHb2fOeBwiAkqARiSGYBkXYULEj877gF+7qL5/KROssMRGr\nhU5FRKxdicFaKPPxYb5famF239prfHjZIELJzx/OWzipLKD12pUo3HvQ/aefsvfXr889oTK3bX33\n9TWEv/39z779nS+R8e7Dy4+/fndt588+ud+uF5HKEZToPbr7spSnx1OaXS9967iuuTkRaD4cpPI0\n4f7hVEXdsyc1rtvaX1r21cIMhZbKdxP11b9utr1et3WNtRPj8XF6++nTdn75rX/wD69bvl5771l1\nfvvJm+nxk3M327bTwyNibc+vAkRbnajMRYht654AYi4l4CDOiT/59ptPn07R1usWGTaprJu/76cv\n/vV/9w/9m39W746pBy4Z61ccDYmAEwsguy6LCJnhndIZ8OjIzOiICCS7u28MIJp7T/ew1a1Hu7h7\n3wLR3dzD3I2CY2TRDBXxsPhl3ui5NwCa5xhWYK98OZaATBiTydyh28SDuj5uiJupMT3AFGFESaQR\nTuSZY/kng1Nzq2IgIPkbmWU3jywRIwcEKYZmOnOMZo2IQLrLgAZCNwDq6ZICDFFPIEmRIRwWJq1L\nQeiiWkO+E1Q53wObhAEcKeyB0rNHiOV2jf6e5jd0eMz9bgVVZH3M80r9JREMOAsnYbvGfPqFf/vf\n+vlf/aW/93/9j86//bf5+nreunkW4VNVKhK2BXVOyUz2SPLU1LmUWqw7Z5NMloLmQbRZKyg0aamF\nlbM3hFEOFyRHGCJkqiQCSRGydev90t2DqIwMQgvPqMzozTLdLXNPrkgguzeLcFZOUkphIphtRJxE\nxJq9Q1wLJ4l7NLskQlVPBz3Noko9cst0D+EipWoRgLNHCyQRhCeWMnGd52SOvvXezA3IqpOWSVT3\n4AWkkChLFSmVRCRhZhTIHsCgCXCSyPDUmLmB13Z1CyFmoTJVoSk9Nr8mMOIHiwgzW5SmibCxw2Kw\n55iUO8aoP4NIQAOiK5kxCiWSRYQoRBgQpBGhCBPlFmy0EIGpcyVSYip7HJnUtE4BCmiZxk2RQRQC\nGLQmM0WSUMB96wSBGodyv4RMkErptXDWOT0yr+CjDG6JGRHlCH5gCDFVte7i4ulr29TbDJO5lDBs\nay3cGzMn1ZJ1oXIYJSrBGYRBM8xOECZBUEQP38btTWD6zifH3Wp1401/HDbuveA3p6MffREpCbtN\nJIF9pkQfkWzMSdDIYOIk31VbGUTEtwPyx/r6EWXK2LPjiNTcRBTk+54uAQq+0VDHBIk9kmQ8KGjE\nShBFxq4V3RFb+1qYOCllpEElAcED5EiQJKcUHYZBCYlSGF9M+QvH+e0ch4Ij/KijsIMEQuwWkXQ3\nFZ20SI1ck5Airy+r1kIIdw6CFHXfCooKc2wyl9PDQ2vb5dp7dyXu15VK5eP8ybfvP3/7tF6uH54v\nP/rxC6ItMr18/bJMqiqiSQAVXg66UPXe1xbPr9vWvVmBNJ1Pp5OcjsfD/Ry+mZGRZC6vdl1bXLYt\nLEXlUHXWvFzW8yWv27W9tnD75G7+9g8eKe3ydVtf2/V6EdaX1/Xhk/v5eEfzwTQu3i/PF+HS+/Z0\n93A9v1tY5lqD4K2368ooSiD3jVLuDl/+4NvHQtv52rrXMnm2y0U+yLf/2F/6X3z281/yfE9lIiIM\nsglhx/uJchIQkUHkZIhsyNEVGqwlHBEU2FqUSaI9pzfvl9y23pu31qzDPayZjR2gD8sPEVmmDFVI\njjAGH3xnER0MvcFt33PqMLYFkunMuh8AR1FiITAN4fjw3gx/xb4RHM2iZg7xZxAV7IZ1CfKxKdwr\n3+23Ywiyx0vvfp8dobRHpNxaNOyMC2MuzLzX9ptXl1l4DEgAkQkIZQRrkSg/+HP8+EZWa7/7j0Cv\nMniHXFI4h12WlGlmyUCh4xMtDwOttN/kHXZ+4TxT+sgMdSQQQhnr+R//53/rv/0r/7slfnhYymEi\nIlBGRiKdWD0ygtJMpyKiMYy7EZ6enm0LzyRILayHUmjqsXI6FOQSnp4GoMxV60JTEZLu5t0jLXng\nn5MxuOaQyOibWZDwIEmqVE/0rfW+TUVJMJWa3iNTUMAAk4eneWb01voazbbI5JClik5UWTdrEUyl\nusSkkxCHm5tF7907WMusc1ViVRF37z3DHUSkg/fIGYT0QHBimavqgKMlORxq5sHkHiLEBcI1FRnq\nGeFomemIMJaYSwUXTws3dyMuRVSEiMVCzKn3LTP8FsHarGN4SNE1ISLC1Rl5e9zvWOckJkgR4iqs\njgRYpmrhST17IQxuTHBVoILhkd5aJjW7WutVCtKVRAvVUr3O7Ik0rpO7U3p6kICKllJVDjwXqcyi\nIPFmbo3cBsIl4OnofeUR4ltIWHmusdGHl/fP7796/uqHB5K7Y71/Ok71oRwObTvDrmU6ynInZU6R\nsSbbj7wKKcrCUmbiQkTeW/jGWkQnokLf+eR4M8JTDt/TTQIn34x9uU14blVznxSNVosSzIPUtN+z\nt0KYhCFKS9rFmHTL/8uPQpXxmkQ0gFVD8zLeksPlFvt+W9EQmBABJvYM7AfyIL895oRpYIBi933s\n092B9jdm8XRmiXCMcGV2pAZFJYmMN1P8YC4/c1ce1Y+AZi6zLFoyPXvP5LZZVXp6exe9b1uPMnl6\nYW3hp0UZYcEBcuxBsOzhvp7ul7vHw+Vla1cL0OuW7z88P93Pn37x8Ol3vljm+eX5+d3z6+vzyu5C\n8ebTL9999RO7ropwj6fPDnfzhMzr1S+rtRbXlpt5JJWZnx7ujodSlLr3jQrpIUWu3c6X1bu7WynT\nVLlSPL+en8+2vW5wi207neh73/9OFXz4+vn6aufXs9OsIse7eT4dtuQG88jrNc7n50WLpVFkEbo/\nLoUzDG3tk0JFzXHe7PT5/fd/9vOwvl7Mtj4AkNfLKb74+T/2l/7y/WdfYDkQT5wZQ465w7Lpdo0B\nw/k+RFO+AchwYKOIDEeLy/HN/ekB599bf/Lbtl22vtl1C/dwcm9hnt7Cx2XjBHXvYzkdMZxCIwVz\nRKOEcsl92kH8jZyw2AekoFuWG8Zo8xvnwo83BVP5eI8Q8/DNjr03swCCMahnIsjOpYdh/yO66VLl\n5hHim+b0NnsZ89pxF+xDFVBSyvBkMzN//Az3Mz+LDs89q0sCfHr6QX38vL382O2VuYxNOUiYBWRg\nBStQiAtLEB8xP2E5/dSJ78gtov2Iow/DItJBHAnO9d1v/e7/+z/83z+8/0daVrNVkUaDViVuTjGO\npvsug6UkRfTY1r6tXaSwoEyFRZDJaZnJKoiMyBTSqchcqx4tLDzcGifATlQiEOgiByJDD2tbDsP7\nPBFLssDhGe7OhadSVSja1ZqRFCYWlR6IdA5v3dtq63pR9qlMU5WiCMi2mqVJrTqdStUdhLNuZj3N\nQKmiZVatxQPkaekAaOBfWNPgbkP2RRyT8jxNQ+PerVvnsa9mIRUVFZHCLD17OFbPJI5wTU3pwly4\n2hg4p6VlrXOpk1M0897MExKD/UIjpCLRAyoIJucgEYCXRFiGWUeKw5gILCyqLGBiZqJMqiTMRTMp\ncuDixg4bNuw/JGGWHhbW7VqcmZVgpZZpOWCeKMt2vY4tk4XFZqWKlAIloQlFq4yE+mQao/uxPKCk\nTikRIyLZQcEkkGgN6+X88v6rdl0nybupLJOWWsZdgGku00GmA2kBaUeSBQ/bJYNLEVXRGcLpLT2S\nnEm4TKq6d4SRHyVjowI5swz6xMfT6HhQ7Hn0AIKSiT56+4gIycN/NNR0kcwyHi5DNSNEtiNdxoJE\ncDNgjFdmlojddzksgCPC6eZA/vg2kENdeHPxD1g2xaiRIEJGxDe+YDy1mAjoGMG8+6NPMbbWKIE+\nq34x42cP9ZMDP8FOxExxN8nxODWzsIiO67aVWR8OJT3Pq3nlWqattWB6OhQFoNI2DxJLW8rUr9fk\nePPZ/Uz8+vzaHNvFLtezSS2TfP8H3/rk808I+f7Du6+/OieCNo7uJu3+8c32+kwriGw5lbdPh77F\n62Xtjc/r1oI9KZiL8vFOHpa7RLOkDjadHIct1t5aZCDoeLojdor48OH18rKtLxdvnZNU6OFRn45T\nW1vb8nK2zdrp9HR6mrTWLXxrfgnfLuu6uqfdFXl5bUJ4uD8sPKwAdiw1I9dma8bjtx6/8/0vsvVL\n29KJPZrlh3b49A//67/y7/2Pp8d71gN46EoK4JnGJOO4NM4zGT78whFGmYhAesIQln4dmXpyd5fX\n99uHn7TrpbertS16erS0cI8cqQJjC40bYg1B0LEHGMEpRDKwQoSRmTmCI3alTEaQDCnnQFyPujO+\n/uNlCAInJZBEdScNYsdnMwcwttw07FMET+wN4h4MyDn6wLGnzkzanbz5sayOTCm5cf0SDmLsSj7O\nEXq4L/XHrpCHxTb2UayIKlPy6DNFtczMNHT0Nykpj0kJsSaDUEiJqKQIy4kOj6jzT++kHjif098N\nZDrlR3NT85fX3/hr/8nXf+c/eZgugSBH6BD2cibSfOQukrJCI/t67t2Rg9ZKRJQMUs4kEypElN2p\niixlXh5CxPtmrWePJFfOwf/EYJkNo3jrvW0g5qJcKgERBE/jVC1cigqne1oL7xgfkkzgGt771tdr\ns34pLBPjUFlUembr7h1UqJyO5TCRS5in9bAeZgxihaqQUHcJH5j0jUgJNTjCnSKQIKHKpBOpqpJG\nhHts3bpxCy9USqWpCmuBsAWaR4a06EICSpUChLsNmwuHRphqSq2Ztaf3tmUIKIkLiCLMLUa7vI/h\n4DIW2Kie7uarNU4FCRRMVFS0lCQZwg4hhjJYWSaPfeSBRLeOdCIKEiYKH5Cm4TCMIoUL1fmACqbT\nul3DNubimeGBNGGlKgQBOaXUIlJQyiRaICqi6Z0yWAU8tdZsWzMizKxdMmPta79uyqQks6pSo9io\nzNBjWRaZjqksJE7i4XBENmWGqtZJSyVRArltI+ZPtYKJy0Q/8/YYkTmQnTcoDIbP6Ru15+Ov2yZj\nD1jLDKLdWs/MfFsI0kdm22jExsYFBCBiCLg5drX6eL2Pb4A/xvB+nILejsSS6TtBDRRIoTJebWTP\nMcTDCAoK9yAeYyXZ50tIAScyOBApxMEYKllP5+BTwXcn/gMnfjuXyn4UOhWe1O+Od+e2kQA9z++3\nMss0izsyUmfVOl97y7RJiggOU+299aR186rM5DrX+8Ps1p7fb+Gxrv2y5d19vf90fvzk8eHuvkd/\n//XLu588Z+ZcZFt9bT4vRZASeVjK3d2Ebh55vrZt861Tt6QiQE5TOd2d6pKtU3h2KSbSE83IukUG\nod2//fZn3/5y29r5/Vdf/c5v95e1X87eQCVOhxnpbLR1a4ZuW707vP3kszrR5r4lzpft6/eXbeuw\nnCeVcBI9FJYIFZHix6ISeG3x4doO94cf/ML3QKs4oJKG3vGTvvzsn/uLv/Cn/pQsB9UluebtWqD9\nr3WfPGRE5kaRmTaYLhmG7JlBbulbOEWs6JvZxS6r22rdrVtYt97cDbHDzHYh161xIsSYVrCMK1ZA\nhEgajCuiCB95I+Fgzhy7K2BQ00Y3OZyqY4iEfQ457PAfgYvjMMuUDhqkIqLROYGBdMLwhUUmiHgf\nvQwL/mgucVuN3xSnGKTb3CvuKJIf/4EkHMHgFOwwVKI6sF3gghFbTaPUjMNgMhUe0TMqpVSWAi3Y\ntwclJYkUzGAhJqaSIlROmJ6o1n11G8hLo+0dqO0ZIEEUHezw+O2/9V/8zn/6Hx7jh4lOqsxiQJhb\nGyNYBjLMu20ehSQYbE5mockqxgolhMMJtU7lUPR4Amaz1a4Xsy6pJJ7eLIJJhDiZOdhj875LD1gG\nUZ8jwVKhRDLyS02ojkOyhwORogju3dt6tuZFp1p0qoRobtF7jK3y9Hiq870nW7tmv3IqeidKKczg\nTNoQA5IsDNVCBHc3H7E2pBRZcahL0aFSoO65rdHTvUcqLVqnKlJKpkeQUVnDhTg4GMKMsEjA3RCe\nFIrCGpwcY7QY7OHMYE5mNafeemYK7/3WGJZxAOxg7a275zacViJBEJUiIlqYhUac+TiN6URcAsmi\nESM4eBviapbJkfCwdo0IZkG4sGhRLRUsRLX5RiAicaR192hCwVpFF8sV3RhYFpmXo9STCIOE2QhB\nXLhUi1zPW7u+Zj+7mwfcW+Gq4aUKiNg2hGdRnY40jfzqQkmkbEnZzX1lRpkmqbPoRCpjsGTeWXhg\ne1WL3g6euM0tB3p63O7/ch3cx0HDWhQZwh99USMDc5/d3B4NINqfTfQNa9ROW7tFQmRmpuMWfLg/\nE2+PSNrJoRwxKvToJIDd5sj7M4Uywwtxjxjh0SDPW8neT/npO2CcNADKGCKdwnSo+ovVPy/6JLmE\n3U98fyzp7uC1Wy16fW29e8t+nObLdq06R2JtPEmfCivJNBEfSnFJ1NZhvZVCX37/02jX9z9+XVfq\njr7ZdbPjm7vPPr17++m9q3x4Pr98uDy/XIqkIF/en+/uH7X2XF0Ljo90r7xeX7YrdQprct1iI9fl\nIIQqfHyY64S1R6cJlSJ4621dG7IGOQfVw3I6nagmbdd+eUe29mu7bLGU6c2baqudL7Z2CvNp0fvH\nh4enB2Ld4JvR63X7+vm6btv62lQnZDvW5a4qwqnIsmjJ0rZ2AZ09fOLHh7v3zx+WwzwLxWqty3v6\n1r/yl//nX/7yz/HhBJ2H93MHOCIGZxqRQBJG3GNkeoYnfI8PTENYdItulJv3q29rbxff3O0yJifW\n1kyKCIp0CoKE9zE+ZNYc6pbbYCKRlEm026WHOhM0HjKdSDNl2FKJiHkk+gzNl49Urn34P5rN/YQH\nYHjkR8APIXW/fYgygwlgUVBQuhtEKWn3UUAIOcb8H499Q80F8LALJYb1aOwvbqfWcdrcC+hItAOB\nEJ0EcAU1gBBBXNxz+DI5OSk9OzNLpnuGOBdjnfacxKGQCwEjhYOMIuDPsIZ4ovm4t9GnCv081x+R\nb4CRFJBmMrh/91/7Uw+ffu/v/5/+N8f2W6x9PJMiqEySyIQ2726ROIomcSSCPCcJZU8QOzfqKmWa\neDqeZNIQteZhRukqjPDsIzkJRAlh9L517umaKEWYpXcLZxGimUkLYNYssldlaIiKRxCPuGlqkdft\njBbzVJYDqbJbbJu3HuE5FZpPc51nSLF1tRYSClgpYC6B9EzrlgxksoCkJFm3gKdblyQSqlPhIkyI\nCHc0C4/0QBIXXbJwKYVLRnjrvScoGhUhKpUls3hs5qt3AOAMVgZRDAhnbEQlBaVUGSDsmDxMhCON\nx6qMFYEIc+tSKHvPSBAJKxNZJjLhnoQwpIRy3i5eASIpRvpEpmWaUiaUhEUUvgWoanGEGw0mhFlE\nrFSUKJM68ZQ0EXpmZKJbVgURFFMnRHtxUdfKsiYpCRMKOBOBaGERtqX33iMj5/kwTfe+NfQXZUpR\n8ISEc0Kn5EwqBASIHYnedz225H7Y5IhAtBE+Ne55Zo6gn0qlx00ozBHjCPzTbuyjRubjmHSkxisz\nj3TAjJFQlTwWjTs1Znzj24F6h+APj0SMkNu8sdSGOoF2TQLtcA27IXmHMQJ7iOA4lmcQKfYNzbgr\nBquKwEPZusswCMkqET5E7gwkD50CASLRvzXr9yb9XOKupnr7/JO3nK9tjcu1PTyeom/bRq0N49H8\n4fm6HEoYfTg/f/rtt0VwONTTXZlYiOtXX71aynV79/D5/Q9+5u3rV1+vWyOBcLyeVy362dv7+8fl\n8e54bXF9ae/+f1T92a9lW5beh31jjDnnWmvvfbqIOHH7m5VZyapisUpFsUiVSEikQYjqbFGiSJm2\nXuwnwX7yf+C/wgIBG7ZsU25g2NKDZBhwAxggDBmSKNug2JXMMllNVt4mIk6z915rzTkaP8y1T9yK\nh0RGZpwmTqy5xhxjfN/v+/4pvB6K1HNrMeRpqqpsUSa+ebUfwp8en0x5Xq0GaTVnLmUipnGI26ud\nhq8qyKNZGKxaPZ3WVmMYkBMOu7GhtuXp3T/50JZTfVrWlZ+Xtkvy+u1en07Pi51P6kG76/H+s1fj\nNK2qHXX/PB/PiwrRSEUzFeHraRwTlay73XBzfSCr3747nSPPoXkab6ZEbDKUsYjPy/GMev2jf/a/\n+W+/+oWvKB+QChNHlx4TOiCoZ7pQIKDmxgEPhUWE9mISuiDcWoW10KXVU6tNW/O6aq21tWgW3sLC\nAfcGjyDyMACJ5eXpfRlIbMu1i4GvW/KZ+YXlTptkjCMMzNYuYGRARKIbUS+fF0S+6ZARPdaQgshB\nqSfM9j6yXwq7ppSi+1U3q56hO4fCoVs5vlQ3pgLgpQEloktccIT51gv2cwEg+vaRQAGhDujtmWF9\nCgMEkXvkCHQgtwOB5m7JmxuRNMmZUyYeiDxYyRKRBFpwBjmr+bFJPWD3ilIKAkZwfuunE/RDWGNm\ngkSQEW5++tVv/jv/g7/3H/z7/If/hVFYqGSAJAitaTQDE6ypUsou8JIFyOxY12bQNNFuGGWIVMK5\nRNOoC8USRKRqzm4q0bMfyZvVWpu6MCSNZu4e1VrPy+lUUrPm5ikzd9k5SFIJhLWmrekaTJJ3XIYs\nqZi7NdcVHjRkzhmMVJuHmaqTNhGkjryJps6+XaWCRTgliqhrJWRrYLAwjaNIkoC5y9LMDGaGiMQp\njZyTSR7CVdXVaF3DVFmIQynDBeForWll0yYpmBOBA601gZmBc0bJlBMCHEER2vsccgJcJLlHeJg2\n0jDygICImTKLNQ5Tg8I9jNxNKMMYkrpoDYjwSgJiuczvMhOLZA+wZDKNTQipAfJg6rG+DiIzcM9j\ndzVHVWtZ8qYPd82SaLxyPYeZtjnxxKWIDIFQr21e21KX41F1rWaj8CA8CC92DjdoCGfLGU6JE5MY\n2HRhSUTc3Nw8cSYeiAMhbgFfCGwmiEak7szMYR4c9PX93i8LuAjvhtt4Eb1cZqEvlbKXsd5tAd4d\niOibWaZtphrO8YN3wMbmeOn/6OXLfZyeEoGdiOHRp00XqcDHP+MX5hpv5baLDnomIgc0RefWk0G3\nNnXDWW29KYU4KVNmODO7222ST1P8+DDcuB0mHkmv9qMAx+NTHqcsPCZKkpl9aXE2Y6UiATJJfH0Y\nmOLqbjdM7GusC80ep9NayT7//O6m0MP7D8QjmR4f1g+nSkx39zevXu8FOJ7xNLdlPtuq+ylbbWZB\nwsIU4YfbcnddqNHzca6L1+anSgrAY9oVzn51vZ+yqFMNiZJrSG2murbazqtJzrfjmERr1XMLRiNW\n8TSfltOx6upjYfbUrNXV5+V8/8X9m/vrPIyzmZmd5/Z0PDJJWFtXDXKxXKSYPe/z8PaLu7urobXl\n6bE+N52NyuHgthYZbm92N1c7W47v37X47Dd+66/9tZsvP5e06+E1vQptW6LLb4FGHhZOoXAPKNQj\nGkDQoyvcFm9nb7OrtaVWXaOpredWo7XVERQR3gIMN/LYGHyXX5dbnnNwfJxSyEWxBcBjk8hsupgO\njgEIcOYM9yDrpJjYBqC0mQWJtzV4JyhuX0sJhQPezTl9AAHpw34RIUovcjC7pPQinCj171x7MwqA\n+eX7DydOEtZbWmPq3ru+LsqelLw7K5SAIJHgIAApohIlgEAVJEEZPa8FzIwusSFJ0nWHMnIRSSU4\nE3puMAUHiTDGzXAlIw+vYjhs8JxAnBvm90SLixCIXNHjQo/Hf/B//I/e/Zf/96uhGoh8XVoNFbMF\nliyManCyMedtP2yuiLHkPBKJBFMq+wDcWmi4LVipooVSMJKDwoOpVVVyNifkYPFYI9gsShnLWDSa\nu5pDmIecSIhSYi4spVlt1bQ1mJdshSU4qZmaaTUiZ6NEgURIowkTCVsiXpghEBAsoqeXkFFICIVF\n9OUckbBbKZIl5Zw9nBGn1apzFwNPBVMuXAqRGIcpzc3Voraq6pkSyLgIiYePrS2hTcC5RE5FjZy4\ngkidOZVBhomJtmSrnvoUDrPuSoIH4NLamS06RABCQRIBMwTC3HsXJImZSXiAdCOPmwPEoMQsAIgp\ncSfoOktZXUOBnulIcK1EiQhgdxoECCZJoEgRUdvZPEpOuQypTKEGV3fzWqdRhp2k6XoY90iZkZfl\ndDw+LQ8f2nLSVoVw2O2m6ZoT1eN7CaO842mAMGFnFMRMWQgJTEYCNcAkDSmlkMFsTgRwzlKMzN1J\nTRIRkSFEhL56c6DuWEciWFe7dJVBv7tvek7CpSx1OkX07WB0A3IQiPQHKz1iUEA4qAN+ehfAcLeL\nMK/PYy+ItQBx57WGuScWBNwJ1BDc9ezh2AQym2C8Kwv5I+o4olu/lMBbciskyDvEESqU+8a3m5/v\nM3054MfXpVjdpVKkZQLc1VgKZWat6/39VUkUQU/PS0rCEE5akpShSLbr3VUqcj4dq4WpPDyfqAxf\nfnXvy/N8WoQzqZ2O57XSzHrzevfpm9dm8nw6Hp/93eMpS0xgyuKuFJpY8oi7q6tSuK2rmZyWVhdb\nqqnRMHBAhr3c3V1TEo0IytVMnVZHPS3qjUuB42oqoXGq8+NxLVx3ZY9aT6e6rjrkBOB8XKux6tpy\n/mN/7JNX17dzW45Vm8bp9Hh6jjJhv7t69/CkSz0MZXCogWT9hS/eHPZ8rvZ01hAZDgdO+3lt5vPr\n28P1lNfz+bsPS/ryn/6tf/PfOtzf03Do5u7wS8pHCPgyYIBGBHnnFLmHwRvcYQ2qhoWaaX1uTX1d\ntZ2thtazaW3adG1ATyN085UpR4fGk1EIC7pLkF72g0FBjHAwB7i7qfoSIC6iqq1sdjBE5xj1veDL\n1GTryC6AJOqXXQInpgD12hMUqa8Lt/lSv1oC4E0B/eJLinDmtNn/IH1nSIBtOSpERLiYKLjr8LeO\nUaJHXvRsMaDbkCls2zkigwzoe0q6JLenuGQEE5KTMQsRiSRiYklEiYQ555yL8E45uONtRCD9VUjE\nhUVQ9hjeoPQAYcTiMb8jLNi6owA7XH19/t2/9bd+9v/8P414XJbWmsKNIeHVGzgacRJikobAICMP\nlkphInUODknSp0RaQ+u6qgYkR7AEUQp1rWtPoyIWVzXrXlCWwt1dbvAACYUk4T7NS5mYIqyphzGT\nMpBShnt1ckXoIomESFtYa+5BSSgXSbkkFxhLNqcOaACEO0Q9TK2F++WyxEWkFGQRA9pKNZpWWxUc\nNJQ0jHkcRCR5+NFaq9ldEayICKjp6k6ePJFEaNOsbSrgzMHUnOEUwszCSUrO3JOdPBM4gaNQOAXI\nVDWcwRFhrRILE7GwE7rSyi28KxKJWRjkWRJJMZhQ9ujw+K7mEGER9J8fgwicqiqcHO5uFNBQduKE\nzm+L0IutqGMrg4VzniQFU2Yhd7K1ki2lYNjtyjjK7sDpsLZ1Pc7H99/Nz+9raxTtlni83omM1Y5Y\nlcuOC+eyjzIEBEwkiVLvldm9uVfhUsoIycJJrRFZj+tiTuHmrtTvgXAAqUdXBcBhfRzq0Zn6Fw1M\n79jC+0ARG4+6Kx4M3ZvZw7s/KuAjup1KAfILuaPnN0HdiKUb2gO0WZVoq5HYxDDkodSDJeHbKKmL\nK4ANuNbdWxQEp+jqPNZtRaNEicgJ7BwB7u+8PlFNnPYxf5blR9e7n1xPejylJCMpjJZmJBHmVV0L\n37++GQeZj7MTppLS4CmlXc5el/FqQA6vdjpV5L1wbbXurvL1bnf87ntKNI7j6f3p6ThL2dMuf/l2\nfxjKvLSH43Ka27KuU5E4L41lSgJzTjnnePt2n53Op9ld1hbrYmFwUNmVNPFh3O0OWc3VYEksMLuf\nz8t5sV1GGsuU0kCoVp+X5fG0DOCbUs7H+d3z4/2be5HngeR8qi5gtfH28Ou/9OV+pON5PjU9r/70\neHx6fnh1+9l4mE5trmYTJdY1pf0w4ZNPDsnw/cO8VDTG7c2tlKG1dTfxq7t7sbWe54dHKj/6Z/+Z\nv/JX9vf3kvfE3JMj4jISxWXOwNRxMN3lYOEOa4QWauE1tIYetZm2RdeTra01tbq2triamZGTRUXP\neUCK7qAJIYgQWbiA3R2Mbc3mAery760y0CXYiUL6A3jxzEmfdQFwOPUbMTr25aUmbqIwt47nNaN+\nxrjjYDZADAjoEOUg3pR7ROQRwkwkFgFQ+JZKffnMkBepWvRqyrHZjQK0HcDgIGYL64Ub4U4eHaob\nIOhF00OXBrTvGDeEL29/Qw8ijcbEHsGdptMizFIyKbu4kN/IW4SDJVwBYH0kbeRvqIzBwMic7v34\nyPbsZMQcDkKm4foX/+K/gGH/9/+j//1o33T+IUjZiVI1ZVZfzcZJpoOUXJJQbbVaA5UtkY1ZFW3x\nRY05D70EhkY1M+twcwD9ThUEdS9ps3a6N7DkVEAGgCkRJw9yRSc0lcRJshBbeA00JXcdck6Jw1Wt\nrrUBkSim3VAymHN4Mq3eAGFK0sG4cK11oSAiJi7MPkikDMkR5rWGBpnCIlImYcoDpwImqtXmamuY\naRCnnDEInLLNrs3VlkBKxurqHlGDzKFQOElKJeWxJElwaIhrkDcSVsnRhJiqGwzqSOKIQB7CPQjN\nvePg+iPvFMwSJMSd7SkUTEw9uBBk1ilL5EJMzKaKVPqxJhZ3C7hIMnWhDOk/EYpQJlJtQuQRYBJJ\nkkru162IiEgycDFdgyAgCWQ4zGxd2nx8Op+ea21j4swlpYJga9XURQYDgwcD+icGE6cxUh8chjXj\nzojtxGwWcI//ZAZA6l3wxQ1IRAS3DW7U+zjiiw0CJpx942hcbrWwPuG5VDv0XSA2FXoKGHWS3TYx\niRCCuV/u/9EPNDG8jzmDtyV/r81MHYVHgl4mI6iT+8m6QBXkjBTUsL2n+rnqonUEgT2IGe59QERE\nCO2Zo4REIczxWuyrYfjJTfnieqR69oG91XE3nJ7XpenAo1O7utm9vdl5tQ8Px3Hc77JPV3l/KHo8\nuxLv9oQ4P5zSeEWc0aq6SeLlaf3Z+2/ffno9jen4cH5eqkspO3r15gqS3p3a0/N5OdtSl30Z5vP5\nvNr1jt1qiN7eH26mglXnpa7Oy9rms7eIMqRpyGXC69evmXhxs5wrSLVpw/G8msf1vmRJ08ix1Hnl\nD8tq63qdaEB6elyXtd6/uXt9GB9r+3BqAZHU3nxyc3NzKCkej+sSfJ7b49P63YenTz75bJimp+Nz\nXed9UELknHaT3r+5amaPz+tTdQ+ebne1IXS+vS1v7+9tXZ6P58dnlh/9+p/6a39tf/NGyhQoQf2g\nbT3IJnoMA+CucKVO6gtFNMBdG9rqUb2tTedYamtzXZaotfZcUVV3CwR7eBj3eUC4EFsQg42abzIv\n5z7QQJAHeq4ISdeibBFi27Lw8oxTz+AlOHxTO1+Q98ALaKJv0BGxpfSCCL3eh3ntE0V0Thr1xfam\nxIaDmHtgt3t0S6sjwNRToLHhC7sktQt2hMCb43AjOlgEEXr0Skjn0AWMnInDAyzhDZIQjcEg9y4K\n6KA1RHgn4NSObGNiOBkZK0iaNyaJEA93g0k+MA9u1IPRg51kBcwDEhLHb2i6jemGGJFA1zf2nEjf\nw4Klw3Rcqfzon/utvN//7f/Nv7e3bwkq8MQeSO6xVs05xpL3ZeQstS6tRVNLiTglNwprrZpZFCZK\nRMTMYqqgoETh5EptrRHM2cClDIOqsjqzS05EJGQOpJzBZBTG4Q5JSYRKEo60djmigzNnT4kQhtpc\nlSMAlmEYhwyWDOujU44gIR+Ye5ykmYeBhSAxZM+JWARuWqU179IUEUl5IIEIF2GnNCsvdZ1r6xjb\nMpgkcWKok4uwUmTCtlCfTZsKyCwCiKRSqOWsBvfoETiaOHkYbw+2MzFkGGUhgFkMCAyhte/IYR1P\n1AlJLsRBTiTm2pnTYQEOs0h98sFQt0zUR6kAwxXE3eEUESzJ4YkRMGIOcKinFBQknCyaYBBCYgoK\nUu0xFQ7lLERZg0jV1mLtaT49nR+f2rJktmFIiUsmVsygRMOOpEQAyME5wGCXPEpO3gtLt0RuW3HN\n4hFG7MwlYETZfA1USrlXnW45p194c9iasO6Sh3eHlbj4pR/s3VyHn/VRJLoLQsit561hU9l1vufH\nzWK8bGt8u3CDLzFP/ctyn6vGtibsHygXI7H3pMTuUw4JMvgmZEJIJji/vMMsQ4xBHiy0vdH6ZCsi\ngoR9dP9s5J/s5cur/OX1lGJmiFWtzXXGzGZVZKTXb25vJnr6cJI8DGnOw3B3P+2HvM6nNnu4rNWa\n++2rq3Vpc6u7/W49n89Pq6Z48/og1Z+eZp4KsXBOh6urU9UP57Wd4/npKadUKHIZn9fzQLxjTsU+\n+/TuMOD4uB7nk6A8LWtTBzKPKSW5vr65u5mqW7WkQs44zXVe6nJemDGWXc56lUf1+v1zXVa1dX57\nc1vPy/OzVnv67KvP7q723/z+N98+nc+NpsP01Sd3Y2FznD2q6/GkpzVabUPOZTedj7Ouc3JNoHC7\nf3N4NY3qdlKtzTCMTMSSWOj2bvfmaq9W12V5eAr+/Dd+66/+t6fXrykPIUTIXcVxcUtsFNlAI4uA\nwt1DEQGvcO3zL6i2drJa1c92qnU91rr6Us2gruFGgFkfNdolyKE3URzWQrotweDcp+/bBvriMABL\nhLMQum4F3i3tvVV0M2be3PcIZoYHUeqjyQjtZO1eCy+zfSZIsBEujJjLLyJiFnBfuQuAoC6T2T4P\nEQPsvciFbTzSizyNu9ydOyfTebsf9niqhN73QiNSjzBDNwp5319eND0X9/3ls1KfswLo7kOSdOHS\nEJFQCqZCAogIFcm0++W/RFxOv/t7Ob4nbsQCzj2YA0wkA5crDK+QkzM44M8z9D112D1awE2dyL/5\n+//f/8f/5G+80p+NeWWX1clr5YQhy2E/DGO28HleqoqzjpKl5Ahr1TycISYxpJ3CKThgidi0zWtd\nZpi1UEOK/TiRSN88iQhL/+eOVDLzoD1vKlEpknucb9jSzFozYiZhEV+a1rWpQclMQTomHqciQ5bg\nVb21yhEp55QipUENzdXWCo9cUJhEOJUS6q3Z3DSCAJM8cKapjJwJgDmta61Vl9Vri2HMJec8JJbU\nmi51MSX1KjIKocGXxev5FP3tSkHB6p7GMpRCAkQ20yIQ8jQMjAEceUiOyFQIypKAHqVULbKFAT3X\n3LYnVSRJUTeRZOax4SzQZd45JRa5+I8YUJB4EHPq3E0PdIlKRIRrEvTHllkcFEoiyaPCQzIn6dt1\nAG6McId74iSFAfag5VxbXev8DG+H60PJeyCwroaVuISUXEqEBTmlUVIWLjxQGiaTFE3ZY5mfcsrE\nAhJJG1vdI5iLM5taaCUmSYkC4Y280Y/fHPr40t0uGsvNU9RfNCDuLOKIYPR00kAkD8fmaKALEcO7\nhw+XzeKL0Gab+fRp6MWt2GUscnFWAOhXcmISyCWhqd9pqQ+UQE4bDa47pTp9lLr6nVICwA4rSEq6\njacABDuNtPzK1e4ne7kfyy7VuyklmJTx4funeXWL3BhwlJ2UMcNlQMscw0j391e7Aq3WPHTWp+Ms\nu/Lq1Zvj6dQorvf75fH5u/fvD3d3b99cnR8e58VlzNPVbkilLe1p0cezr3Wl0JHSOi8KevXmri7n\nSbCbcPdqny2ej+tSvTWvK6q57BJLDEO5vt2Nw2iNKtgoecjq69NpPh7P+90uJxkkDTu0c32ezw/H\n5XqYJqHj8/L8fBrH8vUvf5Hb+u13H56e4ujt+uZwXUoeCyKfUEMlCr79cBSncbdba9P5TM6oZ7fY\nTeX+9X7K+fh8Ws1kHFMaQrjpOu2HN2+uxX09NY92qrvxi1/70//6X813tzFMJMPGStlUkJcMh/5g\ndIg2PKxejIMW6xreoLO1trbV13Nbz77Oyzxrc1c1b+H9Ba/9fXu5UHFsdJi6HVwYCdFL1xfCSYjE\nQynYORAC2vyv1LfcDKKEi8rmhwLpPtTse58IJSovJQVBEc7CoF6rTLqgoC+tL8VHRLqyWXjoIlS8\nKJ+Jtztizwvs/WkEU2xoqF7FNz/khuJxBuzjGoJZPBqCe0xEbLExfTUQEdaDZdDnO0wU5LBLJe67\n/yBm77SAHj3FfcklwrSW+/tf+3O21PVnv53wHCzEOZiIE8hYMqhw3mG4wzhtR/zcbP0565ESE0qQ\nhYNA3/3D3/5P/md/4279ffjqjsyccuQkWWAUa3V1hXOenHlAmKpT71k5UUrmvkW7JiHjdT0f17Y8\nt4AllpSJgZzTkJPDNscVE0lJiSBiSCQ8DGkoAwtUW1PXWrvpk2lwtnps7byYNiJK5EPpXK5sEaqh\nTRk2DKkME0tyIg93NfIg0sKeEnV2cfXQBtcliDmVkoY8IpfBCe6xLrauTVu0QM6UMgVYOBFI3de2\nchB33orwPGPV6rWFOzM7UfVqDofkaXRiU4N5ST6WlHOhKM4tp8SSEsAp9TmxwshdpPTwPCYxawHx\nMJbEKfeH3hxWLcjd+o42uo4KQNc8ighxAXWLg8KChCknluLWXBuAIRXqUVtJwBlVQY2DQzxJ8Va3\nl3KPuIjctIFWd6/qXrXVM5tPUx52wzheN/Na1bQlQirZ80ggwLvVNaXCOUEyJYFprZUikhCIeRi4\nGyeEIRyJmXdmEbqEWUqZOGk7CgX96PU+CL7hsH3DaVLv7hQAU0a4d20dsFl+cQEv9p3dpd71vWJE\n/PBVcnmhfFyCfGRnA4n4BdfRcxCZpKfw9jQMBvtFbh7oS8CuX+qUbAKMfxCd02nB/rKPChePTwb+\n9bvxTaFdipvEd3tKginvH56e5kWXeVUtLcU0FkocHlOOQj5O+fWrSWCh5iR11rYa7fJhNzw+ncrN\n4Xoczsf5u/cf9je3b6/K83E+z23aTbubfZJ4fp6X6mv15/N6OIz23GatQx6YfTeUlHB9M+4K1bq2\nORxpXeuyzDVKpJhKPuyn/T6Xcn1cTkYIGs9hzWM5tdPxnIe0m9JQBkYsOj98N0fyHUemcjweo9Hu\navzkszuK9btv5+OpPnv9/O2r2900u63G7qzBplVNl4ZhNzRrp4fng4jWulTf7/mzu30YLbUtVdM4\njPupqTljfzjcv7lKps/P83k+WhuHn/yp3/jL/9b0+jUPO7BwcCekbCgxJIQD4dF6fHWERRisISzU\nYNV0MZ+9Lr4ubT36si7r0pZFWzMDR5hbH3MCHqZd3hn9PITio0S0S6/6bJJAFBYiGYALIdDNTX08\n27Nwu8l8K9k/aJ2wTUM2zHYvLdiuiWm76r0QIdijpwoGYhMI8GX0ut0JutkaHNi2FfJSzIi4dzp9\nq7D1hX3DAQSSEHm07auHBOBw7sKU7t2/hKZcDloClC62x493z74s7EOSi093SwnlLUaRhYMiEYMF\nwiTMPAy7W6Jg7vkyQ5/gBDNx4pQJBC40vcHusDHBl+bnP2RU4iEokVtYA9f3v/07/9nf/J+mx9+Z\n8jzkzAEKcveqtZpTkqEMlIKc1JWROCVwT2zvillicMC0yrnZqiuZA85BS6tTkpK15CEix8ZYt8yC\nTJQyhHMpKbNwaeoaaqZClEQQZEZNa5vruiwEZJGhgIgpCQIW1Fog1sSUs5Q8cCoazsRMLhQiIA8K\naNXVrMa2vxq5yIiSRSRFSHVdq7cWWp1BqQRxIgnXLjZMphUUJYsQOfLqXldvukpAiImhYau7NrKU\nmMXZLRIhBsbATBJEOUhzKtxFUBJh8Ai1VShLzkTgnALsbu7uECJOnFzCzdyh6mreG36iyFk8aGtH\niYgkp11EeIRhhQWEKSX0iYQhoubUaXFjd/DCzKyyMNEkgtDFA5s+S7Kbh9Wmi+ShOXxdsJ4SfJiK\nlEKYmKOaNjVmzqVwmeKyKgsi5oFTAILwLRmGRYQlJUrZ1eHKAnCGMGRMMq3r0dox58KQCBWAvnqz\nv2zl43LyQ7ZNYa98gQCLXARRm4rGPWy7dcdLC9hJHJfPdnFu/SCMfqtW20dRhCfqN9Au8+xvsY6l\nikD3QHbiYG8cu5KPfKP6E6Jtx4OZqF+GKShE2dlAMhC+GuJXrg47P1+ltMsYpyRYroe9RTP1OquB\n1ZAPY1VPmQdTIX315uqQxVo1wA0RYR6UclgNzldvDox5eVhOijefvs0S33/zoZk7xe3VDUIdtNY4\nLioZB2Ft/t2HWSRKpsLpalcOBx6H4fxYZ11qNTdqCqOUM41TGqeyG4tIeap1JfYoa3irttZlXZpF\n3N5M16WY24en0/OyTIz76+vnh+dlXsd9fnN/jVrX1Z7mdp7r/u7q9moYy7Q6nQ1Ls6oRra6zKlEe\nJqWZljoYuVd4kmSv7q7assynFpnKfscizU1E3ry9v73a6fq0nOvxrMuK8cvf+M1/4791+OSOywFS\nAvwyCI0u0QV6soO7cRh5c7fwhjCy5qZu56hnW9fWZq9rW+Z6ntc6ezW1CESoIZjY4YagoIboKe3S\nFwDbG/5SAGND/fX2JIWQdFEpSIg8GOjRtanbOLoJL2KjumCLjCBiuYR5baWEOfdc5z6ioK1qbmwl\n4m68kJdi2pu62EaeQkyObtUX4pfp6EXDA0THKvWeMTosoudK4OJLjMudkyk6rXATlParaWyMw9yF\nSJ0g4WBEYxIw+wZQpYvH1z/+8KhD2mjTcYswCEySkzATF8kj5wwqIGOmkAKWjaQoxSlLucHhLvrN\ndnabf06hxAySgEc7CcX3v/OP//b//G+88t9hKtpWU5iBxVLKnY0REUwWEolySAEoVJ1DOAfBDcvS\n1uaGJsRZcjWtsyjNe46S+waT1XsELhKXPEZKISlz3hmoageCQ2RIBUJQrdqwVvVmmUBUmYiZmFkt\nQOIIUCE3Ye1gV8g2Ts2MTAQOU7OqS3N1ijCWlEvKhUrKJKJhTWPRFo3cghg5j5JVaKh1Ve1pgmDq\ntPCsYeaxOJsCsDEREQxYW22uCaNLZmHzFh5ClKUg2NCYmEvKIixEkSwqIK2ZRRMqqSTJwhs5iHVt\n6s7kLCmCPVztgickBkXYZiVweEecsySRwXqwGZyio/IFG6MURGACsYmUlDMCuix9+8VlJCGouYMT\nmHNAzFari+REw25/deUrlg8/E185ATyaUUiEsoYRESXKw4FzQXi4gdmUs1BQF0+TJAbllFiGwpRb\na9ydeAgqQVw4jVaXtiySIucCD+5Y/djW/htHKqIzm2AU3UjBgJoGIYH7aEJDQduapYcybTMlR4fT\nAICnIH1BbfTxaS+0m7d6ewsQEZsZEdFm0E8g860Oh0fXEFCPnuizawBBhuBAIu4xG53E2GU9DCYJ\nelvo60G/GEvSM5GR51Wf5bQ/2iw3OcMk5XGSpWkuw6z1aj/tCmXm62mfPE7HI0uKEBqQONu8qq9X\nn91cD+Pp/dPDshyubz7dlyL4/pvjrDHs9nuy9fm4NLv/9JNlfnd1VYQwPy7NSJJM4gJ9/frV3VXx\ndj6f1+pY1tBVmrsU7EcaRhmnPAxJg06qjcmMF+PHdW3LDIrMvBvykMrxuHw4Lcu6XO/Tlzd33/78\nDzXK1e3+zduDnucPT+t8tFn1cLN/c3eTSqpGs1lzqHs9n9rSjJJmruenwjGJeF01/HYa97v09HD6\n9jgPJb3aX+3GXTUrSb/4/O0gvDw/Lcu6zNbCcf0Lf+pf/Svj/RXyjQlzjzp/2UrhosVyZzjBw82s\nE9SMTNWVtEZb2jLrcrI666Lr8ryuq5mysrtH9NBThwf6egNC3L07fTbhAMGMKcVLpFFsEimEspO7\nEDUg2SUgDACRbox+ZxD1JT/CO5YsIsJ7d9dHiBkwwAPGJB3at/39unwGW73rBvrLmYC5s3RRqvfF\nNaFLclbixIxu5HiZnHSNjiFig+dSp20TCdwAeA9audh0e/N4ESLFluG5gZns4+0ziEC+fdTm64V7\nXKajAMI2CHD0ZE5oC0opea0QIXGQeRQRIukn1zq5AixOjQm+PjKMdm8igSYWeuvze/g5NnVnmMeb\nX/zRn/3v/Pf/3v/23435u2Zn9zRNKOPBXdwWdWPinDNxI+JqDjBxSkJMbKHeoAYm4kgpS1OszpVt\nAFIqEcrEFgCRqQIxFhsEKeUWsi61klEUZnASFlK3GmjKXtXNhmRD4qDiHrBQhQFMkVPxAEHNqlAm\nKvDIQgJnIkdoba1FNfeqLJlozIlzjpIKErfa1qbrsgSY05BLySU4jCibVdWVIjNDthA7qxoOaoZW\n5ywDBIGAFGtB8EQ2CrQ3D5K8+3kcYRoMypxSLjl7aLgSxII4pTCARd3Z2FxZgiDMCAt1iCPgHmre\nmIbgEEoBD1gQazjAAqIQgN3Dg9zdwork7osgQpAzxHtEEEbh5N5IwRQeDTwEavZR4RFCwdbWCGtV\n3SJlKRlDEgP7rmCpHsHhEHHvSqUlkEkYoYD0uaSZgnINFSTinqyouUgEu2sgPCoj90giLE1YI6Er\nvd3Io0+zLV16tY8QmS5Xi20CuulJ+6DHAhuvDAR4H6q8jJLcTRAg2oSi3cC17UQ40OF1dMl1u8yg\n4ofMmn6hMI9AyMdNP0WEu1+kUIS+v4yw3jEKJQrthTNIiJ28/bFD+cmYRsSOYct8c3s1QlO6UdP7\nw57aytPu5m53fF4QkbLd55RFD1dpN0x6WtdzOq+Rrv3u+vV6fDiezjTk+09fDym9/+b7RfXu/tX+\nMNbT8s23HypPt7e7kenxYXUlFjnNp2m3X2tt2iioMCFBOL746s31mI/HuTYLoLWGxAwbkYeRh5F2\n+4O5VUwzYCmvTU8ax+W4rG0kzkn2Q9K1vX/3NJ9nNX+VJVf97g++IbHX98Phan969+F8TI9nbxKv\nPrm7OVynPK7rMqueq3rEMh+X2apiRZtMrjKTxunxPBa6uRmnaTjNzx9OVUCHceSRVm+7q/Lm7m5w\nno/P87KqNW/k+x/96X/tr0+f3MtwHYkYCfDu642e4Q4EWgQI4dHgEd76mBSuUA1t0FNb5lab16XN\n5/lstTZ0HZ9pn0NEUHhQ2EWCCYQxmduLaad/rTC37iXvJPmOsQomgSoZuxKReYhI74eY+yZZvN8F\nmaMD3bldIiPoMkXUiCDKtOVXB6j/fRkIkBHyNv8I7cKZXmyYsfVekUDGIDcjU5BTkLkzMaCUB4ot\nXAIXMk5ssWFdnOMBdNOzmQX7xYbv0Qlw2xHyLpHdoqXRcXCbIQwfTSx9FSIvZscL4KmneTbhwUM4\noFolF9NIYQx2X9xDIBHCPiKJUxNjwF0ag3V9kAja30dCjIn9tc7GNlMMGG6oNXi9/vGPfvmv/vf+\n83//fzT4WiYfcg4PsxawLMIJQPQxDMKDTRIDpC1WzyCQgEM4eK1tVjZtDEhGuEoX04dmT1ESyzrl\nnASq2kybCUoqJXIqNZMqmkk086pwGzKVLIkBz81MvRkBLFmycLhR1QgWsDBREk7ojmV4paZhpmig\nlEM4J+RCXDjC/Yzz2qo1oiSSSkk5CXEz89ZIVeFgjlyICGFpVXMzdQ0UyUzd+uPkrQUlTkOCcOQe\ncGdqFNm1EZhJwlv4YO4G29QV27+4ppSJJKKFKUQQ5OotzMyIWGGwlaRQSFiLRIYGQkDCNEBwpUip\ndKAtzFVIBAhzZgQ1BBGEBIkYFCLO8KAENjhSJATYslFTOFNsu39Tc2VJIiiA6eoNrKsHzDmoUkyl\nTKqap2v3ZKQb3SI640kJyFLAIkXMU5sfSJESofUeMTtzRHioa3WiFHCBdOZoqDmYhD5/tduOSx/K\n0FZKxClejM/9ZouOpIofyD7jsmC4uHm2/eD2CaOv6jbTSoeJfpTP9FeYoM+AXt4p1o9ub1P7n3QC\nAV231JuAfl9nRJ9oEUtmAZFTUOgN+0/3uy+GuBEkNYcdpnQ7jdYaxKectK2H2+uS5cOHc4hc77Od\nlv2b4XrKUd2aqdPD4+nq7athn+v7YwWu317fXQ3P7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Kn7CD/eWH+wL+SPl97NBPzyy2OL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G/LZDz0CQimcc757na7r7yqOgz6xctH+10pHL/865/0LjffvXt7v0qzkxe/ePzi5bPlIpfV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\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yzt78na/sf/Ub2391ufro4vIP/p8nf/Z/+eTDH9989P0eS2S6udwPtLEaCyGzVgQQV3Mv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\nfWu6yH0nOZuaiYpDNsYM5iCoZQp4aH3trXNXsI6f//X3VyeO/et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r9an1rq6q9eneIqZE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NDMGShzia0ilTp9ASQvnbnlYVNonAjoFZIiUPH/wULhuthBFjBkk9XAK2wjRiYGNMVI6hUV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\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MF/1Wr5W4PARgm5ow2Bo8Ohqzh36W5VwX5XzQ7w96SW/Qe3ZyGkXx3JtRrKo1OfGicToI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\nnoxn1WqVJTaO03a7Vaxmw+2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3T+7++CcH+4fDjR6ImT4+1hA2X3phcPNlT+buO+8c3Xt6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NU7b9w+nYx4PPnlG/3dmxum0W4HfpwWaVHmcXU+mirjN8P0q7/4xt6d158fzc6P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\nmZYwq7gAZJACJCaMHVw6LkQcggBprjv5SCJGsPxZRgvBIJUCtSNCC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Q7MhorFiyhFIN4r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0s+IFmCQSACUCSF4AMQBEIGBvkBq7Dt9ZfxABScC9R7I16Rh/10rE5lqyOUeMoG3iO4A1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\nSUVORK5CYII=\n",
-       "prompt_number": 24,
-       "text": [
-        "Image([[[ 21,  13,   8],\n",
-        "        [ 21,  13,   9],\n",
-        "        [ 20,  11,   8],\n",
-        "        ..., \n",
-        "        [228, 182, 138],\n",
-        "        [231, 185, 142],\n",
-        "        [228, 184, 140]],\n",
-        "\n",
-        "       [[ 21,  13,   7],\n",
-        "        [ 21,  13,   9],\n",
-        "        [ 20,  14,   7],\n",
-        "        ..., \n",
-        "        [228, 182, 136],\n",
-        "        [231, 185, 139],\n",
-        "        [229, 183, 137]],\n",
-        "\n",
-        "       [[ 21,  14,   7],\n",
-        "        [ 23,  13,  10],\n",
-        "        [ 20,  14,   9],\n",
-        "        ..., \n",
-        "        [228, 182, 136],\n",
-        "        [228, 184, 137],\n",
-        "        [229, 185, 138]],\n",
-        "\n",
-        "       ..., \n",
-        "       [[189, 124,  77],\n",
-        "        [214, 155, 109],\n",
-        "        [197, 141, 100],\n",
-        "        ..., \n",
-        "        [165,  86,  37],\n",
-        "        [161,  82,  41],\n",
-        "        [143,  67,  29]],\n",
-        "\n",
-        "       [[207, 148, 102],\n",
-        "        [201, 142,  99],\n",
-        "        [196, 140,  97],\n",
-        "        ..., \n",
-        "        [154,  74,  37],\n",
-        "        [147,  66,  33],\n",
-        "        [145,  65,  31]],\n",
-        "\n",
-        "       [[197, 141, 100],\n",
-        "        [195, 137,  99],\n",
-        "        [193, 138,  98],\n",
-        "        ..., \n",
-        "        [158,  73,  38],\n",
-        "        [144,  64,  30],\n",
-        "        [143,  60,  29]]], dtype=uint8)"
-       ]
-      }
-     ],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def edit_image(image, sigma=0.1, r=1.0, g=1.0, b=1.0):\n",
-      "    new_image = filter.gaussian_filter(image, sigma=sigma, multichannel=True)\n",
-      "    new_image[:,:,0] = r*new_image[:,:,0]\n",
-      "    new_image[:,:,1] = g*new_image[:,:,1]\n",
-      "    new_image[:,:,2] = b*new_image[:,:,2]\n",
-      "    new_image = io.Image(new_image)\n",
-      "    display(new_image)\n",
-      "    return new_image"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "lims = (0.0,1.0,0.01)\n",
-      "w = interactive(edit_image, image=fixed(i), sigma=(0.0,10.0,0.1), r=lims, g=lims, b=lims)\n",
-      "display(w)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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J/Wah/7oW/6uKaG8LNCmiSAiSEuJbgUQB1IN0ASkVaYBQBXSjVU0RkbHQ9voWWYSLgpLW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\nlv2b4XrKUd2aqdPD4+nq7athn+v7YwWu317fXQ3P7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Kn7CD/eWH+wL+SPl97NBPzyy2OL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G/LZDz0CQimcc757na7r7yqOgz6xctH+10pHL/865/0LjffvXt7v0qzkxe/ePzi5bPlIpfV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\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yzt78na/sf/Ub2391ufro4vIP/p8nf/Z/+eTDH9989P0eS2S6udwPtLEaCyGzVgQQV3Mv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\nfWu6yH0nOZuaiYpDNsYM5iCoZQp4aH3trXNXsI6f//X3VyeO/et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r9an1rq6q9eneIqZE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NDMGShzia0ilTp9ASQvnbnlYVNonAjoFZIiUPH/wULhuthBFjBkk9XAK2wjRiYGNMVI6hUV\n0KlB5bwSggkEpwrmhHO4/JMLpxan1lwdOLdqoNST9qyOeTHv5l1fnkoZAQCCFouRqpESzdtMRIim\nCuWlRhSG5IABQbgMucWQFIgRzKkJKiN5wJtW3kxEYIWrBQoFNQQAkDGXa3MpXQLCXIWRuiiFGopk\nDMLMDACsKgBGaCaWAge1pAiSERCziDq4yb0pMgoDK7NoEzM0lKXf0ZjZUJvKN84NDq0OVvef2+13\nOs0ITGBoSuS5UBGWw0QCADQtoousQ7aBJ1f5mA3ImtD4KuScDhzcP5m10EljkC2LWZujOTZdqjrR\nwRwsrI+OH9s37zqBOKybRaLYXaGgA67adlpVg9T3h9bqT77/Pb//b7607/DR82+dHVQ+rFQxWu5V\nzTrrrUu14/JWhUsfhopA8awgOoIokgFAwEwMLDnyBorgDBGYAFzJKqWkRKQmJbntfEDUouVAAHIO\nDVJOVVX1bcuBEZFgSe1ldqgIpGSgoowugxC6AkZn9il1zlUC5aXKYozsg4owOyu9CiJkpBKm7tnY\nVNWPR2mRLQkZVOyRUT1ZH/3qSLNAEtEezWWDwXCUCbCTpJMF1PPsLPXIfrG3t2992DQhRtdJ1qwO\nFX09m02dsxZcbI3QQrDp5mZnOtXEg/WxEovORSmEWgxUMlCb28b7lcoXSiGha6MCkFElCkQQc4fA\ngamum8l8Rh4qB95bBvPlZixg3hzbyA+mizmxOs878840qPfbu1NSGwxr8g6zzafdwBtknUpaJCB2\nhoAqRN4QEkjEmqVHIWYeDoaTSVsPBpS7rs1NRc7x3mxSVTxPKqq169fW9rUqK4NBUlHgWdcNBpWr\nCOshoi2mM4eWpU9JWbAOPJ3PRoHXRqMk9NbZs1XT1JWXTtyNzfyuj/zC5uXXr555lXVvpdL1Bnd3\ndi9dtrDaHF4Z3IAtEQ4xv/STn2iEnd3JLIau326cu3jm/ObOLmrynanoMKzfuPjGuz723g9+/GMv\nP/n1yxevHLrt/s/+zf/11deeO/XSc5/5rb/5yhN/sbvXP/KJLxza1zz+h//DxsXL6wcOrB08Hvt8\n7fqVlX37Ttx6u2+aa2+d7ifb25fPrd5y6/7jJ868clolAXRrVT04un/96OFzb5yKiWsMHGddnX/2\nr/7t2XTjpR9/54mvfefg/gP3P/a2p554djpfrB88cmC9mU6uz1OOnlvRCMoVaWbnsmYarI4tz3Gi\nrnKgJJvP7J2GwT1gVFeD9S7A2i2HJqeui7nYR2OPIKYEBPsPHtjZ3gERtcjsRLKIhqo2ElNDgBIO\nZCRgMAXnPIDlbAg3TfHlTDUldkCY+77rpK68awZVzeByanl3b5cwdH2XVJcJHLJmMJxM9rLqcktR\nkKdIgkaIJgAEMSsiRy3QLzbDnODtjzz6wotPpqgAqMZMis6poQkqgAj2YIImhd4iaMhm2Uo5QIvR\n3tQI2ZZnHdLSt1fK6WpKYMUxa6X4XujSSxi0IQCoY0BdviQYYS+2jZBNT4RwtOL1ADWAgBqRLRML\nDARZMwKgsSPKqkYgwit16q5dMdH9lY0z96BzsYjYKYo47XLDvldR1AxA5hInVConAgFk0yymbMaQ\nDQgQErBPAZUwRBBQIXBqGgwNC9RFxcq/sAECE5kaEWYrukGxpcHBytygeIcLz80Qskrp1DOSKiTI\nBIIKFgERDRMaElIfUwGgLLsKgJrUlh+lLKnK7VtMMRcIDjAgAtqods45YK9ql69tbU+s7Wbqa9IE\nqoxAjKioCFTWgkagyRF5iMOajx099PCjt9WD0VtvXrpy/ura2kqXeWPWru0fN1yfPX82IBU131KQ\nUS5SoAmhy3Dk9lu50ig5qjAG8VkRzcAj9pAGVCvklMRjLXn+K7/0ydsP33rx+R874t3F5MiBA/2i\nw0xq6AiSGLsCalAEcchIqGIGGQGqwESlZ2IIWGyWgKwqzC7GvnzlEIEIEMrA0pgZQEUspc45rwiW\nExICWte2NyOgbKU/vyzLKhpktfJrUnMeTDIigQoZWzZQzJbVkDmUVaWALmH2qGIODCGLkAStsqR2\nd8JIggqGSdPa+qHZzo4A5a1d9t6CNwEAMs3A5rVepPmsl0k3R6LxqKooiEw9OTU/nU8h59XV9UlH\n3WLhK0c8vHzh8mhcMTc5SczdpUnXRxzSfG19hcehn3QVmGOeLaTLbePD0CFSQgiuquezdtYtAN3Q\nu2pYx773XJEjVO37+SBQqH0Sk5ibYYOOF13PCEPfiJpZZEeA2PcqZpHbGJkQm9FQFXwfHThk8K6a\nzrqIUgUfHC+6rm4GopKzAXBdSV0FdDYYjHYnqWtFZQddYEeilrv+wLjZm+R5K95BtbY+18ziiVGT\n9qKOHWEerK75aryYTriyFFGNCawJkPssQIOBIcu1jRv1aAUVNCVfBfe2977v4mtPXj9/cfXWu3/x\n5z8/v3r2mR8+xTlNN3f2V0d3qK9dE72zFOfbk+1ZP2tb0nTk8KHUzm9cus69DSsjRkHoF1d/++/9\nncV85wdf+crW9tY7P/FXbr3rxHf/5PfZjR75yKf35pvv+cxv37ix2e1d/uqffXW+yHfefe/q4VsW\ns/nO1rXx6spt993PzDvXLs02zu9cvX7gtvuQmxd/+GNAL2l2YG20duTEcN/a+TfOZBxWtJDd3XDo\n+Ic//plnfvzVC6+fOfv6Gx/49M+fPHHiyb/87sVr0ztO3pLybrN6Yu3k7Qnl2W//WIiSBcasplVV\nZ4u7V7abkXOs3lzsbdzMXn3iqcfuebf5IVWrYd8tzb59wKqJXYAsgsQKSkY3rlxzvkJkMFIVRw5I\nc07snGTLkr1nENZiJGMUyaVNUWSBhahl5ByaikhEY0ehanN37MSRnb2twcrq6gEXGpzO4zCMurbX\nhMoxt3EymQEwESCx5GxWqvMgQGigoKCopoZmCCo5SZGtwltnXjdFUzBEgZgzgpIhmrKqKjCAz5YV\nMoBDVEOSjEBAWlS3FhXJwJYjLAJdwrLLDbFoFopSiG7q8JaMMAZQMiUEKYWrElUHgwzYKw4Abgm2\nwjbwqIoBuJQXiUuJzBgcossq3owJzUSZIhAjOQYFywzRYJhpASrgprF3Hr1RjdSaAWqmzICAJUhE\naOqQDDCJqiESsBKARFFiYhXBouHQYr8DwozgsWySSugCb1bOlAEQUE0VgBEVjZaV8+IWNCx5ijJM\nB5dMS7C2xOgRS3albJwzGS479mZi9tNXChMzonILBP2pDrr8dSQ4WlsZmeakkFIHwAY8m+4pJlMk\nNOdcAASQZLl2ntAFtuC5CX593+jAwZUjx1bvuu9+GNQX3zz98U89duSe+946c/lLf/gNSZFlcP7c\n5eC4V8uKjJQUtERLkMQwax4fWt+Y7TSxHg8rNutzog68Y0dqgBIVGgvKlfcqyM6R5mff/MnxB982\neeU1Yre7szccDFPqHKCUiGcJNaM4Rwiu7CAIPSOJAlBxaJaSSUAkEGAuyAguqTSCQoMAEbh5KBJA\n0WwTGSAhEi0XYIbOe0labMcMUE5AzeCZlBABWJABkFgBsiiBMrIrwVrJ6NhMENHETJWc6/u+8QiW\nkTwTIoBvKmIPMXnXLLops085ASEK+rUViJkI/Xh9utUmS0yUTWcKMcKa14Q4m7RQ94cPr2Xzab4Q\n9QIym3eecTAczvvctrODh/YDWIo5qSCiEwWzhkPfti5Lo4nNnOODK6O9+YIy1E6apgKqpotWVMNS\naOq6vjMFRkQj5xlARDSB393a05SHw2a+iIgYHDLmXgSpIbbUZpVEda0Ri6vEE6FZzRY8MFBMHVXM\nPeScFxLJcbbovV8s5qPADZtDbMars7ZzgxWP5DT00WLssuXDq4NIvpNddjhYrYGMLfg6LPo4Sx0i\nSC/r6/U4IGDnRvXurqr2TYWeGAU7TBql68PebAeEhuOwaHs2HAdy1069fP3qtdve+aGHPvL+Cz/5\n4bnTb9xyx637DrZv3ZjMu13Hg8leu703Xd0/TpbmsW+7NG/lyrXTd4/rQ4gTzmujtY0bV25/8PZP\nfebjz/zg8YsXLq0cue0zv/l3Z9fP/uk//gd33vP2wfGT42O3N5y/8Uf/n36ePMl0Qfc+/JiZ9rPJ\nzuaVQ7fde+d9925cOjvd2mn3tkI1uO1tP7Ozs3X93KlRUzvnqvrw0dvuiZbfOn1GlCBPAHR014P7\nDhx84vt/tndtq59OP/W5z/V9+uaXvzzdXNx99+HKh5O3vfP18xcOueFzT/+460IykwwMwMCxzyDU\nxXRw/QD0U00WKJHp5NlX4dffbWGIocHBWr2+IqTOMJoCOismHURUH2Mu36beeSmdXURAQBBXkFoI\nRCiSrAgWDBALFw0AgMADiKo5N0CPwD6nBXpedHn95EmCRZ71q6tHdzZOR5b5YlGx7xe9ZhGNzEHN\nUAWQBMXVIafsEHNZThGa4rLYC0tbSVKezvbW1tauXd90FMQYmZOJZFEiMzSQjADIIiZGxCA5IbEK\nCiYkMOWC5FSjgjshIltWzEBAAQkUYdnARgAjoAymAKAIZojZAIHAKUYwUASA5GA1ycmRPxBo2HiE\nVDGqEZbZnxXhApeRJjNmM1DMVEgflMkMUQGy4QA5EjRmfVJ2XGebi3aIDEVGiAm0NyxAHMQibrJC\nxctgBBLYiWimsv1DQ1Aq/+MidlCVzOQIkAqxG0uWG/0yTg9ooACEJCBWkK5QsCqqpgWIQ5jxZtQQ\nQABIVAmlXNUBS3kTBcGMHC27OKIIxCWdhICCZKBqBsSg2TtbGTaM0BkwUOMxqTjERNpGJgBGaLwj\nT4RuLXhXh6YOVfCj1VEzcORcr3mrhb2Xzl998433ve+RR3/tb0130+N/8F9Cu/3X/uqnv/K1b0KM\nRMSmWVUhG7JAWe2pEuw/emzRz5u63p13e31aq1093C8aCwI75UjsVCChqSr5RYUhq1KOhx+8ffvy\nG81eQoez+RyR1MCROmRNQoDknIggCYGxUYYE5n35hlx6C9FMshlCAvAGpBqREAFUBIhgiXZyImW2\nKo6ZyBAxZWNWcggCphqjlsWFc15zMmPnULXs5a1YnQFEDUChciy58A0BgIlZckR25U0BHKlqCJWB\nknPMJBoFTJKZoEZh65umTn1OixYQc0rQimcngJiEvJt18VBw89gPQjWupBmvnL++QUjecdf2tfPo\ncDQabG/37Ww6XHXq1+ez3aah4ejAjc2rlfWDmnmwr0vb7WyBKI4rB7lijxpdcPNFVzMQEah1rc7n\nm2aAhR6plPtYD5lCSFESZTEXPCG7ft4GB53W23vtoKKaEZH6TgL5ed+nDEauN5N5mqc4Ho5q57Hr\nAmJdkSL0OVs25yBUOUOQaGUlUWEa1aFunPcZ0V2+elUzadwZ1a4eVJktCw6HTZfDjemuqwf1Ug/j\nFbVtF72Sr8La6liAMMdusgjj0d5sRpE85JXQKOYO42q9tp0ms/nU+eCCB5O6ds5oPPDu6tWLH/ml\nv+V4+if/4H8Hw0Pv/sSnKgQkmz/9+JUrXT/rsrbMaB3tzTomz3GOO/NH7zgcpJu184Mr47296z/3\nix+/+/6Tz/74e9tXNw/dfv9HfumLz37rT86+dvoXfvPvXbp+8Z2f+twbP/ryk3/6x2H95KGjh7Yu\nX9h36PaUJHWzxHzrA++6/c67zrz8zHxra/vGjQOHDu07cvjcmauEfPDgkX6xHer66G13XbxyxlrJ\nk20kXNm/dstdb+v3Nt569aV2b2/R51/4rb/71is/fP35FyjRHXccbReT5uC+RWbPK898/4fXNnZT\ntjWmedu6GpLGoEGtd1ovtuejtarf22Ny3tnVl37o8LciDzk0LdWjg0fcmFMEh5wFsygzF3CyY1Yr\n+nYyMBUJvs65X7Zry4+LAnMlIqpCgMtLEAASmEnxDkUVTGBZj952eDqfbG5cG0VZWfV7W5sVxbqq\nN65t1r7JUBIq5n3NSFlFVCUKIhqbChqhqikYasHHO9QyuVQhhxbnbR6vgKklRAMRoawiZkyUFBXI\nJCkvZ3s5oSAKaDIDJMla5n0KJuXzLx7aIqIpdtmbE1NYuh3KAxx4eXSBEpOUhr3hzZoaCR8Pdntl\n+yqsLCE5QkVQIscGqgmAlkq/JbcaBZQBl5V4KN15c2X8ysZqntETGPFUZCY6E0RDUegIkVAAxEBK\nUhbBgwARJB2tNF3fVr5uYxeYnSnctMEbljgHoEEwALOi5MISAkLswcyQwRhRFQgMkG4OSaH8PRFL\nRIpuQiyXkkgFBGBZolvQbhZRAcgRFDdvkVeoWSpvVGCAJgBspArBu9WRD7XPlo/uX1PpUlaEajhk\n5mpje7azuyBHdQWrK8PVcdh/YAXJq+qs7aaz7urGPKdejOd9Gno8dvTQop98+9/+uz/4vT850cz+\n+x+flmivvPiRyfVpN1UBQIZstWiPhmLqGA8cOTjrF8h+p+06tdTOU67m8fL9d5xgs37RujoIqFlO\nAqnrXGCsMashjV8+e/pjn//1v/h//7M14hE1YpHZEyOIGaNKInCEvkh6TQydF1UmZFRCVhUiE02B\nagACgBh7VguuEhFGsqKCAcPiA0niPQNQVmEiMrCc0TEx6U1CKRjknMyKe6QwgzOyVxV2rDkSsC3x\nfuCIxARU1ZTZi4njgmhHZhZJzjsiM0jo2ZEHyWiGwYmIqVAg9jQ6eWLv9AUA0ZzI1X3fMTQQaRaz\nCyP2ZsQb23v7VtYGAWLMm1s7q4O4dmD/3nS3QqHGtZOOur3x6gp7v7W9xXHejAY8GFzb2GCuxk0V\nHHe5J/VgPXm3mHWEUDVV18qi6xERsSKCLD17TwpdTjgHdNlVHtT6KIAQEOrgJ4sYYw9JufFYexIA\n0KyQxbIkUGqzxMwSE3KECnOOTKEDbKVjIhealCfoG1WoXMg5GvnZtN234inHaeZ5uzceDI0y1Doe\n1tV4ZW9zY60eXN/d8sijZlgP6+liwuDNJMWYIoDD1bXBYHVU8XC2c8Mjbm9vp8wjVw+952DReEgD\nJc2xqzxBAASFlGvvh6Omny/cp3/tt089/s3nX335V/63//WVC2+tHr6tCnr97MtHbr3j/JknF9HS\nZLq9N08HnONwY2O3jna8pnNXr7/7PW+PZ05DyL/4uV9pJ5t/+m//4Pa7Hjp29119pj/93X+4/+At\nj378M7GqH370nf/uv/37GxuzD33ubx44tm88qF/8wROvPPOM52Oe3P0fed9oYE9+7UuQYHPj8p33\n3h97eu7Zl48dPWxdq123sn5wuLJ+8dJFMENnVVMdvP2eA8fveOPFpy+fe2uFuRqufeQTH37xB189\n++Kp0Kys7Ku62B+584GNqxde+MvT83nfdnkQwuot4+nObLbbDjxV3qslBI6xm88puIFhTl1GdCE9\n010C2FcxOApr9b5RaLwqJVTNgkRlL6ImKFmBnC86TEAlMTUlA0B05UejCOtLwhMAVEVVybny37IK\nUGnfmWduZ7FpGt5P/dbk6lYc719x6CaTSe0opS5mMoNs6oySJJGcTUtPUbX42GAZ4UQyVQUgolKi\nKGyMwE2M4lwp4BukDB5QMYkocBQAYs0JgETych/IRIyKufAdAdAAydQQ6GahUNUAsTBMBIyADKBU\nqAzB1IqjzSmKWUY1RW8umSiqMzxqcvdgcEul44qK1A+NCAxUcJkzKGvGAn7UMu3SpeNKEDmTcpk/\nsnNqiRAZGThlHKExggd0IB0CKHqgDjWDCpQ7AAARIlaBCdHVdbcQARI1z2hops5My/W1pGfALJSF\nKAABsv40FlTGBaQo5dgkBDBQAyAyNTAtVG5E/qlnCrFEfQEAjIqiQAjJlhcZziCMZCpiJf/LiEui\nCpgJcuXy+sivroUqNOtDOnbrLRtb2wByz723HTh5BMxNZ9M3X3nz8vkdcrQyGlZNBYSKMJksdvbm\nXTcHqpogJ4/deuHqRqhsIfDMS2f59PYK0j1vvye5AxDgY7/xt0YH/vS7X322m0dBzdCLopisrzXH\njh+fTKYt8fXZrMsK5IB4t0uTXvOZy/ceu2V1WNWE7Kp5Sg2DCkBWnS7QkWje3Zm/fuG0GzAm16I6\ngHrQdO08qzKgo8pMARMA5awMSEpMZJaZPBgQMymU7KjjZZraMSVVQhQzZFIDEWVCz6jsAASW3mKj\nQDkqGokIU2ElLolyhGRgIoqI7JyKEIOIkBF5T+wWsz1GUljuKAtr25EzWSKlVLV4S5wLKcfBuE6L\njhhBQJOQGiEhau7z4sb1mBdVMyZBUFOWZly117VGx94k415K0HaVp3r//m5ntjLYHwZu49qW5Z4H\nlYhb9Ip55k3bhXTTbnUERra36GeTefDz9X3HJjubTUCT1EXc3dwZr1aD4aCqqvnuVooZCVwAVDDN\naAwMq6HqetXUZ4S96Xx9bT1HjKZqqVto5UOzzzNpcOCHg+miywtxCByaizuzKFijDcOAYuLgBV2b\nu0TI5B37lMSIEFxTQ5/Es8t955yPgBZhJ/UHK1d7UzAfvPPVYnt7JTSTaRsceePhgGazPWRGxK5N\ne/PpwbWVMB46z3ExQcwrw3o62xuN10Gyi5JSEiauAoBbTHab2qloTMlBXlsb1OPh9uaNo/vW3Jd/\n75/Vx+7/9f/LPzr39I/9eLVx3dVXXnr9zbPnXnhx1rnx2ooeqJv1Q+cuX7/8/2frT6M1va77PnDv\nfc55xne6c81VKIwEQYIgAYKjxEEiNdCSrUiWHTmWnagtJ469nKxOO3F6sJ1eshMnqz2k204cRbEc\nDxpsK5IcijRJcRQnjAQKQAGouerOwzs+0zl77/5w3qJWenV9qIWFW7ioqnvf9zxn7///9zuYpSyo\nLZ67ePH08MpLL73z3Q9/6IPve/XrX9k7Onnv+z85n0wPD3ZbaJ9+9pk3btxbe+CJWy9/6Xe+8vur\nDz/7x/7yz66t5d/67V+/+dpbs6bbWNmouPvUv/Nz0h489/ufPTiYpmXx9Mc/fbR/r67Cww8+EtpZ\nU6TnLj4ymx1OJovgGVWcTc6/61EN8OIXf6eaVGtrK+vrm7XKq1/+/L23b7msOHuqL7k9s37hyutv\ndvNWFdZHIzBJWvbeunlv+3D6uLOi6FWdUVVCxNAEnwMrklg0vtTdr/7aP/7Ef/LzkKbGWMhHxcZK\nc7sGFSKS5Y1HrbWqahCBID4YglXxLMrWpixBwBM4WiY8jarGsKgAMIclicbaEDoEAEJWnm0fXnjg\n3Mnx7nC0Sq3r5m2L3lGOVoH8oor+MxQREUVyRkQFFZAIjDHMsUtOCgjGGdAQCx5RngCgqtWijgTK\nEEdEiozIIh2zEAZWJaMsKBC7tEGFFDpdulHjel+MiUmXJU9u2btSRCKIyNNYMFQCUsR45Cw1sxHo\nSUpBFDBXfSKDB1IY5S6yGYEYZSlnUFa8D6teOhdQo+vdRIyaEAHF2Y4StCAWCRFY1SoABQCwADmo\nFfKi1kDFAgrN8mxVRYp+EGN1Ma/BmuW2UyFQnLkFNMQCSyKAildkEKtgARIyTPfjQBB1uwBKUdur\nigJMYIDjVZbovos3hlVYlCha5yUyv6OTMXqXWVEUAA0vQaikCgKowFaR42Vfw8ZqmaZpklKCevr8\nWjEq8ejoqWefevaHP5FtnucmdG391A/s1bPxYjyrKs8h6ar5dH5yfHCc7ewktM7YJMVQWR+6vD6r\nmqNJ1xusHR/X+zfuPP3EEJquK5InPv5zr333a6NR2taCCsK8dWrFpplad/fwqPFtkfbODFeOmtms\nCV1QRFI1h7Nu/Oatc8OVc1v99dUs1N0U2RGlZIKiClhwEtybO3sf/PiPvvW5L0gn5Ozh8XRjo1xM\nG1FgDATGUNp1C2udBjJWQYnI9sr+bDZVllixUAAOiqTGEMfyEpIIg6JqlDgJIBlDXeedBWFFQ8Rg\nDDlDXfB6X5gcIqEGFIAxGle8GIvsOa4DQudVOyIyZJkDpYl0AoZUxKAaovglVGAkQoRI9G5mNZKF\nwOqDARtAgcRRzNMIWoQQwJArypbaC0899dUrb6RZUbE2C5+6MNwY1oj7h8dFPuiIj47myl2W5sEL\nOdd1C0ryk91Dq5r3h8bae3dOhqu9i5cuVV17cG9nWKS9spxNeDHf7w+GBgMJHB1ODLqyNJ5FfScK\nKKDsiWzlW2cdUNp03WBQOOIO3Mm4MeL7fZMlUJSJsZlzNnggNYxdQKo6IbT9REujiaEeOszNeNGq\nSRBh0OvNmq4NXVm6Ii+atu7nReKgWSgLZ6lrBYbWoaF6UhVlymhmB/vDXl77KidIs8Tl+e7RSWLs\nyqA8mEy7YPrD0uVZP0vmbWvIogu7B+PEZqk2qUEkpCyzNlY7JEUMiakqPyiyxGEvz/2iWukX6cqG\nPfueH3nvhx75/P/8d9/x/k+PevmLn//897539dbte3WXXL7Yv3393ukz527cuVuP55eo29xa6Y1O\nL+bz7Zff/vGf+tHCye/+2m+efeCRZz727M2rr27v7D3zg59ZGbk3Xn3VGfvS5//pyfjkIz/75971\n7LN3rnznN37lt7rgiuHq8f6bsHnq6U9++vj2K9e/910Qt76x+cDDj9668WbuijKD8cHN/sqpwXD9\n7r17pJ1huxjvD9ZOnXrgod2d2/P947zsD1Y3e2vD+f7J7auv2Q4T5JUzaw++/0PV+Pg733hh+2hc\nUhq6UIzyvcn86s7R9sEkKDXOBOHMJcoBCYlRWFr2o/XhYneGBsl0429+FX7hT3ljXLIOxXB4+sIx\nvYTsog0bQMkghwBAcbAVvDfWsrAhMmBFOHLYMCYrl0NCCSG+KgjIqqoAS2BF64wJEoK0Bmjn5l0g\nhELms1neG6yvrt24dlWbBBGtsyEwqXJ0O4SgGG16pAE6YAM2sAAgqgJpYAYyhAQQfOeJSAgCBzJG\nQ0CAAIBI7CUgMmgrHsFAQDFkHLYhZjCWzBFBEIkjObiP9AaMP8XVFy4V82SAJSbz6f6EDywgiBiz\nFN4aFQtgRR9yeLk0mxmmJGQcylIIcP/sA4LliJUQWQLG4RcZRbUKTASqjkgJRcCpgqiqODQxZ2hA\nWlAi7JF4sMzxVGNR04IYQAlsUkS0QXwMs4TofVBkQWsUgGTZlIhf/TiihIAghMxsI6Fruc2M0RhY\nuiwgYsCVEVBI7lPLNe5QBSK3JVbYFFCXN8c4dMWYxoyNBzYBASUyWAHD8hqJozJNqHFsJ5OmlyT1\nvGuaSesxXRn0Tz8Im48lyUaeJkOwaOySewsgCuCb+c61w7e+enzvWvAwOR7fvr29vXOyvX9Ut/7e\n7YPxwWzVCVlrUCyASXrv/OAH5kfVZ//V5x575wM2Nelg5WvPvdlC4oXbgAJNP7UXBv1j2xzXdRcg\nLfPxZKbGbU/ms7ZeORpvroxWepkPLQlyK3mvv3eyv2h5snf9sdNnjrVZT/ImeGfp+Hia21S0A0gJ\nIUhLximAseIDWzId6/HJJHMEBiFOHZQZyCgudxOG4iNhhB2ikqKKCgQxxoiCscYsGaFGBKxzEhRU\nQdUaoyAWAdQEFmsIiULg+49/oKjGECCxiDE2DirizJqsBZEgQCAGAABsbGji8o6PRMaRdAwYsuGA\nLIBIu1erZ3AhGEqrxdzXtH5B1R7X7e7to/XR8PTWWieeO0icqxZ1VbXD0jKkItRVlel8kmdHh/ME\nsOiZgcPWILmkbprczxOXr68NMxJC6rrjPHFd0zqHrRHhgEoCqoENoSoYTAwA2cRXkvTTrmmKtLTG\nK3HwkqfWgOSZy63LXIJJNpvMA0On3CjNF7JofEJaWigN9BNMnV0ED8ZyCHmezqqmZelnae6gmU2S\nnIZF0TSLrJ9PJotFEzKXoCME7K2Nat8uxrMiyU7mrXVGkfNefzKtKLGpTZt6kSZJtrLaNfOcYe/w\nMMsHbLhZsEvLXNvMUBfEknXGOAPzLjibERbjo0NnTJa7NMubRWVMMK6cHc9sUVa//g/+u8tPfPje\n1Rd/84u/v7+7CMCDja1H333pcOd6Ffx4585qu9h4qP/0e5/YubN3cHBw8fTwU596/8HeyUu3r3/4\nx//o/Hjn2ivPHS78Mz/8R9ifPPfN19xo/T0feOray6+svu/p3mDwe//jfzM+mYCmBOxPdj7x458e\nXriQp/zqc6+eHDfnHrl4/qGH33rhO+I56duT3aPB+vm0l49nkyTJxC/Ge/ujUw9snr94880rDGzz\nPC/LXlke793au7FdTUOeJVsPP7J5+fJrz3/76tU7i9qX/WGR4zvf8/gioF2M7N09j253b79SYlVm\nIbRmyf2C3CTz7RNrExEhFbh9RY+PYVACee2vjs5ukYvveGBj6FCVjI25cQWwzgGAIQuwPC0AEZTi\nTovQACiSWY5UFVUZgAwZIA2szCyqAsriAVEVT45nXrqR7V99/bWy32OPoauZUUGWuiMlRKOgKsTq\nAQzFNA4AS0xeI5H13ALYclQA06SaCYtEs6waAWEBZVGyIvHTWlFgQOb71GUwnTKgqgSLxmO8By7P\n9oioBBBAIEQWiWuzGCE1VkUE1CDGnh4SErKSUQUSBWdoJPJomW6mUibWkiBBJF5DzJqBSnTbxk+J\nFJXevGxyAACa5eJsOZi0aAQ5ArhcHMsqAlHQuCdSBmRWH7k5iuzDJ59+dOfo3rW92gcFRB+UCQUQ\nFEhhGYOIDwFAy/4eLBd7EoEB0ZcUr4uIcYEaD7C4LGUFVRYCUAj3iTURIROv6qwopMKKEGAJQUGE\naGDXSDhTtPFPAxGQA2AAjIVRP01z8+kffPAf/8Z322SQb5/MG3Gp7Nw9PLxza2RX3MYQaBDfwJce\n4Bi2SbL87DvL8fR4f3Hr2huvf+/le3cP5otWWCcnMx43idOin919e++bv/n/ev+/91+4fmE3f+it\nu//6yfc/sbKWrqytVov62Xc//PWX3kB0yKETOfbdsMCtXrbez/fG9eF0lhgQ5DawNtyy3j24tTEq\nHrnw4O69PUrsbG+faw2kvnGf/cZ3P/ODH7v75W/0irStO0PIDERWlsQmITQqwZAFQGtc5xdAJogY\nZCJASkwkxTALLz1W8QWKJgLRUJTv72SjhgJEAygSGkPUeR/NkaCQppkhDcEbYygecQhoMC7dY9Yq\nUkYNRvScKIOx5LsGCNiLdYRACAjIEhtUQLSMShkS8hrSwdCHQI23jlyigzPn6sMTEglGUOS7X/49\nb3Red5urvbWVlaPJQtWmicnyQdU1TAt0vd6omJ/ME0vNXPx8stYj76GXZdNFB67eOnNmfDKfHU9X\nt8ypRx462tk9uns7J1sZDIGZyGQlBGLfhuCDFxFNMwOG6tBpp41nhnmeGGtDlrjZrOM2JBbyPEUI\nLk3QacfBG6nnddWZKQsZcJZzDTmYYZpi5pomTBuxhEVZ7h1OBkW60s9RQr0Iee5S506mU0oSZOj1\nhz7MgdWRL5ICnSnyFTXl4mC/7GW2CI1GWwABAABJREFULMDz3sm8VxTni97h8dhYm2Z5QLEKu/Oq\nTNOuCWLC+sYGTxvDSVe3itBILcQ2SXtlnqb5vb3DBGhrc8TqF5MxqPZtjqDTxdwiFE9/5Ee/+YXP\nv/j8VTL5Wq8fUJyTo8Px8d2JqX1dLz74/netbfSuXbvOAR959+NlP3v9tat74+ZnfvEXtl9/8XD/\n2K2cGZbtzu03F4fbF554/6nTZ/7g878bgvF3b7298OvnHhAbunl1+eEHHn/22WJt/eDO9S/+3hf2\n7+3/wI//1Mbq4IV/+9vF6GyaD/e2D08//K40g/lkNuyvtNWk60x/6/LpixfeevWlzGaj1eFgcIqh\nvvH6q9DydFqPVgZbD1688Og7v/2VL+3vz9OsWO3nlHUPPf6eGzduvPzq2/vTCoLM5iE3KEgdi1hB\nDUmWV1Xrg867quhlZb/sxpX3nZvfPXrrjdUPvAPdQqXsn30ATetMGbEqACBewBAhBhHCiM+OrSOJ\nKwrmYIyNEYkIXVERIEIkRIp2NVZUBWYPSEAUyweqEJRbDyS0c+82UlbNGzQJB1BmJSUyKigaa2sq\n92FguET/emNcvJQIMyIomOmkDqG11olEq6xXm2gAIWyDMCojqCiziojNMt8GQscQJJoSRSIbTWFJ\ncySQaDC/f0XEILwchxJopDVrrGfH0wMVgUUFFQEtaAAsgj6R48UkrBQ5uWBdwkGVgIMQWQBVASRR\nhvt3TwagZWdx2YZWREQyy4KlsUIg3hAwx4SuRfUKpMLoQBCxNZADVIoIYHz4T//8T/RG/rnneefk\n9rhV0rgUlDg2ZQSDyCCghN+/B8MyCiQiIBBJ5SDiYKkWRIyP/6ga/0FBUeLtGAFAYssldgEZFCMt\nJeb40SwLpoSqjIQgoPEBgQPj0khLABH01svMrPKL+Xw8yyoPC2C/fUImyzO9ceXN1888f6GtRwd7\n2epm0r+M5RpmBEQAEDjsXTl5+Yu/cfO1Lx5u39s92hU1Vdu1IpP9ipvaJUQJCYNSMd6+enLtxq/9\n8j978Qt/7/zpC76RpkEOsLrZ39y6ePP2/s6s9hkvqgYoCfO2anxvaFcK1yvWt0+O0SRVE5iFMaDi\nvArfeenF4XAEigJeGXzbBYHQdrdO9t251fm9w5QcGWXpECyrMFqUqC4GRXAuCYGdS4SVUZLMaVDh\nDogAomVpeRtfbl5ZEZVVIm8J7m8GyDgWVVY06IMHBGuscVYCe98Fgw5IMKafREWXs1KIoG1DZCSE\nuDBfkvWEnXWqYC0tH2JESAlBWh/yPGva2qILXSBDhpBnlTEWc4NEaoXrjn1wSSKAeZrd278L5DKk\nvKCTWbVo60FZVE11cHy0urYOZb6YT9MchxsrB4dH4GeDXqbis2J4+2DswNrU7OwcdU2VK7eZqwdz\nsZj3R74NwA0ZQOVuduI9gvfGWk0Vgm8DNO2sVxZzH1RRO9MKSuiqhpu27RWZsyLgnU3Jkg/kOUjH\nHdg5M/huvchSlwBCZm2SJzvH46AONRT52p39wyKzg54blubgpDVWkDEwu9SOx+Ne2e/1s7YBDm3W\nT6eMpp73V/PZ9KToFWmRkrM7RyeJg9WenS4mxkiS5q1n7SpEHPX7hGHRBALTHC1SA01bo6i1wN6z\ncpbmeZoezqfOut4ArMF61jpMTApZmbQS2Cr+7p//wB98+bmts6c2RqOK/ZkLpxZHs5deuHrShUvD\nQX+l+NiPP7vz1vWdvWky6r/nyXdce/2l+bGcff/7n3zfe7/4v/4P80qGq6vjo5kxGRb6sR/92e99\n83N3Xn3TO/rIj3wqM+H1F1+7fXv/pJ7+wI/86LM//Km9t1997aXvvfHiy5mzP/zTPz85vPHmi99x\nlENgSdP1Rx7bOn1q++oVm6b1eNLrDyRxJu1Xs3kzPiqLNO/1q8XC+7aZ1yfbd9fXti488XhVVzde\neX139zgtR1V1cunCeUj9bI7703q+qJl1fDK36LhdtA0/oLyemMyZ0IayzJp60R/1N0+vNUczl4Rm\nyh7yUz/7nz7yU59Js1pkAne++42/+Z+3d8ouaCAIQWziQGNIBXLXq7t5fHlAzKeJqEqUs4AaEVYE\nkbhCQEQiIhEOoiEEa20QIWOAmUGZxcW3KlGjEECRyCG1wasiWVSPYXmvUI7RGVQVFBJCq6rig5KJ\ngu2AkdSOBu0yoBj1gmBa9hKwIw1AHgXU1sGLAiswGSMGMDCg18Bs1EIIwoAMoIJigEU5XmdUWTVa\nyySuWJDub/gwTgrjew8pNPFOhQgij1h8up+cLRGw/dgP/+D1N18KU1fNG7CKbMEIKol0oGZ5QQJl\n8YQpcxe5kYYoogwMIAuDEoOiqIegajoRBBMx010XApLvtEKZMkxVxnP5qT/zQ70zkJnRF3//xc99\n4wYYQtFA6kFV1SlaRBsZeff98qpRfb78eUlEQDAISSzqAS7Xf5GtBxLDMHTf/7r88ytAHPLGx5g4\nM41/KlbFKNJSADCwXHcpIYkogMTjgHCUUeJMCGhJjbHz0CJaALU2ya3JS1pbHb3zPZcvPvjw8PT6\n5sbZYnUjH6wQ5bPj+Ytf/8qd62/euX5zf/+gqWuvSTFM7x4cHdw7WbMoQS0lIO25s8kzn/jEnb39\nqpo7kzpD1mJArqb15trqgw+dHfXTk1n47Je/e/Nwdmvn2GY95uAsEGKfkjw3LWNQmdedl2CQUkxS\nl3ZdBcqJSyIrNU1yRa6q4EL1r37lH/6jv/Kf6aRKHFmk1JAhckQSGocGMCTGKSoJImrqEmFfpC52\nW6whjFQDUBDA5bYXKMJ5DQGoMaQqaZ4t5gsCivVWkWAQOZK4YwFGJE1z39UKUmSF940qMqhFy+yN\nMd4HRIr1/PjwwwGsi6MMy8qgasiFtkFEMERGnUubZmHRKoIlBGaTGgR1qVFjRNk3bdkfsngVCWl6\na77Yq3jr4pkZK5OzBJ4xtcboIi3yYlD6TgXFpaVTMVBVXrq51hXfPRyn1hqCUVnWoc2d9doORr2t\nC6dNPjg8PpnunpiK62aBhCDsfbfwaqzNk/RkMhlkxhW942mbOUOoRGqtmS88AfRyl2QIymmaqkqa\nlUE5kD3cX3QMOfjVPDGWfd30ymLRhlnwmaWsN9o9ms4XzdbqILeBADCIGElspsjBC6j2Cqo6nVc0\nLBFTtaYwRe/urbu5yfOssy45OJkNs3x1rZzM56yUJIWieCEJoUwoS/JJU3Vs2fvcAHMoHZRFMvMy\nm9e9nE5vnbt3MkaFMkuRgZuJcerynnVuPD2BtGiDw7//qYtl3ssH7vTZs8bZ44Md63kxm0swZx4+\ndfbsqdde+O646p79wU90TfP2a1cCwQc//dMHN66+/OUv9Qaj0fqobevrb917+oc+c+rC2nO/99uz\no0WSl+/88A8203s7N292Jp0t6iee/eijzzyz/caLr3zn26+89MbDj7/zUz/56esvv7Bz6w5BMhlP\nH37fk+/64A/cu3Nnvn1799pbxdpodWMzK0cHR3vTo7m1XdnrG3J1M54c1vXBUWLbU+948vKDF2/d\nuDa+u3O4c+QhLUq7unmmM7qzfzRfdPsHs6aTzbWBFXz7xq0kzeq2O921F4phadSH5tTWxvHBkbNJ\nkpExST+Veh5Qdf7ID/z4f/VLps+hW9jD773wy790/J2jprUAGJSTzLJn5hi2Btaliixe/hAJEYMP\n1jog6rqOCFWJwd93iyqoJmlWVQtCJ2QAODReEDpmYmVUgxgCg7FGBcixtCoWURGNZ49IEq+ZimAM\nBJWI2iZUFmZiDYQGSEQMA6MYRo5q9QCiAkIQOupIWgFR8aCsxKwMEUxqAIFVArIoemHCJAgIsgow\noUBkg6OKwveLEwQsqmqQSDTE6088Fz0CxcEcAauso/1Ajy6XulIUgWZPfejDtR7j3N54+RYaQbBA\nAioqQcXG9zIkFRDgWLkTUTEQFQCGg4/7M1YGUVYGMAEVBboQP4u2rMwwVa3VzH146qPvbfJu89Sp\nr3/3xuefezsnCAAJkiB0EQGgaEWTZf86nmIUGxcCSvcrIqoKolHK6hRMxLVADDBpvEMTxCcFVVou\n6ZAFjC4LFRrJAhBL2Abo+6NejL6HqLpS4PvVFEYoAS6fXp80i85jLa2wknEWjJKQodQZY8VRnhpe\nWekVw+zMmdN5fxBaFcHDk3E9nR7s7zUCrIbVr62tX7l2d3tveqpwpgve67NPn33iw8+++dZb3AUg\no6DMPjWJcZTY7Ggyqet2Y+Seee97wIrv8Oob11+5fXTl9r7n1tqchK0uXWAAXeL6Ifgktb7tkIEB\nbeq878BqilnXLlJXVqER37qW//mv/oP/9y/+h6OEUptYVLesx3sHBgykJlXqjFqVQEQGNUFrDVIU\nExoCUTL3p9ugZJyXYBQMQmz40HKmIWSMqnCItzyNX1AiWs5XYnmGCEAw6qKXNKVgTKIKGoJx1lhq\nW6+qRCaO8AmtxTgzssCNWmcBKTWd78phKYuKAaUT54wYNYjkFDQBAu06SsGg64JI6nZmC9g8ewL+\nZOpHw7wD183mK0PXHwwXs3E56KnrewqGw2pp8+HW3nhSLRaTw3EzDwrOUlc4YzJTz0PVdb2SHnvq\nqYVyborJZHHnylWrXT8vq67K8qxtu8gDJgzO0qJSBjVEHLr+Sr/pfNd0jiyKgKMEwWVOyYICWDup\nfGh8z+HQcZE7URavddd26kwaTP/UrTvbvbSwxNYQSTtIU0WDBgDtdDFX0GHmiLDtQAkLp4OVUkdn\nrrz6pjSaO02dXczr1WFyfrV3bzYRb7wxqc2atnHW9LNSDEsAoWBNspi1iXPzZnFpa4CEtw/Ga71k\nNFzbOZ507Ee94Xh2lDBmBrN+AcYeHx/avGwBM0D8O5+6cPmBy+cfemBytN/Mpif7u7nLWukefuKp\n+cHtne2d9YsXHn3sqZ2bV27fufPI+3/g1PrKc1/8XDVerKyv5aPebLJ469adH/tTf56a/W/8b/+y\na1xSugcef1fXTN668rrJB0U/f+8nP72+uf69r33hle++cjI++Xd+8S9cfujBb3z2X473JgTu3t6d\nT/7cL5w/f+bf/savyLjqmtnKmbOjjVNFf/X6m2+CNL7uzj748NqZc9PDnRuvvwGN3zhdnnvoMU/5\n4u6dl668XBTDZrz/6BNPDdbOLgDuXLv+xts3bIt57qrZTDovqlmxcrKYVJXvLRaraNcyJTY2MYaY\nNEkKmxKgEfUMzLvu/B/7O/8wuTBEEVNff+PX/tb1f/0Sd5kigiAmgIjWJq1vtYtXtBhSCQAGQclY\nZgYh1mggR6+qIoac2G5lbSUjO5ssqpatFRGoak8BmOICcRmUZBGDVmJO/34LQxUUA0BEUMb3ZxRR\nIFBGUUEyIV4dYtkDidUT2qAAoAFUWEVVjFWvHXJQU2sAJa8gAjHhqWoEA6JhFV1W7ohZGIwoMolq\n1DZJtC4BAKsqEseoHmhkjguDLk9WjSAQISxZnhkmD2V0pu+cISJUp73+2uH2rkNDREBirY3sLhBk\n6RANWUKMHUZGJUXR6OdYwjeVWQQMIgFwYEEiFg0qPoBX7JS7DqYGmk4vvu9iR0Yw/+qrN17fmeQG\nRQ2iWEAEaQAFlQCdgAVAK8CoSgHFCCrB90uA+IcbNyBUMuBUTWwMUoSrxhAHRcWtqJgllPT+cFRB\nSSnGegUYZZlDWq6ZY0dFVZUxtilBUInh/Ppg5JpTmxsv3RzPuEFFaxNUIVwKbMFgbowYKJ3J0rTM\n0rbjLnSs3IpR9O9//7PPvfBi2UuLYuUPvvv6cd2lrCOCH/rU02pQCA+PDxFMVpg0zSyRb70PwRrq\nAleLul8WXWhXB8WDD14I3tssOT6sX3lr540727O6A5HUJFXwCmAJraI1hoWX+SkEEQ6wvPIaAmMS\nAK1DwI4vDPEf/+4X/8ZHPnxmvaQgDg2E1llCVUAyBgggcQ5VQMQ4axEdorMEqiocA7cEoKCEikom\noWURNf7FAxpHzMy+I5uCGGep62oRITCASPG8i18eRBEEUhfv9CqIpISAYI0R762xLBJZUdYYZiYk\nUDAWFCO/VckgEZCCGIBOrSOWYMAgKhjM+jn7QKA+aFoWzKFtZ1SuXT04sqc2DxqOh3LWKxACIaWW\n8jw9nlWBbK+frfX6aHU8nbkksS4Nno73D0O3qOftoFcwADB04lPjEuvaUFFiQ9Awa/IyFRZlJWvR\nJiLsjCaJTZLs6GQOIr2ea1ruujaoFlmeGTtrWlbJEjTWCBhBN503mdFekafYrJZJkpdHJ9PxrPXC\nw7KgzNze3u31Rv3eoKoWqcPMUm4peHZZUgddtGxUrEqaGpMkTVuvrhRJb3N/Mp0v6nbcoG9s4vp5\ntr6SLupFxzCZ187lrQ+9Ihn2+0GY2SOaNLEY2kAmtGKNGquiSs7kef/2vd0s7eU5su9CW/d7PZek\nPoT9g6MkK5Os8N28l6T4pf/yJ/tlvnvruq+7xWy+ujEq+8loMNq/d3cyl0ff94HTZ4ZXnv+OV/Pw\nu99bn5y89sK3R6Mz5x84e7K/t3dw0GD5Qz/1U9/7xu/de/GNWTN5+iMfsZS8dfX1tEiV0t75hz7y\nwx+/9sK3br159cYb3zv30Lv/yF/4T/Zuvv4H/+o3Fie+8WAdfupP/ZlusffV3/qNwWijyFIV6J0+\nXZa9a997XllHpzc3Tj/o2+7mG1cmB+OV1fTshVNrZy41Xq6+9PxsPG/rpiiLp555/6Kubu3sTubN\nvVvb/SzZWt9om4oDEDCSPTieSKD9WdNMZg8AruaYESHoYNCbj2s0sLKWk6hzSTefH1XJs3/1b53+\n6HvJEun05LlffeFv/y9+nnsBSpY5NI62h6BxeMXCtBzWCSIxx5eHqlFlJZN53+RF4hXOXzxfLQ5P\n9scghWDojcrDvSNlA8pAhiXOT1E4cimJgxhDIXTWWIlCQETReP0iAUEkLyGKyllFZNngFvUEiUcG\niNF8CMAAhkG9MKhrQVBNw50HDZp4lVjj6zQgYCeAFpVF0AQVBQ2KsV0uGpGbSxcAGBIVVYyh1Zgi\nUV6G0QGQI5SS0Ik8npt3lni+n/Qyu4wTAQT2iEaFSRWNJUug6FW0DWQIJGK0ACmqXxXNsrQQ2k4E\nmRUEJMZVglqiAKyqgTUotZ5rlc7bE6hPPXa5sjDt5NtvHmyfLBKDomABFNAiAkoTVXiIJJCoGkPK\nIKpiACVCtKNzcnkKUqxDKChoQmgg3o3jHwxj04MjCwExapvvP9fE6arEG2H88f1O4f/hR1x7EQAo\nqM0wvOvy6VDt/sAnPvpb/+a53TaoQGagzFwIgdARAiAYY63TwqUCwfsgAK4o6qoGSjrlQaGPPvzI\n4aT94ldeCoit6A89feljn/jAN5+7giqNrxCQ0BSZNQi9QZHn+dHBeD6fE9KwLIS16hpgAbSjlZFz\nVM+bo/F80sp4Mq2brhNpOw0qTMBekZjQxRaPiQ96hE1oEY0BNESiSAiKtq7n77t86m//7b/7d/70\nz65bRwQpkAWJVSVLqKC5dQqCGsXz7BAyl6AIGgpBKD57opilVT4u95QousqsYvw+N547ZyyCCizj\nNAhLNmkb2BCRMW3jE2dBBaxCAHJWNIDERS4jAnslq8oUCzLxIEUAAYzVQ2ctghDGkoxjqq1JtFOb\nms532TAPVSegSC7LyXv1wqYcfvPNG/1HLyxa9RLKJAMjADmRZCnM54tyOOo8roxcOdg4mExE0WnT\nG40Od4/9YpEltmFp5gzAuSVGmi8WZZqSA0OJ+ro/6o1ObR4ejk/2DhLKbIKAAEwiTdEvkbJ2UQVp\nhalqQ2JNv2dVgQEIFNVNOi8eWlFQPrPRM0wpNqujIlD29p0dFeoXJrNUCWjny2Hv6HiSJGmakgTC\nUOVJxgnWXgOkvJiPMpOVKaAv+iudSQ4PT1Jr0dpm0SyqLsdweqV30kz7STGpO0PpeN6kBlYGGVkT\nAIw1ubGeW4KkDZyhsNXpfL46zIvByr17xxJ4da1HqL7zJkkSV1rn7ty507aaZMap5hbLXmrLjKrj\nk8LoXj07fen85YcuHh8cHR0difaf/tR75kf7X/6tz28+8M6HH7x8943v7dy5t7q+vn5h6+bVK/fu\nHG499uSpMvutf/TfY1W5Qf4n/vJ/cXjr1e98/qvJYDPbXDlz/iGn+G/+p/++qbrx4uRHfv4vnnnH\no8/9xi8///UXRqM16Rar6xvv+vDHrnz19/bv3CxMMT86Gj70YH9rSwT2b19Ps3J05sLmxUtczW++\nfUUYLl46vXrh9Prmxbtvv/b21dfy3nA46qVntx565LG9/d1gipOD8c69g5Wyl/bNfDpTw2srw6at\n62qRGDrwcnVvIqBny5SDD9aljquqDQFdoqOzFxbjXV7UoLSW4rVvfPnM+98HFsRk/YeetuU/6Wao\nGCNhNi1TFm2bjllVAi6laPFSSCF4AEBDsUMNjjR0eZImWQbU2MyOVh8U3OXFbDJbdFVaZLn33Pi4\n4RcVZImMT0QSa4k5GGNicEOUnUs5cFy3gcR2WQxfiAKyBiIDwkhWJYYc0RjwwkRWRFAU1ACIBjlz\nbv329r5nEx9YlSGgiDrEgITeK8UjX0EiiEUULEqINhnjmSHGQmkZKgUARBPhxSIxRImAGt2oGwme\nz2A1N73UxToakZHIOVNVtugAQADEZUnuTAgUQpCWVRSAQhdc4oxL0YiziUgQTYBVDasXUlEmIBEJ\niEaBlCQET2Q5hDm0K6fP7E/qMXev7td74yohZCCzFAnGxAoRixqQmNWJ3LRl0oUIY/AF42hUASLx\nLCAYUUQMqkKQ6lITwcKI6JV1eWKKRjYoLhOoAnCfbwMAyMtA6PIwlNg8jFULBCskxIycJqZrGaEc\nT4MHdUgeuVXNRPMs5+AtOo8BlMWL2MQLOJs89sRDh+PZm9f3NIzf98iFpz/+sf/5V3/n+rV9Viwd\n/sVf/OM3b7351a9/O8lT4eAo8V4UJLQejc1T+1/+N//ol/6ff23y/LcxoQBokJI05dZ3ivd2jiSw\nClWhrrvOkksS1JYx1853zNpiUKROfbyXGSSHxKLGGCOk8ZsdQKRTUnX04s3DP/un/t3//XNf+es/\n/WPlom4cOgSH6FSVUS3HSK4hKxgjVSCqoGIUjCMNcn/hh6DC3BGQoipYY4wEIYshKCGb6ORLndGg\n4i3lta9cxExAIEw1cGJRQmeMjbgE7xtrLIgARskfgYsTliVyQlhAwTgkUER0RMIcxJfFyEsDihy6\nXjma+wpVnEuNtZCoiAdlNAkEjyQNh0YgDcFisrHR6wKcnNTI02yQtHaFMkMgg+HApG7vaNb5eq3I\nMevdu7OTee3nZrQyevvekUawFLngm7JMDZEgdhKyot+2jaPk3MWHyBXN8VFuICCwxUQz8IK2zrKk\n6eIIC0C56TxRIizOmKbzIKENGIB6CZH4MkVrbR2kaiuybrVIksQh2qaaloPBrK6sc4k1ne+Ipd8r\n2yYA07Aox4t52e/1Cxf81LlMbLJ757CXUWbxaDLRQINMNlZPzU+OszSftL4VRd+lFgxg24pVTVNT\nZraeV0EYLeepaZrAlV/rFUm+cuvmdkYpAiedN5lmw7641aN7t6bTsVCxaBarLh/1XFYmbeutbwNC\ndXB49I53P7V1duPm1St3b++defSdjzz8wNsvv7S/c/DUJ39ycXL31W9/NYi7+PA7rHO3X/geO/Po\nU+/eu3un2fEZyul3v+eDf+JPzHauvfrlF+36w+cfubwxzI/vbd/YuVNmfVviH/2P//L2Gy/927/3\nX7cNXDh7blbNBpcuPvzwg89/4bdSddL69NTq5UvvdmVZhfrk9rXQ4dZj7+gNVhbHe/vXrh/dvrt1\namPl9HqSlC997QuLRZf3Rqzdu9/9nooXR62v2dy88uLhraOHL26JQWeTUNLq+qCaTS+cPj89Pn7u\n4PbzN06mJnOhPe58aUzhQEVZBSkA08HN3WIAxpKmiK3ffu1FmS5s0UdNcHBx8NCZ+e6uwQxU86JY\ntPOi11cCRm8l9d6L6LK2TqiRLMGIJgkSCIDFluuZU7M49jvN7TR1zaLthNHm5JyEFtCqemts8ELG\nqCjH5aMudfMEyIrOZW234PCHcznrXPASA4bWErMnVdWQpilZaj2TBxZRQYtJCAENiJJXUg1C5vb2\n/srqyvb+mJEEgoAVFJUOyLKo0H3clyohhjhvErA28RJEouBmKfwTiN4JRACi5eERpXGkGIB6EB7O\n3BlnVjOrHGIOPUgHQgjI4CNDtb9WrD+0la71wYID57tOgkIQ9h6CkiHRUC3m8+NmbbBytHPQnARi\nVjBeIagnyoDCknfFFRpgNUpJf7WcmXCykGvjZm9aocGgqCRACt6IisXoJSdWjsVAUTCwnOYRxK47\n4PdTLzHgEuGYABK5AgANggMkVUPGC8fVIN0vL3AM8seb3jKDszz47g9dYRm5RYoJLFCwAIIxrooG\niLlDkHt3bllga4k7EpXGi0qbOmsTk2emzPJLF86+eX17Np/PuHnlxt727v5m4X/yJz6WlKO/8Uv/\nuPaq6P7Yjzzz+Huf/M4L3/GLmhKHCogJawCDIKblzpjk5GDv5tu3T5951+3XXjYY2q7JnUNgNWnb\ntG1XZTZr2av41CZtI8KdIhoJo/7KwfGBRSOqAPZ+RR07DoSEELnahlENKFKqiLmhjsKbNXzkox99\n4aXb//EHL1xU4MSiSIdcmiQodCwGQZHJROEndBhG/ZWmWyiLoji0gBS8pzhDAEIDotD54KwxSIqM\nAKjUy9Om6yIzMEiwSCZiYpLEovGtB1XrrARBVGNSUvK+SWwa6zq8XA0YZkHCJHFNaBMwiOCVjaKI\nKmHm0qadsYolNZqFwJ20DnNjMHRd3LdY53znteswNdNFBc5gah2kJ7PGe5zPJ6c3Vpmgnc4GeZI5\nx745qrt6OusP86RMGLDIiyTreoPepJVenpGT1ntpfeKsErZeAZWEgzYuL1594bur586un7s8Bpjv\n76WJTQ2EaM4JIU+1vzY8OG5sfJVgXlchcMhSXXTBkE0AC/VbRdJLqNcfHhwftNo1Cv0CMmvmdVeH\n2rPU8zGlppcXwTe9lJwUi/mCCIe9ollUoyxFwGo+zYcl5P2jw9lqP8tLnEzZd5IlBsjtHR4YBNti\nCqbxXa8oDbl6Mfehy9NkpVfOqxmJBIKtwXA8PeGGR72U02x3e6cPEMIicdaHhXOjyaybzG7duHkz\nLfOiZ4f9lb6R1Jm2bREsfuEvfaCa18/88Kenk3s3rrzZBHjyQx9YXd188Sv/e4fZuUuPTvYOFpOT\ntN9fGazOJscn48OyHJy68MCtV148OjoYrm898uSTYM21l5+/c/26av/hJy9trK9cv3bbmXRydGhG\nm3/0z/y55z/3v25feSP4DCmkRZasX7r81JPNwc17b14Ngr2V4WBra7CxdXLv3tGdm+XKyuqZS4tq\n4o9O7l67VmSmP+z3Tj/QdDLeu5sWg+nJXr83LEeZyzcZzJtvvjq7e1RaNDaMhoODgyNI01Nnzgnx\n1tapNrRv3hz/zueeO/BQteCh/eiwuAC6ldrMooBYIFBNe1lvNcW6I9BQd0cw/PB/9tdWn30SDVn0\nO//ml1795d8LXeoVXOJsniYDB6LT/YlzzhhczDvfBrEGBIUbIBKxZEnaVhFGp9ZDNWla2nro/Nrq\nYOfatelJhS5dzGeqRiXCLNCB8SwKoiKqGBnwEkehESMjqBD3UMrKBmwny1msoTSoiAQVQmUkpyqg\n1EKIw74gqCBBGDGtOACgR2XRtdV0seDDmRdDghBUURKwXhW9MAIEBUYCAFHh+PBOGDgoYNz0AAIT\nGI1AZRNfTcKKECljoAiW4VKG7yrt6ax773sfP947rmaLEAKwCcpgQJmQ+NQjp0+/++HexbPF2hrl\nfeKKWeK7m3WFTVKxxrJvpuNqf//o2u2T1++c3DlSJmZlFQUKKsjCCCzh8qOXpm399vWDecBupbi7\nCNsVv3k4B2sQ5PvCDECkIKmSJQ2CnZGYf0VVp0hLRoiixgOeY8/h+/vRPxxhIqJCROQQAMZOJ0Qy\nNCy7ifGmD8AY6wDL+yUsJbMkpMsLZ0yXqiKYKFcWVFAapbBRDgd586f/5I/9/pe//c3Xd5XSwNpy\nZ41xSokzNsHeYMX1izvbB01bo7TnN9Yunu4/+b73fvZL3/3aC7cya5oQ/tIv/umbd1+TLjB3HQcE\ndJQghC4ElUDGIQozk2KSml5/rasXjjiIiJroNphN570sLa1FC+NqcTQJdc2iWrMsuENBCRr7s4yo\npIF9tO9aUCJLCggYRAnJEIYQyNgAzKzey0D97/7yr/zdv/Rnz6UZIyZWSdUSGUBjARQT61g6C4ZI\nLFprBNEAamJttGEDK4C6GH8UQQRSZO3IJDGhTWAZOjIEgBoCOQsARKqqBgwAcWiNtYTgmZ2xPniM\n3xHKiUnatgFDpGAoUWAGSZyRIIwAS0HncoggqoAcghokk0hWltwwWEUbWYVMxhFoYG97vav7sx2k\ntUce6KrGtwsUF7zH1LadOPanzgwx7+/vj1GpLHGwvnm4e5yn2h8OukUznk3Yw0q/MDbdO5w4awD4\n6GSRJi5LCRCNAnea5IaMKYalTQbNvPLVOHVWtatb4U6TBJIs84zzRW0QBaCufBA7a+a9LB2W2HOQ\nGmON7Y+SoNnO0XHbcpLlhZMQ0KO2tUdkwmRQuq6r+0VS9odv3dodlbklAEtlkgQN46NZ2Ssaha6F\nfolbp88eHZ/UVeWyfDZu2qZyieQuyZK07TpnoByU7LULwaGsb4z2T06gNVmOo7X1veNDqerNzdPB\n2aO9I0JE4CSwSUwxKIyz05aPDo7TvNdZ48D0IKTQKWrS68+qxsLwwid+7oePrr9w7+17DfY/9sf/\nyHz39md//ddOPfTudz1y8a0rL08n05XhGZfS0fGOF/fg+z5EOrvyB1/var34+JNnLp1vFoc3X3hj\n/2DaP33hBz79R9c2829+/rNrp88e3Lr5jg988IGHH/nSr/637azN84G3XblyKhut7R8df/f3fsdI\nAKBTDzzw0JPvPti7e/vV7+3e3XnkyWc2Lpw53ttdnEzuXL1y6fIDxfC06/fHO7ePtnch7fnp5PT5\n8ybNNx94RzOvvvPVL832D1dW+pubA+y6DuGRU48v5jPw/szF09s7+7/3pZduHLeQFVZ8kqk2OBPu\nDDTKjiyKgsW8zH0NQmLzBFkSY/KTdnz9+uhdj9oEgtOVsw+b4nPagXWJEFNGNrdAWjapMHbMSZ6C\nAAN2KqAGNXJJkVxiHboiG231D+/unOzePbzDaLPBqY2TvSMCF3xQEUKQwF3EiShGhj2RUxVjLLMi\nGiKI8nKDqOIJooEweoio7ToyCLqEZUTsGSASmtjgJpSgimiE9H7QEwBx3mFv2DusTgIQCAMpmCaE\nOHgFQIOw/FTxYioEHILiUjKB94OUqgBqIJL5FUhJIzoMABS3iC7nupVYhXbznQ/cunn17NlHb964\nBoCITiAYxGSUbb734dXH3pOf3rJpgS5FdECKlFCaqLNEGToLAGl7Uh7vpytXKEA7bWZH9fJuymLJ\nMnrDRCvOJ3hw8zgY02Zu76S9F/j6UQ2JiXMAYsGI7I6qGfjD8hkAxvjg99ec95lsyw0fxnfcpW8Y\nY9td4n437kpVEQmio+q+SCmeiMtzUZYmeL1/E8RlQeM+sBuW3g6CyDSJ5X5tGDoJmU1dNvyZn/+j\nx3//1+8c1cqAkKOBUb8kgoDchMXR/vGKS3rD4eXzaysr5d7M/4P/5V+m5Xqe4aQJn/nQY7PFdmIk\nHRrf5ZNZ3XQdkY9fU5flyGzItAxJ6oi0qSbWOo4qMVZABk3IuuGouHh6zYCezGq6uTdFPKymXoIR\nUmZGMWgRmLW16hBNAFneooQJSAHJGkRqfeesDRyMMWTAez8j+yf/L//n//CP/fGrv/PrI5vJ/UdC\nS5aZFZC72hnLBKwKKmggjdEtH1yeL+bzhKwxhoWFGePGF4EoAVFS7XxnnRKhCgF2aE18NGGOmUlG\nEAQTfGfIxiyMIYO4zD0JSlqWwp2xJIFZAQTbLgAiGUpc2tZ1dDkwe4zRNUtkjIIgGK9tkfSUgq8b\nxZAlRRe8CrFoFQSyQTVZnEzrlHh9ZXQik7Zuy7Qwhk7GdZhx12nqvDPF0eGREYbOGxwZlwzSzBgF\nA5DR6kZZTUPbdYm1hoy1QCbT0LoMGu97xlSTYw3Ho9XB8NxGs/DtYkYOGblDnC8qVGeStKnnxpgO\ndVJXKdq+1a0yKdIsANSLdvtg0bYLL0rOSugqNZ4lTRNjgEOwSVcHSm3mBW7tHKTOAAGCpqguNcfH\nDbp04XE6n68Nckh6t3cO1beps5PxfHoyH/SLxPnhsFwsmiwxSUrjWeWczdSsrKaLtq5nPi8lTVf3\nDydt26yU/Uk9mxxUpcnzPG3bOhiTlQPfSR3qRav5+ogp03G1NtLRoKxbG8AcHp7kWWJPvft9YJzN\nBnbzwic/+olXvvLbr3z3xU//yX8vd8lLX/psULt5/rL4djJfoC22LjxAYN785rdU03e878m1M+t7\nb1+5d+1mC+6RZ59+/JkP7t698eJvv1idzNt27yOf+enEtt/6rX9xcrLYPHfGdjUkq17h2pWX8mJ1\nWJZ11Tz+4R88dXHrzZe+dfeNt/Le4L0f/QEke+/6zend69Xs5KkPfCQYBIHda2/Mx5Uq5km7ur4Z\nBMvVc6889+1bb7xW2uT0+rDs5VbVlL2V1ZVqejza6DvTe/v23jeef3Pq6fSp9aNJ7UCMl61eEUAW\noCusCsaAwSD1vO6a0JNTaAKqNgtfGrz6nd8/9/GPmpUckl7x4OP90/2TWe19p4CFcWQtJaSDtq7Y\n1UkLHVrr61qBkIg1KEhqTKdttroWFhMabGCZnN5YP7h3qGLG+4f1rBEkAUGDHDhiKKLGh2K0RR0g\nyPIhWoUZFRFM0IilNBAEVFQVDSXWAWpgNmQEvu8xEFUWsYwcP7MqIYuS+qBAELzMTxasVpdlN0BA\nZUNGgyDEOoYuSaGR4K0KZAxzdCqhCsZdVxzmgS7d39GagKgqmIGc77szKfYyLJLha1//3qkzj9y9\ndQvAGCQgMIRAun7x/ODMpWxtk1yBaQ8cEWRAVq0FVxjXgyRTa1EFKDMr6eiyn9y+Y9+8w0fzeLyQ\nSdiyZdeh4AKPd2pOelXT3Jv73cbfXoRgTPTqRgClLtv/sQyJ3x/yomrUMOl93Nn3e4CwdG9835+h\nsUcff33UTggvJVyqEu9896+Afzj9pOX/fflv5P7iEBAAJWIYADEGo0gjMhMBtBWsfaPu1D/85V97\n8p0XH7ywTjoe11WIY1oDSWJPrY/ycnVv916W2I2NUVmkL715+/jw6Mc++Yl/+tkvHTbys59473Bg\n69k4SXIv3dF8ZgQyA0HUIqCGzFgxpmM/GJS9Mg2hcbY/mY2ttd77LE2wxUqhadqjI10rS+vQpKXL\nC9e0BsAgtdoiGWnBJoQqPTdQDl1orRoUCsAAaI3pNH5jBzVYh1YRMobEQmcIEjyY1//db/7G//3n\n/sQrv/Ev1rIM0RBCCMElMSdKhKgczFL4QJ7ZJRYCh65LrBNmFTWAoGqMBWEFFRUEMMYiGYugYBRD\n/At3SSLeEyK3wdgEUETEWCesyrwc+BMBa1AgwRAaJPCNIqGqagAFTYzxnTS+xmWPVsBaMoQsubPs\nxQcffw8cWgV0aQImbdsmPmOJSTyE0SAVMlUrhsJ4PmeWQVaidIrasvVNl6VZkZlFF7qu3lhbqxt/\n4+bdlUF/ZTiou9ZaR66fFsX88LVRnqyuDjqPs8nEhY4cskJqTd16MJq5JHjP04nJSwkpsyfLqtnJ\n3K8ViTTzsuzNm1AFBqTMwjDRQWm9b5vGH9d6PK36ec7WtPOmNMQyHwyGi9kMAEbD0Wwx7xeu9Xx4\nOF8d5QkZDKHMTUc4HXfOuqpeEMDWSunS/N7BkVO7MUhSY2dhdmqll/dMWqzOJtWsWpxbW5kGNmBM\n4KS0J7X3ddcvs6woDk5OkgT7WTbn1ldQGgtGF23ru865vOkaAZk3tS1XPSVStyPXpdaKybAY7u+O\nUbRXOPvgux++8e1vjL3trW/96t/86+n6A//+X/2b1577wu9//vPDsw8+8Og7ROpOMeuneTFoDnde\ne/X53tq59z3zZJqk11/67r27O/nq+rNPf6gosi/+5j9ZVKzzqre+9ZN/9i/efOkLd69cPdyebZzb\n6A978yOeN3We2VOXLs3mNaXmQ5/8iZP9m1/5tS8sFt3ahTPPfPSH7m7fkXYe5rsr670HHn+kZuuy\nPMxmzhkg6Q83Bxuj3uqG2vT21avz3cNTo9XChbJI61aKM6dGa/3x4Wzt7IPt7OT5l17fPpyPhptF\nEeb1vAAdlNnGqXR7XG1Pm43CNho8U0rELMo+s/1IQgptxbXYVMPunfruoRlupYJcbJ56/MHjN14g\n6IWuriZtf5Db1OCwj6ZppUFWmzor3LUtgEFyAr63Mmw6k6RYrm3MJjPs9Gh8YnsWG5gcdgAm4qBC\n6ECscRS4MUnKrWfkJaUEGMCKMoFTjGZwURFDxvugAIAWIYiKsCdrlu/ogIIoKggaVI1RVFBBUWRg\nEVLEhEwDsSxlj2eLiNBHMhwrgMKgJvI4LFHMoQICKcQIqCECBVYFAgNGQKIqFhRILahKZHkAGZHL\nOZ3LzUoGOYmone1O5jCJmGGxUb5rFZQVPaNwh5JaJUFQUbCILieXST6g/sNoFRZ74I9ZGIWXBxAp\niygalRYDAKZAQcEcHh1N1ezU3XYIuwut+X5OBeOnVgCDuqSZcSwCKliMIy2E+zdE/f8b5gT8PlQo\n7vkQYoGSEIBZ0QAtn0eWG8LlzU/uH4eiFJFA9IctcLx/kRfQKKRAQSAxet9FJTJr4Pbe0dk1fPL8\nZl1CgJAe6WzBi4C1cGjVzprj8c08z1dXh01XvfnWjTNba+968D3/9uXrx3P9Ex9/13F11Eri8pyr\nCoB8w61ollqDkBr/k5/5zLWDk2uvvKJtp2lo29ArV9rGS2AgZwHbpu1CCK2y6qz2b93aW988tXtw\nQ4LZn87nIaBCz+ZVU1uDGSohtdW0V/aFeq0EQG0CIBpESIE6CZ3EtSkQUgBEj2VqWjWtzhvK/6t/\n9pt/5Wf+2K3f/mxu1atPnWXVpUkPgCiC0EQDgDNA1IXGilhjoxpCRCi2e1UNgCGjqhEhG4JAHFoQ\nJUnifaNMzhqbQJAArADggxAQGQIFax0LL63B6lWVMFNpAI2wOCIvGlQAmMiiYDwhkVU4GAvBs2pI\nksTXdZY4FVWrIbDVPMmoqVpywInZPqnLMtTT+ahfzirYPjxZKUvhKklT8eys5oYHOUlipfW9/vp0\nXilDfzCsmsW8aVd7Kx3p/HB/dnxcZNb1BmqA69oQsbKwTWwKVprJHBFJNU+NDz4hGA7yzmHT8qLu\nLBoEZeOO5n5aNalNisz2bCDSZlED0rjlWSWdIKT5fHxYFiNjZdTLm5YTl6ysFVe3D88O+vMuHB8c\nnxr0DCiERVYmLi/mVYME00Xlg64PoNejpu76aZEbosROJrN+L02dTcv+/uFBWfYG6WiyUM/Sz0zW\nT+pZpS5NimyYD+7s3UsxF4ZFF9rQrI/WwHTSQSvsymyUDNpmNutqMxgxOcMhqSe9fuJZpdUb925g\nwM3VktnYu9/6vS9//rtzb+bH+5cef8+P/cSPvvTFf/HW1dtn3/mBiw9d2tvbS0crpy9cqo+2Ry77\n6tdeeOQDH7t86fz2rWv7N7fn83rroUcvv+Mxf7j7e7/9tVllwmL+sZ/6ifXz55/73G++/fIVY8zW\n1sr5hy/W3o998+C734debr32ej4cPPz44ze+9/XdG/cWQZ/9+MeT1D7/ta/0+it+uuvbOt3Yams5\n3L3V1bPx8Tjr9U5dON/bOF2unrrx6veOju46gV7mc0hsllpHqxtnz1y8fOU7X9w4/9BkenT3+ISJ\nfCtk64Kw6bozI0rIThfdnXl7Zy4rLmw5J2o1AW3ZQQrEx/tHWw9ukTdFH0LbJVV799VvP/rYZ7Bp\ntCwGjz2B+QtQiSCGZuG7vgmYpgn7ypYmU6qnDRIWRaGKPoQyzdvFop5UzVxPf/DREELbNe147ue6\nmHfOWQEmImaO7UDvQdGBZ7K27VoTfXyikdckEpvkgawFxICxxGtYA5ENoQVMYl84MEc3N6gJIAbp\n/lCPIholpsQDighHnwMzrgx7k8UiCCKoWjGChMCqCJhY65lBhNAImJh3gFgliHEPZBExSqKoRkAE\nEAyqMKLR0w4ezM26w75zAkEAidCoqHoFCxzQZkSowR/f2V2/e+iKQRaABBVKQ85gAtaJG9Hqwxod\n88UGTV8zXZgdH8+PJ4tpB5oCCQIog1giZBFqO98w7Ae52/oDpmkUH2l0DAoQxFMlJt1NHF8iWKIo\nH4/SAnNfsqGgESOnqvj9c0wBlyu9ZUGQosoH0QCgCIKSonw/XRNR28uvqAL9oZgxfk5ejkhBBdAs\nt4eEqmqWkitViyisJ1WLojw8JTS7cOn0u596kCy9cXXvWy+8mSW9IksUCkrNtZ3Dgrv3PfmO0pnf\nvz75+mvXf+ZDTxwuZmma1LWfVwKgzN4QpcYZQ4kDVfDp+ts33jDGlP3MuhKk65qFIbMy7Cth8M5P\nK8VkOp/MFy0ZaILfG78tQnXrLWECWLGKekAsbPf45ub5YTfaeOA7L72hZW9cp52yrdWrW7QVkFmO\nlAXUGBFGgMxCXq5WsxNDrvMNuuzv/cb/9tNPPjZ//Wo/sUtTGKuAWAOIFgwYNEHYhKCojlzQEJ8q\ngBWQggjF7k2ssAQxhkTQJalyUGVjSDqvohZBuFUkAhIARMyzPHQeQHzwLKzKNs2Vg0h0PPkkS9LU\nLRYLAHRESEZiicNo1zVFkrGwNcarWlVSS5YkpqeQCZwAMDdAToERbctmztgsagI+tZEezLpRNlAI\nXk3wEpq2RCmH5WLWEavngLIA8Urc76/ULj0+Ory7c2CkdUh5luepzXqjNijaajhMteVFCEGaXlnW\ndYoqrrCdCoipJ/OyLKyFQbE5rQ5WhqnB9GhaTauFeC7ApDY4o0iJUnYwnY1rD84N81J83S/LLCVn\nqfKCJMOcdu7tXxjadFic3D08tT4iEGEdDdaE1LPPnDlpGzG2X/Dm6bNHk4PWV8P19aaav3XvcKOX\n9tIkyYs727u9rGcAAqNX7heSJsnsuHY2saq93nBnbz/BxFnoCFNnCsgdhLruQttKmlpFDvNJqKBX\nFv2VblqnvuUsmXlO0R3MjnsuW10ddr6Znczwn/xHH7x5e1KUhUp48qn3ntoo3n7jyjue/KD68ZzJ\nJP2Hn3l/03Qrp1e//E9/5dKDT505N3zza7//+ndeFMKH3veei6fPvnXlyt3tw1Fp0kH+zA/99L2b\nL77wpW8djX2ZmADTT//svx/mR29fu/aej/zoztUXXvjWN594z5MWwt3tw8XBUbm6+v5Pfvr44MaN\nqzc3Nk9P926xl3OPPZbnvdevvprn/cPDozNnThf9fpHnx4d7JyczX7fd9KRwThyOsmw2OxpungOF\ng527D7/vwwH4+tvXdnbn1fgwL/tS1fPjWaDkwqXTd3YOv3zl3o3WIySbpv3ooHfBwkqRaGiAyAZS\nR4ONYWimBRjPQq3pHnjsg3/lr5bDHFLCnT/4xi/91/O7EpRsarONLN/qGZsawvZk3lR1Ow9dp76t\nVWwA6PVztFofzRZ1WNvqTWeL9a3Thzt70Jqqq1SRrFVVDhK3ggAqwmRA2BhLwXfRUxELabGjHuG9\nskwbMgCxikHyGgwmXegQjSowiFFUhKAEBCAgRN63XpTRMGBAkhBa0SDIhF20RRAKixpkQUJVllbU\npq5AnyTFwaJjpADCwqDIgAzMAIAoQKoRB02qSub+HQcxCeHpYfpQJpv9IlFmpHhmkAHhJZMTSdLU\nMqsIUD+/+Mw7i7OnBuceKNZWitUNM1qjJA9uy557LA4Suenw+u/xyfbuK9++9eUXtl9+G8GQUuwv\nihESqkUntd/38HbV3WQ46BgCBaOoKCgoGH1QCAigsUpPig7BKHb4hwFOAEC9LxCIpx8gLZuC3z8R\nFZaX8Pv0tfhRiCA2NMIS62WIHsQAgsJS6fj90zEejcu/kdjIXF7qjQKAElhFXnYr0LBKyxBEi8I9\ncXHtw08/+O/+wp+vOvhr/7f/x829edXWYe5PryaPXDplDeQOr7f9X//Scz/+wXdV1WFmXd106ysb\nDbez2SzPnCMlY9I0cyTOWiTKXQKiNnGGkFDT1GWZaVpf141N8v2DSfCwaNrdg0NjKLHGmbTp2tYz\nd1Fj6Y4nU+SwMei984Hhz/3Cn1vdOn/72vV/+D/+o73DmUcNjAG0qrpWmBEoShCM65SBhYAvrJ3e\nOzlciCgogcnIXBryf/DpH772r/5NmWdIYmLoxiAKWIcJJEqeVMhZq0gIhqwyK0dNJCCgCFskiPJM\nFZck3IXYHHQJhYatjacyKAJpLMBEnwsvUWnAFB0hiIggErtMAUGNIWYlg8JoU9c1VWoSSE03bRCF\nnBUvoOKcJQcCal1CAAoeIfEQDBoWtmVyfdq8etgko5VODSMXWZqKtqiGKENMKNjU1a3PXWrKpK19\nkZkyc56g8ZIXRVGMtu9tc105hGSQWEobH1ySafAITOJFTWBhUVLjuxbIkKMyT8sUk9RWTRtc73B/\ngsod87QKwfvCJkODZWEAtNcrj6tZNQ5oNUkNoUIHxllDIORcWYZ6MZ7OnEvSIhEILuIjRNMUwBHX\n4fLFMzePpvXCq5fV9aFKm6GzWTJvmsnR3DrIjV1ZX7+9s5Ontp8k86ollLWytLnOZ40C5GW/lXAy\nrsokTRJljy4BC2hQuy6gBUxym+ZNNa59MIMVk47ybmGp69rAjXShBg5JnhdFIsqdmHpW4//n558Z\nH9ZK/MwPfvLJdz741lvfe/CJ99tu9tbVN9KNBx5557vTrbW9t16bv/XiN7979dyFx5u9F2Y742Jl\n8P4f+lTZL1/76tduvHV1OBiefeSxrcsPXHvlm+PtvZODejaZnH3nEz/0Z35xfP25K8+/9Mh7P3F0\n99VXn//uxz71Uybpbr3yvYPtg8Hm+fd85IO33r4y3j8kl+jsZDoNH/zJn51P7r783ecHwy1QP5nu\nP/DAI51vZ/MZCh7t3C1MYsE7a10/K3rDorc6nxz70GxefPTa3Xvbt/fayQmrP3/xfHV4MD2YdB1P\nF/7GYf3mpJp6miFjoD7IR9Z6Z6TaGpamC9YmKJ2iS2ySDNNHHr9w+8pVW8MB5R/6y39j+MQpLbNS\nD177+3/95tfudGxdAf1zW5hrkg4Agl/M2qbt5qGtW2Ab2iAWijwH0mo2Xzl9Oi+Lxd7h8Xg8m9cr\n5dpkfoKUMLNwADCiDGyElFAVmDD9PuSZlREcRoePAe+DgFjjRCCoJyBREhUCUXIKwOxVCcCIeiWj\nrGAAgJg5oDaswgSgLQIH9ahBTKeiaBQFgQxiw52zKUhzemN9MZ/XrYKFtY31O7v7Uw9dzMKQioAo\nMCKgSpRQxN8JkqiAKlkChstG3zdKThdYJilo4KAKBo1Y42DJL45HCEWJsYqSc8nqcP2RyxvvemR4\n/lJ56qwbriFmjT2XP3gZGKo3vuEmL55cu7n7wss3v/pyNw8eO2sy0U4BjDXksnkIO4eL64twrdVd\nLwtDyCIApMgoBgjvt9gRxAggolWyEW6nsd6AGs8rBYyMrsh5iYVKvD/njIkiQEWB5aRzmZOJIRkT\nd4pAS6TM/88PXX44Mk0hQsWVaflL1VCklaKiBgQWUgCvKgJKgAoNihFowFTMGWErigg5wgcfOP3A\n+dKqTfvZm7v1v3zu6sefenQgi8amBQc2JEA+NJYsAmYpr5Rk0tyzsyoatNfPCbDumjLJybSrW5tG\nyTozGx/l5Wh3/3gxbw6OFtO6RZbEmWGRjycTDxQ6qXzw7EUkS3NFXU2xzNOTWTerFh2gglEEZhDW\nICwKoBSIgwRDrpNWwSBwQnlK4EVqERUGMCl3P/TeJ5+Qib+zZySAS4wyAqAhZxAVUcUikIWEksSa\npm2cMYARDKGRnB4BoQCoIkS0VIQQIxEJoEH2bK3lECLPFgDAEkhARCInGFSEgITFWocUi7SIwoEV\nAW2Shq5DRyhqBRiUVVGVkIAkDlNc6RSCQ2IVMBZUVEQEGbqsP3r+znG3cWZ3MZtUvufSjbW8bUPd\nVKtl2c/TQLh/dFy4ZLUsjhvPTTMc5WUvD0gIyixN1/XSvpemqetT50+LUj1fNI13xhgCEAwhCPvO\nd6pOCCyRgl8dFsgenR3XeDjpSMF7rrraWZOgccADR6OcbK/YP5ogJMzeJcazqGJiKM+zuu1qD9P5\nrJdlSNixgoRekZHpkiR1xs0WswTxwulh1cmdvUpsAhQylVEvz3pp10lVdUY1TeF41szrsFakvSxr\nJSSk/SzNMppVLSG61ILNj4/HKZmyn3Y+hGDytCt6K7vbe3mGVPQdJW0zr1h6wzVypa2rrNCgxiXZ\n7HjcVNM8z4wjJGkbZSJjnQ2QrmzkdnTeFWvffO7K6QuPv/3mdfbt7n7vZ/7Mz+/deOnVX/+HN2/c\n29s5NApfefHXBlnvHU+/+wc+/QN337r6nd/6etO0K+fOPv6epwLLy1//GgVNk/zBhwarlz92/p1P\nv/3Cl3eu3XT51h987resn/7gJz9z7/rVyd5eADn9jsfPPvDQrbevHFy/M5kvzm5uhsHGhz718Tdf\n/Nqdt68pFmgm1umjjz++mLZdCKS6f+/2mQvnuvm8KPoALi3SxPaaybax6bl3f+A7X/u3t9/cs2ky\nWNsaDROo6+FqsTKy04nsXD2sHZY96yeLttOeJUXcX7SbWRY6cs60oXVgADrNit7WqgxMPnSLtko7\n+8q/+dfvf8efTzoJSbH64IUb37qFQZqZ9jyZAYTWEwWkhIwkhVVQ7sA411ZNVVero1VaMZ58s3cQ\nmkaF+ulgthgDREk9IBkViE+fhijCShUkGuYjcDJyhJkDIRAS4bJ/BogKy6oZg1rRTuPWMCK/DSgr\nUgAgYUVSCU6xQ0ZKSIVtMIohDuhARBWARdGQ8d6f31pNrfZOj/rJ8NrO3el8XrdBLCHHd22Ik1OI\nocolEkwARFSiZENZhqoXc7uaQp4moKhgNL4HgQozGWOMAUQNYh2xSmwscheq7cOb945237z20Aef\nWXtiNrxwwfYL57fnX/62QJPw0dHu7snrb9x9/rVm1iISqREJgAY8QG48qPdh0ulux0cKLRFKuM8M\nWd7S7k84FZfAl5hGias+IIWw1C0CI8RytFFQVSBQQEGI2JK4TBTVeAsEACSN69QlfZbUxg8Rgi4v\nfxrLEveL+Uv6FyORKKCJZkMCUhRFJWhUVTDCC4xFZWU0nuI1lbwhEUkNEmFpwRqXSndha70szbjm\nr7+8/d07+0+eXdnMufIO61nt8oxQQ5e6hICSBC9s9f6j//xv/fKv/KPDN9+wSRqMF+2syZyznbaj\n/ohFs9SkmVs0eRu8UV5dW2vbtmsXdWCyiYKurAz3xzMgIMIUky5I07WOwOcrO5OqYa+UhbYVDeRc\nF0KQ5Zw6ggoMWhVFMAlQi2IFTGrrpkY0ggLCYu2XXnqleOaxrTxxjUcJiCgQMso6L5bEWkrLfrOY\nAkkTgiFSQbR2GQCOoVMRFokyemMNe5+mmXgAVAYmtEikKugsS4gUbyQWEWccEfrAhixLQKS2achQ\nkiRd60FUUfNeAagJIDrbdZ1nRVBiAANBg2E11oqAXzQmIcmRFIDZ5f2qm1NAU6TepnPfdYrjeXd6\ntccCBrVI8sLq6igbT+tJG1bXVqcns5uHCxS/MciVQzVnm7JaqGceJRQrUEnuer2Dvf1h4kYra21u\n7m3fC77LbWqdDV5F0BoMXXA9izZpAxADgjmcN4fzmoIgwcClvQSdNSQ6TFPnYFIvjDPDfrqYa92x\nV7XWdb7tGjCErOKcO7NZzlo/nTTZMO9qxcC9QV436kTPrJeBfTVrEotoIU2T4aDM8vTk6LipQpqZ\n1SSrkDk0G4OeIfXcDoqkLPKmaY4WkgAJokvcwcFRWRR5ZhTQOC6yzPRGN27fQ5Cs3PDB19MpG8n7\nA1AH8+N8kHdkKChLXTfT0bCnJoCatgnggxogA7ZtpVwbLaZ3//mvfvPK27dXVganRqO94+3/63/7\nP/0P/6dPbs/9aLSyc2f3wqkL43s3KGCxOrh4+fKXf/NfzPYOTl+8cP70qfOXH97dP9i+8TahFr10\n5dSZC0881bb48lc+u71z0Hiq9q8+8PDDT33kM899+YuTo+NFW7/vwx8bbawe3bl9cPMGS7h85tRc\nzdmHHv7GZ/95NauNKQGg3882T23V9XwyOYG2qxfTleHK/OA4NZaKzPV6GmrVeuWRp9BmX/nd3y6S\nwSMPPdogHE3G6BX8LMmGg83LL1178cVbJ43K+jC/fHmjCebG7sGdVu/67nzhiq7LXEbi1VKSFG7Y\n659Zt3kyvHDWldPFTj3eu+GPF24NA3D/sUdc9mVoki7I9Gi6sXE6aFtXlWXDHXvvAU0+zOpqXtge\nq3pm730/LWZNw21oq9ralIwFiT08EBCMDQRPoBjFQhH/CwBoDDMjqg8qolZiepCEIHheJgsRDJII\nSaTJAKiqtYmqdCwWjVEGpcAeKKoMTNCgABZsqxy9ehw5X4qqAgaQoAncz+3GMH/7xq1ZS4fTCdkk\nMDMIRk+6LJmoSPd5xAC0HONpUHCAl3Jzpufy3JESS7dkiZGqCAAoAiEJsSEbRITAggUEFgYDFrDd\nOXjlNz93+sbdSx9+3+DsFqaGRH01Pdk7nF29fvfVa4u9mcQ9H0LngzEuoO9q9Nwdt3Cv42OhWlRU\nFCkab+PSTr/fDUGKZxII26XMCoiAZclziVwcAIz/OSyfTZb1d42og6WiUeI1UpeZoTjHFQViUBs/\noMtmJRKY5TmrESIaG+cqCEt6mKqagBJA1WO3vFAIKYpQC0FQRQQBvaIEBjKGoncEvQRW/FffeoUN\nTD0QmBUD73r8wcnRDovxYBxrVrrWQi8tqvnMaD6u9NUrb5/sj7NeKcJpkrJ0lGrubJplzjlfV7bI\nB/3Vsj/a3jnor63PprM0yQcDSFgX07piQRCI31qWvA8uwRTLqluMmyoCB4PvBDRJqOk8EQF3zAgG\ngQWsYfYJWVDqFNFCK2wh8SwCUXGJnRARfe65K7/4iY/Xz381FYUkMeBa36XGEYARV02mLiG9H5qO\n3FFS6EKXFVnXcpIk0nUx6BRCANXAnUIAsYZSjZMOVVQERrSOKZR5EkLgwCLeoQudZ1akQNYAKAsD\nqEkskUiE0qtBZJvY0NVp2W/mC2sc+FD7NmO0jpCIDAkLIpJxbVMBgQpD7hqEoy6YZr7Z67caUnCL\nRbO1VnQtTqtwXHlLurmxdXQyG5U5kTPOsooEn5aDyXxBCmR1MhdXOjRUZKlNHWiXl/287HFdZ3ki\nZNQpdHVibJYGm2eH40kC3lk7acL2pO2YyctqluZGpa7zYW/Qz3zb3R533PmHz235jPzCV8z94ZA4\ntAyJaVfXNtpKnDOT6cwHKYvSL+ZlnjvXc94XifRWBgwwrSXNhwsKqYbUUp7Zqu7ahV8f2KI/rIIP\n49np9YFx2f7+3Y3+aH1t0Crubx9HdUWe5ZNxlWTOB5ZaHSJDommY3dtLszLtleC5bjmglMP1zre9\n5ohSWy8WxrqY7sqyxBA3wp03ENQSJsZV8xb/wofO7xzMZk2TJ0m/3zOkhFYgcDN/8PxWlpb3Dk7q\nRZcx9xN9/1PvDM2srheDftlbWbn0rkdH/eSNl96YTZvUURfqJ5/5YN4rX3nupZZ9lhU3r90YH+z+\n1C/8+dVTa1/9nX8WKlzMqw/86B/pD3tXv/3lvas32sQ9+OgT/bVNZH7+m1+ziSXQwPD4U8+unj/T\n1vPXv/2NblH1yz4mzkHnVJPeSlKks+OjfHU1Xzl7685O4Hkv7V9/8/rqqZXFyfFarwc8W1k/vzed\nfuGL37m521Uqw9Rd2BjdvXdyq232G+gUnZePbxSPop7e6KVSqySAkpX9dLPor/dktlgcT0uXNLb/\n6Kf/g8F7zthBkcLe83/nb24/dzdIGihsPnLJFdA1jQU0tqiniyDeV61BB4RgUKwdZuW969eM9lr2\nGnwQZeFYIlYViJYCJA1x2aYQwUixUKYByKrGI2+JsdD4momZfGFBKxzQQPBgnAuhMcYiGhH17EUM\nRIshsxeoxCCAh+ABgqBg8GJb0bA0ySIRdByAXJEnpzd7j11e/9YfXDnuLBjpgtZgg0elILEWoBDv\nPhI9vYiqKooxKrlq9elB+lCfhqmFAJ4DROo/ICAEIUA0qMaAISJ08XIWZGlvIqK4mFFVzNKNxx7q\nn1kjhXoymW/vzO4dNY0nYkTnRZb0M/Vq0tq30xZuVvpWVe8GnYMRiD7bOLVctuDx/oUPYgsexAk6\nACQ0CK1Id/++GO9mCEvWKC15OQqAkZ0DABgBXBE7ihJjpxDVgSBkwSyvi1FabwQZ7l/5demhRyEx\ninw/g0qAEjQAsSovt5sq8b5CCHpfDiUGQeIAlxAUUBh5qcjQFuws+D/7qQ/PTq4H6idWFs0MQm4N\n54WzHMS6uqpWRut5maCEAOqMa9tmfdjrDfptu1Dv015x6dJDic12d64lRI26qqr3dg6blg8Pxyll\ndVtz8GgMEDhKZ21T++DbDgCMo9kiIIEEL2JcoiSm7UJQVkPaYXwQEfHOGh84GASFTkOiaZmbqu1q\nBgeSZkkTOFVqQnh0lHzm/DncuY0IEAPMAJk1okKKzlCeGERBNfEEjVheiGthAYpIWEIgFK9EQOb+\npJrFWuvFq0rqcgRRJbXsEgQwpKgsQZCDRwTrEg0iIoCcZRlzEBGXJwbTpm1CaI0QioJofJk30uXO\nIWrZL5l9F1pnHFqrAEFD2urZH3z697/9xvdu7tvzG7LAgJIznt/QjY3TO7Px4f5crUstWkoa4WFi\nRaVtPCXGGBMEpJkNin4nXfABs2RtbW1ldRi6UPmaFHrDtXm1qMeLuqn6qwNLycH+YQiqYH01LYqi\n9bI9a7y1w8FImtCDRaHI3K4OM2aYzSslGq31QLuTk1oga1RC246Gg4TaYlDOZt6R2xsfWM17RKR1\nf1gkSWocJOg3tjZ3xuO29sY6YQfaOoNudbU5mQGHJLMOwaPOj9pyYFy6cni0V1LS61tXuOOjBQgl\npGBFOiGTmsQ01cy5tG2bore6ezQeljYtsrXVlTsH47odj7bO5CbP/KJpGgndoD8Yzyck5ApIk/6s\natqmyTAj4gBwMp9tFqXdP6lbD2Vv0CMqEhsM5ZZG/WJt5dL29s727v7keDoq86LAj37gSalOZtPJ\ncLC6dvbsE8+8ezE/vvXW7fk8oEVEvnT50ZP9vddeuN2q662vXX3+W5ceevRnfvEXTw63v/WFL9x4\n+97Z9TMXLzz60pe/0M1OEkenLl8yaW/93IOdn7398rcTpH6xNpsfbz54wa0Mxgd7b7/0HT+ZDtY2\ne6srWYJd1w4Go3o6mR7url58pMYwmRxYrce7B3eP3sKkrwtdybO2Plo/d+H6Wze/8vy1g5N6pde7\nkLn1vn17d7rvZeKNGARmb3Cs4EmbqnOp7ULInLXCzWyaOHTiy6KQzrPM77z+9Uce+TFwSC6UGyO1\nd7kzqKGZVgiJsZkAc6hAO6PQBm2b+crmxryd2YA7+9s2y6cHEzJREyigEOIVw5CqqpAqExoyqIGD\nilEjyyEdiQJK1BwCITIzoQEkUWVho1aAEUlEyIBKQLQcLT9KABZRFLTjIKqIYEk7QWBQUoPo2Qqr\nIVSAeP4IUCwUzqvq2u329vZR0yIQpFgwVxqHcxFiCPHOhEQWVBBUWBUNgSoqBT1XmDO56ecJSmAA\nAAohIEYbERrCqKxgNgAU0BuIK0YEREICgOiZMwhYdYcvvL73YkdkAqpVx9oSGVVUYREBJTSGFYMP\nVdB5wN2mmrKtOYhlBPIIBpQhLiQjNnu5hYurvgjxjkqJmIBFZTbxyFxiVJEwwnqWt0QAEmRUBYj6\nnuXU+r6lXklRGQGEdVkNFYlI2BjA0KX0FZclfTYBIMRb4hK8huE+eCbamON8TzWqm5QElbwCgYpR\nlPtnPCGIYKtai17uJ6GedJIt5tPRyvpsdtRL82yYrvazn/gjP8l27dd/7ZdD5+uJTxLMimJ9a+vg\n4GAxm2XWFP08s33Pne+qk+Ptorc6G59gIgjWGKfSphkaFSO2boNB7pUZoc0ZMkoWCsziWVMAa12L\nwmpNV0O3SNMsd0UQboC7TlDCsJe33rceVYTAYVx7m6RVJrRBQFpWEq9qSLcnYf+RfDV1SRP0/ncM\ngyECZVSizocsS7zvjKIqWiSAuAwTjQwD1Wg4iU6KIi/qpiYimyRt19nEIUvouiRJGbokccJiVNgH\nmyQo3hICqAS/rNgb9G1nEqvAPnjvhX0Xo74iagCJyDjEBhBBQLumI0M2LdR7g8jMKjjzJ+uXn73+\nu1/vrRQerIgvklCmhgD2x4cC1jiXJERg2qbqDcj1BpPD49F6YdLBdFyTVKubG+2iyq1r1CDrdO+w\nnk6zfFD5qsyLyWzRKgRFk6agdjKd156DiFRVr1+cNN24kUkIOZJtFoXB1FgIXWrxaFp3SIO8SBy1\nLY+nXDdha8W1zcI6zBPHyt6H9bX+4c7BIOkZY7p2sdbPzpwZnUxr9rP1S+ffvnM8PZitjIqg1FR1\n1gNHyfRoYqQdlakS1Y3uHcyKhGYV4OIwd25QJiJdMw9GQ3+1L52MT6a9LM1We/PZLEkzz3XRK46n\ne6NhmVkzyKD146ygfPVcYY2fHHTGpIkp1raOjw6JKCuzLM/m0wkqokAgjpCM85uraZLYumovX9zq\n5pPjBRgwq7lbWe2vjAZvvvxaSMp5XfXz5H0PbibQ7N2+UxSpLezZd1x69D1Pbt9683D3YHI0QfFb\nF86cOnfx3u3t46Nx0R9N9/Z4f/Yjf/yP9zc2rj3/9ddeeOnW7Ts/+MkfP3V+/cXvfHe4urrx6AOe\n215/pRgNb7zx8nj7gJIB2NnU1w8+86HRxtpkXN1545Uwmw3XNodrazazIrq5ubV/9zqoKbfO7+0f\nLaaT1kPnw+7dfZdm59dzCIcXnv34A+9610uf/+wbN3eMS9//zs21keuP1l999ea9cXXgjQKkwmsJ\nMqS70/poJe9JyDER9pBi23mXurZapBe2Rnk+vn7TNeR3bvjJwhQZm97phx95+0uvEnNQnZ9M0+Eq\n+K5rW0OGW/adZ+bBcHh4cLyyvtEtxtqFupI0SQRAvCcywhxnNwQYOMSlmoIG76NegdkDGkIbp29E\nRpd6WECM6gMCYYy6XYz7DwUANEpqQxAVJRUklYBA6KwVEVYIXTDLACR6ZVBUwkDKgkQI0QKkqAoB\nHUrQIJZMEJm2TSDSKGm7f4+6f8URUAAFQ8DC8bLSN3oqN6WTlEjEMIb7OZHv6/dilYEEADgQkZcQ\n76QOliEGVKvAAgGQBAyig6UBMMR1D6L1y0WRknqkRNizyFEDY4apqkcDqktlhxKAUBTn0pLnEpsK\n9xvyKCCoSACEaBVVgRUFQVERlJaVwfv4VQDBJXFH4wl3vy2Iy2pGPGIBRO8DZIyoooJyHL8J3AcQ\n6DJqGq//AACydFUIRZc9xBoGEt6f0zIQLQ9RRuT71Q5U/P/S9WdBlm1Jeh7m7mutPZ35xIkx5+HO\nY81DV/UMgARIAGp2k+AkCnqQ8EAzGimakQ/SA4w06YkmGY0UBxFqmgQjBAHdQKMHNLqru6q65ro1\n3Ft3zMybc2TMceazp7XcXQ/7xO2W0RhmNzPDMi3u2ftELN/u/v/fL6q1QIFIEr7wxU+Px6cChmJa\nFStUAAcO1NfFzq2Xfu/3/sDayJgqtXZ7b/dsujw7PTPIl65fztI4SRM0kCbu0uWbR0dHadotikoA\njCtMZIqqFs9ENrLYa5kobi+LHLVot1IM0Djci7piS6p1Yl3E4Td+7Zcu3br9P/yPv73I/TLULiBG\nRgKiamqTGmuAylgKDKC8ygsVEvEGSYRjQwoYUFXpj955+9dffBme3I/JAKAKGwLP4owVCUJU1pUl\nIjS6HneTrJ8hBBFEhEMzI0BRrkMFiCxeOfQHw+ns3BmHAgLsjKurKmt18mVhyBa1Fw5xnITaG1Db\n/BSgAwfo0FHc6g0nB8cIQIZEVEGJrAYOAGDRNE8oFgLXJK432ijKQlY1BSm75mfv/XCWS6uTaQgu\nknYcWRfnyHGWGaGeUC0qwltbPa8SGRp0O0kUPT0dl8Xy+pUtYDEWRdggEYGQWRa+zo97G50osWeT\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MF/1Wr5W4PARgm5ow2Bo8Ohqzh36W5VwX5XzQ7w96SW/Qe3ZyGkXx3JtRrKo1OfGicToI\nVVn70I3NaPfy3Oskn/qiTCK3mC9Ta6PYVksuq5Ji1+n1l/OV3Uz55mdf+av/4X8w/ugH3/ud31tK\ndHZ+/MKLV9pxxip+Pk8S/PJf/w3j+Pvf+jMWyrq90Wa/1c0Wyxw1R5N0En365O7l7d3tvctstSxC\n3BlVZXnl+U+dHT6888MfjDZ3PIS65Otf+HJ3s/X2t7+1omzYzkzgoHrt9nOrxfjtb/xwMZu1Wn1w\nSaebvv3H/+xzX/2V2y/TvR98/epLr9diPvjJjwWSfm9YTMbG4aXd4eaVq0mrU5b52cnZ3UfvJmn/\nhRtkKdx+5ZXB9taTDz5+duIfn81Jicvza/1OsNmHD1dVkJ00McASxcu82ve6o7pZe882cSFIwGCZ\nuJ4Vy6lPIowSKkpLdXH47lvbn3k529qgzet7L1x9cHSv0pYyF8uqM2jledFutbPI8UxDqBkg5CGm\nxFIEgAy6lpkAIqmCEl6IDtddADf8JxY2ajwHImDQRtehhAaaeRo1BYg+sSKAAtmmMIXgsZE3ilg0\nQKQqDFLUtY2NIaxUhcUAlUCgSqSeNQgArUWajcZNG5O84hrmCCTCTILQoErXosbmv6Z6rlk3QA54\naEzbijHMAWWNjW5CFgnRopEmEFzZX/BlGtGQKlHwwRmDiESkDX3ONNdIRo1q46eMQIMvqubJHRAA\nTVX7Es1pXY3VLIMHJLhwpzeedQAihIuivU4ub6S6phns4nr8K6ZR0AOJRkikEhDrT4qWIK0vuunp\nARWiZvG5Ln544YdYlz1tBsp/sQR+osH5BE7TaGEakyN+YkdpUHXrJ4OmY12PZpttMQIDqGClXCsG\nUlYwnpAgZ/hf/7XPv/v+h9ZZLENkE6PcyXRrOHx0eP7wf/gvBv3OarEkpatXd0Dh8f7HN55/iX34\n4hdequqq1Ymaezfcu0btTq9/RTXjyenrX/7LP/7ON/PJhEyaxpyvQjdp9/eiELw10UY3IRe328nZ\n2WI2m6UGZ1UlQk2+EqAErq3LQu1F697mxvm82D89NEICVAXPAdSAFRuBkApEceAg4q3EjtbPBSrW\nIntUb+FgPt/JEipKD96yqdE7g4poreMQqsCxdUSA6IL3ZEAa/isJIiGRQmD2kUtAmnxtbvfb+WIV\nx1EdaosGAH3wBBYQDUjQYNghB0BCMkQkiFVZgkIapwBcFhWKWmPRiHIAURHG5vtLxMQJaAmWhMUl\n5EMgiHyo1Kvttv7kN/+rybzKhkObZZWoBTQGMUmcUCxxrAwBydYhlIHS6WTcT3vqkgQ4rnwZKmCP\nJKsK6lAZikNdGANFzYqGMPRaHeOi4JcGlUHqUAInRZUv8zxG6nRaq7LIElt4AAVl9QyoupPGaUSr\nVdWKdW/QWvq65Pzq5uB0Mq1XRWs4OjpbdLOoZbUUqeu6E3eWdR5nsUAwAM6kq/ms222fT5dZCLeu\n7IxXy7oOy7IadfsPDyfE0u3a6ao0ZK+MRhvtaOWLkiEhHHRtXXOomGgx2N7ZP5yJ5yR2QVDrqt/v\n9XqdLHPHJ6e1tzm5NkuRzxNLcRR1u4P7j54q8yCLtob9eVmg1/p8nrpkNjk3AEmS+EqKVQ0iILAs\nFla8/dy//Itf/ld/4/2v/ZP3vv69Dx6cUpr8pb/ylV4/eXb/8XIy6Y66v/qv/cazhx998P6dinl7\n0H/jM58xFibLMu4PiOjo6WP2q+dvXt177pXJZDI/PPEcNne2u1uje2999/DJ47TdwnqF7e7rX/3q\n4wfv/Ox7D588OxvtXc7z0B+MXnrp5Sf33l6envhi2Ukii2IMnT199OILLx/efZ8Br37uF63o97/2\n+53+FvpifHQax7GLsv7utajXVnGTqnpydDQ/n3cH7edujC7dvFks+cOfvfv++09WS7+Vxbt7g61h\n/4N7T9999HRR0W5GmTVzMfcmq7wGj+bZsn6xi5Oi2Gk7CUIGLbokwunR4a3XblWbp3XpIyjD5Pjh\n9z947iuvTbq8/dLt/Z/cqZdWMC9zl6RJ5Iz3IeSVBGgSayeTWeKMwjorl0UIFVSMiQRCE9gqzNBk\nADUHpbAKBQjNesOigjESGMB6DhakEhGuW5FThUYiqIL0CZOSFICaIAsB1BBYBAygGF+HJmYW0TCA\nIVIfWI02SEtVZlh75LSxyAkSsIIggagSiq7B2grrYiLQdFgAjYtcUYlMCJk1gEYCehTrnHJAa+VC\nMGKMU5UL6wheJB2piIKqIcvMTVOKF8CBpv40WhVofIHN0JF5PZ0VDawT708rmbFWiqhgGgUmoKrQ\nOhgRjKxHYXph9FMEaQB33FDUCFUMoEcVVNdsDxG8akSECGY9DkYD0Fz0hRdDAS4iJHTdI1+Uxos/\nw0UT+T/7wOZym5epa2UqAihDI6VZV2FRoAbv1bSb4FW9SEkQoHnngIwAkmG5sZeksHX/8cE0jy9f\nvpxGuHnp6rsfffTsycMr13fnea3ChsNqXkbO9NvtzU70/HMv7N28oa0r2dZtX1dRMlxNz9PRUG0K\nZd5ub7/3o28uF6ft3qDIl8a5tAWGchN1DYRPvfr89dsv3Hl4+OTjO1E37g/bWkOvFYIkD08OWMUA\n7ox2IJT961c//OCj+wdVXs67Lr15eftgPDtbVGB45ZlQHUFFBL5SUTVUa42KhlQDAnglF5Gg2LvH\nB1e3NmH/AATjViQoFJr0QwAGNCQiFglAjLMq6gwFHxAFQUWqNEnYgyI3CDxAN50sDYn3QYSTdm+1\nXCIoqBdWih2oCos2yWZg1BAiEAsiCoExLpQVGINWuArIjTMXjSEARrLB18BqTFBh8WgIRT2sLTh2\nqehjKGuhoo6csQECM0yKjWFmrEniVJFFvVg3O50ZclUdLHESx8HXEtjX3ostymARiMQbDIxIIL4c\nDVsAYbWauyxd5BULR1knz/35ItdaN7OUuFJkRtwYdr33q6Kscj9IbRJF54ulNdRKWzNWFH/70qX7\nz/ajuC3UPj1dtBNIiIwDi64ouajKKIqLmkPu05ZBgCTKihASKi9f3zyYnudLYQm7o8Gzcd6yvLPX\nXxQalSZ1Ouoky9V5t9W2SJ3OKISK1Qjkg97g7HQGzL2slbMv61XayfrtFhl8eHiqJjGR2yAtqooA\nghSd0XC2GpNgL7Y7u4NlWVsbYdSJ07BczCxJJ46LildF2fBkyirvZHGWGPupL3/hT/7e/+3+vXv5\nUm/fuDK6fgWlOvz4fjnOb3/pC5/+7Oc/+Mmf3Xv3btQavHj95u7VS2Lt9ORk//DYJHECYXswins3\nB5uXDvbvzien4nXr0nWR8sPvfL0cV4bg0qUdr9hp9370rd+PyFaLvN/pRBief+nNTr/9vT/5vWqx\nHA6Gve297UtXjh7uV8tpC6v503u9yzcGl289/tmPHt+9z56hlhDqwajbbkXZoG+zTja4/OTh/cf3\nDg+fja9f2bz+3LWXP/Wl/Yd33nn7rSePVxbh6m7v8pVhpz34/vfffXyyiDHe3MhcDE8m9cfTeUHG\nRQCgS+FTTbqsNYtT8eylMAKcRM4zuU7PJoehpBjt4Q++d/uN5wrjXadnugkuKsBYWX0ZXGQQCMSJ\nCeoBVNPUKbN4YggGjCFVMSwWkBGa07vZ6K2h2M2EiyiIGgAVZSQSDihWsJn0WUuCJgoNhaWxGJCo\nAKFh8Ig2SKDIiSgHj2Sw4U8TaeOS4IZnIijCgKCsYBSAFbXRjkKTkweEyNC0O+h1TV/UZvYJTY7R\nX8hURwBQFkXlxEDbKgGwqiGjqBQ5IpKgqgBNrj0iklMJIooEIGAMIqhpjntsRJhGQKgZ1jY+k7X/\nQAGJlAHBuiiEoIpeNQcaez0PtAgcLiCgTXtlCEGFABv6C0hjndB1IVxfZpOjKggBlVg02MZmgpaA\nRJGARLkxokkT84jaFLpP/meCBKDNjcRG5KQGURs+LKJc9Inr2SytZTvrnu/C0bHuNS/mt7zuuJER\n0CAiBNHG1qiCpapHDWsnPxBAALAq//pXPvf7X//hX/7ypS994Y0//pOP7jw7COQ+eHAKqINOb//x\nIZF1EV7a3ipXM3BJq7f383/93+xuDbn/avMSLIAAZNnldeObtpKt523/XZoc+6piVUUo6+Ll1z61\nOHv8uV/+G6/8pV/zbkDvv/fciz94/50Pj49/jxEqgNPpiSUUURE4n87Koj6a5DuXru0/e+Bcy6DM\nlkVV1RutZLIsGx1tXrMBrICiiELNAY2qGDABxRChMAGgoQf7Z7033yhOThE9gFhr6qKw1iGpczFC\nsGQai7x4sVEURBonkpA6cnVVK6uLY0APCKiMIkQOBaxzRb1K0rgqCsVGhwumyVSxSKqNz4h9TTYN\nXJKJ6mUdRVm5mDibAq4DOk1kVFgDm9ggEFkgQUEy1il7BQghMBKZ+KwIUacDJPPZMkujbpqRSjvL\ngHSyrDGWVn8wmS6X07LKfW8QFXWdRNH0fBGTRmiUDWlIHZEhQsjrxgsF7VaUhzJqdcVgXrOCLBY5\naDEtgwHNCOtq6Qzs9rtRqzXOi1Y3TQgZXeX5bF5hKDv9wbKoo5oHw9bZYt6J2nGv8/j9p3vDbDRq\nF750UZqkSKGKo3hZ1cu87PdSl6Uns1VkMDUwHG5MvT9bGcN4c3fjbBUWq8XVwaavcTKbtOJsq59M\nz88AvGnX/X5/Ol4gEVdVp7t5Nh6nFpLYLTmUXgedbNBNXav3s4/uMtqdzW5KtljOjIHIapz1zs7G\nRRWGvXi02Z+sltWq7I6yIKII/VY3X4Yi6HKVG2cNSmDe7MbdVlo6sP/i7/1myPOdvau3Xn1Dw/Lj\nO3dSTMTrZ/7qr197fuPPfvsfnc6KT3/x50NYbW3vleVsevJ0NfW7V58nmQ5GnWJVZZ30yb0PJ4cH\nab+7c+PacrkgplbWp+qoNdzRyMRR9ujBg1baz3rd1WJxeXv39hd+rlw8+8nX/8XRs9ntF58b7exi\nlX/rd//p1t71K5c2xwePb37mi71L13/0zW+cHZ0Xnl588zXH8zA57+104/5WNtpblfU3/uh3Dz58\n6j1ubiRXbtx+/ed+8e0//mdv//TedF60O9GV7es3b12ajCff//6PyQ0GrTqNPEn44GDxMNfaInu2\nxmYAkwoe1LCHsqp9xzmQwJxrIEvu4fv3br15u54ugl/weSXj/Wfv3xm9fqU7uLr3wgur43dY+szB\nez8YppOzvKprYAQAFlnMVoQUhJEMWqMCooII1tggIuLJGJCwVh8CigZDThQMCmsTMISsBghUlAiV\nwDDJ+uS9kFUogopaoIBNiL16D4AWrZcGtCYIDCBEiIwCIgiqaAAMWWQI2gjvAZG8cLPekjX6WVWl\n8fMRUlBZ63zQgAqhCKKANFlMzYAzsRA36aYqymIT52KDxgFL8L6qANE0CX9EhhvX3FoR2dg3wnru\nq8EYt74JHIhItRlSAagXNM4YUd/qZGVZh1UxrfSg1HPVEshCI9FEArAXw9BwYWgwppFdYFOaCcE0\n74ACATY2dzFrW0JhIVWODBoBRGJVAbWGGi0umKYZbro41Gavh8aINH2krAtlYweVdSenDWBmXT2x\n2V+KfhJVv+5jLx4xjEBo6HvrOg6MSEwBpAbxRNo8HxCQrplv7PVf/qVf+Ke//eGjR8/+5n/yf335\nLy/+9q//mpor3ZZblflgc2Pg+0+fPE6y1tGzx6+/8XolQmFazY/4xuty8er+vG+9eNyJ4vbWcFMX\nk6PiURxHG6Ptu/c+mi3m126/sPviL/lsFwCuvPzZYtg6nY5fffXFDz94ILOQxlnNixBC8JJkMSBW\nXO8/eypqjYQa6HiyMMbVdbEz2rx3cii1IAowRgbqAIIoEkQQiQ0aCY2CxoOEVQmVS4LRVuLSbroa\nr9BFYAAYgL2sRSmAiFGUeO8BgaUmMsK1qBXQ2FqRQEjS8BKQLcY115FFEzAvFoaixnmhgF7YucT7\nypI1YMQHroNFL2QQkAB9vlBWL7UhC6KILKyWDBtERDBgrQvAkYmlDtYaJFpxmbTSVWU/2n86b/cd\n1LubG6DmbHL23OWRsbicc5rERusoSmeLE2sjF/t2lnEws/mUGSKHbJ3UeRJZTygUypzQWQSsOUxL\ndc5Nq7zT62RJnOf+ZFqMi6oT2c04YpEkpqydxsPh0ckhoSOuI+TKIgkOM5tEQwb0NTLy5Dy02yaJ\n5ODp/kbH9fqO2SedLrFA7Xv99mRZs9dLW22N0/mCQ1lFScw22j8cezWg6GJ752Q6meVXhr08BAOy\n3Ws7Y2bzBSF0OnGv1326f5y6GAyUZM5Pj1s2Ttrt0ufLJXd7Nsu6TDI5H6tLXPCxl9n0uOPiOLNZ\nfzidzIuVpBG1utnSqzLGaQsBufbdTrvMFy5N6rKyEcZRFGqOnbWRgU66f/+A8kI3br70S7/xb2ad\nZHG+aKfZ8MreX/87/8lwO/nd//F/zFfLV567qlW5vXdlujgeT3MPUWdrGKrDVisrckVKjx7vr2ZH\nu7f3dq5ePz08PH/y8OmH7zhnOju71B3Zzujx/f1WaziZnx4//bi3vXfrzV+88/aPv/eNH56Mw/bG\n6Pjx0x/8yR+/+/0fbVy+/dIbn/FV9dl/5W8enp/9wT/+h8fHZ+Vq9vzNq9XJfcnnyWgru/zC9c//\n6nI5f+tP/2x87+TsdPbG65d++W/+S698+ktf+we/+fTRGYT6+s3hFz5984VPvbj/9OlHH97f2Lp+\nfPB02G/1u539STgKzAZJYcs5i7ASMcacLMoFYM1aQ1CGCK1BqyLFyWRxNDeZJWO959jpnW98k1Zh\nUft0MFASAjDKdelrJpY6sHpVa0zzOM/KxhgA4MBN2IKA1CEIM6FRAUORsDAroUW0LCAgoAYBhYyq\nIjACKCmrqioDAygS4ppA0qQqIQixoAgQOUBCRCWJnAVgi83DuCgrIDASimGV0LgqUBTRIJlPhPpN\nelCj+WgkqJ9IGRu8CiKgBFAAo4qElhABlVmd6simsbGCXk1TGpyJEpMajcnEcRRZsgTQwLiCI2cM\nkUEyhERAREQBGiAL+YbVoaiqgZnVKhKiU7QIqIIlrPp7wyA1k55JPRUtAiiobwT0QEDr8AAldM3u\nzaBHFSIAcAAWqKn21HBTEYhI8cJiCCqizJAoRAaM0chARmiMEqkhsIpWERUsklE1gCpNjW80PkKN\n7hXRIFhVq0j65yGF9OeLQ/2kTVx32+vfQBEYVQibrbIIBAVVqJRzlRpQLhA5Zs27AQPWAGBxb2PU\nlnq1nNXR5Z//4pd+weISMTo9O3jy+PDxs2ci5CjYhCaLyebGRr+TpL0dMH368wL4//ehALYVl6zL\n1bSsirIOp6fz0ejSMp+Ndi5/+JM/owIUAByQi6+/8OaXfumvvfLaZxOMi3wFnh2YdmKubY267dRA\nZFCJtA3ldrfFEorCLyrz4PgweEXR2LrYkEUEZWFxNnZ2HXhliL2KCgGBAag0OpjPt7Y2QoWWHIka\nAOeMSyNFNYjOICGF4LHRa4F1zlnnwJq0lYkBROtDAGBAJBcFZaPkKy2rYCHi2jODdSQa4iRRrp1x\nhIwIYKA9GmpkrFP1HNgzMwChqPigLKQGEYWVyPm6NgAsGmpvE4cp1RbEWVA0cXI4nc+CiLWe4fR4\nXK7OX7h6mRCWqzLi2th6llfTyarX7aPlbmZX40Ve5YZcN3ZINF+t1BrGKHgmIDFIdUEItfetNC0Y\n6oqNrwTNeDzj2o+SeCuJ08h2OmZjFJOlBw/3nUn7Wdxu945O50VZZzGgeBHNi5yIkDFrmdFwdHKa\nD/r9NDEhIMaJM1mxXKGz56u6WK6yri3RTCarqlj1e+72lUsJhNSlsXW9fpZXuFqGS5sDDYSo/U7a\n6yTT5ZzItmOzMRw+O5+TonpVQMe+lUSdjbYYPZ0vFLwE2j96tn94VtScmrgbZfly3GnHrQ71N3Y/\nvre///Q4EU1jnK+Wi8kMLba6vTz3VVH4spBQofp2O07b/cW8cAhJ7M4X5d3398NM6I1f/tKrX/3y\nvQ/e/v43/vT8/Gz7ynOXrt/e//itd7/3vdGVK6+8+eb29ZvZaCNnXdV2WdVJ2o5YW90N1+75oPP5\nxLXjy7dfBmo9vf8QWLYub7zy6Zc8yOa1V4MPs/3H7TQqZ2dxkO29G6PtnZ99558dffx4drSIBMrl\nuFhNLu1duX7rWmp9jss3/tq/+u4Pv3f86Jkjq/mq1Uqr5UkrScDZK69+5tIrr3/4o2/+7v/nd/f3\nZ7XhX//3/uZXfu3fCp6/9S/+0VkOaPTaja3Xnru9tXP5g+/95Nn+abc9enLvnc9+7qU4dR88PTkM\nNYD2DFxpdUvxXiVGJCvi4NjaaeCyVjDIJEG4LqsE0sXZPNm8wXUlqmAwzZfj+0+rivpXdrLNxCgY\nMsiQzxZZlIKistaeBQhUm/h1wKaGqYAaNBYJkcgSEiCKtcY4hCbXCBgFK66rtTcbEA2CkgARCYKq\nNkBDRFoftWSAGlkmEJBeSClUNdRBBRSNQSQyDdMNlZAYgUDQXACoWYVhfZ4iICmAUjM/JABQFGy6\nDoUGiHURFaSC6wQJBTToUDP1Btipk4BK5nw+7u5s2ZisIxsDxpEYRENoCNEJCjeQzcYUb60QElGD\naQUgERHxAMQCIdQheBYBEMUgACxxp9/vX91YoJvkZqZaNyxRggqlImmknURkmszbRmki4EFqVVC0\nuvY1KGEjugURQWAEbXIERRJnmlmqAUREQTCKjtAY0EbY2hAImlJFzR1CIiIDxq7tkXDhBrzg1KxN\njZ+Uw0a8g41xcL25VEFlUG5yOVSbXjCw1iwlagPzDqKhke4okEKM0FbqEoxP3s8SHW30VtMZRO0X\nb17bbNvjk5Nee8Nzzerb7XhrZ6fT7SxXxenZqYfQ6Q4+GTT8zz+aMcT1V/7KlZsvtlr90uuqns6m\nJ3m+enxwhlz80d/7Pz75xm/zwYdRBKPrr3aHW91ee/PybrfttjrJdj+9tDU6n87mq0IBrMHLPfq3\n/o2/3E40iVtsQx2q2osIGAPOeDToRYEQDQUOFg0oKrAoOQQ0RByG3fTh4QEjAFjbdhSjjak96iSp\nHWwO43aCBhnERcY4g8agRWuw1UoMAir4UCmL9yUgqRpgBg/iA6MoKAirMBEF1rwIwhqqGhEFRRQ4\n1MqwnM+Vaw5KBkkgTjIACd6rCpIYtwatG0INiBbJgIviYr6CAIiqBimKKqTDVaiTyDmTRKlGrpXG\nDuvGBWtia9I2RZ3lYr6ajBPl/nDzdFHOz2biK1ZxWRrFzrPM8qJWuyg4trbd6rNIr5PM5vlyWbZT\nC0oPP75fC7WjbCuJiXRR+jRNk+EouKjXiTu9yDgzmy82ehuGIZ+ulsu8yAsXZ5OyZPBni+XDg4Nu\nv1ejzVodtck418f7D8TEk0VZ5avh1uaqFGEyDjba7tL2xmp57FFLKdIEi2XFWm6PsrooIlu1nEGg\n6bIabmymiXU2Gp+MY4Th5qbL0rIo86IeDvrT+fLBk0kSpZf6/cKXsxVU1EZRCzw9P2m5yFmwSXrv\n/iPLstfvkOVV4VfzvNXpGJvMZ3NreHu7F5glcJq2kt72suR2t8/kliuP3gr7G9e6du/a9a/9k38y\nPTveu3T5za/+PIf63ttvL1aLwag92hpuX35hslrly+Xp/qN+u7/ZacfWb+5eXfhqtpirL5NWOtzY\nnoxn1WqVJTaO03a7Vaxmw+2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3T+7++CcH+4fDjR6ImT4+1hA2X3phcPNlT+buO+8c3Xt6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NU7b9w+nYx4PPnlG/3dmxum0W4HfpwWaVHmcXU+mirjN8P0q7/4xt6d158fzc6P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\nmZYwq7gAZJACJCaMHVw6LkQcggBprjv5SCJGsPxZRgvBIJUCtSNCC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Q7MhorFiyhFIN4r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0s+IFmCQSACUCSF4AMQBEIGBvkBq7Dt9ZfxABScC9R7I16Rh/10rE5lqyOUeMoG3iO4A1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\nSUVORK5CYII=\n",
-       "text": [
-        "Image([[[ 0.08235294,  0.05098039,  0.03137255],\n",
-        "        [ 0.08235294,  0.05098039,  0.03529412],\n",
-        "        [ 0.07843137,  0.04313725,  0.03137255],\n",
-        "        ..., \n",
-        "        [ 0.89411765,  0.71372549,  0.54117647],\n",
-        "        [ 0.90588235,  0.7254902 ,  0.55686275],\n",
-        "        [ 0.89411765,  0.72156863,  0.54901961]],\n",
-        "\n",
-        "       [[ 0.08235294,  0.05098039,  0.02745098],\n",
-        "        [ 0.08235294,  0.05098039,  0.03529412],\n",
-        "        [ 0.07843137,  0.05490196,  0.02745098],\n",
-        "        ..., \n",
-        "        [ 0.89411765,  0.71372549,  0.53333333],\n",
-        "        [ 0.90588235,  0.7254902 ,  0.54509804],\n",
-        "        [ 0.89803922,  0.71764706,  0.5372549 ]],\n",
-        "\n",
-        "       [[ 0.08235294,  0.05490196,  0.02745098],\n",
-        "        [ 0.09019608,  0.05098039,  0.03921569],\n",
-        "        [ 0.07843137,  0.05490196,  0.03529412],\n",
-        "        ..., \n",
-        "        [ 0.89411765,  0.71372549,  0.53333333],\n",
-        "        [ 0.89411765,  0.72156863,  0.5372549 ],\n",
-        "        [ 0.89803922,  0.7254902 ,  0.54117647]],\n",
-        "\n",
-        "       ..., \n",
-        "       [[ 0.74117647,  0.48627451,  0.30196078],\n",
-        "        [ 0.83921569,  0.60784314,  0.42745098],\n",
-        "        [ 0.77254902,  0.55294118,  0.39215686],\n",
-        "        ..., \n",
-        "        [ 0.64705882,  0.3372549 ,  0.14509804],\n",
-        "        [ 0.63137255,  0.32156863,  0.16078431],\n",
-        "        [ 0.56078431,  0.2627451 ,  0.11372549]],\n",
-        "\n",
-        "       [[ 0.81176471,  0.58039216,  0.4       ],\n",
-        "        [ 0.78823529,  0.55686275,  0.38823529],\n",
-        "        [ 0.76862745,  0.54901961,  0.38039216],\n",
-        "        ..., \n",
-        "        [ 0.60392157,  0.29019608,  0.14509804],\n",
-        "        [ 0.57647059,  0.25882353,  0.12941176],\n",
-        "        [ 0.56862745,  0.25490196,  0.12156863]],\n",
-        "\n",
-        "       [[ 0.77254902,  0.55294118,  0.39215686],\n",
-        "        [ 0.76470588,  0.5372549 ,  0.38823529],\n",
-        "        [ 0.75686275,  0.54117647,  0.38431373],\n",
-        "        ..., \n",
-        "        [ 0.61960784,  0.28627451,  0.14901961],\n",
-        "        [ 0.56470588,  0.25098039,  0.11764706],\n",
-        "        [ 0.56078431,  0.23529412,  0.11372549]]])"
-       ]
-      }
-     ],
-     "prompt_number": 26
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Image Manipulation with skimage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example builds a simple UI for performing basic image manipulation with [scikit-image](http://scikit-image.org/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.html.widgets import interact, interactive, fixed\n",
+    "from IPython.display import display"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import skimage\n",
+    "from skimage import data, filter, io"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "i = data.coffee()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w.result"
-     ],
-     "language": "python",
+     "data": {
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+       "SUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "Image([[[ 21,  13,   8],\n",
+       "        [ 21,  13,   9],\n",
+       "        [ 20,  11,   8],\n",
+       "        ..., \n",
+       "        [228, 182, 138],\n",
+       "        [231, 185, 142],\n",
+       "        [228, 184, 140]],\n",
+       "\n",
+       "       [[ 21,  13,   7],\n",
+       "        [ 21,  13,   9],\n",
+       "        [ 20,  14,   7],\n",
+       "        ..., \n",
+       "        [228, 182, 136],\n",
+       "        [231, 185, 139],\n",
+       "        [229, 183, 137]],\n",
+       "\n",
+       "       [[ 21,  14,   7],\n",
+       "        [ 23,  13,  10],\n",
+       "        [ 20,  14,   9],\n",
+       "        ..., \n",
+       "        [228, 182, 136],\n",
+       "        [228, 184, 137],\n",
+       "        [229, 185, 138]],\n",
+       "\n",
+       "       ..., \n",
+       "       [[189, 124,  77],\n",
+       "        [214, 155, 109],\n",
+       "        [197, 141, 100],\n",
+       "        ..., \n",
+       "        [165,  86,  37],\n",
+       "        [161,  82,  41],\n",
+       "        [143,  67,  29]],\n",
+       "\n",
+       "       [[207, 148, 102],\n",
+       "        [201, 142,  99],\n",
+       "        [196, 140,  97],\n",
+       "        ..., \n",
+       "        [154,  74,  37],\n",
+       "        [147,  66,  33],\n",
+       "        [145,  65,  31]],\n",
+       "\n",
+       "       [[197, 141, 100],\n",
+       "        [195, 137,  99],\n",
+       "        [193, 138,  98],\n",
+       "        ..., \n",
+       "        [158,  73,  38],\n",
+       "        [144,  64,  30],\n",
+       "        [143,  60,  29]]], dtype=uint8)"
+      ]
+     },
+     "execution_count": 24,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "png": 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J/Wah/7oW/6uKaG8LNCmiSAiSEuJbgUQB1IN0ASkVaYBQBXSjVU0RkbHQ9voWWYSLgpLW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\nlv2b4XrKUd2aqdPD4+nq7athn+v7YwWu317fXQ3P7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Kn7CD/eWH+wL+SPl97NBPzyy2OL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G/LZDz0CQimcc757na7r7yqOgz6xctH+10pHL/865/0LjffvXt7v0qzkxe/ePzi5bPlIpfV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\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yzt78na/sf/Ub2391ufro4vIP/p8nf/Z/+eTDH9989P0eS2S6udwPtLEaCyGzVgQQV3Mv\nTIAiKc8iwK0QoQG6u+0G9CDhEHFzgCHUIcxdKmIg5BTODaWUcjctOt0VCdxryi0LE4TpEK6mjg5G\nRCwcAm6cBALdlMnNFEiM3SG7K6cUiOo1bGQSVQNQZDMdpWkxIXDLwkDE2HjUICSkYAYKEWZJweTV\nApQxwC2YGNlsRDQjDKJr+KBIGVfU96Hs7JSzAW37dQZiBSYyV0mQU2pnuaY09Efhm3BV1XGzqZen\nqf0IllV3p7P53rCZ9UNJXFJKSK0I/In6CiGIAqd8dkCt6OiIziBDqeiQoVmvL8ieJmZO4iTy6EyH\nnmZLcjDhtpXqta6ruBZv88Gi2/R9QNy7dzLr0v7h4dGrJ8uT+5ePPjvf1Bebcb0ad1fnpcfq4gwX\nV7sUumdBY4k96sn3bh7MO+zavedPTl9cbIByP8TBfnPjoPn888dtk91K09Lh/o3RhqJ6tdu9+95r\n0e8ePTvfDbg1rWpHi9mNGydnl6vNZnVw0H71K29tzs+/eLhZXznkfOfO4f487c7Pzzb1YrNpmvnJ\n8SJJt746tTIip+VivtutNmfnUqMWu/+Vry5v3bx68XDcXOwu14Tzq7LtN6s0X4TVg72cdHz4k4f7\n1Bka87L05wYkwvuzFDoGEAZahPYjBFlEhqgTBIkIm/bw5s3N6gUoar/DCmHGkwGcxBGnzQRzNlO8\nBlLhpPxD5D85AvE6eQcRYKbMEjFVIDACichdAUgnatQkQwonIAAQpnAjYlXEqOGBRAhw7XkHcPPJ\nJKsaGBlgZAwDM5f11vKSusyEZk4f/u4fv/3zfzof7XM7o+TkBUUieEJKIuKUmA0DxAnXSDhh94XT\n4a3ZjfvLO/fGF2dgRO7XEdZpf4baZAkzN4Dw2aJ1VGHJmZELhUzyPWB0s5yb6mU6xie2DjIB2vST\nCggKtFqFxYmECYWtTOPE0DrmtiXQ6oEQBk4oKA1EiGnUSTZjwS0lEQD1kahxB0bUfouUKOXpyHeG\nAIJwluSOjFRtBBFGoqiGwG0XkzMHiRjCCBJwyoHCxBRmtk2JLYCIONhYSXJMdZoIQRnHniVZYJoM\nt0Qg4dU5d+EDqTorATN1NRRJmkXrqsBk/QgR0nYoFSdCFbjkZHUADJQcNpbz59licfvVvr9Ac4ey\nvP/e1effTU1SJjJjyobOibp5KylVHwGvoyqSUtXKxJCyhYYpMhPF9QbPDHVELCANEHkohiMnhwD3\nSZE3UUkJJFyIIDVNszjh7hglIwUCI4RPiRpSiCYQIKb0KEA4hkUohGHp18/Of/xHPwZIEVhGSwiJ\nXCYiamCtlRPFS530dJpO9XmAYGYP55emX0JAEtXqESTsalPfMSG5mwMIIhBPnxzgfP7J2Sf/9B+/\nO1/6a/N3//Jf/9bHH/CDjx7/9u+9+/Ufv/sX/vpH/69P3uKmJKk5bVa7YdT9PAfCqDqOGw1kMG4l\nCTOABoWDY1TkKBqgIgmIwgDVHI0gVRALC1fmJLM998nfN7A0SAKB7iOjEwahk6up1jqq1kQCIFOY\njpMLs2q14AgAUyZJQsbinJlFtbo7IhNJgGkpLJPxY0iuWLYSSOAOgciSiIRhIl2hk4ihDHXLUMKR\nIUWtSElEXKtzALQO4VYJc25mPo7jMHiMedalds6btu6uBKkvfUIyajRWuzoOffF+XUyRBFPlhKdP\nH3dXO0/fGa5GijFyirGqjR4KlMFdixILO3J0pdTqo5XwCLMBM4VrgEQwYRRTAkws4NVDJS3kareZ\n7e/tv3rgl1cVYqUonPtx281mkCAwWOjG4XL/uDs4PNi/e2vRdZ9/7/efr/xqM5xdXm7PdkWRch6L\nXV5escXJQuZttxv5+Xq8ff/w9dduPvn0iwdnw2rApllE2O3XbwjgZw+f7Xdd1zC6IPFuu3226XcB\nJ8vlwy+ej+qjx2LeHo6Y9w8uNqsHnz9oGn/1/s27t26eXpxGJOeZz/s3Xr/bga/O1v2g602/tzi4\ncfPE1a8uX9RaTm7cbpv2/MWDzcWqa+fYydvf+LUm+8WTz/T8cnt1VWRxud05jkdHN6GR+6/cevH8\n2YtPH7dKwoyUdCxEgpzyTNqDuV/tai0eHFEtIAKyJCL0ogGukr72V/5K5IAf/PF4ufJzrz6iQ0TU\nsCQCDuHgZqrKPI0WjSiHj4AyHYpTlJSZI0JrBWJEUlVmYGZ39QA1ZRaISJLBg4CQEIgmkLdahAWi\nM6OZT31D8EBCJnYzZo5wCJyAwO7mwA5IgM8ePbr/9snlE3xxcaEOl6ebD37/Dw9evdO2h8Qt5RQW\ngIQ8PQB5OqoRMSbjRyRABSJ0ofaoO7538Po74/MX22fPtdpE5s+NRNRESOCKigDNopGlsGBiD6sI\nCcgJOWVxwuldnUlgUqyCgI+ImUiAaNrJgTlxQkCcXA5lJGREN60iqerA1BE5gzgxBrHwWNazpqlO\niAhRSRDRvYwI6FERBVMGQ6QpcBREmZnMTdJcdUgiZpExDXVkoOq14aaWEhiUmrBqxcfdMM8z5SDB\nAIMkUMGdULh6CIFQVotpgsfVawyIZNW5SYBQ+4EJrXp/dXZw7/UAViROjVlF75kQjIOZKZkWzuxq\n6gVRWEEDEJ1UgwjcKJAxS5O0bNeffdbefQVmJ213XM+eHtx+a/PgY0ar4RElFIloNsumQ2JWjHBL\nbeMOJOJBBBWZjAgA3QIIwBU8tOxSd2iMHs5T9FNH5ETXs3yKUAIDSBFGJGnWyvwA8zI4QeTr5h9w\nRGDIVDd6ybatEBZTsTxxGVYvPvz4wcPPFg23TdqdrpZtyiTMwcyI6GAcdP03TJWl6V/AJMQWFh5T\npJmJvRaSNO3JY+LaQxBRIBKSAysqmUJAImYKjO70h58cvfbHN45ejcO33vpLf/vj/+z/CM/7h//k\n/37v7/xvD9/+ufOffLcl3NRSzLum45zQxl5tKstDJ0EYIS/h2RTQeB2IORMji0O4aZijazGvqIAN\nS9OkxDmP6mUYbBzDNCinlESYMVuN8AEZtbobUCg4cQIA5UQiJCm1SDoqQoCr6UgyJ0QWsGoUSmRI\nWd2IE7EHBiCaG9aaHSwm2y5w4tQkEAlCNAyu0jTIS9Y8rp81wBEeoSQQFK5Rq7NoOFsoE6kpmKc8\nL/02+mG+vzdfHGyHHbiiayBDGYZiHuvdUEoZnIJFmiarVbvMp4++CN/O2iN3piCQGeD0ZCN1C9Mk\nIihsYHUsal5xqEWy2FjUESMQS4lCyF03Q84R7FqDqpzcObx3+2B7vhoKqQ9Y2RwP9/bHiLaTxXwO\nKe6+8/be/uxg3l69ePxH33mqvPf0xcVmPayvNm4pdd3q8mqzXh2kto3CAcWlmB7dPCItDz55uO5J\nIyENTHy0aNer3djb4f5+Sx6QtzYSixbPnNssfdERohY9XHQny26F9OjsydHxwfvvvHm436y3/eU4\nOiZzXh6kd47vnZ6fPri8ariliDe/9BoxXzx+frG+IElvvv0zZuPZiwd11+eU924e33z1S6vLx6fn\nL8bzZ3Oab0fqx35vNmub7nBvppwePX6EOmyevriFe040ak1Ng6kN0cOjvbI6r9tRsINQB2LmWkZV\nMgUE9MD23htjypx07423t08e9pvPdVUB2dyISM0QGNCJBa7hLI7I7hbAUyOhWk2cIrBWZRZKGc2B\nCJkitNaSUnbVbjbfbNc5ZXcXRHVjJLMKwIAAYUSCyB4TBxndHejlGTulNBHIApkMjZmxBiIy5tNH\nT7/01tdFzloG8yCD7/73v/POz/1sc7igrkHcRwQk9TCGJq7LXhjg7BgQkzoAwwPFidL+q3v33yrn\nT7eXVxI0jQQJApsM4ZRSHmMc13m+EHaiyiIU7BIUglTDI5AJp6IIIgaEmw4sQixAQOgBFiRMGdAw\nKEDDg0gwJwhITQokgkzADmBjSd0ETh85PBwpEdqEDke3gSlZBIu4QaiJJCBDQgvAKVJhgCmhDXUY\ngCE4pdSaV+Fm4qXUQDLPJGq1W8yH9aqZ7YFWRPdqCAzMEMEew9jPmhmQugUEQWZWNwRiIeRqpUlJ\nEZqu5UzuBZlSTlo3wnNzrWPfdIfuVTUQAcyIeOrHqSonqtZoRjYHyRCGtWy3u6Zttb+oVw2b8KK1\nnNbDut3f66/Ok5hqZgkDA2QMUatN06kHEELUxEhEqgSErkEUqkpBoFELpjqKbSGSULr2+xEFxTQm\npgiPqctJRAg5571bMjtAEQCexiEQhET/2sGLCB4Y5uEQ4W7o4/Di4dnHn/zwt/8H2Gl3NPPqQ6+z\nxJmREEwVhbumtdBrQj0iQOD1L9HAACARAxgEgAUQudtLk9TEORczNYxEyaHKJJdA1zDCpFqy7T/8\n5u91J7n9pb998xu/8vgPfke/9831H/3Ef+kP7//5v3P26HPaPE60SGyNYC1V3aN6ADbSOiFgCiCE\nZG4BBYgkg8QMsLiNbhDTJquqB044UuYGiD2CE2WYjSOU3YqTcQ6QjiV7ibI5VzKXJJFQGndCgMRk\nYGbAEZJzGaqXHQlTkwUhpqVDI0YdRbFrOqOkzGPZgRmnoKgTLytCEZC5M0oJxIFQwiNUR+bMpGBA\nswYmKRWAYegkqUTXWkAhONwdUMZ+q0OBkKvTZxiCwBgGAGF9oQ6DxrBBZbvRnJzn5F50qEN/ocWY\nfITNFPoGREAj4qr9tPjNSYDLWMlrHXWM2rBgADmkCNNw9CAHbBA9MIIZyQAtqEN+8MnZ06dbcKmF\nQLJS7Nzblk/29xYH83e/9s7JcdfY+MlHn77YLS9tdr6rp6frx0/OalC1WF2tfOxPEE+a8sbrN5ez\nZij97Vt7AmXo/cmVXoywHdf7e/M2ycVWgdPxzf00SzvA73zx7PngQ/h8r42EI3KtMW4BgG/duXmx\n61/sVu++/8av/7mvH57MTlfj1RC7virLW19+7c7B/tXV1XZTBBoLuHH3HmNcnT46vbjoDm6+9c57\nFxcPnj16cHW5XRzfeeW99/Zv33zx6Isnn/0kVtvF/PjZrtB8drRYxrDqltwbX4x9AbDTq65mJAst\nZReXq3W7dzRr2mG1XuwfCAmGGThSQBgiehAGGKgturd+5df2bpxsLi4wUXu4nzqUPJF1cSIsA1Z1\n9TBmivBpIzi9xkaEahFiNLiOJk/PDJYpLoDBAGjuJKxakyQt5SWxMRCRADmAAIlYJg84THdEIpI/\n2TL+yVMmGKspEGdK7EAAHuoK49o5YZbkCAN62diPf/db/emz0p857KbUD8W0XnmZ6LkO9hWAAGQX\nBEhE4nnWvfLG8vW3j16/D1AwlBkpM2OkJiMYc8wO9pGInCQnYgRyCudEZmhITIiIQZ4ECINS4rYl\nIQgLG5FcUko8mQYJmZgbkow5IZGheSiCa+2hbjFL6loHQ3IJYumCBD2CY2q8kbQvw/QSXgjNowz9\n2utOQN3NAwhVNysMppQAADw4SGROjkioXpmImZ1TzhmQcnug4UYcMgNqgmK63KjXnBrzkQMD2IDA\nw0g8EKLWuiUkJQZEc+WuBcpuEIjc7EPOabnoFgcBmnM7LdYMwCAyi7kZAbAwB9VR0cLRS+Gcm8Uc\nRcyKNJzmvDs/E4ca7lQpUTdfCHiEtd1SiJMwIalWwgC3lPL1rCJnbhMJpixtKxgOEuiGNaJuEXaA\nFUOnYTwCEwSGGpgDTDHgQG7me83eDW4WwBlZkKcGDVzHq4EiAj0gPMIwLMzJDczG/vLZBz/8/MOP\nUKRJZiRFI0timsB5QgyqSoB07cIAmMCEHuGKHgQRE8Ya8bp5RAxBL8tOGOE4ceQiCNP0QZ+m3WMt\n7liC+6f65Pd+iF/8YcD5u3/z79bjm+UqffT//X/orNz5uT8T0XQ47ndzCxlqNSWgRIKYJTXEGVny\n1KQiJGGUxChugXUwKEY2VSWv5VUiDQBWHdXUI6iV1ObcHTXCHCwRbmo6lGI6GpoyT9uTmJamiARB\nVU01+p2OfdFq4RhegEDaFhrJXUvcIKKDKqoSIBKjcShgDszEDeeZpBQEzDgVWdRDq9owDldnMey6\nWYsMJNimhljQQVIHJFVreEAgTq+szSyalqGpu2F9/mTYvogYSTDN9rjZR24j5RGb0VzaTgAPchIR\n7Xe1DuCq1Yf1ppadayFKgMmtCmPmLuXsFa2nOmxKvyl9j3UdrmGDuYUCYYQPnDJRZiQkZOZAthjl\n7HwD0PUFh6sSYYtWqpYbN/ePZ8tmmY5v3p7N4fzJmaZFD/npF188fHr6/NG5Bea8N4yD9buMvAc0\nm+WT24f9btNHuX1zOfSFEFJiMZgJ7C0OwmU7jiQY7ONYqtrlbpztdW2SRPniqgwG6+GyyR0kTdR9\n/vCZQ//1n33zxtHB5fPVrmpxX222h7cOX7tz4+ryzDCPFQ3g+OaNlBpTO3v24mq7ufna/b15+/z5\n06EfneBLX32/TWm7Gs9fPChXZ3POZvzwYs25jVoGuzq5eWOzg13Z7urqT7//9e//wfePpFMKCySB\nUNpuLzselidHUI2S1F4z81irgniwoXIgIedm9uC739y7ebK8eXvYrWk3Jsk6m/l6tJfGwUDCqTEY\nNHV3iEi1AiAzAwgBOQETEnGpo6TsrkxEIE7OQREGBmZggFMqcjKLlVKYEa+Vm+iEk/ztZeMQJsrG\nlKC7fqIhAmIlV1MgMnUkJKInj5+0nSSBTDQ4aPgPv/nHr3/t/bR/0DXHOLOgiULoAAQohOhRrnk5\nbpON7jpgTxlmt2av/sz+Zt1fnunZ5TS8RCFKgMUCDMFz20jDUxqcwpEoIJrZnAg8JshcAALRBHbz\nlNgD41pxAI7OMk3qDBmRGcisVOKEwqHGnIEyYgQaIRIyEDJYgAJyKFAmUIfQqEZdQgSW5ODI3M7m\nYRCcKRQQag/MAoB8XZkKxYpmUUfELJwwAMrgSDQhgRKiIxPX/iI1+4YJAFAAzSfbgdcKksDqtSII\nHKjJkmrdMlApO5aklZpmPphJbkodmKiqSk4Q0V9dSdsmTg5mru6eJBMBYALYgTDh9NLTWC0pNdtx\nu8Bm9eBjPr+c3X3NzQ/2jnQNhYYIjZappu3leV7MmIWTDLs+PDChFZWUFBwiKECuyZceblPNx1W5\nKrfiMH3S6pQrNndhxIndEgDoTdPNDm6mxT7mFjAhIEICAEKcAsnXL2rT698kJQsP0/BxvOqffvKs\n38W8nbXMZ5sNgDQxXSswzImImYmnM/XlwJMmZCVCBAGSJA8XTuGG6LVMHMTpbWpq1XO4AYaZYsIw\nQ4DJljZZojT4+feeDON//uV/b9m9+m+98st/7dE//E/sgxfP/tX/+ZVf/HvPPvhJ9+QPrREPDkuC\nwcLAwgLMTUpzJ9I6hjunjsNDS0wthRqBiIKGRJlciVMGJvcRg5CZwslLk3MQASSEQEMttYwVQByv\n7TCSEyOrD2oVUJAQHNSV03RFJrfRK6amAU4ZPXx0UCNC2bNhjHEE1yBxZsYMQVPolrhFJLfpmz4A\ngAAHsFvhUJAZABC6RYmoHoqQpkEskwRQgHFO1LUMM4NS1ki7HmrN3ZyIKAnmpVvdFQxSaZroa5NS\nYJ5OboTGvU6rn1J7kdJOJ2uEI0QUREQspRYvdYK0FXIcAoDGQDMQiq5pkYIogCCLUG6A8rC9kuIS\ngJ6TGnBK7R68un8oCWZ7cHC8H7X89HvPjPOLy/PVrqw2u/OzFXHXCWxXO0RdUiw5lnttYr68vDi6\nfXxv0T19fKaqbe62Oh50vGjbs8t1NWw7zgREadPXbVVkPJp349CPFSOl0QozHs0lnC7WqzuvvfLe\nu+95tdX5+rzvEZsk9Oa7d/dTe365HjRVHVqWV19/nYXPHj9cX/Xr3t79uV8WLZ989NNxN86Plq+/\n+SUJWG8vhn5NEIc37wyb4ersKjwQ+iwwWx49eHI6unfL5a//5b+1/fbvdEDM3bDr+wmFgZEzvPr6\nl84/+wIFJWerVh0R02QOpOky0aZuPivbbYzLi0efRT+Eu+QubDO1gyMmIhojRZJkVqdOb8RkGYCq\nisiORkAegRCTpGkKeRI6kLh7gBExAiZkd58yWcWcRaZYgV0T18jAEREiXv4DAl5OGN0tAjDJlKFL\ndJ0aCOftOFLCdrnXNDvojQlrqA3lh7/9OwevvdJ1x7MsyBGcJywvsYTj9OI/FQkjADFHTEhvRJ43\nJ3f37r9ZL569+MEfU/EQInb0SNQox4SCkoREiARIZGHCjYNdo5OJhSg8EHW6RitGIrGIKZEC7mqU\nmoaYg4GAAiN3jYULsjNAJooplS/AEy5adAJ6uos0wABm7jWSBIC5ITICgAEgghskC0PwgKk96aJm\nwCTEaAUCIrUorUOtw1YoRzhI4zYQIOfGtQg1ERURk8igJUmyYkgBIJSyFQBXIHMgodDSw9RxyzMm\nNK21FsRQHYQpkNJs4WZETgnQPcDDDTiQoPRDni8AUzijRwiyGSK6ZHDvchtAXkbJg/dFI6jlYW2L\nw5v92bMmEi06yexh4e4eKedpJA2C7k4Agc6cHNtQy21TiFzDvMZIUpxjNGwokAADKCBkYgmGQyhw\nDsY0XzT7x5gPgDpAAkpOMHVSAx1jGpBM2zq4dm66hQ3j9uzii4ef/egjN2skGm7W2zWhNQhMia4X\nDT4tAqZEs2MwEU1YXgeh6QUGCalqwQAiZgYEjAB/OZ5xN5pWW8huylNeiIgwwB0qgFDiZNpJexTj\n5e1f+saz7/4mfvbZ+T/5/9z66l9//Tf+xif/+U9T2TV5vwISFgSVlDkxEoZrLSMwpdQwoZUx1N0t\nRgtXIwTIKbfmQEyVFUCEu0n1HWUkQUyGuWPqqmktw243TJsW4TbAERHDAAKqBhpC68wBI1ISBof8\nEmyAWDYibSAMtZpydeY2o5gXRUrBDjRzFIhBEJkxiK+N9cMOUUNS6uYAmQAcCoGihQUBAHgaq1nd\noqTrrHsgMIRDaM0E2rD23ubkOgKFEZaxEGlf3ao3LPMZBzdWXUNRSb1OgNhwHYupQ1iElxwoAKox\nFidGHtkBIJAohRt4VBtR0qgFESIJlsjYYoQLYSLKmbu82e1I2ThLmC/mdPfu8uaNI27k3pv3b53c\n3K7WD56e6fzg+cXFi9PV48eXz59ciszE63az2WvttQUcJgCGYrHuh8Pjk7reff7FqXpyoO1Y5oIZ\n7PxiNV8277x74/7JIjtuR71ab/YPZ8f7i/V2CGmM27PV5dF89qUbh5kszeFX/9zPfuNP3Y9RX5ye\nr7bjWCLP+e137lOxzx+9WG/LbnOVcrr7xlvdrFtfPNdhNPKf/bVfmZF/+tH3hr4/ODl498vvR9GL\n8xe71WWSZrl/dHVxdXqxdpd5wvv3X0+zxeePn29LvP31r/zZv/K3Lz//4Wff/ONlXjhaDZvsLYG2\n5Lx68qSUoiXqroYDBJgbTgisAABC5mG9omqhENvRdrsYarnaUjDlCdwwfTYj5QTTNg2JhAEALAAi\nibBcg8sIYepZTxsLokScAZyZmdJL7oYTTkJLJYg8XQEjCImZq6lP4Xy4FtYCoEd4gHsQsSQ2K9OU\nNNgNbEqSo0eiZdPkxYI6CggT4NHjg+/88NEf/XB38XjcrkM1TB10Sv4gIqBMrb/JqhHTWYwUmIIk\nmhuz2+8u3nxn+frbnBtJRMjs7j4Sq7QpzxpmocyBgUSMhIjCyKlBunbJBRhyAiZAiqE6RBBFBKUE\nUwbaDRjDQ8uAAG4jhhkDMqHrlKIESYCCZmo9URAKMAFuw9QZmBOa1zowX4ePmDIyMxGpYhAlFmFX\nBStRB1QPVbep4rIDUEbhnD00pdZgkBTk6FqRGHIDhODFHIQ4wIIRnRHBvSIhghNTng5IQGY2NyYK\ndwKBGFNqSHKgcKD1vZZdQKRGkDPm6SSRABBOXouXDXofDFCs1h5RmJtADDMrOya8ev5k+/RDxsJN\nN+/mtfZ5b46NuPUB6uZMlHOT2qxWINSjsBClBig5AwlQml7JIkIRmUn63Qbd8Bp4BIDXKvQJhwSM\nhJTbeXt8h+cnmBKQTDwfCIIp0hwC+JJCExY4UpRwUxuj7Lanjx9++7sPPv2MiIWtahkHbTlnmMr7\nTISBfg3jxWkUe710DHMGZKAwCwiYHs1AFj7RlQCNaEJVOBO7VY7A8DBA5GAEDCQMJsXw8Opp9fGL\nj/+z/zg+/1fp5sGX/9a/v2IZnvH57/6D/ddv5Ld/LXmd09DOhBJREhRAWVrguFt7sTa3uZ2pjTZW\nN4zq7iVCcQrUoQUnQhbqECHYJLUAQjjFkAhNDfpwrRHIAjACaACxEGMJgAgCIgwAVx2H8JCE4RBh\nMa1eI3wctB+GYVc1+hLDsCmbCyZKsyU1DaUOmTTGAEIEydh0DZOoDWMZ+77UEqVcQ3CYBJHU3HQM\nrxVAHXQcwRQBw9VtjHCJSFrH7UX0V8lGFC422nCldVxdbS5PN8PFmsc+2TZ5aZK0zUw4g1uMxq5E\nxE0iITUbtS9lrAbmpKOVwU/Xu7PzzWa9qw7VfDOMq3EoYaUqmHEgqFXdmauOow+lbLdQtQ59Sknm\nnPt+6Dq8fWf/6MZe6uT2jaN+6D98+EzmxzIrZ0+enD68uBjKONiyne9223G7e+3uwT7W3bi6ce/O\nxx89URBw/OjROS6aWc66W71+48b6/GqxaMsYB/v86us3zz57enY1nK5qzvQzX37j6dlqp2X/1n6/\n037sf/5Pf/3i8ae7vr76zqvvvf9aWP3i00dXVyUYIdHdW/sHi/b8xenFpieXYdjefeX1k3tfkug/\n+PB7Zy+uXn/77TsnB5tnnz794kOg5Vs/88ZiOTt98cDUEcp8NrMKZ+enu2KlbBft3vHJ0bC5fPbi\nMu0d/dqv/XqpV9/+rX/Yf/Zxt8HIUTXCABJhwGye3eowDOQSaqo+FbeZyQPcAgUtQPshO9RxCKuc\nmcOvLi5v3LqjY02VHNGZyBwItSpP9V1CM50kgmDhEBEwZY6nmdK0f5v2GbUWTk2tRUQImZmGokSE\n/rLYTtMpdm2ZYGYzIyaIZOFCpOoTzxCv8d9TNy6YiYMSYTWLAML0yYeffv3ga7O9+exiXI89cTJ1\nDPmDf/bPbr1xLy+OJc+Z8zSKDDBiQ6QAikiIGhGAet1dRsaAIKTlzcWrX7G+XA6jnj8Rlhobp0gs\nTQss08nPKWUHwwgWjjC3yonD1VRZmAgwKCIk5ZSSejAaRCCzSGumTEw5EncaTkXVK3mgKWZ2AyFG\nZp+6hKFujsKgDtB4GFEGyUQI1evYJ6YAMa5RjJkVEK24AeUGEYIzWWipkhkzAzB48gD0kpErkSEI\nJN1uOLGOO2maYPKxAJDpJYqAtBymWpkbRANkQESQ4io5TR4m2+0IjUggp6n8gUABtXrN0tUSLIHh\nVno0YBZMqI4oBG4IjtJWSzITrFI2qyZnZzHkTGgo8xs3D+69d/7k88XhwTBslomu1msbBhaQaZRp\nqmVkaZqmMTNCICL1SgihAWwEBARMQDlZHQiIgagOlPYCIwAdgUGFuxoWpQhzUJqdHHdHt7A9RG6m\nRd617gAQJsAOILgHIESQgXuAjehj7TdXj5588O2PqvLhfrfXNrtd9YhFMmCYfmIASMCBUNWZASI8\nHK/DVjSZDQHRbUIeYri7OTJPm253n27tkwcGEc0MEVVrgCcmJDSLiGBjkyIxf/ZHT47f/Nb+yZfm\nb969/Qu/fPF7f/jsn/yT/a/+6ut/+a9++sk3x8vTfGuvpwaEsJuhEShQaBIWQRLEoRljJ2EOCohB\n4MBmjhTCGRmB4GUHdkxJEDM4mhevXgZyH6sKoUi7RA8mDQyPFrw6TCRvnGQdEQkCibl6JKImNQCu\nNtp27U0OgrCIqqo7yQIy4zS3cRuhcO1H5em/W6vVCkUZwCgC1A1GJAORCFDHKTkYbMSz1DEmRBQI\nqFaZJ4eJQwnVLZEjIZJs+2KbbT/2zJwbgYAYqYyFMiNSwsYMWAhAkDnQuGnIFPT6YmvECnExrKtV\n8JZKLN2SZEPWcABomi4MJKUIQ8pCQgF1HIBQ7bS4NpwlrB4czPeOeLlPB4eL5Wx+9uT0dL2j+d7l\nxYta9cnDZ+u15GZm1F+uzm/ttzeODq4uX4zdvJ0fPntxdvP2ne1u2PX+guk/+F/9/e/9g//dQT06\nPX0ewrvel7eWy2X74vHqyelO0d7/ymuXZ6ff/+lnkdqTk4OLy03TNT//Kz+3evao2U/v//L7dw/2\nnz958PDJph9sMO1mzZfeuLfbrtaFBgMMaBbd3Rv3++3Fj37/v2+yXazGn/mFf2N52O3Onq8uz/Zu\nvXr/ra/6cPX89LlVR8yzxeEwjrNbx/2DD9pEN770RjM7uvvG29/9o2+9cv/e3tHJh9/91tPHj4YB\nl0/6w9msjnVQpCQlIqXUEQQqqIuQeSBTOJi7iGipyFMIOxBRawWEctlXHXPXNNKdPnk+gUKsGkoA\nX3NEISDCKZiAUpOnE0tdEdF1QiyCuyES4NSgBwQwK5KbcAt3VWCexq3TYoxUp5rE5EN1jKlpcO20\njwgieGmfgClvRZhiynpSUAQSWFRy1koff/rFl//Ua7vSP7tYu4sQVovzZ6c/+Of/8udvn3DbdJkZ\neZJmBAUwQjBFnTAp12GH6/ABUEDgsjl6bf7qOQxX5z9e63rjgYLcZKQkgEo8vZU7BYOAW2XiieAG\nSJx4IiJOl6kgxgDXEZpE6AxsWoCAIsZxEJ57aBBRapGnZBiBARLZsEOZXpYlIjwMCD0cYbKC75gS\nCvL0/CUlTxDmVSE3AIEiSMIMWgsT5cwOECBuA6cOagWm4h51lEhBFMyI2LQzJ4IIbuZEYnUwVzQv\nOkhuGMOQAANzo2UnWcINMdw95QbRzZyhcoBrkdQgJuZsHt1y7uYexoyRMADDkdCn3AwyO0BuZCzb\nJs1hVgmSBUgjOFDi0LFfP/1g/+j2sNtkkmJOBKnrPIDAQgIkg3oEQzhjeBg6NJSCYjoLKItLNJjK\nMFphQFEdy26b5lAjAiEjmnt4EJojOqZmf687viezI0yzCWIQL8NbAXzNcp+wagHuilEj1Dys9uPV\n5ZOffPLw0ycB0GSeSXM29Koxa1ISEwaadg0IghQIPsF+MdghJJgnRg0yJb++6F37sad15DXuaeLg\nhyHxdY8iggKAOQB1ggEhA4MpqFeJ9rN/+Z0v376bfv5vvv23/r0/+uDb5fzk0//2//bOf/QL9NV/\nJ//e/9WH8zQ7JMnhtttuhWvXJKs+7sbcpYACARYVOUtCu166w9STRWYEFQiLkYEpKCgFGRj0BQMK\nkUimcG8Tcc7oEqBkgJIgAFCAWK0HbATRajVTVBC81t9YUK1jIgASZCB0hMbMiErKexBWhkuBBBgA\nNFSKWq3W4hzcMAOQm4/sggGI7CEO6hFWB0BOHSIRk1DDLBmGguAMwBYkBMUQgrvWq28uX0AdADgl\n6KQTiGEcPQa31DQz1wGYRMQD1eqoo1XIQTo9iBUxobo33ZLMbbRdHXw7zJpC0oAjYUNmLNwwgrR4\nvcwyJrFiVTeBgVRlebJ49ZWbmP21d95DXT377PFaU3u4vz49v3h2tdnYUBJl2fa9ld37rxzPqW63\nu3v37pZeNaDt9h88Oxsco22tbP+H//T/cAP9yYtnuc0a4/LGcjFrPvvw8fl6PDzef+P20bOHTx6e\n9mnv1vJwdvn82Uzw1vHBpz/97uuvvXr/zqtW+p988MXVdgxrlMvB3uz27eP1el3DdVyXbWlm7XKx\n3G2u6lB02FG++St/8RdtXL/47MPTZw8Pbrx2cvvu+uLJ1dmL3HQpd3mxtHZ5IO3w4otZN7t/711s\n4osPP/rm5z+6e+9+FH366SerbQHleR0WlKFiNUSAYkCMTaIa5caNG+tHz4HYYXoqGjOb2mQncPcA\n9Lg23IbtmGhYbVLKYW6AxRSZENDxOgIAhAzsEERkpTrG1OFDIEcjYGTyaji9DhJf023DzUKIfIoR\nTASlcCIxVwQCn+a0QHitHIwgYdIJpu+BhO4uwuYOEBaKTiiUiQUd3QWkQkTAw8+f/tJf/pV+fXnv\ntH7x7MpQGDmAf/Dt797/+le7/b3UtESCIBGA5EBzQA4QQJ1W1wjhLx9yTkQQnhbtrXdhKFaG1Y++\nR+bE5GLCRCJmJVF2ABYMdSIBnlZ0wDkxNqXuRBpACjdyNeLcNm6RUnKhAMKwUE2pNUTCNLUdUI0l\n2QSoqybcOWqQuBu5ElIAEAugggP6dA8IZAYA0xEpwdTuQsWoYABAHkpyjQcAYPJwddMezdGAmhba\n2eRWkNROj9gACDMCM9gBiqQUHpyXyGxqwEjgTg0JhBpMYVcUxBxCwlzLOuVuu1svUExNmgZddRwQ\nktrIIjqsm9lerbVfX87nh8GJAymLj31i8TIisOKUnzJvM4YIYxQrqzWTQM46bNvcmXhdXWl4u1jU\nokwWWpGTolMkJAt0M4spKkQa1cFruGZJgHVKM4UXgMwkGiaUI0YIIEwymy1uvJb3X4l8AEiIjCAw\nUdUQgBxi4kW4u6EDhYar6+h1TWV78ejRj7/13c2oB8s2Ywz9MKgGT+tEAkAHo0DwCAJKGHWSQrKG\nt5imfTwhuWuEOyAGBkyMtRBEm2xoEUhIgO5BLzcfBgERDFPQKsKNlfS6UQPrJ+PTb/7+a6++F/vf\nOH7na8+/+6H8+MfDd3/rnX/n3//JB/9SLz6eL4+Uue+rjSU1YshVS1mdeh24WaTc+KhhypIQOKZW\nEBByuFYIq+GpQUF29wh1j7EoOpAIJ2JGCJTERMG5URMiDHYwDHBEYhJwA8ooTYwG03ffC1J2Y5Gm\nqokAaEysNQPBsIiRUiJtZGL5KlhVjwookoi7PP3QkDJBWGgpo4WreqhhEEZov83IwY7RAgQJJ0gB\nAK4YBTlzmwyTsVUVdG0zJuKqAzABo1WxWkFXDRFnAWgCcSwx1sqBAQbhjCBgE/X3aP+gp7zdFOyb\nsl1vt6XpEhFGqLB2WSSLBnlVDANkNQ90QonwYiavv7pYHu+fvP6K2PjFRw/W3u4dHT3//JOnT652\ng6+2vtOiCmLDncWsXl69CNOQPFZGPLtYDzUVwtQ1rdSv3L87PHz4lGHWtRBwfDhfX26ePbnqx/H2\n3cOb+8sHD55slW/cv/f0/JzP+5NlHoZ+vXrxc7/01VcO9x59+tknjy4M8jCUcLh963CepN+MSE3d\nXAL50Y2T/aObq8uz5w+/uLi8eOXdn3n/q984ffDRk08/GKref/ereb68OH+s223TtIJpME/Y1vPn\np48+T5IWBwef/fRHF6un7ezo1S+9P8P8+dOfbvsdVHvjtXtPv/mHM27dXQPUxbwgYmqaxV7HqQmP\nqvXaCkMAzLXUCQCFxAFRvETQ9GBVty6lUsbw67PsWhJhARzTPp+IJuXyRB8Smn4TETg8zKqwmPv0\nhQUEJBJEDDC3aW3vrhA27dqIJNyQKdwJcYLRpKaxqrX6yzEPAqKHhhoQqDsDRrgFCjOQxURLIw9H\nIXj66fniYO/2XX30YuXgAGgGMeIf/tPfXNy7KbMZpU5mM0QNB9QRUkKciGNpIodNdEkAg0BHREjU\n3mjvvOXa22a3+uiHTTIWIHRCJmkAaHIViOQIC5ucw0iAVSsiASILqVfMOTzcPIicGM0QMTwcCFnA\nolptqCGWQA5iDEUwJwIyMENB8lE4V1eAEaExIlRjztebXIjw6hrQ+FTRiwiUjJy1jkRCEKGDa0VJ\nHgHswhjQ2DiQV2TxSZhFgCFeCkmjESGIkDB4KlxaGVGIMbntgHPYjrE1DARDbJEhTL1A4Mipq8WW\n7Z6GgoRbAYgwJ6wpzcIt5w6qkvusWxITMIMFWCUSA3B08mA0B0SnKcbqZYQmSyPuCLVCHft+3Xad\nzDs0MysUGACUxMIRcMKcQbhIh0w+ETtpTE3jTt73IZRTjurgxhThlSgHOTqDu4IenNyc3bxH3SFw\nQ9fbgWlEMs2Wp8WgwbSEn7SBpl63ULaXTx5+/v0ff/6Tx0G010nHNPQ6DNoiJg6EQCfJHO7CzHyt\nlZjWBUnkOqeGaNeVeSaIgGBEJnJT9WAWAxNOpqY2CbnCzZGYiQ0mFXYgkAHoFKhxDEBTevr9p/O7\n//T4Lxy+8Zf+Fw8//V/D0/1P/t//5Zd/5s+03/i3Nv/y/wSrZ3T4OvouMVhE1B4UbIRoghAlk6oE\nEGIAMwBREodwILfKiIyRhAiTW7U6agGI61YwmQMlRHE3IjFzYnFEiho04RsdmcwCrRAjSmYg9T6H\n9MM4Vk1CwFDKEMCU2jybOVDoiFbbWSO0l8DdFE0hHJhFGiTvujz1FD3cSlRTdw7zUncJk0h2cCu9\nkibzKGgGTOxgiDiWnoClYeLMmGbzve28950KE4T7MHpuck5WyY0c3QjDzcZhQPCKQg0CkAQiIogj\nJqac0uiAFPMZ8mK5avPlixfZAyGVotG6SEKAsHrNodQaMfW+MIjDTA5uvHLz3q3Tzz949uQKu32v\n5cGHPx1WxU3W210x0LHuJ+FgL2N0y/X68t6rJ7vNZhhgLEANzHJ3e69rbbx8/nw2W4KVpp3XMpQ+\nLvo6a+XN115//ODRT882s5ODV27c+ezzj99+9bhcrYTa7jj/3J/++uE8P/j0k88fn/UlmZX9ve7m\njX0GGEy0DMPV+fLGzeM7t4f17vmjL05Pn+b9m3/uz//V8cUnP/z93xo3O5ktv/yzP7u7Or989sBL\nCUIR6Fer52dr+OILGMf9g8Pw+tlnD8cx9m996Rd/4688/ew7H33nB5dXlzdOjvYW+/3zx6TILVUN\nIpqesA2L11E8rz55hIwYMnkiWFhrIWZ3B4fgMHdwaESqGwGYWz8C/YlP+zrMqYkTQBDj9GVEIngp\nfzCzyUvIzIiEDjhhuUMTiYVOEbqJQIwgEdPskwDCQ6fmgVlFAPMgYpZUypilsbAwcJ/Sp0pCAUgE\nicVcw8PdmJApmMANJkxxpu4Hf/Dtv/h3/uzli4uDvebsapg0BoPbg48ff/Dbf9Ad70tqON1ECEAJ\nUKQCkiOYyCdERwDBS8wVAAQKIsre3fbuEP0Iddw9+Ylk4iZDGISIiIeCeyABMjIQJCAIiNy0qr0j\nuqk0jaIL4kQItVoRHSUQInFyx0DKDVNwGXepzddN7iQUyeuOhMDBEIyQIKtVARJECwwNTBjTQz6k\nadk8ODUalRyZyCbAjZp5JSbOmaXbbV803dyBCQOxjSAEEA43J5IwQ4iwigA+1gAV7oAEgprMBsVt\nCAhkAe2BK2HUYk2mqgXAWKQONcKilJ5YOCEJirhVlMYD3UbGlNq5WqB7WAmLsfQ5NRQamCBChK1U\nDwYkD6VA1UoibDFcnqflfmpaEZkadhjeNG0p1XByYYYwukBYWAkHpymDFRqYWJIOPYKjEFj4BI4L\nregICYnBcZJU7B3fm998S5Y3YZIVv6y6OUxE0bjWakYEBPkkXapoA6r1m8v1gyff+5e/t1OfdZLI\nOWRb3SzmKWUKIRSa9NMvexcACEGMiJyZq1UIQ4dpcADgRJgQHMnUEZk4IAzi+ttCfJ2/JqLwMK9I\nYeETDWoKsQIAMiECgZRVffjHPzr++Sf++tff+oVff/jP/oX89BP9w3/85l/73/zwu/8inv0gZjuE\nEHFTIyEDIwwfSo01CnHOYe5WGTgIw6twduK8lAAgK5nQgqDgOBQNQnQJokqQhMIcCINrNW4YfQI6\nMgRF1HAPMwgPCzciap0VuRvMS6keVnQMowiXhKlr8mKvlCFNql2EbjbTUsxVidRCCBIps6CbsEwP\nQI8Ig0AbbYygAHcyNw4St7AKYQPAkJd74zAh7CsuDohkRLRSyLxNCVLLAu5mgGLR15FTG4FC4W7j\nCMUQSBGySElsxJ0aAQQRU24qsGrV0LY7QpTl/v6w2wmMiEEY5GC1koC5IWDoCNwQi+pASsEGwHLv\n7dcefe8HV4NjvjWuN+v1xdVZuViNo+nFUHPYl1+9UbfrxeJES1lvS9POLs4GybjalK3gcSfvv3rj\nyecPtyFIUMb++GC51Ti9XFeNw4PlUdd8/OnnI8u9L73e95vHn3/49v3XttuzSnHntZO7r9+wYffB\np0+fPb9iatuGum6x1yVTXo07t03ZDvff/fL8xi0t26vtxeX26uSV+3ffePvs8WfnDz8/ffz8ldff\nuf3G/e3myebylCJ188W4XV9e7hKkVig0br71ppN88dFPhzreuPHKjVcOf/w//neXZ0/Pn5zu3b5x\n+633Vp9+cfnZwyU2CFR0cJkEAsQdzrr27OyZwAyVEAzDw4Nz9sk8yhwU7h6BAWRqyNNWD6ZHmEd4\nXEObmCSnXHWMCGYppbxcjkzh0Oty/TSQmfIs7i5CqspJ0F2rIiaPCUMakgh8Kg4yhqsqEU5oKwuj\nEAJUn8KV7DChUTjcE3O1UsyJKK6BpsjECBYBADYl09Znqxdnm+Wtozd3fPntjxQJPQBMUP7wt3/r\n+O3b3aKTNsn8JlNFDNJJj9tMLHm0GtdtsGvoNQA4McYs7b8J9yygphR68bkrkDBGQBhMvXQhCCPE\nAANnyhwIIikiKLUByLEVbD0xREQtlDonQq8OFShHqVGV2r0kHQKEExGCIZAhSnAGByZEFGBHyMEN\np4RUh37X4CxCAJ0yARGYRTgDB4VO5mRATITAwskiqvapXQZNAFQKq8QZkB3GCd5MwrWMyA0jq22E\nG5yuJqRFaYrEupmNAyaGwDJabhoFY8lhjhEinaPl2UzB0My9TjxMCIuqmBqvfVUGQmbUqpzbJgcY\ngJkHOAVURskRyCQKhojklhw13LVCTipom14wMGXwqHW8jl4RYAAgTzqnkNGLATiGUG4hwsEoJfQx\nCrmpkAZi1J6lc5xg5Sk8qGm6O3fT8V1oDohbQEbMjlPFnl72B6fSUIBHQAl304JuruP62dOf/sF3\nHn38uFe70c0bbIbtdgy2sEyJAERkUhxPWUgi8TBiIpp4TBMVEAOdAlnQHDDIwKYFoE+d2gAioiBg\nUi9EEq6If4LndkRUdQQnYAcnFDNnCAdCSf3T7fNv/c7B0f1Xf+0vffLHf7h82H3/v/mvfu7P/93l\nL/6Ns3/0UXP1GPdu+2hEFk4BBLb1ASKqdC3NDupmY+HMyExaIdS4gdwuiFG3NtroFr17DWJTB1Pg\ntmXi5GaUG2AJH0xHJvEpgDvVL2uJMJbMjDY1NSGpx1AqAiRujFJAhAcQEnMmoFbQKKwCmBM6yTgy\nOqIN7h7QKTpBIIAjKIJBVA9gZpkpjqpRewNxapIXUDfQmsjr0API0O/ariNAyYuhVt1dmQ4N95oR\nkNpuf7e9UAhXCxxzYpSkdRzGWqshRRJ0C8ActVbdEbSUGnOrlAEDreaog1vf950QBmZKiUUdxlKx\nlgAIzLntiAiANcQDwSlA5YPf/dblzreq4C9iwN1ludrqxrBfj6/ePDhOfnV1+dr9V148fzGMnnJ7\ntdn1uaSCvsB3bt/Iuv3kpx/ePr53uT29eXQkbh89fZr2juZtezDrasS5oe0dvHr7zqOHX7zx+t1l\ni8+ePDy8dfRrv/KncvjzR0+fv7jQ3maShtDlct41DTD2/ab2o5m9/fVfSOLPH3yw2ejQl/vvfJl8\n+/G3fvPqxUVv+vVf/DUlvDo7D+tzu2iaxWZ97tBSKrAbu/3ljZvvPHn82dXZGaD8qT/zZ4fVxeXV\n6sEHP92N5f1f/jfefve9D37wO7o5h6vSzBf9bjBgNywWTSMW/cGN23V3lbrcrzeI5IDMXM1VKwSE\nkCsAQZjz1FILELl2NtUwBmJK5hWRAmAsPfNE8XOCqTsMYR6EiGRmACGS3D0w3G1KyjCJ6xSQAqOp\ndw9m12YZc8VAc0+UNKpBMAsFoAbnpDVi2kq+HNFWrcjMLODgEPT/J+q/YnXb0vQ87EtjzPn//8p7\n7Zz32SeHqjqVq7qqurrJJrubFE2RlEkRFnRhyTcOMGHBMCzBBmwYujAEA74wBAiCZZMyKdLNbrKL\nrE6Vu/JJVSennePKa/1hzjG+4Iu5Dn2zL845OHulueYY3/e+zyPs7o4gCOCGJEMlV8EE+MNXPvjr\nf/+vlcOfjxqc9YO/JvUY1ulrf/ydjVPnuB1PeIySGTywgi4QE0oGQABEtxjusnF8mx32mCxtPnk5\nose66GNRp/vMxztQTg16DMA6CxNpAgy0BjMlCrMIIyQg9nAMCCaGHKGIiQIHYEHKyYMQ0NwFnABd\nzSkjEoiHFZbGzQndDROKe9Xi4JHbMcbwhQkMCA8BAUFfzFAIgNxrQDBmdLVaKCULB0DSjInAqhei\nFJgDCgYAopeusjQQgIkZRwgAilqKcHJ3TOwamFK4uSthllGLiFE6TOARtVaCtJjPIwW2jANvhQAI\nVA00OAFydhxKc5aZIAwCUTiowepEAEjMycHd+kxsIUhgRJQaKkVrRyE5S1eUwYkpMIcbcdLaSxIH\nDHCvGo6paUrpU5PDQUSAGbUMMDN3dwVXg9rlsRGk4SsAKGtnro1OXqHJJlADhAbA5Ag8vGHik9Ug\nhHtUdHcvpAFW1ebzo63Dj++89f2fLSquTiYjwiHcUiwwKCEwBpoDknsVZgN3dxlAE+4BITxMDiE0\nglBdEQdg9DAJpwhVA4JhymDVjJmGqw4RmRkzAyCimNUsuarSoFMBIkYHpUCqo7uv/GrlqSv+/P/4\nwuc+vfPgYWzv1Js/vfQ3/1cP//KPxgevZsBDNSJgRHBgbGB46vsqvEAMZkjSDLh8CgMKdA13JCoG\noQjFyUugWPhqI8gUGA7AhEAE1ciRWzGFoAgtxEQpeRzHWsIh2A2penGAxNnj2FYcEKhGAW5FhAO9\nonofjgzcUsp1Pi2LrmmTmebMEBDAxEnLvK8+L5DEwxfMOUJrWA5J1MqItF+4VY/wWhCJKNU61wLo\nbKW3coTu5A6EDKilRyC3CElgFQDVNXhETDk7QB2gyRpmtRBLAHT9nLVxXDhDoFlxyuPp4d1Wi0gI\nVAh0wM6AWRwkk7hbkJsPzC0wr33taB9XC7eMPop8uDvbPizTufZdd3p91WY7NBltrK7cunkvgBLI\n4/1ue75AGgfFmY21xc7ubK9v83IHi+tXz5fp0aPdmjEvg6+1+eBo8eDgaGltPQBv375x7ey5vXt3\nHm/tblzY/MLXviDW3X7/4+2tA+vD3RT9xMnNJNJX7brFYjGdrIyfeekLzPXRnRsfvvHm3lH/3Je+\nofPpg/dv7+0cNBtnvva3/8F4eWW+/1j7WWJp2rzo50EyPdqHvh+vrreTpYf3bpbD/Twaf+43fyei\nPnjw8MY77zd59IXf/v3N9eU3vv8vD2/dPbqznbzVTtFDslStlEhIN1ZW9rYeYyTr+txmbnIaNUhE\nxNyMDI8L8eDHq4iBYDGfdwISHmgeEcPrzb0ObTQmUXNTJ8aU0vFxeACkETHLQC4DD4xB48PmBgFM\nKeck6BEaQ1Aw3CGOIVHB7gMpGNQqBgxdaiQmcKLj7zciSkoxBMCHU64PIsFEFEkGYD9FEDJjkp27\n248f7TQnxk8+eQmhDPNH9ODcPLj54JU/+vb0zoPFwVZ0R2EaplA1tIBjYAupAU5DORrBAsHZaWgZ\nAjuvpFNPtJefH139VLN6FoFQBtfAcBcOkIZSY6VilhhmxYDMDQ56O2RiRoSBrexhHA4ESVpn9sF9\nBygsThjScG6YGdCQs9CwjyFEQKYAR2AGBzA39dABWudWw6pbZ/2RWQde0QpWEwcrC0L0cDMnzBRC\nEgAWxnv7+3s7h1ERU0JptHrOjYxabkdghQAcbKAMqBfEgBoAw6SOOY0wIVqxfgZsSMSUmNi8n2ws\npZbQNGpBIkoZgRiZRRCViMrsiFECMzRNiJRFAVMgotwyJRIENPRCYUiBDMc1VQjCREQh4ilJTqEY\nyBAhIgiRm8bUEGC4wnBCJGlyRjISsPABHEgIwJCEmUlyJgNEdg8C1ODJqQujc9dl9SymhCyASSjF\nMVCUAwWOs6IRw285VzQ378N6Xyymd+6/8+PX7z+azlyXWkpM84M5UluqSkIhYxLmgaYWak7EiRMR\nIQKCM/NQHHR3YITjNDVEeLUCYcdBbiBzMwv3odoJYQHEA7QhAuIYQ0pmlQcqBcKwvXJHCzSD8rB/\n8MPvxPa75z77xW7tQqoWd29bhkt/93+3kE2f7ibh4BwUDkZNw5LNMIrWxSFacBohoyMgI6ZEwOCu\n4VUjqlupSCxJcoMrK0uUkjIrEDOmJJSYpKXcAAoSoyuECoGIkGR11wAkBrD5vNMKiShJQ6mBoHAm\nkpQgwGNwQRKHY+37bjFEJQjRU5M5ZZJEwK6uVgJQgfuiBjUCKAjDEOoo56ZpRAiIMTWUR4piqmYe\nnPoFlqNDXczdHBXAPYA5sta6mB8ezea1aqi7R3VwSAQ0ytI0lFNOmZvRhNpJapbavA7SGuNs0c8X\nh2U+wwCLHsrR0qgtpmYwL2VR+r6quVQjh2ph1bl0huYUYVX7boZm0ndzXMzI4uHjg8f7ujtbTBq8\ndmbZ5xVX1hazvjvcP7G22tW4P5tratbHq6srxLE035uORmNTz40vjyYPHh4eOaWWTp85fTA/OqoV\nEieQuzfvbJ5YXltbv/XRB5NT65/7/IsXzqw/+ujdB4/nh9MOwsNheX15eTQ+mvddcL+YA9YrV54e\nTybz6d79ux9uPd579ovfnExGN1//ydbj+33ff/qbv/fkk1c/fuW7u1sHxGMHt6Cj3RkRlmrN8tKZ\ns6cPp7PD+w/66eHq5WvXn37u/bdeeXR/q+zPT5w5/fRLv9GX3Tvvvr63vXV6dOrOow832lWsUSEW\ns84cmobb1GycPvXw3oc5mkJBxrX2ARSIpnWgOIUjBLgbCTOmMLdQZq5RGRkcVetAt0ZkBHeEYIbS\nQwggalF3Z2ID/eQGZcjIImYx6PGqVcZ/tzsZ8DGfxOw8mMACkTCJqzkGMSUzdQSCAXPsA3Q4DJAG\n3yF6DHUJg+PoHvlxs/4YjSyABQIBXeuvfvbWb//tb3j/67U7j3f3iiECoJki0wev/Wrt4sZnRn81\npdQyEYw9IdYZMKGsBjXIORBDHSMQAlwC7Bj7QIi4lk5coyhAOL9b8nQfSGPIiUJgOGJAkwgckwDy\nEIEjoiHfDyzD2zwiWBoHAg6LjNBxWla18J6BEAjYPYIxBTgWVQkKR0a1wsDD3+RWkITQbNYzc7UZ\nNy14kEEIiIwcKDBY0KtDqGkQfzJFTIHQuJqWcmfX0A4nE2kmEySgREHkZmH9wJ1DRCREA6bGvToU\ndvFagwNAHIQ4Owm41doTNUQpN+wKrook0Ai4Q1VnwJTcewwJq2k89lAg9EgMSAmreQIIW4AQeAKG\noEQUxgTGwGHmZNXAIYTdzAzNDve2llc3kal6lSQ2SB2rOiCICCTVLiAYKMD82CWFTsQMkdwAhRGg\nCzOUBAyj1TPjC5fz5kVKawgZQADIkRA80BHycPoJd8OK7hAeURwKWHjppzsPH75z4/Wf/HLhuDYZ\nLaWUIeZQO9ejriyllBCPyWoyGHZIwgF0ODgReqgS8nBijAgHR0cK8HAi9nDCYdQxIMQ4wAnA1YmI\nhnANxiAyiwhD5wCGQGKEYa0fhIgeViHlpd239pYvfOvUb/+9a7/zVx7/sw/e+n//dy/91v/m5Oe/\nePO/v7RmdyCBaQ1nCHDwwGH67lAiLQNLNjVJhCjDcxBAXtVNzUAN0GvO1IySI2hJAMbSEDsR5/Ha\n1PbRNShI0LXJyBBhVmut5nn4m8wgC5sHcAqElBgZGZxzYkogjRkGBgSryWK2YN1vR0sYDSK07UiE\nEMEcLNQdwHowBCQGQSDDoZYSIA6IpsYJUsO901ATbcYrXalKU1aisuDRhMbLjn3GpHOfllmvbn2H\nohmzhyPbSABE+r423AJEoNN4RZrlqF1Rj1m0oyUgPDrYajl80ZdizCVzo3m0mB8IIRJoeIPQpDY8\nIIX2M6LojUspDMpCLCPBxXx/f7H18LDz5vDo8Ozm2vqYlkfSEWzvdU0LTz773N7h0Z0btyeTk73P\nT5xai929zrFzeriz+9K18/3e3se3HtGYT22unByPHh92vcJoqa3VqD968vS5o8XR7mE9de3yZ7/y\nuUbKjffe3b67Mz2qgIlXls+dPeHd4bzvSy1a6ng8Pn/l2vRgf2vn8fzowJm+9jf+rk0Pbrz/5tsf\n3ljZOPfX/+F/mO3w9T/7g+5oXixtXj7pAQ/390M1haxcvHjt2rUP3n7jaGfnsJt//W/9RwKzd375\nwwcfPaq1e+bFF2Rl43D/zoPHtxdb8/NXrudZ34YgQe9awDlnshhllhXodbqysj6zaSrYex0i2uBh\n5oMv3qJ6HHcSDDEzu1aDYGQmVnQCImZ1TZRC3SLMFkRiFmbOw/tAggYUIoCDiCTVCghEFAAiKTzc\njJCCwi1gAPS7A4L50Mo3SRmsDBz9oacIFOrAQu4OECTk6iln1YoRHoEexAzuRsiIgpCEoCgiGgyx\nGQeWvVs77fLplQsPLl85t7X7fsQIMQavW0B+/ds/OXXuIjUj5FFeY0GMhF7mhAK8AoyILUagdeEI\nOGixGcEAwqGB9gRuPpEgTQD6e+/CwSMetKyEwAJkjAJWw50YzRUFEYIAVC2TOAYASWpclVHUKwpi\nqWaHlLIZwNCpNkeAoBJah9s2YIRWya1WQzSAbig+uquTAQKnhgIQySUYyVTVjDkX68CQmZHAai+M\nCIUiM0iQpSZ/9qlT88U8tS0AuhYCJJKAAGqqKqeM2gM55uTmiMQsCIRhx/I/INU5cgIgjMAICzsu\nwwQhMVgN7Rw5giOqSEIaJM081GskJXcjJkIOiJAcviAgMjdXw4TRMxEgoWS1wiwW5kOHDmNzc8OA\nzPokQh4ehdPYwoW4zmYiAO7EBKAYIiKB7H0vQMbOzKAQEYJNAAIK5qXJ+avt5hPUrgULIAENCzc7\nDmMcD1UD0MjBvQ76TdQSpovZ1v6NO6/9xY92DnQOcWl1qcnWHVYRmtfqDg1DIgR0IgzAoECElCUC\nCcDdJOfqGjFIMCMRBopq74CIFGGIx49MBNDQXER0QMcYjoxmhiQ0EHIA5HjD4OgDEmMY3BxHMlLK\ntcjjV19def6Lm9fP3pqcpXvv7f7p//HU3/ivr/ze/3b3j/9j8RFWg1BzG4zUBBaBxEjcAIl7D1HS\nqCUW1dL3Vt04kIglA7knEatUCZAY2IElMIiZGJqmnR/tJou2YQXXCEJQNSImIjXUY3YdIoF5ZEZC\nzE2KCEyJhZlTDYS+C4teVVUpVMuM04hzCgwFQHWLzhGK9hAgo9XcjkpBLZXcgSlcwjzMOGEoVARE\nwDQWZiPkNk3ihHoXCl6VCNvRBPJIxzJ1N9shdQavdWbmbbPOyMyUVpbczEJdE3M0jcy9UesN3YtO\nRquAJ63rUTvXGYgAKTEhZzMnYFCsfRng0ahdk1FrTPt5mC+NsyRxRDnsYP+g3z3qm1RfunqSQVma\nxw92+8rjtby5sfmrDz+G3CydWKHozrYTmx9G2xztTzOnS2vj3e3tvqJM5Aufe3r77v1bjxeeefXM\n+YPD/Tana5dHD+4/gnb01AtPPHH9ymz77vt37hzOObfLHB01ozObK1037Sppv+jL/Oylq20znu0e\nPN7ZmZX+xZe/sLk6uffxr7cePdw/mD7zwgu//Xf/4aN3fvHO26/tbU1PXrm2kcdH80MzxwpEsXpi\nzcv8Vz/78eMHd1c3N3/jr/2P+r2bb/76tRsf3zt59tJXvvlXY//hKz//4dEUGP36Cy8xLm798u3l\ndjknODiIGEFmxow0aSZLvLK0fO/edljUAUJhAHBMmsDhGOSO6EmSWnCEmQbRkCWtXiEMkMw0STIz\nRmRmCw91DxWRYd1VSmUmwFANRJwtpokFUMyGWK8jDFkSsCC3KsThA7UqhLOFE3G1gkOIC/D/j5Wh\ngTJKEBEWgGGqRGKgEIDM5KEA4WYRxCLhmTkEtBoZOBJ4eOl++oO//MpvveyPF2/8+v1wtACAIBCF\nKH3/vX/6R399ZVVGI2LmlUHTilGmhAiyAjgixoBA0OFdCABDdTrACVscnW1ONAgYBEZs0330HqMS\ngyG7BRITEyIJEVHq66KRETdm4AAQoWoR4AbAkimoNktce3fjANMS7sLZSdAU3Ltu0U5G5BjAao7E\noMYRTmYQODA81QSpuoUXDjHsAZzAMYaBqocjDA5EU0R097I4RAyQsVFIQoji2hOLmUYNBFxMD0dr\n61UXZTadrJxgEWmavd1Hk6WGQlSKV4ck6Bjh4ApAFEbBIVGnXUjOzAjQu3NuwHqBgGbkhkAeFQCd\nmcNjWNWZBSNiZkDxsOh7Tw1hg8DI2d0TsQcJyXF1PFCQihvJCLUyNRC9qiEDog2G99FoUvoFp+Ru\nwmKMcFxESCFGBOYW5oTmoQkCRivrV19oTl/n8cnAliAdd2WHpmvQJ6+vAZTqGIoBHjXMI6L0e/MH\nD2/97FfvvP3BHHFz0iaqaGiqIlQcRowtS6AyMFAEICMQIaKEqQWwQK0dURtmg8CraqREQ6yUCBDJ\nNYhp8B7SgCkKhIBP4DJAREBstQ59CxzwuTjg5sNCMQQJBiLC/HDmrfAjf/Tj75z//DfTuXP5o5v3\n/l//5Mxv/xcb3/zCvT+cLHHNqDMlRFEtAoiM2CBnRPQIBUrITJSQR4FJyzRMHQAYOVhSBjIwiDCE\ngpHQKrWJm8TokkQkYWhRM1eobuQirEABYqaEQcgWDgFMBAjIaF6DJDGJjFzY1EoxXRQAx2GA4ZY4\nV2S3Ho0AsKqFRSmWMmB0TV5Wq4ZVVVFaR80SLFhL7bsKrE0zSth6kJfSNohLuUaeH+yZats0klKk\nTGk8mnRYZgFausowyglV67xMGxonGaXRRJXdZ2S1zA8RUuJR5a7O9yvQymiswlDANDxEzTCC02he\njqjoKGUL73t1iBi37qmGqVVhBnBXRWbZ2e4ePNzaWF174eLG/uGORt7d2tmd26WzJ7eODvb276el\nZjafXTx10uaH1s2qw1Fn6+Nm0jR7+1NumsvPbFw5f/LjN9+7tdddeurT+7Pt+49ur43a5Lx3WJdO\nrT7/meuT5Ns337l9dyua1SDtPdZPbiw1cjSbb+8dGpSVPLl8/cVzl6599NYrd259DGn85W/+jncH\nH7396sPbO9KkL/3+37t07Ylff/+fz/dnD+7dPX/5yQjd3d938ugPBfO5y888enBza3tnMT/61Fd/\n88zp87fee+3B+x/tHBx85a/+e5evXvvgnR/ffeeDrfvTlc3l8+evfvzBOytIZXd/lTdn07lniCqL\nvh+Ns9RS9u3u1oEX45xDvSsKw7M2ADUjXAdqNtRaEGR4AQ3LHkHqh2FjOLOYKQCoOxNFODGNqFWv\nHu4eQhJux1h7YQEBpAEB4x5DZx8Dh+I8BCDgcFsgADUDgnAn9CE0PlhlzOLYjjTQ1CIioh2PS189\nYphLElCFgjD8PuNAJxYiXR5NpmXWhVKQIxQtD9++sfT734y2v3h2/dbdIyDRoQk37EQP+p/+4bd+\nc32VKVMiHjGjDbcwRoE8ARoFEuIRaO9OhA6gAASDiTUSjE+mYYLBOL/3HhzsAkQ4EAGSAR0LawBz\nhKZmEg4GBmoAgZIBA4MREkR1UNJwU0BxdfdCxAFVY5ExR+KxLA+Rb0SEgQ+XxIsOYIwghOoMCIFk\nAMRISJAdArAIZS8LEvYwJqylZySMKF5YUrixVghLLAHoiO4KNQIhsI6XV8KDCJrJGKK4lUq+cfrs\nfNGFi+TJzu2PVs9sOHI7Sg5D0dQ8gCzxiKJ2GpgFBQSBgDEizIcPydDAoicaESXvSmAwi2vRrjbN\nKlICGRIDqV/MxsvLJCMPC1UkJKHMCYGdCC1rN6U0CjeMRE0CwVAHDq+KQcgEPvy0MCL6cMFnNBhC\nu5QRVSuBODdrF66PTl3DpQ1II0IBgkCGcIQETAZBARAeoOCAYe5G7lEXqVnqFnu2P9/68PbPfvTj\neUkQ/bm1ZnmcFo+PBnfhvFQATHGMcmNuxc0hhDjcmMWtj2AADu8BkrsyJ3cdDrLH+Bg3DAJHAiDG\niGCSQbYlROBhEYEMYSKC6MB0TKYIxKEjTE2gAqCqM4HXaKiYpcN33zv93Kee/f3fefu/+QD3Hzz8\nxf/z5Nf/82u/+5/d+PZ/NRlxj2RFEck82oyUxzkhEVu4pIQYxOyILCJ5VCBAjcI5JwAIRXdDAHIK\n6ZhbhAA1o9I2I2ubcApzrR7hTKSAirm6h1sIhyBiwoHnyxRhYWwKyJ7aCEAzqLVgWMOYxrkWLX2H\nNIsILYpxjK1yYIUeFaQUtBmHVqtEJlh4JJLIaswXfTgJClQrOHXEnBoLSShE2IyXvCoLATF4uM5z\nIuem516EFOuwH/GFhfbFog2gtMQy6stBeEHCpiXEtY7bEVTwuiRNp2KI1WZEYy89BovkiEUpHSn0\n5k6IgNoAM6fUMnNgACV1lK3Hj6+eP3H+dNsv+oK2vjJCpzzp7j/c9nFObeKGz41X5tPD5baZTg+V\n8vkTo8N5v71/MLP65U8/1Ya+8+ZHcvLciy9duPPhu/3e0Qrm7rBqC+efOPnEpTN6OL29tTPtfdYF\nWt8sTzY3xn4029sv09K79ifXT2ycOY9S3/7Fnz+4/eDktSefe+HlO+/+6sGt29W7c08998Jv/q74\n0et/9o+3bm/1hT77zb+1c/fmjY9uYG5XNzfOX3thvDR+/ODevXv3lpbWvvI3/4Z1D37xnT95fPdh\nOxp/5bf+JoN+9w/+ye72AXJ6+tNPJG8ePLofWstBGcXYhy6TkoEKIFjBNB6P1+aHh8akGu7IkiBg\nSKOZViRSdUcQTgNcbRCBB4G5ASIiM5KHRUAM6wdC8ACHwKheWTgQAYIRVQERidnt2GXExIiBSH6s\nTDIAIDQQHvCj7gokQXj8poRBoU4BxwsPN0c6DmoSIjGVrg5BuGOmIjghgbvjcdCaGTBgejTVGJga\nFsAOae/+o4/ffq9dW9o8tXb3zsEw1QIwxwBMXcTtdz967Y+//fm/8zck5dEJAJwwpqizcEHKkUaE\nDQABzEkX6BHIg8sQIIASRGB7Sk4IIk4wdfAOHD0YMF4CNPjERbh6h8ObiliMncLREcLdOEJrgczs\n5GREBK7BIql1dyBCDUcTZAcPREYKB8IhnmshFNZjagEpSgcsRQsPqiyLGi4pE0ApnalzmzDUtBKQ\nBZFwqh0iYRrZQoEB3TElJoHqNQohGQa4IgmEI6EaMgJaWKlNcnTc3t6pSO3ymvfFQSGAIqqFSGO6\nQEyclwPN6wKCkAOOX0cUam6QmKqGLWZpslQFJciAmd0hgxeyGK42ENS2Y9OOiUko2EwLuFBDYAWY\nkCU3q2ozPF6oIVoMBpbBO0Ek4Y5g4YHoJMkjiFlSDqCEql0PIjierF5+enLuaVo9B2kFKcHxSdER\nCMhgiDJ/AuQmDw8FN1eNiDrflWofvfPWK9/63q27+1OFyxtLTZKyqEV1JKJBfYnVJlEYkyRCgBog\nAI54vMxjEvehjUGCFpLCLBAoEBn92NDCcWwsBNWB3GvggDCUnhAB3F1dFXl49kUIEGAQjYVHKCIF\nGAIM10rtAzPv3NlbeuvVy3/r+dGnv7H40R/u/vf/982v/+frf+sf/Prf/Ndr3SNJq9oQcU6MxMTA\nGsqGQQoQSOwGSEpC0o4AgzNpPwsNJKzq4YAByJDyJElWt77rMmVsliSPtJiqA1L1Wo20ryhkgzyL\nErIwo8gIhQVNtbMaaoaq/WxuIMWQXTK7ZEJAkbbreo0hM8/olRCzSAQDihWtiwWmwAh2kkiCIcx9\n3y3mc2RqmzESdX2BsNSkcAizij0ijSfLVTsyAmrVLTzQFZmSSHGXiiiCTKX2/WI+Dp8ZVSwJOCQa\nQkSHsphMltu2jVksFouudL3Xedcztx4CqDnZuE29r+mikql6AQR1LAaZkIAggqX1oL6EPHFudP7k\nytHBYbTj69evP755R0uY82RDKsH1S5s3P3pw5Hz29GS6v7+2spZGaW93f2dP184tf+1T1/Yf7b55\nZ3fj+pM84vdf+3mGnCxvH8xWTy89++knzm7S/p2trb1ZDe96csqnNk/lJvr5ovSxd7jPOZ07d2Fl\n9USZ7928eZPS+OK1q5O1yZs//NOt7Z3RxvqLX/ub164+ff/mK/fefm13az65eO3rv/X7N9788Yfv\nvjtZ36DUnLt89fTZC++9/tOPP3rr6pWnl1dW3/z5d/d3Hh3sdVeefP6ZF569+e5rBw+3rLdTJzc2\nL19DxBs3PkySz13avPOjV5aRQdyra7hBCKUktDKeYLjWHkLcBqcL+PFpkAJ52KUPzw8iBXNUDSEC\nGuATzATHGDRn4kF7FKBDX97czCwiRESPocAB7p+YLOCTOcwwOBrmSYEoGB7hgNSOxqXUQeztjg4V\nQ8wUiQD9k+1FDLRCUyNAd8DBTYcpoveIYw4pcjjknIpXpkCHAapMgABKhKW3n/34jb/x9//6ztlH\nS43v9jUgBRAShNcANEqv/OgXS+vrLzCH0BiFgyI3BkfcAcImpBEkASJCAF2E27EQIIwCHANJsDlB\nJ55NkoGzPhrr3l3UuVIMuCs9VuFE1U6kcfJAIcfhw0QkThgEGBWrQYRSEA4qAyTEIMRqkQKDIYqD\nuBlx5iS1OxJMgRJGzGwY06PDydIquppaSg0xW51jICgwhZcFpEySrJqVPvNYOQUEeHBCdU+jiZoR\nkkuTkAKcSTigLhZECMIQAZ6sVMU+5bF7t77CTZq4QoSntY3S9WgqrmQAzSS6aljQAAzMe2qbAIgA\nhEDmgVeeZRLdwi2YxNxQyJFliENJcndCnB4cLk8mzowCgRbhqZ3087nQqBqHVRIPFoTERO6VkB0A\namhfmfNgcuito8QQBrmJAEncdz2knCLU1AmM8/q1F0eXn6PV85BXgQFimIQPjTwHz0CD/quiY8DQ\nGlS3QqahC5sfPb753s2f/fq1196aYzPKevHs6ohl++5DJgGkrqpGZCThAHIiIYSAof0QA4AXWRjd\nqpKgDothiOPOUgQTW5i7I4qjC+Kws9MIdU/HS1kkAkFATR6RkgxqveOHCBxxAOrGAPglQCcKCoeZ\n+PL+ux9f+vrHl7/0+Xde+0F/49HB9/+r9hv/6NTL//7hD/5v7fKRL62HpDY3jsEGVdW8J0oA4Gau\nRVgRJ9KOgAL7wpmiqJkGYRiAAOZMOQWSQ5gW8cbDkMVMvFpRswBdzJASoBoAkRhGwuG44EkYIUM1\nwAWylb7O5jMAAGpGGZuUg4G5KdH7ol/UmeSGgEItZ0agCENua5Tk1SwANDEKAIFaLXVRS6+jNrtF\nWE+IJJk4lVKQMygHabZKZo4OZYpI4QwETWaFHBSVqGkal3Gd96F7RZGxN0RhFAcNNugT0WTctJvr\nzYVLjx4/3Lt/t847d3IoHDBOCciFc7O0VBbQHR0kxHAjgIhi5ugS7PNQJLYg2TixfLA/PXHuhE7r\n7ffu3tk9Go9PNBNfGY2WGrhz4xFDLDc8Pzw8e/qUq20dLR5tz5/7zLW1iTzeOby7ddCcPf348U6/\n9/Dk+ondBzvGfObqxpe/+hwtDh5+tPdwd+HMQYwtXj5zjqUeHhzWnmvt15dX10+tu/mNj94q0zra\nWHvyqef27997cPPm0eHi1JNPPvXy59dafuP7//zBrZvOK+c/9fWXPv/SK9/+x4/vHI7adrS89swL\nL9y/9f5fvvrz2aJ/8VO/mTDeefv1+SyWTm5++dNPtrl59Qff7Y8OWfjcxcuTE6NHtz6+vXs0krx2\n6uTh3W2bVcAWKoE2TDURENHyeNwdHZauY2nc0cLcLQiHkIiZfSLQhiAiSgrKSMQ0REjcI0JTSh7B\nTGbDCEVsaC8ReQQjeUTTtKX0Q8uOCAepZiCCg7sdoxGZw4+lNmRWfbg8Qt8vCCgCgTmg5JS1+AC7\nQSSKgMEhB4YomyfP7O4+DlChbOFm3YBbI0SH4c+wqhwgTKAViEQhEByAQoLj7lsfES+df/nJpx4f\n/vJnHxdIMIQ0BuE6IRu89mc/yK081RKRwepp4mAHO26GnEQeoQwz0hbqIXg3cKIiDDwHKRBRc8LW\nKcEYUyZuy86HjEGg7gPhTMJDoj3eonkABQChkIMDc3gfAQEOKMIMJO4GXixYQDBlgwWgETYRGKEA\nRU2lGYdbuHo5iraRPFni1rUnYo8+nBwdUZAJIUxt4H6bOwHk3CoGEIZWJAwiTuIBCG5WIaJGbbgh\nYquVMhMxeL+9VbQuTpxZlTSKMkvNpFNdWpkYTIGFkAFzGmcCKfOu9geT8cq82wvtObccHK6BQikD\nVCzh2DO3CoDNyCMyJ4Po+2mWFpkgxEzDDaRZ2lgb+AkOiDUAvNauGY2DQiQIuNaOKQTBXAeAO4Rh\nQrLwUiVlM004KEeAzFMeVS8ZIVANEnIOoRNPv7x09QvNxtPerCBxgADTgI0AAIA0YNQjBsm5QZib\nQ6hbBatW5vtb9+//+p0ff/cney6l61948jSRTnfnGIDBnH2xcCZgdEIXyh5IFgwxZI0dPaU8BPCJ\nEYldPVx5wGcTqVYauA2ICAgGFQKZqjsCZOJjxz3yMFYBMGEeXrGBQUhhhkMYYGC10aCICDCHQGE2\nwH5r8egXPz777z2/9OwXu1/98L1/8n/53Df+0Yv/4H/5nZ/8s9X9dycr6zNVF5GczRWZCcSsACYi\ndDWoQbBgQgEKQMCkBOFhVhi5TUzoUTpD4dySTJClGlTHRenBS2fqpomEkDWUgHPTYErDdMshHAJq\nBUbJ4gur1WuvDJEzsEhVyJwRw9xLUYJqABUQvNY55FEhcAcRBgYEV8mMSGhhCuYFwbPIovaNKTec\nZATAbrX0C0mQsph73/U+yLBEgZkhNQmtGQNFOOUlbzMVQRpt9CLT2cOmQjNuMbiGWq0AVqHykU+W\n2uUTk/z0C0sb13ZuvDfd3VuUPYIA4BiAkVY9kJsWA+JoH4VDBILMwhxQq0gQkSweHSBx2Z89uD97\nPFdPy9P+6ML1K7Z3+HhnlgJGq+MLZ1YPD6Yz7efThQJ86asv8tLooCvN8uTJ9Us/+8WPTi4vb66s\n7x0dnjhz8vyTm9curtnh0cM7Bzu9aUWoZenE0tr62nwx1SAzJrLTp9aavNR57O4/7Hs78/Qzn/rM\ni++9/pM7Dx+aNNe+8dsvf/kLj379g59875WDWUfL5z/z9W9S3fn2P/lvumn18MvXnj57Zu32r35Z\nnc9cfG7j5MY7b/58ftgd1YPV5RPPfvrTZf/g9Vd/vr91OF4aX3/ymTKbP3ywtTiaXlidgLfVD2F2\n1BhZAqtWoZgLsi4vjar1Lcl4stwVMzNATCmDhNWKxKpetQIAUDJTABu68Dz49dyIGBFVFcAByN0A\nHDHDgAY0RaThpF5rcXBmdo9wBIBjwR5ixABhjPBjN9MAmsm5Ua0BFBaOFhEQDoil9EKN/TsDMNMx\nMtgiLHa3HyMBIQ9jW+YUYeZEGCnlUjoSwYCEKIREhBpBgZAcDKIiRerhz//oW//+/+zvlZ3ZW7/6\n2LvowSjAIggAXZxwvrCffeu7k5UNQQrwMZyyhpgUCgYKNhypgdQEIOEEenQvCArAgRUhADMKEwOd\nWYWGOY1hzPboptU5cUbvIJCQLbpwx9wm594XHG59oZzBDAExgHLj7oBA7kTigOBuFDmxaSJEcEVw\nEgAWBA4rzCOTYOAACjdA5ZQgIOUUqkPq3hUhBANSTg4O4ZTYEMnQHVnEwwkjdAgEGiJFYAp2rZEA\nkJByCIXD+tllcvWoAARa5/12btsyPZB2bH3fdQts2p3720vjZRi1DTcamttlQ6E2+3zBgCBSa29a\n2naZWEyNU6qlDPnS0qukhpEUUVggNNFIQSFIcCB4kXk3O6rjcQpyB86Jwhwx0IuTgAYQAQ3vKUx5\npChInBOU+WKA2UJwrQWInMUDWO0IdP3Fz02ufDZvXodmFWSEIhiMABEDKjYNQ10ARXADAzfwEl7B\nCtai3cF87+GDN9984zuv3N89qCbnNprTp0bQ+d50XwgI1VzmBVokJgCCREgQAI4QTomOT6kIAEQU\nBu4+dDUQKWDId6N7iEiEDkebgeQkSOpuw1FqIIUxAUBKYqqOnFgCDBxo8IERmpkGkgNwwuG4LFR1\nOB6393/+3uan3li7dObBu7m5+ajc+JZc/f3Vz/4d+M5/aQczXmpBzWAOIaltGXkxr6Y95TYCLDQK\nIpKSolFABICbByAldiDzCiUoEKOHHGYctZoKQK5a0DHziMCHey1zCgRKYl3hUAjWriBlYjJK6qBW\nCepECKkSSBbxMDsGkDY2t6JH3IwJslZVL0IxasbhaqachLklor4sqlliEQkzrt2Ux+OV5RPQNNPD\nTo+6LEhmAQ5VK/pgMAlVJkQG7Bc5c2fC2bVq31Va4VHG8dLl/qHUvbsTXcyVkVLbkgGU2WLhsy28\nt+htdOp8Hq1D29BoEjYP7w0Nw7CKhzLmqqF9BREHaiEhEXGy2iODo3ugnLlwZndr78b9gz4ERpN2\nVJ+6eGH7/qPwlNxWToxObq50tXAzYoAT62teS+++eHAP0+j2o629raOzK2vgttPP1zbalz//LCz2\nDu9vbW9Ppx0d9iYjPLW5OZksz+fzWutsOl89sXH2/Gnt64PbH9aFadN87e/9g9U2vfLdbz18uLVx\n/upzX/rmydNrH/38z3/95ju+evmZr3/m8pUL+3fffO/dN+88mKY0+sZf+Z1+59abr/+qXyyeePZT\nkvnNN35yNO1c45lnP3P1iasfvvvmwcO9+VyfePaZE6srWw8ezuddSnj21IWDfu4ha5P0YP+DzBKu\nZpQlOYISjVLowg3MMUzRLQJAvZgGAYabqiYRj7A4ZomaGRGaqYULydDVxeHY60QYyKzViAECBtOL\nHb9AEIHCnImH3j0BB7jFIAoZEF3DGVYRIQJUHTwBGg08Syb36oFIDISuxz5v1SqS3Z0BAZ0pm1UA\nPK4n8nALtXB0juGxtjBAZgTGY1lG9QIIgORuFnrzjfcevv94dGbzxc899csf3uyANDChGYFEsIex\n14X++F/9a27oIghBalfZsaWYg4cHI57AlECyoxAw1GmYc0RAE1EJargTtuDK69eBR5CgyFp9+CGW\nLWYyVyclAEKJIIvKOBxwc9gQRo2gBinY0KxEGtUwIQwg96K1hvcxoNZEMASGoJEj+Zw4GYNrIRyO\ntg5AjA1QALlFQnK2cGT1YBn4k6FVhTmEAzOjhwZoAe8qUtvk0h8JSwCDWfjAESLAxEAhgkgREMQC\no7pYAKGqJoQKBRe2Mlk3ND86iNESY3J0FgmH3E7MCiCl3GBWAEAU5BJaGLHMZ+CQR2O3/vjI3yTk\nkZkBJUIwrQOlAXOzfnLFtKeUINzU3TU1I3AAD+WBeYboweBBhExRizkAIBASJydg5iBEQzfTyeqZ\nyy/mcy/IyScjjyGNidIQIoswxBRAiAjAER7hNBiiXd0rqJqq22JxsPfo/Ru//rNf/uSV9xaRxfrn\nrp8bTZq7Dx8JkYQTSq/lqPSbo7ZFYJFwIyYgBEoETselGogAd0NAoIG+S8f/FEiQNAZ/D8UwXh8K\nSe7hQxjOiNgG8g24Kwpn1w6kcfsk9/qJyz5ioAubgQeAhA+FUBNsqhy88+a5r/79x79+Re7VX/3X\n//BL/+X25d/622//8L9tD7aWTlzuiYafAlcDMPXqQFBKYgojD6tdH+iMEhjhjoI5TSCw6CLckwCk\nCFdQqtZLztBumNr0qJ/QoJSkwCDgCGAAgpAmB7i5OTD5goKrYomSG2maZYrFkG+oHgHiThYBzp33\nUUFikZcYKmCdSRq5Va0lIggqYHVIWoxkuB3n4oVlKSXxJGm0nssMrbJ1Bo11amgSEMJgRYSFmQAd\nsWhknCknN/cKMO3TUjca8cb6ienioNfeq7aTxGjNZJ14aX6wszfbGm9PVw92lSdW3X1RtDezFJhF\nEDnEJWHGcdU+Abi5RkQpuQnOSTLmnAJI9neO9guYTA7ns0uX1jcyHTzc1xqY4MzZ1TY3O0eHB3t1\ntNqsjfNib35Y+qU2tvYOjxbby5PJBPDooMeWLz917smL6zR/vLU9O5xGdZ+rTtaWT51YL7Xu7E8x\nsFc4ffHqyVMndh/dme7vzQ7KmetPP/25LywOH/zkRz/d3927/Pynnn7h5dnOrW9/7w/XLj9/5fN/\nZbS6vjpu7r7/48e3b938+O6JtRPnz128897ru1vbpS+f+drXytHu26++cTDrz5+/+tKXvuI2ffPn\nv9g/OBw1zbPPPzM/mt388EMZLTcraxTd0XwGQheuXR/Hzu3DxQTWDWtBOpzOUxqtriTtC404iSyO\nFgBIgmEeAwgRkQZ0ubkjRRzvN4ZkJtGxBxSBwwE4ImjYhXg4kQNwgGGQ2uDDCSSOgUwNAE5ONpz5\nGCXYGcHDiJObE1MM8RREF0NHisET544IPmRTK2EEEJOYk0guZUFNg6jaFzxGKQZAEIoFJqDA8FqZ\nKQyAMCAS0ycYkaAhagCISJio3+t+8Rff//Rfe/n02TPLo49nc0QkCyLHwOpMGGIMBzuLH/+Lf/tl\ni0sYEdGubUCzSqDRbxEZ4EmUAQ+5TMhQ2W2Og1URCAdMDDHiGJfOiAjIGudR//h9nz5G69kJmCIM\ntA+iMATCIIZgAnTrLCoYMiJjBouhfwEICG6qgIzYcMyH0lpgBWdCMgWQYa2UwWFIGAZQoIGbW02U\n3Y+/RBRpsCOAY2KOCHJwX5hVkYlLIpQ0sHHyCpBTeAy/xqJ66dRKbpdSGsfg8g5Ad5YmWI4rJpRA\nqPYzFuamDTDvNAQgN+4lpUmEuBuAYu05jZWAeaLWoXtemphpqgUYKWf0FF7ClChjsJkRoEMIOgSY\n9YHqkJCSe0/E4Q6gNrRKiNA1S9ZQ7RfC2RmJyYmGyA/nhMBDxxxHG8tXXqRzz/H6E9CuIo0pjX2w\nI3kBwAHfOpxVIGy48YD37hW8hKqXRZ0e7N+689qf/fCnf/naFEK0fPrZU2cvbs4Poz+YZ5BAzBS7\nXbBji8hoPDDqAY/PNkYkFAgaRjHsJiM0YNg6YCBQhENidjRzRAwAG9aHFEAycLcj0BHh2LgSxGgR\nRGlIiQ/VqTAopaZGQpXQkYgG3UNEgJmjWNgc7v3indHmT5tx6gFX3jmojz9Ye/FZvPIb+P4/5sW5\ntNyiJDOZ14PogZJIzmBGkp2QQsPD1FAYEYmQiRC968CNwzQEE+UYjGGqQBS1J27DGUCHoy0CMgJk\nFkZyR2nclVMiEui97/quGpoJamoFYimIizn0QalaUF+1lF4VkACJojgKZl5qSPpSZAB7KM0WPeKC\nc3YjB6gs7aQNHzNXm89soQ6REgfmar27QJiBkWLO2SFUK4aZ13APZkGmlEO9lqLd4bwGGE3G466H\nnDKS5jRBTsFhmNq0Xmt9/Gi/HZVmvIqRoVcRRsildNQUhkkiyatjErayKFqs1NKrajQZBmUzUJKP\nHs3n1bWp3/jCiwf37h3taJiOR01CmlerqNNpOXf5ZIOws3XYaZrN6uOdx5jp9KmTYKFjaCfp5Mm1\ns6eWFgeHB7vT/QU6oyGcPL+5vDza2551pqOlpdL7pScuipWP33pjejStpTzzwvPrJ1df/7M/vHvn\nXttOnvz8Fy5cuPDRqz+7s71/+YWvAtru/tZq1vfefGv//vZ7H9y6ePnp5eXm7oOb3veTpfGnvvil\nB3dufvz+x2p49Zknn33+uaPd2x+/9+7RYddOJufPbO4+erg46ufVL1zcmB9uI3O/mK+tnnpw+73u\n9q1kDVCohpq3bevaj9sTOepoeelwdweRzT3AWdgGcWqEWhCzGQDF0CFjHoaWNnQlADUiEDgskJCR\nLSohAaJDIOEAChtSne6GNKgnaiAMYHsmJ3Dw0FDmXOtQADgeKQXEkAl1pHBFYAYMQjclZPMgZA8X\nIS0FwrNItQB0C0fi4SkGsAFyCiBDGWBQ6qBHI9KwVDvmBIRDIAyKJmF5/7U3v/zN38QWTp9bn358\n0CkZGQJgSIBThJsRy9b23vf/hz/8DYxrn6MAb1chtSsE4fNdCsJmM9qECYBHQYQFoc4jHJEDzN2I\nUoQDLfkIeWNEuaFmtT78oO5/wLV6XRDnoAjzcEMgCLMAlIHa5UxjiNDohQmhAS0eJTiIKJiqmaSx\n1p7Rh3wgBgDgAF0zVxnux31hEfNKQIQ5PrEWEEmoBShyigAkdu3QgykZUIBDGEujVsMLIMHwsxJD\nXJGb0RLbAoNcOwgHEASHGGDmhmBBAhEDBzPcKSwcCNGZwMxK1/eqVlPOzimNJuEY2qlXbht3Szlj\nMeuqayU2Ig5sEDN6OA/6SqlWnAIG74mMwwMZOLVeuwEAR0xErVsND8uJMEs4hBEmdxUMJSAgrwot\nQaBPTk3OP59PPQ0bV0DGRBPIGbwit6Hlk3uwHk8sYwgwGrqHG7h7UauzMtvZvnnjvZ/+8pXv/mq3\nAoqcnsSTT26sbqx99M7biZOpEqIGHJTIiZg90HmACyKIeyglYT8GEx77fhHIw459TEMNEFCrDexf\n9xji3fDvEEUs6O4ex1v2IbNt6GDMNHhkzIPCIJAluVo4AsvQJBGGcEgBiAQeZUG6tdh949ULz//G\nex9+nBebD/7Z//rS/+LbV/7Kf3z37X8+OzgcLY/6StWt6whDm2CARNIA1gALdM6NeBAGsWBK7m5W\nwWbEBJJJuJoSeHUvpesWXV5JzXKTmkm/2EeMlIPzEmcaogbgFgGYEnOqbsaY2yZnxIoYHQCTpOLe\nm1OQOKmBGxBhkxtmYUoAltgzpyDBmBNADQtPRGBaSNGCuwLIPmpTcU+SrCpAeNSqFiGIGoiuCkjo\nzkSAaVF7HpCHyJzGGIgImFjGoGVW+x4BKTNWMIdMCMSlX5DqKCc1U6i1JyAzn6qhcEoJNXpAUiOq\nBSWjFiGTlslaHS3H9EDARAQJqtambcRHfPJUeuLi+ccfv8ewbGo5Z0Pf2j8cxagxvXblzO6DrQf7\nc2lGj/d3fZyfeulpKP3u7kGMsghcvXaO+9nj2ztTc4jGkgfhE9evY93f2+mMeTJuAuPJZ67PD/d3\ntx8/fPBoY+PUZ77wxYe3bj6+d3fr4KhZO/XiV7682tIbP/iTu0eygLZ/57V+drDc5tffPnx8/6jr\n9atf+20Gvf3B+6XG9edfOrEyefeNn2/vKufR08+/+IWvf+Ptn/zxrfdvE483Tpw+sbF05+adrkbK\nzRNXLu/tPk5CtSw2z5ze2t4Cta17BydoFBGGsLq+fLi/QzLWWcdNHG3vuWEADRMSN8DjCEy4G1IC\nUHMf2ngRbubMDBCAw+9xQOJ2PJrP5gABiB4mAw4fcGjfIRFTDtDByI0ENOBJmcCBmBFjeEoJaUjW\neECEC5K7QwDS8AcM81hEcgdGDIxwGxqNiLCYzZwRPZCAiFSNPukFE5PGQBGnMEUYoqvGn5hsIJgQ\nNEJj+CihzOxP//kf/9Z/8Jva4+7BKw8eLQIwUI5L9AAIGSkQ4Ohg8aN/8S12uPilT1nAZB0Fx4jg\nix2MADwBqQFElBYGJbdO3eugiQercBzFa3E8wiyR2tQkniwvHn1Idd/rFEBIWvTOzZkEQ71W5hwR\n7j0EcjhYdVImxEjMjBY+vNgcwsG8pzy2Y6wADfUYAQwLA0i5MTVEDFUAQsluHWHr1QKCqQF0QCRI\nIKq1IDCFEwaQGABJA7V47UEEIQE5RMUACG3S2AxKP1ft8mhpEFiazsKAmuzzI5ZUfCZ5CcGs7zER\n5BbCAlEQwlQEAUEkDbAYHA5T4ZxbtxoBMmqDsgQ5hZsTDkhmCELDkDx2DWFSVQsndRjm1gTgAY4g\nZNYjAjKSdrVW0ljM55PV1YBw4lBz7yxz5vXaLo8vvpBOPcNLZyGtACXIApCBJWyOx915iAFFFA5R\nwQOiD6tovWuttffpwf6dO3d//uYP/uVf3J0tIFG2+vLLV09dOL291XdHxT3GRBHQaemqbwplDCGW\nIGYkFKJIwubGyI6BiGZGyBGDywUBjruZjDRMOWAAQ4QP0LtPhMCKPjBlnPBYloiAEeDuBIzgMERU\nBzgkIAF66PBBIACEA4qjowJgQMXtD+9OVj5qJtmsuffjP7n8nzw88dmX32gvX4aZl64yejARQjBy\nAnPzziFyHjk6RCVpmAQR0BzJA1HGDQJFRbdCFBYU1boOatXVNOU2Ly8vHfTTljwn4UaIknk1rQIQ\nMadI1W3ghouMJDeVNJSQohZTDcYEyMVNVZuE2LThVLQweBo6J2FowJhKN3MCDODUBIKDFUUzT4Dz\n6ZRYJAmPx6rRL7SWSm6UkBgCPFwgsCwUi0LiAJDEgMKqhmAATWYW5jQWSQHIODpaTMFqGIAbhLbS\n9h5dnQNyk7H03aKbtjRJ2ZI0qFixcCQHqLpQK2CoXttm0o4bwAkueiRwN3BzNXnh2QuHD7e2Pt6a\nTqmz/aXlcemrA8xLeeLCxXHV27fuUprklSUj/PxLX8Akj+/c7x2b0WhpPV86sXR4sLM/g6nWgpF5\ntH5icvLE8tFs//Cw39/fTiTrTz53+fqT+w9v3/vw3ce706c/+8W1Uf7Va6/NixrWzavXvvLbv3N4\n/9Zbr7364KjMDhbb+3ds7UTgYhayvfX42pPPfeFLn3/39R9u3d4DkSefunr0+NbObTiY1rOXLr/w\n1W+srTTv/PjffPz+Ta946dok5v277zys1c6eOz1qeL44HK8ste1o3h09ePSoQ37p0oWD195rZayo\no3Z8sL/H3EqyydKya2fkjFm1MnNEBCFYmBmThEfTNvPeMQKP9UnD1v1YK8iM7h4Ri9lseWltvjhC\nxONrhHvgAFWjCPDQYzQ/ogMAqPAxdmtY5qsqs0mSYRBnpkgcASmlofMEgOHByP5Jxs+G7QvRMAQb\n/iI0HB78WguzEB1nvj1wWL6De9O0pRZCzAQt4zSiOiJgPZ4FHX+qSPTgxt3Dvbp0fvX6s+eP9j/Y\n7zFINQKRHYBpCAxBlma+P//+P/3Xv8V85uVCUCdrp3HkKAU6xyjcnoHcAoPnTJSQE+oMaw9QAj85\n0GOO0OANXm1BlihvjNsl3f/Idu+QVoyqQ2kMFCg4KKAykZqJE7Y5FIRStflQ+BJs3JWFDQObFotT\nOFo4Kklr/RRBIBzQmZLWQpwhEAQo2BER2/BKiIQ5MNQ1UeNRyCMRg1Cohzl4CUcQsVLDiVkCzbx4\n77nJVTskDAoPHy2vYEJbFGBEmpAAkJO0AZjbcbi7GWX2JBAwP5hNmsYR3JyJCFPUWQnEYjQeCUt1\nE7NwkJSZRbUMkX6WsPCcslnPRG4QVslREdANmTkzQgS6eiRqQkoAMaOHokUFYybBxCdGEc6ctRQJ\n1SDKG3jy6vjUdVq/TkunIY0COGREIRjVoxyLgIZLlQ9TQ8OwcAM3sKqq3hebbm3f+vDWT375Z//i\nux/tdy6IFV56YvXS9Qvt8ur+m+9rXwXR3UXSvFMnY244qiAREgQ6KCGpB3pARgLU8CEkRYM71pCE\nceAjwtCaQB6ObAAUgOimDojC6IEexswQSBAGYBAMpGo0WKKDwisiDokZCIeQFMTso9Xl+f4MLIZl\nKCAgSD3op1u3T5w9e2vxwclZ3n71X61/5T9defFvpgf/9GDmuErM3iJHJEJKHKUYAljtObG7oxgg\nxrDChC4xs+S+79zABitN9L2KK0B43yvNj3J7qh03KSLlBMSAHsgszFnMfD6d1s5ybiiHudbuSAZ5\npoI5IpMwd4taXCE850lQcuPW5okUxTBIS88M6qZaUVyEA5xZHKXB3Jc+VAFAGuOUSCTqwtSYcliP\n0NBAF4EK4RZGkIhMAZBbAlxor44gLkRuKJJAEnKuvY1Gq9JUh7mjRbAyomSURtCrzwdRh/JRgwkp\nEA0sAlQ8RQkkNQ0FrbTAWSVzYXRENHTEWqrs3n0U1Y5m0dXFxupG1a64Ly8vX3vqxMHtnYfq47Vl\nD2vz6NK1q/v7+wdbtVd0igtnV8ajtLU3WyhF2xLDMtaTGxug/f5+V52s2tLS2pVnP7157uTB3Y/e\n+unPVCZf/q3f3b5345fvvjG3vL556ktf/fKF06df/f63b9y+11s9fersE5dWPvr4g4MjsCr7h9tf\n/ivfuHzy3Huv/uXWg93J6sqVp6/fv3XLe63d/OSZzU9//jOHj99+/U9f7ac2Wlm6cP2qut6fPozw\nK+fPHMyP+oWOMm6efmoxmx1uT9vJyosvPysP77IbIgpBv+jdsB03GWrfHY1yExalVAfAY4cnRAzv\nmyChvl8MUc7hcUoyLnU++CHDCRBzTrVWQpwfHQSGgSMCoxCJOxBKYB9ugDLIawkEiCjQTZEYEGqt\nRMyUicBtkOlh5tRrMeLQoRFvAD7spwRIIQiQCAAIIJrc9t0CkQhEwXgw8RC4u3vNuQUg9YqEA36i\n9D0gABNAJMKEXKlGCELEkDRAhMH6VOt3/ujf/MP/7H/K7vuPpu9+8KgPdAxHRAt3cwBGqV6JfTrv\nvv//+Vdf1d87/3kAi3ZtU0bjSBFhZIBwAvIyMUDCoAlRBuzcDlA7PLY2FRrgODDm8SXIK5hGNFq2\nvFH3PvbpPqIRQkAgJwpW7kMxN6NQ91pBWnAbauDo4ajE5N5DOGHm3IQDgHoglAEgoEhiWgQxwDCs\nmgmGoaIDCYeyAw7dx0wjVZMkNjQI3Z3IPbQsUm5IA5hq1PCeiAhHMgY1M7WFL/oFECbV2XhphNRE\nOCcgYl0suu5oNF42VWSINjMIlM6sb9qREbj2zE0gmilzlrBK4GVRBjxCM8FEiLma8lBp5CDMFDVi\n8F1Abke19pyEHIerjUcQgTuxsGlBiKPp4ahZTokiSXS95Ik6gFYcqjsoSu7NenPuyebs87z+RIw2\nUMYDR5SggnvA8Q0rjq+Zw4nR0MPRh7UraInS6cH+9p2P7/z8jT//g+/f3FkYcJheXJLPfeWJ05dO\nPrjzeOfhDhoTKIETwn4NchQ0ScgMgJ/ck00pgHmYJAAFDPt8d2fm4S3CzMM8eHgxWwzVCEAKH3yh\nMJDogym5G0B4oAMAxnHK1F3NhQcgLoKHuRMC04AjxOn+0eC1DgtHIg/icODD7f2zT19u4tD5+p3/\n4b84+ZX/8MW/85+89X/4f5x/+bnd2TbI0sINwQANMbFk0+LuYZZShqZhhWLqaq7uDF77QSKsHqo1\nCCXl4kYgrl1EbtBtNA5dIOcwcAwMwEzAElpQnaygQxhprZJGQaGlAjCTOJJqBXPwKgS1r80oIRXH\nOXplHlNOk9OnD3cOu8NHBBjBQUvHl2NJbo41UiZhyM0kpdFsdugGaBhhAd6QM6kJR6lEQIBWFn1x\npKThTY6FOmhkaAwLCJaygK4CAxG1GYTHIJNatJaiWsGRiLQURqkE2hdyEFLMiYlGNLIK1UrnGcCE\ngzDPe+fSISljQyDHhm3MspjV7YN+ssQnNzaq9dg0G6N8aqPdubE7RcAkk/X1ldV1YL5/f2t/b+qp\naSajF66e0On04dYepI2VU6v7s/2N0UiwTA/3gNJsXoJwbW317PVnJfp3vv+n7773/rMvvnxic+Pd\n1388r0yjlafOXXj6xZcWe3f/4od/evPeloyWPvfZz033t+/df3B4NOvmZfPU2te++ldNF698598c\nzvTCk9dawTs3brmRgV3/0m9ce/rSWz/9we7DHbN85vTyaNLu7jx69HhnPF5+9tlrt+/eF8kra2vr\nGxtHe7tbW4+XTp56+tkXDnZvffjTXyVqR0vtYhEWWrSnBY2XR6BQevdwSYkTqQ0YtOHhqURgge4E\n6CLiHg4BroIUxAOuFzDMBkMDDMrHgd5iEcDAhBbFIxKKY7gAmGgEASHh0JRAGnaIMRSTjheQhATA\ngoLsYYAIyP+OiK9mxOxuQMHUmNdSe4Bh0OpMaKaIPLSEEdlUB40sEoMOFcUgQiJ0d2EWUg5Sc0FQ\nZne3cCYmgjDYv7P7kz/+yy//3mcuvvTg4cPtnQPwsGPn7fAhIyTzoR15cDD9zr/4o69002tffHnV\nvF3flDFwAgtH6NlPR7OGAiAIlAGFLAEehi4gAjAAyCMQMVhA1ohaGC1Du0pLy/r4Pkzv62KPpAUc\nbOQTSMUtAo+/oB7OnMwM3FyQKEMwhropIQ5dF4bMSObgHpKJsB1q6Q5BGEMjPQjdeo6IwDLv2tG4\nYk1JDCCAHXSYngEA5wZl5AToIJho+FdYHXlrZ37x2S89/uDHJzY2Z/10abJutXdbMOeoZlAArZ0s\nE3IgAFIsFthMHCECKUx4BJIdKioYcHB278CUKBEKJvCotoimCQIqDk3TICXXOQYi0KKfjydLVspA\ntrPoSBiDiPiYBFs79CBMcLyhLCQ5NWOrPQIGOXI2UFWn1SuTc8/hmSu4ds2aFeSESIgUkOB4mz3M\n6AHAIYYRoZODRwVT0Aq20L6rh1s7Nz+8+dOf/8Uf/eDGo/ksAVusYHz+0+dPP3XVLW09nh0eTAWR\nAAi8U9srtt5Qy8gQFgMAWNGPVRAQ6O4Gwf9uAcBobsfjUEAb7k1uxzDeIEQUSV3pjwmoQyDWzT0Q\nBvIRug1sbo9j8N2AGMNAFyYIQGEzIwVECjc8lokZMhcLJup25of7D06cvLJ7RPj6471/+38d/8b/\n/BBOPE11z3NR9VrAXDKpDygq58G0AuB9UQ/vNdzdQ2s/yMoYuDCZBRMheSMNiklicqle2/HyfGpW\nioUbEgg3mF3NVcNjiJu7BjGCqRqYGgA6LRw4LKLOsWr1OIq9dtSujtqcGqgOtZeJbGyczJNLh1tb\nBAhApe85iQOmAHRtGpZEzIJo3XxK7mamGH03HwkjmFu4krAEVKtVLTSMRUpfw0ADGTjqHJsJITol\nY/D50Wi5Cc7AwM1SsVJqp7UMAwfJLVL03ucU4NnLQqWQNMjOWQ46MKuEHklSK6RKiAmFwCWBsEhK\nQCKHfSxNlpbGYa7quD6hscTde497ak+dXz1z9iwn6qbdrbuPpp3lUXPh3InNcZru7u0flYqJYLH9\nYO/EiQ0hQ2o50eHhYW5l8+xJ0Nj5+O27N26awxe++k0Of+WXb3ierKymp555anl5cvftV95/79bD\nx3tXn376xWeffnT3/Rv37846T83yUy9cuXLx9I1fv/5wa79tVy+dXcFSH+/Oi1qb+JnPfv7smc1X\n//RfHe4t2nZ0+tzpvLyyv797sD87c/H8KOGDe3fXVk6vn9pYTA8e3L5TTVdOXTl38dwH772Nh1uP\nbm6vpyU3XSwWpeLS0opbR4MMUp2l6fuuL4bEAMQUGGRAajrANRywqmIEMVdTYnSwAdjF4ABIARYG\nQSRDgIIwGMgiggLzUvYx5hKL3bkkHDbw6CRMC63sAm5EFGCEAjTEq4+bvDjYXhHdh0MuIZLT4PkU\nAw83QkLE5fXNrj+CYjVsGLoSEZG4KxKpKTGE2ZAVHGxu7obAzN40MptXZooIPKYSh8Ox6z2Rf/Da\nq1dfunrq6qXrz+3NXr1T+gTuHsYD6dTNUWD4YAjmB92P/+BPFkezp37zc+u1jjZP5nHPaQkANJBD\nIa9BEpchYtAiMlCD9SiGyOeAiQOkSC6JV5cgrUG7CqNTsHfCdz/CxR5GxPFOdNG0Y0ZSC3QbmJwB\nJimDi0VFwIChbWmDW9WsEmWmRIncejBHSR5R6yw3owjG4Z1pA4E5mskoKCCouhMFH8+6Y6AlDN+d\nAETUQAMk00KUQnXSpp0bb46WRtV1NBl7VFNH4iB0rUNHAkEMQzDMC0ne3z4KwNW1bATDgTqQI0Go\nktcazuMJESGRmSJgyuGA3hXzgu0IEVhGpqXvCqeRQ0IyCAUQSdlqT9waKAWHQ6lVSCz6leXWdIHc\nmAEhugfnBgixmtJSc+ainH6KN5+MpVPEq0HDZ02ADMeOS45j8o0PPdcIgNBAB/fwElatLOp0Z//u\njRs/eePP/uV3b++UBWAYtKEvXFt68sVrxM3Du9vb93ewdyRCQKJ2f7EI4IaPbfIJmQYq77EHAh2O\n60MDUPT4aRmCnIhVC5MMm8KByjt0k2rpCRAJ3RwNLYaWMA3dw7AhLXvcAB5WD0OOJjEPJQ1w8ABg\nIg9nZ0jgoeFkyOKIFAWayXh88al73//DDTn34b/8bz//u/9o5Tf+04+//78fPfN5jQlGBxEwgMOH\nfDgjgUeN3imqckLACC1EUmyYMmMMxPNwU8upoZQD3IPcIyFEJLW5BxcKMXIFtz6qobqhMeVEScG6\n0oH7EJGaL3oNFAisGl61VlvUo8Oj9uRqXlk3cMK0OOwexMddnzmlCOvLIsugla7FSm4bDEIAInIH\n8C4Qa3CtnfYzjFyAVaEPc24wIjyAiKQxBSt9pMAWAdPQdQakldX1vtfOKgF6MKKUflFUzSpT9lII\nPKcEOS3RqEPR2RQwvPQE4MAs1CSq6EFAYKB9EhRG4URIWSAgpTYbZCklpj4jXpoe7Vw6d8p6ezAt\nKxurJ1eWzl49pfO6c3//5tbMkdoJX7t4jvvpo0cH0xLMGdA5fP30KZTWq/WL2fRoOl5feemLX7fZ\nwa333nvz129duHL9qc991vujX/3gRzXw3LlTT7300rnTp1750b+5996D7b2jr/zVb1574uKvXvlx\nMbl0+dmq0yeeenGx8/gX3/suUXvq9Ln11dXbt+7sHc4EY/PsyS9842tbD2/+4Nv/NiXeOLOxMl7b\n2T268/HHrvbk9eulP7p359FkfXNtfXLz43dQUaOeunBlNGl+9dr3GljK06NBAtMvihsQUy0qCQkJ\nwZCw1i4iWNLw+7dauCoQDPAqhwCIRAIEbopDMQ1joIg6Db5cZ0wOjsGJm1oLwgCqpyYnXh9d+PLL\nL3/5c2/+6299+J23FIXAgql3I2SioQ5sMkQ/IhAEKRA5wkqpIgxDKZrAzZBweE0hiochITOH2nR3\nBxMOxiHEIIII01o58dAFxkHsBISDVsYCgTgRmWXiRFoNIpxpwNU7IuuQy3Se7Sx+/ic/+r3/ye+d\neubxxseP4rEeeRyXE90/CScAc3gYo5QOfv7H36vz+XO//bVVs/HJE+04cQR61ZizFhptALUAgIyR\nMyQBJqxHod1x+g/cURFHkMfEIxyNcXLaJpsy2fCdm7Z/R6CGl3a8dMx85eH+Vxw9NY0DgBGTILL6\ncZoXiUJ7CHNQACAX5AbEIhQpUm4cgCk0PHESCCAyU+IEbuglkNHZbI4syAIBgBxevS6MgKVlDAwg\nZPWSZDReCdDCTAAuLKZBFAGKRsTiEOANRjC4p4bDonSj1dZKddco7KlnQY+InKBJOBiSwYwDnXkY\nc0IiomKLo8ODJi3FiFFGRpCXVmbT3SaLqYH1xG6UKC95KHbVoQ8QhOFWpx5AnAEigdfB6uVWgLBd\nHZ19zk9c4o2r1JxxEUQJlIBACADEGOQSQ74ZAxzABqM4RI3wUDPtvO/76e7B7Vu3fvHGX/zr73z8\ncDEVJIY28Mxac/3pCxvnT8/mdHh/+/DxPmIiUMJw4sMaWVDAGYIZsxC6kQgGAijgsaczYmjNUwQc\n75s9wpFxOC4MftphdwkR7kBo7hYQEcIM6KGDvdndh/8WAYh4CAxEWGYZxidDYk7DGBg0AoOBUchV\nRQMG5IWLgj187e31T9nKZDw9ys29W/Nv/Z+f+Nxv3PrztdmHH6Vr17FpoSqgEzqCUGoGObtbgBd0\nNO25GXM0tSu1UhoJGqHrcL/KTSujTCLhWKwm98Vioap9MUQsbpIdE2eE3q2G8pAaJlZVVBNANVt4\nPZovOCjQUmKAkpgel1IWi7bJgk1qU6nFIx/s3kMnZyy1pCH3XtVM83hCqYlq4RpepVlhloW7Wgem\nFFwtipVawxyNOhFGimrulkPDwsDmFGk8QQ0igyYJaB9lJgkAGuCICC0mJDlnN/UwVA10VxcgEoTU\nWHUT8tqhS/CozakR6EFJbSTiFA0nzo25UgRnoZS1hiSxxTQ+unf7+UsXH+8erKy3KytLJ69dWmnL\n/tbe9uPFnf0jBX7iydPn1sfd/uFuKRa5OnTRXbl0epSao0Wdzg+8xuHh0ZnzVz77G1+/9/4vP373\nrYPt+rW/9R9dfeb63bd//NoPf7C/V579whc/9fVv9Ad3fvwn/9/7t/fHS0tf/9KXu0X3kz/7s9LX\nNmdAuH71+tuv/Hjr0a6wXLp6rRnFu7/+cLqw5bWl85evPvXck2/84C/uPno8yuMrl04JLT3a3j88\nXJw5ffHCE+fK0VyavHrizOFs8XBre7y0Ml5ZPXny9N7W3a2btyYwbtsMHS5JG1L7kIAUoU1qmAAx\nrLr6kFdkrc6JU9NYsXCp2hERBoYDEGgoOQNSMxrP51PigTWIEDgajbpuARaEYlYHRCkIgjNgVIUy\nt8V+Pz06yG1GAIhaI1KIEBGTuhECIQ3OCkBW64iICIGQOX0SQDhebyAiMnoguGakWodRogOiABPo\nJ90IAhxib5wkldKjgYfjgEU1MLCAAKjM2CRMg5ZCqFQ7ZqX6oDBFo5Dg3VtbH7xx++z5K9eefHxw\ncIMWECERwGiBpBADFxUxqxtiRUpvfO8X/d78+d/95qZ3cKLm5U12R3S3h2GF/VQ04xBABnCCtAKc\nqe9Aj8yHHiQHFCiBmEA2YTJKkw1qTvjoVFo5bYe3/PAxlQ4GzBZhmc04ZU4jCCIo7jXCQBII62zO\nxMZAnNENtHI79nBED9Lhi89pHOChPaO4W2AISYQP93WQEYSAK4oAJjMImzVpAtIGBoJ5QFgxNOTR\n8Ibg0DjeT3XeI1ocr5ajBiQgxkANldwgBLghSiLKq20AoRey5ImQEwQkpPDARMe/iRlC3bQiAETb\ntO3m5DRJ080ey9JpzhNwXVpejwiUATNesUciq2GMyawc7W+fuP6FezdeP7csgZOoJaCaTIACiZ3a\ntHFRNp6A9Sd45Tw0Y6c8XP4QEYGPu7DDyi0IIgIqugeogw+KQTDzql7m3eHO4YM7N1955Tt/8L33\n7x10iZGckTcaObssFy6c7FSPDg63H027zloMDCeGw77uF1hJ2lJK7CID7hIBA4IQ5fi4pEoizKha\nc8pVlZkdHIdnaXj9mUcM9llUGxL8yESqQ8jm2NM0xLaBGM3dPRCZyUyFh/+/55xrLRhACoRspERo\nUCWEkUII0QPDsLAk77XW6akLT7/3/tubeOrtb//xZ/5P//DG6U9N6nveTYOXBVAwEhNwcmTtFn1f\nSTEgyKLqwgHDba7mzjZ3xmxgCJiQRUgIUMSCmpz6vi/FvJbelMEaJEGnqMBt9QW4EYlZX44WxCms\ndqqGooYMqZYuqBBT2/BhT4fzBfZ97dW6mciI04Q4xqtj67FWhygIqn3VwHY0RuKICDNAiPAkCCmz\n4dIYoeG9nbCiFmXe9Rowzg0l7ouHOUAH6Dxq+t6XwrHw4B0NtTo/AEXCZNFbnwzmTdtgyu7jvnYk\nCL6otUDfDzkmwmpCfa2JkgZlXTTSIHIjiQIFvHo4R1gP5grQW0UtQY2Y0cqyPHHpSmacTDaxwc2L\nG8uNbd872O59bsqT/PzViydGON2fzypYMBDLqF6//GTt5nvzuljMFtOpRX7y5S888fQzb/3k3954\n4x1YXfvm3/8P5nt3fvoH37t/++5c47Pf/GsXn7h645UfvP/qq7t79eyVq88/++zb779ZZot+Nhf0\nM6c3F73+8Hvf3do6OHPu0tUrZ7YfPtg67JPI2QvrTzz7YtXpL3/wp2XuVy9du3rtwo0P3r378M6i\nwjPPPr+81t5+6+3pUbd5+fx0d3vrcNqkfOr0qXFqPnzzVzqvQn76/Im1zdNvfet9V7z02Wcfvfte\nNe+xNMHt0qhNMrOOEMzRLZgYwLR0WswNE7EFBIRFjQAiAcBA6MqUiT2UmMPAw7pZhwQo5F4haCgL\nDsz7MCaCbmv6/r/67tYPfjafzTONEX0k6B5+LB8KcAYKigHxYEQ8JOTRCcl7DaJgJgjEQLMYQjKO\nGFGJB00UsIh6RRQQC0NO4Y6S0+CsDox2NFEtXkpVBQBmRnBzR5GWQATIooYxcfE6kPUBwAOjaBHU\n2eyNv/je6He/uHb+1KWt+Ucfbs2ruQczezgBAXKYI8XAkEPAHuitN97qZovP/N7XNp6r45O2tHEG\neARQyR+bT8nOQrvuQsHACOEtQhuSuc7CFhGVAtxrIFBeQmW3Ma1d4dFqHS/x0kmY3Iv9h2X3JiMA\nY55MAhwBQRUSINPgVwOr0DQQwOAhTLiEqoPwGNCBOBAQJawf4F0YFkEgPAgLAxxS9gioRl5I2JHI\nCpIoDq/JhIERFUXAMFQJ0UpBYSKOMGYJqEjDVcbBEcEAHJAwKiLVRc0skAICTVUSuXuAAmRwcO0w\niTlxCJSIxpETCjNALWbWdf1ssrKEqDLZIGT34lFIh2QEkZETE4R7D9QGRBJYXT81vffuSibg1qxn\noXBxXwBNfHxKTl3jk1dgvMmjjUgToDyQ3QOHjrkNRoaIEsOh/Rgtqu4WrujoWq0uQmfzg93pnVs3\nXn39+3/43Xfu7lZBR0iIE4yVBGfPr9HqxNwPt452HjwgNwEaCpDz4gHQ4uALFq/GbQ4wMCUYjPcp\nyInZwyCAiGtVIgKPLE21ioAaFoHCpGbLK6vT2WEaePpwrJYcPpFBazgQ8MF8AGpAGAYzkroO35tB\nURFESByqAzqfMA2wPbcIjyBMScIVMyP3J5/90od3bvQzPbp9DxrLn/5rh//2J6Pa4ZXrAURNw5jc\nNayvBaNGNSeBYkVNm8TFCI5VaxheCC3lMTCYOhYL6jmN1LQvtfRVu+JQEidi7OdVa5jNtI/UcgCU\neQ8oSaS41eLMASRByikJwKjBDnHaeStpc3UyGTfEKYapQ3VseLS6MuJGDnI92u8Wh9I06pWjMQ0k\npJxApK9GWDFgMhnXmsYr1h11LGOgxXzvkM1qNY+UOIH3ws1YlkYULAqhEBC1FKpMASgaEEq9l1YS\nMkJYO2ooS2qizlkPDxQMPSiUJUUpAKk3A4++wwpTFmxTwwh9xZQ4C2GzWrtFf3gACKBO5LI6QiZP\npMVh5eTyuSsn2zz66MNbM2967VZWll46u45hjx8fzitKIkRq2tGlM+e7WemCaymLo2mzefLTn/tS\nv/fo9T/9g+3tnae/9teffvGF27/6y/dff2N6uFg/fe4rX/rG6XOnf/7n//L/x9WfBWuWZfd92Br2\n3uecb7hzZt6cK8eqzKyxa+puNLsbDXSDgJoYCBESRYmWbcrhCE/h4KP1wnA4bL8owg8MM4IhW1KY\nICkRAtkECKDRjR6qeqx5ysrKec47T99wztl7r7X8cL4CZd+nqoiszJt1v+/bZ6/1//9+N6/e8oPB\nlVdeHg6Ld37548k4OcfHz5xcmhs8unPv7vp+Bjh/6RI5Nx63kavBIhxbXl1YOXKwvX7v2lVjd/T4\niVPnz1y/em1jY7y4fPTZcxebyfbdq1cNwsrqSRMUV8wt0JHDx1Jq7t+7p1nKnjvx1Lk2Th7cvzvd\nPWB/8pV/9F/9m//l35Yn+/PHVl2aLhxdoPGIajIhJgNVZDREUUEyh0EtaU5IDsyQEAg1d907FlPm\nrhRIhNadi2jdxlyAnXOccyJghUTgHMJcr5cbDNwDFAcs2YjVjJgcQJaOZgPgCKnbIUpWYgVxwJ6d\ndLUHoE5mhIBE1tHG0LooHYoI4KzkbwyGRB25BQnUSLCdTLJKVRRtM/HsjQwQQZDJQamhoZAsG2UA\nRhbognK5S+6ACBDt7bUf/+LTF7946cjJ3s4WpV1FRe16ymT6+bCUVBFYs3lmZLhz497BH45e/Z1v\nnXg5mFs+cvyMWWp2H3EzsfyI84SqI+oLYFAHxGDSQwqYS8gj1WSW0LI0NWMBJYmoG1TOFVYdoeqw\n9h/TcEn3NrTeAc1o0czQl10AVSGaJESPHEwFQMGSQUbXJ2hNMiGT95KNCLIkiTlg9xMDmDVOQC2T\nJFAFRAXWqBQAXDBI3QcjAICg5G595gEJVcy6xD4COFIUaVQbF3oxThEAqSBQYqYCRTX0KshZNREG\nJm/IgA6MzJDIiDyYoGYFdBREwbRFdppEYlSkqiwBDMkYMOc2g2idyDvvi9g27JmUDBS4x2BgIK4E\n1KIkF4aa1JEXNXI+SihWL/DySZw/AYNj7AdAZffq7+icXS7GwHV1dOhqoGCogmZqCiqzdEwWi7HZ\n2dm7f/P+2x/+6N+++enj3RGQA3BgPaJ5z8M+rJ46hK5sRs3ju1sH+03FzmDGpNmoo0cqiDwCMxJz\nUnEIBA4JGJgYMsAs4mJg2j21ACKknOBzF0sHSPPIzWTC0K2JZx2hbljaeSpxhvBWdoyOc4zOMYAh\noeveqx0WGFA7Sh8hgKBAx8xQAEVDRgaEbMiswPXt9c3i+jBom3rzu+v7f/hfXfiNf3T1h//Y4dQ3\nE+gNPAKYtDmrmIGIJFCKWZGcQUZXeOdSs+sJowkhO4fkVcGJIiphArWUs2DCmDQ2017wviCRJEJt\nG7GLQ8dCRR1zFqnrKagBECKLCIKF4KqiiliPx3XFfuX0UVApQ09FcwYFMREmLSpD5uHy0QMeFrCG\n0IL3USKZFMH7UAC7nJM1tWOWlpip12N0PvQP04FJ86kZOueJGEHAQskFkgY0VSMmzAoumbCSBwEx\nFOTSIaNhagEN2RUhOKbCuYac5ih1bSlmAQA055CcQ1MDxy5lMDIjjO3EUeWLucHRw23rDtxDbSeW\nE5k4y7p0ZCFxPn366Mrhle21zVuP79eAgLSyNHd6da4eT7YOmhrQOcmaBosrq4cP748PUtTRZD/H\n9sIrXzr/9NM33/r+tY8+cP1jz33lbwaMv/zzf7G+tpNSvvLqy4dOncnTnR/98XfWHu0dOrZ65cXn\n99YOrr/7wdrG7tLKyrnzpz3b++++N8m0fOzoMxeejs3e4ydbN9cezA8HV176Qh/1w3d+srszqULv\nxPFjEpu/+vPv+mpw5PBK8NXD6+9Ppwdzg5WUdXP94WB5ZTgsFw6t7jx8XE+aXtlnj0tLg4PxqKlr\nG+3bhMXv3f2//59ov20dUT0uCzd9vAVqZb8vMU8mDWERc+t8UDXJhlIrgXM+SkYiQ1OVWcvcCNFU\nMzFa565WNTNhYyRBJaA2tUyskpldVmFmUGNCAIzSyRk6IyKAZGC0nI0QOuUvWkAWEADVDtaFXeMe\ntQt3MxGxoRJgpz8EwKzJz3gSkMC421+qSTJC9s6XVdHUUzNrYvSucAAGpllBMbZt1SvL4EZN6tY9\nCEBICmIGnb+GidXEmrhxd/f6/IMjJ+ePPtXEdndn3GT7a2aHAdIsxcIKAK1FL56ZtvZ2f/Kv/vSl\nuv3GP/z2/vqnTlru/CQmYolywv6S+Xn0hAjgwNhBGqIvMY5NpiDRrDZ1ZsBUqACGOeSBFQPsL/Nw\nBRe2Yfe+7T3Q6T5YciSoiIxogQBEs0lC7HTHDruFkYlzTgkMEB2BJV8VXitUVcgAhDDjZyGw5gxd\n8ogSG5qkzs7aJZlSapk8hwBgSKBiIok4EJiIIHBODRC4MFDNzvsObm6IAgoEBKgSO1ejiYBjMlRX\naBQAVCXibp3XYeMS+QoMBJSKUBVF7riamnNMVJSIfO+z+6eu/Pre+keL1UiBg3NmSEwiMas4E8Ki\nrTOiSykXlDKxUB/6R/qrZ3HxNPaPQ7mErgRkha7YgmYdOLSbE3RcFlBQ6Fy2ICYzjihKlJhSO51u\nr+0+uHn7l++98Z03b21Pmu7EAKiIe+hKkqOr83NHD6Hrrd+7tX7vUQEOEdCAmXaaPE02KC0QGAgS\n+o6tZmamxEFhhgzt7nFoXRu+Q7p1aFAgAyM0MxF1iKazKr2p2czYSKraBW1EFIi6MapK7dABGoPL\nmghoZsHq+J9M2DUtkMFhRmEAMgAxJOzARZrNGeb94vHV9yrSsUTOfOetn778HysePz8HTzaaXd+f\ni20rOadkLgSHlow0SkIhdmEwZwCimZgQMfgChBEjGKtmQqcSGVhi0mySFCQV7PtzAwLNucutW/CV\niLRNE5yLyTQLBwZSVRtNayIoS9/rVepcbCEQDEoi77NVBqKa29qSRQIssGhh31WJq/n5Q0uNszTZ\nB8I2tkYo5DMw5ETEsalJJKYs5Ih50F9aOnGqmqKMt4gpp33Jgs4VhIGZPMVpdAzE1hgEU0CfxSRp\nMgle2JXs1KzrYbeacygGFIL3hyS3yY9wfOCUJhzRxKFDBMmpKGh+YVj2em0zbXea2E6knUvNlMN8\n1Vvgaj7Wu5prh6XOHZurlstBWHh0+8mj7f1WXVXh2QsrOKoP9uuNg3GiwrH1BosrR5Yrb1tbO/s7\nW/U09ZYXX/r6rxUyfetP/7snjzYOnbhw9tnnR9trd+7fOjjYXzx07OnXXltdPfLZL35w84OP6yY/\n/+qr80tLj+4+eHj37sG4fer8hYvPXhptb1+/fpV7i4cHxamjK3c//XAUkxhfvvTsmfPn6p21d975\ncDTOw4WVF1548d7tzzY3x+cvP88WAbQeTQVhZfWpwvPaowcU+MSZU4H4+qfvlhZ293dPX7y4vHro\n0e0HB6O636OFsrdp6nq0vb3bjGrAIJranAIHzFqPx0QMZmIxhCJr7lIo7LxojEnQ+5xaAyRCVCIP\nlhUAiTplEooYzpYRWbF7bEyeHAAg+5nu0wAIVRVMHDtiAjOabfTBsiARghGxghEBcghgiFCFMB1P\nZjVlnC0xGAFMc8zAbDTLu3nyJkLsmrZxIShA8EGykAM1ZBfqdoJCwXvtXPYIhsCeCQCRNGrh0BFQ\nVwQTQBPAbqFliKwiAApEdduu3Vobzp8+dOpoO07xbjtNmM2JgKoCCygAMmr37F0kziDiXWkq93/+\n8Uff+ZMzX3jqYLQR+lUYzoXQBxBQAJlSNaHyMLhgDgABAph4okWQHsYadVezoSlIPWPzoMMwYB5a\nOcDeKvXnZP4Yj9bTzn2NY4DxrBkGhBQI0NJUmdkPNDeIyNBTzYhGhFkSQMdHMLEOLImqOdVtKAoA\nQ+QuFYLoFRSckJWgSgQpZ2KEmRPIUoreOUU105SEyCAgSxBGKklqcewkR2KHvtAcVURzJnLmHKJH\nZVURjcA0nUyKXul816hBFyo0qdPYpQYcc9a9/SaEMFgcqmpuGhFfWgYHK8uLT669sXJ44HrBWRcy\nbGIyDmwpJwqchQHapi16ZeReuXgkLJyipVM0OIzFIfU9wgIRDZWQZzmYrq9naGjw+UUYTRFURQwS\niJlE1NSmGpr90caT3Tv3PnvjF2987+37+23EbsEIBXGJ1nPp2GJx5pmnhqsnH997/OTB1nSSAmFQ\nYSTlcquuMYQeRY/mZwNMIXSAqGJRUuGZkURErXM1uS5WjYimGdERkWgGoM5fLTCrHs2OT0Q1MTVi\nzqqMgAyMqGpmqSoqUIWOvaZg2JkIBdGDmzFLUbsZNzIYEQqYLwtpImH3SsIcE4aCmn23uOikNT+c\nrG8299/on38p33/S42UgP5FoomiQ22TKKmpECobokwBKZmQmhyCqSgpggiBMlLIoWIGsIrGeoHEV\nrBjMGVE9rSU1lgWQkcyyOSY1FckGSuoBeBqnkmSu50oOAOL90EOVFQhzrmvnQ5Yco2YV9i4wkfJ0\nXEM97c+L72csCtQ+Sip9AWAgMbetL1ySugylpDajTOupDyEUvcnoYOvxVm/gkav9g8QmFWuvIqSS\ny773IGks2oKpoI+CACo5eV+iiMAUsXAFA/lUR4DMWAD6qnT1tIWyyNrziD5zHVPbJsw5oHcUqyL1\nF4aVHWoj1juPD7Z3plGBnwRXDHoVEHCo3MkXzp8799TawyfXbt0+qCGRf+qplcWAOG2aLNttjmU5\nj35/snns0sV2PLr7eLMe1eDL1YuXnn3l5Z077//yjR+Porv0wlfKflh/cPfB/ZtZ8MorX1s9c7ak\n+qOf/MntT+5xb+Gbf/d3XWre+t5f3X+4ubC8eOmp+f7c0trdW6PRvrFevHBB0sG96zey6y0eXjx0\n6PThk8d31m5ffe8DyPnw4WPPPPvC7Wu/3NobLR9ewfZgtHfAg2FdN6E3F0VTrtmXl557TXD80S9+\n5nyRvbz21a9tb289uH5re3Nz6fDxE8dPv/vv/rgKIThau7M2zZYgrvRXwGozLftV27ZEyKWLrRWF\nlyaTAjJLNlUFI1N17AHEjMyZzhrDJJqRCQC64BaSEaBIBmDqMGymrCSgzK5bAntkc0FMVQRIQZWQ\nDbHs96fTCSKGMoBa29Z1M2LvidhiTeRUFdSAOg9oB7UBI+ze9gA2mB/WTa0NmGlZFkmUmXNsEcjM\nEQJYJkAzUUVVJQMhgAKWV49uPLo/7M+jQJqmwuFUshozdbdRVjZLNoueI2VQB9BM6s272+euPLVy\naj7V7dp6exATgjKRGQEykAJwNgGLQUKGLG0y5Aun5j757n/z6Q+qCy+/uHTm+GDlsCwcdn0NThRK\nU9XUuGKZqjllRgRgEAJ0BVBByhBbk2l3BVHLBAzAxmw8z37eeivY37C5QzS3KqMdHa/r/mPUMSoI\nClEQDETOtCYEVhQGNDATU8fsVdUMzKJBF11CIuf7iBA0ZVWZYVMAHbtkGcCLZlUhX3BRAYqmKDk7\nCqBGvjLJRF41oQCXDi1LzggsOQE4MaC2Mee8cWvCzhuhoZqxMTswwabqBewV1iaLyv0SFczUe4Kk\nsTVx3lFh1kpuwTwS7W+PcSH70O9VwTvPFmWa2LNZAiDmkCatkaIwWkKngCUun+gvnIaFVRwcod4K\nuDkjj+QNFBABGI06hnnnfURMs9I6MKCYZDU1zZDFIIpEa2Kabo6ePN668dknb7z9yx9/8qDONSEZ\nMBqx9c33ncwVdOjY0vHLZycHe1v3nqw/2HUERIjKBeJuG7cbC0EKT8ExorEpGasqiLEjJJUMjgkJ\nTNDUuGBN0tXnRYEso/PdTp2YFJQVTIWZzbocDJoZGormTg9D1EEKrcvxmwISACoxkEPNagCaBci4\n82ezqSiRy0RsBmYpJgMVwDDrHWFKGWOuSzBt1PXC3l5+40/mXviPtj/4F8uXv7K7v0bolUlSG1ML\nimKCvrCMUbPP6MlCr0pguU2qIGkU3CCrgLVqVTajlFIUlRxYq1Cot3HdUMqULSHk1Ia2QM0KZmaM\nYMS11HFKOadhzwXHTEGTtnEdBNmSGqKjppk0MatYCFyG0nHR1CMRA7IWRzGJ49KzuuDVCqRoCVRF\nc8xZfGEUqmY6QSxAeDoeH4xHMp5Q5cCwqlxFhXdeLUtSTZHJUSDIHsxiqwhKpj32AMbGMTaSQRSJ\nzAEamDRjci5SS5CpKKUr8WjhClGqR7t7kifBq+SimY6KfoHeYRHUlfUkokzFo06BQzAj/PD//Jvb\nGwfjJk5GUSEdP760GMqcdFTXe3Xrej1tJ73h4rmnr2w+vnH37j0Fz9z7wje+tVDJxz/9y8cPd4rF\nQ1cuX9raXHNlf7wzThJPXnn53IvPrn3yk1vvvf3w9pPVZ6585Vt/8+H1D298/OmTJztFCBfPXySG\n9UcP9w5Gg6XFpy4+u7v1ZO3x3ao3XDl09PCxk8S09viz21ev9/qLq6sr9XSyvb1Tlv3hfNWrBntb\nezlH7pUuDJm4Ge31FobHT519cufWk/u3mkk8eubUiZOnHty7vbO5K0gXLl7e3X68fvfh+ObaIeh5\np9O6NguRZFhVg0BM1iER20bYo+hMP6Ri3dLLAFQlqiIgs0NE7d4cCmKps9WjkrGSOlWbQazUHDkj\n7dRlznmAz387MyTqdICG4NmJdI16QHACEQEYHRCCmoIgkqGBooFxN+ZhzjESdwBF0GyASsyAUA4r\n54vm4ECSZhVmx4XPbWRichBc1UwnKuAC5RQNIPjCs4HrykuiqhJtdypbddtmSmIZUbrlTzIDRebP\nFVGOmYdz1fKJwZlzJ7bX1h/e2FhbG2d0YxH+vNVO5EENUAHAGQEJInHpvvEbX3t473qzOz778hdO\nvfbs/OpqtbwUBnOumKfQMwbmARVLVK2AK8B3Utlu9QkkBnkKaWrSmHXFbUUk7DTGhKhZ81jbPZzu\nyOSxjTZlsmt7mxa3QTMhAHNXNstRkIHIq0TypWgkJdDcjUKRIcWWTIF8h6JgZhNRzWDGzmmn2Oue\nf0CzJXKIymbdGABBI8QIzhND0uTJKxg5BskWk7FXRMdlspoUgTr9JAAxABOCZlCIRRhmUsktkeuS\nKIaqorubB5/B4UW1o8VkbrlgRBGIcVr1FprpyLPL7ZgWnt57cvPwvFdfomWgoJqYSwBTkYNMiycv\n02CR50/icJXKRfB95QLBdUUY6GQ+xh2TbPaFgkpqimqG2UQAkmUFFdMIqU1p2uzt7N6/ufbZjQ/+\n6t1337+9LVITgmIgJYMBu7nAlcsnjsxdfuH06pVnntzZ+vStj7Yfj8i0QPXEPUc39pvHDQ8wrpZ+\n1at3NChcYODPpYOeDZAcO8BuBGhMINpxxdFMGcCIu4xJNy7tmp7df56zMJGBiYpjhwAGCtCFhDu/\nTAc+hNl5Sd3AX5nYsGPPgHUfCAC+YJDuj+hAHMaGCoqE5JPD4AZlEVyTMkz2bTG8+g//y1/+039B\n+rg6dnJEpWPKbds2NSJg8Ko2GUdEZkZfOC6GZmbtNE5GjGTE4BwyZHFZVWdSs1wgY69KOYsKmEGW\nlBQslUUlapKTOUTiJNYYYtLAGggdk3dlRmibxgQIhRBcWdVJ25Sq4IuS2Rc51poTYRADAzS2yoVq\nft5VJSgYGqNvpgcGOq4lADCTooTQa0VAAEkdYtLsfL8MLpSVSJTUjsfTgtCFKpTQJqsNRuMJ1LlX\neCRjZORg1kqMZrk/GLqyn1NGE2OXk2XQQTmgslAAQAXnR1Od7OxqMwpeymoOPOUI9WgqTV0MCjUk\nNSKhzroVgrt/d2fU5kk9OX5s/qmjq2lSN40e7I0aa1JGTO3xc5fmFgY3Pv759takhXD6wrlzl65o\nu/fpex/df7A5PHzmxddfrrceTvYOJmnr0JFDV178xnBh6eabf/bwxs398c4Xfuv3n3n28ns/+M79\n6w/299ul5eWTZ45ORtPNzUcK1clLLw4qvHvt/a3tUb/ChX5YXl7e2bhx++Nbotof9i4+fXp7Z388\nnc7PLywuHR6PNtcfPioHw5VT5w8dO/7xOz8jzSuHV4cLh7bXbkz214fLq6cuLDHp/Vt39qa7h0+u\nLg4GNz/9YGN394Vnnvn02sPs8tLSvK/LvVEsfBgMK4wTAoYGMysTiVrRGzTtyDJ0qBdSzJhDKCS2\nRM4MTMQ57pTWRITgO+4ad5wRR2wIxuQIzYg4ZzPAnJTYCMjAkAgMUmy7kF0SgVnWzhCVABG6rH1H\nOqRZ33amjwBTM8zeByQTVRMApMXllZ3tLWLXjGui6H0AzYiAjmJOwQeDjISKiTxCzirA6DXnBFmj\n+dKFflmPDhAAGQZV2G1bNUUiU2WgqEaebNa7mOkSVWw8mpS7tLm+eej4qirF+uHuWCuzrKqABF4t\nM5IAGWQCRPNoZq1+70/+6siRhdy07333hxt37j3ztdeXzz/VXzlULUVfDqicA5tky5QnXCxiuQDO\nowMEMAYgNOojV5hryFOTCJbATAk6mQ4go59nHlqxwv0jNtzhdseWN/PBmoy2dboLeQqW2HVyHlVF\n9t40dTQvQ+6EkQqJECwpkAhkcoVaBstEBZjEdspYqJiRMrFa5FCRsVrsemqQc7fWVVbmCtE0CxOp\nGlAxqhN7Hwo1aQtXmqPuxz4Lm4BK2wKVwNJ1yth1J6sSoKiJQTmszi09tX/rbvCOQJkdena+RxTL\nKphh8MtxfP/woTkAo5wVzVkG8moAZc8tnFxZOIGDI9RbxmqRfA+gAPKIjEAEHSLGABls1isAUARQ\n1a5MYgAmAtpBRLOlmKXR+mCys75z9+GT96++8d2f3nq4s6MmwKyZCAPSMLjS5ZB9v3BLh6rhodXp\npF17+Gi61zIaooGRUx1F2GqyghUEDILoGQUBmT4Pt5BBN3ghNUAiE7UOsorA2rHKCToojKp1O84u\nHQMAs/EpGCIyzLaeqMieoVNIdshW7jSKhoiShYAJuRuqEHz+qwAQUWKmGQIATVUkAzMYAqBiYUgx\nR184RhGudFLvXH9r+dLzWz+6EYvtYmW1bdqUchbxzilVbRal5EzNIDbqdOSLAhlCWYoCEHaGJmnb\nLMYEauKYjTC2tZqhL9B8zqPgTYVjippy4V0SaZOZcS9Yb7FiDuODg5yEKYliyt3DV+19z9BFbTyT\n907QZwFTZsdMTpPmnAlyRoqxUcbggvdBNbN3kMSHrLWpGbGmekxcMphICr2q5AEgkdPukULMmMuc\nx2yFYFDEtp3mrJ44x+QLbFU4m1nqJtspNRNRyugYG8Ii9OPBaJJHBWooexyKYuEIV+aBCr+igJOD\n/bIsJ7Ge5Bg0SWuu9I69AuUkJgY5urX9fUd45crRY3OL9f54fz/uT6fiRKXoLZbPv/DapBlt3L05\n3t1vWrj0K1+5+PSFBx++tzlev3/rwYtf+80Lz75w94M3bn16zffmXvri1w6fOFtv3/3oz/7i1r1H\nxdyhX/2D/60v4pv/+v915/qjZP0Ll8+FENYebbbNQSgGK4cPte3mwyfjnb3x4vziU2dPs8d3f/Jj\nBZrr95eWl4ZLhx6vP9Yo88srK4eP3P/06sFo/+jxp5z3T27e/+yTa2D5+RdfANHttYejvf1+f2Xp\n0Eo93r97935RLbz4+tea3Y0bn3ySBV68fFH29k29A5weNAeTBqgIBfue7/cXxts7HIL3ITaJCZvx\nCMiZJUKKOX1+aEXCTgWHAJrEum2FmTk2VVXArNlBMFFA17V7JQshYJe1MzPFboxJaArK5Bw5gVk7\noVMeIgB0jbPPM4jQ+bU75Nos+IGmqqgARkiiwoS7eztMBGCl6+WYhAQYFKl0Abn7TnzR74FYnNTA\nPMtbgHoFIiJ2k/G+QzImAur1yjCdugSpAyECM6FKBmDo6N5oCp2olNppGu9M2e/1h8Xq0fn2/l5u\nVAQMwCibkRgBAqLrMOMOWLMi4dra1rCsgNydq3d27q2dfe3Z8198eXjqcH/lsJtrLcxxEMgiqcG0\nz+Ui+AUIDASGYA6ACbgPvqKUQCaQWsDajLsEB6AZeaQAfkjVssU97B11w6O+OZCDNR1v58mOQc1t\nm/LEO9JZeH62FARDJgZDYpfZgq8MsgEieFNVi92yhsgQzXlWAXJlpxhhcjElj4LSkC/MeybOOQEC\nconOS6pZpDUEdS5OxRFglizsSdTQnEkm05zbUBVERZZIIsTMRB1Tm9k75xDY7d9YPlUELIFQAFhB\ngUw68ggZ5iIUTCyawZegpn7OqjIsnsK5JeqtQu8wlUN0faVg6K1jrHVX3C4sZZ3yj2B2CpqpmAmq\nmCmYGkTLCpItt5LaZrQ5Xn+yfe/B7Z999OMfvvtwbzwmUkDPnduIKqalMtQpM8ejR5efevpMtHrv\n9tZ4bZwlAygJMgER7EadCJYOHNIwOGZzjkkVlbs4THcOfZ5jQlVDMETUnJGpMw4CIBKpavdQawZA\nKGbY0WBmzmRAIrA8gxuYdRXF2eU3G7J1pHsAyyaMpGDkHCDabCYAiErEZNjlS4uyqJsaFQCNCVST\nWHA++34p2VNzkDLl3Paf/fLmG/+SU0xt2yQUhWio5m3aQs4e0bmQtbGk6DIppKSIGFwhHlNKua1V\ngEwsCgGyx2zYZiAUXzK0AiIK0LYRTObLMgPmrG3Ugsx5RXWixAiOCRRSFpEGsxVVDz2axp4vusJJ\n00yBKDgmhCQaRcGAkcm7pMCi6iyJgChhSDCljMYSJedpLp0PZQSilHPRxmK5R1ClWIulrBkBiEyd\nG8cIKTkOORk0WRGEwGUDxyBmjhHQgaZGlFITs/MBPYJHZAaEZtqiIYchCkJuqsrPVf3sB+NJrJtU\n9Ya5ic47LphMESil1DTJUvJO3fKwuHBmNU5H2+s7O3sH4wzgGMAdO3f+9OVn9h7cfXz3xtbalkL1\nN377b5U8/cW//W/XNseD1eO//Q/+D96lq2/+ySeffnrx8svnXnxl6fDyw3e/f/O9D29df3z+lS99\n8W9+e7Rx7af/5i+3H0yqhSMXnzljOd+7eaduRmcvXT557uze+sb6rce727tLh49ffO5KM9m5+eEn\n7HrLh5cXe240Gu3evdWfK5dPn9G2vfX2u1oOvvjNv/34xntPHj0Y7U54sf/ClZf3Nu6tPVkHXw7m\n+vNHjkzrg7W1teGRIy88ffH2tY9v33yycuzwyRPHHt2+tfv4SSAmx6qqmItQkPPO+/HGGpIzg3oy\nZufMGIDUVBFBlACsc7igSxKLUKqqqjF6NQUzRJckA1rXQddOb60RrLMc+Zhihw9FRCQQTTRLrbCY\nALDBbFZjpkbYieyzCKigQzACMCInORF2cRbs6tAhlG2qDRWQwBQFFJlQmzw1hIJKdF2IWF2ApeHK\nwf6OgUz3D0TUGSMYOOcBnJmaxbqm2ScNucB1XQ9C0dR1NiB0BuqQFCEbEHJXJ2DSjnoTo7QTne42\nJ169JKQxQn6yYx1B3HQWKUdQTYAlmibIhOpckESjsYSCkWCriTvf++nWzQfnv/Hq6jNP944s9ReX\nXG8ulAtgHiFajFiMuZyHsIAE5gEQwAECARWmHnyCNAWJJg2CzKA6hBmJcGBVj6vjqGdsssPDDW72\nXL0tzY7ubVGzJXHqGDXWZoZGZpnYAXS724weDUnMiEBScp4xGTEzgjlADJJzt7VFcgaA2bwzQTRE\nIkbylqZEmNG8d5hbUcw8XBwyeVIpNdaqZAoOQZPllD1nya2v+ooGaiCGjswQVNEhgss5I2mWxNZ1\nPpCMwWpkB2YmGTtDNAAhJ605zKub57lFPzgFgznqLWO5CFVP3RAhKHRQCCJkm934YQb06/ryhmAK\n3bciCiigACpmGXIDojk3OY7aza2DtQePrt384K/efveDO1sGERGNHHVWTwxg3jRpYqPVld7zv/Lc\n6jPP3L12a3frUd20oApG5GIAR67c3t537ArLFTkG8eQQLLAjBDUjpA7S1eWZu2tfziYGiK5rr6op\nMTlAxVmElBCzKphBN+QEJeZu3MLeMVOOGQCQyUS6HPisx6uKCITIzIAKhoCao3SPQoSI6DQlII+o\nptw2LYKqonNmKiikaJINkWSy59ghY3zwaP7pPlZz7aSVcr9FB1gyFphUTSSL84TMnoJpMkhZfKuo\nlllj20YGZmNGVXQx5Sowe5eELSbM0KRdh+iRE6iBK4LPwAk0CpNlAtBErTWEtSNHLpgqg3nnwakr\nHDvHRZ8RY06xiUkSCgYrsiihd8yiLVIFxCYqKYuJdk87gPWkRVREzpIZgSAB+7bNCqaatZ0Ac9Ys\nOSOzITHlDD7Wkb1jzDnFtm3LQAbIEMQUApXoocBmvAvOy+dePKhH42YMagkIHWPWnCbt7h6iF8x7\nzajOSVJ0oM5TNXCBFnv9lfF4P9V7SaSJOdejuYLcheNLeTydJI4i42yK6Ap37tmXT108e/OXf7l5\n/8l4ZAtHL77ylS/ev/aLt9+7ujnRF7/+669+7etrn/z8k5+/sb41+fJv/u6JS0+320/e+td/+PD6\nvUnUr/+9/9XZZ5/67Ed/9t6bvxg3dPj0ubNXnoZ2fPWtD4mqK6996fjRE59++O7G5rpGufLFrzz1\n1PH3fvaT3a29UJTLc0Mm2J/k1niw3D9+4syjWzc2798YLh1aXD1y7Zc/3tjZ39pae+Wll3pldfP9\nt6cxU6+8eOW5uYWFydbmaG/73OWXesPq3uO79x4/vvzyC6Xp3VufHj56ePr4QWYvIqTssbDuLW9p\nML842T1oQQHZjERAwWaxcEJTAqOuFc4dzkxFVYESIv+1588UiM2QunQidT4gJLHUhbSBQFWoQ2Ag\nGgOiIHIWQQa1ma4XMHSqM1UtQ2FmiqoAKbegClQiqokgMjLU7dh5571vckuC3QDGeWeWAX1R+cl4\nahkPrR7a2dycUmzQjh85NNrcRSPwLJpUJZQlgGqsLctgftjGuk05JjWjfi8c1HFaZzLJ4IwMSclM\nzSkCmKqqp2CWwVzbtqHlm1evnb103hqZtFPaTSMgE8pgJgKEhgGxe6qgrIBJAZVMsggosYiyv3P3\nyeY//9Pzr9w5/cUXlk6c7C0v20J0vQUqE1BGiJZH5CdQLCD2EQB4djtEI6QCOEBuSfqQJmoNQFLr\nMPcMSEDz4JSLeYCnrJ1YvYbNBizsSb0O0zHFsY62Ur3pSVw5EBFF1FybGkGBCgKZtDsLxZFDYsuA\nprOed/d3zFklGjkiCuy0GIg0pgJIBghYgGoWwZycM1eWBtlxYY7EMqmLKTJR6LERk4aEszY3cQct\nYwBA8pqRIVJGKzznLpvLotHYITlkBBGFmAXJF8lXxfIq9A656hAND1GYt3IOfQVUIHlCNgDA7nbF\n8PldUBEAhIwBpPtpGySzhEpgClkMklrEmCTHlJt2vDveeHRwf/3OLz9468fv3VrfG4Nr0Li7TyIg\nEJj10CNIjnmh0ONnFsHp49v3n9x6kCcxS8pJCRCN2WCjrg/UkIWRSnZI2RSZBYjEEgGBmXPc2TBV\nTE0ZyDlGJNXcFfs9saSE3MELrRuHBudy7iCl1s1FiMg5J5LAyAyIQDXZTMTVeblniF8zc11fgkiz\nMrOoekAFAUDs4L2BAXLHl0CPospAwMCqiNzujVgwk3mpHn/0Wf/U95fOXtq+el33DvpHj48iSrtb\nlHMqyqimZpotp8JRMkFEh9QkmKZkCv1KnQ8iZtCWXAGYsXlzim3hKIMJAjJDnSpPiFjn1DQJgMFy\nMgVgb5jZKjKURCGIKYI3M0AuSh9KaKI0KbF3ledA2KG6HYELmMibyxoBmGNGUkeQpHvRBGcASQHJ\n+QCu6H7PzIRFWSRhme4qWs4ExEUBnw/YDRFStMl4kjUG9UBBkFCxrHqtZckiFNBYJPlQiLQpZcom\nSkCq2GgUHwlxgkzsqPADNWEgNW4nu96FnOJ0spHHY2lzjC157tOQcu1E8sbuSHwZJ9M6xrPPvXD8\nwllCufrDP964u7u9dXDy+S88ff70L/7dv7p3+5FbXvnNv/+3Dy8O3/o3//X9Ww99Uf36H/xnK4cW\nPvizP7x19ebeflo9efrv/O//19O9ez/9l//tR29djVy+/OUvLh5enmw+vnf908HC4tkrL4z21t75\n+Q8ORs3iocWLzzy7u/bw+//jTyYRT5w+deb06Z3NJ5sb60W1cGT1SD3au/qzN9uUT5+/Uh8cXHvv\nk61pW/X8a1/8EgJvTg4GK0fmquLYmdOBafPRzd39vWdeeHW6tXHng+tNe/D0uXOjJ3dvrK9dfPH1\nPsP+xshBGRxq0phjD/vDhR5SU+83UQSBQ+VMTFUdcDZVtW7yCWaCJNYVjHK3Y58JOxFFPk+ydBdH\nQCYW1RCC5EyAyAwwA3YKZERUEAI2ASAj9CKRLAtCEvVknR3JOZdzRhNAJkeqiGSSIjpCINXkQzBL\n3aEZOIgmAOhVvRjbnNW5rMpIxr44GB2Uiz02G1RzO0+eeCCeK7TW3AkeMB8+f2H91qcYCQtvzTiE\nSnPrqx4RVvswJZqqogkasSM2anI2ha7TmFWcYyIks3o65eC2Hz6mghePDEQmZmZNBOVMqoaGOQOC\naUZgAgFDwDyL482afWp80Mr7b7zz4Oqnl1597eSrz6Zjo3JpKczPFdUSqhEXIlsaD7AdULGAYaAO\nuWMyOTBA4tLUkEuSiWkNSQSly7Ca1qgV2ABYNZTkhzw8gfNi04fY7EDa58lWL43sYNvaPWvGliNh\nZRDbNj3aqAtnRw6R9x5AAEkkmQkyiSiAEfsOsg7E6Eoza1LjkEAQgzdpER0zgzbZwPfmpB3H6ci7\noL5VVR+CKQfnTVtizEDIzpmSuaRTxGBmgO7zD24G8IrmqTAfESBla+vGE1EFAoY+oDvqhos4PBSK\nRZ5bBj/EYk7DUKnHzpsREBkAAVl33ALPboEAAEbGn08dVU3RACyCqlkyjSZqqqG3MK3XpN5rdrf2\n1tY2Prv90Q9//va7t3eSNshqhghIBmYG5AD75AIIgXqE1WO9YxdORYWdtUepndZNI01iQ29YEhRV\nubVxEBF7gBWSI2UABEFiBNSOa2GzmWQnCgNRAzJNADxLUxuKxi7dNpufMqFCNlUzQuw2C904HCyb\nos5mM0BAwJCyhOBym5GIHSKwc6imjpyampkn7tblSIhijskYVASUyaFzoFmxYyECZcyF803dhuCJ\nBY3SBPfuXp9ffWHro2swnnA276sqlKltHUoCQJFmMvZMWQE7vZ9qk3JsNRQOxKJNyByTUyITyTEo\nZEfoHcbEZqSWgDEbtUmaRj1xUThBkmxRkwtF4ThLDKEyI2QHlpmKomTnfGxiM40EzM6qfqXq8rQ1\niORRJbPzBBoBkqZgCKAZoKAKHAKxIGLdAkBRlsQkImVZxawilNViVCMRU7MScjDVNmdjOxiPrTU0\nKZxHCAoCCOyKlJKICMo0tojMyNI2ZtDUqQTNps45Q0zaQiwQjNgkA1nLRUEGZkoYgMmya/f227ae\nNuAClGXBJjrJbn2Usy/jaIrl4NJrz548eXr7yd0nt6/t3V2XEF7/W7/ncfr2z368s7N1/le++ivf\n+s39u2+98Uff2dndeerpVy6//sWD9bs/+O5/P96dtLV+6Td+6+mXX1m/9rNffu/7Dx7szK0ceu2l\nl6bj/ZvvXEvt9Owzzx4/ffbhvdsbj9enTTx67vKlS+fvffL2tas3gcPZS0+dO3Xy+kfvbe+MkMPh\no4Othw+219fKhYUTx888enI3tjiaNufPnjxy5NDG5uO1zT2J6ey508fOXe5V1Z2rb3Nv7rUv//rd\nT35x59MbiydWjg4PPbp3a2Fp9cVXf3Vn+9Gje4/iRCpQEMoGCnYwnS7AENQIfHdN782t1JMxpBrA\nQNR7JyLKaGJmMnuiRPwcBIpgpiaOnWhG4u6iYzkLGLFLOSGwcy5LBCMiAswE3C0eiFCRTUEtIQKi\nZyTwiZk0ZwUlIiRUgxnJojuzmMysy6iaWZesyFm6uQyYTZsJIRSVJx8E42B53sg0VEVAdb1+yWsb\na1k4HhywK9n5lGqKsP7ZjaKqksU4bcg5zYnQt+OpAQx7vYPpQZfRUzAwdjMTsaoSgCkCM0OHkQfI\ndbP5KB89c6yaywutxZiyem0yGCcEM0UAJTZTQBJVRCIjBTEiEmUgw5zFEML6djv6858++OzmM19+\n6fClC9XR473FNgzHHOaoGDiKqi3ECYYBhSH4AXie3Q4ZjBHYmcyTDcAnziPIZtoCRssZsUdWMqkp\nAQdwwRUV5ja3O244hrhhi2Nrp1wfaD2SeED1tk3b089/fXr/I9n/lKEL2TNSBwVlz14MwJxoCwSM\nJSABZs5oqujZSC2bmXaSEjLNTeNdUG0rV8sTAAEAAElEQVQVlICw8/uAsSORwpAYVJMCqlImLEwF\nUUHAsLMfKFhGcGIZsiXLJgRYSDlv1cAvzPv+UewPMKxgOYfFEF0PXIXkGBnMG+isE9+dGNipphnw\n8yY6mAKSkloyU9TcFRhVI6qiJNPENDCtrd45eHh3+/aDe+9efftn7312f2eMoMRdr47Q0FDA2MSr\nC6xo6okPL/kXvvwshvJgr87TCBomo1FshEwQqUKNRbXV7AUfGLJz4AHZ0HlnFgFcR/IL7IC6Xr4S\n+ZSAGVRJVZhdV7iHbsbbsWBQ0chMQTqniIaiMI1glJMxEczq9TN2BCp2dm7vnKjmJIgUozoHIsmF\nwhAU0QBNDbKiYUblmXk7ee61bUPICKaqjqQ36LdN7cmorIQBsqFRu7ExODugEl0q23ZaLvfNCsc8\nne4zu5hb50ojTeRAITc5ZgHLniloVvUI2LTRh4JYU5LA3gm0KU6NcxYiIHCGMKobybHnQ1WwD9hk\nbCyX5Akw51wFD0xJWhFg78rKM1OKuZ5Ou54kAWrbKiYFQWZzDrMAEASvbS5c8K7IBmQKxFkEUg4+\nFK5qYiuayfeZfM6SFUbTKXtC8oRoIo2laWrJiAnQENlVvaBGMUcwIecAnRkIkfMQG3EuSGyENLct\nEwNiStkICckYAUhMNKtH50FZnANo1AjFuaBAKjkqTpNmkz74ihQA1ZFrmyRJikPHXvjqN3ouX3/n\np08ePBK0U8++fvL86s233791/44vB1/6/f/8zNmTn/7gf7z22bV+mPvyt/+T5ZWFu5+8s3bjxoNH\nOyunzv7Gf/Dby8tz13/5vXf/6o1RTVdeeuXy02ev3fg0UEHO/co3f98H+ujnb+6u76kPZy69cP6Z\n8+98/08fPFgbrBy59NLLZ44cfvO7/3ptY3T89KljJ1Zuf3JzHOtzz73QL6s7N28fTFRMvvT1LyI0\nTx49Wd8Zo/MXXnjplVdfv3/jg89uf3bl1a8vLhY33/75o5u3jp441S/d+oMHR5eOQlk+vH19P+59\n+Vd/9c8/uNNp/TRpykmVBnNz2sboGsOMWOw+Xici6J5kif66NaFACKymMLsGKs0A9h0scmb7REBQ\nJuaOw4Idp0kUgZHMUMGAHJt1YUvJmsuybFs1QJVsBsxeNQMgkesezb0Puc2KhsgAQEwAaqrOuSyK\nQJ01m4myaPDoexV2zoeqdCWGfr+eNEUR2GCamqWlQ5aBHSk4ApCUCBkcG2rnz0AHTKFt2mQtugI1\nMWpV+lYkmwIICALngj1AFgCxjIAiIoQKWdXlZMay/WRr7tBCWXoxiQ92xXjSZDK0zn2DjAACSugE\nVCCDmVfkblnZEZIdgtEY8Pbt9Sf3/+L05ZsXv/LSodMne0dWyrllN7+EYYAhKGXINcY98nNQzJEf\nUmDo9qoEQGDGSIyuhJxAG5UWdGo6NY3GBWLoIvGIwTwzOgiLakucouappRHHmtMI0mQQDyzt4/wi\nhfOoY8iS49STN+uKTYiIIBlyJk8qCUHAARFItq65zC4oKBgwkRUeOkw2ehBCR0AO2TrtNxgZSEc2\nMSIUBQQk97kpMFk2JJ8FiT0WQcJwt6aFw6eqQeUGQ1ctY6jQD7QYsu+pL5n6RtiNFMy0S/VTF56E\nLk7CQGhECGygqGoqoBFM0QxMTMU0mSqIZGstJclTaZ70p67dWL/2k59e+9kn7713azNKRhTssumA\naIJIAA6sMO4hE2Q0GAQ7/8xxCWUzquN4Ik2a7IzTtAURQgig5vH2xkEiLDsPFoljdETYbbCg+45R\nNDMyEpqhZO3Gt8zc5RpTioiMYKYACDmlDlVhBt0FEgktJyBCAgISFfY+59z9ghkxB7Dr0Xd5N1VV\nFQOPjLFtACy4Qk0JQNmZdIYvNOtegsCIZoLschaADOOGyNQBmlkUMLIsBDo8cd58FffHerBPiwvj\nGAMYMQOTL7xFFUQfXFO3qEDkSK0kc94pQFYCUkPMWR2TI82aBSBOswuMSG1KddKcpfKu760ovaCx\n4kJRJEzZsAAydkmwieI8l2UJZvV02oXxiqrKKbcxmkBwZILsShMAF0w0Re1cxE0zcuSJLLWtAJeh\nCC46V4k5y6a5ZVeCabc7shwdABZFK1liRCQAMTbCMOh5Nh8VuSVnxsSfP6GpGnoCT6BQNjkyk+OA\nTgEBJIIKIYuJZgEwiUgOW42tJmQGAiUWR4kkey7nFgqmgoCJSCUrOFU4dO7Sc7/yNyaPPnnzx99z\nPEClp7/wypGj8+9+70eb25P5Y+e+9nu/g5P1H/2zf7K5MVp86tw3/sP/Ra4fXP3edx4/2FpfG738\nm3/7xW98a7x+7fpbP3z/zZ/FRF/9+lfqg/13f/5zZNRh78u/9puPHty8d/2zybieP3L61W/8BrU7\nb//lH63d3146cuL85ct5Z+2P/vI73g/OnD8/XBhc//hq0vDSl74WrP3ovfdjhIXFuYvPXZxMp/vb\ncVTHlZWFy1/4G0uHhx+98ecbj++/+NrXJ4/vP3r/7uM7d5dXToy3DlKZ5hbmxq3t7z3BcviN3/2f\n7d/+CBWQOGYRTRxCoWFva3t+nj1Scl5MHHsEEDNTBFQCnEXvELKaKDk3AwmaAhuaKTCikpF29jLF\nSOqTtWCMSEAi0GmJuuWi5pyD46QKqiCYszCjGYCDEHzOufOydssMM4uxYVdQ96+oAKzaiZNsFv8W\nYCYTKENooe7P9wSyJ2IObq7HpV84dKRAHu1tn1o9t3nzMzBBV7ICCYAnJm5yevFLr1/74O3CB7Ek\nBkXZnzT7LALeB6Y5yZM6pswRRDATBA7oM1oLigRmIAierLvmgpJSO01tk4YL/aOni73NSWpj5Vyd\njEAVEEyUAIGyqaERgEMGQGEwQzZARI0KTsXQCKeK1z+4sXb7/pnnLjz12nOLp88Vh3aHwxU3t+TC\ngDioZcgZmgMIFZRLFIbs2Bi7PLw5AANkD+A5D0D7kCPIFGQERoIOyRtYdwUCCoZsnMiGZiuYksII\nYvSSLCVeHmOaaLsD7ZSaGqyh5gCbieasGo0ihU5zJQDICkmSMjIVHXmFoRBtEEsiBUcmJVIicFkT\nI0ByaskYEDwamiihaRZAUgUQhcBIJVI/F+SqRV8NXFjSqqrC0JXLRTUEUOSKfN/KIZnDctixxcHo\n8x6qIbQdkBYAAJ12iGnyaISopoYmJqrWkoJaNFXQbNqaGkjOOWlucj2p9zanmxuf3Lx7573r7/zs\ng4c70ylTJgZQRkNG654wCFgcYfTEbMam8wW89NpTqxfPjA/2m1HMOTV1HO3XKSY2cEwOczi0dP/h\nOhUOTQNbZRrYeRLooqsIpAhIDBlVjKEsB23bEjECKGQ0li7ULdoB4LsNB5h1ckkERERyiDqbZXRP\nBSIy23p0wDhQ1eRdkTWbCqFHRGYneWapcM6JJTXoklSdf0bUzJSRmmlDDMxsBo5IIxupFoACbV37\ngdeI7Gjv3v7g9o8GSwujvb1276B3JJFySmJOEJgAWs0EEFuMSRyYK6AXMKhvNIuBWevBgxpodr50\ngSRrm3NB3D1wiwobDT0VBXkXskiTE5KpMzQOAEbUKraxJSbniiS5bXJMTRV8VVQu9FN7wMTEZACu\nU3wESFGnMXZUJ2yy946ctk1DHEK/13dBOIGodz6KtG078AUqkip7H1NOmGJtuY0qjXOVqCChx+75\nzByRUiAQJs5tRgVFUaIAHhHZqWoyj8zAWBn2Yp4046lAZo/gmcyDoEBuoKFMjp1iBgUaDjiEAK5X\nBEKPJhDHLnhCc6svvHT+zLHrb/7Rg1u3nF/urxx99rkXewF++m/++aPt5uVv/f5zv/Ly9e9/5+r7\nH1Gv9+Xf+09PPHNh7ZMfvv+Dv5q2FVfFb/0XfzAcFm9/559u3nm4u74Grve7f+8Prr7zxqPH+47p\n2NOnV4+d+PCXP95c3/Xl4Oylp889++KjT39658OPtneao0dXFgb0y+9/d9LEo6tnLlw++/DBjWsf\nf3ryued+9etf27z30aM7dxO55TOHLz59rjloxwdbTTs9c+n5y6984frbb1x94/3BwqFTR5+6/su3\nnjzZXF1dKJI+/uzq4rHDxw4fvX7roZbl6ZdePnf24vW3/mLn2n3myhhJkgqwoQ+UJhMrekJKngwc\nOZOsnjG1akpmmZGlW5wQMJKCkIGBMgfJCQlMACB9Hq9zs2PSjAgQWBWQFJFE1Uy7C5904wom6moI\npl1SO8doZuhw5n8BQEQi/9cRPiZSBe6awQZGQOTYd0G1rMDV8jwG9OgT8MKxU1LmXrHSxvF4e12o\nzDmNd7Zj5OCiZHCGoAhE8wvDz955q5wbpvG04CI7aEaT4AtFDgUc7IyKour1cj1JJAxKIsYMhQto\nCjmLdWsbIyQjIGJTMYHJ9iiQjvbHh1cXifd2NscluwYQzUQBwZllRSRgM5nVYEVnegdAJBPpFDgI\nRspuZ1RPf/7pzY8+ffaLrxx79VI6ema4MvXDvuvN+XIoWIEXq1uII3MDLuYwDCFU2vUOCZC6FgAq\nVOQr0DlMLWhCnUAcdwohs9lNEsADAkC2EBgq8wCWUIW0tdSg1ZBaSK1C7XJtbQO50ZQkT1jEYnKW\nVFpLY4xNAMoxSVs7X7TtmCBQl3YBaKUpXJEUwYLnqo4T7wcCBOjQMTlvjpGJcIBFcDyAMoCr0JWF\nD+j76Ev0fXA986HkwnEvAxEz8hDLeaj3TY3IqSE5b6qYE0g2QkNAKgyh47oDIRibZbMMKiINaAY1\n1QzWgmZVU0maJKeJNgf1wWiyub376N7D929++POP76zv7opEZjUkZLAOTGiAZKQoqqgVuh4IADiw\nKy+ePHTmaJvjdBpRQVKspzKZ1ARGRB4zMxTHL8mH6wzQFWQKHxBULJfouNM+ebacgRkZDCRrBgA1\nJXRgDGKC0v3FiF1H4emkUSkm5x0zgwgoiAAyZMldFDtLIqBu42jW1X29ZEBwBrFrM+FsegyINDMA\nd0aITvuCSkymhJ0UBoSZ1IzImRMCyjEhgBt6YK8kIsAU9q69v3rihdGj9TKmPD7IZV/BcsoDchGx\n8sU0NbnJJooeGdUXRWzlYJQktaFQ79GT9qpAwccmg0C/qNRyN/IFdI67VBG2OQKaGcda0Qs6SMIZ\nk2oqQq+sAvrQRkki2Jmo2cV2Sg4DBWJq2jrFtlcUZBgF20aRFc2KQAwymdQ5p7kBVB78oMdgOWfN\nrGqE5fbOGLwhEKoyFDm1otF5l6E0NSKvABlaJxh4iKjkuWnamHOKLWZj8sbcZmCPwYWy6ItIl5AS\nlja5pACWTCBAEQKmnLrrDDuIknPOpU0DSjU3VzkCMEChlIqy54JrW+9Or86//5f/LjbQHxxaeuri\nsdNPPfr4px9/9NnKibO//w/+82L86K/+yf/lwYPNY+de/MKv/XqvZ2/9y//nraufNcm98PVXnnv9\ny7fe/+mbH7y//XhXWn3py6+fPXfy5z/4843N3YUjSy98+VcdpAc3rm6t7ygUF19+zUP7wRt/trux\nzkleeOGZOuOtO/dawaevPBeK4rNrn26Nd7/59//+fEifvvnv6v16Y298+sVXTx0+fPezTx4/ftCf\nXzp96YWzZ0989svvP7h9/fKzrx3sbT+5/WAc06UXz+/ce1AbX/nGVz3ynduf4Nzcr/z23ykxfvaT\nH2/ceTjZOFB0rFkMkCEl8QU4c5O9MRtlgRk4yVSli6OhZJdVACBnMBT2rKoiSsSgaqazwQsBocsq\nZNgZFxCdSkZEVWF0M7MgQKfgAejeZQrEmrJzHgirsppORsycu0QookgmIEBDZtXsvRcRAO4qwM55\nQgXAnBpwjoAyQTCt64nrh95gTthVvf50tFcurIS5+bqV4fJ8+DSQd62iM0DPZRlSis14RFzG8ZQZ\n25whJa78YHleojSxXTl5eDraX1DfRNlXMYUc1TliB54hA0lCVchZUxYvDlGBnCqknPZ3p4fPnkzt\nFAuaNq2OrEKKQmZiJt2dCCAjzi4oBNTBKgDAIANpF8ZBIhQV4jFhbuCdv/rl7Y8/vfz6K4cvn587\ndrxaWe4NY7X6BXfiVLzzHugURZOMMXp0c+SHGAbgnSEAQ9eGmWVqfGlWYh6gN5AJWFRp4N/TJAHR\nmQKAGnbUV0NXUJg3U1NBU7AEGlGjpcTakCTQKYigguVoMA1iIOg1Qs5A6FLLEDtckaOiD1lB0FhB\nkco+KHaMS3LInYarMgbCgXhw3BdiohJcieyYCmWHGDKaI68IakQIRAWAB1HggryDbADR2hoxGSCw\nM1ICNuLPozGKCmoJJIK2KgKqANlMQUxzRLUUG5CmnRy0B9vj7d2D9bW1q7fe/dmH9+7vjlWnoBEI\nEBgNKIoBGSByNiVDj1BBWWgL6HyOf/e/+LX+sZWNJ9vT3b0O2tI0ur85ggyEUoJHheNPH//eJ7eo\nU1QjlmYFkSconGMHCECO2CSpAaqpUfAgYAKMPIMYABA5UEUkVXVIzhc5RVP1PhikWSLIwDnXLSPM\nzAz4c2Xh5wBhVBHEDivou6UGmqp19zybnZdMkjJat7rHmRv48w5ijIkYkc0QsqkqFt5LzGkq3gUu\nq5xifLhWHXsFg7kaJ5MaqoItIGFmRBUFJUMyYHZEiiZN4mmdU4JeCGUpaEhg7Hg8nsTJZL4sQuBW\nfd00omygTJCzmhoRmqc2tYho5po6Jc2h8EUoi+DKwUDM0PeBgrQjdKGNCcyCM0+smF3wFgFJgkNj\nzOQlK5KZ2aQ1Vhw4LslAWsJEBorARN5zk1KTm9zkogxIhphVldGDQZZUVD0iCiFAymYEIN6xqBK5\nnGqNkZwTZ2gduA0yt4GJXI/ZCSTrKqqhP5lkT2QZxFoAyGqO2LCcNtlSJqgBKVCzsLJcDY/sbW1b\nbhHVVKoquA9+/L2Y/ZGLF46unkht++FPv/fk8fprv/OfXXzp9Vs//ucf/ejNqfCXfvMPzj73zNrN\nD957572799dPHn36yldeJ5Dv/Q//zebmaDLNKyurl5+/0kwPvv8n/3rS5Msvf/n5L39p78mNh9c/\nu393Y+ns01/+2t94cvvjmx9+vL+7f/LEsUEo7t9fH5sW/eqpp86MR7s3bl079fyr3/z6Pxjt3Hv/\n5z9ttqdTqp752rcCTN795Y939/aXT5y4+NyrvZB//J1/weXi13/3799+56+217aqlblFXz66fb9a\nqC6dv6ImN69/Wh09+dXf+jv7Dz/5+I3vjnbHq0dWZWecSRCJFNSUnRFmh5hbQQdE4KpKU6pCbzLe\nQ8cxRhNySIIzNC+okCGyF8ldawLMkJxoBGeIJKIK6tiLSLein8F5iRCRELNkEWEmVUHgrkqYJYHi\neJSYkYhoVtwwntnqhYCAUMRUzSARBTPNOXrvu6AUIWJVoMf+8gr3imJ+aESaIlCfvOOysnpvMhov\nl37n0WYhZVH5ZtqC2nSa2VFV9gxJAbVtmIuYwAtOdkbeOWYXYy6Gi3NLZYaN6eOdREhAbSNIWPhA\nTCatGGSBNmbnsR96ZqZkaGqxWL/38NzlC6TY7Mf1tH/QtmTokHVGqzRTJOy6mqAqiKgA3RSri+sy\ngkAycyjgzBJbNlrfnmz/2Y8Ov/vh2S+8fOq5p3vnL5+9fCSv3UzTHQCEkDA7cyVSNNojV2EYou+h\nG4BH5c9JBZ2yJ6B5RBuaAopAzmQNpBYxmwmAKSGYojEgmaWusA3MAIDQB1AEsRIQMkAXMTGYzfCy\nqBAhTPfARE28qpk4U+hAcIaEhkrEgMizsQKRIiEzogf0RgjoHSCwJyAiVlQApzPsJzCAAZEhohNQ\nUwWsLU1Rg8mke8RAYuMegJ+5k5DQxEBRzUTMWu02WJpAFbQzNCTMSXKrKbfT7WZ3fby5dfBk7fHN\ne9feu37z1vp+lBYxARh0/HE0MFMkIgCZbRbMvHKJEZkKiV/82oXBicOmRX1w0E0p62naWxuNJzWb\neSBCcSzVxXOf/eD78xU75ABWeSpRPXE3SAHIaopgFLhb3HUiejQEsCyxq72bKnS5ZhFDyBZNhZ2z\nlLsmsWOnlnMWIu4qEN3Z2K3/swojI3YUM05ROrcIAVKnXjHp3nmmZlmIGQBUBJmhy+MYsOuemRUR\nARktMwERGWQSxVwaIXS7QCmb9gCtRXV5OuoXh2rwhKY5Axqk7JC5MBHLKU0zRdHpNDvWUHqGgGQZ\ncOegbceT+V7l+1USSW1KhqbinUfRnIWZUCk3kV0gAnIMBkTIZVn0PHu2rsAF4hgbQbW2qirNImYp\nRibuLx8a40HIiQghaWXAhcvgowpiLIm9QxFpD8axaZG9WOE8OOckoUHh1Bhs0rYM4l2RCUByCAFR\ne2VBjjFUzIbKbW4+lyQH9hpjW7CfhcsJPLounmGQy6ry5YIt2876XtSJz2IahYiQCQgVNKmKqGRL\n4LOEnCtHhMlpVhBpcmyl6vVcsXTmyqUrkOtHt65uPHys5dzf+gf/R5bRL/7Z/+3ep7d7h0/9+m/9\nBwXre9/94yd3n2yM4lNXnrv41Mn7d69vbx3kJs8vLpy4uPjc0898/NbPNzZ2OAxf+OKXXv7KV67/\n7Dt3Pvpo0rrLX/76ibOnr7/3s/W79yaT5tS5i/M9vv7hx9sTObJ6tKrcjRvX1PDC5SvHjy+/+90/\n2tkbzy0eOsB09OzT248frz952F848pVv/O6hw/MHD2786F//q7NXnv/CF79x971f3P3kwWBlsV+E\nnY2NU8+cP7R65M5nn+01k/Ov/epzv/La3R//xZ3339nbn5569uWTp1au/+IttkVVEVXTRN5ZNM2x\nLMvcxnJ+LlsCtP39HSLS1I2sQQ2yZAOcZesITYHIGWgXoDRD77yKABoSOXB/HeZWVWZCRNEMosrU\neV7MOrcBqYoBOB9MwUzNQDqCNJBjlCxmwMzalbsJyTEBCmjwRc4RmWJsimFPSIqAw9Xl/f1pP/RI\nxGFC9gTcn1+I020Qt3rp+fmgc/Nl2ocYkwGwAFeBVGNKphnRkw+EFCxQYPI82T1IOTkOkowKWFgc\n7NfF7k5SRSWYthERfcGVUh1VDSRBbC2EyGWBwKDaQOMbfHL3fjnsDZerZtrmbW1QNBkaI2VnKGiE\nZgBiXX5jpjboJAcZENUAnRKYqWRFUWKHYplpfe1g97s/fPTxra/+w9exgI33f5DiOAyqXn+Jq8qX\nA3MD4GSSIE+YC3WF4z4UQyoqcN0EtgNkASAgATiGwKYFeQBJaB0/ujZpzIQMAUlBkVzHAkJDgxl3\nxZDRCnTODNF3zqbozIA8uDkgR6YqNRkCdM83AOTUMplZN7clMTOYoUlmkJvuzkGI0EUeu9o7AKIA\nYsf/RANAVRPqDHwddwcJuYfIne5kduyDgSXIrZqQgUo2TaDZtDMoqZmCJMtZcmtt3UwPprt7k80H\nu/cfPrx6++7Ve7fvre1FaImUWMAITLsDwoyQ9PNHDBM1hAIwgBmgM1s+XB45uZxSWrv7QHMiQEiw\n82S0tbGHQg6AwSDnK7/62lqaZWEQlRELYCAzNAdkJoDIQADZYZd80SJUamYgpkLIiKQmpkLEgGBi\nCuqZyXkDUZp5srtoLH3uoyfCnIRdp4UB+uu6hYFqZsZuEYhopgnREaMiEICaAAATZ0lEHWHfOvOx\nqSoCAgOYiBA6UWNmUxUCNAHpZh+KXmvLVAypqCGOCnATJEs1GajlDm0Q2EVysck5N7VZcOwDgEYL\nftJIC+IxFMGrg8TlqB4RIysTK1rKksqyEjHJ4lxQNvIhI1FgR54gEZVEQaI20ykVLmcC9KZiQs5x\nbmsCyir7W5tt3ULhYIJZxPsSgVI9ybmZ6/eqfs8I4+igbaWZtEXfuZDbltIUMkAZUI0clyWZiJga\nEzpfuuCQzTEjBybIygQg2RgRA1E5bOoArpW2tmxI6smTC0ogCpWvSl96j/2lI8BVWUE6GKe6Fs3A\nTIBNjBzbng9cFSTAUpdYQjNF9oXXJkpM2ra1TGu3emRl7do7W9N2aX5u4amzl1549e5Pv3Pv05vt\nQT716utXvvDq3vqNn/34rdFUfb/30pdePnby6O0bN/ZGk9wexJifef7Lx04u3/zFD5/ce9xfPf36\nr3976eTqx2/+6Yc/f+/w0pGv/uY3e/3w/g/+5PHDdTM4e/aCJ/zgvY9cb+Hs6cPN7s7a2s7w8JEL\n5y4413709pvrm9OUEwe+/PwLb73544OtyWBp5cy5I/euvfPzP7+1t735W9/+nR6kH/zhP7n/YPv5\nF59rxs325t6ZZ16gwu7durU9ar74t/7jxUPDN//f/7jZ2hORy1/4WnHsMORxOxEPuQASMESPpshk\nito0qNhOJ9X8MMWISGScVZgIkdtUi0hwZRe41q4yaDKbgXTQYpPuU6dDY6N17ThSMCYgQpXuY0gA\n0Ltg2HWTO+6vmRkxgDHMQqiGAKJKjhFATE2AGBFJJfnQE6ldGXqhShKdeiyw7A0Gqyv13n5voe/7\nHLzP7IjdeLLdqxasKPLOeOnw8UfvfC+NJhkGJSK6DpaWY04EDArIqW2T9z6bFcjj3Xb11ImDrR2p\no0FGDdWwv3iome42iRyBiWCM5giKUGRoU0YVjG1qAxaMrigNDRRypulBHbOcvXyhqO7I1fW9g1qz\nKIKiGTBZZph1NLs2CMzAcjDTvc4+vgFAZ1NTtaTG5tCZEG9sr//x//UfvfbeL+fnduvt8fzqyuDw\nUlieD/1F35sLVV99Yc4DtZYw4QG0u+CDFoe5rNDN7lX//gsBGIwBwAN4U0AdgBhLAmlQp516HFBn\nz0bdtsgAgBEAVJAKQFbLBqEDPRs6oh5oS0iKggZAZN1Rh2yzP98MmbrRN2H3v8CgC8A6BQXseu4d\ng88AuoQnIgqaA2ICBmJARvTGDOaho3vNynEJutMuTUEby5INAAUEFIXENCe1pNks12m62072x7u7\n9fbOzoPHG5/d/vitqw+3RpMMDVDmTp+ppKA4k052eRTBDiKE3XHjrYMt5SNL1auvn6/m++sPHlus\nPTowWtva2trYNTMGMTYPzhfxyOuX//KP3huWiAakUjGVhSsISEzYHHF31nTvHJHsnEs5QRcBVVTT\njoLNwGSkmp0PHcIXqLu4Qs6JiBUAu0GozezWIXhiTCmqfB6nRQJVVTDT2ZsVlBjRQEQAIAMyM4Ko\npg7izeRMcsfg9YgIDKBm5oMXURIA05xiQR6zMDtBQO+s1t2H9+bnl7Y2HrO5dm8C1RCyTKcSBhbI\nAVjhXDZOAB6LYWFB1dgMeNzmbN65UJCa4WhSJzVfFASune4zeSQqB4VnmLamZgKmSRVjUQ4VTCW3\ndfQ+Wc5dKhiSQ4m9AhALApPU+hDMbDI66FKySY3AHKLlNpq0cVqWnphaMWLnqkXM05T25geF5BCj\n1lGIGCkbAWHb7/eElqcHOxJbF5iImdhAnIlmtz/ZddnMsqj4EEJZ9Hkg+zkDStt65iytImeAYNQC\nWJ1NVfQhClb9eUcBwtjGBw4kCiAwk/QCh7LIKXszMjNp0gRVkNABKFElzcg5D7xyolh/cvaVb8T9\nR59977+/d3+t6M99+T/6nWpO7l979/ZnN/Yn8fiFZ1752tdGexv3rl99ePsBioLSF7/920EO3vve\nn+ztjE6//PqXvvU3b/zyu9/9F29MRvXJC5f+xu/93cnugzsf3bp288HpU2dOHj/5+OHd6SSvnDhz\n8vypG++9S8XgxDMXnjp5emP9/p3bGwcTOHT2zKtf+UYaPX7nR29Mnmz2S3dkITy+c/Xh+kZ/OPyd\n3/uDnfvX33r/Qyx7L7768uaj9YWjJy5fOryx+eT2B9ePXnrht//+/2Z674O3/viPpxs72fEr3/zN\nts1bD2/wwQgjEEpWVEZtG0ceqRtnERQ+BL+3vl1WhagJSvdRnHNCJPKkhl1DFomJSCGrdlVAUMvd\nIA8JTRTQBIyIwLQMXkRU1QAQ1Aycc7PHTJUutqazrw5Y2D3DFoSaJXeWVxFFEEPuxLBiWviiPyhG\nzUEoCvB+uLpiKZN3iydOZ1YyqOaW66bB3PrCpemO5gEOl1cuXj649mMWM3MJUn9hrt3ZI1doNjR0\nhROJjlkVCl+gcOn1YGMr1i2hC4NgLa89XDvz/OW2vTG5u6NQkEmOcaJa9ELJAS2PczIN9VTYIVNC\nx90neYqgltefPALm4fJAVBIkjYbKyUDZ5U6sCJ3RjboroYIhmHWIExPr+pQEaAQGZJxTVAEFCUV1\nfNXvPnhz7WBycH8TyZ+4eObIlbP9oyvDxUXuDcv5FV/2NPTBOWPB3Ghy2uzhpAIeWnGYex7d/89R\n+O//maALrwAE0GA2BxlQBDWCZuiqBRC76xSAAURNkdAQfacrQgoALBLRFFEQGP4nR68BoTkDMVAC\n1y2eQZ1hRjIwbx0EGr3OZnfdwYuGhOgI0RARg5EzYyCEDo5KGZTBFCChRVAxaUEFpNE8RokGBkBm\nhioAkpNoSpbr6Xiv2d2a7myPNrd27z96cv3e1Y/ubR40o5Qb5ARKODsSAEm5Q29/TuDsVEUGGZQA\ngkLBoUfp+PHe6197Piz0p+MmtuqwIKeTvelkvwUFUyTS0pxa/c3/9Pfq6tCNd99nMmcQEAOBF0UP\nDqjDNqAZYmYmUSHi7trXfbgTGiFpRykBJSJGBlNC6s41VUMzRkYi73wbW4ROFAGeIWn05ru3LwBK\nlioUrUVVBQW1FDgYIXhCYmghp0zQYeOQAJkICFWzgZoyolcU0MTMYJCa7LxLqXbB+aonEA2MANgR\nCExj47c3F84cRge91jfjfdcrs/e+RxajeaWSRvV02maHVjoqCtct2SeC9bgdDKrCEbTjCABcVK5g\n7zw4rIYgjQ8ck02T5JysqzqDmWFwFByPdzIC5HZK3qskRgcGHWTSMCURzXVba5swptwvnUr2vSEj\ndYQ6Rl1cKUMoUtaUIisJkGCq5uZyLqYttO00lMWobgmlX3jyHtSry0VvIcK+xJQVwHsfQpNSTNMS\ni5zHOddiSBmj1hkpxcaiEFM2lTp6RfCsHHJskBhNSVDAA4Bzvbm5lZFpnE68Rx88tdMeYoxNn0MA\nsdROtqO6ffY+q2MOvvDksrt/406xeub13/vdBz///t0PP9jbr8+88JVnvvwFPRg9uPbhrQ8+mwK/\n+LVvnDi5ev/qOxtb254D7O/Nnbn4pa9+9drb39t9tC3MX/rtv4Np+uP/zz++eWt9efXwr/+dbx9e\nPXz97R/d/PCTUd2++tVvHTt28pOffffB5u5TZy95HF99/z3P5ZHjp4YLw5s3rz94tF7XzZd+7VvP\nv/b6/Q9++u4P/zzV7gtfevmZF15cf3z/7bffuXj23PNPn7v91k9v37176vylwvv7j+4+dfmZ00dP\nf/DOL7b3xl/6nb978uK5h+/88M4n76WdydKFK+fPPv3kye3J3ngy2oGDKCm4gM5hrA2c56IsC0fe\nNGbvKyrJHwQzDEU1Ho2r0I/adiJOAxYUzw4MJSd1Do0BMjtUEWYXU/aeAdB5n7M5JBMDpyl3O3mH\nJISkhjmpWgLDbg/RSbEJfdbknEtNAkCibNYJ6mc5NyISUWCm4E0kEU4mUxeKsLQ4d+hIK62rKOfW\nMxS9nrZpf3+TCGRvDJWjLGhExSHX53vvvU+uypaKKJODESBSnIIABdasRVW2TQLoUpydUgDIuWp+\nUJZh48kWJJ8m9dLp4skjS0kzoJm1knKr8/0hm5YuiErdKrWMLKX3ph2lVS3y1qPRYKEcLAbTQSMH\ntUGuW0YDczOvjRkgiiGAdMgQxE78jYAOEbJmIsYOeWmCRKrYTiXWkygPv/XtX729d3WSdDLa3Nnc\ndm+9ffzsqeMXLyydOV6tLPm5ud7cYhgucuhRUTJXyCQpEzXWbtlBIUWf3Rz4OSjxrw9F+/87GgkA\nABlM2aBCBdOOsA1gQJa7mAmLABBoMhBCAEmAjjohOjjErgtDn98CAWdSEeoSGd3piejBEIkBu4sL\ncEehNgdE3e5kpjygbksHaAQgABnMNAtaC1KbJJUWVU0jdB1NU80GlhXULGNU1Wk7PWj3DprR3v7W\n2vjJzpOrd+5+cvvOk+2DVhrDBJiAjAi0w3MqoRPrcg2kndro83utAjJiAVAChRxPnx585VtfgsqN\nR22sE4EBUTtNG4/3RzsTNSgMCbCnZvN0+vd/642/eHd/Yit9JGUmrYJHUDbyTK4bTLOQUbeGR4Su\nO+8AhfFz/LYyIBIzkc1WERqAZhh2ZjWdNUVsNt3saAZgJDRzE6oKM7W56WCryN0bAxBJooLrnBQA\nAFnEyDxyNnHgTGZlDJ3dKxkEjWYvJR8KgASSTRQKEE3e9RRyMRzadEoJDTMAaJYSGgv9UUzm0Tnf\nZkCiokBpMzCq5uCrnZhikwfB9UFA2gwABEMPTsHaiRZF1fOQzSyJtZaAEEVVyLF3WPiYUhxPcxTP\nSgapbptWHWfXE4NC1CRHFBWgNisAALk2aa9CQmFHQAV56w0GzvlpsjStJark1jvnfakI4zZZLqeN\nRssEGpwrikFKsc0JYmIK7IKkzJYBfNvEKFO0Sl0yCo5kOBxqFk0pWxZJQCxZFbu3BpMAEYIhJDTD\nlAXYgBi5NSZXDc0M2rZ0hBjaOEVUM4OC1FTbZK0kIDe3FEqP0giTW3j6hfNPHX/w0z+7+rO3e3NL\nr3/rW4fPXdh+fPX2L376+NGuDRa+8bd+n+P++2/8YJJxftBff3D3td/5e0uL7qMf/tu1R1tHzl66\n8toXx7v3r/78zfXt6Utf/bUvfPMbjz78/l/81/9sfWN/6fDqSy+8/uTujY9/8qODSbzwzDP1/u7V\nm9eOnTr6/KuvHezv3r91fXNrd/HIkd/91rcJpj/4w//Hk9trg8VDL7z20tyhw/tra599+u7rL79e\neH33J9+LmV945dXJOG7sb730zW8fPzT/s7/4y4M6f/N//r/r9eH6m3/y8NO7ntOR009hUb71o+8n\naXvUTxYX55cBVdEIvOIUhdp6AuAG/XmxWO/tu6nP1HUMs/Mu5QiijnzEpJAhEXpWMEWwpMTA5MC6\nqgAyk4p0UG1CEFByHZ+XuOvlAotkcg4AHRWSxcDAyBQROaWErGZWVlWOrZqSMlJGYgAgAiIMvbJp\nmqoo2LhNbTOeVgvVdLxH3pVLK877/vAIAQJBbEXr2vsQ27renwwWDjlCGq4A+VhrvxzWsRUEA3Dk\nkDBRAlXvXFM3koCZc5v6g/nBoYV6PBnt7k9290ZAqsqm927efe3b30i773z03iNgBiCUBNmPx5PQ\n88GRAbc5SWw1uBwTB5cFmFhFCGR/Ow2Xh8MlBLDtjdrMUpMdQkZTM0Aw7GauCGBk0Mkfu6Fbtzbs\nvKloIASUBREZnYHtb+3+D//dHy0tLoFJkpBAXQs3Pnp099OHc4vVifOnDj99dvn4qWJlzg9W3CBU\n1Rz1D5X9pdiOkRUwg0wEt5Qdj3rAAUMfQ99cQR7sf3J9AwTVzzUM1GlA0brZLXpUACiw25OZdRD1\nrrSHBgRdsFDREECADNQxaKd0mN0+jQAVMcDnq78udQUIf73nM4CZVm/2bXX99wyGoLVpBBGwpDma\ntaiqKtSN8dQUVCVSzqpZYx3TKE7r5mCn3t6brq9v3X9478bDezcfbm5OR6atQjbMiNolmkyIIIMS\nsmiHusYOfqpgruNJoAVDD9gzrCg/c2XptW99iYpib2urmdaMykiNwPb6/v7WQRahpIy5omJik//y\nT/5irZF3fvJP++ycGZiWTAWkKgTC7hRWQGUiAp4NpBEAQbuGrSVgxtkDgRET6Aw7Q4jGZKpA2KGa\nU4fPJgYAE2UyUzSdzeE71w+C0ueRN0AQAyKnKmCYWnHMbK5Lp3ZVQkbULNqtuHHmKVPtHmqBzEQE\nVbsUnCNvoD6UKUU2klQH9qjJOcqq1jYF9w7abBYRi3GNbRrND+fNKIMgOQN3MB7XKZeu6BWsltI0\nFt6VZdWkNra1cV4Y9OaOn9ldX5tub4C4bDxtamLoFzacK5rodvfHohmSoPrJJE+bFkUGFWdVc2Bk\njsomTs0EvScgJO3PzSGapqhICNNAPXJBARCwX1XKYhGRvGCOTcrZJuP98XRa/n/Z+rNgy6/rvBNc\nw97/4Zxzp5wzkYlEYk7MIwGCAEkQFEiRNElTkyVTsl2uasvVZberyjV0RPVLPzi6Ijq6H7rKHV2u\nag+yZFm2JIsSJUIEOJMYiRnIRAI5z/fmnc7wH/bea61+2Cchd0ffpxtx53Pu2Wuvtb7v98mgdAqk\n4/VNJfWDgQGUaLUHHo4IOEkAREeFqFiy0iO6gjlLGz325Fl7CbEXQCsdISQVUyIsi4SmYRqVSs++\nLHrpCKku63K0kIhiG1iFqCBW8qTEqoboQt8XRVW5IlmI0pqiu2FgP/l3//LimTN3PvLkHY8/Plpa\n3Dj7wXuvvHLt7GzX3fd88rPPnHn/hx++eRJ8gaqytPsXv/mfrp8+/tJf/KTr+d7Pfe3Gw7vefuXH\nG1dWabTv67/+a8v7F3/+rd85/8GZcnHfLUsHBvXCu++8g2TKfPjm2z46drxL3f2PP/XI44+f/fCt\nsydPtlHueOSJ+x995OTrL5547dVLly/v2nvo6KOPMMArf/EtLqsH73t89fKlzatX6sHw6B13rV69\nOkP3mV/6JnYbz//hH1S7b/rKN387bJ9/7/kXVk+fq/1g/+2PmOmZ428XXKmKMuzesXv7yjrAgAA6\nSWoGTN7VnhCSkhEAiYgjrxjrxeFkY9uUFJMlBDOHpISqEYB9UaqIqiDQXM5BBh9rFJTz9C5nQBOQ\ngaops/OuQNIkCnkyptm4D4ieHaslSQmA5q+0nOdj5hyLGoDFrmekvkuDBT9aWOpURvt2sXflwkig\nd1WFhHE2bcer3bh1fjBLTUrIXMYupGa2b2kHbK41a1ehq4thBX2nhABIngpGX7jQ9KOV5WY8CV3j\nCjfb2rYpcFHs3Ld3fX3D+pRjBFMTrnxwac/NO3dduLa2FpQMgWPSaEa99yUSQAEsmkKbGJEAyHkA\nJVRRAsNm0u3eu6fpuuGC702jSUjRqWdU4rIccLUyuHRuXY0hg3lAcxnIGHRCMjVBNjNFcgRigsAI\nDCqbG1u1c8aaVQJA2Ir012abq++Vb7+/Z9/hfXcc3Hn4cLV3ebSyZ/99+3j3jXr+Q2ouRD8A9s4V\ngCY8QfTQOyBGLAxrLBbNF1YykUdiVICYdYCYZTIfV6tcqjRb1Q0BMg7oekUDZ9f7pnlhnUcEzd/J\n7///vtn1Ca1cL44ZaaSGoBCT2cxEzYKmZBBBEqoZtKYMEE0MQJIkM4GUVGJKjbZdbJt+Op5tbbTr\nW9tXV69+dP7c8TPnLq5PWglMrakCqZmiAYJnJIOcxUXgkooZXQ9nyjJZSAaM6gFKlEKpwnT//fue\n/sYX4nDh0rET3XZTeFZTA+i3J1vXtswwiAwlwKAaNbN9n/90vP1Tp3/3fz15/HJZARg6pyWaQyei\npQMic0xkAGKKmh9fZpc5E2pqxKZmqszk2SGhqABQ3lpaUjMTTUxZnao5ZVBSIiJTUhVEy00zzqU0\nKKqAyKCmNhhVfRtU1RGZoYliPhaMCQnneduogCjIPivAwEANmACISaVnP0BUJEsqkDS1HZUMhcOY\nktG1i2cLhEgOe5VuCsCMZUwpdk3lGSHL1MAhCIFRuVCgI4rJYgQ26lJ0YCIWA9YDYxbFUOy4YXN1\nS2PcbFrVdv9ox2hUKnLb92jomLFEFGxCatpuoSgMGFRKjFT6XhQJIIH2UZCK0nlfOC6AFCQigIhu\nb15zVBW1Zyx4sNCBiLiUOgXtNXVtT8mkbXum2EqMWtes0iXWZEUaWElkqS8di2jUQEq9JIrGdSH9\njMuhGTDFwIxGRgSiClgWhZkAUuoDE0RDB0IOxVIKjjnNUuNdaUgA4OqiQkSNAtCEWFKFjFiOqDCT\nhiMi+SYG99pz31nfap78pd+66b4Hu+1Lx3727TPvnV7dnD7xpV87eNPuD37+fLO+VfCgnTY33HPP\nHUdvefv5f3Plwtae2+966omnt66dfOm5P1y/vLb/7kcf/Oxn4uTid/+X/21rnG67/YF+/ezGtdmx\n0xcH9RIRLwxHF06c2Hnghkc+9UmRrR/86b/d3pr0yT7/q39r1w7/vX/7zy5fWB2U1S333POJJz93\n+eQHP/vhD/becOiWm44ce/8tdu6GWw5V9eLlC+er5R333HTH5XdffOO1Vx/+4i8f3rvn2A//9PKJ\nd7pZc/Nt90jXnTv9Ueo660mtXdm9vLLn8Jn3X2uuTjywWiqMvSvNwDMRgWrKcZ3sSwOVqGHWMrMh\ngLKaGgIQOucBfAh9ytNOQFUhdmagYogIygDAnkXFVOc6GDJCJvIh9DkOhoktAxgJFcg0ZWk1cWGq\nAKoqlBPmkFQsinBFi6PF2XQmSRNrmzpeHuzcscucX9i1K4WpAnNJzeqljYsXB4MRJe5mWwzJjIV8\nwRg667sNWx+ggHc+SewllM5RXfazFgRTDGpYDBernQupS33XhO1x32nsW0KnIRBmvBwUrvzwrfd/\n8bd/LbXpxRfe76JPqAgGYl0XFIvCARCgYggdojgeglNklMhECIgxpM3Vde+qahF3IIBK13KKUQES\nhGe+8rW11bNbV8dNH7O/Ip/685KQw+dz9A4qAwZRTwyQGQZACCGE7DIwEEID1HphaTZtU6/nT1++\ncObc0vI7e2++eXTo4P57PqfXLszOvhNtVnLhBoNYjNgTFRUyEzK5UgkQvIU1ZEJ0gt7YI3lEnxOX\nAAtgj8iZTYdABkgIc0ULXs+Z/Li8/X/XubkyUa/XP7w+jUUwyc2JAoApoiXQCGaggpBUA0JQERBT\niyAR5rmAksW1AGoJFKJJUulBUoythaafTfvpdru53W/Ptq6sbZ2/dOnU6umz57a2wky49xQRk5mp\nMQMRsaljEsnMB3OAKW8x53/ZXNiFgIxAxt7UAVWUHn/80Be/+Y3O/Ac/fUn6xheFEptRaLvZNDjn\nggXXK5S2qH4ygn/0u88H8JP1a2vnzw9dhRbZlBmZzJMjNRAEMkQSU2dG3mfaGRE59m3snVqS5Njl\nciYpZnG2aUJ22TBC2SYh5pxXk5QiGhrY9XMA7WPiuFl+nWbQjJm1TWNKWfPJDlXw4882E0KfdyVg\nSujUBPPIgAkzEIOQodQU2ZMyj5aW2/GGETETirhRCTE54KiCAKzAWkTUybTxBZdD74sSwZGkBJCQ\nJQVKjD41Kc2UvLjKEnvQqKIyqAeD4agZSyNnB8tHoFzs4nrhaVAs1nXZNf1MpO3ToCpF+qTcaU8g\nKwuDgXdlhfWCVyXgyroIda1RumZGoCbSjDcGg0X0jOiMXNMl54i5x2RYO01gwEAAzJLMsPC1JBRT\niBFi6j1QC4J9UhUshAy1JwWDUYHJha735FA1koVZYwg+KmqWIhuyJ+glRSWMPbuCiCwECUE8O64I\nyfoQu9AbMoEUcdMBurIcVUOzqGpp1oOqFgLoCz9kiNYrkBERg7mZ2OO/9JuHH7hr8/jrp15/6dL5\nK2mw9Fv/7T88/8ZLP//zn8yujluJkapnvv4robn8+p9/a73vHv/yr95w5Mixn37n7PG3r6039z77\nSw88+cSpH//xSz/48e0P/eKh2/q4duXChWtNsL27FrcnzaJf3rh28cY7b7v7vnuvnDt2/sratAl+\neeUrX/jyxvmPvv1nr0xnnUd84HPP3nzLra/96b85fuz47Ucfln58/Phr3pU3HNifDJum2X1gP/ry\nnZefnzTdN/7OP1o/98bLf/G9NNsui8Ft931yu9kcN1OcxTTdLkb1wcNHQmo++PkPd+1dHtpic3qG\nxlagzOZGNVIEQVUAwCSRmXzhTbQa1H0bkwQEKMm1oVMWREeYOcK5EKpIJCpUEzmnoGBmwTJiJWvq\nEEHBRNIcE0PM7MQ0b4ZUldmLRjOe0x4ZvB+kFFWTgCFSWZS+qsbjmavcwq6iWl60wMOVxd7FYrhg\nGIWdY9+PJYKt7FyZrG1qG1PSCETStDHKdAqLA2a//tarPqVgkVRGo8W+b2zcl4OBRUkmnmjjwqnY\nWelGY9k6dNNtvi4unj7bTWfOOAk4X+TMJmf42g9fuvOp229d3Xj71UvAZIKQtWUdWY0lMxMDsar1\nfSoKJCFmiMmYgBT6EEi0Lmod4S6i7XGcTTUmMbHv/MEfO+80mlMSNMgpswhqYjAPGTcDMUAAMyIC\nzSEJqAKYZ5ag5AySBCQ/HA37ZsbOy1ze5KYhxvPndqSeD93Vra6tvfLi2HCpIre8XIwGfjh0w0VX\nM/uKfOmKimiAbAgOHSAWmnUU6BBJAZE8kEPHDE6zRJBZEQgdgAMkyxBSpDzzBHRg12OYMdduRciT\nYMnIvDxQQAOwXlXJVDUaBBK03OFZMkii+UbGRj3k3k1F58VSVSIkkdSmvg3NWNq2m036yWS2uTW5\ncnXr3OVz51cvnV4f96kR60SEXMAcMAZI5hAwk3XnnKN5X5ryDQ80F6UkBoRIwIhOoGKzBCNKn3/m\n7l037zt99uqVs6c5RUcMhGIiMaYuSRdNIPZpwJ3SjmK28Zl/8n/ZZl/P4MN3jyUDwMRgJXMNUJJH\nNCTIdwwyAEQjnofAIAKAqJCKqqEDArRMqPFlSoJABA4AUowO2QiQEQlFAntPRBI1R0aqCiKbZRUc\nZe88AYAmAyIkMcq5jHOiBbGKzcukmpiQJ0S0CIBGc6UXqyQgQsx6AgZDMXGKzfamZppdVSAxpijJ\nWt/5qvTR9V139er6tcLVWBal826AGCV0hlQMRtIHjdSFmUUvxGVd63jMBRFxTJEdDUdFpyKaZKLT\n5mLTzvq+Xx7U9cDPun7WCaEUJYiEEC32kVSWlxcBgFGrISnwpDXyATyhIxItS1f4Qk3ZQdReIjlf\nMjCheFeUjpDATBIiFwNQKQZoGMgEKTjnYrQAysSoCmKQFBAJoiZiDGjabHbD0dCVZWo7E4vop920\nZEYfwaAqPbqiqmvnFCsLKaQoiMaEwChRPGBUSxEjsZmSGWd8EgMjhnZCbKrOzNizel+U6JxaYgMB\nFTApRwvu2d/+x8NCPvr+H55/5+2z1/rP/crfXR6Ed77zh5dOXwzBYkgH77//rvuPnj/59nh9uxnu\nePbXf4XT+hvP/d7xN9/ec8ejX//NX124Yefqy99656cv7thza42bNONTxy8mrG657Qbn3WI9vnzl\n8r0PP1aU7vzp45dWz3dRbjn60L2PPvr2D/7y5IkPl1d2Le674ekvff3qmbf/4P/+T5iW7n7k0+P1\nNVTYfeCmQ4f3nf/oLJbu0K1HN69unT3x3o49hx+9774Tr7xw+dz7lbj9Bw8NVvaunbsya7ZQrJs1\n+286uO/mg1fPXlhbvfLg554pfXj/e68yM2NvUqhIURYqxoU3yg5SZWAwNg0q1IWWsyaaKaooIwhn\nT6pBRlEYZuS8GRFjJm6qGrBYQiICRCBJlg9u9g6FBaNIMjNizqv+JJGI2JGqMrNzlFIkQgIvpmgu\n9lFQfeFd5YNAzZQq3Nq+4ofLw5Vq1lxZ3LFnvLo6HKw07YR6aSeddMEDiJIGUQFeKGMwQ97e2jal\nJMaUgEJKvXNlaGZJZO+hfZura4PByjRNk8SS6yvnLqrG4Y6FHQf2n3zjvZJLlYQMBDSsFi8fP/fo\nF75455OzrWvTM6e3iYskCoQm0TqHnp2bx84oWAqRuUQkZgPLyG5To86i90Wx6AUnla8ns77pRAL0\nKUctIlhAYNWUDQM4T0YnyERTU56fVjliVS1v2gxAtTdAJkzmyjJ1rfYBTISMufDO1QvVaKH8s3/4\nN8db22HtosyauqjckHce2Ld8cE+1a0e5OCyHQz8Y+qLisqCidFVFvmLnjB1jBYxKBOYI2QiJ5noX\nzbhOQgWaG+QJc4toyIh+3h5itt47wAhGmB8YRBHJFR4+BpWpJYgAhipqzlBQ0VCz69Q0ZcmjSI4V\nEoszkRi7RvouztqumabJrN1ab7bG49Vr25e2z5y8uLY9Dk2aiHWAvSkopbz/YxADR0g5lwhRDFAM\ncs/HhICkIkBgiEgJBEARyQMMDJmkNF8X/d/8+1/ac/PB7c1w7qMPUFQZGQjVSC1Fa8dNN+m216cQ\nk3m3C1LxyD2P/fZ/FwC2Lp45/tprFRMYMqoDLhAY1VSIHFHGLakvOMVEBKrqiypXJhEpuIgWjLM6\ndB7bCgiGRoAuQ+ZgLp8hyrdPZQKmLNGiuXcT0cw8c4gJeb4zVABVMJM8EmdAVcnQIUIidiIqYqSA\nannymScXRJQHrEAgIh6yGodMJNOHiUhiMFFmV/hSJBhYXdbTrq+H1ahgApMwA+eISWJLqdQMT/MD\n81DwyKQdjAaiSQFK76uBizITJTFVLMfbm0i0Y3lYsG/7btIFQFd6BoSgDCrDihmYyXzlQ582xiG5\nlCLUXFsPal01GoVePDEAQuGAK1NBYu8dOAaQIIhAEgIzFQUTYumcK3BqXdFi25kzYc9NFxyzASbR\n0qNXqb1PTLOuJ7PQRF8VRVVvzbpu2ghCB5iSDUpvyMgEqS9KVw6qpmv7rg0pamJjwrKKHaWm9UXe\npih7X3kPzhUIg4KSOA1RNcQkKLpQmSsqZg8MoY8YDUzM9a67fPzdV3969t0Txf7b/sY//Jurp99+\n+7X3t1Y3p1Mtdy4/8fTTO1fql5//Yb247HYefPapp8dX3nvluRcur20+9tW/d8dnPmX95MM/++dv\nvfjK7v233nDowObW9vlTHxj5W+68OXbNpYuXrCg/+ewzs831yfb65StXZtH9wjf+ho7XvvcH/3xt\nfba4tLDj8KHHPv3pU688/9aPf7zrwO3Le1auXTmL4A/dccew5osnTy3u2bu8Y+/amXOnzn1w58NP\nleyOvfj90GwvjFYOHrylbbu1K1f6FCBBCJMbb79596HDp95+e7vpHvz8l61tTr7zWt+1jlgNIWkC\nLUhQRBOSL4EUyWGJFk2dG+xaBpN2bRvByCgmZHbkUFTZ+ZRSDCkjGBDB8t4m0z2IAZTYZyuxAgIa\nMYgoKCfrCUlViRgMCu8ln3eaVLP5WlIy5zjGSATsEEF9NYwaQ+w9FdVwMO265V17/eIexwWVxWK9\nv1m7VhYL21euDoaDzatXK+IeimbaggUz82UxXl0rbzpyYN+e918+D4JcOVTrm+ALL1lK7sqtjfXR\nyvLWxhZzIRYkJaKCwc2utVtbHx6+86ZLZy9aYIioYB3MSpXv/fv/8Ox/9tePPra1fvWVSQeMHCQy\no8bUqwqwd4TEKmrqJeWsCwDNob6oqk7BJJAjU/OlWyR0Ls1mCYKCmaoyOgUjYpujXDKzShQzkAaU\nzUAZGVTM1KEzEwKMBqzzI2bj6kZZFjH0hoio0DchBFdVKzv3lYu+nU22Qj8dd6lvknb0/tmidLt2\nLi/sGC3uWxntWhwu76wXFvzCqFwYUVX4YkCVd74mV6EncsxcIZI4QvSICEDzWBIEzBgXTJatDuDn\n/Bz72CtpCDDXK6Ihgprh3IOR/ZO50gdSjpxYe7WUP26aTJNphChJWosSQpu6NnZNbLo4nXbjcbvV\njVevrl9evXL+6nh9Nut12scg2BOKUgKTDLkly+nshvNjP2/VDMDUEgDnZFcwU1OgrLcUhGhWIDOk\nCtiDQqeHjww+84uPVkuDy2evbly6iiBA2VBIoGaCoYnbW/14u7eUaodWVtPV8T/4n/7DBKACOHXq\ng2vnNjxTDugoUB2xc+zAHAKoAhg7L0k8e0OZL0sNCNC5QtWYPCjg9QBe57ykBIgZ9QWI17FnisBg\npmIwJ9zPdcveUVLNzBJEQiJTybWWmE1zVmiGml7HIZnNQyeSGqIrChXJW8P56Nsh5lk3gzIxmIQI\njIJSYqF9j8RWUpz2wAisQCB97xmWK0ceJUg2EyVzYGySuCpRHahVddW10URSiuD9cDBAk6A9WqbX\nlzHYjpGryzoAdug6c91svfYIwF03K8vKV2WSYJhKRgNqo/Ri9XBYlmXfTaqiKIrKVcx+AZIysTrO\nDQAbMTJwQsQUY2gEJBXDCt1i2wRyxI5ShzGpoSlJ30dNKREamQIo9oBlUG27RsEbU9PHgU+dFYgW\nYnClo5IqsoGjoq7NE3S5g04ISly5slZEABNpzcwU+7YFpNrXzslosBRFVDohr4Yq6jwzGMYe+5Yr\nRFImn1jBmcZgMbiTb7x+4q33bnn8Fx77/OePv/jcxROn+zZtTSZ3P/7pex97+NrpEy/8u2/B8pG7\nv/Dru5bx5I///M2f/rT2O7/69/7xYGk0OfvO+8/9+xMfnLzrnidKl95987W+4R379y4uLjSb62fP\nXT18/0N3Hj169cJHnaS1rUm568Zv/OrfvHjshyfffVsTLS6u3PWpJ245dODVP/4XFy+sHr77sb4d\nXzp9th6WR++//8KZj9Zjf8M991WiH7z+Sof88FNf2Lp44erF87N2czRc2HPopstXLiOi9n3YXK+X\nhrc+8oSm9P7rL4727H3ss89eOv7q9gzu/9w3fvZ7/yKYegADYwDmsqiUxJBVRVLE4cJQNSkb1OTI\n0lpSIDEx1KTZh6sIwExIZJAtz4RogKRijNepu6pmhkRgJpYkZX3D/AXjvM/0bRDQHFGIrHMzMkhM\nqlKUJSKCI/BAHrzi0spOGgzJ+6p2flCpaUipwJjCNDYhcPQFb549HRrQIKDZV8OpU0IrR4MUuiun\n3tu8dJmSWt+bChAakCefVNgxGk+2ZwQupYTomEiQEVJVueXlpY+Onbn51tsvnTtrQsYagzDw5gen\nz75/6cZH773r0tVXf3g6sGdGNQQEVdVWoSgkRfWKnGMPjJkV1a7r9mJUcOQB66ru+g4810CFo82p\najSJaprHhqioSKQ508oIQBAIWJKkubkyMyHhergeoIIYIpqKYtv2YDaXERJgsKsXrmz/0Xejpx07\nl0FSl6KYqpghzzrZPneFzrDnc0RxuFAvLy8vLFc7btgz3LOrXlzwC7Wry2K4yFy40mFRFmUFLOQH\nhATkgdkxIzMSZ+E9oRdij5jmuhpFyz2uGrBA5v9qBpTB/KiV3CkCqFiDikmlV0EJmlSTaowWmr6f\npSihnfXTTkPotmZha7K5urZ9dfvatWvj9c3tVps+CnBCp6hBIJlA3orNQQA5AQ6yBlbVAHhOWhUl\nQptvKedqVyAURUCKgp6wRi3NExiL3HX3nnsfv1nLwbWr2810CmBqyITsvcWoYjHG2dZs49JmSlIw\necQ93fS+/9s/q26/BQw0yfbJl2aTWVEwAxKBIyYmVSlLnu8WHKmZZ0dgishz3AtlapOYqCYmPwfx\nACRRJFYQRywpZROwiJghManq9QIJplINBv20CTGvBslUcxAb5ug1RNXIrjCDFINnhpzWoZrj7PNX\nqaqKfswXQEIRYefMjFyGlKqKOWITY3KAGFSrUW1NX+9bjE3vkQUE1anOlkdVFNdqE6OBKIOiITOa\nxKr0DlwTo6pkvtGoKDwFIAiJPFdiFvuwUPvhYDEBxmCdOSjdYNFkNp3GFk2tRLO2m0V05AudNW0y\nRGK0ZL6sB8PaozkSYyKgApJpipGJfFlKCF1oCucITYViTH3fp8l0cTG6spIAXYh9HzUT6ZIUDr1n\nElI0TQG5iMqzaS8CRKIiw6qIgZN5cLjzwK4Ye0V1VBAjaGB1RiQGbd/H0HpfFEUp5PuUQpLQRSAS\n0YFHdFKzI0lsDMZJtJ31DpMhFM4zuaYds2cbUjRzxOKKFFpW546/9dbDn/vavptvfOt7/2Fro5m0\n0rf9w89+6ebbbnzr+T84ceLUgbs++Ylnvnj5g++//tYbp06dvufxTz/x9NfXzr3x4c9PnX//56F3\nDzz+BezG77973C/tvPGmgyBhNlmfdt39j3/Ku9l7L32n1YGgXz58+ycff+K1P/+Xm+PZxur2sB48\n9szXS934zr/6p10o73vgyRPvvRbF7rj/gYNHbjz++ovOFUfuvnvjyvljH52ud+2+6677L3x0fLJ2\ntUi2a8eOulw4/d4HVT0CmWoIe47effujn9w4e/LU8dcO3nnv3v03vf/yd9xw/92PPHbq9R+snV91\nuEBG0dATJtCDNx7YvHAxqdaDgU762dbMFzWWvHjgRuZxu7o96yMAEFjpS02aTUMGkFJfjxbadjbf\n/mRbvSQkBwai4tmLiZpVRZUkK0pzmgRKtsEQgZlnF1PKuWe5VlBBAKCWPBUKoJ1y7ZXJ+brr+8Fw\nOFheMgGkqCl0m9thNkWsoJ+GaWrHCgE0RjEz8o5NvYGDFPtisFANbby1VVAJ7DQJFVwWdehadg5F\nYkhZ6KiAmgK7LEqRadsZU+WKi2fOZgVcSsYOe02jhaVX//Dbv/o//P3bP/XglYvbp09P1SC7/sBQ\nDEJI4CgpRA0D8GAgouQJMmLSEFRJIWlA5sIXahYpAtGCDbq270AxCgOjMZEaQLKk81UVAiBqXuTI\nnPBilt9XA0aW+U+wLMxERAOglJTAgTBgKCNGWL10VRQdkjLnZ4cBo4IxtSpgNN5s1tZnBlC/caoq\nqF4cjnaNRovVYHGhHo5Gy0vFaIhV4eqFoqrAJ++InUdf+qJAVwEDOiEYoCN2BRgTQ85lQFRHlI9k\nNKc5DBJ7MxOJpqgiqgkSJesgGYRWkqXUWYx928Qgqe3azc1uazqbrM02YHN9c7I129qezUJULKMl\nAOoMRVnIIAmSZRGX5J7UCLM1HrMcN2e+QzaPW96FZ5+HARoaZVMo5GVmRckjeiAwqyjdcmTwxDP3\nW1nMZjE2U6eazMCUwAOACklI3Xa/enEtJSFgsuQdbrrikd/4TQUgAFU5c/wYE6KCBynNRkyFJU8u\n7wIZGUBQwViMKFt4RcV7pyoxBmbnwZkqMWcYhZogcsYVMTsDSykxuSzD0txzo0kCYupDB0Rgys6J\nCDGhqIGZCTMhkokxQhItvIP5K9oyenvOYFNl5vybECDkWXfu/S3vOuivyEAIxFmlIdL2QNBDoFGt\n24133EIsAUd1vSWAUrfNlCjWZYVIolKVniBFpRiSdrGuoVqpFEzIVVVZhmDo27YFcFW1YNZNp9OE\n3o9WprPIYCGkypMvipSSKoAjIgtRoxICMJGJ1BVAlt0mIyZFjhobBUkwqjj00bMXkTZ1qKaiiGxW\nxa5PZVvUg2bcb21sKasXSDE5Au8dFWXbKmlflFUbtAPrU96v9gPvvWMsq4oHKTV16VZ27mn6pu+m\nkqAEB3NAcMdqRTmg2rEr+ogBUlUN2nYcu35Q10XpHBgaioqkICrjrg+zbmk0rIQK54Il5wcxGFpX\n+FK9JXBYDkLfuS/97/7rrQvH33/tlXa73Vq/xssrX/wbf0fbK6/92e+dvNR+/m/853tuXDr56o9e\n+8GLvfE3/vZ/tXPP0kc/f+7iRyfXzlzYceimZ77w9Y/eeOH08eN+ZdeNR25qNre2x5PRjr0PPHx4\n/dxH29OwvhWx6J744uc9hRf+8J81Yx1vXnnwoYcWdxw6+/PvnjrxwZG7HhgNygsn32HDR5/8lNn4\n5e/9+d7D9+zYOTj9wQebm+M7HvqExu7Me2/Ntq+MqsHOgzc4z2sXr3jCMN7kim976vMH7nnw4qs/\n/PCtN+5+8tl2vP7GD/797lseXVxcfOP7f+TbVvsQDR05gj4hcdTNq9cAtCjK2Wxixll5kRQFi7oe\nlgNq1ue6RZWA2dUAaGjsfNe1Ga9FxKopK+gRUSWBWtCEzKYxSWJ2KUVmJ6LeVX3fE5tjZwBJJPO3\nRBW1Z66QjYir4VCTkvbguJk1oz01eBxUi1xQ7Gboy8LjbDLFaT9evbq4tDfMutl40s36sqy48LGL\nEnpwxOxjSsOFxZbdzqVdKFZURW+JXSGpbzWQll1oq3LIzhOiAGpMiMbszChnZ3RNP9fuK3LhLCr7\nElrp+6YP+sM/+uFnfuMX7v/cxsa/+8nmGBAdaVJgIAwpITiRGIKqWPShrIrCyDnnnBcAY84TpswW\nATTvHQA56xdcWfhi1vQhJk0xw5QdeAA11ARgoEA476wMAUwJSCCHkQcTRoLrgY15gCa5nzTMqBXq\nYU65Ng1klELMQQQIzIWYmUnOJ1QgQ+wBLJhfa3BtggaIVBRYMpYF1oOqqqrRUuVGC0VR+NqNdqz4\nIZXVICH5ih0PjT2glVVtpsAGxpCpeuzNBNSUhMCpFJLapB0JpK5XFQ0UYyvtJMRgMxtvX+vb0M/i\ndHs82dqe9DqbtJ0AAEXEpE7ADAsRUdCcAwbZa4Cm1/ukuWsDc6eXxS+W6X4f61uJKBs1cseDzEkE\nAPOWjREqJUIujAvuHnn4pv2HFqbNFFpWNVNDJIPIrM5jiAlUpQtXzl6bbMfSIScoELVt/9YffxcG\nZY60np2/9v7LLzH53PZXDtghcXYdCJE3M0RCQgNRtbwyzIm6ZnkIOQ8vU0nILoN8WYEYQdTyi20u\n1sWUonPFHAaEOBiOmnaaJAGjmaUUGTwDYRaLiIhEIk5JEU0kpwGbzakXOacCAEAk5xCDQTKdpzOZ\nJMdgZqpGXBRVaWYMqNCLKAL4qkzTBG23+46jl8fvlSDE6Klo2ymWy1S7BaOu2/KeRVBD0pgiQBOj\nmRVFGowWAtC0CeywZzQsJ5tTYlmoqmk7njQSo9R1kcazMG0gYFJ1zCWXnWpdxqIomj5oEo8QTU1o\nMKiGJSfB2BE4ZZfzHBj6FEJaa2ZDx1gURIbopn2XmtD3Ecj7AhVpuj2Z9a1oBCXjgjEwQVlWQeKg\n9pqQgfqksW89FiqqZq4ozHFZFpYCO2RVsWk1GopA3220KkmoQFVv4InQWBAJHaOABqSiqutqsFRX\npcNKlRzEtldJE4mhU8+AqkguaGcqXNYptZxSD6oBy6pWMLew4i6891pz6fz61SvrW/Gxr/7KbUdv\n+vBH33nvw/cOHPzEL/3vn3HUvvfj7/UbzZG7737k6We76eorz/1l35bO1bfcfmu1sufP/vn/I5js\nO3R4WA2b8aRptm666/5q6LeunAMadKm74bZb773vgTdffn714pXxte0b9u584PFHLl68eun06a3t\n6S233z9ZX1s/ua5Ijz399Pql02dOnrrpwUcWq+GHx9/bjvLkF7929aM3ty5cSl23e9e+lb0HJPbN\n1ibGMJvMlnYs3vzIY8PF5Tf+4H+ZtPGxL/6Nd17846Tl/c/+rc2zH5x548etVk987stn3j7unQOL\nIhDRIPRhglVNBFgWZYwKziFqsVD7YWEUyA2NOlRz7OYUCkRCFFMzJWYwVLWsVjA1JBKVwnlRFYXM\nPTGzGHrH3sxMUEmZWU0g68wAIQ9KwArvQ4ho6n2xtb5W+tKVbs+NB6KEaT9JzXhlZalcWErRPKbt\nS+e5GNjQ79i5Z3xtS5SkTYO66vsUQ2DCqqxTCmrGzJvr64NhefalVyEkBQD1on1RFSEEAfG+UIli\nSMxF6TQlcq7te4c+SM9U9LH1WKiBkTkmFewnM0VUJnJ07Mev3v2Zx4489NDGhY0Xv/9OjByN1MQx\nUk7I0WRETdf1yRUJKuHRkA2EmcjAkLIRGkFVFJ0rS3ZgsU9WAKNvI3dNTJLMLKlkkSgQ5aCgnJJg\nMN/vCOVWiwQy1JxRkyFi9nVinqhaylpTixlxDUCiki8ykg/Y+RqJHbFgMpzjqHMJETBGRMMYaRoE\nW4RxYzZGNQBjQABxTKO6dt5xYQ65clW9UCzsWXQFNU1KCRwxFwZmzlVJLCXK/2VROovUtX3bTKUN\noddZ14uJ9tRJUIOoajn4gFgUoolZCaigIEhGyQzm42QEULBMVSEgy7RuUDDBfHvQzM5EpJSECDXD\nYZEZcw8IYiAGAJBEFAAMCbUwc1gwAkPYuxPvf+jOfYf3FYPh1sa6pcQEhiYqJEKuSElBIbbp0oX1\n8XZTMpkZOzHVwcGFXZ/6fL6pEMDFMyfOnjhr5kqWwqgUKwE8OgNw3hsAkYoKoXdoAISsSZRy/Lyp\nZzRkmF8u2USdd5Yh4qqOON+D5vBWUyIXQvSezJAY2q6xBMQeQMGMHGcID6nmp74ohiJxHqxdeMi0\nNoT5pN+UiFUhhymjGQEbGBGkFIhLUzUG8miifduQkamhMzJhJosCGNEN+sk2E5bD4awdW8TZ1iyM\nPHFBaHW5YIwiYgazXjRIUdeeZVANuSoiDteunYPYVAgrC4OlyqOrp0HXp10K/aD0fZ+8t8IgGlTe\n9yIcY02GQKJYusLA+hgZuSqNWKaTmXfsfCno+hAZnWeIDrSJlrRLgUJfFkVCm03bZrv1jIuLSFXp\nnBccVFgU0EiIBA5LvzCqurZHMzJh5xW5VFeyp7Lom9ZSAJtJGkwnwIquRF+UqU3YTau6Ytg1G4+n\n/daI6sqRQwRiRTCRzgSNnEBVlIWLC4PKsUuhE7A+dU3XkfeOwTOLxa7XQVWpcyrB1wNwFEOLSD2K\nWaiHK+7Se+9Oxg0PFp/9zV8qq/DKn/7rc+c2H/yFX77pnjvidO3Uz392+v0Ti4fv/tSnP3PspRfW\nz19tw2xULo0W6vHm+OTJl5CKu+86KhLWN9eHO3c8+PQXt66e37iyMV5bXb26+ehXfvngwT3v/eTP\nLhz/aLYV73nk/ptuO3z57Mm1C1cGB4488eQzZ15/cXzp/IFbDt945Oaz77y80fZP/urf3rpw8r3X\nXx3suuELj3/q3Ve/L7MpuWL/LXdWddm2afPSpdhPQtvsOnzznQ89evX8B+/++C9377thucLn/vX/\nfOThx4/eed/5d368dvnKwaOP3TQc/Pzl73UtVBYBISkQgvOlsgE4SQJZ8EgGyObrwY6lftaSU0Mx\ncqCqSQgYEc0AjQzMVIgIiZKkvCdHZgSI+bA2JXN5Q0CcqYzGHpP0jgs0zqwZNEOGLKhHZl/yYGHQ\nN5N6YUhEvq6nqS8HzK4eLu1SiynMsKia7cloaW9oO2n72ayFaP10zFT20zYh7ty7t5s1k+1tz4UB\nmfalH1KM3caaRgwgyjIY1UAESTQaExiSabYegyQU0KxPKYpSkxauNANVIXCz2ayoqsiaVFOSYjhY\nrP2f/LPf+eZ/99u3PHbn2sWrx99ddexVQUwBOYkaEKWk5kQkDytTjAvDYVkSkyNHQC6jk9XMiSA4\nLgt0DH0kckgRJfWRuqCMhGrADs2QID/uBE7n6pF5VyP5zq8GlDQ/+MCZGTLfckHGpGBSZaKP4WBq\nZjoHSSGzWhRLeQmExGgMlCT7+5ENNfteDJMBqRkhIFAwAHCWcDzuo/YFI4KCbu/bvRCvrPVtKtxg\nezoGRTQ0tGSGqGYEpuQoMy9ExYx0Hm1JhgaGAZQMDFkVDDElRQAFhiwcygNpzWVF0HLbY4pgAKQA\noCkTCuaMngw8AgUAVURO2S4ASIzJgAUV86+BpnMjf04FK8kXJEsebjy858bb9+3cu1PJbY3HaMRM\nCoAgSRM5NgBKmmbd+uWtdrtz7JIGsETqWbpf/j/9l3S9A0WAk+++ah14hwwMoI4BQZCAkVCReI7S\ncchmQIwiyuyI2MxygpLPna8ZgLLzZpLB2TmkkJxKzCgZVSUiZEZVs6z9UFDLoZMEAGSELm/0AB0r\npBg7YDSbx2KLCjs2VVAjRCOXNTXIzNfdF4hzpEz2XDmEZJnOPk93EoR8v3ZlGfsZbelmf6YcDNhX\nfbpGWEjT+aHr23EM6gYDEwWgZApm6LgqCoe9EfrFEVR75NzVEfULpVUlsqZZ0l4rQWRXFEyqqgkH\nnlrQcZsqBvYRyUVlACOipo8pQDUgdhCSRAGJioUIOVTEAqVT6sLQSQ8uBeUKDHXWzSxRVbphQeS9\ncNl0sSqagrwQESqBKGrTtn0Inh0gJ1IyQM+OK+eBFrCdmRooYgpiCBLIcUgpAZJLwVc1l87HUlNv\nWvkCIkoyDDGKgqWEKAVxRd7FKLGBpLNg29OGMA0cF2VBlswY2QkAqJSVg0Hl6hWWpDGl0FkxaDtx\nXaSDd9/74Gc/t372vZ8//1KL5ed+7Zve64WfP3/59MnJ9uSWT3zu0NG7L51+v90ObZ/27lgpuF69\nfGHWhT1Hbrn73k9cunQitv62T9x/8417f/4Xf7w+tc31a8t79n3+m/8Z2/T7f/DP169sOqZPPv3p\nxR2LH7391tra5qH772NNrz/3LSzc0ac+tXvnrndf+tnowK3PPvWZYz/9zrkLl4488OSuHYuvv/yC\nJx7tPlAXpXbTcx+9A8h925njmx56fM9Nh8++8+r66pW7n/hCmG1dPHPyE1/6DbD+2I//MvVbNz30\njNf2tR8///BDj1546z3dRGfgmSQlMUXy5cJAuykDAzpmSg78YMDVoIglZU6mGSAqombxQsbLI4lJ\n5nswMXpSVc26agUzZcScRyYpEXFROEDou977Iq8TVJCyQA9IJbrChxDZVW3X+0HBVQVo5MwgQjVY\n2LFMSsSI4Hxdp8mk29wO7QQi9psTixqaAABUOgq2dunqYGVhZd9uiLa9cQ2x0ChiGGfCRCCpckUz\naZldVTkrVIUkKTGmoLFPTC6lJJByLKICZAMXEsQYmMo+JNGcKey3J7PB4lJzaevD1947cO++Wx65\nfzp9+dTpbUMPZgKaVXeiDpOwI+nFjFR5O7TV0NWlVFWhSOiQHaGgSkJUAShLlyRBQih4QIU1UUxS\nAlEDSUSG6gomFYigCKDK80VMnpIiGaHkYKuM6TRUUEacRwhcF39ky4tmFgxybv7AwCxlD5kaoHFW\nAVMiMAMiNTVQyBNazcA0MwDReQKDWh6cYy+aQTRXp+2BPSuKzXQWWjWA/OsBIWZ4mSJZyrrCebg7\nKGfZDJqYQZ7QznVVME9pcvMmdT7+zbr/OX4tmwpyIzQ3fUNG3BiAqPF80wqY4ylySUI0NbL8hWAG\nSREBK0RHxgYV8VLFSyv+c19+0g190zQpSt82jGBEmqViKg4QjSxB14bx1nZqxZG0Gk3NgQ+hf/Tp\nO3fe83houmJYCUCaNFfef4kQHRFBqNFXTirnvBOH4DhzrTEPSFXzVCa/Hk1SYmaaryeMyGW8LWLu\njglQVSE/x5n/SYTzYohZXJWnpuLQRVFEMgVAIEBzGX7hMuAN0ZwrkggCiCRHLqvDcotJ5FLq2TkQ\nhTkwyJxzZqCG0QQU1UBNmD2DsRkaqGg/nfGgdsptal0xbJtm4KsYOfa9tdsABEQgxmx96rmo0FFZ\nOmBMieti2DRdN7164Ib9xWx159Jg3KZ2u59pWFxe9rWbra+Td5gAY4qeojGgKzx6R0m9aYoKTTth\n4soXzhkgdb0Aoxq2W6GuPBcuRQTBIBEMVHrnsYssMYhg6RLyIGiStqPourbtC0DQwpeawLtuNu1E\nsfAWBSAheQ4xJgSPakHUu2I0ZOupHkGvYTIugZrpzBEBYhOCbY+Hi0tuZXm6uhn7rkM1dLPYd9HQ\nsPI4LHxJLCH13ZhQYtBxG81Vjgk1hjYMh4OF0VLsO9VOTb15SNJ2U/al5TQxUVRzj/7yV4d98+Z3\n/+j8R+dXbr33S1/7xdmFYx+8+PZke9P88qd/7e8ajS+//+Lx198Y1cs7lkdbs3Z87TIXxcF7Hty3\nb9e7b/6w2nX4wc883ayd+t6/+Z1rG821rfFTX/vG0XvvPf3WSx+8/V7pyjvuuf/Qkdtn7fjDd98a\nN+GhX/jK5tl3zx3/gBYWP/vlr14+8fbbL/7k4N2PH7rp0M/+5HcurzUPffpzzcaZ4x+tL67sIScV\nwWz9Ski6cuQWDW0zmRy87+HhYPHYT36Azj/8ma+fPvbqrGluf/jTFz94NcRULi/ffssjp95/f+3a\n1Uf/+m/deGD40h/8LhipBBUQRItp1tLiojhXSKcEwq6EQiWKr4sklWGqS+46AAQmTMly9FK+gTI7\niQnUiBhEkTkHozHTfNkCAGiOEUyTJAArvAewZIbks3Esizjqwcg0ld4hYTkqejQuuEQOIMPlnbWr\nZu2sWlgxkdg121cuUiIJjQXAoGTQtWkwGPVdkD4iouOiXW86NxssLzhfqKCBpe3ZajMVgaquUgrM\nbBo3tpsdO/a3zWbpi+zfUNFkCcDUCFRSSogMiKqqKAIgJgkhagKgpNEXZTOdKuOf/qs/+oXf+Mqt\ndx3pm43J5jtb2xQgoCEQWU6Pdc6SCpkGMaeJuNkOS8NaFIqBZ0NPPmlUzKXFRLCsCmOJatZVpgoA\nbaNmAuhUhQnVjAi8UkTLtcCur38EjcTmrrDsPgQFgJQTQuaaeyEiM4dzE/tcHamQR20ACkoGktsk\nNNV8vUdDAECwZEroshN+fmjmzEpERlLNi0xTM2fctfrhmavsHCgCkGW3NeRwIEw5vQSMyWfSKhoo\nsJnMJ5jZ6J0tiPlTARCuo/3mKUI5O2/+SCgYECCCCgKhQE7UvP5hwlw+50GKiAyYciSYoRGhXv92\nKGxQgRs4RwA7Fnj//urWe48s3rDbueHa+29zXjKiQwMzgBg1JWREgL4P/TSkTkClj0mjObWCQjGU\nu7/8JMYpzI5Dfb8RjlcvNVsTpLrAQEgFqgNAMCJmpJQCOwRDJLge8YG5mDnnskbLEePcApiLECIS\nIohkpSUZAhHHFEAtT3CygjTH7eZ1o6RI7BHAEM2AiDTnCDLkKTsAiihYIvYZZWSiQPkGDClFM5Qk\nfL3GzltDRhEp2AEKWObxKQKaYJC+qktD0JiMtBhVGkLqo+SLXXIoLnLy5lT6PmJM0azbuWtXsTTS\nqFhVkjR0SYAHFotBGQzNKPoKujbMxgy4UJTeMRL20HmPyShFA9FWyRMk00kbBr4Ylhxi6JPvRKvS\nkaPOEBGCaGgCU/SuUHAmqS7KGFPTJWD20FeDetrEpgsVgbOuLsxz1XaNxNa5woIiOtE+JGBKhTcS\nhwiezQMAQI1kBM5XWA7XN64WaiEIkylF58okIJKcbnNZDFcWZ5vTpmmRqI3YdO3CwJeuKsyhCpfk\nitJCWptuRHSeGRlC6B2yF5F+aoZojrULzVR1aCG6IoqZIhlYbDu3XLuff//HH548++xv/aOdu4tT\nr7xw9ey5tmuPPvLlpRv3aRxf+eC1c6fP79t9g8a4ubkZu3Yw3HXwzpunaxdfO3N8920PfPKpJ975\nwZ+efPtYF8sLa7Nf/y/+D6O6/8mf/dtzx84xxTvvvOPgDXs+fPenG6vbe++4++6jd5195ycXPvro\n4F0P7d2z85Xn/ziof/yZL62e/fDnz781jvbop59cvfQRKIxWlgHDEN3WxlZEWdi5J4TODRcfe+qv\nnXz/Zx+98fKuA7fuWll642ff3Xvk6KE795w//vZw5+6Vqh4Mlo+/9TKVo8/8yt92CwuXXn91vAU1\npAEXgB2iktbLi55MQdFVrm8sxLi0a4c7fIhHNWyFwcLSVbgKXMcYDI3Q5wHTfNMuqSiKlPIJllfm\nc+LUX93NwQCAmbMZTud7cyAGM2AGNTSVvmsNxXFJUU2KelhYQeXCImrHFIMvlnfd0I63Qpq5VrWZ\nSUsaO0kQpqLSo7EEYOc0JTBWtKLyJtpuzVSIWETTgqsMZf/BfdurmzElAGL0VTnc3tooyzqkRA6U\nDIkL5r4PzOx90baz6x2GuaIQ7Y1ZQi9qMYVkqgomICqFH775ozd37dtz430Pzib9a999JyamHM0K\nAkAmrGQOQASQCS2q2va0SwmqJKNhkRxQ4UDADAnADLwnZSx40MHMmR+RQ+imPaVeAChZmtc9IlDN\nDVH2A2TK/7xaAcyZPnn+OJ+bZRGpiSlBLpJzFYl+3FQBACEApKyPUkPLoQKUzFQVCWjurgHM8zhD\nQ1JTUXOUkQqcQ+iBAVQdEKolUAIGyEvn+cOboS0GaJoVmsiAZEYEYmTzKJJ5GcuvW0NIZqAocwuc\nGmUc+HU+WJaBKnysGFIEZ6iggOiANOshCRhQAKNCNP24oKmZAhLYALBARpWB451L1YEbhofvuskt\n7rh06rxKckigguTzzU5TylsDMbUY4qyLTYqtTidtapViIC5i6h959kEdFJcvXlrgYbHrXgd84Z13\njr35CjKpQcHGwJRDBUWNrmfkQg70nCt8UkpEjpDNQnboZnmxGTAhzDGkqmp5QqlJFKJjVkRiUAER\nybUwi0JVlMgZgIg45yBjdlTIeTBUEASguUzcZ7hM1sEx0fWGG4goezFF8oBUc1QKmGaoaca2GQBV\nZde3rhoAq1nC0dAmQWcSoCHCwWAw3pxJL44VkJIgYEIhD25px7ICTjZXh4MFXy1GB5DcIClpoQba\nW4hWsHJdUTmYzraGHkZVMUuqVIkoO608gpIaJZCU4spSUTgvUcWwnc6KqoxMHmihrGRpMG1mrIYK\nCEoEXehVfTJNZqQgRJNZiFE8ERVlF4MvMPQdmxmQ9C0XpXFCKMzAkbFaVZInl1IAFAY0UdQE4Gbd\n+lLlNHqwRpKmGM0Q3YAEUET7zvoYEJDKtp1OJx0ARVDxoFwxDbisGPny5rXtiRZDpNj3XT/wXNbO\nxPpJVy2UhC4ACrBGRUOzHn1ZVGVoW4rqvv+7/7bB+pf//n+/efWDN3/+znh1DPXCQ7/4a45t+/z7\n7//sFQhhVJWbm1e6ADPVOx55dPeOHeeOvX32ytqnvvpLw7j5nf/X/5VoOBl3Y6Jf+vv/NcPm83/0\nx31INxzceePNt6mkl7//vSi27/At2LVvfPuPwPlPf/U3r3707qvf/+GBO+697dab3nn1p81sevDI\n7bfv2X3y2HtVNVKZLg0PAsvFC2eWl/fXGDfX12976BGi7mfP/aGB3PXo0+O1S5dXrzz8uS+vXTr/\n4ZuvcTnYv/PQ9tbVcyfeWzx89NYHH7PZ6hvf/tb22Ut5SJdEydWaIrEmBQXQvjPHflSnmBRdsbRC\nNMBiwa8MmMQkCZNJDvrE0XC4tbnlizL1EZQIAQGd82FukxezhIioTkw5Y4dUMhUE0AwVqUA0UTEh\nYqiqMqXki9pMXMl9FzTGxaWFFntfkyHWo1G7fS01sd/eUvRpKtoHFIt9pwIxqpkxGTE5LkWU1OYJ\nt4bGYojVYCScGPy5c1dWRsvW9Uw+hYAOC3IqySEmUUsWtR8OF5zzIXVh1rN3KQh6J5IwJDDSEJKA\n82XilJrIhAHAuaJNcvKjC8Mfvfz0V5+57YnHppPZOz85E0hFkIGEMC+tIiABSzLniQAgwaxpREoV\nqQdlgUCMTIzIhjGXsCTBlX5YVP20MStFsY2hE2WgiIaaQ1gNroM/cnJ7vnTr9fXaHFRtHxNLQVWz\ng0WvuzFy85O/kZkhUKaSIJKCGVDuF0Xm5Vfn4a+EuYNEdsiikgMIxQDNODP2socjlztDNjQ0BQGg\neYtnbEhmakAKYmaoKLnrUxCTnLKUcpuLCCB5AoxACZIBiQghsYBlqyUYceanEKLM2xUDxvniCgAy\niBUJACEJiJHkNeq8jCIYlASFAQEuVm5lUO/aXe2/cefiyoKYS+OxpB7RAB2y//hr0EzIoYl2IfWQ\ngkjom1k7bTvuRBgJ+5vuPHDzM0+vrk9v27GULh2zQ8+Ug8WT77105tT2ngGbESoMK/SMjAoGkoAd\nMhEkADZCng/CyYFB0pQ0VVwamJrlG4haht7Hj+eT2duO4PJSTlUAMvKJxBQJRRISiyREY0TQpEjO\nMSRgxBgDkRNTQHKOzACM81JVRPI8BRSJOQ/bc0ah5fRpyrhiImYWSwxkymWpiA7cnOWjptOgZAWj\nL6qunWCUnBMCcz6cQGfMyQ0wEoSgiCUSq6ok9EZUes8YWjfZnjBG5rIY+NY8FnWMtt2kKCEERXam\nQIX3zjE5B2E0GIkAUC3Qx1nj2CPyrOsGbqAUBkWxWA/bBE27bUEcoPO+aSIzzIPkjNC0YDBykpIj\ni13w7IAAzKwAcz70sfRl6FIvPaFKzUjOOR96idos1ENHvksRxOp6mCDGHlxZREWPrBjVAoO/Nh6H\nCWJJQMWk7bXXojZPTkQURFMvfRhHEhXv2YMUqh7ZYazQgVBd19q3yTAUdRCuCyepk2RkSUkkJl+A\nG+w9/Pj993/4yncn19avXrl2+yc+dcdD9109d2Jzdf3c6VO7FxYr55rt8axP9e7Dn/z0U+P1M8df\nfXEC+OyvfHNy/p033j+21eDG1uUHn/nSQ088fubdn732s1e7ALffde/eHTu21i5cOHVu5cD+svD9\nbNY0mzfe9eCB2297/6cvrE+mn/jaX1vy/MPvPjfcsefeow+unnv/vZ+fGNRLyGnXgUNtM4WAi0u7\nickPRrfcfPv66tXtq+d27Tu0vHvPyWMvr+w5fPS+B8+8++rm1UuDlV1LO1Y+fPd1K/zdT35xx4FD\nm1dPffT95xs3+tTXf/Xfv/lPjEqGtpOo0KMNmr7ds3dvL4nIG+LiTUd2HL2j3LOLvKLTamHkBoPQ\nA6jMa2iEyWSa6WjOe09FSC3NNdSoIs4zIktK7HjekBj6wveZHGHKzACmqoX3ogoGXBTOUYiRBw6U\nnIkUoB48QzFa5oInl86B5MQe62ctEoDarG0RwBWlWkDIEm0KMRC43Iyqzn3Z6HyKgQHUZKEaNbMp\nMSfpi7ISEDA0SwJspkBAQE3TIJJ3tXekFhVzKpvvYo9IMYkApBDU0JBmamacooSU2Nfvv/nB7Xfe\nftN9hx545pMQ0puvXNZ8ZisCxszeyQcHApEDQFOgtk8hahtwUWI18M7X3qW80kIgxmTGzqXl5Z3r\n61uRBKjUPkoAB2rgg4lkjxxdf8QBJMtPckrB3M1v8HGrl4X2Ogdd4/UOfr5Au75vy5ZoVcnzuKyo\nyH7EuVwQPv4GaAZREyCaISFHUSRKqgZAxDk4I7ec+lfeBTRDzT2cqQAgqKGBkc5r91zjqPOIKtRM\nC7OPt302B86hA1NBIeT8Z6na9T8EEQgQCC0Lgih7RAEF0DQLbUFyxBBQ3hQyACMXZiXDqMZdi3Tk\n9r2H7jzsy2K8OQ6pw0zbAgLDXDxVkwZRFUPSTvreJCQRmzShnUVNSh4rZCtk6Z47fv7GO2XFqW8P\nH1xeaK5eOPHiDcs7VIBAPOGQ0YHluBF2Banl8YpzxA5BwTtvYgaGjEDoyM+Hm6ZiCknJMTOrGpJl\n/ss8IRkp72CJCkliZrETYof4caoBEGKejoBqEskYP2YHeVOHGGM0MI9OiYhKMjUEVSRmMwGzHBSs\npugJ1BgA8kg3GRBqSq4uQ9d7TIbqmFIXipWl4Y6y2rVfQhyfOV9BIURogYD7vk3qAHRxqTBTKfys\nFQReWGS1QpIwQhOakmpww5mGalgwl8DcNQJh6jW2ZhqipFjVlQMpnWdnRnWMoeTCUEWiURKDqqpU\nIFpaHAxnTR8noRyJq4ZJek1GSAlg2kaL0dXonUsi7EAFQJEtFAUT8bAu+z4mFWbiagDgll0Vo/Rg\nZhANyoSjlcVJ20k7BuCg2PQtO3Rl3YXO2hYZ68q5YMQ+xKBq0yaEWUSC2FMbY+HBD3noacBcMqvA\n2qxnxwZWVQ4LQuuGriB2KZr2abhYgU2JeNJrm1rv62amalqQi/2s6HpE7oTdw4/d89oLL2zNelct\nfvrXf3O0UJx586W1tbWt1e2dCwMO8dLla21vtz366KE7b1479fb7b721c/9tX/vKF9/54Z9dPLM2\n2dzmhZWv/92/szDQn/zRv752aaMe8q233720Z9+1C2dXV68dvP0OkHDt6pWF0eKhO+5nR2+98Kfi\nhp/8hS/Mrl168c03Dt9ydMfupbUzJ8gP9x7eWxRcD6rttQ0ix6zjK+d3HLp9YcfuM++82ooefegT\n1rUn3v3JXY88k5r191/+wWS8vrL/0MrC3g/eff3gPQ/f9slniuFg6/TL3/+931s+8uDDT37+wusv\n9AqDDL82E3NA5pTDpGNGBdEozdZWvR2X7trlykToTdGh6yQImANgdAAKYuQgxgBIQQIzk2OJwA41\ngYkYohGqGTs20RhTlmybzaXqWaClqgSoCG07c+jY+WpYqwpxiaXHypUrO0zUusB1zcyx6RkhzibF\noBqvbxSucGXVTWY5+0ctqTomzhp/FUEEA3PEqW+ZWaIikGgHwCrGxAZgyZCY2BGRCcxZWyoAKJJA\nIUpCKlT7FCOw9/Vg1m8rYgTrJQb0QROJJeAELkFsp/rCX770t+444pd2HP3Mw6F/+Z03LpkVYqDk\nQAUBIRv1BaKpdzxPLFJsmpASLUYaLgSiwrFTiIQkyIRkAiLGztVlURMUpcVWm5Y7jWqaRBFBFBGz\nSIKR59Qrzrk8iGikllwOu1dlJgC0TNLCPLG+PtuC+WYIMmATSXUOjM3F8uOqOa+X14fhhJhjWJNp\nxghB3twZqikgfdyRZi1LVqcYofxHrSpgNt7MY5k0t6+5JqNyvnKB6TzdMLeHRmhIZHP1qyCRCgDq\nfD6MgGYEmNCAMKllnpupAooaEBAaOgQiIFMH4MAx6rBwe1eK2+7ad+PttyWL08nEtmZqEdHQjMgh\nZp4ciCQJmrPr+9BpEzRFQJxtz8bXJiJKgCXhaGWAu5aPn11lp/v3Le3de+PatW147blz589eOrE6\ncMDEHsirVkzM5tlZEnIeQUUEHIAaKGoEQgZCYh+7LiPcVDU71NERICQRpLmkiZyXFIlIJIoAe5dS\nDscmRDLVKOKck9QXRZlSIqKUEiEjzK2oCkaWvzuZCrNXUZhfJSEmc35u3bE8mVVlRIWcHAvJxBGb\nmpBU7A3yGFuRSZMCSJw0wtO63tWOty2JsgvSFx45ISQBV9QVUu1E0dgXBVXOKZO2DTFgOarrEblq\n0oR2OikXBjysLSHQtCLuIHj2bRB23oyZaTgYCLvYp8qzWmravhXG6dau5ZFU9bRJBOW0nYWuX6wK\nS9qHZuDJ166PMOuSRQGJXUuAfVWi97VbHnYbY0m9afTFqO0DYT7eVFLYtbIEUG+sb6J1fTJkFyRt\nbW74ciElQc/TrgNNNRYYkkjvmA0kJgAAtSiKXcSumdaDQVLxiAtlXReVacCuKZmqumhVg6nOYulY\n0QqvtS+Gg4VZ25FoPWRnmognHYybCMSp73uQyqPKtC6dJGm7iWPvXn/hR6tXZss33/bJzz4zm165\n9OG5i2cvT7evHdy1RxQ2rm5udulTX/rSaMmfevUnx46dfPzLv3zw0O4f/vt/DnGwtbFx58OPHv3E\nk1c+fP2F57/LtLR71/INt90U+/DWT59DV996x9GNy+e004WdO+958JGzJ949f/bswr6b777voYsn\n3zpz4sTRhz7FjFfPnSurpa5tFkY7/bC8eOqdwWBF4mzWTm976ouLzr32/W8Pd9/4qad+4cM3fzDZ\n2H7gK9/sLpz46O23lnbsvu/eh7dWrx5//+2DD37q6MOPbl0+8fpf/un5Uxce+vJv3HTwwE//5F9N\nL62FDnDRWS8MHEHREAeuGA2tm5lIPViAxcV+7cJ49cZi/0JOsYqagMAZi6YESqC+qJBMVRWIiSQl\nEwQTModoyJzdVugshEDoiP0cLgpzUaNqQgRDc1yCiCVKKOy9CVEB1WghqfrKQ0zEJhItCJSQZlNt\nZVDVk/GEiNu2dykZEHIG3foYY15I5O2XqhKhamJ23nFMScSuI8LBcro3CSDGKAgAxKpqYOycKsQU\nmFjQJLZVXTdNG5N1s1kC7GJv4GLiaJqisCuSJQEsXInOzp68/OrP3nn8mfsXDhy6+ymJ4bW337pE\nVKJBThOXOaUE1AjFIClhPoAl9roRZDprRguDwbAoa0YHDsgogdFsOiPEwXBgJk44BONx6DeCJDPL\nOX8JIPNd52s0cmxpPrvJx1Oy+XpJVBkIAQTATF0eEcJ1H4aSzUsV5yKV28MsKYLrS8ePVZp/9TkI\nACiWM13nipV5czb/5HntJCAxMzPOiYYGebKXkTNzM8h1CpoBgCIDCaqBEYAzSqAJTM3Y0EhzUIRm\nu4oIACIQmFMMZACMmjSLTvOwFwEN0AEiABkQmkMjAI/OGXuPo1r37S7uvPfWYmk0m036tpU8eQAg\n5zM/1ExBQdU0qGpUsWbaoTGI+LJau7i5vjqLQR1JQX6wgOWh3a+dvLK4c6fjOF7f3LX70M79y2+/\n+uLm1moaL1aFc2gVUe2pQHG5xJqKimOXnyFfFikomLDDXiJjYcBJUn5aVHN6LiKAY866MAA0FQQm\n5lzhJImCZmBNvqE6z4jgfWlm5MjE8vAGLatp8h6YFOR6+CQaAgFlBE+2tuUnChEZKGpiZgbQ3Ifm\nULeCMFF0QEkcknqQFH1RmkQGUKpXT53wxUANDNT7QroWDSrHfWVUDDpRSVJWI8dcuKKT1iFx4cnZ\nLGg/2QYHg1HFZd20SsbD4YBHDG0Zp+1o5BF8iBGKojOQLpQOu2SzcR9iWFjY4T0mtC4okMv34GFR\nIlITk4c0GI06xr5pDVwwG1WDtuvA+oBclK40KYdFP0tmNO36PppHJIAQU8WGmpq+K71rjAoHzkOI\n5ixtbF0umDHIYDAEZGaMsSdCAueKYeraBAmAZ530XSB0ktSh9wMelm4wLEOTkrmqrqOIMe07sKsN\n2K1dqciGO+qycuNJKxJ27KhLZ+VwuDmVzXYcWnFOe00Jy9TMdg89RAkKaG5hMHSXNyfFyJexe/1H\n3wEL09Wrhfc333z7+uWLk1Z6X33t7/y9y8d+/v4b71/cbH7tt//bD97/wev/4dUgOJ1ufe43vrl7\n18prz/3hxbOX9+y6cbhz+b4nP3Pi1Z+cfO/Enltu3bPnwOUTr8+a9qHPf3nPvp1v//SF2TTd8eBT\nw8XFV3/4XEK679FnLp56qyhoUNTTyfqBI/c009n2+OpgsLy9ubXz8E333nvPpfdeeumtNw/cct++\nG/b/6I/+6eKBuz/5jb914fXvnHvjzeFtR+9/6DM//vbvr29sPfn131retePCuz859uIPp0116xPP\nzC6f/dd/+rueiofvu6u9tAaNRG0BPKMpqm91ut0UlqKISqOIC3c+vOfmW6BbI0sB43B5YOO2EzGE\nCjkBiQQwYi5MJV+9QYEcSUrOeVUlIDWNIXyMsDBTUyViA2NiZlYQUxRTQiQ0IhejUOjqehhClyXb\nGhszISkISyAsaTju16vlUWo1qVZlFUJQUkrqqEiSiCgb4QgJAPMvYwZmomJRowE7ZERAtJSCmXfE\n4BHMiqJu+obIMXEUJXbeFZbmq6/JbBYUDCjE2KsRlSnEDjRGJldEkwAYVWKfAAi5/IPf/97hO27Z\ne6BYOXLLPZ9upuN4/sxGQADjZDrHUpkYZrMBilKWjiRKAC7MqG+3x+VgtFAMR2lQITmPpMQ+xmQq\nzhdc2KDidtwZoSoaomBUQIAcGHTd9aBaFA7mWLs5bRRydQI1IEAlYlBUnNsddL5cikSUS2JW3Mx7\nK/qrru4/KpC5dJnOcZS5Rs6XU7mbnAta7brsEBgNAdO8mhKaqmZrtlrGWl6vsmpmnBtZVDJQBMPr\nAb2ZUpqdgmTZmaiGhnlCbqhRERkxijCj2lx2mbtsI2YTR0AGaEjmAKUAWR76stLbjx7asXdQFF5V\nYgi5KhCAcZ6FmmpCxJgkRpUYUaRtW+1BU2+Ytjcmqxc3JcmoKsB8VYabHn3kL9754GJn1y5cu//O\nG++879aL17YurJ7atfdoXS0fP3+epSToHEDhrHAFkmQJTL68ZPtEEslMT0lCPL/M8DwYi5DUwD4m\nGpa+UJVs3EOAKD0T23yPyIjIiEmzZCnluGwVQUfI80AYS0DzYEuXh8aWEppBEkZWAEIxg0E9CnFm\n0cxMERgViIxAk0Ietc4ZfwBgJIKMQmpAZKCSpUbkBNxg5KqF8bVVcqioSYGZigrr3Yttb12no3pU\nOtebdrGrgPxCZQ6bRsL6NS5cURfAC7NWmFPpC1+hmXngAY1SnNb1MCTs+zCLyYFg4NBLlDQcuKUh\nR/KihGoJvaEvasMuhBhqVziijXEzaaJ3g9jNUoxXx+OqLhx7Fehm7cKwYoN6YbEZb6FCsoTJqhIc\nF5r6jc3tvBUqhoUlhaQ5w6esi4GvFNXQPJGpOkf10iKCSyFAJLJi1oeoxszapZiSOqAuSpIm9HVd\nYsHbTQsAvuQbdu+4fLX1HlcWBoOFYUQudbJYdc5BWdRtkEBIwxGn1hcQ+6DNzFcuqTWqZt3Syo7t\ndoav/J+/9uGbb1K5Y7R7R+2Uukns0qyL17ZXK7/zzoceWLt0anZt0w1HN95868UzHxZUXLxw5Z7P\nfuaBz37m/Bs//uDlF2c9Oe9ueOixh5546v3nf/+lH7x0+72fNGk2Vi/uOHj4kc99YfPS6RNvvbFy\n4Kab7zx68cTbx46fPnDL/Udu2n363ddHi3ua6bViMNi75+DpD99JSQqh81cuPfjs1w7etOfN5/+k\nm/b3fvarhetffuHbD3zml5d2jd59/lvnz5x98tf+waDo3vz2729B/ex/8n/U2cW3f/Ct1ZOru47c\ncuiWO9fOHX/7xZewKB749GfixtWf/Ztv73K7JcxiJFOpHHPJyytLlDqNCQzjjpX7v/Gf4KFh5fvN\nsx/1a2fWXzu2fmJTgMWAjKJFj3wdVYwfqywcUJ9iURQiSQWBjdEjmUoypaTinENQxy5KVFNkIHAG\n6l0JhMVCWRQ+anAFl0vL1WAQtGMujIxchWLt1lWdxDCJXdv4epC6zoSSJAQWSUSsmuV+6NilFJG8\nWQRTAzISNorX56UMwOxEhNknU4dU1GXbNs5XfWzQPACISpSERKImKs67ttderUvaJlNmjbE3VsWU\n3VIgiXxIqoiq0CWsF/Af/w//6WjkUeOFd955+bnXrl7pkgKCBzNByagOMsqHVzLw6AgNQAgAiRhI\nLfiyWF52g8FCUbEjAPJqhqjE1Ey6c1emG5OmC5ZXdNmpHEEZiQg8WlW5ovBkJmAiGpOEyGo9GlNu\nzFAxJ8UhADrToHOLwfWCR6gINI+y0Kxd/NikMZ90W27pFDF3pSS5O5vrX64vHZFAFTEnDc5LJQDI\nf6RT1Y91PYSooGiKoJYNkHOKGJKZzpeZlgkywIaSj/gszEHMLvhc3gHnK6q5aNYA2NRlR4EZIrKh\nIqDZQkErS35lYbDzhqU9B3ahQ42CIIZODUQSCLjSEyMCEmKUBAGapuvabrbVGhiatZNOxGbTNvZ2\nw/4d0nftbOPhzz7yg3PrP3zr4vJwYGTLVblviXbvXY4su5Z33n30wd//n/4FTrTAtKP0u6pQoSMi\nTFZWjgE9MZKAUFHOk6qYnIG6PAQnMhEEQFPHTlUcEyq5mkIXC/LGAgmAWUSyFSbbLJzjIBEAmFlU\nQCkPFZgUgUXVEWWTYn7KDRUFFSH/E6IqkQsiRIbI81cD2PzWNbdweNAkqsTIhGLGWa0EAEwIwkiA\nxg6KxZKBgpF2fT2qoko7bWDXknpyR45MxjNFHIyWQ9ciqURj4pJRa6etgCY1jGhtG03ijj07PY80\nzSSoOugms6KkYrTYdBAk9c3EGfUhkHMVgSNzjpNBBOsVVT2isxSajWsDSqOFxa1OtyctFVz5amtz\nU4xRpCo5G4TKgupK6mKBSycG062ZgTqEqipD0qjKxmjJ1yVy2UwbRt5uNgso1dmgLnbu2om+2trc\n0NgsLy2MlndMptJtr0NISSWKBnR9sDBtBiWWrh56AUJT9UV1rZ0mtZqgZHCEzrsdC2VZVeo8lN5T\nlWbbKbbFYLTZ8iREAKMo5aDc2NiwhLFrFoZlWZVFVTZhpoDuyF1Hrl6+Nqy9SucFGoFWyu1+et+j\nz1IVr314Ymtz/Zajd062xlcunFtb25bSf/2/+e+rIr3957934eR5xeqG2w7f/sTTg4p++of/6/lj\nF+96/Olm8yqj7L/9nvsff+TUOz85c/rCQ09+nqR/7UcvkB898PhTITQfvfP6wA8mk2s33nLbeG3j\nnZd/Wvuyn41n1fDpb/79wqZvfeffzSJ/+hu/eexH37m8Pfnsr/+DzeM///63f4DV0tO/9V9Orp74\n8V9868DRB5/6xKeuvPPd0z9/7cr51due/NyRO297+0d/ceGDs0v79z/81LPrq5fXLq6rAhJSjk4i\nFKRBWWo7owLdoFAxRLny0alDtzyECN4x+qpr2oKpFQMzIuD8Oskp82aoZggCigjVoE4xAhHmy70z\nVSXnAEyj+YIlanbyMjMTiVrW1nsmMI3SF4PaHBmjABTVMKWQ+i5tT8q6IqMEKERIrpt1pGRghIVY\nXFxamG5PISMsEFMfFM0hpGSASCRlVfZNRDBPLplgRmx4n8lbojJrpt75uVDcApFXM3ZFHwKxSwpR\nrE8WTJKZIfUxoHkBETAjRDRR7PtU1vWs63uRnmz7GvzJH/3413/rFxK0B+49+nDXv/bCuxevNEBJ\n0RDQIwXLdCwlFUdoGiHrSiB3rcjoUkzXVsNgYIMB12VZFMp1dmLReGvathKDIDrT3LjlmzuZkUAs\nqUDHpXO+YCX1BCnodBqmPaegiZABzTg3AgbCKmaIRIJC9lfqSTAQmG8S7T9aEKoZAGfMDGJmE6kC\nqgpeX1b+R0Hn9DEGc94+msHcColggIQfC2TmP4gADMiAjJTRxIA0c2IQc/Jztv7n7oclGRLQXMdv\ndN3sMe9UFT6+uREAEZOCqREyKzpUJloc+aUFt7Dobr/71nKhbqadpR4BcihvfoLYcRYkJTWTJALN\nxrib9bOpbGzPLPWlL5pZUtUkYf/ysoV2srX5xFfue2vD/fmLp5eXBtFMZrPk/eYUdu4dDDwxikW7\neqU9uFhWDBWlkitPmYdJSE5TBHKSzDGIAJgRuZTEeVJRRWCRuWeGGAByDAUThKAAkCyRspqgCBFl\nnBARm6Q+xOzKV1UmBgJJRgQEbKhMCIC+KEQke1uQyEwYyAABUAxynmgWoIIZkVeNhkRMIAmZzRKi\nKihBHtUQRAM2RSqLSlLji6rvW0YUAFSytnVlmdRYNKrcevSeD46/x3G2sFgmBTB1jiSFsqwMbNpr\nmFwblKN6YdQlaCYThri0Y9kQU+z7vjM1FCrJmcJsayuZI1eWvooavXrH6CgFJQWPHFNINdfRUds3\nywuLtdsbZ+3l9e0+KhGxWTOdIsCAGUyZEvsSgPoYJJUbzUwsDUYLAOTBqkFl5DR0DlxKBk5YRFNX\nlK5pImIdQu/MJ4ohdJh1wsPRaHH3eDLr2xbZdRa848L5WStdkLr0zsGAtR75FHTSh/G0bTsbDQsg\naUP0Snt3lqBuNttGVxU6EggA1Ijb3ppFqiGlPB7rp2nXyo7Vq1fq2tUFDutivZ0JsSV0H75zDGMz\nTX7fgb3nP/rI83A8W//EM1/q243Tb7wFXD3w6Wdnk23j5tKlMzfc9eBnv/6lzdPvv/nDH3StlYu7\ndtx45OhTT7eXT/z5v/y3xks33/dA6qdEcOjuR284uOftn3531uCDjz+xevqdE+9/tP/Wuw8evvH8\nh29BlMFoEUhvveWON198cXtzsnf3vvPnjx+868FnvvDXP3r3R2fef+/wnY/cd/PNP/w3/8/l2x/9\nxa9/873n/+Dc2+/uvfdT937u2Y9++mfvvvTqw1/9tb1H7oHm6pk3X9tcm9726BM143P/7/85NPHQ\n3ffdfPTuU++++tF77+0oF1VAskoiIWh0VKS+o6IswJmqRQE/uv2X/htcOZauXnTVIGBW8BkBglGU\nxOgE8jzJAAE9g6gjJyogERBVhIiIXRJxzCklQGB2ZmYEKvlkRFUhcgbGBJKSs3n0c1lVzpNpbMYT\nVHDVcLRr5+za1dnGljYYooEAZrxkpkkR9ZqiShbuGyEzEUBKPSABpJRkx2h3O7mKjrJIPzcNYIAG\nLgv6iUNISInJE2GMyYxMkqpFlV7yPw8K5FxVMGCZr+IwmZBxEAPmadv7ug5N5yQu1PVz33/rwP7d\nT3/xkb6dHnnkgRilff6trY3o0CmqQhayW85ERiUAFFBTZXKqSkxEVjISlbHpNhsdu3Y4GA6Hrh4W\nXeyaxpq+90g9WNYEKgiAUXYsq2tFe0h9F8vaV8zkHXk3XMDOVBQkT2kJACyBgTGYA0ygCkiGDNnG\nAM5AASRfgACJmCFP54AVVFQJ8bqPEYEAmNUMzXIXdr2BzP1B7uaAgQDnBEvN0JHMN0X4//OGYPPx\nHSASzNmfkBvW3H8gZM75x31m/mD27HOe7DJgBrHM3bCIiOjUigIWK7ewUA4HdV3zjr1LURK0HYCY\nArnarJeQkAvHACoAJmJd14VZmGx1a1fHTdulqADIZKHrojJpOrhnebQ8unLu/JNfedLtOfS7v/f7\nvh4eWXGsNi0WtyZbMKrXr22WpRQ0nG2MCwCPWikuVAPkpElziVeJZJRSIsoaISF0gEDEzmU1tBgh\nCXw8zUbAnIGIlHWylqujQkIgyjleGog8IyZR7xgMTc1MiTCpAZgvOCUxSKoOmSXFwrOCOVeqhBw3\naMaESgZRlebzmJARNfONSEpAbGLOeZcFXJYy6Z3B+unEVZxMciOLkjE/pGAEBqbssWtnkAIPasUa\nE0nfOsKiHvRJJLlmsl3XVRRtJpOkRGgLCyvMRZ9i00+IKoBuQImGZdsHCZI0iUSiwiFDmUMaa0NJ\nEkLT+GLg2GtMtcXKpTgoVtfWZ7OmIHZVYaqIVhZlCMGhepTKWwRCLE0UTE15vDndtVz7sjDkbjb1\nrk5mSXqNqilUg4oYHJkQ1otlTMn7smk6JBGRvoF1WQUyNhYVA+qjtSJdkFHpKpJKlcA0tMGqWbAY\nxHlibyCOCRyBmbSdEMDAmbRTQJccJgC1SmNnKgbczSZFWfcbG2VZjQaVs9iGYAKWbHvauwsnzlRL\nu26++cjJ48dS8rYw/NJX//q7P/7W+bOXdu296ehDD7/36k82NjbJ01O/9M1bH7rr7T//VydfPVYv\n717cs+vGBx6+8Y7bjv/4T17+3k+PPPS5/fv3XDv7obF75Nm/1myvvfi974x27n704QfOfnT8ynpz\n52NPlxVePPVhWS0BN/XyzoWqeum7zxXFaKmqz545d/dTX7770Xtefe53r5y/9OgXf9na1W//3j99\n5K//5zfesHDsud85dfzMY7/8XyzuKH7wv/2PG+P+K3/3v6KqmJ569eXvfNsXKzfeccf6uTPdeGO0\nuPuRX/2SyezYq69dPHm2Ho527l6+UiMomioAmVnS5B0vriykbpKiYdJmaxy216uVqiyqDsB7Xw+q\nYCKihEaMSQSvg0kE5yuf/JIAATNlyrV2nuNNRGBkmkQtO4lM5oFlTGAKRugKF6TzVFBZGUBKgbEE\nUzLYvnI+VIPp+vpCvbzVT/KoTiQxemVFYkrWjHsDSqQQFQwV52gpgpzuUG5ubCI55xkFU+q9L4A4\nhN5zEVNkIjBi9KJBzVJKkgwZkoqYtkkVKaVkWPamMVkrKGisCXPEMWCSpGBmDAZ905R1qcltz5pB\n5f7F7z2///Cu22/b1wS5+eF7Q9u/9P23xzMjnIcFEKgBkV63sWdbgER2mMQqR6PBoChBSt+Hrkk6\nm4zDDLqFYRTtulAQBxDKm0bMiQcumjBiRHAKEGliOG47R1QVvizUFaUBqYgaAnCyxFkCgXPkmAKB\nKWNu4lwyIQICNMuxkaI5qQgg94iEed4sWeGS0xyysub6nvB6Bq/ZHMIHEEFpDlzIf/icdpqd1wKW\nDfUAOSjJAOfhVtn6D2CUJxsKRHlGCog0n8zlldp13AyjICDnmqCGBg4RiFS1ZhsM/eJCUVe+KqAY\nwG133d7GKajE2IERAJt0kCksmGeDKEGaSbe1Prty6VrXxhhRNImJcwUmFEue4qj2QLx2aePxLz40\nPHTj7/z+T7vOjqzopz9xz6c//fR7r7507Mra8dOXN7e2b75xV+XrMx+eGjhES857FUkqAMSM7LJ1\ngdAUEdSCoxIgp0pQSAkNmBABfV1IStloIgJl5UL8/zD1p8GaZdd5JraGvfc55xvukFPlXPNcKBRQ\nKBDzREwkQAokBIgUxcmSuqlWhBy2bLfb7XDb7lZ0t6OltuRWUw6JlEXJFN0kKDYFEDOIoQDUXKg5\nK6uych7v/E3nnL33Wss/9pdQ/8mIrMi4lffmvWefvdb7Po8ywrI6YSpq7IKVBC6VvKhqUlz6IpZ9\nFtViSIKc1FBJUUxUAAFyyuxd33VIROQBAFktqxg6ZrNsBt6zCJT3Ow5VShFL7kZNHVUhpBQVDdTI\neyAjgZw79HntgXsn569pSshkqmQMjA6ajUuXHWo92re3Pd3d3Diwb4xMMeG8T0px9cA+Y5vuztvZ\nfDwYN+OqN2inHXnh4FltWNcUqE/cdrroZ5K1Hg6HQ8/MuZsvBUwZup6CGzMkgVRXGIYrEXhnt0Wg\nldXVbjaHwikQADJG7KMAALAli2SkWU1BJJthyoQV5y6JouUI4BAJ2KcUXZfCsOKamgq9ADhmIEQm\nx+RC7lsnibkWyz1azFnQA9i44aEHn6mPSQ1jb23fSZK6qoIn7RXAqooppu3p3sDXtaNuEd1ovIi5\nS4ihEolEOInY9/MgtNiZhcaNm2C5y6jGXimD2KAGd+cj7+ln89dOneZoq7fe9uBDDz7/F3905vKV\n937yl3O7/fIzT8wXszbbz//qb6w19vjv/5OrF682bnjgzgfvfsf7Kt9979/8s0vnrn7g53/lxvU3\nLpy6ILzy8c9//tyrT7743HMP/MyHb7312OtP/fDytet3P/TI9vULoPsP33Lk4plT9z32HjZ59fHH\nq9FoNpkTjj/8Vz6jsvjK7/6jQ7fe+fEv/voz3/nznd32s3/7P1tsnf3+v/mDhfGn/vb/fvvcy7/3\nj/+Ht3/ki1/4nS+0V9944d//2Xx3fvD4PWjpyhuvLDpZPXr43T/7sevnzrz+zJN7u2k4Xj9468ma\nUrGEGyIZChiY9sS+rtJ0R4E8gWeeXNsKj96uk+vgKqImq6Q8Zx5kERATs2WT1jlQiUmq4Oq66RfJ\nqKyo0CwjhiIkAwBmyAmMSoxCiFELGDmbooIok3OhRmawHKrVYmcVV5OIbypmXw9Wul588M652XwB\n7E0VxVSSKTICGKoUYpflnNGhQ59zcs4njfv33TLZ3oy9ILqsACkSkiHlMuBlMrEMktQAVQwAKaYs\naJpZULKBsu+zKICCEGMWy8gZSlOOBVENE4ECdFn7WW9kSgQIUOE/+n9+6b/8v//H+9d9F/Hu9z/S\nx/TUd1/rpFJUVEFwBqaYi8AKCxIaMYk5pJRz1dDKuqtx0PZdO+/atm9jnnUyaztRBEOHglwZJBE1\n9IbgbHkvMIAEBbYCAjjt0jRBNWvNoQICgVlGcGYi5eBAIAAw1NK7JqeaDUvVDxXFSuZC9eYCEICX\n9QZEFsslk1lKnQqAN+lC9tPkS0mGLgnPy1tZWeP9L26NAIBqJiXOqQJWMJuq/+FYBDMgQiAToPIB\nbFkfWZ6EBIhlGIdsoGQOIQMjiXlmcnk4aEaBhnUDDry3lX2jg0cP9mluqkhlNI3KoEI/NczHGNt5\n2rm+t3VjujPpulZKc9x7byY5RwfOITg2Bbp6deNnf/6R1ZN3/O5//4c33JgIlJpvPHvuD7/5X99x\n8JbAFHy1MgiDoR+PD7z02hvBoUMipPXRoOtmJfolYIyWJFXemVrZZpKiMYAZauGnITtGopQyMxds\nS99nx04xkxGYZVUCpzkjEnuIMRMDqRM1YizDGjNwgZEYUnK+FkmEZESeMcVMTCpKJgZI7EGSohIQ\nEBlxke0i+j6nwK6AhyQJgYPl90IycV02dBgqzimlLruKuK5kET3Rxsunw7BZP3TkxqVzgOj9eNEu\nBtV47/peN9ZedZFdU4fQNKIhtXvSTgfDERBs702dwNrqioLkbCZRQNgPADlU5gIJVX0bs7KaryoI\nzhpEYrHROOVeky1yDuV5RDwAq8arXZf2Jnt9zs2gnkzmvYgCIZXEcHaeHI/6vp1MI7I1PpggAFb1\nYLpo510WjYNBHbyPvS26OZFHiU09VFDNidgNh03OprMuqQ5CyLknV/m6SjGmvmcEQ47kQXRc1esD\nBdB+oV7YlHrogXHgnK/rybzF2I6aUR+TRfXkOtGJdMNmQLogFxSpnbfz2VzJJaXauX4+p4rJMqqf\nL1owXvTTirleqZvRqjt/6tR8Z7eX9PBj7277vae/+metwWd/83fOPPHtvT47FwZHbvvFv/abV37y\n7We/95ONzclgsH70trvuedcjN179yx9861urR27/2C9/4dybpya708O33/3Ao+999pv/v/NXNz/y\ny3/Dzaff+5//JNPg4Xd/dLZ7ZXW0b+PC6/nwbe/7G3/r2ouPv/70c+iGkxuXD5688563PXLx9Rdu\nXLn8wAc+ubpaf/crf3Tvez79wXe9863v//lrjz9x53s+df+H3/fWj7725X/3tb/6n/zDxi0uPPnV\nJ7/2NSA+eOLOlHK/u5eM7nvv+/YfOvrmC89sX7o+XjvaLa7vP3J0c2OLU5tdSK1WAIaGyAaCC9y4\ndmNtWKnAaDDY2upHaw8joxKRp0xaj4chdG1UQ0AmpwgArkg7AQM5Auvb1gBNgUvR3vnyq5pqBoEM\nqM6cmhKQqjpmNKeqzjGhFVA9mEGWbjpTyo58TC0khwrzbq+d9HVVpRj7RfZ1FWMmWmInVRMQIS9h\nz4QAzAh4M0EjxL7ygQCrKpiRSDIDKcFJ1QhAUYqz2wxErGTwM6Aoaln+ZVA2MRMBV1V97KHU5RQM\nKamBOcHEBmKQqWwayRxpNlPbtfx/+69+/x/8F78zGOYkwwc/+O7Yty8+eXnSArAtn3UAAMVJW0QK\nRKimWTLt7kyHK+OwylyPukUEHoqLbd+1DM4IK8W+Yk7e0bAZ783aRZcIQcAjqpLhkqtW0CGgAB0Y\nZKRiMgQ0yAoAxgbmybIooTM0JALNBGDF/4BExKKlN13OKjAiKQ+AQmC3Jcd5eT27Wb23/3BHXF77\n8Cb1rQgvym9VtLwtlZEl3KxZFFLOMuwKYoq2BBotsTdoACWAAcvOIJK5n3JUSnTG0DgxECvWDe4b\nV6FqmJ1jJAIg3be+snZwNWn0RuQIqBQFARWLvAjMZtPF3lZ74/p0e3u+mGUtWayiys0toiMwIBJT\nl6rZ3vwX/vp7R0eO/d7/+Ke1X9lNXQjcqU02dslGb2xMxpUbDOp2ke6463A9WJlubA9cxRAr1sls\n5pwyOrPMhIjobmoHpViygMsyVVUccZmIxj4iomUAAgVwDokYCiqdKauaycqBfdPdXQBmT6AGqMyE\nhJ7ZMUvKRCKqAoYSPbssQmwI4H3ImonBzDlMIMmFqu/npbMPlgjZKBig9y4lYyZQK2XBm7JCb1md\nMwDNrZHj7EQMdTEnopgsGIukna0bznkA6GYTF5qym9RoaT45ePhYmrJAavvJoHah2j+dd7tXriza\neGB91AxqtZBzUmdNPSx3UBfqHLjvNak5lOGo8ZS9wz6biq6MHInN82KAkAzLDVkU2vl02gmwO3zk\nlraf7c6nHDwYcOWJQGMESSWWa4qexERBUzVo1DLlHkiBqe9TqJwh+NqxBnNqlAjYFBkkd33OqtJV\noc7dAgggRR+ajUVbMZNRr5FQBw5XuRsCqWsWuhCQpBaz3VwsB8TkBw2yVMDeh5Riny2T3+m7AVg1\nDAlzl2zeac6pCtyJQu1qhoHzferabFlZO2jGPKqbLvfuxubW0duP3XH8tsvnzl29emV89Oj7H3vX\nqR9+e28i9eraLbffeud9t7347//F3uZ2tzsPRA9+8IPrh08+9/UvvfnmxUc/9csnjhx87bknc2dH\n7n/7fW9/5NT3v3Jlc/bBz/6qTi5/4yt/fvKB9952932L7evzrc12tn37uz56/N77T33vK1dOn0Hz\nk82tux7+mf1HDr354tNt1777s18AThfPX/zg3/zPRty++D/97pk3Lz32y79xYG31e3/wT8+8efnj\nv/jbWxeev/HmubdO/+RdH//M3ffceerZp65euET14B2f+rTm/uwLT9y4dKauGl/Vt993//XLFxZd\nNx7UDIQsZCimZGDGSfPh43ftXj1jELrZdDBcSyGtjo5H3/i6Vg6u8aItYyWi2ZjQAFC0wJpNrejl\nCRBVxW5Wb4luAu2pwH8pSXLsTZUQU06AWGTZaCxZcgdIRA59YFDLKQ2qZm8yoQyW0BmlLiKwG5Cl\nRIRQattgzCymOct/SDICLTP6CJpEJEfJhBCl4L4wqbAt444EREgZJPYpF4y4QC58UKaUVEEBTQRT\nlMi6GmroOgEDpwQek6lpMgCjjNqrigEBJ1MwFEBDYsQbi+7/+g/++f/p7/+NtX0g1eDtP/shwh89\n/+Pz0x4Vl92AstAyU0AQFEREIRNsZ7p3rV0ZHtjZ2tqb9VdnaWvRg3NguG9Uz7vOUepb3b9az9qZ\nC6GSLIiUNKGw4rJQYcDlTqZYNoiCSkv3T3HbgzM9uLIyWcz7DGJoKQuCQy65fUAAEATgJdul3A2X\n7LZiRlzq3RHt5qJviYYprUFcotjL0Qw3ETbLfwo1JCxMsALT5sIdQABQWfYpyicCCGRl10yGaoyG\nDksup3TZ0JbfAwYGyAVciiDeuyrgcEB1xaGqzFQhr6wM19aHdeORi0uofLpoKgUao8L9PG1d2b1y\nfWu6yH0nOZuaiYpDNsYM5iCoZQp4aH3trXNXsI6f//X3VyeO/et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r9an1rq6q9eneIqZE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NDMGShzia0ilTp9ASQvnbnlYVNonAjoFZIiUPH/wULhuthBFjBkk9XAK2wjRiYGNMVI6hUV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\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MF/1Wr5W4PARgm5ow2Bo8Ohqzh36W5VwX5XzQ7w96SW/Qe3ZyGkXx3JtRrKo1OfGicToI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\nnoxn1WqVJTaO03a7Vaxmw+2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3T+7++CcH+4fDjR6ImT4+1hA2X3phcPNlT+buO+8c3Xt6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NU7b9w+nYx4PPnlG/3dmxum0W4HfpwWaVHmcXU+mirjN8P0q7/4xt6d158fzc6P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\nmZYwq7gAZJACJCaMHVw6LkQcggBprjv5SCJGsPxZRgvBIJUCtSNCC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Q7MhorFiyhFIN4r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0s+IFmCQSACUCSF4AMQBEIGBvkBq7Dt9ZfxABScC9R7I16Rh/10rE5lqyOUeMoG3iO4A1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\nKxHPaEyUDG0pt7oLFQTdcdAab9alavWzs0+3xS7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-       "prompt_number": 27,
-       "text": [
-        "Image([[[ 0.08235294,  0.05098039,  0.03137255],\n",
-        "        [ 0.08235294,  0.05098039,  0.03529412],\n",
-        "        [ 0.07843137,  0.04313725,  0.03137255],\n",
-        "        ..., \n",
-        "        [ 0.89411765,  0.71372549,  0.54117647],\n",
-        "        [ 0.90588235,  0.7254902 ,  0.55686275],\n",
-        "        [ 0.89411765,  0.72156863,  0.54901961]],\n",
-        "\n",
-        "       [[ 0.08235294,  0.05098039,  0.02745098],\n",
-        "        [ 0.08235294,  0.05098039,  0.03529412],\n",
-        "        [ 0.07843137,  0.05490196,  0.02745098],\n",
-        "        ..., \n",
-        "        [ 0.89411765,  0.71372549,  0.53333333],\n",
-        "        [ 0.90588235,  0.7254902 ,  0.54509804],\n",
-        "        [ 0.89803922,  0.71764706,  0.5372549 ]],\n",
-        "\n",
-        "       [[ 0.08235294,  0.05490196,  0.02745098],\n",
-        "        [ 0.09019608,  0.05098039,  0.03921569],\n",
-        "        [ 0.07843137,  0.05490196,  0.03529412],\n",
-        "        ..., \n",
-        "        [ 0.89411765,  0.71372549,  0.53333333],\n",
-        "        [ 0.89411765,  0.72156863,  0.5372549 ],\n",
-        "        [ 0.89803922,  0.7254902 ,  0.54117647]],\n",
-        "\n",
-        "       ..., \n",
-        "       [[ 0.74117647,  0.48627451,  0.30196078],\n",
-        "        [ 0.83921569,  0.60784314,  0.42745098],\n",
-        "        [ 0.77254902,  0.55294118,  0.39215686],\n",
-        "        ..., \n",
-        "        [ 0.64705882,  0.3372549 ,  0.14509804],\n",
-        "        [ 0.63137255,  0.32156863,  0.16078431],\n",
-        "        [ 0.56078431,  0.2627451 ,  0.11372549]],\n",
-        "\n",
-        "       [[ 0.81176471,  0.58039216,  0.4       ],\n",
-        "        [ 0.78823529,  0.55686275,  0.38823529],\n",
-        "        [ 0.76862745,  0.54901961,  0.38039216],\n",
-        "        ..., \n",
-        "        [ 0.60392157,  0.29019608,  0.14509804],\n",
-        "        [ 0.57647059,  0.25882353,  0.12941176],\n",
-        "        [ 0.56862745,  0.25490196,  0.12156863]],\n",
-        "\n",
-        "       [[ 0.77254902,  0.55294118,  0.39215686],\n",
-        "        [ 0.76470588,  0.5372549 ,  0.38823529],\n",
-        "        [ 0.75686275,  0.54117647,  0.38431373],\n",
-        "        ..., \n",
-        "        [ 0.61960784,  0.28627451,  0.14901961],\n",
-        "        [ 0.56470588,  0.25098039,  0.11764706],\n",
-        "        [ 0.56078431,  0.23529412,  0.11372549]]])"
-       ]
-      }
-     ],
-     "prompt_number": 27
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "io.Image(i)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def edit_image(image, sigma=0.1, r=1.0, g=1.0, b=1.0):\n",
+    "    new_image = filter.gaussian_filter(image, sigma=sigma, multichannel=True)\n",
+    "    new_image[:,:,0] = r*new_image[:,:,0]\n",
+    "    new_image[:,:,1] = g*new_image[:,:,1]\n",
+    "    new_image[:,:,2] = b*new_image[:,:,2]\n",
+    "    new_image = io.Image(new_image)\n",
+    "    display(new_image)\n",
+    "    return new_image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
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+       "SUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "Image([[[ 0.08235294,  0.05098039,  0.03137255],\n",
+       "        [ 0.08235294,  0.05098039,  0.03529412],\n",
+       "        [ 0.07843137,  0.04313725,  0.03137255],\n",
+       "        ..., \n",
+       "        [ 0.89411765,  0.71372549,  0.54117647],\n",
+       "        [ 0.90588235,  0.7254902 ,  0.55686275],\n",
+       "        [ 0.89411765,  0.72156863,  0.54901961]],\n",
+       "\n",
+       "       [[ 0.08235294,  0.05098039,  0.02745098],\n",
+       "        [ 0.08235294,  0.05098039,  0.03529412],\n",
+       "        [ 0.07843137,  0.05490196,  0.02745098],\n",
+       "        ..., \n",
+       "        [ 0.89411765,  0.71372549,  0.53333333],\n",
+       "        [ 0.90588235,  0.7254902 ,  0.54509804],\n",
+       "        [ 0.89803922,  0.71764706,  0.5372549 ]],\n",
+       "\n",
+       "       [[ 0.08235294,  0.05490196,  0.02745098],\n",
+       "        [ 0.09019608,  0.05098039,  0.03921569],\n",
+       "        [ 0.07843137,  0.05490196,  0.03529412],\n",
+       "        ..., \n",
+       "        [ 0.89411765,  0.71372549,  0.53333333],\n",
+       "        [ 0.89411765,  0.72156863,  0.5372549 ],\n",
+       "        [ 0.89803922,  0.7254902 ,  0.54117647]],\n",
+       "\n",
+       "       ..., \n",
+       "       [[ 0.74117647,  0.48627451,  0.30196078],\n",
+       "        [ 0.83921569,  0.60784314,  0.42745098],\n",
+       "        [ 0.77254902,  0.55294118,  0.39215686],\n",
+       "        ..., \n",
+       "        [ 0.64705882,  0.3372549 ,  0.14509804],\n",
+       "        [ 0.63137255,  0.32156863,  0.16078431],\n",
+       "        [ 0.56078431,  0.2627451 ,  0.11372549]],\n",
+       "\n",
+       "       [[ 0.81176471,  0.58039216,  0.4       ],\n",
+       "        [ 0.78823529,  0.55686275,  0.38823529],\n",
+       "        [ 0.76862745,  0.54901961,  0.38039216],\n",
+       "        ..., \n",
+       "        [ 0.60392157,  0.29019608,  0.14509804],\n",
+       "        [ 0.57647059,  0.25882353,  0.12941176],\n",
+       "        [ 0.56862745,  0.25490196,  0.12156863]],\n",
+       "\n",
+       "       [[ 0.77254902,  0.55294118,  0.39215686],\n",
+       "        [ 0.76470588,  0.5372549 ,  0.38823529],\n",
+       "        [ 0.75686275,  0.54117647,  0.38431373],\n",
+       "        ..., \n",
+       "        [ 0.61960784,  0.28627451,  0.14901961],\n",
+       "        [ 0.56470588,  0.25098039,  0.11764706],\n",
+       "        [ 0.56078431,  0.23529412,  0.11372549]]])"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Python 3 only: Function annotations"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lims = (0.0,1.0,0.01)\n",
+    "w = interactive(edit_image, image=fixed(i), sigma=(0.0,10.0,0.1), r=lims, g=lims, b=lims)\n",
+    "display(w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "image/png": [
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+       "SUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "Image([[[ 0.08235294,  0.05098039,  0.03137255],\n",
+       "        [ 0.08235294,  0.05098039,  0.03529412],\n",
+       "        [ 0.07843137,  0.04313725,  0.03137255],\n",
+       "        ..., \n",
+       "        [ 0.89411765,  0.71372549,  0.54117647],\n",
+       "        [ 0.90588235,  0.7254902 ,  0.55686275],\n",
+       "        [ 0.89411765,  0.72156863,  0.54901961]],\n",
+       "\n",
+       "       [[ 0.08235294,  0.05098039,  0.02745098],\n",
+       "        [ 0.08235294,  0.05098039,  0.03529412],\n",
+       "        [ 0.07843137,  0.05490196,  0.02745098],\n",
+       "        ..., \n",
+       "        [ 0.89411765,  0.71372549,  0.53333333],\n",
+       "        [ 0.90588235,  0.7254902 ,  0.54509804],\n",
+       "        [ 0.89803922,  0.71764706,  0.5372549 ]],\n",
+       "\n",
+       "       [[ 0.08235294,  0.05490196,  0.02745098],\n",
+       "        [ 0.09019608,  0.05098039,  0.03921569],\n",
+       "        [ 0.07843137,  0.05490196,  0.03529412],\n",
+       "        ..., \n",
+       "        [ 0.89411765,  0.71372549,  0.53333333],\n",
+       "        [ 0.89411765,  0.72156863,  0.5372549 ],\n",
+       "        [ 0.89803922,  0.7254902 ,  0.54117647]],\n",
+       "\n",
+       "       ..., \n",
+       "       [[ 0.74117647,  0.48627451,  0.30196078],\n",
+       "        [ 0.83921569,  0.60784314,  0.42745098],\n",
+       "        [ 0.77254902,  0.55294118,  0.39215686],\n",
+       "        ..., \n",
+       "        [ 0.64705882,  0.3372549 ,  0.14509804],\n",
+       "        [ 0.63137255,  0.32156863,  0.16078431],\n",
+       "        [ 0.56078431,  0.2627451 ,  0.11372549]],\n",
+       "\n",
+       "       [[ 0.81176471,  0.58039216,  0.4       ],\n",
+       "        [ 0.78823529,  0.55686275,  0.38823529],\n",
+       "        [ 0.76862745,  0.54901961,  0.38039216],\n",
+       "        ..., \n",
+       "        [ 0.60392157,  0.29019608,  0.14509804],\n",
+       "        [ 0.57647059,  0.25882353,  0.12941176],\n",
+       "        [ 0.56862745,  0.25490196,  0.12156863]],\n",
+       "\n",
+       "       [[ 0.77254902,  0.55294118,  0.39215686],\n",
+       "        [ 0.76470588,  0.5372549 ,  0.38823529],\n",
+       "        [ 0.75686275,  0.54117647,  0.38431373],\n",
+       "        ..., \n",
+       "        [ 0.61960784,  0.28627451,  0.14901961],\n",
+       "        [ 0.56470588,  0.25098039,  0.11764706],\n",
+       "        [ 0.56078431,  0.23529412,  0.11372549]]])"
+      ]
+     },
+     "execution_count": 27,
      "metadata": {},
-     "source": [
-      "In Python 3, you can use the new function annotation syntax to describe widgets for interact:"
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Python 3 only: Function annotations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In Python 3, you can use the new function annotation syntax to describe widgets for interact:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "lims = (0.0,1.0,0.01)\n",
-      "\n",
-      "@interact\n",
-      "def edit_image(image: fixed(i), sigma:(0.0,10.0,0.1)=0.1, r:lims=1.0, g:lims=1.0, b:lims=1.0):\n",
-      "    new_image = filter.gaussian_filter(image, sigma=sigma, multichannel=True)\n",
-      "    new_image[:,:,0] = r*new_image[:,:,0]\n",
-      "    new_image[:,:,1] = g*new_image[:,:,1]\n",
-      "    new_image[:,:,2] = b*new_image[:,:,2]\n",
-      "    new_image = io.Image(new_image)\n",
-      "    display(new_image)\n",
-      "    return new_image"
-     ],
-     "language": "python",
+     "data": {
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+      "text/plain": [
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+       "        [ 0.22229563,  0.17469964,  0.0585694 ]]])"
+      ]
+     },
      "metadata": {},
-     "outputs": [
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yjYvRy1ZV8isxWxVmKSqTMZRLZpynmow2RSg80zOxfharinis+A1fTpVxOeaK9yuRWdNw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\n",
-       "metadata": {},
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TOUfW+GHQp79kfItawje7AmGBiGLNjUsfkfoO79tu/wUK3re9/eTtl3q/HRQj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drlinpSU7tZlbxjkSh3jSgX04EwVk1THxFTEPi+40l/y8KoKdWb6i8UoNQ55Viq0o1puARp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-       "text": [
-        "Image([[[ 0.031225  ,  0.03386275,  0.01439672],\n",
-        "        [ 0.03130634,  0.03407508,  0.01462769],\n",
-        "        [ 0.03143893,  0.03439138,  0.01484771],\n",
-        "        ..., \n",
-        "        [ 0.33656628,  0.46619193,  0.2511235 ],\n",
-        "        [ 0.33772221,  0.46884855,  0.25273993],\n",
-        "        [ 0.33857568,  0.47098706,  0.25403225]],\n",
-        "\n",
-        "       [[ 0.03117078,  0.03383425,  0.01409231],\n",
-        "        [ 0.03126383,  0.03405914,  0.01435435],\n",
-        "        [ 0.03140891,  0.03438642,  0.01461365],\n",
-        "        ..., \n",
-        "        [ 0.33594563,  0.4650567 ,  0.25017022],\n",
-        "        [ 0.33712154,  0.46765368,  0.25172959],\n",
-        "        [ 0.33801104,  0.46978134,  0.25301513]],\n",
-        "\n",
-        "       [[ 0.03107764,  0.03373585,  0.01378239],\n",
-        "        [ 0.03118522,  0.03397299,  0.01406746],\n",
-        "        [ 0.03134598,  0.03431106,  0.01435835],\n",
-        "        ..., \n",
-        "        [ 0.33511674,  0.46352495,  0.24903393],\n",
-        "        [ 0.33627453,  0.46595131,  0.25044242],\n",
-        "        [ 0.33717093,  0.46797833,  0.25164205]],\n",
-        "\n",
-        "       ..., \n",
-        "       [[ 0.28695644,  0.34935413,  0.17136882],\n",
-        "        [ 0.28855329,  0.35272033,  0.1736226 ],\n",
-        "        [ 0.2903627 ,  0.35673107,  0.17621886],\n",
-        "        ..., \n",
-        "        [ 0.22352221,  0.17962763,  0.05981787],\n",
-        "        [ 0.22403522,  0.17977049,  0.0599102 ],\n",
-        "        [ 0.22408975,  0.17943402,  0.05985967]],\n",
-        "\n",
-        "       [[ 0.28858483,  0.35243006,  0.17384366],\n",
-        "        [ 0.2900826 ,  0.35564267,  0.17601813],\n",
-        "        [ 0.29182592,  0.35955218,  0.17857115],\n",
-        "        ..., \n",
-        "        [ 0.2236212 ,  0.17897503,  0.059783  ],\n",
-        "        [ 0.22364767,  0.17825777,  0.05955546],\n",
-        "        [ 0.22318354,  0.17708305,  0.05921115]],\n",
-        "\n",
-        "       [[ 0.29026098,  0.35578943,  0.17654416],\n",
-        "        [ 0.29163166,  0.35874772,  0.17856208],\n",
-        "        [ 0.29328954,  0.36246859,  0.18100227],\n",
-        "        ..., \n",
-        "        [ 0.2236058 ,  0.17805625,  0.05964427],\n",
-        "        [ 0.22319839,  0.17658488,  0.05914965],\n",
-        "        [ 0.22229563,  0.17469964,  0.0585694 ]]])"
-       ]
-      }
-     ],
-     "prompt_number": 28
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "lims = (0.0,1.0,0.01)\n",
+    "\n",
+    "@interact\n",
+    "def edit_image(image: fixed(i), sigma:(0.0,10.0,0.1)=0.1, r:lims=1.0, g:lims=1.0, b:lims=1.0):\n",
+    "    new_image = filter.gaussian_filter(image, sigma=sigma, multichannel=True)\n",
+    "    new_image[:,:,0] = r*new_image[:,:,0]\n",
+    "    new_image[:,:,1] = g*new_image[:,:,1]\n",
+    "    new_image[:,:,2] = b*new_image[:,:,2]\n",
+    "    new_image = io.Image(new_image)\n",
+    "    display(new_image)\n",
+    "    return new_image"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:d75ab1c53fa3389eeac78ecf8e89beb52871950f296aad25776699b6d6125037"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Index.ipynb b/examples/Interactive Widgets/Index.ipynb
index 741af72..55df639 100644
--- a/examples/Interactive Widgets/Index.ipynb
+++ b/examples/Interactive Widgets/Index.ipynb
@@ -1,4 +1,89 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"../images/ipython_logo.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Back to the main [Index](../Index.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Interactive Widgets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython includes an architecture for interactive widgets that tie together Python code running in the kernel and JavaScript/HTML/CSS  running in the browser. These widgets enable users to explore their code and data interactively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tutorials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- [Using Interact](Using Interact.ipynb)\n",
+    "- [Widget Basics](Widget Basics.ipynb)  \n",
+    "- [Widget Events](Widget Events.ipynb)  \n",
+    "- [Widget Placement](Widget Placement.ipynb)  \n",
+    "- [Widget Styling](Widget Styling.ipynb)    \n",
+    "- [Custom Widget](Custom Widget - Hello World.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examples of custom widgets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- [Variable Inspector](Variable Inspector.ipynb)  \n",
+    "- [Export As (nbconvert)](Export As (nbconvert%29.ipynb)  \n",
+    "- [Nonblocking Console](Nonblocking Console.ipynb)  \n",
+    "- [File Upload Widget](File Upload Widget.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examples using `interact`/`interactive`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* [Beat Frequencies](Beat Frequencies.ipynb)\n",
+    "* [Exploring Graphs](Exploring Graphs.ipynb)\n",
+    "* [Factoring](Factoring.ipynb)\n",
+    "* [Image Browser](Image Browser.ipynb)\n",
+    "* [Image Processing](Image Processing.ipynb)\n",
+    "* [Lorenz Differential Equations](Lorenz Differential Equations.ipynb)"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "codemirror_mode": {
@@ -9,103 +94,8 @@
    "language": "python",
    "name": "python2"
   },
-  "name": "",
   "signature": "sha256:8e469f292b096d750dc8eeb71caa8c08b26bc722708314a26a7684c380ccd20f"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<img src=\"../images/ipython_logo.png\">"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Back to the main [Index](../Index.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Interactive Widgets"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython includes an architecture for interactive widgets that tie together Python code running in the kernel and JavaScript/HTML/CSS  running in the browser. These widgets enable users to explore their code and data interactively."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Tutorials"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "- [Using Interact](Using Interact.ipynb)\n",
-      "- [Widget Basics](Widget Basics.ipynb)  \n",
-      "- [Widget Events](Widget Events.ipynb)  \n",
-      "- [Widget Placement](Widget Placement.ipynb)  \n",
-      "- [Widget Styling](Widget Styling.ipynb)    \n",
-      "- [Custom Widget](Custom Widget - Hello World.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Examples of custom widgets"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "- [Variable Inspector](Variable Inspector.ipynb)  \n",
-      "- [Export As (nbconvert)](Export As (nbconvert%29.ipynb)  \n",
-      "- [Nonblocking Console](Nonblocking Console.ipynb)  \n",
-      "- [File Upload Widget](File Upload Widget.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Examples using `interact`/`interactive`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "* [Beat Frequencies](Beat Frequencies.ipynb)\n",
-      "* [Exploring Graphs](Exploring Graphs.ipynb)\n",
-      "* [Factoring](Factoring.ipynb)\n",
-      "* [Image Browser](Image Browser.ipynb)\n",
-      "* [Image Processing](Image Processing.ipynb)\n",
-      "* [Lorenz Differential Equations](Lorenz Differential Equations.ipynb)"
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Lorenz Differential Equations.ipynb b/examples/Interactive Widgets/Lorenz Differential Equations.ipynb
index 59b68d1..41130b0 100644
--- a/examples/Interactive Widgets/Lorenz Differential Equations.ipynb
+++ b/examples/Interactive Widgets/Lorenz Differential Equations.ipynb
@@ -1,377 +1,13436 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:c6ccfb14927d633933da8b5c0f776a8da756d833654b5a3f8b6121b390ca6424"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Exploring the Lorenz System of Differential Equations"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In this Notebook we explore the Lorenz system of differential equations:\n",
-      "\n",
-      "$$\n",
-      "\\begin{aligned}\n",
-      "\\dot{x} & = \\sigma(y-x) \\\\\n",
-      "\\dot{y} & = \\rho x - y - xz \\\\\n",
-      "\\dot{z} & = -\\beta z + xy\n",
-      "\\end{aligned}\n",
-      "$$\n",
-      "\n",
-      "This is one of the classic systems in non-linear differential equations. It exhibits a range of different behaviors as the parameters ($\\sigma$, $\\beta$, $\\rho$) are varied."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Imports"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "First, we import the needed things from IPython, NumPy, Matplotlib and SciPy."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.html.widgets import interact, interactive\n",
-      "from IPython.display import clear_output, display, HTML"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "from scipy import integrate\n",
-      "\n",
-      "from matplotlib import pyplot as plt\n",
-      "from mpl_toolkits.mplot3d import Axes3D\n",
-      "from matplotlib.colors import cnames\n",
-      "from matplotlib import animation"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Computing the trajectories and plotting the result"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We define a function that can integrate the differential equations numerically and then plot the solutions. This function has arguments that control the parameters of the differential equation ($\\sigma$, $\\beta$, $\\rho$), the numerical integration (`N`, `max_time`) and the visualization (`angle`)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def solve_lorenz(N=10, angle=0.0, max_time=4.0, sigma=10.0, beta=8./3, rho=28.0):\n",
-      "\n",
-      "    fig = plt.figure()\n",
-      "    ax = fig.add_axes([0, 0, 1, 1], projection='3d')\n",
-      "    ax.axis('off')\n",
-      "\n",
-      "    # prepare the axes limits\n",
-      "    ax.set_xlim((-25, 25))\n",
-      "    ax.set_ylim((-35, 35))\n",
-      "    ax.set_zlim((5, 55))\n",
-      "    \n",
-      "    def lorenz_deriv((x, y, z), t0, sigma=sigma, beta=beta, rho=rho):\n",
-      "        \"\"\"Compute the time-derivative of a Lorentz system.\"\"\"\n",
-      "        return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]\n",
-      "\n",
-      "    # Choose random starting points, uniformly distributed from -15 to 15\n",
-      "    np.random.seed(1)\n",
-      "    x0 = -15 + 30 * np.random.random((N, 3))\n",
-      "\n",
-      "    # Solve for the trajectories\n",
-      "    t = np.linspace(0, max_time, int(250*max_time))\n",
-      "    x_t = np.asarray([integrate.odeint(lorenz_deriv, x0i, t)\n",
-      "                      for x0i in x0])\n",
-      "    \n",
-      "    # choose a different color for each trajectory\n",
-      "    colors = plt.cm.jet(np.linspace(0, 1, N))\n",
-      "\n",
-      "    for i in range(N):\n",
-      "        x, y, z = x_t[i,:,:].T\n",
-      "        lines = ax.plot(x, y, z, '-', c=colors[i])\n",
-      "        plt.setp(lines, linewidth=2)\n",
-      "\n",
-      "    ax.view_init(30, angle)\n",
-      "    plt.show()\n",
-      "\n",
-      "    return t, x_t"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's call the function once to view the solutions. For this set of parameters, we see the trajectories swirling around two points, called attractors. "
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exploring the Lorenz System of Differential Equations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this Notebook we explore the Lorenz system of differential equations:\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\dot{x} & = \\sigma(y-x) \\\\\n",
+    "\\dot{y} & = \\rho x - y - xz \\\\\n",
+    "\\dot{z} & = -\\beta z + xy\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "This is one of the classic systems in non-linear differential equations. It exhibits a range of different behaviors as the parameters ($\\sigma$, $\\beta$, $\\rho$) are varied."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we import the needed things from IPython, NumPy, Matplotlib and SciPy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.html.widgets import interact, interactive\n",
+    "from IPython.display import clear_output, display, HTML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy import integrate\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "from matplotlib.colors import cnames\n",
+    "from matplotlib import animation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Computing the trajectories and plotting the result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We define a function that can integrate the differential equations numerically and then plot the solutions. This function has arguments that control the parameters of the differential equation ($\\sigma$, $\\beta$, $\\rho$), the numerical integration (`N`, `max_time`) and the visualization (`angle`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def solve_lorenz(N=10, angle=0.0, max_time=4.0, sigma=10.0, beta=8./3, rho=28.0):\n",
+    "\n",
+    "    fig = plt.figure()\n",
+    "    ax = fig.add_axes([0, 0, 1, 1], projection='3d')\n",
+    "    ax.axis('off')\n",
+    "\n",
+    "    # prepare the axes limits\n",
+    "    ax.set_xlim((-25, 25))\n",
+    "    ax.set_ylim((-35, 35))\n",
+    "    ax.set_zlim((5, 55))\n",
+    "    \n",
+    "    def lorenz_deriv((x, y, z), t0, sigma=sigma, beta=beta, rho=rho):\n",
+    "        \"\"\"Compute the time-derivative of a Lorentz system.\"\"\"\n",
+    "        return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]\n",
+    "\n",
+    "    # Choose random starting points, uniformly distributed from -15 to 15\n",
+    "    np.random.seed(1)\n",
+    "    x0 = -15 + 30 * np.random.random((N, 3))\n",
+    "\n",
+    "    # Solve for the trajectories\n",
+    "    t = np.linspace(0, max_time, int(250*max_time))\n",
+    "    x_t = np.asarray([integrate.odeint(lorenz_deriv, x0i, t)\n",
+    "                      for x0i in x0])\n",
+    "    \n",
+    "    # choose a different color for each trajectory\n",
+    "    colors = plt.cm.jet(np.linspace(0, 1, N))\n",
+    "\n",
+    "    for i in range(N):\n",
+    "        x, y, z = x_t[i,:,:].T\n",
+    "        lines = ax.plot(x, y, z, '-', c=colors[i])\n",
+    "        plt.setp(lines, linewidth=2)\n",
+    "\n",
+    "    ax.view_init(30, angle)\n",
+    "    plt.show()\n",
+    "\n",
+    "    return t, x_t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's call the function once to view the solutions. For this set of parameters, we see the trajectories swirling around two points, called attractors. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "t, x_t = solve_lorenz(angle=0, N=10)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {
-        "png": {
-         "height": 459,
-         "width": 734
-        }
-       },
-       "output_type": "display_data",
-       "png": 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D9ZlMb1kE3QCaEZetF0Of6Nngx2owM00+JuCZ+kJqoE5ODlK3Nu7573nSv/+d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\nkjEY+zsjcXXtyboN5xk2bAunTvlHhptMOua8oKL12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ScNqVMnKvskmpIELgsqRzKCMwXB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\nlrnuO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hnHoOzpA08f6WVi4cupX/k7gX+8yT27Ogh0d/NJQtc0486StjbaPvxQmHfewLNq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\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GL79k8fjj8YLw5MkWunRxsW9f/idQK1KVjLsWEKzcUSiXQl6csx7E+sdbuUmbLwUlgk1o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lq1/CxbFiobEQ7nyZP82KRJWIkHb7F87Fn3QVYSS6OQecVxF5X8sZkd3C4iX+iP6a/fQ8Zu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XZp+JdS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\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8sB/iGJ/IVVXGRRe1xahRXXHBBa3ZtuRkcLtge/5RWP87O2zKSE6B+4Xp0M4fEIPCiIga\nLgbeRERERAQAOHhQwty5FsyZY0FubuUhhyQJDBoUxAMP+HDKKVVrd+KWglhqzsESaxaOyP5KjzML\nGWcFmmGAvyXa6lVsIk7USGXLLiw3H8Rv5oM4rPgAAAN8LXGtt3OMK6NYE0JgzZo8LFy4HQsXbkVB\nge+Yx3fokIhRo7ph+PDOSEtz1FKVDZ/661LYH7gdSvb+sLngGefCPXkGRFrzGFRGRNSwMfAmIiIi\nohCaBnz5pQlvvmnB0qWVr7ZeurSkyv29K84NA2tM+VhiycJ2U9Exj22rxWOAvyXODKTBAiWqP4eo\noSqQfBUhd5bqCpt3GiZMLT4fKrgytzHaubMQH3ywDR9+uA27dxcf81ibTcVll3XA6NHdcNZZ6ZAk\ntpU6aUpLYH/uP7DMnxM2JSQJvvH/gm8CW5gQEdUUBt5ERERE9P/t3Xl0VOed5vHn3rq3SruQhIRA\nQoARq0FGYDaDwSY22Ak4Xojj7M50d9pO5yTOMpPubGfSGXen05mke9LTTibdk20SO46NkxjHxjY7\neGOzASMwi9jRgoQkkFTLXeYPAbENkgoslaSr7+ecOiX5vu+9v4NLSz331e/t1FtvmfrZzyJ69NGw\nWlr+HKDNnOlo1aqu+79250jorF6MHNPL4RoljC42TPMszY8P16JYqYZ7rDzE4HPOiOs1u04vh2u6\nvVEkSV88O03TnKEpqAz9QW1tq1aseEtPPLFPO3bUdTt+2rQifeIT1+qee8YrJyeJjRlwRaxNaztW\ndZ88fskxd9Q1avvHH8mZObcPKgOAwYPAGwAAAN1qbZWefDKs//zPiHbtsvTjH7fq3nvjPXLus0Zc\n68MntbqbdieSNDExRAviJZoZL1KYVd8IsJhcbbfr9Uq4RrvsBrlG12+jbN/UtMRQzY0XqyIxVDYr\nvAOtpSWmlSsP6ne/26eNG4/L87p+feTmRnTvvRP08Y9fq6lTC1NU5SBztkUZ//QtRX77y0sO+Yah\n2P0PqP2LX5fSM/qgOAAYXAi8AQAAkDTfl7ZuDWnqVFdpaT17blee3rAbtCZyXLutBvld/MaZ4Vma\nGy/WwniJRrnZPVsI0EcceXrTatTL4RptD9d3udGrJBm+dK2Tr7nxYs2IFyldtEcIsra2hFavPqIV\nK97SqlXVika7fn2Ew6ZuvXW0li+foMWLxyg9nddHb7E2vKjMrz8ks+bkJcfc0WPV+t0fyZ0xpw8q\nA4DBicAbAAAA/U6d2aa1kRPaGD6ps2aiy7FjnBzdGBuhOYlhyvQ77zkO9EcJedpjNWp7uF7b7Lpu\nX++SNNbJ1dz4MM2KD1OuT0uKIGtpien55w/rj388oNWrj6i93elyvGFI8+aVaPnyCbrjjnINGdLD\ndybxDkZLs9L/8RuKPPHrS475hqHYf/ms2h/6mpSW3gfVAcDgReANAACAfisuV1vDdVobPtFt72LL\nN1SZKNT82AhNdfIVoqUD+qk2OXrDPq3t4XrttE8r2s1Kbkka7mZobrxYc+LFGubREiHIGhvb9ac/\nHdLTTx/U+vVHFY93vsfBBVOnDtXy5RN1993jVFLCX72kgrXuBWV+4yGZtacuOeZeM65jVXflrD6o\nDABA4A0AAIAB4aTZqvWRE9ocPtXtKthcL6y58WLNiw9XGS1P0A80GlHtsE9re7hOVdaZbntyS1K+\nF9Gc8yF3mZsl4+rfhqGfq6lp1TPPHNTKlQe1adNxuW73r49Ro3K0fPkE3XPPeE2cWJCCKiFJRnOT\n0h/+uiJPPXrJMd80FfuLz6n9819lVTcA9CECbwAAAAwojjztsOu1LnJCb1qNXfb6lqRSN/NiaFjo\nEUAgNXz5OmW2aVu4Ttvteh2yWpKal+lZmpkYprnxYo13hsgk5A4k3/f11ltntGpVtZ599pBee+2U\n/CTeKZeUZGnp0nLdeec4zZpVLMPg9ZFK9urnlPGtL8msq7nkmDt2vFq/+29yp13fB5UBAN6OwBsA\nAAADVr3ZrpfCp7QxfEr1ofZux5c7uZobL9as+DDl+OEUVIjBxJGnQ6EW7QjXa7tdr5pQW1Lzhnhh\nVSYKNSNepElOniza8QRSNOpo8+YTeuGFw1q1qlpHjiR3E+Saa3K1bFm5li0rV2VlESF3HzCazij9\n4a8p8vvfXnLMN01FP/MFRT/3X6UIPdMBoD8g8AYAAMCA58vX/lCzNkVO6rVwrdq76Yls+oYmOnma\nGS/SjEQhG//hqtWabdptNWq33aA9dmNS/biljp7c08+//sa4OazkDqhTp87phRcO6/nnD2v9+mNq\nbe1+U1JJmjSpQMuWjdUdd5Rr0qQCQu6+4vuyn/uDMr7zdzLray857I6b2LGqu2J6HxQHAOgMgTcA\nAAACJSZX2+16bY6c0ptWo7xueiUbvjTBydPMRJFmxIuUR/iNLrTJUZXdqN1Wo3bZDUn9ZcEFY50c\nzYgXqTJRqBFeZi9Wib7iup527Kg7H3JX64036pOeW1lZpGXLyrV06ViVl+f1YpVIhrl/rzK+87ey\nX95wyTE/FFL0Mw8p+jdfkSL8zACA/obAGwAAAIHVYsS1JVyrl8M12m81dzve8KVr3FxVJoaqMl6o\nEi+TjQIHOU++qkMt2m03aJfdoIOhlm5volwQ8g1NdvI1PV6oykQhN1MCqrq6SevWHdPatUe1ceNx\nNTfHkpoXiYQ0f36pliwZrSVLxmjkyJxerhRJOdui9B/9kyK/+qkMx7nksDNhstq++29yp0zrg+IA\nAMkg8AYAAMCgUG+261W7Vq+Ea3TMOpfUnEI3vSP8ThRqvDOE3sqDgCtPR0Jn9ZbVpLesJu21zqjV\nvDT06ky2Z2tKokDTEkNVkRiqDFm9WC36QlNTVBs2HNe6dUe1du3RpHtxS9Lw4ZlavHiMFi8erQUL\nRioz0+7FSnFFPE/hPzyu9O/9d5mn6y457FuWog98UdEHvyyF2QMCAPozAm8AAAAMOjVmq7aE67TF\nrtMR62xSc9L8kCYn8jUlUaCpTr6KvIxerhKpEJOrg1az9llN2m816YDVrFiSfbglyfINjXeGaEqi\nQFOcAo10s+jHHTDRqKOtW2u0fn3HKu7XX6+T5yX3ttYwpOnTh2nJkjFasmSMpkwZSj/ufij05hvK\n+PZ/k7Vjy2WPJ25YqLZvflde+YQUVwYAuBoE3gAAABjUas02bbXrtCVcp2or+ZWaw9wMTU3k61on\nXxOcPGX6rNQcCM4Zcb1lNV0MuA+HzspNskXJBSPcTE05f/NjopOniEK9VC36Qnu7oy1bTmnz5hPa\nvPm4tm2rVSyW/E2Q/Pw0LVgwUrfeOlq33DJKhYXcHOuvjDONSv/hwwo/9nMZ/qXfB9wRpWr/2sNK\nLF7acfcCADAgEHgDAAAA5zUaUb1hn9aOcL32WGeUMLyk5hm+VOZma6KTp4lOniY4QwjA+4F2OTpi\nnVV1qEWHrRYdDp1VTajtis+T6dm61snvuMGRKFCBn9YL1aKvtLUltGVLjTZtOq6XXjqhbdtqFI8n\n97UvSeGwqdmzR+imm0bq5ptHqaKiUKZJONqvua7Cv/2l0n/4P2Q2nbnksB+OKPqZzyv6mS9I6dyw\nAICBhsAbAAAAuIyoHO22G7XDrtcuu0HNZjzpuYYvlbpZKndzNdbJVbmTq2Ivgw0we9GFcPtwqCPY\nrrZarirclqQcL6zxzhBNcIZovDNEZW42bUoC5PTpNm3ZUqPXXjulV145qe3ba5VIJB9wS9KkSQXn\nA+4yzZ1bQi/uASS0/VVlfPursvbsvOzx+PtuV/vXHpZXNjq1hQEAegyBNwAAANANT76Ohc5pt9Wg\nnXaD9ltNV9wGI9OzVe7maIyTo1FutkY5Ocr3I4TgV8iRp1qzTSdDrToRatXJUKuOhs6qxmyTf5X/\nlEVu+tsC7jwN89L5/xIQnudr375GvfbaKb322ilt2XJKBw40XfF5SkuzNX9+iRYuHKmFC8tUXJzZ\nC9WiNxn1tUr/528r8tRjlz3ujh6rtm/8g5yFt6a4MgBATyPwBgAAAK5QuxxV2WdUZTWqyjqjY9a5\nqzpPlmerzM3WKDdbI90sjXAzNdzNUJqsHq544InLVU2oTSfN1neE27Vm2xXfbHg7w5dGulma4ORp\n/PkV3EP8SA9Wjr7U3BzTjh212rq15nzAXaPm5tgVn2fUqBzdcEOJ5s8v1bx5JSory+mFapESiYQi\n/++nSv/X78povfR7tZ+RqfbPflmx+x+UInwvAIAgIPAGAAAA3qNzRlx7rSbttc5o73sIwC/I9yLn\nw+9MjXAzVeila6iXpgIvTeGAbJDoyFOjGVW9GdVps131ZrtOm1HVh9p12mxX0xW0kOmM4UvDvUyN\ndrI1+vzq+jI3ixsKARGLOdq9+7S2b6+9+Ni//9J+zMkYMyb3fMBdonnzSlVamt3D1aIvWC+tV8Z3\n/lahA/suezz+gbvV9tVvyx9ekuLKAAC9icAbAAAA6GGtRkIHQ806YHU8DlnNajfcHjl3rhfWUC9N\nQ7105XtpyvHCyvXD73jO8cN90nPal6+oXLWYcTUb8Y5nM/bnj424ms24moyYGs3oVbcguRzDl4q9\nDI1xcjTazdFoN1tlTrbSCbcDwXE8vfVWo954o17bt9do+/Za7d59+op7b0tSKGRoypShmj17hGbO\nHK45c4arpISAO0jMA/uU/oOHFX5h5WWPu+Mnqe1b/yRn9vwUVwYASAUCbwAAAKCXefJ10mzVQatZ\nR0JndcQ6q2Ohc4r1UAj+boYvZfiW0n1Lab6lNIWUfv7zdD+ksB+SKUMhGQr5559lKuQbMmTIky/X\n8OTK73gYvlx1fJ4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-       "text": [
-        "<matplotlib.figure.Figure at 0x10c01fc90>"
-       ]
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+       "bwAAAAAAAABAIBB4AwAAAAAAAAACgcAbAAAAAAAAABAIBN4AAAAAAAAAgEAg8AYAAAAAAAAABAKB\n",
+       "NwAAAAAAAAAgEAi8AQAAAAAAAACBQOANAAAAAAAAAAgEAm8AAAAAAAAAQCAQeAMAAAAAAAAAAoHA\n",
+       "GwAAAAAAAAAQCATeAAAAAAAAAIBAIPAGAAAAAAAAAAQCgTcAAAAAAAAAIBAIvAEAAAAAAAAAgUDg\n",
+       "DQAAAAAAAAAIBAJvAAAAAAAAAEAgEHgDAAAAAAAAAAKBwBsAAAAAAAAAEAgE3gAAAAAAAACAQCDw\n",
+       "BgAAAAAAAAAEAoE3AAAAAAAAACAQCLwBAAAAAAAAAIFA4A0AAAAAAAAACAQCbwAAAAAAAABAIBB4\n",
+       "AwAAAAAAAAACgcAbAAAAAAAAABAIBN4AAAAAAAAAgEAg8AYAAAAAAAAABAKBNwAAAAAAAAAgEAi8\n",
+       "AQAAAAAAAACBQOANAAAAAAAAAAgEAm8AAAAAAAAAQCAQeAMAAAAAAAAAAoHAGwAAAAAAAAAQCATe\n",
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+       "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
+       "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
+       "AAAAAAAAAAAAAAAAAPSJ/w99Q3OUzWUfZAAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c01fc90>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 459,
+       "width": 734
       }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Using IPython's `interactive` function, we can explore how the trajectories behave as we change the various parameters."
-     ]
-    },
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "t, x_t = solve_lorenz(angle=0, N=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using IPython's `interactive` function, we can explore how the trajectories behave as we change the various parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w = interactive(solve_lorenz, angle=(0.,360.), N=(0,50), sigma=(0.0,50.0), rho=(0.0,50.0))\n",
-      "display(w)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {
-        "png": {
-         "height": 459,
-         "width": 734
-        }
-       },
-       "output_type": "display_data",
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yg7wCQMZIuS1S11qpa50a0r4WLZqmO++cqfnzR530qnzfSD/Jx/RvmYQ2DtBy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\ng7flmJTs3lD80GC8b1DeHaC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uPthRo92H9euusYz+sz6rW4eoX/cvf7lB/+f/PKZMMFwa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h7Vl3bTLHP4icfxFYXFV1VIJyCep+nzj0e1YvV77xXSXXYzME77PZ+bGxbyO/jO0nxWatiP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\nK9BtQdTaOcM7LsLkANkMR4DcXkUr3W7dtDkx8QAt/3t4Y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rBQDkA9sCc8yCFFx/dr+4pU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\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0qB3edUfo8HY0V1o3/cdZ8D6SSBN1kcqEkPkF16j6qyAuJ52sR0Km4K0jro40Sfu5vgzv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hjOHD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Y+YX3AVrmzIn+wcg2KZ1K+USURA9kDRLBAHWH10L7eYqz3BC+aXUmWiDCUzLJQBSm+tDdoe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M09LcKFFbois0mTtqbU1ZQnLVVT0Ykr8Vgk40ycDU1jMEH9BgZSV7HnjgmNts0lrS2vNX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1KbX8VaP70NHRqd3ogreOjo6Ojk4tZZthK7Mds1CEmNghqAJDfWfTOnJKwj6KChPL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02T/v0T3r8kSeXDpk0Or70WZeJEg0ik4HM0DQYMcPPAAx7q1y+efQuKgxZcTGDLrWiRNXEx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\ncgsWU+lOSB+LqfVJcHRCCCGOhiS8hRBCCCFEjbR2i8ppV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-       "text": [
-        "<matplotlib.figure.Figure at 0x10c08c690>"
-       ]
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+       "NwAAAAAAAAAgEAjeAAAAAAAAAIBAIHgDAAAAAAAAAAKB4A0AAAAAAAAACASCNwAAAAAAAAAgEAje\n",
+       "AAAAAAAAAIBAIHgDAAAAAAAAAAKB4A0AAAAAAAAACASCNwAAAAAAAAAgEAjeAAAAAAAAAIBAIHgD\n",
+       "AAAAAAAAAAKB4A0AAAAAAAAACASCNwAAAAAAAAAgEAjeAAAAAAAAAIBAIHgDAAAAAAAAAAKB4A0A\n",
+       "AAAAAAAACASCNwAAAAAAAAAgEAjeAAAAAAAAAIBAIHgDAAAAAAAAAAKB4A0AAAAAAAAACASCNwAA\n",
+       "AAAAAAAgEAjeAAAAAAAAAIBAIHgDAAAAAAAAAAKB4A0AAAAAAAAACASCNwAAAAAAAAAgEAjeAAAA\n",
+       "AAAAAIBAIHgDAAAAAAAAAAKB4A0AAAAAAAAACASCNwAAAAAAAAAgEAjeAAAAAAAAAIBAIHgDAAAA\n",
+       "AAAAAAKB4A0AAAAAAAAACASCNwAAAAAAAAAgEAjeAAAAAAAAAIBAIHgDAAAAAAAAAAKB4A0AAAAA\n",
+       "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
+       "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
+       "AAAAAAAAAAAAQ+L/AQTPKT0WqXhXAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c08c690>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 459,
+       "width": 734
       }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The object returned by `interactive` is a `Widget` object and it has attributes that contain the current result and arguments:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "t, x_t = w.result"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "w = interactive(solve_lorenz, angle=(0.,360.), N=(0,50), sigma=(0.0,50.0), rho=(0.0,50.0))\n",
+    "display(w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The object returned by `interactive` is a `Widget` object and it has attributes that contain the current result and arguments:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "t, x_t = w.result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w.kwargs"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "{u'N': 25,\n",
+       " u'angle': 180.0,\n",
+       " u'beta': 2.6666666666666665,\n",
+       " u'max_time': 4.0,\n",
+       " u'rho': 25.0,\n",
+       " u'sigma': 25.0}"
+      ]
+     },
+     "execution_count": 8,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 8,
-       "text": [
-        "{u'N': 25,\n",
-        " u'angle': 180.0,\n",
-        " u'beta': 2.6666666666666665,\n",
-        " u'max_time': 4.0,\n",
-        " u'rho': 25.0,\n",
-        " u'sigma': 25.0}"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.kwargs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After interacting with the system, we can take the result and perform further computations. In this case, we compute the average positions in $x$, $y$ and $z$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "xyz_avg = x_t.mean(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "(25, 3)"
+      ]
+     },
+     "execution_count": 10,
      "metadata": {},
-     "source": [
-      "After interacting with the system, we can take the result and perform further computations. In this case, we compute the average positions in $x$, $y$ and $z$."
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xyz_avg.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Creating histograms of the average positions (across different trajectories) show that on average the trajectories swirl about the attractors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "xyz_avg = x_t.mean(axis=1)"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x10c0387d0>"
+      ]
+     },
+     "execution_count": 11,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "xyz_avg.shape"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "(25, 3)"
-       ]
+     "data": {
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+       "Bu9BkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\n",
+       "kiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\n",
+       "kiRJkiRJkiRJkiQ19//zUyDZtRgtUwAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c08c610>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 391,
+       "width": 586
       }
-     ],
-     "prompt_number": 10
-    },
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(xyz_avg[:,0])\n",
+    "plt.title('Average $x(t)$')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x10cadb610>"
+      ]
+     },
+     "execution_count": 12,
      "metadata": {},
-     "source": [
-      "Creating histograms of the average positions (across different trajectories) show that on average the trajectories swirl about the attractors."
-     ]
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.hist(xyz_avg[:,0])\n",
-      "plt.title('Average $x(t)$')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 11,
-       "text": [
-        "<matplotlib.text.Text at 0x10c0387d0>"
-       ]
-      },
-      {
-       "metadata": {
-        "png": {
-         "height": 391,
-         "width": 586
-        }
-       },
-       "output_type": "display_data",
-       "png": 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hbnk7Pl1M+uckVyf59aZpljvu/LZtPz7A9QAAAABYoCGC0qb+875JXj7lmO1J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\nkiRJkiRJkiRJkiQ19//zUyDZtRgtUwAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10c08c610>"
-       ]
+     "data": {
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+       "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
+       "AAAAAAAAAAAAAGB+/wvBfl7+2FfVJwAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c033e10>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 391,
+       "width": 586
       }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.hist(xyz_avg[:,1])\n",
-      "plt.title('Average $y(t)$')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 12,
-       "text": [
-        "<matplotlib.text.Text at 0x10cadb610>"
-       ]
-      },
-      {
-       "metadata": {
-        "png": {
-         "height": 391,
-         "width": 586
-        }
-       },
-       "output_type": "display_data",
-       "png": 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-       "text": [
-        "<matplotlib.figure.Figure at 0x10c033e10>"
-       ]
-      }
-     ],
-     "prompt_number": 12
+     },
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "plt.hist(xyz_avg[:,1])\n",
+    "plt.title('Average $y(t)$')"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:c6ccfb14927d633933da8b5c0f776a8da756d833654b5a3f8b6121b390ca6424"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Nonblocking Console.ipynb b/examples/Interactive Widgets/Nonblocking Console.ipynb
index 84b3ef5..46a9701 100644
--- a/examples/Interactive Widgets/Nonblocking Console.ipynb
+++ b/examples/Interactive Widgets/Nonblocking Console.ipynb
@@ -1,4 +1,220 @@
 {
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Console related imports.\n",
+    "from subprocess import Popen, PIPE\n",
+    "import os\n",
+    "from IPython.utils.py3compat import bytes_to_str, string_types\n",
+    "\n",
+    "# Widget related imports.\n",
+    "from IPython.html import  widgets\n",
+    "from IPython.display import display"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define function to run a process without blocking the input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def read_process(process, append_output):\n",
+    "    \"\"\" Try to read the stdout and stderr of a process and render it using \n",
+    "    the append_output method provided\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    process: Popen handle\n",
+    "    append_output: method handle\n",
+    "        Callback to render output.  Signature of\n",
+    "        append_output(output, [prefix=])\"\"\"\n",
+    "    \n",
+    "    try:\n",
+    "        stdout = process.stdout.read()\n",
+    "        if stdout is not None and len(stdout) > 0:\n",
+    "            append_output(stdout, prefix='    ')\n",
+    "    except:\n",
+    "        pass\n",
+    "        \n",
+    "    try:\n",
+    "        stderr = process.stderr.read()\n",
+    "        if stderr is not None and len(stderr) > 0:\n",
+    "            append_output(stderr, prefix='ERR ')\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "\n",
+    "def set_pipe_nonblocking(pipe):\n",
+    "    \"\"\"Set a pipe as non-blocking\"\"\"\n",
+    "    try:\n",
+    "        import fcntl\n",
+    "        fl = fcntl.fcntl(pipe, fcntl.F_GETFL)\n",
+    "        fcntl.fcntl(pipe, fcntl.F_SETFL, fl | os.O_NONBLOCK)\n",
+    "    except:\n",
+    "        pass\n",
+    "\n",
+    "kernel = get_ipython().kernel\n",
+    "def run_command(command, append_output, has_user_exited=None):\n",
+    "    \"\"\"Run a command asyncronously\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    command: str\n",
+    "        Shell command to launch a process with.\n",
+    "    append_output: method handle\n",
+    "        Callback to render output.  Signature of\n",
+    "        append_output(output, [prefix=])\n",
+    "    has_user_exited: method handle\n",
+    "        Check to see if the user wants to stop the command.\n",
+    "        Must return a boolean.\"\"\"\n",
+    "    \n",
+    "    # Echo input.\n",
+    "    append_output(command, prefix='>>> ')\n",
+    "    \n",
+    "    # Create the process.  Make sure the pipes are set as non-blocking.\n",
+    "    process = Popen(command, shell=True, stdout=PIPE, stderr=PIPE)\n",
+    "    set_pipe_nonblocking(process.stdout)\n",
+    "    set_pipe_nonblocking(process.stderr)\n",
+    "    \n",
+    "    # Only continue to read from the command \n",
+    "    while (has_user_exited is None or not has_user_exited()) and process.poll() is None:\n",
+    "        read_process(process, append_output)\n",
+    "        kernel.do_one_iteration() # Run IPython iteration.  This is the code that\n",
+    "                                  # makes this operation non-blocking.  This will\n",
+    "                                  # allow widget messages and callbacks to be \n",
+    "                                  # processed.\n",
+    "        \n",
+    "    # If the process is still running, the user must have exited.\n",
+    "    if process.poll() is None:\n",
+    "        process.kill()\n",
+    "    else:\n",
+    "        read_process(process, append_output) # Read remainer\n",
+    "        \n",
+    "    \n",
+    "        \n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the console widgets without displaying them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "console_container = widgets.VBox(visible=False)\n",
+    "console_container.padding = '10px'\n",
+    "\n",
+    "output_box = widgets.Textarea()\n",
+    "output_box.height = '400px'\n",
+    "output_box.font_family = 'monospace'\n",
+    "output_box.color = '#AAAAAA'\n",
+    "output_box.background_color = 'black'\n",
+    "output_box.width = '800px'\n",
+    "\n",
+    "input_box = widgets.Text()\n",
+    "input_box.font_family = 'monospace'\n",
+    "input_box.color = '#AAAAAA'\n",
+    "input_box.background_color = 'black'\n",
+    "input_box.width = '800px'\n",
+    "\n",
+    "console_container.children = [output_box, input_box]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hook the process execution methods up to our console widgets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def append_output(output, prefix):\n",
+    "    if isinstance(output, string_types):\n",
+    "        output_str = output\n",
+    "    else:\n",
+    "        output_str = bytes_to_str(output)\n",
+    "    output_lines = output_str.split('\\n')\n",
+    "    formatted_output = '\\n'.join([prefix + line for line in output_lines if len(line) > 0]) + '\\n'\n",
+    "    output_box.value += formatted_output\n",
+    "    output_box.scroll_to_bottom()\n",
+    "    \n",
+    "def has_user_exited():\n",
+    "    return not console_container.visible\n",
+    "\n",
+    "def handle_input(sender):\n",
+    "    sender.disabled = True\n",
+    "    try:\n",
+    "        command = sender.value\n",
+    "        sender.value = ''\n",
+    "        run_command(command, append_output=append_output, has_user_exited=has_user_exited)\n",
+    "    finally:\n",
+    "        sender.disabled = False\n",
+    "        \n",
+    "input_box.on_submit(handle_input)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create the button that will be used to display and hide the console.  Display both the console container and the new button used to toggle it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "toggle_button = widgets.Button(description=\"Start Console\")\n",
+    "def toggle_console(sender):\n",
+    "    console_container.visible = not console_container.visible\n",
+    "    if console_container.visible:\n",
+    "        toggle_button.description=\"Stop Console\"\n",
+    "        input_box.disabled = False\n",
+    "    else:\n",
+    "        toggle_button.description=\"Start Console\"\n",
+    "toggle_button.on_click(toggle_console)\n",
+    "\n",
+    "display(toggle_button)\n",
+    "display(console_container)"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "codemirror_mode": {
@@ -9,225 +225,8 @@
    "language": "python",
    "name": "python2"
   },
-  "name": "",
   "signature": "sha256:3dfbc0dcb1fefc9ef028022760916b0300e14f71bf8e27589e61800841d5839c"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Console related imports.\n",
-      "from subprocess import Popen, PIPE\n",
-      "import os\n",
-      "from IPython.utils.py3compat import bytes_to_str, string_types\n",
-      "\n",
-      "# Widget related imports.\n",
-      "from IPython.html import  widgets\n",
-      "from IPython.display import display"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Define function to run a process without blocking the input."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def read_process(process, append_output):\n",
-      "    \"\"\" Try to read the stdout and stderr of a process and render it using \n",
-      "    the append_output method provided\n",
-      "    \n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    process: Popen handle\n",
-      "    append_output: method handle\n",
-      "        Callback to render output.  Signature of\n",
-      "        append_output(output, [prefix=])\"\"\"\n",
-      "    \n",
-      "    try:\n",
-      "        stdout = process.stdout.read()\n",
-      "        if stdout is not None and len(stdout) > 0:\n",
-      "            append_output(stdout, prefix='    ')\n",
-      "    except:\n",
-      "        pass\n",
-      "        \n",
-      "    try:\n",
-      "        stderr = process.stderr.read()\n",
-      "        if stderr is not None and len(stderr) > 0:\n",
-      "            append_output(stderr, prefix='ERR ')\n",
-      "    except:\n",
-      "        pass\n",
-      "\n",
-      "\n",
-      "def set_pipe_nonblocking(pipe):\n",
-      "    \"\"\"Set a pipe as non-blocking\"\"\"\n",
-      "    try:\n",
-      "        import fcntl\n",
-      "        fl = fcntl.fcntl(pipe, fcntl.F_GETFL)\n",
-      "        fcntl.fcntl(pipe, fcntl.F_SETFL, fl | os.O_NONBLOCK)\n",
-      "    except:\n",
-      "        pass\n",
-      "\n",
-      "kernel = get_ipython().kernel\n",
-      "def run_command(command, append_output, has_user_exited=None):\n",
-      "    \"\"\"Run a command asyncronously\n",
-      "    \n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    command: str\n",
-      "        Shell command to launch a process with.\n",
-      "    append_output: method handle\n",
-      "        Callback to render output.  Signature of\n",
-      "        append_output(output, [prefix=])\n",
-      "    has_user_exited: method handle\n",
-      "        Check to see if the user wants to stop the command.\n",
-      "        Must return a boolean.\"\"\"\n",
-      "    \n",
-      "    # Echo input.\n",
-      "    append_output(command, prefix='>>> ')\n",
-      "    \n",
-      "    # Create the process.  Make sure the pipes are set as non-blocking.\n",
-      "    process = Popen(command, shell=True, stdout=PIPE, stderr=PIPE)\n",
-      "    set_pipe_nonblocking(process.stdout)\n",
-      "    set_pipe_nonblocking(process.stderr)\n",
-      "    \n",
-      "    # Only continue to read from the command \n",
-      "    while (has_user_exited is None or not has_user_exited()) and process.poll() is None:\n",
-      "        read_process(process, append_output)\n",
-      "        kernel.do_one_iteration() # Run IPython iteration.  This is the code that\n",
-      "                                  # makes this operation non-blocking.  This will\n",
-      "                                  # allow widget messages and callbacks to be \n",
-      "                                  # processed.\n",
-      "        \n",
-      "    # If the process is still running, the user must have exited.\n",
-      "    if process.poll() is None:\n",
-      "        process.kill()\n",
-      "    else:\n",
-      "        read_process(process, append_output) # Read remainer\n",
-      "        \n",
-      "    \n",
-      "        \n",
-      "        "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Create the console widgets without displaying them."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "console_container = widgets.VBox(visible=False)\n",
-      "console_container.padding = '10px'\n",
-      "\n",
-      "output_box = widgets.Textarea()\n",
-      "output_box.height = '400px'\n",
-      "output_box.font_family = 'monospace'\n",
-      "output_box.color = '#AAAAAA'\n",
-      "output_box.background_color = 'black'\n",
-      "output_box.width = '800px'\n",
-      "\n",
-      "input_box = widgets.Text()\n",
-      "input_box.font_family = 'monospace'\n",
-      "input_box.color = '#AAAAAA'\n",
-      "input_box.background_color = 'black'\n",
-      "input_box.width = '800px'\n",
-      "\n",
-      "console_container.children = [output_box, input_box]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Hook the process execution methods up to our console widgets."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "\n",
-      "def append_output(output, prefix):\n",
-      "    if isinstance(output, string_types):\n",
-      "        output_str = output\n",
-      "    else:\n",
-      "        output_str = bytes_to_str(output)\n",
-      "    output_lines = output_str.split('\\n')\n",
-      "    formatted_output = '\\n'.join([prefix + line for line in output_lines if len(line) > 0]) + '\\n'\n",
-      "    output_box.value += formatted_output\n",
-      "    output_box.scroll_to_bottom()\n",
-      "    \n",
-      "def has_user_exited():\n",
-      "    return not console_container.visible\n",
-      "\n",
-      "def handle_input(sender):\n",
-      "    sender.disabled = True\n",
-      "    try:\n",
-      "        command = sender.value\n",
-      "        sender.value = ''\n",
-      "        run_command(command, append_output=append_output, has_user_exited=has_user_exited)\n",
-      "    finally:\n",
-      "        sender.disabled = False\n",
-      "        \n",
-      "input_box.on_submit(handle_input)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Create the button that will be used to display and hide the console.  Display both the console container and the new button used to toggle it."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "toggle_button = widgets.Button(description=\"Start Console\")\n",
-      "def toggle_console(sender):\n",
-      "    console_container.visible = not console_container.visible\n",
-      "    if console_container.visible:\n",
-      "        toggle_button.description=\"Stop Console\"\n",
-      "        input_box.disabled = False\n",
-      "    else:\n",
-      "        toggle_button.description=\"Start Console\"\n",
-      "toggle_button.on_click(toggle_console)\n",
-      "\n",
-      "display(toggle_button)\n",
-      "display(console_container)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Using Interact.ipynb b/examples/Interactive Widgets/Using Interact.ipynb
index 8374c9d..3b4b5de 100644
--- a/examples/Interactive Widgets/Using Interact.ipynb
+++ b/examples/Interactive Widgets/Using Interact.ipynb
@@ -1,4 +1,609 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using Interact"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `interact` function (`IPython.html.widgets.interact`) automatically creates user interface (UI) controls for exploring code and data interactively. It is the easiest way to get started using IPython's widgets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "from IPython.html.widgets import interact, interactive, fixed\n",
+    "from IPython.html import widgets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "As of IPython 2.0, the widgets in this notebook won't show up on http://nbviewer.ipython.org. To view the widgets and interact with them, you will need to download this notebook and run it with an IPython Notebook server.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic `interact`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At the most basic level, `interact` autogenerates UI controls for function arguments, and then calls the function with those arguments when you manipulate the controls interactively. To use `interact`, you need to define a function that you want to explore. Here is a function that prints its only argument `x`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def f(x):\n",
+    "    print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you pass this function as the first argument to `interact` along with an integer keyword argument (`x=10`), a slider is generated and bound to the function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "interact(f, x=10);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you move the slider, the function is called and the current value of `x` is printed.\n",
+    "\n",
+    "If you pass `True` or `False`, `interact` will generate a checkbox:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "interact(f, x=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you pass a string, `interact` will generate a text area."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "interact(f, x='Hi there!');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`interact` can also be used as a decorator. This allows you to define a function and interact with it in a single shot. As this example shows, `interact` also works with functions that have multiple arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "@interact(x=True, y=1.0)\n",
+    "def g(x, y):\n",
+    "    print(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fixing arguments using `fixed`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are times when you may want to explore a function using `interact`, but fix one or more of its arguments to specific values. This can be accomplished by wrapping values with the `fixed` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def h(p, q):\n",
+    "    print(p, q)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we call `interact`, we pass `fixed(20)` for q to hold it fixed at a value of `20`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "interact(h, p=5, q=fixed(20));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that a slider is only produced for `p` as the value of `q` is fixed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Widget abbreviations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you pass an integer valued keyword argument (`x=10`) to `interact`, it generates an integer valued slider control with a range of $[-10,+3\\times10]$. In this case `10` is an *abbreviation* for an actual slider widget:\n",
+    "\n",
+    "```python\n",
+    "IntSliderWidget(min=-10,max=30,step=1,value=10)\n",
+    "```\n",
+    "\n",
+    "In fact, we can get the same result if we pass this `IntSliderWidget` as the keyword argument for `x`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "interact(f, x=widgets.IntSliderWidget(min=-10,max=30,step=1,value=10));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This examples clarifies how `interact` proceses its keyword arguments:\n",
+    "\n",
+    "1. If the keyword argument is `Widget` instance with a `value` attribute, that widget is used. Any widget with a `value` attribute can be used, even custom ones.\n",
+    "2. Otherwise, the value is treated as a *widget abbreviation* that is converted to a widget before it is used.\n",
+    "\n",
+    "The following table gives an overview of different widget abbreviations:\n",
+    "\n",
+    "<table class=\"table table-condensed table-bordered\">\n",
+    "  <tr><td><strong>Keyword argument</strong></td><td><strong>Widget</strong></td></tr>  \n",
+    "  <tr><td>`True` or `False`</td><td>CheckboxWiget</td></tr>  \n",
+    "  <tr><td>`'Hi there'`</td><td>TextareaWidget</td></tr>\n",
+    "  <tr><td>`value` or `(min,max)` or `(min,max,step)` if integers are passed</td><td>IntSliderWidget</td></tr>\n",
+    "  <tr><td>`value` or `(min,max)` or `(min,max,step)` if floats are passed</td><td>FloatSliderWidget</td></tr>\n",
+    "  <tr><td>`('orange','apple')` or `{'one':1,'two':2}`</td><td>DropdownWidget</td></tr>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You have seen how the checkbox and textarea widgets work above. Here, more details about the different abbreviations for sliders and dropdowns are given.\n",
+    "\n",
+    "If a 2-tuple of integers is passed `(min,max)` a integer valued slider is produced with those minimum and maximum (inclusive) values. In this case, the default step size of `1` is used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "interact(f, x=(0,4));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If a 3-tuple of integers is passed `(min,max,step)` the step size can also be set."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "interact(f, x=(0,8,2));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A float valued slider is produced if the elements of the tuples are floats. Here the minimum is `0.0`, the maximum is `10.0` and step size is `0.1` (the default)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "interact(f, x=(0.0,10.0));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The step size can be changed by passing a 3rd element in the tuple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "interact(f, x=(0.0,10.0,0.01));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For both integer and float valued sliders, you can pick the initial value of the widget by passing a default keyword argument to the underlying Python function. Here we set the initial value of a float slider to `5.5`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "@interact(x=(0.0,20.0,0.5))\n",
+    "def h(x=5.5):\n",
+    "    print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dropdown menus can be produced by passing a tuple of strings. In this case, the strings are both used as the names in the dropdown menu UI and passed to the underlying Python function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "interact(f, x=('apples','oranges'));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want a dropdown menu that passes non-string values to the Python function, you can pass a dictionary. The keys in the dictionary are used for the names in the dropdown menu UI and the values are the arguments that are passed to the underlying Python function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "interact(f, x={'one': 10, 'two': 20});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using function annotations with `interact`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are using Python 3, you can also specify widget abbreviations using [function annotations](https://docs.python.org/3/tutorial/controlflow.html#function-annotations). This is a convenient approach allows the widget abbreviations to be defined with a function.\n",
+    "\n",
+    "Define a function with an checkbox widget abbreviation for the argument `x`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def f(x:True):\n",
+    "    print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, because the widget abbreviation has already been defined, you can call `interact` with a single argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "interact(f);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you are running Python 2, function annotations can be defined using the `@annotate` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.utils.py3compat import annotate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "@annotate(x=True)\n",
+    "def f(x):\n",
+    "    print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "interact(f);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `interactive`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In addition to `interact` IPython provides another function, `interactive`, that is useful when you want to reuse the widget that are produced or access the data that is bound to the UI controls."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is a function that returns the sum of its two arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def f(a, b):\n",
+    "    return a+b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unlike `interact`, `interactive` returns a `Widget` instance rather than immediately displaying the widget."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w = interactive(f, a=10, b=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The widget is a `Box`, which is a container for other widgets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "type(w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The children of the `Box` are two integer valued sliders produced by the widget abbreviations above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.children"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To actually display the widgets, you can use IPython's `display` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import display\n",
+    "display(w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At this point, the UI controls work just like they would if `interact` had been used. You can manipulate them interactively and the function will be called. However, the widget instance returned by `interactive` also give you access to the current keyword arguments and return value of the underlying Python function.\n",
+    "\n",
+    "Here are the current keyword arguments. If you rerun this cell after manipulating the sliders, the values will have changed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.kwargs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is the current return value of the function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.result"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "codemirror_mode": {
@@ -9,597 +614,8 @@
    "language": "python",
    "name": "python3"
   },
-  "name": "",
   "signature": "sha256:1bbd16f32ec50ec64d4dd646cd6868aeace8091cac989107f2026fc688ca3701"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Using Interact"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The `interact` function (`IPython.html.widgets.interact`) automatically creates user interface (UI) controls for exploring code and data interactively. It is the easiest way to get started using IPython's widgets."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from __future__ import print_function\n",
-      "from IPython.html.widgets import interact, interactive, fixed\n",
-      "from IPython.html import widgets"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<div class=\"alert alert-success\">\n",
-      "As of IPython 2.0, the widgets in this notebook won't show up on http://nbviewer.ipython.org. To view the widgets and interact with them, you will need to download this notebook and run it with an IPython Notebook server.\n",
-      "</div>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Basic `interact`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "At the most basic level, `interact` autogenerates UI controls for function arguments, and then calls the function with those arguments when you manipulate the controls interactively. To use `interact`, you need to define a function that you want to explore. Here is a function that prints its only argument `x`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def f(x):\n",
-      "    print(x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When you pass this function as the first argument to `interact` along with an integer keyword argument (`x=10`), a slider is generated and bound to the function."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "interact(f, x=10);"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When you move the slider, the function is called and the current value of `x` is printed.\n",
-      "\n",
-      "If you pass `True` or `False`, `interact` will generate a checkbox:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "interact(f, x=True);"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If you pass a string, `interact` will generate a text area."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "interact(f, x='Hi there!');"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "`interact` can also be used as a decorator. This allows you to define a function and interact with it in a single shot. As this example shows, `interact` also works with functions that have multiple arguments."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "@interact(x=True, y=1.0)\n",
-      "def g(x, y):\n",
-      "    print(x, y)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Fixing arguments using `fixed`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "There are times when you may want to explore a function using `interact`, but fix one or more of its arguments to specific values. This can be accomplished by wrapping values with the `fixed` function."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def h(p, q):\n",
-      "    print(p, q)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When we call `interact`, we pass `fixed(20)` for q to hold it fixed at a value of `20`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "interact(h, p=5, q=fixed(20));"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Notice that a slider is only produced for `p` as the value of `q` is fixed."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Widget abbreviations"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When you pass an integer valued keyword argument (`x=10`) to `interact`, it generates an integer valued slider control with a range of $[-10,+3\\times10]$. In this case `10` is an *abbreviation* for an actual slider widget:\n",
-      "\n",
-      "```python\n",
-      "IntSliderWidget(min=-10,max=30,step=1,value=10)\n",
-      "```\n",
-      "\n",
-      "In fact, we can get the same result if we pass this `IntSliderWidget` as the keyword argument for `x`:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "interact(f, x=widgets.IntSliderWidget(min=-10,max=30,step=1,value=10));"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This examples clarifies how `interact` proceses its keyword arguments:\n",
-      "\n",
-      "1. If the keyword argument is `Widget` instance with a `value` attribute, that widget is used. Any widget with a `value` attribute can be used, even custom ones.\n",
-      "2. Otherwise, the value is treated as a *widget abbreviation* that is converted to a widget before it is used.\n",
-      "\n",
-      "The following table gives an overview of different widget abbreviations:\n",
-      "\n",
-      "<table class=\"table table-condensed table-bordered\">\n",
-      "  <tr><td><strong>Keyword argument</strong></td><td><strong>Widget</strong></td></tr>  \n",
-      "  <tr><td>`True` or `False`</td><td>CheckboxWiget</td></tr>  \n",
-      "  <tr><td>`'Hi there'`</td><td>TextareaWidget</td></tr>\n",
-      "  <tr><td>`value` or `(min,max)` or `(min,max,step)` if integers are passed</td><td>IntSliderWidget</td></tr>\n",
-      "  <tr><td>`value` or `(min,max)` or `(min,max,step)` if floats are passed</td><td>FloatSliderWidget</td></tr>\n",
-      "  <tr><td>`('orange','apple')` or `{'one':1,'two':2}`</td><td>DropdownWidget</td></tr>\n",
-      "</table>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You have seen how the checkbox and textarea widgets work above. Here, more details about the different abbreviations for sliders and dropdowns are given.\n",
-      "\n",
-      "If a 2-tuple of integers is passed `(min,max)` a integer valued slider is produced with those minimum and maximum (inclusive) values. In this case, the default step size of `1` is used."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "interact(f, x=(0,4));"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If a 3-tuple of integers is passed `(min,max,step)` the step size can also be set."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "interact(f, x=(0,8,2));"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "A float valued slider is produced if the elements of the tuples are floats. Here the minimum is `0.0`, the maximum is `10.0` and step size is `0.1` (the default)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "interact(f, x=(0.0,10.0));"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The step size can be changed by passing a 3rd element in the tuple."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "interact(f, x=(0.0,10.0,0.01));"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "For both integer and float valued sliders, you can pick the initial value of the widget by passing a default keyword argument to the underlying Python function. Here we set the initial value of a float slider to `5.5`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "@interact(x=(0.0,20.0,0.5))\n",
-      "def h(x=5.5):\n",
-      "    print(x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Dropdown menus can be produced by passing a tuple of strings. In this case, the strings are both used as the names in the dropdown menu UI and passed to the underlying Python function."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "interact(f, x=('apples','oranges'));"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If you want a dropdown menu that passes non-string values to the Python function, you can pass a dictionary. The keys in the dictionary are used for the names in the dropdown menu UI and the values are the arguments that are passed to the underlying Python function."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "interact(f, x={'one': 10, 'two': 20});"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Using function annotations with `interact`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If you are using Python 3, you can also specify widget abbreviations using [function annotations](https://docs.python.org/3/tutorial/controlflow.html#function-annotations). This is a convenient approach allows the widget abbreviations to be defined with a function.\n",
-      "\n",
-      "Define a function with an checkbox widget abbreviation for the argument `x`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def f(x:True):\n",
-      "    print(x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Then, because the widget abbreviation has already been defined, you can call `interact` with a single argument."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "interact(f);"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If you are running Python 2, function annotations can be defined using the `@annotate` function."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.utils.py3compat import annotate"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "@annotate(x=True)\n",
-      "def f(x):\n",
-      "    print(x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "interact(f);"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "`interactive`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In addition to `interact` IPython provides another function, `interactive`, that is useful when you want to reuse the widget that are produced or access the data that is bound to the UI controls."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is a function that returns the sum of its two arguments."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def f(a, b):\n",
-      "    return a+b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Unlike `interact`, `interactive` returns a `Widget` instance rather than immediately displaying the widget."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w = interactive(f, a=10, b=20)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The widget is a `Box`, which is a container for other widgets."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "type(w)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The children of the `Box` are two integer valued sliders produced by the widget abbreviations above."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w.children"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To actually display the widgets, you can use IPython's `display` function."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import display\n",
-      "display(w)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "At this point, the UI controls work just like they would if `interact` had been used. You can manipulate them interactively and the function will be called. However, the widget instance returned by `interactive` also give you access to the current keyword arguments and return value of the underlying Python function.\n",
-      "\n",
-      "Here are the current keyword arguments. If you rerun this cell after manipulating the sliders, the values will have changed."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w.kwargs"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is the current return value of the function."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w.result"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Variable Inspector.ipynb b/examples/Interactive Widgets/Variable Inspector.ipynb
index 844d1be..24b3285 100644
--- a/examples/Interactive Widgets/Variable Inspector.ipynb
+++ b/examples/Interactive Widgets/Variable Inspector.ipynb
@@ -1,4 +1,190 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Variable Inspector Widget"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A short example implementation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook demonstrates how one can use the widgets already built-in to IPython to create a working variable inspector much like the ones seen in popular commercial scientific computing environments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.html import widgets # Loads the Widget framework.\n",
+    "from IPython.core.magics.namespace import NamespaceMagics # Used to query namespace.\n",
+    "\n",
+    "# For this example, hide these names, just to avoid polluting the namespace further\n",
+    "get_ipython().user_ns_hidden['widgets'] = widgets\n",
+    "get_ipython().user_ns_hidden['NamespaceMagics'] = NamespaceMagics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class VariableInspectorWindow(object):\n",
+    "    instance = None\n",
+    "    \n",
+    "    def __init__(self, ipython):\n",
+    "        \"\"\"Public constructor.\"\"\"\n",
+    "        if VariableInspectorWindow.instance is not None:\n",
+    "            raise Exception(\"\"\"Only one instance of the Variable Inspector can exist at a \n",
+    "                time.  Call close() on the active instance before creating a new instance.\n",
+    "                If you have lost the handle to the active instance, you can re-obtain it\n",
+    "                via `VariableInspectorWindow.instance`.\"\"\")\n",
+    "        \n",
+    "        VariableInspectorWindow.instance = self\n",
+    "        self.closed = False\n",
+    "        self.namespace = NamespaceMagics()\n",
+    "        self.namespace.shell = ipython.kernel.shell\n",
+    "        \n",
+    "        self._popout = widgets.Popup()\n",
+    "        self._popout.description = \"Variable Inspector\"\n",
+    "        self._popout.button_text = self._popout.description\n",
+    "\n",
+    "        self._modal_body = widgets.VBox()\n",
+    "        self._modal_body.overflow_y = 'scroll'\n",
+    "\n",
+    "        self._modal_body_label = widgets.HTML(value = 'Not hooked')\n",
+    "        self._modal_body.children = [self._modal_body_label]\n",
+    "\n",
+    "        self._popout.children = [\n",
+    "            self._modal_body, \n",
+    "        ]\n",
+    "        \n",
+    "        self._ipython = ipython\n",
+    "        self._ipython.events.register('post_run_cell', self._fill)\n",
+    "        \n",
+    "    def close(self):\n",
+    "        \"\"\"Close and remove hooks.\"\"\"\n",
+    "        if not self.closed:\n",
+    "            self._ipython.events.unregister('post_run_cell', self._fill)\n",
+    "            self._popout.close()\n",
+    "            self.closed = True\n",
+    "            VariableInspectorWindow.instance = None\n",
+    "\n",
+    "    def _fill(self):\n",
+    "        \"\"\"Fill self with variable information.\"\"\"\n",
+    "        values = self.namespace.who_ls()\n",
+    "        self._modal_body_label.value = '<table class=\"table table-bordered table-striped\"><tr><th>Name</th><th>Type</th><th>Value</th></tr><tr><td>' + \\\n",
+    "            '</td></tr><tr><td>'.join(['{0}</td><td>{1}</td><td>{2}'.format(v, type(eval(v)).__name__, str(eval(v))) for v in values]) + \\\n",
+    "            '</td></tr></table>'\n",
+    "\n",
+    "    def _ipython_display_(self):\n",
+    "        \"\"\"Called when display() or pyout is used to display the Variable \n",
+    "        Inspector.\"\"\"\n",
+    "        self._popout._ipython_display_()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "inspector = VariableInspectorWindow(get_ipython())\n",
+    "inspector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a = 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "b = 3.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "c = a * b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "d = \"String\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "del b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "inspector.close()"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "codemirror_mode": {
@@ -9,194 +195,8 @@
    "language": "python",
    "name": "python2"
   },
-  "name": "",
   "signature": "sha256:474731659fb14b86672d1dafb2b497fa280082ab40a8a82fe2cde1b6d9b88a6e"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Variable Inspector Widget"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "A short example implementation"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This notebook demonstrates how one can use the widgets already built-in to IPython to create a working variable inspector much like the ones seen in popular commercial scientific computing environments."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.html import widgets # Loads the Widget framework.\n",
-      "from IPython.core.magics.namespace import NamespaceMagics # Used to query namespace.\n",
-      "\n",
-      "# For this example, hide these names, just to avoid polluting the namespace further\n",
-      "get_ipython().user_ns_hidden['widgets'] = widgets\n",
-      "get_ipython().user_ns_hidden['NamespaceMagics'] = NamespaceMagics"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class VariableInspectorWindow(object):\n",
-      "    instance = None\n",
-      "    \n",
-      "    def __init__(self, ipython):\n",
-      "        \"\"\"Public constructor.\"\"\"\n",
-      "        if VariableInspectorWindow.instance is not None:\n",
-      "            raise Exception(\"\"\"Only one instance of the Variable Inspector can exist at a \n",
-      "                time.  Call close() on the active instance before creating a new instance.\n",
-      "                If you have lost the handle to the active instance, you can re-obtain it\n",
-      "                via `VariableInspectorWindow.instance`.\"\"\")\n",
-      "        \n",
-      "        VariableInspectorWindow.instance = self\n",
-      "        self.closed = False\n",
-      "        self.namespace = NamespaceMagics()\n",
-      "        self.namespace.shell = ipython.kernel.shell\n",
-      "        \n",
-      "        self._popout = widgets.Popup()\n",
-      "        self._popout.description = \"Variable Inspector\"\n",
-      "        self._popout.button_text = self._popout.description\n",
-      "\n",
-      "        self._modal_body = widgets.VBox()\n",
-      "        self._modal_body.overflow_y = 'scroll'\n",
-      "\n",
-      "        self._modal_body_label = widgets.HTML(value = 'Not hooked')\n",
-      "        self._modal_body.children = [self._modal_body_label]\n",
-      "\n",
-      "        self._popout.children = [\n",
-      "            self._modal_body, \n",
-      "        ]\n",
-      "        \n",
-      "        self._ipython = ipython\n",
-      "        self._ipython.events.register('post_run_cell', self._fill)\n",
-      "        \n",
-      "    def close(self):\n",
-      "        \"\"\"Close and remove hooks.\"\"\"\n",
-      "        if not self.closed:\n",
-      "            self._ipython.events.unregister('post_run_cell', self._fill)\n",
-      "            self._popout.close()\n",
-      "            self.closed = True\n",
-      "            VariableInspectorWindow.instance = None\n",
-      "\n",
-      "    def _fill(self):\n",
-      "        \"\"\"Fill self with variable information.\"\"\"\n",
-      "        values = self.namespace.who_ls()\n",
-      "        self._modal_body_label.value = '<table class=\"table table-bordered table-striped\"><tr><th>Name</th><th>Type</th><th>Value</th></tr><tr><td>' + \\\n",
-      "            '</td></tr><tr><td>'.join(['{0}</td><td>{1}</td><td>{2}'.format(v, type(eval(v)).__name__, str(eval(v))) for v in values]) + \\\n",
-      "            '</td></tr></table>'\n",
-      "\n",
-      "    def _ipython_display_(self):\n",
-      "        \"\"\"Called when display() or pyout is used to display the Variable \n",
-      "        Inspector.\"\"\"\n",
-      "        self._popout._ipython_display_()\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "inspector = VariableInspectorWindow(get_ipython())\n",
-      "inspector"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Test"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = 5"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = 3.0"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c = a * b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "d = \"String\""
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "del b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "inspector.close()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Widget Basics.ipynb b/examples/Interactive Widgets/Widget Basics.ipynb
index 8aea4f1..f4e56e0 100644
--- a/examples/Interactive Widgets/Widget Basics.ipynb
+++ b/examples/Interactive Widgets/Widget Basics.ipynb
@@ -1,4 +1,426 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Index](Index.ipynb) - [Next](Widget List.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple Widget Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What are widgets?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Widgets are elements that exists in both the front-end and the back-end.\n",
+    "\n",
+    "![Kernel & front-end diagram](../images/FrontendKernel.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What can they be used for?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "You can use widgets to build **interactive GUIs** for your notebooks.  \n",
+    "You can also use widgets to **synchronize stateful and stateless information** between Python and JavaScript."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using widgets  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "To use the widget framework, you need to **import `IPython.html.widgets`**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.html.widgets import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### repr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Widgets have their own display `repr` which allows them to be displayed using IPython's display framework.  Constructing and returning an `IntSlider` automatically displays the widget (as seen below).  Widgets are **displayed inside the `widget area`**, which sits between the code cell and output.  **You can hide all of the widgets** in the `widget area` by clicking the grey *x* in the margin."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "IntSlider()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### display()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also explicitly display the widget using `display(...)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import display\n",
+    "w = IntSlider()\n",
+    "display(w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Multiple display() calls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you display the same widget twice, the displayed instances in the front-end **will remain in sync** with each other."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "display(w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Why does displaying the same widget twice work?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Widgets are **represented in the back-end by a single object**.  Each time a widget is displayed, **a new representation** of that same object is created in the front-end.  These representations are called **views**.\n",
+    "\n",
+    "![Kernel & front-end diagram](images/WidgetModelView.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Closing widgets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can close a widget by calling its `close()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "display(w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Widget properties"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "All of the IPython widgets **share a similar naming scheme**.  To read the value of a widget, you can query its `value` property."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w = IntSlider()\n",
+    "display(w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, to set a widget's value, you can set its `value` property."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.value = 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Keys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In addition to `value`, most widgets share `keys`, `description`, `disabled`, and `visible`.  To see the entire list of synchronized, stateful properties, of any specific widget, you can **query the `keys` property**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.keys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Shorthand for setting the initial values of widget properties"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "While creating a widget, you can set some or all of the initial values of that widget by **defining them as keyword arguments in the widget's constructor** (as seen below)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Text(value='Hello World!', disabled=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linking two similar widgets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "If you need to display the same value two different ways, you'll have to use two different widgets.  Instead of **attempting to manually synchronize the values** of the two widgets, you can use the `traitlet` `link` function **to link two properties together**.  Below, the values of three widgets are linked together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.utils.traitlets import link\n",
+    "a = FloatText()\n",
+    "b = FloatSlider()\n",
+    "c = FloatProgress()\n",
+    "display(a,b,c)\n",
+    "\n",
+    "\n",
+    "mylink = link((a, 'value'), (b, 'value'), (c, 'value'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Unlinking widgets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Unlinking the widgets is simple.  All you have to do is call `.unlink` on the link object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "mylink.unlink()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Index](Index.ipynb) - [Next](Widget List.ipynb)"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "codemirror_mode": {
@@ -9,437 +431,8 @@
    "language": "python",
    "name": "python2"
   },
-  "name": "",
   "signature": "sha256:c8af7f5d30b29ee52fe6a79cf0d573c9c2d5b2f522b04731249e3208671741d3"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Index](Index.ipynb) - [Next](Widget List.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Simple Widget Introduction"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "What are widgets?"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Widgets are elements that exists in both the front-end and the back-end.\n",
-      "\n",
-      "![Kernel & front-end diagram](../images/FrontendKernel.png)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "What can they be used for?"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "You can use widgets to build **interactive GUIs** for your notebooks.  \n",
-      "You can also use widgets to **synchronize stateful and stateless information** between Python and JavaScript."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Using widgets  "
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "To use the widget framework, you need to **import `IPython.html.widgets`**."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.html.widgets import *"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "repr"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Widgets have their own display `repr` which allows them to be displayed using IPython's display framework.  Constructing and returning an `IntSlider` automatically displays the widget (as seen below).  Widgets are **displayed inside the `widget area`**, which sits between the code cell and output.  **You can hide all of the widgets** in the `widget area` by clicking the grey *x* in the margin."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "IntSlider()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "display()"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can also explicitly display the widget using `display(...)`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import display\n",
-      "w = IntSlider()\n",
-      "display(w)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Multiple display() calls"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If you display the same widget twice, the displayed instances in the front-end **will remain in sync** with each other."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display(w)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Why does displaying the same widget twice work?"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Widgets are **represented in the back-end by a single object**.  Each time a widget is displayed, **a new representation** of that same object is created in the front-end.  These representations are called **views**.\n",
-      "\n",
-      "![Kernel & front-end diagram](images/WidgetModelView.png)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Closing widgets"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can close a widget by calling its `close()` method."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display(w)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w.close()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Widget properties"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "All of the IPython widgets **share a similar naming scheme**.  To read the value of a widget, you can query its `value` property."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w = IntSlider()\n",
-      "display(w)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w.value"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Similarly, to set a widget's value, you can set its `value` property."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w.value = 100"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Keys"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In addition to `value`, most widgets share `keys`, `description`, `disabled`, and `visible`.  To see the entire list of synchronized, stateful properties, of any specific widget, you can **query the `keys` property**."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w.keys"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Shorthand for setting the initial values of widget properties"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "While creating a widget, you can set some or all of the initial values of that widget by **defining them as keyword arguments in the widget's constructor** (as seen below)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "Text(value='Hello World!', disabled=True)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Linking two similar widgets"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "If you need to display the same value two different ways, you'll have to use two different widgets.  Instead of **attempting to manually synchronize the values** of the two widgets, you can use the `traitlet` `link` function **to link two properties together**.  Below, the values of three widgets are linked together."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.utils.traitlets import link\n",
-      "a = FloatText()\n",
-      "b = FloatSlider()\n",
-      "c = FloatProgress()\n",
-      "display(a,b,c)\n",
-      "\n",
-      "\n",
-      "mylink = link((a, 'value'), (b, 'value'), (c, 'value'))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Unlinking widgets"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Unlinking the widgets is simple.  All you have to do is call `.unlink` on the link object."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "mylink.unlink()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Index](Index.ipynb) - [Next](Widget List.ipynb)"
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Widget Events.ipynb b/examples/Interactive Widgets/Widget Events.ipynb
index 45ce1fa..da4841e 100644
--- a/examples/Interactive Widgets/Widget Events.ipynb
+++ b/examples/Interactive Widgets/Widget Events.ipynb
@@ -1,4 +1,210 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Index](Index.ipynb) - [Back](Widget List.ipynb) - [Next](Widget Styling.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Widget Events"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Special events"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `Button` is not used to represent a data type.  Instead the button widget is used to **handle mouse clicks**.  The **`on_click` method** of the `Button` can be used to register function to be called when the button is clicked.  The doc string of the `on_click` can be seen below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.html import widgets\n",
+    "print(widgets.Button.on_click.__doc__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since button clicks are **stateless**, they are **transmitted from the front-end to the back-end using custom messages**.  By using the `on_click` method, a button that prints a message when it has been clicked is shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import display\n",
+    "button = widgets.Button(description=\"Click Me!\")\n",
+    "display(button)\n",
+    "\n",
+    "def on_button_clicked(b):\n",
+    "    print(\"Button clicked.\")\n",
+    "\n",
+    "button.on_click(on_button_clicked)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### on_sumbit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The **`Text`** also has a special **`on_submit` event**.  The `on_submit` event **fires when the user hits return**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "text = widgets.Text()\n",
+    "display(text)\n",
+    "\n",
+    "def handle_submit(sender):\n",
+    "    print(text.value)\n",
+    "\n",
+    "text.on_submit(handle_submit)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Traitlet events"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Widget properties are IPython traitlets** and **traitlets are eventful**.  To handle  changes, the **`on_trait_change` method** of the widget can be used to **register a callback**.  The doc string for `on_trait_change` can be seen below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(widgets.Widget.on_trait_change.__doc__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Signatures"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mentioned in the doc string, the callback registered can have **4 possible signatures**:\n",
+    "\n",
+    "- callback()\n",
+    "- callback(trait_name)\n",
+    "- callback(trait_name, new_value)\n",
+    "- callback(trait_name, old_value, new_value)\n",
+    "\n",
+    "Using this method, an example of how to output an `IntSlider`'s value as it is changed can be seen below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "int_range = widgets.IntSlider()\n",
+    "display(int_range)\n",
+    "\n",
+    "def on_value_change(name, value):\n",
+    "    print(value)\n",
+    "\n",
+    "int_range.on_trait_change(on_value_change, 'value')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Index](Index.ipynb) - [Back](Widget List.ipynb) - [Next](Widget Styling.ipynb)"
+   ]
+  }
+ ],
  "metadata": {
   "cell_tags": [
    [
@@ -15,220 +221,8 @@
    "language": "python",
    "name": "python2"
   },
-  "name": "",
   "signature": "sha256:05a3e92089b37f68e3134587ffef6ef73830e5f8b3c515ba24640d7c803820c3"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Index](Index.ipynb) - [Back](Widget List.ipynb) - [Next](Widget Styling.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Widget Events"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Special events"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from __future__ import print_function"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The `Button` is not used to represent a data type.  Instead the button widget is used to **handle mouse clicks**.  The **`on_click` method** of the `Button` can be used to register function to be called when the button is clicked.  The doc string of the `on_click` can be seen below."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.html import widgets\n",
-      "print(widgets.Button.on_click.__doc__)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Example"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Since button clicks are **stateless**, they are **transmitted from the front-end to the back-end using custom messages**.  By using the `on_click` method, a button that prints a message when it has been clicked is shown below."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import display\n",
-      "button = widgets.Button(description=\"Click Me!\")\n",
-      "display(button)\n",
-      "\n",
-      "def on_button_clicked(b):\n",
-      "    print(\"Button clicked.\")\n",
-      "\n",
-      "button.on_click(on_button_clicked)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "on_sumbit"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The **`Text`** also has a special **`on_submit` event**.  The `on_submit` event **fires when the user hits return**."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "text = widgets.Text()\n",
-      "display(text)\n",
-      "\n",
-      "def handle_submit(sender):\n",
-      "    print(text.value)\n",
-      "\n",
-      "text.on_submit(handle_submit)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Traitlet events"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Widget properties are IPython traitlets** and **traitlets are eventful**.  To handle  changes, the **`on_trait_change` method** of the widget can be used to **register a callback**.  The doc string for `on_trait_change` can be seen below."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print(widgets.Widget.on_trait_change.__doc__)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Signatures"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Mentioned in the doc string, the callback registered can have **4 possible signatures**:\n",
-      "\n",
-      "- callback()\n",
-      "- callback(trait_name)\n",
-      "- callback(trait_name, new_value)\n",
-      "- callback(trait_name, old_value, new_value)\n",
-      "\n",
-      "Using this method, an example of how to output an `IntSlider`'s value as it is changed can be seen below."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "int_range = widgets.IntSlider()\n",
-      "display(int_range)\n",
-      "\n",
-      "def on_value_change(name, value):\n",
-      "    print(value)\n",
-      "\n",
-      "int_range.on_trait_change(on_value_change, 'value')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Index](Index.ipynb) - [Back](Widget List.ipynb) - [Next](Widget Styling.ipynb)"
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Widget List.ipynb b/examples/Interactive Widgets/Widget List.ipynb
index 6fbfd0a..52f12cc 100644
--- a/examples/Interactive Widgets/Widget List.ipynb
+++ b/examples/Interactive Widgets/Widget List.ipynb
@@ -1,4 +1,568 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Index](Index.ipynb) - [Back](Widget Basics.ipynb) - [Next](Widget Events.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Widget List"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Complete list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "For a complete list of the widgets available to you, you can list the classes in the widget namespace (as seen below).  `Widget` and `DOMWidget`, not listed below, are base classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.html import widgets\n",
+    "[n for n in dir(widgets) if not n.endswith('Widget') and n[0] == n[0].upper() and not n[0] == '_']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Numeric widgets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are 8 widgets distributed with IPython that are designed to display numeric values.  Widgets exist for displaying integers and floats, both bounded and unbounded.  The integer widgets share a similar naming scheme to their floating point counterparts.  By replacing `Float` with `Int` in the widget name, you can find the Integer equivalent."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### FloatSlider"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "widgets.FloatSlider(\n",
+    "    value=7.5,\n",
+    "    min=5.0,\n",
+    "    max=10.0,\n",
+    "    step=0.1,\n",
+    "    description='Test:',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sliders can also be **displayed vertically**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "widgets.FloatSlider(\n",
+    "    value=7.5,\n",
+    "    min=5.0,\n",
+    "    max=10.0,\n",
+    "    step=0.1,\n",
+    "    description='Test',\n",
+    "    orientation='vertical',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### FloatProgress"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "widgets.FloatProgress(\n",
+    "    value=7.5,\n",
+    "    min=5.0,\n",
+    "    max=10.0,\n",
+    "    step=0.1,\n",
+    "    description='Loading:',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### BoundedFloatText"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "widgets.BoundedFloatText(\n",
+    "    value=7.5,\n",
+    "    min=5.0,\n",
+    "    max=10.0,\n",
+    "    description='Text:',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### FloatText"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "widgets.FloatText(\n",
+    "    value=7.5,\n",
+    "    description='Any:',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Boolean widgets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are two widgets that are designed to display a boolean value."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ToggleButton"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "widgets.ToggleButton(\n",
+    "    description='Click me',\n",
+    "    value=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Checkbox"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "widgets.Checkbox(\n",
+    "    description='Check me',\n",
+    "    value=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Selection widgets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are four widgets that can be used to display single selection lists.  All four inherit from the same base class.  You can specify the **enumeration of selectables by passing a list**.  You can **also specify the enumeration as a dictionary**, in which case the **keys will be used as the item displayed** in the list and the corresponding **value will be returned** when an item is selected."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Dropdown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import display\n",
+    "w = widgets.Dropdown(\n",
+    "    values=[1, 2, 3],\n",
+    "    value=2,\n",
+    "    description='Number:',\n",
+    ")\n",
+    "display(w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following is also valid:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w = widgets.Dropdown(\n",
+    "    values={'One': 1, 'Two': 2, 'Three': 3},\n",
+    "    value=2,\n",
+    "    description='Number:',\n",
+    ")\n",
+    "display(w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "w.value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### RadioButtons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "widgets.RadioButtons(\n",
+    "    description='Pizza topping:',\n",
+    "    values=['pepperoni', 'pineapple', 'anchovies'],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Select"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "widgets.Select(\n",
+    "    description='OS:',\n",
+    "    values=['Linux', 'Windows', 'OSX'],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### ToggleButtons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "widgets.ToggleButtons(\n",
+    "    description='Speed:',\n",
+    "    values=['Slow', 'Regular', 'Fast'],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## String widgets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are 4 widgets that can be used to display a string value.  Of those, the **`Text` and `Textarea` widgets accept input**.  The **`Latex` and `HTML` widgets display the string** as either Latex or HTML respectively, but **do not accept input**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "widgets.Text(\n",
+    "    description='String:',\n",
+    "    value='Hello World',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Textarea"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "widgets.Textarea(\n",
+    "    description='String:',\n",
+    "    value='Hello World',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Latex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "widgets.Latex(\n",
+    "    value=\"$$\\\\frac{n!}{k!(n-k)!} = \\\\binom{n}{k}$$\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### HTML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "widgets.HTML(\n",
+    "    value=\"Hello <b>World</b>\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Button"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "widgets.Button(description='Click me')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Index](Index.ipynb) - [Back](Widget Basics.ipynb) - [Next](Widget Events.ipynb)"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "codemirror_mode": {
@@ -9,579 +573,8 @@
    "language": "python",
    "name": "python2"
   },
-  "name": "",
   "signature": "sha256:83b39d018a7a6ae0a324b9f3d38debafbfb2ed0a114e4bbd357fb318f8f23438"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Index](Index.ipynb) - [Back](Widget Basics.ipynb) - [Next](Widget Events.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Widget List"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Complete list"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "For a complete list of the widgets available to you, you can list the classes in the widget namespace (as seen below).  `Widget` and `DOMWidget`, not listed below, are base classes."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.html import widgets\n",
-      "[n for n in dir(widgets) if not n.endswith('Widget') and n[0] == n[0].upper() and not n[0] == '_']"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Numeric widgets"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "There are 8 widgets distributed with IPython that are designed to display numeric values.  Widgets exist for displaying integers and floats, both bounded and unbounded.  The integer widgets share a similar naming scheme to their floating point counterparts.  By replacing `Float` with `Int` in the widget name, you can find the Integer equivalent."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "FloatSlider"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widgets.FloatSlider(\n",
-      "    value=7.5,\n",
-      "    min=5.0,\n",
-      "    max=10.0,\n",
-      "    step=0.1,\n",
-      "    description='Test:',\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Sliders can also be **displayed vertically**."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widgets.FloatSlider(\n",
-      "    value=7.5,\n",
-      "    min=5.0,\n",
-      "    max=10.0,\n",
-      "    step=0.1,\n",
-      "    description='Test',\n",
-      "    orientation='vertical',\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "FloatProgress"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widgets.FloatProgress(\n",
-      "    value=7.5,\n",
-      "    min=5.0,\n",
-      "    max=10.0,\n",
-      "    step=0.1,\n",
-      "    description='Loading:',\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "BoundedFloatText"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widgets.BoundedFloatText(\n",
-      "    value=7.5,\n",
-      "    min=5.0,\n",
-      "    max=10.0,\n",
-      "    description='Text:',\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "FloatText"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widgets.FloatText(\n",
-      "    value=7.5,\n",
-      "    description='Any:',\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Boolean widgets"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "There are two widgets that are designed to display a boolean value."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "ToggleButton"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widgets.ToggleButton(\n",
-      "    description='Click me',\n",
-      "    value=False,\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Checkbox"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widgets.Checkbox(\n",
-      "    description='Check me',\n",
-      "    value=True,\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Selection widgets"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "There are four widgets that can be used to display single selection lists.  All four inherit from the same base class.  You can specify the **enumeration of selectables by passing a list**.  You can **also specify the enumeration as a dictionary**, in which case the **keys will be used as the item displayed** in the list and the corresponding **value will be returned** when an item is selected."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Dropdown"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import display\n",
-      "w = widgets.Dropdown(\n",
-      "    values=[1, 2, 3],\n",
-      "    value=2,\n",
-      "    description='Number:',\n",
-      ")\n",
-      "display(w)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w.value"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The following is also valid:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w = widgets.Dropdown(\n",
-      "    values={'One': 1, 'Two': 2, 'Three': 3},\n",
-      "    value=2,\n",
-      "    description='Number:',\n",
-      ")\n",
-      "display(w)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "w.value"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "RadioButtons"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widgets.RadioButtons(\n",
-      "    description='Pizza topping:',\n",
-      "    values=['pepperoni', 'pineapple', 'anchovies'],\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Select"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widgets.Select(\n",
-      "    description='OS:',\n",
-      "    values=['Linux', 'Windows', 'OSX'],\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "ToggleButtons"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widgets.ToggleButtons(\n",
-      "    description='Speed:',\n",
-      "    values=['Slow', 'Regular', 'Fast'],\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "String widgets"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "There are 4 widgets that can be used to display a string value.  Of those, the **`Text` and `Textarea` widgets accept input**.  The **`Latex` and `HTML` widgets display the string** as either Latex or HTML respectively, but **do not accept input**."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Text"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widgets.Text(\n",
-      "    description='String:',\n",
-      "    value='Hello World',\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Textarea"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widgets.Textarea(\n",
-      "    description='String:',\n",
-      "    value='Hello World',\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Latex"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widgets.Latex(\n",
-      "    value=\"$$\\\\frac{n!}{k!(n-k)!} = \\\\binom{n}{k}$$\",\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "HTML"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widgets.HTML(\n",
-      "    value=\"Hello <b>World</b>\"\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Button"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widgets.Button(description='Click me')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Index](Index.ipynb) - [Back](Widget Basics.ipynb) - [Next](Widget Events.ipynb)"
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Interactive Widgets/Widget Styling.ipynb b/examples/Interactive Widgets/Widget Styling.ipynb
index 2924a30..7156d05 100644
--- a/examples/Interactive Widgets/Widget Styling.ipynb
+++ b/examples/Interactive Widgets/Widget Styling.ipynb
@@ -1,4 +1,734 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Index](Index.ipynb) - [Back](Widget Events.ipynb) - [Next](Custom Widget - Hello World.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%html\n",
+    "<style>\n",
+    ".example-container { background: #999999; padding: 2px; min-height: 100px; }\n",
+    ".example-container.sm { min-height: 50px; }\n",
+    ".example-box { background: #9999FF; width: 50px; height: 50px; text-align: center; vertical-align: middle; color: white; font-weight: bold; margin: 2px;}\n",
+    ".example-box.med { width: 65px; height: 65px; }   \n",
+    ".example-box.lrg { width: 80px; height: 80px; }   \n",
+    "</style>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.html import widgets\n",
+    "from IPython.display import display"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Widget Styling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basic styling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The widgets distributed with IPython can be styled by setting the following traits:\n",
+    "\n",
+    "- width  \n",
+    "- height  \n",
+    "- fore_color  \n",
+    "- back_color  \n",
+    "- border_color  \n",
+    "- border_width  \n",
+    "- border_style  \n",
+    "- font_style  \n",
+    "- font_weight  \n",
+    "- font_size  \n",
+    "- font_family  \n",
+    "\n",
+    "The example below shows how a `Button` widget can be styled:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "button = widgets.Button(\n",
+    "    description='Hello World!',\n",
+    "    width=100, # Integers are interpreted as pixel measurements.\n",
+    "    height='2em', # em is valid HTML unit of measurement.\n",
+    "    color='lime', # Colors can be set by name,\n",
+    "    background_color='#0022FF', # and also by color code.\n",
+    "    border_color='red')\n",
+    "display(button)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Parent/child relationships"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To display widget A inside widget B, widget A must be a child of widget B.  Widgets that can contain other widgets have a **`children` attribute**.  This attribute can be **set via a keyword argument** in the widget's constructor **or after construction**.  Calling display on an **object with children automatically displays those children**, too."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import display\n",
+    "\n",
+    "float_range = widgets.FloatSlider()\n",
+    "string = widgets.Text(value='hi')\n",
+    "container = widgets.Box(children=[float_range, string])\n",
+    "\n",
+    "container.border_color = 'red'\n",
+    "container.border_style = 'dotted'\n",
+    "container.border_width = 3\n",
+    "display(container) # Displays the `container` and all of it's children."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### After the parent is displayed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Children **can be added to parents** after the parent has been displayed.  The **parent is responsible for rendering its children**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "container = widgets.Box()\n",
+    "container.border_color = 'red'\n",
+    "container.border_style = 'dotted'\n",
+    "container.border_width = 3\n",
+    "display(container)\n",
+    "\n",
+    "int_range = widgets.IntSlider()\n",
+    "container.children=[int_range]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Fancy boxes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you need to display a more complicated set of widgets, there are **specialized containers** that you can use.  To display **multiple sets of widgets**, you can use an **`Accordion` or a `Tab` in combination with one `Box` per set of widgets** (as seen below).  The \"pages\" of these widgets are their children.  To set the titles of the pages, one must **call `set_title` after the widget has been displayed**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Accordion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "name1 = widgets.Text(description='Location:')\n",
+    "zip1 = widgets.BoundedIntText(description='Zip:', min=0, max=99999)\n",
+    "page1 = widgets.Box(children=[name1, zip1])\n",
+    "\n",
+    "name2 = widgets.Text(description='Location:')\n",
+    "zip2 = widgets.BoundedIntText(description='Zip:', min=0, max=99999)\n",
+    "page2 = widgets.Box(children=[name2, zip2])\n",
+    "\n",
+    "accord = widgets.Accordion(children=[page1, page2])\n",
+    "display(accord)\n",
+    "\n",
+    "accord.set_title(0, 'From')\n",
+    "accord.set_title(1, 'To')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### TabWidget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "name = widgets.Text(description='Name:')\n",
+    "color = widgets.Dropdown(description='Color:', values=['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet'])\n",
+    "page1 = widgets.Box(children=[name, color])\n",
+    "\n",
+    "age = widgets.IntSlider(description='Age:', min=0, max=120, value=50)\n",
+    "gender = widgets.RadioButtons(description='Gender:', values=['male', 'female'])\n",
+    "page2 = widgets.Box(children=[age, gender])\n",
+    "\n",
+    "tabs = widgets.Tab(children=[page1, page2])\n",
+    "display(tabs)\n",
+    "\n",
+    "tabs.set_title(0, 'Name')\n",
+    "tabs.set_title(1, 'Details')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Popup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unlike the other two special containers, the `Popup` is only **designed to display one set of widgets**.  The `Popup` can be used to **display widgets outside of the widget area**.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "counter = widgets.IntText(description='Counter:')\n",
+    "popup = widgets.Popup(children=[counter], description='Popup Demo', button_text='Popup Button')\n",
+    "display(popup)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "counter.value += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "counter.value += 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "popup.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Alignment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most widgets have a **`description` attribute**, which allows a label for the widget to be defined.\n",
+    "The label of the widget **has a fixed minimum width**.\n",
+    "The text of the label is **always right aligned and the widget is left aligned**:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "display(widgets.Text(description=\"a:\"))\n",
+    "display(widgets.Text(description=\"aa:\"))\n",
+    "display(widgets.Text(description=\"aaa:\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "If a **label is longer** than the minimum width, the **widget is shifted to the right**:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "display(widgets.Text(description=\"a:\"))\n",
+    "display(widgets.Text(description=\"aa:\"))\n",
+    "display(widgets.Text(description=\"aaa:\"))\n",
+    "display(widgets.Text(description=\"aaaaaaaaaaaaaaaaaa:\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "If a `description` is **not set** for the widget, the **label is not displayed**:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "display(widgets.Text(description=\"a:\"))\n",
+    "display(widgets.Text(description=\"aa:\"))\n",
+    "display(widgets.Text(description=\"aaa:\"))\n",
+    "display(widgets.Text())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Flex boxes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Widgets can be aligned using the `FlexBox`, `HBox`, and `VBox` widgets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Application to widgets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Widgets display vertically by default:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "buttons = [widgets.Button(description=str(i)) for i in range(3)]\n",
+    "display(*buttons)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Using hbox"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make widgets display horizontally, you need to **child them to a `HBox` widget**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "container = widgets.HBox(children=buttons)\n",
+    "display(container)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By setting the width of the container to 100% and its `pack` to `center`, you can center the buttons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "container.width = '100%'\n",
+    "container.pack = 'center'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Visibility"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes it is necessary to **hide or show widgets** in place, **without having to re-display** the widget.\n",
+    "The `visibility` property of widgets can be used to hide or show **widgets that have already been displayed** (as seen below)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "string = widgets.Latex(value=\"Hello World!\")\n",
+    "display(string) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "string.visible=False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "string.visible=True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Another example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the example below, a form is rendered, which conditionally displays widgets depending on the state of other widgets.  Try toggling the student checkbox."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "form = widgets.VBox()\n",
+    "first = widgets.Text(description=\"First Name:\")\n",
+    "last = widgets.Text(description=\"Last Name:\")\n",
+    "\n",
+    "student = widgets.Checkbox(description=\"Student:\", value=False)\n",
+    "school_info = widgets.VBox(visible=False, children=[\n",
+    "    widgets.Text(description=\"School:\"),\n",
+    "    widgets.IntText(description=\"Grade:\", min=0, max=12)\n",
+    "    ])\n",
+    "\n",
+    "pet = widgets.Text(description=\"Pet's Name:\")\n",
+    "form.children = [first, last, student, school_info, pet]\n",
+    "display(form)\n",
+    "\n",
+    "def on_student_toggle(name, value):\n",
+    "    if value:\n",
+    "        school_info.visible = True\n",
+    "    else:\n",
+    "        school_info.visible = False\n",
+    "student.on_trait_change(on_student_toggle, 'value')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Index](Index.ipynb) - [Back](Widget Events.ipynb) - [Next](Custom Widget - Hello World.ipynb)"
+   ]
+  }
+ ],
  "metadata": {
   "cell_tags": [
    [
@@ -15,723 +745,8 @@
    "language": "python",
    "name": "python2"
   },
-  "name": "",
   "signature": "sha256:8bb091be85fd5e7f76082b1b4b98cddec92a856334935ac2c702fe5ec03f6eff"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Index](Index.ipynb) - [Back](Widget Events.ipynb) - [Next](Custom Widget - Hello World.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%html\n",
-      "<style>\n",
-      ".example-container { background: #999999; padding: 2px; min-height: 100px; }\n",
-      ".example-container.sm { min-height: 50px; }\n",
-      ".example-box { background: #9999FF; width: 50px; height: 50px; text-align: center; vertical-align: middle; color: white; font-weight: bold; margin: 2px;}\n",
-      ".example-box.med { width: 65px; height: 65px; }   \n",
-      ".example-box.lrg { width: 80px; height: 80px; }   \n",
-      "</style>"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.html import widgets\n",
-      "from IPython.display import display"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Widget Styling"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Basic styling"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The widgets distributed with IPython can be styled by setting the following traits:\n",
-      "\n",
-      "- width  \n",
-      "- height  \n",
-      "- fore_color  \n",
-      "- back_color  \n",
-      "- border_color  \n",
-      "- border_width  \n",
-      "- border_style  \n",
-      "- font_style  \n",
-      "- font_weight  \n",
-      "- font_size  \n",
-      "- font_family  \n",
-      "\n",
-      "The example below shows how a `Button` widget can be styled:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "button = widgets.Button(\n",
-      "    description='Hello World!',\n",
-      "    width=100, # Integers are interpreted as pixel measurements.\n",
-      "    height='2em', # em is valid HTML unit of measurement.\n",
-      "    color='lime', # Colors can be set by name,\n",
-      "    background_color='#0022FF', # and also by color code.\n",
-      "    border_color='red')\n",
-      "display(button)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Parent/child relationships"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To display widget A inside widget B, widget A must be a child of widget B.  Widgets that can contain other widgets have a **`children` attribute**.  This attribute can be **set via a keyword argument** in the widget's constructor **or after construction**.  Calling display on an **object with children automatically displays those children**, too."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import display\n",
-      "\n",
-      "float_range = widgets.FloatSlider()\n",
-      "string = widgets.Text(value='hi')\n",
-      "container = widgets.Box(children=[float_range, string])\n",
-      "\n",
-      "container.border_color = 'red'\n",
-      "container.border_style = 'dotted'\n",
-      "container.border_width = 3\n",
-      "display(container) # Displays the `container` and all of it's children."
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "After the parent is displayed"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Children **can be added to parents** after the parent has been displayed.  The **parent is responsible for rendering its children**."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "container = widgets.Box()\n",
-      "container.border_color = 'red'\n",
-      "container.border_style = 'dotted'\n",
-      "container.border_width = 3\n",
-      "display(container)\n",
-      "\n",
-      "int_range = widgets.IntSlider()\n",
-      "container.children=[int_range]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Fancy boxes"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If you need to display a more complicated set of widgets, there are **specialized containers** that you can use.  To display **multiple sets of widgets**, you can use an **`Accordion` or a `Tab` in combination with one `Box` per set of widgets** (as seen below).  The \"pages\" of these widgets are their children.  To set the titles of the pages, one must **call `set_title` after the widget has been displayed**."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Accordion"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "name1 = widgets.Text(description='Location:')\n",
-      "zip1 = widgets.BoundedIntText(description='Zip:', min=0, max=99999)\n",
-      "page1 = widgets.Box(children=[name1, zip1])\n",
-      "\n",
-      "name2 = widgets.Text(description='Location:')\n",
-      "zip2 = widgets.BoundedIntText(description='Zip:', min=0, max=99999)\n",
-      "page2 = widgets.Box(children=[name2, zip2])\n",
-      "\n",
-      "accord = widgets.Accordion(children=[page1, page2])\n",
-      "display(accord)\n",
-      "\n",
-      "accord.set_title(0, 'From')\n",
-      "accord.set_title(1, 'To')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "TabWidget"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "name = widgets.Text(description='Name:')\n",
-      "color = widgets.Dropdown(description='Color:', values=['red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet'])\n",
-      "page1 = widgets.Box(children=[name, color])\n",
-      "\n",
-      "age = widgets.IntSlider(description='Age:', min=0, max=120, value=50)\n",
-      "gender = widgets.RadioButtons(description='Gender:', values=['male', 'female'])\n",
-      "page2 = widgets.Box(children=[age, gender])\n",
-      "\n",
-      "tabs = widgets.Tab(children=[page1, page2])\n",
-      "display(tabs)\n",
-      "\n",
-      "tabs.set_title(0, 'Name')\n",
-      "tabs.set_title(1, 'Details')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Popup"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Unlike the other two special containers, the `Popup` is only **designed to display one set of widgets**.  The `Popup` can be used to **display widgets outside of the widget area**.  "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "counter = widgets.IntText(description='Counter:')\n",
-      "popup = widgets.Popup(children=[counter], description='Popup Demo', button_text='Popup Button')\n",
-      "display(popup)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "counter.value += 1"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "counter.value += 1"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "popup.close()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Alignment"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Most widgets have a **`description` attribute**, which allows a label for the widget to be defined.\n",
-      "The label of the widget **has a fixed minimum width**.\n",
-      "The text of the label is **always right aligned and the widget is left aligned**:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display(widgets.Text(description=\"a:\"))\n",
-      "display(widgets.Text(description=\"aa:\"))\n",
-      "display(widgets.Text(description=\"aaa:\"))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "If a **label is longer** than the minimum width, the **widget is shifted to the right**:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display(widgets.Text(description=\"a:\"))\n",
-      "display(widgets.Text(description=\"aa:\"))\n",
-      "display(widgets.Text(description=\"aaa:\"))\n",
-      "display(widgets.Text(description=\"aaaaaaaaaaaaaaaaaa:\"))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "If a `description` is **not set** for the widget, the **label is not displayed**:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "display(widgets.Text(description=\"a:\"))\n",
-      "display(widgets.Text(description=\"aa:\"))\n",
-      "display(widgets.Text(description=\"aaa:\"))\n",
-      "display(widgets.Text())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Flex boxes"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Widgets can be aligned using the `FlexBox`, `HBox`, and `VBox` widgets."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Application to widgets"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Widgets display vertically by default:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "buttons = [widgets.Button(description=str(i)) for i in range(3)]\n",
-      "display(*buttons)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Using hbox"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To make widgets display horizontally, you need to **child them to a `HBox` widget**."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "container = widgets.HBox(children=buttons)\n",
-      "display(container)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "By setting the width of the container to 100% and its `pack` to `center`, you can center the buttons."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "container.width = '100%'\n",
-      "container.pack = 'center'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Visibility"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Sometimes it is necessary to **hide or show widgets** in place, **without having to re-display** the widget.\n",
-      "The `visibility` property of widgets can be used to hide or show **widgets that have already been displayed** (as seen below)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "string = widgets.Latex(value=\"Hello World!\")\n",
-      "display(string) "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "string.visible=False"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "string.visible=True"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Another example"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In the example below, a form is rendered, which conditionally displays widgets depending on the state of other widgets.  Try toggling the student checkbox."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "form = widgets.VBox()\n",
-      "first = widgets.Text(description=\"First Name:\")\n",
-      "last = widgets.Text(description=\"Last Name:\")\n",
-      "\n",
-      "student = widgets.Checkbox(description=\"Student:\", value=False)\n",
-      "school_info = widgets.VBox(visible=False, children=[\n",
-      "    widgets.Text(description=\"School:\"),\n",
-      "    widgets.IntText(description=\"Grade:\", min=0, max=12)\n",
-      "    ])\n",
-      "\n",
-      "pet = widgets.Text(description=\"Pet's Name:\")\n",
-      "form.children = [first, last, student, school_info, pet]\n",
-      "display(form)\n",
-      "\n",
-      "def on_student_toggle(name, value):\n",
-      "    if value:\n",
-      "        school_info.visible = True\n",
-      "    else:\n",
-      "        school_info.visible = False\n",
-      "student.on_trait_change(on_student_toggle, 'value')\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Index](Index.ipynb) - [Back](Widget Events.ipynb) - [Next](Custom Widget - Hello World.ipynb)"
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Notebook/Connecting with the Qt Console.ipynb b/examples/Notebook/Connecting with the Qt Console.ipynb
index 14dc2cc..f620bb8 100644
--- a/examples/Notebook/Connecting with the Qt Console.ipynb
+++ b/examples/Notebook/Connecting with the Qt Console.ipynb
@@ -1,149 +1,139 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:9a1dd30de04270174c09ef33ca214e5b15ca3721547420087c1563ad557d78e3"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Connecting to an existing IPython kernel using the Qt Console"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "The Frontend/Kernel Model"
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Connecting to an existing IPython kernel using the Qt Console"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Frontend/Kernel Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The traditional IPython (`ipython`) consists of a single process that combines a terminal based UI with the process that runs the users code.\n",
+    "\n",
+    "While this traditional application still exists, the modern IPython consists of two processes:\n",
+    "\n",
+    "* Kernel: this is the process that runs the users code.\n",
+    "* Frontend: this is the process that provides the user interface where the user types code and sees results.\n",
+    "\n",
+    "IPython currently has 3 frontends:\n",
+    "\n",
+    "* Terminal Console (`ipython console`)\n",
+    "* Qt Console (`ipython qtconsole`)\n",
+    "* Notebook (`ipython notebook`)\n",
+    "\n",
+    "The Kernel and Frontend communicate over a ZeroMQ/JSON based messaging protocol, which allows multiple Frontends (even of different types) to communicate with a single Kernel. This opens the door for all sorts of interesting things, such as connecting a Console or Qt Console to a Notebook's Kernel.  For example, you may want to connect a Qt console to your Notebook's Kernel and use it as a help\n",
+    "browser, calling `??` on objects in the Qt console (whose pager is more flexible than the\n",
+    "one in the notebook).  \n",
+    "\n",
+    "This Notebook describes how you would connect another Frontend to a Kernel that is associated with a Notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Manual connection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To connect another Frontend to a Kernel manually, you first need to find out the connection information for the Kernel using the `%connect_info` magic:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The traditional IPython (`ipython`) consists of a single process that combines a terminal based UI with the process that runs the users code.\n",
-      "\n",
-      "While this traditional application still exists, the modern IPython consists of two processes:\n",
-      "\n",
-      "* Kernel: this is the process that runs the users code.\n",
-      "* Frontend: this is the process that provides the user interface where the user types code and sees results.\n",
-      "\n",
-      "IPython currently has 3 frontends:\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"stdin_port\": 52858, \n",
+      "  \"ip\": \"127.0.0.1\", \n",
+      "  \"hb_port\": 52859, \n",
+      "  \"key\": \"7efd45ca-d8a2-41b0-9cea-d9116d0fb883\", \n",
+      "  \"shell_port\": 52856, \n",
+      "  \"iopub_port\": 52857\n",
+      "}\n",
       "\n",
-      "* Terminal Console (`ipython console`)\n",
-      "* Qt Console (`ipython qtconsole`)\n",
-      "* Notebook (`ipython notebook`)\n",
-      "\n",
-      "The Kernel and Frontend communicate over a ZeroMQ/JSON based messaging protocol, which allows multiple Frontends (even of different types) to communicate with a single Kernel. This opens the door for all sorts of interesting things, such as connecting a Console or Qt Console to a Notebook's Kernel.  For example, you may want to connect a Qt console to your Notebook's Kernel and use it as a help\n",
-      "browser, calling `??` on objects in the Qt console (whose pager is more flexible than the\n",
-      "one in the notebook).  \n",
-      "\n",
-      "This Notebook describes how you would connect another Frontend to a Kernel that is associated with a Notebook."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Manual connection"
+      "Paste the above JSON into a file, and connect with:\n",
+      "    $> ipython <app> --existing <file>\n",
+      "or, if you are local, you can connect with just:\n",
+      "    $> ipython <app> --existing kernel-b3bac7c1-8b2c-4536-8082-8d1df24f99ac.json \n",
+      "or even just:\n",
+      "    $> ipython <app> --existing \n",
+      "if this is the most recent IPython session you have started.\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To connect another Frontend to a Kernel manually, you first need to find out the connection information for the Kernel using the `%connect_info` magic:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%connect_info"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "{\n",
-        "  \"stdin_port\": 52858, \n",
-        "  \"ip\": \"127.0.0.1\", \n",
-        "  \"hb_port\": 52859, \n",
-        "  \"key\": \"7efd45ca-d8a2-41b0-9cea-d9116d0fb883\", \n",
-        "  \"shell_port\": 52856, \n",
-        "  \"iopub_port\": 52857\n",
-        "}\n",
-        "\n",
-        "Paste the above JSON into a file, and connect with:\n",
-        "    $> ipython <app> --existing <file>\n",
-        "or, if you are local, you can connect with just:\n",
-        "    $> ipython <app> --existing kernel-b3bac7c1-8b2c-4536-8082-8d1df24f99ac.json \n",
-        "or even just:\n",
-        "    $> ipython <app> --existing \n",
-        "if this is the most recent IPython session you have started.\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can see that this magic displays everything you need to connect to this Notebook's Kernel."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Automatic connection using a new Qt Console"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can also start a new Qt Console connected to your current Kernel by using the `%qtconsole` magic. This will detect the necessary connection\n",
-      "information and start the Qt Console for you automatically."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = 10"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%qtconsole"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
     }
    ],
-   "metadata": {}
+   "source": [
+    "%connect_info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can see that this magic displays everything you need to connect to this Notebook's Kernel."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Automatic connection using a new Qt Console"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also start a new Qt Console connected to your current Kernel by using the `%qtconsole` magic. This will detect the necessary connection\n",
+    "information and start the Qt Console for you automatically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%qtconsole"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:9a1dd30de04270174c09ef33ca214e5b15ca3721547420087c1563ad557d78e3"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Notebook/Converting Notebooks With nbconvert.ipynb b/examples/Notebook/Converting Notebooks With nbconvert.ipynb
index b10a5ef..e9a73b3 100644
--- a/examples/Notebook/Converting Notebooks With nbconvert.ipynb
+++ b/examples/Notebook/Converting Notebooks With nbconvert.ipynb
@@ -1,496 +1,485 @@
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "NbConvert"
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NbConvert"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Command line usage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`NbConvert` is the library, and the command line tool that allow to convert from notebook to other formats.\n",
+    "It is a technological preview in 1.0 but is already usable and highly configurable.\n",
+    "It ships already with many default available formats : `html`, `latex`, `markdown`, `python`, `rst` and  `slides`\n",
+    "which are fully base on Jinja templating engine, so writing a converter for your custom format or tweeking the existing \n",
+    "one should be extra simple."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can invoke nbconvert by doing\n",
+    "\n",
+    "```bash\n",
+    "$ ipython nbconvert <options and arguments>\n",
+    "```\n",
+    "\n",
+    "Call `ipython nbconvert` with the `--help` flag or no aruments to get basic help on how to use it.\n",
+    "For more information about configuration use the `--help-all` flag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Basic export"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will be converting `Custom Display Logic.ipynb`. \n",
+    "Be sure to have runed some of the cells in it to have output otherwise you will only see input in nbconvert.\n",
+    "Nbconvert **do not execute the code** in the notebook files, it only converts what is inside."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Command line usage"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[NbConvertApp] Using existing profile dir: u'/Users/bussonniermatthias/.ipython/profile_default'\n",
+      "[NbConvertApp] Converting notebook 04 - Custom Display Logic.ipynb to html\n",
+      "[NbConvertApp] Support files will be in 04 - Custom Display Logic_files/\n",
+      "[NbConvertApp] Loaded template html_full.tpl\n",
+      "[NbConvertApp] Writing 221081 bytes to 04 - Custom Display Logic.html\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "ipython nbconvert  '04 - Custom Display Logic.ipynb'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Html is the default value (that can be configured) , so the verbose form would be "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "`NbConvert` is the library, and the command line tool that allow to convert from notebook to other formats.\n",
-      "It is a technological preview in 1.0 but is already usable and highly configurable.\n",
-      "It ships already with many default available formats : `html`, `latex`, `markdown`, `python`, `rst` and  `slides`\n",
-      "which are fully base on Jinja templating engine, so writing a converter for your custom format or tweeking the existing \n",
-      "one should be extra simple."
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[NbConvertApp] Using existing profile dir: u'/Users/bussonniermatthias/.ipython/profile_default'\n",
+      "[NbConvertApp] Converting notebook 04 - Custom Display Logic.ipynb to html\n",
+      "[NbConvertApp] Support files will be in 04 - Custom Display Logic_files/\n",
+      "[NbConvertApp] Loaded template html_full.tpl\n",
+      "[NbConvertApp] Writing 221081 bytes to 04 - Custom Display Logic.html\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "ipython nbconvert  --to=html '04 - Custom Display Logic.ipynb'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also convert to latex, which will take care of extractin the embeded base64 encoded png, or the svg and call inkscape to convert those svg to pdf if necessary :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can invoke nbconvert by doing\n",
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[NbConvertApp] Using existing profile dir: u'/Users/bussonniermatthias/.ipython/profile_default'\n",
+      "[NbConvertApp] Converting notebook 04 - Custom Display Logic.ipynb to latex\n",
+      "[NbConvertApp] Support files will be in 04 - Custom Display Logic_files/\n",
+      "Setting Language: .UTF-8\n",
       "\n",
-      "```bash\n",
-      "$ ipython nbconvert <options and arguments>\n",
-      "```\n",
+      "(process:26432): Gtk-WARNING **: Locale not supported by C library.\n",
+      "\tUsing the fallback 'C' locale.\n",
+      "Setting Language: .UTF-8\n",
       "\n",
-      "Call `ipython nbconvert` with the `--help` flag or no aruments to get basic help on how to use it.\n",
-      "For more information about configuration use the `--help-all` flag"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Basic export"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We will be converting `Custom Display Logic.ipynb`. \n",
-      "Be sure to have runed some of the cells in it to have output otherwise you will only see input in nbconvert.\n",
-      "Nbconvert **do not execute the code** in the notebook files, it only converts what is inside."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%bash\n",
-      "ipython nbconvert  '04 - Custom Display Logic.ipynb'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "[NbConvertApp] Using existing profile dir: u'/Users/bussonniermatthias/.ipython/profile_default'\n",
-        "[NbConvertApp] Converting notebook 04 - Custom Display Logic.ipynb to html\n",
-        "[NbConvertApp] Support files will be in 04 - Custom Display Logic_files/\n",
-        "[NbConvertApp] Loaded template html_full.tpl\n",
-        "[NbConvertApp] Writing 221081 bytes to 04 - Custom Display Logic.html\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Html is the default value (that can be configured) , so the verbose form would be "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%bash\n",
-      "ipython nbconvert  --to=html '04 - Custom Display Logic.ipynb'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "[NbConvertApp] Using existing profile dir: u'/Users/bussonniermatthias/.ipython/profile_default'\n",
-        "[NbConvertApp] Converting notebook 04 - Custom Display Logic.ipynb to html\n",
-        "[NbConvertApp] Support files will be in 04 - Custom Display Logic_files/\n",
-        "[NbConvertApp] Loaded template html_full.tpl\n",
-        "[NbConvertApp] Writing 221081 bytes to 04 - Custom Display Logic.html\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can also convert to latex, which will take care of extractin the embeded base64 encoded png, or the svg and call inkscape to convert those svg to pdf if necessary :"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%bash\n",
-      "ipython nbconvert  --to=latex '04 - Custom Display Logic.ipynb'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "[NbConvertApp] Using existing profile dir: u'/Users/bussonniermatthias/.ipython/profile_default'\n",
-        "[NbConvertApp] Converting notebook 04 - Custom Display Logic.ipynb to latex\n",
-        "[NbConvertApp] Support files will be in 04 - Custom Display Logic_files/\n",
-        "Setting Language: .UTF-8\n",
-        "\n",
-        "(process:26432): Gtk-WARNING **: Locale not supported by C library.\n",
-        "\tUsing the fallback 'C' locale.\n",
-        "Setting Language: .UTF-8\n",
-        "\n",
-        "(process:26472): Gtk-WARNING **: Locale not supported by C library.\n",
-        "\tUsing the fallback 'C' locale.\n",
-        "Setting Language: .UTF-8\n",
-        "\n",
-        "(process:26512): Gtk-WARNING **: Locale not supported by C library.\n",
-        "\tUsing the fallback 'C' locale.\n",
-        "Setting Language: .UTF-8\n",
-        "\n",
-        "(process:26552): Gtk-WARNING **: Locale not supported by C library.\n",
-        "\tUsing the fallback 'C' locale.\n",
-        "Setting Language: .UTF-8\n",
-        "\n",
-        "(process:26592): Gtk-WARNING **: Locale not supported by C library.\n",
-        "\tUsing the fallback 'C' locale.\n",
-        "[NbConvertApp] Loaded template latex_article.tplx\n",
-        "[NbConvertApp] Writing 41196 bytes to 04 - Custom Display Logic.tex\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You should just have to compile the generated `.tex` file. If you get the required packages installed, if should compile out of the box.\n",
+      "(process:26472): Gtk-WARNING **: Locale not supported by C library.\n",
+      "\tUsing the fallback 'C' locale.\n",
+      "Setting Language: .UTF-8\n",
       "\n",
-      "For convenience we allow to run extra action after the conversion has been done, in particular for `latex` we have a `pdf` post-processor. \n",
-      "You can define the postprocessor tu run with the `--post` flag."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%bash\n",
-      "ipython nbconvert  --to=latex '04 - Custom Display Logic.ipynb' --post=pdf"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "[NbConvertApp] Using existing profile dir: u'/Users/bussonniermatthias/.ipython/profile_default'\n",
-        "[NbConvertApp] Converting notebook 04 - Custom Display Logic.ipynb to latex\n",
-        "[NbConvertApp] Support files will be in 04 - Custom Display Logic_files/\n",
-        "Setting Language: .UTF-8\n",
-        "\n",
-        "(process:26658): Gtk-WARNING **: Locale not supported by C library.\n",
-        "\tUsing the fallback 'C' locale.\n",
-        "Setting Language: .UTF-8\n",
-        "\n",
-        "(process:26698): Gtk-WARNING **: Locale not supported by C library.\n",
-        "\tUsing the fallback 'C' locale.\n",
-        "Setting Language: .UTF-8\n",
-        "\n",
-        "(process:26738): Gtk-WARNING **: Locale not supported by C library.\n",
-        "\tUsing the fallback 'C' locale.\n",
-        "Setting Language: .UTF-8\n",
-        "\n",
-        "(process:26778): Gtk-WARNING **: Locale not supported by C library.\n",
-        "\tUsing the fallback 'C' locale.\n",
-        "Setting Language: .UTF-8\n",
-        "\n",
-        "(process:26818): Gtk-WARNING **: Locale not supported by C library.\n",
-        "\tUsing the fallback 'C' locale.\n",
-        "[NbConvertApp] Loaded template latex_article.tplx\n",
-        "[NbConvertApp] Writing 41196 bytes to 04 - Custom Display Logic.tex\n",
-        "[NbConvertApp] Building PDF: ['pdflatex', '04 - Custom Display Logic.tex']\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Have a look at `04 - Custom Display Logic.pdf`, toward the end, where we compared `display()` vs `display_html()` and returning the object.\n",
-      "See how the cell where we use `display_html` was not able to display the circle, whereas the two other ones were able to select one of the oher representation they know how to display."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Customizing template"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "let's look at the first 20 lines of the `python` exporter"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pyfile = !ipython nbconvert --to python '04 - Custom Display Logic.ipynb' --stdout\n",
-      "for l in pyfile[20:40]:\n",
-      "    print l"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "# 1. Implementing special display methods such as `_repr_html_`.\n",
-        "# 2. Registering a display function for a particular type.\n",
-        "# \n",
-        "# In this Notebook we show how both approaches work.\n",
-        "\n",
-        "# Before we get started, we will import the various display functions for displaying the different formats we will create.\n",
-        "\n",
-        "# In[54]:\n",
-        "\n",
-        "from IPython.display import display\n",
-        "from IPython.display import (\n",
-        "    display_html, display_jpeg, display_png,\n",
-        "    display_javascript, display_svg, display_latex\n",
-        ")\n",
-        "\n",
-        "\n",
-        "### Implementing special display methods\n",
-        "\n",
-        "# The main idea of the first approach is that you have to implement special display methods, one for each representation you want to use. Here is a list of the names of the special methods and the values they must return:\n",
-        "# \n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We see that the non-code cell are exported to the file. To have a cleaner script, we will export only the code contained in the code cells.\n",
+      "(process:26512): Gtk-WARNING **: Locale not supported by C library.\n",
+      "\tUsing the fallback 'C' locale.\n",
+      "Setting Language: .UTF-8\n",
+      "\n",
+      "(process:26552): Gtk-WARNING **: Locale not supported by C library.\n",
+      "\tUsing the fallback 'C' locale.\n",
+      "Setting Language: .UTF-8\n",
       "\n",
-      "To do so, we will inherit the python template, and overwrite the markdown blocks to be empty."
+      "(process:26592): Gtk-WARNING **: Locale not supported by C library.\n",
+      "\tUsing the fallback 'C' locale.\n",
+      "[NbConvertApp] Loaded template latex_article.tplx\n",
+      "[NbConvertApp] Writing 41196 bytes to 04 - Custom Display Logic.tex\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "ipython nbconvert  --to=latex '04 - Custom Display Logic.ipynb'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You should just have to compile the generated `.tex` file. If you get the required packages installed, if should compile out of the box.\n",
+    "\n",
+    "For convenience we allow to run extra action after the conversion has been done, in particular for `latex` we have a `pdf` post-processor. \n",
+    "You can define the postprocessor tu run with the `--post` flag."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%writefile simplepython.tpl\n",
-      "{% extends 'python.tpl'%}\n",
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[NbConvertApp] Using existing profile dir: u'/Users/bussonniermatthias/.ipython/profile_default'\n",
+      "[NbConvertApp] Converting notebook 04 - Custom Display Logic.ipynb to latex\n",
+      "[NbConvertApp] Support files will be in 04 - Custom Display Logic_files/\n",
+      "Setting Language: .UTF-8\n",
       "\n",
-      "{% block markdowncell -%}\n",
-      "{% endblock markdowncell %}\n",
+      "(process:26658): Gtk-WARNING **: Locale not supported by C library.\n",
+      "\tUsing the fallback 'C' locale.\n",
+      "Setting Language: .UTF-8\n",
       "\n",
-      "## we also want to get rig of header cell\n",
-      "{% block headingcell -%}\n",
-      "{% endblock headingcell %}\n",
+      "(process:26698): Gtk-WARNING **: Locale not supported by C library.\n",
+      "\tUsing the fallback 'C' locale.\n",
+      "Setting Language: .UTF-8\n",
       "\n",
-      "## and let's change the appearance of input prompt\n",
-      "{% block in_prompt %}\n",
-      "# This was input cell with prompt number : {{ cell.prompt_number if cell.prompt_number else ' ' }}\n",
-      "{%- endblock in_prompt %}"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Overwriting simplepython.tpl\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pyfile = !ipython nbconvert --to python '04 - Custom Display Logic.ipynb' --stdout --template=simplepython.tpl\n",
+      "(process:26738): Gtk-WARNING **: Locale not supported by C library.\n",
+      "\tUsing the fallback 'C' locale.\n",
+      "Setting Language: .UTF-8\n",
       "\n",
-      "for l in pyfile[4:40]:\n",
-      "    print l\n",
-      "print '...'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "# This was input cell with prompt number : 54\n",
-        "from IPython.display import display\n",
-        "from IPython.display import (\n",
-        "    display_html, display_jpeg, display_png,\n",
-        "    display_javascript, display_svg, display_latex\n",
-        ")\n",
-        "\n",
-        "\n",
-        "# This was input cell with prompt number : 55\n",
-        "get_ipython().magic(u'load soln/mycircle.py')\n",
-        "\n",
-        "\n",
-        "# This was input cell with prompt number : 56\n",
-        "class MyCircle(object):\n",
-        "    \n",
-        "    def _repr_html_(self):\n",
-        "        return \"&#x25CB; (<b>html</b>)\"\n",
-        "\n",
-        "    def _repr_svg_(self):\n",
-        "        return \"\"\"<svg width='100px' height='100px'>\n",
-        "           <circle cx=\"50\" cy=\"50\" r=\"20\" stroke=\"black\" stroke-width=\"1\" fill=\"blue\"/>\n",
-        "        </svg>\"\"\"\n",
-        "    \n",
-        "    def _repr_latex_(self):\n",
-        "        return r\"$\\bigcirc \\LaTeX$\"\n",
-        "\n",
-        "    def _repr_javascript_(self):\n",
-        "        return \"alert('I am a circle!');\"\n",
-        "\n",
-        "\n",
-        "# This was input cell with prompt number : 57\n",
-        "c = MyCircle()\n",
-        "\n",
-        "\n",
-        "# This was input cell with prompt number : 58\n",
-        "...\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I'll let you read Jinja manual for the exact syntax of the template."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Template that use cells metadata"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Notebook fileformat support attaching arbitrary JSON metadata to each cell of a notebook. In this part we will use those metadata."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "First you need to choose another notebook you want to convert to html, and tag some of the cell with metadata.\n",
-      "You can see the file `soln/celldiff.js` for a solution on how to tag, or follow the javascript tutorial to see how to do that. Use what we have written there to tag cells of some notebooks to `Easy`|`Medium`|`Hard`|`<None>`, and convert this notebook using your template."
+      "(process:26778): Gtk-WARNING **: Locale not supported by C library.\n",
+      "\tUsing the fallback 'C' locale.\n",
+      "Setting Language: .UTF-8\n",
+      "\n",
+      "(process:26818): Gtk-WARNING **: Locale not supported by C library.\n",
+      "\tUsing the fallback 'C' locale.\n",
+      "[NbConvertApp] Loaded template latex_article.tplx\n",
+      "[NbConvertApp] Writing 41196 bytes to 04 - Custom Display Logic.tex\n",
+      "[NbConvertApp] Building PDF: ['pdflatex', '04 - Custom Display Logic.tex']\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "ipython nbconvert  --to=latex '04 - Custom Display Logic.ipynb' --post=pdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Have a look at `04 - Custom Display Logic.pdf`, toward the end, where we compared `display()` vs `display_html()` and returning the object.\n",
+    "See how the cell where we use `display_html` was not able to display the circle, whereas the two other ones were able to select one of the oher representation they know how to display."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Customizing template"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "let's look at the first 20 lines of the `python` exporter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "you might need the following : \n",
-      "```\n",
-      "{% extends 'html_full.tpl'%}\n",
-      "{% block any_cell %}\n",
-      "{{ super() }}\n",
-      "<div style=\"background-color:red\">\n",
-      "<div style='background-color:orange'>\n",
-      "```\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# 1. Implementing special display methods such as `_repr_html_`.\n",
+      "# 2. Registering a display function for a particular type.\n",
+      "# \n",
+      "# In this Notebook we show how both approaches work.\n",
+      "\n",
+      "# Before we get started, we will import the various display functions for displaying the different formats we will create.\n",
+      "\n",
+      "# In[54]:\n",
       "\n",
-      "`metadata` might not exist, be sure to :\n",
+      "from IPython.display import display\n",
+      "from IPython.display import (\n",
+      "    display_html, display_jpeg, display_png,\n",
+      "    display_javascript, display_svg, display_latex\n",
+      ")\n",
       "\n",
-      "`cell['metadata'].get('example',{}).get('difficulty','')`\n",
       "\n",
-      "tip: use `%%writefile` to edit the template in the notebook :-)"
+      "### Implementing special display methods\n",
+      "\n",
+      "# The main idea of the first approach is that you have to implement special display methods, one for each representation you want to use. Here is a list of the names of the special methods and the values they must return:\n",
+      "# \n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%bash\n",
-      "# ipython nbconvert --to html <your chosen notebook.ipynb> --template=<your template file>"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%loadpy soln/coloreddiff.tpl"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# ipython nbconvert --to html '04 - Custom Display Logic.ipynb' --template=soln/coloreddiff.tpl"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
+    }
+   ],
+   "source": [
+    "pyfile = !ipython nbconvert --to python '04 - Custom Display Logic.ipynb' --stdout\n",
+    "for l in pyfile[20:40]:\n",
+    "    print l"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that the non-code cell are exported to the file. To have a cleaner script, we will export only the code contained in the code cells.\n",
+    "\n",
+    "To do so, we will inherit the python template, and overwrite the markdown blocks to be empty."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Get rid of all command line flags."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting simplepython.tpl\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "%%writefile simplepython.tpl\n",
+    "{% extends 'python.tpl'%}\n",
+    "\n",
+    "{% block markdowncell -%}\n",
+    "{% endblock markdowncell %}\n",
+    "\n",
+    "## we also want to get rig of header cell\n",
+    "{% block headingcell -%}\n",
+    "{% endblock headingcell %}\n",
+    "\n",
+    "## and let's change the appearance of input prompt\n",
+    "{% block in_prompt %}\n",
+    "# This was input cell with prompt number : {{ cell.prompt_number if cell.prompt_number else ' ' }}\n",
+    "{%- endblock in_prompt %}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "As of all of IPython nbconvert can be configured using profiles and passing the `--profile` flag. \n",
-      "Moreover if a `config.py` file exist in current working directory nbconvert will use that, or read the config file you give to it with the `--config=<file>` flag. \n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "# This was input cell with prompt number : 54\n",
+      "from IPython.display import display\n",
+      "from IPython.display import (\n",
+      "    display_html, display_jpeg, display_png,\n",
+      "    display_javascript, display_svg, display_latex\n",
+      ")\n",
+      "\n",
+      "\n",
+      "# This was input cell with prompt number : 55\n",
+      "get_ipython().magic(u'load soln/mycircle.py')\n",
+      "\n",
+      "\n",
+      "# This was input cell with prompt number : 56\n",
+      "class MyCircle(object):\n",
+      "    \n",
+      "    def _repr_html_(self):\n",
+      "        return \"&#x25CB; (<b>html</b>)\"\n",
+      "\n",
+      "    def _repr_svg_(self):\n",
+      "        return \"\"\"<svg width='100px' height='100px'>\n",
+      "           <circle cx=\"50\" cy=\"50\" r=\"20\" stroke=\"black\" stroke-width=\"1\" fill=\"blue\"/>\n",
+      "        </svg>\"\"\"\n",
+      "    \n",
+      "    def _repr_latex_(self):\n",
+      "        return r\"$\\bigcirc \\LaTeX$\"\n",
+      "\n",
+      "    def _repr_javascript_(self):\n",
+      "        return \"alert('I am a circle!');\"\n",
+      "\n",
+      "\n",
+      "# This was input cell with prompt number : 57\n",
+      "c = MyCircle()\n",
       "\n",
-      "In the end, if you are often running nbconvert on the sam project, `$ ipython nbconvert` should be enough to get you up and ready."
+      "\n",
+      "# This was input cell with prompt number : 58\n",
+      "...\n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "pyfile = !ipython nbconvert --to python '04 - Custom Display Logic.ipynb' --stdout --template=simplepython.tpl\n",
+    "\n",
+    "for l in pyfile[4:40]:\n",
+    "    print l\n",
+    "print '...'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I'll let you read Jinja manual for the exact syntax of the template."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Template that use cells metadata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notebook fileformat support attaching arbitrary JSON metadata to each cell of a notebook. In this part we will use those metadata."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First you need to choose another notebook you want to convert to html, and tag some of the cell with metadata.\n",
+    "You can see the file `soln/celldiff.js` for a solution on how to tag, or follow the javascript tutorial to see how to do that. Use what we have written there to tag cells of some notebooks to `Easy`|`Medium`|`Hard`|`<None>`, and convert this notebook using your template."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "you might need the following : \n",
+    "```\n",
+    "{% extends 'html_full.tpl'%}\n",
+    "{% block any_cell %}\n",
+    "{{ super() }}\n",
+    "<div style=\"background-color:red\">\n",
+    "<div style='background-color:orange'>\n",
+    "```\n",
+    "\n",
+    "`metadata` might not exist, be sure to :\n",
+    "\n",
+    "`cell['metadata'].get('example',{}).get('difficulty','')`\n",
+    "\n",
+    "tip: use `%%writefile` to edit the template in the notebook :-)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "# ipython nbconvert --to html <your chosen notebook.ipynb> --template=<your template file>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%loadpy soln/coloreddiff.tpl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# ipython nbconvert --to html '04 - Custom Display Logic.ipynb' --template=soln/coloreddiff.tpl"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get rid of all command line flags."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As of all of IPython nbconvert can be configured using profiles and passing the `--profile` flag. \n",
+    "Moreover if a `config.py` file exist in current working directory nbconvert will use that, or read the config file you give to it with the `--config=<file>` flag. \n",
+    "\n",
+    "In the end, if you are often running nbconvert on the sam project, `$ ipython nbconvert` should be enough to get you up and ready."
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Notebook/Custom Keyboard Shortcuts.ipynb b/examples/Notebook/Custom Keyboard Shortcuts.ipynb
index 53640b4..6cb38ce 100644
--- a/examples/Notebook/Custom Keyboard Shortcuts.ipynb
+++ b/examples/Notebook/Custom Keyboard Shortcuts.ipynb
@@ -1,149 +1,147 @@
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Keyboard Shortcut Customization"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Starting with IPython 2.0 keyboard shortcuts in command and edit mode are fully customizable. These customizations are made using the IPython JavaScript API. Here is an example that makes the `r` key available for running a cell:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "\n",
-      "IPython.keyboard_manager.command_shortcuts.add_shortcut('r', {\n",
-      "    help : 'run cell',\n",
-      "    help_index : 'zz',\n",
-      "    handler : function (event) {\n",
-      "        IPython.notebook.execute_cell();\n",
-      "        return false;\n",
-      "    }}\n",
-      ");"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "javascript": [
-        "\n",
-        "IPython.keyboard_manager.command_shortcuts.add_shortcut('r', {\n",
-        "    help : 'run cell',\n",
-        "    help_index : 'zz',\n",
-        "    handler : function (event) {\n",
-        "        IPython.notebook.execute_cell();\n",
-        "        return false;\n",
-        "    }}\n",
-        ");"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Javascript at 0x10e8d1890>"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "There are a couple of points to mention about this API:\n",
-      "\n",
-      "* The `help_index` field is used to sort the shortcuts in the Keyboard Shortcuts help dialog. It defaults to `zz`.\n",
-      "* When a handler returns `false` it indicates that the event should stop propagating and the default action should not be performed. For further details about the `event` object or event handling, see the jQuery docs.\n",
-      "* If you don't need a `help` or `help_index` field, you can simply pass a function as the second argument to `add_shortcut`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "\n",
-      "IPython.keyboard_manager.command_shortcuts.add_shortcut('r', function (event) {\n",
-      "    IPython.notebook.execute_cell();\n",
-      "    return false;\n",
-      "});"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "javascript": [
-        "\n",
-        "IPython.keyboard_manager.command_shortcuts.add_shortcut('r', function (event) {\n",
-        "    IPython.notebook.execute_cell();\n",
-        "    return false;\n",
-        "});"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Javascript at 0x1019baf90>"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Keyboard Shortcut Customization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Starting with IPython 2.0 keyboard shortcuts in command and edit mode are fully customizable. These customizations are made using the IPython JavaScript API. Here is an example that makes the `r` key available for running a cell:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "application/javascript": [
+       "\n",
+       "IPython.keyboard_manager.command_shortcuts.add_shortcut('r', {\n",
+       "    help : 'run cell',\n",
+       "    help_index : 'zz',\n",
+       "    handler : function (event) {\n",
+       "        IPython.notebook.execute_cell();\n",
+       "        return false;\n",
+       "    }}\n",
+       ");"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript at 0x10e8d1890>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Likewise, to remove a shortcut, use `remove_shortcut`:"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%javascript\n",
+    "\n",
+    "IPython.keyboard_manager.command_shortcuts.add_shortcut('r', {\n",
+    "    help : 'run cell',\n",
+    "    help_index : 'zz',\n",
+    "    handler : function (event) {\n",
+    "        IPython.notebook.execute_cell();\n",
+    "        return false;\n",
+    "    }}\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are a couple of points to mention about this API:\n",
+    "\n",
+    "* The `help_index` field is used to sort the shortcuts in the Keyboard Shortcuts help dialog. It defaults to `zz`.\n",
+    "* When a handler returns `false` it indicates that the event should stop propagating and the default action should not be performed. For further details about the `event` object or event handling, see the jQuery docs.\n",
+    "* If you don't need a `help` or `help_index` field, you can simply pass a function as the second argument to `add_shortcut`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "\n",
-      "IPython.keyboard_manager.command_shortcuts.remove_shortcut('r');"
-     ],
-     "language": "python",
+     "data": {
+      "application/javascript": [
+       "\n",
+       "IPython.keyboard_manager.command_shortcuts.add_shortcut('r', function (event) {\n",
+       "    IPython.notebook.execute_cell();\n",
+       "    return false;\n",
+       "});"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript at 0x1019baf90>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "javascript": [
-        "\n",
-        "IPython.keyboard_manager.command_shortcuts.remove_shortcut('r');"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Javascript at 0x10e8d1950>"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%javascript\n",
+    "\n",
+    "IPython.keyboard_manager.command_shortcuts.add_shortcut('r', function (event) {\n",
+    "    IPython.notebook.execute_cell();\n",
+    "    return false;\n",
+    "});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Likewise, to remove a shortcut, use `remove_shortcut`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "application/javascript": [
+       "\n",
+       "IPython.keyboard_manager.command_shortcuts.remove_shortcut('r');"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript at 0x10e8d1950>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "If you want your keyboard shortcuts to be active for all of your notebooks, put the above API calls into your `<profile>/static/custom/custom.js` file."
-     ]
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "%%javascript\n",
+    "\n",
+    "IPython.keyboard_manager.command_shortcuts.remove_shortcut('r');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want your keyboard shortcuts to be active for all of your notebooks, put the above API calls into your `<profile>/static/custom/custom.js` file."
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Notebook/Importing Notebooks.ipynb b/examples/Notebook/Importing Notebooks.ipynb
index 8ceabf5..a58e94c 100644
--- a/examples/Notebook/Importing Notebooks.ipynb
+++ b/examples/Notebook/Importing Notebooks.ipynb
@@ -1,715 +1,717 @@
 {
- "metadata": {
-  "gist_id": "6011986",
-  "name": "",
-  "signature": "sha256:26d4e13b833b84557da8501e68fc5119f11a3c1cdbecba63c0ab613e0cb0619f"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Importing IPython Notebooks as Modules"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "It is a common problem that people want to import code from IPython Notebooks.\n",
-      "This is made difficult by the fact that Notebooks are not plain Python files,\n",
-      "and thus cannot be imported by the regular Python machinery.\n",
-      "\n",
-      "Fortunately, Python provides some fairly sophisticated [hooks](http://www.python.org/dev/peps/pep-0302/) into the import machinery,\n",
-      "so we can actually make IPython notebooks importable without much difficulty,\n",
-      "and only using public APIs."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import io, os, sys, types"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.nbformat import current\n",
-      "from IPython.core.interactiveshell import InteractiveShell"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Import hooks typically take the form of two objects:\n",
-      "\n",
-      "1. a Module **Loader**, which takes a module name (e.g. `'IPython.display'`), and returns a Module\n",
-      "2. a Module **Finder**, which figures out whether a module might exist, and tells Python what **Loader** to use"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def find_notebook(fullname, path=None):\n",
-      "    \"\"\"find a notebook, given its fully qualified name and an optional path\n",
-      "    \n",
-      "    This turns \"foo.bar\" into \"foo/bar.ipynb\"\n",
-      "    and tries turning \"Foo_Bar\" into \"Foo Bar\" if Foo_Bar\n",
-      "    does not exist.\n",
-      "    \"\"\"\n",
-      "    name = fullname.rsplit('.', 1)[-1]\n",
-      "    if not path:\n",
-      "        path = ['']\n",
-      "    for d in path:\n",
-      "        nb_path = os.path.join(d, name + \".ipynb\")\n",
-      "        if os.path.isfile(nb_path):\n",
-      "            return nb_path\n",
-      "        # let import Notebook_Name find \"Notebook Name.ipynb\"\n",
-      "        nb_path = nb_path.replace(\"_\", \" \")\n",
-      "        if os.path.isfile(nb_path):\n",
-      "            return nb_path\n",
-      "            "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Notebook Loader"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here we have our Notebook Loader.\n",
-      "It's actually quite simple - once we figure out the filename of the module,\n",
-      "all it does is:\n",
-      "\n",
-      "1. load the notebook document into memory\n",
-      "2. create an empty Module\n",
-      "3. execute every cell in the Module namespace\n",
-      "\n",
-      "Since IPython cells can have extended syntax,\n",
-      "the IPython transform is applied to turn each of these cells into their pure-Python counterparts before executing them.\n",
-      "If all of your notebook cells are pure-Python,\n",
-      "this step is unnecessary."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class NotebookLoader(object):\n",
-      "    \"\"\"Module Loader for IPython Notebooks\"\"\"\n",
-      "    def __init__(self, path=None):\n",
-      "        self.shell = InteractiveShell.instance()\n",
-      "        self.path = path\n",
-      "    \n",
-      "    def load_module(self, fullname):\n",
-      "        \"\"\"import a notebook as a module\"\"\"\n",
-      "        path = find_notebook(fullname, self.path)\n",
-      "        \n",
-      "        print (\"importing IPython notebook from %s\" % path)\n",
-      "                                       \n",
-      "        # load the notebook object\n",
-      "        with io.open(path, 'r', encoding='utf-8') as f:\n",
-      "            nb = current.read(f, 'json')\n",
-      "        \n",
-      "        \n",
-      "        # create the module and add it to sys.modules\n",
-      "        # if name in sys.modules:\n",
-      "        #    return sys.modules[name]\n",
-      "        mod = types.ModuleType(fullname)\n",
-      "        mod.__file__ = path\n",
-      "        mod.__loader__ = self\n",
-      "        sys.modules[fullname] = mod\n",
-      "        \n",
-      "        # extra work to ensure that magics that would affect the user_ns\n",
-      "        # actually affect the notebook module's ns\n",
-      "        save_user_ns = self.shell.user_ns\n",
-      "        self.shell.user_ns = mod.__dict__\n",
-      "        \n",
-      "        try:\n",
-      "          for cell in nb.worksheets[0].cells:\n",
-      "            if cell.cell_type == 'code' and cell.language == 'python':\n",
-      "                # transform the input to executable Python\n",
-      "                code = self.shell.input_transformer_manager.transform_cell(cell.input)\n",
-      "                # run the code in themodule\n",
-      "                exec(code, mod.__dict__)\n",
-      "        finally:\n",
-      "            self.shell.user_ns = save_user_ns\n",
-      "        return mod\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "The Module Finder"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The finder is a simple object that tells you whether a name can be imported,\n",
-      "and returns the appropriate loader.\n",
-      "All this one does is check, when you do:\n",
-      "\n",
-      "```python\n",
-      "import mynotebook\n",
-      "```\n",
-      "\n",
-      "it checks whether `mynotebook.ipynb` exists.\n",
-      "If a notebook is found, then it returns a NotebookLoader.\n",
-      "\n",
-      "Any extra logic is just for resolving paths within packages."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class NotebookFinder(object):\n",
-      "    \"\"\"Module finder that locates IPython Notebooks\"\"\"\n",
-      "    def __init__(self):\n",
-      "        self.loaders = {}\n",
-      "    \n",
-      "    def find_module(self, fullname, path=None):\n",
-      "        nb_path = find_notebook(fullname, path)\n",
-      "        if not nb_path:\n",
-      "            return\n",
-      "        \n",
-      "        key = path\n",
-      "        if path:\n",
-      "            # lists aren't hashable\n",
-      "            key = os.path.sep.join(path)\n",
-      "        \n",
-      "        if key not in self.loaders:\n",
-      "            self.loaders[key] = NotebookLoader(path)\n",
-      "        return self.loaders[key]\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Register the hook"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now we register the `NotebookFinder` with `sys.meta_path`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "sys.meta_path.append(NotebookFinder())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "After this point, my notebooks should be importable.\n",
-      "\n",
-      "Let's look at what we have in the CWD:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ls nbpackage"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "__init__.py       mynotebook.ipynb  \u001b[34mnbs\u001b[m\u001b[m/\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "So I should be able to `import nbimp.mynotebook`.\n"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Aside: displaying notebooks"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is some simple code to display the contents of a notebook\n",
-      "with syntax highlighting, etc."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from pygments import highlight\n",
-      "from pygments.lexers import PythonLexer\n",
-      "from pygments.formatters import HtmlFormatter\n",
-      "\n",
-      "from IPython.display import display, HTML\n",
-      "\n",
-      "formatter = HtmlFormatter()\n",
-      "lexer = PythonLexer()\n",
-      "\n",
-      "# publish the CSS for pygments highlighting\n",
-      "display(HTML(\"\"\"\n",
-      "<style type='text/css'>\n",
-      "%s\n",
-      "</style>\n",
-      "\"\"\" % formatter.get_style_defs()\n",
-      "))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "\n",
-        "<style type='text/css'>\n",
-        ".hll { background-color: #ffffcc }\n",
-        ".c { color: #408080; font-style: italic } /* Comment */\n",
-        ".err { border: 1px solid #FF0000 } /* Error */\n",
-        ".k { color: #008000; font-weight: bold } /* Keyword */\n",
-        ".o { color: #666666 } /* Operator */\n",
-        ".cm { color: #408080; font-style: italic } /* Comment.Multiline */\n",
-        ".cp { color: #BC7A00 } /* Comment.Preproc */\n",
-        ".c1 { color: #408080; font-style: italic } /* Comment.Single */\n",
-        ".cs { color: #408080; font-style: italic } /* Comment.Special */\n",
-        ".gd { color: #A00000 } /* Generic.Deleted */\n",
-        ".ge { font-style: italic } /* Generic.Emph */\n",
-        ".gr { color: #FF0000 } /* Generic.Error */\n",
-        ".gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
-        ".gi { color: #00A000 } /* Generic.Inserted */\n",
-        ".go { color: #888888 } /* Generic.Output */\n",
-        ".gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
-        ".gs { font-weight: bold } /* Generic.Strong */\n",
-        ".gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
-        ".gt { color: #0044DD } /* Generic.Traceback */\n",
-        ".kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
-        ".kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
-        ".kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
-        ".kp { color: #008000 } /* Keyword.Pseudo */\n",
-        ".kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
-        ".kt { color: #B00040 } /* Keyword.Type */\n",
-        ".m { color: #666666 } /* Literal.Number */\n",
-        ".s { color: #BA2121 } /* Literal.String */\n",
-        ".na { color: #7D9029 } /* Name.Attribute */\n",
-        ".nb { color: #008000 } /* Name.Builtin */\n",
-        ".nc { color: #0000FF; font-weight: bold } /* Name.Class */\n",
-        ".no { color: #880000 } /* Name.Constant */\n",
-        ".nd { color: #AA22FF } /* Name.Decorator */\n",
-        ".ni { color: #999999; font-weight: bold } /* Name.Entity */\n",
-        ".ne { color: #D2413A; font-weight: bold } /* Name.Exception */\n",
-        ".nf { color: #0000FF } /* Name.Function */\n",
-        ".nl { color: #A0A000 } /* Name.Label */\n",
-        ".nn { color: #0000FF; font-weight: bold } /* Name.Namespace */\n",
-        ".nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
-        ".nv { color: #19177C } /* Name.Variable */\n",
-        ".ow { color: #AA22FF; font-weight: bold } /* Operator.Word */\n",
-        ".w { color: #bbbbbb } /* Text.Whitespace */\n",
-        ".mf { color: #666666 } /* Literal.Number.Float */\n",
-        ".mh { color: #666666 } /* Literal.Number.Hex */\n",
-        ".mi { color: #666666 } /* Literal.Number.Integer */\n",
-        ".mo { color: #666666 } /* Literal.Number.Oct */\n",
-        ".sb { color: #BA2121 } /* Literal.String.Backtick */\n",
-        ".sc { color: #BA2121 } /* Literal.String.Char */\n",
-        ".sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\n",
-        ".s2 { color: #BA2121 } /* Literal.String.Double */\n",
-        ".se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */\n",
-        ".sh { color: #BA2121 } /* Literal.String.Heredoc */\n",
-        ".si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */\n",
-        ".sx { color: #008000 } /* Literal.String.Other */\n",
-        ".sr { color: #BB6688 } /* Literal.String.Regex */\n",
-        ".s1 { color: #BA2121 } /* Literal.String.Single */\n",
-        ".ss { color: #19177C } /* Literal.String.Symbol */\n",
-        ".bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
-        ".vc { color: #19177C } /* Name.Variable.Class */\n",
-        ".vg { color: #19177C } /* Name.Variable.Global */\n",
-        ".vi { color: #19177C } /* Name.Variable.Instance */\n",
-        ".il { color: #666666 } /* Literal.Number.Integer.Long */\n",
-        "</style>\n"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.HTML at 0x1072303d0>"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def show_notebook(fname):\n",
-      "    \"\"\"display a short summary of the cells of a notebook\"\"\"\n",
-      "    with io.open(fname, 'r', encoding='utf-8') as f:\n",
-      "        nb = current.read(f, 'json')\n",
-      "    html = []\n",
-      "    for cell in nb.worksheets[0].cells:\n",
-      "        html.append(\"<h4>%s cell</h4>\" % cell.cell_type)\n",
-      "        if cell.cell_type == 'code':\n",
-      "            html.append(highlight(cell.input, lexer, formatter))\n",
-      "        else:\n",
-      "            html.append(\"<pre>%s</pre>\" % cell.source)\n",
-      "    display(HTML('\\n'.join(html)))\n",
-      "\n",
-      "show_notebook(os.path.join(\"nbpackage\", \"mynotebook.ipynb\"))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<h4>heading cell</h4>\n",
-        "<pre>My Notebook</pre>\n",
-        "<h4>code cell</h4>\n",
-        "<div class=\"highlight\"><pre><span class=\"k\">def</span> <span class=\"nf\">foo</span><span class=\"p\">():</span>\n",
-        "    <span class=\"k\">return</span> <span class=\"s\">&quot;foo&quot;</span>\n",
-        "</pre></div>\n",
-        "\n",
-        "<h4>code cell</h4>\n",
-        "<div class=\"highlight\"><pre><span class=\"k\">def</span> <span class=\"nf\">has_ip_syntax</span><span class=\"p\">():</span>\n",
-        "    <span class=\"n\">listing</span> <span class=\"o\">=</span> <span class=\"err\">!</span><span class=\"n\">ls</span>\n",
-        "    <span class=\"k\">return</span> <span class=\"n\">listing</span>\n",
-        "</pre></div>\n",
-        "\n",
-        "<h4>code cell</h4>\n",
-        "<div class=\"highlight\"><pre><span class=\"k\">def</span> <span class=\"nf\">whatsmyname</span><span class=\"p\">():</span>\n",
-        "    <span class=\"k\">return</span> <span class=\"n\">__name__</span>\n",
-        "</pre></div>\n"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.HTML at 0x10775a150>"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "So my notebook has a heading cell and some code cells,\n",
-      "one of which contains some IPython syntax.\n",
-      "\n",
-      "Let's see what happens when we import it"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from nbpackage import mynotebook"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "importing IPython notebook from nbpackage/mynotebook.ipynb\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Hooray, it imported!  Does it work?"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "mynotebook.foo()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 12,
-       "text": [
-        "'foo'"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Hooray again!\n",
-      "\n",
-      "Even the function that contains IPython syntax works:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "mynotebook.has_ip_syntax()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 13,
-       "text": [
-        "['Animations Using clear_output.ipynb',\n",
-        " 'Connecting with the Qt Console.ipynb',\n",
-        " 'Importing Notebooks.ipynb',\n",
-        " 'Progress Bars.ipynb',\n",
-        " 'Raw Input.ipynb',\n",
-        " 'SymPy.ipynb',\n",
-        " 'Trapezoid Rule.ipynb',\n",
-        " 'nbpackage']"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Notebooks in packages"
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Importing IPython Notebooks as Modules"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is a common problem that people want to import code from IPython Notebooks.\n",
+    "This is made difficult by the fact that Notebooks are not plain Python files,\n",
+    "and thus cannot be imported by the regular Python machinery.\n",
+    "\n",
+    "Fortunately, Python provides some fairly sophisticated [hooks](http://www.python.org/dev/peps/pep-0302/) into the import machinery,\n",
+    "so we can actually make IPython notebooks importable without much difficulty,\n",
+    "and only using public APIs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import io, os, sys, types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.nbformat import current\n",
+    "from IPython.core.interactiveshell import InteractiveShell"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import hooks typically take the form of two objects:\n",
+    "\n",
+    "1. a Module **Loader**, which takes a module name (e.g. `'IPython.display'`), and returns a Module\n",
+    "2. a Module **Finder**, which figures out whether a module might exist, and tells Python what **Loader** to use"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def find_notebook(fullname, path=None):\n",
+    "    \"\"\"find a notebook, given its fully qualified name and an optional path\n",
+    "    \n",
+    "    This turns \"foo.bar\" into \"foo/bar.ipynb\"\n",
+    "    and tries turning \"Foo_Bar\" into \"Foo Bar\" if Foo_Bar\n",
+    "    does not exist.\n",
+    "    \"\"\"\n",
+    "    name = fullname.rsplit('.', 1)[-1]\n",
+    "    if not path:\n",
+    "        path = ['']\n",
+    "    for d in path:\n",
+    "        nb_path = os.path.join(d, name + \".ipynb\")\n",
+    "        if os.path.isfile(nb_path):\n",
+    "            return nb_path\n",
+    "        # let import Notebook_Name find \"Notebook Name.ipynb\"\n",
+    "        nb_path = nb_path.replace(\"_\", \" \")\n",
+    "        if os.path.isfile(nb_path):\n",
+    "            return nb_path\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notebook Loader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we have our Notebook Loader.\n",
+    "It's actually quite simple - once we figure out the filename of the module,\n",
+    "all it does is:\n",
+    "\n",
+    "1. load the notebook document into memory\n",
+    "2. create an empty Module\n",
+    "3. execute every cell in the Module namespace\n",
+    "\n",
+    "Since IPython cells can have extended syntax,\n",
+    "the IPython transform is applied to turn each of these cells into their pure-Python counterparts before executing them.\n",
+    "If all of your notebook cells are pure-Python,\n",
+    "this step is unnecessary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class NotebookLoader(object):\n",
+    "    \"\"\"Module Loader for IPython Notebooks\"\"\"\n",
+    "    def __init__(self, path=None):\n",
+    "        self.shell = InteractiveShell.instance()\n",
+    "        self.path = path\n",
+    "    \n",
+    "    def load_module(self, fullname):\n",
+    "        \"\"\"import a notebook as a module\"\"\"\n",
+    "        path = find_notebook(fullname, self.path)\n",
+    "        \n",
+    "        print (\"importing IPython notebook from %s\" % path)\n",
+    "                                       \n",
+    "        # load the notebook object\n",
+    "        with io.open(path, 'r', encoding='utf-8') as f:\n",
+    "            nb = current.read(f, 'json')\n",
+    "        \n",
+    "        \n",
+    "        # create the module and add it to sys.modules\n",
+    "        # if name in sys.modules:\n",
+    "        #    return sys.modules[name]\n",
+    "        mod = types.ModuleType(fullname)\n",
+    "        mod.__file__ = path\n",
+    "        mod.__loader__ = self\n",
+    "        sys.modules[fullname] = mod\n",
+    "        \n",
+    "        # extra work to ensure that magics that would affect the user_ns\n",
+    "        # actually affect the notebook module's ns\n",
+    "        save_user_ns = self.shell.user_ns\n",
+    "        self.shell.user_ns = mod.__dict__\n",
+    "        \n",
+    "        try:\n",
+    "          for cell in nb.worksheets[0].cells:\n",
+    "            if cell.cell_type == 'code' and cell.language == 'python':\n",
+    "                # transform the input to executable Python\n",
+    "                code = self.shell.input_transformer_manager.transform_cell(cell.input)\n",
+    "                # run the code in themodule\n",
+    "                exec(code, mod.__dict__)\n",
+    "        finally:\n",
+    "            self.shell.user_ns = save_user_ns\n",
+    "        return mod\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Module Finder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The finder is a simple object that tells you whether a name can be imported,\n",
+    "and returns the appropriate loader.\n",
+    "All this one does is check, when you do:\n",
+    "\n",
+    "```python\n",
+    "import mynotebook\n",
+    "```\n",
+    "\n",
+    "it checks whether `mynotebook.ipynb` exists.\n",
+    "If a notebook is found, then it returns a NotebookLoader.\n",
+    "\n",
+    "Any extra logic is just for resolving paths within packages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class NotebookFinder(object):\n",
+    "    \"\"\"Module finder that locates IPython Notebooks\"\"\"\n",
+    "    def __init__(self):\n",
+    "        self.loaders = {}\n",
+    "    \n",
+    "    def find_module(self, fullname, path=None):\n",
+    "        nb_path = find_notebook(fullname, path)\n",
+    "        if not nb_path:\n",
+    "            return\n",
+    "        \n",
+    "        key = path\n",
+    "        if path:\n",
+    "            # lists aren't hashable\n",
+    "            key = os.path.sep.join(path)\n",
+    "        \n",
+    "        if key not in self.loaders:\n",
+    "            self.loaders[key] = NotebookLoader(path)\n",
+    "        return self.loaders[key]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Register the hook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we register the `NotebookFinder` with `sys.meta_path`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sys.meta_path.append(NotebookFinder())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After this point, my notebooks should be importable.\n",
+    "\n",
+    "Let's look at what we have in the CWD:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "__init__.py       mynotebook.ipynb  \u001b[34mnbs\u001b[m\u001b[m/\r\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We also have a notebook inside the `nb` package,\n",
-      "so let's make sure that works as well."
+    }
+   ],
+   "source": [
+    "ls nbpackage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So I should be able to `import nbimp.mynotebook`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aside: displaying notebooks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is some simple code to display the contents of a notebook\n",
+    "with syntax highlighting, etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style type='text/css'>\n",
+       ".hll { background-color: #ffffcc }\n",
+       ".c { color: #408080; font-style: italic } /* Comment */\n",
+       ".err { border: 1px solid #FF0000 } /* Error */\n",
+       ".k { color: #008000; font-weight: bold } /* Keyword */\n",
+       ".o { color: #666666 } /* Operator */\n",
+       ".cm { color: #408080; font-style: italic } /* Comment.Multiline */\n",
+       ".cp { color: #BC7A00 } /* Comment.Preproc */\n",
+       ".c1 { color: #408080; font-style: italic } /* Comment.Single */\n",
+       ".cs { color: #408080; font-style: italic } /* Comment.Special */\n",
+       ".gd { color: #A00000 } /* Generic.Deleted */\n",
+       ".ge { font-style: italic } /* Generic.Emph */\n",
+       ".gr { color: #FF0000 } /* Generic.Error */\n",
+       ".gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
+       ".gi { color: #00A000 } /* Generic.Inserted */\n",
+       ".go { color: #888888 } /* Generic.Output */\n",
+       ".gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
+       ".gs { font-weight: bold } /* Generic.Strong */\n",
+       ".gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
+       ".gt { color: #0044DD } /* Generic.Traceback */\n",
+       ".kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
+       ".kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
+       ".kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
+       ".kp { color: #008000 } /* Keyword.Pseudo */\n",
+       ".kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
+       ".kt { color: #B00040 } /* Keyword.Type */\n",
+       ".m { color: #666666 } /* Literal.Number */\n",
+       ".s { color: #BA2121 } /* Literal.String */\n",
+       ".na { color: #7D9029 } /* Name.Attribute */\n",
+       ".nb { color: #008000 } /* Name.Builtin */\n",
+       ".nc { color: #0000FF; font-weight: bold } /* Name.Class */\n",
+       ".no { color: #880000 } /* Name.Constant */\n",
+       ".nd { color: #AA22FF } /* Name.Decorator */\n",
+       ".ni { color: #999999; font-weight: bold } /* Name.Entity */\n",
+       ".ne { color: #D2413A; font-weight: bold } /* Name.Exception */\n",
+       ".nf { color: #0000FF } /* Name.Function */\n",
+       ".nl { color: #A0A000 } /* Name.Label */\n",
+       ".nn { color: #0000FF; font-weight: bold } /* Name.Namespace */\n",
+       ".nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
+       ".nv { color: #19177C } /* Name.Variable */\n",
+       ".ow { color: #AA22FF; font-weight: bold } /* Operator.Word */\n",
+       ".w { color: #bbbbbb } /* Text.Whitespace */\n",
+       ".mf { color: #666666 } /* Literal.Number.Float */\n",
+       ".mh { color: #666666 } /* Literal.Number.Hex */\n",
+       ".mi { color: #666666 } /* Literal.Number.Integer */\n",
+       ".mo { color: #666666 } /* Literal.Number.Oct */\n",
+       ".sb { color: #BA2121 } /* Literal.String.Backtick */\n",
+       ".sc { color: #BA2121 } /* Literal.String.Char */\n",
+       ".sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\n",
+       ".s2 { color: #BA2121 } /* Literal.String.Double */\n",
+       ".se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */\n",
+       ".sh { color: #BA2121 } /* Literal.String.Heredoc */\n",
+       ".si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */\n",
+       ".sx { color: #008000 } /* Literal.String.Other */\n",
+       ".sr { color: #BB6688 } /* Literal.String.Regex */\n",
+       ".s1 { color: #BA2121 } /* Literal.String.Single */\n",
+       ".ss { color: #19177C } /* Literal.String.Symbol */\n",
+       ".bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
+       ".vc { color: #19177C } /* Name.Variable.Class */\n",
+       ".vg { color: #19177C } /* Name.Variable.Global */\n",
+       ".vi { color: #19177C } /* Name.Variable.Instance */\n",
+       ".il { color: #666666 } /* Literal.Number.Integer.Long */\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML at 0x1072303d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pygments import highlight\n",
+    "from pygments.lexers import PythonLexer\n",
+    "from pygments.formatters import HtmlFormatter\n",
+    "\n",
+    "from IPython.display import display, HTML\n",
+    "\n",
+    "formatter = HtmlFormatter()\n",
+    "lexer = PythonLexer()\n",
+    "\n",
+    "# publish the CSS for pygments highlighting\n",
+    "display(HTML(\"\"\"\n",
+    "<style type='text/css'>\n",
+    "%s\n",
+    "</style>\n",
+    "\"\"\" % formatter.get_style_defs()\n",
+    "))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h4>heading cell</h4>\n",
+       "<pre>My Notebook</pre>\n",
+       "<h4>code cell</h4>\n",
+       "<div class=\"highlight\"><pre><span class=\"k\">def</span> <span class=\"nf\">foo</span><span class=\"p\">():</span>\n",
+       "    <span class=\"k\">return</span> <span class=\"s\">&quot;foo&quot;</span>\n",
+       "</pre></div>\n",
+       "\n",
+       "<h4>code cell</h4>\n",
+       "<div class=\"highlight\"><pre><span class=\"k\">def</span> <span class=\"nf\">has_ip_syntax</span><span class=\"p\">():</span>\n",
+       "    <span class=\"n\">listing</span> <span class=\"o\">=</span> <span class=\"err\">!</span><span class=\"n\">ls</span>\n",
+       "    <span class=\"k\">return</span> <span class=\"n\">listing</span>\n",
+       "</pre></div>\n",
+       "\n",
+       "<h4>code cell</h4>\n",
+       "<div class=\"highlight\"><pre><span class=\"k\">def</span> <span class=\"nf\">whatsmyname</span><span class=\"p\">():</span>\n",
+       "    <span class=\"k\">return</span> <span class=\"n\">__name__</span>\n",
+       "</pre></div>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML at 0x10775a150>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def show_notebook(fname):\n",
+    "    \"\"\"display a short summary of the cells of a notebook\"\"\"\n",
+    "    with io.open(fname, 'r', encoding='utf-8') as f:\n",
+    "        nb = current.read(f, 'json')\n",
+    "    html = []\n",
+    "    for cell in nb.worksheets[0].cells:\n",
+    "        html.append(\"<h4>%s cell</h4>\" % cell.cell_type)\n",
+    "        if cell.cell_type == 'code':\n",
+    "            html.append(highlight(cell.input, lexer, formatter))\n",
+    "        else:\n",
+    "            html.append(\"<pre>%s</pre>\" % cell.source)\n",
+    "    display(HTML('\\n'.join(html)))\n",
+    "\n",
+    "show_notebook(os.path.join(\"nbpackage\", \"mynotebook.ipynb\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So my notebook has a heading cell and some code cells,\n",
+    "one of which contains some IPython syntax.\n",
+    "\n",
+    "Let's see what happens when we import it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "importing IPython notebook from nbpackage/mynotebook.ipynb\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ls nbpackage/nbs"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "__init__.py  other.ipynb\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Note that the `__init__.py` is necessary for `nb` to be considered a package,\n",
-      "just like usual."
+    }
+   ],
+   "source": [
+    "from nbpackage import mynotebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hooray, it imported!  Does it work?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'foo'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mynotebook.foo()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hooray again!\n",
+    "\n",
+    "Even the function that contains IPython syntax works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Animations Using clear_output.ipynb',\n",
+       " 'Connecting with the Qt Console.ipynb',\n",
+       " 'Importing Notebooks.ipynb',\n",
+       " 'Progress Bars.ipynb',\n",
+       " 'Raw Input.ipynb',\n",
+       " 'SymPy.ipynb',\n",
+       " 'Trapezoid Rule.ipynb',\n",
+       " 'nbpackage']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mynotebook.has_ip_syntax()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notebooks in packages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We also have a notebook inside the `nb` package,\n",
+    "so let's make sure that works as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "__init__.py  other.ipynb\r\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "show_notebook(os.path.join(\"nbpackage\", \"nbs\", \"other.ipynb\"))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<h4>markdown cell</h4>\n",
-        "<pre>This notebook just defines `bar`</pre>\n",
-        "<h4>code cell</h4>\n",
-        "<div class=\"highlight\"><pre><span class=\"k\">def</span> <span class=\"nf\">bar</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">):</span>\n",
-        "    <span class=\"k\">return</span> <span class=\"s\">&quot;bar&quot;</span> <span class=\"o\">*</span> <span class=\"n\">x</span>\n",
-        "</pre></div>\n"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.HTML at 0x10775a250>"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from nbpackage.nbs import other\n",
-      "other.bar(5)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "importing IPython notebook from nbpackage/nbs/other.ipynb\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 16,
-       "text": [
-        "'barbarbarbarbar'"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "So now we have importable notebooks, from both the local directory and inside packages.\n",
-      "\n",
-      "I can even put a notebook inside IPython, to further demonstrate that this is working properly:"
+    }
+   ],
+   "source": [
+    "ls nbpackage/nbs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the `__init__.py` is necessary for `nb` to be considered a package,\n",
+    "just like usual."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<h4>markdown cell</h4>\n",
+       "<pre>This notebook just defines `bar`</pre>\n",
+       "<h4>code cell</h4>\n",
+       "<div class=\"highlight\"><pre><span class=\"k\">def</span> <span class=\"nf\">bar</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">):</span>\n",
+       "    <span class=\"k\">return</span> <span class=\"s\">&quot;bar&quot;</span> <span class=\"o\">*</span> <span class=\"n\">x</span>\n",
+       "</pre></div>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML at 0x10775a250>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_notebook(os.path.join(\"nbpackage\", \"nbs\", \"other.ipynb\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "importing IPython notebook from nbpackage/nbs/other.ipynb\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import shutil\n",
-      "from IPython.utils.path import get_ipython_package_dir\n",
-      "\n",
-      "utils = os.path.join(get_ipython_package_dir(), 'utils')\n",
-      "shutil.copy(os.path.join(\"nbpackage\", \"mynotebook.ipynb\"),\n",
-      "            os.path.join(utils, \"inside_ipython.ipynb\")\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "'barbarbarbarbar'"
+      ]
+     },
+     "execution_count": 16,
      "metadata": {},
-     "source": [
-      "and import the notebook from `IPython.utils`"
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from nbpackage.nbs import other\n",
+    "other.bar(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So now we have importable notebooks, from both the local directory and inside packages.\n",
+    "\n",
+    "I can even put a notebook inside IPython, to further demonstrate that this is working properly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "from IPython.utils.path import get_ipython_package_dir\n",
+    "\n",
+    "utils = os.path.join(get_ipython_package_dir(), 'utils')\n",
+    "shutil.copy(os.path.join(\"nbpackage\", \"mynotebook.ipynb\"),\n",
+    "            os.path.join(utils, \"inside_ipython.ipynb\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and import the notebook from `IPython.utils`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "importing IPython notebook from /Users/bgranger/Documents/Computing/IPython/code/ipython/IPython/utils/inside_ipython.ipynb\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.utils import inside_ipython\n",
-      "inside_ipython.whatsmyname()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "importing IPython notebook from /Users/bgranger/Documents/Computing/IPython/code/ipython/IPython/utils/inside_ipython.ipynb\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 18,
-       "text": [
-        "'IPython.utils.inside_ipython'"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "'IPython.utils.inside_ipython'"
+      ]
+     },
+     "execution_count": 18,
      "metadata": {},
-     "source": [
-      "This approach can even import functions and classes that are defined in a notebook using the `%%cython` magic."
-     ]
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "from IPython.utils import inside_ipython\n",
+    "inside_ipython.whatsmyname()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This approach can even import functions and classes that are defined in a notebook using the `%%cython` magic."
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "gist_id": "6011986",
+  "signature": "sha256:26d4e13b833b84557da8501e68fc5119f11a3c1cdbecba63c0ab613e0cb0619f"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Notebook/Index.ipynb b/examples/Notebook/Index.ipynb
index 6a744f3..44baf4e 100644
--- a/examples/Notebook/Index.ipynb
+++ b/examples/Notebook/Index.ipynb
@@ -1,85 +1,76 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"../images/ipython_logo.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Back to the main [Index](../Index.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The IPython Notebook is a web-based interactive computing system that enables users to author documents that include live code, narrative text, LaTeX equations, HTML, images and video. These documents contain a full record of a computation and its results and can be shared on email, [Dropbox](http://dropbox.com), version control systems (like git/[GitHub](http://github.com)) or [nbviewer.ipython.org](http://nbviewer.ipython.org)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tutorials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* [What is the IPython Notebook](What is the IPython Notebook.ipynb)\n",
+    "* [Running the Notebook Server](Running the Notebook Server.ipynb)\n",
+    "* [Notebook Basics](Notebook Basics.ipynb)\n",
+    "* [Running Code](Running Code.ipynb)\n",
+    "* [Working With Markdown Cells](Working With Markdown Cells.ipynb)\n",
+    "* [Custom Keyboard Shortcuts](Custom Keyboard Shortcuts.ipynb)\n",
+    "* [JavaScript Notebook Extensions](JavaScript Notebook Extensions.ipynb)\n",
+    "* [Notebook Security](Notebook Security.ipynb)\n",
+    "* [Converting Notebooks With nbconvert](Converting Notebooks With nbconvert.ipynb)\n",
+    "* [Using nbconvert as a Library](Using nbconvert as a Library.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* [Importing Notebooks](Importing Notebooks.ipynb)\n",
+    "* [Connecting with the Qt Console](Connecting with the Qt Console.ipynb)\n",
+    "* [Typesetting Equations](Typesetting Equations.ipynb)"
+   ]
+  }
+ ],
  "metadata": {
-  "name": "",
   "signature": "sha256:5a3c5ebae9154e13957e7c9d28bd3b7697c4b14f965482feb288d2cdf078983e"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<img src=\"../images/ipython_logo.png\">"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Back to the main [Index](../Index.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Notebook"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The IPython Notebook is a web-based interactive computing system that enables users to author documents that include live code, narrative text, LaTeX equations, HTML, images and video. These documents contain a full record of a computation and its results and can be shared on email, [Dropbox](http://dropbox.com), version control systems (like git/[GitHub](http://github.com)) or [nbviewer.ipython.org](http://nbviewer.ipython.org)."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Tutorials"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "* [What is the IPython Notebook](What is the IPython Notebook.ipynb)\n",
-      "* [Running the Notebook Server](Running the Notebook Server.ipynb)\n",
-      "* [Notebook Basics](Notebook Basics.ipynb)\n",
-      "* [Running Code](Running Code.ipynb)\n",
-      "* [Working With Markdown Cells](Working With Markdown Cells.ipynb)\n",
-      "* [Custom Keyboard Shortcuts](Custom Keyboard Shortcuts.ipynb)\n",
-      "* [JavaScript Notebook Extensions](JavaScript Notebook Extensions.ipynb)\n",
-      "* [Notebook Security](Notebook Security.ipynb)\n",
-      "* [Converting Notebooks With nbconvert](Converting Notebooks With nbconvert.ipynb)\n",
-      "* [Using nbconvert as a Library](Using nbconvert as a Library.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Examples"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "* [Importing Notebooks](Importing Notebooks.ipynb)\n",
-      "* [Connecting with the Qt Console](Connecting with the Qt Console.ipynb)\n",
-      "* [Typesetting Equations](Typesetting Equations.ipynb)"
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Notebook/JavaScript Notebook Extensions.ipynb b/examples/Notebook/JavaScript Notebook Extensions.ipynb
index 6655bd4..b7b8ed3 100644
--- a/examples/Notebook/JavaScript Notebook Extensions.ipynb
+++ b/examples/Notebook/JavaScript Notebook Extensions.ipynb
@@ -1,770 +1,755 @@
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Embrasing web standards"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "One of the main reason that allowed us to developp the current notebook web application \n",
-      "was to embrase the web technology. \n",
-      "\n",
-      "By beeing a pure web application using HTML, Javascript and CSS, the Notebook can get \n",
-      "all the web technology improvement for free. Thus, as browsers support for different \n",
-      "media extend, The notebook web app should be able to be compatible without modification. \n",
-      "\n",
-      "This is also true with performance of the User Interface as the speed of javascript VM increase. "
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The other advantage of using only web technology is that the code of the interface is fully accessible to the end user, and modifiable live.\n",
-      "Even if this task is not always easy, we strive to keep our code as accessible and reusable as possible.\n",
-      "This should allow with minimum effort to develop small extensions that customize the behavior of the web interface. "
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Tempering with Notebook app"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The first tool that is availlable to you and that you shoudl be aware of are browser \"developpers tool\". The exact naming can change across browser, and might require the installation of extensions. But basically they can allow you to inspect/modify the DOM, and interact with the javascript code that run the frontend.\n",
-      "\n",
-      " - In Chrome and safari Developper tools are in the menu [Put mmenu name in english here] \n",
-      " - In firefox you might need to install [Firebug](http://getfirebug.com/)\n",
-      " - others ?\n",
-      " \n",
-      "Those will be your best friends to debug and try different approach for your extensions."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Injecting JS"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 4,
-     "metadata": {},
-     "source": [
-      "using magics"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Above tools can be tedious to edit long javascipt files. Hopefully we provide the `%%javascript` magic. This allows you to quickly inject javascript into the notebook. Still the javascript injected this way will not survive reloading. Hence it is a good tool for testing an refinig a script.\n",
-      "\n",
-      "You might see here and there people modifying css and injecting js into notebook by reading file and publishing them into the notebook.\n",
-      "Not only this often break the flow of the notebook and make the re-execution of the notebook broken, but it also mean that you need to execute those cells on all the notebook every time you need to update the code.\n",
-      "\n",
-      "This can still be usefull in some cases, like the `%autosave` magic that allows to control the time between each save. But this can be replaced by a Javascript dropdown menu to select save interval."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "## you can inspect the autosave code to see what it does.\n",
-      "%autosave??"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 4,
-     "metadata": {},
-     "source": [
-      "custom.js"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To inject Javascript we provide an entry point: `custom.js` that allow teh user to execute and load other resources into the notebook.\n",
-      "Javascript code in `custom.js` will be executed when the notebook app start and can then be used to customise almost anything in the UI and in the behavior of the notebook.\n",
-      "\n",
-      "`custom.js` can be found in IPython profile dir, and so you can have different UI modification on a per profile basis, as well as share your modfication with others."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 5,
-     "metadata": {},
-     "source": [
-      "Because we like you...."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You have been provided with an already existing profile folder with this tutorial...\n",
-      "start the notebook from the root of the tutorial directory with :\n",
-      "\n",
-      "```bash\n",
-      "$ ipython notebook --ProfileDir.location=./profile_euroscipy\n",
-      "```"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 5,
-     "metadata": {},
-     "source": [
-      "but back to theory"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "profile_dir = ! ipython locate\n",
-      "profile_dir = profile_dir[0]\n",
-      "profile_dir"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 1,
-       "text": [
-        "'/Users/bussonniermatthias/.ipython'"
-       ]
-      }
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "and custom js is in "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import os.path\n",
-      "custom_js_path = os.path.join(profile_dir,'profile_default','static','custom','custom.js')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#  my custom js\n",
-      "with open(custom_js_path) as f:\n",
-      "    for l in f: \n",
-      "        print l,"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "// we want strict javascript that fails\n",
-        "// on ambiguous syntax\n",
-        "\"using strict\";\n",
-        "\n",
-        "// notebook loaded is not perfect as it is re-triggerd on\n",
-        "// revert to checkpoint but this allow extesnsion to be loaded\n",
-        "// late enough to work.\n",
-        "$([IPython.events]).on('notebook_loaded.Notebook', function(){\n",
-        "\n",
-        "\n",
-        "    /**  Use path to js file relative to /static/ dir without leading slash, or\n",
-        "     *  js extension.\n",
-        "     *  Link directly to file is js extension isa simple file\n",
-        "     *\n",
-        "     *  first argument of require is a **list** that can contains several modules if needed.\n",
-        "     **/\n",
-        "\n",
-        "    //require(['custom/noscroll']);\n",
-        "    // require(['custom/clean_start'])\n",
-        "    // require(['custom/toggle_all_line_number'])\n",
-        "    // require(['custom/gist_it']);\n",
-        "    // require(['custom/autosavetime']);\n",
-        "\n",
-        "    /**\n",
-        "     *  Link to entrypoint if extesnsion is a folder.\n",
-        "     *  to be consistent with commonjs module, the entrypoint is main.js\n",
-        "     *  here youcan also trigger a custom function on load that will do extra\n",
-        "     *  action with the module if needed\n",
-        "     **/\n",
-        "    require(['custom/slidemode/main'])\n",
-        "\n",
-        "    // require(['custom/autoscroll']);\n",
-        "\n",
-        "    //require(['custom/css_selector/main'])\n",
-        "    require(['custom/pre_exec_strip']);\n",
-        "    // require(['custom/no_exec_dunder']);\n",
-        "    // load_ext('nbviewer_theme')\n",
-        "\n",
-        "\n",
-        "    require(['custom/clippytip/main']);\n",
-        "\n",
-        "    IPython.toolbar.add_buttons_group([\n",
-        "        {\n",
-        "             'label'   : 'run qtconsole',\n",
-        "             'icon'    : 'icon-paper-clip', // select your icon from http://jqueryui.com/themeroller/\n",
-        "             'callback': function(){\n",
-        "                IPython.tooltip.remove_and_cancel_tooltip(true)\n",
-        "                $('#tooltip').empty() \n",
-        "                $('#tooltip').attr('style','') \n",
-        "                IPython.tooltip = new IPython.ClippyTip()\n",
-        "             }\n",
-        "        },\n",
-        "        {\n",
-        "             'label'   : 'run qtconsole',\n",
-        "             'icon'    : 'icon-th-large', // select your icon from http://jqueryui.com/themeroller/\n",
-        "             'callback': function(){\n",
-        "                IPython.tooltip.remove_and_cancel_tooltip(true)\n",
-        "                $('#tooltip').empty() \n",
-        "                $('#tooltip').attr('style','')\n",
-        "                IPython.tooltip = new IPython.Tooltip()\n",
-        "            }\n",
-        "        }\n",
-        "        // add more button here if needed.\n",
-        "        ]);\n",
-        "    //\n",
-        "\n",
-        "});\n",
-        "\n",
-        "/*\n",
-        "$([IPython.events]).on('notebook_loaded.Notebook', function(){\n",
-        "    IPython.toolbar.add_buttons_group([\n",
-        "                    {\n",
-        "                'label'   : 'run qtconsole',\n",
-        "                'icon'    : 'ui-icon-calculator',\n",
-        "                'callback': function(){IPython.notebook.kernel.execute('%qtconsole')}\n",
-        "                }\n",
-        "            ]);\n",
-        "});\n",
-        "*/\n",
-        "\n",
-        "//$([IPython.events]).on('notebook_loaded.Notebook', function(){\n",
-        "//    mobile_preset = []\n",
-        "//    var edit = function(div, cell) {\n",
-        "//            var button_container = $(div);\n",
-        "//            var button = $('<div/>').button({icons:{primary:'ui-icon-pencil'}});\n",
-        "//                button.click(function(){\n",
-        "//                    cell.edit()\n",
-        "//                        })\n",
-        "//            button_container.append(button);\n",
-        "//    }\n",
-        "//\n",
-        "//    IPython.CellToolbar.register_callback('mobile.edit',edit);\n",
-        "//    mobile_preset.push('mobile.edit');\n",
-        "//\n",
-        "//    IPython.CellToolbar.register_preset('Mobile',mobile_preset);\n",
-        "//});\n"
-       ]
-      }
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Note that `custom.js` is ment to be modified by user, when writing a script, you can define it in a separate file and add a line of configuration into `custom.js` that will fetch and execute the file."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Warning** : even if modification of `custom.js` take effect immediately after browser refresh (except if browser cache is aggressive), *creating* a file in `static/` directory need a **server restart**."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Exercise :"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      " - Create a `custom.js` in the right location with the following content:\n",
-      "```javascript\n",
-      "alert(\"hello world from custom.js\")\n",
-      "```\n",
-      "\n",
-      " - Restart your server and open any notebook.\n",
-      " - Be greeted by custom.js"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Have a look at [default custom.js](https://github.com/ipython/ipython/blob/1.x/IPython/html/static/custom/custom.js), to see it's content and some more explanation."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 4,
-     "metadata": {},
-     "source": [
-      "For the quick ones : "
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We've seen above that you can change the autosave rate by using a magic. This is typically something I don't want to type everytime, and that I don't like to embed into my workwlow and documents. (reader don't care what my autosave time is), let's build an extension that allow to do it.  "
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "foo": true
-     },
-     "source": [
-      "Create a dropdow elemement in the toolbar (DOM `IPython.toolbar.element`), you will need \n",
-      "\n",
-      "- `IPython.notebook.set_autosave_interval(miliseconds)`\n",
-      "- know that 1min = 60 sec, and 1 sec = 1000 ms"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "```javascript\n",
-      "\n",
-      "var label = jQuery('<label/>').text('AutoScroll Limit:');\n",
-      "var select = jQuery('<select/>')\n",
-      "     //.append(jQuery('<option/>').attr('value', '2').text('2min (default)'))\n",
-      "     .append(jQuery('<option/>').attr('value', undefined).text('disabled'))\n",
-      "\n",
-      "     // TODO:\n",
-      "     //the_toolbar_element.append(label)\n",
-      "     //the_toolbar_element.append(select);\n",
-      "     \n",
-      "select.change(function() {\n",
-      "     var val = jQuery(this).val() // val will be the value in [2]\n",
-      "     // TODO\n",
-      "     // this will be called when dropdown changes\n",
-      "\n",
-      "});\n",
-      "\n",
-      "var time_m = [1,5,10,15,30];\n",
-      "for (var i=0; i < time_m.length; i++) {\n",
-      "     var ts = time_m[i];\n",
-      "                                          //[2]   ____ this will be `val` on [1]  \n",
-      "                                          //     | \n",
-      "                                          //     v \n",
-      "     select.append($('<option/>').attr('value', ts).text(thr+'min'));\n",
-      "     // this will fill up the dropdown `select` with\n",
-      "     // 1 min\n",
-      "     // 5 min\n",
-      "     // 10 min\n",
-      "     // 10 min\n",
-      "     // ...\n",
-      "}\n",
-      "```"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 4,
-     "metadata": {},
-     "source": [
-      "A non interactive example first"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I like my cython to be nicely highlighted\n",
-      "\n",
-      "```javascript\n",
-      "IPython.config.cell_magic_highlight['magic_text/x-cython'] = {}\n",
-      "IPython.config.cell_magic_highlight['magic_text/x-cython'].reg = [/^%%cython/]\n",
-      "```\n",
-      "\n",
-      "`text/x-cython` is the name of CodeMirror mode name, `magic_` prefix will just patch the mode so that the first line that contains a magic does not screw up the highlighting. `reg`is a list or regular expression that will trigger the change of mode."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 4,
-     "metadata": {},
-     "source": [
-      "Get more docs"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Sadly you will have to read the js source file (but there are lots of comments) an/or build the javascript documentation using yuidoc.\n",
-      "If you have `node` and `yui-doc` installed:"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "```bash\n",
-      "$ cd ~/ipython/IPython/html/static/notebook/js/\n",
-      "$ yuidoc . --server\n",
-      "warn: (yuidoc): Failed to extract port, setting to the default :3000\n",
-      "info: (yuidoc): Starting YUIDoc@0.3.45 using YUI@3.9.1 with NodeJS@0.10.15\n",
-      "info: (yuidoc): Scanning for yuidoc.json file.\n",
-      "info: (yuidoc): Starting YUIDoc with the following options:\n",
-      "info: (yuidoc):\n",
-      "{ port: 3000,\n",
-      "  nocode: false,\n",
-      "  paths: [ '.' ],\n",
-      "  server: true,\n",
-      "  outdir: './out' }\n",
-      "info: (yuidoc): Scanning for yuidoc.json file.\n",
-      "info: (server): Starting server: http://127.0.0.1:3000\n",
-      "```\n",
-      "\n",
-      "and browse http://127.0.0.1:3000 to get docs"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 4,
-     "metadata": {
-      "foo": true
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Embrasing web standards"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the main reason that allowed us to developp the current notebook web application \n",
+    "was to embrase the web technology. \n",
+    "\n",
+    "By beeing a pure web application using HTML, Javascript and CSS, the Notebook can get \n",
+    "all the web technology improvement for free. Thus, as browsers support for different \n",
+    "media extend, The notebook web app should be able to be compatible without modification. \n",
+    "\n",
+    "This is also true with performance of the User Interface as the speed of javascript VM increase. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The other advantage of using only web technology is that the code of the interface is fully accessible to the end user, and modifiable live.\n",
+    "Even if this task is not always easy, we strive to keep our code as accessible and reusable as possible.\n",
+    "This should allow with minimum effort to develop small extensions that customize the behavior of the web interface. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tempering with Notebook app"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first tool that is availlable to you and that you shoudl be aware of are browser \"developpers tool\". The exact naming can change across browser, and might require the installation of extensions. But basically they can allow you to inspect/modify the DOM, and interact with the javascript code that run the frontend.\n",
+    "\n",
+    " - In Chrome and safari Developper tools are in the menu [Put mmenu name in english here] \n",
+    " - In firefox you might need to install [Firebug](http://getfirebug.com/)\n",
+    " - others ?\n",
+    " \n",
+    "Those will be your best friends to debug and try different approach for your extensions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Injecting JS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### using magics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Above tools can be tedious to edit long javascipt files. Hopefully we provide the `%%javascript` magic. This allows you to quickly inject javascript into the notebook. Still the javascript injected this way will not survive reloading. Hence it is a good tool for testing an refinig a script.\n",
+    "\n",
+    "You might see here and there people modifying css and injecting js into notebook by reading file and publishing them into the notebook.\n",
+    "Not only this often break the flow of the notebook and make the re-execution of the notebook broken, but it also mean that you need to execute those cells on all the notebook every time you need to update the code.\n",
+    "\n",
+    "This can still be usefull in some cases, like the `%autosave` magic that allows to control the time between each save. But this can be replaced by a Javascript dropdown menu to select save interval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "## you can inspect the autosave code to see what it does.\n",
+    "%autosave??"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### custom.js"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To inject Javascript we provide an entry point: `custom.js` that allow teh user to execute and load other resources into the notebook.\n",
+    "Javascript code in `custom.js` will be executed when the notebook app start and can then be used to customise almost anything in the UI and in the behavior of the notebook.\n",
+    "\n",
+    "`custom.js` can be found in IPython profile dir, and so you can have different UI modification on a per profile basis, as well as share your modfication with others."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Because we like you...."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You have been provided with an already existing profile folder with this tutorial...\n",
+    "start the notebook from the root of the tutorial directory with :\n",
+    "\n",
+    "```bash\n",
+    "$ ipython notebook --ProfileDir.location=./profile_euroscipy\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### but back to theory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/Users/bussonniermatthias/.ipython'"
+      ]
      },
-     "source": [
-      "Some convenience methods"
-     ]
-    },
-    {
-     "cell_type": "markdown",
+     "execution_count": 1,
      "metadata": {},
-     "source": [
-      "By browsing the doc you will see that we have soem convenience methods that avoid to re-invent the UI everytime :\n",
-      "```javascript\n",
-      "IPython.toolbar.add_buttons_group([\n",
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "profile_dir = ! ipython locate\n",
+    "profile_dir = profile_dir[0]\n",
+    "profile_dir"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and custom js is in "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os.path\n",
+    "custom_js_path = os.path.join(profile_dir,'profile_default','static','custom','custom.js')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "// we want strict javascript that fails\n",
+      "// on ambiguous syntax\n",
+      "\"using strict\";\n",
+      "\n",
+      "// notebook loaded is not perfect as it is re-triggerd on\n",
+      "// revert to checkpoint but this allow extesnsion to be loaded\n",
+      "// late enough to work.\n",
+      "$([IPython.events]).on('notebook_loaded.Notebook', function(){\n",
+      "\n",
+      "\n",
+      "    /**  Use path to js file relative to /static/ dir without leading slash, or\n",
+      "     *  js extension.\n",
+      "     *  Link directly to file is js extension isa simple file\n",
+      "     *\n",
+      "     *  first argument of require is a **list** that can contains several modules if needed.\n",
+      "     **/\n",
+      "\n",
+      "    //require(['custom/noscroll']);\n",
+      "    // require(['custom/clean_start'])\n",
+      "    // require(['custom/toggle_all_line_number'])\n",
+      "    // require(['custom/gist_it']);\n",
+      "    // require(['custom/autosavetime']);\n",
+      "\n",
+      "    /**\n",
+      "     *  Link to entrypoint if extesnsion is a folder.\n",
+      "     *  to be consistent with commonjs module, the entrypoint is main.js\n",
+      "     *  here youcan also trigger a custom function on load that will do extra\n",
+      "     *  action with the module if needed\n",
+      "     **/\n",
+      "    require(['custom/slidemode/main'])\n",
+      "\n",
+      "    // require(['custom/autoscroll']);\n",
+      "\n",
+      "    //require(['custom/css_selector/main'])\n",
+      "    require(['custom/pre_exec_strip']);\n",
+      "    // require(['custom/no_exec_dunder']);\n",
+      "    // load_ext('nbviewer_theme')\n",
+      "\n",
+      "\n",
+      "    require(['custom/clippytip/main']);\n",
+      "\n",
+      "    IPython.toolbar.add_buttons_group([\n",
       "        {\n",
       "             'label'   : 'run qtconsole',\n",
-      "             'icon'    : 'icon-terminal', // select your icon from \n",
-      "                                          // http://fortawesome.github.io/Font-Awesome/icons/\n",
-      "             'callback': function(){IPython.notebook.kernel.execute('%qtconsole')}\n",
+      "             'icon'    : 'icon-paper-clip', // select your icon from http://jqueryui.com/themeroller/\n",
+      "             'callback': function(){\n",
+      "                IPython.tooltip.remove_and_cancel_tooltip(true)\n",
+      "                $('#tooltip').empty() \n",
+      "                $('#tooltip').attr('style','') \n",
+      "                IPython.tooltip = new IPython.ClippyTip()\n",
+      "             }\n",
+      "        },\n",
+      "        {\n",
+      "             'label'   : 'run qtconsole',\n",
+      "             'icon'    : 'icon-th-large', // select your icon from http://jqueryui.com/themeroller/\n",
+      "             'callback': function(){\n",
+      "                IPython.tooltip.remove_and_cancel_tooltip(true)\n",
+      "                $('#tooltip').empty() \n",
+      "                $('#tooltip').attr('style','')\n",
+      "                IPython.tooltip = new IPython.Tooltip()\n",
+      "            }\n",
       "        }\n",
       "        // add more button here if needed.\n",
       "        ]);\n",
-      "```\n",
-      "with a [lot of icons] you can select from. \n",
+      "    //\n",
       "\n",
-      "[lot of icons]: http://fortawesome.github.io/Font-Awesome/icons/"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "foo": true
-     },
-     "source": [
-      "Cell Metadata"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "foo": true
-     },
-     "source": [
-      "The most requested feature is generaly to be able to distinguish individual cell in th enotebook, or run specific action with them.\n",
-      "To do so, you can either use `IPython.notebook.get_selected_cell()`, or rely on `CellToolbar`. This allow you to register aset of action and graphical element that will be attached on individual cells."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Cell Toolbar"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can see some example of what can be done by toggling the `Cell Toolbar` selector in the toolbar on top of the notebook. It provide two default `presets` that are `Default` and `slideshow`. Default allow edit the metadata attached to each cell manually."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "First we define a function that takes at first parameter an element on the DOM in which to inject UI element. Second element will be the cell this element will be registerd with. Then we will need to register that function ad give it a name.\n"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 4,
-     "metadata": {},
-     "source": [
-      "Register a callback"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "var CellToolbar = IPython.CellToolbar\n",
-      "var toggle =  function(div, cell) {\n",
-      "     var button_container = $(div)\n",
-      "\n",
-      "     // let's create a button that show the  current value of the metadata\n",
-      "     var button = $('<button/>').addClass('btn btn-mini').text(String(cell.metadata.foo));\n",
-      "\n",
-      "     // On click, change the metadata value and update the button label\n",
-      "     button.click(function(){\n",
-      "                 var v = cell.metadata.foo;\n",
-      "                 cell.metadata.foo = !v;\n",
-      "                 button.text(String(!v));\n",
-      "             })\n",
-      "\n",
-      "     // add the button to the DOM div.\n",
-      "     button_container.append(button);\n",
-      "}\n",
+      "});\n",
       "\n",
-      " // now we register the callback under the name foo to give the\n",
-      " // user the ability to use it later\n",
-      " CellToolbar.register_callback('tuto.foo', toggle);"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "javascript": [
-        "var CellToolbar = IPython.CellToolbar\n",
-        "var toggle =  function(div, cell) {\n",
-        "     var button_container = $(div)\n",
-        "\n",
-        "     // let's create a button that show the  current value of the metadata\n",
-        "     var button = $('<button/>').addClass('btn btn-mini').text(String(cell.metadata.foo));\n",
-        "\n",
-        "     // On click, change the metadata value and update the button label\n",
-        "     button.click(function(){\n",
-        "                 var v = cell.metadata.foo;\n",
-        "                 cell.metadata.foo = !v;\n",
-        "                 button.text(String(!v));\n",
-        "             })\n",
-        "\n",
-        "     // add the button to the DOM div.\n",
-        "     button_container.append(button);\n",
-        "}\n",
-        "\n",
-        " // now we register the callback under the name foo to give the\n",
-        " // user the ability to use it later\n",
-        " CellToolbar.register_callback('tuto.foo', toggle);"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Javascript at 0x10c94a590>"
-       ]
-      }
-     ],
-     "prompt_number": 51
-    },
-    {
-     "cell_type": "heading",
-     "level": 4,
-     "metadata": {},
-     "source": [
-      "Registering a preset"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This function can now be part of many `preset` of the CellToolBar."
+      "/*\n",
+      "$([IPython.events]).on('notebook_loaded.Notebook', function(){\n",
+      "    IPython.toolbar.add_buttons_group([\n",
+      "                    {\n",
+      "                'label'   : 'run qtconsole',\n",
+      "                'icon'    : 'ui-icon-calculator',\n",
+      "                'callback': function(){IPython.notebook.kernel.execute('%qtconsole')}\n",
+      "                }\n",
+      "            ]);\n",
+      "});\n",
+      "*/\n",
+      "\n",
+      "//$([IPython.events]).on('notebook_loaded.Notebook', function(){\n",
+      "//    mobile_preset = []\n",
+      "//    var edit = function(div, cell) {\n",
+      "//            var button_container = $(div);\n",
+      "//            var button = $('<div/>').button({icons:{primary:'ui-icon-pencil'}});\n",
+      "//                button.click(function(){\n",
+      "//                    cell.edit()\n",
+      "//                        })\n",
+      "//            button_container.append(button);\n",
+      "//    }\n",
+      "//\n",
+      "//    IPython.CellToolbar.register_callback('mobile.edit',edit);\n",
+      "//    mobile_preset.push('mobile.edit');\n",
+      "//\n",
+      "//    IPython.CellToolbar.register_preset('Mobile',mobile_preset);\n",
+      "//});\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%javascript\n",
-      "IPython.CellToolbar.register_preset('Tutorial 1',['tuto.foo','default.rawedit'])\n",
-      "IPython.CellToolbar.register_preset('Tutorial 2',['slideshow.select','tuto.foo'])"
-     ],
-     "language": "python",
-     "metadata": {
-      "foo": true,
-      "slideshow": {
-       "slide_type": "subslide"
-      }
+    }
+   ],
+   "source": [
+    "#  my custom js\n",
+    "with open(custom_js_path) as f:\n",
+    "    for l in f: \n",
+    "        print l,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that `custom.js` is ment to be modified by user, when writing a script, you can define it in a separate file and add a line of configuration into `custom.js` that will fetch and execute the file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Warning** : even if modification of `custom.js` take effect immediately after browser refresh (except if browser cache is aggressive), *creating* a file in `static/` directory need a **server restart**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise :"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " - Create a `custom.js` in the right location with the following content:\n",
+    "```javascript\n",
+    "alert(\"hello world from custom.js\")\n",
+    "```\n",
+    "\n",
+    " - Restart your server and open any notebook.\n",
+    " - Be greeted by custom.js"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Have a look at [default custom.js](https://github.com/ipython/ipython/blob/1.x/IPython/html/static/custom/custom.js), to see it's content and some more explanation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### For the quick ones : "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We've seen above that you can change the autosave rate by using a magic. This is typically something I don't want to type everytime, and that I don't like to embed into my workwlow and documents. (reader don't care what my autosave time is), let's build an extension that allow to do it.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "foo": true
+   },
+   "source": [
+    "Create a dropdow elemement in the toolbar (DOM `IPython.toolbar.element`), you will need \n",
+    "\n",
+    "- `IPython.notebook.set_autosave_interval(miliseconds)`\n",
+    "- know that 1min = 60 sec, and 1 sec = 1000 ms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```javascript\n",
+    "\n",
+    "var label = jQuery('<label/>').text('AutoScroll Limit:');\n",
+    "var select = jQuery('<select/>')\n",
+    "     //.append(jQuery('<option/>').attr('value', '2').text('2min (default)'))\n",
+    "     .append(jQuery('<option/>').attr('value', undefined).text('disabled'))\n",
+    "\n",
+    "     // TODO:\n",
+    "     //the_toolbar_element.append(label)\n",
+    "     //the_toolbar_element.append(select);\n",
+    "     \n",
+    "select.change(function() {\n",
+    "     var val = jQuery(this).val() // val will be the value in [2]\n",
+    "     // TODO\n",
+    "     // this will be called when dropdown changes\n",
+    "\n",
+    "});\n",
+    "\n",
+    "var time_m = [1,5,10,15,30];\n",
+    "for (var i=0; i < time_m.length; i++) {\n",
+    "     var ts = time_m[i];\n",
+    "                                          //[2]   ____ this will be `val` on [1]  \n",
+    "                                          //     | \n",
+    "                                          //     v \n",
+    "     select.append($('<option/>').attr('value', ts).text(thr+'min'));\n",
+    "     // this will fill up the dropdown `select` with\n",
+    "     // 1 min\n",
+    "     // 5 min\n",
+    "     // 10 min\n",
+    "     // 10 min\n",
+    "     // ...\n",
+    "}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### A non interactive example first"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I like my cython to be nicely highlighted\n",
+    "\n",
+    "```javascript\n",
+    "IPython.config.cell_magic_highlight['magic_text/x-cython'] = {}\n",
+    "IPython.config.cell_magic_highlight['magic_text/x-cython'].reg = [/^%%cython/]\n",
+    "```\n",
+    "\n",
+    "`text/x-cython` is the name of CodeMirror mode name, `magic_` prefix will just patch the mode so that the first line that contains a magic does not screw up the highlighting. `reg`is a list or regular expression that will trigger the change of mode."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Get more docs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sadly you will have to read the js source file (but there are lots of comments) an/or build the javascript documentation using yuidoc.\n",
+    "If you have `node` and `yui-doc` installed:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```bash\n",
+    "$ cd ~/ipython/IPython/html/static/notebook/js/\n",
+    "$ yuidoc . --server\n",
+    "warn: (yuidoc): Failed to extract port, setting to the default :3000\n",
+    "info: (yuidoc): Starting YUIDoc@0.3.45 using YUI@3.9.1 with NodeJS@0.10.15\n",
+    "info: (yuidoc): Scanning for yuidoc.json file.\n",
+    "info: (yuidoc): Starting YUIDoc with the following options:\n",
+    "info: (yuidoc):\n",
+    "{ port: 3000,\n",
+    "  nocode: false,\n",
+    "  paths: [ '.' ],\n",
+    "  server: true,\n",
+    "  outdir: './out' }\n",
+    "info: (yuidoc): Scanning for yuidoc.json file.\n",
+    "info: (server): Starting server: http://127.0.0.1:3000\n",
+    "```\n",
+    "\n",
+    "and browse http://127.0.0.1:3000 to get docs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "foo": true
+   },
+   "source": [
+    "#### Some convenience methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By browsing the doc you will see that we have soem convenience methods that avoid to re-invent the UI everytime :\n",
+    "```javascript\n",
+    "IPython.toolbar.add_buttons_group([\n",
+    "        {\n",
+    "             'label'   : 'run qtconsole',\n",
+    "             'icon'    : 'icon-terminal', // select your icon from \n",
+    "                                          // http://fortawesome.github.io/Font-Awesome/icons/\n",
+    "             'callback': function(){IPython.notebook.kernel.execute('%qtconsole')}\n",
+    "        }\n",
+    "        // add more button here if needed.\n",
+    "        ]);\n",
+    "```\n",
+    "with a [lot of icons] you can select from. \n",
+    "\n",
+    "[lot of icons]: http://fortawesome.github.io/Font-Awesome/icons/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "foo": true
+   },
+   "source": [
+    "## Cell Metadata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "foo": true
+   },
+   "source": [
+    "The most requested feature is generaly to be able to distinguish individual cell in th enotebook, or run specific action with them.\n",
+    "To do so, you can either use `IPython.notebook.get_selected_cell()`, or rely on `CellToolbar`. This allow you to register aset of action and graphical element that will be attached on individual cells."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Cell Toolbar"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can see some example of what can be done by toggling the `Cell Toolbar` selector in the toolbar on top of the notebook. It provide two default `presets` that are `Default` and `slideshow`. Default allow edit the metadata attached to each cell manually."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First we define a function that takes at first parameter an element on the DOM in which to inject UI element. Second element will be the cell this element will be registerd with. Then we will need to register that function ad give it a name.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Register a callback"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "var CellToolbar = IPython.CellToolbar\n",
+       "var toggle =  function(div, cell) {\n",
+       "     var button_container = $(div)\n",
+       "\n",
+       "     // let's create a button that show the  current value of the metadata\n",
+       "     var button = $('<button/>').addClass('btn btn-mini').text(String(cell.metadata.foo));\n",
+       "\n",
+       "     // On click, change the metadata value and update the button label\n",
+       "     button.click(function(){\n",
+       "                 var v = cell.metadata.foo;\n",
+       "                 cell.metadata.foo = !v;\n",
+       "                 button.text(String(!v));\n",
+       "             })\n",
+       "\n",
+       "     // add the button to the DOM div.\n",
+       "     button_container.append(button);\n",
+       "}\n",
+       "\n",
+       " // now we register the callback under the name foo to give the\n",
+       " // user the ability to use it later\n",
+       " CellToolbar.register_callback('tuto.foo', toggle);"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript at 0x10c94a590>"
+      ]
      },
-     "outputs": [
-      {
-       "javascript": [
-        "IPython.CellToolbar.register_preset('Tutorial 1',['tuto.foo','default.rawedit'])\n",
-        "IPython.CellToolbar.register_preset('Tutorial 2',['slideshow.select','tuto.foo'])"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<IPython.core.display.Javascript at 0x10c94a510>"
-       ]
-      }
-     ],
-     "prompt_number": 54
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You should now have access to two presets :\n",
-      "\n",
-      "  - Tutorial 1\n",
-      "  - Tutorial 2\n",
-      "  \n",
-      "And check that the buttons you defin share state when you toggle preset. \n",
-      "Check moreover that the metadata of the cell is modified when you clisk the button, and that when saved on reloaded the metadata is still availlable."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 4,
-     "metadata": {},
-     "source": [
-      "Exercise:"
-     ]
-    },
-    {
-     "cell_type": "markdown",
      "metadata": {},
-     "source": [
-      "Try to wrap the all code in a file, put this file in `{profile}/static/custom/<a-name>.js`, and add \n",
-      "\n",
-      "```\n",
-      "require(['custom/<a-name>']);\n",
-      "```\n",
-      "\n",
-      "in `custom.js` to have this script automatically loaded in all your notebooks.\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "`require` is provided by a [javascript library](http://requirejs.org/) that allow to express dependency. For simple extension like the previous one we directly mute the global namespace, but for more complexe extension you could pass acallback to `require([...], <callback>)` call, to allow the user to pass configuration information to your plugin.\n",
-      "\n",
-      "In Python lang, \n",
-      "\n",
-      "```javascript\n",
-      "require(['a/b', 'c/d'], function( e, f){\n",
-      "    e.something()\n",
-      "    f.something()\n",
-      "})\n",
-      "```\n",
-      "\n",
-      "could be read as\n",
-      "```python\n",
-      "import a.b as e\n",
-      "import c.d as f\n",
-      "e.something()\n",
-      "f.something()\n",
-      "```\n",
-      "\n",
-      "\n",
-      "See for example @damianavila [\"ZenMode\" plugin](https://github.com/ipython-contrib/IPython-notebook-extensions/blob/master/custom.example.js#L34) :\n",
-      "\n",
-      "```javascript\n",
-      "\n",
-      "// read that as\n",
-      "// import custom.zenmode.main as zenmode\n",
-      "require(['custom/zenmode/main'],function(zenmode){\n",
-      "    zenmode.background('images/back12.jpg');\n",
-      "})\n",
-      "```\n"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 4,
-     "metadata": {},
-     "source": [
-      "For the quickest"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Try to use [the following](https://github.com/ipython/ipython/blob/1.x/IPython/html/static/notebook/js/celltoolbar.js#L367) to bind a dropdown list to `cell.metadata.difficulty.select`. \n",
-      "\n",
-      "It should be able to take the 4 following values :\n",
-      "\n",
-      " - `<None>`\n",
-      " - `Easy`\n",
-      " - `Medium`\n",
-      " - `Hard`\n",
-      " \n",
-      "We will use it to customise the output of the converted notebook depending of the tag on each cell"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load soln/celldiff.js"
-     ],
-     "language": "python",
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%javascript\n",
+    "var CellToolbar = IPython.CellToolbar\n",
+    "var toggle =  function(div, cell) {\n",
+    "     var button_container = $(div)\n",
+    "\n",
+    "     // let's create a button that show the  current value of the metadata\n",
+    "     var button = $('<button/>').addClass('btn btn-mini').text(String(cell.metadata.foo));\n",
+    "\n",
+    "     // On click, change the metadata value and update the button label\n",
+    "     button.click(function(){\n",
+    "                 var v = cell.metadata.foo;\n",
+    "                 cell.metadata.foo = !v;\n",
+    "                 button.text(String(!v));\n",
+    "             })\n",
+    "\n",
+    "     // add the button to the DOM div.\n",
+    "     button_container.append(button);\n",
+    "}\n",
+    "\n",
+    " // now we register the callback under the name foo to give the\n",
+    " // user the ability to use it later\n",
+    " CellToolbar.register_callback('tuto.foo', toggle);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Registering a preset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function can now be part of many `preset` of the CellToolBar."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": false,
+    "foo": true,
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "IPython.CellToolbar.register_preset('Tutorial 1',['tuto.foo','default.rawedit'])\n",
+       "IPython.CellToolbar.register_preset('Tutorial 2',['slideshow.select','tuto.foo'])"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript at 0x10c94a510>"
+      ]
+     },
      "metadata": {},
-     "outputs": []
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "%%javascript\n",
+    "IPython.CellToolbar.register_preset('Tutorial 1',['tuto.foo','default.rawedit'])\n",
+    "IPython.CellToolbar.register_preset('Tutorial 2',['slideshow.select','tuto.foo'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You should now have access to two presets :\n",
+    "\n",
+    "  - Tutorial 1\n",
+    "  - Tutorial 2\n",
+    "  \n",
+    "And check that the buttons you defin share state when you toggle preset. \n",
+    "Check moreover that the metadata of the cell is modified when you clisk the button, and that when saved on reloaded the metadata is still availlable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Exercise:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Try to wrap the all code in a file, put this file in `{profile}/static/custom/<a-name>.js`, and add \n",
+    "\n",
+    "```\n",
+    "require(['custom/<a-name>']);\n",
+    "```\n",
+    "\n",
+    "in `custom.js` to have this script automatically loaded in all your notebooks.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`require` is provided by a [javascript library](http://requirejs.org/) that allow to express dependency. For simple extension like the previous one we directly mute the global namespace, but for more complexe extension you could pass acallback to `require([...], <callback>)` call, to allow the user to pass configuration information to your plugin.\n",
+    "\n",
+    "In Python lang, \n",
+    "\n",
+    "```javascript\n",
+    "require(['a/b', 'c/d'], function( e, f){\n",
+    "    e.something()\n",
+    "    f.something()\n",
+    "})\n",
+    "```\n",
+    "\n",
+    "could be read as\n",
+    "```python\n",
+    "import a.b as e\n",
+    "import c.d as f\n",
+    "e.something()\n",
+    "f.something()\n",
+    "```\n",
+    "\n",
+    "\n",
+    "See for example @damianavila [\"ZenMode\" plugin](https://github.com/ipython-contrib/IPython-notebook-extensions/blob/master/custom.example.js#L34) :\n",
+    "\n",
+    "```javascript\n",
+    "\n",
+    "// read that as\n",
+    "// import custom.zenmode.main as zenmode\n",
+    "require(['custom/zenmode/main'],function(zenmode){\n",
+    "    zenmode.background('images/back12.jpg');\n",
+    "})\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### For the quickest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Try to use [the following](https://github.com/ipython/ipython/blob/1.x/IPython/html/static/notebook/js/celltoolbar.js#L367) to bind a dropdown list to `cell.metadata.difficulty.select`. \n",
+    "\n",
+    "It should be able to take the 4 following values :\n",
+    "\n",
+    " - `<None>`\n",
+    " - `Easy`\n",
+    " - `Medium`\n",
+    " - `Hard`\n",
+    " \n",
+    "We will use it to customise the output of the converted notebook depending of the tag on each cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%load soln/celldiff.js"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Notebook/Notebook Basics.ipynb b/examples/Notebook/Notebook Basics.ipynb
index 4024524..ff28b01 100644
--- a/examples/Notebook/Notebook Basics.ipynb
+++ b/examples/Notebook/Notebook Basics.ipynb
@@ -1,4 +1,237 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Notebook Basics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook assumes that you already have IPython [installed](http://ipython.org/install.html) and are able to start the notebook server by running:\n",
+    "\n",
+    "    ipython notebook\n",
+    "\n",
+    "For more details on how to run the notebook server, see [Running the Notebook Server](Running the Notebook Server.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Notebook dashboard"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you first start the notebook server, your browser will open to the notebook dashboard. The dashboard serves as a home page for the notebook. Its main purpose is to display the notebooks in the current directory. For example, here is a screenshot of the dashboard page for the `examples` directory in the IPython repository:\n",
+    "\n",
+    "<img src=\"images/dashboard_notebooks_tab.png\" />\n",
+    "\n",
+    "The top of the notebook list displays clickable breadcrumbs of the current directory. By clicking on these breadcrumbs or on sub-directories in the notebook list, you can navigate your file system.\n",
+    "\n",
+    "To create a new notebook, click on the \"New Notebook\" button at the top of the list.\n",
+    "\n",
+    "Notebooks can be uploaded to the current directory by dragging a notebook file onto the notebook list or by the \"click here\" text above the list.\n",
+    "\n",
+    "The notebook list shows a red \"Shutdown\" button for running notebooks and a \"Delete\" button for stopped notebooks. Notebook remain running until you explicitly click the \"Shutdown\" button; closing the notebook's page is not sufficient.\n",
+    "\n",
+    "To see all of your running notebooks along with their directories, click on the \"Running\" tab:\n",
+    "\n",
+    "<img src=\"images/dashboard_running_tab.png\" />\n",
+    "\n",
+    "This view provides a convenient way to track notebooks that you start as you navigate the file system in a long running notebook server."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview of the Notebook UI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert\">\n",
+    "As of IPython 2.0, the user interface has changed significantly. Because of this we highly recommend existing users to review this information after upgrading to IPython 2.0. All new users of IPython should review this information as well.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you create a new notebook or open an existing one, you will be taken to the notebook user interface (UI). This UI allows you to run code and author notebook documents interactively. The notebook UI has the following main areas:\n",
+    "\n",
+    "* Menu\n",
+    "* Toolbar\n",
+    "* Notebook area and cells\n",
+    "\n",
+    "IPython 2.0 has an interactive tour of these elements that can be started in the \"Help:User Interface Tour\" menu item."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modal editor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Starting with IPython 2.0, the IPython Notebook has a modal user interface. This means that the keyboard does different things depending on which mode the Notebook is in. There are two modes: edit mode and command mode."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Edit mode"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Edit mode is indicated by a green cell border and a prompt showing in the editor area:\n",
+    "\n",
+    "<img src=\"images/edit_mode.png\">\n",
+    "\n",
+    "When a cell is in edit mode, you can type into the cell, like a normal text editor."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "Enter edit mode by pressing `Enter` or using the mouse to click on a cell's editor area.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Command mode"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Command mode is indicated by a grey cell border:\n",
+    "\n",
+    "<img src=\"images/command_mode.png\">\n",
+    "\n",
+    "When you are in command mode, you are able to edit the notebook as a whole, but not type into individual cells. Most importantly, in command mode, the keyboard is mapped to a set of shortcuts that let you perform notebook and cell actions efficiently. For example, if you are in command mode and you press `c`, you will copy the current cell - no modifier is needed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-error\">\n",
+    "Don't try to type into a cell in command mode; unexpected things will happen!\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "Enter command mode by pressing `Esc` or using the mouse to click *outside* a cell's editor area.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mouse navigation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All navigation and actions in the Notebook are available using the mouse through the menubar and toolbar, which are both above the main Notebook area:\n",
+    "\n",
+    "<img src=\"images/menubar_toolbar.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first idea of mouse based navigation is that **cells can be selected by clicking on them.** The currently selected cell gets a grey or green border depending on whether the notebook is in edit or command mode. If you click inside a cell's editor area, you will enter edit mode. If you click on the prompt or output area of a cell you will enter command mode.\n",
+    "\n",
+    "If you are running this notebook in a live session (not on http://nbviewer.ipython.org) try selecting different cells and going between edit and command mode. Try typing into a cell."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second idea of mouse based navigation is that **cell actions usually apply to the currently selected cell**. Thus if you want to run the code in a cell, you would select it and click the <button class='btn btn-default btn-xs'><i class=\"fa fa-play icon-play\"></i></button> button in the toolbar or the \"Cell:Run\" menu item. Similarly, to copy a cell you would select it and click the <button class='btn btn-default btn-xs'><i class=\"fa fa-copy icon-copy\"></i></button> button in the toolbar or the \"Edit:Copy\" menu item. With this simple pattern, you should be able to do most everything you need with the mouse.\n",
+    "\n",
+    "Markdown and heading cells have one other state that can be modified with the mouse. These cells can either be rendered or unrendered. When they are rendered, you will see a nice formatted representation of the cell's contents. When they are unrendered, you will see the raw text source of the cell. To render the selected cell with the mouse, click the <button class='btn btn-default btn-xs'><i class=\"fa fa-play icon-play\"></i></button> button in the toolbar or the \"Cell:Run\" menu item. To unrender the selected cell, double click on the cell."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Keyboard Navigation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The modal user interface of the IPython Notebook has been optimized for efficient keyboard usage. This is made possible by having two different sets of keyboard shortcuts: one set that is active in edit mode and another in command mode.\n",
+    "\n",
+    "The most important keyboard shortcuts are `Enter`, which enters edit mode, and `Esc`, which enters command mode.\n",
+    "\n",
+    "In edit mode, most of the keyboard is dedicated to typing into the cell's editor. Thus, in edit mode there are relatively few shortcuts:\n",
+    "\n",
+    "<img src=\"images/edit_shortcuts.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In command mode, the entire keyboard is available for shortcuts, so there are many more:\n",
+    "\n",
+    "<img src=\"images/command_shortcuts.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We recommend learning the command mode shortcuts in the following rough order:\n",
+    "\n",
+    "1. Basic navigation: `enter`, `shift-enter`, `up/k`, `down/j`\n",
+    "2. Saving the notebook: `s`\n",
+    "2. Cell types: `y`, `m`, `1-6`, `t`\n",
+    "3. Cell creation and movement: `a`, `b`, `ctrl+k`, `ctrl+j`\n",
+    "4. Cell editing: `x`, `c`, `v`, `d`, `z`, `shift+=`\n",
+    "5. Kernel operations: `i`, `.`"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "codemirror_mode": {
@@ -9,255 +242,8 @@
    "language": "python",
    "name": "python3"
   },
-  "name": "",
   "signature": "sha256:6e4f35a8bc2cb1408b995bc3c006369dd3c3e31a3e6212088f3d1e2abef36407"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Notebook Basics"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This notebook assumes that you already have IPython [installed](http://ipython.org/install.html) and are able to start the notebook server by running:\n",
-      "\n",
-      "    ipython notebook\n",
-      "\n",
-      "For more details on how to run the notebook server, see [Running the Notebook Server](Running the Notebook Server.ipynb)."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "The Notebook dashboard"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When you first start the notebook server, your browser will open to the notebook dashboard. The dashboard serves as a home page for the notebook. Its main purpose is to display the notebooks in the current directory. For example, here is a screenshot of the dashboard page for the `examples` directory in the IPython repository:\n",
-      "\n",
-      "<img src=\"images/dashboard_notebooks_tab.png\" />\n",
-      "\n",
-      "The top of the notebook list displays clickable breadcrumbs of the current directory. By clicking on these breadcrumbs or on sub-directories in the notebook list, you can navigate your file system.\n",
-      "\n",
-      "To create a new notebook, click on the \"New Notebook\" button at the top of the list.\n",
-      "\n",
-      "Notebooks can be uploaded to the current directory by dragging a notebook file onto the notebook list or by the \"click here\" text above the list.\n",
-      "\n",
-      "The notebook list shows a red \"Shutdown\" button for running notebooks and a \"Delete\" button for stopped notebooks. Notebook remain running until you explicitly click the \"Shutdown\" button; closing the notebook's page is not sufficient.\n",
-      "\n",
-      "To see all of your running notebooks along with their directories, click on the \"Running\" tab:\n",
-      "\n",
-      "<img src=\"images/dashboard_running_tab.png\" />\n",
-      "\n",
-      "This view provides a convenient way to track notebooks that you start as you navigate the file system in a long running notebook server."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Overview of the Notebook UI"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<div class=\"alert\">\n",
-      "As of IPython 2.0, the user interface has changed significantly. Because of this we highly recommend existing users to review this information after upgrading to IPython 2.0. All new users of IPython should review this information as well.\n",
-      "</div>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If you create a new notebook or open an existing one, you will be taken to the notebook user interface (UI). This UI allows you to run code and author notebook documents interactively. The notebook UI has the following main areas:\n",
-      "\n",
-      "* Menu\n",
-      "* Toolbar\n",
-      "* Notebook area and cells\n",
-      "\n",
-      "IPython 2.0 has an interactive tour of these elements that can be started in the \"Help:User Interface Tour\" menu item."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Modal editor"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Starting with IPython 2.0, the IPython Notebook has a modal user interface. This means that the keyboard does different things depending on which mode the Notebook is in. There are two modes: edit mode and command mode."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Edit mode"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Edit mode is indicated by a green cell border and a prompt showing in the editor area:\n",
-      "\n",
-      "<img src=\"images/edit_mode.png\">\n",
-      "\n",
-      "When a cell is in edit mode, you can type into the cell, like a normal text editor."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<div class=\"alert alert-success\">\n",
-      "Enter edit mode by pressing `Enter` or using the mouse to click on a cell's editor area.\n",
-      "</div>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Command mode"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Command mode is indicated by a grey cell border:\n",
-      "\n",
-      "<img src=\"images/command_mode.png\">\n",
-      "\n",
-      "When you are in command mode, you are able to edit the notebook as a whole, but not type into individual cells. Most importantly, in command mode, the keyboard is mapped to a set of shortcuts that let you perform notebook and cell actions efficiently. For example, if you are in command mode and you press `c`, you will copy the current cell - no modifier is needed."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<div class=\"alert alert-error\">\n",
-      "Don't try to type into a cell in command mode; unexpected things will happen!\n",
-      "</div>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<div class=\"alert alert-success\">\n",
-      "Enter command mode by pressing `Esc` or using the mouse to click *outside* a cell's editor area.\n",
-      "</div>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Mouse navigation"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "All navigation and actions in the Notebook are available using the mouse through the menubar and toolbar, which are both above the main Notebook area:\n",
-      "\n",
-      "<img src=\"images/menubar_toolbar.png\">"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The first idea of mouse based navigation is that **cells can be selected by clicking on them.** The currently selected cell gets a grey or green border depending on whether the notebook is in edit or command mode. If you click inside a cell's editor area, you will enter edit mode. If you click on the prompt or output area of a cell you will enter command mode.\n",
-      "\n",
-      "If you are running this notebook in a live session (not on http://nbviewer.ipython.org) try selecting different cells and going between edit and command mode. Try typing into a cell."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The second idea of mouse based navigation is that **cell actions usually apply to the currently selected cell**. Thus if you want to run the code in a cell, you would select it and click the <button class='btn btn-default btn-xs'><i class=\"fa fa-play icon-play\"></i></button> button in the toolbar or the \"Cell:Run\" menu item. Similarly, to copy a cell you would select it and click the <button class='btn btn-default btn-xs'><i class=\"fa fa-copy icon-copy\"></i></button> button in the toolbar or the \"Edit:Copy\" menu item. With this simple pattern, you should be able to do most everything you need with the mouse.\n",
-      "\n",
-      "Markdown and heading cells have one other state that can be modified with the mouse. These cells can either be rendered or unrendered. When they are rendered, you will see a nice formatted representation of the cell's contents. When they are unrendered, you will see the raw text source of the cell. To render the selected cell with the mouse, click the <button class='btn btn-default btn-xs'><i class=\"fa fa-play icon-play\"></i></button> button in the toolbar or the \"Cell:Run\" menu item. To unrender the selected cell, double click on the cell."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Keyboard Navigation"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The modal user interface of the IPython Notebook has been optimized for efficient keyboard usage. This is made possible by having two different sets of keyboard shortcuts: one set that is active in edit mode and another in command mode.\n",
-      "\n",
-      "The most important keyboard shortcuts are `Enter`, which enters edit mode, and `Esc`, which enters command mode.\n",
-      "\n",
-      "In edit mode, most of the keyboard is dedicated to typing into the cell's editor. Thus, in edit mode there are relatively few shortcuts:\n",
-      "\n",
-      "<img src=\"images/edit_shortcuts.png\">"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In command mode, the entire keyboard is available for shortcuts, so there are many more:\n",
-      "\n",
-      "<img src=\"images/command_shortcuts.png\">"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We recommend learning the command mode shortcuts in the following rough order:\n",
-      "\n",
-      "1. Basic navigation: `enter`, `shift-enter`, `up/k`, `down/j`\n",
-      "2. Saving the notebook: `s`\n",
-      "2. Cell types: `y`, `m`, `1-6`, `t`\n",
-      "3. Cell creation and movement: `a`, `b`, `ctrl+k`, `ctrl+j`\n",
-      "4. Cell editing: `x`, `c`, `v`, `d`, `z`, `shift+=`\n",
-      "5. Kernel operations: `i`, `.`"
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Notebook/Notebook Security.ipynb b/examples/Notebook/Notebook Security.ipynb
index 519dba7..8b28ec3 100644
--- a/examples/Notebook/Notebook Security.ipynb
+++ b/examples/Notebook/Notebook Security.ipynb
@@ -1,9 +1,8 @@
 {
+ "cells": [],
  "metadata": {
-  "name": "",
   "signature": "sha256:0abf067a20ebda26a671db997ac954770350d292dff7b7d6a4ace8808f70aca1"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": []
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Notebook/Running Code.ipynb b/examples/Notebook/Running Code.ipynb
index 76f29b0..b0926dd 100644
--- a/examples/Notebook/Running Code.ipynb
+++ b/examples/Notebook/Running Code.ipynb
@@ -1,896 +1,885 @@
 {
- "metadata": {
-  "kernelspec": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Running Code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First and foremost, the IPython Notebook is an interactive environment for writing and running code. IPython is capable of running code in a wide range of languages. However, this notebook, and the default kernel in IPython 2.0, runs Python code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Code cells allow you to enter and run Python code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run a code cell using `Shift-Enter` or pressing the <button class='btn btn-default btn-xs'><i class=\"icon-play fa fa-play\"></i></button> button in the toolbar above:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
    },
-   "display_name": "IPython (Python 3)",
-   "language": "python",
-   "name": "python3"
+   "outputs": [],
+   "source": [
+    "a = 10"
+   ]
   },
-  "name": "",
-  "signature": "sha256:55c4f2c75812ec42ca18dcd7e895a056a3faa0e6d567893faf8c85345016be06"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Running Code"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "First and foremost, the IPython Notebook is an interactive environment for writing and running code. IPython is capable of running code in a wide range of languages. However, this notebook, and the default kernel in IPython 2.0, runs Python code."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Code cells allow you to enter and run Python code"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Run a code cell using `Shift-Enter` or pressing the <button class='btn btn-default btn-xs'><i class=\"icon-play fa fa-play\"></i></button> button in the toolbar above:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = 10"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print(a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "10\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "There are two other keyboard shortcuts for running code:\n",
-      "\n",
-      "* `Alt-Enter` runs the current cell and inserts a new one below.\n",
-      "* `Ctrl-Enter` run the current cell and enters command mode."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Managing the IPython Kernel"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Code is run in a separate process called the IPython Kernel.  The Kernel can be interrupted or restarted.  Try running the following cell and then hit the <button class='btn btn-default btn-xs'><i class='icon-stop fa fa-stop'></i></button> button in the toolbar above."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import time\n",
-      "time.sleep(10)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If the Kernel dies you will be prompted to restart it. Here we call the low-level system libc.time routine with the wrong argument via\n",
-      "ctypes to segfault the Python interpreter:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import sys\n",
-      "from ctypes import CDLL\n",
-      "# This will crash a Linux or Mac system\n",
-      "# equivalent calls can be made on Windows\n",
-      "dll = 'dylib' if sys.platform == 'darwin' else 'so.6'\n",
-      "libc = CDLL(\"libc.%s\" % dll) \n",
-      "libc.time(-1)  # BOOM!!"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Cell menu"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The \"Cell\" menu has a number of menu items for running code in different ways. These includes:\n",
-      "\n",
-      "* Run and Select Below\n",
-      "* Run and Insert Below\n",
-      "* Run All\n",
-      "* Run All Above\n",
-      "* Run All Below"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Restarting the kernels"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The kernel maintains the state of a notebook's computations. You can reset this state by restarting the kernel. This is done by clicking on the <button class='btn btn-default btn-xs'><i class='fa fa-repeat icon-repeat'></i></button> in the toolbar above."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "sys.stdout and sys.stderr"
-     ]
-    },
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The stdout and stderr streams are displayed as text in the output area."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print(\"hi, stdout\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "hi, stdout\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from __future__ import print_function\n",
-      "print('hi, stderr', file=sys.stderr)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "hi, stderr\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
+    }
+   ],
+   "source": [
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are two other keyboard shortcuts for running code:\n",
+    "\n",
+    "* `Alt-Enter` runs the current cell and inserts a new one below.\n",
+    "* `Ctrl-Enter` run the current cell and enters command mode."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Managing the IPython Kernel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Code is run in a separate process called the IPython Kernel.  The Kernel can be interrupted or restarted.  Try running the following cell and then hit the <button class='btn btn-default btn-xs'><i class='icon-stop fa fa-stop'></i></button> button in the toolbar above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "time.sleep(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If the Kernel dies you will be prompted to restart it. Here we call the low-level system libc.time routine with the wrong argument via\n",
+    "ctypes to segfault the Python interpreter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "from ctypes import CDLL\n",
+    "# This will crash a Linux or Mac system\n",
+    "# equivalent calls can be made on Windows\n",
+    "dll = 'dylib' if sys.platform == 'darwin' else 'so.6'\n",
+    "libc = CDLL(\"libc.%s\" % dll) \n",
+    "libc.time(-1)  # BOOM!!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cell menu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The \"Cell\" menu has a number of menu items for running code in different ways. These includes:\n",
+    "\n",
+    "* Run and Select Below\n",
+    "* Run and Insert Below\n",
+    "* Run All\n",
+    "* Run All Above\n",
+    "* Run All Below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Restarting the kernels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The kernel maintains the state of a notebook's computations. You can reset this state by restarting the kernel. This is done by clicking on the <button class='btn btn-default btn-xs'><i class='fa fa-repeat icon-repeat'></i></button> in the toolbar above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## sys.stdout and sys.stderr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The stdout and stderr streams are displayed as text in the output area."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Output is asynchronous"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hi, stdout\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "print(\"hi, stdout\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "All output is displayed asynchronously as it is generated in the Kernel. If you execute the next cell, you will see the output one piece at a time, not all at the end."
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "hi, stderr\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import time, sys\n",
-      "for i in range(8):\n",
-      "    print(i)\n",
-      "    time.sleep(0.5)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n",
-        "1\n",
-        "2\n",
-        "3\n",
-        "4\n",
-        "5\n",
-        "6\n",
-        "7\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
+    }
+   ],
+   "source": [
+    "from __future__ import print_function\n",
+    "print('hi, stderr', file=sys.stderr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Output is asynchronous"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All output is displayed asynchronously as it is generated in the Kernel. If you execute the next cell, you will see the output one piece at a time, not all at the end."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Large outputs"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "import time, sys\n",
+    "for i in range(8):\n",
+    "    print(i)\n",
+    "    time.sleep(0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Large outputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To better handle large outputs, the output area can be collapsed. Run the following cell and then single- or double- click on the active area to the left of the output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To better handle large outputs, the output area can be collapsed. Run the following cell and then single- or double- click on the active area to the left of the output:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "10\n",
+      "11\n",
+      "12\n",
+      "13\n",
+      "14\n",
+      "15\n",
+      "16\n",
+      "17\n",
+      "18\n",
+      "19\n",
+      "20\n",
+      "21\n",
+      "22\n",
+      "23\n",
+      "24\n",
+      "25\n",
+      "26\n",
+      "27\n",
+      "28\n",
+      "29\n",
+      "30\n",
+      "31\n",
+      "32\n",
+      "33\n",
+      "34\n",
+      "35\n",
+      "36\n",
+      "37\n",
+      "38\n",
+      "39\n",
+      "40\n",
+      "41\n",
+      "42\n",
+      "43\n",
+      "44\n",
+      "45\n",
+      "46\n",
+      "47\n",
+      "48\n",
+      "49\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for i in range(50):\n",
-      "    print(i)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n",
-        "1\n",
-        "2\n",
-        "3\n",
-        "4\n",
-        "5\n",
-        "6\n",
-        "7\n",
-        "8\n",
-        "9\n",
-        "10\n",
-        "11\n",
-        "12\n",
-        "13\n",
-        "14\n",
-        "15\n",
-        "16\n",
-        "17\n",
-        "18\n",
-        "19\n",
-        "20\n",
-        "21\n",
-        "22\n",
-        "23\n",
-        "24\n",
-        "25\n",
-        "26\n",
-        "27\n",
-        "28\n",
-        "29\n",
-        "30\n",
-        "31\n",
-        "32\n",
-        "33\n",
-        "34\n",
-        "35\n",
-        "36\n",
-        "37\n",
-        "38\n",
-        "39\n",
-        "40\n",
-        "41\n",
-        "42\n",
-        "43\n",
-        "44\n",
-        "45\n",
-        "46\n",
-        "47\n",
-        "48\n",
-        "49\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
+    }
+   ],
+   "source": [
+    "for i in range(50):\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Beyond a certain point, output will scroll automatically:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Beyond a certain point, output will scroll automatically:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "3\n",
+      "7\n",
+      "15\n",
+      "31\n",
+      "63\n",
+      "127\n",
+      "255\n",
+      "511\n",
+      "1023\n",
+      "2047\n",
+      "4095\n",
+      "8191\n",
+      "16383\n",
+      "32767\n",
+      "65535\n",
+      "131071\n",
+      "262143\n",
+      "524287\n",
+      "1048575\n",
+      "2097151\n",
+      "4194303\n",
+      "8388607\n",
+      "16777215\n",
+      "33554431\n",
+      "67108863\n",
+      "134217727\n",
+      "268435455\n",
+      "536870911\n",
+      "1073741823\n",
+      "2147483647\n",
+      "4294967295\n",
+      "8589934591\n",
+      "17179869183\n",
+      "34359738367\n",
+      "68719476735\n",
+      "137438953471\n",
+      "274877906943\n",
+      "549755813887\n",
+      "1099511627775\n",
+      "2199023255551\n",
+      "4398046511103\n",
+      "8796093022207\n",
+      "17592186044415\n",
+      "35184372088831\n",
+      "70368744177663\n",
+      "140737488355327\n",
+      "281474976710655\n",
+      "562949953421311\n",
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      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for i in range(500):\n",
-      "    print(2**i - 1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n",
-        "1\n",
-        "3\n",
-        "7\n",
-        "15\n",
-        "31\n",
-        "63\n",
-        "127\n",
-        "255\n",
-        "511\n",
-        "1023\n",
-        "2047\n",
-        "4095\n",
-        "8191\n",
-        "16383\n",
-        "32767\n",
-        "65535\n",
-        "131071\n",
-        "262143\n",
-        "524287\n",
-        "1048575\n",
-        "2097151\n",
-        "4194303\n",
-        "8388607\n",
-        "16777215\n",
-        "33554431\n",
-        "67108863\n",
-        "134217727\n",
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-        "6243497100631984462763194459586332611497196285329942301718313919250743477639531240240612206126983942319653862242813245790895951358576570612580351\n",
-        "12486994201263968925526388919172665222994392570659884603436627838501486955279062480481224412253967884639307724485626491581791902717153141225160703\n",
-        "24973988402527937851052777838345330445988785141319769206873255677002973910558124960962448824507935769278615448971252983163583805434306282450321407\n",
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-        "99895953610111751404211111353381321783955140565279076827493022708011895642232499843849795298031743077114461795885011932654335221737225129801285631\n",
-        "199791907220223502808422222706762643567910281130558153654986045416023791284464999687699590596063486154228923591770023865308670443474450259602571263\n",
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-        "799167628880894011233688890827050574271641124522232614619944181664095165137859998750798362384253944616915694367080095461234681773897801038410285055\n",
-        "1598335257761788022467377781654101148543282249044465229239888363328190330275719997501596724768507889233831388734160190922469363547795602076820570111\n",
-        "3196670515523576044934755563308202297086564498088930458479776726656380660551439995003193449537015778467662777468320381844938727095591204153641140223\n",
-        "6393341031047152089869511126616404594173128996177860916959553453312761321102879990006386899074031556935325554936640763689877454191182408307282280447\n",
-        "12786682062094304179739022253232809188346257992355721833919106906625522642205759980012773798148063113870651109873281527379754908382364816614564560895\n",
-        "25573364124188608359478044506465618376692515984711443667838213813251045284411519960025547596296126227741302219746563054759509816764729633229129121791\n",
-        "51146728248377216718956089012931236753385031969422887335676427626502090568823039920051095192592252455482604439493126109519019633529459266458258243583\n",
-        "102293456496754433437912178025862473506770063938845774671352855253004181137646079840102190385184504910965208878986252219038039267058918532916516487167\n",
-        "204586912993508866875824356051724947013540127877691549342705710506008362275292159680204380770369009821930417757972504438076078534117837065833032974335\n",
-        "409173825987017733751648712103449894027080255755383098685411421012016724550584319360408761540738019643860835515945008876152157068235674131666065948671\n",
-        "818347651974035467503297424206899788054160511510766197370822842024033449101168638720817523081476039287721671031890017752304314136471348263332131897343\n",
-        "1636695303948070935006594848413799576108321023021532394741645684048066898202337277441635046162952078575443342063780035504608628272942696526664263794687\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
     }
    ],
-   "metadata": {}
+   "source": [
+    "for i in range(500):\n",
+    "    print(2**i - 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
   }
- ]
+ ],
+ "metadata": {
+  "kernelspec": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "display_name": "IPython (Python 3)",
+   "language": "python",
+   "name": "python3"
+  },
+  "signature": "sha256:55c4f2c75812ec42ca18dcd7e895a056a3faa0e6d567893faf8c85345016be06"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Notebook/Running the Notebook Server.ipynb b/examples/Notebook/Running the Notebook Server.ipynb
index 602ebec..38ada9a 100644
--- a/examples/Notebook/Running the Notebook Server.ipynb
+++ b/examples/Notebook/Running the Notebook Server.ipynb
@@ -1,389 +1,363 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:ee4b22b4c949fe21b3e5cda24f0916ba59d8c09443f4a897d98b96d4a73ac335"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Running the Notebook Server"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The IPython notebook server is a custom web server that runs the notebook web application. Most of the time, users run the notebook server on their local computer using IPython's command line interface."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Starting the notebook server using the command line"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can start the notebook server from the command line (Terminal on Mac/Linux, CMD prompt on Windows) by running the following command: \n",
-      "\n",
-      "    ipython notebook\n",
-      "\n",
-      "This will print some information about the notebook server in your terminal, including the URL of the web application (by default, `http://127.0.0.1:8888`). It will then open your default web browser to this URL.\n",
-      "\n",
-      "When the notebook opens, you will see the **notebook dashboard**, which will show a list of the notebooks and subdirectories in the directory where the notebook server was started. As of IPython 2.0, the dashboard allows you to navigate to different subdirectories. Because of this, it is no longer necessary to start a separate notebook server for each subdirectory. Most of the time, you will want to start a notebook server in the highest directory in your filesystem where notebooks can be found. Often this will be your home directory.\n",
-      "\n",
-      "You can start more than one notebook server at the same time. By default, the first notebook server starts on port 8888 and later notebook servers search for open ports near that one.\n",
-      "\n",
-      "You can also specify the port manually:\n",
-      "\n",
-      "    ipython notebook --port 9999\n",
-      "\n",
-      "Or start notebook server without opening a web browser.\n",
-      "\n",
-      "    ipython notebook --no-browser\n",
-      "\n",
-      "The notebook server has a number of other command line arguments that can be displayed with the `--help` flag: \n",
-      "\n",
-      "    ipython notebook --help\n",
-      "\n",
-      "<div class=\"alert alert-failure\">\n",
-      "It used to be possible to specify kernel options, such as <code>--pylab inline</code> from the command line. This is deprecated in IPython 2.0 and will be removed in IPython 3.0. To enable matplotlib based plotting for the Python kernel use the <code>%matplotlib</code> magic command.\n",
-      "</div>\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Configuring the IPython Notebook"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The notebook web server can also be configured using IPython profiles and configuration files. The Notebook web server configuration options are set in a file named `ipython_notebook_config.py` in your IPython *profile directory*. The profile directory is a subfolder of your IPython directory, which itself is usually `.ipython` in your home directory.\n",
-      "\n",
-      "You can display the location of your default profile directory by running the command:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!ipython profile locate default"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "/Users/bgranger/.ipython/profile_default\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The default version of `ipython_notebook_config.py` lists all of the options available along with documentation for each. Changes made to that file will affect all notebook servers run under that profile. Command line options always override those set in configuration files.\n",
-      "\n",
-      "You can create a new profile:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!ipython profile create my_profile"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[ProfileCreate] Generating default config file: u'/Users/bgranger/.ipython/profile_my_profile/ipython_config.py'\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[ProfileCreate] Generating default config file: u'/Users/bgranger/.ipython/profile_my_profile/ipython_qtconsole_config.py'\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[ProfileCreate] Generating default config file: u'/Users/bgranger/.ipython/profile_my_profile/ipython_notebook_config.py'\r\n",
-        "[ProfileCreate] Generating default config file: u'/Users/bgranger/.ipython/profile_my_profile/ipython_nbconvert_config.py'\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And then view its location:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!ipython profile locate my_profile"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "/Users/bgranger/.ipython/profile_my_profile\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To start the notebook server using a given profile, run the following:\n",
-      "\n",
-      "    ipython notebook --profile=my_profile"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "More details about IPython configuration files and profiles can be found [here](http://ipython.org/ipython-doc/dev/config/intro.html)."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Securing the notebook server"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The IPython Notebook allows arbitrary code execution on the computer running it. Thus, the notebook web server should never be run on the open internet without first securing it. By default, the notebook server only listens on local network interface (`127.0.0.1`) There are two steps required to secure the notebook server:\n",
-      "\n",
-      "1. Setting a password\n",
-      "2. Encrypt network traffic using SSL"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Setting a password"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can protect your notebook server with a simple single password by setting the `NotebookApp.password` configurable. You can prepare a hashed password using the function `IPython.lib.passwd`:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.lib import passwd\n",
-      "password = passwd(\"secret\")\n",
-      "password"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 1,
-       "text": [
-        "'sha1:6c2164fc2b22:ed55ecf07fc0f985ab46561483c0e888e8964ae6'"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can then add this to your `ipython_notebook_config.py`:\n",
-      "\n",
-      "```python\n",
-      "# Password to use for web authentication\n",
-      "c = get_config()\n",
-      "c.NotebookApp.password = \n",
-      "u'sha1:6c2164fc2b22:ed55ecf07fc0f985ab46561483c0e888e8964ae6'\n",
-      "```"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Using SSL/HTTPS"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When using a password, it is a good idea to also use SSL, so that your \n",
-      "password is not sent unencrypted by your browser to the web server. When running the notebook on the public internet this is absolutely required.\n",
-      "\n",
-      "The first step is to generate an SSL certificate. A self-signed certificate can be generated with ``openssl``.  For example, the following command will create a certificate valid for 365 days with both the key and certificate data written to the same file:\n",
-      "\n",
-      "    openssl req -x509 -nodes -days 365 -newkey rsa:1024 -keyout mycert.pem -out mycert.pem\n",
-      "\n",
-      "In most cases, you should run this command in your profile directory, which will make it easy to use the generated key and certificate.\n",
-      "\n",
-      "When you connect to a notebook server over HTTPS using a self-signed certificate, your browser will warn you of a dangerous certificate because it is self-signed.  If you want to have a fully compliant certificate that will not raise warnings, it is possible (but rather involved) to obtain one,\n",
-      "as explained in detail in [this tutorial](http://arstechnica.com/security/news/2009/12/how-to-get-set-with-a-secure-sertificate-for-free.ars)\n",
-      "\t\n",
-      "When you enable SSL support, you will need to access the notebook server over ``https://``, rather than plain ``http://``.  The startup message from the notebook server prints the correct URL, but it is easy to overlook and think the server is for some reason non-responsive.\n",
-      "\n",
-      "Once you have generated the key and certificate, you can configure the notebook server to use them, by adding the following to `ipython_notebook_config.py`:\n",
-      "\n",
-      "```python\n",
-      "# The full path to an SSL/TLS certificate file.\n",
-      "c.NotebookApp.certfile = u'/Users/bgranger/.ipython/profile_my_profile/mycert.crt'\n",
-      "\n",
-      "# The full path to a private key file for usage with SSL/TLS.\n",
-      "c.NotebookApp.keyfile = u'/Users/bgranger/.ipython/profile_my_profile/mycert.key'\n",
-      "```"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Running a public notebook server"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<div class=\"alert alert-error\">\n",
-      "Don't run a public notebook server unless you first secure it with a password and SSL/HTTPS as described above\n",
-      "</div>\n",
-      "\n",
-      "By default the notebook server only listens on the `localhost/127.0.0.1` network interface. If you want to connect to the notebook from another computers, or over the internet, you need to configure the notebook server to listen on all network interfaces and not open the browser. You will often also want to disable the automatic launching of the web browser.\n",
-      "\n",
-      "This can be accomplished by passing a command line options.\n",
-      "\n",
-      "    ipython notebook --ip=* --no-browser\n",
-      "\n",
-      "You can also add the following to your`ipython_notebook_config.py` file:\n",
-      "\n",
-      "```python\n",
-      "c.NotebookApp.ip = '*'\n",
-      "c.NotebookApp.open_browser = False\n",
-      "```"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Running with a different URL prefix"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The notebook dashboard typically lives at the URL `http://localhost:8888/tree`. If you prefer that it lives, together with the \n",
-      "rest of the notebook web application, under a base URL prefix, such as `http://localhost:8888/ipython/tree`, you can do so by adding the following lines to your `ipython_notebook_config.py` file.\n",
-      "\n",
-      "```python\n",
-      "c.NotebookApp.base_url = '/ipython/'\n",
-      "c.NotebookApp.webapp_settings = {'static_url_prefix':'/ipython/static/'}\n",
-      "```"
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Running the Notebook Server"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The IPython notebook server is a custom web server that runs the notebook web application. Most of the time, users run the notebook server on their local computer using IPython's command line interface."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Starting the notebook server using the command line"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can start the notebook server from the command line (Terminal on Mac/Linux, CMD prompt on Windows) by running the following command: \n",
+    "\n",
+    "    ipython notebook\n",
+    "\n",
+    "This will print some information about the notebook server in your terminal, including the URL of the web application (by default, `http://127.0.0.1:8888`). It will then open your default web browser to this URL.\n",
+    "\n",
+    "When the notebook opens, you will see the **notebook dashboard**, which will show a list of the notebooks and subdirectories in the directory where the notebook server was started. As of IPython 2.0, the dashboard allows you to navigate to different subdirectories. Because of this, it is no longer necessary to start a separate notebook server for each subdirectory. Most of the time, you will want to start a notebook server in the highest directory in your filesystem where notebooks can be found. Often this will be your home directory.\n",
+    "\n",
+    "You can start more than one notebook server at the same time. By default, the first notebook server starts on port 8888 and later notebook servers search for open ports near that one.\n",
+    "\n",
+    "You can also specify the port manually:\n",
+    "\n",
+    "    ipython notebook --port 9999\n",
+    "\n",
+    "Or start notebook server without opening a web browser.\n",
+    "\n",
+    "    ipython notebook --no-browser\n",
+    "\n",
+    "The notebook server has a number of other command line arguments that can be displayed with the `--help` flag: \n",
+    "\n",
+    "    ipython notebook --help\n",
+    "\n",
+    "<div class=\"alert alert-failure\">\n",
+    "It used to be possible to specify kernel options, such as <code>--pylab inline</code> from the command line. This is deprecated in IPython 2.0 and will be removed in IPython 3.0. To enable matplotlib based plotting for the Python kernel use the <code>%matplotlib</code> magic command.\n",
+    "</div>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configuring the IPython Notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The notebook web server can also be configured using IPython profiles and configuration files. The Notebook web server configuration options are set in a file named `ipython_notebook_config.py` in your IPython *profile directory*. The profile directory is a subfolder of your IPython directory, which itself is usually `.ipython` in your home directory.\n",
+    "\n",
+    "You can display the location of your default profile directory by running the command:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Using a different notebook store"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/bgranger/.ipython/profile_default\r\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "!ipython profile locate default"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The default version of `ipython_notebook_config.py` lists all of the options available along with documentation for each. Changes made to that file will affect all notebook servers run under that profile. Command line options always override those set in configuration files.\n",
+    "\n",
+    "You can create a new profile:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "By default, the notebook server stores the notebook documents that it saves as files in the working directory of the notebook server, also known as the\n",
-      "`notebook_dir`. This  logic is implemented in the `FileNotebookManager` class. However, the server can be configured to use a different notebook manager class, which can store the notebooks in a different format. \n",
-      "\n",
-      "The [bookstore](https://github.com/rgbkrk/bookstore) package currently allows users to store notebooks on Rackspace CloudFiles or OpenStack Swift based object stores.\n",
-      "\n",
-      "Writing a notebook manager is as simple as extending the base class `NotebookManager`. The [simple_notebook_manager](https://github.com/khinsen/simple_notebook_manager) provides a great example\n",
-      "of an in memory notebook manager, created solely for the purpose of\n",
-      "illustrating the notebook manager API."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ProfileCreate] Generating default config file: u'/Users/bgranger/.ipython/profile_my_profile/ipython_config.py'\r\n",
+      "[ProfileCreate] Generating default config file: u'/Users/bgranger/.ipython/profile_my_profile/ipython_qtconsole_config.py'\r\n",
+      "[ProfileCreate] Generating default config file: u'/Users/bgranger/.ipython/profile_my_profile/ipython_notebook_config.py'\r\n",
+      "[ProfileCreate] Generating default config file: u'/Users/bgranger/.ipython/profile_my_profile/ipython_nbconvert_config.py'\r\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "!ipython profile create my_profile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then view its location:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Known issues"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/bgranger/.ipython/profile_my_profile\r\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "!ipython profile locate my_profile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To start the notebook server using a given profile, run the following:\n",
+    "\n",
+    "    ipython notebook --profile=my_profile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "More details about IPython configuration files and profiles can be found [here](http://ipython.org/ipython-doc/dev/config/intro.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Securing the notebook server"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The IPython Notebook allows arbitrary code execution on the computer running it. Thus, the notebook web server should never be run on the open internet without first securing it. By default, the notebook server only listens on local network interface (`127.0.0.1`) There are two steps required to secure the notebook server:\n",
+    "\n",
+    "1. Setting a password\n",
+    "2. Encrypt network traffic using SSL"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting a password"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can protect your notebook server with a simple single password by setting the `NotebookApp.password` configurable. You can prepare a hashed password using the function `IPython.lib.passwd`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "'sha1:6c2164fc2b22:ed55ecf07fc0f985ab46561483c0e888e8964ae6'"
+      ]
+     },
+     "execution_count": 1,
      "metadata": {},
-     "source": [
-      "When behind a proxy, especially if your system or browser is set to autodetect the proxy, the notebook web application might fail to connect to the server's websockets, and present you with a warning at startup. In this case, you need to configure your system not to use the proxy for the server's address.\n",
-      "\n",
-      "For example, in Firefox, go to the Preferences panel, Advanced section,\n",
-      "Network tab, click 'Settings...', and add the address of the notebook server\n",
-      "to the 'No proxy for' field."
-     ]
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "from IPython.lib import passwd\n",
+    "password = passwd(\"secret\")\n",
+    "password"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can then add this to your `ipython_notebook_config.py`:\n",
+    "\n",
+    "```python\n",
+    "# Password to use for web authentication\n",
+    "c = get_config()\n",
+    "c.NotebookApp.password = \n",
+    "u'sha1:6c2164fc2b22:ed55ecf07fc0f985ab46561483c0e888e8964ae6'\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using SSL/HTTPS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When using a password, it is a good idea to also use SSL, so that your \n",
+    "password is not sent unencrypted by your browser to the web server. When running the notebook on the public internet this is absolutely required.\n",
+    "\n",
+    "The first step is to generate an SSL certificate. A self-signed certificate can be generated with ``openssl``.  For example, the following command will create a certificate valid for 365 days with both the key and certificate data written to the same file:\n",
+    "\n",
+    "    openssl req -x509 -nodes -days 365 -newkey rsa:1024 -keyout mycert.pem -out mycert.pem\n",
+    "\n",
+    "In most cases, you should run this command in your profile directory, which will make it easy to use the generated key and certificate.\n",
+    "\n",
+    "When you connect to a notebook server over HTTPS using a self-signed certificate, your browser will warn you of a dangerous certificate because it is self-signed.  If you want to have a fully compliant certificate that will not raise warnings, it is possible (but rather involved) to obtain one,\n",
+    "as explained in detail in [this tutorial](http://arstechnica.com/security/news/2009/12/how-to-get-set-with-a-secure-sertificate-for-free.ars)\n",
+    "\t\n",
+    "When you enable SSL support, you will need to access the notebook server over ``https://``, rather than plain ``http://``.  The startup message from the notebook server prints the correct URL, but it is easy to overlook and think the server is for some reason non-responsive.\n",
+    "\n",
+    "Once you have generated the key and certificate, you can configure the notebook server to use them, by adding the following to `ipython_notebook_config.py`:\n",
+    "\n",
+    "```python\n",
+    "# The full path to an SSL/TLS certificate file.\n",
+    "c.NotebookApp.certfile = u'/Users/bgranger/.ipython/profile_my_profile/mycert.crt'\n",
+    "\n",
+    "# The full path to a private key file for usage with SSL/TLS.\n",
+    "c.NotebookApp.keyfile = u'/Users/bgranger/.ipython/profile_my_profile/mycert.key'\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running a public notebook server"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-error\">\n",
+    "Don't run a public notebook server unless you first secure it with a password and SSL/HTTPS as described above\n",
+    "</div>\n",
+    "\n",
+    "By default the notebook server only listens on the `localhost/127.0.0.1` network interface. If you want to connect to the notebook from another computers, or over the internet, you need to configure the notebook server to listen on all network interfaces and not open the browser. You will often also want to disable the automatic launching of the web browser.\n",
+    "\n",
+    "This can be accomplished by passing a command line options.\n",
+    "\n",
+    "    ipython notebook --ip=* --no-browser\n",
+    "\n",
+    "You can also add the following to your`ipython_notebook_config.py` file:\n",
+    "\n",
+    "```python\n",
+    "c.NotebookApp.ip = '*'\n",
+    "c.NotebookApp.open_browser = False\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Running with a different URL prefix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The notebook dashboard typically lives at the URL `http://localhost:8888/tree`. If you prefer that it lives, together with the \n",
+    "rest of the notebook web application, under a base URL prefix, such as `http://localhost:8888/ipython/tree`, you can do so by adding the following lines to your `ipython_notebook_config.py` file.\n",
+    "\n",
+    "```python\n",
+    "c.NotebookApp.base_url = '/ipython/'\n",
+    "c.NotebookApp.webapp_settings = {'static_url_prefix':'/ipython/static/'}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using a different notebook store"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, the notebook server stores the notebook documents that it saves as files in the working directory of the notebook server, also known as the\n",
+    "`notebook_dir`. This  logic is implemented in the `FileNotebookManager` class. However, the server can be configured to use a different notebook manager class, which can store the notebooks in a different format. \n",
+    "\n",
+    "The [bookstore](https://github.com/rgbkrk/bookstore) package currently allows users to store notebooks on Rackspace CloudFiles or OpenStack Swift based object stores.\n",
+    "\n",
+    "Writing a notebook manager is as simple as extending the base class `NotebookManager`. The [simple_notebook_manager](https://github.com/khinsen/simple_notebook_manager) provides a great example\n",
+    "of an in memory notebook manager, created solely for the purpose of\n",
+    "illustrating the notebook manager API."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Known issues"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When behind a proxy, especially if your system or browser is set to autodetect the proxy, the notebook web application might fail to connect to the server's websockets, and present you with a warning at startup. In this case, you need to configure your system not to use the proxy for the server's address.\n",
+    "\n",
+    "For example, in Firefox, go to the Preferences panel, Advanced section,\n",
+    "Network tab, click 'Settings...', and add the address of the notebook server\n",
+    "to the 'No proxy for' field."
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:ee4b22b4c949fe21b3e5cda24f0916ba59d8c09443f4a897d98b96d4a73ac335"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Notebook/Typesetting Equations.ipynb b/examples/Notebook/Typesetting Equations.ipynb
index b75c1e3..df729bd 100644
--- a/examples/Notebook/Typesetting Equations.ipynb
+++ b/examples/Notebook/Typesetting Equations.ipynb
@@ -1,260 +1,253 @@
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The Markdown parser included in IPython is MathJax-aware.  This means that you can freely mix in mathematical expressions using the [MathJax subset of Tex and LaTeX](http://docs.mathjax.org/en/latest/tex.html#tex-support).  [Some examples from the MathJax site](http://www.mathjax.org/demos/tex-samples/) are reproduced below, as well as the Markdown+TeX source."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Motivating Examples\n",
-      "\n",
-      "---\n",
-      "\n",
-      "## The Lorenz Equations\n",
-      "### Source\n",
-      "```\\begin{align}\n",
-      "\\dot{x} & = \\sigma(y-x) \\\\\n",
-      "\\dot{y} & = \\rho x - y - xz \\\\\n",
-      "\\dot{z} & = -\\beta z + xy\n",
-      "\\end{align}\n",
-      "```\n",
-      "### Display\n",
-      "\\begin{align}\n",
-      "\\dot{x} & = \\sigma(y-x) \\\\\n",
-      "\\dot{y} & = \\rho x - y - xz \\\\\n",
-      "\\dot{z} & = -\\beta z + xy\n",
-      "\\end{align}"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## The Cauchy-Schwarz Inequality\n",
-      "### Source\n",
-      "```\\begin{equation*}\n",
-      "\\left( \\sum_{k=1}^n a_k b_k \\right)^2 \\leq \\left( \\sum_{k=1}^n a_k^2 \\right) \\left( \\sum_{k=1}^n b_k^2 \\right)\n",
-      "\\end{equation*}\n",
-      "```\n",
-      "### Display\n",
-      "\\begin{equation*}\n",
-      "\\left( \\sum_{k=1}^n a_k b_k \\right)^2 \\leq \\left( \\sum_{k=1}^n a_k^2 \\right) \\left( \\sum_{k=1}^n b_k^2 \\right)\n",
-      "\\end{equation*}"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## A Cross Product Formula\n",
-      "### Source\n",
-      "```\\begin{equation*}\n",
-      "\\mathbf{V}_1 \\times \\mathbf{V}_2 =  \\begin{vmatrix}\n",
-      "\\mathbf{i} & \\mathbf{j} & \\mathbf{k} \\\\\n",
-      "\\frac{\\partial X}{\\partial u} &  \\frac{\\partial Y}{\\partial u} & 0 \\\\\n",
-      "\\frac{\\partial X}{\\partial v} &  \\frac{\\partial Y}{\\partial v} & 0\n",
-      "\\end{vmatrix}  \n",
-      "\\end{equation*}\n",
-      "```\n",
-      "### Display\n",
-      "\\begin{equation*}\n",
-      "\\mathbf{V}_1 \\times \\mathbf{V}_2 =  \\begin{vmatrix}\n",
-      "\\mathbf{i} & \\mathbf{j} & \\mathbf{k} \\\\\n",
-      "\\frac{\\partial X}{\\partial u} &  \\frac{\\partial Y}{\\partial u} & 0 \\\\\n",
-      "\\frac{\\partial X}{\\partial v} &  \\frac{\\partial Y}{\\partial v} & 0\n",
-      "\\end{vmatrix}  \n",
-      "\\end{equation*}"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## The probability of getting \\(k\\) heads when flipping \\(n\\) coins is\n",
-      "### Source\n",
-      "```\\begin{equation*}\n",
-      "P(E)   = {n \\choose k} p^k (1-p)^{ n-k} \n",
-      "\\end{equation*}\n",
-      "```\n",
-      "### Display\n",
-      "\\begin{equation*}\n",
-      "P(E)   = {n \\choose k} p^k (1-p)^{ n-k} \n",
-      "\\end{equation*}"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## An Identity of Ramanujan\n",
-      "### Source\n",
-      "```\\begin{equation*}\n",
-      "\\frac{1}{\\Bigl(\\sqrt{\\phi \\sqrt{5}}-\\phi\\Bigr) e^{\\frac25 \\pi}} =\n",
-      "1+\\frac{e^{-2\\pi}} {1+\\frac{e^{-4\\pi}} {1+\\frac{e^{-6\\pi}}\n",
-      "{1+\\frac{e^{-8\\pi}} {1+\\ldots} } } } \n",
-      "\\end{equation*}\n",
-      "```\n",
-      "### Display\n",
-      "\\begin{equation*}\n",
-      "\\frac{1}{\\Bigl(\\sqrt{\\phi \\sqrt{5}}-\\phi\\Bigr) e^{\\frac25 \\pi}} =\n",
-      "1+\\frac{e^{-2\\pi}} {1+\\frac{e^{-4\\pi}} {1+\\frac{e^{-6\\pi}}\n",
-      "{1+\\frac{e^{-8\\pi}} {1+\\ldots} } } } \n",
-      "\\end{equation*}"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## A Rogers-Ramanujan Identity\n",
-      "### Source\n",
-      "```\\begin{equation*}\n",
-      "1 +  \\frac{q^2}{(1-q)}+\\frac{q^6}{(1-q)(1-q^2)}+\\cdots =\n",
-      "\\prod_{j=0}^{\\infty}\\frac{1}{(1-q^{5j+2})(1-q^{5j+3})},\n",
-      "\\quad\\quad \\text{for $|q|<1$}. \n",
-      "\\end{equation*}\n",
-      "```\n",
-      "### Display\n",
-      "\\begin{equation*}\n",
-      "1 + \\frac{q^2}{(1-q)}+\\frac{q^6}{(1-q)(1-q^2)}+\\cdots =\n",
-      "\\prod_{j=0}^{\\infty}\\frac{1}{(1-q^{5j+2})(1-q^{5j+3})},\n",
-      "\\quad\\quad \\text{for $|q|<1$}. \n",
-      "\\end{equation*}"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Maxwell's Equations\n",
-      "### Source\n",
-      "```\\begin{align}\n",
-      "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\   \\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
-      "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
-      "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
-      "\\end{align}\n",
-      "```\n",
-      "### Display\n",
-      "\\begin{align}\n",
-      "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\   \\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
-      "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
-      "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
-      "\\end{align}"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Equation Numbering and References\n",
-      "\n",
-      "---\n",
-      "\n",
-      "Equation numbering and referencing will be available in a future version of IPython."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Inline Typesetting (Mixing Markdown and TeX)\n",
-      "\n",
-      "---\n",
-      "\n",
-      "While display equations look good for a page of samples, the ability to mix math and *formatted* **text** in a paragraph is also important.\n",
-      "\n",
-      "## Source\n",
-      "``` This expression $\\sqrt{3x-1}+(1+x)^2$ is an example of a TeX inline equation in a **[Markdown-formatted](http://daringfireball.net/projects/markdown/)** sentence.  \n",
-      "```\n",
-      "## Display\n",
-      "This expression $\\sqrt{3x-1}+(1+x)^2$ is an example of a TeX inline equation in a **[Markdown-formatted](http://daringfireball.net/projects/markdown/)** sentence.  "
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Other Syntax\n",
-      "\n",
-      "---\n",
-      "\n",
-      "You will notice in other places on the web that `$$` are needed explicitly to begin and end MathJax typesetting.  This is **not** required if you will be using TeX environments, but the IPython notebook will accept this syntax on legacy notebooks.  \n",
-      "\n",
-      "### Source\n",
-      "```\n",
-      "$$\n",
-      "\\begin{array}{c}\n",
-      "y_1 \\\\\\\n",
-      "y_2 \\mathtt{t}_i \\\\\\\n",
-      "z_{3,4}\n",
-      "\\end{array}\n",
-      "$$\n",
-      "```\n",
-      "\n",
-      "```\n",
-      "$$\n",
-      "\\begin{array}{c}\n",
-      "y_1 \\cr\n",
-      "y_2 \\mathtt{t}_i \\cr\n",
-      "y_{3}\n",
-      "\\end{array}\n",
-      "$$\n",
-      "```\n",
-      "\n",
-      "```\n",
-      "$$\\begin{eqnarray} \n",
-      "x' &=& &x \\sin\\phi &+& z \\cos\\phi \\\\\n",
-      "z' &=& - &x \\cos\\phi &+& z \\sin\\phi \\\\\n",
-      "\\end{eqnarray}$$\n",
-      "```\n",
-      "\n",
-      "```\n",
-      "$$\n",
-      "x=4\n",
-      "$$\n",
-      "```\n",
-      "\n",
-      "### Display\n",
-      "$$\n",
-      "\\begin{array}{c}\n",
-      "y_1 \\\\\\\n",
-      "y_2 \\mathtt{t}_i \\\\\\\n",
-      "z_{3,4}\n",
-      "\\end{array}\n",
-      "$$\n",
-      "\n",
-      "$$\n",
-      "\\begin{array}{c}\n",
-      "y_1 \\cr\n",
-      "y_2 \\mathtt{t}_i \\cr\n",
-      "y_{3}\n",
-      "\\end{array}\n",
-      "$$\n",
-      "\n",
-      "$$\\begin{eqnarray} \n",
-      "x' &=& &x \\sin\\phi &+& z \\cos\\phi \\\\\n",
-      "z' &=& - &x \\cos\\phi &+& z \\sin\\phi \\\\\n",
-      "\\end{eqnarray}$$\n",
-      "\n",
-      "$$\n",
-      "x=4\n",
-      "$$"
-     ]
-    }
-   ],
-   "metadata": {}
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Markdown parser included in IPython is MathJax-aware.  This means that you can freely mix in mathematical expressions using the [MathJax subset of Tex and LaTeX](http://docs.mathjax.org/en/latest/tex.html#tex-support).  [Some examples from the MathJax site](http://www.mathjax.org/demos/tex-samples/) are reproduced below, as well as the Markdown+TeX source."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Motivating Examples\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## The Lorenz Equations\n",
+    "### Source\n",
+    "```\\begin{align}\n",
+    "\\dot{x} & = \\sigma(y-x) \\\\\n",
+    "\\dot{y} & = \\rho x - y - xz \\\\\n",
+    "\\dot{z} & = -\\beta z + xy\n",
+    "\\end{align}\n",
+    "```\n",
+    "### Display\n",
+    "\\begin{align}\n",
+    "\\dot{x} & = \\sigma(y-x) \\\\\n",
+    "\\dot{y} & = \\rho x - y - xz \\\\\n",
+    "\\dot{z} & = -\\beta z + xy\n",
+    "\\end{align}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Cauchy-Schwarz Inequality\n",
+    "### Source\n",
+    "```\\begin{equation*}\n",
+    "\\left( \\sum_{k=1}^n a_k b_k \\right)^2 \\leq \\left( \\sum_{k=1}^n a_k^2 \\right) \\left( \\sum_{k=1}^n b_k^2 \\right)\n",
+    "\\end{equation*}\n",
+    "```\n",
+    "### Display\n",
+    "\\begin{equation*}\n",
+    "\\left( \\sum_{k=1}^n a_k b_k \\right)^2 \\leq \\left( \\sum_{k=1}^n a_k^2 \\right) \\left( \\sum_{k=1}^n b_k^2 \\right)\n",
+    "\\end{equation*}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A Cross Product Formula\n",
+    "### Source\n",
+    "```\\begin{equation*}\n",
+    "\\mathbf{V}_1 \\times \\mathbf{V}_2 =  \\begin{vmatrix}\n",
+    "\\mathbf{i} & \\mathbf{j} & \\mathbf{k} \\\\\n",
+    "\\frac{\\partial X}{\\partial u} &  \\frac{\\partial Y}{\\partial u} & 0 \\\\\n",
+    "\\frac{\\partial X}{\\partial v} &  \\frac{\\partial Y}{\\partial v} & 0\n",
+    "\\end{vmatrix}  \n",
+    "\\end{equation*}\n",
+    "```\n",
+    "### Display\n",
+    "\\begin{equation*}\n",
+    "\\mathbf{V}_1 \\times \\mathbf{V}_2 =  \\begin{vmatrix}\n",
+    "\\mathbf{i} & \\mathbf{j} & \\mathbf{k} \\\\\n",
+    "\\frac{\\partial X}{\\partial u} &  \\frac{\\partial Y}{\\partial u} & 0 \\\\\n",
+    "\\frac{\\partial X}{\\partial v} &  \\frac{\\partial Y}{\\partial v} & 0\n",
+    "\\end{vmatrix}  \n",
+    "\\end{equation*}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The probability of getting \\(k\\) heads when flipping \\(n\\) coins is\n",
+    "### Source\n",
+    "```\\begin{equation*}\n",
+    "P(E)   = {n \\choose k} p^k (1-p)^{ n-k} \n",
+    "\\end{equation*}\n",
+    "```\n",
+    "### Display\n",
+    "\\begin{equation*}\n",
+    "P(E)   = {n \\choose k} p^k (1-p)^{ n-k} \n",
+    "\\end{equation*}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## An Identity of Ramanujan\n",
+    "### Source\n",
+    "```\\begin{equation*}\n",
+    "\\frac{1}{\\Bigl(\\sqrt{\\phi \\sqrt{5}}-\\phi\\Bigr) e^{\\frac25 \\pi}} =\n",
+    "1+\\frac{e^{-2\\pi}} {1+\\frac{e^{-4\\pi}} {1+\\frac{e^{-6\\pi}}\n",
+    "{1+\\frac{e^{-8\\pi}} {1+\\ldots} } } } \n",
+    "\\end{equation*}\n",
+    "```\n",
+    "### Display\n",
+    "\\begin{equation*}\n",
+    "\\frac{1}{\\Bigl(\\sqrt{\\phi \\sqrt{5}}-\\phi\\Bigr) e^{\\frac25 \\pi}} =\n",
+    "1+\\frac{e^{-2\\pi}} {1+\\frac{e^{-4\\pi}} {1+\\frac{e^{-6\\pi}}\n",
+    "{1+\\frac{e^{-8\\pi}} {1+\\ldots} } } } \n",
+    "\\end{equation*}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A Rogers-Ramanujan Identity\n",
+    "### Source\n",
+    "```\\begin{equation*}\n",
+    "1 +  \\frac{q^2}{(1-q)}+\\frac{q^6}{(1-q)(1-q^2)}+\\cdots =\n",
+    "\\prod_{j=0}^{\\infty}\\frac{1}{(1-q^{5j+2})(1-q^{5j+3})},\n",
+    "\\quad\\quad \\text{for $|q|<1$}. \n",
+    "\\end{equation*}\n",
+    "```\n",
+    "### Display\n",
+    "\\begin{equation*}\n",
+    "1 + \\frac{q^2}{(1-q)}+\\frac{q^6}{(1-q)(1-q^2)}+\\cdots =\n",
+    "\\prod_{j=0}^{\\infty}\\frac{1}{(1-q^{5j+2})(1-q^{5j+3})},\n",
+    "\\quad\\quad \\text{for $|q|<1$}. \n",
+    "\\end{equation*}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Maxwell's Equations\n",
+    "### Source\n",
+    "```\\begin{align}\n",
+    "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\   \\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
+    "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
+    "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
+    "\\end{align}\n",
+    "```\n",
+    "### Display\n",
+    "\\begin{align}\n",
+    "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\   \\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
+    "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
+    "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
+    "\\end{align}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Equation Numbering and References\n",
+    "\n",
+    "---\n",
+    "\n",
+    "Equation numbering and referencing will be available in a future version of IPython."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Inline Typesetting (Mixing Markdown and TeX)\n",
+    "\n",
+    "---\n",
+    "\n",
+    "While display equations look good for a page of samples, the ability to mix math and *formatted* **text** in a paragraph is also important.\n",
+    "\n",
+    "## Source\n",
+    "``` This expression $\\sqrt{3x-1}+(1+x)^2$ is an example of a TeX inline equation in a **[Markdown-formatted](http://daringfireball.net/projects/markdown/)** sentence.  \n",
+    "```\n",
+    "## Display\n",
+    "This expression $\\sqrt{3x-1}+(1+x)^2$ is an example of a TeX inline equation in a **[Markdown-formatted](http://daringfireball.net/projects/markdown/)** sentence.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Other Syntax\n",
+    "\n",
+    "---\n",
+    "\n",
+    "You will notice in other places on the web that `$$` are needed explicitly to begin and end MathJax typesetting.  This is **not** required if you will be using TeX environments, but the IPython notebook will accept this syntax on legacy notebooks.  \n",
+    "\n",
+    "### Source\n",
+    "```\n",
+    "$$\n",
+    "\\begin{array}{c}\n",
+    "y_1 \\\\\\\n",
+    "y_2 \\mathtt{t}_i \\\\\\\n",
+    "z_{3,4}\n",
+    "\\end{array}\n",
+    "$$\n",
+    "```\n",
+    "\n",
+    "```\n",
+    "$$\n",
+    "\\begin{array}{c}\n",
+    "y_1 \\cr\n",
+    "y_2 \\mathtt{t}_i \\cr\n",
+    "y_{3}\n",
+    "\\end{array}\n",
+    "$$\n",
+    "```\n",
+    "\n",
+    "```\n",
+    "$$\\begin{eqnarray} \n",
+    "x' &=& &x \\sin\\phi &+& z \\cos\\phi \\\\\n",
+    "z' &=& - &x \\cos\\phi &+& z \\sin\\phi \\\\\n",
+    "\\end{eqnarray}$$\n",
+    "```\n",
+    "\n",
+    "```\n",
+    "$$\n",
+    "x=4\n",
+    "$$\n",
+    "```\n",
+    "\n",
+    "### Display\n",
+    "$$\n",
+    "\\begin{array}{c}\n",
+    "y_1 \\\\\\\n",
+    "y_2 \\mathtt{t}_i \\\\\\\n",
+    "z_{3,4}\n",
+    "\\end{array}\n",
+    "$$\n",
+    "\n",
+    "$$\n",
+    "\\begin{array}{c}\n",
+    "y_1 \\cr\n",
+    "y_2 \\mathtt{t}_i \\cr\n",
+    "y_{3}\n",
+    "\\end{array}\n",
+    "$$\n",
+    "\n",
+    "$$\\begin{eqnarray} \n",
+    "x' &=& &x \\sin\\phi &+& z \\cos\\phi \\\\\n",
+    "z' &=& - &x \\cos\\phi &+& z \\sin\\phi \\\\\n",
+    "\\end{eqnarray}$$\n",
+    "\n",
+    "$$\n",
+    "x=4\n",
+    "$$"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Notebook/Using nbconvert as a Library.ipynb b/examples/Notebook/Using nbconvert as a Library.ipynb
index 6c1bfd3..31e58ca 100644
--- a/examples/Notebook/Using nbconvert as a Library.ipynb
+++ b/examples/Notebook/Using nbconvert as a Library.ipynb
@@ -1,1054 +1,1542 @@
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "NbConvert, Python library"
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# NbConvert, Python library"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this Notebook, I will introduce you to the programatic API of nbconvert to show you how to use it in various context. \n",
+    "\n",
+    "For this I will use one of [@jakevdp](https://github.com/jakevdp) great [blog post](http://jakevdp.github.io/blog/2013/04/15/code-golf-in-python-sudoku/).\n",
+    "I've explicitely chosen a post with no javascript tricks as Jake seem to be found of right now, for the reason that the becommings of embeding javascript in nbviewer, which is based on nbconvert is not fully decided yet. \n",
+    "\n",
+    "\n",
+    "This will not focus on using the command line tool to convert file. The attentive reader will point-out that no data are read from, or written to disk during the conversion process. Indeed, nbconvert as been though as much as\n",
+    "possible to avoid IO operation and work as well in a database, or web-based environement."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Quick overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<pre style='background-color:#FDD'> Warning, Do use 1.0 or 1.x branch and not master naming have changed.</pre>\n",
+    "\n",
+    "<pre style='background-color:#FDD'> Warning, NbConvert is a Tech-Preview, API will change within the next 6 month.</pre>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Credit, Jonathan Freder (@jdfreder on github)\n",
+    "\n",
+    "<center>\n",
+    " ![nbca](files/nbconvert_arch.png)\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The main principle of nbconvert is to instanciate a `Exporter` that controle\n",
+    "a pipeline through which each notebook you want to export with go through."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start by importing what we need from the API, and download @jakevdp's notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'{\\n \"metadata\": {\\n  \"name\": \"XKCD_plots\"\\n },\\n \"nbformat\": 3,\\n...'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "response = requests.get('http://jakevdp.github.com/downloads/notebooks/XKCD_plots.ipynb')\n",
+    "response.content[0:60]+'...'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you do not have request install downlad by hand, and read the file as usual."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We read the response into a slightly more convenient format which represent IPython notebook. \n",
+    "There are not real advantages for now, except some convenient methods, but with time this structure should be able to\n",
+    "guarantee that the notebook structure is valid. Note also that the in-memory format and on disk format can be slightly different. In particual, on disk, multiline strings might be spitted into list of string to be more version control friendly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{u'cell_type': u'heading',\n",
+       " u'level': 1,\n",
+       " u'metadata': {},\n",
+       " u'source': u'XKCD plots in Matplotlib'}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.nbformat import current as nbformat\n",
+    "jake_notebook = nbformat.reads_json(response.content)\n",
+    "jake_notebook.worksheets[0].cells[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we have here Jake's notebook in a convenient form, which is mainly a Super-Powered dict and list nested.\n",
+    "You don't need to worry about the exact structure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The nbconvert API exposes some basic exporter for common format and default options. We will start\n",
+    "by using one of them. First we import it, instanciate an instance with most of the default parameters and fed it\n",
+    "the downloaded notebook. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import IPython.nbconvert"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.config import Config\n",
+    "from IPython.nbconvert import HTMLExporter\n",
+    "\n",
+    "## I use `basic` here to have less boilerplate and headers in the HTML.\n",
+    "## we'll see later how to pass config to exporters.\n",
+    "exportHtml = HTMLExporter(config=Config({'HTMLExporter':{'default_template':'basic'}}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "(body, resources) = exportHtml.from_notebook_node(jake_notebook)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exporter returns a tuple containing the body of the converted notebook, here raw HTML, as well as a resources dict.\n",
+    "The resource dict contains (among many things) the extracted PNG, JPG [...etc] from the notebook when applicable.\n",
+    "The basic HTML exporter does keep them as embeded base64 into the notebook, but one can do ask the figures to be extracted. Cf advance use. So for now the resource dict **should** be mostly empty, except for 1 key containing some css, and 2 others whose content will be obvious.\n",
+    "\n",
+    "Exporter are stateless, you won't be able to extract any usefull information (except their configuration) from them.\n",
+    "You can directly re-use the instance to convert another notebook. Each exporter expose for convenience a `from_file` and `from_filename` methods if you need."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['inlining', 'output_extension', 'metadata']\n",
+      "defaultdict(None, {'name': 'Notebook'})\n",
+      "html\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In this Notebook, I will introduce you to the programatic API of nbconvert to show you how to use it in various context. \n",
-      "\n",
-      "For this I will use one of [@jakevdp](https://github.com/jakevdp) great [blog post](http://jakevdp.github.io/blog/2013/04/15/code-golf-in-python-sudoku/).\n",
-      "I've explicitely chosen a post with no javascript tricks as Jake seem to be found of right now, for the reason that the becommings of embeding javascript in nbviewer, which is based on nbconvert is not fully decided yet. \n",
+    }
+   ],
+   "source": [
+    "print resources.keys()\n",
+    "print resources['metadata']\n",
+    "print resources['output_extension']\n",
+    "# print resources['inlining'] # too lng to be shown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\n",
+      "<div class=\"text_cell_render border-box-sizing rendered_html\">\n",
+      "<h1 id=\"XKCD-plots-in-Matplotlib\">XKCD plots in Matplotlib<a class=\"anchor-link\" href=\"#XKCD-plots-in-Matplotlib\">&#182;</a></h1>\n",
+      "</div>\n",
       "\n",
-      "This will not focus on using the command line tool to convert file. The attentive reader will point-out that no data are read from, or written to disk during the conversion process. Indeed, nbconvert as been though as much as\n",
-      "possible to avoid IO operation and work as well in a database, or web-based environement."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 4,
-     "metadata": {},
-     "source": [
-      "Quick overview"
+      "<div class=\"text_cell_render border-box-sizing rendered_html\">\n",
+      "<p>This notebook originally appeared as a blog post at <a href=\"http://jakevdp.github.com/blog/2012/10/07/xkcd-style-plots-in-matplotli...\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<pre style='background-color:#FDD'> Warning, Do use 1.0 or 1.x branch and not master naming have changed.</pre>\n",
+    }
+   ],
+   "source": [
+    "# Part of the body, here the first Heading\n",
+    "start = body.index('<h1 id', )\n",
+    "print body[:400]+'...'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can directly write the body into an HTML file if you wish, as you see it does not contains any body tag, or style declaration, but thoses are included in the default HtmlExporter if you do not pass it a config object as I did."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Extracting Figures"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When exporting one might want to extract the base64 encoded figures to separate files, this is by default what does the RstExporter does, let see how to use it. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.nbconvert import RSTExporter\n",
+    "\n",
+    "rst_export = RSTExporter()\n",
+    "\n",
+    "(body,resources) = rst_export.from_notebook_node(jake_notebook)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "<pre style='background-color:#FDD'> Warning, NbConvert is a Tech-Preview, API will change within the next 6 month.</pre>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Credit, Jonathan Freder (@jdfreder on github)\n",
+      "XKCD plots in Matplotlib\n",
+      "========================\n",
       "\n",
-      "<center>\n",
-      " ![nbca](files/nbconvert_arch.png)\n",
-      "</center>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The main principle of nbconvert is to instanciate a `Exporter` that controle\n",
-      "a pipeline through which each notebook you want to export with go through."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's start by importing what we need from the API, and download @jakevdp's notebook."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import requests\n",
-      "response = requests.get('http://jakevdp.github.com/downloads/notebooks/XKCD_plots.ipynb')\n",
-      "response.content[0:60]+'...'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 1,
-       "text": [
-        "'{\\n \"metadata\": {\\n  \"name\": \"XKCD_plots\"\\n },\\n \"nbformat\": 3,\\n...'"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If you do not have request install downlad by hand, and read the file as usual."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We read the response into a slightly more convenient format which represent IPython notebook. \n",
-      "There are not real advantages for now, except some convenient methods, but with time this structure should be able to\n",
-      "guarantee that the notebook structure is valid. Note also that the in-memory format and on disk format can be slightly different. In particual, on disk, multiline strings might be spitted into list of string to be more version control friendly."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.nbformat import current as nbformat\n",
-      "jake_notebook = nbformat.reads_json(response.content)\n",
-      "jake_notebook.worksheets[0].cells[0]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 2,
-       "text": [
-        "{u'cell_type': u'heading',\n",
-        " u'level': 1,\n",
-        " u'metadata': {},\n",
-        " u'source': u'XKCD plots in Matplotlib'}"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "So we have here Jake's notebook in a convenient form, which is mainly a Super-Powered dict and list nested.\n",
-      "You don't need to worry about the exact structure."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The nbconvert API exposes some basic exporter for common format and default options. We will start\n",
-      "by using one of them. First we import it, instanciate an instance with most of the default parameters and fed it\n",
-      "the downloaded notebook. "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import IPython.nbconvert"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.config import Config\n",
-      "from IPython.nbconvert import HTMLExporter\n",
       "\n",
-      "## I use `basic` here to have less boilerplate and headers in the HTML.\n",
-      "## we'll see later how to pass config to exporters.\n",
-      "exportHtml = HTMLExporter(config=Config({'HTMLExporter':{'default_template':'basic'}}))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "(body, resources) = exportHtml.from_notebook_node(jake_notebook)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The exporter returns a tuple containing the body of the converted notebook, here raw HTML, as well as a resources dict.\n",
-      "The resource dict contains (among many things) the extracted PNG, JPG [...etc] from the notebook when applicable.\n",
-      "The basic HTML exporter does keep them as embeded base64 into the notebook, but one can do ask the figures to be extracted. Cf advance use. So for now the resource dict **should** be mostly empty, except for 1 key containing some css, and 2 others whose content will be obvious.\n",
+      "This notebook originally appeared as a blog post at `Pythonic\n",
+      "Perambulations <http://jakevdp.github.com/blog/2012/10/07/xkcd-style-plots-in-matplotlib/>`_\n",
+      "by Jake Vanderplas.\n",
       "\n",
-      "Exporter are stateless, you won't be able to extract any usefull information (except their configuration) from them.\n",
-      "You can directly re-use the instance to convert another notebook. Each exporter expose for convenience a `from_file` and `from_filename` methods if you need."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print resources.keys()\n",
-      "print resources['metadata']\n",
-      "print resources['output_extension']\n",
-      "# print resources['inlining'] # too lng to be shown"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "['inlining', 'output_extension', 'metadata']\n",
-        "defaultdict(None, {'name': 'Notebook'})\n",
-        "html\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Part of the body, here the first Heading\n",
-      "start = body.index('<h1 id', )\n",
-      "print body[:400]+'...'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "<div class=\"text_cell_render border-box-sizing rendered_html\">\n",
-        "<h1 id=\"XKCD-plots-in-Matplotlib\">XKCD plots in Matplotlib<a class=\"anchor-link\" href=\"#XKCD-plots-in-Matplotlib\">&#182;</a></h1>\n",
-        "</div>\n",
-        "\n",
-        "<div class=\"text_cell_render border-box-sizing rendered_html\">\n",
-        "<p>This notebook originally appeared as a blog post at <a href=\"http://jakevdp.github.com/blog/2012/10/07/xkcd-style-plots-in-matplotli...\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can directly write the body into an HTML file if you wish, as you see it does not contains any body tag, or style declaration, but thoses are included in the default HtmlExporter if you do not pass it a config object as I did."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 4,
-     "metadata": {},
-     "source": [
-      "Extracting Figures"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When exporting one might want to extract the base64 encoded figures to separate files, this is by default what does the RstExporter does, let see how to use it. "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.nbconvert import RSTExporter\n",
+      " *Update: the matplotlib pull request has been merged! See* `*This\n",
+      "post* <http://jakevdp.github.io/blog/2013/07/10/XKCD-plots-in-matplotlib/>`_\n",
+      "*for a description of the XKCD functionality now built-in to\n",
+      "matplotlib!*\n",
       "\n",
-      "rst_export = RSTExporter()\n",
+      "One of the problems I've had with typical matplotlib figures is that\n",
+      "everything in them is so precise, so perfect. For an example of what I\n",
+      "mean, take a look at this figure:\n",
+      "In[1]:\n",
+      ".. code:: python\n",
       "\n",
-      "(body,resources) = rst_export.from_notebook_node(jake_notebook)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print body[:970]+'...'\n",
-      "print '[.....]'\n",
-      "print body[800:1200]+'...'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "XKCD plots in Matplotlib\n",
-        "========================\n",
-        "\n",
-        "\n",
-        "This notebook originally appeared as a blog post at `Pythonic\n",
-        "Perambulations <http://jakevdp.github.com/blog/2012/10/07/xkcd-style-plots-in-matplotlib/>`_\n",
-        "by Jake Vanderplas.\n",
-        "\n",
-        " *Update: the matplotlib pull request has been merged! See* `*This\n",
-        "post* <http://jakevdp.github.io/blog/2013/07/10/XKCD-plots-in-matplotlib/>`_\n",
-        "*for a description of the XKCD functionality now built-in to\n",
-        "matplotlib!*\n",
-        "\n",
-        "One of the problems I've had with typical matplotlib figures is that\n",
-        "everything in them is so precise, so perfect. For an example of what I\n",
-        "mean, take a look at this figure:\n",
-        "In[1]:\n",
-        ".. code:: python\n",
-        "\n",
-        "    from IPython.display import Image\n",
-        "    Image('http://jakevdp.github.com/figures/xkcd_version.png')\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        ".. image:: output_3_0.png\n",
-        "\n",
-        "\n",
-        "\n",
-        "Sometimes when showing schematic plots, this is the type of figure I\n",
-        "want to display. But drawing it by hand is a pain: I'd rather just use\n",
-        "matplotlib. The problem is, matplotlib is a bit...\n",
-        "[.....]\n",
-        "owing schematic plots, this is the type of figure I\n",
-        "want to display. But drawing it by hand is a pain: I'd rather just use\n",
-        "matplotlib. The problem is, matplotlib is a bit too precise. Attempting\n",
-        "to duplicate this figure in matplotlib leads to something like this:\n",
-        "In[2]:\n",
-        ".. code:: python\n",
-        "\n",
-        "    Image('http://jakevdp.github.com/figures/mpl_version.png')\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        ".. image:: output_5_0.png\n",
-        "\n",
-        "\n",
-        "\n",
-        "It just doesn'...\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here we see that base64 images are not embeded, but we get what look like file name. Actually those are (Configurable) keys to get back the binary data from the resources dict we havent inspected earlier.\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "So when writing a Rst Plugin for any blogengine, Sphinx or anything else, you will be responsible for writing all those data to disk, in the right place. \n",
-      "Of course to help you in this task all those naming are configurable in the right place."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "let's try to see how to get one of these images"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "resources['outputs'].keys()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "[u'output_13_1.text',\n",
-        " u'output_18_0.text',\n",
-        " u'output_3_0.text',\n",
-        " u'output_18_1.png',\n",
-        " u'output_12_0.text',\n",
-        " u'output_5_0.text',\n",
-        " u'output_5_0.png',\n",
-        " u'output_13_1.png',\n",
-        " u'output_16_0.text',\n",
-        " u'output_13_0.text',\n",
-        " u'output_18_1.text',\n",
-        " u'output_3_0.png',\n",
-        " u'output_16_0.png']"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We have extracted 5 binary figures, here `png`s, but they could have been svg, and then wouldn't appear in the binary sub dict.\n",
-      "keep in mind that a object having multiple _repr_ will store all it's repr in the notebook. \n",
+      "    from IPython.display import Image\n",
+      "    Image('http://jakevdp.github.com/figures/xkcd_version.png')\n",
       "\n",
-      "Hence if you provide `_repr_javascript_`,`_repr_latex_` and `_repr_png_`to an object, you will be able to determine at conversion time which representaition is the more appropriate. You could even decide to show all the representaition of an object, it's up to you. But this will require beeing a little more involve and write a few line of Jinja template. This will probably be the subject of another tutorial.\n",
       "\n",
-      "Back to our images,\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import Image\n",
-      "Image(data=resources['outputs']['output_3_0.png'],format='png')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "png": 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UrFgRvr6++N///gc3Nzf4+flh37592LVrV65DhzKJiYkAIFLoBQKBoIAIA8uAvIC5ePHi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\nRU5ErkJggg==\n",
-       "prompt_number": 11,
-       "text": [
-        "<IPython.core.display.Image at 0x10d2bbf90>"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Yep, this is indeed the image we were expecting, and I was able to see it without ever writing or reading it from disk. I don't think I'll have to show to you what to do with those data, as if you are here you are most probably familiar with IO."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Extracting figures with HTML Exporter ?"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Use case:\n",
       "\n",
-      "> I write an [awesome blog](http://jakevdp.github.io/) in HTML, and I want all but having base64 embeded images. \n",
-      "Having one html file with all inside is nice to send to coworker, but I definitively want resources to be cached !\n",
-      "So I need an HTML exporter, and I want it to extract the figures !"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Some theory"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The process of converting a notebook to a another format with the nbconvert Exporters happend in a few steps:\n",
       "\n",
-      "  - Get the notebook data and other required files. (you are responsible for that)\n",
-      "  - Feed them to the exporter that will\n",
-      "      - sequentially feed the data to a number of `Transformers`. Transformer only act on the **structure**\n",
-      "      of the notebook, and have access to it all. \n",
-      "      - feed the notebook through the jinja templating engine\n",
-      "          - the use templates are configurable.\n",
-      "          - templates make use of configurable macros called filters.\n",
-      "  - The exporter return the converted notebook as well as other relevant resources as a tuple.\n",
-      "  - Write what you need to disk, or elsewhere. (You are responsible for it)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here we'll be interested in the `Transformers`. Each `Transformer` is applied successively and in order on the notebook before going through the conversion process.\n",
       "\n",
-      "We provide some transformer that do some modification on the notebook structure by default.\n",
-      "One of them, the `ExtractOutputTransformer` is responsible for crawling notebook,\n",
-      "finding all the figures, and put them into the resources directory, as well as choosing the key\n",
-      "(`filename_xx_y.extension`) that can replace the figure in the template.\n",
+      ".. image:: output_3_0.png\n",
       "\n",
       "\n",
-      "The `ExtractOutputTransformer` is special in the fact that it **should** be availlable on all `Exporter`s, but is just inactive by default on some exporter."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# second transformer shoudl be Instance of ExtractFigureTransformer\n",
-      "exportHtml._transformers # 3rd one shouel be <ExtractOutputTransformer>"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 12,
-       "text": [
-        "[<function IPython.nbconvert.transformers.coalescestreams.wrappedfunc>,\n",
-        " <IPython.nbconvert.transformers.svg2pdf.SVG2PDFTransformer at 0x10d2a7490>,\n",
-        " <IPython.nbconvert.transformers.extractoutput.ExtractOutputTransformer at 0x10d2a7ad0>,\n",
-        " <IPython.nbconvert.transformers.csshtmlheader.CSSHTMLHeaderTransformer at 0x10d2a7b50>,\n",
-        " <IPython.nbconvert.transformers.revealhelp.RevealHelpTransformer at 0x10d29dd90>,\n",
-        " <IPython.nbconvert.transformers.latex.LatexTransformer at 0x10d29db50>,\n",
-        " <IPython.nbconvert.transformers.sphinx.SphinxTransformer at 0x10d2a7b90>]"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To enable it we will use IPython configuration/Traitlets system. If you are have already set some IPython configuration options, \n",
-      "this will look pretty familiar to you. Configuration option are always of the form:\n",
       "\n",
-      "   ClassName.attribute_name = value\n",
-      "   \n",
-      "A few ways exist to create such config, like reading a config file in your profile, but you can also do it programatically usign a dictionary. Let's create such a config object, and see the difference if we pass it to our `HtmlExporter`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.config import Config\n",
+      "Sometimes when showing schematic plots, this is the type of figure I\n",
+      "want to display. But drawing it by hand is a pain: I'd rather just use\n",
+      "matplotlib. The problem is, matplotlib is a bit...\n",
+      "[.....]\n",
+      "owing schematic plots, this is the type of figure I\n",
+      "want to display. But drawing it by hand is a pain: I'd rather just use\n",
+      "matplotlib. The problem is, matplotlib is a bit too precise. Attempting\n",
+      "to duplicate this figure in matplotlib leads to something like this:\n",
+      "In[2]:\n",
+      ".. code:: python\n",
       "\n",
-      "c =  Config({\n",
-      "            'ExtractOutputTransformer':{'enabled':True}\n",
-      "            })\n",
+      "    Image('http://jakevdp.github.com/figures/mpl_version.png')\n",
       "\n",
-      "exportHtml = HTMLExporter()\n",
-      "exportHtml_and_figs = HTMLExporter(config=c)\n",
       "\n",
-      "(_, resources)          = exportHtml.from_notebook_node(jake_notebook)\n",
-      "(_, resources_with_fig) = exportHtml_and_figs.from_notebook_node(jake_notebook)\n",
       "\n",
-      "print 'resources without the \"figures\" key :'\n",
-      "print resources.keys()\n",
       "\n",
-      "print ''\n",
-      "print 'Here we have one more field '\n",
-      "print resources_with_fig.keys()\n",
-      "resources_with_fig['outputs'].keys() "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "resources without the \"figures\" key :\n",
-        "['inlining', 'output_extension', 'metadata']\n",
-        "\n",
-        "Here we have one more field \n",
-        "['outputs', 'inlining', 'output_extension', 'metadata']\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 13,
-       "text": [
-        "[u'output_13_1.text',\n",
-        " u'output_18_0.text',\n",
-        " u'output_3_0.text',\n",
-        " u'output_18_1.png',\n",
-        " u'output_12_0.text',\n",
-        " u'output_5_0.text',\n",
-        " u'output_5_0.png',\n",
-        " u'output_13_1.png',\n",
-        " u'output_16_0.text',\n",
-        " u'output_13_0.text',\n",
-        " u'output_18_1.text',\n",
-        " u'output_3_0.png',\n",
-        " u'output_16_0.png']"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "So now you can loop through the dict and write all those figures to disk in the right place... "
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 4,
-     "metadata": {},
-     "source": [
-      "Custom transformer"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Of course you can imagine many transformation that you would like to apply to a notebook. This is one of the reason we provide a way to register your own transformers that will be applied to the notebook after the default ones.\n",
       "\n",
-      "To do so you'll have to pass an ordered list of `Transformer`s to the Exporter constructor. \n",
+      ".. image:: output_5_0.png\n",
       "\n",
-      "But what is an transformer ? Transformer can be either *decorated function* for dead-simple `Transformer`s that apply\n",
-      "independently to each cell, for more advance transformation that support configurability You have to inherit from\n",
-      "`Transformer` and define a `call` method as we'll see below.\n",
       "\n",
-      "All transforers have a magic attribute that allows it to be activated/disactivate from the config dict."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.nbconvert.transformers import Transformer\n",
-      "import IPython.config\n",
-      "print \"Four relevant docstring\"\n",
-      "print '============================='\n",
-      "print Transformer.__doc__\n",
-      "print '============================='\n",
-      "print Transformer.call.__doc__\n",
-      "print '============================='\n",
-      "print Transformer.transform_cell.__doc__\n",
-      "print '============================='"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Four relevant docstring\n",
-        "=============================\n",
-        " A configurable transformer\n",
-        "\n",
-        "    Inherit from this class if you wish to have configurability for your\n",
-        "    transformer.\n",
-        "\n",
-        "    Any configurable traitlets this class exposed will be configurable in profiles\n",
-        "    using c.SubClassName.atribute=value\n",
-        "\n",
-        "    you can overwrite transform_cell to apply a transformation independently on each cell\n",
-        "    or __call__ if you prefer your own logic. See corresponding docstring for informations.\n",
-        "\n",
-        "    Disabled by default and can be enabled via the config by\n",
-        "        'c.YourTransformerName.enabled = True'\n",
-        "    \n",
-        "=============================\n",
-        "\n",
-        "        Transformation to apply on each notebook.\n",
-        "        \n",
-        "        You should return modified nb, resources.\n",
-        "        If you wish to apply your transform on each cell, you might want to \n",
-        "        overwrite transform_cell method instead.\n",
-        "        \n",
-        "        Parameters\n",
-        "        ----------\n",
-        "        nb : NotebookNode\n",
-        "            Notebook being converted\n",
-        "        resources : dictionary\n",
-        "            Additional resources used in the conversion process.  Allows\n",
-        "            transformers to pass variables into the Jinja engine.\n",
-        "        \n",
-        "=============================\n",
-        "\n",
-        "        Overwrite if you want to apply a transformation on each cell.  You \n",
-        "        should return modified cell and resource dictionary.\n",
-        "        \n",
-        "        Parameters\n",
-        "        ----------\n",
-        "        cell : NotebookNode cell\n",
-        "            Notebook cell being processed\n",
-        "        resources : dictionary\n",
-        "            Additional resources used in the conversion process.  Allows\n",
-        "            transformers to pass variables into the Jinja engine.\n",
-        "        index : int\n",
-        "            Index of the cell being processed\n",
-        "        \n",
-        "=============================\n"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "***\n",
-      "We don't provide convenient method to be aplied on each worksheet as the **data structure** for worksheet will be removed. (not the worksheet functionnality, which is still on it's way)\n",
-      "***"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Example"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I'll now demonstrate a specific example [requested](https://github.com/ipython/nbconvert/pull/137#issuecomment-18658235) while nbconvert 2 was beeing developped. The ability to exclude cell from the conversion process based on their index. \n",
       "\n",
-      "I'll let you imagin how to inject cell, if what you just want is to happend static content at the beginning/end of a notebook, plese refer to templating section, it will be much easier and cleaner."
+      "It just doesn'...\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.utils.traitlets import Integer"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class PelicanSubCell(Transformer):\n",
-      "    \"\"\"A Pelican specific transformer to remove somme of the cells of a notebook\"\"\"\n",
-      "    \n",
-      "    # I could also read the cells from nbc.metadata.pelican is someone wrote a JS extension\n",
-      "    # But I'll stay with configurable value. \n",
-      "    start = Integer(0, config=True, help=\"first cell of notebook to be converted\")\n",
-      "    end   = Integer(-1, config=True, help=\"last cell of notebook to be converted\")\n",
-      "    \n",
-      "    def call(self, nb, resources):\n",
+    }
+   ],
+   "source": [
+    "print body[:970]+'...'\n",
+    "print '[.....]'\n",
+    "print body[800:1200]+'...'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we see that base64 images are not embeded, but we get what look like file name. Actually those are (Configurable) keys to get back the binary data from the resources dict we havent inspected earlier.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So when writing a Rst Plugin for any blogengine, Sphinx or anything else, you will be responsible for writing all those data to disk, in the right place. \n",
+    "Of course to help you in this task all those naming are configurable in the right place."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "let's try to see how to get one of these images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[u'output_13_1.text',\n",
+       " u'output_18_0.text',\n",
+       " u'output_3_0.text',\n",
+       " u'output_18_1.png',\n",
+       " u'output_12_0.text',\n",
+       " u'output_5_0.text',\n",
+       " u'output_5_0.png',\n",
+       " u'output_13_1.png',\n",
+       " u'output_16_0.text',\n",
+       " u'output_13_0.text',\n",
+       " u'output_18_1.text',\n",
+       " u'output_3_0.png',\n",
+       " u'output_16_0.png']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "resources['outputs'].keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have extracted 5 binary figures, here `png`s, but they could have been svg, and then wouldn't appear in the binary sub dict.\n",
+    "keep in mind that a object having multiple _repr_ will store all it's repr in the notebook. \n",
+    "\n",
+    "Hence if you provide `_repr_javascript_`,`_repr_latex_` and `_repr_png_`to an object, you will be able to determine at conversion time which representaition is the more appropriate. You could even decide to show all the representaition of an object, it's up to you. But this will require beeing a little more involve and write a few line of Jinja template. This will probably be the subject of another tutorial.\n",
+    "\n",
+    "Back to our images,\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
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+       "RU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Image at 0x10d2bbf90>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(data=resources['outputs']['output_3_0.png'],format='png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Yep, this is indeed the image we were expecting, and I was able to see it without ever writing or reading it from disk. I don't think I'll have to show to you what to do with those data, as if you are here you are most probably familiar with IO."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extracting figures with HTML Exporter ?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use case:\n",
+    "\n",
+    "> I write an [awesome blog](http://jakevdp.github.io/) in HTML, and I want all but having base64 embeded images. \n",
+    "Having one html file with all inside is nice to send to coworker, but I definitively want resources to be cached !\n",
+    "So I need an HTML exporter, and I want it to extract the figures !"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Some theory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The process of converting a notebook to a another format with the nbconvert Exporters happend in a few steps:\n",
+    "\n",
+    "  - Get the notebook data and other required files. (you are responsible for that)\n",
+    "  - Feed them to the exporter that will\n",
+    "      - sequentially feed the data to a number of `Transformers`. Transformer only act on the **structure**\n",
+    "      of the notebook, and have access to it all. \n",
+    "      - feed the notebook through the jinja templating engine\n",
+    "          - the use templates are configurable.\n",
+    "          - templates make use of configurable macros called filters.\n",
+    "  - The exporter return the converted notebook as well as other relevant resources as a tuple.\n",
+    "  - Write what you need to disk, or elsewhere. (You are responsible for it)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we'll be interested in the `Transformers`. Each `Transformer` is applied successively and in order on the notebook before going through the conversion process.\n",
+    "\n",
+    "We provide some transformer that do some modification on the notebook structure by default.\n",
+    "One of them, the `ExtractOutputTransformer` is responsible for crawling notebook,\n",
+    "finding all the figures, and put them into the resources directory, as well as choosing the key\n",
+    "(`filename_xx_y.extension`) that can replace the figure in the template.\n",
+    "\n",
+    "\n",
+    "The `ExtractOutputTransformer` is special in the fact that it **should** be availlable on all `Exporter`s, but is just inactive by default on some exporter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<function IPython.nbconvert.transformers.coalescestreams.wrappedfunc>,\n",
+       " <IPython.nbconvert.transformers.svg2pdf.SVG2PDFTransformer at 0x10d2a7490>,\n",
+       " <IPython.nbconvert.transformers.extractoutput.ExtractOutputTransformer at 0x10d2a7ad0>,\n",
+       " <IPython.nbconvert.transformers.csshtmlheader.CSSHTMLHeaderTransformer at 0x10d2a7b50>,\n",
+       " <IPython.nbconvert.transformers.revealhelp.RevealHelpTransformer at 0x10d29dd90>,\n",
+       " <IPython.nbconvert.transformers.latex.LatexTransformer at 0x10d29db50>,\n",
+       " <IPython.nbconvert.transformers.sphinx.SphinxTransformer at 0x10d2a7b90>]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# second transformer shoudl be Instance of ExtractFigureTransformer\n",
+    "exportHtml._transformers # 3rd one shouel be <ExtractOutputTransformer>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To enable it we will use IPython configuration/Traitlets system. If you are have already set some IPython configuration options, \n",
+    "this will look pretty familiar to you. Configuration option are always of the form:\n",
+    "\n",
+    "   ClassName.attribute_name = value\n",
+    "   \n",
+    "A few ways exist to create such config, like reading a config file in your profile, but you can also do it programatically usign a dictionary. Let's create such a config object, and see the difference if we pass it to our `HtmlExporter`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "resources without the \"figures\" key :\n",
+      "['inlining', 'output_extension', 'metadata']\n",
       "\n",
-      "        #nbc = deepcopy(nb)\n",
-      "        nbc = nb\n",
-      "        # don't print in real transformer !!!\n",
-      "        print \"I'll keep only cells from \", self.start, \"to \", self.end, \"\\n\\n\"\n",
-      "        for worksheet in nbc.worksheets :\n",
-      "            cells = worksheet.cells[:]\n",
-      "            worksheet.cells = cells[self.start:self.end]                    \n",
-      "        return nbc, resources"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# I create this on the fly, but this could be loaded from a DB, and config object support merging...\n",
-      "c =  Config({\n",
-      "            'PelicanSubCell':{\n",
-      "                            'enabled':True,\n",
-      "                            'start':4,\n",
-      "                            'end':6,\n",
-      "                             }\n",
-      "            })"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I'm creating a pelican exporter that take `PelicanSubCell` extra transformers and a `config` object as parameter. This might seem redundant, but with configuration system you'll see that one can register an inactive transformer on all exporters and activate it at will form its config files and command line. "
+      "Here we have one more field \n",
+      "['outputs', 'inlining', 'output_extension', 'metadata']\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pelican = RSTExporter(transformers=[PelicanSubCell], config=c)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print pelican.from_notebook_node(jake_notebook)[0]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I'll keep only cells from  4 to  6 \n",
-        "\n",
-        "\n",
-        "\n",
-        "Sometimes when showing schematic plots, this is the type of figure I\n",
-        "want to display. But drawing it by hand is a pain: I'd rather just use\n",
-        "matplotlib. The problem is, matplotlib is a bit too precise. Attempting\n",
-        "to duplicate this figure in matplotlib leads to something like this:\n",
-        "In[2]:\n",
-        ".. code:: python\n",
-        "\n",
-        "    Image('http://jakevdp.github.com/figures/mpl_version.png')\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        "\n",
-        ".. image:: output_5_0.png\n",
-        "\n",
-        "\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Programatic example of extending templates / cutom filters"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.nbconvert.filters.highlight import _pygment_highlight\n",
-      "from pygments.formatters import HtmlFormatter\n",
+     "data": {
+      "text/plain": [
+       "[u'output_13_1.text',\n",
+       " u'output_18_0.text',\n",
+       " u'output_3_0.text',\n",
+       " u'output_18_1.png',\n",
+       " u'output_12_0.text',\n",
+       " u'output_5_0.text',\n",
+       " u'output_5_0.png',\n",
+       " u'output_13_1.png',\n",
+       " u'output_16_0.text',\n",
+       " u'output_13_0.text',\n",
+       " u'output_18_1.text',\n",
+       " u'output_3_0.png',\n",
+       " u'output_16_0.png']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.config import Config\n",
+    "\n",
+    "c =  Config({\n",
+    "            'ExtractOutputTransformer':{'enabled':True}\n",
+    "            })\n",
+    "\n",
+    "exportHtml = HTMLExporter()\n",
+    "exportHtml_and_figs = HTMLExporter(config=c)\n",
+    "\n",
+    "(_, resources)          = exportHtml.from_notebook_node(jake_notebook)\n",
+    "(_, resources_with_fig) = exportHtml_and_figs.from_notebook_node(jake_notebook)\n",
+    "\n",
+    "print 'resources without the \"figures\" key :'\n",
+    "print resources.keys()\n",
+    "\n",
+    "print ''\n",
+    "print 'Here we have one more field '\n",
+    "print resources_with_fig.keys()\n",
+    "resources_with_fig['outputs'].keys() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So now you can loop through the dict and write all those figures to disk in the right place... "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Custom transformer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course you can imagine many transformation that you would like to apply to a notebook. This is one of the reason we provide a way to register your own transformers that will be applied to the notebook after the default ones.\n",
+    "\n",
+    "To do so you'll have to pass an ordered list of `Transformer`s to the Exporter constructor. \n",
+    "\n",
+    "But what is an transformer ? Transformer can be either *decorated function* for dead-simple `Transformer`s that apply\n",
+    "independently to each cell, for more advance transformation that support configurability You have to inherit from\n",
+    "`Transformer` and define a `call` method as we'll see below.\n",
+    "\n",
+    "All transforers have a magic attribute that allows it to be activated/disactivate from the config dict."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Four relevant docstring\n",
+      "=============================\n",
+      " A configurable transformer\n",
       "\n",
-      "from IPython.nbconvert.exporters import HTMLExporter\n",
-      "from IPython.config import Config\n",
+      "    Inherit from this class if you wish to have configurability for your\n",
+      "    transformer.\n",
       "\n",
-      "from IPython.nbformat import current as nbformat"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here we define a dustom 'highlight' filter that apply a custom class to code in css. We register this filter with a already existing name, so it will replace the default one."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def my_highlight(source, language='ipython'):\n",
-      "    formatter = HtmlFormatter(cssclass='highlight-ipynb')\n",
-      "    return _pygment_highlight(source, formatter, language)\n",
+      "    Any configurable traitlets this class exposed will be configurable in profiles\n",
+      "    using c.SubClassName.atribute=value\n",
+      "\n",
+      "    you can overwrite transform_cell to apply a transformation independently on each cell\n",
+      "    or __call__ if you prefer your own logic. See corresponding docstring for informations.\n",
+      "\n",
+      "    Disabled by default and can be enabled via the config by\n",
+      "        'c.YourTransformerName.enabled = True'\n",
+      "    \n",
+      "=============================\n",
+      "\n",
+      "        Transformation to apply on each notebook.\n",
       "        \n",
-      "c = Config({'CSSHtmlHeaderTransformer':\n",
-      "                    {'enabled':False, 'highlight_class':'highlight-ipynb'}})\n",
+      "        You should return modified nb, resources.\n",
+      "        If you wish to apply your transform on each cell, you might want to \n",
+      "        overwrite transform_cell method instead.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        nb : NotebookNode\n",
+      "            Notebook being converted\n",
+      "        resources : dictionary\n",
+      "            Additional resources used in the conversion process.  Allows\n",
+      "            transformers to pass variables into the Jinja engine.\n",
+      "        \n",
+      "=============================\n",
       "\n",
-      "exportHtml = HTMLExporter( config=c , filters={'highlight2html': my_highlight} )\n",
-      "(body,resources) = exportHtml.from_notebook_node(jake_notebook)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "i = body.index('highlight-ipynb')\n",
-      "body[i-12:i+50]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 22,
-       "text": [
-        "u'<div class=\"highlight-ipynb\"><pre><span class=\"kn\">from</span>'"
-       ]
-      }
-     ],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Programatically make templates"
+      "        Overwrite if you want to apply a transformation on each cell.  You \n",
+      "        should return modified cell and resource dictionary.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        cell : NotebookNode cell\n",
+      "            Notebook cell being processed\n",
+      "        resources : dictionary\n",
+      "            Additional resources used in the conversion process.  Allows\n",
+      "            transformers to pass variables into the Jinja engine.\n",
+      "        index : int\n",
+      "            Index of the cell being processed\n",
+      "        \n",
+      "=============================\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from jinja2 import DictLoader\n",
+    }
+   ],
+   "source": [
+    "from IPython.nbconvert.transformers import Transformer\n",
+    "import IPython.config\n",
+    "print \"Four relevant docstring\"\n",
+    "print '============================='\n",
+    "print Transformer.__doc__\n",
+    "print '============================='\n",
+    "print Transformer.call.__doc__\n",
+    "print '============================='\n",
+    "print Transformer.transform_cell.__doc__\n",
+    "print '============================='"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "***\n",
+    "We don't provide convenient method to be aplied on each worksheet as the **data structure** for worksheet will be removed. (not the worksheet functionnality, which is still on it's way)\n",
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I'll now demonstrate a specific example [requested](https://github.com/ipython/nbconvert/pull/137#issuecomment-18658235) while nbconvert 2 was beeing developped. The ability to exclude cell from the conversion process based on their index. \n",
+    "\n",
+    "I'll let you imagin how to inject cell, if what you just want is to happend static content at the beginning/end of a notebook, plese refer to templating section, it will be much easier and cleaner."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.utils.traitlets import Integer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "class PelicanSubCell(Transformer):\n",
+    "    \"\"\"A Pelican specific transformer to remove somme of the cells of a notebook\"\"\"\n",
+    "    \n",
+    "    # I could also read the cells from nbc.metadata.pelican is someone wrote a JS extension\n",
+    "    # But I'll stay with configurable value. \n",
+    "    start = Integer(0, config=True, help=\"first cell of notebook to be converted\")\n",
+    "    end   = Integer(-1, config=True, help=\"last cell of notebook to be converted\")\n",
+    "    \n",
+    "    def call(self, nb, resources):\n",
+    "\n",
+    "        #nbc = deepcopy(nb)\n",
+    "        nbc = nb\n",
+    "        # don't print in real transformer !!!\n",
+    "        print \"I'll keep only cells from \", self.start, \"to \", self.end, \"\\n\\n\"\n",
+    "        for worksheet in nbc.worksheets :\n",
+    "            cells = worksheet.cells[:]\n",
+    "            worksheet.cells = cells[self.start:self.end]                    \n",
+    "        return nbc, resources"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# I create this on the fly, but this could be loaded from a DB, and config object support merging...\n",
+    "c =  Config({\n",
+    "            'PelicanSubCell':{\n",
+    "                            'enabled':True,\n",
+    "                            'start':4,\n",
+    "                            'end':6,\n",
+    "                             }\n",
+    "            })"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I'm creating a pelican exporter that take `PelicanSubCell` extra transformers and a `config` object as parameter. This might seem redundant, but with configuration system you'll see that one can register an inactive transformer on all exporters and activate it at will form its config files and command line. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "pelican = RSTExporter(transformers=[PelicanSubCell], config=c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I'll keep only cells from  4 to  6 \n",
       "\n",
-      "dl = DictLoader({'html_full.tpl': \n",
-      "\"\"\"\n",
-      "{%- extends 'html_basic.tpl' -%} \n",
       "\n",
-      "{% block footer %}\n",
-      "FOOOOOOOOTEEEEER\n",
-      "{% endblock footer %}\n",
-      "\"\"\"})\n",
       "\n",
+      "Sometimes when showing schematic plots, this is the type of figure I\n",
+      "want to display. But drawing it by hand is a pain: I'd rather just use\n",
+      "matplotlib. The problem is, matplotlib is a bit too precise. Attempting\n",
+      "to duplicate this figure in matplotlib leads to something like this:\n",
+      "In[2]:\n",
+      ".. code:: python\n",
       "\n",
-      "exportHtml = HTMLExporter( config=None , filters={'highlight': my_highlight}, extra_loaders=[dl] )\n",
-      "(body,resources) = exportHtml.from_notebook_node(jake_notebook)\n",
-      "for l in body.split('\\n')[-4:]:\n",
-      "    print l"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "<p>This post was written entirely in an IPython Notebook: the notebook file is available for download <a href=\"http://jakevdp.github.com/downloads/notebooks/XKCD_plots.ipynb\">here</a>. For more information on blogging with notebooks in octopress, see my <a href=\"http://jakevdp.github.com/blog/2012/10/04/blogging-with-ipython/\">previous post</a> on the subject.</p>\n",
-        "</div>\n",
-        "FOOOOOOOOTEEEEER\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Real World Use"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "@jakevdp use Pelican and IPython Notebook to blog. Pelican [Will use](https://github.com/getpelican/pelican-plugins/pull/21) nbconvert programatically to generate blog post. Have a look a [Pythonic Preambulations](http://jakevdp.github.io/) for Jake blog post."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "@damianavila Wrote a Nicholas Plugin to [Write blog post as Notebook](http://www.damian.oquanta.info/posts/one-line-deployment-of-your-site-to-gh-pages.html) and is developping a js-extension to publish notebooks in one click from the web app."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<center>\n",
-      "<blockquote class=\"twitter-tweet\"><p>As <a href=\"https://twitter.com/Mbussonn\">@Mbussonn</a> requested... easieeeeer! Deploy your Nikola site with just a click in the IPython notebook! <a href=\"http://t.co/860sJunZvj\">http://t.co/860sJunZvj</a> cc <a href=\"https://twitter.com/ralsina\">@ralsina</a></p>&mdash; Dami\u00e1n Avila (@damian_avila) <a href=\"https://twitter.com/damian_avila/statuses/370306057828335616\">August 21, 2013</a></blockquote>\n",
-      "</center>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And finaly,  what you just did, is replicate what [nbviewer](http://nbviewer.ipython.org) does. WHich to fetch a notebook from url, convert it and send in back to you as a static html."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 5,
-     "metadata": {},
-     "source": [
-      "A few gotchas"
+      "    Image('http://jakevdp.github.com/figures/mpl_version.png')\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      "\n",
+      ".. image:: output_5_0.png\n",
+      "\n",
+      "\n",
+      "\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Jinja blocks use `{% %}`by default which does not play nicely with $\\LaTeX$, hence thoses are replaced by `((* *))` in latex templates."
+    }
+   ],
+   "source": [
+    "print pelican.from_notebook_node(jake_notebook)[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Programatic example of extending templates / cutom filters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.nbconvert.filters.highlight import _pygment_highlight\n",
+    "from pygments.formatters import HtmlFormatter\n",
+    "\n",
+    "from IPython.nbconvert.exporters import HTMLExporter\n",
+    "from IPython.config import Config\n",
+    "\n",
+    "from IPython.nbformat import current as nbformat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we define a dustom 'highlight' filter that apply a custom class to code in css. We register this filter with a already existing name, so it will replace the default one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def my_highlight(source, language='ipython'):\n",
+    "    formatter = HtmlFormatter(cssclass='highlight-ipynb')\n",
+    "    return _pygment_highlight(source, formatter, language)\n",
+    "        \n",
+    "c = Config({'CSSHtmlHeaderTransformer':\n",
+    "                    {'enabled':False, 'highlight_class':'highlight-ipynb'}})\n",
+    "\n",
+    "exportHtml = HTMLExporter( config=c , filters={'highlight2html': my_highlight} )\n",
+    "(body,resources) = exportHtml.from_notebook_node(jake_notebook)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "u'<div class=\"highlight-ipynb\"><pre><span class=\"kn\">from</span>'"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "i = body.index('highlight-ipynb')\n",
+    "body[i-12:i+50]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Programatically make templates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<p>This post was written entirely in an IPython Notebook: the notebook file is available for download <a href=\"http://jakevdp.github.com/downloads/notebooks/XKCD_plots.ipynb\">here</a>. For more information on blogging with notebooks in octopress, see my <a href=\"http://jakevdp.github.com/blog/2012/10/04/blogging-with-ipython/\">previous post</a> on the subject.</p>\n",
+      "</div>\n",
+      "FOOOOOOOOTEEEEER\n",
+      "\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
     }
    ],
-   "metadata": {}
+   "source": [
+    "from jinja2 import DictLoader\n",
+    "\n",
+    "dl = DictLoader({'html_full.tpl': \n",
+    "\"\"\"\n",
+    "{%- extends 'html_basic.tpl' -%} \n",
+    "\n",
+    "{% block footer %}\n",
+    "FOOOOOOOOTEEEEER\n",
+    "{% endblock footer %}\n",
+    "\"\"\"})\n",
+    "\n",
+    "\n",
+    "exportHtml = HTMLExporter( config=None , filters={'highlight': my_highlight}, extra_loaders=[dl] )\n",
+    "(body,resources) = exportHtml.from_notebook_node(jake_notebook)\n",
+    "for l in body.split('\\n')[-4:]:\n",
+    "    print l"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Real World Use"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "@jakevdp use Pelican and IPython Notebook to blog. Pelican [Will use](https://github.com/getpelican/pelican-plugins/pull/21) nbconvert programatically to generate blog post. Have a look a [Pythonic Preambulations](http://jakevdp.github.io/) for Jake blog post."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "@damianavila Wrote a Nicholas Plugin to [Write blog post as Notebook](http://www.damian.oquanta.info/posts/one-line-deployment-of-your-site-to-gh-pages.html) and is developping a js-extension to publish notebooks in one click from the web app."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<center>\n",
+    "<blockquote class=\"twitter-tweet\"><p>As <a href=\"https://twitter.com/Mbussonn\">@Mbussonn</a> requested... easieeeeer! Deploy your Nikola site with just a click in the IPython notebook! <a href=\"http://t.co/860sJunZvj\">http://t.co/860sJunZvj</a> cc <a href=\"https://twitter.com/ralsina\">@ralsina</a></p>&mdash; Dami\u00e1n Avila (@damian_avila) <a href=\"https://twitter.com/damian_avila/statuses/370306057828335616\">August 21, 2013</a></blockquote>\n",
+    "</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And finaly,  what you just did, is replicate what [nbviewer](http://nbviewer.ipython.org) does. WHich to fetch a notebook from url, convert it and send in back to you as a static html."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### A few gotchas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Jinja blocks use `{% %}`by default which does not play nicely with $\\LaTeX$, hence thoses are replaced by `((* *))` in latex templates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Notebook/What is the IPython Notebook.ipynb b/examples/Notebook/What is the IPython Notebook.ipynb
index ee05fae..f957098 100644
--- a/examples/Notebook/What is the IPython Notebook.ipynb
+++ b/examples/Notebook/What is the IPython Notebook.ipynb
@@ -1,180 +1,168 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# What is the IPython Notebook?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The IPython Notebook is an **interactive computing environment** that enables users to author notebook documents that include: \n",
+    "- Live code\n",
+    "- Interactive widgets\n",
+    "- Plots\n",
+    "- Narrative text\n",
+    "- Equations\n",
+    "- Images\n",
+    "- Video\n",
+    "\n",
+    "These documents provide a **complete and self-contained record of a computation** that can be converted to various formats and shared with others using email, [Dropbox](http://dropbox.com), version control systems (like git/[GitHub](http://github.com)) or [nbviewer.ipython.org](http://nbviewer.ipython.org)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Components"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The IPython Notebook combines three components:\n",
+    "\n",
+    "* **The notebook web application**: An interactive web application for writing and running code interactively and authoring notebook documents.\n",
+    "* **Kernels**: Separate processes started by the notebook web application that runs users' code in a given language and returns output back to the notebook web application. The kernel also handles things like computations for interactive widgets, tab completion and introspection. \n",
+    "* **Notebook documents**: Self-contained documents that contain a representation of all content visible in the notebook web application, including inputs and outputs of the computations, narrative\n",
+    "text, equations, images, and rich media representations of objects. Each notebook document has its own kernel."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Notebook web application"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The notebook web application enables users to:\n",
+    "\n",
+    "* **Edit code in the browser**, with automatic syntax highlighting, indentation, and tab completion/introspection.\n",
+    "* **Run code from the browser**, with the results of computations attached to the code which generated them.\n",
+    "* See the results of computations with **rich media representations**, such as HTML, LaTeX, PNG, SVG, PDF, etc.\n",
+    "* Create and use **interactive JavaScript wigets**, which bind interactive user interface controls and visualizations to reactive kernel side computations.\n",
+    "* Author **narrative text** using the [Markdown](https://daringfireball.net/projects/markdown/) markup language.\n",
+    "* Build **hierarchical documents** that are organized into sections with different levels of headings.\n",
+    "* Include mathematical equations using **LaTeX syntax in Markdown**, which are rendered in-browser by [MathJax](http://www.mathjax.org/).\n",
+    "* Start **parallel computing** clusters that work with IPython's interactive parallel computing libraries `IPython.parallel`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Kernels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Through IPython's kernel and messaging architecture, the Notebook allows code to be run in a range of different programming languages.  For each notebook document that a user opens, the web application starts a kernel that runs the code for that notebook. Each kernel is capable of running code in a single programming language and there are kernels available in the following languages:\n",
+    "\n",
+    "* Python(https://github.com/ipython/ipython)\n",
+    "* Julia (https://github.com/JuliaLang/IJulia.jl)\n",
+    "* R (https://github.com/takluyver/IRkernel)\n",
+    "* Ruby (https://github.com/minrk/iruby)\n",
+    "* Haskell (https://github.com/gibiansky/IHaskell)\n",
+    "* Scala (https://github.com/Bridgewater/scala-notebook)\n",
+    "* node.js (https://gist.github.com/Carreau/4279371)\n",
+    "* Go (https://github.com/takluyver/igo)\n",
+    "\n",
+    "The default kernel runs Python code. When it is released in the Summer/Fall of 2014, IPython 3.0 will provide a simple way for users to pick which of these kernels is used for a given notebook. \n",
+    "\n",
+    "Each of these kernels communicate with the notebook web application and web browser using a JSON over ZeroMQ/WebSockets message protocol that is described [here](http://ipython.org/ipython-doc/dev/development/messaging.html). Most users don't need to know about these details, but it helps to understand that \"kernels run code.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Notebook documents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notebook documents contain the **inputs and outputs** of an interactive session as well as **narrative text** that accompanies the code but is not meant for execution. **Rich output** generated by running code, including HTML, images, video, and plots, is embeddeed in the notebook, which makes it a complete and self-contained record of a computation. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you run the notebook web application on your computer, notebook documents are just **files on your local filesystem with a `.ipynb` extension**. This allows you to use familiar workflows for organizing your notebooks into folders and sharing them with others using email, Dropbox and version control systems."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notebooks consist of a **linear sequence of cells**. There are four basic cell types:\n",
+    "\n",
+    "* **Code cells:** Input and output of live code that is run in the kernel\n",
+    "* **Markdown cells:** Narrative text with embedded LaTeX equations\n",
+    "* **Heading cells:** 6 levels of hierarchical organization and formatting\n",
+    "* **Raw cells:** Unformatted text that is included, without modification, when notebooks are converted to different formats using nbconvert\n",
+    "\n",
+    "Internally, notebook documents are **[JSON](http://en.wikipedia.org/wiki/JSON) data** with **binary values [base64](http://en.wikipedia.org/wiki/Base64)** encoded. This allows them to be **read and manipulated programmatically** by any programming language. Because JSON is a text format, notebook documents are version control friendly.\n",
+    "\n",
+    "**Notebooks can be exported** to different static formats including HTML, reStructeredText, LaTeX, PDF, and slide shows ([reveal.js](http://lab.hakim.se/reveal-js/#/)) using IPython's `nbconvert` utility.\n",
+    "\n",
+    "Furthermore, any notebook document available from a **public URL on or GitHub can be shared** via [nbviewer](http://nbviewer.ipython.org). This service loads the notebook document from the URL and renders it as a static web page. The resulting web page may thus be shared with others **without their needing to install IPython**."
+   ]
+  }
+ ],
  "metadata": {
   "celltoolbar": "Slideshow",
-  "name": "",
   "signature": "sha256:86e99da10fb4a123108149196f182861fa434fb59520b256405c6052601c2486"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "What is the IPython Notebook?"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Introduction"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The IPython Notebook is an **interactive computing environment** that enables users to author notebook documents that include: \n",
-      "- Live code\n",
-      "- Interactive widgets\n",
-      "- Plots\n",
-      "- Narrative text\n",
-      "- Equations\n",
-      "- Images\n",
-      "- Video\n",
-      "\n",
-      "These documents provide a **complete and self-contained record of a computation** that can be converted to various formats and shared with others using email, [Dropbox](http://dropbox.com), version control systems (like git/[GitHub](http://github.com)) or [nbviewer.ipython.org](http://nbviewer.ipython.org)."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Components"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The IPython Notebook combines three components:\n",
-      "\n",
-      "* **The notebook web application**: An interactive web application for writing and running code interactively and authoring notebook documents.\n",
-      "* **Kernels**: Separate processes started by the notebook web application that runs users' code in a given language and returns output back to the notebook web application. The kernel also handles things like computations for interactive widgets, tab completion and introspection. \n",
-      "* **Notebook documents**: Self-contained documents that contain a representation of all content visible in the notebook web application, including inputs and outputs of the computations, narrative\n",
-      "text, equations, images, and rich media representations of objects. Each notebook document has its own kernel."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Notebook web application"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The notebook web application enables users to:\n",
-      "\n",
-      "* **Edit code in the browser**, with automatic syntax highlighting, indentation, and tab completion/introspection.\n",
-      "* **Run code from the browser**, with the results of computations attached to the code which generated them.\n",
-      "* See the results of computations with **rich media representations**, such as HTML, LaTeX, PNG, SVG, PDF, etc.\n",
-      "* Create and use **interactive JavaScript wigets**, which bind interactive user interface controls and visualizations to reactive kernel side computations.\n",
-      "* Author **narrative text** using the [Markdown](https://daringfireball.net/projects/markdown/) markup language.\n",
-      "* Build **hierarchical documents** that are organized into sections with different levels of headings.\n",
-      "* Include mathematical equations using **LaTeX syntax in Markdown**, which are rendered in-browser by [MathJax](http://www.mathjax.org/).\n",
-      "* Start **parallel computing** clusters that work with IPython's interactive parallel computing libraries `IPython.parallel`."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Kernels"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Through IPython's kernel and messaging architecture, the Notebook allows code to be run in a range of different programming languages.  For each notebook document that a user opens, the web application starts a kernel that runs the code for that notebook. Each kernel is capable of running code in a single programming language and there are kernels available in the following languages:\n",
-      "\n",
-      "* Python(https://github.com/ipython/ipython)\n",
-      "* Julia (https://github.com/JuliaLang/IJulia.jl)\n",
-      "* R (https://github.com/takluyver/IRkernel)\n",
-      "* Ruby (https://github.com/minrk/iruby)\n",
-      "* Haskell (https://github.com/gibiansky/IHaskell)\n",
-      "* Scala (https://github.com/Bridgewater/scala-notebook)\n",
-      "* node.js (https://gist.github.com/Carreau/4279371)\n",
-      "* Go (https://github.com/takluyver/igo)\n",
-      "\n",
-      "The default kernel runs Python code. When it is released in the Summer/Fall of 2014, IPython 3.0 will provide a simple way for users to pick which of these kernels is used for a given notebook. \n",
-      "\n",
-      "Each of these kernels communicate with the notebook web application and web browser using a JSON over ZeroMQ/WebSockets message protocol that is described [here](http://ipython.org/ipython-doc/dev/development/messaging.html). Most users don't need to know about these details, but it helps to understand that \"kernels run code.\""
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_type": "slide"
-      }
-     },
-     "source": [
-      "Notebook documents"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Notebook documents contain the **inputs and outputs** of an interactive session as well as **narrative text** that accompanies the code but is not meant for execution. **Rich output** generated by running code, including HTML, images, video, and plots, is embeddeed in the notebook, which makes it a complete and self-contained record of a computation. "
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When you run the notebook web application on your computer, notebook documents are just **files on your local filesystem with a `.ipynb` extension**. This allows you to use familiar workflows for organizing your notebooks into folders and sharing them with others using email, Dropbox and version control systems."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Notebooks consist of a **linear sequence of cells**. There are four basic cell types:\n",
-      "\n",
-      "* **Code cells:** Input and output of live code that is run in the kernel\n",
-      "* **Markdown cells:** Narrative text with embedded LaTeX equations\n",
-      "* **Heading cells:** 6 levels of hierarchical organization and formatting\n",
-      "* **Raw cells:** Unformatted text that is included, without modification, when notebooks are converted to different formats using nbconvert\n",
-      "\n",
-      "Internally, notebook documents are **[JSON](http://en.wikipedia.org/wiki/JSON) data** with **binary values [base64](http://en.wikipedia.org/wiki/Base64)** encoded. This allows them to be **read and manipulated programmatically** by any programming language. Because JSON is a text format, notebook documents are version control friendly.\n",
-      "\n",
-      "**Notebooks can be exported** to different static formats including HTML, reStructeredText, LaTeX, PDF, and slide shows ([reveal.js](http://lab.hakim.se/reveal-js/#/)) using IPython's `nbconvert` utility.\n",
-      "\n",
-      "Furthermore, any notebook document available from a **public URL on or GitHub can be shared** via [nbviewer](http://nbviewer.ipython.org). This service loads the notebook document from the URL and renders it as a static web page. The resulting web page may thus be shared with others **without their needing to install IPython**."
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Notebook/Working With Markdown Cells.ipynb b/examples/Notebook/Working With Markdown Cells.ipynb
index 2544a18..d8f3dc6 100644
--- a/examples/Notebook/Working With Markdown Cells.ipynb
+++ b/examples/Notebook/Working With Markdown Cells.ipynb
@@ -1,319 +1,304 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Markdown Cells"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Text can be added to IPython Notebooks using Markdown cells.  Markdown is a popular markup language that is a superset of HTML. Its specification can be found here:\n",
+    "\n",
+    "<http://daringfireball.net/projects/markdown/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Markdown basics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can make text *italic* or **bold**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can build nested itemized or enumerated lists:\n",
+    "\n",
+    "* One\n",
+    "    - Sublist\n",
+    "        - This\n",
+    "  - Sublist\n",
+    "        - That\n",
+    "        - The other thing\n",
+    "* Two\n",
+    "  - Sublist\n",
+    "* Three\n",
+    "  - Sublist\n",
+    "\n",
+    "Now another list:\n",
+    "\n",
+    "1. Here we go\n",
+    "    1. Sublist\n",
+    "    2. Sublist\n",
+    "2. There we go\n",
+    "3. Now this"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can add horizontal rules:\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is a blockquote:\n",
+    "\n",
+    "> Beautiful is better than ugly.\n",
+    "> Explicit is better than implicit.\n",
+    "> Simple is better than complex.\n",
+    "> Complex is better than complicated.\n",
+    "> Flat is better than nested.\n",
+    "> Sparse is better than dense.\n",
+    "> Readability counts.\n",
+    "> Special cases aren't special enough to break the rules.\n",
+    "> Although practicality beats purity.\n",
+    "> Errors should never pass silently.\n",
+    "> Unless explicitly silenced.\n",
+    "> In the face of ambiguity, refuse the temptation to guess.\n",
+    "> There should be one-- and preferably only one --obvious way to do it.\n",
+    "> Although that way may not be obvious at first unless you're Dutch.\n",
+    "> Now is better than never.\n",
+    "> Although never is often better than *right* now.\n",
+    "> If the implementation is hard to explain, it's a bad idea.\n",
+    "> If the implementation is easy to explain, it may be a good idea.\n",
+    "> Namespaces are one honking great idea -- let's do more of those!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And shorthand for links:\n",
+    "\n",
+    "[IPython's website](http://ipython.org)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Headings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want, you can add headings using Markdown's syntax:\n",
+    "\n",
+    "# Heading 1\n",
+    "# Heading 2\n",
+    "## Heading 2.1\n",
+    "## Heading 2.2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**BUT most of the time you should use the Notebook's Heading Cells to organize your Notebook content**, as they provide meaningful structure that can be interpreted by other tools, not just large bold fonts."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Embedded code"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can embed code meant for illustration instead of execution in Python:\n",
+    "\n",
+    "    def f(x):\n",
+    "        \"\"\"a docstring\"\"\"\n",
+    "        return x**2\n",
+    "\n",
+    "or other languages:\n",
+    "\n",
+    "    if (i=0; i<n; i++) {\n",
+    "      printf(\"hello %d\\n\", i);\n",
+    "      x += 4;\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LaTeX equations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Courtesy of MathJax, you can include mathematical expressions both inline: \n",
+    "$e^{i\\pi} + 1 = 0$  and displayed:\n",
+    "\n",
+    "$$e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Github flavored markdown (GFM)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Notebook webapp support Github flavored markdown meaning that you can use triple backticks for code blocks \n",
+    "<pre>\n",
+    "```python\n",
+    "print \"Hello World\"\n",
+    "```\n",
+    "\n",
+    "```javascript\n",
+    "console.log(\"Hello World\")\n",
+    "```\n",
+    "</pre>\n",
+    "\n",
+    "Gives \n",
+    "```python\n",
+    "print \"Hello World\"\n",
+    "```\n",
+    "\n",
+    "```javascript\n",
+    "console.log(\"Hello World\")\n",
+    "```\n",
+    "\n",
+    "And a table like this : \n",
+    "\n",
+    "<pre>\n",
+    "| This | is   |\n",
+    "|------|------|\n",
+    "|   a  | table| \n",
+    "</pre>\n",
+    "\n",
+    "A nice Html Table\n",
+    "\n",
+    "| This | is   |\n",
+    "|------|------|\n",
+    "|   a  | table| "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## General HTML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because Markdown is a superset of HTML you can even add things like HTML tables:\n",
+    "\n",
+    "<table>\n",
+    "<tr>\n",
+    "<th>Header 1</th>\n",
+    "<th>Header 2</th>\n",
+    "</tr>\n",
+    "<tr>\n",
+    "<td>row 1, cell 1</td>\n",
+    "<td>row 1, cell 2</td>\n",
+    "</tr>\n",
+    "<tr>\n",
+    "<td>row 2, cell 1</td>\n",
+    "<td>row 2, cell 2</td>\n",
+    "</tr>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Local files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you have local files in your Notebook directory, you can refer to these files in Markdown cells directly:\n",
+    "\n",
+    "    [subdirectory/]<filename>\n",
+    "\n",
+    "For example, in the images folder, we have the Python logo:\n",
+    "\n",
+    "    <img src=\"images/python-logo.svg\" />\n",
+    "\n",
+    "<img src=\"images/python-logo.svg\" />\n",
+    "\n",
+    "and a video with the HTML5 video tag:\n",
+    "\n",
+    "    <video controls src=\"images/animation.m4v\" />\n",
+    "\n",
+    "<video controls src=\"images/animation.m4v\" />\n",
+    "\n",
+    "These do not embed the data into the notebook file, and require that the files exist when you are viewing the notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Security of local files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that this means that the IPython notebook server also acts as a generic file server\n",
+    "for files inside the same tree as your notebooks.  Access is not granted outside the\n",
+    "notebook folder so you have strict control over what files are visible, but for this\n",
+    "reason it is highly recommended that you do not run the notebook server with a notebook\n",
+    "directory at a high level in your filesystem (e.g. your home directory).\n",
+    "\n",
+    "When you run the notebook in a password-protected manner, local file access is restricted\n",
+    "to authenticated users unless read-only views are active."
+   ]
+  }
+ ],
  "metadata": {
-  "name": "",
   "signature": "sha256:3b7cae0c0936f25e6ccb7acafe310c08a4162a1a7fd66fa9874a52cffa0f64f9"
  },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Markdown Cells"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Text can be added to IPython Notebooks using Markdown cells.  Markdown is a popular markup language that is a superset of HTML. Its specification can be found here:\n",
-      "\n",
-      "<http://daringfireball.net/projects/markdown/>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Markdown basics"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can make text *italic* or **bold**."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can build nested itemized or enumerated lists:\n",
-      "\n",
-      "* One\n",
-      "    - Sublist\n",
-      "        - This\n",
-      "  - Sublist\n",
-      "        - That\n",
-      "        - The other thing\n",
-      "* Two\n",
-      "  - Sublist\n",
-      "* Three\n",
-      "  - Sublist\n",
-      "\n",
-      "Now another list:\n",
-      "\n",
-      "1. Here we go\n",
-      "    1. Sublist\n",
-      "    2. Sublist\n",
-      "2. There we go\n",
-      "3. Now this"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can add horizontal rules:\n",
-      "\n",
-      "---"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is a blockquote:\n",
-      "\n",
-      "> Beautiful is better than ugly.\n",
-      "> Explicit is better than implicit.\n",
-      "> Simple is better than complex.\n",
-      "> Complex is better than complicated.\n",
-      "> Flat is better than nested.\n",
-      "> Sparse is better than dense.\n",
-      "> Readability counts.\n",
-      "> Special cases aren't special enough to break the rules.\n",
-      "> Although practicality beats purity.\n",
-      "> Errors should never pass silently.\n",
-      "> Unless explicitly silenced.\n",
-      "> In the face of ambiguity, refuse the temptation to guess.\n",
-      "> There should be one-- and preferably only one --obvious way to do it.\n",
-      "> Although that way may not be obvious at first unless you're Dutch.\n",
-      "> Now is better than never.\n",
-      "> Although never is often better than *right* now.\n",
-      "> If the implementation is hard to explain, it's a bad idea.\n",
-      "> If the implementation is easy to explain, it may be a good idea.\n",
-      "> Namespaces are one honking great idea -- let's do more of those!"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And shorthand for links:\n",
-      "\n",
-      "[IPython's website](http://ipython.org)"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Headings"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If you want, you can add headings using Markdown's syntax:\n",
-      "\n",
-      "# Heading 1\n",
-      "# Heading 2\n",
-      "## Heading 2.1\n",
-      "## Heading 2.2"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**BUT most of the time you should use the Notebook's Heading Cells to organize your Notebook content**, as they provide meaningful structure that can be interpreted by other tools, not just large bold fonts."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Embedded code"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can embed code meant for illustration instead of execution in Python:\n",
-      "\n",
-      "    def f(x):\n",
-      "        \"\"\"a docstring\"\"\"\n",
-      "        return x**2\n",
-      "\n",
-      "or other languages:\n",
-      "\n",
-      "    if (i=0; i<n; i++) {\n",
-      "      printf(\"hello %d\\n\", i);\n",
-      "      x += 4;\n",
-      "    }"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "LaTeX equations"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Courtesy of MathJax, you can include mathematical expressions both inline: \n",
-      "$e^{i\\pi} + 1 = 0$  and displayed:\n",
-      "\n",
-      "$$e^x=\\sum_{i=0}^\\infty \\frac{1}{i!}x^i$$"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Github flavored markdown (GFM)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The Notebook webapp support Github flavored markdown meaning that you can use triple backticks for code blocks \n",
-      "<pre>\n",
-      "```python\n",
-      "print \"Hello World\"\n",
-      "```\n",
-      "\n",
-      "```javascript\n",
-      "console.log(\"Hello World\")\n",
-      "```\n",
-      "</pre>\n",
-      "\n",
-      "Gives \n",
-      "```python\n",
-      "print \"Hello World\"\n",
-      "```\n",
-      "\n",
-      "```javascript\n",
-      "console.log(\"Hello World\")\n",
-      "```\n",
-      "\n",
-      "And a table like this : \n",
-      "\n",
-      "<pre>\n",
-      "| This | is   |\n",
-      "|------|------|\n",
-      "|   a  | table| \n",
-      "</pre>\n",
-      "\n",
-      "A nice Html Table\n",
-      "\n",
-      "| This | is   |\n",
-      "|------|------|\n",
-      "|   a  | table| "
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "General HTML"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Because Markdown is a superset of HTML you can even add things like HTML tables:\n",
-      "\n",
-      "<table>\n",
-      "<tr>\n",
-      "<th>Header 1</th>\n",
-      "<th>Header 2</th>\n",
-      "</tr>\n",
-      "<tr>\n",
-      "<td>row 1, cell 1</td>\n",
-      "<td>row 1, cell 2</td>\n",
-      "</tr>\n",
-      "<tr>\n",
-      "<td>row 2, cell 1</td>\n",
-      "<td>row 2, cell 2</td>\n",
-      "</tr>\n",
-      "</table>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Local files"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If you have local files in your Notebook directory, you can refer to these files in Markdown cells directly:\n",
-      "\n",
-      "    [subdirectory/]<filename>\n",
-      "\n",
-      "For example, in the images folder, we have the Python logo:\n",
-      "\n",
-      "    <img src=\"images/python-logo.svg\" />\n",
-      "\n",
-      "<img src=\"images/python-logo.svg\" />\n",
-      "\n",
-      "and a video with the HTML5 video tag:\n",
-      "\n",
-      "    <video controls src=\"images/animation.m4v\" />\n",
-      "\n",
-      "<video controls src=\"images/animation.m4v\" />\n",
-      "\n",
-      "These do not embed the data into the notebook file, and require that the files exist when you are viewing the notebook."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Security of local files"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Note that this means that the IPython notebook server also acts as a generic file server\n",
-      "for files inside the same tree as your notebooks.  Access is not granted outside the\n",
-      "notebook folder so you have strict control over what files are visible, but for this\n",
-      "reason it is highly recommended that you do not run the notebook server with a notebook\n",
-      "directory at a high level in your filesystem (e.g. your home directory).\n",
-      "\n",
-      "When you run the notebook in a password-protected manner, local file access is restricted\n",
-      "to authenticated users unless read-only views are active."
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Notebook/nbpackage/mynotebook.ipynb b/examples/Notebook/nbpackage/mynotebook.ipynb
index cdc7d43..daf9299 100644
--- a/examples/Notebook/nbpackage/mynotebook.ipynb
+++ b/examples/Notebook/nbpackage/mynotebook.ipynb
@@ -1,59 +1,51 @@
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "My Notebook"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def foo():\n",
-      "    return \"foo\""
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def has_ip_syntax():\n",
-      "    listing = !ls\n",
-      "    return listing"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def whatsmyname():\n",
-      "    return __name__"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    }
-   ],
-   "metadata": {}
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# My Notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def foo():\n",
+    "    return \"foo\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def has_ip_syntax():\n",
+    "    listing = !ls\n",
+    "    return listing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def whatsmyname():\n",
+    "    return __name__"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Notebook/nbpackage/nbs/other.ipynb b/examples/Notebook/nbpackage/nbs/other.ipynb
index 33f63f6..5db9938 100644
--- a/examples/Notebook/nbpackage/nbs/other.ipynb
+++ b/examples/Notebook/nbpackage/nbs/other.ipynb
@@ -1,33 +1,26 @@
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This notebook just defines `bar`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def bar(x):\n",
-      "    return \"bar\" * x"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    }
-   ],
-   "metadata": {}
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook just defines `bar`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def bar(x):\n",
+    "    return \"bar\" * x"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Parallel Computing/Data Publication API.ipynb b/examples/Parallel Computing/Data Publication API.ipynb
index c136df4..46ec906 100644
--- a/examples/Parallel Computing/Data Publication API.ipynb
+++ b/examples/Parallel Computing/Data Publication API.ipynb
@@ -1,276 +1,2653 @@
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "IPython's Data Publication API"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython has an API that allows IPython Engines to publish data back to the Client.  This Notebook shows how this API works."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Setup"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We begin by enabling matplotlib plotting and creating a `Client` object to work with an IPython cluster."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.parallel import Client"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "c = Client()\n",
-      "dv = c[:]\n",
-      "dv.block = False\n",
-      "dv"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "<DirectView [0, 1, 2, 3,...]>"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Simple publication"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is a simple Python function we are going to run on the Engines.  This function uses `publish_data` to publish a simple Python dictionary when it is run."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def publish_it():\n",
-      "    from IPython.kernel.zmq.datapub import publish_data\n",
-      "    publish_data(dict(a='hi'))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We run the function on the Engines using `apply_async` and save the returned `AsyncResult` object:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ar = dv.apply_async(publish_it)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The published data from each engine is then available under the `.data` attribute of the `AsyncResult` object."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ar.data"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 6,
-       "text": [
-        "[{'a': 'hi'},\n",
-        " {'a': 'hi'},\n",
-        " {'a': 'hi'},\n",
-        " {'a': 'hi'},\n",
-        " {'a': 'hi'},\n",
-        " {'a': 'hi'},\n",
-        " {'a': 'hi'},\n",
-        " {'a': 'hi'}]"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Each time `publish_data` is called, the `.data` attribute is updated with the most recently published data."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Simulation loop"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In many cases, the Engines will be running a simulation loop and we will want to publish data at each time step of the simulation.  To show how this works, we create a mock simulation function that iterates over a loop and publishes a NumPy array and loop variable at each time step. By inserting a call to `time.sleep(1)`, we ensure that new data will be published every second."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def simulation_loop():\n",
-      "    from IPython.kernel.zmq.datapub import publish_data\n",
-      "    import time\n",
-      "    import numpy as np\n",
-      "    for i in range(10):\n",
-      "        publish_data(dict(a=np.random.rand(20), i=i))\n",
-      "        time.sleep(1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Again, we run the `simulation_loop` function in parallel using `apply_async` and save the returned `AsyncResult` object."
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# IPython's Data Publication API"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython has an API that allows IPython Engines to publish data back to the Client.  This Notebook shows how this API works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We begin by enabling matplotlib plotting and creating a `Client` object to work with an IPython cluster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.parallel import Client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ar = dv.apply_async(simulation_loop)"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "<DirectView [0, 1, 2, 3,...]>"
+      ]
+     },
+     "execution_count": 3,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c = Client()\n",
+    "dv = c[:]\n",
+    "dv.block = False\n",
+    "dv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simple publication"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is a simple Python function we are going to run on the Engines.  This function uses `publish_data` to publish a simple Python dictionary when it is run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def publish_it():\n",
+    "    from IPython.kernel.zmq.datapub import publish_data\n",
+    "    publish_data(dict(a='hi'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We run the function on the Engines using `apply_async` and save the returned `AsyncResult` object:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ar = dv.apply_async(publish_it)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The published data from each engine is then available under the `.data` attribute of the `AsyncResult` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "[{'a': 'hi'},\n",
+       " {'a': 'hi'},\n",
+       " {'a': 'hi'},\n",
+       " {'a': 'hi'},\n",
+       " {'a': 'hi'},\n",
+       " {'a': 'hi'},\n",
+       " {'a': 'hi'},\n",
+       " {'a': 'hi'}]"
+      ]
+     },
+     "execution_count": 6,
      "metadata": {},
-     "source": [
-      "New data will be published by the Engines every second.  Anytime we access `ar.data`, we will get the most recently published data."
-     ]
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ar.data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each time `publish_data` is called, the `.data` attribute is updated with the most recently published data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation loop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In many cases, the Engines will be running a simulation loop and we will want to publish data at each time step of the simulation.  To show how this works, we create a mock simulation function that iterates over a loop and publishes a NumPy array and loop variable at each time step. By inserting a call to `time.sleep(1)`, we ensure that new data will be published every second."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def simulation_loop():\n",
+    "    from IPython.kernel.zmq.datapub import publish_data\n",
+    "    import time\n",
+    "    import numpy as np\n",
+    "    for i in range(10):\n",
+    "        publish_data(dict(a=np.random.rand(20), i=i))\n",
+    "        time.sleep(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, we run the `simulation_loop` function in parallel using `apply_async` and save the returned `AsyncResult` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ar = dv.apply_async(simulation_loop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "New data will be published by the Engines every second.  Anytime we access `ar.data`, we will get the most recently published data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib.pyplot as plt"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x10c41e910>"
+      ]
+     },
+     "execution_count": 10,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "data = ar.data\n",
-      "for i, d in enumerate(data):\n",
-      "    plt.plot(d['a'], label='engine: '+str(i))\n",
-      "plt.title('Data published at time step: ' + str(data[0]['i']))\n",
-      "plt.legend()"
-     ],
-     "language": "python",
+     "data": {
+      "image/png": [
+       "iVBORw0KGgoAAAANSUhEUgAAAlYAAAGKCAYAAADOsQ/WAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
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+       "B/HOO+8A4N5EDx8+jEWLFiV5VWQ+cLvd2LlzJ8xmM9avXw8AqK6unvH1JSFbgRUVFTh79iy2bt0K\n",
+       "gCu0qHGdTMRgMMBisWDr1q1gWRbr1q1DQUFBspdF5qmcnBysWbMGTz/9dHi01p133pnsZZF5KiMj\n",
+       "A4FAAE899RSkUilWrlwJs9mc7GWReaChoQE//fQTXC4XPvnkEwDAmjVrZnx9oZE2hBBCCCFxQgGh\n",
+       "hBBCCCFxQoUVIYQQQkicUGFFCCGEEBInVFgRQgghhMQJFVaEEEIIIXFChRUhhBBCSJxQYUUIIYQQ\n",
+       "EidUWBFCCCGExAkVVoQQQgghcfL/u8ZDblhTSdUAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c41e850>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "<matplotlib.legend.Legend at 0x10c41e910>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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bb79tcxwDQ0+DIqSLqsgxMDB0mKKiIqSlpeH27dtdvRSGHsInn3yCw4cP49NPP+3qpTAw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3+edaaFFcCt2eNxYK84m2Z5JmzE+hnYe4wDU8UqLgHMFkOLn0t34crwBnZXd+TCk4O7LE9e\nafCOWh7DnTVcn7qktNsnZxFMyu99wWfjKFMm0cQMGcbAXpPk+qhF+X6zi/19Vv/i3n02E0n+p8+/\nNvKLOpMqYndpb+6Zp/1I5XIHW2zCGSBbTqA3yqzd6TRPy7JcZ1gN1rFK3388lJ9GMbAPF2UUTR8x\n7vAjip3F22Wfjc0nW1R+/BbGv/hXGh/3vvFqS7xNu3Ibe+gdNsz+wbAWoypVrPLaopu/95M9Sm2d\nq1tTk3wYDFLrAZW1ryyQPSFy4TBdwmM1tHgoksUiVUnCY+n/3tcysEfqhVa6VMOsFzHVUQSZE0bZ\nNKt5HHjvvX1MZj1rdwbPCj2p1j8OsXy5ewdj1mWiXJMIafisVNQCKEXinMvFXroHcmF3C2l3E3F6\nDqFLR2oQT1yznNcukBqwsEoUqrhSCawjMKxUiYFppCGRC3Ik1NGxclrHsFuchAYIjPeegnldkmrs\nZ3O4zWY8lv5NBteYheQALKtkPN/wEw8i27KyALY8buFpfTNwbogw5tY4m/6jvWypyrt7ab58bvjo\nm0+izr6wavJZ9Yqzada4b5DMwLmuhVWyWMVWkztao7rzq9TWP0aWBmt3Anid3SGhj/fvcLEHGNR5\n42I9ePk4H1AvClyftPNhU9fqqt/Hg3BkIDPmUabEmPV4lV0JZO5eWL1/cMg/+fAO/+CrbwzU1u+m\nXtucgiDgWD1Puq1rpRP1TDgDXHnFwdvfXafadrNXRz4WDV+QltL3H+O8NoC/qi7R5UEasmOlRtlo\n6ZLfhvet/wvjm38R0X18gRh/5QWyaxuUo9o/K3n7/jMdA6oKZcu8OONkZcLCv/yw9TU7YbUybrHy\nNN7972Ga9FIrlignunt0+kmTuJ5UiOuDFPUKy6rZwH4cxhxtY1ilTxhl0yy7w0QuXaJSqfHxRwd8\n9etXWbt71HOJ5rQky7ImZqFZgiBwNdAZbyPXjeu+qePuUs/NQNcYgl5P9cN3EHsa14csrPpE2zQr\nni5gzaQwT/YehfWSOAIkVIoebwQ2a1AC+2nQ12OZMJJpnmv+wXyB7oE7Vv2p682yLiksyJV6FioM\nXlhVJSVWakysIdeq6Oz9C7rvbyS4NePAaR79HvRJ0pkXVpd8No7SZZKFCplkEWcPhpUqBRLa+wVs\nXZ4j3yUzMFWsYq5UOyp4welGcHuQdgdPz55wdY+1UTcC2yXLMqn6KFBaX0Ncvtjy+ZvTjpbCymu1\nYjUY2OnhjVC1FS+y2FQw+qddJOPaPqtgNsvf/M73+Luvf4lp58mAa5l0sQMO2izn6goZDZ+V3z0D\ntgwTfjt3320thJOx3HCohXuDoRZUjeKxiqQO8XUprFYLh/gPH2P42i+1fFw0GRn/3ItEvveO5tc9\na+O6qlA96PbXPzvDW0/iDUaNqn4+K0EQsJ+bJ7c+us9qJ1lkwa0RZTPAdhlosayMDZZVpIm6Xs0X\nKOwHsa0sjPxYm2VzKqPAJw+C+KddnLvsw2DUcbA7nEF6FD388ACDSYc30Ps9qowDW8eTiVges9WI\nxXpccPb1Wc0uUX37uz03AneSgzGsVDmunCP7eKur77VZsZ0QFZcL0TD6TVYYYRSodKw6u2RL/osD\n+ay8tpMHMQcTu1RNM339VapcHgvJAZALyXhhyI7V7HHHKtbMsuo/CozmyoxZ9UgxBQ46yPX7/y+m\ndVVnXljpRYEbU3ZuH2TqBugB1kEdJmpVqacpW10Z1VKqUEVfqmqa+Yb1WXXrWFWqZbZCa6xMdUaU\n5LcP0NutmLyeeseqtbC6VTewN3eoBh0HbtdRC43fR6fNsyrXavzGW9/hL19b5eXZTsbWsMqmunus\nABxXOjtWoBRWocQ+r71xnnf/dJNy6fjCG48OvhFYyxfJ7x1iv7A48GNWRoHDFVb/H3tvGudGQp17\nP1XaVdq3bvXu3tt2t+2xPTPMDgNmCFsgIYEAuSEsCRCSkJBAwsyFYUhIAr9AeAM3cCHLe0MgYRm2\nXGaBYRhmH894adu9u1u9amktVdq3qvuhVN1aqqSSVG23B/7frFZLslqqOnXOc54nSG+Kdqw4joP9\nB/+K7wyeQoyrPSF4Tt2C4EOP19wOlAorhaNsxAglcvCYNLAbNHjX9V347M9XK4TfJ7ydON3Az4oa\nbU9ntRJNi24EylnbByRGgQm+s1mur0rMLME0MtDWybkcYRR4/tk1HLmhFwRBYOKoV3SErSQhfxw/\n+9EcXvNmccuWcsSMQgMbu/oqgf46HSuAF7Bz0TDI/iHJ+/hkWi0IqI0GGHq9SMw3vmiNrWyiKENf\nVA+yowtcaKup6UO9jpWczUC3kR8FtmOi64+tIQ47Jjvk/f/ldqyaEa8Du5ZFHSYt8kW+AyW3YyXo\nq7LBCLQy9FWL2ynEs0Uc69rfburNcNULK4DfhHvORyOZyMJkaeywTRAEHO76AnZ+FCh+ZU1nCkAq\nt7MRWA5fWMnXWUlprC4HZuB1DMCgrdUH7dgsxCLg0qkaZ2OvRQe9hsRydPdDLLewWo6kMVDV8hXT\nWf3140/AYzTiXcfER5XNwnespP92lsnKaBuBDlsvArF1uL1m9A87cfrxlZ2fxbaTssOX4zOL/Im0\nCR+oVsTrIYnCqnj2GXBMDMGjt1fYZQi473wJwo89V7NynmcSSK9uwXxIfPtKKXIFFvFsEY7SWPUV\nIw6YdWp8+8LuZ+p4lxent7bqnhhMbWYGio0CV5oYK9V0rEy77uvlo0BGAWPQckxmHcLBBOhoGkNj\n/Elv4kgX5i/4UZTYtGyXXLaAH/zHWdzx6nG4PPVzJgGgz65HMlfc0ZwBJcf17srCSk7HCiQJsm9Q\n9OfNWC2UI3ccmFz3g+hsfQwIAIRWB8JiB7ctT5vKe1gFazRWAO/A7gvOg21QpFFaLVRtuq+vh9cQ\nzmsw4ZKnt+TF6/U7VrlsAflcUXZ6BQDoOl0oJlIoxJMYchqwGE6j26rHFpOtuBgTwx/n80hzoQh0\nMjYCH5gP49Soo27qwrXGviisTnRbML0ak/RBEsPZoLDSd3nA5guIz9Wu+MYyBRQSOdGZs2rsMNi5\nadlXHQ6zB3QqgkKx0qhQKh8QABihsFqahWpoTNTZ+Hh3pe3ClMxomxURV+tqndW3Z2ZxemMLf33n\nSxXZwOA43jG/2nW9HGqoH5nNAApVrfIOe8+O+/rNLx/BC0/6djQ0zXhYNTsGBFozCRUrrLhiEblv\nfAW6t7wbY50WzIrkBmpddpgmhhB58kzF7fSZS7DskTFoxetO5uGkNDsHL4Ig8Ee39OK/zgWwUWrv\nd5nN0KvVWI5Jj7eokX4k51sbBRZYDptMFr0iVgsDNnlOy6JBzInaUaASUTblUGYdgltxTB7vAVk6\nRlntBjjcFFYWm8/ybATHcXjo/ovoHrDj0DHxSJlqSILAZNV2YLnVgkA9jRXAe/qpDh4FoRU/EW8l\nEnDKtFooxzI5CuZ87cVVNdkNP7Rt6KsEmtkMFPOwEjDpLbAY7diK1k/zANoXsM9sh+A1amHUyDse\nGIwasCyLTJ2A5FhJX9XMsZ4gSV5ntbMZmIJeTcJu0MDfwNPNH8+WhOvhhh2rbIHFT5ei13yETTX7\norDqMGvhIAANpW185xIOT30vK4IkMfgHb8f8X/1Tzc/oTAFpJiM6cyZcHYBKDS4gT/ioItWwUU6E\n44GK22fXz2JUxBgUAOhzM7zj+uJMhXC9nOuqbBcOul1YjESRLUhrFLIFFqFEria8slxnNR0M4rNP\nP4PPv+oUTFr573c90qk8NBoVNHXcckmNGqbRA4jPLFXcLowCAb6tPXHUi6cf5YvhaDgl28OKNwZt\n7kTayihQrLAqPPYgCIsNqqM34KCHwqWg+IHVc+pmhKrGgfwYcO/1VcEEfxVZjteiw1uOduJzj6/u\nXEicaGC70E7HapPOwkVpoFfvHnY4jsNqjEa/TL1ObcdKi2BpFMgHMPMnJMHDSim0ejUSTAaTJyvH\n5hNHu/ZkO/Dcs2sIBxN42WvFjw9STHWaMF0qrDiWQ2CzUrgO8COrVD6PhERnhfT2wPCRv5V8jlWa\nkf33KsfaIDNQgN0KwtiiOWg5pLcb7Ja8zUAxD6ty5AvY27NcWKbTOCQz9B7gL5CsjvqZgXST+ioB\nwXJhyGnA4na5gL1RYVWyWtiONtwIfGIlhjG3ER6TMuei/cK+KKwAYNSsRUZmtwooCdhFOgPl9L3j\njUjMLCLy9NmK25lkHvlsASaR1ihBEFCNHUZxTn4gMx9ts3twZTkW8xvnMNYj4rheLIKZXoD1yDjY\npdka4brA0S4zZoLJnbV4g0aDfpsVs9vS9gC+WAbdVh3UZOWViaCzmp0L4I8feBgfv/02DNqbT42X\nIiFzm5N3YK/UWXVYu7HNbO202W+8YwiXzmyCjqYRbSLOptmNQIBfVuDitGwdRjafRiqbgI3aPVhw\nmTRy3/n/oX3Le/jMQA+Fhe3UjultOZ5TtyL48BMV3dDYFfCvAnaF69X86iE30nkWD87zBeaJLi9O\nb0kb3hr6u5ANbqOYbt6JfCWWRn+VD1MolYJBo4FZJ29MYaR0FRoru0GNVK6IbIEtaay0YAsFJGYv\nKzpe9a/RIAiixkdq9HAnludDFdrAdgls0Hji4QW89reO1r1YEWPKa97ZDIxFUtAb1DBWXbASBIFe\nqwVrdbpW9fDRNPqa2AgUMB8aQfziYkUguxhkMATrQPuFFeHtlW0SKuZhVY5cB3YPZUSoxc1AlmOx\nlVXjZI+4d5gUNnv9cSBvtSBfXyVADfMLYMMuIxbD8i0X/IncTseqkYfVj+bCuOtF1q0C9lFh1aMl\nsd2EVKGRxgrgHX9HPvwezH3iCzsns2yBhSZXgMWmB0GKt0b5wkq+gN1l8WKb3j0ZbUV80GuNcJpr\nr4ASCyvQdTihNhtRvDwP1aB4YUVpVRhyGCrclBvlBq5E0hiQECh2D9jxXz8/j9eOjuDlg/IF3nJo\ntBEoYJ4cBXOxchSg1ehhNth2On6UWYdjN/bh4e9egFpN1uSiicFmc0gsrMA8Li22FYNQqwGjCVxc\n3glmm/HDZfGCJHa/NvkffQuq8SmoBvluGaVVocOkxbLIgY4a6QepUe9sR3Isi9jzF69IYRVKihdW\nKpLAB2/tw1ef20Q4lcfJri6c3tyUHIWTajWM/T2S+sV6iOqrmhCuA7UdK5Ig4KI0CCVzfE6gUYPU\n0hp0nS6oTc3FMtVj5twWOHA1liBGSovuATsWLgUkfrM5spk8fvD1c7jzdQfhkNmtLeeAQw86U0A4\nlS8FL4u/t3y0TeMtYzHkhi9Xo7GaofM46ua4AoB2exuOA/LGn/UgO3vkjwJFPKzK4QurxpuBrjZG\ngZF4ABlVB455m/u/Wx2GugJ2OpKC1d58x8o4yFsWdVt0iGUKSGQL/GZgg8IqUNJYZUP1xeubTBYr\n0Qxu7G/+s7Tf2TeFlYnjECywiMu88rM5jIgzGRQaWOx733gKbC6HwA8fBcCPAZ3g6rZGyVYE7Mzu\ndpBUPiAgOK6Pg91YBWGzgzBLt9T5eJvdceCUx1M32mYlmqkRrgs8nvKDYkh84PoTjf47TROnMzDL\nWDqwSDiwd9p5AbvAiVsHENhg5AvX55Zh7O+Byti4uKuGbMIkNFgVZcPGIsg9eD+0b3pHxf0mOqid\nNIFyCILgzUIf5MeBycVVaCympkJKW0UQlIox5DTgV8ad+OJT6+izWngtVLxOZNRoa9E24oVVDANN\nnKQ1WhU4rjJexm3S4nIkDTVJwKhVgbm4ALNC/lUALwBPxrOgTOKZgQePKDMO5DgOD3z7AgZGXRif\naq1jQxIEDnfw48DAJl0jXBfgBeytdaxW6easFsppJGAvsiyoaAQdw0oUVvJNQqU2AgUOdIzDF5xH\nka1/fmpnFHg56EMORgw5mpsmNBoFxpp0XRfgLRfWoCIJHHAYsBRJN+xY5QosmGwBTqMGuQbmoA/N\nh3HnsB3aJiZV1wr75n+UYrLwuCicEYnbEEOlImG1GxAN1/8QEySJsbvfh/lP/RPYfAGxTAE2cKIb\ngQJkdz+4BANWpv7GbancDJyTyAcEAPqMELwsra8SON5txvMbZQL2BpuBfPhybXHxwOISfhxZhbmg\nRi6j3MhCIMFk5XWsDg7xXjZVOjFPmc4KAHR6DW45NYruAXkHGH4M2JpQmbA6wMXkbQby4cu7+qrc\n/f8HmltfCdJTeRI86KFENwMBwPPKWxB8mC+srtQYcJ3O4JlVGlNe6c2ytx7txHIkjSd9dGkcWF9n\n1Yrlgk9kscJHM7KF6wBfnFaPAz2UBpcCyd0omwvzTTnwN+L8s2uYOtkDk0UvWlgNTbixtRZrO6j5\nhSd9YGJp3PEr4l1suUx5eaNQ/0btRqAAL2BvsWPVpNVCOZYGOqvIxjYKWi0M5va7jYTLA46hZQWt\nS3lYCRh1ZjhMHmyGV+o+jptq3ST0ufVlePRFqEWWmephcxhA1/GyijVpDipgPMCnl3Ach+GSzqq3\nVFhJdbQDCT6rU0USyIYi0LnFj+FFlsND85EX5RgQ2EeFVZxOY7zPitPr8vO3nO7GDuwA4LrjBhh6\nOrH+798HnS6AKhTrenoQJAnV6CGwMseBrqrCanb9rORGoGC1UKyjrxIYcRkRTuURTvLi3AM2G8Lp\nNKJp8S/RcpTPCCxnIRzBfY89js/ddUpWbmArxBt4WAmoTRR0XneNI36HbXczUODI9b24/S55mile\nuN6cvkqAsNllbwYGYxs75qDs5ioKz/4c2te/peZ+4x4jZiU+l/brjyC9soGMP3RFjEELLIe/fdSH\n3z7uRVedrqJWTeKPb+nDF55cx5S7A89tSPszUcP9TQvY+S5YtmaxwicjfLma6nGgx8Tnjbr3QLie\nyxYwe34Lkyd6KmJtytFo1Rga92BuWlqb1ojN1RiefvQyXveWo1Cr2zssT3pNOL8ZR3CTQUdXnY6V\nRKxNPQSrhd4mrRYELIfrbwaGLm8g7VDmZEuQKpAdXWADjb3GGnWsAHnjQI+x9VHgxVAYg5bmC6B6\nHSuW5RCPpSsycSi3eW0AACAASURBVOWitVtA6njbhGEnr7Oy6tXQqEhE0uIX6P44H77McRyyIemt\nwOc3GLgojeSE5Vqn4Tf4+9//Pu655x7cc889uP/++2t+/sQTT+CjH/0oPv7xj+PTn/40ki1W6wyd\nwfEhB06vMfKtDtyU7JT5sXvej6XP/gtiEQa6fLFhBc/rrOSNA8vF67HENhIZBt2uWg8YNpdHfO4y\nzJOjsjpWKpLAsa7drpWKJHHY48Z0MFRzXyZTQCbP7pxcAIDJZvGHDzyED9/8Eky4XbJyA1shwWTq\nWi2UIzYOrB4FNkt8eqGNwkr+ZmB5xyr7n1+F9jW/CcJUe4Lps+lBZ4qIiaxAkxo1XC+9EaGHn7wi\nG4FfP+uHSavCaycabxpNeU24oc8CX1jfuGPVpOXCBp2Bm9Lu5PgJrJTibJqh2iTUTWmxsJ2Gi+IP\n6EqOAmfObaF30AGTRQ9TWRBzNRNHvbjUolloOpXDD79xFqfecKhuJ10uQw4DknQGaq0KRpN4Md1q\nx0qwWtC1GH9lLvnZcRKZlLGVTRTc7ZmDlkN0doNrMA6s52FVzmDnRMPNQA9Ftey+vsxkcNjTfF6o\nxWZAgs6ALda+p3E6A6NJB3WTSxACgtH2cMnLCgD66uisAiXhejGRAkGQUFPin+cfvcic1qupW1jN\nzMxgeXkZ9913H+677z74/X5MT+92cYrFIl544QV87GMfw8c//nFMTU3hxz/+cdMvolBgkU3nMeg1\nQ60i4IvKC3p0eBoL2AUsk6Nw3HoC6X//DlSZPGwNxHzNOLA7zR2IJIJg2SLmNs5htGuqQuAsEJ9Z\ngrG/GyqCBRvy80Z8DTheZbsw1SHuZ7US5Y1BBa8SluPwFz/5KW7p68HrxvixSKPcwFbhtwLlbXWZ\nD4+AqYq26bD1wC8j5FQMtlBA/NJiy5lwvJeVzFFgyWqhODsN1rcEzSteL3o/kiAw7jZiRsJ2wX3q\nFmx+6wGk17YUNbGsZjaYxPcvbeNPb+uT7WHzruu7MRfMIpFWS440qKE+pHzrNSPdeoiNAQssi/V4\nHL1N6nXELBcKLAc3pUF2KwSCIKBTINCa4zice3YNR67vAwDJjhUA9A85wcT4TdamnoPl8KNvTWP0\ncCdGDsrLh2uEiiQwriOhqXOM6zSZQGeySOel/Y/E4MOXW+tWAYDO5YDaZER6VbwIjfu2gE7lCive\ny6pBYVXHw6ocOQ7sgni9Ffd1f1aDG3qbXyxSq0kYTTrE6drzJt2k43o11CAfxtxv18PPZJEpsOi1\nSuusdjys6piDRtN5nNtM4PZB5TbT9xt1C6szZ87gzjvv3Pn3nXfeiRdeeGHn3yqVCh/4wAegLfkh\nZTIZuFu42kjQGVBmPVQqEid7LHhuXV6L2uE2IRKU17ECgNGPvAeq7/4Q6u1ow44VeWAErH8DXKrx\ngVKj1sJssCGSCNXXVwljwMvzIPuHJJPjy7mu24IXNuJgS1/UKY+4zorPCNw9cX3p+RdAZ7L4s5te\nsnNbvdzAdog327GarhwFdNh6EIitt3QwSi6tQud1Q92iJqOZvMAQvQm3xYvs178M7ZveAaKOD9iE\nR1zADgDul96A2PMXYJkaVSxypZpMgcXf/cyHP7ippyKYuBGUVoU/uLkHVtUAnlkTP/mpjHro3E6k\nffI7NL5YrXB9K56Ay9i80WSNSaiJ79K6jJqdbpUSxrf+DQbZdB4Dw/yVdb3CilSRGJvsxMz55kTs\nzz2+jEwqj1tfqZwmDAA6ORYJnfRGLUkQ6LGYsc7Il14AvIdVK1YL5VgmxyTHgZmNANRdyhSYQGkz\nsFHHqoGHlcBAxxjWthdrzKDLoTSaltzXt+JxFDlgqqu1Cy2b04iYyCZyLJJqSbguYBziw5g1KhK9\nNj1WIrzOSqpjtRtnIz0G/PFCBDf1W0FpW+uiXQvULazi8TjM5t38HovFAlqiffzYY49hfX0dN910\nU9MvIl7mg3SiahOuHg4Xhch2SnbCvKHXi+jNt8Bz5lHo9PXX+Am1BqrBMRQXGnuXALsC9rn1cxiT\nclwvC15WDckz/uswa2HRqXYSxgUBe3URslKmr3rMt4r/ujiDz77y5dCqdj+8UrmB7ZDNFMBxgE5m\nKjlvubBQ8fopvRlatQ50qvluWiuO6+WQdqesJYV0LolsPgPqwgWgWIT6JS+te/96hZXGZoH9hqN7\nmg/4lWc3MOoytnRVeFO/DZ0WNb57Sfp9oUabMwpdiWbQX+243kSUTTliQcwA4KK0JX2VMkWKIFoX\nbFnqjQIB4ODRLsyclbaqqGZ9JYLTj6/gNW8+IjtxQi66ZA6+BvY1jaJtxGjVaqEcy+SoaLwVABS2\nAjD0SIvIm0WOSWgjDysBg5aCy+LFRrh+3qGHan4z8OnVRVgQhUHXWhFktRtAR2ufMxZJtzVe5k1C\neU3skNOAhe1UXZPQQCKHDrMWuZC4OSjHcXhg/sU9BgQaFFZmsxlMmcCRYRhYRESL3/72t+Hz+fD+\n97+/pRfB0GmYSwfdI14TZkJJZBrYKAD8yVxvUIMRaYFK4XvpKViWLyG52FgjQjbhZ+W2dmEjfBnr\n4SUMdYoXTeXC9erg5Xoc79mNt3FTFAwadY3wdLk0almlaXz0kUfx96deDjdV28VRWmeVYHjhutwO\ngc7jBEGSyG5V6sR4B/bmx4HtbAQCpY6VDPH6Nr2FTnMncv/1z9C+5d2iMUTljHuMmN9OSeZqjd/7\nAfT9zhtbes2NeG6NwVM+Gn9wU+vh2u886cValNwp6KsxjfQj2YTlAm+1UNkl9jURZVNO9VYgpVWB\n0qrgpjSlKJv2x6vZTB7zF/w4fHz3PazXsQKAzh4rOJaPkWlEKpHFD79xDnf92mSN6Wi7cByH+HYC\n61wpF1WCRtE2YqzSNPpa3AgUqLcZSARCsPS1bw4qIIwC6xW7jTysyhnsaGwU6m5BwH56wwevofXM\nSZvDiJjIhjzdojmoADXUh+Rl/rg84jJiKZyu62XFd6y0JeF67UXdpUAS4IBDHcp5zO1H6p4drrvu\nOjzyyCM7/37kkUdw/PjxnX8Xi0V86UtfAkVRePvb397yiyjvWBm1Koy6jDi3JW/E53DXj7apJlHU\nIHPbnZj/1Jca3rcZAbvL0omnZh9Gv3sUWk3tWKyYyiC1vA7TxCDYxRmQMjtWgIjOqmocyHEcViJp\ndJjV+MMHHsL7Th7HMa/4FZjSOqs4XT98uRqCICR1Vq0I2JnpubZW6+XaLQTpTdwRMYDs6oX60LGG\n9zfr1HBRGlGjUIDfjjL0KndlLsBkCvj7n6/iQ7f1w6Rrfcx4fY8HKW4Tn/7ZimhxSDURbZMvstiK\nZ9Fb9Tnx0TQGWjhJG01aJBOVo5aPvmwAvTZ9KXy5/Y7VpTOb6B9xVQTXUmYdEoz0RRxBEJg46sXM\nufojUpbl8N//dR6HjnVjcEw5PZEAE01Do1FhpNuCC37pY2Of1Qof02THqsU4m3KEUaBYsaMJbcN2\nQLnCCiYLQBBAXPr/KWcjUICPtmmwGdiC+/qlUARD1tY7S1aHATERywXedb2NUeBAD9K+TXDFIt+x\nCqfgMWkRzxWRzFU2P9L5IjL5Imx6NR/A7K7tSj0wH8Yrx5yKjOr3M3ULq/HxcQwMDODuu+/G3Xff\nDY/Hg8nJSXzta18DwzA4c+YMnn32WTzzzDO49957ce+99+L73/9+0y8iHsvAUraGfaLHgtMydVbO\nJgTsAFBIZKF/7atAn7mE6On63SjV8EGwKwvg8o3n5W5rFy6tnpbUVzEX5kGNDYCIRwGSBOGUf0Cd\n8powF0ohXeriVftZBRN5GDQqfOapJ3HQ5cKbDx2UfCyldVYJJivLaqEcy+FRMNU6K3vzhRXHsmCm\n2/QsMlJAsdDQ6yYSWMb1CxFof/Ndsh/6YJ1x4F7AcRw+/8Qabh+04Vi3ufEv1EFNkpjwqFHkCrj/\nYq2mj98MXJH1WBt0Fh0mLbQiG4GtjwIrO0cneixgE0nkQhFQg6136oCSaP25NRw52VtxO2XSIpPK\ni25fCUwc6cLseT/YOvKEZx5dQrHA4uaXKxe5U46/ZLMw1VkZyFxNsx2rQptWCwK6ThdAoKZrXUgk\nQRTycHnbXzwQIAgCpLen7jiwkYdVOXKibVyUsalRIMtxWInnMNnRevC0zWEUdV+n2xwFqgw6aN12\npNf9GHIYsBrNgOWAXmtt18ofz6HDrANBEMiKmIOmckU8sULjFSP1Y25eDDS8pH3961+P17++cvvp\nrW99KwDgxIkT+OpXv9r2i4jTGRwY3f0yneix4L6f1J9jCzhcFEL+JgSYqTzcXXZ0/fm7MH/fF3H9\nd78oWT0TBiPIrl6wl+ehGqu/Fu+yeMGBk3Rcp0v6qmKpW9VMxW7QqDDmNuL8VgI39Fkx6fHgwaVn\ndn6+Ek1Dq87jcjSGr73x9XUfu1xnNazAFpLcOJtyLIdH4P/BTytu67D14Pzy0009TmplAxqbGVpn\n8+MkAYIgeMsFOlp3K8j5xJOIDPbD1cTWzoSHwgV/Aq89qHxXQoxHlqLwRTP4s9ubyxqT4mS3FxtM\nAt84y+GmfluFDxY1MoDEog8cxzX8LK+ICNf525uLsxGotlsQiF9chHliCISqPVHs1loMxTyLvsHK\nEwCpIqE3apBK5iSXNRxuCiaLDqtLYQyM1BYIvsUwzj6zhre//yUg98hxOrDBoKPbCqvXhH98Urqg\n6LM0p7Hyt2m1IEAQxM44UN+1W0yk1/2IWx1wNrFsIQdBwC51DG+mY9XvGcP69mUUinmoVeI6XY+R\nwlZC/hRlJRaDlshjyN16oW111OYFZtJ5sCwLg7FxLFg9qME+JJfW4O7vhseshS+a4QXsdBbjnt2R\nXqA0BgRQirOpHAX+7HIUU14T7DJiyq519oVBaLwqxHfQoUcmX8QG3djJ2CHTJBQAckU+J9DjptD9\nplchT8cRfPDndX9Hru2CEHUyKlVYlfRV7NJsQ/8qMY73WHbMUw963FiIRJArhZn+fCWItXgIn7/r\nlKwNKyV1Vgla/kaggPnQKJhqLytb815WvJ6mdeG6QCOdFbsdRN+sD8wrTjX1uBMeCpckLBeUJpjI\n4Z+e3sCH7+iv8YpqleNdXlza3sRvHunAPzy+VjG60dotUOl1NV0HMXwiwvVsoYBQKoVuc/OdNQPF\nd46ql1biF+cV8a8698wapq7vFc0SbSRgB/iu1cy52u3ABJPB//3mefzKm6aa/s40Q2CDj7IZdRmx\nyWQlY8K8ZhO2U+md40gj2rVaKIePtqnsWseWN8HYHTBqlD0t1etYyfWwEtBrDeiw9WAttCh5H0+T\n7uvTwRDMiKLT1tv4zhIYjBoUiywyZd55vOO6se2xGzXYi1RJwD7iNGIpnBKNtvEnsjuFVS4YrulY\n/SKI1gX2T2FVJuAkCILfDpQhAnV6KERkescwmQKoIgub0whCpcLo3e/F/F/9r7p+PCqZuYEdth78\njzs/BItRfAtrR7i+ONOUcF3geLcZL5TeD0qjQb/VgtntMPyJBH60sI5fPzSIbou8k5SSOqs4U1kU\ny4Ea7EEuFEGe2b2qa0VjxZxv3XG9nEYmoblv/Que69bB3tPcyLHPpkcsna8rIFYCluPw6Z/58MbD\nbgy72jeYFJj0eLAcjeHUqA2JbAEPLVS+RyaZm4G+aG04+BoTR7fZ3HR8B8B3XbU6NdJVBqzM9ELb\nUTaZdB6LM0Ecuk48q66RgB0Axqc6sXgpgHzZAg5bZPHf/3keR27oRf/w3p1cOI4rhS9boFGRmHBT\nuOgXPz6qSRJekwnrMh3YV2mmbeG6gGVyFPT5SgF7ZGULeZdLcf1N3cKKiYHQGxp6WJUz2Dle14G9\nWfH6+UAAquwqOuytj7AJgqgZB9KRdFv6KgHjUN9OcDa/GSguYPeXd6y2I9CV2S2sRNMIJvI42aNM\nYb7fueqFVT5XQCFfrGlXnuiV52dFmXUo5FlZmqFoMgdNobhTCLjvvAk6twMb3/hvyd8hRw+huHAR\nHFv/qk6t0uBVx2vjTQAgzySQ3QrBOOAFu7YM8kDzB/8hpwFMtohAnP9/TnV04PmtLfzxgw/Dobfh\nFcNdDR5hFyV1VgmZAczlECoVzBNDiF/cveqzUk7kClmksvLHusz0fFsbgTuvx+oAKyFgL64sojj9\nPH5gpytyAuWgIgmMu/deZ3X/hRDyLIffmFLO/wcAtCoVDnncOB8M4k9u68NXnt1EJLVbzFAyMwPF\nwpd9sVhb3Y9qywUAYBToWF18YQODY24YJcZRcgork0WPzh4rLs/satOe+MkiSBWBG+8Yauv1NYKJ\nZaBSkzsdsclSbqAUzeisfDEa/RaFCiuRzcD46hbYDuXH5kRnj6T7Ohfyg5DZrRIY7DyIpYC0zqpZ\n9/WzW1twqFIwaNvblKsWsMfa3AgU4C0XyjcDU+iz1npZ8UHvOj7OJhiB1rVbWD0wF8apUQdUIl3g\nFyNXvbBiSmPA6quU67rMmN5KIFdHKArwlbrDLU/A7g8lwWnVO54xBEFg9J73Y/EzX0UhJS5eJq12\n/sS7Jk/zJQZzfpY/4G+uguzoaurqaOd1EASuK+taTXrc+Pwzz8FtMCKTI9Bnk981UtLPKt7CKBDg\nHdjjF3dHAQRBlCwX5HWtOI5rKyOwHFIiL5DjOOS+8b/BvebXkSQKMBua13JNdOxtYbUSTeMb5wL4\n89v79+SgdaLLi9ObWxhyGnHXmBNffGr372Ma6UeigeVCrsjCn8ihu2ojcIWmW7JaEDBSWqTLdFZs\nLo/k0irMY43TDKQQnNanrpceycgZBQL8OPBSaRx4eS6Eiy9s4NW/MQVyj08sgU26Ih9wymvCuS3p\ni5Vmom18ClgtCBh6vSimMsiGdr93qbUtqDtbF3BLQXZ0gQ1uiV4cs9sBkDL1VQK8A7t0x8pVGgXK\n8TPLFYtYisYwZGvffqC2Y5VSJCKJGtodBQ46DFiKpNFp0cKfyCFfdn4WOlYFOg6VTguVgf/O54ss\nfrIYxStfpIHLYlz1wioey+x4WJVj0avRb9dLtrHLccosrLa3UyCMlVeitmMHYb/+CHxf/k/J3+Nt\nFy42fHwpKoOXm9dXCRzvNuN0yXbhhu5uHPN24r0nXgK3qTaDrRG9BxxYbXMcWMgXkcsWJK/u62E5\nNFKjs+LDmOUVVpmNAEi1GnoFoksIm1N0FFicfh7sdhDho5PwWLtbGlFMeIx7Vljliyz+9lEffvdE\n/YDldjjR1YXnN/kC4W3HOrEUTuNJXwyAvI7VBs3rLrQqkY3ANk7SRlOl+3pifhnG3i6ojK1rl9ZX\noiAA9AxIm6pSZh2STOPCauRQB9YuRxDcYvDAt6fx6t88IpnbpyS8cH23sBpzG7EWy9asxgvwJqHy\nR4HtmoMKEATBG4WW6azymwHoupW3ICF0ehAWG7jt2u1WLhQAIXMjUKDfPYLN8ApyBfHPAaXRQE2S\nstzXZ7fDcBtU6LaLj56boVrAHlNoFKjv6UQ2GEExnYVFr4ZFp8Z2Mg83pcVm6bvAcVxZnE0U2jJ9\n1VOrNAbs+j07Ru1Hrn5hRWdgtop3cOTaLjg88rys6EgKGpGD28hf/B5WvvyfyG2Ld3BUY4fByjQK\nFX3ec7OwHhkvBS83r68SON5twdnNOIosh16rBf/8utcgGC/iQAtZUL2D7QvYE/EsKIteVOTbCLOI\n+3IzYczM9DzMk8o4bIuJ1zm2iNzXvwzdm9+FYMK/s5zQLONuCvMhaaPQdvg/L/jhpjR7Kgg90uHB\n7PY2MoUCdGoSH7y1F194ch3JXBGmkQEkGlguiAnXgdY9rASMJl2Fl1VcgeDl86VuVb0CmrI0HgUC\nvHlx/4gT3/vaGZy4eQC9B67MirmwESigVZEYcxtxMSB+fOyzyOtY7VottGfjUU7NONAfgqlP+cIK\nkNZZsdv+pjtWWo0enfbeBgJ2ee7rF4JBeLRZdNr7mnoNYtgcBsTKOla8eL39USCpVsPQ50XKx79/\nwy4DFrfTpTBm/rsQz/KFu0mrQi4Uhq5sI/DBF3ngshj7pLASv8qUmxvocFMIy+gKJGIZGEWeizrQ\nA++vvgKLn/1X0d8TNgNbybIDAKbCcb31jpWT0sBp1GBhe/fLw4cvN3+V3tFtRSzcns4q3oK+SsA8\nNojkog9sblez04yAnR8DKlRY2WpNQgs//zFgMEJ1/CaE6K2m9VUCFr0aTqMGKzKDxeVy0Z/AQ/Nh\nfPBW+QHLrWDUaDDidOBcIAAAmPKacbLHgq8+twmd141iJotcVPo7uhJN1ziuA617WO28riqNFXNh\nAZY2Aq1TyRwuz4UkResCckeBAMAWOWTTBZy8tflg3VbgheuVo0CAHwdK+VnJ7VgpZbVQDr8ZyBdW\nbC4PgmFg61FWJyhAdvaA3apNduA7Vs0/Z6NAZrdRnknodDAEig2j0976RqCA1WHcKayKRRZJJqOY\nq79guQAAw04jFqs2AwMJPiOQIAg+gLlkDhpM5DAbSuGWgdbH/tciV72wYmJpycJqxGVEJJVHKFn/\n5C9XY5VlMjBLdHeG/uR3sPWdB5FaqT2xE+5OgCDABZsLWAWA3HYUeToBg9PEb6B42/sCHe+2VLiw\nL0cyNRtXclCpSHT327Dehs4q0YKH1c7zG/Uw9HVVbJU1E2uj1EYgIBRW4Z1/c9kMct/6V+h+6/dA\nEARC9CY8LRZWADDuoTCr4DgwlSvi737mwwdu7r0injCCzkrgXdd34WkfjQuBJEzD/XXHgWLhy4lc\nDslcDh6RyCW5VAcxMxfa62BefGEDwxMe6Bu8n5RZL6tjtXgpgOAmDQ4c4nXc2pUkTmdAkARMVRc7\nk3WMQrstZmwlEsg3sFxQ0mpBwDo5uhPGnNkKIme1wWHeGxsKwtsDzr9RczvfsWq+SzbUOVHXgd1D\nydsMnA4EQWZWFCmsLDYDEnQGbJEFE0uDsugVy6A0DvUidVkorAxYDPNhzEJhVbERGIzsBDA/NB/G\nHYN2xSxgrhWu+v+2XsdKRRK4rrtxKLPNYUScyaDQIF+wkMzBLjFz1rkcGHjPmzH/qS/X/IwgCNl+\nVtXQ52ZhmRoDu7wA1dB4w4y5RpzoMVeMR5ejaRxooWMFlPys2tBZxenmXdfLsRwaQbxsHNjUKPDC\nPKxTChVWFhu4OL0jbs0/8B2QIwd3uotBerPljhXAO7BfUrCw+tIzG5jymnDzFboKPOH17uisAMCk\nU+P9N/Xgsz9fhXZssK7lgi+awUDVKHC1FL5MttFpK+9YRZ4+i+TiKqxHWhuzcxy3MwZsBGXSIpnI\n1g1+pyMpPPTdi3jtW45h7HAnZkU8rfaCQMlxvbqDOeGhsBLN7CQ3lKNVqeChKGw2MLT0xZQTrgsY\nB3uRC8eQjzG8OajdAUebZpZSCCah5QgeVoSzecH8YOdBLAfqWC7IGAXGs1n4k0mkmHl02NpLCwAA\ntZqE0aRDnM4oZrUgQA32IrnEC9iHXUYsbqd493VaKKzKPKxCEeg8DrAchwfnI79wY0BgnxRWljpd\nj+pCQgyVioTVbkBUJISyHCKdh9stfZXc/3u/iegzZ0Gfqf3CkE3kBpazK1yfATnUur5K4HCnCZcj\naSRzRaTzRUTTBXjNrY3jegedbemsEkxrG4EClsOVRqFOcweYVBS5fP0r/GwwDDaThb5HGT0GodYA\nBgpcnAHHxJD70beh+43f3fl5iN6E29J6fpmSm4FP+Wic2YzjvTe2fyCWyzFvJ84HQxVGkjcP2DBg\n1+Ox8ROSHatcgUVAbCOwTeE6sBvEnN2O4Nx7P4bJz30UGmtr+p+1yxGoNPymbCPUGhW02loPLYFC\ngcUPvn4WN9w+iK4+GyaOduFSg+xApajWVwno1CRGXAY+AFcEOZYLSgrXBQiShPnQMJgL88is+xGz\n2OE0KjdqLIfXWFV2w1vxsBLoc49gK7IqeayS42V1IbSNEbsFBo0eRp2p6dcghs1hRCySVsxqQYAa\n3A1jdho1UJEEDBoV1mJZsBxXGgUKHaswtG4Hzm7GYdKpMKKgt961wlUtrDiOK5mDSp+cj/dYcKYk\n2K6Hs4EDeyadB8dxcNd5LrXRgOE/fSfmPvnFGj1Vyx0rwXF9cRYqBQornZrEhIfC2c04fNEM+qy6\nltfsO7otoNvws0rU6TbKgbdc2C2sSFIFp6UTQbr+iYiZ5h3XldQWkSWT0Nz9/w7NTS8D2clrbTiO\n4zVWtta3dvptekRSeTBtGoXG0nn8w+Or+LPb+2HUthfb0gwWnQ59VgsuhbYrbn//Tb14QufCwqa4\n+HmNzsJr1kEjthHY5kla2Ao8/7570f0bvwL3nS9p+bHOPsvnAsr9PNXbDPzZj2Zhthpw3U18rFBP\nvx3ZdKG52K0WERzXxZjsNEkG2/M6q/oCdh9No0/hwgrYDWROrG4harHDot+bwopwecAxNLjsbiHU\nioeVgEatRZdzAL7QgujP5bivTweC6DdpFBGuC1gdBtDRFOg2w5erMQ71IrW0W5gOO43Yimdh1JDY\nTub5UWBpMSwXikLnceKBuQju+gWyWCjnqhZW2UwBBEFAp5du/zqNGnhMWsw20FA53BTCdTYDY5EU\nMhoVbA1azd1veTWywW1sP1KZW0f2DIBjaLB1HLqr4TgO9NkZWKbGULw8q0jHChBc2ONYjqQx0MZV\nCe9nZW9ZZxVnMjV6jmYQLBfKi1g540Cl/KvKIWwOFOemkX/qp9C+4W07tyczJbd7XevbUCqSwKjb\n2PAzXA+O4/C5x9fwihEHJjuVubpthhPeSp0VwH833zZixrcGrhO98PFF0+IZgW1uBAK8xioRTYIr\nFDH8Z+9s+XGS8Sx8C9s4eEz+qNdkERewz53fwuW5EF75a4d3ijSCJDB+xIuZs3vbteI4bmcUKMZU\nHaPQq9WxAgDr1CiY6Tkwvi0U3e62xsP1IEgVSI8XbGD379CKh1U59QKZ5ZiETgeDcGnS6GzDcb0a\n607Hqr3wZcbkHQAAIABJREFU5Wp0HieKmSzyMf5zMuwyYKmks1qLZSo1VqEwClYrnltn8LJhaeuS\nFzNXtbCS8rCq5kRZTp4UjQTs0XAKSbUKZl39K31SrcboX74Xc/d9AVzZ6IMgSahGD4Kdl+9nld0K\nASwHHZkHQZlBWpX5kAk2FMvRTMv6KoF24m3q6ePkoHXZoaIMSK/tnrA7bD3wNxCwK+W4Xg5htSP3\nzX+B9tVvAmHePYGE6E24rd62u2MHPZTkKEYOD85H4I/n8PbjrY8k2+FkdxdOb9VqhV574wGoU0nc\nL1I4+GIZDIgUVr5Ye+agABB/9gyKRQ4H/7//CbKNTbULL2xg9HBn3Yu7asTc1yPbSfz4+5fwurcc\nrRHAHyxlB9bTZbVLgsmC4yD5fTzoobAUTiNTqDVcbtSx2gurBQFhMzC5ugXCu7dh5YS3B1zZOLAV\nD6tyhjqkNwPljAKng0EY2RA62sgIrMZmN4COpBSzWhAgCALUUC+SyyXLBacRC9v8ZqAvlkEgnkVH\nWQDzs0kSN/RaYNbtTQdyv3N1Cys6XVdfJSBHZ8V7WUmfuALBJIo6jawrIs9dt0JtMWHzWw9U3N7s\nOHBnDHi5PZuFagbsemQLLJ5epVvysCqn1UBmtsgilcyBalHfJcAL2Hfb6XLCmJnzc21nwlVD2Jwg\nDBQ0r3xDxe1BehMea/vmffxmYGuBzFtMFl99bhMfuaO/xmjzSnHc24kzW34U2coTs0qjwevPPoav\nnw/CX1VsiHlYcRwHH91enE3GH8KFD9wHg1EDlmr9ZM+x8kXr5fCF1e5IKZ8v4gf/cRY3v2JEVOPk\n9pqh06ux7ms/6UAKwWZB6gJAr1FhyGHAjEhx39+gY7UVV95qQYAaGUB6I4Dskg+aPTAHLYcXsO9u\nBrbiYVUO37GaFf1ZI/f1QCKJfJFFJrmu8CiQt1xQehQIlMKYBQG7c7djNe1PQK9RwaBRgWNZ5Laj\neChY+IUUrQtc1cKKkdnxOOihsBbLICYhGAUAh4vvWEldFYbDSZAyHcIJgsDYPe/Hwt99BcX07slC\n1aSAnT47A+sxPnhZqTGg8PqO91jgj+dasloop1WdVTKRg8GobXud13x4BEyZzqqRl1UuyiAXoWEc\nVO4qDwDUN94B/fv+AoS2slAUOlbtMuGhMBtKNm0UWmQ5/N3PfHjzkY62xr7t4jAY4KGMmAuHa37W\n67XiVdokPvf4WsWJZEUkIzCayYAAAZu+tU4nWyjg3O//T/S949dgdpiQSrTuw7ayGIbOoEGnhC5J\nimovq5/+cAZOjwlH6hRoE0e79nQ7MLDJSOqrBKRyA3ssFmzE4zVFs8Aq0/6ygRSkRg3z2CC4cBSm\nvS6sqkxC29FYAUCvexiB2Doyudo4NMF9ncmKfz6ng0FMdngQiK0qYrUgYHMase2PgyTJhtYhzWIc\n2hWwd5q1SOVZOAxqnNmI74wB81EGhMGABFSY8l55ycJ+4eqPAmUUVhoViaNdvK5ICp1eDb1BDYYW\n39KgI2lom+iu2E9OwnpkHL5//ubObeSBUbBba+DS8kY69LlZWI6Mg23TGFSM67rNMOtUcLS5RdOq\nzirBtG4OWo7lUGWsRYe9B4GY9CgwfmEelsMjbdtWVKM6MALV+GTN7SFmS5GOlVWvht2g2fF9kcs3\np4NQkwTecHhvxyRyON5Vq7MCANPIAG5ZnwGTKeDhBb77mS2w2E7m0F31/RY2AlsdrS78zZehMugx\n+Ee/LRrE3Aznn13DkQZO62KUjwIvntnA2nIEp95wqO7jjE95MX/Bj6LIKE4JqqNsxDgiYRSqV6vh\nMOjhT4gf13wxGn0Ke1iVY5kaA2u1wG5vPy+vHtWbgWwo0JKHlYBapUGPaxC+4Jzozz0UJTkOnA4G\nMel2wx9dU1RjZTBqQKpIRTcCBXjLBf79IwgCw04DCiyHVJ5Fp6k0BtyOIG2x4JWjzj3Ty10LXOVR\noHScTTVy4m0cbulomySdhrHJQmD0L38fy1/8jx1naUKjBXlgFMUFaf8SAY7jwJybgWXiAFj/Bsh+\nZVPtb+yz4vdv7FFkM64VnVW8DXPQciyTlZuBbmsXthk/iqz4Bh2/EajsGLAeQXpDkY4VwOcGNuNn\ntRRO4dvTQXzotv59cZASE7ADpXHOwgo+eGsfvvLsJqLpPNZiGXgtOqirNlZ9NI2BFkXQwYefwNb9\nD2PqHz8GgiRLJqEtbrQyGawtRzBxpPm/rVBYbQcTePS/Z/G63zoGbQMtidVugNNjwvLCdt37tQov\nXK//vh70UFjYTiEnprOySOus9kq4LmCZHEPW5dozDysB3n19nfev4jhw24GWPKzKqRfI7KGk3den\nA0EMWvXQafQwtrEYUw1BELA5jIoK1wWowb6dMGYAGHIaEEjkYNSQO/qqxNY2wjoKp0avTITTfmUf\nFFbyTs68zioOtk6sjJSAnWU5ZBM5mJu096eG+9H56jtw+fP/tnMbPw5srLNKrWxARRmhSWyD7BkA\noWk+qLjua9Oq8IoRZT68reisEnS2LQ8rAUNfF/J0HLkIf1DXqnWwGp3YZvyi99+LjcB6tBNnU81E\nEw7suQKLv3nUh9+7oXvnoHW1Oe714vSWv0Y3YhrpR3LehxGXEadGHfjiU+u8cF1kMaVVD6v0mh8X\nPvjXOPJPn4DWyQvf+Y6VvHiZaqZPr2NssrNhQSSGyawDHU3jB/9xFrfdNQZ3p7wT415tByYY3m3b\n0mARyKhVod+ux4zIMbLeZuBeWS0IeO66FSuvfR3shj0WOputAEEAcRocHQVhMLbkYVXOUJ3NQCkB\nO8txuBjahkOVQqeCwnUBq8OwJ4WVcbAHyaXdcf+Iy4jLJZ1VZ2kadP7SOjQuB9wyZTcvVq6yxiot\naysQADrNOph1KiyFa+fZAk6JwipOZ0Dq1LCbmv9jD3/ondj4xn/vbK7xAvbGOivm3AysR8ZRXJxR\nfAyoNK3orOJMexuBAgRJ8gL2S/J0Vsz0HCwKOa43gvewUkZjBQAHO+Q7sP/L6S302fS4cx+tK3vN\nJpi0GixFYxW3U0P9SPnWwRYKeNt1Xixsp/DdiyH0iW4ExpruWLG5PM6+524ceP/bYD+5O641lExC\nm4VlOZx/br1p0boAZdYhwWTR2WPF4ePyx8Rjk51Ynt9Gtk0/s2qEMaCc7rVUbmCf1QofI96x8tHt\n+47VQ+d2YGn8CJx73LEiCAJkZzdY/zrfrWpDXyUw2HkQlyUc2KXc11diMdj0eqTTfkWF6wJjhztx\nYNSl+ONqrGaojHpkA3zXdchpwEI4jTcccuNYF39xMTu7ga6Bvcl7vJa4aoUVx3JIMM1FojQaBzrc\nJoSDtQcNOpICZ9DA2oL5nM7jRN/v/joW/paPulGNTIBdngeXr39Ap8/OwFIKXlZSuL4XtKKzStCZ\ntuJsyjEfHgUzXbYZaBcvrAqJJDIbQVAj/Yo8byPi6RjUKo1irfoBuwHbycZGoWc343j0chR/dHPz\n+p+95mRXV0W8DQCoDDroPC6kfZvQq0n88S19mAulRD2sfHTzVgtzn/hH6DqcGPj9N1fc3qrGank+\nBMqsk/R8aoRWp8btrxrDy1830dTfx2DUoveAHQuXAi09rxSBTQaeBmNAgSmJ3ECpjlWBZbEZT+yJ\n1UI5kVR+z0eBgKCz2gAbam8jUKDbeQDbzBbSudoLJimT0OlgCJMeXl/VoaC+SmDiaBd6D+zNKI4a\n6tvJDOy16hFO5XFDnxXdVh3W6QyyoQj6Bq+OJcx+4qoVVqlkDlqtCpomHKQbFVZOD4XIdu0HmY6m\nkdeqWyqsAODAe9+C8GOnwVyYB2Gg+C/nsrjj7s5z7jiu7/+OFdC8zipOt2cOWo7l8AjiF8sE7BJh\nzPGLizCND7blW9QMoTYzAqtRkQRGXUbMhaRtF5K5Ij7zmA8fvLVvz1yo2+G4t1PUz4oa6d/JDDza\nZcaHbuvDdV2VJ2OW47BKM01lzvl/8FMEH3oCk5/7aE0R06rG6lxJtN4OJ289AI22+b/PxNEuxceB\n9RzXqzncacJcKIVcsVJnJeVltZdWCwIsxyGWKez9KBAA6e3lF5C22/OwElCrNOh1DWMlUCtglxoF\nTgf4jUB/dHVPRoF7ibFMwK4iCRyw63emSA/OhTGIDAwdv9j6KuAqFlZ8lE1z8+0prwmLYT4nTwzK\nrEMhz9aMtOhICmmNCrYWv7hqE4XBD/4O5j75RQCNx4FcsQhmegGWXhdQLIJoY/PkStGszirBZBUZ\nBQKAueTALtAh4b7O66uunHA9RG/Co2BhBfA6q3q5gV94cg039Fpxfe/ebWG1w4nSZqCozmrBt/Pv\nU6NOmKr0S4FEEiatFpRGXmciubyOSx/5DI5++T5obLXvhxBr0wxMLI1NXwzjU1fnOzk07oF/na4x\nGG2HwCaDTpndN0qrQq9Vh/mq4r7XasE6U6th3UurBQEmU4BRQ9ZEH+0FwihQqY4VIAjYa3VWUu7r\n08EgJj0e+GPr6HQoPwrcS6ihXiTLBOwjLiOWwikUWQ4PL0TQkU9B5/7F9a8SuLqFVZMnZp2axKEO\nCmckbBcIghAVsMeiaTAk2XLHCgB63/Z6pH2b2H7suYYC9sSiDzqPA2RoDeTQ+L4b54jRjM6K47i2\nA5jLMY8dQGplfcczTEpjJWQEXimCCnesAL6wktJZPXY5iplgCu++XtnnVJJeiwUcOKwxlZ1jamRg\np2MlRTNRNsV0Fmff9VEMf+idsB4V7/hSJl3To8Dp0+uYOOptqdukBBqtCsMTHsyeV8bTKhnPopBn\nYWnCz27Ka64ZB1IaDUxabc3oaq+tFgAgnCrAfgXGgABAdPaA828oprECgCGJzUC30YhgqvL9zBWL\nWIxEMe50IhBdu+Y6VtRg307HCuB1VovhNJ5dY9Bp0YGMxqDz/LKwuqYKK0DGOFCksKIjKURBwNZG\nYUVq+Kib+fu+AHLkEIrzF8FJGOoxpTFgcVF5/6q9ohmdVTqVh1rT3Bi3HqROC2qwD4m5ywB2C6vq\nrgh9/sp3rNwWZfUCEx5+FFjdGQin8vjHJ9fx4Tv6oddcuYDlZiEIQtR2wTQygOT8St3fXW0iymbm\nns/CNNKP3t95g+R9DBSvsZJyt66GLbKYPr2OIyev7slMyXGgMAZs5uJNKjdQTGfl22OrBQA4vxXf\n8UHaa8jObrCBTbCBrbY8rMrhHdhrCysXZUQomar4fM5uh9FvsyKXZ6BWaUDp91a7pjTGod4Ky4Vh\nlxGL2yk8MBfGXaNO5EIRaN37Z+HmanHVCismJi/OppoTPRac3mAkD6Z8x6ryoEFH0ogAbecWdbzm\nDhAaDfyPnAZhsYJdXxG9H32mVFgtKeu4vtfI1VklmAxMVmX0VQLlDuxGnQk6jQGx5K7nTzGdRWp5\nDabxQUWftx5KxdmUYzNoYNWrKoxCOY7D3z/mw2smXBj37K1JohKc6PLi+a1KOwxqZACJRV/dImdF\nZpTNxjcfQOSpszj0mQ/XLRjUahIajUr2lt3SXAgWmwEumfYIe0XfoANxJiuqB20W/yYDT5Mi/MOd\n/Di6UJUC0C+is1rd443As5txfP1sAO+5QdnvmRSETg/CYgUX2Gjbw0qgyzmASCKIVLZykkJpNNCo\nVBXu68IYMBBdU9Rx/Uph7O9Ges0PtsB/5wbsemwwWUz7E7it34xcJAat85eF1TXXseq16kCAwFpM\nXKPg8JgQLutY5bIFZLMFaA0aqMj2RnI7UTd/82WQwwclx4H0uVlYDo+AXVmEavDKja7aRa7OSsmN\nQIHqzEA+jHl3HJiYvQxqqB8qvbIFXT2UtFooZ9xNVWS2/XBmG3SmiN86tv+1eMCuzqocrd0ClV7H\nB49LwHtY1e9YJeaWMfvxz+PYV/4KalPjIrMZAfv5Z9dw5IarfzIjVSTGJjsVibgJbsjXVwmYdWp4\nzbU6K/GO1d55WK3GMvjrR1bwly8bQK9M2x0lIDt7QFhsbXtYCahINfo9o1gO1OYG8gL23e/6dCC4\nsxF4rY0BAUCl10HncSK9xl9YaVUkeqx63HLABlUiAbXVDFKz/5ZurjRXWbze/JeJIAic6DHjOYlx\noMNNIVKmYaGjaRitelgVmuE7XnIUpvFBxIIZsCICdjaXR2L2MkwOHUhXBwjj/u9ACMjVWcUZZcxB\ny7FMjoK5ULkZGCzTWV1p4TrHcdhm/IprrADez2qmdFJbpzP4t+e38OE7+mtcyvcrg3Y74rkc/InK\nzrBptL7OaiVWX2NVSKZw5t0fxdg974N5Ql5SgVyTUDqSgn+dxujh/VG8ThztwqWzm7LHmFLwGYHN\nFz5i48A+i6WiY7WXVgt0poD/+dAS3nl9F452XdkOIuntVUxfJSA1DvSUxoECvNWCB/7Y2p54WF0J\njEO7YcwA8KYpD3590oNcMAKd+5cbgcDVLqxkxtlUU09nZXMYEWcyKOT5zUE6koLOrGtLX1XN6N3v\nxeX7n0Zh5nzNgTE+swRDfxeIjWWQ14i+SkCuzirRYrexHuaDw4hfWtrRrXVWbQbSV9AYFADoZBg6\njR56rfIOxhMevmNVZDn87aM+/PZx7xW9Ym8XkiBwwtspOg5MShRW+WIRW4kEei3i3RWO43Dxzz8N\n27FD6Hnza2S/FqNMk9Dzz63j4LEuaPaJfk0Ifvavi5tyyiGVyCKXLcDaQi7cVKcJ01uVoyvecmH3\nuLpXVgu5Iot7H76MWw/Y8crRKy90Jrw9im0ECkhtBropCsGS5QKTzSKQSGDIYd8zD6srATW4G8YM\nAHcOO9Bn0yMbDP+ysCpxVQortsgimci27IN0tMuMS8EkMiKZVyoVCavdgGiY/zDT0TRIo7atjcBq\nzGODsN5xC4rJFLhQ5cmFOTcL65EJ3nH9GtJXCcjRWcUZ5TysBDQ2C7QOC1IrGwCAYe8knph5AIkM\nf6C/4lE2jHJRNtUMOviMrf/97AZMWhVeO6G8S/JeIzYONI30I1FmuVDOZjwBD2WEViVe2Kx/7fuI\nX1zAwU/9aVOvQ45JaLHAYvr51p3W9wKCIHDwaBdm2hgH+ptwXK9m0mvCxVJxL9Br5TtWwsWij1be\naoHjOHz256uwGzR4x4mrYySpue0UtG/5PUUfs95moNCxuhgMYcLtgpokeQ+ra1BjBdRaLghkQ1Fo\nf7kRCOAqFVaJeBYGoxaqFn1LKK0Kw06jqIMwUJkZGGvDdb0ew3/+bsSCWaSffqLidsEYtLg0e811\nrAB5OqtW9XGN4P2s+HHg0cGbcHz4NvzD9z6CfDaDxNwyzAeVDbKux154WAmoSAIjLiMeXojgT2/r\nuybsOKoRK6zqdaxW6kTZMBfmMf+pL+HYV/4KKmNznys5GqvFmSCcbhOcblNTj73XTBzxYvb8Ftii\n+HZxI+QEL0th1avhMWmxGN4dU1l0OujUaoTTvOHjXgjX/+NsAGuxLP7sjqsXLE4YKJAuZYTrAl57\nH+hkZOdCUKDcfX06GMJkhwccx8EfXb9mR4HUYB9SS7UGzrlgGLpfbgQCuEqFlRIn5pM9ZslxoNNt\n2tkMpCNp5HRq2AzK+qTovW6oD06B/t73K26nz87AOt4PLroNsufKRK8oiRydVbNRRHKxHB6tELC/\n9Y4/AkEQ+NfvfRKGnk6oKeXHclLshYdVOa876MJH7hiA6xoNKx1zOhFMJhFJ72Z3mkYGkJCwXFih\nxYXreSaBs+++GxOf/CCo4ea/L3JMQs89u4ap6/ff2MXuomCxGeBbai4AXSCwQbccywOI5waWC9h9\nNKOoh9WjS1H8aG4b954ahF59VWNqFYckVRjoGMNyVdeq3H19OhjElMeDeDoGFamCSb8/TYAbYRzq\nrRgFCmS3I780By1x1QqrRknsjains3K4qZ3NQDqSQkqlUrxjBQCud/wPaOgtxGeXAADFVAbJ5TUY\ntTmoDoyCIPeHnqMZ5Ois4nQGpr3oWJVZLgD8ts0fvu5TOL/+LJZfoswGj1yUjrOp5vZBO07uU3d1\nOahIEkc7Oyp0VjqvG8VMFrlo7ffSJyJc5zgOF/7kr+G8/SS63vCKll6H0VRfYxXdTiLkj2Pk0P4Q\nrVczccSLmXOteVoFNxnZUTZiiOUGlkfbKNmxuhRI4gtPreMTp4b2PGz5aiGmsyp3XxesFrau4TEg\nABi6O5ALR1FMZSpuzwYj0P5SYwXgqnas2jtRDjoNSOaK2GJqr1YdHhMioSQ4jgMdSyNOkoqK1wW0\nEweho7RY+uTnAQDMxQWYRg+A8y1eU/5V1dTTWeWyBbAsB90evJ+WQ5UdKwAw6S14U+Qm/Mw9j9n1\nM4o/pxR7OQp8sVA9DiQIAqbhftFx4IrISXr1q99Ees2PiXv/qOXX0Ehjdf65NRy+rhvqfdohGZ/y\nYmkmiLxETJcUqWQO2UwBNkfrXdxJrwkXqnRWlR0rZawW/PEsPvGTy/jQbX0YbEFof60gthnopnj3\n9UAiiQLLostsumY9rAQIlQrGvm6kVirTMXKhCHSeXxZWwDU8CiQJQrJr5XDxGqsEk4VGqwZdYPek\nY0WQKqgPToHYXEHkyTMV+qprxXFdjHo6qziTgdmq2xNdkL6nA2w2i2yo8rm15/34nYl343Pf+whC\ntDJRII3Y61HgiwExB3ZKwnKhumMVe+Eilj77bzj65ftA6lofh9bTWBUKLC68sLkvx4AClFkHb68V\nS7PBpn4vsMEbgxJtWHTYDRo4jGpcjuyOc4WOlVJWC8lcEfc8eBlvPtKJG/r21sH9ajPUeRCXA7Wj\nwGAyhfOlbhVBELyH1TVcWAGlcWCVziobDP8yzqbEVSmsmFi6JQ+rak70mHF6vTY3UKdXQ29QY305\nApvDgFimAOseJaerJ6bQc9tBzH3iH0GfuQTrkfFrznG9mno6qwStXEZgNQRBwFzVteKKRcQvLuLG\nW9+I117/dnzm/j9BJpeu8yjtw3IswowfLsv+HB/tFw553PDRNOLZ3a6xSUTAnikUEEmn4TXx4vFc\nhMbZ99yNQ5/5MIz97Tlu1+tYLVz0w+M1w+7c315yE0eaj7gJbLanrxKoHgf2lzpWW/EEXMb2rBaK\nLIdP/mQZR7pM+NVD7rZf636nw96LRJoBk9qVURg1GmhVKjyxtoZJDy+Yv5Y9rAR4y4XKzcBcKPrL\nUWCJq6exUkCjc123Bee24siLbNU43CZcng/B6jCCThf2pGMFAKqxSRgK/Iqy/wePwNrnBKHTg7Rf\nu5V7PZ1VnMnuyUaggOXwCJiLu0ahycvr0Lrs0FjN+JUTb8WAZwz/6/9+DCzX2iaVHGKJbRj1Zug0\nL96xhRJoVSpMedw44w/s3EaJWC6s0jR6LGaoSBIcy2L6A/eh8zUvRcerbmv7Nej1GuSyBRRFrFfO\nPbO2rywWpBg51IG15aisAHSBwAajTGHlNWG6zCi0z2qFj6bbHgNyHIcvPLUOkgDee+P+7RgqCUmQ\nONAxXuPA7jEa8ciyD4c9fHHpj66hw3ZtvyfUUC9SZQJ2tlBAnmagdby4u5JyuWZHgQC/Mtxr0+Ni\noDZzy+GmsDK/DYvNgHh27wor8sAo2K01jP35O3m7fzZxTY8BBaR0VnsRZ1OO+fAomOndjhUzPQfL\nFO+4ThAE3nXqLxFNhPCdJ7+yZ69hL8KXX6wcr9JZiYUxl0fZLH/xa8jTDEY/+j5Fnp8gCRgobU1R\nEg4mEA2nMDyh7Fr9XqDVqXFg1IW5aX/jO5do1XG9mqlOM6b9iZ1QcKtOB4IAzgeCbQnX778YwgV/\nAn/5sgNtR4ldS/A6q0oBu5syYjuVwqTHXbJauLbF6wBgHOxFssx9PReOQWO3gpDwqftF46oUVpl0\nHkaTMgaTJyV0Vk43hXQqD71FB4NGtWdxIYRWC3JgGFaPHref/g645blregwoIKWzitPKm4OWYzk8\ngnhZx4qZnoPl8G6UjUatxZ/86qfx0/PfwzNzP9mT1xBi/l97dxrdVpneAfx/rzZr927Ju+UsdhLL\nxnHCpAOBEAgDhNLkcIbCadpSyoEOB0opJIUTlhkXkgOZk0PnTBgOTDsfIFDKMklZEghmCSWQzc5C\n7Bji2E5kW3Zsa7OtXf1wc2VJ3m1dybp5fl84iu3oPUa5enTf5/0/3chNT8xQ2FRXl2/Eka7RbSxl\nST48ff1RJ4babXaU6vUYONSE9lf/G9Wv1sd1nphKLcdQTJ/VySMXsGx5wayz8hKtsiYfzU3T6x8c\nGfbCPexFRtbc40ey1DLoFFK0D3L/vxiGQbFOj4OdnbOOWviuw47/OdmL+nXlUMuvrDfa8U4G5qjV\nKNLpkKFUwjliA8Ow0Conn5k536nLo9PXvb0D1F8VISlXHY02DWycCh2ugX1sn1Xm5TBAViVHukD9\nVTzJ4ioEzp6GTK9F4KfUblznTdRn5XIIEw7KUy8owYjFCv8w10flONk6JnE9XZONxzfswOufvoB2\n69m4r6HXZqE7VtNkzs1Fa/8Ahn0+AAArlUJVUhj1abbDbkehRIoTv3oWVS9vhbIgvuNEuCyr0dep\nzxfAmcYumFekzl2BsoXZGOhzwT44df+g1eJArnFujeuRuDyr0WtosV436ztW5/qH8duDnXj2xjLk\naVMzo20uuDtW0VuBOSoVqi5vA1ptF1Ny+HIseXYGQj4/vANcNIentx9yCgcNS0phFc835sU5KvQN\nedE/5Iv686xcrmE1kBb/1PVYksXLEDx7CiGvB0FLB9jShYI+XyJIJCwKSsb2WTkdHkEyrHisTMoF\nTTafQygUmnD4cpmhEv9w079hxwePwTbUH9c1CDnORmyUMhkqsrNwwjp6qk29qCTqZGC7zQbfa++i\n4K5bkXPDz+K+hthBzK2nemAo1EOfkTo9chIpi0XLDGiZRqZVvLYBebFBocV6PULAjMfZ9A/58Myn\nbXj4LwpRkTu/DwwIJS+9EG7vUNQ16baF5fgbcxUApHyGFY9hGG4Y83nurpX3Et2xipScwiqOA2cl\nLINQtXXEAAAQhUlEQVTafC2OWaK3A9VaBa5aVQyPVJhw0Kg1LFyKQNtZBM+1gC0oBiMXbqsskYrK\nxvZZOQXusQK4BHbHqVaMdHZBolZOONhzVcVNWL10PXb+eTN8/uk3/k6FMqxmpi7fiGOxfVYRhVVb\ntxV5LjcWPH6fIM8fO4iZS1pPvTevyhojzpzoHjPYPZbVYp9TMGisKoMGp3qGws9bpNeBAVConX7U\ngtsXwDOfncP6ymysNl25dy4YhkGZoTIqgX1JTg5qDNxd2lTPsIqkNhWHIxc8vQNQZNOJQF5SCqt4\nnAiMVFekw5GYPiuGYbD29iVweAOChINGPZdKDdZQAN+BvZCUp/42IC+2gd3vD8Lr9kEl8BgWPoHd\ncWrsNmCsO695ADpVOv742fYp35CmizKsZmZ5TJ6VeuFoltX5z7+F2+fH6pe3gp3D0f3JREYu9PU4\n4bCNoHxx6h3vLyjOgM/jR1/P2NaGSPE6EcjL1cihlLHosHF9VsV6HYxazbSjFoKhELZ/2YHSDCX+\nujq+27ypyDTBQGbg8olA0RRWo8OYPX0DkFM4aFjKbwUCwPICLY5bnFEJwjy7gBlWkSSLq+A//E1K\nDl6eSG6+DvbB0T6rIYcbaq0ibr0dE9EtXQjnaa6w0psnL6xYhsVDt9WjrecH7Dv29pyfOxgMYMBp\nRRZlWE1brdGA07298Aa49HDNwhIMtXbA3d2Hr17YhRK9Dml52YI9f2RI6InDF1BVVwg2RZrWIzEs\ng4rqyZvY3SM+DA95kZEd3622yDyrmrw8/O6Wm6f9s3880gWXN4BHrylKyYHi8WYyVI4JCuX1DF6A\nMcUzrHiq8tFhzNwAZiqseEnaCoxv70O2Wo5slQytl4bHfM0mYIZVJMniZUAoCIkITgTywn1W57k+\nK6dDuHDQSNqlC+BqaYO98cy4/VWx0uQqPLFxJ/Z8/yecPH9oTs894OqDVpkOuVQc27mJoJHLUZaR\njtO9fQAAdXkJhjssaHrgaQTuWIPyAmEPAvB3rHxeP1pOdKOqLnUzgpZUG9FyshuhcT4kAqON6/E6\n/MMzGzU4dbmwkrAsKrOnVwh/0nIJ37bb8czaMshSsJgVgilvbOQCr8eW+hlWPO6O1eWtwL5B6rGK\nIIo7VgC3HThe7ILdnZjCiq0wgy0sBWMQ1zH9yNgFl13YcFCeVKOGwpCNgf87PuVWIC9Hn49//stt\n+P1Hz6B7oGPqH5iA0MOXxSpybqBEqYAiLxtSjQrDV5vHDF+ON5VajhGXBy0ne1BQkg5dnD+4JVK2\nQYs0pQwXO8Yfgm7tim9/Fc9s1OBkj2tG2+mNFif+62g36m82QZeAa2yqyNHnw+f3YsDVF/XnzhEb\nQqFgykct8NQmLiQ0FAzC09dPW4ERRFNYrSjU4ciF8QsroXusAIDVZ0C1/TXR3QqP7LNK1B0rgOuz\nkqXroDBOv1emsqgWv7zmn/DS+49h2DN5n8pEKBx0dri5gaMn2pbu2ALz759Dp8OB0jgM8p2MSqPA\nkMuLE4cvoDoFm9ZjVVYbJxxxY7U4BCmsDFoFZBIGF+xjh9qPp9PmxrYv2rF1bSkKE/BhK5UwDANT\nTAM7gMszAotF8x4h1aoh1arh6bnEDWCm5vWwpBRWSpUs7n/nkjw1Om1uONz+qD+3u30JuWMlVpF9\nVlxifmK2yHRLF0JbtXDGF6G1NRtRVXo1Xt77FILBwIyft9feReGgs1BrNKCxxwp/kBstk716BeQZ\nOrTbbOHUdaGo1HI4HW4MuzwoXZR6TeuxKqqNaD1thX+cMT3xblyPZDaMbgdOxu7245lPz+EfV+bD\nbJzbkGaxGi8o1Gq7KJoTgTxVeRGcLW3wu4YhyxDmdZmKklJYCVGxyyUszEYNjlui71TYRvyCB4SK\nWWSflcshfNQCr+CXt2LRlgdm9bOb1vwL/AEfdn/1HzP+WbpjNTsZSiWMWg1aLo3m94RCIW6cjcB3\nrGRyCWQyCapWFMW99ygZdOlKZOdpcL41eivJPeLDkMsTDj+ON7NRi5M9kxdW3kAQz33WhtVlGVi3\niHpqJmIyLBnTwN4z2CmKcNBIalMxBg+fgDwrHQxLPXY8Uf0m6gqjYxdCoRDsbj/t/88R32fltAsb\nDhopLT8X+qtmd8JSKpHh0Tu248iPX+Kr0/87o5/ts9M4m9mqi4lduDQyArlEAn2a8Hc5K6uNMKdw\n03osbsRN9HZgb5cDOQatYMVj1eWTgRP1WYVCIew82IlMlQx/X0cfPiZjMlSgrac56nfZI6IMK57a\nVITB75qocT2GqAorfm4gP1DU5Q1AIWUhp9Mqc8L3WbkS2GM1V1plOp7YuBNvfvkyWi0np/1zfXYa\nZzNbdflGHO0eLaw6bXbBG9d56zYsg1ornpOci5blof3HfnjcoxMluMR14bZb8nVcPl2XY/yw3d1N\nVly0e/DEdSVgRdInJJQsrQGhUBADrtGJBGLKsOKpyotgO34GcopaiCKqisOoU0Alk+D8ADdvy+6m\nbcB44PushpweaFLozasw24QHb3kWO/dsRr/TOuX3B4J+DLj6KMNqlvgEdv6DTbtd+G1AsVKq5Cg2\nZeLHH0Zft1zjunC/T4ZhuNiFcbYDvzg3gH1n+/Hrm0xIk4rqbUMQXAN7dJ+VmDKseGpTMUJeH2VY\nxRDdvxBuO5Drs7InKMNK7Pg+qzSVDJIUu6jWll+LW5bfjR3vPwaPb/IBtwPOXqSrsyGVxP9wxZUg\nV62GPk2BcwNcVEC7zZawO1ZiVFmTjzMRYaFWix0GgRrXebEDmQHgB6sLuw5Z8Jt1JmQKcPBIrLiB\nzFyflWvEjkAwIJqoBZ6qJB9gWSqsYqTWu+Q0rCjS4tjlPitbgjKsrgRFZZkJybASwu0r/xaFWSb8\n4ZPfTJrT02u3IEdP24BzUWcc3Q5st9kFPxEoZqaKHFgtdrgcbnjcPjgdHmTmCDvc2GzQ4EREn1WP\n04P6z8/jietKUJaZuvlgycAFhXKFFXcisFA0UQs8ViGHssgIOfVYRRFdYWU2aNB6aRjD3kDCwkGv\nBIuqDCmbaM0wDO7/xVb02bvw5+/+c8Lv67N3UzjoHNXlG3HkcgN7h90ueIaVmMlkEixYkoeWkz3o\n7XZyjesC94sW6hXwB0OwurxwefzYur8Nd1cbsLKIjtLPFL8VGAqF0D3YCYPItgF5mkWlSJtB3uCV\nQHSFVZpMgspcNZq6nQkLB70SpGeqUHN16l4Y5FIFHtuwAwea3sPRH78c93v67F3IpcJqTvgE9kAw\niAt2B4r19IY8F5XVRjSf6ILVIkzieiyGYWA2aNBoceLfG9pxVb4WdyylN83ZyNDkQMJKccnRA+ug\n+DKseObfPY3cX1yb7GXMK6IrrAD+dKDzcoYV9QQQTqYmB4/91Ut4dV89Ovt+HPP1XhpnM2cFWi2k\nLIPvLV3IUKZBKaN/f3NRXJ4Fl8ODlhPdMCSgsAKAKqMGf/jeAgnD4MGfUfTIbPEJ7G09Z0SZYcWT\npevASukGRiRRFlZ1hVocueCgrUAyRrlxKf5u7ePY8f6/wjEcPY+NmxNIPVZzwTAM6oxGvNfcQtuA\nccCyDCrMBvRYHMjLT8zvc2WRDmajBk/dUAqJCAJXk4nfDuyxiS/DikxMlIVVcXoagqEQfrAOUWFF\nxrhmyS1YVXETdu7ZDH9gNCeI2wqkT+hztTzfiANt56lxPU4qa/IhlbHIzBW2cZ1n0CpQv64carkk\nIc8nZqa8SrRZW0SZYUUmJsrCimEY1BXqYHV5oaccKzKOu1Y/BJVcgz8deAmhUAj+gA/24QFkanOT\nvbSUV5dvhC8YpAyrODEU6HHvo9dCQkHHKafMUIFWywn4/F7oVRRJcKUQ7b/UukKuH4Ga11PX2bNn\nBfu7WYbFQ+vr0WJpwmdN76Lf0RNuNiVzY0pPR6YyLeEZVkK+XpJNn0FRB/GUqNdKhiYHaoUWhsxi\n0UUtkImJtrC6Kl8DlYylrcAU1traKujfr1Jo8MSG3+K9b1/DF6f2IkdHjevxwDAMnl9zPVbkJ7Zf\nTejXCxGPRL5WTIYlMKSnZlQNmZ0pq469e/fiyJEjAIDa2lps2LAh6usHDx7E/v37wbIsysrKcO+9\n9wqz0hnSKKTYffcyKFIsKZwkVl5GER5e/zxeeOchXLv01mQvRzSuKy1J9hIImReqSlfC5x9//iIR\np0kLq+bmZpw/fx719fUAgFdeeQWnTp1CVVUVAKC3txeff/456uvrwTAM3n33XTQ0NOCGG24QfuXT\noKLmSzINy0pW4Fe3/RppclWyl0IIEZmba+9K9hJIgk16O6exsRFr164NP167di2OHz8eftzU1ITV\nq1eH945vvPFGHDt2TKClEiKca5bcgroF1yV7GYQQQlLcpIWV0+mEVqsNP9bpdLDb7eHHLpcLOp0u\n6usOh0OAZRJCCCGEzH+TbgVqtdqoQsnhcEQVUlN9fSKRd70ImUhBQQG9Vsi00euFTBe9VoiQJi2s\namtrsX///nBPVUNDA37+85+Hv15TU4Ndu3bh+uuvB8uyOHDgAJYvXz7pE0ZuLRJCCCGEiAkTCoVC\nk33Dnj17ok4Fbty4EW+++SZuv/126HQ6fP3119i/fz8kEglKSkpw3333JWThhBBCCCHzzZSFFSGE\nEEIImR4KeSKEEEIIiRMqrAghhBBC4oQKK0IIIYSQOEnYIL2pRuMQEumRRx5BVlZW+PHDDz+MzEya\nDk8Av9+P3bt3o7m5Gdu2bQMwf0drkeQb7/Xy3HPPIRQKgWW5ewubNm2CyWRK5jLJPPHxxx/jm2++\ngVQqhdFoxP33349Dhw7N6PqSkMJqqtE4hMRSq9V49tlnk70MMg+99dZbWLZsGZqbmwHM/9FaJLli\nXy8ANyj8ySefhEKhSOLKyHzjcrnQ2dmJ559/HgzD4I033sBHH32ExsbGGV1fErIVONVoHEJi+Xw+\n1NfXY/Pmzdi3b1+yl0PmkU2bNqG2tjb8mEZrkcnEvl4AQCKR4MUXX8SWLVvw9ttvJ2llZL7RaDR4\n8MEHw9cSj8eDUCg04+tLQu5YTTUah5BY27Ztg0wmg8/nw/bt21FRUYHS0tJkL4vMQy6XC8XFxeHH\nNFqLTGXLli2QyWQIBoPYtWsXjh49irq6umQvi8wjH3zwAZRKJYLB4IxH9yXkjtVsR9+QK5dMJgv/\nd8WKFejo6Ejyish8RdcXMlP89YVlWaxatQrt7e3JXRCZN4LBIF5//XXIZDLcc889s7q+JKSwqq2t\nRUNDQ/hxQ0PDlKNvyJXr4sWL+PDDDwFwjaeNjY1YsGBBkldF5quamhocPHgQwWAQAKY1WotcuQYH\nB/HOO+8A4N5EDx8+jEWLFiV5VWQ+cLvd2LlzJ8xmM9avXw8AqK6unvH1JSFbgRUVFTh79iy2bt0K\ngCu0qHGdTMRgMMBisWDr1q1gWRbr1q1DQUFBspdF5qmcnBysWbMGTz/9dHi01p133pnsZZF5KiMj\nA4FAAE899RSkUilWrlwJs9mc7GWReaChoQE//fQTXC4XPvnkEwDAmjVrZnx9oZE2hBBCCCFxQgGh\nhBBCCCFxQoUVIYQQQkicUGFFCCGEEBInVFgRQgghhMQJFVaEEEIIIXFChRUhhBBCSJxQYUUIIYQQ\nEidUWBFCCCGExAkVVoQQQgghcfL/u8ZDblhTSdUAAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10c41e850>"
-       ]
-      }
-     ],
-     "prompt_number": 10
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "data = ar.data\n",
+    "for i, d in enumerate(data):\n",
+    "    plt.plot(d['a'], label='engine: '+str(i))\n",
+    "plt.title('Data published at time step: ' + str(data[0]['i']))\n",
+    "plt.legend()"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Parallel Computing/Index.ipynb b/examples/Parallel Computing/Index.ipynb
index 94519b2..6f49df0 100644
--- a/examples/Parallel Computing/Index.ipynb
+++ b/examples/Parallel Computing/Index.ipynb
@@ -1,330 +1,354 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:1e9336d35cc07875300c5b876df6ce1f1971c2ee94870788c6ea32bbb789c42b"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"../images/ipython_logo.png\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Back to the main [Index](../Index.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Parallel Computing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython includes an architecture and library for interactive parallel computing. The enables Python functions, along with their arguments, to be run in parallel a multicore CPU, cluster or cloud using a simple Python API."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tutorials"
+   ]
+  },
   {
-   "cells": [
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* [Data Publication API](Data Publication API.ipynb) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* [Monitoring an MPI Simulation - 1](Monitoring an MPI Simulation - 1.ipynb)\n",
+    "* [Monitoring an MPI Simulation - 2](Monitoring an MPI Simulation - 2.ipynb)\n",
+    "* [Parallel Decorator and map](Parallel Decorator and map.ipynb)\n",
+    "* [Parallel Magics](Parallel Magics.ipynb)\n",
+    "* [Using Dill](Using Dill.ipynb)\n",
+    "* [Using MPI with IPython Parallel](Using MPI with IPython Parallel.ipynb)\n",
+    "* [Monte Carlo Options](Monte Carlo Options.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Non-notebook examples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This directory also contains examples that are regular Python (`.py`) files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/html": [
+       "<a href='customresults.py' target='_blank'>customresults.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/customresults.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "<img src=\"../images/ipython_logo.png\">"
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/html": [
+       "<a href='dagdeps.py' target='_blank'>dagdeps.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/dagdeps.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Back to the main [Index](../Index.ipynb)"
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "heading",
-     "level": 1,
+     "data": {
+      "text/html": [
+       "<a href='dependencies.py' target='_blank'>dependencies.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/dependencies.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Parallel Computing"
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/html": [
+       "<a href='fetchparse.py' target='_blank'>fetchparse.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/fetchparse.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "IPython includes an architecture and library for interactive parallel computing. The enables Python functions, along with their arguments, to be run in parallel a multicore CPU, cluster or cloud using a simple Python API."
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "text/html": [
+       "<a href='iopubwatcher.py' target='_blank'>iopubwatcher.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/iopubwatcher.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Tutorials"
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/html": [
+       "<a href='itermapresult.py' target='_blank'>itermapresult.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/itermapresult.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "* [Data Publication API](Data Publication API.ipynb) "
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "text/html": [
+       "<a href='nwmerge.py' target='_blank'>nwmerge.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/nwmerge.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Examples"
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/html": [
+       "<a href='phistogram.py' target='_blank'>phistogram.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/phistogram.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "* [Monitoring an MPI Simulation - 1](Monitoring an MPI Simulation - 1.ipynb)\n",
-      "* [Monitoring an MPI Simulation - 2](Monitoring an MPI Simulation - 2.ipynb)\n",
-      "* [Parallel Decorator and map](Parallel Decorator and map.ipynb)\n",
-      "* [Parallel Magics](Parallel Magics.ipynb)\n",
-      "* [Using Dill](Using Dill.ipynb)\n",
-      "* [Using MPI with IPython Parallel](Using MPI with IPython Parallel.ipynb)\n",
-      "* [Monte Carlo Options](Monte Carlo Options.ipynb)"
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
+      "text/html": [
+       "<a href='task_profiler.py' target='_blank'>task_profiler.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/task_profiler.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Non-notebook examples"
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/html": [
+       "<a href='throughput.py' target='_blank'>throughput.py</a><br>"
+      ],
+      "text/plain": [
+       "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/throughput.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "This directory also contains examples that are regular Python (`.py`) files."
-     ]
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%run ../utils/list_pyfiles.ipy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "More substantial examples can be found in subdirectories:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "daVinci Word Count/<br>\n",
+       "&nbsp;&nbsp;<a href='daVinci Word Count/pwordfreq.py' target='_blank'>pwordfreq.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='daVinci Word Count/wordfreq.py' target='_blank'>wordfreq.py</a><br>"
+      ],
+      "text/plain": [
+       "daVinci Word Count/\n",
+       "  pwordfreq.py\n",
+       "  wordfreq.py"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "interengine/<br>\n",
+       "&nbsp;&nbsp;<a href='interengine/bintree.py' target='_blank'>bintree.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='interengine/bintree_script.py' target='_blank'>bintree_script.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='interengine/communicator.py' target='_blank'>communicator.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='interengine/interengine.py' target='_blank'>interengine.py</a><br>"
+      ],
+      "text/plain": [
+       "interengine/\n",
+       "  bintree.py\n",
+       "  bintree_script.py\n",
+       "  communicator.py\n",
+       "  interengine.py"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%run ../utils/list_pyfiles.ipy"
-     ],
-     "language": "python",
+     "data": {
+      "text/html": [],
+      "text/plain": []
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<a href='customresults.py' target='_blank'>customresults.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/customresults.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='dagdeps.py' target='_blank'>dagdeps.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/dagdeps.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='dependencies.py' target='_blank'>dependencies.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/dependencies.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='fetchparse.py' target='_blank'>fetchparse.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/fetchparse.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='iopubwatcher.py' target='_blank'>iopubwatcher.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/iopubwatcher.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='itermapresult.py' target='_blank'>itermapresult.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/itermapresult.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='nwmerge.py' target='_blank'>nwmerge.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/nwmerge.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='phistogram.py' target='_blank'>phistogram.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/phistogram.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='task_profiler.py' target='_blank'>task_profiler.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/task_profiler.py"
-       ]
-      },
-      {
-       "html": [
-        "<a href='throughput.py' target='_blank'>throughput.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "/Users/bgranger/Documents/Computing/IPython/code/ipython/examples/Parallel Computing/throughput.py"
-       ]
-      }
-     ],
-     "prompt_number": 1
+     "output_type": "display_data"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/html": [
+       "pi/<br>\n",
+       "&nbsp;&nbsp;<a href='pi/parallelpi.py' target='_blank'>parallelpi.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='pi/pidigits.py' target='_blank'>pidigits.py</a><br>"
+      ],
+      "text/plain": [
+       "pi/\n",
+       "  parallelpi.py\n",
+       "  pidigits.py"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "More substantial examples can be found in subdirectories:"
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%run ../utils/list_subdirs.ipy"
-     ],
-     "language": "python",
+     "data": {
+      "text/html": [
+       "rmt/<br>\n",
+       "&nbsp;&nbsp;<a href='rmt/rmt.ipy' target='_blank'>rmt.ipy</a><br>\n",
+       "&nbsp;&nbsp;<a href='rmt/rmt.ipynb' target='_blank'>rmt.ipynb</a><br>\n",
+       "&nbsp;&nbsp;<a href='rmt/rmtkernel.py' target='_blank'>rmtkernel.py</a><br>"
+      ],
+      "text/plain": [
+       "rmt/\n",
+       "  rmt.ipy\n",
+       "  rmt.ipynb\n",
+       "  rmtkernel.py"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "daVinci Word Count/<br>\n",
-        "&nbsp;&nbsp;<a href='daVinci Word Count/pwordfreq.py' target='_blank'>pwordfreq.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='daVinci Word Count/wordfreq.py' target='_blank'>wordfreq.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "daVinci Word Count/\n",
-        "  pwordfreq.py\n",
-        "  wordfreq.py"
-       ]
-      },
-      {
-       "html": [
-        "interengine/<br>\n",
-        "&nbsp;&nbsp;<a href='interengine/bintree.py' target='_blank'>bintree.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='interengine/bintree_script.py' target='_blank'>bintree_script.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='interengine/communicator.py' target='_blank'>communicator.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='interengine/interengine.py' target='_blank'>interengine.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "interengine/\n",
-        "  bintree.py\n",
-        "  bintree_script.py\n",
-        "  communicator.py\n",
-        "  interengine.py"
-       ]
-      },
-      {
-       "html": [],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": []
-      },
-      {
-       "html": [
-        "pi/<br>\n",
-        "&nbsp;&nbsp;<a href='pi/parallelpi.py' target='_blank'>parallelpi.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='pi/pidigits.py' target='_blank'>pidigits.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "pi/\n",
-        "  parallelpi.py\n",
-        "  pidigits.py"
-       ]
-      },
-      {
-       "html": [
-        "rmt/<br>\n",
-        "&nbsp;&nbsp;<a href='rmt/rmt.ipy' target='_blank'>rmt.ipy</a><br>\n",
-        "&nbsp;&nbsp;<a href='rmt/rmt.ipynb' target='_blank'>rmt.ipynb</a><br>\n",
-        "&nbsp;&nbsp;<a href='rmt/rmtkernel.py' target='_blank'>rmtkernel.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "rmt/\n",
-        "  rmt.ipy\n",
-        "  rmt.ipynb\n",
-        "  rmtkernel.py"
-       ]
-      },
-      {
-       "html": [
-        "wave2D/<br>\n",
-        "&nbsp;&nbsp;<a href='wave2D/communicator.py' target='_blank'>communicator.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='wave2D/parallelwave-mpi.py' target='_blank'>parallelwave-mpi.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='wave2D/parallelwave.py' target='_blank'>parallelwave.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='wave2D/RectPartitioner.py' target='_blank'>RectPartitioner.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='wave2D/wavesolver.py' target='_blank'>wavesolver.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "wave2D/\n",
-        "  communicator.py\n",
-        "  parallelwave-mpi.py\n",
-        "  parallelwave.py\n",
-        "  RectPartitioner.py\n",
-        "  wavesolver.py"
-       ]
-      },
-      {
-       "html": [
-        "workflow/<br>\n",
-        "&nbsp;&nbsp;<a href='workflow/client.py' target='_blank'>client.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='workflow/job_wrapper.py' target='_blank'>job_wrapper.py</a><br>\n",
-        "&nbsp;&nbsp;<a href='workflow/wmanager.py' target='_blank'>wmanager.py</a><br>"
-       ],
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "workflow/\n",
-        "  client.py\n",
-        "  job_wrapper.py\n",
-        "  wmanager.py"
-       ]
-      }
-     ],
-     "prompt_number": 2
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "wave2D/<br>\n",
+       "&nbsp;&nbsp;<a href='wave2D/communicator.py' target='_blank'>communicator.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='wave2D/parallelwave-mpi.py' target='_blank'>parallelwave-mpi.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='wave2D/parallelwave.py' target='_blank'>parallelwave.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='wave2D/RectPartitioner.py' target='_blank'>RectPartitioner.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='wave2D/wavesolver.py' target='_blank'>wavesolver.py</a><br>"
+      ],
+      "text/plain": [
+       "wave2D/\n",
+       "  communicator.py\n",
+       "  parallelwave-mpi.py\n",
+       "  parallelwave.py\n",
+       "  RectPartitioner.py\n",
+       "  wavesolver.py"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "workflow/<br>\n",
+       "&nbsp;&nbsp;<a href='workflow/client.py' target='_blank'>client.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='workflow/job_wrapper.py' target='_blank'>job_wrapper.py</a><br>\n",
+       "&nbsp;&nbsp;<a href='workflow/wmanager.py' target='_blank'>wmanager.py</a><br>"
+      ],
+      "text/plain": [
+       "workflow/\n",
+       "  client.py\n",
+       "  job_wrapper.py\n",
+       "  wmanager.py"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "%run ../utils/list_subdirs.ipy"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:1e9336d35cc07875300c5b876df6ce1f1971c2ee94870788c6ea32bbb789c42b"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Parallel Computing/Monitoring an MPI Simulation - 1.ipynb b/examples/Parallel Computing/Monitoring an MPI Simulation - 1.ipynb
index b64cb38..2fdd029 100644
--- a/examples/Parallel Computing/Monitoring an MPI Simulation - 1.ipynb
+++ b/examples/Parallel Computing/Monitoring an MPI Simulation - 1.ipynb
@@ -1,543 +1,866 @@
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "source": [
+    "# Interactive monitoring of a parallel MPI simulation with the IPython Notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from IPython.display import display\n",
+    "from IPython.parallel import Client, error\n",
+    "\n",
+    "cluster = Client(profile=\"mpi\")\n",
+    "view = cluster[:]\n",
+    "view.block = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
+     "data": {
+      "text/plain": [
+       "[0, 1, 2, 3]"
+      ]
      },
-     "source": [
-      "Interactive monitoring of a parallel MPI simulation with the IPython Notebook"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline\n",
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "\n",
-      "from IPython.display import display\n",
-      "from IPython.parallel import Client, error\n",
-      "\n",
-      "cluster = Client(profile=\"mpi\")\n",
-      "view = cluster[:]\n",
-      "view.block = True"
-     ],
-     "language": "python",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "cluster.ids"
-     ],
-     "language": "python",
+     "execution_count": 2,
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 2,
-       "text": [
-        "[0, 1, 2, 3]"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "source": [
-      "Now, we load the MPI libraries into the engine namespaces, and do a simple printing of their MPI rank information to verify that all nodes are operational and they match our cluster's real capacity.  \n",
-      "\n",
-      "Here, we are making use of IPython's special `%%px` cell magic, which marks the entire cell for parallel execution.  This means that the code below will not run in this notebook's kernel, but instead will be sent to *all* engines for execution there.  In this way, IPython makes it very natural to control your entire cluster from within the notebook environment:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%px\n",
-      "# MPI initialization, library imports and sanity checks on all engines\n",
-      "from mpi4py import MPI\n",
-      "import numpy as np\n",
-      "import time\n",
-      "\n",
-      "mpi = MPI.COMM_WORLD\n",
-      "bcast = mpi.bcast\n",
-      "barrier = mpi.barrier\n",
-      "rank = mpi.rank\n",
-      "print(\"MPI rank: %i/%i\" % (mpi.rank,mpi.size))"
-     ],
-     "language": "python",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[stdout:0] MPI rank: 3/4\n",
-        "[stdout:1] MPI rank: 2/4\n",
-        "[stdout:2] MPI rank: 0/4\n",
-        "[stdout:3] MPI rank: 1/4\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "source": [
-      "We write a utility that reorders a list according to the mpi ranks of the engines, since all gather operations will return data in engine id order, not in MPI rank order.  We'll need this later on when we want to reassemble in IPython data structures coming from all the engines: IPython will collect the data ordered by engine ID, but our code creates data structures based on MPI rank, so we need to map from one indexing scheme to the other.  This simple function does the job:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ranks = view['rank']\n",
-      "rank_indices = np.argsort(ranks)\n",
-      "\n",
-      "def mpi_order(seq):\n",
-      "    \"\"\"Return elements of a sequence ordered by MPI rank.\n",
-      "\n",
-      "    The input sequence is assumed to be ordered by engine ID.\"\"\"\n",
-      "    return [seq[x] for x in rank_indices]"
-     ],
-     "language": "python",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "source": [
-      "MPI simulation example"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "source": [
-      "This is our 'simulation', a toy example that computes $\\sin(f(x^2+y^2))$ for a slowly increasing frequency $f$ over a gradually refined mesh.  In a real-world example, there typically is a 'simulate' method that, afer setting up initial parameters, runs the entire computation.  But having this simple example will be sufficient to see something that changes visually as the computation evolves and that is quick enough for us to test.\n",
-      "\n",
-      "And while simple, this example has a realistic decomposition of the spatial domain in one array per MPI node that requires care in reordering the data for visualization, as would be needed in a real-world application (unless your code accumulates data in the rank 0 node that you can grab directly)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%px\n",
-      "\n",
-      "stop = False\n",
-      "nsteps = 100\n",
-      "delay = 0.1\n",
-      "\n",
-      "xmin, xmax = 0, np.pi\n",
-      "ymin, ymax = 0, 2*np.pi\n",
-      "dy = (ymax-ymin)/mpi.size\n",
-      "\n",
-      "def simulation():\n",
-      "    \"\"\"Toy simulation code, computes sin(f*(x**2+y**2)) for a slowly increasing f\n",
-      "    over an increasingly fine mesh.\n",
-      "\n",
-      "    The purpose of this code is simply to illustrate the basic features of a typical\n",
-      "    MPI code: spatial domain decomposition, a solution which is evolving in some \n",
-      "    sense, and local per-node computation.  In this case the nodes don't really\n",
-      "    communicate at all.\n",
-      "    \"\"\"\n",
-      "    # By making these few variables global, we allow the IPython client to access them\n",
-      "    # remotely for interactive introspection\n",
-      "    global j, Z, nx, nyt\n",
-      "    freqs = np.linspace(0.6, 1, nsteps)\n",
-      "    for j in range(nsteps):\n",
-      "        nx, ny = 2+j/4, 2+j/2/mpi.size\n",
-      "        nyt = mpi.size*ny\n",
-      "        Xax = np.linspace(xmin, xmax, nx)\n",
-      "        Yax = np.linspace(ymin+rank*dy, ymin+(rank+1)*dy, ny, endpoint=rank==mpi.size)\n",
-      "        X, Y = np.meshgrid(Xax, Yax)\n",
-      "        f = freqs[j]\n",
-      "        Z = np.cos(f*(X**2 + Y**2))\n",
-      "        # We add a small delay to simulate that a real-world computation\n",
-      "        # would take much longer, and we ensure all nodes are synchronized\n",
-      "        time.sleep(delay)\n",
-      "        # The stop flag can be set remotely via IPython, allowing the simulation to be\n",
-      "        # cleanly stopped from the outside\n",
-      "        if stop:\n",
-      "            break"
-     ],
-     "language": "python",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "source": [
-      "IPython tools to interactively monitor and plot the MPI results"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "source": [
-      "We now define a local (to this notebook) plotting function that fetches data from the engines' global namespace.  Once it has retrieved the current state of the relevant variables, it produces and returns a figure:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import clear_output\n",
-      "\n",
-      "def plot_current_results(in_place=True):\n",
-      "    \"\"\"Makes a blocking call to retrieve remote data and displays the solution mesh\n",
-      "    as a contour plot.\n",
-      "    \n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    in_place : bool\n",
-      "        By default it calls clear_output so that new plots replace old ones.  Set\n",
-      "        to False to allow keeping of all previous outputs.\n",
-      "    \"\"\"\n",
-      "    \n",
-      "    # We make a blocking call to load the remote data from the simulation into simple named \n",
-      "    # variables we can read from the engine namespaces\n",
-      "    #view.apply_sync(load_simulation_globals)\n",
-      "    # And now we can use the view to read these variables from all the engines.  Then we\n",
-      "    # concatenate all of them into single arrays for local plotting\n",
-      "    try:\n",
-      "        Z = np.concatenate(mpi_order(view['Z']))\n",
-      "    except ValueError:\n",
-      "        print(\"dimension mismatch in Z, not plotting\")\n",
-      "        ax = plt.gca()\n",
-      "        return ax.figure\n",
-      "        \n",
-      "    nx, nyt, j, nsteps = view.pull(['nx', 'nyt', 'j', 'nsteps'], targets=0)\n",
-      "    fig, ax = plt.subplots()\n",
-      "    ax.contourf(Z)\n",
-      "    ax.set_title('Mesh: %i x %i, step %i/%i' % (nx, nyt, j+1, nsteps))\n",
-      "    plt.axis('off')\n",
-      "    # We clear the notebook output before plotting this if in-place plot updating is requested\n",
-      "    if in_place:\n",
-      "        clear_output(wait=True)\n",
-      "    display(fig)\n",
-      "    return fig"
-     ],
-     "language": "python",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "outputs": [],
-     "prompt_number": 6
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cluster.ids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "source": [
+    "Now, we load the MPI libraries into the engine namespaces, and do a simple printing of their MPI rank information to verify that all nodes are operational and they match our cluster's real capacity.  \n",
+    "\n",
+    "Here, we are making use of IPython's special `%%px` cell magic, which marks the entire cell for parallel execution.  This means that the code below will not run in this notebook's kernel, but instead will be sent to *all* engines for execution there.  In this way, IPython makes it very natural to control your entire cluster from within the notebook environment:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "source": [
-      "It will also be useful to be able to check whether the simulation is still alive or not.  Below we will wrap the main simulation function into a thread to allow IPython to pull data from the engines, and we will call this object `simulation_thread`.  So to check whether the code is still running, all we have to do is call the `is_alive` method on all of our engines and see whether any of them returns True:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[stdout:0] MPI rank: 3/4\n",
+      "[stdout:1] MPI rank: 2/4\n",
+      "[stdout:2] MPI rank: 0/4\n",
+      "[stdout:3] MPI rank: 1/4\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "%%px\n",
+    "# MPI initialization, library imports and sanity checks on all engines\n",
+    "from mpi4py import MPI\n",
+    "import numpy as np\n",
+    "import time\n",
+    "\n",
+    "mpi = MPI.COMM_WORLD\n",
+    "bcast = mpi.bcast\n",
+    "barrier = mpi.barrier\n",
+    "rank = mpi.rank\n",
+    "print(\"MPI rank: %i/%i\" % (mpi.rank,mpi.size))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "source": [
+    "We write a utility that reorders a list according to the mpi ranks of the engines, since all gather operations will return data in engine id order, not in MPI rank order.  We'll need this later on when we want to reassemble in IPython data structures coming from all the engines: IPython will collect the data ordered by engine ID, but our code creates data structures based on MPI rank, so we need to map from one indexing scheme to the other.  This simple function does the job:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ranks = view['rank']\n",
+    "rank_indices = np.argsort(ranks)\n",
+    "\n",
+    "def mpi_order(seq):\n",
+    "    \"\"\"Return elements of a sequence ordered by MPI rank.\n",
+    "\n",
+    "    The input sequence is assumed to be ordered by engine ID.\"\"\"\n",
+    "    return [seq[x] for x in rank_indices]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "source": [
+    "## MPI simulation example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "source": [
+    "This is our 'simulation', a toy example that computes $\\sin(f(x^2+y^2))$ for a slowly increasing frequency $f$ over a gradually refined mesh.  In a real-world example, there typically is a 'simulate' method that, afer setting up initial parameters, runs the entire computation.  But having this simple example will be sufficient to see something that changes visually as the computation evolves and that is quick enough for us to test.\n",
+    "\n",
+    "And while simple, this example has a realistic decomposition of the spatial domain in one array per MPI node that requires care in reordering the data for visualization, as would be needed in a real-world application (unless your code accumulates data in the rank 0 node that you can grab directly)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%px\n",
+    "\n",
+    "stop = False\n",
+    "nsteps = 100\n",
+    "delay = 0.1\n",
+    "\n",
+    "xmin, xmax = 0, np.pi\n",
+    "ymin, ymax = 0, 2*np.pi\n",
+    "dy = (ymax-ymin)/mpi.size\n",
+    "\n",
+    "def simulation():\n",
+    "    \"\"\"Toy simulation code, computes sin(f*(x**2+y**2)) for a slowly increasing f\n",
+    "    over an increasingly fine mesh.\n",
+    "\n",
+    "    The purpose of this code is simply to illustrate the basic features of a typical\n",
+    "    MPI code: spatial domain decomposition, a solution which is evolving in some \n",
+    "    sense, and local per-node computation.  In this case the nodes don't really\n",
+    "    communicate at all.\n",
+    "    \"\"\"\n",
+    "    # By making these few variables global, we allow the IPython client to access them\n",
+    "    # remotely for interactive introspection\n",
+    "    global j, Z, nx, nyt\n",
+    "    freqs = np.linspace(0.6, 1, nsteps)\n",
+    "    for j in range(nsteps):\n",
+    "        nx, ny = 2+j/4, 2+j/2/mpi.size\n",
+    "        nyt = mpi.size*ny\n",
+    "        Xax = np.linspace(xmin, xmax, nx)\n",
+    "        Yax = np.linspace(ymin+rank*dy, ymin+(rank+1)*dy, ny, endpoint=rank==mpi.size)\n",
+    "        X, Y = np.meshgrid(Xax, Yax)\n",
+    "        f = freqs[j]\n",
+    "        Z = np.cos(f*(X**2 + Y**2))\n",
+    "        # We add a small delay to simulate that a real-world computation\n",
+    "        # would take much longer, and we ensure all nodes are synchronized\n",
+    "        time.sleep(delay)\n",
+    "        # The stop flag can be set remotely via IPython, allowing the simulation to be\n",
+    "        # cleanly stopped from the outside\n",
+    "        if stop:\n",
+    "            break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "source": [
+    "## IPython tools to interactively monitor and plot the MPI results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "source": [
+    "We now define a local (to this notebook) plotting function that fetches data from the engines' global namespace.  Once it has retrieved the current state of the relevant variables, it produces and returns a figure:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import clear_output\n",
+    "\n",
+    "def plot_current_results(in_place=True):\n",
+    "    \"\"\"Makes a blocking call to retrieve remote data and displays the solution mesh\n",
+    "    as a contour plot.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    in_place : bool\n",
+    "        By default it calls clear_output so that new plots replace old ones.  Set\n",
+    "        to False to allow keeping of all previous outputs.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    # We make a blocking call to load the remote data from the simulation into simple named \n",
+    "    # variables we can read from the engine namespaces\n",
+    "    #view.apply_sync(load_simulation_globals)\n",
+    "    # And now we can use the view to read these variables from all the engines.  Then we\n",
+    "    # concatenate all of them into single arrays for local plotting\n",
+    "    try:\n",
+    "        Z = np.concatenate(mpi_order(view['Z']))\n",
+    "    except ValueError:\n",
+    "        print(\"dimension mismatch in Z, not plotting\")\n",
+    "        ax = plt.gca()\n",
+    "        return ax.figure\n",
+    "        \n",
+    "    nx, nyt, j, nsteps = view.pull(['nx', 'nyt', 'j', 'nsteps'], targets=0)\n",
+    "    fig, ax = plt.subplots()\n",
+    "    ax.contourf(Z)\n",
+    "    ax.set_title('Mesh: %i x %i, step %i/%i' % (nx, nyt, j+1, nsteps))\n",
+    "    plt.axis('off')\n",
+    "    # We clear the notebook output before plotting this if in-place plot updating is requested\n",
+    "    if in_place:\n",
+    "        clear_output(wait=True)\n",
+    "    display(fig)\n",
+    "    return fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "source": [
+    "It will also be useful to be able to check whether the simulation is still alive or not.  Below we will wrap the main simulation function into a thread to allow IPython to pull data from the engines, and we will call this object `simulation_thread`.  So to check whether the code is still running, all we have to do is call the `is_alive` method on all of our engines and see whether any of them returns True:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def simulation_alive():\n",
+    "    \"\"\"Return True if the simulation thread is still running on any engine.\n",
+    "    \"\"\"\n",
+    "    return any(view.apply_sync(lambda : simulation_thread.is_alive()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "source": [
+    "Finally, this is a convenience wrapper around the plotting code so that we can interrupt monitoring at any point, and that will provide basic timing information:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def monitor_simulation(refresh=5.0, plots_in_place=True):\n",
+    "    \"\"\"Monitor the simulation progress and call plotting routine.\n",
+    "\n",
+    "    Supress KeyboardInterrupt exception if interrupted, ensure that the last \n",
+    "    figure is always displayed and provide basic timing and simulation status.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    refresh : float\n",
+    "      Refresh interval between calls to retrieve and plot data.  The default\n",
+    "      is 5s, adjust depending on the desired refresh rate, but be aware that \n",
+    "      very short intervals will start having a significant impact.\n",
+    "\n",
+    "    plots_in_place : bool\n",
+    "       If true, every new figure replaces the last one, producing a (slow)\n",
+    "       animation effect in the notebook.  If false, all frames are plotted\n",
+    "       in sequence and appended in the output area.\n",
+    "    \"\"\"\n",
+    "    import datetime as dt, time\n",
+    "    \n",
+    "    if not simulation_alive():\n",
+    "        plot_current_results(in_place=plots_in_place)\n",
+    "        plt.close('all')\n",
+    "        print('Simulation has already finished, no monitoring to do.')\n",
+    "        return\n",
+    "    \n",
+    "    t0 = dt.datetime.now()\n",
+    "    fig = None\n",
+    "    try:\n",
+    "        while simulation_alive():\n",
+    "            fig = plot_current_results(in_place=plots_in_place)\n",
+    "            plt.close('all') # prevent re-plot of old figures\n",
+    "            time.sleep(refresh) # so we don't hammer the server too fast\n",
+    "    except (KeyboardInterrupt, error.TimeoutError):\n",
+    "        msg = 'Monitoring interrupted, simulation is ongoing!'\n",
+    "    else:\n",
+    "        msg = 'Simulation completed!'\n",
+    "    tmon = dt.datetime.now() - t0\n",
+    "    if plots_in_place and fig is not None:\n",
+    "        clear_output(wait=True)\n",
+    "        plt.close('all')\n",
+    "        display(fig)\n",
+    "    print(msg)\n",
+    "    print('Monitored for: %s.' % tmon)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "source": [
+    "## Making a simulation object that can be monitored interactively"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%px\n",
+    "from threading import Thread\n",
+    "stop = False\n",
+    "nsteps = 100\n",
+    "delay=0.5\n",
+    "# Create a thread wrapper for the simulation. The target must be an argument-less\n",
+    "# function so we wrap the call to 'simulation' in a simple lambda:\n",
+    "simulation_thread = Thread(target = lambda : simulation())\n",
+    "# Now we actually start the simulation\n",
+    "simulation_thread.start()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def simulation_alive():\n",
-      "    \"\"\"Return True if the simulation thread is still running on any engine.\n",
-      "    \"\"\"\n",
-      "    return any(view.apply_sync(lambda : simulation_thread.is_alive()))"
-     ],
-     "language": "python",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
+     "data": {
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+       "SAUoU4QQQgghFfj/AcxNvvk8Uc7VAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1141a1450>"
+      ]
      },
-     "outputs": [],
-     "prompt_number": 7
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "source": [
-      "Finally, this is a convenience wrapper around the plotting code so that we can interrupt monitoring at any point, and that will provide basic timing information:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Simulation completed!\n",
+      "Monitored for: 0:00:50.653178.\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "monitor_simulation(refresh=1);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "source": [
+    "If you execute the following cell before the MPI code is finished running, it will stop the simulation at that point, which you can verify by calling the monitoring again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false,
+    "slideshow": {
+     "slide_start": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "view['stop'] = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def monitor_simulation(refresh=5.0, plots_in_place=True):\n",
-      "    \"\"\"Monitor the simulation progress and call plotting routine.\n",
-      "\n",
-      "    Supress KeyboardInterrupt exception if interrupted, ensure that the last \n",
-      "    figure is always displayed and provide basic timing and simulation status.\n",
-      "\n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    refresh : float\n",
-      "      Refresh interval between calls to retrieve and plot data.  The default\n",
-      "      is 5s, adjust depending on the desired refresh rate, but be aware that \n",
-      "      very short intervals will start having a significant impact.\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "  \"stdin_port\": 65310, \n",
+      "  \"ip\": \"127.0.0.1\", \n",
+      "  \"control_port\": 58188, \n",
+      "  \"hb_port\": 58187, \n",
+      "  \"key\": \"e4f5cda8-faa8-48d3-a62c-dbde67db9827\", \n",
+      "  \"shell_port\": 65083, \n",
+      "  \"transport\": \"tcp\", \n",
+      "  \"iopub_port\": 54934\n",
+      "}\n",
       "\n",
-      "    plots_in_place : bool\n",
-      "       If true, every new figure replaces the last one, producing a (slow)\n",
-      "       animation effect in the notebook.  If false, all frames are plotted\n",
-      "       in sequence and appended in the output area.\n",
-      "    \"\"\"\n",
-      "    import datetime as dt, time\n",
-      "    \n",
-      "    if not simulation_alive():\n",
-      "        plot_current_results(in_place=plots_in_place)\n",
-      "        plt.close('all')\n",
-      "        print('Simulation has already finished, no monitoring to do.')\n",
-      "        return\n",
-      "    \n",
-      "    t0 = dt.datetime.now()\n",
-      "    fig = None\n",
-      "    try:\n",
-      "        while simulation_alive():\n",
-      "            fig = plot_current_results(in_place=plots_in_place)\n",
-      "            plt.close('all') # prevent re-plot of old figures\n",
-      "            time.sleep(refresh) # so we don't hammer the server too fast\n",
-      "    except (KeyboardInterrupt, error.TimeoutError):\n",
-      "        msg = 'Monitoring interrupted, simulation is ongoing!'\n",
-      "    else:\n",
-      "        msg = 'Simulation completed!'\n",
-      "    tmon = dt.datetime.now() - t0\n",
-      "    if plots_in_place and fig is not None:\n",
-      "        clear_output(wait=True)\n",
-      "        plt.close('all')\n",
-      "        display(fig)\n",
-      "    print(msg)\n",
-      "    print('Monitored for: %s.' % tmon)"
-     ],
-     "language": "python",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "source": [
-      "Making a simulation object that can be monitored interactively"
+      "Paste the above JSON into a file, and connect with:\n",
+      "    $> ipython <app> --existing <file>\n",
+      "or, if you are local, you can connect with just:\n",
+      "    $> ipython <app> --existing kernel-64604.json \n",
+      "or even just:\n",
+      "    $> ipython <app> --existing \n",
+      "if this is the most recent IPython session you have started.\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%px\n",
-      "from threading import Thread\n",
-      "stop = False\n",
-      "nsteps = 100\n",
-      "delay=0.5\n",
-      "# Create a thread wrapper for the simulation. The target must be an argument-less\n",
-      "# function so we wrap the call to 'simulation' in a simple lambda:\n",
-      "simulation_thread = Thread(target = lambda : simulation())\n",
-      "# Now we actually start the simulation\n",
-      "simulation_thread.start()"
-     ],
-     "language": "python",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "outputs": [],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "monitor_simulation(refresh=1);"
-     ],
-     "language": "python",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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ZVNESJliUKUJCkFLiTJhufYqYDwGrLF6ehYuS1ZlK4uUgXC6yVUS0nCTLFG8K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\nSAUoU4QQQgghFfj/AcxNvvk8Uc7VAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x1141a1450>"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Simulation completed!\n",
-        "Monitored for: 0:00:50.653178.\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "source": [
-      "If you execute the following cell before the MPI code is finished running, it will stop the simulation at that point, which you can verify by calling the monitoring again:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "view['stop'] = True"
-     ],
-     "language": "python",
-     "metadata": {
-      "slideshow": {
-       "slide_start": false
-      }
-     },
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%px --target 0\n",
-      "from IPython.parallel import bind_kernel; bind_kernel()\n",
-      "%connect_info"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "{\n",
-        "  \"stdin_port\": 65310, \n",
-        "  \"ip\": \"127.0.0.1\", \n",
-        "  \"control_port\": 58188, \n",
-        "  \"hb_port\": 58187, \n",
-        "  \"key\": \"e4f5cda8-faa8-48d3-a62c-dbde67db9827\", \n",
-        "  \"shell_port\": 65083, \n",
-        "  \"transport\": \"tcp\", \n",
-        "  \"iopub_port\": 54934\n",
-        "}\n",
-        "\n",
-        "Paste the above JSON into a file, and connect with:\n",
-        "    $> ipython <app> --existing <file>\n",
-        "or, if you are local, you can connect with just:\n",
-        "    $> ipython <app> --existing kernel-64604.json \n",
-        "or even just:\n",
-        "    $> ipython <app> --existing \n",
-        "if this is the most recent IPython session you have started.\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%px --target 0\n",
-      "%qtconsole"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 13
     }
    ],
-   "metadata": {}
+   "source": [
+    "%%px --target 0\n",
+    "from IPython.parallel import bind_kernel; bind_kernel()\n",
+    "%connect_info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%px --target 0\n",
+    "%qtconsole"
+   ]
   }
- ]
-}
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/examples/Parallel Computing/Monitoring an MPI Simulation - 2.ipynb b/examples/Parallel Computing/Monitoring an MPI Simulation - 2.ipynb
index adb8b29..915a903 100644
--- a/examples/Parallel Computing/Monitoring an MPI Simulation - 2.ipynb
+++ b/examples/Parallel Computing/Monitoring an MPI Simulation - 2.ipynb
@@ -1,412 +1,515 @@
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Interactive visualization of MPI simulaitons"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In this example, which builds on our previous one of interactive MPI monitoring, we now demonstrate how to use the IPython data publication APIs."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Load IPython support for working with MPI tasks"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If you have not done so yet, use [the cluster tab in the Dashboard](/#tab2) to start your `mpi` cluster, it should be OK to leave the number of engines field empty (IPython will auto-detect the number of cores on your machine), unless you want to limit the run to use less cores than available in total.  Once your MPI cluster is running, you can proceed with the rest of the code.\n",
-      "\n",
-      "We begin by creating a cluster client that gives us a local handle on the engines running in the (possibly remote) MPI cluster.  From the client we make a `view` object, which we set to use blocking mode by default as it is more convenient for interactive control.  Since the real computation will be done over MPI without IPython intervention, setting the default behavior to be blocking will have no significant performance impact.\n",
-      "\n",
-      "**Note:** if on first try the following cell gives you an error message, wait a few seconds and run it again. It's possible that the system is simply initializing all your MPI engines, which may take a bit of time to be completely ready if you hadn't used any MPI libraries recently and the disk cache is cold."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.parallel import Client, error\n",
-      "cluster = Client(profile=\"mpi\")\n",
-      "view = cluster[:]\n",
-      "view.block = True"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's also load the plotting and numerical libraries so we have them ready for visualization later on."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline\n",
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now, we load the MPI libraries into the engine namespaces, and do a simple printing of their MPI rank information to verify that all nodes are operational and they match our cluster's real capacity.  \n",
-      "\n",
-      "Here, we are making use of IPython's special `%%px` cell magic, which marks the entire cell for parallel execution.  This means that the code below will not run in this notebook's kernel, but instead will be sent to *all* engines for execution there.  In this way, IPython makes it very natural to control your entire cluster from within the notebook environment:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%px\n",
-      "# MPI initialization, library imports and sanity checks on all engines\n",
-      "from mpi4py import MPI\n",
-      "# Load data publication API so engines can send data to notebook client\n",
-      "from IPython.kernel.zmq.datapub import publish_data\n",
-      "import numpy as np\n",
-      "import time\n",
-      "\n",
-      "mpi = MPI.COMM_WORLD\n",
-      "bcast = mpi.bcast\n",
-      "barrier = mpi.barrier\n",
-      "rank = mpi.rank\n",
-      "print(\"MPI rank: %i/%i\" % (mpi.rank,mpi.size))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[stdout:0] MPI rank: 2/4\n",
-        "[stdout:1] MPI rank: 0/4\n",
-        "[stdout:2] MPI rank: 3/4\n",
-        "[stdout:3] MPI rank: 1/4\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We write a utility that reorders a list according to the mpi ranks of the engines, since all gather operations will return data in engine id order, not in MPI rank order.  We'll need this later on when we want to reassemble in IPython data structures coming from all the engines: IPython will collect the data ordered by engine ID, but our code creates data structures based on MPI rank, so we need to map from one indexing scheme to the other.  This simple function does the job:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ranks = view['rank']\n",
-      "engine_mpi = np.argsort(ranks)\n",
-      "\n",
-      "def mpi_order(seq):\n",
-      "    \"\"\"Return elements of a sequence ordered by MPI rank.\n",
-      "\n",
-      "    The input sequence is assumed to be ordered by engine ID.\"\"\"\n",
-      "    return [seq[x] for x in engine_mpi]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "MPI simulation example"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This is our 'simulation', a toy example that computes $\\sin(f(x^2+y^2))$ for a slowly increasing frequency $f$ over a gradually refined mesh.  In a real-world example, there typically is a 'simulate' method that, afer setting up initial parameters, runs the entire computation.  But having this simple example will be sufficient to see something that changes visually as the computation evolves and that is quick enough for us to test.\n",
-      "\n",
-      "And while simple, this example has a realistic decomposition of the spatial domain in one array per MPI node that requires care in reordering the data for visualization, as would be needed in a real-world application (unless your code accumulates data in the rank 0 node that you can grab directly)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%px\n",
-      "\n",
-      "# Global flag in the namespace\n",
-      "stop = False\n",
-      "\n",
-      "def simulation(nsteps=100, delay=0.1):\n",
-      "    \"\"\"Toy simulation code, computes sin(f*(x**2+y**2)) for a slowly increasing f\n",
-      "    over an increasingly fine mesh.\n",
-      "\n",
-      "    The purpose of this code is simply to illustrate the basic features of a typical\n",
-      "    MPI code: spatial domain decomposition, a solution which is evolving in some \n",
-      "    sense, and local per-node computation.  In this case the nodes only communicate when \n",
-      "    gathering results for publication.\"\"\"\n",
-      "    # Problem geometry\n",
-      "    xmin, xmax = 0, np.pi\n",
-      "    ymin, ymax = 0, 2*np.pi\n",
-      "    dy = (ymax-ymin)/mpi.size\n",
-      "\n",
-      "    freqs = np.linspace(0.6, 1, nsteps)\n",
-      "    for j in range(nsteps):\n",
-      "        nx, ny = 2+j/4, 2+j/2/mpi.size\n",
-      "        nyt = mpi.size*ny\n",
-      "        Xax = np.linspace(xmin, xmax, nx)\n",
-      "        Yax = np.linspace(ymin+rank*dy, ymin+(rank+1)*dy, ny, endpoint=rank==mpi.size)\n",
-      "        X, Y = np.meshgrid(Xax, Yax)\n",
-      "        f = freqs[j]\n",
-      "        Z = np.cos(f*(X**2 + Y**2))\n",
-      "        \n",
-      "        # We are now going to publish data to the clients. We take advantage of fast\n",
-      "        # MPI communications and gather the Z mesh at the rank 0 node in the Zcat variable:\n",
-      "        Zcat = mpi.gather(Z, root=0)\n",
-      "        if mpi.rank == 0:\n",
-      "            # Then we use numpy's concatenation to construct a single numpy array with the\n",
-      "            # full mesh that can be sent to the client for visualization:\n",
-      "            Zcat = np.concatenate(Zcat)\n",
-      "            # We now can send a dict with the variables we want the client to have access to:\n",
-      "            publish_data(dict(Z=Zcat, nx=nx, nyt=nyt, j=j, nsteps=nsteps))\n",
-      "            \n",
-      "        # We add a small delay to simulate that a real-world computation\n",
-      "        # would take much longer, and we ensure all nodes are synchronized\n",
-      "        time.sleep(delay)\n",
-      "        # The stop flag can be set remotely via IPython, allowing the simulation to be\n",
-      "        # cleanly stopped from the outside\n",
-      "        if stop:\n",
-      "            break"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "IPython tools to interactively monitor and plot the MPI results"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We now define a local (to this notebook) plotting function that fetches data from the engines' global namespace.  Once it has retrieved the current state of the relevant variables, it produces and returns a figure:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython.display import display, clear_output\n",
-      "\n",
-      "def plot_current_results(ar, in_place=True):\n",
-      "    \"\"\"Makes a blocking call to retrieve remote data and displays the solution mesh\n",
-      "    as a contour plot.\n",
-      "    \n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    ar : async result object\n",
-      "\n",
-      "    in_place : bool\n",
-      "        By default it calls clear_output so that new plots replace old ones.  Set\n",
-      "        to False to allow keeping of all previous outputs.\n",
-      "    \"\"\"\n",
-      "    # Read data from MPI rank 0 engine\n",
-      "    data = ar.data[engine_mpi[0]]\n",
-      "    \n",
-      "    try:\n",
-      "        nx, nyt, j, nsteps = [data[k] for k in ['nx', 'nyt', 'j', 'nsteps']]\n",
-      "        Z = data['Z']\n",
-      "    except KeyError:\n",
-      "        # This can happen if we read from the engines so quickly that the data \n",
-      "        # hasn't arrived yet.\n",
-      "        fig, ax = plt.subplots()\n",
-      "        ax.plot([])\n",
-      "        ax.set_title(\"No data yet\")\n",
-      "        display(fig)\n",
-      "        return fig\n",
-      "    else:\n",
-      "    \n",
-      "        fig, ax = plt.subplots()\n",
-      "        ax.contourf(Z)\n",
-      "        ax.set_title('Mesh: %i x %i, step %i/%i' % (nx, nyt, j+1, nsteps))\n",
-      "        plt.axis('off')\n",
-      "        # We clear the notebook output before plotting this if in-place \n",
-      "        # plot updating is requested\n",
-      "        if in_place:\n",
-      "            clear_output(wait=True)\n",
-      "        display(fig)\n",
-      "        \n",
-      "        return fig"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Interactive visualization of MPI simulaitons"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example, which builds on our previous one of interactive MPI monitoring, we now demonstrate how to use the IPython data publication APIs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load IPython support for working with MPI tasks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you have not done so yet, use [the cluster tab in the Dashboard](/#tab2) to start your `mpi` cluster, it should be OK to leave the number of engines field empty (IPython will auto-detect the number of cores on your machine), unless you want to limit the run to use less cores than available in total.  Once your MPI cluster is running, you can proceed with the rest of the code.\n",
+    "\n",
+    "We begin by creating a cluster client that gives us a local handle on the engines running in the (possibly remote) MPI cluster.  From the client we make a `view` object, which we set to use blocking mode by default as it is more convenient for interactive control.  Since the real computation will be done over MPI without IPython intervention, setting the default behavior to be blocking will have no significant performance impact.\n",
+    "\n",
+    "**Note:** if on first try the following cell gives you an error message, wait a few seconds and run it again. It's possible that the system is simply initializing all your MPI engines, which may take a bit of time to be completely ready if you hadn't used any MPI libraries recently and the disk cache is cold."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.parallel import Client, error\n",
+    "cluster = Client(profile=\"mpi\")\n",
+    "view = cluster[:]\n",
+    "view.block = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's also load the plotting and numerical libraries so we have them ready for visualization later on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we load the MPI libraries into the engine namespaces, and do a simple printing of their MPI rank information to verify that all nodes are operational and they match our cluster's real capacity.  \n",
+    "\n",
+    "Here, we are making use of IPython's special `%%px` cell magic, which marks the entire cell for parallel execution.  This means that the code below will not run in this notebook's kernel, but instead will be sent to *all* engines for execution there.  In this way, IPython makes it very natural to control your entire cluster from within the notebook environment:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Finally, this is a convenience wrapper around the plotting code so that we can interrupt monitoring at any point, and that will provide basic timing information:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[stdout:0] MPI rank: 2/4\n",
+      "[stdout:1] MPI rank: 0/4\n",
+      "[stdout:2] MPI rank: 3/4\n",
+      "[stdout:3] MPI rank: 1/4\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def monitor_simulation(ar, refresh=5.0, plots_in_place=True):\n",
-      "    \"\"\"Monitor the simulation progress and call plotting routine.\n",
-      "\n",
-      "    Supress KeyboardInterrupt exception if interrupted, ensure that the last \n",
-      "    figure is always displayed and provide basic timing and simulation status.\n",
-      "\n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    ar : async result object\n",
-      "\n",
-      "    refresh : float\n",
-      "      Refresh interval between calls to retrieve and plot data.  The default\n",
-      "      is 5s, adjust depending on the desired refresh rate, but be aware that \n",
-      "      very short intervals will start having a significant impact.\n",
-      "\n",
-      "    plots_in_place : bool\n",
-      "       If true, every new figure replaces the last one, producing a (slow)\n",
-      "       animation effect in the notebook.  If false, all frames are plotted\n",
-      "       in sequence and appended in the output area.\n",
-      "    \"\"\"\n",
-      "    import datetime as dt, time\n",
-      "    \n",
-      "    if ar.ready():\n",
-      "        plot_current_results(ar, in_place=plots_in_place)\n",
-      "        plt.close('all')\n",
-      "        print('Simulation has already finished, no monitoring to do.')\n",
-      "        return\n",
-      "    \n",
-      "    t0 = dt.datetime.now()\n",
-      "    fig = None\n",
-      "    try:\n",
-      "        while not ar.ready():\n",
-      "            fig = plot_current_results(ar, in_place=plots_in_place)\n",
-      "            plt.close('all') # prevent re-plot of old figures\n",
-      "            time.sleep(refresh)\n",
-      "    except (KeyboardInterrupt, error.TimeoutError):\n",
-      "        msg = 'Monitoring interrupted, simulation is ongoing!'\n",
-      "    else:\n",
-      "        msg = 'Simulation completed!'\n",
-      "    tmon = dt.datetime.now() - t0\n",
-      "    if plots_in_place and fig is not None:\n",
-      "        clear_output(wait=True)\n",
-      "        plt.close('all')\n",
-      "        display(fig)\n",
-      "    print(msg)\n",
-      "    print('Monitored for: %s.' % tmon)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
+    }
+   ],
+   "source": [
+    "%%px\n",
+    "# MPI initialization, library imports and sanity checks on all engines\n",
+    "from mpi4py import MPI\n",
+    "# Load data publication API so engines can send data to notebook client\n",
+    "from IPython.kernel.zmq.datapub import publish_data\n",
+    "import numpy as np\n",
+    "import time\n",
+    "\n",
+    "mpi = MPI.COMM_WORLD\n",
+    "bcast = mpi.bcast\n",
+    "barrier = mpi.barrier\n",
+    "rank = mpi.rank\n",
+    "print(\"MPI rank: %i/%i\" % (mpi.rank,mpi.size))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We write a utility that reorders a list according to the mpi ranks of the engines, since all gather operations will return data in engine id order, not in MPI rank order.  We'll need this later on when we want to reassemble in IPython data structures coming from all the engines: IPython will collect the data ordered by engine ID, but our code creates data structures based on MPI rank, so we need to map from one indexing scheme to the other.  This simple function does the job:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ranks = view['rank']\n",
+    "engine_mpi = np.argsort(ranks)\n",
+    "\n",
+    "def mpi_order(seq):\n",
+    "    \"\"\"Return elements of a sequence ordered by MPI rank.\n",
+    "\n",
+    "    The input sequence is assumed to be ordered by engine ID.\"\"\"\n",
+    "    return [seq[x] for x in engine_mpi]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MPI simulation example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is our 'simulation', a toy example that computes $\\sin(f(x^2+y^2))$ for a slowly increasing frequency $f$ over a gradually refined mesh.  In a real-world example, there typically is a 'simulate' method that, afer setting up initial parameters, runs the entire computation.  But having this simple example will be sufficient to see something that changes visually as the computation evolves and that is quick enough for us to test.\n",
+    "\n",
+    "And while simple, this example has a realistic decomposition of the spatial domain in one array per MPI node that requires care in reordering the data for visualization, as would be needed in a real-world application (unless your code accumulates data in the rank 0 node that you can grab directly)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%px\n",
+    "\n",
+    "# Global flag in the namespace\n",
+    "stop = False\n",
+    "\n",
+    "def simulation(nsteps=100, delay=0.1):\n",
+    "    \"\"\"Toy simulation code, computes sin(f*(x**2+y**2)) for a slowly increasing f\n",
+    "    over an increasingly fine mesh.\n",
+    "\n",
+    "    The purpose of this code is simply to illustrate the basic features of a typical\n",
+    "    MPI code: spatial domain decomposition, a solution which is evolving in some \n",
+    "    sense, and local per-node computation.  In this case the nodes only communicate when \n",
+    "    gathering results for publication.\"\"\"\n",
+    "    # Problem geometry\n",
+    "    xmin, xmax = 0, np.pi\n",
+    "    ymin, ymax = 0, 2*np.pi\n",
+    "    dy = (ymax-ymin)/mpi.size\n",
+    "\n",
+    "    freqs = np.linspace(0.6, 1, nsteps)\n",
+    "    for j in range(nsteps):\n",
+    "        nx, ny = 2+j/4, 2+j/2/mpi.size\n",
+    "        nyt = mpi.size*ny\n",
+    "        Xax = np.linspace(xmin, xmax, nx)\n",
+    "        Yax = np.linspace(ymin+rank*dy, ymin+(rank+1)*dy, ny, endpoint=rank==mpi.size)\n",
+    "        X, Y = np.meshgrid(Xax, Yax)\n",
+    "        f = freqs[j]\n",
+    "        Z = np.cos(f*(X**2 + Y**2))\n",
+    "        \n",
+    "        # We are now going to publish data to the clients. We take advantage of fast\n",
+    "        # MPI communications and gather the Z mesh at the rank 0 node in the Zcat variable:\n",
+    "        Zcat = mpi.gather(Z, root=0)\n",
+    "        if mpi.rank == 0:\n",
+    "            # Then we use numpy's concatenation to construct a single numpy array with the\n",
+    "            # full mesh that can be sent to the client for visualization:\n",
+    "            Zcat = np.concatenate(Zcat)\n",
+    "            # We now can send a dict with the variables we want the client to have access to:\n",
+    "            publish_data(dict(Z=Zcat, nx=nx, nyt=nyt, j=j, nsteps=nsteps))\n",
+    "            \n",
+    "        # We add a small delay to simulate that a real-world computation\n",
+    "        # would take much longer, and we ensure all nodes are synchronized\n",
+    "        time.sleep(delay)\n",
+    "        # The stop flag can be set remotely via IPython, allowing the simulation to be\n",
+    "        # cleanly stopped from the outside\n",
+    "        if stop:\n",
+    "            break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## IPython tools to interactively monitor and plot the MPI results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now define a local (to this notebook) plotting function that fetches data from the engines' global namespace.  Once it has retrieved the current state of the relevant variables, it produces and returns a figure:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import display, clear_output\n",
+    "\n",
+    "def plot_current_results(ar, in_place=True):\n",
+    "    \"\"\"Makes a blocking call to retrieve remote data and displays the solution mesh\n",
+    "    as a contour plot.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    ar : async result object\n",
+    "\n",
+    "    in_place : bool\n",
+    "        By default it calls clear_output so that new plots replace old ones.  Set\n",
+    "        to False to allow keeping of all previous outputs.\n",
+    "    \"\"\"\n",
+    "    # Read data from MPI rank 0 engine\n",
+    "    data = ar.data[engine_mpi[0]]\n",
+    "    \n",
+    "    try:\n",
+    "        nx, nyt, j, nsteps = [data[k] for k in ['nx', 'nyt', 'j', 'nsteps']]\n",
+    "        Z = data['Z']\n",
+    "    except KeyError:\n",
+    "        # This can happen if we read from the engines so quickly that the data \n",
+    "        # hasn't arrived yet.\n",
+    "        fig, ax = plt.subplots()\n",
+    "        ax.plot([])\n",
+    "        ax.set_title(\"No data yet\")\n",
+    "        display(fig)\n",
+    "        return fig\n",
+    "    else:\n",
+    "    \n",
+    "        fig, ax = plt.subplots()\n",
+    "        ax.contourf(Z)\n",
+    "        ax.set_title('Mesh: %i x %i, step %i/%i' % (nx, nyt, j+1, nsteps))\n",
+    "        plt.axis('off')\n",
+    "        # We clear the notebook output before plotting this if in-place \n",
+    "        # plot updating is requested\n",
+    "        if in_place:\n",
+    "            clear_output(wait=True)\n",
+    "        display(fig)\n",
+    "        \n",
+    "        return fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, this is a convenience wrapper around the plotting code so that we can interrupt monitoring at any point, and that will provide basic timing information:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def monitor_simulation(ar, refresh=5.0, plots_in_place=True):\n",
+    "    \"\"\"Monitor the simulation progress and call plotting routine.\n",
+    "\n",
+    "    Supress KeyboardInterrupt exception if interrupted, ensure that the last \n",
+    "    figure is always displayed and provide basic timing and simulation status.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    ar : async result object\n",
+    "\n",
+    "    refresh : float\n",
+    "      Refresh interval between calls to retrieve and plot data.  The default\n",
+    "      is 5s, adjust depending on the desired refresh rate, but be aware that \n",
+    "      very short intervals will start having a significant impact.\n",
+    "\n",
+    "    plots_in_place : bool\n",
+    "       If true, every new figure replaces the last one, producing a (slow)\n",
+    "       animation effect in the notebook.  If false, all frames are plotted\n",
+    "       in sequence and appended in the output area.\n",
+    "    \"\"\"\n",
+    "    import datetime as dt, time\n",
+    "    \n",
+    "    if ar.ready():\n",
+    "        plot_current_results(ar, in_place=plots_in_place)\n",
+    "        plt.close('all')\n",
+    "        print('Simulation has already finished, no monitoring to do.')\n",
+    "        return\n",
+    "    \n",
+    "    t0 = dt.datetime.now()\n",
+    "    fig = None\n",
+    "    try:\n",
+    "        while not ar.ready():\n",
+    "            fig = plot_current_results(ar, in_place=plots_in_place)\n",
+    "            plt.close('all') # prevent re-plot of old figures\n",
+    "            time.sleep(refresh)\n",
+    "    except (KeyboardInterrupt, error.TimeoutError):\n",
+    "        msg = 'Monitoring interrupted, simulation is ongoing!'\n",
+    "    else:\n",
+    "        msg = 'Simulation completed!'\n",
+    "    tmon = dt.datetime.now() - t0\n",
+    "    if plots_in_place and fig is not None:\n",
+    "        clear_output(wait=True)\n",
+    "        plt.close('all')\n",
+    "        display(fig)\n",
+    "    print(msg)\n",
+    "    print('Monitored for: %s.' % tmon)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interactive monitoring in the client of the published data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can monitor the published data. We submit the simulation for execution as an asynchronous task, and then monitor this task at any frequency we desire."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Create the local client that controls our IPython cluster with MPI support\n",
+    "from IPython.parallel import Client\n",
+    "cluster = Client(profile=\"mpi\")\n",
+    "# We make a view that encompasses all the engines\n",
+    "view = cluster[:]\n",
+    "# And now we call on all available nodes our simulation routine,\n",
+    "# as an asynchronous task\n",
+    "ar = view.apply_async(lambda : simulation(nsteps=10, delay=0.1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
+     "data": {
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+       "CQAgkXACAEgknAAAEgknAIBEwgkAINH/Acd8GCUQEYlkAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10b00b350>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "Interactive monitoring in the client of the published data"
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now, we can monitor the published data. We submit the simulation for execution as an asynchronous task, and then monitor this task at any frequency we desire."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Simulation completed!\n",
+      "Monitored for: 0:00:01.229672.\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Create the local client that controls our IPython cluster with MPI support\n",
-      "from IPython.parallel import Client\n",
-      "cluster = Client(profile=\"mpi\")\n",
-      "# We make a view that encompasses all the engines\n",
-      "view = cluster[:]\n",
-      "# And now we call on all available nodes our simulation routine,\n",
-      "# as an asynchronous task\n",
-      "ar = view.apply_async(lambda : simulation(nsteps=10, delay=0.1))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "monitor_simulation(ar, refresh=1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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AAGzPU3fkO97I2zu3nnACADot76Apd8IJAKpQrQVNVxFOANCFBE1lE04AEIKGNMIJgLIn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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10b00b350>"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Simulation completed!\n",
-        "Monitored for: 0:00:01.229672.\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
     }
    ],
-   "metadata": {}
+   "source": [
+    "monitor_simulation(ar, refresh=1)"
+   ]
   }
- ]
-}
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/examples/Parallel Computing/Monte Carlo Options.ipynb b/examples/Parallel Computing/Monte Carlo Options.ipynb
index 6de6ff6..c32dbc5 100644
--- a/examples/Parallel Computing/Monte Carlo Options.ipynb
+++ b/examples/Parallel Computing/Monte Carlo Options.ipynb
@@ -1,562 +1,2541 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:1b19dedc6473d4e886e549020c6710f2d14c17296168a02e7e7fa9673912b893"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Parallel Monto-Carlo options pricing"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This notebook shows how to use `IPython.parallel` to do Monte-Carlo options pricing in parallel. We will compute the price of a large number of options for different strike prices and volatilities."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Problem setup"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline\n",
-      "import matplotlib.pyplot as plt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "import sys\n",
-      "import time\n",
-      "from IPython.parallel import Client\n",
-      "import numpy as np"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here are the basic parameters for our computation."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "price = 100.0  # Initial price\n",
-      "rate = 0.05  # Interest rate\n",
-      "days = 260  # Days to expiration\n",
-      "paths = 10000  # Number of MC paths\n",
-      "n_strikes = 6  # Number of strike values\n",
-      "min_strike = 90.0  # Min strike price\n",
-      "max_strike = 110.0  # Max strike price\n",
-      "n_sigmas = 5  # Number of volatility values\n",
-      "min_sigma = 0.1  # Min volatility\n",
-      "max_sigma = 0.4  # Max volatility"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "strike_vals = np.linspace(min_strike, max_strike, n_strikes)\n",
-      "sigma_vals = np.linspace(min_sigma, max_sigma, n_sigmas)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print \"Strike prices: \", strike_vals\n",
-      "print \"Volatilities: \", sigma_vals"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Strike prices:  [  90.   94.   98.  102.  106.  110.]\n",
-        "Volatilities:  [ 0.1    0.175  0.25   0.325  0.4  ]\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Monte-Carlo option pricing function"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The following function computes the price of a single option.  It returns the call and put prices for both European and Asian style options."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def price_option(S=100.0, K=100.0, sigma=0.25, r=0.05, days=260, paths=10000):\n",
-      "    \"\"\"\n",
-      "    Price European and Asian options using a Monte Carlo method.\n",
-      "\n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    S : float\n",
-      "        The initial price of the stock.\n",
-      "    K : float\n",
-      "        The strike price of the option.\n",
-      "    sigma : float\n",
-      "        The volatility of the stock.\n",
-      "    r : float\n",
-      "        The risk free interest rate.\n",
-      "    days : int\n",
-      "        The number of days until the option expires.\n",
-      "    paths : int\n",
-      "        The number of Monte Carlo paths used to price the option.\n",
-      "\n",
-      "    Returns\n",
-      "    -------\n",
-      "    A tuple of (E. call, E. put, A. call, A. put) option prices.\n",
-      "    \"\"\"\n",
-      "    import numpy as np\n",
-      "    from math import exp,sqrt\n",
-      "    \n",
-      "    h = 1.0/days\n",
-      "    const1 = exp((r-0.5*sigma**2)*h)\n",
-      "    const2 = sigma*sqrt(h)\n",
-      "    stock_price = S*np.ones(paths, dtype='float64')\n",
-      "    stock_price_sum = np.zeros(paths, dtype='float64')\n",
-      "    for j in range(days):\n",
-      "        growth_factor = const1*np.exp(const2*np.random.standard_normal(paths))\n",
-      "        stock_price = stock_price*growth_factor\n",
-      "        stock_price_sum = stock_price_sum + stock_price\n",
-      "    stock_price_avg = stock_price_sum/days\n",
-      "    zeros = np.zeros(paths, dtype='float64')\n",
-      "    r_factor = exp(-r*h*days)\n",
-      "    euro_put = r_factor*np.mean(np.maximum(zeros, K-stock_price))\n",
-      "    asian_put = r_factor*np.mean(np.maximum(zeros, K-stock_price_avg))\n",
-      "    euro_call = r_factor*np.mean(np.maximum(zeros, stock_price-K))\n",
-      "    asian_call = r_factor*np.mean(np.maximum(zeros, stock_price_avg-K))\n",
-      "    return (euro_call, euro_put, asian_call, asian_put)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We can time a single call of this function using the `%timeit` magic:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%timeit -n1 -r1 print price_option(S=100.0, K=100.0, sigma=0.25, r=0.05, days=260, paths=10000)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "(12.478072469211625, 7.5692079226372924, 6.9498346596114704, 4.5592719279729934)\n",
-        "1 loops, best of 1: 111 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Parallel computation across strike prices and volatilities"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The Client is used to setup the calculation and works with all engines."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "rc = Client()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "A `LoadBalancedView` is an interface to the engines that provides dynamic load\n",
-      "balancing at the expense of not knowing which engine will execute the code."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "view = rc.load_balanced_view()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Submit tasks for each (strike, sigma) pair. Again, we use the `%%timeit` magic to time the entire computation."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "async_results = []"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "%%timeit -n1 -r1\n",
-      "\n",
-      "for strike in strike_vals:\n",
-      "    for sigma in sigma_vals:\n",
-      "        # This line submits the tasks for parallel computation.\n",
-      "        ar = view.apply_async(price_option, price, strike, sigma, rate, days, paths)\n",
-      "        async_results.append(ar)\n",
-      "\n",
-      "rc.wait(async_results)  # Wait until all tasks are done."
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 1: 810 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "len(async_results)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 18,
-       "text": [
-        "30"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Parallel Monto-Carlo options pricing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook shows how to use `IPython.parallel` to do Monte-Carlo options pricing in parallel. We will compute the price of a large number of options for different strike prices and volatilities."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Problem setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import time\n",
+    "from IPython.parallel import Client\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here are the basic parameters for our computation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "price = 100.0  # Initial price\n",
+    "rate = 0.05  # Interest rate\n",
+    "days = 260  # Days to expiration\n",
+    "paths = 10000  # Number of MC paths\n",
+    "n_strikes = 6  # Number of strike values\n",
+    "min_strike = 90.0  # Min strike price\n",
+    "max_strike = 110.0  # Max strike price\n",
+    "n_sigmas = 5  # Number of volatility values\n",
+    "min_sigma = 0.1  # Min volatility\n",
+    "max_sigma = 0.4  # Max volatility"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "strike_vals = np.linspace(min_strike, max_strike, n_strikes)\n",
+    "sigma_vals = np.linspace(min_sigma, max_sigma, n_sigmas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Process and visualize results"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Strike prices:  [  90.   94.   98.  102.  106.  110.]\n",
+      "Volatilities:  [ 0.1    0.175  0.25   0.325  0.4  ]\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "print \"Strike prices: \", strike_vals\n",
+    "print \"Volatilities: \", sigma_vals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Monte-Carlo option pricing function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following function computes the price of a single option.  It returns the call and put prices for both European and Asian style options."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def price_option(S=100.0, K=100.0, sigma=0.25, r=0.05, days=260, paths=10000):\n",
+    "    \"\"\"\n",
+    "    Price European and Asian options using a Monte Carlo method.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    S : float\n",
+    "        The initial price of the stock.\n",
+    "    K : float\n",
+    "        The strike price of the option.\n",
+    "    sigma : float\n",
+    "        The volatility of the stock.\n",
+    "    r : float\n",
+    "        The risk free interest rate.\n",
+    "    days : int\n",
+    "        The number of days until the option expires.\n",
+    "    paths : int\n",
+    "        The number of Monte Carlo paths used to price the option.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    A tuple of (E. call, E. put, A. call, A. put) option prices.\n",
+    "    \"\"\"\n",
+    "    import numpy as np\n",
+    "    from math import exp,sqrt\n",
+    "    \n",
+    "    h = 1.0/days\n",
+    "    const1 = exp((r-0.5*sigma**2)*h)\n",
+    "    const2 = sigma*sqrt(h)\n",
+    "    stock_price = S*np.ones(paths, dtype='float64')\n",
+    "    stock_price_sum = np.zeros(paths, dtype='float64')\n",
+    "    for j in range(days):\n",
+    "        growth_factor = const1*np.exp(const2*np.random.standard_normal(paths))\n",
+    "        stock_price = stock_price*growth_factor\n",
+    "        stock_price_sum = stock_price_sum + stock_price\n",
+    "    stock_price_avg = stock_price_sum/days\n",
+    "    zeros = np.zeros(paths, dtype='float64')\n",
+    "    r_factor = exp(-r*h*days)\n",
+    "    euro_put = r_factor*np.mean(np.maximum(zeros, K-stock_price))\n",
+    "    asian_put = r_factor*np.mean(np.maximum(zeros, K-stock_price_avg))\n",
+    "    euro_call = r_factor*np.mean(np.maximum(zeros, stock_price-K))\n",
+    "    asian_call = r_factor*np.mean(np.maximum(zeros, stock_price_avg-K))\n",
+    "    return (euro_call, euro_put, asian_call, asian_put)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can time a single call of this function using the `%timeit` magic:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Retrieve the results using the `get` method:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(12.478072469211625, 7.5692079226372924, 6.9498346596114704, 4.5592719279729934)\n",
+      "1 loops, best of 1: 111 ms per loop\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "results = [ar.get() for ar in async_results]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 19
-    },
+    }
+   ],
+   "source": [
+    "%timeit -n1 -r1 print price_option(S=100.0, K=100.0, sigma=0.25, r=0.05, days=260, paths=10000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parallel computation across strike prices and volatilities"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Client is used to setup the calculation and works with all engines."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "rc = Client()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `LoadBalancedView` is an interface to the engines that provides dynamic load\n",
+    "balancing at the expense of not knowing which engine will execute the code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "view = rc.load_balanced_view()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Submit tasks for each (strike, sigma) pair. Again, we use the `%%timeit` magic to time the entire computation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "async_results = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Assemble the result into a structured NumPy array."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 1: 810 ms per loop\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "%%timeit -n1 -r1\n",
+    "\n",
+    "for strike in strike_vals:\n",
+    "    for sigma in sigma_vals:\n",
+    "        # This line submits the tasks for parallel computation.\n",
+    "        ar = view.apply_async(price_option, price, strike, sigma, rate, days, paths)\n",
+    "        async_results.append(ar)\n",
+    "\n",
+    "rc.wait(async_results)  # Wait until all tasks are done."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "prices = np.empty(n_strikes*n_sigmas,\n",
-      "    dtype=[('ecall',float),('eput',float),('acall',float),('aput',float)]\n",
-      ")\n",
-      "\n",
-      "for i, price in enumerate(results):\n",
-      "    prices[i] = tuple(price)\n",
-      "\n",
-      "prices.shape = (n_strikes, n_sigmas)"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 18,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 20
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(async_results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Process and visualize results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Retrieve the results using the `get` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "results = [ar.get() for ar in async_results]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assemble the result into a structured NumPy array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "prices = np.empty(n_strikes*n_sigmas,\n",
+    "    dtype=[('ecall',float),('eput',float),('acall',float),('aput',float)]\n",
+    ")\n",
+    "\n",
+    "for i, price in enumerate(results):\n",
+    "    prices[i] = tuple(price)\n",
+    "\n",
+    "prices.shape = (n_strikes, n_sigmas)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the value of the European call in (volatility, strike) space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x1100a3290>"
+      ]
+     },
+     "execution_count": 21,
      "metadata": {},
-     "source": [
-      "Plot the value of the European call in (volatility, strike) space."
-     ]
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.figure()\n",
-      "plt.contourf(sigma_vals, strike_vals, prices['ecall'])\n",
-      "plt.axis('tight')\n",
-      "plt.colorbar()\n",
-      "plt.title('European Call')\n",
-      "plt.xlabel(\"Volatility\")\n",
-      "plt.ylabel(\"Strike Price\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 21,
-       "text": [
-        "<matplotlib.text.Text at 0x1100a3290>"
-       ]
-      },
-      {
-       "metadata": {
-        "png": {
-         "height": 407,
-         "width": 563
-        }
-       },
-       "output_type": "display_data",
-       "png": 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9yTOT3KOUckSt9eINPnpekr/a5LBnTv0DwJSEGegPYQbaY2oGdsbUDOzMThpC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\nElEG2IkoA9MkzgCDI8gAq0iYAXYizMB0iTPAIAgywKoSZYC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-       "text": [
-        "<matplotlib.figure.Figure at 0x10ffc7c10>"
-       ]
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+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10ffc7c10>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 407,
+       "width": 563
       }
-     ],
-     "prompt_number": 21
-    },
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.contourf(sigma_vals, strike_vals, prices['ecall'])\n",
+    "plt.axis('tight')\n",
+    "plt.colorbar()\n",
+    "plt.title('European Call')\n",
+    "plt.xlabel(\"Volatility\")\n",
+    "plt.ylabel(\"Strike Price\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the value of the Asian call in (volatility, strike) space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x11009b3d0>"
+      ]
+     },
+     "execution_count": 22,
      "metadata": {},
-     "source": [
-      "Plot the value of the Asian call in (volatility, strike) space."
-     ]
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.figure()\n",
-      "plt.contourf(sigma_vals, strike_vals, prices['acall'])\n",
-      "plt.axis('tight')\n",
-      "plt.colorbar()\n",
-      "plt.title(\"Asian Call\")\n",
-      "plt.xlabel(\"Volatility\")\n",
-      "plt.ylabel(\"Strike Price\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 22,
-       "text": [
-        "<matplotlib.text.Text at 0x11009b3d0>"
-       ]
-      },
-      {
-       "metadata": {
-        "png": {
-         "height": 407,
-         "width": 562
-        }
-       },
-       "output_type": "display_data",
-       "png": 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HpFmb5k9qre+cxXdjHzEGmJYYA6tNjAHGIcYwzDgNIc3kjTsn+UKaCR4nlFKy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\nPY495r2BPjrggAPulOTIJD+f5MZJvpjkj5I8s9b6o5Xrn3r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-       "text": [
-        "<matplotlib.figure.Figure at 0x10ffc7210>"
-       ]
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+       "AABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10ffc7210>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 407,
+       "width": 562
       }
-     ],
-     "prompt_number": 22
-    },
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.contourf(sigma_vals, strike_vals, prices['acall'])\n",
+    "plt.axis('tight')\n",
+    "plt.colorbar()\n",
+    "plt.title(\"Asian Call\")\n",
+    "plt.xlabel(\"Volatility\")\n",
+    "plt.ylabel(\"Strike Price\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the value of the European put in (volatility, strike) space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x1109ef450>"
+      ]
+     },
+     "execution_count": 23,
      "metadata": {},
-     "source": [
-      "Plot the value of the European put in (volatility, strike) space."
-     ]
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.figure()\n",
-      "plt.contourf(sigma_vals, strike_vals, prices['eput'])\n",
-      "plt.axis('tight')\n",
-      "plt.colorbar()\n",
-      "plt.title(\"European Put\")\n",
-      "plt.xlabel(\"Volatility\")\n",
-      "plt.ylabel(\"Strike Price\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 23,
-       "text": [
-        "<matplotlib.text.Text at 0x1109ef450>"
-       ]
-      },
-      {
-       "metadata": {
-        "png": {
-         "height": 407,
-         "width": 573
-        }
-       },
-       "output_type": "display_data",
-       "png": 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l7FHOjD2SloKhJzPGHuXGdXukOcYelcrYo5w53SOpboaeDHkpl3Jk7JESY49K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\n4dkPdTKjCz1JfiNN5Hl/khPXpndKKVdI8uwkj0ryJ0lO3uS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-        "<matplotlib.figure.Figure at 0x1109e0310>"
-       ]
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+       "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAjNv/D946C4GGg8YDAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1109e0310>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 407,
+       "width": 573
       }
-     ],
-     "prompt_number": 23
-    },
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.contourf(sigma_vals, strike_vals, prices['eput'])\n",
+    "plt.axis('tight')\n",
+    "plt.colorbar()\n",
+    "plt.title(\"European Put\")\n",
+    "plt.xlabel(\"Volatility\")\n",
+    "plt.ylabel(\"Strike Price\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the value of the Asian put in (volatility, strike) space."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x1109e3b10>"
+      ]
+     },
+     "execution_count": 24,
      "metadata": {},
-     "source": [
-      "Plot the value of the Asian put in (volatility, strike) space."
-     ]
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.figure()\n",
-      "plt.contourf(sigma_vals, strike_vals, prices['aput'])\n",
-      "plt.axis('tight')\n",
-      "plt.colorbar()\n",
-      "plt.title(\"Asian Put\")\n",
-      "plt.xlabel(\"Volatility\")\n",
-      "plt.ylabel(\"Strike Price\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 24,
-       "text": [
-        "<matplotlib.text.Text at 0x1109e3b10>"
-       ]
-      },
-      {
-       "metadata": {
-        "png": {
-         "height": 407,
-         "width": 562
-        }
-       },
-       "output_type": "display_data",
-       "png": 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LpD0D/aM9A/1xyKofAGCT7bvR0RcLaQAW5bCbHnOxBg3QD8eedP2LhTTAdtGUAeiA9gyw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\nhTTLoABYcqZmgCGypAlg+QxyUmaMY9rb48Y+q1V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-       "text": [
-        "<matplotlib.figure.Figure at 0x110dd2c50>"
-       ]
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+       "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAo/1/MrL3Cbz9qMUAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x110dd2c50>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 407,
+       "width": 562
       }
-     ],
-     "prompt_number": 24
+     },
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "plt.figure()\n",
+    "plt.contourf(sigma_vals, strike_vals, prices['aput'])\n",
+    "plt.axis('tight')\n",
+    "plt.colorbar()\n",
+    "plt.title(\"Asian Put\")\n",
+    "plt.xlabel(\"Volatility\")\n",
+    "plt.ylabel(\"Strike Price\")"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:1b19dedc6473d4e886e549020c6710f2d14c17296168a02e7e7fa9673912b893"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Parallel Computing/Parallel Decorator and map.ipynb b/examples/Parallel Computing/Parallel Decorator and map.ipynb
index 6f96038..f288318 100644
--- a/examples/Parallel Computing/Parallel Decorator and map.ipynb
+++ b/examples/Parallel Computing/Parallel Decorator and map.ipynb
@@ -1,120 +1,115 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:8781781d8835d77bf0f71b535b72ee2718405543c48e87ad0408242119cdb3cc"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load balanced map and parallel function decorator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "from IPython.parallel import Client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "rc = Client()\n",
+    "v = rc.load_balanced_view()"
+   ]
+  },
   {
-   "cells": [
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Load balanced map and parallel function decorator"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Simple, default map:  [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "from __future__ import print_function\n",
-      "from IPython.parallel import Client"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "rc = Client()\n",
-      "v = rc.load_balanced_view()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "result = v.map(lambda x: 2*x, range(10))\n",
-      "print(\"Simple, default map: \", list(result))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Simple, default map:  [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ar = v.map_async(lambda x: 2*x, range(10))\n",
-      "print(\"Submitted tasks, got ids: \", ar.msg_ids)\n",
-      "result = ar.get()\n",
-      "print(\"Using a mapper: \", result)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Submitted tasks, got ids:  ['b4d86123-967a-4f21-b9c5-8a77a69a3ced', '97401998-891d-4729-8288-ae1b97c28235', '1586cf7e-32c7-4864-bd0e-6aab16e07a3f', 'f6770223-59c3-4344-a69a-5d555d1dfb7f', '0ebb71da-6e16-44ac-917a-be978fd3787d', '582443c5-937e-45b0-9f13-5607c53cba60', '8d453d87-d70d-4fbd-a2c4-994ab3a8b6c7', '0a680757-1a59-4826-969c-523a45f3d76f', '63a3e983-a205-4f07-b494-ec86b2cdd004', '79b34cbb-aa93-4d11-8746-28969dbdd3ec']\n",
-        "Using a mapper:  [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
+    }
+   ],
+   "source": [
+    "result = v.map(lambda x: 2*x, range(10))\n",
+    "print(\"Simple, default map: \", list(result))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "@v.parallel(block=True)\n",
-      "def f(x): return 2*x\n",
-      "\n",
-      "result = f.map(range(10))\n",
-      "print(\"Using a parallel function: \", result)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Using a parallel function:  [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Submitted tasks, got ids:  ['b4d86123-967a-4f21-b9c5-8a77a69a3ced', '97401998-891d-4729-8288-ae1b97c28235', '1586cf7e-32c7-4864-bd0e-6aab16e07a3f', 'f6770223-59c3-4344-a69a-5d555d1dfb7f', '0ebb71da-6e16-44ac-917a-be978fd3787d', '582443c5-937e-45b0-9f13-5607c53cba60', '8d453d87-d70d-4fbd-a2c4-994ab3a8b6c7', '0a680757-1a59-4826-969c-523a45f3d76f', '63a3e983-a205-4f07-b494-ec86b2cdd004', '79b34cbb-aa93-4d11-8746-28969dbdd3ec']\n",
+      "Using a mapper:  [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n"
+     ]
+    }
+   ],
+   "source": [
+    "ar = v.map_async(lambda x: 2*x, range(10))\n",
+    "print(\"Submitted tasks, got ids: \", ar.msg_ids)\n",
+    "result = ar.get()\n",
+    "print(\"Using a mapper: \", result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using a parallel function:  [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n"
+     ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "@v.parallel(block=True)\n",
+    "def f(x): return 2*x\n",
+    "\n",
+    "result = f.map(range(10))\n",
+    "print(\"Using a parallel function: \", result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
   }
- ]
+ ],
+ "metadata": {
+  "signature": "sha256:8781781d8835d77bf0f71b535b72ee2718405543c48e87ad0408242119cdb3cc"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Parallel Computing/Parallel Magics.ipynb b/examples/Parallel Computing/Parallel Magics.ipynb
index e54255a..09474ef 100644
--- a/examples/Parallel Computing/Parallel Magics.ipynb
+++ b/examples/Parallel Computing/Parallel Magics.ipynb
@@ -1,490 +1,503 @@
 {
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Using Parallel Magics"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython has a few magics for working with your engines.\n",
-      "\n",
-      "This assumes you have started an IPython cluster, either with the notebook interface,\n",
-      "or the `ipcluster/controller/engine` commands."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython import parallel\n",
-      "rc = parallel.Client()\n",
-      "dv = rc[:]\n",
-      "rc.ids"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Creating a Client registers the parallel magics `%px`, `%%px`, `%pxresult`, `pxconfig`, and `%autopx`.  \n",
-      "These magics are initially associated with a DirectView always associated with all currently registered engines."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now we can execute code remotely with `%px`:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%px a=5"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%px print(a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%px a"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "with dv.sync_imports():\n",
-      "    import sys"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%px from __future__ import print_function\n",
-      "%px print(\"ERROR\", file=sys.stderr)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You don't have to wait for results.  The `%pxconfig` magic lets you change the default blocking/targets for the `%px` magics:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pxconfig --noblock"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%px import time\n",
-      "%px time.sleep(5)\n",
-      "%px time.time()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "But you will notice that this didn't output the result of the last command.\n",
-      "For this, we have `%pxresult`, which displays the output of the latest request:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pxresult"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Remember, an IPython engine is IPython, so you can do magics remotely as well!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pxconfig --block\n",
-      "%px %matplotlib inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%px\n",
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "`%%px` can also be used as a cell magic, for submitting whole blocks.\n",
-      "This one acceps `--block` and `--noblock` flags to specify\n",
-      "the blocking behavior, though the default is unchanged.\n"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "dv.scatter('id', dv.targets, flatten=True)\n",
-      "dv['stride'] = len(dv)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%px --noblock\n",
-      "x = np.linspace(0,np.pi,1000)\n",
-      "for n in range(id,12, stride):\n",
-      "    print(n)\n",
-      "    plt.plot(x,np.sin(n*x))\n",
-      "plt.title(\"Plot %i\" % id)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pxresult"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "It also lets you choose some amount of the grouping of the outputs with `--group-outputs`:\n",
-      "\n",
-      "The choices are:\n",
-      "\n",
-      "* `engine` - all of an engine's output is collected together\n",
-      "* `type` - where stdout of each engine is grouped, etc. (the default)\n",
-      "* `order` - same as `type`, but individual displaypub outputs are interleaved.\n",
-      "  That is, it will output the first plot from each engine, then the second from each,\n",
-      "  etc."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%px --group-outputs=engine\n",
-      "x = np.linspace(0,np.pi,1000)\n",
-      "for n in range(id+1,12, stride):\n",
-      "    print(n)\n",
-      "    plt.figure()\n",
-      "    plt.plot(x,np.sin(n*x))\n",
-      "    plt.title(\"Plot %i\" % n)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When you specify 'order', then individual display outputs (e.g. plots) will be interleaved.\n",
-      "\n",
-      "`%pxresult` takes the same output-ordering arguments as `%%px`, \n",
-      "so you can view the previous result in a variety of different ways with a few sequential calls to `%pxresult`:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pxresult --group-outputs=order"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Single-engine views"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When a DirectView has a single target, the output is a bit simpler (no prefixes on stdout/err, etc.):"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from __future__ import print_function\n",
-      "\n",
-      "def generate_output():\n",
-      "    \"\"\"function for testing output\n",
-      "    \n",
-      "    publishes two outputs of each type, and returns something\n",
-      "    \"\"\"\n",
-      "    \n",
-      "    import sys,os\n",
-      "    from IPython.display import display, HTML, Math\n",
-      "    \n",
-      "    print(\"stdout\")\n",
-      "    print(\"stderr\", file=sys.stderr)\n",
-      "    \n",
-      "    display(HTML(\"<b>HTML</b>\"))\n",
-      "    \n",
-      "    print(\"stdout2\")\n",
-      "    print(\"stderr2\", file=sys.stderr)\n",
-      "    \n",
-      "    display(Math(r\"\\alpha=\\beta\"))\n",
-      "    \n",
-      "    return os.getpid()\n",
-      "\n",
-      "dv['generate_output'] = generate_output"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can also have more than one set of parallel magics registered at a time.\n",
-      "\n",
-      "The `View.activate()` method takes a suffix argument, which is added to `'px'`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "e0 = rc[-1]\n",
-      "e0.block = True\n",
-      "e0.activate('0')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%px0 generate_output()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%px generate_output()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "As mentioned above, we can redisplay those same results with various grouping:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pxresult --group-outputs order"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pxresult --group-outputs engine"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Parallel Exceptions"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "When you raise exceptions with the parallel exception,\n",
-      "the CompositeError raised locally will display your remote traceback."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%px\n",
-      "from numpy.random import random\n",
-      "A = random((100,100,'invalid shape'))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Remote Cell Magics"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Remember, Engines are IPython too, so the cell that is run remotely by %%px can in turn use a cell magic."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%px\n",
-      "%%timeit\n",
-      "from numpy.random import random\n",
-      "from numpy.linalg import norm\n",
-      "A = random((100,100))\n",
-      "norm(A, 2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Local Execution"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "As of IPython 1.0, you can instruct `%%px` to also execute the cell locally.\n",
-      "This is useful for interactive definitions,\n",
-      "or if you want to load a data source everywhere,\n",
-      "not just on the engines."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%px --local\n",
-      "import os\n",
-      "thispid = os.getpid()\n",
-      "print(thispid)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using Parallel Magics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython has a few magics for working with your engines.\n",
+    "\n",
+    "This assumes you have started an IPython cluster, either with the notebook interface,\n",
+    "or the `ipcluster/controller/engine` commands."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython import parallel\n",
+    "rc = parallel.Client()\n",
+    "dv = rc[:]\n",
+    "rc.ids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Creating a Client registers the parallel magics `%px`, `%%px`, `%pxresult`, `pxconfig`, and `%autopx`.  \n",
+    "These magics are initially associated with a DirectView always associated with all currently registered engines."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can execute code remotely with `%px`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%px a=5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%px print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%px a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "with dv.sync_imports():\n",
+    "    import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%px from __future__ import print_function\n",
+    "%px print(\"ERROR\", file=sys.stderr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You don't have to wait for results.  The `%pxconfig` magic lets you change the default blocking/targets for the `%px` magics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%pxconfig --noblock"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%px import time\n",
+    "%px time.sleep(5)\n",
+    "%px time.time()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But you will notice that this didn't output the result of the last command.\n",
+    "For this, we have `%pxresult`, which displays the output of the latest request:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%pxresult"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember, an IPython engine is IPython, so you can do magics remotely as well!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%pxconfig --block\n",
+    "%px %matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%px\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`%%px` can also be used as a cell magic, for submitting whole blocks.\n",
+    "This one acceps `--block` and `--noblock` flags to specify\n",
+    "the blocking behavior, though the default is unchanged.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dv.scatter('id', dv.targets, flatten=True)\n",
+    "dv['stride'] = len(dv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%px --noblock\n",
+    "x = np.linspace(0,np.pi,1000)\n",
+    "for n in range(id,12, stride):\n",
+    "    print(n)\n",
+    "    plt.plot(x,np.sin(n*x))\n",
+    "plt.title(\"Plot %i\" % id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%pxresult"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It also lets you choose some amount of the grouping of the outputs with `--group-outputs`:\n",
+    "\n",
+    "The choices are:\n",
+    "\n",
+    "* `engine` - all of an engine's output is collected together\n",
+    "* `type` - where stdout of each engine is grouped, etc. (the default)\n",
+    "* `order` - same as `type`, but individual displaypub outputs are interleaved.\n",
+    "  That is, it will output the first plot from each engine, then the second from each,\n",
+    "  etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%px --group-outputs=engine\n",
+    "x = np.linspace(0,np.pi,1000)\n",
+    "for n in range(id+1,12, stride):\n",
+    "    print(n)\n",
+    "    plt.figure()\n",
+    "    plt.plot(x,np.sin(n*x))\n",
+    "    plt.title(\"Plot %i\" % n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you specify 'order', then individual display outputs (e.g. plots) will be interleaved.\n",
+    "\n",
+    "`%pxresult` takes the same output-ordering arguments as `%%px`, \n",
+    "so you can view the previous result in a variety of different ways with a few sequential calls to `%pxresult`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%pxresult --group-outputs=order"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Single-engine views"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When a DirectView has a single target, the output is a bit simpler (no prefixes on stdout/err, etc.):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from __future__ import print_function\n",
+    "\n",
+    "def generate_output():\n",
+    "    \"\"\"function for testing output\n",
+    "    \n",
+    "    publishes two outputs of each type, and returns something\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    import sys,os\n",
+    "    from IPython.display import display, HTML, Math\n",
+    "    \n",
+    "    print(\"stdout\")\n",
+    "    print(\"stderr\", file=sys.stderr)\n",
+    "    \n",
+    "    display(HTML(\"<b>HTML</b>\"))\n",
+    "    \n",
+    "    print(\"stdout2\")\n",
+    "    print(\"stderr2\", file=sys.stderr)\n",
+    "    \n",
+    "    display(Math(r\"\\alpha=\\beta\"))\n",
+    "    \n",
+    "    return os.getpid()\n",
+    "\n",
+    "dv['generate_output'] = generate_output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also have more than one set of parallel magics registered at a time.\n",
+    "\n",
+    "The `View.activate()` method takes a suffix argument, which is added to `'px'`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "e0 = rc[-1]\n",
+    "e0.block = True\n",
+    "e0.activate('0')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%px0 generate_output()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%px generate_output()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As mentioned above, we can redisplay those same results with various grouping:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%pxresult --group-outputs order"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%pxresult --group-outputs engine"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parallel Exceptions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you raise exceptions with the parallel exception,\n",
+    "the CompositeError raised locally will display your remote traceback."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%px\n",
+    "from numpy.random import random\n",
+    "A = random((100,100,'invalid shape'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Remote Cell Magics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember, Engines are IPython too, so the cell that is run remotely by %%px can in turn use a cell magic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%px\n",
+    "%%timeit\n",
+    "from numpy.random import random\n",
+    "from numpy.linalg import norm\n",
+    "A = random((100,100))\n",
+    "norm(A, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Local Execution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As of IPython 1.0, you can instruct `%%px` to also execute the cell locally.\n",
+    "This is useful for interactive definitions,\n",
+    "or if you want to load a data source everywhere,\n",
+    "not just on the engines."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%px --local\n",
+    "import os\n",
+    "thispid = os.getpid()\n",
+    "print(thispid)"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Parallel Computing/Using Dill.ipynb b/examples/Parallel Computing/Using Dill.ipynb
index d2e3bab..7615a25 100644
--- a/examples/Parallel Computing/Using Dill.ipynb
+++ b/examples/Parallel Computing/Using Dill.ipynb
@@ -1,534 +1,542 @@
 {
- "metadata": {
-  "gist_id": "5241793",
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Using dill to pickle anything"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython.parallel doesn't do much in the way of serialization.\n",
-      "It has custom zero-copy handling of numpy arrays,\n",
-      "but other than that, it doesn't do anything other than the bare minimum to make basic interactively defined functions and classes sendable.\n",
-      "\n",
-      "There are a few projects that extend pickle to make just about anything sendable, and one of these is [dill](http://www.cacr.caltech.edu/~mmckerns/dill).\n",
-      "\n",
-      "To install dill:\n",
-      "        \n",
-      "    pip install --pre dill"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "First, as always, we create a task function, this time with a closure"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def make_closure(a):\n",
-      "    \"\"\"make a function with a closure, and return it\"\"\"\n",
-      "    def has_closure(b):\n",
-      "        return a * b\n",
-      "    return has_closure"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "closed = make_closure(5)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "closed(2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "10"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import pickle"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Without help, pickle can't deal with closures"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pickle.dumps(closed)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "PicklingError",
-       "evalue": "Can't pickle <function has_closure at 0x10412c6e0>: it's not found as __main__.has_closure",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mPicklingError\u001b[0m                             Traceback (most recent call last)",
-        "\u001b[1;32m<ipython-input-5-0f1f376cfea0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclosed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[1;32m/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36mdumps\u001b[1;34m(obj, protocol)\u001b[0m\n\u001b[0;32m   1372\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1373\u001b[0m     \u001b[0mfile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1374\u001b[1;33m     \u001b[0mPickler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1375\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1376\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36mdump\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    222\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproto\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    223\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPROTO\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mchr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproto\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 224\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    225\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSTOP\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    284\u001b[0m         \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m             \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    287\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave_global\u001b[1;34m(self, obj, name, pack)\u001b[0m\n\u001b[0;32m    746\u001b[0m             raise PicklingError(\n\u001b[0;32m    747\u001b[0m                 \u001b[1;34m\"Can't pickle %r: it's not found as %s.%s\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 748\u001b[1;33m                 (obj, module, name))\n\u001b[0m\u001b[0;32m    749\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    750\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mklass\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;31mPicklingError\u001b[0m: Can't pickle <function has_closure at 0x10412c6e0>: it's not found as __main__.has_closure"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "But after we import dill, magic happens"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import dill"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pickle.dumps(closed)[:64] + '...'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 7,
-       "text": [
-        "\"cdill.dill\\n_load_type\\np0\\n(S'FunctionType'\\np1\\ntp2\\nRp3\\n(cdill.dill...\""
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "So from now on, pretty much everything is pickleable."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Now use this in IPython.parallel"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "As usual, we start by creating our Client and View"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from IPython import parallel\n",
-      "rc = parallel.Client()\n",
-      "view = rc.load_balanced_view()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now let's try sending our function with a closure:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "view.apply_sync(closed, 3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "ValueError",
-       "evalue": "Sorry, cannot pickle code objects with closures",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-        "\u001b[1;32m<ipython-input-9-23a646829fdc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mview\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_sync\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclosed\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/view.pyc\u001b[0m in \u001b[0;36mapply_sync\u001b[1;34m(self, f, *args, **kwargs)\u001b[0m\n",
-        "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/view.pyc\u001b[0m in \u001b[0;36mspin_after\u001b[1;34m(f, self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     73\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mspin_after\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m     \u001b[1;34m\"\"\"call spin after the method.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 75\u001b[1;33m     \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     76\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mspin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/view.pyc\u001b[0m in \u001b[0;36mapply_sync\u001b[1;34m(self, f, *args, **kwargs)\u001b[0m\n\u001b[0;32m    245\u001b[0m          \u001b[0mreturning\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m         \"\"\"\n\u001b[1;32m--> 247\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_really_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    248\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m     \u001b[1;31m#----------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/view.pyc\u001b[0m in \u001b[0;36m_really_apply\u001b[1;34m(self, f, args, kwargs, block, track, after, follow, timeout, targets, retries)\u001b[0m\n",
-        "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/view.pyc\u001b[0m in \u001b[0;36msync_results\u001b[1;34m(f, self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     64\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_in_sync_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     67\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     68\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_in_sync_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/view.pyc\u001b[0m in \u001b[0;36m_really_apply\u001b[1;34m(self, f, args, kwargs, block, track, after, follow, timeout, targets, retries)\u001b[0m\n",
-        "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/view.pyc\u001b[0m in \u001b[0;36msave_ids\u001b[1;34m(f, self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     49\u001b[0m     \u001b[0mn_previous\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m         \u001b[0mnmsgs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mn_previous\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/view.pyc\u001b[0m in \u001b[0;36m_really_apply\u001b[1;34m(self, f, args, kwargs, block, track, after, follow, timeout, targets, retries)\u001b[0m\n\u001b[0;32m   1049\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1050\u001b[0m         msg = self.client.send_apply_request(self._socket, f, args, kwargs, track=track,\n\u001b[1;32m-> 1051\u001b[1;33m                                 metadata=metadata)\n\u001b[0m\u001b[0;32m   1052\u001b[0m         \u001b[0mtracker\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtrack\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mFalse\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'tracker'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1053\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/client.pyc\u001b[0m in \u001b[0;36msend_apply_request\u001b[1;34m(self, socket, f, args, kwargs, metadata, track, ident)\u001b[0m\n\u001b[0;32m   1252\u001b[0m         bufs = serialize.pack_apply_message(f, args, kwargs,\n\u001b[0;32m   1253\u001b[0m             \u001b[0mbuffer_threshold\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_threshold\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1254\u001b[1;33m             \u001b[0mitem_threshold\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitem_threshold\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1255\u001b[0m         )\n\u001b[0;32m   1256\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/kernel/zmq/serialize.pyc\u001b[0m in \u001b[0;36mpack_apply_message\u001b[1;34m(f, args, kwargs, buffer_threshold, item_threshold)\u001b[0m\n\u001b[0;32m    163\u001b[0m     \u001b[0minfo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnargs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnarg_bufs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg_bufs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkw_keys\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkw_keys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    164\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 165\u001b[1;33m     \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    166\u001b[0m     \u001b[0mmsg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    167\u001b[0m     \u001b[0mmsg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg_bufs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/utils/codeutil.pyc\u001b[0m in \u001b[0;36mreduce_code\u001b[1;34m(co)\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mreduce_code\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mco\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mco\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mco_freevars\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mco\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mco_cellvars\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Sorry, cannot pickle code objects with closures\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m     args =  [co.co_argcount, co.co_nlocals, co.co_stacksize,\n\u001b[0;32m     40\u001b[0m             \u001b[0mco\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mco_flags\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mco\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mco_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mco\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mco_consts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mco\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mco_names\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-        "\u001b[1;31mValueError\u001b[0m: Sorry, cannot pickle code objects with closures"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Oops, no dice. For IPython to work with dill,\n",
-      "there are one or two more steps. IPython will do these for you if you call `DirectView.use_dill`:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "rc[:].use_dill()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "<AsyncResult: use_dill>"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This is equivalent to\n",
-      "\n",
-      "```python\n",
-      "from IPython.utils.pickleutil import use_dill\n",
-      "use_dill()\n",
-      "rc[:].apply(use_dill)\n",
-      "```"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now let's try again"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "view.apply_sync(closed, 3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 11,
-       "text": [
-        "15"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Yay! Now we can use dill to allow IPython.parallel to send anything.\n",
-      "\n",
-      "And that's it! We can send closures and other previously non-pickleables to our engines.\n",
-      "\n",
-      "Let's give it a try now:"
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using dill to pickle anything"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "IPython.parallel doesn't do much in the way of serialization.\n",
+    "It has custom zero-copy handling of numpy arrays,\n",
+    "but other than that, it doesn't do anything other than the bare minimum to make basic interactively defined functions and classes sendable.\n",
+    "\n",
+    "There are a few projects that extend pickle to make just about anything sendable, and one of these is [dill](http://www.cacr.caltech.edu/~mmckerns/dill).\n",
+    "\n",
+    "To install dill:\n",
+    "        \n",
+    "    pip install --pre dill"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, as always, we create a task function, this time with a closure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def make_closure(a):\n",
+    "    \"\"\"make a function with a closure, and return it\"\"\"\n",
+    "    def has_closure(b):\n",
+    "        return a * b\n",
+    "    return has_closure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "closed = make_closure(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "closed(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Without help, pickle can't deal with closures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "PicklingError",
+     "evalue": "Can't pickle <function has_closure at 0x10412c6e0>: it's not found as __main__.has_closure",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mPicklingError\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-5-0f1f376cfea0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclosed\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36mdumps\u001b[1;34m(obj, protocol)\u001b[0m\n\u001b[0;32m   1372\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1373\u001b[0m     \u001b[0mfile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1374\u001b[1;33m     \u001b[0mPickler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1375\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1376\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36mdump\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    222\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproto\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    223\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPROTO\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mchr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mproto\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 224\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    225\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSTOP\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    226\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m    284\u001b[0m         \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    285\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 286\u001b[1;33m             \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Call unbound method with explicit self\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    287\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/pickle.pyc\u001b[0m in \u001b[0;36msave_global\u001b[1;34m(self, obj, name, pack)\u001b[0m\n\u001b[0;32m    746\u001b[0m             raise PicklingError(\n\u001b[0;32m    747\u001b[0m                 \u001b[1;34m\"Can't pickle %r: it's not found as %s.%s\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 748\u001b[1;33m                 (obj, module, name))\n\u001b[0m\u001b[0;32m    749\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    750\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mklass\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mPicklingError\u001b[0m: Can't pickle <function has_closure at 0x10412c6e0>: it's not found as __main__.has_closure"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "remote_closure = view.apply_sync(make_closure, 4)\n",
-      "remote_closure(5)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 12,
-       "text": [
-        "20"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "But wait, there's more!\n",
-      "\n",
-      "At this point, we can send/recv all kinds of stuff"
+    }
+   ],
+   "source": [
+    "pickle.dumps(closed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But after we import dill, magic happens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import dill"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"cdill.dill\\n_load_type\\np0\\n(S'FunctionType'\\np1\\ntp2\\nRp3\\n(cdill.dill...\""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pickle.dumps(closed)[:64] + '...'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So from now on, pretty much everything is pickleable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Now use this in IPython.parallel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As usual, we start by creating our Client and View"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython import parallel\n",
+    "rc = parallel.Client()\n",
+    "view = rc.load_balanced_view()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's try sending our function with a closure:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Sorry, cannot pickle code objects with closures",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-9-23a646829fdc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mview\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_sync\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclosed\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/view.pyc\u001b[0m in \u001b[0;36mapply_sync\u001b[1;34m(self, f, *args, **kwargs)\u001b[0m\n",
+      "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/view.pyc\u001b[0m in \u001b[0;36mspin_after\u001b[1;34m(f, self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     73\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mspin_after\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m     \u001b[1;34m\"\"\"call spin after the method.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 75\u001b[1;33m     \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     76\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mspin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/view.pyc\u001b[0m in \u001b[0;36mapply_sync\u001b[1;34m(self, f, *args, **kwargs)\u001b[0m\n\u001b[0;32m    245\u001b[0m          \u001b[0mreturning\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m         \"\"\"\n\u001b[1;32m--> 247\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_really_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    248\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m     \u001b[1;31m#----------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/view.pyc\u001b[0m in \u001b[0;36m_really_apply\u001b[1;34m(self, f, args, kwargs, block, track, after, follow, timeout, targets, retries)\u001b[0m\n",
+      "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/view.pyc\u001b[0m in \u001b[0;36msync_results\u001b[1;34m(f, self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     64\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_in_sync_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     67\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     68\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_in_sync_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/view.pyc\u001b[0m in \u001b[0;36m_really_apply\u001b[1;34m(self, f, args, kwargs, block, track, after, follow, timeout, targets, retries)\u001b[0m\n",
+      "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/view.pyc\u001b[0m in \u001b[0;36msave_ids\u001b[1;34m(f, self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     49\u001b[0m     \u001b[0mn_previous\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     50\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     52\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m         \u001b[0mnmsgs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mn_previous\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/view.pyc\u001b[0m in \u001b[0;36m_really_apply\u001b[1;34m(self, f, args, kwargs, block, track, after, follow, timeout, targets, retries)\u001b[0m\n\u001b[0;32m   1049\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1050\u001b[0m         msg = self.client.send_apply_request(self._socket, f, args, kwargs, track=track,\n\u001b[1;32m-> 1051\u001b[1;33m                                 metadata=metadata)\n\u001b[0m\u001b[0;32m   1052\u001b[0m         \u001b[0mtracker\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtrack\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mFalse\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'tracker'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1053\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/parallel/client/client.pyc\u001b[0m in \u001b[0;36msend_apply_request\u001b[1;34m(self, socket, f, args, kwargs, metadata, track, ident)\u001b[0m\n\u001b[0;32m   1252\u001b[0m         bufs = serialize.pack_apply_message(f, args, kwargs,\n\u001b[0;32m   1253\u001b[0m             \u001b[0mbuffer_threshold\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer_threshold\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1254\u001b[1;33m             \u001b[0mitem_threshold\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitem_threshold\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1255\u001b[0m         )\n\u001b[0;32m   1256\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/kernel/zmq/serialize.pyc\u001b[0m in \u001b[0;36mpack_apply_message\u001b[1;34m(f, args, kwargs, buffer_threshold, item_threshold)\u001b[0m\n\u001b[0;32m    163\u001b[0m     \u001b[0minfo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnargs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnarg_bufs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg_bufs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkw_keys\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkw_keys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    164\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 165\u001b[1;33m     \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    166\u001b[0m     \u001b[0mmsg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    167\u001b[0m     \u001b[0mmsg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg_bufs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/utils/codeutil.pyc\u001b[0m in \u001b[0;36mreduce_code\u001b[1;34m(co)\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mreduce_code\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mco\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mco\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mco_freevars\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mco\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mco_cellvars\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Sorry, cannot pickle code objects with closures\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m     args =  [co.co_argcount, co.co_nlocals, co.co_stacksize,\n\u001b[0;32m     40\u001b[0m             \u001b[0mco\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mco_flags\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mco\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mco_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mco\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mco_consts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mco\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mco_names\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Sorry, cannot pickle code objects with closures"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def outer(a):\n",
-      "    def inner(b):\n",
-      "        def inner_again(c):\n",
-      "            return c * b * a\n",
-      "        return inner_again\n",
-      "    return inner"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "So outer returns a function with a closure, which returns a function with a closure.\n",
-      "\n",
-      "Now, we can resolve the first closure on the engine, the second here, and the third on a different engine,\n",
-      "after passing through a lambda we define here and call there, just for good measure."
+    }
+   ],
+   "source": [
+    "view.apply_sync(closed, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Oops, no dice. For IPython to work with dill,\n",
+    "there are one or two more steps. IPython will do these for you if you call `DirectView.use_dill`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AsyncResult: use_dill>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rc[:].use_dill()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is equivalent to\n",
+    "\n",
+    "```python\n",
+    "from IPython.utils.pickleutil import use_dill\n",
+    "use_dill()\n",
+    "rc[:].apply(use_dill)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's try again"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "view.apply_sync(closed, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Yay! Now we can use dill to allow IPython.parallel to send anything.\n",
+    "\n",
+    "And that's it! We can send closures and other previously non-pickleables to our engines.\n",
+    "\n",
+    "Let's give it a try now:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "remote_closure = view.apply_sync(make_closure, 4)\n",
+    "remote_closure(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But wait, there's more!\n",
+    "\n",
+    "At this point, we can send/recv all kinds of stuff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def outer(a):\n",
+    "    def inner(b):\n",
+    "        def inner_again(c):\n",
+    "            return c * b * a\n",
+    "        return inner_again\n",
+    "    return inner"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So outer returns a function with a closure, which returns a function with a closure.\n",
+    "\n",
+    "Now, we can resolve the first closure on the engine, the second here, and the third on a different engine,\n",
+    "after passing through a lambda we define here and call there, just for good measure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "view.apply_sync(lambda f: f(3),view.apply_sync(outer, 1)(2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And for good measure, let's test that normal execution still works:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5, 5, 5, 5, 5, 5, 5, 5]\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "view.apply_sync(lambda f: f(3),view.apply_sync(outer, 1)(2))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 14,
-       "text": [
-        "6"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "[10, 10, 10, 10, 10, 10, 10, 10]"
+      ]
+     },
+     "execution_count": 15,
      "metadata": {},
-     "source": [
-      "And for good measure, let's test that normal execution still works:"
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%px foo = 5\n",
+    "\n",
+    "print(rc[:]['foo'])\n",
+    "rc[:]['bar'] = lambda : 2 * foo\n",
+    "rc[:].apply_sync(parallel.Reference('bar'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And test that the `@interactive` decorator works"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting testdill.py\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%px foo = 5\n",
-      "\n",
-      "print(rc[:]['foo'])\n",
-      "rc[:]['bar'] = lambda : 2 * foo\n",
-      "rc[:].apply_sync(parallel.Reference('bar'))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[5, 5, 5, 5, 5, 5, 5, 5]\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 15,
-       "text": [
-        "[10, 10, 10, 10, 10, 10, 10, 10]"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And test that the `@interactive` decorator works"
+    }
+   ],
+   "source": [
+    "%%file testdill.py\n",
+    "from IPython.parallel import interactive\n",
+    "\n",
+    "@interactive\n",
+    "class C(object):\n",
+    "    a = 5\n",
+    "\n",
+    "@interactive\n",
+    "class D(C):\n",
+    "    b = 10\n",
+    "\n",
+    "@interactive\n",
+    "def foo(a):\n",
+    "    return a * b\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import testdill"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5 10\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%file testdill.py\n",
-      "from IPython.parallel import interactive\n",
-      "\n",
-      "@interactive\n",
-      "class C(object):\n",
-      "    a = 5\n",
-      "\n",
-      "@interactive\n",
-      "class D(C):\n",
-      "    b = 10\n",
-      "\n",
-      "@interactive\n",
-      "def foo(a):\n",
-      "    return a * b\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Overwriting testdill.py\n"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import testdill"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "v = rc[-1]\n",
-      "v['D'] = testdill.D\n",
-      "d = v.apply_sync(lambda : D())\n",
-      "print d.a, d.b"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "5 10\n"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "v['b'] = 10\n",
-      "v.apply_sync(testdill.foo, 5)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 19,
-       "text": [
-        "50"
-       ]
-      }
-     ],
-     "prompt_number": 19
     }
    ],
-   "metadata": {}
+   "source": [
+    "v = rc[-1]\n",
+    "v['D'] = testdill.D\n",
+    "d = v.apply_sync(lambda : D())\n",
+    "print d.a, d.b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v['b'] = 10\n",
+    "v.apply_sync(testdill.foo, 5)"
+   ]
   }
- ]
+ ],
+ "metadata": {
+  "gist_id": "5241793"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Parallel Computing/Using MPI with IPython Parallel.ipynb b/examples/Parallel Computing/Using MPI with IPython Parallel.ipynb
index 5012a90..7ef3538 100644
--- a/examples/Parallel Computing/Using MPI with IPython Parallel.ipynb
+++ b/examples/Parallel Computing/Using MPI with IPython Parallel.ipynb
@@ -1,187 +1,188 @@
 {
- "metadata": {
-  "name": "parallel_mpi"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Simple usage of a set of MPI engines"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This example assumes you've started a cluster of N engines (4 in this example) as part\n",
-      "of an MPI world.  \n",
-      "\n",
-      "Our documentation describes [how to create an MPI profile](http://ipython.org/ipython-doc/dev/parallel/parallel_process.html#using-ipcluster-in-mpiexec-mpirun-mode)\n",
-      "and explains [basic MPI usage of the IPython cluster](http://ipython.org/ipython-doc/dev/parallel/parallel_mpi.html).\n",
-      "\n",
-      "\n",
-      "For the simplest possible way to start 4 engines that belong to the same MPI world, \n",
-      "you can run this in a terminal:\n",
-      "\n",
-      "<pre>\n",
-      "ipcluster start --engines=MPI -n 4\n",
-      "</pre>\n",
-      "\n",
-      "or start an MPI cluster from the cluster tab if you have one configured.\n",
-      "\n",
-      "Once the cluster is running, we can connect to it and open a view into it:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "from IPython.parallel import Client\n",
-      "c = Client()\n",
-      "view = c[:]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's define a simple function that gets the MPI rank from each engine."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "@view.remote(block=True)\n",
-      "def mpi_rank():\n",
-      "    from mpi4py import MPI\n",
-      "    comm = MPI.COMM_WORLD\n",
-      "    return comm.Get_rank()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "mpi_rank()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "[2, 3, 1, 0]"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To get a mapping of IPython IDs and MPI rank (these do not always match),\n",
-      "you can use the get_dict method on AsyncResults."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "mpi_rank.block = False\n",
-      "ar = mpi_rank()\n",
-      "ar.get_dict()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "{0: 2, 1: 3, 2: 1, 3: 0}"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "With %%px cell magic, the next cell will actually execute *entirely on each engine*:"
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simple usage of a set of MPI engines"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example assumes you've started a cluster of N engines (4 in this example) as part\n",
+    "of an MPI world.  \n",
+    "\n",
+    "Our documentation describes [how to create an MPI profile](http://ipython.org/ipython-doc/dev/parallel/parallel_process.html#using-ipcluster-in-mpiexec-mpirun-mode)\n",
+    "and explains [basic MPI usage of the IPython cluster](http://ipython.org/ipython-doc/dev/parallel/parallel_mpi.html).\n",
+    "\n",
+    "\n",
+    "For the simplest possible way to start 4 engines that belong to the same MPI world, \n",
+    "you can run this in a terminal:\n",
+    "\n",
+    "<pre>\n",
+    "ipcluster start --engines=MPI -n 4\n",
+    "</pre>\n",
+    "\n",
+    "or start an MPI cluster from the cluster tab if you have one configured.\n",
+    "\n",
+    "Once the cluster is running, we can connect to it and open a view into it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.parallel import Client\n",
+    "c = Client()\n",
+    "view = c[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's define a simple function that gets the MPI rank from each engine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "@view.remote(block=True)\n",
+    "def mpi_rank():\n",
+    "    from mpi4py import MPI\n",
+    "    comm = MPI.COMM_WORLD\n",
+    "    return comm.Get_rank()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "%%px\n",
-      "from mpi4py import MPI\n",
-      "\n",
-      "comm = MPI.COMM_WORLD\n",
-      "size = comm.Get_size()\n",
-      "rank = comm.Get_rank()\n",
-      "\n",
-      "if rank == 0:\n",
-      "   data = [(i+1)**2 for i in range(size)]\n",
-      "else:\n",
-      "   data = None\n",
-      "data = comm.scatter(data, root=0)\n",
-      "\n",
-      "assert data == (rank+1)**2, 'data=%s, rank=%s' % (data, rank)"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "[2, 3, 1, 0]"
+      ]
+     },
+     "execution_count": 3,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mpi_rank()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get a mapping of IPython IDs and MPI rank (these do not always match),\n",
+    "you can use the get_dict method on AsyncResults."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "view['data']"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "{0: 2, 1: 3, 2: 1, 3: 0}"
+      ]
+     },
+     "execution_count": 4,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 6,
-       "text": [
-        "[9, 16, 4, 1]"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mpi_rank.block = False\n",
+    "ar = mpi_rank()\n",
+    "ar.get_dict()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With %%px cell magic, the next cell will actually execute *entirely on each engine*:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%%px\n",
+    "from mpi4py import MPI\n",
+    "\n",
+    "comm = MPI.COMM_WORLD\n",
+    "size = comm.Get_size()\n",
+    "rank = comm.Get_rank()\n",
+    "\n",
+    "if rank == 0:\n",
+    "   data = [(i+1)**2 for i in range(size)]\n",
+    "else:\n",
+    "   data = None\n",
+    "data = comm.scatter(data, root=0)\n",
+    "\n",
+    "assert data == (rank+1)**2, 'data=%s, rank=%s' % (data, rank)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "[9, 16, 4, 1]"
+      ]
+     },
+     "execution_count": 6,
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
+     "output_type": "execute_result"
     }
    ],
-   "metadata": {}
+   "source": [
+    "view['data']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file
diff --git a/examples/Parallel Computing/rmt/rmt.ipynb b/examples/Parallel Computing/rmt/rmt.ipynb
index 4e02800..d646850 100644
--- a/examples/Parallel Computing/rmt/rmt.ipynb
+++ b/examples/Parallel Computing/rmt/rmt.ipynb
@@ -1,308 +1,803 @@
 {
- "metadata": {
-  "name": "rmt"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Eigenvalue distribution of Gaussian orthogonal random matrices"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The eigenvalues of random matrices obey certain statistical laws. Here we construct random matrices \n",
-      "from the Gaussian Orthogonal Ensemble (GOE), find their eigenvalues and then investigate the nearest\n",
-      "neighbor eigenvalue distribution $\\rho(s)$."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from rmtkernel import ensemble_diffs, normalize_diffs, GOE\n",
-      "import numpy as np\n",
-      "from IPython.parallel import Client"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Wigner's nearest neighbor eigenvalue distribution"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The Wigner distribution gives the theoretical result for the nearest neighbor eigenvalue distribution\n",
-      "for the GOE:\n",
-      "\n",
-      "$$\\rho(s) = \\frac{\\pi s}{2} \\exp(-\\pi s^2/4)$$"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "def wigner_dist(s):\n",
-      "    \"\"\"Returns (s, rho(s)) for the Wigner GOE distribution.\"\"\"\n",
-      "    return (np.pi*s/2.0) * np.exp(-np.pi*s**2/4.)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "def generate_wigner_data():\n",
-      "    s = np.linspace(0.0,4.0,400)\n",
-      "    rhos = wigner_dist(s)\n",
-      "    return s, rhos"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "s, rhos = generate_wigner_data()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plot(s, rhos)\n",
-      "xlabel('Normalized level spacing s')\n",
-      "ylabel('Probability $\\rho(s)$')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 17,
-       "text": [
-        "&lt;matplotlib.text.Text at 0x3828790&gt;"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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oUeqcDCkYQljOrILx8ssvs2DBAlq1asWMGTOIc/Kps1Iwai83N5gxQ33KcIGH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\nSUVORK5CYII=\n"
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Serial calculation of nearest neighbor eigenvalue distribution"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In this section we numerically construct and diagonalize a large number of GOE random matrices\n",
-      "and compute the nerest neighbor eigenvalue distribution. This comptation is done on a single core."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "def serial_diffs(num, N):\n",
-      "    \"\"\"Compute the nearest neighbor distribution for num NxX matrices.\"\"\"\n",
-      "    diffs = ensemble_diffs(num, N)\n",
-      "    normalized_diffs = normalize_diffs(diffs)\n",
-      "    return normalized_diffs"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "serial_nmats = 1000\n",
-      "serial_matsize = 50"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Eigenvalue distribution of Gaussian orthogonal random matrices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The eigenvalues of random matrices obey certain statistical laws. Here we construct random matrices \n",
+    "from the Gaussian Orthogonal Ensemble (GOE), find their eigenvalues and then investigate the nearest\n",
+    "neighbor eigenvalue distribution $\\rho(s)$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from rmtkernel import ensemble_diffs, normalize_diffs, GOE\n",
+    "import numpy as np\n",
+    "from IPython.parallel import Client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Wigner's nearest neighbor eigenvalue distribution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Wigner distribution gives the theoretical result for the nearest neighbor eigenvalue distribution\n",
+    "for the GOE:\n",
+    "\n",
+    "$$\\rho(s) = \\frac{\\pi s}{2} \\exp(-\\pi s^2/4)$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def wigner_dist(s):\n",
+    "    \"\"\"Returns (s, rho(s)) for the Wigner GOE distribution.\"\"\"\n",
+    "    return (np.pi*s/2.0) * np.exp(-np.pi*s**2/4.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def generate_wigner_data():\n",
+    "    s = np.linspace(0.0,4.0,400)\n",
+    "    rhos = wigner_dist(s)\n",
+    "    return s, rhos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "s, rhos = generate_wigner_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%timeit -r1 -n1 serial_diffs(serial_nmats, serial_matsize)"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "&lt;matplotlib.text.Text at 0x3828790&gt;"
+      ]
+     },
+     "execution_count": 17,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 1: 1.19 s per loop"
-       ]
-      }
-     ],
-     "prompt_number": 8
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "serial_diffs = serial_diffs(serial_nmats, serial_matsize)"
-     ],
-     "language": "python",
+     "data": {
+      "image/png": [
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+       "cCIjI+nSpQsdO3Ysd9ycOXPo0qULoaGh+Pr6cscdd1T430U4J1l8UAgnlZ2dzeDBg9mxY4ddrnfp\n",
+       "0iWaNGlCXl4e99xzDx9//DG33367Xa4tHIP0YQjhxOy541xsbCzr1q3Dzc2N//u//5NiUQvJE4YQ\n",
+       "QgizSB+GEEIIs0jBEEIIYRYpGEIIIcwiBUMIIYRZpGAIIYQwixQMIYQQZvl/CHrf0nQauboAAAAA\n",
+       "SUVORK5CYII=\n"
+      ]
+     },
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(s, rhos)\n",
+    "xlabel('Normalized level spacing s')\n",
+    "ylabel('Probability $\\rho(s)$')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Serial calculation of nearest neighbor eigenvalue distribution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this section we numerically construct and diagonalize a large number of GOE random matrices\n",
+    "and compute the nerest neighbor eigenvalue distribution. This comptation is done on a single core."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def serial_diffs(num, N):\n",
+    "    \"\"\"Compute the nearest neighbor distribution for num NxX matrices.\"\"\"\n",
+    "    diffs = ensemble_diffs(num, N)\n",
+    "    normalized_diffs = normalize_diffs(diffs)\n",
+    "    return normalized_diffs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "serial_nmats = 1000\n",
+    "serial_matsize = 50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The numerical computation agrees with the predictions of Wigner, but it would be nice to get more\n",
-      "statistics. For that we will do a parallel computation."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loops, best of 1: 1.19 s per loop"
      ]
-    },
+    }
+   ],
+   "source": [
+    "%timeit -r1 -n1 serial_diffs(serial_nmats, serial_matsize)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "serial_diffs = serial_diffs(serial_nmats, serial_matsize)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The numerical computation agrees with the predictions of Wigner, but it would be nice to get more\n",
+    "statistics. For that we will do a parallel computation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "hist_data = hist(serial_diffs, bins=30, normed=True)\n",
-      "plot(s, rhos)\n",
-      "xlabel('Normalized level spacing s')\n",
-      "ylabel('Probability $P(s)$')"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "&lt;matplotlib.text.Text at 0x3475bd0&gt;"
+      ]
+     },
+     "execution_count": 10,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "&lt;matplotlib.text.Text at 0x3475bd0&gt;"
-       ]
-      },
-      {
-       "output_type": "display_data",
-       "png": 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A+a990KKNYetQSnEu+Ry+n/jy299+o0X9FmW8RwhhaprNVrto0SJmzZpVkVWUm7kUjMzc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\nrkJggg==\n"
-      }
-     ],
-     "prompt_number": 10
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "image/png": [
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+       "+vpy4sQJ/fOEhARcXFxKbV9YYMz5MXfuXM0zSE7JKDklZ+GjvMyyYGzatImkpCTWrVuHm5ub1pGE\n",
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+       "rkJggg==\n"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "## Parallel calculation of nearest neighbor eigenvalue distribution"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hist_data = hist(serial_diffs, bins=30, normed=True)\n",
+    "plot(s, rhos)\n",
+    "xlabel('Normalized level spacing s')\n",
+    "ylabel('Probability $P(s)$')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parallel calculation of nearest neighbor eigenvalue distribution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we perform a parallel computation, where each process constructs and diagonalizes a subset of\n",
+    "the overall set of random matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def parallel_diffs(rc, num, N):\n",
+    "    nengines = len(rc.targets)\n",
+    "    num_per_engine = num/nengines\n",
+    "    print \"Running with\", num_per_engine, \"per engine.\"\n",
+    "    ar = rc.apply_async(ensemble_diffs, num_per_engine, N)\n",
+    "    diffs = np.array(ar.get()).flatten()\n",
+    "    normalized_diffs = normalize_diffs(diffs)\n",
+    "    return normalized_diffs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "client = Client()\n",
+    "view = client[:]\n",
+    "view.run('rmtkernel.py')\n",
+    "view.block = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "parallel_nmats = 40*serial_nmats\n",
+    "parallel_matsize = 50"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here we perform a parallel computation, where each process constructs and diagonalizes a subset of\n",
-      "the overall set of random matrices."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running with 10000 per engine.\n",
+      "1 loops, best of 1: 14 s per loop"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "def parallel_diffs(rc, num, N):\n",
-      "    nengines = len(rc.targets)\n",
-      "    num_per_engine = num/nengines\n",
-      "    print \"Running with\", num_per_engine, \"per engine.\"\n",
-      "    ar = rc.apply_async(ensemble_diffs, num_per_engine, N)\n",
-      "    diffs = np.array(ar.get()).flatten()\n",
-      "    normalized_diffs = normalize_diffs(diffs)\n",
-      "    return normalized_diffs"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "client = Client()\n",
-      "view = client[:]\n",
-      "view.run('rmtkernel.py')\n",
-      "view.block = False"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "parallel_nmats = 40*serial_nmats\n",
-      "parallel_matsize = 50"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%timeit -r1 -n1 parallel_diffs(view, parallel_nmats, parallel_matsize)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Running with 10000 per engine.\n",
-        "1 loops, best of 1: 14 s per loop"
-       ]
-      }
-     ],
-     "prompt_number": 14
     }
    ],
-   "metadata": {}
+   "source": [
+    "%timeit -r1 -n1 parallel_diffs(view, parallel_nmats, parallel_matsize)"
+   ]
   }
- ]
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 0
 }
\ No newline at end of file