From 92b295a42ce0519ee0267edeef61b36451e41e10 2024-12-10 13:33:37
From: M Bussonnier <bussonniermatthias@gmail.com>
Date: 2024-12-10 13:33:37
Subject: [PATCH] Remove all references to notebooks (#14572)

IPython repo is just the CLI, and those are unmaintained. Reduce the
size of the repo, and if anything those should migrate to notebook or
lab, or be a separate example repo.
---

diff --git a/examples/Embedding/Index.ipynb b/examples/Embedding/Index.ipynb
deleted file mode 100644
index ae87ef1..0000000
--- a/examples/Embedding/Index.ipynb
+++ /dev/null
@@ -1,210 +0,0 @@
-{
- "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": [
-    "# 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": [
-    {
-     "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": {},
-     "output_type": "display_data"
-    },
-    {
-     "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": {},
-     "output_type": "display_data"
-    },
-    {
-     "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": {},
-     "output_type": "display_data"
-    },
-    {
-     "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": {},
-     "output_type": "display_data"
-    },
-    {
-     "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": {},
-     "output_type": "display_data"
-    },
-    {
-     "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": {},
-     "output_type": "display_data"
-    },
-    {
-     "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": {},
-     "output_type": "display_data"
-    },
-    {
-     "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": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%run ../utils/list_pyfiles.ipy"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/Animations Using clear_output.ipynb b/examples/IPython Kernel/Animations Using clear_output.ipynb
deleted file mode 100644
index 850988d..0000000
--- a/examples/IPython Kernel/Animations Using clear_output.ipynb
+++ /dev/null
@@ -1,1359 +0,0 @@
-{
- "cells": [
-  {
-   "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": [
-    {
-     "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": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " 100/100 tasks finished after    6 s\n",
-      "done\n"
-     ]
-    }
-   ],
-   "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": [
-    {
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-       "tARBEARBEJqJSLQEQRAEQRCaiUi0BEEQBEEQmolItARBEARBEJqJSLQEQRAEQRCaiUi0BEEQBEEQ\n",
-       "msn/AbPOyGwnPY5DAAAAAElFTkSuQmCC\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10f01b310>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "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": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/Background Jobs.ipynb b/examples/IPython Kernel/Background Jobs.ipynb
deleted file mode 100644
index 22e22fd..0000000
--- a/examples/IPython Kernel/Background Jobs.ipynb
+++ /dev/null
@@ -1,404 +0,0 @@
-{
- "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",
-   "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... %i' % 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": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "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"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<BackgroundJob #3: printfunc(1,3)>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "In the background... 0\n",
-      "In the background... 1\n",
-      "In the background... 2\n",
-      "All done!\n"
-     ]
-    }
-   ],
-   "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": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Completed jobs:\n",
-      "0 : <function sleepfunc at 0x10521f2f0>\n",
-      "2 : <function sleepfunc at 0x10521f2f0>\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": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'args': (), 'interval': 4, 'kwargs': {}}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "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": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting job # 4 in a separate thread.\n",
-      "Starting job # 5 in a separate thread.\n"
-     ]
-    }
-   ],
-   "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": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "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/Users/minrk/dev/ip/mine/IPython/lib/backgroundjobs.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self)\u001b[0m\n",
-      "\u001b[0;32m    489\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m    490\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--> 491\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-1-169e49434ce0>\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"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Status of diejob1: %s\" % 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": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Traceback for: <BackgroundJob #4: <function diefunc at 0x10521f7b8>>\n",
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
-      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)\n",
-      "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/lib/backgroundjobs.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self)\u001b[0m\n",
-      "\u001b[0;32m    489\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m    490\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--> 491\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-1-169e49434ce0>\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 0x10521f7b8>>\n",
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
-      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)\n",
-      "\u001b[1;32m/Users/minrk/dev/ip/mine/IPython/lib/backgroundjobs.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self)\u001b[0m\n",
-      "\u001b[0;32m    489\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m    490\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--> 491\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-1-169e49434ce0>\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"
-     ]
-    }
-   ],
-   "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": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Flushing 3 Completed jobs.\n",
-      "Flushing 2 Dead jobs.\n"
-     ]
-    }
-   ],
-   "source": [
-    "jobs.flush()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After that, the status is simply empty:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "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": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting job # 0 in a separate thread.\n"
-     ]
-    }
-   ],
-   "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": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/Beyond Plain Python.ipynb b/examples/IPython Kernel/Beyond Plain Python.ipynb
deleted file mode 100644
index 50c123f..0000000
--- a/examples/IPython Kernel/Beyond Plain Python.ipynb
+++ /dev/null
@@ -1,2522 +0,0 @@
-{
- "cells": [
-  {
-   "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"
-     ]
-    }
-   ],
-   "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’re dealing with. Simply type `object_name.<TAB>` to view the object’s attributes. Besides Python objects and keywords, tab completion also works on file and directory names."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "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"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2+10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "22"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "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"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "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"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "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"
-     ]
-    }
-   ],
-   "source": [
-    "from __future__ import print_function\n",
-    "\n",
-    "print('last output:', _)\n",
-    "print('next one   :', __)\n",
-    "print('and next   :', ___)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false,
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'_10 == Out[10]'"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "In[11]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'In[11]'"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "_i"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'In[11]'"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "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"
-     ]
-    }
-   ],
-   "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"
-     ]
-    }
-   ],
-   "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": [
-      "/Users/minrk/dev/ip/mine/examples/IPython Kernel\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pwd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "My current directory's files:\n",
-      "['Animations Using clear_output.ipynb', 'Background Jobs.ipynb', 'Beyond Plain Python.ipynb', 'Capturing Output.ipynb', 'Cell Magics.ipynb', 'Custom Display Logic.ipynb', 'Index.ipynb', 'Old Custom Display Logic.ipynb', 'Plotting in the Notebook.ipynb', 'Raw Input in the Notebook.ipynb', 'Rich Output.ipynb', 'Script Magics.ipynb', 'SymPy.ipynb', 'Terminal Usage.ipynb', 'Third Party Rich Output.ipynb', 'Trapezoid Rule.ipynb', 'Working With External Code.ipynb', '__pycache__', 'data', 'example-demo.py', 'gui', 'ipython-completion.bash', 'ipython-get-history.py', 'ipython-qtconsole.desktop', 'ipython.desktop', 'mod.py', 'test.txt']\n"
-     ]
-    }
-   ],
-   "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": [
-      "[Animations Using clear_output.ipynb, Background Jobs.ipynb, Beyond Plain Python.ipynb, Capturing Output.ipynb, Cell Magics.ipynb, Custom Display Logic.ipynb, Index.ipynb, Old Custom Display Logic.ipynb, Plotting in the Notebook.ipynb, Raw Input in the Notebook.ipynb, Rich Output.ipynb, Script Magics.ipynb, SymPy.ipynb, Terminal Usage.ipynb, Third Party Rich Output.ipynb, Trapezoid Rule.ipynb, Working With External Code.ipynb, __pycache__, data, example-demo.py, gui, ipython-completion.bash, ipython-get-history.py, ipython-qtconsole.desktop, ipython.desktop, mod.py, test.txt]\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "!echo $files"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ANIMATIONS USING CLEAR_OUTPUT.IPYNB\r\n"
-     ]
-    }
-   ],
-   "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": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "00 - Animations Using clear_output\n",
-      "01 - Background Jobs\n",
-      "02 - Beyond Plain Python\n",
-      "03 - Capturing Output\n",
-      "04 - Cell Magics\n",
-      "05 - Custom Display Logic\n",
-      "06 - Index\n",
-      "07 - Old Custom Display Logic\n",
-      "08 - Plotting in the Notebook\n",
-      "09 - Raw Input in the Notebook\n",
-      "10 - Rich Output\n",
-      "11 - Script Magics\n",
-      "12 - SymPy\n",
-      "13 - Terminal Usage\n",
-      "14 - Third Party Rich Output\n",
-      "15 - Trapezoid Rule\n",
-      "16 - Working With External Code\n",
-      "--\n",
-      "--\n",
-      "--\n",
-      "--\n",
-      "--\n",
-      "--\n",
-      "--\n",
-      "--\n",
-      "--\n",
-      "--\n"
-     ]
-    }
-   ],
-   "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": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%magic"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Line vs cell magics:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10000 loops, best of 3: 19.3 µs per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit list(range(1000))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "100000 loops, best of 3: 2.78 µs per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "list(range(10))\n",
-    "list(range(100))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Line magics can be used even inside code blocks:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "size: 100 100000 loops, best of 3: 1.86 µs per loop\n",
-      "size: 200 100000 loops, best of 3: 2.49 µs per loop\n",
-      "size: 300 100000 loops, best of 3: 4.04 µs per loop\n",
-      "size: 400 100000 loops, best of 3: 6.21 µs per loop\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in range(1, 5):\n",
-    "    size = i*100\n",
-    "    print('size:', size, end=' ')\n",
-    "    %timeit list(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": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "My shell is: /usr/local/bin/bash\n",
-      "My disk usage is:\n",
-      "Filesystem      Size   Used  Avail Capacity  iused   ifree %iused  Mounted on\n",
-      "/dev/disk1     233Gi  216Gi   16Gi    94% 56788108 4190706   93%   /\n",
-      "devfs          190Ki  190Ki    0Bi   100%      656       0  100%   /dev\n",
-      "map -hosts       0Bi    0Bi    0Bi   100%        0       0  100%   /net\n",
-      "map auto_home    0Bi    0Bi    0Bi   100%        0       0  100%   /home\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%bash\n",
-    "echo \"My shell is:\" $SHELL\n",
-    "echo \"My disk usage is:\"\n",
-    "df -h"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another interesting cell magic: create any file you want locally from the notebook:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwriting 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": 29,
-   "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..."
-     ]
-    }
-   ],
-   "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": 30,
-   "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",
-        "clear": "KernelMagics",
-        "colors": "BasicMagics",
-        "config": "ConfigMagics",
-        "connect_info": "KernelMagics",
-        "cp": "Other",
-        "debug": "ExecutionMagics",
-        "dhist": "OSMagics",
-        "dirs": "OSMagics",
-        "doctest_mode": "BasicMagics",
-        "ed": "Other",
-        "edit": "KernelMagics",
-        "env": "OSMagics",
-        "gui": "BasicMagics",
-        "hist": "Other",
-        "history": "HistoryMagics",
-        "install_default_config": "DeprecatedMagics",
-        "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",
-        "namespace": "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",
-        "set_env": "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  %cat  %cd  %clear  %colors  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %install_default_config  %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  %namespace  %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  %set_env  %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  %%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": 30,
-     "metadata": {},
-     "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": 31,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n",
-      "1\n",
-      "2\n",
-      "3\n",
-      "5\n",
-      "8\n"
-     ]
-    }
-   ],
-   "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": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 1 2 3 4 5 6 7 8 9 "
-     ]
-    }
-   ],
-   "source": [
-    "In [1]: for i in range(10):\n",
-    "   ...:     print(i, end=' ')\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": 33,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Overwriting mod.py\n"
-     ]
-    }
-   ],
-   "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": 34,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "float division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-40-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/Users/minrk/dev/ip/mine/examples/IPython Kernel/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/Users/minrk/dev/ip/mine/examples/IPython Kernel/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"
-     ]
-    }
-   ],
-   "source": [
-    "import mod\n",
-    "mod.g(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Exception reporting mode: Plain\n"
-     ]
-    },
-    {
-     "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-41-8932f4bf53fa>\"\u001b[0m, line \u001b[0;32m2\u001b[0m, in \u001b[0;35m<module>\u001b[0m\n    mod.g(0)\n",
-      "  File \u001b[0;32m\"/Users/minrk/dev/ip/mine/examples/IPython Kernel/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\"/Users/minrk/dev/ip/mine/examples/IPython Kernel/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"
-     ]
-    }
-   ],
-   "source": [
-    "%xmode plain\n",
-    "mod.g(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Exception reporting mode: Verbose\n"
-     ]
-    },
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "float division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-42-539f73e80e01>\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;34m'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 0x105887d08>\u001b[0m\n",
-      "\u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/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 0x105887d90>\u001b[0m\u001b[1;34m\n        \u001b[0m\u001b[0;36my\u001b[0m \u001b[1;34m= 0\u001b[0m\n",
-      "\u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/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"
-     ]
-    }
-   ],
-   "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": 37,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Exception reporting mode: Context\n"
-     ]
-    }
-   ],
-   "source": [
-    "%xmode context"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Running code in other languages with special `%%` magics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "July"
-     ]
-    }
-   ],
-   "source": [
-    "%%perl\n",
-    "@months = (\"July\", \"August\", \"September\");\n",
-    "print $months[0];"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hello World!\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%ruby\n",
-    "name = \"world\"\n",
-    "puts \"Hello #{name.capitalize}!\""
-   ]
-  },
-  {
-   "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": 40,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "float division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-46-5e708f13c839>\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/Users/minrk/dev/ip/mine/examples/IPython Kernel/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/Users/minrk/dev/ip/mine/examples/IPython Kernel/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"
-     ]
-    }
-   ],
-   "source": [
-    "mod.g(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "> \u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m(3)\u001b[0;36mf\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32m      2 \u001b[1;33m\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[0m\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[1;32m      4 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
-      "ipdb> up\n",
-      "> \u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m(6)\u001b[0;36mg\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32m      4 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[1;32m      5 \u001b[1;33m\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[0m\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\n",
-      "ipdb> down\n",
-      "> \u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m(3)\u001b[0;36mf\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32m      2 \u001b[1;33m\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[0m\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[1;32m      4 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
-      "ipdb> bt\n",
-      "  \u001b[1;32m<ipython-input-46-5e708f13c839>\u001b[0m(1)\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\n",
-      "  \u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m(6)\u001b[0;36mg\u001b[1;34m()\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[0;32m      3 \u001b[0m    \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[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\n",
-      "> \u001b[1;32m/Users/minrk/dev/ip/mine/examples/IPython Kernel/mod.py\u001b[0m(3)\u001b[0;36mf\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32m      1 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[1;32m      2 \u001b[1;33m\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[0m\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[1;32m      4 \u001b[1;33m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[1;32m      5 \u001b[1;33m\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[0m\n",
-      "ipdb> exit\n"
-     ]
-    }
-   ],
-   "source": [
-    "%debug"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Don't forget to exit your debugging session. Raw input can of course be used to ask for user input:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Are you enjoying this tutorial? yes\n",
-      "enjoy is: yes\n"
-     ]
-    }
-   ],
-   "source": [
-    "enjoy = 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": 43,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
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-       "NnPy9WQz184Enp+mqbPXVAf/9qp0aZp+FDgLuJrszaP3AKcAv5ym6StqXJrmWJqm24EzgLeTvUn0\n",
-       "L8BfkFUCPiJN00trXJ7a4bnAncA7i7rgqn37/LstSZIkSZKkZmp75ZUkSZIkSZIazPBKkiRJkiRJ\n",
-       "jWV4JUmSJEmSpMYyvJIkSZIkSVJjGV5JkiRJkiSpsQyvJEmSJEmS1FiGV5IkSZIkSWoswytJkiRJ\n",
-       "kiQ1luGVJEmSJEmSGsvwSpIkSZIkSY1leCVJkiRJkqTGMrySJEmSJElSYxleSZIkSZIkqbEMryRJ\n",
-       "kiRJktRYhleSJEmSJElqLMMrSZIkSZIkNZbhlSRJkiRJkhrL8EqSJEmSJEmNZXglSZIkSZKkxvr/\n",
-       "gUJ07DcuELgAAAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x106c2a9b0>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 392,
-       "width": 599
-      }
-     },
-     "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": 46,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{\n",
-      "  \"stdin_port\": 62401,\n",
-      "  \"key\": \"64c935a7-64e8-4ab7-ab22-6e0f3ff84e02\",\n",
-      "  \"hb_port\": 62403,\n",
-      "  \"transport\": \"tcp\",\n",
-      "  \"signature_scheme\": \"hmac-sha256\",\n",
-      "  \"shell_port\": 62399,\n",
-      "  \"control_port\": 62402,\n",
-      "  \"ip\": \"127.0.0.1\",\n",
-      "  \"iopub_port\": 62400\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-25383540-ce7f-4529-900a-ded0e510d5d8.json \n",
-      "or even just:\n",
-      "    $> ipython <app> --existing \n",
-      "if this is the most recent IPython session you have started.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%connect_info"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can automatically connect 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": 47,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%qtconsole"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/Capturing Output.ipynb b/examples/IPython Kernel/Capturing Output.ipynb
deleted file mode 100644
index b04580b..0000000
--- a/examples/IPython Kernel/Capturing Output.ipynb
+++ /dev/null
@@ -1,505 +0,0 @@
-{
- "cells": [
-  {
-   "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": 1,
-   "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": 2,
-   "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": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%%capture captured\n",
-    "print('hi, stdout')\n",
-    "print('hi, stderr', file=sys.stderr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<IPython.utils.capture.CapturedIO at 0x1076c9310>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "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": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hi, stdout\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "hi, stderr\n"
-     ]
-    }
-   ],
-   "source": [
-    "captured()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'hi, stdout\\n'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "captured.stdout"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'hi, stderr\\n'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "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": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "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": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "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"
-     ]
-    },
-    {
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-       "qXxDhV5E5Hfly8M995iLxxs0gMsvN6P87dttJzvbsWPmspWiKHGhnz9/Po0bN6Z8+fJ89tlnhT63\n",
-       "bNkyYmJiqF+/Ps8880xJ305EpMxcfDH8z//Av/9tTsO88kq48Ub49FP7Pfyffzb7AaKjzdHMRVHi\n",
-       "Qt+0aVMWLFhA+/btC30mPz+fYcOGsWzZMjZv3kxqaipbtmwp6VsGDY/HYzuCY+h7cYq+F6eU1ffi\n",
-       "0kthwgTYscMcfTxwIDRuDM8+C99/XyYRAHM37vLl0L8/1KljWkzz5kF6etE+v8SFPiYmhgYNGpzz\n",
-       "mczMTKKjo4mKiqJChQr07duXRYsWlfQtg4b+Qp+i78Up+l6cUtbfi0qV4N57YetWmDrV/Nq4MVx1\n",
-       "FUyaBF995fuR/oEDsGABDB4M4eHw6KPmfP3t2yE11bSVisqvF4/k5uYSGRl58uOIiAjWrl3rz7cU\n",
-       "EfGbE9cVJiSYYxQ8HliyxByYdvCgafFceaW53aphQ3OoWlFOzNy7F/7v/8xr3TpYs8b8vm1bc2TD\n",
-       "o49CvXolz33OQt+lSxd27dp11j+fOHEiPXr0OO8XDwkJKXkyEREHu+giuPZa80pJgZwcU6AzMuCV\n",
-       "V8yoPzcXqlUzxyzUqAEXXGA+1+uF/fvNaZp79ph/1qCBebVqZVb8tGwJF17oo7DeUkpMTPRu2LCh\n",
-       "wD9bs2aNt2vXric/njhxonfSpEkFPluvXj0voJdeeumlVzFe9erVO2+d9knrxltIcyo+Pp5t27ax\n",
-       "Y8cOateuzdy5c0lNTS3w2e1OXL8kIuICJZ6MXbBgAZGRkWRkZNC9e3euu+46AL7//nu6d+8OQGho\n",
-       "KCkpKXTt2pVGjRrRp08fYmNjfZNcRESKxDFHIIiIiH9Y3xmrDVWnDBo0iLCwMJoGyiEbfpKdnU2H\n",
-       "Dh1o3LgxTZo04aWXXrIdyZpff/2VNm3a0KJFCxo1asQjjzxiO5J1+fn5xMXFFWlBiJtFRUXRrFkz\n",
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-       "165dtGjRgl9++YVWrVqxcOHCoP3v4tChQ1SsWJFjx47Rrl07nnvuOdq1a2c7ljUvvPACGzZs4MCB\n",
-       "AyxevNh2HGvq1q3Lhg0bqF69+nmftTqi14aq0yUkJFCtWjXbMayrWbMmLVq0AKBy5crExsbyfVlu\n",
-       "Q3SYihUrAnDkyBHy8/OL9BfbrXJyckhLS+OOO+7QabcUvhDmTFYLfUEbqnJzcy0mEqfZsWMHWVlZ\n",
-       "tGnTxnYUa44fP06LFi0ICwujQ4cONGrUyHYka+6//34mT55MuXLWu87WhYSE0LlzZ+Lj45k+ffo5\n",
-       "n7X63dKGKjmXX375hZtvvpkpU6ZQuXJl23GsKVeuHJ9//jk5OTl88sknQXsUwpIlS7jsssuIi4vT\n",
-       "aB5IT08nKyuLpUuX8ve//51Vq1YV+qzVQh8eHk52dvbJj7Ozs4mIiLCYSJzi6NGj3HTTTdx66630\n",
-       "7NnTdhxHqFKlCt27d2f9+vW2o1ixevVqFi9eTN26denXrx8fffQRt99+u+1Y1tSqVQuAP/3pT9x4\n",
-       "441kZmYW+qzVQv/HDVVHjhxh7ty5JCUl2YwkDuD1ehk8eDCNGjVi5MiRtuNYtWfPHvbt2wfA4cOH\n",
-       "+eCDD4iLi7Ocyo6JEyeSnZ3Nt99+y5w5c+jYsSOzZs2yHcuKQ4cOceDAAQAOHjzI8uXLz7laz2qh\n",
-       "14aq0/VNJLb3AAAAlUlEQVTr14+2bduydetWIiMjmTFjhu1IVqSnpzN79mxWrlxJXFwccXFxLFu2\n",
-       "zHYsK3bu3EnHjh1p0aIFbdq0oUePHnTq1Ml2LEcI5tZvXl4eCQkJJ/+7uP7667nmmmsKfV4bpkRE\n",
-       "XE5T1yIiLqdCLyLicir0IiIup0IvIuJyKvQiIi6nQi8i4nIq9CIiLqdCLyLicv8PoaUAhzXYTeQA\n",
-       "AAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10866ae90>"
-      ]
-     },
-     "metadata": {},
-     "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": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "hello, stderr\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%capture cap --no-stderr\n",
-    "print('hi, stdout')\n",
-    "print(\"hello, stderr\", file=sys.stderr)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'hi, stdout\\n'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cap.stdout"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "''"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cap.stderr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cap.outputs"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/Cell Magics.ipynb b/examples/IPython Kernel/Cell Magics.ipynb
deleted file mode 100644
index e9be41f..0000000
--- a/examples/IPython Kernel/Cell Magics.ipynb
+++ /dev/null
@@ -1,688 +0,0 @@
-{
- "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 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",
-        "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",
-        "clear": "KernelMagics",
-        "colors": "BasicMagics",
-        "config": "ConfigMagics",
-        "connect_info": "KernelMagics",
-        "cp": "Other",
-        "debug": "ExecutionMagics",
-        "dhist": "OSMagics",
-        "dirs": "OSMagics",
-        "doctest_mode": "BasicMagics",
-        "ed": "Other",
-        "edit": "KernelMagics",
-        "env": "OSMagics",
-        "gui": "BasicMagics",
-        "hist": "Other",
-        "history": "HistoryMagics",
-        "install_default_config": "DeprecatedMagics",
-        "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",
-        "set_env": "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  %clear  %colors  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %install_default_config  %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  %set_env  %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": 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: 7.06 ms per loop\n"
-     ]
-    }
-   ],
-   "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: 7.4 ms per loop\n"
-     ]
-    }
-   ],
-   "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"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "and stderr\n"
-     ]
-    }
-   ],
-   "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"
-     ]
-    }
-   ],
-   "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"
-     ]
-    }
-   ],
-   "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 2.7.9 (default, Jan 29 2015, 06:27:40) \n",
-      "[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.56)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%script python2\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: 3.4.2 |Continuum Analytics, Inc.| (default, Oct 21 2014, 17:42:20) \n",
-      "[GCC 4.2.1 (Apple Inc. build 5577)]\n"
-     ]
-    }
-   ],
-   "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 2.0.0\n"
-     ]
-    }
-   ],
-   "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"
-     ]
-    }
-   ],
-   "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": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hi, stdout\n"
-     ]
-    },
-    {
-     "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": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%%bash --out output --err error\n",
-    "echo \"hi, stdout\"\n",
-    "echo \"hello, stderr\" >&2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hello, stderr\n",
-      "\n",
-      "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": 17,
-   "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": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<_io.BufferedReader name=52>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ruby_lines"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "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"
-     ]
-    }
-   ],
-   "source": [
-    "print(ruby_lines.read().decode('utf8'))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/Custom Display Logic.ipynb b/examples/IPython Kernel/Custom Display Logic.ipynb
deleted file mode 100644
index 447c34a..0000000
--- a/examples/IPython Kernel/Custom Display Logic.ipynb
+++ /dev/null
@@ -1,976 +0,0 @@
-{
- "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": true
-   },
-   "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": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.ion()"
-   ]
-  },
-  {
-   "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 a JSONable dict\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 \"$\".\n",
-    "* `_repr_mimebundle_`: return a full mimebundle containing the mapping from all mimetypes to data "
-   ]
-  },
-  {
-   "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": true
-   },
-   "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:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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a4Ji99kmDY88zY8peK66WeWQvSQ0w7CWpAYa9JDXAsJekBhj2ktQAw16SGmDYS1IDvM5e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\n1ADDXpIa8P8BEb7Ae9JKdQAAAAAASUVORK5CYII=\n",
-      "text/latex": [
-       "$\\mathcal{N}(\\mu=2, \\sigma=1),\\ N=1000$"
-      ],
-      "text/plain": [
-       "<__main__.Gaussian at 0x116fe76d8>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "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": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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a4Ji99kmDY88zY8peK66WeWQvSQ0w7CWpAYa9JDXAsJekBhj2ktQAw16SGmDYS1IDvM5e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\n1ADDXpIa8P8BEb7Ae9JKdQAAAAAASUVORK5CYII=\n",
-      "text/latex": [
-       "$\\mathcal{N}(\\mu=2, \\sigma=1),\\ N=1000$"
-      ],
-      "text/plain": [
-       "<__main__.Gaussian at 0x116fe76d8>"
-      ]
-     },
-     "metadata": {},
-     "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": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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a4Ji99kmDY88zY8peK66WeWQvSQ0w7CWpAYa9JDXAsJekBhj2ktQAw16SGmDYS1IDvM5e\nzfP6e7XAI3tJaoBhL0kNMOwlqQGO2Wuf55j8ZM3Xn97jfvXyyF6SGmDYS1IDDHtJaoBj9lqTZo8N\nOy4/fUvpY8fwV4+xjuyTnJ7kniT3Jdk2qaIkSZO15LBPsg74PeAM4FjgzUmOnVRhkqTJGefI/kTg\nvqp6oKr+HrgcOHsyZUmSJilVtbQVkzcAp1fVL/bTbwV+rKoumLXcVmBrP/nDwB1LL3fZHAp8ZaWL\nGMFaqHMt1AjWOWnWOVmvqKoDx9nAOCdoM2Tesz45qmo7sB0gya6q2jzGay4L65yctVAjWOekWedk\nJdk17jbGGcZ5EHjxwPSRwEPjlSNJmoZxwv4vgaOTHJVkf+Bc4NrJlCVJmqQlD+NU1dNJLgA+BqwD\nLq2qOxdYbftSX2+ZWefkrIUawTonzTona+w6l3yCVpK0dni7BElqgGEvSQ2YeNgneWOSO5M8k2Tz\nrLZ39LdWuCfJz86x/lFJbkpyb5Ir+pO/U9W/zq39Y3eSW+dYbneSz/XLjX0p1BLqvCjJlwdqPXOO\n5VbsNhZJ/muSu5PcnuTqJAfNsdyK9OVCfZPkgH5/uK/fDzcuV20DNbw4ySeT3NX/X3rbkGVOSfL4\nwL7wG8tdZ1/HvO9jOv+978/bk7xqBWp8xUA/3Zrk60nePmuZFenPJJcm2ZPkjoF5hyTZ2WfgziQH\nz7Huln6Ze5NsWfDFqmqiD+AY4BXAp4DNA/OPBW4DDgCOAu4H1g1Z/0rg3P75+4F/OekaF6j/t4Hf\nmKNtN3DoctYz6/UvAv7dAsus6/v2ZcD+fZ8fu4w1ngas75+/G3j3aunLUfoG+FfA+/vn5wJXrMD7\nfATwqv75gcDnh9R5CnDdcte22PcROBP4KN3v5ZwE3LTC9a4D/gZ46WroT+CngFcBdwzM+y1gW/98\n27D/Q8AhwAP9z4P75wfP91oTP7Kvqruq6p4hTWcDl1fVN6vqr4H76G658B1JArwa+FA/awdwzqRr\nnEv/+m8C/mS5XnMKVvQ2FlX18ap6up/8C7rfv1gtRumbs+n2O+j2w1P7/WLZVNXDVfXZ/vkTwF3A\ni5azhgk6G/ij6vwFcFCSI1awnlOB+6vqCytYw3dU1aeBr82aPbgPzpWBPwvsrKqvVdWjwE7g9Ple\naznH7F8EfGlg+kGevQP/APDYQFgMW2aafhJ4pKrunaO9gI8nubm/DcRKuKD/OnzpHF/vRunn5XIe\n3VHdMCvRl6P0zXeW6ffDx+n2yxXRDyOdANw0pPkfJbktyUeTvHJZC/uuhd7H1bQ/Qvdtba6DudXQ\nnwCHV9XD0H3wA4cNWWbR/bqk6+yT/F/ghUOaLqyqa+Zabci82dd9jnQLhqUYseY3M/9R/clV9VCS\nw4CdSe7uP5knZr46gfcBv0nXJ79JN+R03uxNDFl3otfXjtKXSS4EngYum2MzU+/LIVZ0H1ysJM8H\nPgy8vaq+Pqv5s3RDEU/2524+Ahy93DWy8Pu4mvpzf+B1wDuGNK+W/hzVovt1SWFfVa9Zwmqj3F7h\nK3Rf89b3R1UTuwXDQjUnWQ/8E+BH59nGQ/3PPUmuphsWmGhAjdq3Sf4QuG5I09RvYzFCX24BXguc\nWv0A45BtTL0vhxilb2aWebDfJ17As79mT12S/eiC/rKqump2+2D4V9UNSX4/yaFVtaw39RrhfVxN\nt1U5A/hsVT0yu2G19GfvkSRHVNXD/ZDXniHLPEh3nmHGkXTnSee0nMM41wLn9lc7HEX3qfmZwQX6\nYPgk8IZ+1hZgrm8Kk/Ya4O6qenBYY5LnJTlw5jndichlvYPnrLHO18/x+it6G4skpwO/Bryuqv52\njmVWqi9H6Ztr6fY76PbDT8z1gTUt/TmCS4C7qup35ljmhTPnEpKcSPd/+avLV+XI7+O1wC/0V+Wc\nBDw+M0SxAub85r4a+nPA4D44VwZ+DDgtycH9cO5p/by5TeHs8uvpPnW+CTwCfGyg7UK6qyHuAc4Y\nmH8D8IP985fRfQjcB/wf4IBJ1zhH3R8EfnnWvB8Ebhio67b+cSfdkMVyn7n/Y+BzwO39DnHE7Dr7\n6TPpruC4f7nr7N+3LwG39o/3z65xJftyWN8A76T7cAJ4br/f3dfvhy9bgff5J+i+kt8+0I9nAr88\ns48CF/R9dxvdifAfX4E6h76Ps+oM3R85ur/fdzcvd519Hf+ALrxfMDBvxfuT7sPnYeBbfW6eT3eO\n6Ebg3v7nIf2ym4EPDKx7Xr+f3gf884Vey9slSFID/A1aSWqAYS9JDTDsJakBhr0kNcCwl6QGGPaS\n1ADDXpIa8P8BEb7Ae9JKdQAAAAAASUVORK5CYII=\n"
-     },
-     "metadata": {},
-     "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": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/latex": [
-       "$\\mathcal{N}(\\mu=0, \\sigma=2),\\ N=2000$"
-      ],
-      "text/plain": [
-       "<__main__.Gaussian at 0x116fe7668>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "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": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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-     },
-     "metadata": {},
-     "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": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Polynomial([ 1.,  2.,  3.], [-10.,  10.], [-1,  1])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "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": true
-   },
-   "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": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'$P(x)=1+2 x+3 x^2$, $x \\\\in [-10,\\\\ 10]$'"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "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": {},
-   "outputs": [
-    {
-     "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": {},
-     "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 `poly_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": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              text/plain : PlainTextFormatter\n",
-      "               text/html : HTMLFormatter\n",
-      "           text/markdown : MarkdownFormatter\n",
-      "           image/svg+xml : SVGFormatter\n",
-      "               image/png : PNGFormatter\n",
-      "         application/pdf : PDFFormatter\n",
-      "              image/jpeg : JPEGFormatter\n",
-      "              text/latex : LatexFormatter\n",
-      "        application/json : JSONFormatter\n",
-      "  application/javascript : JavascriptFormatter\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": true
-   },
-   "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": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on method for_type in module IPython.core.formatters:\n",
-      "\n",
-      "for_type(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"
-     ]
-    }
-   ],
-   "source": [
-    "help(latex_f.for_type)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on method for_type_by_name in module IPython.core.formatters:\n",
-      "\n",
-      "for_type_by_name(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": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "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": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "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": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "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": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "p2 = np.polynomial.Polynomial([-20, 71, -15, 1])\n",
-    "p2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Custom Mimetypes with `_repr_mimebundle_`\n",
-    "\n",
-    "Available on IPython 5.4+ and 6.1+.\n",
-    "\n",
-    "For objects needing full control over the `repr` protocol may decide to implement the `_repr_mimebundle_(include, exclude)` method.\n",
-    "Unlike the other `_repr_*_` methods must return many representation of the object in a mapping object which keys are _mimetypes_ and value are associated data. The `_repr_mimebundle_()` method, may also return a second mapping from _mimetypes_ to metadata. \n",
-    "\n",
-    "Example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "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_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 _compute_mathml(self):\n",
-    "        return \"\"\"\n",
-    "        <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n",
-    "          <mrow class=\"MJX-TeXAtom-ORD\">\n",
-    "            <mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">N</mi>\n",
-    "          </mrow>\n",
-    "          <mo stretchy=\"false\">(</mo>\n",
-    "          <mi>&#x03BC;<!-- μ --></mi>\n",
-    "          <mo>=</mo>\n",
-    "          <mn>{mu}</mn>\n",
-    "          <mo>,</mo>\n",
-    "          <mi>&#x03C3;<!-- σ --></mi>\n",
-    "          <mo>=</mo>\n",
-    "          <mn>{sigma}</mn>\n",
-    "          <mo stretchy=\"false\">)</mo>\n",
-    "          <mo>,</mo>\n",
-    "          <mtext>&#xA0;</mtext>\n",
-    "          <mi>N</mi>\n",
-    "          <mo>=</mo>\n",
-    "          <mn>{N}</mn>\n",
-    "        </math>\n",
-    "        \"\"\".format(N=self.size, mu=self.mean, sigma=self.std)\n",
-    "        \n",
-    "    def _repr_mimebundle_(self, include, exclude, **kwargs):\n",
-    "        \"\"\"\n",
-    "        repr_mimebundle should accept include, exclude and **kwargs\n",
-    "        \"\"\"\n",
-    "        if self._png_data is None:\n",
-    "            self._png_data = self._figure_data('png')\n",
-    "        math = r'$\\mathcal{N}(\\mu=%.2g, \\sigma=%.2g),\\ N=%d$' % (self.mean,\n",
-    "                                                                 self.std, self.size)\n",
-    "        data = {'image/png':self._png_data,\n",
-    "                'text/latex':math,\n",
-    "                'application/mathml+xml': self._compute_mathml()\n",
-    "                }\n",
-    "        if include:\n",
-    "            data = {k:v for (k,v) in data.items() if k in include}\n",
-    "        if exclude:\n",
-    "            data = {k:v for (k,v) in data.items() if k not in exclude}\n",
-    "        return data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/mathml+xml": "\n        <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n          <mrow class=\"MJX-TeXAtom-ORD\">\n            <mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">N</mi>\n          </mrow>\n          <mo stretchy=\"false\">(</mo>\n          <mi>&#x03BC;<!-- μ --></mi>\n          <mo>=</mo>\n          <mn>0.0</mn>\n          <mo>,</mo>\n          <mi>&#x03C3;<!-- σ --></mi>\n          <mo>=</mo>\n          <mn>1</mn>\n          <mo stretchy=\"false\">)</mo>\n          <mo>,</mo>\n          <mtext>&#xA0;</mtext>\n          <mi>N</mi>\n          <mo>=</mo>\n          <mn>1000</mn>\n        </math>\n        ",
-      "image/png": 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uesPj7av3nn1VHaiq+4Y8tRX4UFV9t6r+FbifwS0Xvi9JgJcA13eLdgOX913jUrr9vx74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\nDfgfS9fLKUqMYTsAAAAASUVORK5CYII=\n",
-      "text/latex": [
-       "$\\mathcal{N}(\\mu=0, \\sigma=1),\\ N=1000$"
-      ],
-      "text/plain": [
-       "<__main__.Gaussian at 0x11a614e80>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# that is definitively wrong as it should show the PNG. \n",
-    "display(Gaussian())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the above example, the 3 mimetypes are embedded in the notebook document this allowing custom extensions and converters to display the representation(s) of their choice.\n",
-    "\n",
-    "For example, converting this noetebook to _epub_ may decide to use the MathML representation as most ebook reader cannot run mathjax (unlike browsers). \n",
-    "\n",
-    "\n",
-    "### Implementation guidelines\n",
-    "\n",
-    "The `_repr_mimebundle_` methods is also given two keywords parameters :  `include` and `exclude`. Each can be a  containers (e.g.:`list`, `set` ...) of mimetypes to return or `None`, This allows implementation to avoid computing potentially unnecessary and expensive mimetypes representations. \n",
-    "\n",
-    "When `include` is non-empty (empty `list` or None), `_repr_mimebundle_` may decide to returns only the mimetypes in include.\n",
-    "When `exclude` is non-empty, `_repr_mimebundle_` may decide to not return any mimetype in exclude. \n",
-    "If both `include` and `exclude` and overlap, mimetypes present in exclude may not be returned. \n",
-    "\n",
-    "If implementations decide to ignore the `include` and `exclude` logic and always returns a full mimebundles, the IPython kernel will take care of removing non-desired representations.\n",
-    "\n",
-    "The `_repr_mimebundle_` method should accept arbitrary keyword arguments for future compatiility.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$\\mathcal{N}(\\mu=0, \\sigma=1),\\ N=1000$"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display(Gaussian(), include={'text/latex'}) # only show latex"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/mathml+xml": "\n        <math xmlns=\"http://www.w3.org/1998/Math/MathML\">\n          <mrow class=\"MJX-TeXAtom-ORD\">\n            <mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">N</mi>\n          </mrow>\n          <mo stretchy=\"false\">(</mo>\n          <mi>&#x03BC;<!-- μ --></mi>\n          <mo>=</mo>\n          <mn>0.0</mn>\n          <mo>,</mo>\n          <mi>&#x03C3;<!-- σ --></mi>\n          <mo>=</mo>\n          <mn>1</mn>\n          <mo stretchy=\"false\">)</mo>\n          <mo>,</mo>\n          <mtext>&#xA0;</mtext>\n          <mi>N</mi>\n          <mo>=</mo>\n          <mn>1000</mn>\n        </math>\n        ",
-      "text/latex": [
-       "$\\mathcal{N}(\\mu=0, \\sigma=1),\\ N=1000$"
-      ],
-      "text/plain": [
-       "<__main__.Gaussian at 0x116fe7550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display(Gaussian(), exclude={'image/png'}) # exclude png"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<__main__.Gaussian at 0x11a8a0b38>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display(Gaussian(), include={'text/plain', 'image/png'}, exclude={'image/png'}) # keep only plain/text"
-   ]
-  },
-  {
-   "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": 25,
-   "metadata": {
-    "collapsed": true
-   },
-   "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(list(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": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div id=\"c6929609-3cb6-4443-9574-d9f71791a987\" style=\"height: 300px; width:80%;\"></div>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "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], [0.6122448979591837, 0.5747060412161791], [0.8163265306122449, 0.7286347834693503], [1.0204081632653061, 0.8523215697196184], [1.2244897959183674, 0.9406327851124867], [1.4285714285714286, 0.9899030763721239], [1.6326530612244898, 0.9980874821347183], [1.836734693877551, 0.9648463089837632], [2.0408163265306123, 0.8915592304110037], [2.2448979591836737, 0.7812680235262639], [2.4489795918367347, 0.6385503202266021], [2.6530612244897958, 0.469329612777201], [2.857142857142857, 0.28062939951435684], [3.0612244897959187, 0.0802816748428135], [3.2653061224489797, -0.12339813736217871], [3.4693877551020407, -0.3219563150726187], [3.673469387755102, -0.5071517094845144], [3.8775510204081636, -0.6712977935519321], [4.081632653061225, -0.8075816909683364], [4.285714285714286, -0.9103469443107828], [4.4897959183673475, -0.9753282860670456], [4.6938775510204085, -0.9998286683840896], [4.8979591836734695, -0.9828312039256306], [5.1020408163265305, -0.9250413717382029], [5.3061224489795915, -0.8288577363730427], [5.510204081632653, -0.6982723955653996], [5.714285714285714, -0.5387052883861563], [5.918367346938775, -0.35677924089893803], [6.122448979591837, -0.16004508604325057], [6.326530612244898, 0.04333173336868346], [6.530612244897959, 0.2449100710119793], [6.73469387755102, 0.4363234264718193], [6.938775510204081, 0.6096271964908323], [7.142857142857143, 0.7576284153927202], [7.346938775510204, 0.8741842988197335], [7.551020408163265, 0.9544571997387519], [7.755102040816327, 0.9951153947776636], [7.959183673469388, 0.9944713672636168], [8.16326530612245, 0.9525518475314604], [8.36734693877551, 0.8710967034823207], [8.571428571428571, 0.7534867274396376], [8.775510204081632, 0.6046033165061543], [8.979591836734695, 0.43062587038273736], [9.183673469387756, 0.23877531564403087], [9.387755102040817, 0.03701440148506237], [9.591836734693878, -0.1662827938487564], [9.795918367346939, -0.3626784288265488], [10.0, -0.5440211108893699]]\");\n",
-       "          console.log(line);\n",
-       "          $.plot(\"#c6929609-3cb6-4443-9574-d9f71791a987\", [line]);\n",
-       "        });\n",
-       "        "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "x = np.linspace(0,10)\n",
-    "y = np.sin(x)\n",
-    "FlotPlot(x, np.sin(x))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/IPython Kernel/Importing Notebooks.ipynb b/examples/IPython Kernel/Importing Notebooks.ipynb
deleted file mode 100644
index 9d091aa..0000000
--- a/examples/IPython Kernel/Importing Notebooks.ipynb
+++ /dev/null
@@ -1,763 +0,0 @@
-{
- "cells": [
-  {
-   "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](https://peps.python.org/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": [
-    "import nbformat\n",
-    "\n",
-    "from IPython import get_ipython\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 notebook from %s\" % path)\n",
-    "                                       \n",
-    "        # load the notebook object\n",
-    "        nb = nbformat.read(path, as_version=4)\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",
-    "        mod.__dict__['get_ipython'] = get_ipython\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.cells:\n",
-    "            if cell.cell_type == 'code':\n",
-    "                # transform the input to executable Python\n",
-    "                code = self.shell.input_transformer_manager.transform_cell(cell.source)\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       \u001b[34m__pycache__\u001b[m\u001b[m/      mynotebook.ipynb  \u001b[34mnbs\u001b[m\u001b[m/\r\n"
-     ]
-    }
-   ],
-   "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",
-       ".mb { color: #666666 } /* Literal.Number.Bin */\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 object>"
-      ]
-     },
-     "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": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<h4>markdown 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 object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def show_notebook(fname):\n",
-    "    \"\"\"display a short summary of the cells of a notebook\"\"\"\n",
-    "    nb = nbformat.read(fname, as_version=4)\n",
-    "    html = []\n",
-    "    for cell in nb.cells:\n",
-    "        html.append(\"<h4>%s cell</h4>\" % cell.cell_type)\n",
-    "        if cell.cell_type == 'code':\n",
-    "            html.append(highlight(cell.source, 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": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "importing notebook from /Users/minrk/dev/ip/mine/examples/IPython Kernel/nbpackage/mynotebook.ipynb\n"
-     ]
-    }
-   ],
-   "source": [
-    "from nbpackage import mynotebook"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Hooray, it imported!  Does it work?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'foo'"
-      ]
-     },
-     "execution_count": 11,
-     "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": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['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",
-       " 'Importing Notebooks.ipynb',\n",
-       " 'Index.ipynb',\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",
-       " '__pycache__',\n",
-       " 'data',\n",
-       " 'example-demo.py',\n",
-       " 'gui',\n",
-       " 'ipython-completion.bash',\n",
-       " 'ipython-get-history.py',\n",
-       " 'ipython.desktop',\n",
-       " 'nbpackage']"
-      ]
-     },
-     "execution_count": 12,
-     "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": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "__init__.py  \u001b[34m__pycache__\u001b[m\u001b[m/ other.ipynb\r\n"
-     ]
-    }
-   ],
-   "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": 14,
-   "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 object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "show_notebook(os.path.join(\"nbpackage\", \"nbs\", \"other.ipynb\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "importing notebook from /Users/minrk/dev/ip/mine/examples/IPython Kernel/nbpackage/nbs/other.ipynb\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'barbarbarbarbar'"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "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": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/Users/minrk/dev/ip/mine/IPython/utils/inside_ipython.ipynb'"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import shutil\n",
-    "from IPython.paths 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": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "importing notebook from /Users/minrk/dev/ip/mine/IPython/utils/inside_ipython.ipynb\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'IPython.utils.inside_ipython'"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "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",
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/Index.ipynb b/examples/IPython Kernel/Index.ipynb
deleted file mode 100644
index 6da3e93..0000000
--- a/examples/IPython Kernel/Index.ipynb
+++ /dev/null
@@ -1,201 +0,0 @@
-{
- "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": [
-    "# 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)\n",
-    "* [Importing Notebooks](Importing Notebooks.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": [
-    {
-     "data": {
-      "text/html": [
-       "<a href='example-demo.py' target='_blank'>example-demo.py</a><br>"
-      ],
-      "text/plain": [
-       "/Users/minrk/dev/ip/mine/examples/IPython Kernel/example-demo.py"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<a href='ipython-get-history.py' target='_blank'>ipython-get-history.py</a><br>"
-      ],
-      "text/plain": [
-       "/Users/minrk/dev/ip/mine/examples/IPython Kernel/ipython-get-history.py"
-      ]
-     },
-     "metadata": {},
-     "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": [
-    {
-     "data": {
-      "text/html": [
-       "data/<br>\n",
-       "&nbsp;&nbsp;<a href='data/flare.json' target='_blank'>flare.json</a><br>"
-      ],
-      "text/plain": [
-       "data/\n",
-       "  flare.json"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "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-gtk4.py' target='_blank'>gui-gtk4.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-gtk4.py\n",
-       "  gui-pyglet.py\n",
-       "  gui-qt.py\n",
-       "  gui-tk.py\n",
-       "  gui-wx.py"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%run ../utils/list_subdirs.ipy"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/Plotting in the Notebook.ipynb b/examples/IPython Kernel/Plotting in the Notebook.ipynb
deleted file mode 100644
index 645c2d8..0000000
--- a/examples/IPython Kernel/Plotting in the Notebook.ipynb
+++ /dev/null
@@ -1,2837 +0,0 @@
-{
- "cells": [
-  {
-   "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": [
-    {
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-       "rn+U7YcFXyPpxyR9g1xS/39I+mVJL6WCBOjEablfYL8tabHhv7ds/1IAHaEFGOhInucPSPpCSa+T\n",
-       "9M2SfkPS90t6m6TPy/P8/RGXBwyaH3HxdyT9hNwM19dL+h5Jb5A0yfP89ojLA4bogra8rszz/JWS\n",
-       "vlMuaM7lNgx7m6S/7jdPaezYhQu8lgUAAAAAAEBaxl6pBQAAAAAAgAQRagEAAAAAACA5hFoAAAAA\n",
-       "AABIDqEWAAAAAAAAkkOoBQAAAAAAgOQQagEAAAAAACA5hFoAAAAAAABIDqEWAAAAAAAAkkOoBQAA\n",
-       "AAAAgOQQagEAAAAAACA5hFoAAAAAAABIDqEWAAAAAAAAkkOoBQAAAAAAgOQQagEAAAAAACA5hFoA\n",
-       "AAAAAABIDqEWAAAAAAAAkkOoBQAAAAAAgOQQagEAAAAAACA5hFoAAAAAAABIzv8PtKN7Annmb8kA\n",
-       "AAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x106622080>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 392,
-       "width": 602
-      }
-     },
-     "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": [
-    {
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-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x106d1b198>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 403,
-       "width": 596
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# %load http://matplotlib.org/mpl_examples/showcase/integral_demo.py\n",
-    "\"\"\"\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"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Matplotlib 1.4 introduces an interactive backend for use in the notebook,\n",
-    "called 'nbagg'. You can enable this with `%matplotlib notebook`.\n",
-    "\n",
-    "With this backend, you will get interactive panning and zooming of matplotlib figures in your browser."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib notebook"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "            fig.waiting = false;\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width);\n",
-       "        canvas.attr('height', height);\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'];\n",
-       "    var y0 = fig.canvas.height - msg['y0'];\n",
-       "    var x1 = msg['x1'];\n",
-       "    var y1 = fig.canvas.height - msg['y1'];\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x;\n",
-       "    var y = canvas_pos.y;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
-       "    fig.send_message('closing', {});\n",
-       "    fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-danger\" href=\"#\" title=\"Close figure\"><i class=\"fa fa-times icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Close figure', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        event.shiftKey = false;\n",
-       "        // Send a \"J\" for go to next cell\n",
-       "        event.which = 74;\n",
-       "        event.keyCode = 74;\n",
-       "        manager.command_mode();\n",
-       "        manager.handle_keydown(event);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "<img 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\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "x = np.linspace(0, 5 * np.pi, 1000)\n",
-    "for n in range(1, 4):\n",
-    "    plt.plot(np.sin(n * x))\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/Raw Input in the Notebook.ipynb b/examples/IPython Kernel/Raw Input in the Notebook.ipynb
deleted file mode 100644
index fd49fb3..0000000
--- a/examples/IPython Kernel/Raw Input in the Notebook.ipynb
+++ /dev/null
@@ -1,151 +0,0 @@
-{
- "cells": [
-  {
-   "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": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "What is your name? Sir Robin\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'Sir Robin'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "name = raw_input(\"What is your name? \")\n",
-    "name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\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: division by zero"
-     ]
-    }
-   ],
-   "source": [
-    "def div(x, y):\n",
-    "    return x/y\n",
-    "\n",
-    "div(1,0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "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> bt\n",
-      "  \u001b[1;32m<ipython-input-4-a5097cc0c0c5>\u001b[0m(4)\u001b[0;36m<module>\u001b[1;34m()\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[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\n",
-      "> \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\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\n",
-      "ipdb> exit\n"
-     ]
-    }
-   ],
-   "source": [
-    "%debug"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/Rich Output.ipynb b/examples/IPython Kernel/Rich Output.ipynb
deleted file mode 100644
index d294daf..0000000
--- a/examples/IPython Kernel/Rich Output.ipynb
+++ /dev/null
@@ -1,3313 +0,0 @@
-{
- "cells": [
-  {
-   "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": [
-    {
-     "data": {
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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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-    "display(i)"
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-   "metadata": {},
-   "source": [
-    "An image can also be displayed from raw data or a URL."
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-   "cell_type": "markdown",
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-   "source": [
-    "SVG images are also supported out of the box."
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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 today's image."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
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-       "gHawxyTgUpdhYK+ZkgH09808RyyEKjqP72fmloEPVYoHXMZcgZIPINFcwabdBZtgG0bdo4PNRck7\n",
-       "Q0hnn2ix+V5IxHgr81LXUDLtkYhW7j8e9TUmXoLbauF2MQq8gZ9qrzosblYZGx3Hfiqi3eyWqEzg\n",
-       "mJ1dQCxx254qILUB1ZwCo7c4qkyDSMQigYOT7e9S/CB05X+oPxXQWWLaYGPaeSORxmrEM8T8qh+u\n",
-       "PmpAso/mSDfk7SScewFGoUDadoL9gTSsAHkhWMhjh/wwKAupJjKliec/FIBHmNG5RCBv7/hRLKZQ\n",
-       "x8zjPahiFsRvDKM/PuKat22GUIdo5wff86OxiyCzA7AN+R9MCsZ5EZgZT2xQBnnO4UH7oOO3tS5p\n",
-       "o1YDHbkfT4oSESJBLkYJIOTtPGaadiAJjB74xzigBfmqo2tkD5qBLggrkjPIBooC5b3LR8gEn3qy\n",
-       "GMoZiAFKg7aliFMWRAwxgjIGe/8A3ml5YL/w857nHGfihIYqeRZINinBU5zmq0LZ4kJyRwB7Cr9h\n",
-       "GSyqfSGCilG4CY5zj3+tNBYMlyp9h3zz3NCfLkkDAlecc0uihvk5baxIAHJx3NMjCLmRkLZ/Ol2B\n",
-       "PmgjLKMDkDGKBwTGGiPPwe9PoQMauMlm7c1G9iuM8Ae9MAHTzeN2T78/9/NVmAUlWXNNCZm9SoUd\n",
-       "6YrbGzvBz2NAIY7mRdoPehLFFIJ/CkISyiRu/POT3qq6OmNwqkMzBY4UHJ7cUaxkA/hTEGImHJ4A\n",
-       "Gc1JcEFMknNAzEZhnb2pgYk5Dc4oAcsJdjI57g/rU7hGQQcnNLsZetCUQlic5yK6rQF6buLGRNRm\n",
-       "EUu7azGUqHGcjFKCTewd+xafS+iP/wB1Cf8A+4P+9cneW1taXUqQypPGpxG6twRROvYEmuyutsCT\n",
-       "Jnj2GaKWQsMEDaOM1ndldArK6rgHHfjtUG6cDAIJ+PcGirHYxXMi8IQ47n5qV8yNeM5+DS/ISYDy\n",
-       "LKNjsf8Ac0carH/DVc57FuOKp6QWLuJRGAi+3akmTIHlA5PByKKE9gtPgsp7+7VIlLIBjnNFE9DE\n",
-       "kYKMjHPFGZG5LAHPvSaAxpWVSh5B5GTRRT7c5HJFKtAh8M2T6+SfzohNCSxAxngZ4FRQ7AkfyWDO\n",
-       "q8nt35pb3CM+xAdvvnmqW9jIW4Tt90A8VdjuEc7ynA+uP1pSQDpcXEYUuobvnNIEm1/JHsMgngGp\n",
-       "ivYTQF7pkUu4hdr47itVJptwo2qMgc8VrCaa2Cdk/Z5EYhgdw7YHFWolJZdmA2PVxjJ+asY9EcOR\n",
-       "uz7/AKmodmUZAJVjgmpsRXnl8xi0mV5HpB/1qGnlTEyuNx7gjnFAWLMyyplj/Nn5oJGQcZyO4yMG\n",
-       "gAhJ7iIHcPStEs58ry8gZ7/7UUMxZC+OTgc8HkVDyjJLLxjt9KAFhg5yGwB/LRyxGXDKrYIHNO6E\n",
-       "gxEUZl2+o+wzk0GJGk5O3A9qSYAvmIFwwLJzlj9filpPuP3VB9j80+wsZ5ku0Yw31HtTUuJPvA/g\n",
-       "BSaAswtIWwz8nHBHb/vFWUfaq7zliRn4qWBRlDK7Huo47VTedRIfL9PyatbELeYAZyuPnNJ8x3b1\n",
-       "n0t2piCkxjjse1WIXhCA7Dn3JoZSJedW9POD7UMcwPG59tJIYcjR8qNxx8+1LD4y2/J/rQINpsjJ\n",
-       "5PYUHmoBy2QKYdit4Y8Z70L5IGe1VRIsj+YdhU8gZUZHxQAzLY3N6R2qVIkBzjgVIwkjVVJBPIx+\n",
-       "fzUSxwsAxAOB2oEKVXjG4E4xwPehabdwFIwfimhoVOzkbkJYHA7UhCwbmqGNLEjIPB+TTrd0B5oE\n",
-       "Pk3lQ6HK9qtW1s11hQ21lOCDzUtpIoeYXRQBLuK9qLqGx+wQWNxkBLqEPj4bA3D+v9aUWBphcRju\n",
-       "wrZWMifZd6lSd3enLoB6t5mWzx759hWGZI08vvker2zWdAYvlSHs2MYxmgl2bgyj73HIoVgKlumV\n",
-       "+cHHHBrIZpp2JMnH1/0qq1YBhSrYNMWYJxk/T3pPYFeZw8gOGBHzzxRC4BGPTwOMe1FaArNJhtzA\n",
-       "Dd8UayDPaqJ7HREMSSMAUbyp/JgkDvipdgKkyB6DnAz3qIpiODg8Yo7QGNNt5V85HIFYs5HKk/l8\n",
-       "0UMyadpfS+SD2b4rIyFAwT9Cf9aKodjEUEja+4DvxT5RNGojhcEEZ+tJgHDvICsMNnjB4q+koVNr\n",
-       "sAcfzdqzkJH/2Q==\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 0x1058e5cf8>"
-      ]
-     },
-     "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"
-    }
-   ],
-   "source": [
-    "from IPython.display import HTML\n",
-    "from base64 import b64encode\n",
-    "with open(\"../images/animation.m4v\", \"rb\") as f:\n",
-    "    video = f.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=\"https://jupyter.org\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x1059ae7b8>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import IFrame\n",
-    "IFrame('https://jupyter.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": 32,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<a href='Cell Magics.ipynb' target='_blank'>Cell Magics.ipynb</a><br>"
-      ],
-      "text/plain": [
-       "/Users/minrk/dev/ip/mine/examples/IPython Kernel/Cell Magics.ipynb"
-      ]
-     },
-     "execution_count": 32,
-     "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": 33,
-   "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='./foo.py' target='_blank'>foo.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='./mod.py' target='_blank'>mod.py</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='./test.txt' target='_blank'>test.txt</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",
-       "./.ipynb_checkpoints/<br>\n",
-       "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Animations Using clear_output-checkpoint.ipynb' target='_blank'>Animations Using clear_output-checkpoint.ipynb</a><br>\n",
-       "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Background Jobs-checkpoint.ipynb' target='_blank'>Background Jobs-checkpoint.ipynb</a><br>\n",
-       "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Beyond Plain Python-checkpoint.ipynb' target='_blank'>Beyond Plain Python-checkpoint.ipynb</a><br>\n",
-       "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Capturing Output-checkpoint.ipynb' target='_blank'>Capturing Output-checkpoint.ipynb</a><br>\n",
-       "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Cell Magics-checkpoint.ipynb' target='_blank'>Cell Magics-checkpoint.ipynb</a><br>\n",
-       "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Custom Display Logic-checkpoint.ipynb' target='_blank'>Custom Display Logic-checkpoint.ipynb</a><br>\n",
-       "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Index-checkpoint.ipynb' target='_blank'>Index-checkpoint.ipynb</a><br>\n",
-       "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Plotting in the Notebook-checkpoint.ipynb' target='_blank'>Plotting in the Notebook-checkpoint.ipynb</a><br>\n",
-       "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Raw Input in the Notebook-checkpoint.ipynb' target='_blank'>Raw Input in the Notebook-checkpoint.ipynb</a><br>\n",
-       "&nbsp;&nbsp;<a href='./.ipynb_checkpoints/Rich Output-checkpoint.ipynb' target='_blank'>Rich Output-checkpoint.ipynb</a><br>\n",
-       "./__pycache__/<br>\n",
-       "&nbsp;&nbsp;<a href='./__pycache__/mod.cpython-34.pyc' target='_blank'>mod.cpython-34.pyc</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-gtk4.py' target='_blank'>gui-gtk4.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",
-       "  foo.py\n",
-       "  Index.ipynb\n",
-       "  ipython-completion.bash\n",
-       "  ipython-get-history.py\n",
-       "  ipython-qtconsole.desktop\n",
-       "  ipython.desktop\n",
-       "  mod.py\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",
-       "  test.txt\n",
-       "  Third Party Rich Output.ipynb\n",
-       "  Trapezoid Rule.ipynb\n",
-       "  Working With External Code.ipynb\n",
-       "./.ipynb_checkpoints/\n",
-       "  Animations Using clear_output-checkpoint.ipynb\n",
-       "  Background Jobs-checkpoint.ipynb\n",
-       "  Beyond Plain Python-checkpoint.ipynb\n",
-       "  Capturing Output-checkpoint.ipynb\n",
-       "  Cell Magics-checkpoint.ipynb\n",
-       "  Custom Display Logic-checkpoint.ipynb\n",
-       "  Index-checkpoint.ipynb\n",
-       "  Plotting in the Notebook-checkpoint.ipynb\n",
-       "  Raw Input in the Notebook-checkpoint.ipynb\n",
-       "  Rich Output-checkpoint.ipynb\n",
-       "./__pycache__/\n",
-       "  mod.cpython-34.pyc\n",
-       "./data/\n",
-       "  flare.json\n",
-       "./gui/\n",
-       "  gui-glut.py\n",
-       "  gui-gtk.py\n",
-       "  gui-gtk3.py\n",
-       "  gui-gtk4.py\n",
-       "  gui-pyglet.py\n",
-       "  gui-qt.py\n",
-       "  gui-tk.py\n",
-       "  gui-wx.py"
-      ]
-     },
-     "execution_count": 33,
-     "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](https://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 rendered 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": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/Script Magics.ipynb b/examples/IPython Kernel/Script Magics.ipynb
deleted file mode 100644
index a4ffdb6..0000000
--- a/examples/IPython Kernel/Script Magics.ipynb
+++ /dev/null
@@ -1,466 +0,0 @@
-{
- "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": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hello from Python 2.7.9 (default, Jan 29 2015, 06:27:40) \n",
-      "[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.56)]\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%script python2\n",
-    "import sys\n",
-    "print 'hello from Python %s' % sys.version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hello from Python: 3.4.2 |Continuum Analytics, Inc.| (default, Oct 21 2014, 17:42:20) \n",
-      "[GCC 4.2.1 (Apple Inc. build 5577)]\n"
-     ]
-    }
-   ],
-   "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": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hello from Ruby 2.0.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%ruby\n",
-    "puts \"Hello from Ruby #{RUBY_VERSION}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "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": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hi, stdout\n"
-     ]
-    },
-    {
-     "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": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "hello, stderr\n",
-      "\n",
-      "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": [
-    {
-     "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": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<open file '<fdopen>', mode 'rb' at 0x10a4be660>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ruby_lines"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "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"
-     ]
-    }
-   ],
-   "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": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.333333333333\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%script python2 -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": [
-    {
-     "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": [
-    {
-     "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"
-     ]
-    }
-   ],
-   "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 appear 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": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/SymPy.ipynb b/examples/IPython Kernel/SymPy.ipynb
deleted file mode 100644
index dd7efaf..0000000
--- a/examples/IPython Kernel/SymPy.ipynb
+++ /dev/null
@@ -1,3980 +0,0 @@
-{
- "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",
-   "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": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\frac{3 \\pi}{2} + \\frac{e^{i x}}{x^{2} + y}$$"
-      ],
-      "text/plain": [
-       "        ⅈ⋅x \n",
-       "3⋅π    ℯ    \n",
-       "─── + ──────\n",
-       " 2     2    \n",
-       "      x  + y"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "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": [
-    {
-     "data": {
-      "text/latex": [
-       "$$-1.0$$"
-      ],
-      "text/plain": [
-       "-1.00000000000000"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "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": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Add(Symbol('x'), Mul(Integer(2), Symbol('y')))\""
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "srepr(e)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$262537412640768743.99999999999925007259719818568888$$"
-      ],
-      "text/plain": [
-       "262537412640768743.99999999999925007259719818568888"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "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": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\left(x + 1\\right) \\left(x + y\\right)^{2}$$"
-      ],
-      "text/plain": [
-       "               2\n",
-       "(x + 1)⋅(x + y) "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "eq = ((x+y)**2 * (x+1))\n",
-    "eq"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "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⋅x ⋅y + x  + x⋅y  + 2⋅x⋅y + y "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expand(eq)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\frac{1}{x} \\left(x \\sin{\\left (x \\right )} - 1\\right) + \\frac{1}{x}$$"
-      ],
-      "text/plain": [
-       "x⋅sin(x) - 1   1\n",
-       "──────────── + ─\n",
-       "     x         x"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "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": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\sin{\\left (x \\right )}$$"
-      ],
-      "text/plain": [
-       "sin(x)"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "simplify(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$x^{3} + 2 x^{2} + 4 x + 8 = 0$$"
-      ],
-      "text/plain": [
-       " 3      2              \n",
-       "x  + 2⋅x  + 4⋅x + 8 = 0"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "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": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\left [ -2, \\quad - 2 i, \\quad 2 i\\right ]$$"
-      ],
-      "text/plain": [
-       "[-2, -2⋅ⅈ, 2⋅ⅈ]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "solve(eq, x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\sum_{n=a}^{b} \\left(2^{n} + 6 n^{2}\\right)$$"
-      ],
-      "text/plain": [
-       "  b              \n",
-       " ___             \n",
-       " ╲               \n",
-       "  ╲   ⎛ n      2⎞\n",
-       "  ╱   ⎝2  + 6⋅n ⎠\n",
-       " ╱               \n",
-       " ‾‾‾             \n",
-       "n = a            "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "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": [
-    {
-     "data": {
-      "text/latex": [
-       "$$- \\frac{1}{6}$$"
-      ],
-      "text/plain": [
-       "-1/6"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "limit((sin(x)-x)/x**3, x, 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "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⋅x     ⎛ 6⎞\n",
-       "1 + ── + ──── + O⎝x ⎠\n",
-       "    2     24         "
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(1/cos(x)).series(x, 0, 6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "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": [
-       "         ⎛ 2⎞    ⎛ 2⎞      2⎛ 2⎞\n",
-       "  4⋅x⋅sin⎝x ⎠⋅cos⎝x ⎠   cos ⎝x ⎠\n",
-       "- ─────────────────── - ────────\n",
-       "         x + 1                 2\n",
-       "                        (x + 1) "
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "diff(cos(x**2)**2 / (1+x), x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$-2 + \\frac{\\pi^{2}}{4}$$"
-      ],
-      "text/plain": [
-       "      2\n",
-       "     π \n",
-       "-2 + ──\n",
-       "     4 "
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "integrate(x**2 * cos(x), (x, 0, pi/2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "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⋅f(x) + ───(f(x)) = 1\n",
-       "           2          \n",
-       "         dx           "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "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) = C₁⋅sin(3⋅x) + C₂⋅cos(3⋅x) + 1/9"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "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": [
-    {
-     "data": {
-      "image/png": [
-       "iVBORw0KGgoAAAANSUhEUgAABK8AAAL7CAYAAAA7y6Y/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n",
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-       "m0k2JdlXVYsTlsYqVlVfkmxPciV9k+huktPpnYBbq+r9hOUxHxaSfEtybaUeuGZpyXsbAAAAgDHN\n",
-       "e+cVAAAAAAMTXgEAAAAwLOEVAAAAAMMSXgEAAAAwLOEVAAAAAMMSXgEAAAAwLOEVAAAAAMMSXgEA\n",
-       "AAAwLOEVAAAAAMMSXgEAAAAwLOEVAAAAAMMSXgEAAAAwLOEVAAAAAMMSXgEAAAAwLOEVAAAAAMMS\n",
-       "XgEAAAAwLOEVAAAAAMMSXgEAAAAwLOEVAAAAAMP6Duf8ovqEvPzUAAAAAElFTkSuQmCC\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1078c5e80>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 381,
-       "width": 599
-      }
-     },
-     "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": [
-    {
-     "data": {
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-       "Z4QAAAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x108276278>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 381,
-       "width": 599
-      }
-     },
-     "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": [
-    {
-     "data": {
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-       "ao3FJ0mSJEmSJLXG4pMkSZIkSZJaY/FJkiRJkiRJrbH4JEmSJEmSpNZYfJIkSZIkSVJrLD5JkiRJ\n",
-       "kiSpNRafJEmSJEmS1BqLT5IkSZIkSWqNxSdJkiRJkiS1xuKTJEmSJEmSWvMfdhLgUx9xCjkAAAAA\n",
-       "SUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x109cac9b0>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 376,
-       "width": 591
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "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": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/Terminal Usage.ipynb b/examples/IPython Kernel/Terminal Usage.ipynb
deleted file mode 100644
index 935bdd4..0000000
--- a/examples/IPython Kernel/Terminal Usage.ipynb
+++ /dev/null
@@ -1,285 +0,0 @@
-{
- "cells": [
-  {
-   "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": [
-    {
-     "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: %s' % z)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "z is: 30\n"
-     ]
-    }
-   ],
-   "source": [
-    "%run script"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "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": [
-    {
-     "data": {
-      "text/plain": [
-       "12"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "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/main/examples/lib)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Embedding IPython in a terminal application"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing simple-embed.py\n"
-     ]
-    }
-   ],
-   "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": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/Third Party Rich Output.ipynb b/examples/IPython Kernel/Third Party Rich Output.ipynb
deleted file mode 100644
index ad7354e..0000000
--- a/examples/IPython Kernel/Third Party Rich Output.ipynb
+++ /dev/null
@@ -1,540 +0,0 @@
-{
- "cells": [
-  {
-   "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 objects rich output by default when used in the Notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "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": 2,
-   "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": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing data.csv\n"
-     ]
-    }
-   ],
-   "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": 4,
-   "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": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "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": {},
-     "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": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from sympy.interactive.printing import init_printing\n",
-    "init_printing(use_latex='mathjax')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "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": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\frac{3 \\pi}{2} + \\frac{e^{i x}}{x^{2} + y}$$"
-      ],
-      "text/plain": [
-       "        ⅈ⋅x \n",
-       "3⋅π    ℯ    \n",
-       "─── + ──────\n",
-       " 2     2    \n",
-       "      x  + y"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Rational(3,2)*pi + exp(I*x) / (x**2 + y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\frac{1}{x} \\left(x \\sin{\\left (x \\right )} - 1\\right) + \\frac{1}{x}$$"
-      ],
-      "text/plain": [
-       "x⋅sin(x) - 1   1\n",
-       "──────────── + ─\n",
-       "     x         x"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "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": [
-    {
-     "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⋅x     ⎛ 6⎞\n",
-       "1 + ── + ──── + O⎝x ⎠\n",
-       "    2     24         "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "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.io/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": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import vincent\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "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()})"
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36.04}, {\"col\": \"AAPL\", \"idx\": 1388476800000, \"val\": 78.19}, {\"col\": \"IBM\", \"idx\": 1388476800000, \"val\": 181.85}, {\"col\": \"YHOO\", \"idx\": 1388476800000, \"val\": 40.44}, {\"col\": \"MSFT\", \"idx\": 1388476800000, \"val\": 36.16}]}], \"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: \"#vise33c59d182bd4093bad74a04ddf24501\"}).update();\n",
-       "       });\n",
-       "     };\n",
-       "     _do_plot();\n",
-       "   })();\n",
-       "</script>\n",
-       "<style>.vega canvas {width: 100%;}</style>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<vincent.charts.Line at 0x107badb38>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "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": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/Trapezoid Rule.ipynb b/examples/IPython Kernel/Trapezoid Rule.ipynb
deleted file mode 100644
index 5ccd13f..0000000
--- a/examples/IPython Kernel/Trapezoid Rule.ipynb
+++ /dev/null
@@ -1,971 +0,0 @@
-{
- "cells": [
-  {
-   "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": false
-   },
-   "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, 8 # the left and right boundaries\n",
-    "N = 5 # the number of points\n",
-    "xint = np.linspace(a, b, N)\n",
-    "yint = f(xint)"
-   ]
-  },
-  {
-   "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": [
-    {
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-       "D88ocBnIBY7j/NpiNESR4zh1CiyY+7ACHy68IOlnCsycO9JxnJUW4yF2bpTUIOnvtoMgdoJLbJwj\n",
-       "6Q8KrAX7vALNipcluRzH2WwxHmLEGHOSAjPjfuk4TmU0jpnk9/PzHgAAAAAAAOxI5JlTAAAAAAAA\n",
-       "6OJoTgEAAAAAAMAamlMAAAAAAACwhuYUAAAAAAAArKE5BQAAAAAAAGtoTgEAAAAAAMAamlMAAAAA\n",
-       "AACwhuYUAAAAAAAArKE5BQAAAAAAAGtoTgEAAAAAAMAamlMAAAAAAACwhuYUAAAAAAAArKE5BQAA\n",
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-       "AAAAAACw5v8DPWODrRZIv60AAAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10664cfd0>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 381,
-       "width": 595
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(x, y, lw=2)\n",
-    "plt.axis([0, 9, 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": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The integral is: 565.2499999999999 +/- 6.275535646693696e-12\n",
-      "The trapezoid approximation with 5 points is: 559.890625\n"
-     ]
-    }
-   ],
-   "source": [
-    "from __future__ import print_function\n",
-    "from scipy.integrate import quad\n",
-    "integral, error = quad(f, a, b)\n",
-    "integral_trapezoid = sum( (xint[1:] - xint[:-1]) * (yint[1:] + yint[:-1]) ) / 2\n",
-    "print(\"The integral is:\", integral, \"+/-\", error)\n",
-    "print(\"The trapezoid approximation with\", len(xint), \"points is:\", integral_trapezoid)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/Updating Displays.ipynb b/examples/IPython Kernel/Updating Displays.ipynb
deleted file mode 100644
index 33b8667..0000000
--- a/examples/IPython Kernel/Updating Displays.ipynb
+++ /dev/null
@@ -1,337 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Updatable Displays\n",
-    "\n",
-    "Note: This feature requires notebook >= 5.0 or JupyterLab, and \n",
-    "\n",
-    "\n",
-    "IPython 6 implements a new API as part of the Jupyter Protocol version 5.1 for easily updating displays.\n",
-    "\n",
-    "When you display something, you can now pass a `display_id` argument to attach an id to that output.\n",
-    "\n",
-    "Any future display with the same ID will also update other displays that had the same ID.\n",
-    "\n",
-    "`display` with a `display_id` will return a `DisplayHandle`\n",
-    "object, which gives you easy access to update the output:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import display, update_display"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'z'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<DisplayHandle display_id=update-me>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "handle = display('x', display_id='update-me')\n",
-    "handle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When we call `handle.display('y')`, we get a new display of 'y',\n",
-    "but in addition to that, we updated the previous display."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'z'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "handle.display('y')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also *just* update the existing displays,\n",
-    "without creating a new display:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "handle.update('z')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You don't have to generate display_ids yourself,\n",
-    "if you specify `display_id=True`, then a unique ID will be assigned:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'hello'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<DisplayHandle display_id=118a56127f42348f8893440da7181c57>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "handle = display(\"hello\", display_id=True)\n",
-    "handle"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Calling `handle.display(obj)` is the same as calling `display(obj, handle.display_id)`,\n",
-    "so you don't need to use the handle objects if you don't want to:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'z'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display('x', display_id='here');"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'z'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display('y', display_id='here');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And just like `display`, there is now `update_display`,\n",
-    "which is what `DisplayHandle.update` calls:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "update_display('z', display_id='here')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## More detailed example\n",
-    "\n",
-    "One of the motivating use cases for this is simple progress bars.\n",
-    "\n",
-    "Here is an example ProgressBar using these APIs:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<progress style='width:60ex' max='10' value='10'></progress>"
-      ],
-      "text/plain": [
-       "[============================================================] 10/10"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from IPython.display import ProgressBar\n",
-    "\n",
-    "bar = ProgressBar(10)\n",
-    "bar.display()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And the ProgressBar has `.display` and `.update` methods:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<progress style='width:60ex' max='10' value='10'></progress>"
-      ],
-      "text/plain": [
-       "[============================================================] 10/10"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import time\n",
-    "\n",
-    "bar.display()\n",
-    "\n",
-    "for i in range(11):\n",
-    "    bar.progress = i\n",
-    "    bar.update()\n",
-    "    time.sleep(0.25)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The ProgressBar also has an update built into iteration:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<progress style='width:60ex' max='10' value='10'></progress>"
-      ],
-      "text/plain": [
-       "[============================================================] 10/10"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for i in ProgressBar(10):\n",
-    "    time.sleep(0.25)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We would encourage any updatable-display objects that track their own display_ids to follow-suit with `.display()` and `.update()` or `.update_display()` methods."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/examples/IPython Kernel/Working With External Code.ipynb b/examples/IPython Kernel/Working With External Code.ipynb
deleted file mode 100644
index c5e5c84..0000000
--- a/examples/IPython Kernel/Working With External Code.ipynb
+++ /dev/null
@@ -1,691 +0,0 @@
-{
- "cells": [
-  {
-   "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": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Be careful not to fall off!\n"
-     ]
-    }
-   ],
-   "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": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%load?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%load http://matplotlib.org/mpl_examples/showcase/integral_demo.py"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
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-       "AACgTqQCAAAAoE6kAgAAAKBOpAIAAACgTqQCAAAAoE6kAgAAAKBOpAIAAACgTqQCAAAAoE6kAgAA\n",
-       "AKBOpAIAAACg7nD7BQAAAAD4940xziW5lORbko0kV5NcS3Isyckkm3POt/v9fZEKAAAAgLXGGKeS\n",
-       "XJlzXl+tt5JsJ7mc3Z16L5K8TvJovzNs9wMAAADgT24kub1nfTTJpznndpL3SR4m2VoyYMw5lzwP\n",
-       "AAAAwH9ujLEx53y3Z/0hyZM5592DmuFLKgAAAADW+iVQnUlyIsnzg5whUgEAAADwN84n+ZLk5c8L\n",
-       "qzOrFhGpAAAAAPitMcaRMcb9McbZ1aULSd7MOXdW9w8lubV0jn/3AwAAAGCdi0luJnk1xvia5HSS\n",
-       "z3vu38nCQ9MTB6cDAAAAsMYY43iSB0k+Jvme5F6Sx0l2srvt7+mc89niOSIVAAAAAG3OpAIAAACg\n",
-       "TqQCAAAAoE6kAgAAAKBOpAIAAACgTqQCAAAAoE6kAgAAAKBOpAIAAACgTqQCAAAAoE6kAgAAAKBO\n",
-       "pAIAAACgTqQCAAAAoE6kAgAAAKBOpAIAAACgTqQCAAAAoE6kAgAAAKBOpAIAAACgTqQCAAAAoE6k\n",
-       "AgAAAKBOpAIAAACg7gefISOV+L+GHQAAAABJRU5ErkJggg==\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1066447f0>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 403,
-       "width": 596
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# %load http://matplotlib.org/mpl_examples/showcase/integral_demo.py\n",
-    "\"\"\"\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": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/data/flare.json b/examples/IPython Kernel/data/flare.json
deleted file mode 100644
index b976cd1..0000000
--- a/examples/IPython Kernel/data/flare.json
+++ /dev/null
@@ -1,380 +0,0 @@
-{
- "name": "flare",
- "children": [
-  {
-   "name": "analytics",
-   "children": [
-    {
-     "name": "cluster",
-     "children": [
-      {"name": "AgglomerativeCluster", "size": 3938},
-      {"name": "CommunityStructure", "size": 3812},
-      {"name": "HierarchicalCluster", "size": 6714},
-      {"name": "MergeEdge", "size": 743}
-     ]
-    },
-    {
-     "name": "graph",
-     "children": [
-      {"name": "BetweennessCentrality", "size": 3534},
-      {"name": "LinkDistance", "size": 5731},
-      {"name": "MaxFlowMinCut", "size": 7840},
-      {"name": "ShortestPaths", "size": 5914},
-      {"name": "SpanningTree", "size": 3416}
-     ]
-    },
-    {
-     "name": "optimization",
-     "children": [
-      {"name": "AspectRatioBanker", "size": 7074}
-     ]
-    }
-   ]
-  },
-  {
-   "name": "animate",
-   "children": [
-    {"name": "Easing", "size": 17010},
-    {"name": "FunctionSequence", "size": 5842},
-    {
-     "name": "interpolate",
-     "children": [
-      {"name": "ArrayInterpolator", "size": 1983},
-      {"name": "ColorInterpolator", "size": 2047},
-      {"name": "DateInterpolator", "size": 1375},
-      {"name": "Interpolator", "size": 8746},
-      {"name": "MatrixInterpolator", "size": 2202},
-      {"name": "NumberInterpolator", "size": 1382},
-      {"name": "ObjectInterpolator", "size": 1629},
-      {"name": "PointInterpolator", "size": 1675},
-      {"name": "RectangleInterpolator", "size": 2042}
-     ]
-    },
-    {"name": "ISchedulable", "size": 1041},
-    {"name": "Parallel", "size": 5176},
-    {"name": "Pause", "size": 449},
-    {"name": "Scheduler", "size": 5593},
-    {"name": "Sequence", "size": 5534},
-    {"name": "Transition", "size": 9201},
-    {"name": "Transitioner", "size": 19975},
-    {"name": "TransitionEvent", "size": 1116},
-    {"name": "Tween", "size": 6006}
-   ]
-  },
-  {
-   "name": "data",
-   "children": [
-    {
-     "name": "converters",
-     "children": [
-      {"name": "Converters", "size": 721},
-      {"name": "DelimitedTextConverter", "size": 4294},
-      {"name": "GraphMLConverter", "size": 9800},
-      {"name": "IDataConverter", "size": 1314},
-      {"name": "JSONConverter", "size": 2220}
-     ]
-    },
-    {"name": "DataField", "size": 1759},
-    {"name": "DataSchema", "size": 2165},
-    {"name": "DataSet", "size": 586},
-    {"name": "DataSource", "size": 3331},
-    {"name": "DataTable", "size": 772},
-    {"name": "DataUtil", "size": 3322}
-   ]
-  },
-  {
-   "name": "display",
-   "children": [
-    {"name": "DirtySprite", "size": 8833},
-    {"name": "LineSprite", "size": 1732},
-    {"name": "RectSprite", "size": 3623},
-    {"name": "TextSprite", "size": 10066}
-   ]
-  },
-  {
-   "name": "flex",
-   "children": [
-    {"name": "FlareVis", "size": 4116}
-   ]
-  },
-  {
-   "name": "physics",
-   "children": [
-    {"name": "DragForce", "size": 1082},
-    {"name": "GravityForce", "size": 1336},
-    {"name": "IForce", "size": 319},
-    {"name": "NBodyForce", "size": 10498},
-    {"name": "Particle", "size": 2822},
-    {"name": "Simulation", "size": 9983},
-    {"name": "Spring", "size": 2213},
-    {"name": "SpringForce", "size": 1681}
-   ]
-  },
-  {
-   "name": "query",
-   "children": [
-    {"name": "AggregateExpression", "size": 1616},
-    {"name": "And", "size": 1027},
-    {"name": "Arithmetic", "size": 3891},
-    {"name": "Average", "size": 891},
-    {"name": "BinaryExpression", "size": 2893},
-    {"name": "Comparison", "size": 5103},
-    {"name": "CompositeExpression", "size": 3677},
-    {"name": "Count", "size": 781},
-    {"name": "DateUtil", "size": 4141},
-    {"name": "Distinct", "size": 933},
-    {"name": "Expression", "size": 5130},
-    {"name": "ExpressionIterator", "size": 3617},
-    {"name": "Fn", "size": 3240},
-    {"name": "If", "size": 2732},
-    {"name": "IsA", "size": 2039},
-    {"name": "Literal", "size": 1214},
-    {"name": "Match", "size": 3748},
-    {"name": "Maximum", "size": 843},
-    {
-     "name": "methods",
-     "children": [
-      {"name": "add", "size": 593},
-      {"name": "and", "size": 330},
-      {"name": "average", "size": 287},
-      {"name": "count", "size": 277},
-      {"name": "distinct", "size": 292},
-      {"name": "div", "size": 595},
-      {"name": "eq", "size": 594},
-      {"name": "fn", "size": 460},
-      {"name": "gt", "size": 603},
-      {"name": "gte", "size": 625},
-      {"name": "iff", "size": 748},
-      {"name": "isa", "size": 461},
-      {"name": "lt", "size": 597},
-      {"name": "lte", "size": 619},
-      {"name": "max", "size": 283},
-      {"name": "min", "size": 283},
-      {"name": "mod", "size": 591},
-      {"name": "mul", "size": 603},
-      {"name": "neq", "size": 599},
-      {"name": "not", "size": 386},
-      {"name": "or", "size": 323},
-      {"name": "orderby", "size": 307},
-      {"name": "range", "size": 772},
-      {"name": "select", "size": 296},
-      {"name": "stddev", "size": 363},
-      {"name": "sub", "size": 600},
-      {"name": "sum", "size": 280},
-      {"name": "update", "size": 307},
-      {"name": "variance", "size": 335},
-      {"name": "where", "size": 299},
-      {"name": "xor", "size": 354},
-      {"name": "_", "size": 264}
-     ]
-    },
-    {"name": "Minimum", "size": 843},
-    {"name": "Not", "size": 1554},
-    {"name": "Or", "size": 970},
-    {"name": "Query", "size": 13896},
-    {"name": "Range", "size": 1594},
-    {"name": "StringUtil", "size": 4130},
-    {"name": "Sum", "size": 791},
-    {"name": "Variable", "size": 1124},
-    {"name": "Variance", "size": 1876},
-    {"name": "Xor", "size": 1101}
-   ]
-  },
-  {
-   "name": "scale",
-   "children": [
-    {"name": "IScaleMap", "size": 2105},
-    {"name": "LinearScale", "size": 1316},
-    {"name": "LogScale", "size": 3151},
-    {"name": "OrdinalScale", "size": 3770},
-    {"name": "QuantileScale", "size": 2435},
-    {"name": "QuantitativeScale", "size": 4839},
-    {"name": "RootScale", "size": 1756},
-    {"name": "Scale", "size": 4268},
-    {"name": "ScaleType", "size": 1821},
-    {"name": "TimeScale", "size": 5833}
-   ]
-  },
-  {
-   "name": "util",
-   "children": [
-    {"name": "Arrays", "size": 8258},
-    {"name": "Colors", "size": 10001},
-    {"name": "Dates", "size": 8217},
-    {"name": "Displays", "size": 12555},
-    {"name": "Filter", "size": 2324},
-    {"name": "Geometry", "size": 10993},
-    {
-     "name": "heap",
-     "children": [
-      {"name": "FibonacciHeap", "size": 9354},
-      {"name": "HeapNode", "size": 1233}
-     ]
-    },
-    {"name": "IEvaluable", "size": 335},
-    {"name": "IPredicate", "size": 383},
-    {"name": "IValueProxy", "size": 874},
-    {
-     "name": "math",
-     "children": [
-      {"name": "DenseMatrix", "size": 3165},
-      {"name": "IMatrix", "size": 2815},
-      {"name": "SparseMatrix", "size": 3366}
-     ]
-    },
-    {"name": "Maths", "size": 17705},
-    {"name": "Orientation", "size": 1486},
-    {
-     "name": "palette",
-     "children": [
-      {"name": "ColorPalette", "size": 6367},
-      {"name": "Palette", "size": 1229},
-      {"name": "ShapePalette", "size": 2059},
-      {"name": "SizePalette", "size": 2291}
-     ]
-    },
-    {"name": "Property", "size": 5559},
-    {"name": "Shapes", "size": 19118},
-    {"name": "Sort", "size": 6887},
-    {"name": "Stats", "size": 6557},
-    {"name": "Strings", "size": 22026}
-   ]
-  },
-  {
-   "name": "vis",
-   "children": [
-    {
-     "name": "axis",
-     "children": [
-      {"name": "Axes", "size": 1302},
-      {"name": "Axis", "size": 24593},
-      {"name": "AxisGridLine", "size": 652},
-      {"name": "AxisLabel", "size": 636},
-      {"name": "CartesianAxes", "size": 6703}
-     ]
-    },
-    {
-     "name": "controls",
-     "children": [
-      {"name": "AnchorControl", "size": 2138},
-      {"name": "ClickControl", "size": 3824},
-      {"name": "Control", "size": 1353},
-      {"name": "ControlList", "size": 4665},
-      {"name": "DragControl", "size": 2649},
-      {"name": "ExpandControl", "size": 2832},
-      {"name": "HoverControl", "size": 4896},
-      {"name": "IControl", "size": 763},
-      {"name": "PanZoomControl", "size": 5222},
-      {"name": "SelectionControl", "size": 7862},
-      {"name": "TooltipControl", "size": 8435}
-     ]
-    },
-    {
-     "name": "data",
-     "children": [
-      {"name": "Data", "size": 20544},
-      {"name": "DataList", "size": 19788},
-      {"name": "DataSprite", "size": 10349},
-      {"name": "EdgeSprite", "size": 3301},
-      {"name": "NodeSprite", "size": 19382},
-      {
-       "name": "render",
-       "children": [
-        {"name": "ArrowType", "size": 698},
-        {"name": "EdgeRenderer", "size": 5569},
-        {"name": "IRenderer", "size": 353},
-        {"name": "ShapeRenderer", "size": 2247}
-       ]
-      },
-      {"name": "ScaleBinding", "size": 11275},
-      {"name": "Tree", "size": 7147},
-      {"name": "TreeBuilder", "size": 9930}
-     ]
-    },
-    {
-     "name": "events",
-     "children": [
-      {"name": "DataEvent", "size": 2313},
-      {"name": "SelectionEvent", "size": 1880},
-      {"name": "TooltipEvent", "size": 1701},
-      {"name": "VisualizationEvent", "size": 1117}
-     ]
-    },
-    {
-     "name": "legend",
-     "children": [
-      {"name": "Legend", "size": 20859},
-      {"name": "LegendItem", "size": 4614},
-      {"name": "LegendRange", "size": 10530}
-     ]
-    },
-    {
-     "name": "operator",
-     "children": [
-      {
-       "name": "distortion",
-       "children": [
-        {"name": "BifocalDistortion", "size": 4461},
-        {"name": "Distortion", "size": 6314},
-        {"name": "FisheyeDistortion", "size": 3444}
-       ]
-      },
-      {
-       "name": "encoder",
-       "children": [
-        {"name": "ColorEncoder", "size": 3179},
-        {"name": "Encoder", "size": 4060},
-        {"name": "PropertyEncoder", "size": 4138},
-        {"name": "ShapeEncoder", "size": 1690},
-        {"name": "SizeEncoder", "size": 1830}
-       ]
-      },
-      {
-       "name": "filter",
-       "children": [
-        {"name": "FisheyeTreeFilter", "size": 5219},
-        {"name": "GraphDistanceFilter", "size": 3165},
-        {"name": "VisibilityFilter", "size": 3509}
-       ]
-      },
-      {"name": "IOperator", "size": 1286},
-      {
-       "name": "label",
-       "children": [
-        {"name": "Labeler", "size": 9956},
-        {"name": "RadialLabeler", "size": 3899},
-        {"name": "StackedAreaLabeler", "size": 3202}
-       ]
-      },
-      {
-       "name": "layout",
-       "children": [
-        {"name": "AxisLayout", "size": 6725},
-        {"name": "BundledEdgeRouter", "size": 3727},
-        {"name": "CircleLayout", "size": 9317},
-        {"name": "CirclePackingLayout", "size": 12003},
-        {"name": "DendrogramLayout", "size": 4853},
-        {"name": "ForceDirectedLayout", "size": 8411},
-        {"name": "IcicleTreeLayout", "size": 4864},
-        {"name": "IndentedTreeLayout", "size": 3174},
-        {"name": "Layout", "size": 7881},
-        {"name": "NodeLinkTreeLayout", "size": 12870},
-        {"name": "PieLayout", "size": 2728},
-        {"name": "RadialTreeLayout", "size": 12348},
-        {"name": "RandomLayout", "size": 870},
-        {"name": "StackedAreaLayout", "size": 9121},
-        {"name": "TreeMapLayout", "size": 9191}
-       ]
-      },
-      {"name": "Operator", "size": 2490},
-      {"name": "OperatorList", "size": 5248},
-      {"name": "OperatorSequence", "size": 4190},
-      {"name": "OperatorSwitch", "size": 2581},
-      {"name": "SortOperator", "size": 2023}
-     ]
-    },
-    {"name": "Visualization", "size": 16540}
-   ]
-  }
- ]
-}
diff --git a/examples/IPython Kernel/nbpackage/__init__.py b/examples/IPython Kernel/nbpackage/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/examples/IPython Kernel/nbpackage/__init__.py
+++ /dev/null
diff --git a/examples/IPython Kernel/nbpackage/mynotebook.ipynb b/examples/IPython Kernel/nbpackage/mynotebook.ipynb
deleted file mode 100644
index fd3920c..0000000
--- a/examples/IPython Kernel/nbpackage/mynotebook.ipynb
+++ /dev/null
@@ -1,69 +0,0 @@
-{
- "cells": [
-  {
-   "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": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/IPython Kernel/nbpackage/nbs/__init__.py b/examples/IPython Kernel/nbpackage/nbs/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/examples/IPython Kernel/nbpackage/nbs/__init__.py
+++ /dev/null
diff --git a/examples/IPython Kernel/nbpackage/nbs/other.ipynb b/examples/IPython Kernel/nbpackage/nbs/other.ipynb
deleted file mode 100644
index dad7e73..0000000
--- a/examples/IPython Kernel/nbpackage/nbs/other.ipynb
+++ /dev/null
@@ -1,44 +0,0 @@
-{
- "cells": [
-  {
-   "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": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/Index.ipynb b/examples/Index.ipynb
deleted file mode 100644
index 30e201a..0000000
--- a/examples/Index.ipynb
+++ /dev/null
@@ -1,61 +0,0 @@
-{
- "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](https://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",
-    "* [Embedding](Embedding/Index.ipynb): Embedding and reusing IPython's components into other applications\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/tools/tests/ANSI Test.ipynb b/tools/tests/ANSI Test.ipynb
deleted file mode 100644
index 98c91ff..0000000
--- a/tools/tests/ANSI Test.ipynb
+++ /dev/null
@@ -1,560 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook tests the processing of ANSI and VT100 color escapes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from __future__ import print_function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "ESC = '\\x1b['\n",
-    "RESET = ESC + \"00m\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plain ANSI 16-color"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[01;40;30mtext \u001b[01m\u001b[01;40;31mtext \u001b[01m\u001b[01;40;32mtext \u001b[01m\u001b[01;40;33mtext \u001b[01m\u001b[01;40;34mtext \u001b[01m\u001b[01;40;35mtext \u001b[01m\u001b[01;40;36mtext \u001b[01m\u001b[01;40;37mtext \u001b[01m\n",
-      "\u001b[01;41;30mtext \u001b[01m\u001b[01;41;31mtext \u001b[01m\u001b[01;41;32mtext \u001b[01m\u001b[01;41;33mtext \u001b[01m\u001b[01;41;34mtext \u001b[01m\u001b[01;41;35mtext \u001b[01m\u001b[01;41;36mtext \u001b[01m\u001b[01;41;37mtext \u001b[01m\n",
-      "\u001b[01;42;30mtext \u001b[01m\u001b[01;42;31mtext \u001b[01m\u001b[01;42;32mtext \u001b[01m\u001b[01;42;33mtext \u001b[01m\u001b[01;42;34mtext \u001b[01m\u001b[01;42;35mtext \u001b[01m\u001b[01;42;36mtext \u001b[01m\u001b[01;42;37mtext \u001b[01m\n",
-      "\u001b[01;43;30mtext \u001b[01m\u001b[01;43;31mtext \u001b[01m\u001b[01;43;32mtext \u001b[01m\u001b[01;43;33mtext \u001b[01m\u001b[01;43;34mtext \u001b[01m\u001b[01;43;35mtext \u001b[01m\u001b[01;43;36mtext \u001b[01m\u001b[01;43;37mtext \u001b[01m\n",
-      "\u001b[01;44;30mtext \u001b[01m\u001b[01;44;31mtext \u001b[01m\u001b[01;44;32mtext \u001b[01m\u001b[01;44;33mtext \u001b[01m\u001b[01;44;34mtext \u001b[01m\u001b[01;44;35mtext \u001b[01m\u001b[01;44;36mtext \u001b[01m\u001b[01;44;37mtext \u001b[01m\n",
-      "\u001b[01;45;30mtext \u001b[01m\u001b[01;45;31mtext \u001b[01m\u001b[01;45;32mtext \u001b[01m\u001b[01;45;33mtext \u001b[01m\u001b[01;45;34mtext \u001b[01m\u001b[01;45;35mtext \u001b[01m\u001b[01;45;36mtext \u001b[01m\u001b[01;45;37mtext \u001b[01m\n",
-      "\u001b[01;46;30mtext \u001b[01m\u001b[01;46;31mtext \u001b[01m\u001b[01;46;32mtext \u001b[01m\u001b[01;46;33mtext \u001b[01m\u001b[01;46;34mtext \u001b[01m\u001b[01;46;35mtext \u001b[01m\u001b[01;46;36mtext \u001b[01m\u001b[01;46;37mtext \u001b[01m\n",
-      "\u001b[01;47;30mtext \u001b[01m\u001b[01;47;31mtext \u001b[01m\u001b[01;47;32mtext \u001b[01m\u001b[01;47;33mtext \u001b[01m\u001b[01;47;34mtext \u001b[01m\u001b[01;47;35mtext \u001b[01m\u001b[01;47;36mtext \u001b[01m\u001b[01;47;37mtext \u001b[01m\n"
-     ]
-    }
-   ],
-   "source": [
-    "for bg in range(40,48):\n",
-    "    for fg in range(30,38):\n",
-    "        print (\"{ESC}01;{bg};{fg}mtext {RESET}\".format(**locals()), end='')\n",
-    "    print ()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 256-color"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[00;38;5;0m000 \u001b[01m\u001b[00;38;5;1m001 \u001b[01m\u001b[00;38;5;2m002 \u001b[01m\u001b[00;38;5;3m003 \u001b[01m\u001b[00;38;5;4m004 \u001b[01m\u001b[00;38;5;5m005 \u001b[01m\u001b[00;38;5;6m006 \u001b[01m\u001b[00;38;5;7m007 \u001b[01m\n",
-      "\u001b[00;38;5;8m008 \u001b[01m\u001b[00;38;5;9m009 \u001b[01m\u001b[00;38;5;10m010 \u001b[01m\u001b[00;38;5;11m011 \u001b[01m\u001b[00;38;5;12m012 \u001b[01m\u001b[00;38;5;13m013 \u001b[01m\u001b[00;38;5;14m014 \u001b[01m\u001b[00;38;5;15m015 \u001b[01m\n",
-      "\n",
-      "\u001b[00;38;5;16m016 \u001b[01m\u001b[00;38;5;17m017 \u001b[01m\u001b[00;38;5;18m018 \u001b[01m\u001b[00;38;5;19m019 \u001b[01m\u001b[00;38;5;20m020 \u001b[01m\u001b[00;38;5;21m021 \u001b[01m\n",
-      "\u001b[00;38;5;22m022 \u001b[01m\u001b[00;38;5;23m023 \u001b[01m\u001b[00;38;5;24m024 \u001b[01m\u001b[00;38;5;25m025 \u001b[01m\u001b[00;38;5;26m026 \u001b[01m\u001b[00;38;5;27m027 \u001b[01m\n",
-      "\u001b[00;38;5;28m028 \u001b[01m\u001b[00;38;5;29m029 \u001b[01m\u001b[00;38;5;30m030 \u001b[01m\u001b[00;38;5;31m031 \u001b[01m\u001b[00;38;5;32m032 \u001b[01m\u001b[00;38;5;33m033 \u001b[01m\n",
-      "\u001b[00;38;5;34m034 \u001b[01m\u001b[00;38;5;35m035 \u001b[01m\u001b[00;38;5;36m036 \u001b[01m\u001b[00;38;5;37m037 \u001b[01m\u001b[00;38;5;38m038 \u001b[01m\u001b[00;38;5;39m039 \u001b[01m\n",
-      "\u001b[00;38;5;40m040 \u001b[01m\u001b[00;38;5;41m041 \u001b[01m\u001b[00;38;5;42m042 \u001b[01m\u001b[00;38;5;43m043 \u001b[01m\u001b[00;38;5;44m044 \u001b[01m\u001b[00;38;5;45m045 \u001b[01m\n",
-      "\u001b[00;38;5;46m046 \u001b[01m\u001b[00;38;5;47m047 \u001b[01m\u001b[00;38;5;48m048 \u001b[01m\u001b[00;38;5;49m049 \u001b[01m\u001b[00;38;5;50m050 \u001b[01m\u001b[00;38;5;51m051 \u001b[01m\n",
-      "\n",
-      "\u001b[00;38;5;52m052 \u001b[01m\u001b[00;38;5;53m053 \u001b[01m\u001b[00;38;5;54m054 \u001b[01m\u001b[00;38;5;55m055 \u001b[01m\u001b[00;38;5;56m056 \u001b[01m\u001b[00;38;5;57m057 \u001b[01m\n",
-      "\u001b[00;38;5;58m058 \u001b[01m\u001b[00;38;5;59m059 \u001b[01m\u001b[00;38;5;60m060 \u001b[01m\u001b[00;38;5;61m061 \u001b[01m\u001b[00;38;5;62m062 \u001b[01m\u001b[00;38;5;63m063 \u001b[01m\n",
-      "\u001b[00;38;5;64m064 \u001b[01m\u001b[00;38;5;65m065 \u001b[01m\u001b[00;38;5;66m066 \u001b[01m\u001b[00;38;5;67m067 \u001b[01m\u001b[00;38;5;68m068 \u001b[01m\u001b[00;38;5;69m069 \u001b[01m\n",
-      "\u001b[00;38;5;70m070 \u001b[01m\u001b[00;38;5;71m071 \u001b[01m\u001b[00;38;5;72m072 \u001b[01m\u001b[00;38;5;73m073 \u001b[01m\u001b[00;38;5;74m074 \u001b[01m\u001b[00;38;5;75m075 \u001b[01m\n",
-      "\u001b[00;38;5;76m076 \u001b[01m\u001b[00;38;5;77m077 \u001b[01m\u001b[00;38;5;78m078 \u001b[01m\u001b[00;38;5;79m079 \u001b[01m\u001b[00;38;5;80m080 \u001b[01m\u001b[00;38;5;81m081 \u001b[01m\n",
-      "\u001b[00;38;5;82m082 \u001b[01m\u001b[00;38;5;83m083 \u001b[01m\u001b[00;38;5;84m084 \u001b[01m\u001b[00;38;5;85m085 \u001b[01m\u001b[00;38;5;86m086 \u001b[01m\u001b[00;38;5;87m087 \u001b[01m\n",
-      "\n",
-      "\u001b[00;38;5;88m088 \u001b[01m\u001b[00;38;5;89m089 \u001b[01m\u001b[00;38;5;90m090 \u001b[01m\u001b[00;38;5;91m091 \u001b[01m\u001b[00;38;5;92m092 \u001b[01m\u001b[00;38;5;93m093 \u001b[01m\n",
-      "\u001b[00;38;5;94m094 \u001b[01m\u001b[00;38;5;95m095 \u001b[01m\u001b[00;38;5;96m096 \u001b[01m\u001b[00;38;5;97m097 \u001b[01m\u001b[00;38;5;98m098 \u001b[01m\u001b[00;38;5;99m099 \u001b[01m\n",
-      "\u001b[00;38;5;100m100 \u001b[01m\u001b[00;38;5;101m101 \u001b[01m\u001b[00;38;5;102m102 \u001b[01m\u001b[00;38;5;103m103 \u001b[01m\u001b[00;38;5;104m104 \u001b[01m\u001b[00;38;5;105m105 \u001b[01m\n",
-      "\u001b[00;38;5;106m106 \u001b[01m\u001b[00;38;5;107m107 \u001b[01m\u001b[00;38;5;108m108 \u001b[01m\u001b[00;38;5;109m109 \u001b[01m\u001b[00;38;5;110m110 \u001b[01m\u001b[00;38;5;111m111 \u001b[01m\n",
-      "\u001b[00;38;5;112m112 \u001b[01m\u001b[00;38;5;113m113 \u001b[01m\u001b[00;38;5;114m114 \u001b[01m\u001b[00;38;5;115m115 \u001b[01m\u001b[00;38;5;116m116 \u001b[01m\u001b[00;38;5;117m117 \u001b[01m\n",
-      "\u001b[00;38;5;118m118 \u001b[01m\u001b[00;38;5;119m119 \u001b[01m\u001b[00;38;5;120m120 \u001b[01m\u001b[00;38;5;121m121 \u001b[01m\u001b[00;38;5;122m122 \u001b[01m\u001b[00;38;5;123m123 \u001b[01m\n",
-      "\n",
-      "\u001b[00;38;5;124m124 \u001b[01m\u001b[00;38;5;125m125 \u001b[01m\u001b[00;38;5;126m126 \u001b[01m\u001b[00;38;5;127m127 \u001b[01m\u001b[00;38;5;128m128 \u001b[01m\u001b[00;38;5;129m129 \u001b[01m\n",
-      "\u001b[00;38;5;130m130 \u001b[01m\u001b[00;38;5;131m131 \u001b[01m\u001b[00;38;5;132m132 \u001b[01m\u001b[00;38;5;133m133 \u001b[01m\u001b[00;38;5;134m134 \u001b[01m\u001b[00;38;5;135m135 \u001b[01m\n",
-      "\u001b[00;38;5;136m136 \u001b[01m\u001b[00;38;5;137m137 \u001b[01m\u001b[00;38;5;138m138 \u001b[01m\u001b[00;38;5;139m139 \u001b[01m\u001b[00;38;5;140m140 \u001b[01m\u001b[00;38;5;141m141 \u001b[01m\n",
-      "\u001b[00;38;5;142m142 \u001b[01m\u001b[00;38;5;143m143 \u001b[01m\u001b[00;38;5;144m144 \u001b[01m\u001b[00;38;5;145m145 \u001b[01m\u001b[00;38;5;146m146 \u001b[01m\u001b[00;38;5;147m147 \u001b[01m\n",
-      "\u001b[00;38;5;148m148 \u001b[01m\u001b[00;38;5;149m149 \u001b[01m\u001b[00;38;5;150m150 \u001b[01m\u001b[00;38;5;151m151 \u001b[01m\u001b[00;38;5;152m152 \u001b[01m\u001b[00;38;5;153m153 \u001b[01m\n",
-      "\u001b[00;38;5;154m154 \u001b[01m\u001b[00;38;5;155m155 \u001b[01m\u001b[00;38;5;156m156 \u001b[01m\u001b[00;38;5;157m157 \u001b[01m\u001b[00;38;5;158m158 \u001b[01m\u001b[00;38;5;159m159 \u001b[01m\n",
-      "\n",
-      "\u001b[00;38;5;160m160 \u001b[01m\u001b[00;38;5;161m161 \u001b[01m\u001b[00;38;5;162m162 \u001b[01m\u001b[00;38;5;163m163 \u001b[01m\u001b[00;38;5;164m164 \u001b[01m\u001b[00;38;5;165m165 \u001b[01m\n",
-      "\u001b[00;38;5;166m166 \u001b[01m\u001b[00;38;5;167m167 \u001b[01m\u001b[00;38;5;168m168 \u001b[01m\u001b[00;38;5;169m169 \u001b[01m\u001b[00;38;5;170m170 \u001b[01m\u001b[00;38;5;171m171 \u001b[01m\n",
-      "\u001b[00;38;5;172m172 \u001b[01m\u001b[00;38;5;173m173 \u001b[01m\u001b[00;38;5;174m174 \u001b[01m\u001b[00;38;5;175m175 \u001b[01m\u001b[00;38;5;176m176 \u001b[01m\u001b[00;38;5;177m177 \u001b[01m\n",
-      "\u001b[00;38;5;178m178 \u001b[01m\u001b[00;38;5;179m179 \u001b[01m\u001b[00;38;5;180m180 \u001b[01m\u001b[00;38;5;181m181 \u001b[01m\u001b[00;38;5;182m182 \u001b[01m\u001b[00;38;5;183m183 \u001b[01m\n",
-      "\u001b[00;38;5;184m184 \u001b[01m\u001b[00;38;5;185m185 \u001b[01m\u001b[00;38;5;186m186 \u001b[01m\u001b[00;38;5;187m187 \u001b[01m\u001b[00;38;5;188m188 \u001b[01m\u001b[00;38;5;189m189 \u001b[01m\n",
-      "\u001b[00;38;5;190m190 \u001b[01m\u001b[00;38;5;191m191 \u001b[01m\u001b[00;38;5;192m192 \u001b[01m\u001b[00;38;5;193m193 \u001b[01m\u001b[00;38;5;194m194 \u001b[01m\u001b[00;38;5;195m195 \u001b[01m\n",
-      "\n",
-      "\u001b[00;38;5;196m196 \u001b[01m\u001b[00;38;5;197m197 \u001b[01m\u001b[00;38;5;198m198 \u001b[01m\u001b[00;38;5;199m199 \u001b[01m\u001b[00;38;5;200m200 \u001b[01m\u001b[00;38;5;201m201 \u001b[01m\n",
-      "\u001b[00;38;5;202m202 \u001b[01m\u001b[00;38;5;203m203 \u001b[01m\u001b[00;38;5;204m204 \u001b[01m\u001b[00;38;5;205m205 \u001b[01m\u001b[00;38;5;206m206 \u001b[01m\u001b[00;38;5;207m207 \u001b[01m\n",
-      "\u001b[00;38;5;208m208 \u001b[01m\u001b[00;38;5;209m209 \u001b[01m\u001b[00;38;5;210m210 \u001b[01m\u001b[00;38;5;211m211 \u001b[01m\u001b[00;38;5;212m212 \u001b[01m\u001b[00;38;5;213m213 \u001b[01m\n",
-      "\u001b[00;38;5;214m214 \u001b[01m\u001b[00;38;5;215m215 \u001b[01m\u001b[00;38;5;216m216 \u001b[01m\u001b[00;38;5;217m217 \u001b[01m\u001b[00;38;5;218m218 \u001b[01m\u001b[00;38;5;219m219 \u001b[01m\n",
-      "\u001b[00;38;5;220m220 \u001b[01m\u001b[00;38;5;221m221 \u001b[01m\u001b[00;38;5;222m222 \u001b[01m\u001b[00;38;5;223m223 \u001b[01m\u001b[00;38;5;224m224 \u001b[01m\u001b[00;38;5;225m225 \u001b[01m\n",
-      "\u001b[00;38;5;226m226 \u001b[01m\u001b[00;38;5;227m227 \u001b[01m\u001b[00;38;5;228m228 \u001b[01m\u001b[00;38;5;229m229 \u001b[01m\u001b[00;38;5;230m230 \u001b[01m\u001b[00;38;5;231m231 \u001b[01m\n",
-      "\n",
-      "\n",
-      "\u001b[00;38;5;232m232 \u001b[01m\u001b[00;38;5;233m233 \u001b[01m\u001b[00;38;5;234m234 \u001b[01m\u001b[00;38;5;235m235 \u001b[01m\u001b[00;38;5;236m236 \u001b[01m\u001b[00;38;5;237m237 \u001b[01m\u001b[00;38;5;238m238 \u001b[01m\u001b[00;38;5;239m239 \u001b[01m\u001b[00;38;5;240m240 \u001b[01m\u001b[00;38;5;241m241 \u001b[01m\u001b[00;38;5;242m242 \u001b[01m\u001b[00;38;5;243m243 \u001b[01m\n",
-      "\u001b[00;38;5;244m244 \u001b[01m\u001b[00;38;5;245m245 \u001b[01m\u001b[00;38;5;246m246 \u001b[01m\u001b[00;38;5;247m247 \u001b[01m\u001b[00;38;5;248m248 \u001b[01m\u001b[00;38;5;249m249 \u001b[01m\u001b[00;38;5;250m250 \u001b[01m\u001b[00;38;5;251m251 \u001b[01m\u001b[00;38;5;252m252 \u001b[01m\u001b[00;38;5;253m253 \u001b[01m\u001b[00;38;5;254m254 \u001b[01m\u001b[00;38;5;255m255 \u001b[01m\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "t = \"{ESC}00;38;5;{i}m{i:03} {RESET}\"\n",
-    "for i in range(16):\n",
-    "    print (t.format(**locals()), end='')\n",
-    "    if i % 8 == 7:\n",
-    "        print ()\n",
-    "print ()\n",
-    "\n",
-    "for i in range(16,232):\n",
-    "    print (t.format(**locals()), end='')\n",
-    "    if (i-16) % 6 == 5:\n",
-    "        print ()\n",
-    "    if (i-16) % 36 == 35:\n",
-    "        print ()\n",
-    "\n",
-    "print ()\n",
-    "\n",
-    "for i in range(232,256):\n",
-    "    print (t.format(**locals()), end='')\n",
-    "    if (i-232) % 12 == 11:\n",
-    "        print ()\n",
-    "print ()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 256-color background"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[00;48;5;0m000 \u001b[01m\u001b[00;48;5;1m001 \u001b[01m\u001b[00;48;5;2m002 \u001b[01m\u001b[00;48;5;3m003 \u001b[01m\u001b[00;48;5;4m004 \u001b[01m\u001b[00;48;5;5m005 \u001b[01m\u001b[00;48;5;6m006 \u001b[01m\u001b[00;48;5;7m007 \u001b[01m\n",
-      "\u001b[00;48;5;8m008 \u001b[01m\u001b[00;48;5;9m009 \u001b[01m\u001b[00;48;5;10m010 \u001b[01m\u001b[00;48;5;11m011 \u001b[01m\u001b[00;48;5;12m012 \u001b[01m\u001b[00;48;5;13m013 \u001b[01m\u001b[00;48;5;14m014 \u001b[01m\u001b[00;48;5;15m015 \u001b[01m\n",
-      "\n",
-      "\u001b[00;48;5;16m016 \u001b[01m\u001b[00;48;5;17m017 \u001b[01m\u001b[00;48;5;18m018 \u001b[01m\u001b[00;48;5;19m019 \u001b[01m\u001b[00;48;5;20m020 \u001b[01m\u001b[00;48;5;21m021 \u001b[01m\n",
-      "\u001b[00;48;5;22m022 \u001b[01m\u001b[00;48;5;23m023 \u001b[01m\u001b[00;48;5;24m024 \u001b[01m\u001b[00;48;5;25m025 \u001b[01m\u001b[00;48;5;26m026 \u001b[01m\u001b[00;48;5;27m027 \u001b[01m\n",
-      "\u001b[00;48;5;28m028 \u001b[01m\u001b[00;48;5;29m029 \u001b[01m\u001b[00;48;5;30m030 \u001b[01m\u001b[00;48;5;31m031 \u001b[01m\u001b[00;48;5;32m032 \u001b[01m\u001b[00;48;5;33m033 \u001b[01m\n",
-      "\u001b[00;48;5;34m034 \u001b[01m\u001b[00;48;5;35m035 \u001b[01m\u001b[00;48;5;36m036 \u001b[01m\u001b[00;48;5;37m037 \u001b[01m\u001b[00;48;5;38m038 \u001b[01m\u001b[00;48;5;39m039 \u001b[01m\n",
-      "\u001b[00;48;5;40m040 \u001b[01m\u001b[00;48;5;41m041 \u001b[01m\u001b[00;48;5;42m042 \u001b[01m\u001b[00;48;5;43m043 \u001b[01m\u001b[00;48;5;44m044 \u001b[01m\u001b[00;48;5;45m045 \u001b[01m\n",
-      "\u001b[00;48;5;46m046 \u001b[01m\u001b[00;48;5;47m047 \u001b[01m\u001b[00;48;5;48m048 \u001b[01m\u001b[00;48;5;49m049 \u001b[01m\u001b[00;48;5;50m050 \u001b[01m\u001b[00;48;5;51m051 \u001b[01m\n",
-      "\n",
-      "\u001b[00;48;5;52m052 \u001b[01m\u001b[00;48;5;53m053 \u001b[01m\u001b[00;48;5;54m054 \u001b[01m\u001b[00;48;5;55m055 \u001b[01m\u001b[00;48;5;56m056 \u001b[01m\u001b[00;48;5;57m057 \u001b[01m\n",
-      "\u001b[00;48;5;58m058 \u001b[01m\u001b[00;48;5;59m059 \u001b[01m\u001b[00;48;5;60m060 \u001b[01m\u001b[00;48;5;61m061 \u001b[01m\u001b[00;48;5;62m062 \u001b[01m\u001b[00;48;5;63m063 \u001b[01m\n",
-      "\u001b[00;48;5;64m064 \u001b[01m\u001b[00;48;5;65m065 \u001b[01m\u001b[00;48;5;66m066 \u001b[01m\u001b[00;48;5;67m067 \u001b[01m\u001b[00;48;5;68m068 \u001b[01m\u001b[00;48;5;69m069 \u001b[01m\n",
-      "\u001b[00;48;5;70m070 \u001b[01m\u001b[00;48;5;71m071 \u001b[01m\u001b[00;48;5;72m072 \u001b[01m\u001b[00;48;5;73m073 \u001b[01m\u001b[00;48;5;74m074 \u001b[01m\u001b[00;48;5;75m075 \u001b[01m\n",
-      "\u001b[00;48;5;76m076 \u001b[01m\u001b[00;48;5;77m077 \u001b[01m\u001b[00;48;5;78m078 \u001b[01m\u001b[00;48;5;79m079 \u001b[01m\u001b[00;48;5;80m080 \u001b[01m\u001b[00;48;5;81m081 \u001b[01m\n",
-      "\u001b[00;48;5;82m082 \u001b[01m\u001b[00;48;5;83m083 \u001b[01m\u001b[00;48;5;84m084 \u001b[01m\u001b[00;48;5;85m085 \u001b[01m\u001b[00;48;5;86m086 \u001b[01m\u001b[00;48;5;87m087 \u001b[01m\n",
-      "\n",
-      "\u001b[00;48;5;88m088 \u001b[01m\u001b[00;48;5;89m089 \u001b[01m\u001b[00;48;5;90m090 \u001b[01m\u001b[00;48;5;91m091 \u001b[01m\u001b[00;48;5;92m092 \u001b[01m\u001b[00;48;5;93m093 \u001b[01m\n",
-      "\u001b[00;48;5;94m094 \u001b[01m\u001b[00;48;5;95m095 \u001b[01m\u001b[00;48;5;96m096 \u001b[01m\u001b[00;48;5;97m097 \u001b[01m\u001b[00;48;5;98m098 \u001b[01m\u001b[00;48;5;99m099 \u001b[01m\n",
-      "\u001b[00;48;5;100m100 \u001b[01m\u001b[00;48;5;101m101 \u001b[01m\u001b[00;48;5;102m102 \u001b[01m\u001b[00;48;5;103m103 \u001b[01m\u001b[00;48;5;104m104 \u001b[01m\u001b[00;48;5;105m105 \u001b[01m\n",
-      "\u001b[00;48;5;106m106 \u001b[01m\u001b[00;48;5;107m107 \u001b[01m\u001b[00;48;5;108m108 \u001b[01m\u001b[00;48;5;109m109 \u001b[01m\u001b[00;48;5;110m110 \u001b[01m\u001b[00;48;5;111m111 \u001b[01m\n",
-      "\u001b[00;48;5;112m112 \u001b[01m\u001b[00;48;5;113m113 \u001b[01m\u001b[00;48;5;114m114 \u001b[01m\u001b[00;48;5;115m115 \u001b[01m\u001b[00;48;5;116m116 \u001b[01m\u001b[00;48;5;117m117 \u001b[01m\n",
-      "\u001b[00;48;5;118m118 \u001b[01m\u001b[00;48;5;119m119 \u001b[01m\u001b[00;48;5;120m120 \u001b[01m\u001b[00;48;5;121m121 \u001b[01m\u001b[00;48;5;122m122 \u001b[01m\u001b[00;48;5;123m123 \u001b[01m\n",
-      "\n",
-      "\u001b[00;48;5;124m124 \u001b[01m\u001b[00;48;5;125m125 \u001b[01m\u001b[00;48;5;126m126 \u001b[01m\u001b[00;48;5;127m127 \u001b[01m\u001b[00;48;5;128m128 \u001b[01m\u001b[00;48;5;129m129 \u001b[01m\n",
-      "\u001b[00;48;5;130m130 \u001b[01m\u001b[00;48;5;131m131 \u001b[01m\u001b[00;48;5;132m132 \u001b[01m\u001b[00;48;5;133m133 \u001b[01m\u001b[00;48;5;134m134 \u001b[01m\u001b[00;48;5;135m135 \u001b[01m\n",
-      "\u001b[00;48;5;136m136 \u001b[01m\u001b[00;48;5;137m137 \u001b[01m\u001b[00;48;5;138m138 \u001b[01m\u001b[00;48;5;139m139 \u001b[01m\u001b[00;48;5;140m140 \u001b[01m\u001b[00;48;5;141m141 \u001b[01m\n",
-      "\u001b[00;48;5;142m142 \u001b[01m\u001b[00;48;5;143m143 \u001b[01m\u001b[00;48;5;144m144 \u001b[01m\u001b[00;48;5;145m145 \u001b[01m\u001b[00;48;5;146m146 \u001b[01m\u001b[00;48;5;147m147 \u001b[01m\n",
-      "\u001b[00;48;5;148m148 \u001b[01m\u001b[00;48;5;149m149 \u001b[01m\u001b[00;48;5;150m150 \u001b[01m\u001b[00;48;5;151m151 \u001b[01m\u001b[00;48;5;152m152 \u001b[01m\u001b[00;48;5;153m153 \u001b[01m\n",
-      "\u001b[00;48;5;154m154 \u001b[01m\u001b[00;48;5;155m155 \u001b[01m\u001b[00;48;5;156m156 \u001b[01m\u001b[00;48;5;157m157 \u001b[01m\u001b[00;48;5;158m158 \u001b[01m\u001b[00;48;5;159m159 \u001b[01m\n",
-      "\n",
-      "\u001b[00;48;5;160m160 \u001b[01m\u001b[00;48;5;161m161 \u001b[01m\u001b[00;48;5;162m162 \u001b[01m\u001b[00;48;5;163m163 \u001b[01m\u001b[00;48;5;164m164 \u001b[01m\u001b[00;48;5;165m165 \u001b[01m\n",
-      "\u001b[00;48;5;166m166 \u001b[01m\u001b[00;48;5;167m167 \u001b[01m\u001b[00;48;5;168m168 \u001b[01m\u001b[00;48;5;169m169 \u001b[01m\u001b[00;48;5;170m170 \u001b[01m\u001b[00;48;5;171m171 \u001b[01m\n",
-      "\u001b[00;48;5;172m172 \u001b[01m\u001b[00;48;5;173m173 \u001b[01m\u001b[00;48;5;174m174 \u001b[01m\u001b[00;48;5;175m175 \u001b[01m\u001b[00;48;5;176m176 \u001b[01m\u001b[00;48;5;177m177 \u001b[01m\n",
-      "\u001b[00;48;5;178m178 \u001b[01m\u001b[00;48;5;179m179 \u001b[01m\u001b[00;48;5;180m180 \u001b[01m\u001b[00;48;5;181m181 \u001b[01m\u001b[00;48;5;182m182 \u001b[01m\u001b[00;48;5;183m183 \u001b[01m\n",
-      "\u001b[00;48;5;184m184 \u001b[01m\u001b[00;48;5;185m185 \u001b[01m\u001b[00;48;5;186m186 \u001b[01m\u001b[00;48;5;187m187 \u001b[01m\u001b[00;48;5;188m188 \u001b[01m\u001b[00;48;5;189m189 \u001b[01m\n",
-      "\u001b[00;48;5;190m190 \u001b[01m\u001b[00;48;5;191m191 \u001b[01m\u001b[00;48;5;192m192 \u001b[01m\u001b[00;48;5;193m193 \u001b[01m\u001b[00;48;5;194m194 \u001b[01m\u001b[00;48;5;195m195 \u001b[01m\n",
-      "\n",
-      "\u001b[00;48;5;196m196 \u001b[01m\u001b[00;48;5;197m197 \u001b[01m\u001b[00;48;5;198m198 \u001b[01m\u001b[00;48;5;199m199 \u001b[01m\u001b[00;48;5;200m200 \u001b[01m\u001b[00;48;5;201m201 \u001b[01m\n",
-      "\u001b[00;48;5;202m202 \u001b[01m\u001b[00;48;5;203m203 \u001b[01m\u001b[00;48;5;204m204 \u001b[01m\u001b[00;48;5;205m205 \u001b[01m\u001b[00;48;5;206m206 \u001b[01m\u001b[00;48;5;207m207 \u001b[01m\n",
-      "\u001b[00;48;5;208m208 \u001b[01m\u001b[00;48;5;209m209 \u001b[01m\u001b[00;48;5;210m210 \u001b[01m\u001b[00;48;5;211m211 \u001b[01m\u001b[00;48;5;212m212 \u001b[01m\u001b[00;48;5;213m213 \u001b[01m\n",
-      "\u001b[00;48;5;214m214 \u001b[01m\u001b[00;48;5;215m215 \u001b[01m\u001b[00;48;5;216m216 \u001b[01m\u001b[00;48;5;217m217 \u001b[01m\u001b[00;48;5;218m218 \u001b[01m\u001b[00;48;5;219m219 \u001b[01m\n",
-      "\u001b[00;48;5;220m220 \u001b[01m\u001b[00;48;5;221m221 \u001b[01m\u001b[00;48;5;222m222 \u001b[01m\u001b[00;48;5;223m223 \u001b[01m\u001b[00;48;5;224m224 \u001b[01m\u001b[00;48;5;225m225 \u001b[01m\n",
-      "\u001b[00;48;5;226m226 \u001b[01m\u001b[00;48;5;227m227 \u001b[01m\u001b[00;48;5;228m228 \u001b[01m\u001b[00;48;5;229m229 \u001b[01m\u001b[00;48;5;230m230 \u001b[01m\u001b[00;48;5;231m231 \u001b[01m\n",
-      "\n",
-      "\n",
-      "\u001b[00;48;5;232m232 \u001b[01m\u001b[00;48;5;233m233 \u001b[01m\u001b[00;48;5;234m234 \u001b[01m\u001b[00;48;5;235m235 \u001b[01m\u001b[00;48;5;236m236 \u001b[01m\u001b[00;48;5;237m237 \u001b[01m\u001b[00;48;5;238m238 \u001b[01m\u001b[00;48;5;239m239 \u001b[01m\u001b[00;48;5;240m240 \u001b[01m\u001b[00;48;5;241m241 \u001b[01m\u001b[00;48;5;242m242 \u001b[01m\u001b[00;48;5;243m243 \u001b[01m\n",
-      "\u001b[00;48;5;244m244 \u001b[01m\u001b[00;48;5;245m245 \u001b[01m\u001b[00;48;5;246m246 \u001b[01m\u001b[00;48;5;247m247 \u001b[01m\u001b[00;48;5;248m248 \u001b[01m\u001b[00;48;5;249m249 \u001b[01m\u001b[00;48;5;250m250 \u001b[01m\u001b[00;48;5;251m251 \u001b[01m\u001b[00;48;5;252m252 \u001b[01m\u001b[00;48;5;253m253 \u001b[01m\u001b[00;48;5;254m254 \u001b[01m\u001b[00;48;5;255m255 \u001b[01m\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "t = \"{ESC}00;48;5;{i}m{i:03} {RESET}\"\n",
-    "for i in range(16):\n",
-    "    print (t.format(**locals()), end='')\n",
-    "    if i % 8 == 7:\n",
-    "        print ()\n",
-    "\n",
-    "print ()\n",
-    "\n",
-    "for i in range(16,232):\n",
-    "    print (t.format(**locals()), end='')\n",
-    "    if (i-16) % 6 == 5:\n",
-    "        print ()\n",
-    "    if (i-16) % 36 == 35:\n",
-    "        print ()\n",
-    "\n",
-    "print ()\n",
-    "\n",
-    "for i in range(232,256):\n",
-    "    print (t.format(**locals()), end='')\n",
-    "    if (i-232) % 12 == 11:\n",
-    "        print ()\n",
-    "print ()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 256-color background and foreground"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[00;48;5;2;38;5;165m165 on 002 \u001b[00m\u001b[00;48;5;2;38;5;102m102 on 002 \u001b[00m\u001b[00;48;5;2;38;5;252m252 on 002 \u001b[00m\u001b[00;48;5;2;38;5;9m009 on 002 \u001b[00m\n",
-      "\u001b[00;48;5;57;38;5;165m165 on 057 \u001b[00m\u001b[00;48;5;57;38;5;102m102 on 057 \u001b[00m\u001b[00;48;5;57;38;5;252m252 on 057 \u001b[00m\u001b[00;48;5;57;38;5;9m009 on 057 \u001b[00m\n",
-      "\u001b[00;48;5;160;38;5;165m165 on 160 \u001b[00m\u001b[00;48;5;160;38;5;102m102 on 160 \u001b[00m\u001b[00;48;5;160;38;5;252m252 on 160 \u001b[00m\u001b[00;48;5;160;38;5;9m009 on 160 \u001b[00m\n",
-      "\u001b[00;48;5;246;38;5;165m165 on 246 \u001b[00m\u001b[00;48;5;246;38;5;102m102 on 246 \u001b[00m\u001b[00;48;5;246;38;5;252m252 on 246 \u001b[00m\u001b[00;48;5;246;38;5;9m009 on 246 \u001b[00m\n"
-     ]
-    }
-   ],
-   "source": [
-    "t = \"{ESC}00;48;5;{bg};38;5;{fg}m{fg:03} on {bg:03} {RESET}\"\n",
-    "\n",
-    "for bg in [2, 57, 160, 246]:\n",
-    "    for fg in [165, 102, 252, 9]:\n",
-    "        print (t.format(**locals()), end='')\n",
-    "    print()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 24-bit RGB"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[00;38;2;0;0;0m000|000|000 \u001b[00m\u001b[00;38;2;0;0;30m000|000|030 \u001b[00m\u001b[00;38;2;0;0;60m000|000|060 \u001b[00m\u001b[00;38;2;0;0;90m000|000|090 \u001b[00m\u001b[00;38;2;0;0;120m000|000|120 \u001b[00m\u001b[00;38;2;0;0;150m000|000|150 \u001b[00m\u001b[00;38;2;0;0;180m000|000|180 \u001b[00m\u001b[00;38;2;0;0;210m000|000|210 \u001b[00m\u001b[00;38;2;0;0;240m000|000|240 \u001b[00m\n",
-      "\u001b[00;38;2;0;30;0m000|030|000 \u001b[00m\u001b[00;38;2;0;30;30m000|030|030 \u001b[00m\u001b[00;38;2;0;30;60m000|030|060 \u001b[00m\u001b[00;38;2;0;30;90m000|030|090 \u001b[00m\u001b[00;38;2;0;30;120m000|030|120 \u001b[00m\u001b[00;38;2;0;30;150m000|030|150 \u001b[00m\u001b[00;38;2;0;30;180m000|030|180 \u001b[00m\u001b[00;38;2;0;30;210m000|030|210 \u001b[00m\u001b[00;38;2;0;30;240m000|030|240 \u001b[00m\n",
-      "\u001b[00;38;2;0;60;0m000|060|000 \u001b[00m\u001b[00;38;2;0;60;30m000|060|030 \u001b[00m\u001b[00;38;2;0;60;60m000|060|060 \u001b[00m\u001b[00;38;2;0;60;90m000|060|090 \u001b[00m\u001b[00;38;2;0;60;120m000|060|120 \u001b[00m\u001b[00;38;2;0;60;150m000|060|150 \u001b[00m\u001b[00;38;2;0;60;180m000|060|180 \u001b[00m\u001b[00;38;2;0;60;210m000|060|210 \u001b[00m\u001b[00;38;2;0;60;240m000|060|240 \u001b[00m\n",
-      "\u001b[00;38;2;0;90;0m000|090|000 \u001b[00m\u001b[00;38;2;0;90;30m000|090|030 \u001b[00m\u001b[00;38;2;0;90;60m000|090|060 \u001b[00m\u001b[00;38;2;0;90;90m000|090|090 \u001b[00m\u001b[00;38;2;0;90;120m000|090|120 \u001b[00m\u001b[00;38;2;0;90;150m000|090|150 \u001b[00m\u001b[00;38;2;0;90;180m000|090|180 \u001b[00m\u001b[00;38;2;0;90;210m000|090|210 \u001b[00m\u001b[00;38;2;0;90;240m000|090|240 \u001b[00m\n",
-      "\u001b[00;38;2;0;120;0m000|120|000 \u001b[00m\u001b[00;38;2;0;120;30m000|120|030 \u001b[00m\u001b[00;38;2;0;120;60m000|120|060 \u001b[00m\u001b[00;38;2;0;120;90m000|120|090 \u001b[00m\u001b[00;38;2;0;120;120m000|120|120 \u001b[00m\u001b[00;38;2;0;120;150m000|120|150 \u001b[00m\u001b[00;38;2;0;120;180m000|120|180 \u001b[00m\u001b[00;38;2;0;120;210m000|120|210 \u001b[00m\u001b[00;38;2;0;120;240m000|120|240 \u001b[00m\n",
-      "\u001b[00;38;2;0;150;0m000|150|000 \u001b[00m\u001b[00;38;2;0;150;30m000|150|030 \u001b[00m\u001b[00;38;2;0;150;60m000|150|060 \u001b[00m\u001b[00;38;2;0;150;90m000|150|090 \u001b[00m\u001b[00;38;2;0;150;120m000|150|120 \u001b[00m\u001b[00;38;2;0;150;150m000|150|150 \u001b[00m\u001b[00;38;2;0;150;180m000|150|180 \u001b[00m\u001b[00;38;2;0;150;210m000|150|210 \u001b[00m\u001b[00;38;2;0;150;240m000|150|240 \u001b[00m\n",
-      "\u001b[00;38;2;0;180;0m000|180|000 \u001b[00m\u001b[00;38;2;0;180;30m000|180|030 \u001b[00m\u001b[00;38;2;0;180;60m000|180|060 \u001b[00m\u001b[00;38;2;0;180;90m000|180|090 \u001b[00m\u001b[00;38;2;0;180;120m000|180|120 \u001b[00m\u001b[00;38;2;0;180;150m000|180|150 \u001b[00m\u001b[00;38;2;0;180;180m000|180|180 \u001b[00m\u001b[00;38;2;0;180;210m000|180|210 \u001b[00m\u001b[00;38;2;0;180;240m000|180|240 \u001b[00m\n",
-      "\u001b[00;38;2;0;210;0m000|210|000 \u001b[00m\u001b[00;38;2;0;210;30m000|210|030 \u001b[00m\u001b[00;38;2;0;210;60m000|210|060 \u001b[00m\u001b[00;38;2;0;210;90m000|210|090 \u001b[00m\u001b[00;38;2;0;210;120m000|210|120 \u001b[00m\u001b[00;38;2;0;210;150m000|210|150 \u001b[00m\u001b[00;38;2;0;210;180m000|210|180 \u001b[00m\u001b[00;38;2;0;210;210m000|210|210 \u001b[00m\u001b[00;38;2;0;210;240m000|210|240 \u001b[00m\n",
-      "\u001b[00;38;2;0;240;0m000|240|000 \u001b[00m\u001b[00;38;2;0;240;30m000|240|030 \u001b[00m\u001b[00;38;2;0;240;60m000|240|060 \u001b[00m\u001b[00;38;2;0;240;90m000|240|090 \u001b[00m\u001b[00;38;2;0;240;120m000|240|120 \u001b[00m\u001b[00;38;2;0;240;150m000|240|150 \u001b[00m\u001b[00;38;2;0;240;180m000|240|180 \u001b[00m\u001b[00;38;2;0;240;210m000|240|210 \u001b[00m\u001b[00;38;2;0;240;240m000|240|240 \u001b[00m\n",
-      "\n",
-      "\u001b[00;38;2;30;0;0m030|000|000 \u001b[00m\u001b[00;38;2;30;0;30m030|000|030 \u001b[00m\u001b[00;38;2;30;0;60m030|000|060 \u001b[00m\u001b[00;38;2;30;0;90m030|000|090 \u001b[00m\u001b[00;38;2;30;0;120m030|000|120 \u001b[00m\u001b[00;38;2;30;0;150m030|000|150 \u001b[00m\u001b[00;38;2;30;0;180m030|000|180 \u001b[00m\u001b[00;38;2;30;0;210m030|000|210 \u001b[00m\u001b[00;38;2;30;0;240m030|000|240 \u001b[00m\n",
-      "\u001b[00;38;2;30;30;0m030|030|000 \u001b[00m\u001b[00;38;2;30;30;30m030|030|030 \u001b[00m\u001b[00;38;2;30;30;60m030|030|060 \u001b[00m\u001b[00;38;2;30;30;90m030|030|090 \u001b[00m\u001b[00;38;2;30;30;120m030|030|120 \u001b[00m\u001b[00;38;2;30;30;150m030|030|150 \u001b[00m\u001b[00;38;2;30;30;180m030|030|180 \u001b[00m\u001b[00;38;2;30;30;210m030|030|210 \u001b[00m\u001b[00;38;2;30;30;240m030|030|240 \u001b[00m\n",
-      "\u001b[00;38;2;30;60;0m030|060|000 \u001b[00m\u001b[00;38;2;30;60;30m030|060|030 \u001b[00m\u001b[00;38;2;30;60;60m030|060|060 \u001b[00m\u001b[00;38;2;30;60;90m030|060|090 \u001b[00m\u001b[00;38;2;30;60;120m030|060|120 \u001b[00m\u001b[00;38;2;30;60;150m030|060|150 \u001b[00m\u001b[00;38;2;30;60;180m030|060|180 \u001b[00m\u001b[00;38;2;30;60;210m030|060|210 \u001b[00m\u001b[00;38;2;30;60;240m030|060|240 \u001b[00m\n",
-      "\u001b[00;38;2;30;90;0m030|090|000 \u001b[00m\u001b[00;38;2;30;90;30m030|090|030 \u001b[00m\u001b[00;38;2;30;90;60m030|090|060 \u001b[00m\u001b[00;38;2;30;90;90m030|090|090 \u001b[00m\u001b[00;38;2;30;90;120m030|090|120 \u001b[00m\u001b[00;38;2;30;90;150m030|090|150 \u001b[00m\u001b[00;38;2;30;90;180m030|090|180 \u001b[00m\u001b[00;38;2;30;90;210m030|090|210 \u001b[00m\u001b[00;38;2;30;90;240m030|090|240 \u001b[00m\n",
-      "\u001b[00;38;2;30;120;0m030|120|000 \u001b[00m\u001b[00;38;2;30;120;30m030|120|030 \u001b[00m\u001b[00;38;2;30;120;60m030|120|060 \u001b[00m\u001b[00;38;2;30;120;90m030|120|090 \u001b[00m\u001b[00;38;2;30;120;120m030|120|120 \u001b[00m\u001b[00;38;2;30;120;150m030|120|150 \u001b[00m\u001b[00;38;2;30;120;180m030|120|180 \u001b[00m\u001b[00;38;2;30;120;210m030|120|210 \u001b[00m\u001b[00;38;2;30;120;240m030|120|240 \u001b[00m\n",
-      "\u001b[00;38;2;30;150;0m030|150|000 \u001b[00m\u001b[00;38;2;30;150;30m030|150|030 \u001b[00m\u001b[00;38;2;30;150;60m030|150|060 \u001b[00m\u001b[00;38;2;30;150;90m030|150|090 \u001b[00m\u001b[00;38;2;30;150;120m030|150|120 \u001b[00m\u001b[00;38;2;30;150;150m030|150|150 \u001b[00m\u001b[00;38;2;30;150;180m030|150|180 \u001b[00m\u001b[00;38;2;30;150;210m030|150|210 \u001b[00m\u001b[00;38;2;30;150;240m030|150|240 \u001b[00m\n",
-      "\u001b[00;38;2;30;180;0m030|180|000 \u001b[00m\u001b[00;38;2;30;180;30m030|180|030 \u001b[00m\u001b[00;38;2;30;180;60m030|180|060 \u001b[00m\u001b[00;38;2;30;180;90m030|180|090 \u001b[00m\u001b[00;38;2;30;180;120m030|180|120 \u001b[00m\u001b[00;38;2;30;180;150m030|180|150 \u001b[00m\u001b[00;38;2;30;180;180m030|180|180 \u001b[00m\u001b[00;38;2;30;180;210m030|180|210 \u001b[00m\u001b[00;38;2;30;180;240m030|180|240 \u001b[00m\n",
-      "\u001b[00;38;2;30;210;0m030|210|000 \u001b[00m\u001b[00;38;2;30;210;30m030|210|030 \u001b[00m\u001b[00;38;2;30;210;60m030|210|060 \u001b[00m\u001b[00;38;2;30;210;90m030|210|090 \u001b[00m\u001b[00;38;2;30;210;120m030|210|120 \u001b[00m\u001b[00;38;2;30;210;150m030|210|150 \u001b[00m\u001b[00;38;2;30;210;180m030|210|180 \u001b[00m\u001b[00;38;2;30;210;210m030|210|210 \u001b[00m\u001b[00;38;2;30;210;240m030|210|240 \u001b[00m\n",
-      "\u001b[00;38;2;30;240;0m030|240|000 \u001b[00m\u001b[00;38;2;30;240;30m030|240|030 \u001b[00m\u001b[00;38;2;30;240;60m030|240|060 \u001b[00m\u001b[00;38;2;30;240;90m030|240|090 \u001b[00m\u001b[00;38;2;30;240;120m030|240|120 \u001b[00m\u001b[00;38;2;30;240;150m030|240|150 \u001b[00m\u001b[00;38;2;30;240;180m030|240|180 \u001b[00m\u001b[00;38;2;30;240;210m030|240|210 \u001b[00m\u001b[00;38;2;30;240;240m030|240|240 \u001b[00m\n",
-      "\n",
-      "\u001b[00;38;2;60;0;0m060|000|000 \u001b[00m\u001b[00;38;2;60;0;30m060|000|030 \u001b[00m\u001b[00;38;2;60;0;60m060|000|060 \u001b[00m\u001b[00;38;2;60;0;90m060|000|090 \u001b[00m\u001b[00;38;2;60;0;120m060|000|120 \u001b[00m\u001b[00;38;2;60;0;150m060|000|150 \u001b[00m\u001b[00;38;2;60;0;180m060|000|180 \u001b[00m\u001b[00;38;2;60;0;210m060|000|210 \u001b[00m\u001b[00;38;2;60;0;240m060|000|240 \u001b[00m\n",
-      "\u001b[00;38;2;60;30;0m060|030|000 \u001b[00m\u001b[00;38;2;60;30;30m060|030|030 \u001b[00m\u001b[00;38;2;60;30;60m060|030|060 \u001b[00m\u001b[00;38;2;60;30;90m060|030|090 \u001b[00m\u001b[00;38;2;60;30;120m060|030|120 \u001b[00m\u001b[00;38;2;60;30;150m060|030|150 \u001b[00m\u001b[00;38;2;60;30;180m060|030|180 \u001b[00m\u001b[00;38;2;60;30;210m060|030|210 \u001b[00m\u001b[00;38;2;60;30;240m060|030|240 \u001b[00m\n",
-      "\u001b[00;38;2;60;60;0m060|060|000 \u001b[00m\u001b[00;38;2;60;60;30m060|060|030 \u001b[00m\u001b[00;38;2;60;60;60m060|060|060 \u001b[00m\u001b[00;38;2;60;60;90m060|060|090 \u001b[00m\u001b[00;38;2;60;60;120m060|060|120 \u001b[00m\u001b[00;38;2;60;60;150m060|060|150 \u001b[00m\u001b[00;38;2;60;60;180m060|060|180 \u001b[00m\u001b[00;38;2;60;60;210m060|060|210 \u001b[00m\u001b[00;38;2;60;60;240m060|060|240 \u001b[00m\n",
-      "\u001b[00;38;2;60;90;0m060|090|000 \u001b[00m\u001b[00;38;2;60;90;30m060|090|030 \u001b[00m\u001b[00;38;2;60;90;60m060|090|060 \u001b[00m\u001b[00;38;2;60;90;90m060|090|090 \u001b[00m\u001b[00;38;2;60;90;120m060|090|120 \u001b[00m\u001b[00;38;2;60;90;150m060|090|150 \u001b[00m\u001b[00;38;2;60;90;180m060|090|180 \u001b[00m\u001b[00;38;2;60;90;210m060|090|210 \u001b[00m\u001b[00;38;2;60;90;240m060|090|240 \u001b[00m\n",
-      "\u001b[00;38;2;60;120;0m060|120|000 \u001b[00m\u001b[00;38;2;60;120;30m060|120|030 \u001b[00m\u001b[00;38;2;60;120;60m060|120|060 \u001b[00m\u001b[00;38;2;60;120;90m060|120|090 \u001b[00m\u001b[00;38;2;60;120;120m060|120|120 \u001b[00m\u001b[00;38;2;60;120;150m060|120|150 \u001b[00m\u001b[00;38;2;60;120;180m060|120|180 \u001b[00m\u001b[00;38;2;60;120;210m060|120|210 \u001b[00m\u001b[00;38;2;60;120;240m060|120|240 \u001b[00m\n",
-      "\u001b[00;38;2;60;150;0m060|150|000 \u001b[00m\u001b[00;38;2;60;150;30m060|150|030 \u001b[00m\u001b[00;38;2;60;150;60m060|150|060 \u001b[00m\u001b[00;38;2;60;150;90m060|150|090 \u001b[00m\u001b[00;38;2;60;150;120m060|150|120 \u001b[00m\u001b[00;38;2;60;150;150m060|150|150 \u001b[00m\u001b[00;38;2;60;150;180m060|150|180 \u001b[00m\u001b[00;38;2;60;150;210m060|150|210 \u001b[00m\u001b[00;38;2;60;150;240m060|150|240 \u001b[00m\n",
-      "\u001b[00;38;2;60;180;0m060|180|000 \u001b[00m\u001b[00;38;2;60;180;30m060|180|030 \u001b[00m\u001b[00;38;2;60;180;60m060|180|060 \u001b[00m\u001b[00;38;2;60;180;90m060|180|090 \u001b[00m\u001b[00;38;2;60;180;120m060|180|120 \u001b[00m\u001b[00;38;2;60;180;150m060|180|150 \u001b[00m\u001b[00;38;2;60;180;180m060|180|180 \u001b[00m\u001b[00;38;2;60;180;210m060|180|210 \u001b[00m\u001b[00;38;2;60;180;240m060|180|240 \u001b[00m\n",
-      "\u001b[00;38;2;60;210;0m060|210|000 \u001b[00m\u001b[00;38;2;60;210;30m060|210|030 \u001b[00m\u001b[00;38;2;60;210;60m060|210|060 \u001b[00m\u001b[00;38;2;60;210;90m060|210|090 \u001b[00m\u001b[00;38;2;60;210;120m060|210|120 \u001b[00m\u001b[00;38;2;60;210;150m060|210|150 \u001b[00m\u001b[00;38;2;60;210;180m060|210|180 \u001b[00m\u001b[00;38;2;60;210;210m060|210|210 \u001b[00m\u001b[00;38;2;60;210;240m060|210|240 \u001b[00m\n",
-      "\u001b[00;38;2;60;240;0m060|240|000 \u001b[00m\u001b[00;38;2;60;240;30m060|240|030 \u001b[00m\u001b[00;38;2;60;240;60m060|240|060 \u001b[00m\u001b[00;38;2;60;240;90m060|240|090 \u001b[00m\u001b[00;38;2;60;240;120m060|240|120 \u001b[00m\u001b[00;38;2;60;240;150m060|240|150 \u001b[00m\u001b[00;38;2;60;240;180m060|240|180 \u001b[00m\u001b[00;38;2;60;240;210m060|240|210 \u001b[00m\u001b[00;38;2;60;240;240m060|240|240 \u001b[00m\n",
-      "\n",
-      "\u001b[00;38;2;90;0;0m090|000|000 \u001b[00m\u001b[00;38;2;90;0;30m090|000|030 \u001b[00m\u001b[00;38;2;90;0;60m090|000|060 \u001b[00m\u001b[00;38;2;90;0;90m090|000|090 \u001b[00m\u001b[00;38;2;90;0;120m090|000|120 \u001b[00m\u001b[00;38;2;90;0;150m090|000|150 \u001b[00m\u001b[00;38;2;90;0;180m090|000|180 \u001b[00m\u001b[00;38;2;90;0;210m090|000|210 \u001b[00m\u001b[00;38;2;90;0;240m090|000|240 \u001b[00m\n",
-      "\u001b[00;38;2;90;30;0m090|030|000 \u001b[00m\u001b[00;38;2;90;30;30m090|030|030 \u001b[00m\u001b[00;38;2;90;30;60m090|030|060 \u001b[00m\u001b[00;38;2;90;30;90m090|030|090 \u001b[00m\u001b[00;38;2;90;30;120m090|030|120 \u001b[00m\u001b[00;38;2;90;30;150m090|030|150 \u001b[00m\u001b[00;38;2;90;30;180m090|030|180 \u001b[00m\u001b[00;38;2;90;30;210m090|030|210 \u001b[00m\u001b[00;38;2;90;30;240m090|030|240 \u001b[00m\n",
-      "\u001b[00;38;2;90;60;0m090|060|000 \u001b[00m\u001b[00;38;2;90;60;30m090|060|030 \u001b[00m\u001b[00;38;2;90;60;60m090|060|060 \u001b[00m\u001b[00;38;2;90;60;90m090|060|090 \u001b[00m\u001b[00;38;2;90;60;120m090|060|120 \u001b[00m\u001b[00;38;2;90;60;150m090|060|150 \u001b[00m\u001b[00;38;2;90;60;180m090|060|180 \u001b[00m\u001b[00;38;2;90;60;210m090|060|210 \u001b[00m\u001b[00;38;2;90;60;240m090|060|240 \u001b[00m\n",
-      "\u001b[00;38;2;90;90;0m090|090|000 \u001b[00m\u001b[00;38;2;90;90;30m090|090|030 \u001b[00m\u001b[00;38;2;90;90;60m090|090|060 \u001b[00m\u001b[00;38;2;90;90;90m090|090|090 \u001b[00m\u001b[00;38;2;90;90;120m090|090|120 \u001b[00m\u001b[00;38;2;90;90;150m090|090|150 \u001b[00m\u001b[00;38;2;90;90;180m090|090|180 \u001b[00m\u001b[00;38;2;90;90;210m090|090|210 \u001b[00m\u001b[00;38;2;90;90;240m090|090|240 \u001b[00m\n",
-      "\u001b[00;38;2;90;120;0m090|120|000 \u001b[00m\u001b[00;38;2;90;120;30m090|120|030 \u001b[00m\u001b[00;38;2;90;120;60m090|120|060 \u001b[00m\u001b[00;38;2;90;120;90m090|120|090 \u001b[00m\u001b[00;38;2;90;120;120m090|120|120 \u001b[00m\u001b[00;38;2;90;120;150m090|120|150 \u001b[00m\u001b[00;38;2;90;120;180m090|120|180 \u001b[00m\u001b[00;38;2;90;120;210m090|120|210 \u001b[00m\u001b[00;38;2;90;120;240m090|120|240 \u001b[00m\n",
-      "\u001b[00;38;2;90;150;0m090|150|000 \u001b[00m\u001b[00;38;2;90;150;30m090|150|030 \u001b[00m\u001b[00;38;2;90;150;60m090|150|060 \u001b[00m\u001b[00;38;2;90;150;90m090|150|090 \u001b[00m\u001b[00;38;2;90;150;120m090|150|120 \u001b[00m\u001b[00;38;2;90;150;150m090|150|150 \u001b[00m\u001b[00;38;2;90;150;180m090|150|180 \u001b[00m\u001b[00;38;2;90;150;210m090|150|210 \u001b[00m\u001b[00;38;2;90;150;240m090|150|240 \u001b[00m\n",
-      "\u001b[00;38;2;90;180;0m090|180|000 \u001b[00m\u001b[00;38;2;90;180;30m090|180|030 \u001b[00m\u001b[00;38;2;90;180;60m090|180|060 \u001b[00m\u001b[00;38;2;90;180;90m090|180|090 \u001b[00m\u001b[00;38;2;90;180;120m090|180|120 \u001b[00m\u001b[00;38;2;90;180;150m090|180|150 \u001b[00m\u001b[00;38;2;90;180;180m090|180|180 \u001b[00m\u001b[00;38;2;90;180;210m090|180|210 \u001b[00m\u001b[00;38;2;90;180;240m090|180|240 \u001b[00m\n",
-      "\u001b[00;38;2;90;210;0m090|210|000 \u001b[00m\u001b[00;38;2;90;210;30m090|210|030 \u001b[00m\u001b[00;38;2;90;210;60m090|210|060 \u001b[00m\u001b[00;38;2;90;210;90m090|210|090 \u001b[00m\u001b[00;38;2;90;210;120m090|210|120 \u001b[00m\u001b[00;38;2;90;210;150m090|210|150 \u001b[00m\u001b[00;38;2;90;210;180m090|210|180 \u001b[00m\u001b[00;38;2;90;210;210m090|210|210 \u001b[00m\u001b[00;38;2;90;210;240m090|210|240 \u001b[00m\n",
-      "\u001b[00;38;2;90;240;0m090|240|000 \u001b[00m\u001b[00;38;2;90;240;30m090|240|030 \u001b[00m\u001b[00;38;2;90;240;60m090|240|060 \u001b[00m\u001b[00;38;2;90;240;90m090|240|090 \u001b[00m\u001b[00;38;2;90;240;120m090|240|120 \u001b[00m\u001b[00;38;2;90;240;150m090|240|150 \u001b[00m\u001b[00;38;2;90;240;180m090|240|180 \u001b[00m\u001b[00;38;2;90;240;210m090|240|210 \u001b[00m\u001b[00;38;2;90;240;240m090|240|240 \u001b[00m\n",
-      "\n",
-      "\u001b[00;38;2;120;0;0m120|000|000 \u001b[00m\u001b[00;38;2;120;0;30m120|000|030 \u001b[00m\u001b[00;38;2;120;0;60m120|000|060 \u001b[00m\u001b[00;38;2;120;0;90m120|000|090 \u001b[00m\u001b[00;38;2;120;0;120m120|000|120 \u001b[00m\u001b[00;38;2;120;0;150m120|000|150 \u001b[00m\u001b[00;38;2;120;0;180m120|000|180 \u001b[00m\u001b[00;38;2;120;0;210m120|000|210 \u001b[00m\u001b[00;38;2;120;0;240m120|000|240 \u001b[00m\n",
-      "\u001b[00;38;2;120;30;0m120|030|000 \u001b[00m\u001b[00;38;2;120;30;30m120|030|030 \u001b[00m\u001b[00;38;2;120;30;60m120|030|060 \u001b[00m\u001b[00;38;2;120;30;90m120|030|090 \u001b[00m\u001b[00;38;2;120;30;120m120|030|120 \u001b[00m\u001b[00;38;2;120;30;150m120|030|150 \u001b[00m\u001b[00;38;2;120;30;180m120|030|180 \u001b[00m\u001b[00;38;2;120;30;210m120|030|210 \u001b[00m\u001b[00;38;2;120;30;240m120|030|240 \u001b[00m\n",
-      "\u001b[00;38;2;120;60;0m120|060|000 \u001b[00m\u001b[00;38;2;120;60;30m120|060|030 \u001b[00m\u001b[00;38;2;120;60;60m120|060|060 \u001b[00m\u001b[00;38;2;120;60;90m120|060|090 \u001b[00m\u001b[00;38;2;120;60;120m120|060|120 \u001b[00m\u001b[00;38;2;120;60;150m120|060|150 \u001b[00m\u001b[00;38;2;120;60;180m120|060|180 \u001b[00m\u001b[00;38;2;120;60;210m120|060|210 \u001b[00m\u001b[00;38;2;120;60;240m120|060|240 \u001b[00m\n",
-      "\u001b[00;38;2;120;90;0m120|090|000 \u001b[00m\u001b[00;38;2;120;90;30m120|090|030 \u001b[00m\u001b[00;38;2;120;90;60m120|090|060 \u001b[00m\u001b[00;38;2;120;90;90m120|090|090 \u001b[00m\u001b[00;38;2;120;90;120m120|090|120 \u001b[00m\u001b[00;38;2;120;90;150m120|090|150 \u001b[00m\u001b[00;38;2;120;90;180m120|090|180 \u001b[00m\u001b[00;38;2;120;90;210m120|090|210 \u001b[00m\u001b[00;38;2;120;90;240m120|090|240 \u001b[00m\n",
-      "\u001b[00;38;2;120;120;0m120|120|000 \u001b[00m\u001b[00;38;2;120;120;30m120|120|030 \u001b[00m\u001b[00;38;2;120;120;60m120|120|060 \u001b[00m\u001b[00;38;2;120;120;90m120|120|090 \u001b[00m\u001b[00;38;2;120;120;120m120|120|120 \u001b[00m\u001b[00;38;2;120;120;150m120|120|150 \u001b[00m\u001b[00;38;2;120;120;180m120|120|180 \u001b[00m\u001b[00;38;2;120;120;210m120|120|210 \u001b[00m\u001b[00;38;2;120;120;240m120|120|240 \u001b[00m\n",
-      "\u001b[00;38;2;120;150;0m120|150|000 \u001b[00m\u001b[00;38;2;120;150;30m120|150|030 \u001b[00m\u001b[00;38;2;120;150;60m120|150|060 \u001b[00m\u001b[00;38;2;120;150;90m120|150|090 \u001b[00m\u001b[00;38;2;120;150;120m120|150|120 \u001b[00m\u001b[00;38;2;120;150;150m120|150|150 \u001b[00m\u001b[00;38;2;120;150;180m120|150|180 \u001b[00m\u001b[00;38;2;120;150;210m120|150|210 \u001b[00m\u001b[00;38;2;120;150;240m120|150|240 \u001b[00m\n",
-      "\u001b[00;38;2;120;180;0m120|180|000 \u001b[00m\u001b[00;38;2;120;180;30m120|180|030 \u001b[00m\u001b[00;38;2;120;180;60m120|180|060 \u001b[00m\u001b[00;38;2;120;180;90m120|180|090 \u001b[00m\u001b[00;38;2;120;180;120m120|180|120 \u001b[00m\u001b[00;38;2;120;180;150m120|180|150 \u001b[00m\u001b[00;38;2;120;180;180m120|180|180 \u001b[00m\u001b[00;38;2;120;180;210m120|180|210 \u001b[00m\u001b[00;38;2;120;180;240m120|180|240 \u001b[00m\n",
-      "\u001b[00;38;2;120;210;0m120|210|000 \u001b[00m\u001b[00;38;2;120;210;30m120|210|030 \u001b[00m\u001b[00;38;2;120;210;60m120|210|060 \u001b[00m\u001b[00;38;2;120;210;90m120|210|090 \u001b[00m\u001b[00;38;2;120;210;120m120|210|120 \u001b[00m\u001b[00;38;2;120;210;150m120|210|150 \u001b[00m\u001b[00;38;2;120;210;180m120|210|180 \u001b[00m\u001b[00;38;2;120;210;210m120|210|210 \u001b[00m\u001b[00;38;2;120;210;240m120|210|240 \u001b[00m\n",
-      "\u001b[00;38;2;120;240;0m120|240|000 \u001b[00m\u001b[00;38;2;120;240;30m120|240|030 \u001b[00m\u001b[00;38;2;120;240;60m120|240|060 \u001b[00m\u001b[00;38;2;120;240;90m120|240|090 \u001b[00m\u001b[00;38;2;120;240;120m120|240|120 \u001b[00m\u001b[00;38;2;120;240;150m120|240|150 \u001b[00m\u001b[00;38;2;120;240;180m120|240|180 \u001b[00m\u001b[00;38;2;120;240;210m120|240|210 \u001b[00m\u001b[00;38;2;120;240;240m120|240|240 \u001b[00m\n",
-      "\n",
-      "\u001b[00;38;2;150;0;0m150|000|000 \u001b[00m\u001b[00;38;2;150;0;30m150|000|030 \u001b[00m\u001b[00;38;2;150;0;60m150|000|060 \u001b[00m\u001b[00;38;2;150;0;90m150|000|090 \u001b[00m\u001b[00;38;2;150;0;120m150|000|120 \u001b[00m\u001b[00;38;2;150;0;150m150|000|150 \u001b[00m\u001b[00;38;2;150;0;180m150|000|180 \u001b[00m\u001b[00;38;2;150;0;210m150|000|210 \u001b[00m\u001b[00;38;2;150;0;240m150|000|240 \u001b[00m\n",
-      "\u001b[00;38;2;150;30;0m150|030|000 \u001b[00m\u001b[00;38;2;150;30;30m150|030|030 \u001b[00m\u001b[00;38;2;150;30;60m150|030|060 \u001b[00m\u001b[00;38;2;150;30;90m150|030|090 \u001b[00m\u001b[00;38;2;150;30;120m150|030|120 \u001b[00m\u001b[00;38;2;150;30;150m150|030|150 \u001b[00m\u001b[00;38;2;150;30;180m150|030|180 \u001b[00m\u001b[00;38;2;150;30;210m150|030|210 \u001b[00m\u001b[00;38;2;150;30;240m150|030|240 \u001b[00m\n",
-      "\u001b[00;38;2;150;60;0m150|060|000 \u001b[00m\u001b[00;38;2;150;60;30m150|060|030 \u001b[00m\u001b[00;38;2;150;60;60m150|060|060 \u001b[00m\u001b[00;38;2;150;60;90m150|060|090 \u001b[00m\u001b[00;38;2;150;60;120m150|060|120 \u001b[00m\u001b[00;38;2;150;60;150m150|060|150 \u001b[00m\u001b[00;38;2;150;60;180m150|060|180 \u001b[00m\u001b[00;38;2;150;60;210m150|060|210 \u001b[00m\u001b[00;38;2;150;60;240m150|060|240 \u001b[00m\n",
-      "\u001b[00;38;2;150;90;0m150|090|000 \u001b[00m\u001b[00;38;2;150;90;30m150|090|030 \u001b[00m\u001b[00;38;2;150;90;60m150|090|060 \u001b[00m\u001b[00;38;2;150;90;90m150|090|090 \u001b[00m\u001b[00;38;2;150;90;120m150|090|120 \u001b[00m\u001b[00;38;2;150;90;150m150|090|150 \u001b[00m\u001b[00;38;2;150;90;180m150|090|180 \u001b[00m\u001b[00;38;2;150;90;210m150|090|210 \u001b[00m\u001b[00;38;2;150;90;240m150|090|240 \u001b[00m\n",
-      "\u001b[00;38;2;150;120;0m150|120|000 \u001b[00m\u001b[00;38;2;150;120;30m150|120|030 \u001b[00m\u001b[00;38;2;150;120;60m150|120|060 \u001b[00m\u001b[00;38;2;150;120;90m150|120|090 \u001b[00m\u001b[00;38;2;150;120;120m150|120|120 \u001b[00m\u001b[00;38;2;150;120;150m150|120|150 \u001b[00m\u001b[00;38;2;150;120;180m150|120|180 \u001b[00m\u001b[00;38;2;150;120;210m150|120|210 \u001b[00m\u001b[00;38;2;150;120;240m150|120|240 \u001b[00m\n",
-      "\u001b[00;38;2;150;150;0m150|150|000 \u001b[00m\u001b[00;38;2;150;150;30m150|150|030 \u001b[00m\u001b[00;38;2;150;150;60m150|150|060 \u001b[00m\u001b[00;38;2;150;150;90m150|150|090 \u001b[00m\u001b[00;38;2;150;150;120m150|150|120 \u001b[00m\u001b[00;38;2;150;150;150m150|150|150 \u001b[00m\u001b[00;38;2;150;150;180m150|150|180 \u001b[00m\u001b[00;38;2;150;150;210m150|150|210 \u001b[00m\u001b[00;38;2;150;150;240m150|150|240 \u001b[00m\n",
-      "\u001b[00;38;2;150;180;0m150|180|000 \u001b[00m\u001b[00;38;2;150;180;30m150|180|030 \u001b[00m\u001b[00;38;2;150;180;60m150|180|060 \u001b[00m\u001b[00;38;2;150;180;90m150|180|090 \u001b[00m\u001b[00;38;2;150;180;120m150|180|120 \u001b[00m\u001b[00;38;2;150;180;150m150|180|150 \u001b[00m\u001b[00;38;2;150;180;180m150|180|180 \u001b[00m\u001b[00;38;2;150;180;210m150|180|210 \u001b[00m\u001b[00;38;2;150;180;240m150|180|240 \u001b[00m\n",
-      "\u001b[00;38;2;150;210;0m150|210|000 \u001b[00m\u001b[00;38;2;150;210;30m150|210|030 \u001b[00m\u001b[00;38;2;150;210;60m150|210|060 \u001b[00m\u001b[00;38;2;150;210;90m150|210|090 \u001b[00m\u001b[00;38;2;150;210;120m150|210|120 \u001b[00m\u001b[00;38;2;150;210;150m150|210|150 \u001b[00m\u001b[00;38;2;150;210;180m150|210|180 \u001b[00m\u001b[00;38;2;150;210;210m150|210|210 \u001b[00m\u001b[00;38;2;150;210;240m150|210|240 \u001b[00m\n",
-      "\u001b[00;38;2;150;240;0m150|240|000 \u001b[00m\u001b[00;38;2;150;240;30m150|240|030 \u001b[00m\u001b[00;38;2;150;240;60m150|240|060 \u001b[00m\u001b[00;38;2;150;240;90m150|240|090 \u001b[00m\u001b[00;38;2;150;240;120m150|240|120 \u001b[00m\u001b[00;38;2;150;240;150m150|240|150 \u001b[00m\u001b[00;38;2;150;240;180m150|240|180 \u001b[00m\u001b[00;38;2;150;240;210m150|240|210 \u001b[00m\u001b[00;38;2;150;240;240m150|240|240 \u001b[00m\n",
-      "\n",
-      "\u001b[00;38;2;180;0;0m180|000|000 \u001b[00m\u001b[00;38;2;180;0;30m180|000|030 \u001b[00m\u001b[00;38;2;180;0;60m180|000|060 \u001b[00m\u001b[00;38;2;180;0;90m180|000|090 \u001b[00m\u001b[00;38;2;180;0;120m180|000|120 \u001b[00m\u001b[00;38;2;180;0;150m180|000|150 \u001b[00m\u001b[00;38;2;180;0;180m180|000|180 \u001b[00m\u001b[00;38;2;180;0;210m180|000|210 \u001b[00m\u001b[00;38;2;180;0;240m180|000|240 \u001b[00m\n",
-      "\u001b[00;38;2;180;30;0m180|030|000 \u001b[00m\u001b[00;38;2;180;30;30m180|030|030 \u001b[00m\u001b[00;38;2;180;30;60m180|030|060 \u001b[00m\u001b[00;38;2;180;30;90m180|030|090 \u001b[00m\u001b[00;38;2;180;30;120m180|030|120 \u001b[00m\u001b[00;38;2;180;30;150m180|030|150 \u001b[00m\u001b[00;38;2;180;30;180m180|030|180 \u001b[00m\u001b[00;38;2;180;30;210m180|030|210 \u001b[00m\u001b[00;38;2;180;30;240m180|030|240 \u001b[00m\n",
-      "\u001b[00;38;2;180;60;0m180|060|000 \u001b[00m\u001b[00;38;2;180;60;30m180|060|030 \u001b[00m\u001b[00;38;2;180;60;60m180|060|060 \u001b[00m\u001b[00;38;2;180;60;90m180|060|090 \u001b[00m\u001b[00;38;2;180;60;120m180|060|120 \u001b[00m\u001b[00;38;2;180;60;150m180|060|150 \u001b[00m\u001b[00;38;2;180;60;180m180|060|180 \u001b[00m\u001b[00;38;2;180;60;210m180|060|210 \u001b[00m\u001b[00;38;2;180;60;240m180|060|240 \u001b[00m\n",
-      "\u001b[00;38;2;180;90;0m180|090|000 \u001b[00m\u001b[00;38;2;180;90;30m180|090|030 \u001b[00m\u001b[00;38;2;180;90;60m180|090|060 \u001b[00m\u001b[00;38;2;180;90;90m180|090|090 \u001b[00m\u001b[00;38;2;180;90;120m180|090|120 \u001b[00m\u001b[00;38;2;180;90;150m180|090|150 \u001b[00m\u001b[00;38;2;180;90;180m180|090|180 \u001b[00m\u001b[00;38;2;180;90;210m180|090|210 \u001b[00m\u001b[00;38;2;180;90;240m180|090|240 \u001b[00m\n",
-      "\u001b[00;38;2;180;120;0m180|120|000 \u001b[00m\u001b[00;38;2;180;120;30m180|120|030 \u001b[00m\u001b[00;38;2;180;120;60m180|120|060 \u001b[00m\u001b[00;38;2;180;120;90m180|120|090 \u001b[00m\u001b[00;38;2;180;120;120m180|120|120 \u001b[00m\u001b[00;38;2;180;120;150m180|120|150 \u001b[00m\u001b[00;38;2;180;120;180m180|120|180 \u001b[00m\u001b[00;38;2;180;120;210m180|120|210 \u001b[00m\u001b[00;38;2;180;120;240m180|120|240 \u001b[00m\n",
-      "\u001b[00;38;2;180;150;0m180|150|000 \u001b[00m\u001b[00;38;2;180;150;30m180|150|030 \u001b[00m\u001b[00;38;2;180;150;60m180|150|060 \u001b[00m\u001b[00;38;2;180;150;90m180|150|090 \u001b[00m\u001b[00;38;2;180;150;120m180|150|120 \u001b[00m\u001b[00;38;2;180;150;150m180|150|150 \u001b[00m\u001b[00;38;2;180;150;180m180|150|180 \u001b[00m\u001b[00;38;2;180;150;210m180|150|210 \u001b[00m\u001b[00;38;2;180;150;240m180|150|240 \u001b[00m\n",
-      "\u001b[00;38;2;180;180;0m180|180|000 \u001b[00m\u001b[00;38;2;180;180;30m180|180|030 \u001b[00m\u001b[00;38;2;180;180;60m180|180|060 \u001b[00m\u001b[00;38;2;180;180;90m180|180|090 \u001b[00m\u001b[00;38;2;180;180;120m180|180|120 \u001b[00m\u001b[00;38;2;180;180;150m180|180|150 \u001b[00m\u001b[00;38;2;180;180;180m180|180|180 \u001b[00m\u001b[00;38;2;180;180;210m180|180|210 \u001b[00m\u001b[00;38;2;180;180;240m180|180|240 \u001b[00m\n",
-      "\u001b[00;38;2;180;210;0m180|210|000 \u001b[00m\u001b[00;38;2;180;210;30m180|210|030 \u001b[00m\u001b[00;38;2;180;210;60m180|210|060 \u001b[00m\u001b[00;38;2;180;210;90m180|210|090 \u001b[00m\u001b[00;38;2;180;210;120m180|210|120 \u001b[00m\u001b[00;38;2;180;210;150m180|210|150 \u001b[00m\u001b[00;38;2;180;210;180m180|210|180 \u001b[00m\u001b[00;38;2;180;210;210m180|210|210 \u001b[00m\u001b[00;38;2;180;210;240m180|210|240 \u001b[00m\n",
-      "\u001b[00;38;2;180;240;0m180|240|000 \u001b[00m\u001b[00;38;2;180;240;30m180|240|030 \u001b[00m\u001b[00;38;2;180;240;60m180|240|060 \u001b[00m\u001b[00;38;2;180;240;90m180|240|090 \u001b[00m\u001b[00;38;2;180;240;120m180|240|120 \u001b[00m\u001b[00;38;2;180;240;150m180|240|150 \u001b[00m\u001b[00;38;2;180;240;180m180|240|180 \u001b[00m\u001b[00;38;2;180;240;210m180|240|210 \u001b[00m\u001b[00;38;2;180;240;240m180|240|240 \u001b[00m\n",
-      "\n",
-      "\u001b[00;38;2;210;0;0m210|000|000 \u001b[00m\u001b[00;38;2;210;0;30m210|000|030 \u001b[00m\u001b[00;38;2;210;0;60m210|000|060 \u001b[00m\u001b[00;38;2;210;0;90m210|000|090 \u001b[00m\u001b[00;38;2;210;0;120m210|000|120 \u001b[00m\u001b[00;38;2;210;0;150m210|000|150 \u001b[00m\u001b[00;38;2;210;0;180m210|000|180 \u001b[00m\u001b[00;38;2;210;0;210m210|000|210 \u001b[00m\u001b[00;38;2;210;0;240m210|000|240 \u001b[00m\n",
-      "\u001b[00;38;2;210;30;0m210|030|000 \u001b[00m\u001b[00;38;2;210;30;30m210|030|030 \u001b[00m\u001b[00;38;2;210;30;60m210|030|060 \u001b[00m\u001b[00;38;2;210;30;90m210|030|090 \u001b[00m\u001b[00;38;2;210;30;120m210|030|120 \u001b[00m\u001b[00;38;2;210;30;150m210|030|150 \u001b[00m\u001b[00;38;2;210;30;180m210|030|180 \u001b[00m\u001b[00;38;2;210;30;210m210|030|210 \u001b[00m\u001b[00;38;2;210;30;240m210|030|240 \u001b[00m\n",
-      "\u001b[00;38;2;210;60;0m210|060|000 \u001b[00m\u001b[00;38;2;210;60;30m210|060|030 \u001b[00m\u001b[00;38;2;210;60;60m210|060|060 \u001b[00m\u001b[00;38;2;210;60;90m210|060|090 \u001b[00m\u001b[00;38;2;210;60;120m210|060|120 \u001b[00m\u001b[00;38;2;210;60;150m210|060|150 \u001b[00m\u001b[00;38;2;210;60;180m210|060|180 \u001b[00m\u001b[00;38;2;210;60;210m210|060|210 \u001b[00m\u001b[00;38;2;210;60;240m210|060|240 \u001b[00m\n",
-      "\u001b[00;38;2;210;90;0m210|090|000 \u001b[00m\u001b[00;38;2;210;90;30m210|090|030 \u001b[00m\u001b[00;38;2;210;90;60m210|090|060 \u001b[00m\u001b[00;38;2;210;90;90m210|090|090 \u001b[00m\u001b[00;38;2;210;90;120m210|090|120 \u001b[00m\u001b[00;38;2;210;90;150m210|090|150 \u001b[00m\u001b[00;38;2;210;90;180m210|090|180 \u001b[00m\u001b[00;38;2;210;90;210m210|090|210 \u001b[00m\u001b[00;38;2;210;90;240m210|090|240 \u001b[00m\n",
-      "\u001b[00;38;2;210;120;0m210|120|000 \u001b[00m\u001b[00;38;2;210;120;30m210|120|030 \u001b[00m\u001b[00;38;2;210;120;60m210|120|060 \u001b[00m\u001b[00;38;2;210;120;90m210|120|090 \u001b[00m\u001b[00;38;2;210;120;120m210|120|120 \u001b[00m\u001b[00;38;2;210;120;150m210|120|150 \u001b[00m\u001b[00;38;2;210;120;180m210|120|180 \u001b[00m\u001b[00;38;2;210;120;210m210|120|210 \u001b[00m\u001b[00;38;2;210;120;240m210|120|240 \u001b[00m\n",
-      "\u001b[00;38;2;210;150;0m210|150|000 \u001b[00m\u001b[00;38;2;210;150;30m210|150|030 \u001b[00m\u001b[00;38;2;210;150;60m210|150|060 \u001b[00m\u001b[00;38;2;210;150;90m210|150|090 \u001b[00m\u001b[00;38;2;210;150;120m210|150|120 \u001b[00m\u001b[00;38;2;210;150;150m210|150|150 \u001b[00m\u001b[00;38;2;210;150;180m210|150|180 \u001b[00m\u001b[00;38;2;210;150;210m210|150|210 \u001b[00m\u001b[00;38;2;210;150;240m210|150|240 \u001b[00m\n",
-      "\u001b[00;38;2;210;180;0m210|180|000 \u001b[00m\u001b[00;38;2;210;180;30m210|180|030 \u001b[00m\u001b[00;38;2;210;180;60m210|180|060 \u001b[00m\u001b[00;38;2;210;180;90m210|180|090 \u001b[00m\u001b[00;38;2;210;180;120m210|180|120 \u001b[00m\u001b[00;38;2;210;180;150m210|180|150 \u001b[00m\u001b[00;38;2;210;180;180m210|180|180 \u001b[00m\u001b[00;38;2;210;180;210m210|180|210 \u001b[00m\u001b[00;38;2;210;180;240m210|180|240 \u001b[00m\n",
-      "\u001b[00;38;2;210;210;0m210|210|000 \u001b[00m\u001b[00;38;2;210;210;30m210|210|030 \u001b[00m\u001b[00;38;2;210;210;60m210|210|060 \u001b[00m\u001b[00;38;2;210;210;90m210|210|090 \u001b[00m\u001b[00;38;2;210;210;120m210|210|120 \u001b[00m\u001b[00;38;2;210;210;150m210|210|150 \u001b[00m\u001b[00;38;2;210;210;180m210|210|180 \u001b[00m\u001b[00;38;2;210;210;210m210|210|210 \u001b[00m\u001b[00;38;2;210;210;240m210|210|240 \u001b[00m\n",
-      "\u001b[00;38;2;210;240;0m210|240|000 \u001b[00m\u001b[00;38;2;210;240;30m210|240|030 \u001b[00m\u001b[00;38;2;210;240;60m210|240|060 \u001b[00m\u001b[00;38;2;210;240;90m210|240|090 \u001b[00m\u001b[00;38;2;210;240;120m210|240|120 \u001b[00m\u001b[00;38;2;210;240;150m210|240|150 \u001b[00m\u001b[00;38;2;210;240;180m210|240|180 \u001b[00m\u001b[00;38;2;210;240;210m210|240|210 \u001b[00m\u001b[00;38;2;210;240;240m210|240|240 \u001b[00m\n",
-      "\n",
-      "\u001b[00;38;2;240;0;0m240|000|000 \u001b[00m\u001b[00;38;2;240;0;30m240|000|030 \u001b[00m\u001b[00;38;2;240;0;60m240|000|060 \u001b[00m\u001b[00;38;2;240;0;90m240|000|090 \u001b[00m\u001b[00;38;2;240;0;120m240|000|120 \u001b[00m\u001b[00;38;2;240;0;150m240|000|150 \u001b[00m\u001b[00;38;2;240;0;180m240|000|180 \u001b[00m\u001b[00;38;2;240;0;210m240|000|210 \u001b[00m\u001b[00;38;2;240;0;240m240|000|240 \u001b[00m\n",
-      "\u001b[00;38;2;240;30;0m240|030|000 \u001b[00m\u001b[00;38;2;240;30;30m240|030|030 \u001b[00m\u001b[00;38;2;240;30;60m240|030|060 \u001b[00m\u001b[00;38;2;240;30;90m240|030|090 \u001b[00m\u001b[00;38;2;240;30;120m240|030|120 \u001b[00m\u001b[00;38;2;240;30;150m240|030|150 \u001b[00m\u001b[00;38;2;240;30;180m240|030|180 \u001b[00m\u001b[00;38;2;240;30;210m240|030|210 \u001b[00m\u001b[00;38;2;240;30;240m240|030|240 \u001b[00m\n",
-      "\u001b[00;38;2;240;60;0m240|060|000 \u001b[00m\u001b[00;38;2;240;60;30m240|060|030 \u001b[00m\u001b[00;38;2;240;60;60m240|060|060 \u001b[00m\u001b[00;38;2;240;60;90m240|060|090 \u001b[00m\u001b[00;38;2;240;60;120m240|060|120 \u001b[00m\u001b[00;38;2;240;60;150m240|060|150 \u001b[00m\u001b[00;38;2;240;60;180m240|060|180 \u001b[00m\u001b[00;38;2;240;60;210m240|060|210 \u001b[00m\u001b[00;38;2;240;60;240m240|060|240 \u001b[00m\n",
-      "\u001b[00;38;2;240;90;0m240|090|000 \u001b[00m\u001b[00;38;2;240;90;30m240|090|030 \u001b[00m\u001b[00;38;2;240;90;60m240|090|060 \u001b[00m\u001b[00;38;2;240;90;90m240|090|090 \u001b[00m\u001b[00;38;2;240;90;120m240|090|120 \u001b[00m\u001b[00;38;2;240;90;150m240|090|150 \u001b[00m\u001b[00;38;2;240;90;180m240|090|180 \u001b[00m\u001b[00;38;2;240;90;210m240|090|210 \u001b[00m\u001b[00;38;2;240;90;240m240|090|240 \u001b[00m\n",
-      "\u001b[00;38;2;240;120;0m240|120|000 \u001b[00m\u001b[00;38;2;240;120;30m240|120|030 \u001b[00m\u001b[00;38;2;240;120;60m240|120|060 \u001b[00m\u001b[00;38;2;240;120;90m240|120|090 \u001b[00m\u001b[00;38;2;240;120;120m240|120|120 \u001b[00m\u001b[00;38;2;240;120;150m240|120|150 \u001b[00m\u001b[00;38;2;240;120;180m240|120|180 \u001b[00m\u001b[00;38;2;240;120;210m240|120|210 \u001b[00m\u001b[00;38;2;240;120;240m240|120|240 \u001b[00m\n",
-      "\u001b[00;38;2;240;150;0m240|150|000 \u001b[00m\u001b[00;38;2;240;150;30m240|150|030 \u001b[00m\u001b[00;38;2;240;150;60m240|150|060 \u001b[00m\u001b[00;38;2;240;150;90m240|150|090 \u001b[00m\u001b[00;38;2;240;150;120m240|150|120 \u001b[00m\u001b[00;38;2;240;150;150m240|150|150 \u001b[00m\u001b[00;38;2;240;150;180m240|150|180 \u001b[00m\u001b[00;38;2;240;150;210m240|150|210 \u001b[00m\u001b[00;38;2;240;150;240m240|150|240 \u001b[00m\n",
-      "\u001b[00;38;2;240;180;0m240|180|000 \u001b[00m\u001b[00;38;2;240;180;30m240|180|030 \u001b[00m\u001b[00;38;2;240;180;60m240|180|060 \u001b[00m\u001b[00;38;2;240;180;90m240|180|090 \u001b[00m\u001b[00;38;2;240;180;120m240|180|120 \u001b[00m\u001b[00;38;2;240;180;150m240|180|150 \u001b[00m\u001b[00;38;2;240;180;180m240|180|180 \u001b[00m\u001b[00;38;2;240;180;210m240|180|210 \u001b[00m\u001b[00;38;2;240;180;240m240|180|240 \u001b[00m\n",
-      "\u001b[00;38;2;240;210;0m240|210|000 \u001b[00m\u001b[00;38;2;240;210;30m240|210|030 \u001b[00m\u001b[00;38;2;240;210;60m240|210|060 \u001b[00m\u001b[00;38;2;240;210;90m240|210|090 \u001b[00m\u001b[00;38;2;240;210;120m240|210|120 \u001b[00m\u001b[00;38;2;240;210;150m240|210|150 \u001b[00m\u001b[00;38;2;240;210;180m240|210|180 \u001b[00m\u001b[00;38;2;240;210;210m240|210|210 \u001b[00m\u001b[00;38;2;240;210;240m240|210|240 \u001b[00m\n",
-      "\u001b[00;38;2;240;240;0m240|240|000 \u001b[00m\u001b[00;38;2;240;240;30m240|240|030 \u001b[00m\u001b[00;38;2;240;240;60m240|240|060 \u001b[00m\u001b[00;38;2;240;240;90m240|240|090 \u001b[00m\u001b[00;38;2;240;240;120m240|240|120 \u001b[00m\u001b[00;38;2;240;240;150m240|240|150 \u001b[00m\u001b[00;38;2;240;240;180m240|240|180 \u001b[00m\u001b[00;38;2;240;240;210m240|240|210 \u001b[00m\u001b[00;38;2;240;240;240m240|240|240 \u001b[00m\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "steps = range(0,256,30)\n",
-    "\n",
-    "t = \"{ESC}00;38;2;{r};{g};{b}m{r:03}|{g:03}|{b:03} {RESET}\"\n",
-    "for r in steps:\n",
-    "    for g in steps:\n",
-    "        for b in steps:\n",
-    "            print (t.format(**locals()), end='')\n",
-    "        print()\n",
-    "    print()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 24-bit RGB background"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[00;48;2;0;0;0m000|000|000 \u001b[00m\u001b[00;48;2;0;0;30m000|000|030 \u001b[00m\u001b[00;48;2;0;0;60m000|000|060 \u001b[00m\u001b[00;48;2;0;0;90m000|000|090 \u001b[00m\u001b[00;48;2;0;0;120m000|000|120 \u001b[00m\u001b[00;48;2;0;0;150m000|000|150 \u001b[00m\u001b[00;48;2;0;0;180m000|000|180 \u001b[00m\u001b[00;48;2;0;0;210m000|000|210 \u001b[00m\u001b[00;48;2;0;0;240m000|000|240 \u001b[00m\n",
-      "\u001b[00;48;2;0;30;0m000|030|000 \u001b[00m\u001b[00;48;2;0;30;30m000|030|030 \u001b[00m\u001b[00;48;2;0;30;60m000|030|060 \u001b[00m\u001b[00;48;2;0;30;90m000|030|090 \u001b[00m\u001b[00;48;2;0;30;120m000|030|120 \u001b[00m\u001b[00;48;2;0;30;150m000|030|150 \u001b[00m\u001b[00;48;2;0;30;180m000|030|180 \u001b[00m\u001b[00;48;2;0;30;210m000|030|210 \u001b[00m\u001b[00;48;2;0;30;240m000|030|240 \u001b[00m\n",
-      "\u001b[00;48;2;0;60;0m000|060|000 \u001b[00m\u001b[00;48;2;0;60;30m000|060|030 \u001b[00m\u001b[00;48;2;0;60;60m000|060|060 \u001b[00m\u001b[00;48;2;0;60;90m000|060|090 \u001b[00m\u001b[00;48;2;0;60;120m000|060|120 \u001b[00m\u001b[00;48;2;0;60;150m000|060|150 \u001b[00m\u001b[00;48;2;0;60;180m000|060|180 \u001b[00m\u001b[00;48;2;0;60;210m000|060|210 \u001b[00m\u001b[00;48;2;0;60;240m000|060|240 \u001b[00m\n",
-      "\u001b[00;48;2;0;90;0m000|090|000 \u001b[00m\u001b[00;48;2;0;90;30m000|090|030 \u001b[00m\u001b[00;48;2;0;90;60m000|090|060 \u001b[00m\u001b[00;48;2;0;90;90m000|090|090 \u001b[00m\u001b[00;48;2;0;90;120m000|090|120 \u001b[00m\u001b[00;48;2;0;90;150m000|090|150 \u001b[00m\u001b[00;48;2;0;90;180m000|090|180 \u001b[00m\u001b[00;48;2;0;90;210m000|090|210 \u001b[00m\u001b[00;48;2;0;90;240m000|090|240 \u001b[00m\n",
-      "\u001b[00;48;2;0;120;0m000|120|000 \u001b[00m\u001b[00;48;2;0;120;30m000|120|030 \u001b[00m\u001b[00;48;2;0;120;60m000|120|060 \u001b[00m\u001b[00;48;2;0;120;90m000|120|090 \u001b[00m\u001b[00;48;2;0;120;120m000|120|120 \u001b[00m\u001b[00;48;2;0;120;150m000|120|150 \u001b[00m\u001b[00;48;2;0;120;180m000|120|180 \u001b[00m\u001b[00;48;2;0;120;210m000|120|210 \u001b[00m\u001b[00;48;2;0;120;240m000|120|240 \u001b[00m\n",
-      "\u001b[00;48;2;0;150;0m000|150|000 \u001b[00m\u001b[00;48;2;0;150;30m000|150|030 \u001b[00m\u001b[00;48;2;0;150;60m000|150|060 \u001b[00m\u001b[00;48;2;0;150;90m000|150|090 \u001b[00m\u001b[00;48;2;0;150;120m000|150|120 \u001b[00m\u001b[00;48;2;0;150;150m000|150|150 \u001b[00m\u001b[00;48;2;0;150;180m000|150|180 \u001b[00m\u001b[00;48;2;0;150;210m000|150|210 \u001b[00m\u001b[00;48;2;0;150;240m000|150|240 \u001b[00m\n",
-      "\u001b[00;48;2;0;180;0m000|180|000 \u001b[00m\u001b[00;48;2;0;180;30m000|180|030 \u001b[00m\u001b[00;48;2;0;180;60m000|180|060 \u001b[00m\u001b[00;48;2;0;180;90m000|180|090 \u001b[00m\u001b[00;48;2;0;180;120m000|180|120 \u001b[00m\u001b[00;48;2;0;180;150m000|180|150 \u001b[00m\u001b[00;48;2;0;180;180m000|180|180 \u001b[00m\u001b[00;48;2;0;180;210m000|180|210 \u001b[00m\u001b[00;48;2;0;180;240m000|180|240 \u001b[00m\n",
-      "\u001b[00;48;2;0;210;0m000|210|000 \u001b[00m\u001b[00;48;2;0;210;30m000|210|030 \u001b[00m\u001b[00;48;2;0;210;60m000|210|060 \u001b[00m\u001b[00;48;2;0;210;90m000|210|090 \u001b[00m\u001b[00;48;2;0;210;120m000|210|120 \u001b[00m\u001b[00;48;2;0;210;150m000|210|150 \u001b[00m\u001b[00;48;2;0;210;180m000|210|180 \u001b[00m\u001b[00;48;2;0;210;210m000|210|210 \u001b[00m\u001b[00;48;2;0;210;240m000|210|240 \u001b[00m\n",
-      "\u001b[00;48;2;0;240;0m000|240|000 \u001b[00m\u001b[00;48;2;0;240;30m000|240|030 \u001b[00m\u001b[00;48;2;0;240;60m000|240|060 \u001b[00m\u001b[00;48;2;0;240;90m000|240|090 \u001b[00m\u001b[00;48;2;0;240;120m000|240|120 \u001b[00m\u001b[00;48;2;0;240;150m000|240|150 \u001b[00m\u001b[00;48;2;0;240;180m000|240|180 \u001b[00m\u001b[00;48;2;0;240;210m000|240|210 \u001b[00m\u001b[00;48;2;0;240;240m000|240|240 \u001b[00m\n",
-      "\n",
-      "\u001b[00;48;2;30;0;0m030|000|000 \u001b[00m\u001b[00;48;2;30;0;30m030|000|030 \u001b[00m\u001b[00;48;2;30;0;60m030|000|060 \u001b[00m\u001b[00;48;2;30;0;90m030|000|090 \u001b[00m\u001b[00;48;2;30;0;120m030|000|120 \u001b[00m\u001b[00;48;2;30;0;150m030|000|150 \u001b[00m\u001b[00;48;2;30;0;180m030|000|180 \u001b[00m\u001b[00;48;2;30;0;210m030|000|210 \u001b[00m\u001b[00;48;2;30;0;240m030|000|240 \u001b[00m\n",
-      "\u001b[00;48;2;30;30;0m030|030|000 \u001b[00m\u001b[00;48;2;30;30;30m030|030|030 \u001b[00m\u001b[00;48;2;30;30;60m030|030|060 \u001b[00m\u001b[00;48;2;30;30;90m030|030|090 \u001b[00m\u001b[00;48;2;30;30;120m030|030|120 \u001b[00m\u001b[00;48;2;30;30;150m030|030|150 \u001b[00m\u001b[00;48;2;30;30;180m030|030|180 \u001b[00m\u001b[00;48;2;30;30;210m030|030|210 \u001b[00m\u001b[00;48;2;30;30;240m030|030|240 \u001b[00m\n",
-      "\u001b[00;48;2;30;60;0m030|060|000 \u001b[00m\u001b[00;48;2;30;60;30m030|060|030 \u001b[00m\u001b[00;48;2;30;60;60m030|060|060 \u001b[00m\u001b[00;48;2;30;60;90m030|060|090 \u001b[00m\u001b[00;48;2;30;60;120m030|060|120 \u001b[00m\u001b[00;48;2;30;60;150m030|060|150 \u001b[00m\u001b[00;48;2;30;60;180m030|060|180 \u001b[00m\u001b[00;48;2;30;60;210m030|060|210 \u001b[00m\u001b[00;48;2;30;60;240m030|060|240 \u001b[00m\n",
-      "\u001b[00;48;2;30;90;0m030|090|000 \u001b[00m\u001b[00;48;2;30;90;30m030|090|030 \u001b[00m\u001b[00;48;2;30;90;60m030|090|060 \u001b[00m\u001b[00;48;2;30;90;90m030|090|090 \u001b[00m\u001b[00;48;2;30;90;120m030|090|120 \u001b[00m\u001b[00;48;2;30;90;150m030|090|150 \u001b[00m\u001b[00;48;2;30;90;180m030|090|180 \u001b[00m\u001b[00;48;2;30;90;210m030|090|210 \u001b[00m\u001b[00;48;2;30;90;240m030|090|240 \u001b[00m\n",
-      "\u001b[00;48;2;30;120;0m030|120|000 \u001b[00m\u001b[00;48;2;30;120;30m030|120|030 \u001b[00m\u001b[00;48;2;30;120;60m030|120|060 \u001b[00m\u001b[00;48;2;30;120;90m030|120|090 \u001b[00m\u001b[00;48;2;30;120;120m030|120|120 \u001b[00m\u001b[00;48;2;30;120;150m030|120|150 \u001b[00m\u001b[00;48;2;30;120;180m030|120|180 \u001b[00m\u001b[00;48;2;30;120;210m030|120|210 \u001b[00m\u001b[00;48;2;30;120;240m030|120|240 \u001b[00m\n",
-      "\u001b[00;48;2;30;150;0m030|150|000 \u001b[00m\u001b[00;48;2;30;150;30m030|150|030 \u001b[00m\u001b[00;48;2;30;150;60m030|150|060 \u001b[00m\u001b[00;48;2;30;150;90m030|150|090 \u001b[00m\u001b[00;48;2;30;150;120m030|150|120 \u001b[00m\u001b[00;48;2;30;150;150m030|150|150 \u001b[00m\u001b[00;48;2;30;150;180m030|150|180 \u001b[00m\u001b[00;48;2;30;150;210m030|150|210 \u001b[00m\u001b[00;48;2;30;150;240m030|150|240 \u001b[00m\n",
-      "\u001b[00;48;2;30;180;0m030|180|000 \u001b[00m\u001b[00;48;2;30;180;30m030|180|030 \u001b[00m\u001b[00;48;2;30;180;60m030|180|060 \u001b[00m\u001b[00;48;2;30;180;90m030|180|090 \u001b[00m\u001b[00;48;2;30;180;120m030|180|120 \u001b[00m\u001b[00;48;2;30;180;150m030|180|150 \u001b[00m\u001b[00;48;2;30;180;180m030|180|180 \u001b[00m\u001b[00;48;2;30;180;210m030|180|210 \u001b[00m\u001b[00;48;2;30;180;240m030|180|240 \u001b[00m\n",
-      "\u001b[00;48;2;30;210;0m030|210|000 \u001b[00m\u001b[00;48;2;30;210;30m030|210|030 \u001b[00m\u001b[00;48;2;30;210;60m030|210|060 \u001b[00m\u001b[00;48;2;30;210;90m030|210|090 \u001b[00m\u001b[00;48;2;30;210;120m030|210|120 \u001b[00m\u001b[00;48;2;30;210;150m030|210|150 \u001b[00m\u001b[00;48;2;30;210;180m030|210|180 \u001b[00m\u001b[00;48;2;30;210;210m030|210|210 \u001b[00m\u001b[00;48;2;30;210;240m030|210|240 \u001b[00m\n",
-      "\u001b[00;48;2;30;240;0m030|240|000 \u001b[00m\u001b[00;48;2;30;240;30m030|240|030 \u001b[00m\u001b[00;48;2;30;240;60m030|240|060 \u001b[00m\u001b[00;48;2;30;240;90m030|240|090 \u001b[00m\u001b[00;48;2;30;240;120m030|240|120 \u001b[00m\u001b[00;48;2;30;240;150m030|240|150 \u001b[00m\u001b[00;48;2;30;240;180m030|240|180 \u001b[00m\u001b[00;48;2;30;240;210m030|240|210 \u001b[00m\u001b[00;48;2;30;240;240m030|240|240 \u001b[00m\n",
-      "\n",
-      "\u001b[00;48;2;60;0;0m060|000|000 \u001b[00m\u001b[00;48;2;60;0;30m060|000|030 \u001b[00m\u001b[00;48;2;60;0;60m060|000|060 \u001b[00m\u001b[00;48;2;60;0;90m060|000|090 \u001b[00m\u001b[00;48;2;60;0;120m060|000|120 \u001b[00m\u001b[00;48;2;60;0;150m060|000|150 \u001b[00m\u001b[00;48;2;60;0;180m060|000|180 \u001b[00m\u001b[00;48;2;60;0;210m060|000|210 \u001b[00m\u001b[00;48;2;60;0;240m060|000|240 \u001b[00m\n",
-      "\u001b[00;48;2;60;30;0m060|030|000 \u001b[00m\u001b[00;48;2;60;30;30m060|030|030 \u001b[00m\u001b[00;48;2;60;30;60m060|030|060 \u001b[00m\u001b[00;48;2;60;30;90m060|030|090 \u001b[00m\u001b[00;48;2;60;30;120m060|030|120 \u001b[00m\u001b[00;48;2;60;30;150m060|030|150 \u001b[00m\u001b[00;48;2;60;30;180m060|030|180 \u001b[00m\u001b[00;48;2;60;30;210m060|030|210 \u001b[00m\u001b[00;48;2;60;30;240m060|030|240 \u001b[00m\n",
-      "\u001b[00;48;2;60;60;0m060|060|000 \u001b[00m\u001b[00;48;2;60;60;30m060|060|030 \u001b[00m\u001b[00;48;2;60;60;60m060|060|060 \u001b[00m\u001b[00;48;2;60;60;90m060|060|090 \u001b[00m\u001b[00;48;2;60;60;120m060|060|120 \u001b[00m\u001b[00;48;2;60;60;150m060|060|150 \u001b[00m\u001b[00;48;2;60;60;180m060|060|180 \u001b[00m\u001b[00;48;2;60;60;210m060|060|210 \u001b[00m\u001b[00;48;2;60;60;240m060|060|240 \u001b[00m\n",
-      "\u001b[00;48;2;60;90;0m060|090|000 \u001b[00m\u001b[00;48;2;60;90;30m060|090|030 \u001b[00m\u001b[00;48;2;60;90;60m060|090|060 \u001b[00m\u001b[00;48;2;60;90;90m060|090|090 \u001b[00m\u001b[00;48;2;60;90;120m060|090|120 \u001b[00m\u001b[00;48;2;60;90;150m060|090|150 \u001b[00m\u001b[00;48;2;60;90;180m060|090|180 \u001b[00m\u001b[00;48;2;60;90;210m060|090|210 \u001b[00m\u001b[00;48;2;60;90;240m060|090|240 \u001b[00m\n",
-      "\u001b[00;48;2;60;120;0m060|120|000 \u001b[00m\u001b[00;48;2;60;120;30m060|120|030 \u001b[00m\u001b[00;48;2;60;120;60m060|120|060 \u001b[00m\u001b[00;48;2;60;120;90m060|120|090 \u001b[00m\u001b[00;48;2;60;120;120m060|120|120 \u001b[00m\u001b[00;48;2;60;120;150m060|120|150 \u001b[00m\u001b[00;48;2;60;120;180m060|120|180 \u001b[00m\u001b[00;48;2;60;120;210m060|120|210 \u001b[00m\u001b[00;48;2;60;120;240m060|120|240 \u001b[00m\n",
-      "\u001b[00;48;2;60;150;0m060|150|000 \u001b[00m\u001b[00;48;2;60;150;30m060|150|030 \u001b[00m\u001b[00;48;2;60;150;60m060|150|060 \u001b[00m\u001b[00;48;2;60;150;90m060|150|090 \u001b[00m\u001b[00;48;2;60;150;120m060|150|120 \u001b[00m\u001b[00;48;2;60;150;150m060|150|150 \u001b[00m\u001b[00;48;2;60;150;180m060|150|180 \u001b[00m\u001b[00;48;2;60;150;210m060|150|210 \u001b[00m\u001b[00;48;2;60;150;240m060|150|240 \u001b[00m\n",
-      "\u001b[00;48;2;60;180;0m060|180|000 \u001b[00m\u001b[00;48;2;60;180;30m060|180|030 \u001b[00m\u001b[00;48;2;60;180;60m060|180|060 \u001b[00m\u001b[00;48;2;60;180;90m060|180|090 \u001b[00m\u001b[00;48;2;60;180;120m060|180|120 \u001b[00m\u001b[00;48;2;60;180;150m060|180|150 \u001b[00m\u001b[00;48;2;60;180;180m060|180|180 \u001b[00m\u001b[00;48;2;60;180;210m060|180|210 \u001b[00m\u001b[00;48;2;60;180;240m060|180|240 \u001b[00m\n",
-      "\u001b[00;48;2;60;210;0m060|210|000 \u001b[00m\u001b[00;48;2;60;210;30m060|210|030 \u001b[00m\u001b[00;48;2;60;210;60m060|210|060 \u001b[00m\u001b[00;48;2;60;210;90m060|210|090 \u001b[00m\u001b[00;48;2;60;210;120m060|210|120 \u001b[00m\u001b[00;48;2;60;210;150m060|210|150 \u001b[00m\u001b[00;48;2;60;210;180m060|210|180 \u001b[00m\u001b[00;48;2;60;210;210m060|210|210 \u001b[00m\u001b[00;48;2;60;210;240m060|210|240 \u001b[00m\n",
-      "\u001b[00;48;2;60;240;0m060|240|000 \u001b[00m\u001b[00;48;2;60;240;30m060|240|030 \u001b[00m\u001b[00;48;2;60;240;60m060|240|060 \u001b[00m\u001b[00;48;2;60;240;90m060|240|090 \u001b[00m\u001b[00;48;2;60;240;120m060|240|120 \u001b[00m\u001b[00;48;2;60;240;150m060|240|150 \u001b[00m\u001b[00;48;2;60;240;180m060|240|180 \u001b[00m\u001b[00;48;2;60;240;210m060|240|210 \u001b[00m\u001b[00;48;2;60;240;240m060|240|240 \u001b[00m\n",
-      "\n",
-      "\u001b[00;48;2;90;0;0m090|000|000 \u001b[00m\u001b[00;48;2;90;0;30m090|000|030 \u001b[00m\u001b[00;48;2;90;0;60m090|000|060 \u001b[00m\u001b[00;48;2;90;0;90m090|000|090 \u001b[00m\u001b[00;48;2;90;0;120m090|000|120 \u001b[00m\u001b[00;48;2;90;0;150m090|000|150 \u001b[00m\u001b[00;48;2;90;0;180m090|000|180 \u001b[00m\u001b[00;48;2;90;0;210m090|000|210 \u001b[00m\u001b[00;48;2;90;0;240m090|000|240 \u001b[00m\n",
-      "\u001b[00;48;2;90;30;0m090|030|000 \u001b[00m\u001b[00;48;2;90;30;30m090|030|030 \u001b[00m\u001b[00;48;2;90;30;60m090|030|060 \u001b[00m\u001b[00;48;2;90;30;90m090|030|090 \u001b[00m\u001b[00;48;2;90;30;120m090|030|120 \u001b[00m\u001b[00;48;2;90;30;150m090|030|150 \u001b[00m\u001b[00;48;2;90;30;180m090|030|180 \u001b[00m\u001b[00;48;2;90;30;210m090|030|210 \u001b[00m\u001b[00;48;2;90;30;240m090|030|240 \u001b[00m\n",
-      "\u001b[00;48;2;90;60;0m090|060|000 \u001b[00m\u001b[00;48;2;90;60;30m090|060|030 \u001b[00m\u001b[00;48;2;90;60;60m090|060|060 \u001b[00m\u001b[00;48;2;90;60;90m090|060|090 \u001b[00m\u001b[00;48;2;90;60;120m090|060|120 \u001b[00m\u001b[00;48;2;90;60;150m090|060|150 \u001b[00m\u001b[00;48;2;90;60;180m090|060|180 \u001b[00m\u001b[00;48;2;90;60;210m090|060|210 \u001b[00m\u001b[00;48;2;90;60;240m090|060|240 \u001b[00m\n",
-      "\u001b[00;48;2;90;90;0m090|090|000 \u001b[00m\u001b[00;48;2;90;90;30m090|090|030 \u001b[00m\u001b[00;48;2;90;90;60m090|090|060 \u001b[00m\u001b[00;48;2;90;90;90m090|090|090 \u001b[00m\u001b[00;48;2;90;90;120m090|090|120 \u001b[00m\u001b[00;48;2;90;90;150m090|090|150 \u001b[00m\u001b[00;48;2;90;90;180m090|090|180 \u001b[00m\u001b[00;48;2;90;90;210m090|090|210 \u001b[00m\u001b[00;48;2;90;90;240m090|090|240 \u001b[00m\n",
-      "\u001b[00;48;2;90;120;0m090|120|000 \u001b[00m\u001b[00;48;2;90;120;30m090|120|030 \u001b[00m\u001b[00;48;2;90;120;60m090|120|060 \u001b[00m\u001b[00;48;2;90;120;90m090|120|090 \u001b[00m\u001b[00;48;2;90;120;120m090|120|120 \u001b[00m\u001b[00;48;2;90;120;150m090|120|150 \u001b[00m\u001b[00;48;2;90;120;180m090|120|180 \u001b[00m\u001b[00;48;2;90;120;210m090|120|210 \u001b[00m\u001b[00;48;2;90;120;240m090|120|240 \u001b[00m\n",
-      "\u001b[00;48;2;90;150;0m090|150|000 \u001b[00m\u001b[00;48;2;90;150;30m090|150|030 \u001b[00m\u001b[00;48;2;90;150;60m090|150|060 \u001b[00m\u001b[00;48;2;90;150;90m090|150|090 \u001b[00m\u001b[00;48;2;90;150;120m090|150|120 \u001b[00m\u001b[00;48;2;90;150;150m090|150|150 \u001b[00m\u001b[00;48;2;90;150;180m090|150|180 \u001b[00m\u001b[00;48;2;90;150;210m090|150|210 \u001b[00m\u001b[00;48;2;90;150;240m090|150|240 \u001b[00m\n",
-      "\u001b[00;48;2;90;180;0m090|180|000 \u001b[00m\u001b[00;48;2;90;180;30m090|180|030 \u001b[00m\u001b[00;48;2;90;180;60m090|180|060 \u001b[00m\u001b[00;48;2;90;180;90m090|180|090 \u001b[00m\u001b[00;48;2;90;180;120m090|180|120 \u001b[00m\u001b[00;48;2;90;180;150m090|180|150 \u001b[00m\u001b[00;48;2;90;180;180m090|180|180 \u001b[00m\u001b[00;48;2;90;180;210m090|180|210 \u001b[00m\u001b[00;48;2;90;180;240m090|180|240 \u001b[00m\n",
-      "\u001b[00;48;2;90;210;0m090|210|000 \u001b[00m\u001b[00;48;2;90;210;30m090|210|030 \u001b[00m\u001b[00;48;2;90;210;60m090|210|060 \u001b[00m\u001b[00;48;2;90;210;90m090|210|090 \u001b[00m\u001b[00;48;2;90;210;120m090|210|120 \u001b[00m\u001b[00;48;2;90;210;150m090|210|150 \u001b[00m\u001b[00;48;2;90;210;180m090|210|180 \u001b[00m\u001b[00;48;2;90;210;210m090|210|210 \u001b[00m\u001b[00;48;2;90;210;240m090|210|240 \u001b[00m\n",
-      "\u001b[00;48;2;90;240;0m090|240|000 \u001b[00m\u001b[00;48;2;90;240;30m090|240|030 \u001b[00m\u001b[00;48;2;90;240;60m090|240|060 \u001b[00m\u001b[00;48;2;90;240;90m090|240|090 \u001b[00m\u001b[00;48;2;90;240;120m090|240|120 \u001b[00m\u001b[00;48;2;90;240;150m090|240|150 \u001b[00m\u001b[00;48;2;90;240;180m090|240|180 \u001b[00m\u001b[00;48;2;90;240;210m090|240|210 \u001b[00m\u001b[00;48;2;90;240;240m090|240|240 \u001b[00m\n",
-      "\n",
-      "\u001b[00;48;2;120;0;0m120|000|000 \u001b[00m\u001b[00;48;2;120;0;30m120|000|030 \u001b[00m\u001b[00;48;2;120;0;60m120|000|060 \u001b[00m\u001b[00;48;2;120;0;90m120|000|090 \u001b[00m\u001b[00;48;2;120;0;120m120|000|120 \u001b[00m\u001b[00;48;2;120;0;150m120|000|150 \u001b[00m\u001b[00;48;2;120;0;180m120|000|180 \u001b[00m\u001b[00;48;2;120;0;210m120|000|210 \u001b[00m\u001b[00;48;2;120;0;240m120|000|240 \u001b[00m\n",
-      "\u001b[00;48;2;120;30;0m120|030|000 \u001b[00m\u001b[00;48;2;120;30;30m120|030|030 \u001b[00m\u001b[00;48;2;120;30;60m120|030|060 \u001b[00m\u001b[00;48;2;120;30;90m120|030|090 \u001b[00m\u001b[00;48;2;120;30;120m120|030|120 \u001b[00m\u001b[00;48;2;120;30;150m120|030|150 \u001b[00m\u001b[00;48;2;120;30;180m120|030|180 \u001b[00m\u001b[00;48;2;120;30;210m120|030|210 \u001b[00m\u001b[00;48;2;120;30;240m120|030|240 \u001b[00m\n",
-      "\u001b[00;48;2;120;60;0m120|060|000 \u001b[00m\u001b[00;48;2;120;60;30m120|060|030 \u001b[00m\u001b[00;48;2;120;60;60m120|060|060 \u001b[00m\u001b[00;48;2;120;60;90m120|060|090 \u001b[00m\u001b[00;48;2;120;60;120m120|060|120 \u001b[00m\u001b[00;48;2;120;60;150m120|060|150 \u001b[00m\u001b[00;48;2;120;60;180m120|060|180 \u001b[00m\u001b[00;48;2;120;60;210m120|060|210 \u001b[00m\u001b[00;48;2;120;60;240m120|060|240 \u001b[00m\n",
-      "\u001b[00;48;2;120;90;0m120|090|000 \u001b[00m\u001b[00;48;2;120;90;30m120|090|030 \u001b[00m\u001b[00;48;2;120;90;60m120|090|060 \u001b[00m\u001b[00;48;2;120;90;90m120|090|090 \u001b[00m\u001b[00;48;2;120;90;120m120|090|120 \u001b[00m\u001b[00;48;2;120;90;150m120|090|150 \u001b[00m\u001b[00;48;2;120;90;180m120|090|180 \u001b[00m\u001b[00;48;2;120;90;210m120|090|210 \u001b[00m\u001b[00;48;2;120;90;240m120|090|240 \u001b[00m\n",
-      "\u001b[00;48;2;120;120;0m120|120|000 \u001b[00m\u001b[00;48;2;120;120;30m120|120|030 \u001b[00m\u001b[00;48;2;120;120;60m120|120|060 \u001b[00m\u001b[00;48;2;120;120;90m120|120|090 \u001b[00m\u001b[00;48;2;120;120;120m120|120|120 \u001b[00m\u001b[00;48;2;120;120;150m120|120|150 \u001b[00m\u001b[00;48;2;120;120;180m120|120|180 \u001b[00m\u001b[00;48;2;120;120;210m120|120|210 \u001b[00m\u001b[00;48;2;120;120;240m120|120|240 \u001b[00m\n",
-      "\u001b[00;48;2;120;150;0m120|150|000 \u001b[00m\u001b[00;48;2;120;150;30m120|150|030 \u001b[00m\u001b[00;48;2;120;150;60m120|150|060 \u001b[00m\u001b[00;48;2;120;150;90m120|150|090 \u001b[00m\u001b[00;48;2;120;150;120m120|150|120 \u001b[00m\u001b[00;48;2;120;150;150m120|150|150 \u001b[00m\u001b[00;48;2;120;150;180m120|150|180 \u001b[00m\u001b[00;48;2;120;150;210m120|150|210 \u001b[00m\u001b[00;48;2;120;150;240m120|150|240 \u001b[00m\n",
-      "\u001b[00;48;2;120;180;0m120|180|000 \u001b[00m\u001b[00;48;2;120;180;30m120|180|030 \u001b[00m\u001b[00;48;2;120;180;60m120|180|060 \u001b[00m\u001b[00;48;2;120;180;90m120|180|090 \u001b[00m\u001b[00;48;2;120;180;120m120|180|120 \u001b[00m\u001b[00;48;2;120;180;150m120|180|150 \u001b[00m\u001b[00;48;2;120;180;180m120|180|180 \u001b[00m\u001b[00;48;2;120;180;210m120|180|210 \u001b[00m\u001b[00;48;2;120;180;240m120|180|240 \u001b[00m\n",
-      "\u001b[00;48;2;120;210;0m120|210|000 \u001b[00m\u001b[00;48;2;120;210;30m120|210|030 \u001b[00m\u001b[00;48;2;120;210;60m120|210|060 \u001b[00m\u001b[00;48;2;120;210;90m120|210|090 \u001b[00m\u001b[00;48;2;120;210;120m120|210|120 \u001b[00m\u001b[00;48;2;120;210;150m120|210|150 \u001b[00m\u001b[00;48;2;120;210;180m120|210|180 \u001b[00m\u001b[00;48;2;120;210;210m120|210|210 \u001b[00m\u001b[00;48;2;120;210;240m120|210|240 \u001b[00m\n",
-      "\u001b[00;48;2;120;240;0m120|240|000 \u001b[00m\u001b[00;48;2;120;240;30m120|240|030 \u001b[00m\u001b[00;48;2;120;240;60m120|240|060 \u001b[00m\u001b[00;48;2;120;240;90m120|240|090 \u001b[00m\u001b[00;48;2;120;240;120m120|240|120 \u001b[00m\u001b[00;48;2;120;240;150m120|240|150 \u001b[00m\u001b[00;48;2;120;240;180m120|240|180 \u001b[00m\u001b[00;48;2;120;240;210m120|240|210 \u001b[00m\u001b[00;48;2;120;240;240m120|240|240 \u001b[00m\n",
-      "\n",
-      "\u001b[00;48;2;150;0;0m150|000|000 \u001b[00m\u001b[00;48;2;150;0;30m150|000|030 \u001b[00m\u001b[00;48;2;150;0;60m150|000|060 \u001b[00m\u001b[00;48;2;150;0;90m150|000|090 \u001b[00m\u001b[00;48;2;150;0;120m150|000|120 \u001b[00m\u001b[00;48;2;150;0;150m150|000|150 \u001b[00m\u001b[00;48;2;150;0;180m150|000|180 \u001b[00m\u001b[00;48;2;150;0;210m150|000|210 \u001b[00m\u001b[00;48;2;150;0;240m150|000|240 \u001b[00m\n",
-      "\u001b[00;48;2;150;30;0m150|030|000 \u001b[00m\u001b[00;48;2;150;30;30m150|030|030 \u001b[00m\u001b[00;48;2;150;30;60m150|030|060 \u001b[00m\u001b[00;48;2;150;30;90m150|030|090 \u001b[00m\u001b[00;48;2;150;30;120m150|030|120 \u001b[00m\u001b[00;48;2;150;30;150m150|030|150 \u001b[00m\u001b[00;48;2;150;30;180m150|030|180 \u001b[00m\u001b[00;48;2;150;30;210m150|030|210 \u001b[00m\u001b[00;48;2;150;30;240m150|030|240 \u001b[00m\n",
-      "\u001b[00;48;2;150;60;0m150|060|000 \u001b[00m\u001b[00;48;2;150;60;30m150|060|030 \u001b[00m\u001b[00;48;2;150;60;60m150|060|060 \u001b[00m\u001b[00;48;2;150;60;90m150|060|090 \u001b[00m\u001b[00;48;2;150;60;120m150|060|120 \u001b[00m\u001b[00;48;2;150;60;150m150|060|150 \u001b[00m\u001b[00;48;2;150;60;180m150|060|180 \u001b[00m\u001b[00;48;2;150;60;210m150|060|210 \u001b[00m\u001b[00;48;2;150;60;240m150|060|240 \u001b[00m\n",
-      "\u001b[00;48;2;150;90;0m150|090|000 \u001b[00m\u001b[00;48;2;150;90;30m150|090|030 \u001b[00m\u001b[00;48;2;150;90;60m150|090|060 \u001b[00m\u001b[00;48;2;150;90;90m150|090|090 \u001b[00m\u001b[00;48;2;150;90;120m150|090|120 \u001b[00m\u001b[00;48;2;150;90;150m150|090|150 \u001b[00m\u001b[00;48;2;150;90;180m150|090|180 \u001b[00m\u001b[00;48;2;150;90;210m150|090|210 \u001b[00m\u001b[00;48;2;150;90;240m150|090|240 \u001b[00m\n",
-      "\u001b[00;48;2;150;120;0m150|120|000 \u001b[00m\u001b[00;48;2;150;120;30m150|120|030 \u001b[00m\u001b[00;48;2;150;120;60m150|120|060 \u001b[00m\u001b[00;48;2;150;120;90m150|120|090 \u001b[00m\u001b[00;48;2;150;120;120m150|120|120 \u001b[00m\u001b[00;48;2;150;120;150m150|120|150 \u001b[00m\u001b[00;48;2;150;120;180m150|120|180 \u001b[00m\u001b[00;48;2;150;120;210m150|120|210 \u001b[00m\u001b[00;48;2;150;120;240m150|120|240 \u001b[00m\n",
-      "\u001b[00;48;2;150;150;0m150|150|000 \u001b[00m\u001b[00;48;2;150;150;30m150|150|030 \u001b[00m\u001b[00;48;2;150;150;60m150|150|060 \u001b[00m\u001b[00;48;2;150;150;90m150|150|090 \u001b[00m\u001b[00;48;2;150;150;120m150|150|120 \u001b[00m\u001b[00;48;2;150;150;150m150|150|150 \u001b[00m\u001b[00;48;2;150;150;180m150|150|180 \u001b[00m\u001b[00;48;2;150;150;210m150|150|210 \u001b[00m\u001b[00;48;2;150;150;240m150|150|240 \u001b[00m\n",
-      "\u001b[00;48;2;150;180;0m150|180|000 \u001b[00m\u001b[00;48;2;150;180;30m150|180|030 \u001b[00m\u001b[00;48;2;150;180;60m150|180|060 \u001b[00m\u001b[00;48;2;150;180;90m150|180|090 \u001b[00m\u001b[00;48;2;150;180;120m150|180|120 \u001b[00m\u001b[00;48;2;150;180;150m150|180|150 \u001b[00m\u001b[00;48;2;150;180;180m150|180|180 \u001b[00m\u001b[00;48;2;150;180;210m150|180|210 \u001b[00m\u001b[00;48;2;150;180;240m150|180|240 \u001b[00m\n",
-      "\u001b[00;48;2;150;210;0m150|210|000 \u001b[00m\u001b[00;48;2;150;210;30m150|210|030 \u001b[00m\u001b[00;48;2;150;210;60m150|210|060 \u001b[00m\u001b[00;48;2;150;210;90m150|210|090 \u001b[00m\u001b[00;48;2;150;210;120m150|210|120 \u001b[00m\u001b[00;48;2;150;210;150m150|210|150 \u001b[00m\u001b[00;48;2;150;210;180m150|210|180 \u001b[00m\u001b[00;48;2;150;210;210m150|210|210 \u001b[00m\u001b[00;48;2;150;210;240m150|210|240 \u001b[00m\n",
-      "\u001b[00;48;2;150;240;0m150|240|000 \u001b[00m\u001b[00;48;2;150;240;30m150|240|030 \u001b[00m\u001b[00;48;2;150;240;60m150|240|060 \u001b[00m\u001b[00;48;2;150;240;90m150|240|090 \u001b[00m\u001b[00;48;2;150;240;120m150|240|120 \u001b[00m\u001b[00;48;2;150;240;150m150|240|150 \u001b[00m\u001b[00;48;2;150;240;180m150|240|180 \u001b[00m\u001b[00;48;2;150;240;210m150|240|210 \u001b[00m\u001b[00;48;2;150;240;240m150|240|240 \u001b[00m\n",
-      "\n",
-      "\u001b[00;48;2;180;0;0m180|000|000 \u001b[00m\u001b[00;48;2;180;0;30m180|000|030 \u001b[00m\u001b[00;48;2;180;0;60m180|000|060 \u001b[00m\u001b[00;48;2;180;0;90m180|000|090 \u001b[00m\u001b[00;48;2;180;0;120m180|000|120 \u001b[00m\u001b[00;48;2;180;0;150m180|000|150 \u001b[00m\u001b[00;48;2;180;0;180m180|000|180 \u001b[00m\u001b[00;48;2;180;0;210m180|000|210 \u001b[00m\u001b[00;48;2;180;0;240m180|000|240 \u001b[00m\n",
-      "\u001b[00;48;2;180;30;0m180|030|000 \u001b[00m\u001b[00;48;2;180;30;30m180|030|030 \u001b[00m\u001b[00;48;2;180;30;60m180|030|060 \u001b[00m\u001b[00;48;2;180;30;90m180|030|090 \u001b[00m\u001b[00;48;2;180;30;120m180|030|120 \u001b[00m\u001b[00;48;2;180;30;150m180|030|150 \u001b[00m\u001b[00;48;2;180;30;180m180|030|180 \u001b[00m\u001b[00;48;2;180;30;210m180|030|210 \u001b[00m\u001b[00;48;2;180;30;240m180|030|240 \u001b[00m\n",
-      "\u001b[00;48;2;180;60;0m180|060|000 \u001b[00m\u001b[00;48;2;180;60;30m180|060|030 \u001b[00m\u001b[00;48;2;180;60;60m180|060|060 \u001b[00m\u001b[00;48;2;180;60;90m180|060|090 \u001b[00m\u001b[00;48;2;180;60;120m180|060|120 \u001b[00m\u001b[00;48;2;180;60;150m180|060|150 \u001b[00m\u001b[00;48;2;180;60;180m180|060|180 \u001b[00m\u001b[00;48;2;180;60;210m180|060|210 \u001b[00m\u001b[00;48;2;180;60;240m180|060|240 \u001b[00m\n",
-      "\u001b[00;48;2;180;90;0m180|090|000 \u001b[00m\u001b[00;48;2;180;90;30m180|090|030 \u001b[00m\u001b[00;48;2;180;90;60m180|090|060 \u001b[00m\u001b[00;48;2;180;90;90m180|090|090 \u001b[00m\u001b[00;48;2;180;90;120m180|090|120 \u001b[00m\u001b[00;48;2;180;90;150m180|090|150 \u001b[00m\u001b[00;48;2;180;90;180m180|090|180 \u001b[00m\u001b[00;48;2;180;90;210m180|090|210 \u001b[00m\u001b[00;48;2;180;90;240m180|090|240 \u001b[00m\n",
-      "\u001b[00;48;2;180;120;0m180|120|000 \u001b[00m\u001b[00;48;2;180;120;30m180|120|030 \u001b[00m\u001b[00;48;2;180;120;60m180|120|060 \u001b[00m\u001b[00;48;2;180;120;90m180|120|090 \u001b[00m\u001b[00;48;2;180;120;120m180|120|120 \u001b[00m\u001b[00;48;2;180;120;150m180|120|150 \u001b[00m\u001b[00;48;2;180;120;180m180|120|180 \u001b[00m\u001b[00;48;2;180;120;210m180|120|210 \u001b[00m\u001b[00;48;2;180;120;240m180|120|240 \u001b[00m\n",
-      "\u001b[00;48;2;180;150;0m180|150|000 \u001b[00m\u001b[00;48;2;180;150;30m180|150|030 \u001b[00m\u001b[00;48;2;180;150;60m180|150|060 \u001b[00m\u001b[00;48;2;180;150;90m180|150|090 \u001b[00m\u001b[00;48;2;180;150;120m180|150|120 \u001b[00m\u001b[00;48;2;180;150;150m180|150|150 \u001b[00m\u001b[00;48;2;180;150;180m180|150|180 \u001b[00m\u001b[00;48;2;180;150;210m180|150|210 \u001b[00m\u001b[00;48;2;180;150;240m180|150|240 \u001b[00m\n",
-      "\u001b[00;48;2;180;180;0m180|180|000 \u001b[00m\u001b[00;48;2;180;180;30m180|180|030 \u001b[00m\u001b[00;48;2;180;180;60m180|180|060 \u001b[00m\u001b[00;48;2;180;180;90m180|180|090 \u001b[00m\u001b[00;48;2;180;180;120m180|180|120 \u001b[00m\u001b[00;48;2;180;180;150m180|180|150 \u001b[00m\u001b[00;48;2;180;180;180m180|180|180 \u001b[00m\u001b[00;48;2;180;180;210m180|180|210 \u001b[00m\u001b[00;48;2;180;180;240m180|180|240 \u001b[00m\n",
-      "\u001b[00;48;2;180;210;0m180|210|000 \u001b[00m\u001b[00;48;2;180;210;30m180|210|030 \u001b[00m\u001b[00;48;2;180;210;60m180|210|060 \u001b[00m\u001b[00;48;2;180;210;90m180|210|090 \u001b[00m\u001b[00;48;2;180;210;120m180|210|120 \u001b[00m\u001b[00;48;2;180;210;150m180|210|150 \u001b[00m\u001b[00;48;2;180;210;180m180|210|180 \u001b[00m\u001b[00;48;2;180;210;210m180|210|210 \u001b[00m\u001b[00;48;2;180;210;240m180|210|240 \u001b[00m\n",
-      "\u001b[00;48;2;180;240;0m180|240|000 \u001b[00m\u001b[00;48;2;180;240;30m180|240|030 \u001b[00m\u001b[00;48;2;180;240;60m180|240|060 \u001b[00m\u001b[00;48;2;180;240;90m180|240|090 \u001b[00m\u001b[00;48;2;180;240;120m180|240|120 \u001b[00m\u001b[00;48;2;180;240;150m180|240|150 \u001b[00m\u001b[00;48;2;180;240;180m180|240|180 \u001b[00m\u001b[00;48;2;180;240;210m180|240|210 \u001b[00m\u001b[00;48;2;180;240;240m180|240|240 \u001b[00m\n",
-      "\n",
-      "\u001b[00;48;2;210;0;0m210|000|000 \u001b[00m\u001b[00;48;2;210;0;30m210|000|030 \u001b[00m\u001b[00;48;2;210;0;60m210|000|060 \u001b[00m\u001b[00;48;2;210;0;90m210|000|090 \u001b[00m\u001b[00;48;2;210;0;120m210|000|120 \u001b[00m\u001b[00;48;2;210;0;150m210|000|150 \u001b[00m\u001b[00;48;2;210;0;180m210|000|180 \u001b[00m\u001b[00;48;2;210;0;210m210|000|210 \u001b[00m\u001b[00;48;2;210;0;240m210|000|240 \u001b[00m\n",
-      "\u001b[00;48;2;210;30;0m210|030|000 \u001b[00m\u001b[00;48;2;210;30;30m210|030|030 \u001b[00m\u001b[00;48;2;210;30;60m210|030|060 \u001b[00m\u001b[00;48;2;210;30;90m210|030|090 \u001b[00m\u001b[00;48;2;210;30;120m210|030|120 \u001b[00m\u001b[00;48;2;210;30;150m210|030|150 \u001b[00m\u001b[00;48;2;210;30;180m210|030|180 \u001b[00m\u001b[00;48;2;210;30;210m210|030|210 \u001b[00m\u001b[00;48;2;210;30;240m210|030|240 \u001b[00m\n",
-      "\u001b[00;48;2;210;60;0m210|060|000 \u001b[00m\u001b[00;48;2;210;60;30m210|060|030 \u001b[00m\u001b[00;48;2;210;60;60m210|060|060 \u001b[00m\u001b[00;48;2;210;60;90m210|060|090 \u001b[00m\u001b[00;48;2;210;60;120m210|060|120 \u001b[00m\u001b[00;48;2;210;60;150m210|060|150 \u001b[00m\u001b[00;48;2;210;60;180m210|060|180 \u001b[00m\u001b[00;48;2;210;60;210m210|060|210 \u001b[00m\u001b[00;48;2;210;60;240m210|060|240 \u001b[00m\n",
-      "\u001b[00;48;2;210;90;0m210|090|000 \u001b[00m\u001b[00;48;2;210;90;30m210|090|030 \u001b[00m\u001b[00;48;2;210;90;60m210|090|060 \u001b[00m\u001b[00;48;2;210;90;90m210|090|090 \u001b[00m\u001b[00;48;2;210;90;120m210|090|120 \u001b[00m\u001b[00;48;2;210;90;150m210|090|150 \u001b[00m\u001b[00;48;2;210;90;180m210|090|180 \u001b[00m\u001b[00;48;2;210;90;210m210|090|210 \u001b[00m\u001b[00;48;2;210;90;240m210|090|240 \u001b[00m\n",
-      "\u001b[00;48;2;210;120;0m210|120|000 \u001b[00m\u001b[00;48;2;210;120;30m210|120|030 \u001b[00m\u001b[00;48;2;210;120;60m210|120|060 \u001b[00m\u001b[00;48;2;210;120;90m210|120|090 \u001b[00m\u001b[00;48;2;210;120;120m210|120|120 \u001b[00m\u001b[00;48;2;210;120;150m210|120|150 \u001b[00m\u001b[00;48;2;210;120;180m210|120|180 \u001b[00m\u001b[00;48;2;210;120;210m210|120|210 \u001b[00m\u001b[00;48;2;210;120;240m210|120|240 \u001b[00m\n",
-      "\u001b[00;48;2;210;150;0m210|150|000 \u001b[00m\u001b[00;48;2;210;150;30m210|150|030 \u001b[00m\u001b[00;48;2;210;150;60m210|150|060 \u001b[00m\u001b[00;48;2;210;150;90m210|150|090 \u001b[00m\u001b[00;48;2;210;150;120m210|150|120 \u001b[00m\u001b[00;48;2;210;150;150m210|150|150 \u001b[00m\u001b[00;48;2;210;150;180m210|150|180 \u001b[00m\u001b[00;48;2;210;150;210m210|150|210 \u001b[00m\u001b[00;48;2;210;150;240m210|150|240 \u001b[00m\n",
-      "\u001b[00;48;2;210;180;0m210|180|000 \u001b[00m\u001b[00;48;2;210;180;30m210|180|030 \u001b[00m\u001b[00;48;2;210;180;60m210|180|060 \u001b[00m\u001b[00;48;2;210;180;90m210|180|090 \u001b[00m\u001b[00;48;2;210;180;120m210|180|120 \u001b[00m\u001b[00;48;2;210;180;150m210|180|150 \u001b[00m\u001b[00;48;2;210;180;180m210|180|180 \u001b[00m\u001b[00;48;2;210;180;210m210|180|210 \u001b[00m\u001b[00;48;2;210;180;240m210|180|240 \u001b[00m\n",
-      "\u001b[00;48;2;210;210;0m210|210|000 \u001b[00m\u001b[00;48;2;210;210;30m210|210|030 \u001b[00m\u001b[00;48;2;210;210;60m210|210|060 \u001b[00m\u001b[00;48;2;210;210;90m210|210|090 \u001b[00m\u001b[00;48;2;210;210;120m210|210|120 \u001b[00m\u001b[00;48;2;210;210;150m210|210|150 \u001b[00m\u001b[00;48;2;210;210;180m210|210|180 \u001b[00m\u001b[00;48;2;210;210;210m210|210|210 \u001b[00m\u001b[00;48;2;210;210;240m210|210|240 \u001b[00m\n",
-      "\u001b[00;48;2;210;240;0m210|240|000 \u001b[00m\u001b[00;48;2;210;240;30m210|240|030 \u001b[00m\u001b[00;48;2;210;240;60m210|240|060 \u001b[00m\u001b[00;48;2;210;240;90m210|240|090 \u001b[00m\u001b[00;48;2;210;240;120m210|240|120 \u001b[00m\u001b[00;48;2;210;240;150m210|240|150 \u001b[00m\u001b[00;48;2;210;240;180m210|240|180 \u001b[00m\u001b[00;48;2;210;240;210m210|240|210 \u001b[00m\u001b[00;48;2;210;240;240m210|240|240 \u001b[00m\n",
-      "\n",
-      "\u001b[00;48;2;240;0;0m240|000|000 \u001b[00m\u001b[00;48;2;240;0;30m240|000|030 \u001b[00m\u001b[00;48;2;240;0;60m240|000|060 \u001b[00m\u001b[00;48;2;240;0;90m240|000|090 \u001b[00m\u001b[00;48;2;240;0;120m240|000|120 \u001b[00m\u001b[00;48;2;240;0;150m240|000|150 \u001b[00m\u001b[00;48;2;240;0;180m240|000|180 \u001b[00m\u001b[00;48;2;240;0;210m240|000|210 \u001b[00m\u001b[00;48;2;240;0;240m240|000|240 \u001b[00m\n",
-      "\u001b[00;48;2;240;30;0m240|030|000 \u001b[00m\u001b[00;48;2;240;30;30m240|030|030 \u001b[00m\u001b[00;48;2;240;30;60m240|030|060 \u001b[00m\u001b[00;48;2;240;30;90m240|030|090 \u001b[00m\u001b[00;48;2;240;30;120m240|030|120 \u001b[00m\u001b[00;48;2;240;30;150m240|030|150 \u001b[00m\u001b[00;48;2;240;30;180m240|030|180 \u001b[00m\u001b[00;48;2;240;30;210m240|030|210 \u001b[00m\u001b[00;48;2;240;30;240m240|030|240 \u001b[00m\n",
-      "\u001b[00;48;2;240;60;0m240|060|000 \u001b[00m\u001b[00;48;2;240;60;30m240|060|030 \u001b[00m\u001b[00;48;2;240;60;60m240|060|060 \u001b[00m\u001b[00;48;2;240;60;90m240|060|090 \u001b[00m\u001b[00;48;2;240;60;120m240|060|120 \u001b[00m\u001b[00;48;2;240;60;150m240|060|150 \u001b[00m\u001b[00;48;2;240;60;180m240|060|180 \u001b[00m\u001b[00;48;2;240;60;210m240|060|210 \u001b[00m\u001b[00;48;2;240;60;240m240|060|240 \u001b[00m\n",
-      "\u001b[00;48;2;240;90;0m240|090|000 \u001b[00m\u001b[00;48;2;240;90;30m240|090|030 \u001b[00m\u001b[00;48;2;240;90;60m240|090|060 \u001b[00m\u001b[00;48;2;240;90;90m240|090|090 \u001b[00m\u001b[00;48;2;240;90;120m240|090|120 \u001b[00m\u001b[00;48;2;240;90;150m240|090|150 \u001b[00m\u001b[00;48;2;240;90;180m240|090|180 \u001b[00m\u001b[00;48;2;240;90;210m240|090|210 \u001b[00m\u001b[00;48;2;240;90;240m240|090|240 \u001b[00m\n",
-      "\u001b[00;48;2;240;120;0m240|120|000 \u001b[00m\u001b[00;48;2;240;120;30m240|120|030 \u001b[00m\u001b[00;48;2;240;120;60m240|120|060 \u001b[00m\u001b[00;48;2;240;120;90m240|120|090 \u001b[00m\u001b[00;48;2;240;120;120m240|120|120 \u001b[00m\u001b[00;48;2;240;120;150m240|120|150 \u001b[00m\u001b[00;48;2;240;120;180m240|120|180 \u001b[00m\u001b[00;48;2;240;120;210m240|120|210 \u001b[00m\u001b[00;48;2;240;120;240m240|120|240 \u001b[00m\n",
-      "\u001b[00;48;2;240;150;0m240|150|000 \u001b[00m\u001b[00;48;2;240;150;30m240|150|030 \u001b[00m\u001b[00;48;2;240;150;60m240|150|060 \u001b[00m\u001b[00;48;2;240;150;90m240|150|090 \u001b[00m\u001b[00;48;2;240;150;120m240|150|120 \u001b[00m\u001b[00;48;2;240;150;150m240|150|150 \u001b[00m\u001b[00;48;2;240;150;180m240|150|180 \u001b[00m\u001b[00;48;2;240;150;210m240|150|210 \u001b[00m\u001b[00;48;2;240;150;240m240|150|240 \u001b[00m\n",
-      "\u001b[00;48;2;240;180;0m240|180|000 \u001b[00m\u001b[00;48;2;240;180;30m240|180|030 \u001b[00m\u001b[00;48;2;240;180;60m240|180|060 \u001b[00m\u001b[00;48;2;240;180;90m240|180|090 \u001b[00m\u001b[00;48;2;240;180;120m240|180|120 \u001b[00m\u001b[00;48;2;240;180;150m240|180|150 \u001b[00m\u001b[00;48;2;240;180;180m240|180|180 \u001b[00m\u001b[00;48;2;240;180;210m240|180|210 \u001b[00m\u001b[00;48;2;240;180;240m240|180|240 \u001b[00m\n",
-      "\u001b[00;48;2;240;210;0m240|210|000 \u001b[00m\u001b[00;48;2;240;210;30m240|210|030 \u001b[00m\u001b[00;48;2;240;210;60m240|210|060 \u001b[00m\u001b[00;48;2;240;210;90m240|210|090 \u001b[00m\u001b[00;48;2;240;210;120m240|210|120 \u001b[00m\u001b[00;48;2;240;210;150m240|210|150 \u001b[00m\u001b[00;48;2;240;210;180m240|210|180 \u001b[00m\u001b[00;48;2;240;210;210m240|210|210 \u001b[00m\u001b[00;48;2;240;210;240m240|210|240 \u001b[00m\n",
-      "\u001b[00;48;2;240;240;0m240|240|000 \u001b[00m\u001b[00;48;2;240;240;30m240|240|030 \u001b[00m\u001b[00;48;2;240;240;60m240|240|060 \u001b[00m\u001b[00;48;2;240;240;90m240|240|090 \u001b[00m\u001b[00;48;2;240;240;120m240|240|120 \u001b[00m\u001b[00;48;2;240;240;150m240|240|150 \u001b[00m\u001b[00;48;2;240;240;180m240|240|180 \u001b[00m\u001b[00;48;2;240;240;210m240|240|210 \u001b[00m\u001b[00;48;2;240;240;240m240|240|240 \u001b[00m\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "steps = range(0,256,30)\n",
-    "\n",
-    "t = \"{ESC}00;48;2;{r};{g};{b}m{r:03}|{g:03}|{b:03} {RESET}\"\n",
-    "for r in steps:\n",
-    "    for g in steps:\n",
-    "        for b in steps:\n",
-    "            print (t.format(**locals()), end='')\n",
-    "        print()\n",
-    "    print()\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/tools/tests/CSS Reference.ipynb b/tools/tests/CSS Reference.ipynb
deleted file mode 100644
index a567cc6..0000000
--- a/tools/tests/CSS Reference.ipynb
+++ /dev/null
@@ -1,8193 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<center><h1> CSS Playground </h1></center>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A notebook that contain most of the things that could be displayed, to test CSS, feel free to add things to it, and send modification"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Title first level"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Title second Level"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Title third level"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### h4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### h5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "###### h6"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# h1\n",
-    "## h2\n",
-    "### h3\n",
-    "#### h4\n",
-    "##### h6\n",
-    "\n",
-    "This is just a sample paragraph\n",
-    "\n",
-    "> With a blockquote\n",
-    "\n",
-    "    def some_code():\n",
-    "        return 'by indenting'\n",
-    "\n",
-    "```\n",
-    "def some_other_code():\n",
-    "    return 'between_backticks'\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can look at different level of nested unordered list \n",
-    "\n",
-    "- level 1\n",
-    "    - level 2\n",
-    "    - level 2\n",
-    "    - level 2\n",
-    "        - level 3\n",
-    "        - level 3\n",
-    "            - level 4\n",
-    "                - level 5\n",
-    "                    - level 6\n",
-    "    - level 2\n",
-    "\n",
-    "- level 1\n",
-    "- level 1\n",
-    "- level 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Ordered list \n",
-    "\n",
-    "1. level 1\n",
-    "    2. level 1\n",
-    "    3. level 1\n",
-    "        4. level 1\n",
-    "        1. level 1\n",
-    "        2. level 1\n",
-    "            2. level 1\n",
-    "            3. level 1\n",
-    "                4. level 1\n",
-    "                1. level 1\n",
-    "                2. level 1\n",
-    "3. level 1\n",
-    "4. level 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "some Horizontal line\n",
-    "\n",
-    "***\n",
-    "\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## copy past from Daring Fireball"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "link : \n",
-    "This is [an example](http://example.com/ \"Title\") inline link.\n",
-    "\n",
-    "[This link](http://example.net/) has no title attribute."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "inline Html\n",
-    "This is a regular paragraph.\n",
-    "\n",
-    "<table>\n",
-    "    <tr>\n",
-    "        <td>Foo</td>\n",
-    "    </tr>\n",
-    "</table>\n",
-    "\n",
-    "This is another regular paragraph."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> This is a blockquote with two paragraphs. Lorem ipsum dolor sit amet,\n",
-    "> consectetuer adipiscing elit. Aliquam hendrerit mi posuere lectus.\n",
-    "> Vestibulum enim wisi, viverra nec, fringilla in, laoreet vitae, risus.\n",
-    "> \n",
-    "> Donec sit amet nisl. Aliquam semper ipsum sit amet velit. Suspendisse\n",
-    "> id sem consectetuer libero luctus adipiscing.\n",
-    "\n",
-    "---\n",
-    "\n",
-    "> This is a blockquote with two paragraphs. Lorem ipsum dolor sit amet,\n",
-    "consectetuer adipiscing elit. Aliquam hendrerit mi posuere lectus.\n",
-    "Vestibulum enim wisi, viverra nec, fringilla in, laoreet vitae, risus.\n",
-    "\n",
-    "> Donec sit amet nisl. Aliquam semper ipsum sit amet velit. Suspendisse\n",
-    "id sem consectetuer libero luctus adipiscing."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> This is the first level of quoting.\n",
-    ">\n",
-    "> > This is nested blockquote.\n",
-    ">\n",
-    "> Back to the first level."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> ## This is a header.\n",
-    "> \n",
-    "> 1.   This is the first list item.\n",
-    "> 2.   This is the second list item.\n",
-    "> \n",
-    "> Here's some example code:\n",
-    "> \n",
-    ">     return shell_exec(\"echo $input | $markdown_script\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "1.  This is a list item with two paragraphs. Lorem ipsum dolor\n",
-    "    sit amet, consectetuer adipiscing elit. Aliquam hendrerit\n",
-    "    mi posuere lectus.\n",
-    "\n",
-    "    Vestibulum enim wisi, viverra nec, fringilla in, laoreet\n",
-    "    vitae, risus. Donec sit amet nisl. Aliquam semper ipsum\n",
-    "    sit amet velit.\n",
-    "\n",
-    "2.  Suspendisse id sem consectetuer libero luctus adipiscing."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*   This is a list item with two paragraphs.\n",
-    "\n",
-    "    This is the second paragraph in the list item. You're\n",
-    "only required to indent the first line. Lorem ipsum dolor\n",
-    "sit amet, consectetuer adipiscing elit.\n",
-    "\n",
-    "*   Another item in the same list."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*   A list item with a blockquote:\n",
-    "\n",
-    "    > This is a blockquote\n",
-    "    > inside a list item."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*   A list item with a code block:\n",
-    "\n",
-    "        <code goes here>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "1986. What a great season.\n",
-    "\n",
-    "1986\\. What a great season."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "See my [About](/about/) page for details.   "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "ref link\n",
-    "This is [an example][id] reference-style link.\n",
-    "\n",
-    "[id]: http://example.com/  \"Optional Title Here\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*single asterisks*\n",
-    "\n",
-    "_single underscores_\n",
-    "\n",
-    "**double asterisks**\n",
-    "\n",
-    "__double underscores__\n",
-    "\n",
-    "un*frigging*believable  // should render partially as bold\n",
-    "\n",
-    "\\*this text is surrounded by literal asterisks\\*\n",
-    "\n",
-    "``There is a literal backtick (`) here.``"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Other Notebook element"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A small tooltip\n",
-    "\n",
-    "<div id=\"tooltip_p\" class=\"ipython_tooltip\" style='position:relative'><div class=\"tooltipbuttons\"><a href=\"#\" role=\"button\" class=\"ui-button\"><span class=\"ui-icon ui-icon-close\">Close</span></a><a href=\"#\" class=\"ui-corner-all\" role=\"button\" id=\"expanbutton\" title=\"Grow the tooltip vertically (press tab 2 times)\" style=\"\"><span class=\"ui-icon ui-icon-plus\">Expand</span></a><a href=\"#\" role=\"button\" class=\"ui-button\" title=\"show the current docstring in pager (press tab 4 times)\"><span class=\"ui-icon ui-icon-arrowstop-l-n\">Open in Pager</span></a><a href=\"#\" role=\"button\" class=\"ui-button\" title=\"Tootip is not dismissed while typing for 10 seconds\" style=\"display: none;\"><span class=\"ui-icon ui-icon-clock\">Close</span></a></div><div class=\"pretooltiparrow\"></div><div class=\"tooltiptext smalltooltip\">And some text inside</div></div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "<div id=\"tooltip_p\" class=\"ipython_tooltip\" style='position:relative'>\n",
-    "    <div class=\"tooltipbuttons\">\n",
-    "        <a href=\"#\" role=\"button\" class=\"ui-button\">\n",
-    "            <span class=\"ui-icon ui-icon-close\">Close</span>\n",
-    "        </a>\n",
-    "        <a href=\"#\" class=\"ui-corner-all\" role=\"button\" id=\"expanbutton\" title=\"Grow the tooltip vertically (press tab 2 times)\" style=\"\">\n",
-    "            <span class=\"ui-icon ui-icon-plus\">Expand</span>\n",
-    "        </a>\n",
-    "        <a href=\"#\" role=\"button\" class=\"ui-button\" title=\"show the current docstring in pager (press tab 4 times)\">\n",
-    "            <span class=\"ui-icon ui-icon-arrowstop-l-n\">Open in Pager</span></a>\n",
-    "        <a href=\"#\" role=\"button\" class=\"ui-button\" title=\"Tootip is not dismissed while typing for 10 seconds\" style=\"display: none;\">\n",
-    "            <span class=\"ui-icon ui-icon-clock\">Close</span>\n",
-    "        </a>\n",
-    "    </div>\n",
-    "    <div class=\"pretooltiparrow\">\n",
-    "    </div>\n",
-    "    <div class=\"tooltiptext bigtooltip\">This one should be big</div>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
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-       "2LBhw4YNGzZs2CthA8QYNmzYsGHDhg0bNmzYsGHDhr0SNkCMYcOGDRs2bNiwYcOGDRs2bNgrYf8/\n",
-       "mJtMBcAFmf0AAAAASUVORK5CYII=\n"
-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d54f550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "# a code cell \n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib.colors import LightSource\n",
-    "\n",
-    "# example showing how to make shaded relief plots\n",
-    "# like Mathematica\n",
-    "# (http://reference.wolfram.com/mathematica/ref/ReliefPlot.html)\n",
-    "# or Generic Mapping Tools\n",
-    "# (http://gmt.soest.hawaii.edu/gmt/doc/gmt/html/GMT_Docs/node145.html)\n",
-    "\n",
-    "# test data\n",
-    "d= 1\n",
-    "def maltc(ax, lambd=1, n=1):\n",
-    "    I0=1\n",
-    "    I= lambda theta,d : I0*(sin(2*theta)*sin(pi*n*d/lambd))**2\n",
-    "    X,Y=np.mgrid[-5:5:0.05,-5:5:0.05]\n",
-    "    Z=np.sqrt(X**2+Y**2)+np.sin(X**2+Y**2)\n",
-    "    \n",
-    "    r= np.sqrt(X**2+Y**2)\n",
-    "    theta = np.angle(X+1.0j*Y)\n",
-    "    \n",
-    "    Iv= np.vectorize(I)\n",
-    "    Z = Iv(r,theta)\n",
-    "    \n",
-    "    # create light source object.\n",
-    "    #ls = LightSource(azdeg=0,altdeg=65)\n",
-    "    # shade data, creating an rgb array.\n",
-    "    #rgb = ls.shade(Z,plt.cm.copper)\n",
-    "    # plot un-shaded and shaded images.\n",
-    "    #plt.figure(figsize=(12,5))\n",
-    "    #plt.subplot(121)\n",
-    "    ax.imshow(Z,cmap=plt.cm.copper)\n",
-    "    ax.set_title('d=%d lambda=%f'%(d,lambd))\n",
-    "    \n",
-    "fig, (axes) = plt.subplots(3,4)\n",
-    "fig.set_figheight(10)\n",
-    "fig.set_figwidth(20)\n",
-    "\n",
-    "flatten = [item for sublist in axes for item in sublist]\n",
-    "\n",
-    "for ax,l in zip(flatten,range(len(flatten))):\n",
-    "   maltc(ax,lambd=(l+1)*pi/8.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "stdout\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "stderr\n"
-     ]
-    }
-   ],
-   "source": [
-    "from __future__ import print_function\n",
-    "import sys\n",
-    "print('stdout')\n",
-    "print('stderr',file=sys.stderr)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<table class='table-striped table-condensed'>\n",
-    "    <thead>\n",
-    "        <tr>\n",
-    "        <td>This is table</td>\n",
-    "        <td>Head</td>\n",
-    "        </tr>\n",
-    "    </thead>\n",
-    "    <tbody>\n",
-    "        <tr>\n",
-    "            <td>but this</td>\n",
-    "            <td>is the </td>\n",
-    "        </tr>\n",
-    "        <tr>\n",
-    "            <td>body</td>\n",
-    "            <td>of </td>\n",
-    "        </tr>\n",
-    "        <tr>\n",
-    "            <td>The</td>\n",
-    "            <td>table</td>\n",
-    "        </tr>\n",
-    "    </tbody>\n",
-    "</table>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/tools/tests/Confined Output.ipynb b/tools/tests/Confined Output.ipynb
deleted file mode 100644
index f767c19..0000000
--- a/tools/tests/Confined Output.ipynb
+++ /dev/null
@@ -1,307 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#  Test notebook for overflowing content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "markdown image:\n",
-    "\n",
-    "<img src=\"http://placehold.it/800x200.png\">\n",
-    "\n",
-    "unconfined markdown image:\n",
-    "\n",
-    "<img src=\"http://placehold.it/800x200.png\" class=\"unconfined\">"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from IPython.display import Image, IFrame"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Overflow image in HTML (non-embedded)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<img src=\"http://placehold.it/800x200.png\"   />"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(url=\"http://placehold.it/800x200.png\", embed=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Overflow image:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAADIBAMAAADioZgpAAAAG1BMVEXMzMyWlpbFxcWxsbGjo6Oc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\nLzkAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(url=\"http://placehold.it/800x200.png\", embed=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Overflow, unconfined"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAADIBAMAAADioZgpAAAAG1BMVEXMzMyWlpbFxcWxsbGjo6Oc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\nLzkAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {
-      "image/png": {
-       "unconfined": true
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(url=\"http://placehold.it/800x200.png\", embed=True, unconfined=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Overflow with explicit height, width (retina):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {
-      "image/jpeg": {
-       "height": 100,
-       "width": 900
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(url=\"http://placehold.it/1800x200.jpg\", embed=True, retina=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Overflowing IFrame:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"900\"\n",
-       "            height=\"400\"\n",
-       "            src=\"https://ipython.org\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.IFrame at 0x105239dd8>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "IFrame(src=\"https://ipython.org\", width=900, height=400)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Overflowing table:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "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>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "      <th>10</th>\n",
-       "      <th>11</th>\n",
-       "      <th>12</th>\n",
-       "      <th>13</th>\n",
-       "      <th>14</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td> column</td>\n",
-       "      <td> column</td>\n",
-       "      <td> column</td>\n",
-       "      <td> column</td>\n",
-       "      <td> column</td>\n",
-       "      <td> column</td>\n",
-       "      <td> column</td>\n",
-       "      <td> column</td>\n",
-       "      <td> column</td>\n",
-       "      <td> column</td>\n",
-       "      <td> column</td>\n",
-       "      <td> column</td>\n",
-       "      <td> column</td>\n",
-       "      <td> column</td>\n",
-       "      <td> column</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       0       1       2       3       4       5       6       7       8   \\\n",
-       "0  column  column  column  column  column  column  column  column  column   \n",
-       "\n",
-       "       9       10      11      12      13      14  \n",
-       "0  column  column  column  column  column  column  "
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "pd.DataFrame([['column'] * 15])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/tools/tests/Markdown Pandoc Limitations.ipynb b/tools/tests/Markdown Pandoc Limitations.ipynb
deleted file mode 100644
index 671aefb..0000000
--- a/tools/tests/Markdown Pandoc Limitations.ipynb
+++ /dev/null
@@ -1,2691 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**WARNING:  This document will not render correctly using nbviewer or nbconvert.  To render this notebook correctly, open in `IPython Notebook` and run `Cell->Run All` from the menu bar.**"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Introduction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The IPython Notebook allows Markdown, HTML, and inline LaTeX in *Mardown Cells*.  The inline LaTeX is parsed with [MathJax](http://www.mathjax.org/) and Markdown is parsed with [marked](https://github.com/chjj/marked).  Any inline HTML is left to the web browser to parse.  NBConvert is a utility that allows users to easily convert their notebooks to various formats.  Pandoc is used to parse markdown text in NBConvert.  Since what the notebook web interface supports is a mix of Markdown, HTML, and LaTeX, Pandoc has trouble converting notebook markdown. This results in incomplete representations of the notebook in nbviewer or a compiled Latex PDF.\n",
-    "\n",
-    "This isn't a Pandoc flaw; Pandoc isn't designed to parse and convert a mixed format document.  Unfortunately, this means that Pandoc can only support a subset of the markup supported in the notebook web interface.  This notebook compares output of Pandoc to the notebook web interface.\n",
-    "\n",
-    "**Changes:**\n",
-    "\n",
-    "05102013\n",
-    "\n",
-    " * heading anchors\n",
-    " * note on remote images\n",
-    "\n",
-    "06102013\n",
-    "\n",
-    " * remove strip_math_space filter\n",
-    " * add lxml test\n",
-    " \n",
-    "<style>\n",
-    " .rendered_html xmp { \n",
-    " white-space: pre-wrap;\n",
-    " }\n",
-    "</style>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Utilities"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Define functions to render Markdown using the notebook and Pandoc."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from IPython.nbconvert.utils.pandoc import pandoc\n",
-    "from IPython.display import HTML, Javascript, display\n",
-    "\n",
-    "from IPython.nbconvert.filters import citation2latex, strip_files_prefix, \\\n",
-    "                                     markdown2html, markdown2latex\n",
-    "\n",
-    "def pandoc_render(markdown):\n",
-    "    \"\"\"Render Pandoc Markdown->LaTeX content.\"\"\"\n",
-    "    \n",
-    "    ## Convert the markdown directly to latex.  This is what nbconvert does.\n",
-    "    #latex = pandoc(markdown, \"markdown\", \"latex\")\n",
-    "    #html = pandoc(markdown, \"markdown\", \"html\", [\"--mathjax\"])\n",
-    "    \n",
-    "    # nbconvert template conversions\n",
-    "    html = strip_files_prefix(markdown2html(markdown))\n",
-    "    latex = markdown2latex(citation2latex(markdown))\n",
-    "    display(HTML(data=\"<div style='display: inline-block; width: 30%; vertical-align: top;'>\" \\\n",
-    "                 \"<div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div>\" \\\n",
-    "                 \"<pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>\" + latex + \"</xmp></pre>\"\\\n",
-    "                 \"</div>\" \\\n",
-    "                 \"<div style='display: inline-block; width: 2%;'></div>\" \\\n",
-    "                 \"<div style='display: inline-block; width: 30%; vertical-align: top;'>\" \\\n",
-    "                 \"<div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div>\" \\\n",
-    "                 \"<div style='display: inline-block; width: 100%;'>\" + html + \"</div>\" \\\n",
-    "                 \"</div>\"))\n",
-    "    javascript = \"\"\"\n",
-    "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n",
-    "\"\"\"\n",
-    "    display(Javascript(data=javascript))\n",
-    "\n",
-    "def notebook_render(markdown):\n",
-    "    javascript = \"\"\"\n",
-    "var mdcell = new IPython.MarkdownCell();\n",
-    "mdcell.create_element();\n",
-    "mdcell.set_text('\"\"\" + markdown.replace(\"\\\\\", \"\\\\\\\\\").replace(\"'\", \"\\'\").replace(\"\\n\", \"\\\\n\") + \"\"\"');\n",
-    "mdcell.render();\n",
-    "$(element).append(mdcell.element)\n",
-    ".removeClass()\n",
-    ".css('left', '66%')\n",
-    ".css('position', 'absolute')\n",
-    ".css('width', '30%')\n",
-    "mdcell.element.prepend(\n",
-    "    $('<div />')\n",
-    "    .removeClass()\n",
-    "    .css('background', '#AAAAFF')\n",
-    "    .css('width', '100 %')\n",
-    "    .html('Notebook Output')\n",
-    "\n",
-    ");\n",
-    "container.show()\n",
-    "\"\"\"\n",
-    "    display(Javascript(data=javascript))\n",
-    "\n",
-    "    \n",
-    "def pandoc_html_render(markdown):\n",
-    "    \"\"\"Render Pandoc Markdown->LaTeX content.\"\"\"\n",
-    "    \n",
-    "    # Convert the markdown directly to latex.  This is what nbconvert does.\n",
-    "    latex = pandoc(markdown, \"markdown\", \"latex\")\n",
-    "    \n",
-    "    # Convert the pandoc generated latex to HTML so it can be rendered in \n",
-    "    # the web browser.\n",
-    "    html = pandoc(latex, \"latex\", \"html\", [\"--mathjax\"])\n",
-    "    display(HTML(data=\"<div style='background: #AAFFAA; width: 40%;'>HTML Pandoc Output</div>\" \\\n",
-    "                 \"<div style='display: inline-block; width: 40%;'>\" + html + \"</div>\"))\n",
-    "    return html\n",
-    "    \n",
-    "def compare_render(markdown):\n",
-    "    notebook_render(markdown)\n",
-    "    pandoc_render(markdown)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Outputs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LXML found!\n"
-     ]
-    }
-   ],
-   "source": [
-    "try:\n",
-    "    import lxml\n",
-    "    print 'LXML found!'\n",
-    "except:\n",
-    "    print 'Warning! No LXML found - the old citation2latex filter will not work'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## General markdown"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Heading level 6 is not supported by Pandoc."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n\\n# Heading 1 \\n## Heading 2 \\n### Heading 3 \\n#### Heading 4 \\n##### Heading 5 \\n###### Heading 6');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac2d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>\\section{Heading 1}\n",
-       "\n",
-       "\\subsection{Heading 2}\n",
-       "\n",
-       "\\subsubsection{Heading 3}\n",
-       "\n",
-       "\\paragraph{Heading 4}\n",
-       "\n",
-       "\\subparagraph{Heading 5}\n",
-       "\n",
-       "Heading 6</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><h1 id=\"heading-1\">Heading 1</h1>\n",
-       "<h2 id=\"heading-2\">Heading 2</h2>\n",
-       "<h3 id=\"heading-3\">Heading 3</h3>\n",
-       "<h4 id=\"heading-4\">Heading 4</h4>\n",
-       "<h5 id=\"heading-5\">Heading 5</h5>\n",
-       "<h6 id=\"heading-6\">Heading 6</h6></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21b9fd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21b9fd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "\n",
-    "# Heading 1 \n",
-    "## Heading 2 \n",
-    "### Heading 3 \n",
-    "#### Heading 4 \n",
-    "##### Heading 5 \n",
-    "###### Heading 6\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Headers aren't recognized by (Pandoc on Windows?) if there isn't a blank line above the headers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n# Heading 1 \\n## Heading 2 \\n### Heading 3 \\n#### Heading 4 \\n##### Heading 5 \\n###### Heading 6 ');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>\\section{Heading 1}\n",
-       "\n",
-       "\\subsection{Heading 2}\n",
-       "\n",
-       "\\subsubsection{Heading 3}\n",
-       "\n",
-       "\\paragraph{Heading 4}\n",
-       "\n",
-       "\\subparagraph{Heading 5}\n",
-       "\n",
-       "Heading 6</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><h1 id=\"heading-1\">Heading 1</h1>\n",
-       "<h2 id=\"heading-2\">Heading 2</h2>\n",
-       "<h3 id=\"heading-3\">Heading 3</h3>\n",
-       "<h4 id=\"heading-4\">Heading 4</h4>\n",
-       "<h5 id=\"heading-5\">Heading 5</h5>\n",
-       "<h6 id=\"heading-6\">Heading 6</h6></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "# Heading 1 \n",
-    "## Heading 2 \n",
-    "### Heading 3 \n",
-    "#### Heading 4 \n",
-    "##### Heading 5 \n",
-    "###### Heading 6 \"\"\")\n",
-    "\n",
-    "print(\"\\n\"*10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If internal links are defined, these will not work in nbviewer and latex as the local link is not existing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n[Link2Heading](http://127.0.0.1:8888/0a2d8086-ee24-4e5b-a32b-f66b525836cb#General-markdown)\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac210>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>\\href{http://127.0.0.1:8888/0a2d8086-ee24-4e5b-a32b-f66b525836cb\\#General-markdown}{Link2Heading}</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p><a href=\"http://127.0.0.1:8888/0a2d8086-ee24-4e5b-a32b-f66b525836cb#General-markdown\">Link2Heading</a></p></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac210>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac210>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "[Link2Heading](http://127.0.0.1:8888/0a2d8086-ee24-4e5b-a32b-f66b525836cb#General-markdown)\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Basic Markdown bold and italic works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\nThis is Markdown **bold** and *italic* text.\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>This is Markdown \\textbf{bold} and \\emph{italic} text.</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p>This is Markdown <strong>bold</strong> and <em>italic</em> text.</p></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "This is Markdown **bold** and *italic* text.\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Nested lists work as well"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n- li 1\\n- li 2\\n    1. li 3\\n    1. li 4\\n- li 5\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac150>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>\\begin{itemize}\n",
-       "\\itemsep1pt\\parskip0pt\\parsep0pt\n",
-       "\\item\n",
-       "  li 1\n",
-       "\\item\n",
-       "  li 2\n",
-       "\n",
-       "  \\begin{enumerate}\n",
-       "  \\def\\labelenumi{\\arabic{enumi}.}\n",
-       "  \\itemsep1pt\\parskip0pt\\parsep0pt\n",
-       "  \\item\n",
-       "    li 3\n",
-       "  \\item\n",
-       "    li 4\n",
-       "  \\end{enumerate}\n",
-       "\\item\n",
-       "  li 5\n",
-       "\\end{itemize}</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><ul>\n",
-       "<li>li 1</li>\n",
-       "<li>li 2\n",
-       "<ol style=\"list-style-type: decimal\">\n",
-       "<li>li 3</li>\n",
-       "<li>li 4</li>\n",
-       "</ol></li>\n",
-       "<li>li 5</li>\n",
-       "</ul></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac150>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac150>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "- li 1\n",
-    "- li 2\n",
-    "    1. li 3\n",
-    "    1. li 4\n",
-    "- li 5\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Unicode support"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\nüberschuß +***^°³³ α β θ\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x22b6950>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>überschuß +\\emph{*}\\^{}°³³ α β θ</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p>überschuß +<em>*</em>^°³³ α β θ</p></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac3d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac3d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(ur\"\"\"\n",
-    "überschuß +***^°³³ α β θ\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pandoc may produce invalid latex, e.g \\sout is not allowed in headings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n\\n# Heading 1 ~~strikeout~~\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac590>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>\\section{Heading 1 \\sout{strikeout}}</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><h1 id=\"heading-1-strikeout\">Heading 1 <del>strikeout</del></h1></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac590>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac590>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "\n",
-    "# Heading 1 ~~strikeout~~\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Horizontal lines work just fine"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\nabove\\n\\n--------\\n\\nbelow\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac150>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>above\n",
-       "\n",
-       "\\begin{center}\\rule{3in}{0.4pt}\\end{center}\n",
-       "\n",
-       "below</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p>above</p>\n",
-       "<hr />\n",
-       "<p>below</p></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "above\n",
-    "\n",
-    "--------\n",
-    "\n",
-    "below\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extended markdown of pandoc"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "(maybe we should deactivate this) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\nThis is Markdown ~subscript~ and ^superscript^ text.\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac150>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>This is Markdown \\textsubscript{subscript} and\n",
-       "\\textsuperscript{superscript} text.</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p>This is Markdown <sub>subscript</sub> and <sup>superscript</sup> text.</p></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac150>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac150>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "This is Markdown ~subscript~ and ^superscript^ text.\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "No space before underline behaves inconsistent (Pandoc extension: intraword_underscores - deactivate?)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\nThis is Markdown not_italic_.\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac5d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>This is Markdown not\\_italic\\_.</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p>This is Markdown not_italic_.</p></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac5d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac5d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "This is Markdown not_italic_.\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pandoc allows to define tex macros which are respected for all output formats, the notebook not. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n\\\\newcommand{\\\\tuple}[1]{\\\\langle #1 \\\\rangle}\\n\\n$\\\\tuple{a, b, c}$\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>\\newcommand{\\tuple}[1]{\\langle #1 \\rangle}\n",
-       "\n",
-       "$\\tuple{a, b, c}$</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p><span class=\"math\">\\(\\langle a, b, c \\rangle\\)</span></p></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "\\newcommand{\\tuple}[1]{\\langle #1 \\rangle}\n",
-    "\n",
-    "$\\tuple{a, b, c}$\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When placing the \\newcommand inside a math environment it works within the notebook and nbviewer, but produces invalid latex (the newcommand is only valid in the same math environment)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n$\\\\newcommand{\\\\foo}[1]{...:: #1 ::...}$\\n$\\\\foo{bar}$\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac590>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>$\\newcommand{\\foo}[1]{...:: #1 ::...}$ $\\foo{bar}$</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p><span class=\"math\">\\(\\newcommand{\\foo}[1]{...:: #1 ::...}\\)</span> <span class=\"math\">\\(\\foo{bar}\\)</span></p></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac590>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac590>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "$\\newcommand{\\foo}[1]{...:: #1 ::...}$\n",
-    "$\\foo{bar}$\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## HTML or LaTeX injections"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Raw HTML gets dropped entirely when converting to $\\LaTeX$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\nThis is HTML <b>bold</b> and <i>italic</i> text.\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac5d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>This is HTML bold and italic text.</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p>This is HTML <b>bold</b> and <i>italic</i> text.</p></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac5d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac5d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "This is HTML <b>bold</b> and <i>italic</i> text.\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Same for something like center"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n<center>Center aligned</center>\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac210>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>Center aligned</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><center>\n",
-       "Center aligned\n",
-       "</center></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac210>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac210>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "<center>Center aligned</center>\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Raw $\\LaTeX$ gets droppen entirely when converted to HTML. (I don't know why the HTML output is cropped here???)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\nThis is \\\\LaTeX \\\\bf{bold} and \\\\emph{italic} text.\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac590>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>This is \\LaTeX \\bf{bold} and \\emph{italic} text.</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p>This is </p></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac590>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac590>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "This is \\LaTeX \\bf{bold} and \\emph{italic} text.\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A combination of raw $\\LaTeX$ and raw HTML"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n**foo** $\\\\left( \\\\sum_{k=1}^n a_k b_k \\\\right)^2 \\\\leq$ <b>b\\\\$ar</b> $$test$$ \\n\\\\cite{}\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac590>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>\\textbf{foo} $\\left( \\sum_{k=1}^n a_k b_k \\right)^2 \\leq$ b\\$ar \\[test\\]\n",
-       "\\cite{}</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p><strong>foo</strong> <span class=\"math\">\\(\\left( \\sum_{k=1}^n a_k b_k \\right)^2 \\leq\\)</span> <b>b$ar</b> <span class=\"math\">\\[test\\]</span> </p></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac590>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac590>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "**foo** $\\left( \\sum_{k=1}^n a_k b_k \\right)^2 \\leq$ <b>b\\$ar</b> $$test$$ \n",
-    "\\cite{}\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tables"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "HTML tables render in the notebook, but not in Pandoc."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n<table>\\n    <tr>\\n        <td>a</td>\\n        <td>b</td>\\n    </tr>\\n    <tr>\\n        <td>c</td>\\n        <td>d</td>\\n    </tr>\\n</table>\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac5d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>a\n",
-       "\n",
-       "b\n",
-       "\n",
-       "c\n",
-       "\n",
-       "d</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "a\n",
-       "</td>\n",
-       "        <td>\n",
-       "b\n",
-       "</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "c\n",
-       "</td>\n",
-       "        <td>\n",
-       "d\n",
-       "</td>\n",
-       "    </tr>\n",
-       "</table></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac5d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac5d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "<table>\n",
-    "    <tr>\n",
-    "        <td>a</td>\n",
-    "        <td>b</td>\n",
-    "    </tr>\n",
-    "    <tr>\n",
-    "        <td>c</td>\n",
-    "        <td>d</td>\n",
-    "    </tr>\n",
-    "</table>\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Instead, Pandoc supports simple ascii tables.  Unfortunately marked.js doesn't support this, and therefore it is not supported in the notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n+---+---+\\n| a | b |\\n+---+---+\\n| c | d |\\n+---+---+\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac210>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>\\begin{longtable}[c]{@{}ll@{}}\n",
-       "\\hline\\noalign{\\medskip}\n",
-       "\\begin{minipage}[t]{0.06\\columnwidth}\\raggedright\n",
-       "a\n",
-       "\\end{minipage} & \\begin{minipage}[t]{0.06\\columnwidth}\\raggedright\n",
-       "b\n",
-       "\\end{minipage}\n",
-       "\\\\\\noalign{\\medskip}\n",
-       "\\begin{minipage}[t]{0.06\\columnwidth}\\raggedright\n",
-       "c\n",
-       "\\end{minipage} & \\begin{minipage}[t]{0.06\\columnwidth}\\raggedright\n",
-       "d\n",
-       "\\end{minipage}\n",
-       "\\\\\\noalign{\\medskip}\n",
-       "\\hline\n",
-       "\\end{longtable}</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><table>\n",
-       "<col width=\"5%\" />\n",
-       "<col width=\"5%\" />\n",
-       "<tbody>\n",
-       "<tr class=\"odd\">\n",
-       "<td align=\"left\"><p>a</p></td>\n",
-       "<td align=\"left\"><p>b</p></td>\n",
-       "</tr>\n",
-       "<tr class=\"even\">\n",
-       "<td align=\"left\"><p>c</p></td>\n",
-       "<td align=\"left\"><p>d</p></td>\n",
-       "</tr>\n",
-       "</tbody>\n",
-       "</table></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac210>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac210>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "+---+---+\n",
-    "| a | b |\n",
-    "+---+---+\n",
-    "| c | d |\n",
-    "+---+---+\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An alternative to basic ascii tables is pipe tables.  Pipe tables can be recognized by Pandoc and are supported by marked, hence, this is the **best way to add tables**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n|Left |Center |Right|\\n|:----|:-----:|----:|\\n|Text1|Text2  |Text3|\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac150>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>\\begin{longtable}[c]{@{}lcr@{}}\n",
-       "\\hline\\noalign{\\medskip}\n",
-       "Left & Center & Right\n",
-       "\\\\\\noalign{\\medskip}\n",
-       "\\hline\\noalign{\\medskip}\n",
-       "Text1 & Text2 & Text3\n",
-       "\\\\\\noalign{\\medskip}\n",
-       "\\hline\n",
-       "\\end{longtable}</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><table>\n",
-       "<thead>\n",
-       "<tr class=\"header\">\n",
-       "<th align=\"left\">Left</th>\n",
-       "<th align=\"center\">Center</th>\n",
-       "<th align=\"right\">Right</th>\n",
-       "</tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr class=\"odd\">\n",
-       "<td align=\"left\">Text1</td>\n",
-       "<td align=\"center\">Text2</td>\n",
-       "<td align=\"right\">Text3</td>\n",
-       "</tr>\n",
-       "</tbody>\n",
-       "</table></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac150>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac150>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "|Left |Center |Right|\n",
-    "|:----|:-----:|----:|\n",
-    "|Text1|Text2  |Text3|\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pandoc recognizes cell alignment in simple tables.  Since marked.js doesn't recognize ascii tables, it can't render this table."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\nRight Aligned Center Aligned Left Aligned\\n------------- -------------- ------------\\n          Why      does      this\\n     actually      work?     Who\\n        knows       ...\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>\\begin{longtable}[c]{@{}lll@{}}\n",
-       "\\hline\\noalign{\\medskip}\n",
-       "Right Aligned & Center Aligned & Left Aligned\n",
-       "\\\\\\noalign{\\medskip}\n",
-       "\\hline\\noalign{\\medskip}\n",
-       "Why & does & this\n",
-       "\\\\\\noalign{\\medskip}\n",
-       "actually & work? & Who\n",
-       "\\\\\\noalign{\\medskip}\n",
-       "knows & \\ldots{} &\n",
-       "\\\\\\noalign{\\medskip}\n",
-       "\\hline\n",
-       "\\end{longtable}</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><table>\n",
-       "<thead>\n",
-       "<tr class=\"header\">\n",
-       "<th align=\"left\">Right Aligned</th>\n",
-       "<th align=\"left\">Center Aligned</th>\n",
-       "<th align=\"left\">Left Aligned</th>\n",
-       "</tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "<tr class=\"odd\">\n",
-       "<td align=\"left\">Why</td>\n",
-       "<td align=\"left\">does</td>\n",
-       "<td align=\"left\">this</td>\n",
-       "</tr>\n",
-       "<tr class=\"even\">\n",
-       "<td align=\"left\">actually</td>\n",
-       "<td align=\"left\">work?</td>\n",
-       "<td align=\"left\">Who</td>\n",
-       "</tr>\n",
-       "<tr class=\"odd\">\n",
-       "<td align=\"left\">knows</td>\n",
-       "<td align=\"left\">...</td>\n",
-       "<td align=\"left\"></td>\n",
-       "</tr>\n",
-       "</tbody>\n",
-       "</table></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "Right Aligned Center Aligned Left Aligned\n",
-    "------------- -------------- ------------\n",
-    "          Why      does      this\n",
-    "     actually      work?     Who\n",
-    "        knows       ...\n",
-    "\"\"\")\n",
-    "\n",
-    "print(\"\\n\"*5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Images"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Markdown images work on both. However, remote images are not allowed in $\\LaTeX$. Maybe add a preprocessor to download these.\n",
-    "The alternate text is displayed in nbviewer next to the image."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n![Alternate Text](https://ipython.org/_static/IPy_header.png)\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x22b6690>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>\\begin{figure}[htbp]\n",
-       "\\centering\n",
-       "\\includegraphics{https://ipython.org/_static/IPy_header.png}\n",
-       "\\caption{Alternate Text}\n",
-       "\\end{figure}</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><div class=\"figure\">\n",
-       "<img src=\"https://ipython.org/_static/IPy_header.png\" alt=\"Alternate Text\" /><p class=\"caption\">Alternate Text</p>\n",
-       "</div></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "![Alternate Text](https://ipython.org/_static/IPy_header.png)\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "HTML Images only work in the notebook."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n<img src=\"https://ipython.org/_static/IPy_header.png\">\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x22b65d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp></xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p><img src=\"https://ipython.org/_static/IPy_header.png\"></p></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "<img src=\"https://ipython.org/_static/IPy_header.png\">\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Math"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Simple inline and displaystyle maths work fine"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\nMy equation:\\n$$ 5/x=2y $$\\n\\nIt is inline $ 5/x=2y $ here.\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x22b6950>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>My equation: \\[ 5/x=2y \\]\n",
-       "\n",
-       "It is inline \\$ 5/x=2y \\$ here.</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p>My equation: <span class=\"math\">\\[ 5/x=2y \\]</span></p>\n",
-       "<p>It is inline $ 5/x=2y $ here.</p></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "My equation:\n",
-    "$$ 5/x=2y $$\n",
-    "\n",
-    "It is inline $ 5/x=2y $ here.\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If the first \\$ is on a new line, the equation is not captured by md2tex, if both \\$s are on a new line md2html fails (Note the raw latex is dropped) but the notebook renders it correctly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n$5 \\\\cdot x=2$\\n\\n$\\n5 \\\\cdot x=2$\\n\\n$\\n5 \\\\cdot x=2\\n$\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x22b66d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>$5 \\cdot x=2$\n",
-       "\n",
-       "\\$ 5 \\cdot x=2\\$\n",
-       "\n",
-       "\\$ 5 \\cdot x=2 \\$</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p><span class=\"math\">\\(5 \\cdot x=2\\)</span></p>\n",
-       "<p>$ 5 x=2$</p>\n",
-       "<p>$ 5 x=2 $</p></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "$5 \\cdot x=2$\n",
-    "\n",
-    "$\n",
-    "5 \\cdot x=2$\n",
-    "\n",
-    "$\n",
-    "5 \\cdot x=2\n",
-    "$\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "MathJax permits some $\\LaTeX$ math constructs without \\$s, of course these raw $\\LaTeX$ is stripped when converting to html.\n",
-    "Moreove, the & are escaped by the lxml parsing [#4251](https://github.com/ipython/ipython/issues/4251)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n\\\\begin{align}\\na & b\\\\\\\\\\nd & c\\n\\\\end{align}\\n\\n\\\\begin{eqnarray}\\na & b \\\\\\\\\\nc & d\\n\\\\end{eqnarray}\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x22b6690>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>\\begin{align}\n",
-       "a &amp; b\\\\\n",
-       "d &amp; c\n",
-       "\\end{align}\n",
-       "\n",
-       "\\begin{eqnarray}\n",
-       "a &amp; b \\\\\n",
-       "c &amp; d\n",
-       "\\end{eqnarray}</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "\\begin{align}\n",
-    "a & b\\\\\n",
-    "d & c\n",
-    "\\end{align}\n",
-    "\n",
-    "\\begin{eqnarray}\n",
-    "a & b \\\\\n",
-    "c & d\n",
-    "\\end{eqnarray}\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There is another lxml issue, [#4283](https://github.com/ipython/ipython/issues/4283)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\n1<2 is true, but 3>4 is false.\\n\\n$1<2$ is true, but $3>4$ is false.\\n\\n1<2 it is even worse if it is alone in a line.\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x22b6950>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>14 is false.\n",
-       "\n",
-       "$14$ is false.\n",
-       "\n",
-       "1</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p>1&lt;2 is true, but 3&gt;4 is false.</p>\n",
-       "<p><span class=\"math\">\\(1&lt;2\\)</span> is true, but <span class=\"math\">\\(3&gt;4\\)</span> is false.</p>\n",
-       "<p>1&lt;2 it is even worse if it is alone in a line.</p></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "1<2 is true, but 3>4 is false.\n",
-    "\n",
-    "$1<2$ is true, but $3>4$ is false.\n",
-    "\n",
-    "1<2 it is even worse if it is alone in a line.\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Listings, and Code blocks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\nsome source code\\n\\n```\\na = \"test\"\\nprint(a)\\n```\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x22b68d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>some source code\n",
-       "\n",
-       "\\begin{verbatim}\n",
-       "a = \"test\"\n",
-       "print(a)\n",
-       "\\end{verbatim}</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p>some source code</p>\n",
-       "<pre><code>a = &quot;test&quot;\n",
-       "print(a)</code></pre></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "some source code\n",
-    "\n",
-    "```\n",
-    "a = \"test\"\n",
-    "print(a)\n",
-    "```\n",
-    "\"\"\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Language specific syntax highlighting by Pandoc requires additional dependencies to render correctly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "var mdcell = new IPython.MarkdownCell();\n",
-       "mdcell.create_element();\n",
-       "mdcell.set_text('\\nsome source code\\n\\n```python\\na = \"test\"\\nprint(a)\\n```\\n');\n",
-       "mdcell.render();\n",
-       "$(element).append(mdcell.element)\n",
-       ".removeClass()\n",
-       ".css('left', '66%')\n",
-       ".css('position', 'absolute')\n",
-       ".css('width', '30%')\n",
-       "mdcell.element.prepend(\n",
-       "    $('<div />')\n",
-       "    .removeClass()\n",
-       "    .css('background', '#AAAAFF')\n",
-       "    .css('width', '100 %')\n",
-       "    .html('Notebook Output')\n",
-       "\n",
-       ");\n",
-       "container.show()\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x22b6850>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #AAFFAA; width: 100%;'>NBConvert Latex Output</div><pre class='prettyprint lang-tex' style='background: #EEFFEE; border: 1px solid #DDEEDD;'><xmp>some source code\n",
-       "\n",
-       "\\begin{Shaded}\n",
-       "\\begin{Highlighting}[]\n",
-       "\\NormalTok{a = }\\StringTok{\"test\"}\n",
-       "\\KeywordTok{print}\\NormalTok{(a)}\n",
-       "\\end{Highlighting}\n",
-       "\\end{Shaded}</xmp></pre></div><div style='display: inline-block; width: 2%;'></div><div style='display: inline-block; width: 30%; vertical-align: top;'><div style='background: #FFAAAA; width: 100%;'>NBViewer Output</div><div style='display: inline-block; width: 100%;'><p>some source code</p>\n",
-       "<pre class=\"sourceCode python\"><code class=\"sourceCode python\">a = <span class=\"st\">&quot;test&quot;</span>\n",
-       "<span class=\"kw\">print</span>(a)</code></pre></div></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "    $.getScript(\"https://google-code-prettify.googlecode.com/svn/loader/run_prettify.js\");\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript at 0x21ac450>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "compare_render(r\"\"\"\n",
-    "some source code\n",
-    "\n",
-    "```python\n",
-    "a = \"test\"\n",
-    "print(a)\n",
-    "```\n",
-    "\"\"\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/tools/tests/Test Output Callbacks.ipynb b/tools/tests/Test Output Callbacks.ipynb
deleted file mode 100644
index 94ec190..0000000
--- a/tools/tests/Test Output Callbacks.ipynb
+++ /dev/null
@@ -1,291 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Basic Output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from IPython.display import display"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "print('hi')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "display('hi')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.plot([1,3,2])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%%javascript\n",
-    "console.log(\"I ran!\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%%html\n",
-    "<b>bold</b>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%%latex\n",
-    "$$\n",
-    "a = 5\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# input_request"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "raw_input(\"prompt > \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# set_next_input"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%%writefile tst.py\n",
-    "def foo():\n",
-    "    pass\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "%load tst.py"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Pager in execute_reply"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "plt?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# object_info"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# press shift-tab after parentheses\n",
-    "int("
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# complete"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# press tab after f\n",
-    "f"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# clear_output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import sys, time\n",
-    "from IPython.display import clear_output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "for i in range(10):\n",
-    "    clear_output()\n",
-    "    time.sleep(0.25)\n",
-    "    print(i)\n",
-    "    sys.stdout.flush()\n",
-    "    time.sleep(0.25)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "for i in range(10):\n",
-    "    clear_output(wait=True)\n",
-    "    time.sleep(0.25)\n",
-    "    print(i)\n",
-    "    sys.stdout.flush()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/tools/tests/heartbeat/gilsleep.ipynb b/tools/tests/heartbeat/gilsleep.ipynb
deleted file mode 100644
index a3a2b0c..0000000
--- a/tools/tests/heartbeat/gilsleep.ipynb
+++ /dev/null
@@ -1,72 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Holding the GIL for too long could disrupt the heartbeat due to non-copying sends.\n",
-    "\n",
-    "The following cell repeatedly calls a function that holds the GIL for five seconds.\n",
-    "\n",
-    "The heartbeat will fail after a few iterations prior to fixing Issue [#1260](https://github.com/ipython/ipython/issues/1260)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "import time\n",
-    "\n",
-    "from cython import inline\n",
-    "\n",
-    "def gilsleep(t):\n",
-    "    \"\"\"gil-holding sleep with cython.inline\"\"\"\n",
-    "    code = '\\n'.join([\n",
-    "        'from posix cimport unistd',\n",
-    "        'unistd.sleep(t)',\n",
-    "    ])\n",
-    "    while True:\n",
-    "        inline(code, quiet=True, t=t)\n",
-    "        print(time.time())\n",
-    "        sys.stdout.flush() # this is important\n",
-    "\n",
-    "gilsleep(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/tools/tests/mpl-switch/Matplotlib Switching.ipynb b/tools/tests/mpl-switch/Matplotlib Switching.ipynb
deleted file mode 100644
index 252fe67..0000000
--- a/tools/tests/mpl-switch/Matplotlib Switching.ipynb
+++ /dev/null
@@ -1,654 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using matplotlib backend: MacOSX\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10ce05350>]"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Should pop up a GUI window\n",
-    "%matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.plot([1,2,3])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10cd5dfd0>]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
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-       "vz5HahVVO+sWSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIk\n",
-       "SZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIk\n",
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-      ],
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10cdeea50>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 379,
-       "width": 600
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Should make an inline figure\n",
-    "%matplotlib inline\n",
-    "plt.plot([1,2,3])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x10cd9cb10>]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# New GUI window--should *NOT* have the visual settings of inline\n",
-    "%matplotlib qt\n",
-    "plt.plot([1,2,3])"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.4.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}