More changes to example notebooks.

examples/notebooks/Progress Bars.ipynb

@@ -0,0 +1,146 b''
+{
+ "metadata": {
+  "name": "Progress Bars"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Two Examples of Progress Bars"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "A Javascript Progress Bar"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Here is a simple progress bar using HTML/Javascript:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import uuid\n",
+      "import time\n",
+      "from IPython.display import HTML, Javascript, display\n",
+      "\n",
+      "divid = str(uuid.uuid4())\n",
+      "\n",
+      "pb = HTML(\n",
+      "\"\"\"\n",
+      "<div style=\"border: 1px solid black; width:500px\">\n",
+      "  <div id=\"%s\" style=\"background-color:blue; width:0%%\">&nbsp;</div>\n",
+      "</div> \n",
+      "\"\"\" % divid)\n",
+      "display(pb)\n",
+      "for i in range(1,101):\n",
+      "    time.sleep(0.1)\n",
+      "    \n",
+      "    display(Javascript(\"$('div#%s').width('%i%%')\" % (divid, i)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The above simply makes a div that is a box, and a blue div inside it with a unique ID \n",
+      "(so that the javascript won't collide with other similar progress bars on the same page).  \n",
+      "\n",
+      "Then, at every progress point, we run a simple jQuery call to resize the blue box to\n",
+      "the appropriate fraction of the width of its containing box, and voil\u00e0 a nice\n",
+      "HTML/Javascript progress bar!"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "ProgressBar class"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "And finally, here is a progress bar *class* extracted from [PyMC](http://code.google.com/p/pymc/), which will work in regular Python as well as in the IPython Notebook"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": true,
+     "input": [
+      "import sys, time\n",
+      "\n",
+      "class ProgressBar:\n",
+      "    def __init__(self, iterations):\n",
+      "        self.iterations = iterations\n",
+      "        self.prog_bar = '[]'\n",
+      "        self.fill_char = '*'\n",
+      "        self.width = 50\n",
+      "        self.__update_amount(0)\n",
+      "\n",
+      "    def animate(self, iter):\n",
+      "        print '\\r', self,\n",
+      "        sys.stdout.flush()\n",
+      "        self.update_iteration(iter + 1)\n",
+      "\n",
+      "    def update_iteration(self, elapsed_iter):\n",
+      "        self.__update_amount((elapsed_iter / float(self.iterations)) * 100.0)\n",
+      "        self.prog_bar += '  %d of %s complete' % (elapsed_iter, self.iterations)\n",
+      "\n",
+      "    def __update_amount(self, new_amount):\n",
+      "        percent_done = int(round((new_amount / 100.0) * 100.0))\n",
+      "        all_full = self.width - 2\n",
+      "        num_hashes = int(round((percent_done / 100.0) * all_full))\n",
+      "        self.prog_bar = '[' + self.fill_char * num_hashes + ' ' * (all_full - num_hashes) + ']'\n",
+      "        pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))\n",
+      "        pct_string = '%d%%' % percent_done\n",
+      "        self.prog_bar = self.prog_bar[0:pct_place] + \\\n",
+      "            (pct_string + self.prog_bar[pct_place + len(pct_string):])\n",
+      "\n",
+      "    def __str__(self):\n",
+      "        return str(self.prog_bar)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "p = ProgressBar(1000)\n",
+      "for i in range(1001):\n",
+      "    time.sleep(0.002)\n",
+      "    p.animate(i)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
  No newline at end of file

examples/notebooks/Animations Using clear_output.ipynb

@@ -12,27 +12,31 b''
      "level": 1,
      "metadata": {},
      "source": [
-      "Simple animations, progress bars, and clearing output"
+      "Simple animations Using clear_output"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Sometimes you want to print progress in-place, but don't want\n",
-      "to keep growing the output area.  In terminals, there is the carriage-return\n",
-      "(`'\\r'`) for overwriting a single line, but the notebook frontend does not support this\n",
-      "behavior (yet).\n",
+      "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 does not support this behavior.\n",
       "\n",
-      "What the notebook *does* support is explicit `clear_output`, and you can use this to replace previous\n",
-      "output (specifying stdout/stderr or the special IPython display outputs)."
+      "To clear output in the Notebook you can use the `clear_output` function."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Simple example"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "A simple example printing our progress iterating through a list:"
+      "Here we show our progress iterating through a list:"
      ]
     },
     {
@@ -44,7 +48,8 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 1
     },
     {
      "cell_type": "code",
@@ -59,7 +64,24 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "9\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "AsyncResult.wait_interactive"
+     ]
     },
     {
      "cell_type": "markdown",
@@ -85,15 +107,38 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " 100/100 tasks finished after   30 s"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "done\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Matplotlib example"
+     ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "You can also use `clear_output()` to clear figures and plots.\n",
-      "\n",
-      "This time, we need to make sure we are using inline pylab (**requires matplotlib**)"
+      "You can also use `clear_output()` to clear figures and plots."
      ]
     },
     {
@@ -104,7 +149,18 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
+        "For more information, type 'help(pylab)'.\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
@@ -127,130 +183,16 b''
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "A Javascript Progress Bar"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "`clear_output()` is still something of a hack, and if you want to do a progress bar in the notebook\n",
-      "it is better to just use Javascript/HTML if you can.\n",
-      "\n",
-      "Here is a simple progress bar using HTML/Javascript:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import uuid\n",
-      "from IPython.display import HTML, Javascript, display\n",
-      "\n",
-      "divid = str(uuid.uuid4())\n",
-      "\n",
-      "pb = HTML(\n",
-      "\"\"\"\n",
-      "<div style=\"border: 1px solid black; width:500px\">\n",
-      "  <div id=\"%s\" style=\"background-color:blue; width:0%%\">&nbsp;</div>\n",
-      "</div> \n",
-      "\"\"\" % divid)\n",
-      "display(pb)\n",
-      "for i in range(1,101):\n",
-      "    time.sleep(0.1)\n",
-      "    \n",
-      "    display(Javascript(\"$('div#%s').width('%i%%')\" % (divid, i)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The above simply makes a div that is a box, and a blue div inside it with a unique ID \n",
-      "(so that the javascript won't collide with other similar progress bars on the same page).  \n",
-      "\n",
-      "Then, at every progress point, we run a simple jQuery call to resize the blue box to\n",
-      "the appropriate fraction of the width of its containing box, and voil\u00e0 a nice\n",
-      "HTML/Javascript progress bar!"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "ProgressBar class"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And finally, here is a progress bar *class* extracted from [PyMC](http://code.google.com/p/pymc/), which will work in regular Python as well as in the IPython Notebook"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "import sys, time\n",
-      "\n",
-      "class ProgressBar:\n",
-      "    def __init__(self, iterations):\n",
-      "        self.iterations = iterations\n",
-      "        self.prog_bar = '[]'\n",
-      "        self.fill_char = '*'\n",
-      "        self.width = 50\n",
-      "        self.__update_amount(0)\n",
-      "\n",
-      "    def animate(self, iter):\n",
-      "        print '\\r', self,\n",
-      "        sys.stdout.flush()\n",
-      "        self.update_iteration(iter + 1)\n",
-      "\n",
-      "    def update_iteration(self, elapsed_iter):\n",
-      "        self.__update_amount((elapsed_iter / float(self.iterations)) * 100.0)\n",
-      "        self.prog_bar += '  %d of %s complete' % (elapsed_iter, self.iterations)\n",
-      "\n",
-      "    def __update_amount(self, new_amount):\n",
-      "        percent_done = int(round((new_amount / 100.0) * 100.0))\n",
-      "        all_full = self.width - 2\n",
-      "        num_hashes = int(round((percent_done / 100.0) * all_full))\n",
-      "        self.prog_bar = '[' + self.fill_char * num_hashes + ' ' * (all_full - num_hashes) + ']'\n",
-      "        pct_place = (len(self.prog_bar) // 2) - len(str(percent_done))\n",
-      "        pct_string = '%d%%' % percent_done\n",
-      "        self.prog_bar = self.prog_bar[0:pct_place] + \\\n",
-      "            (pct_string + self.prog_bar[pct_place + len(pct_string):])\n",
-      "\n",
-      "    def __str__(self):\n",
-      "        return str(self.prog_bar)"
+     "outputs": [
+      {
+       "output_type": "display_data",
+       "png": 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Ij8pHVYeqqNGqBmxb26Jm55qw62oH2za2sKgibTiXF6O7FixZsgQrV64sHHWe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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1082fcbd0>"
+       ]
+      }
      ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "p = ProgressBar(1000)\n",
-      "for i in range(1001):\n",
-      "    time.sleep(0.002)\n",
-      "    p.animate(i)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
+     "prompt_number": 5
     }
    ],
    "metadata": {}

examples/notebooks/Custom Display Logic.ipynb

@@ -12,7 +12,7 b''
      "level": 1,
      "metadata": {},
      "source": [
-      "Using the IPython display protocol for your own objects"
+      "Defining Custom Display Logic for Your Own Objects"
      ]
     },
     {
@@ -22,12 +22,7 b''
       "IPython extends the idea of the ``__repr__`` method in Python to support multiple representations for a given\n",
       "object, which clients can use to display the object according to their capabilities.  An object can return multiple\n",
       "representations of itself by implementing special methods, and you can also define at runtime custom display \n",
-      "functions for existing objects whose methods you can't or won't modify.  In this notebook, we show how both approaches work.\n",
-      "\n",
-      "<br/>\n",
-      "**Note:** this notebook has had all output cells stripped out so we can include it in the IPython documentation with \n",
-      "a minimal file size.  You'll need to manually execute the cells to see the output (you can run all of them with the \n",
-      "\"Run All\" button, or execute each individually)."
+      "functions for existing objects whose methods you can't or won't modify.  In this notebook, we show how both approaches work."
      ]
     },
     {

examples/notebooks/Cython Magics.ipynb

@@ -12,7 +12,7 b''
      "level": 1,
      "metadata": {},
      "source": [
-      "Cython Magic Functions Extension"
+      "Cython Magic Functions"
      ]
     },
     {
@@ -229,6 +229,14 b''
      "prompt_number": 8
     },
     {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "External libraries"
+     ]
+    },
+    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [

examples/notebooks/Data Publication API.ipynb

@@ -19,7 +19,7 b''
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "IPython has an API that allows IPython Engines to publish data back to the Client.  This example shows how this API works."
+      "IPython has an API that allows IPython Engines to publish data back to the Client.  This Notebook shows how this API works."
      ]
     },
     {

examples/notebooks/Octave Magic.ipynb

@@ -12,7 +12,7 b''
      "level": 1,
      "metadata": {},
      "source": [
-      "octavemagic: Octave inside IPython"
+      "Using Octave Inside IPython"
      ]
     },
     {

examples/notebooks/Part 4 - Markdown Cells.ipynb

@@ -168,7 +168,7 b''
      "level": 2,
      "metadata": {},
      "source": [
-      "LaTeX Equations"
+      "LaTeX equations"
      ]
     },
     {
@@ -210,6 +210,59 b''
       "</tr>\n",
       "</table>"
      ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Local files"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "If you have local files in your Notebook directory, you can refer to these files in Markdown cells via relative URLs that are prefixed with `files/`:\n",
+      "\n",
+      "    files/[subdirectory/]<filename>\n",
+      "\n",
+      "For example, in the example Notebook folder, we have the Python logo:\n",
+      "\n",
+      "    <img src=\"files/python-logo.svg\" />\n",
+      "\n",
+      "<img src=\"/files/python-logo.svg\" />\n",
+      "\n",
+      "and a video with the HTML5 video tag:\n",
+      "\n",
+      "    <video controls src=\"files/animation.m4v\" />\n",
+      "\n",
+      "<video controls src=\"/files/animation.m4v\" />\n",
+      "\n",
+      "These do not embed the data into the notebook file, and require that the files exist when you are viewing the notebook."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Security of local files"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Note that this means that the IPython notebook server also acts as a generic file server\n",
+      "for files inside the same tree as your notebooks.  Access is not granted outside the\n",
+      "notebook folder so you have strict control over what files are visible, but for this\n",
+      "reason it is highly recommended that you do not run the notebook server with a notebook\n",
+      "directory at a high level in your filesystem (e.g. your home directory).\n",
+      "\n",
+      "When you run the notebook in a password-protected manner, local file access is restricted\n",
+      "to authenticated users unless read-only views are active."
+     ]
     }
    ],
    "metadata": {}

examples/notebooks/Part 5 - Rich Display System.ipynb

@@ -8,22 +8,127 b''
   {
    "cells": [
     {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "IPython's Rich Display System"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In Python, objects can declare their textual representation using the `__repr__` and `__str__` methods.  IPython expands on this idea and allows objects to declare other, richer representations including:\n",
+      "\n",
+      "* HTML\n",
+      "* JSON\n",
+      "* Images = PNG/JPEG\n",
+      "* SVG\n",
+      "* LaTeX\n",
+      "\n",
+      "A single object can declare some or all of these representations; all are handled by IPython's *display system*. This Notebook shows how you can use this display system to incorporate a broad range of content into your Notebooks."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Basic display imports"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The `display` function is a general purpose tool for displaying different representations of objects. Think of it as `print` for these rich representations."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from IPython.display import display"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 8
+    },
+    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "## Rich displays: include anyting a browser can show\n",
+      "A few points:\n",
       "\n",
-      "Note that we have an actual protocol for this, see the `display_protocol` notebook for further details.\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",
-      "### Images"
+      "If you want to display a particular representationa, there are specific functions for that:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from IPython.display import display_pretty, display_html, display_jpeg, display_png, display_json, display_latex, display_svg"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Images"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To work with images (JPEG, PNG) use the `Image` class."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from IPython.display import Image\n",
-      "Image(filename='../../source/_static/logo.png')"
+      "from IPython.display import Image"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "i = Image(filename='../../docs/source/_static/logo.png')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Returning an `Image` object from an expression will automatically display it:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "i"
      ],
      "language": "python",
      "metadata": {},
@@ -31,13 +136,39 b''
       {
        "output_type": "pyout",
        "png": 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L0amUWggcqrXO8gg2FKHG2CdW4Uem9XvBlUflu7RUaiByU3lPa92ZKN8cSav8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\nHMRRca/E5hVINNIVwI2B56z6/3ExLRI31pXNAAAAAElFTkSuQmCC\n",
-       "prompt_number": 1,
+       "prompt_number": 6,
        "text": [
-        "<IPython.core.display.Image at 0x10faeafd0>"
+        "<IPython.core.display.Image at 0x107ea26d0>"
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Or you can pass it to `display`:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "display(i)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "display_data",
+       "png": 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L0amUWggcqrXO8gg2FKHG2CdW4Uem9XvBlUflu7RUaiByU3lPa92ZKN8cSav8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\nHMRRca/E5hVINNIVwI2B56z6/3ExLRI31pXNAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<IPython.core.display.Image at 0x107ea26d0>"
+       ]
+      }
+     ],
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -159,19 +290,18 b''
      "prompt_number": 3
     },
     {
-     "cell_type": "markdown",
+     "cell_type": "heading",
+     "level": 3,
      "metadata": {},
      "source": [
-      "#### Embedded vs Non-embedded Images"
+      "Embedded vs Non-embedded Images"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "As of IPython 0.13, images are embedded by default for compatibility with QtConsole, and the ability to still be displayed offline.\n",
-      "\n",
-      "Let's look at the differences:"
+      "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."
      ]
     },
     {
@@ -196,8 +326,7 b''
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Today's image from a webcam at Berkeley, (at the time I created this notebook). This should also work in the Qtconsole.\n",
-      "Drawback is that the saved notebook will be larger, but the image will still be present offline."
+      "Here is the embedded version. Note that this image was pulled from the webcam when this code cell was originally run and stored in the Notebook. Unless we rerun this cell, this is not todays image."
      ]
     },
     {
@@ -224,8 +353,7 b''
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Today's image from same webcam at Berkeley, (refreshed every minutes, if you reload the notebook), visible only with an active internet connexion, that should be different from the previous one. This will not work on Qtconsole.\n",
-      "Notebook saved with this kind of image will be lighter and always reflect the current version of the source, but the image won't display offline."
+      "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 lighter and always reflect the current version of the source, but the image won't display offline."
      ]
     },
     {
@@ -254,14 +382,15 b''
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Of course, if you re-run the all notebook, the two images will be the same again."
+      "Of course, if you re-run this Notebook, the two images will be the same again."
      ]
     },
     {
-     "cell_type": "markdown",
+     "cell_type": "heading",
+     "level": 2,
      "metadata": {},
      "source": [
-      "### Video"
+      "Video"
      ]
     },
     {
@@ -557,6 +686,21 b''
      "prompt_number": 8
     },
     {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "HTML"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Python objects can declare HTML representations that will be displayed in the Notebook. If you have some HTML you want to display, simply use the `HTML` class."
+     ]
+    },
+    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
@@ -589,18 +733,19 b''
      "prompt_number": 9
     },
     {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "LaTeX"
+     ]
+    },
+    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "### Mathematics\n",
-      "\n",
       "And we also support the display of mathematical expressions typeset in LaTeX, which is rendered\n",
-      "in the browser thanks to the [MathJax library](http://mathjax.org).  \n",
-      "\n",
-      "Note that this is *different* from the above examples.  Above we were typing mathematical expressions\n",
-      "in Markdown cells (along with normal text) and letting the browser render them; now we are displaying\n",
-      "the output of a Python computation as a LaTeX expression wrapped by the `Math()` object so the browser\n",
-      "renders it.  The `Math` object will add the needed LaTeX delimiters (`$$`) if they are not provided:"
+      "in the browser thanks to the [MathJax library](http://mathjax.org)."
      ]
     },
     {

examples/notebooks/R Magics.ipynb

@@ -12,7 +12,14 b''
      "level": 1,
      "metadata": {},
      "source": [
-      "Rmagic Functions Extension"
+      "Using R Within the IPython Notebok"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Using the `rmagic` extension, users can run R code from within the IPython Notebook. This example Notebook demonstrates this capability. "
      ]
     },
     {

examples/notebooks/Script Magics.ipynb

@@ -37,6 +37,14 b''
      "prompt_number": 1
     },
     {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Basic usage"
+     ]
+    },
+    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [

examples/notebooks/Trapezoid Rule.ipynb

@@ -8,12 +8,17 b''
   {
    "cells": [
     {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Basic Numerical Integration: the Trapezoid Rule"
+     ]
+    },
+    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Basic numerical integration: the trapezoid rule\n",
-      "===============================================\n",
-      "\n",
       "A simple illustration of the trapezoid rule for definite integration:\n",
       "\n",
       "$$\n",
@@ -134,14 +139,6 b''
       }
      ],
      "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
     }
    ],
    "metadata": {}