More changes to example notebooks.

examples/notebooks/Animations Using clear_output.ipynb

@@ -1,259 +1,201 b''
 {
  "metadata": {
   "name": "Animations Using clear_output"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
  "worksheets": [
   {
    "cells": [
     {
      "cell_type": "heading",
      "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:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": true,
      "input": [
       "import sys\n",
       "import time"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 1
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "from IPython.display import clear_output\n",
       "for i in range(10):\n",
       "    time.sleep(0.25)\n",
       "    clear_output()\n",
       "    print i\n",
       "    sys.stdout.flush()"
      ],
      "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",
      "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](/#tab2)**"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "from IPython import parallel\n",
       "rc = parallel.Client()\n",
       "view = rc.load_balanced_view()\n",
       "\n",
       "amr = view.map_async(time.sleep, [0.5]*100)\n",
       "\n",
       "amr.wait_interactive()"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "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."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%pylab inline"
      ],
      "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",
      "collapsed": false,
      "input": [
       "from scipy.special import jn\n",
       "x = np.linspace(0,5)\n",
       "f, ax = plt.subplots()\n",
       "ax.set_title(\"Bessel functions\")\n",
       "\n",
       "for n in range(1,10):\n",
       "    time.sleep(1)\n",
       "    ax.plot(x, jn(x,n))\n",
       "    clear_output()\n",
       "    display(f)\n",
       "\n",
       "# close the figure at the end, so we don't get a duplicate\n",
       "# of the last plot\n",
       "plt.close()"
      ],
      "language": "python",
      "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": {}
   }
  ]
 }
  No newline at end of file

examples/notebooks/Custom Display Logic.ipynb

@@ -1,404 +1,399 b''
 {
  "metadata": {
   "name": "Custom Display Logic"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
  "worksheets": [
   {
    "cells": [
     {
      "cell_type": "heading",
      "level": 1,
      "metadata": {},
      "source": [
-      "Using the IPython display protocol for your own objects"
+      "Defining Custom Display Logic for Your Own Objects"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "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."
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Parts of this notebook need the inline matplotlib backend:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%pylab inline"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Custom-built classes with dedicated ``_repr_*_`` methods"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "In our first example, we illustrate how objects can expose directly to IPython special representations of\n",
       "themselves, by providing methods such as ``_repr_svg_``, ``_repr_png_``, ``_repr_latex_``, etc.  For a full\n",
       "list of the special ``_repr_*_`` methods supported, see the code in ``IPython.core.displaypub``.\n",
       "\n",
       "As an illustration, we build a class that holds data generated by sampling a Gaussian distribution with given mean \n",
       "and variance.  The class can display itself in a variety of ways: as a LaTeX expression or as an image in PNG or SVG \n",
       "format.  Each frontend can then decide which representation it can handle.\n",
       "Further, we illustrate how to expose directly to the user the ability to directly access the various alternate \n",
       "representations (since by default displaying the object itself will only show one, and which is shown will depend on the \n",
       "required representations that even cache necessary data in cases where it may be expensive to compute.\n",
       "\n",
       "The next cell defines the Gaussian class:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "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, 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",
       "        self._svg_data = None\n",
       "        \n",
       "    def _figure_data(self, format):\n",
       "        fig, ax = plt.subplots()\n",
       "        ax.plot(self.data, 'o')\n",
       "        ax.set_title(self._repr_latex_())\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",
       "    # Here we define the special repr methods that provide the IPython display protocol\n",
       "    # Note that for the two figures, we cache the figure data once computed.\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",
       "\n",
       "    def _repr_svg_(self):\n",
       "        if self._svg_data is None:\n",
       "            self._svg_data = self._figure_data('svg')\n",
       "        return self._svg_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)\n",
       "    \n",
       "    # We expose as properties some of the above reprs, so that the user can see them\n",
       "    # directly (since otherwise the client dictates which one it shows by default)\n",
       "    @property\n",
       "    def png(self):\n",
       "        return Image(self._repr_png_(), embed=True)\n",
       "    \n",
       "    @property\n",
       "    def svg(self):\n",
       "        return SVG(self._repr_svg_())\n",
       "        \n",
       "    @property\n",
       "    def latex(self):\n",
       "        return Math(self._repr_svg_())\n",
       "    \n",
       "    # An example of using a property to display rich information, in this case\n",
       "    # the histogram of the distribution.  We've hardcoded the format to be png\n",
       "    # in this case, but in production code it would be trivial to make it an option\n",
       "    @property\n",
       "    def hist(self):\n",
       "        fig, ax = plt.subplots()\n",
       "        ax.hist(self.data, bins=100)\n",
       "        ax.set_title(self._repr_latex_())\n",
       "        data = print_figure(fig, 'png')\n",
       "        plt.close(fig)\n",
       "        return Image(data, embed=True)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Now, we create an instance of the Gaussian distribution, whose default representation will be its LaTeX form:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "x = Gaussian()\n",
       "x"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "We can view the data in png or svg formats:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "x.png"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "x.svg"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Since IPython only displays by default as an ``Out[]`` cell the result of the last computation, we can use the\n",
       "``display()`` function to show  more than one representation in a single cell:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "display(x.png)\n",
       "display(x.svg)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Now let's create a new Gaussian with different parameters"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "x2 = Gaussian(0.5, 0.2, 2000)\n",
       "x2"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "We can easily compare them by displaying their histograms"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "display(x.hist)\n",
       "display(x2.hist)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "## Adding IPython display support to existing objects\n",
       "\n",
       "When you are directly writing your own classes, you can adapt them for display in IPython by \n",
       "following the above example.  But in practice, we often need to work with existing code we\n",
       "can't modify.  \n",
       "\n",
       "We now illustrate how to add these kinds of extended display capabilities to existing objects.\n",
       "We will use the numpy polynomials and change their default representation to be a formatted\n",
       "LaTeX expression.\n",
       "\n",
       "First, consider how a numpy polynomial object renders by default:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "p = np.polynomial.Polynomial([1,2,3], [-10, 10])\n",
       "p"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Next, we define a function that pretty-prints a polynomial as a LaTeX string:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "def poly2latex(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', domain: $[%.2g,\\ %.2g]$' % tuple(p.domain)\n",
       "    return px+dom"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "This produces, on our polynomial ``p``, the following:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "poly2latex(p)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "from IPython.display import Latex\n",
       "Latex(poly2latex(p))"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "But we can configure IPython to do this automatically for us as follows.  We hook into the\n",
       "IPython display system and instruct it to use ``poly2latex`` for the latex mimetype, when\n",
       "encountering objects of the ``Polynomial`` type defined in the\n",
       "``numpy.polynomial.polynomial`` module:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "ip = get_ipython()\n",
       "latex_formatter = ip.display_formatter.formatters['text/latex']\n",
       "latex_formatter.for_type_by_name('numpy.polynomial.polynomial',\n",
       "                                 'Polynomial', poly2latex)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "For more examples on how to use the above system, and how to bundle similar print functions\n",
       "into a convenient IPython extension, see the ``IPython/extensions/sympyprinting.py`` file.  \n",
       "The machinery that defines the display system is in the ``display.py`` and ``displaypub.py`` \n",
       "files in ``IPython/core``.\n",
       "\n",
       "Once our special printer has been loaded, all polynomials will be represented by their \n",
       "mathematical form instead:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "p"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "p2 = np.polynomial.Polynomial([-20, 71, -15, 1])\n",
       "p2"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     }
    ],
    "metadata": {}
   }
  ]
 }
  No newline at end of file

examples/notebooks/Cython Magics.ipynb

@@ -1,270 +1,278 b''
 {
  "metadata": {
   "name": "Cython Magics"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
  "worksheets": [
   {
    "cells": [
     {
      "cell_type": "heading",
      "level": 1,
      "metadata": {},
      "source": [
-      "Cython Magic Functions Extension"
+      "Cython Magic Functions"
      ]
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Loading the extension"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "IPtyhon has a `cythonmagic` extension that contains a number of magic functions for working with Cython code. This extension can be loaded using the `%load_ext` magic as follows:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%load_ext cythonmagic"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 1
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "The %cython_inline magic"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "The `%%cython_inline` magic uses `Cython.inline` to compile a Cython expression. This allows you to enter and run a function body with Cython code. Use a bare `return` statement to return values. "
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "a = 10\n",
       "b = 20"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 2
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%%cython_inline\n",
       "return a+b"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 3,
        "text": [
         "30"
        ]
       }
      ],
      "prompt_number": 3
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "The %cython_pyximport magic"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "The `%%cython_pyximport` magic allows you to enter arbitrary Cython code into a cell. That Cython code is written as a `.pyx` file in the current working directory and then imported using `pyximport`.  You have the specify the name of the module that the Code will appear in. All symbols from the module are imported automatically by the magic function."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%%cython_pyximport foo\n",
       "def f(x):\n",
       "    return 4.0*x"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 4
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "f(10)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 5,
        "text": [
         "40.0"
        ]
       }
      ],
      "prompt_number": 5
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "The %cython magic"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Probably the most important magic is the `%cython` magic.  This is similar to the `%%cython_pyximport` magic, but doesn't require you to specify a module name. Instead, the `%%cython` magic uses manages everything using temporary files in the `~/.cython/magic` directory.  All of the symbols in the Cython module are imported automatically by the magic.\n",
       "\n",
       "Here is a simple example of a Black-Scholes options pricing algorithm written in Cython. Please note that this example might not compile on non-POSIX systems (e.g., Windows) because of a missing `erf` symbol."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%%cython\n",
       "cimport cython\n",
       "from libc.math cimport exp, sqrt, pow, log, erf\n",
       "\n",
       "@cython.cdivision(True)\n",
       "cdef double std_norm_cdf(double x) nogil:\n",
       "    return 0.5*(1+erf(x/sqrt(2.0)))\n",
       "\n",
       "@cython.cdivision(True)\n",
       "def black_scholes(double s, double k, double t, double v,\n",
       "                 double rf, double div, double cp):\n",
       "    \"\"\"Price an option using the Black-Scholes model.\n",
       "    \n",
       "    s : initial stock price\n",
       "    k : strike price\n",
       "    t : expiration time\n",
       "    v : volatility\n",
       "    rf : risk-free rate\n",
       "    div : dividend\n",
       "    cp : +1/-1 for call/put\n",
       "    \"\"\"\n",
       "    cdef double d1, d2, optprice\n",
       "    with nogil:\n",
       "        d1 = (log(s/k)+(rf-div+0.5*pow(v,2))*t)/(v*sqrt(t))\n",
       "        d2 = d1 - v*sqrt(t)\n",
       "        optprice = cp*s*exp(-div*t)*std_norm_cdf(cp*d1) - \\\n",
       "            cp*k*exp(-rf*t)*std_norm_cdf(cp*d2)\n",
       "    return optprice"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 6
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "black_scholes(100.0, 100.0, 1.0, 0.3, 0.03, 0.0, -1)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 7,
        "text": [
         "10.327861752731728"
        ]
       }
      ],
      "prompt_number": 7
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%timeit black_scholes(100.0, 100.0, 1.0, 0.3, 0.03, 0.0, -1)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "1000000 loops, best of 3: 821 ns per loop\n"
        ]
       }
      ],
      "prompt_number": 8
     },
     {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "External libraries"
+     ]
+    },
+    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Cython allows you to specify additional libraries to be linked with your extension, you can do so with the `-l` flag (also spelled `--lib`).  Note that this flag can be passed more than once to specify multiple libraries, such as `-lm -llib2 --lib lib3`.  Here's a simple example of how to access the system math library:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%%cython -lm\n",
       "from libc.math cimport sin\n",
       "print 'sin(1)=', sin(1)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "sin(1)= 0.841470984808\n"
        ]
       }
      ],
      "prompt_number": 9
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "You can similarly use the `-I/--include` flag to add include directories to the search path, and `-c/--compile-args` to add extra flags that are passed to Cython via the `extra_compile_args` of the distutils `Extension` class.  Please see [the Cython docs on C library usage](http://docs.cython.org/src/tutorial/clibraries.html) for more details on the use of these flags."
      ]
     }
    ],
    "metadata": {}
   }
  ]
 }
  No newline at end of file

examples/notebooks/Data Publication API.ipynb

@@ -1,252 +1,252 b''
 {
  "metadata": {
   "name": "Data Publication API"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
  "worksheets": [
   {
    "cells": [
     {
      "cell_type": "heading",
      "level": 1,
      "metadata": {},
      "source": [
       "IPython's Data Publication API"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "IPython has an API that allows IPython Engines to publish data back to the Client.  This 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."
      ]
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Setup"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "We begin by enabling pylab mode and creating a `Client` object to work with an IPython cluster."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%pylab inline"
      ],
      "language": "python",
      "metadata": {},
      "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": 48
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "from IPython.parallel import Client"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 12
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "c = Client()\n",
       "dv = c[:]\n",
       "dv.block = False"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 13
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Simple publication"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Here is a simple Python function we are going to run on the Engines.  This function uses `publish_data` to publish a simple Python dictionary when it is run."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "def publish_it():\n",
       "    from IPython.zmq.datapub import publish_data\n",
       "    publish_data(dict(a='hi'))"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 14
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "We run the function on the Engines using `apply_async` and save the returned `AsyncResult` object:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "ar = dv.apply_async(publish_it)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 15
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "The published data from each engine is then available under the `.data` attribute of the `AsyncResult` object."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "ar.data"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 16,
        "text": [
         "[{'a': 'hi'}, {'a': 'hi'}, {'a': 'hi'}, {'a': 'hi'}]"
        ]
       }
      ],
      "prompt_number": 16
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Each time `publish_data` is called, the `.data` attribute is updated with the most recently published data."
      ]
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Simulation loop"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "In many cases, the Engines will be running a simulation loop and we will want to publish data at each time step of the simulation.  To show how this works, we create a mock simulation function that iterates over a loop and publishes a NumPy array and loop variable at each time step. By inserting a call to `time.sleep(1)`, we ensure that new data will be published every second."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "def simulation_loop():\n",
       "    from IPython.zmq.datapub import publish_data\n",
       "    import time\n",
       "    import numpy as np\n",
       "    for i in range(10):\n",
       "        publish_data(dict(a=np.random.rand(20), i=i))\n",
       "        time.sleep(1)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 57
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Again, we run the `simulation_loop` function in parallel using `apply_async` and save the returned `AsyncResult` object."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "ar = dv.apply_async(simulation_loop)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 58
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "New data will be published by the Engines every second.  Anytime we access `ar.data`, we will get the most recently published data."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "data = ar.data\n",
       "for i, d in enumerate(data):\n",
       "    plot(d['a'], label='engine: '+str(i))\n",
       "title('Data published at time step: ' + str(data[0]['i']))\n",
       "legend()"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 61,
        "text": [
         "<matplotlib.legend.Legend at 0x10a8ed8d0>"
        ]
       },
       {
        "output_type": "display_data",
        "png": 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E3ib3eDzDxbMVFWRrVRUhhJBFFy6QvY7eb29TU0NIVJTHpxlCCRg2uHZ2Nhw5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Ul1yOcQURQ2enqi/8Xvw5iIQqnBZkOTrBktI4+gbkwGxUw1xBXx7XxhYIJ651gn7my6M+6vD8xJ\nMHkyWcyLpAyNBY04qCdX/kyxNNjvn6ZptBha8PTVT2PEbse+yUm8FMIfJpGIKMr7HuPc1hYGdsIL\nAA899JCw1w91J6OV79u3D729vXjvvfew1q9AeeHCBdxwww146aWXUFkZ2dDEEwMDuEunI1mgRgP8\n7GdktNN/fPnCBXIbs3E8Dtx0E1ly7ldKiAlh9XRgzpI0jkEdCF0s5SO/ANy6upOmMWS3QxvHYJol\nkWDG5cLS/HXotbXhmPEY3CM1EGUlLlNvH2tPmKYOzPnAxKNIysC24Q2nqw9MDQAACtML8crICK7O\nzAzdUJFgEinBhL02ePzxx7Ft2zZcfvnl2L59O7Kzs7Fz507s9Gz+/vGPf4zx8XHcfvvtWLlyJdaE\njUq+dFut+HhqCv/Kzt6+8hWyXt3fnnf/fuL3EsfuAKmU9K3Hw+iLV6bO2NPGoUedTShdncuhkQsu\nXd1otyNbKoU0ToNHAOlD1srlyCqRQW1bgrc73sZMXw3sqYkJ6jVZNfh44GPQNI0sZXw6mvxhbJKP\nz8zELVNfkrsEXeNdsDgsYTtgGD2doij80WiMem96tElkUA/7Vbdx40acPev7Yd62bZv3/z/77LN4\nNsKF0gDwG70etxUUQMUWkplJ0y1bgOuvn2vdi/HQUTBuu42oQp2dZAlwLNDrieVuSbipZKmUrLWL\nd6aeXYczo9xujXwz9TqVCla3Gz1WK8o82WD/7GxcpRcGrUwGuc4G1b5mjBYfwtDpGlDynoRl6i36\nFjRqG+PezsiQLpGgQaXCBxMTeDLCK26heIulxuMoUxTjiNkc9FgmqLdbLLhgs+HyKNoCxIIFnanH\nkmmnE/9jNGI715aTFSvmiqYMCQrqKSnAHXfE1uiLkV54faZ37iSTuHEkmPxid9kxaZtETkp421OK\nogKy9f44F0kZdHI5kGWHo2sjMhWZmBrKwhidmEy9LKMMIkqUkM4XNs2eeli095KGgrHhDTeAxLQz\nvjg0hK1RtgWIBZ/aoP680YjNGRkoDtbO9vDDwF//StpCpqeBjg5g5cr4nqSHO+8EXnkFGBoKf2wk\n8JJeGK67Dohz8AkmvxjNRuSm5PIep/bX1Qfi3M7IoJXLYU+3Ybz1Mvx09YsoLqUx7HAgN5h/fwyR\niWUoyyhLmJ7OsCkjA+VKZdw8eIC5DphQmjpN02g1tGJVQSOxBVigXS9sPpVB3U3TeFKvD+3GyLbn\nPXiQBPQE9aXm5gJbtwJPPBGb5xcU1BOANk2LWecsxq3jPrfzlV4YNnsKckxPcrw7Xxi0MhlGYUeK\nQgb5hc+gsM6JdLE4rgGNTX1OPWqzasMfGEOuzMjAO3HYPsWGydRLFAoM2GxwcfSqd090I02Whi6X\nAiliMVakpsb1HCPhUxnU/zE+jjSxGJeEmwz9yldIs/g99yREemHzwAPE6Ku3N7rP63aTi5GFHNQp\nikJtdm2ABMO3SMpQplBAJhKh3ZOVxbtHnUHn8bwuLgb27gVyqhMjvTA8f93zuKnhpoS9PkBa8eLV\no86wJHcJzk+ch9s1i2yplNOHnHFmjKUtQLT5VAb1JwYGcFdhYfg/EFM0PXcu4UG9pATYsQO4++7o\nPm9XF7EWz82N7vNGGy4JJtjGo2B4dXWPBJPITN1gs6GoCNi3D0gvS2xQz1BmQCJauC16sUImlqEu\nuw7HjMeC6uqtg61Ynt+E10ZGAmYcFir5MhlGHQ44OLZKxZqEBPWzMzM4PjODm/lGsZUryVjnlvhP\n2/lz333A2bPA229H7zkXuvTCwBXUhWbqgEdX9xRL423mxaCTy2Gw21FcDPT0AAptYgaPksz1qwdr\na2zRt8CZ2YQVqakL0haACwlFIVcmw2C8HQGRoKD+a70e2woKIBeiX155JZCAgpo/cjnw5JMkWxe4\nLzcoiyaoc3TACNXUATLossdkgt3txojDEWAbEA8KZDLobTYUFhENV5KT2Ez900xjQSNaB7mLpS63\nC0eNR9HizFjwven+JEqCiXtQn3A48PLwMG7namNcJGzZQi4efv7z6Dwfr0nSBUB9Tj3OjPj2qvPx\nffFHJ5cjWyrFP8bHkSuTJaQ9LU0igYSikF1C9uM605NBPVEwCzO4MvVzo+eQo67EgekZ3JAdP5/5\naPCpCerPGY24OisLBYvkMioYjz0G/OY3QHf3/J7H4QBOnCBWvwudMk0ZhmeGMWOfWwMXzks9GJs1\nGrxgNCb0clonl0OusyE9HTBRyaCeKJhiaYEEAUG9xdCCzJIbcXVW1oK2BeDiUxHUnTSN3+j1uDvO\nI+6xoKiILNK46675WfOePg0UFwNpadE7t1ghFolRmVmJ9rF27218vNS52JyRgbfGxhJSJGXQymRI\nL7PhBz8AjI6kpp4oZGIZGnIbMDPVHRDUWw2tGEldsSh60/25TKOJ6yAXQ1yD+pujo9DKZGiK8YKL\neLFjB3D+PPDWW5E/R0vL4pBeGNi6usvtwvDMcERBvVmjgYOmExvU5XKYJHbcfz8wZE9m6omksaAR\n/SNHMGK3++xL3TfSA7MoBVeEWkS/QPlcdjauS4BkFNeg/utww0aLDJmMSDB33022y0XC4cOLo0jK\nwO6AGbWMQi1XQyYWHgyzpVIsT3A3A9OrDhBjsWRQTxyrtatx1NiGQrkcfZ5s3e6y46yocFHYAiwk\n4hrUu6xWXL/Iih3huOwysi71Zz+L7PGLpfOFgR3UI2lnZPODkhJsSWAGppXJYLDb4XC7MeF0IjsB\nFgFJCF67AFav+omhk6DyrsDXtUUJPrvFRVyD+natNq4Wq/HiV78Cnn6auDgKwWIhdjZx9uaaF2z5\nJZJ2RjY35uSgIQab4PnCZOojDgeypVKIk9lgwmjIbUDPRA+KpGJvUH+l/wxUYjFWLgJbgIVEXCPs\nNxdxG2ModDrge98Dvv1tYUXTo0eBhoaE2dlERHVWNXpMPXC4HPPO1BMNM1VqtCeLpIlGJpZhSe4S\nSB3j3l71tyZnsVFhXxS2AAuJuAb1T/Ll7V13Af39wF/+wv8xi016AQCFRAFdmg7dE93zztQTjdYz\nVZoski4MGrWNmDX3oGd2FrNuN7rEWnxDF27BQBJ/PnlaSIKQSolFzY4dwMxM+OOBxRnUgTkJJpLB\no4VEgUyGIbudLKFOBvWE01jQiJGxE+iZncXrQwbQ0124onBFok9r0ZEM6lGkuRlYvx74yU/4Hb/Y\n2hkZ6nPqcXb07KKXX2QiETQSCU6azcmgvgBYrV2N84P70TM7i9/1n0fR7FkoJIm3BllsJIN6lPnl\nL4FnniGmkqGYmACMRqA2sRbaEVGXXYczI2cWvfwCEAnmiNmMvE+wNLhYaMhpQP/YSVhcLrRYHNiU\nKg7/oCQBJIN6lCkoAH7wg/BF09ZW4h8jXoTvW6atcbFn6gCgk8lwNJmpLwikYimW5DQgV+xGvr0X\nl2gTs+VssZMM6jHgzjvJ2rtXXw1+zGLV0wGgNrsW7aPtgr3UFyJauRwzLlcyqC8QVmtXo9g9Alr/\nBlZrVyf6dBYlyaAeAyQSUjS9916yWpWLxeLMyIVaoYZaoYZUJEWKLHF95tFA5+knTQb1hUFjQSMy\n9S9hdPB9NOQ0JPp0FiXJoB4jLr0U2LyZ7M7mYjFn6gCRYBa79AKQXnUAyT71BUKjthFvd7yNZXnL\nIBUn6xyRkAzqMeTRR4E//AE442tBDoMBsNmA0tKEnFZUqMupW/TSC0DkFylFIWOR2bp+UmnIaYBE\nJElKL/MgGdRjSF4e8KMfAd/6lm/RlGllXMyDcktzl6JIvfg9OQrlcuTLZMmpxQWCVCzFivwVaNIu\n4svYBEPR9HzcwAW8EEUhTi+1oHA6iczy3e8CW7eS277/faK7P/RQYs9tPthddszYZ5ChzEj0qcwL\nN03juNmMlYvB0P5TQtd4FwrTC5M96h6Exs5kUI8DBw4AN91EFlanp5N1q3fdBVxzTaLPLEmSJAud\nZFBfoHz964BGQxwds7JIgF+Ey1ySJEkSZ4TGzmR1KE78/OfEkXH9erK6LhnQkyRJEguShdI4kZND\nNPSvfnVxtzImSZJkYZMM6nHkm98EamqAdesSfSZJkiT5pJLU1OPM9DRZipGcdUmSJAkfkoXSJEmS\nJPkEITR2JuWXJEmSJPkEkQzqSZIkSfIJIhnUkyRJkuQTRNigvm/fPtTV1aGqqgpPPvkk5zHf+973\nUF5ejsbGRpwLt/InSVTYs2dPok/hE0Pydxldkr/PxBI2qN99993YuXMndu/ejaeeegqjo6M+9x8+\nfBgffvghWltbcd999+G+++6L2ckmmSP5wYkeyd9ldEn+PhNLyKA+OTkJANiwYQNKSkpw5ZVX4tCh\nQz7HHDp0CDfeeCMyMzOxdetWnD17NnZnmyRJkiRJQhIyqLe0tKCWtRm5vr4eBw8e9Dnm8OHDqK+v\n9/47JycH3d3dUT7NJEmSJEnCh3l7v9A0HdBDGcybOulZHV0eWszevQuM5O8yuiR/n4kjZFBvamrC\n/fff7/336dOncdVVV/kcs3btWpw5cwZbtmwBAIyMjKC8vDzguZKDR0mSJEkSe0LKL2q1GgDpgOnt\n7cV7772HtWvX+hyzdu1avPbaaxgbG8OuXbtQV1cXu7NNkiRJkiQhCSu/PP7449i2bRscDgfuuusu\nZGdnY+fOnQCAbdu2Yc2aNVi/fj1Wr16NzMxMvPjiizE/6SRJkiRJEgQ6xuzdu5eu/f/t3T9I61AU\nBvDvDtVFEEGKxaiDDlFbEpBoF1E6OrSCgy4daidx0c6CozgVddCl2XQSBB20W0Fc6hAcgoP/wEVB\nXAzooHCcX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       }
      ],
      "prompt_number": 61
     }
    ],
    "metadata": {}
   }
  ]
 }
  No newline at end of file