More changes to example notebooks for Python 3 compatibility

examples/notebooks/Part 1 - Running Code.ipynb

@@ -1,439 +1,439
 {
  "metadata": {
   "name": "Part 1 - Running Code"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
  "worksheets": [
   {
    "cells": [
     {
      "cell_type": "heading",
      "level": 1,
      "metadata": {},
      "source": [
       "Running Code in the IPython Notebook"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "First and foremost, the IPython Notebook is an interactive environment for writing and running Python code."
      ]
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Code cells allow you to enter and run Python code"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Run a code cell using `Shift-Enter` or pressing the \"Play\" button in the toolbar above:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "a = 10"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 10
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print a"
+      "print(a)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "10\n"
        ]
       }
      ],
      "prompt_number": 11
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Managing the IPython Kernel"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Code is run in a separate process called the IPython Kernel.  The Kernel can be interrupted or restarted.  Try running the following cell and then hit the \"Stop\" button in the toolbar above."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "import time\n",
       "time.sleep(10)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "ename": "KeyboardInterrupt",
        "evalue": "",
        "output_type": "pyerr",
        "traceback": [
         "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
         "\u001b[0;32m<ipython-input-16-d7b436e260d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
         "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
        ]
       }
      ],
      "prompt_number": 16
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "If the Kernel dies you will be prompted to restart it. Here we call the low-level system libc.time routine with the wrong argument via\n",
       "ctypes to segfault the Python interpreter:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "import sys\n",
       "from ctypes import CDLL\n",
       "# This will crash a Linux or Mac system; equivalent calls can be made on Windows\n",
-      "dll = 'dylib' if sys.platform == 'darwin' else '.so.6'\n",
+      "dll = 'dylib' if sys.platform == 'darwin' else 'so.6'\n",
       "libc = CDLL(\"libc.%s\" % dll) \n",
       "libc.time(-1)  # BOOM!!"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": "*"
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "All of the goodness of IPython works"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Here are two system aliases:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "pwd"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 4,
        "text": [
         "u'/Users/bgranger/Documents/Computation/IPython/code/ipython/examples/notebooks'"
        ]
       }
      ],
      "prompt_number": 4
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "ls"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "01_notebook_introduction.ipynb        Octave Magic.ipynb\r\n",
         "Animations Using clear_output.ipynb   PyLab and Matplotlib.ipynb\r\n",
         "Basic Output.ipynb                    R Magics.ipynb\r\n",
         "Custom Display Logic.ipynb            Running Code.ipynb\r\n",
         "Cython Magics.ipynb                   Script Magics.ipynb\r\n",
         "Data Publication API.ipynb            SymPy Examples.ipynb\r\n",
         "Display System.ipynb                  Trapezoid Rule.ipynb\r\n",
         "JS Progress Bar.ipynb                 Typesetting Math Using MathJax.ipynb\r\n",
         "Local Files.ipynb                     animation.m4v\r\n",
         "Markdown Cells.ipynb                  python-logo.svg\r\n",
         "Notebook Tour.ipynb\r\n"
        ]
       }
      ],
      "prompt_number": 2
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Any command line program can be run using `!` with string interpolation from Python variables:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "message = 'The IPython notebook is great!'\n",
       "# note: the echo command does not run on Windows, it's a unix command.\n",
       "!echo $message"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Tab completion works:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "import numpy\n",
       "numpy.random."
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 9
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Tab completion after `(` brings up a tooltip with the docstring:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "numpy.random.rand("
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Adding `?` opens the docstring in the pager below:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "magic?"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 8
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Exceptions are formatted nicely:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "x = 1\n",
       "y = 4\n",
       "z = y/(1-x)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "ename": "ZeroDivisionError",
        "evalue": "integer division or modulo by zero",
        "output_type": "pyerr",
        "traceback": [
         "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
         "\u001b[0;32m<ipython-input-15-dc39888fd1d2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
         "\u001b[0;31mZeroDivisionError\u001b[0m: integer division or modulo by zero"
        ]
       }
      ],
      "prompt_number": 15
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Working with external code"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "There are a number of ways of getting external code into code cells."
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Pasting code with `>>>` prompts works as expected:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       ">>> the_world_is_flat = 1\n",
       ">>> if the_world_is_flat:\n",
-      "...     print \"Be careful not to fall off!\""
+      "...     print(\"Be careful not to fall off!\")"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "Be careful not to fall off!\n"
        ]
       }
      ],
      "prompt_number": 1
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "The `%load` magic lets you load code from URLs or local files:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%load?"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 14
     },
     {
      "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": 2
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%load http://matplotlib.sourceforge.net/mpl_examples/pylab_examples/integral_demo.py"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 3
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "#!/usr/bin/env python\n",
       "\n",
       "# implement the example graphs/integral from pyx\n",
       "from pylab import *\n",
       "from matplotlib.patches import Polygon\n",
       "\n",
       "def func(x):\n",
       "    return (x-3)*(x-5)*(x-7)+85\n",
       "\n",
       "ax = subplot(111)\n",
       "\n",
       "a, b = 2, 9 # integral area\n",
       "x = arange(0, 10, 0.01)\n",
       "y = func(x)\n",
       "plot(x, y, linewidth=1)\n",
       "\n",
       "# make the shaded region\n",
       "ix = arange(a, b, 0.01)\n",
       "iy = func(ix)\n",
       "verts = [(a,0)] + zip(ix,iy) + [(b,0)]\n",
       "poly = Polygon(verts, facecolor='0.8', edgecolor='k')\n",
       "ax.add_patch(poly)\n",
       "\n",
       "text(0.5 * (a + b), 30,\n",
       "     r\"$\\int_a^b f(x)\\mathrm{d}x$\", horizontalalignment='center',\n",
       "     fontsize=20)\n",
       "\n",
       "axis([0,10, 0, 180])\n",
       "figtext(0.9, 0.05, 'x')\n",
       "figtext(0.1, 0.9, 'y')\n",
       "ax.set_xticks((a,b))\n",
       "ax.set_xticklabels(('a','b'))\n",
       "ax.set_yticks([])\n",
       "show()\n"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     }
    ],
    "metadata": {}
   }
  ]
 }
  No newline at end of file

examples/notebooks/Part 2 - Basic Output.ipynb

@@ -1,1156 +1,1159
 {
  "metadata": {
   "name": "Part 2 - Basic Output"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
  "worksheets": [
   {
    "cells": [
     {
      "cell_type": "heading",
      "level": 1,
      "metadata": {},
      "source": [
       "Basic Output"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "When a cell is run, it can generate *output*. In IPython, the definition of output is quite general; it can be text, images, LaTeX, HTML or JSON. All output is displayed below the code that generated it, in the *output area*.\n",
       "\n",
       "This Notebook describes the basics of output and shows how the `stdout/stderr` streams are handled."
      ]
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Displayhook"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "When a Python object is returned by an expression, Python's `displayhook` mechanism is triggered. In IPython, this results in an output prompt, such as `Out[2]`. These objects are then available under the variables:\n",
       "\n",
       "* `_` (last output)\n",
       "* `__` (second to last output)\n",
       "* `_N` (`Out[N]`)"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import numpy as np"
+      "import numpy as np\n",
+      "import sys"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 24
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "numpy.random.rand(10)"
+      "np.random.rand(10)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 27,
        "text": [
         "array([ 0.94311014,  0.03500265,  0.59873731,  0.63411734,  0.03642486,\n",
         "        0.22090049,  0.33132549,  0.24439398,  0.80084185,  0.25114916])"
        ]
       }
      ],
      "prompt_number": 27
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "np.sin(_)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 28,
        "text": [
         "array([ 0.80938852,  0.0349955 ,  0.56359988,  0.59246668,  0.0364168 ,\n",
         "        0.21910832,  0.32529671,  0.24196836,  0.71794236,  0.24851724])"
        ]
       }
      ],
      "prompt_number": 28
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "sys.stdout and sys.stderr"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "The stdout and stderr streams are displayed as text in the output area."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print \"hi, stdout\""
+      "print(\"hi, stdout\")"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "hi, stdout\n"
        ]
       }
      ],
      "prompt_number": 29
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print >> sys.stderr, 'hi, stderr'"
+      "from __future__ import print_function\n",
+      "print('hi, stderr', file=sys.stderr)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
         "hi, stderr\n"
        ]
       }
      ],
      "prompt_number": 8
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Output is asynchronous"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "All output is displayed asynchronously as it is generated in the Kernel. If you execute the next cell, you will see the output one piece at a time, not all at the end."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "import time, sys\n",
       "for i in range(8):\n",
-      "    print i,\n",
+      "    print(i)\n",
       "    time.sleep(0.5)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "0 "
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "1 "
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "2 "
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "3 "
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "4 "
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "5 "
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "6 "
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "7\n"
        ]
       }
      ],
      "prompt_number": 30
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Large outputs"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "To better handle large outputs, the output area can be collapsed. Run the following cell and then single- or double- click on the active area to the left of the output:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "for i in range(50):\n",
-      "    print i"
+      "    print(i)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "0\n",
         "1\n",
         "2\n",
         "3\n",
         "4\n",
         "5\n",
         "6\n",
         "7\n",
         "8\n",
         "9\n",
         "10\n",
         "11\n",
         "12\n",
         "13\n",
         "14\n",
         "15\n",
         "16\n",
         "17\n",
         "18\n",
         "19\n",
         "20\n",
         "21\n",
         "22\n",
         "23\n",
         "24\n",
         "25\n",
         "26\n",
         "27\n",
         "28\n",
         "29\n",
         "30\n",
         "31\n",
         "32\n",
         "33\n",
         "34\n",
         "35\n",
         "36\n",
         "37\n",
         "38\n",
         "39\n",
         "40\n",
         "41\n",
         "42\n",
         "43\n",
         "44\n",
         "45\n",
         "46\n",
         "47\n",
         "48\n",
         "49\n"
        ]
       }
      ],
      "prompt_number": 4
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Beyond a certain point, output will scroll automatically:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "for i in range(500):\n",
-      "    print 2**i - 1"
+      "    print(2**i - 1)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "0\n",
         "1\n",
         "3\n",
         "7\n",
         "15\n",
         "31\n",
         "63\n",
         "127\n",
         "255\n",
         "511\n",
         "1023\n",
         "2047\n",
         "4095\n",
         "8191\n",
         "16383\n",
         "32767\n",
         "65535\n",
         "131071\n",
         "262143\n",
         "524287\n",
         "1048575\n",
         "2097151\n",
         "4194303\n",
         "8388607\n",
         "16777215\n",
         "33554431\n",
         "67108863\n",
         "134217727\n",
         "268435455\n",
         "536870911\n",
         "1073741823\n",
         "2147483647\n",
         "4294967295\n",
         "8589934591\n",
         "17179869183\n",
         "34359738367\n",
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         "818347651974035467503297424206899788054160511510766197370822842024033449101168638720817523081476039287721671031890017752304314136471348263332131897343\n",
         "1636695303948070935006594848413799576108321023021532394741645684048066898202337277441635046162952078575443342063780035504608628272942696526664263794687\n"
        ]
       }
      ],
      "prompt_number": 6
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Capturing output with <tt>%%capture</tt>"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "IPython has a cell magic, `%%capture`, which captures the stdout/stderr of a cell. With this magic you can discard these streams or store them in a variable."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "import sys"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 9
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "By default, `%%capture` discards these streams.  This is a simple way to suppress unwanted output."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%%capture\n",
-      "print 'hi, stdout'\n",
-      "print >> sys.stderr, 'hi, stderr'"
+      "print('hi, stdout')\n",
+      "print('hi, stderr', file=sys.stderr)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 10
     },
     {
      "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",
      "collapsed": false,
      "input": [
       "%%capture captured\n",
-      "print 'hi, stdout'\n",
-      "print >> sys.stderr, 'hi, stderr'"
+      "print('hi, stdout')\n",
+      "print('hi, stderr', file=sys.stderr)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 11
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "captured"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 12,
        "text": [
         "<IPython.utils.io.CapturedIO at 0x107ea2590>"
        ]
       }
      ],
      "prompt_number": 12
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Calling the object writes the output to stdout/stderr as appropriate."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "captured()"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "hi, stdout\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
         "hi, stderr\n"
        ]
       }
      ],
      "prompt_number": 13
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "captured.stdout"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 14,
        "text": [
         "'hi, stdout\\n'"
        ]
       }
      ],
      "prompt_number": 14
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "captured.stderr"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 15,
        "text": [
         "'hi, stderr\\n'"
        ]
       }
      ],
      "prompt_number": 15
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "`%%capture` only captures stdout/stderr, not other output types, so you can still do plots and use IPython's display system inside `%%capture`"
      ]
     },
     {
      "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": 16
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%%capture wontshutup\n",
       "\n",
-      "print \"setting up X\"\n",
+      "print(\"setting up X\")\n",
       "x = np.linspace(0,5,1000)\n",
-      "print \"step 2: constructing y-data\"\n",
+      "print(\"step 2: constructing y-data\")\n",
       "y = np.sin(x)\n",
-      "print \"step 3: display info about y\"\n",
+      "print(\"step 3: display info about y\")\n",
       "plt.plot(x,y)\n",
-      "print \"okay, I'm done now\""
+      "print(\"okay, I'm done now\")"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "display_data",
        "png": 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8aAr0xo0wfboZ9f/3v1C2rDlmoVw5KFbMfK7bDUePmtM0zzY57rjDfDRoYFb8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\nRU5ErkJggg==\n",
        "text": [
         "<matplotlib.figure.Figure at 0x108356ed0>"
        ]
       }
      ],
      "prompt_number": 17
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "wontshutup()"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "setting up X\n",
         "step 2: constructing y-data\n",
         "step 3: display info about y\n",
         "okay, I'm done now\n"
        ]
       }
      ],
      "prompt_number": 18
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "And you can selectively disable capturing stdout or stderr by passing `--no-stdout/err`."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%%capture cap --no-stderr\n",
-      "print 'hi, stdout'\n",
-      "print >> sys.stderr, \"hello, stderr\""
+      "print('hi, stdout')\n",
+      "print(\"hello, stderr\", file=sys.stderr)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
         "hello, stderr"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
         "\n"
        ]
       }
      ],
      "prompt_number": 19
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "cap.stdout"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 20,
        "text": [
         "'hi, stdout\\n'"
        ]
       }
      ],
      "prompt_number": 20
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "cap.stderr"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 21,
        "text": [
         "''"
        ]
       }
      ],
      "prompt_number": 21
     }
    ],
    "metadata": {}
   }
  ]
 }
  No newline at end of file

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

@@ -1,1129 +1,1130
 {
  "metadata": {
   "name": "Part 5 - Rich Display System"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
  "worksheets": [
   {
    "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__` method.  IPython expands on this idea and allows objects to declare other, richer representations including:\n",
       "\n",
       "* HTML\n",
       "* JSON\n",
       "* PNG\n",
       "* JPEG\n",
       "* SVG\n",
       "* LaTeX\n",
       "\n",
       "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": [
       "A few points:\n",
       "\n",
       "* Calling `display` on an object will send **all** possible representations to the Notebook.\n",
       "* These representations are stored in the Notebook document.\n",
       "* In general the Notebook will use the richest available representation.\n",
       "\n",
       "If you want to display a particular representation, there are specific functions for that:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "from IPython.display import 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"
      ],
      "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": {},
      "outputs": [
       {
        "output_type": "pyout",
        "png": 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        "prompt_number": 6,
        "text": [
         "<IPython.core.display.Image at 0x107ea26d0>"
        ]
       }
      ],
      "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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        "text": [
         "<IPython.core.display.Image at 0x107ea26d0>"
        ]
       }
      ],
      "prompt_number": 9
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "An image can also be displayed from raw data or a url"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "Image(url='http://python.org/images/python-logo.gif')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "html": [
         "<img src=\"http://python.org/images/python-logo.gif\" />"
        ],
        "output_type": "pyout",
        "prompt_number": 2,
        "text": [
         "<IPython.core.display.Image at 0x1060e7410>"
        ]
       }
      ],
      "prompt_number": 2
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "SVG images are also supported out of the box (since modern browsers do a good job of rendering them):"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "from IPython.display import SVG\n",
       "SVG(filename='python-logo.svg')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 3,
        "svg": [
         "<svg height=\"115.02pt\" id=\"svg2\" inkscape:version=\"0.43\" sodipodi:docbase=\"/home/sdeibel\" sodipodi:docname=\"logo-python-generic.svg\" sodipodi:version=\"0.32\" version=\"1.0\" width=\"388.84pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:cc=\"http://web.resource.org/cc/\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:inkscape=\"http://www.inkscape.org/namespaces/inkscape\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\" xmlns:sodipodi=\"http://inkscape.sourceforge.net/DTD/sodipodi-0.dtd\" xmlns:svg=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
         "  <metadata id=\"metadata2193\">\n",
         "    <rdf:RDF>\n",
         "      <cc:Work rdf:about=\"\">\n",
         "        <dc:format>image/svg+xml</dc:format>\n",
         "        <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
         "      </cc:Work>\n",
         "    </rdf:RDF>\n",
         "  </metadata>\n",
         "  <sodipodi:namedview bordercolor=\"#666666\" borderopacity=\"1.0\" id=\"base\" inkscape:current-layer=\"svg2\" inkscape:cx=\"243.02499\" inkscape:cy=\"71.887497\" inkscape:pageopacity=\"0.0\" inkscape:pageshadow=\"2\" inkscape:window-height=\"543\" inkscape:window-width=\"791\" inkscape:window-x=\"0\" inkscape:window-y=\"0\" inkscape:zoom=\"1.4340089\" pagecolor=\"#ffffff\"/>\n",
         "  <defs id=\"defs4\">\n",
         "    <linearGradient id=\"linearGradient2795\">\n",
         "      <stop id=\"stop2797\" offset=\"0\" style=\"stop-color:#b8b8b8;stop-opacity:0.49803922\"/>\n",
         "      <stop id=\"stop2799\" offset=\"1\" style=\"stop-color:#7f7f7f;stop-opacity:0\"/>\n",
         "    </linearGradient>\n",
         "    <linearGradient id=\"linearGradient2787\">\n",
         "      <stop id=\"stop2789\" offset=\"0\" style=\"stop-color:#7f7f7f;stop-opacity:0.5\"/>\n",
         "      <stop id=\"stop2791\" offset=\"1\" style=\"stop-color:#7f7f7f;stop-opacity:0\"/>\n",
         "    </linearGradient>\n",
         "    <linearGradient id=\"linearGradient3676\">\n",
         "      <stop id=\"stop3678\" offset=\"0\" style=\"stop-color:#b2b2b2;stop-opacity:0.5\"/>\n",
         "      <stop id=\"stop3680\" offset=\"1\" style=\"stop-color:#b3b3b3;stop-opacity:0\"/>\n",
         "    </linearGradient>\n",
         "    <linearGradient id=\"linearGradient3236\">\n",
         "      <stop id=\"stop3244\" offset=\"0\" style=\"stop-color:#f4f4f4;stop-opacity:1\"/>\n",
         "      <stop id=\"stop3240\" offset=\"1\" style=\"stop-color:#ffffff;stop-opacity:1\"/>\n",
         "    </linearGradient>\n",
         "    <linearGradient id=\"linearGradient4671\">\n",
         "      <stop id=\"stop4673\" offset=\"0\" style=\"stop-color:#ffd43b;stop-opacity:1\"/>\n",
         "      <stop id=\"stop4675\" offset=\"1\" style=\"stop-color:#ffe873;stop-opacity:1\"/>\n",
         "    </linearGradient>\n",
         "    <linearGradient id=\"linearGradient4689\">\n",
         "      <stop id=\"stop4691\" offset=\"0\" style=\"stop-color:#5a9fd4;stop-opacity:1\"/>\n",
         "      <stop id=\"stop4693\" offset=\"1\" style=\"stop-color:#306998;stop-opacity:1\"/>\n",
         "    </linearGradient>\n",
         "    <linearGradient gradientTransform=\"translate(100.2702,99.61116)\" gradientUnits=\"userSpaceOnUse\" id=\"linearGradient2987\" x1=\"224.23996\" x2=\"-65.308502\" xlink:href=\"#linearGradient4671\" y1=\"144.75717\" y2=\"144.75717\"/>\n",
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        "text": [
         "<IPython.core.display.SVG at 0x10fb998d0>"
        ]
       }
      ],
      "prompt_number": 3
     },
     {
      "cell_type": "heading",
      "level": 3,
      "metadata": {},
      "source": [
       "Embedded vs Non-embedded Images"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "By default, image data is embedded in the Notebook document so that the images can be viewed offline. However it is also possible to tell the `Image` class to only store a *link* to the image. Let's see how this works using a webcam at Berkeley."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "# by default Image data are embedded\n",
       "Embed      = Image(    'http://scienceview.berkeley.edu/view/images/newview.jpg')\n",
       "\n",
       "# if kwarg `url` is given, the embedding is assumed to be false\n",
       "SoftLinked = Image(url='http://scienceview.berkeley.edu/view/images/newview.jpg')\n",
       "\n",
       "# In each case, embed can be specified explicitly with the `embed` kwarg\n",
       "# ForceEmbed = Image(url='http://scienceview.berkeley.edu/view/images/newview.jpg', embed=True)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 4
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Here is the embedded version. Note that this image was pulled from the webcam when this code cell was originally run and stored in the Notebook. Unless we rerun this cell, this is not todays image."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "Embed"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
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+lASoKhSUc4oCA1M0AM+tDNAHPGaG6oAbsVCc1RQpNAmhAZoE0KLm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JsEjY4wSD5ZrN0s+s9D9PaXqHQ/Ul3rd9ds/i313vAgg+nw8Rs7qAQSuMHO0epxzknJKK/3Nc1D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        "output_type": "pyout",
        "prompt_number": 5,
        "text": [
         "<IPython.core.display.Image at 0x10fb99b50>"
        ]
       }
      ],
      "prompt_number": 5
     },
     {
      "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 lighter and always reflect the current version of the source, but the image won't display offline."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "SoftLinked"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "html": [
         "<img src=\"http://scienceview.berkeley.edu/view/images/newview.jpg\" />"
        ],
        "output_type": "pyout",
        "prompt_number": 6,
        "text": [
         "<IPython.core.display.Image at 0x10fb99b10>"
        ]
       }
      ],
      "prompt_number": 6
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Of course, if you re-run this Notebook, the two images will be the same again."
      ]
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "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 (and writing a similar wrapper for other hosted content is trivial):"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "from IPython.display import YouTubeVideo\n",
       "# a talk about IPython at Sage Days at U. Washington, Seattle.\n",
       "# Video credit: William Stein.\n",
       "YouTubeVideo('1j_HxD4iLn8')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "html": [
         "\n",
         "            <iframe\n",
         "                width=\"400\"\n",
         "                height=\"300\"\n",
         "                src=\"http://www.youtube.com/embed/1j_HxD4iLn8\"\n",
         "                frameborder=\"0\"\n",
         "                allowfullscreen\n",
         "            ></iframe>\n",
         "        "
        ],
        "output_type": "pyout",
        "prompt_number": 7,
        "text": [
         "<IPython.lib.display.YouTubeVideo at 0x10fba2190>"
        ]
       }
      ],
      "prompt_number": 7
     },
     {
      "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\n",
       "bar at the bottom with a play/pause button and a location slider."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "from IPython.display import HTML\n",
+      "from base64 import b64encode\n",
       "video = open(\"animation.m4v\", \"rb\").read()\n",
-      "video_encoded = video.encode(\"base64\")\n",
+      "video_encoded = b64encode(video)\n",
       "video_tag = '<video controls alt=\"test\" src=\"data:video/x-m4v;base64,{0}\">'.format(video_encoded)\n",
       "HTML(data=video_tag)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "html": [
         "<video controls alt=\"test\" src=\"data:video/x-m4v;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAAAqiW1kYXQAAAKMBgX//4jcRem9\n",
         "5tlIt5Ys2CDZI+7veDI2NCAtIGNvcmUgMTE4IC0gSC4yNjQvTVBFRy00IEFWQyBjb2RlYyAtIENv\n",
         "cHlsZWZ0IDIwMDMtMjAxMSAtIGh0dHA6Ly93d3cudmlkZW9sYW4ub3JnL3gyNjQuaHRtbCAtIG9w\n",
         "dGlvbnM6IGNhYmFjPTEgcmVmPTMgZGVibG9jaz0xOjA6MCBhbmFseXNlPTB4MzoweDExMyBtZT1o\n",
         "ZXggc3VibWU9NyBwc3k9MSBwc3lfcmQ9MS4wMDowLjAwIG1peGVkX3JlZj0xIG1lX3JhbmdlPTE2\n",
         "IGNocm9tYV9tZT0xIHRyZWxsaXM9MSA4eDhkY3Q9MSBjcW09MCBkZWFkem9uZT0yMSwxMSBmYXN0\n",
         "X3Bza2lwPTEgY2hyb21hX3FwX29mZnNldD0tMiB0aHJlYWRzPTEgc2xpY2VkX3RocmVhZHM9MCBu\n",
         "cj0wIGRlY2ltYXRlPTEgaW50ZXJsYWNlZD0wIGJsdXJheV9jb21wYXQ9MCBjb25zdHJhaW5lZF9p\n",
         "bnRyYT0wIGJmcmFtZXM9MyBiX3B5cmFtaWQ9MiBiX2FkYXB0PTEgYl9iaWFzPTAgZGlyZWN0PTEg\n",
         "d2VpZ2h0Yj0xIG9wZW5fZ29wPTAgd2VpZ2h0cD0yIGtleWludD0yNTAga2V5aW50X21pbj0yNSBz\n",
         "Y2VuZWN1dD00MCBpbnRyYV9yZWZyZXNoPTAgcmNfbG9va2FoZWFkPTQwIHJjPWNyZiBtYnRyZWU9\n",
         "MSBjcmY9MjMuMCBxY29tcD0wLjYwIHFwbWluPTAgcXBtYXg9NjkgcXBzdGVwPTQgaXBfcmF0aW89\n",
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         "\">"
        ],
        "output_type": "pyout",
        "prompt_number": 8,
        "text": [
         "<IPython.core.display.HTML at 0x10fba28d0>"
        ]
       }
      ],
      "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": "code",
      "collapsed": false,
      "input": [
       "from IPython.display import HTML"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 3
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "s = \"\"\"<table>\n",
       "<tr>\n",
       "<th>Header 1</th>\n",
       "<th>Header 2</th>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>row 1, cell 1</td>\n",
       "<td>row 1, cell 2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "<td>row 2, cell 1</td>\n",
       "<td>row 2, cell 2</td>\n",
       "</tr>\n",
       "</table>\"\"\""
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 4
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "h = HTML(s); h"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "html": [
         "<table>\n",
         "<tr>\n",
         "<th>Header 1</th>\n",
         "<th>Header 2</th>\n",
         "</tr>\n",
         "<tr>\n",
         "<td>row 1, cell 1</td>\n",
         "<td>row 1, cell 2</td>\n",
         "</tr>\n",
         "<tr>\n",
         "<td>row 2, cell 1</td>\n",
         "<td>row 2, cell 2</td>\n",
         "</tr>\n",
         "</table>"
        ],
        "output_type": "pyout",
        "prompt_number": 5,
        "text": [
         "<IPython.core.display.HTML at 0x1087a0c10>"
        ]
       }
      ],
      "prompt_number": 5
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Pandas makes use of this capability to allow `DataFrames` to be represented as HTML tables."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "import pandas"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 6
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "By default, `DataFrames` will be represented as text; to enable HTML representations we need to set a print option:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "pandas.core.format.set_printoptions(notebook_repr_html=True)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 9
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Here is a small amount of stock data for APPL:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%%file data.csv\n",
       "Date,Open,High,Low,Close,Volume,Adj Close\n",
       "2012-06-01,569.16,590.00,548.50,584.00,14077000,581.50\n",
       "2012-05-01,584.90,596.76,522.18,577.73,18827900,575.26\n",
       "2012-04-02,601.83,644.00,555.00,583.98,28759100,581.48\n",
       "2012-03-01,548.17,621.45,516.22,599.55,26486000,596.99\n",
       "2012-02-01,458.41,547.61,453.98,542.44,22001000,540.12\n",
       "2012-01-03,409.40,458.24,409.00,456.48,12949100,454.53"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "Writing data.csv\n"
        ]
       }
      ],
      "prompt_number": 11
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Read this as into a `DataFrame`:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "df = pandas.read_csv('data.csv')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 12
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "And view the HTML representation:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "df"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "html": [
         "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
         "<table border=\"1\">\n",
         "  <thead>\n",
         "    <tr>\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",
         "      <td><strong>0</strong></td>\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",
         "      <td><strong>1</strong></td>\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",
         "      <td><strong>2</strong></td>\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",
         "      <td><strong>3</strong></td>\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",
         "      <td><strong>4</strong></td>\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",
         "      <td><strong>5</strong></td>\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>"
        ],
        "output_type": "pyout",
        "prompt_number": 14,
        "text": [
         "         Date    Open    High     Low   Close    Volume  Adj Close\n",
         "0  2012-06-01  569.16  590.00  548.50  584.00  14077000     581.50\n",
         "1  2012-05-01  584.90  596.76  522.18  577.73  18827900     575.26\n",
         "2  2012-04-02  601.83  644.00  555.00  583.98  28759100     581.48\n",
         "3  2012-03-01  548.17  621.45  516.22  599.55  26486000     596.99\n",
         "4  2012-02-01  458.41  547.61  453.98  542.44  22001000     540.12\n",
         "5  2012-01-03  409.40  458.24  409.00  456.48  12949100     454.53"
        ]
       }
      ],
      "prompt_number": 14
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "External sites"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "You can even embed an entire page from another site in an iframe; for example this is today's Wikipedia\n",
       "page for mobile users:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "HTML('<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "html": [
         "<iframe src=http://en.mobile.wikipedia.org/?useformat=mobile width=700 height=350></iframe>"
        ],
        "output_type": "pyout",
        "prompt_number": 9,
        "text": [
         "<IPython.core.display.HTML at 0x1094900d0>"
        ]
       }
      ],
      "prompt_number": 9
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "LaTeX"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "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)."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "from IPython.display import Math\n",
       "Math(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "latex": [
         "$$F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$$"
        ],
        "output_type": "pyout",
        "prompt_number": 10,
        "text": [
         "<IPython.core.display.Math at 0x10fba26d0>"
        ]
       }
      ],
      "prompt_number": 10
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "With the `Latex` class, you have to include the delimiters yourself.  This allows you to use other LaTeX modes such as `eqnarray`:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "from IPython.display import Latex\n",
       "Latex(r\"\"\"\\begin{eqnarray}\n",
       "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
       "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
       "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
       "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
       "\\end{eqnarray}\"\"\")"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "latex": [
         "\\begin{eqnarray}\n",
         "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n",
         "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
         "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
         "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0 \n",
         "\\end{eqnarray}"
        ],
        "output_type": "pyout",
        "prompt_number": 11,
        "text": [
         "<IPython.core.display.Latex at 0x10fba2c10>"
        ]
       }
      ],
      "prompt_number": 11
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Or you can enter latex directly with the `%%latex` cell magic:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%%latex\n",
       "\\begin{aligned}\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{aligned}"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "latex": [
         "\\begin{aligned}\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{aligned}"
        ],
        "output_type": "display_data",
        "text": [
         "<IPython.core.display.Latex at 0x10a617c90>"
        ]
       }
      ],
      "prompt_number": 12
     }
    ],
    "metadata": {}
   }
  ]
 }

examples/notebooks/Progress Bars.ipynb

@@ -1,146 +1,147
 {
  "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": [
+      "from __future__ import print_function\n",
       "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",
+      "        print('\\r', self, end='')\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/R Magics.ipynb

@@ -1,928 +1,929
 {
  "metadata": {
   "name": "R Magics"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
  "worksheets": [
   {
    "cells": [
     {
      "cell_type": "heading",
      "level": 1,
      "metadata": {},
      "source": [
       "Using R Within the IPython Notebok"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Using the `rmagic` extension, users can run R code from within the IPython Notebook. This example Notebook demonstrates this capability. "
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%pylab inline"
      ],
      "language": "python",
      "metadata": {},
      "outputs": []
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Line magics"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "IPython has an `rmagic` extension that contains a some magic functions for working with R via rpy2. This extension can be loaded using the `%load_ext` magic as follows:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": true,
      "input": [
       "%load_ext rmagic "
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 1
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "A typical use case one imagines is having some numpy arrays, wanting to compute some statistics of interest on these\n",
       " arrays and return the result back to python. Let's suppose we just want to fit a simple linear model to a scatterplot."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "import numpy as np\n",
       "import pylab\n",
       "X = np.array([0,1,2,3,4])\n",
       "Y = np.array([3,5,4,6,7])\n",
       "pylab.scatter(X, Y)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 2,
        "text": [
         "<matplotlib.collections.PathCollection at 0x10f32f610>"
        ]
       }
      ],
      "prompt_number": 2
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "We can accomplish this by first pushing variables to R, fitting a model and returning the results. The line magic %Rpush copies its arguments to variables of the same name in rpy2. The %R line magic evaluates the string in rpy2 and returns the results. In this case, the coefficients of a linear model."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%Rpush X Y\n",
       "%R lm(Y~X)$coef"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 3,
        "text": [
         "array([ 3.2,  0.9])"
        ]
       }
      ],
      "prompt_number": 3
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "We can check that this is correct fairly easily:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "Xr = X - X.mean(); Yr = Y - Y.mean()\n",
       "slope = (Xr*Yr).sum() / (Xr**2).sum()\n",
       "intercept = Y.mean() - X.mean() * slope\n",
       "(intercept, slope)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 4,
        "text": [
         "(3.2000000000000002, 0.90000000000000002)"
        ]
       }
      ],
      "prompt_number": 4
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "It is also possible to return more than one value with %R."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%R resid(lm(Y~X)); coef(lm(X~Y))\n"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 5,
        "text": [
         "array([-2.5,  0.9])"
        ]
       }
      ],
      "prompt_number": 5
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "One can also easily capture the results of %R into python objects. Like R, the return value of this multiline expression (multiline in the sense that it is separated by ';') is the final value, which is \n",
       "the *coef(lm(X~Y))*. To pull other variables from R, there is one more magic."
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "There are two more line magics, %Rpull and %Rget. Both are useful after some R code has been executed and there are variables\n",
       "in the rpy2 namespace that one would like to retrieve. The main difference is that one\n",
       " returns the value (%Rget), while the other pulls it to self.shell.user_ns (%Rpull). Imagine we've stored the results\n",
       "of some calculation in the variable \"a\" in rpy2's namespace. By using the %R magic, we can obtain these results and\n",
       "store them in b. We can also pull them directly to user_ns with %Rpull. They are both views on the same data."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "b = %R a=resid(lm(Y~X))\n",
       "%Rpull a\n",
-      "print a\n",
+      "print(a)\n",
       "assert id(b.data) == id(a.data)\n",
       "%R -o a"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "[-0.2  0.9 -1.   0.1  0.2]\n"
        ]
       }
      ],
      "prompt_number": 6
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "%Rpull is equivalent to calling %R with just -o\n"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%R d=resid(lm(Y~X)); e=coef(lm(Y~X))\n",
       "%R -o d -o e\n",
       "%Rpull e\n",
-      "print d\n",
-      "print e\n",
+      "print(d)\n",
+      "print(e)\n",
       "import numpy as np\n",
       "np.testing.assert_almost_equal(d, a)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "[-0.2  0.9 -1.   0.1  0.2]\n",
         "[ 3.2  0.9]\n"
        ]
       }
      ],
      "prompt_number": 7
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "On the other hand %Rpush is equivalent to calling %R with just -i and no trailing code."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "A = np.arange(20)\n",
       "%R -i A\n",
       "%R mean(A)\n"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 8,
        "text": [
         "array([ 9.5])"
        ]
       }
      ],
      "prompt_number": 8
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "The magic %Rget retrieves one variable from R."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%Rget A"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 9,
        "text": [
         "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
         "       17, 18, 19], dtype=int32)"
        ]
       }
      ],
      "prompt_number": 9
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Plotting and capturing output"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "R's console (i.e. its stdout() connection) is captured by ipython, as are any plots which are published as PNG files like the notebook with arguments --pylab inline. As a call to %R may produce a return value (see above) we must ask what happens to a magic like the one below. The R code specifies that something is published to the notebook. If anything is published to the notebook, that call to %R returns None."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "v1 = %R plot(X,Y); print(summary(lm(Y~X))); vv=mean(X)*mean(Y)\n",
-      "print 'v1 is:', v1\n",
+      "print('v1 is:', v1)\n",
       "v2 = %R mean(X)*mean(Y)\n",
-      "print 'v2 is:', v2"
+      "print('v2 is:', v2)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "display_data",
        "text": [
         "\n",
         "Call:\n",
         "lm(formula = Y ~ X)\n",
         "\n",
         "Residuals:\n",
         "   1    2    3    4    5 \n",
         "-0.2  0.9 -1.0  0.1  0.2 \n",
         "\n",
         "Coefficients:\n",
         "            Estimate Std. Error t value Pr(>|t|)  \n",
         "(Intercept)   3.2000     0.6164   5.191   0.0139 *\n",
         "X             0.9000     0.2517   3.576   0.0374 *\n",
         "---\n",
         "Signif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1 \n",
         "\n",
         "Residual standard error: 0.7958 on 3 degrees of freedom\n",
         "Multiple R-squared:  0.81,\tAdjusted R-squared: 0.7467 \n",
         "F-statistic: 12.79 on 1 and 3 DF,  p-value: 0.03739 \n",
         "\n"
        ]
       },
       {
        "output_type": "display_data",
        "png": 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       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "v1 is: [ 10.]\n",
         "v2 is: [ 10.]\n"
        ]
       }
      ],
      "prompt_number": 10
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "What value is returned from %R?"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Some calls have no particularly interesting return value, the magic %R will not return anything in this case. The return value in rpy2 is actually NULL so %R returns None."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "v = %R plot(X,Y)\n",
       "assert v == None"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "display_data",
        "png": 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RokWLiIhYsWJFnH766bW6Rtu2baNt27b7Pde6dWuvKRegLl26xMknnxy33357TJgwIaqq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\nrkJggg==\n"
       }
      ],
      "prompt_number": 11
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Also, if the return value of a call to %R (in line mode) has just been printed to the console, then its value is also not returned."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "v = %R print(X)\n",
       "assert v == None"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "display_data",
        "text": [
         "[1] 0 1 2 3 4\n"
        ]
       }
      ],
      "prompt_number": 12
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "But, if the last value did not print anything to console, the value is returned:\n"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "v = %R print(summary(X)); X\n",
-      "print 'v:', v"
+      "print('v:', v)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "display_data",
        "text": [
         "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
         "      0       1       2       2       3       4 \n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "v: [0 1 2 3 4]\n"
        ]
       }
      ],
      "prompt_number": 13
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "The return value can be suppressed by a trailing ';' or an -n argument.\n"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": true,
      "input": [
       "%R -n X"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 14
     },
     {
      "cell_type": "code",
      "collapsed": true,
      "input": [
       "%R X; "
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 15
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Cell level magic"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Often, we will want to do more than a simple linear regression model. There may be several lines of R code that we want to \n",
       "use before returning to python. This is the cell-level magic.\n",
       "\n",
       "\n",
       "For the cell level magic, inputs can be passed via the -i or --inputs argument in the line. These variables are copied \n",
       "from the shell namespace to R's namespace using rpy2.robjects.r.assign. It would be nice not to have to copy these into R: rnumpy ( http://bitbucket.org/njs/rnumpy/wiki/API ) has done some work to limit or at least make transparent the number of copies of an array. This seems like a natural thing to try to build on. Arrays can be output from R via the -o or --outputs argument in the line. All other arguments are sent to R's png function, which is the graphics device used to create the plots.\n",
       "\n",
       "We can redo the above calculations in one ipython cell. We might also want to add some output such as a summary\n",
       " from R or perhaps the standard plotting diagnostics of the lm."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%%R -i X,Y -o XYcoef\n",
       "XYlm = lm(Y~X)\n",
       "XYcoef = coef(XYlm)\n",
       "print(summary(XYlm))\n",
       "par(mfrow=c(2,2))\n",
       "plot(XYlm)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "display_data",
        "text": [
         "\n",
         "Call:\n",
         "lm(formula = Y ~ X)\n",
         "\n",
         "Residuals:\n",
         "   1    2    3    4    5 \n",
         "-0.2  0.9 -1.0  0.1  0.2 \n",
         "\n",
         "Coefficients:\n",
         "            Estimate Std. Error t value Pr(>|t|)  \n",
         "(Intercept)   3.2000     0.6164   5.191   0.0139 *\n",
         "X             0.9000     0.2517   3.576   0.0374 *\n",
         "---\n",
         "Signif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1 \n",
         "\n",
         "Residual standard error: 0.7958 on 3 degrees of freedom\n",
         "Multiple R-squared:  0.81,\tAdjusted R-squared: 0.7467 \n",
         "F-statistic: 12.79 on 1 and 3 DF,  p-value: 0.03739 \n",
         "\n"
        ]
       },
       {
        "output_type": "display_data",
        "png": 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4+BAeHk6pUqXYsmULrVu31jqWEHlGXFycWdox67nbJUuWAGnXba9fv05wcDBn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\n"
       }
      ],
      "prompt_number": 16
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Passing data back and forth"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Currently, data is passed through RMagics.pyconverter when going from python to R and RMagics.Rconverter when \n",
       "going from R to python. These currently default to numpy.ndarray. Future work will involve writing better converters, most likely involving integration with http://pandas.sourceforge.net.\n",
       "\n",
       "Passing ndarrays into R seems to require a copy, though once an object is returned to python, this object is NOT copied, and it is possible to change its values.\n"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": true,
      "input": [
       "seq1 = np.arange(10)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 17
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%%R -i seq1 -o seq2\n",
       "seq2 = rep(seq1, 2)\n",
       "print(seq2)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "display_data",
        "text": [
         " [1] 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9\n"
        ]
       }
      ],
      "prompt_number": 18
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "seq2[::2] = 0\n",
       "seq2"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 19,
        "text": [
         "array([0, 1, 0, 3, 0, 5, 0, 7, 0, 9, 0, 1, 0, 3, 0, 5, 0, 7, 0, 9], dtype=int32)"
        ]
       }
      ],
      "prompt_number": 19
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%%R\n",
       "print(seq2)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "display_data",
        "text": [
         " [1] 0 1 0 3 0 5 0 7 0 9 0 1 0 3 0 5 0 7 0 9\n"
        ]
       }
      ],
      "prompt_number": 20
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Once the array data has been passed to R, modifring its contents does not modify R's copy of the data."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "seq1[0] = 200\n",
       "%R print(seq1)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "display_data",
        "text": [
         " [1] 0 1 2 3 4 5 6 7 8 9\n"
        ]
       }
      ],
      "prompt_number": 21
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "But, if we pass data as both input and output, then the value of \"data\" in user_ns will be overwritten and the\n",
       "new array will be a view of the data in R's copy."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print seq1\n",
+      "print(seq1)\n",
       "%R -i seq1 -o seq1\n",
-      "print seq1\n",
+      "print(seq1)\n",
       "seq1[0] = 200\n",
       "%R print(seq1)\n",
       "seq1_view = %R seq1\n",
       "assert(id(seq1_view.data) == id(seq1.data))"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "[200   1   2   3   4   5   6   7   8   9]\n",
         "[200   1   2   3   4   5   6   7   8   9]\n"
        ]
       },
       {
        "output_type": "display_data",
        "text": [
         " [1] 200   1   2   3   4   5   6   7   8   9\n"
        ]
       }
      ],
      "prompt_number": 22
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Exception handling\n"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Exceptions are handled by passing back rpy2's exception and the line that triggered it."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "try:\n",
       "    %R -n nosuchvar\n",
       "except Exception as e:\n",
-      "    print e.message\n",
+      "    print(e)\n",
       "    pass"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "parsing and evaluating line \"nosuchvar\".\n",
         "R error message: \"Error in eval(expr, envir, enclos) : object 'nosuchvar' not found\n",
         "\"\n",
         " R stdout:\"Error in eval(expr, envir, enclos) : object 'nosuchvar' not found\n",
         "\"\n",
         "\n"
        ]
       }
      ],
      "prompt_number": 23
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Structured arrays and data frames\n"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "In R, data frames play an important role as they allow array-like objects of mixed type with column names (and row names). In bumpy, the closest analogy is a structured array with named fields. In future work, it would be nice to use pandas to return full-fledged DataFrames from rpy2. In the mean time, structured arrays can be passed back and forth with the -d flag to %R, %Rpull, and %Rget"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": true,
      "input": [
       "datapy= np.array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')],\n",
       "          dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])\n"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 24
     },
     {
      "cell_type": "code",
      "collapsed": true,
      "input": [
       "%%R -i datapy -d datar\n",
       "datar = datapy"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 25
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "datar"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 26,
        "text": [
         "array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')], \n",
         "      dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])"
        ]
       }
      ],
      "prompt_number": 26
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%R datar2 = datapy\n",
       "%Rpull -d datar2\n",
       "datar2"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 27,
        "text": [
         "array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')], \n",
         "      dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])"
        ]
       }
      ],
      "prompt_number": 27
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%Rget -d datar2"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 28,
        "text": [
         "array([(1, 2.9, 'a'), (2, 3.5, 'b'), (3, 2.1, 'c')], \n",
         "      dtype=[('x', '<i4'), ('y', '<f8'), ('z', '|S1')])"
        ]
       }
      ],
      "prompt_number": 28
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "For arrays without names, the -d argument has no effect because the R object has no colnames or names."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "Z = np.arange(6)\n",
       "%R -i Z\n",
       "%Rget -d Z"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 29,
        "text": [
         "array([0, 1, 2, 3, 4, 5], dtype=int32)"
        ]
       }
      ],
      "prompt_number": 29
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "For mixed-type data frames in R, if the -d flag is not used, then an array of a single type is returned and\n",
       "its value is transposed. This would be nice to fix, but it seems something that should be fixed at the rpy2 level (See: https://bitbucket.org/lgautier/rpy2/issue/44/numpyrecarray-as-dataframe)"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%Rget datar2"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 30,
        "text": [
         "array([['1', '2', '3'],\n",
         "       ['2', '3', '2'],\n",
         "       ['a', 'b', 'c']], \n",
         "      dtype='|S1')"
        ]
       }
      ],
      "prompt_number": 30
     },
     {
      "cell_type": "code",
      "collapsed": true,
      "input": [],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 30
     }
    ],
    "metadata": {}
   }
  ]
 }
  No newline at end of file

examples/notebooks/Script Magics.ipynb

@@ -1,482 +1,482
 {
  "metadata": {
   "name": "Script Magics"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
  "worksheets": [
   {
    "cells": [
     {
      "cell_type": "heading",
      "level": 1,
      "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",
      "collapsed": false,
      "input": [
       "import sys"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 1
     },
     {
      "cell_type": "heading",
      "level": 2,
      "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",
      "collapsed": false,
      "input": [
       "%%script python\n",
       "import sys\n",
       "print 'hello from Python %s' % sys.version"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "hello from Python 2.7.1 (r271:86832, Jul 31 2011, 19:30:53) \n",
         "[GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2335.15.00)]\n"
        ]
       }
      ],
      "prompt_number": 2
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%%script python3\n",
       "import sys\n",
       "print('hello from Python: %s' % sys.version)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "hello from Python: 3.2.3 (v3.2.3:3d0686d90f55, Apr 10 2012, 11:25:50) \n",
         "[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]\n"
        ]
       }
      ],
      "prompt_number": 3
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "IPython also creates aliases for a few common interpreters, such as bash, ruby, perl, etc.\n",
       "\n",
       "These are all equivalent to `%%script <name>`"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%%ruby\n",
       "puts \"Hello from Ruby #{RUBY_VERSION}\""
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "Hello from Ruby 1.8.7\n"
        ]
       }
      ],
      "prompt_number": 4
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%%bash\n",
       "echo \"hello from $BASH\""
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "hello from /usr/local/bin/bash\n"
        ]
       }
      ],
      "prompt_number": 5
     },
     {
      "cell_type": "heading",
      "level": 2,
      "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",
      "collapsed": false,
      "input": [
       "%%bash\n",
       "echo \"hi, stdout\"\n",
       "echo \"hello, stderr\" >&2\n"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "hi, stdout\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stderr",
        "text": [
         "hello, stderr\n"
        ]
       }
      ],
      "prompt_number": 6
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%%bash --out output --err error\n",
       "echo \"hi, stdout\"\n",
       "echo \"hello, stderr\" >&2"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 7
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print error\n",
-      "print output"
+      "print(error)\n",
+      "print(output)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "hello, stderr\n",
         "\n",
         "hi, stdout\n",
         "\n"
        ]
       }
      ],
      "prompt_number": 8
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Background Scripts"
      ]
     },
     {
      "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",
      "collapsed": false,
      "input": [
       "%%ruby --bg --out ruby_lines\n",
       "for n in 1...10\n",
       "    sleep 1\n",
       "    puts \"line #{n}\"\n",
       "    STDOUT.flush\n",
       "end"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "Starting job # 0 in a separate thread.\n"
        ]
       }
      ],
      "prompt_number": 9
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "When you do store output of a background thread, these are the stdout/err *pipes*,\n",
       "rather than the text of the output."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "ruby_lines"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "pyout",
        "prompt_number": 10,
        "text": [
         "<open file '<fdopen>', mode 'rb' at 0x10a4be660>"
        ]
       }
      ],
      "prompt_number": 10
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print ruby_lines.read()"
+      "print(ruby_lines.read())"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "line 1\n",
         "line 2\n",
         "line 3\n",
         "line 4\n",
         "line 5\n",
         "line 6\n",
         "line 7\n",
         "line 8\n",
         "line 9\n",
         "\n"
        ]
       }
      ],
      "prompt_number": 11
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Arguments to subcommand"
      ]
     },
     {
      "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",
      "collapsed": false,
      "input": [
       "%%script python -Qnew\n",
       "print 1/3"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "0.333333333333\n"
        ]
       }
      ],
      "prompt_number": 12
     },
     {
      "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",
      "collapsed": false,
      "input": [
       "%%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"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "Starting job # 2 in a separate thread.\n"
        ]
       }
      ],
      "prompt_number": 13
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Remember, since the output of a background script is just the stdout pipe,\n",
       "you can read it as lines become available:"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "import time\n",
       "tic = time.time()\n",
       "line = True\n",
       "while True:\n",
       "    line = bashout.readline()\n",
       "    if not line:\n",
       "        break\n",
       "    sys.stdout.write(\"%.1fs: %s\" %(time.time()-tic, line))\n",
       "    sys.stdout.flush()\n"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "0.0s: line 1\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "1.0s: line 2\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "2.0s: line 3\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "3.0s: line 4\n"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "4.0s: line 5\n"
        ]
       }
      ],
      "prompt_number": 14
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
       "Configuring the default ScriptMagics"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "The list of aliased script magics is configurable.\n",
       "\n",
       "The default is to pick from a few common interpreters, and use them if found, but you can specify your own in ipython_config.py:\n",
       "\n",
       "    c.ScriptMagics.scripts = ['R', 'pypy', 'myprogram']\n",
       "\n",
       "And if any of these programs do not apear on your default PATH, then you would also need to specify their location with:\n",
       "\n",
       "    c.ScriptMagics.script_paths = {'myprogram': '/opt/path/to/myprogram'}"
      ]
     }
    ],
    "metadata": {}
   }
  ]
 }
  No newline at end of file

examples/notebooks/Trapezoid Rule.ipynb

@@ -1,147 +1,148
 {
  "metadata": {
   "name": "Trapezoid Rule"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
  "worksheets": [
   {
    "cells": [
     {
      "cell_type": "heading",
      "level": 1,
      "metadata": {},
      "source": [
       "Basic Numerical Integration: the Trapezoid Rule"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "A simple illustration of the trapezoid rule for definite integration:\n",
       "\n",
       "$$\n",
       "\\int_{a}^{b} f(x)\\, dx \\approx \\frac{1}{2} \\sum_{k=1}^{N} \\left( x_{k} - x_{k-1} \\right) \\left( f(x_{k}) + f(x_{k-1}) \\right).\n",
       "$$\n",
       "<br>\n",
       "First, we define a simple function and sample it between 0 and 10 at 200 points"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "%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": 1
     },
     {
      "cell_type": "code",
      "collapsed": true,
      "input": [
       "def f(x):\n",
       "    return (x-3)*(x-5)*(x-7)+85\n",
       "\n",
       "x = linspace(0, 10, 200)\n",
       "y = f(x)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 2
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Choose a region to integrate over and take only a few points in that region"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": true,
      "input": [
       "a, b = 1, 9\n",
       "xint = x[logical_and(x>=a, x<=b)][::30]\n",
       "yint = y[logical_and(x>=a, x<=b)][::30]"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
      "prompt_number": 3
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Plot both the function and the area below it in the trapezoid approximation"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "plot(x, y, lw=2)\n",
       "axis([0, 10, 0, 140])\n",
       "fill_between(xint, 0, yint, facecolor='gray', alpha=0.4)\n",
       "text(0.5 * (a + b), 30,r\"$\\int_a^b f(x)dx$\", horizontalalignment='center', fontsize=20);"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "display_data",
        "png": 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Hx1NTU0O/fv3UKU4I0S7Fx8cTHx9v8us1RqPR2NIX5ebmMnbsWNLS0gD46aef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\nj8mEXkJ1HTLc9+9XvlxcQLo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       }
      ],
      "prompt_number": 4
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "Compute the integral both at high accuracy and with the trapezoid approximation"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from __future__ import print_function\n",
       "from scipy.integrate import quad, trapz\n",
       "integral, error = quad(f, 1, 9)\n",
-      "print \"The integral is:\", integral, \"+/-\", error\n",
-      "print \"The trapezoid approximation with\", len(xint), \"points is:\", trapz(yint, xint)"
+      "print(\"The integral is:\", integral, \"+/-\", error)\n",
+      "print(\"The trapezoid approximation with\", len(xint), \"points is:\", trapz(yint, xint))"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
         "The integral is: 680.0 +/- 7.54951656745e-12\n",
         "The trapezoid approximation with 6 points is: 621.286411141\n"
        ]
       }
      ],
      "prompt_number": 5
     }
    ],
    "metadata": {}
   }
  ]
 }
  No newline at end of file