intro_jupyter.ipynb

intro_jupyter.ipynb

{
  "cells": [
    {
      "metadata": {
        "toc": "true"
      },
      "cell_type": "markdown",
      "source": "# Table of Contents\n <p><div class=\"lev1\"><a href=\"#An-Intro-to-the-Jupyter-notebook\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>An Intro to the Jupyter notebook</a></div><div class=\"lev2\"><a href=\"#High-Level-Intro-and-Teasers\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>High-Level Intro and Teasers</a></div><div class=\"lev3\"><a href=\"#What-is-a-Jupyter-notebook?\"><span class=\"toc-item-num\">1.1.1&nbsp;&nbsp;</span>What is a Jupyter notebook?</a></div><div class=\"lev3\"><a href=\"#Quick-Feature-Run-down-(to-whet-your-appetite)\"><span class=\"toc-item-num\">1.1.2&nbsp;&nbsp;</span>Quick Feature Run-down (to whet your appetite)</a></div><div class=\"lev4\"><a href=\"#Markdown-Cell-Features\"><span class=\"toc-item-num\">1.1.2.1&nbsp;&nbsp;</span>Markdown Cell Features</a></div><div class=\"lev4\"><a href=\"#Tools\"><span class=\"toc-item-num\">1.1.2.2&nbsp;&nbsp;</span>Tools</a></div><div class=\"lev4\"><a href=\"#Other-Features\"><span class=\"toc-item-num\">1.1.2.3&nbsp;&nbsp;</span>Other Features</a></div><div class=\"lev4\"><a href=\"#Integration-with-other-software\"><span class=\"toc-item-num\">1.1.2.4&nbsp;&nbsp;</span>Integration with other software</a></div><div class=\"lev3\"><a href=\"#What-should-I-use-Jupyter-for?-(a.k.a-is-it-the-only-solution?)\"><span class=\"toc-item-num\">1.1.3&nbsp;&nbsp;</span>What should I use Jupyter for? (a.k.a is it the only solution?)</a></div><div class=\"lev2\"><a href=\"#Jupyter-Basics\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Jupyter Basics</a></div><div class=\"lev2\"><a href=\"#What-is-Jupyter-really-capable-of?\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>What is Jupyter really capable of?</a></div><div class=\"lev3\"><a href=\"#Useful-Magics\"><span class=\"toc-item-num\">1.3.1&nbsp;&nbsp;</span>Useful Magics</a></div><div class=\"lev3\"><a href=\"#Useful-Extensions\"><span class=\"toc-item-num\">1.3.2&nbsp;&nbsp;</span>Useful Extensions</a></div><div class=\"lev4\"><a href=\"#Fortran-Magic-(Get-it-here.)\"><span class=\"toc-item-num\">1.3.2.1&nbsp;&nbsp;</span>Fortran Magic (<a target=\"_blank\" href=\"https://github.com/mgaitan/fortran_magic\">Get it here.</a>)</a></div><div class=\"lev4\"><a href=\"#Contributed-Extensions-(Get-them-here.)\"><span class=\"toc-item-num\">1.3.2.2&nbsp;&nbsp;</span>Contributed Extensions (<a target=\"_blank\" href=\"https://github.com/ipython-contrib/IPython-notebook-extensions\">Get them here.</a>)</a></div><div class=\"lev3\"><a href=\"#Other-uses-of-Jupyter\"><span class=\"toc-item-num\">1.3.3&nbsp;&nbsp;</span>Other uses of Jupyter</a></div>"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# An Intro to the Jupyter notebook\n\nWritten by Steven Murray, June 2016"
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "from ipywidgets import interact\nimport numpy as np",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "This notebook introduces the Jupyter notebook for those who have little to no experience with it. Its aim is to first give a quick overview of the pros and cons of the notebook, with a rundown of the features. This will be useful if you're trying to decide whether to give it a go, or if you've been using it but not aware of some of the really neat things it can do. It *doesn't* go into detail about *how* to do various things.\n\nA disclaimer: some of these things are being developed pretty quickly, so some things may be wrong in even a year, and certainly in not too long there will be much cooler things you can do with the notebook than that outlined here."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## High-Level Intro and Teasers"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### What is a Jupyter notebook?"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "* Browser-based environment for writing *and* running python code.\n* Display comments, text, code, equations, images etc. all in the same place.\n* Two elements: *Jupyter* web-app frontend, *IPython* engine backend.\n* In short: a complete environment for interactive coding."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Quick Feature Run-down (to whet your appetite)"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "* Complete python code environment. Anything you can write with python can be written here:"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def print_the_square(a):\n    print a**2\n\nprint_the_square(2)",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "4\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### Markdown Cell Features"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "* Display markdown, for better narrating of the code: eg. *italics*, **bold**, ``simple_code_style``, [links](http://ipython.org), headings as above, dot points and enumerations. There is even support for writing non-functional syntax-highlighted code:\n\n```python\nprint(\"Hello World\")\n```\n\n* Display latex equations inline: $E = mc^2$, or as display:\n\n$$ c^2 = \\sqrt{a^2 + b^2}.$$\n\n* Show images from URL's: \n\n![Image](http://www.icrar.org/?a=410225)\n\n* Show images from files.\n* Use HTML/CSS to further style things (if you ever care to)."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### Tools"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "* Editing tools including cut/paste/merge cells, show/hide cells, find/replace.\n* Interrupt execution, or restart entire *kernel*\n* Heaps of keyboard shortcuts (remember Esc - m - Enter!)\n* Checkpointing.\n* Save/Export to various formats including HTML and PDF via LaTeX!\n* TAB Completion:"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "np.",
      "execution_count": 3,
      "outputs": [
        {
          "ename": "SyntaxError",
          "evalue": "invalid syntax (<ipython-input-3-a58a88215da2>, line 1)",
          "traceback": [
            "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-3-a58a88215da2>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    np.\u001b[0m\n\u001b[1;37m       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
          ],
          "output_type": "error"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### Other Features\n\n* Python2 or Python3.\n* Not just for running python -- other kernels can be used (Julia, Haskell etc.)\n* Deep configurability\n    * config files can instantly change startup routines, styling, cell metadata etc.\n* Deep extensibility\n    * Add custom JS extensions to add editing/visual features\n    * Add custom \"magics\" which do neat things easily (more on this later).\n* Can run standard python scripts from within notebook using %run\n* Easily profiling and benchmarking\n* Runnable over a network (SSH)\n* IPy widgets can do some pretty cool stuff (more on this later):"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "@interact(x=(0,10))\ndef cube(x):\n    print \"The cube of %d is %d\" %(x,x**3)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "* Use IPython as an extended shell:"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "name = %pwd",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "%ls -la",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "* Easy to find help on functions (even your own!):"
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "np.dot?",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def spell_stuff_backwards(word):\n    \"\"\"\n    A function to write things backward, because why not.\n    \n    Parameters\n    ----------\n    word : the word to write backwards.\n    \"\"\"\n    return word[::-1]\n\nspell_stuff_backwards?",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "* Even look at the actual source code:"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "source(np.sum)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### Integration with other software"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "* Matplotlib can plot inline, which is really useful:"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "## Could also use %matplotlib inline\n%matplotlib inline\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nplt.plot(np.sin(np.linspace(0,1,100)))\nplt.show()",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": "<matplotlib.figure.Figure at 0x7f8a6c1d6610>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "And it can be made interactive:"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "x = np.linspace(0,2*np.pi,100)\n\n@interact(a=(1,10))\ndef plot_sin(a):\n    plt.plot(x,np.sin(a*x))\n    plt.show()",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rF8a7xlVaKCTCbwONJOZqI1GGbm6rLNfhot4ddLYVuG0vbX2ota2SpaMIGgeb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\nQkKPIBF+QkJCQo/g/wMsqgE3gThvcgAAAABJRU5ErkJggg==\n",
            "text/plain": "<matplotlib.figure.Figure at 0x7f8a68e42110>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "* Pandas support for displaying tables etc:"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas\n\ndf = pandas.DataFrame({\"col1\":np.linspace(0,1,15),\"col2\":np.linspace(1,0,15)})\n\ndf",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "* Bokeh interactive plots:"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "from bokeh.plotting import output_notebook, figure, show\nfrom bokeh.models import ColumnDataSource\n\n\noutput_notebook()\n\nfig = figure(plot_height=400)\n\nx = np.linspace(0,6*np.pi,1000)\ny1 = np.sin(x)\ny2 = np.cos(y1)\n\ndef update(a=1):\n    y1 = np.sin(a*x)\n    y2 = np.cos(a*y1)\n    \n    source1.data['y'] = y1\n    source2.data['y'] = y2\n    source1.push_notebook()\n    source2.push_notebook()\n    \nsource1 = ColumnDataSource({\"x\":x,\"y\":y1})\nsource2 = ColumnDataSource({\"x\":x,\"y\":y2})\n\nfig.line(x,y1,source=source1,color=\"red\",line_width=4)\nfig.line(x,y2,source=source2,line_width=4)\n\nshow(fig)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "interact(update,a=(0.1,4))",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "* SymPy pretty-prints with LaTeX:"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "from sympy import *\nfrom sympy.abc import *\ninit_printing(use_latex=True)\n\ny = Integral(x**2 + 2*x**3 + exp(-x)*cos(x),x)\ny",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "y.doit()",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "* All sorts of display options can be added by writing custom methods that convert the object into html/latex/markdown."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### What should I use Jupyter for? (a.k.a is it the only solution?)"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "* Excellent for quick **exploratory data analysis** -- load up a dataset, quickly plot it inline, muck around with it, get your results fast, with the full capability of python.\n* Great for **first steps** at something larger -- first write functions/classes in the notebook, much around with them to see if they're working, then copy them over to a more static library.\n* Good way to start a **new science project** -- code up some bits and pieces in Jupyter, have a look at some results, write extended comments about each plot *alongside the plots*, use LaTeX equations etc. Saves your sanity later (better even than documentation!)\n* Awesome for **collaborative work** -- export files as HTML to share, with all embedded graphics and equations to let everyone know what's going on (oh, they display nicely on github as well!)\n* Good for writing tutorials -- both scientific (e.g. [this 3-body problem tute](http://nbviewer.jupyter.org/github/pjpmarques/Julia-Modeling-the-World/blob/master/Three-Body%20Problem.ipynb)) and python! (eg. [this python tute](nbviewer.jupyter.org/gist/rpmuller/5920182))\n* Look [here](http://nb.bianp.net/sort/views/) for some great examples."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "There's some things that it's not great for also, and some drawbacks:\n\n* Obviously not useful for writing large-scale software.\n* Not useful in a pipeline.\n* Not great for editing code (compared to say an IDE or good text editor)\n* Kind of annoying having to open a browser to edit files."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Jupyter Basics"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "There are lots of basic tutorials to get you started, especially look at [The IPython Minibook](https://github.com/ipython-books/minibook-2nd-code) and the official docs."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## What is Jupyter really capable of?"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Useful Magics"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "There are several in-built magic functions which are really useful, and you can create or download others. Some of the most useful:"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "To see available magic functions"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "%lsmagic",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "## Benchmarking\n\n%timeit np.array([i for i in range(100)])\n%timeit np.arange(100)",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "%%timeit\n\ns = 0\nfor i in range(10):\n    s += i**5",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "%history -l 5",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "%%capture output\n\n%ls",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "output.stdout",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "%%bash\n\nmkdir tmp\ncd tmp\ntouch afile\nls\npwd\ncd ..\nrm -r tmp",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "You can even write simple magics to compile and run C or C++ code written directly in the cell!"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Profiling functions is easy, using the %prun magic:"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "%prun np.linspace(0,1,100)",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": " "
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Useful Extensions"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### Fortran Magic ([Get it here.](https://github.com/mgaitan/fortran_magic))\n\nDefines a magic function which can compile fortran code and import the symbols automatically. If installed, you can run the following:"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "%load_ext fortranmagic",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": "/home/steven/anaconda2/lib/python2.7/site-packages/IPython/utils/path.py:264: UserWarning: get_ipython_cache_dir has moved to the IPython.paths module\n  warn(\"get_ipython_cache_dir has moved to the IPython.paths module\")\n"
        },
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          "output_type": "display_data",
          "data": {
            "application/javascript": "$.getScript(\"https://raw.github.com/marijnh/CodeMirror/master/mode/fortran/fortran.js\", function () {\nIPython.config.cell_magic_highlight['magic_fortran'] = {'reg':[/^%%fortran/]};});\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "collapsed": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "%%fortran\n\nsubroutine f1(x, y, z)\n    real, intent(in) :: x,y\n    real, intent(out) :: z\n\n    z = sin(x+y)\n\nend subroutine f1",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Now the symbol ``f1`` is the fortran subroutine, and can be used in normal python code:"
    },
    {
      "metadata": {
        "collapsed": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "%timeit f1(1,-1)\n%timeit np.sin(1+-1)",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "The slowest run took 25.84 times longer than the fastest. This could mean that an intermediate result is being cached.\n1000000 loops, best of 3: 194 ns per loop\nThe slowest run took 19.38 times longer than the fastest. This could mean that an intermediate result is being cached.\n1000000 loops, best of 3: 984 ns per loop\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "#### Contributed Extensions ([Get them here.](https://github.com/ipython-contrib/IPython-notebook-extensions))"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "These are a bunch of useful extensions for doing various things with Jupyter, e.g. adding a Table of Contents, Collapsible Headings, Codefolding, add auto-run Initialisation Cells, edit Keyboard Shortcuts etc."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Other uses of Jupyter"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "* Writing blogs (e.g. [Jake Vanderplass](https://jakevdp.github.io/))\n* Generating annotated figures for science publications (e.g. [the LIGO GW detection!](nbviewer.jupyter.org/urls/losc.ligo.org/s/events/LOSC_Event_tutorial.ipynb))\n* Example documentation for code libraries (eg. my ``mrpy`` [code examples](http://mrpy.readthedocs.io/en/latest/example_toc.html) which are generated from [these notebooks](https://github.com/steven-murray/mrpy/docs/examples/)) "
    }
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