Part 2

2-talk.ipynb

{
 "metadata": {
  "name": "best practices and efficiency"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Clean Code; Clear Code\n",
      "========================\n",
      "\n",
      "Best practices \n",
      "--------------\n",
      "\n",
      "Style Guide for Python Code: http://www.python.org/dev/peps/pep-0008/\n",
      "\n",
      "It ain't c, c++, fortran -- don't code python like it is or it will be slow.\n",
      "\n",
      "Several examples adapted from: http://www.youtube.com/watch?v=OSGv2VnC0go\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Importing libraries\n",
      "-------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This is bad\n",
      "#from numpy import * \n",
      "\n",
      "import itertools\n",
      "#itertools. \n",
      "\n",
      "import numpy as np\n",
      "#np.array("
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 122
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lists!\n",
      "----------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "blank_list = [] # how to create a blank list"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colors = ['red', 'green', 'blue', 'yellow'] # list\n",
      "print colors"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['red', 'green', 'blue', 'yellow']\n"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# IF YOU SEE THIS\n",
      "for i in range(len(colors)): # for loop\n",
      "    print colors[i]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "red\n",
        "green\n",
        "blue\n",
        "yellow\n"
       ]
      }
     ],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# DO THIS INSTEAD\n",
      "for color in colors:\n",
      "    print color"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "red\n",
        "green\n",
        "blue\n",
        "yellow\n"
       ]
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# If you see this kind of thing in your code\n",
      "for index in range(len(colors)):\n",
      "    print index, '-->', colors[index]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0 --> red\n",
        "1 --> green\n",
        "2 --> blue\n",
        "3 --> yellow\n"
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Do this instead\n",
      "for index, color in enumerate(colors):\n",
      "    print index, '-->', color"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0 --> red\n",
        "1 --> green\n",
        "2 --> blue\n",
        "3 --> yellow\n"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#add list comprehensions\n",
      "dave = [x**2 for x in xrange(5)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print dave"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0, 1, 4, 9, 16]\n"
       ]
      }
     ],
     "prompt_number": 129
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Dictionaries!\n",
      "--------------\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "blank_dict = {} # how to create a blank... dictionary"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 130
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dictionary = {'Spider Man':'blue', 'Dave':'green', 'Jonathan':'red' }\n",
      "print dictionary"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'Dave': 'green', 'Jonathan': 'red', 'Spider Man': 'blue'}\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Keys: \"\n",
      "for key in dictionary:\n",
      "    print key\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Keys: \n",
        "Dave\n",
        "Jonathan\n",
        "Spider Man\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Values: \"\n",
      "for value in dictionary.itervalues():\n",
      "    print value"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Values: \n",
        "green\n",
        "red\n",
        "blue\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# dictionaries are unordered! \n",
      "for key, value in dictionary.items():\n",
      "    print key, '-->', value"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Dave --> green\n",
        "Jonathan --> red\n",
        "Spider Man --> blue\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dictionary_two = {key:dictionary[key]*2 for key in dictionary}\n",
      "dictionary_two.update({\"abbie\":\"purple\"})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print dictionary\n",
      "print dictionary_two"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'Dave': 'green', 'Jonathan': 'red', 'Spider Man': 'blue'}\n",
        "{'Dave': 'greengreen', 'Jonathan': 'redred', 'Spider Man': 'blueblue', 'abbie': 'purple'}\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Other cool ipython notebook features\n",
      "-------------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Read/write using the with \n",
      "\n",
      "with open('text.ascii', 'w') as filehandle:\n",
      "    filehandle.write('hello!')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# http://stackoverflow.com/questions/3394835/args-and-kwargs\n",
      "\n",
      "def print_everything(*args):\n",
      "    \"\"\"Takes a list of arguments and prints an enumerated list. \"\"\"\n",
      "    for count, thing in enumerate(args):\n",
      "        print '{0}. {1}'.format(count, thing)\n",
      "\n",
      "print_everything('apple', 'banana', 'cabbage')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0. apple\n",
        "1. banana\n",
        "2. cabbage\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Similarly, **kwargs allows you to handle named arguments that you have not defined in advance:\n",
      "\n",
      "def table_things(title_string, **kwargs):\n",
      "    \"\"\"Takes a title and a dictionary of elements to print as a table elements.\"\"\" \n",
      "    print title_string\n",
      "    print \"-\" * len(title_string)\n",
      "    for name, value in kwargs.items():\n",
      "        print '{0} = {1}'.format(name, value)\n",
      "    \n",
      "table_things(\"Classifying stuff with Dave\", apple = 'fruit', cabbage = 'vegetable')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Classifying stuff with Dave\n",
        "---------------------------\n",
        "cabbage = vegetable\n",
        "apple = fruit\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "names = ['David', 'Jonathan', 'Fred', 'Bob', 'Steve', 'Spider Man']\n",
      "statuses = ['cool', 'lame', 'lame', 'lame', 'lame', 'cool', ]\n",
      "\n",
      "cooldict = {name:status for name, status in itertools.izip(names, statuses)}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'itertools' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-10-f28645ec1aae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mstatuses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'cool'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lame'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lame'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lame'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lame'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'cool'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mcooldict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstatus\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mitertools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mizip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatuses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mNameError\u001b[0m: name 'itertools' is not defined"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cooldict"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "table_things(\"Cool factor\", **cooldict) # double splat! "
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plotting Workflow Example\n",
      "--------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pylab as pl\n",
      "pl.rcParams['figure.figsize'] = 8, 6  # plotsize "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N_points = 100 # 10, 100\n",
      "x = np.linspace(0, 2*np.pi, N_points)\n",
      "f = np.sin(x) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl.plot(x, f, label=\"sin\") # color, label\n",
      "pl.plot(x, np.cos(x), label=\"cos\", color='red') # color, label\n",
      "pl.legend()\n",
      "pl.ylabel(\"sin(x)\")\n",
      "pl.xlabel(\"radians\")\n",
      "pl.savefig(\"amazing-1.pdf\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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JFh4OLFwoZxMTgQU+TlFRwKxZsrvMoaRPD7RvD/j4qE5CkEkV7mAki1i2TNpS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\n+CAkBOjcWXrys7gbDws8ka1kySKFb+xYKYSOZs8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      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Glimpse of the future\n",
      "---------------------\n",
      "\n",
      "ipython notebook + gist.github.com + nbviewer == easily shareable and reproducible work"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Example\n",
      "----------\n",
      "\n",
      "- Their arxiv link: http://arxiv.org/abs/1303.2690\n",
      "- Their github (git repo): https://github.com/forero/LG_Kinematics\n",
      "- A view of their ipython notebook: http://nbviewer.ipython.org/urls/raw.github.com/forero/LG_Kinematics/master/code/main_analysis.ipynb\n",
      "- Change the hubble constant!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}