notebooks/P35 All Together Now.ipynb

{
 "metadata": {
  "name": "P35 All Together Now"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Using DirectView and LoadBalancedView together"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We mentioned that you can use any number of views of any sort simultaneously.\n",
      "\n",
      "The most common model for doing this is to use a DirectView to initialise the engine namespaces\n",
      "(either defining functions and variables, or loading datasets) with a DirectView,\n",
      "and then submitting work as tasks via the LoadBalancedView.\n",
      "\n",
      "For this example, we are going to define a few functions for fetching data and getting a simple summary on some stocks,\n",
      "then we are going to run those functions on a series of stocks in a load-balanced way."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The usual boilerplate:"
     ]
    },
    {
     "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": false,
     "input": [
      "from IPython import parallel\n",
      "\n",
      "rc = parallel.Client()\n",
      "dv = rc[:]\n",
      "lbv = rc.load_balanced_view()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Use matplotlib to fetch data from Yahoo"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px\n",
      "\n",
      "import urllib2\n",
      "from io import BytesIO\n",
      "\n",
      "from matplotlib import finance\n",
      "\n",
      "def get_yahoo_data(name=None, start=None, end=None):\n",
      "    \"\"\"return historical stock data from yahoo as a recarray.\"\"\"\n",
      "    fp = finance.fetch_historical_yahoo(name, start, end)\n",
      "    return finance.parse_yahoo_historical(fp, asobject=True)\n",
      "\n",
      "def relative_close(records):\n",
      "    \"\"\"return the reative change in closing price over the interval\"\"\"\n",
      "    \n",
      "    close = records['aclose']\n",
      "    return (close[-1] - close[0]) / close[0]\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Setup the timestamps.  We want the past year"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px\n",
      "import datetime\n",
      "end = datetime.datetime.now()\n",
      "start = end - datetime.timedelta(days=365)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Our ticker of stocks to check"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ticker = ['AAPL', 'GOOG', 'MSFT', 'RIMM', 'NOK', 'ORCL', 'AMZN', 'INTC', 'ATVI', 'EA', 'NFLX']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And map a simple function (that depends on the code we have defined on the engines)\n",
      "onto our ticker."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def year_over_year(name):\n",
      "    \"\"\"fetch data, and return year-over-year change (relative to first close)\n",
      " \n",
      "    in percent\n",
      "    \"\"\"\n",
      "    stock = get_yahoo_data(name, start, end)\n",
      "    return relative_close(stock) * 100\n",
      "\n",
      "amr = lbv.map(year_over_year, ticker)\n",
      "amr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<AsyncMapResult: year_over_year>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots()\n",
      "fig.set_figwidth(10)\n",
      "ax.bar(range(len(amr)), amr)\n",
      "ax.set_xticks(np.arange(len(amr))+0.4)\n",
      "ax.set_xticklabels(ticker);\n",
      "ax.axhline(0, c='k')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.lines.Line2D at 0x109a6b3d0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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FKTo6Wnl5ebaKBAAAcKpGh6nc3FxFRETo0ksv9dn+xRdfKCIiwvp3RESESkpKGl8hAACA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\nuEIvvvhii9y/jIw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      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And now we can feel sad for RIM, Nokia, and Netflix."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}