{
 "metadata": {
  "name": "",
  "signature": "sha256:2f588d849599d9daa03443c69bcf3f653019270e7d6328a1be4720c5521afcc2"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "import sklearn as sk\n",
      "import pickle\n",
      "print 'pandas version: ',pd.__version__\n",
      "print 'numpy version:',np.__version__\n",
      "print 'sklearn version:',sk.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "pandas version:  0.13.1\n",
        "numpy version: 1.8.1\n",
        "sklearn version: 0.14.1\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "home_dir='/home/ubuntu/UCSD_BigData'\n",
      "sys.path.append(home_dir+'/utils')\n",
      "\n",
      "from find_waiting_flow import *\n",
      "from AWS_keypair_management import *\n",
      "\n",
      "!pwd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/home/ubuntu/UCSD_BigData/notebooks/weather.mapreduce\r\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## managing AWS Credentials ##\n",
      "It can get confusing with the AWS credentials. So I created a few new scripts that should make the\n",
      "process easier and error-free.\n",
      "\n",
      "Before continuing, you need to get the credentials from AWS by using the IAM console.\n",
      "\n",
      "The credentials are downloaded into files with names such as credentials.csv, credentials (2).csv etc.\n",
      "\n",
      "The LaunchNotebookServer.py script has a command that can help you get these files to your EC2 instance,\n",
      "the option you need is -A and it is used as\n",
      "* `LaunchNotebookServer.py -A ~/Downloads/credentials\\*.csv`\n",
      "\n",
      "The `\\*`, rather than `*` is intentional.\n",
      "\n",
      "The files will be copied through `scp` to the directory `\\home\\ubuntu\\Vault ` on your ec2 instance.\n",
      "\n",
      "You can then run the following command that will read through the files and check which of the credentials is active. It will then\n",
      "generate a dictionary `Cred` that holds all of the key pairs that are currently active.\n",
      "\n",
      "In the following cells all of your secret pairs are printed out. Make sure that the cell output is deleted before you commit it to version to github."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "comment out on purpose"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# creds = pickle.load(open('/home/ubuntu/Vault/Creds.pkl'))\n",
      "# config = creds['mrjob']\n",
      "\n",
      "# key_id = config['key_id']\n",
      "# secret_key = config['secret_key']\n",
      "# s3_bucket = config['s3_logs']\n",
      "# s3_scratch= config['s3_scratch']\n",
      "\n",
      "# print config\n",
      "# print key_id\n",
      "# print secret_key\n",
      "# print s3_bucket\n",
      "# print s3_scratch"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_job_flow_id():\n",
      "    job_flow_id=find_waiting_flow(key_id,secret_key)\n",
      "    return job_flow_id\n",
      "\n",
      "# get_job_flow_id()\n",
      "DEBUG = True # if debug == true, remove all existing files, and regenerate"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## using map-reduce to calculate the total number of measurements for each station, later use this as weight##"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%writefile totalNumber.py\n",
      "\n",
      "from mrjob.job import MRJob\n",
      "\n",
      "class totalNumberOfStations(MRJob):\n",
      "    def mapper(self, _, line):\n",
      "        self.increment_counter('MrJob Counters','mapper',1)\n",
      "        elements=line.split(',')\n",
      "        if len(elements) != 368:\n",
      "            yield 'corrupted data', 1\n",
      "        else:\n",
      "            yield 'useful data', 1\n",
      "            yield(elements[0],1)\n",
      "            \n",
      "    def combiner(self, key, counts):\n",
      "        self.increment_counter('MrJob Counters','combiner',1)\n",
      "        yield key, sum(counts)\n",
      "\n",
      "    def reducer(self, key, counts):\n",
      "        self.increment_counter('MrJob Counters','reducer',1)\n",
      "        yield key, sum(counts)\n",
      "\n",
      "if __name__ == '__main__':\n",
      "    totalNumberOfStations.run()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overwriting totalNumber.py\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# try to run this job on local machien\n",
      "import os\n",
      "if not os.path.exists(\"all_station_count\"):\n",
      "    !python totalNumber.py -r emr --emr-job-flow-id=$job_flow_id hdfs:/weather/weather.csv > all_station_count"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head -10 all_station_count"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\"AJ000037668\"\t28\r\n",
        "\"AJ000037679\"\t29\r\n",
        "\"AJ000037734\"\t56\r\n",
        "\"AJ000037756\"\t105\r\n",
        "\"AJ000037844\"\t110\r\n",
        "\"AJ000037866\"\t85\r\n",
        "\"AJ000037888\"\t27\r\n",
        "\"AJ000037899\"\t94\r\n",
        "\"AJ000037907\"\t154\r\n",
        "\"AM000037627\"\t52\r\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os,sys,re,pickle,coding\n",
      "from numpy import *\n",
      "\n",
      "!gunzip stations.pkl.gz\n",
      "stations=pickle.load(open(\"stations.pkl\", 'rb'))\n",
      "!gzip stations.pkl\n",
      "\n",
      "stations.shape\n",
      "type(stations)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "pandas.core.frame.DataFrame"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stations.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>latitude</th>\n",
        "      <th>longitude</th>\n",
        "      <th>elevation</th>\n",
        "      <th>state</th>\n",
        "      <th>name</th>\n",
        "      <th>GSNFLAG</th>\n",
        "      <th>HCNFLAG</th>\n",
        "      <th>WMOID</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>ACW00011604</th>\n",
        "      <td> 17.1167</td>\n",
        "      <td>-61.7833</td>\n",
        "      <td>   10.1</td>\n",
        "      <td> NaN</td>\n",
        "      <td> ST JOHNS COOLIDGE FLD</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ACW00011647</th>\n",
        "      <td> 17.1333</td>\n",
        "      <td>-61.7833</td>\n",
        "      <td>   19.2</td>\n",
        "      <td> NaN</td>\n",
        "      <td>              ST JOHNS</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AE000041196</th>\n",
        "      <td> 25.3330</td>\n",
        "      <td> 55.5170</td>\n",
        "      <td>   34.0</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   SHARJAH INTER. AIRP</td>\n",
        "      <td> GSN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 41196</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AF000040930</th>\n",
        "      <td> 35.3170</td>\n",
        "      <td> 69.0170</td>\n",
        "      <td> 3366.0</td>\n",
        "      <td> NaN</td>\n",
        "      <td>          NORTH-SALANG</td>\n",
        "      <td> GSN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 40930</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AG000060390</th>\n",
        "      <td> 36.7167</td>\n",
        "      <td>  3.2500</td>\n",
        "      <td>   24.0</td>\n",
        "      <td> NaN</td>\n",
        "      <td>    ALGER-DAR EL BEIDA</td>\n",
        "      <td> GSN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 60390</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 8 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "             latitude  longitude  elevation state                   name  \\\n",
        "ACW00011604   17.1167   -61.7833       10.1   NaN  ST JOHNS COOLIDGE FLD   \n",
        "ACW00011647   17.1333   -61.7833       19.2   NaN               ST JOHNS   \n",
        "AE000041196   25.3330    55.5170       34.0   NaN    SHARJAH INTER. AIRP   \n",
        "AF000040930   35.3170    69.0170     3366.0   NaN           NORTH-SALANG   \n",
        "AG000060390   36.7167     3.2500       24.0   NaN     ALGER-DAR EL BEIDA   \n",
        "\n",
        "            GSNFLAG HCNFLAG  WMOID  \n",
        "ACW00011604     NaN     NaN    NaN  \n",
        "ACW00011647     NaN     NaN    NaN  \n",
        "AE000041196     GSN     NaN  41196  \n",
        "AF000040930     GSN     NaN  40930  \n",
        "AG000060390     GSN     NaN  60390  \n",
        "\n",
        "[5 rows x 8 columns]"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print stations.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(85284, 8)\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# experiment: how to get a single row from stations\n",
      "stations.loc['ACW00011604']\n",
      "\n",
      "var = 'ACW00011604'\n",
      "if var in stations: \n",
      "    print True\n",
      "else:\n",
      "    print False"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "False\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# now correlate the count of stations with the station table\n",
      "from pandas import DataFrame\n",
      "\n",
      "source = {} # create a dictionary for later use\n",
      "ins =  open(\"all_station_count\", \"r\")\n",
      "for line in ins:\n",
      "    line = line.strip().split('\\t')\n",
      "    name= line[0][1:-1]\n",
      "    count= line[1]\n",
      "    if name != \"useful data\":\n",
      "        source[name] = count\n",
      "\n",
      "record_count = DataFrame.from_dict(source, orient=\"index\").rename(columns={0:'count'})\n",
      "record_count.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CA004035200</th>\n",
        "      <td>   87</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>USS0006H19S</th>\n",
        "      <td>  134</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>USC00390043</th>\n",
        "      <td> 1061</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>UY000001086</th>\n",
        "      <td>   20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>UY000001084</th>\n",
        "      <td>   20</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "            count\n",
        "CA004035200    87\n",
        "USS0006H19S   134\n",
        "USC00390043  1061\n",
        "UY000001086    20\n",
        "UY000001084    20\n",
        "\n",
        "[5 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#now join the station table and record_count table\n",
      "all_record = record_count.join(other=stations, how=\"left\", lsuffix=\"lf\", rsuffix=\"rf\").rename(columns={ 0:'count'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# # then we can perform the partitioning work\n",
      "# all_record = all_record.sort(columns='latitude')\n",
      "all_record.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>count</th>\n",
        "      <th>latitude</th>\n",
        "      <th>longitude</th>\n",
        "      <th>elevation</th>\n",
        "      <th>state</th>\n",
        "      <th>name</th>\n",
        "      <th>GSNFLAG</th>\n",
        "      <th>HCNFLAG</th>\n",
        "      <th>WMOID</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CA004035200</th>\n",
        "      <td>   87</td>\n",
        "      <td> 49.1700</td>\n",
        "      <td>-104.5800</td>\n",
        "      <td>  695.0</td>\n",
        "      <td> NaN</td>\n",
        "      <td>            MINTON</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>USS0006H19S</th>\n",
        "      <td>  134</td>\n",
        "      <td> 41.3333</td>\n",
        "      <td>-106.5000</td>\n",
        "      <td> 2572.5</td>\n",
        "      <td>   Y</td>\n",
        "      <td> SOUTH BRUSH CREEK</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>USC00390043</th>\n",
        "      <td> 1061</td>\n",
        "      <td> 43.4892</td>\n",
        "      <td> -99.0631</td>\n",
        "      <td>  512.1</td>\n",
        "      <td>   D</td>\n",
        "      <td>       ACADEMY 2NE</td>\n",
        "      <td> NaN</td>\n",
        "      <td> HCN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>UY000001086</th>\n",
        "      <td>   20</td>\n",
        "      <td>-30.6500</td>\n",
        "      <td> -56.1700</td>\n",
        "      <td>   70.0</td>\n",
        "      <td> NaN</td>\n",
        "      <td>        CHARQUEADA</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>UY000001084</th>\n",
        "      <td>   20</td>\n",
        "      <td>-30.6500</td>\n",
        "      <td> -56.3800</td>\n",
        "      <td>  190.0</td>\n",
        "      <td> NaN</td>\n",
        "      <td>         GUAYUBIRA</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "            count  latitude  longitude  elevation state               name  \\\n",
        "CA004035200    87   49.1700  -104.5800      695.0   NaN             MINTON   \n",
        "USS0006H19S   134   41.3333  -106.5000     2572.5     Y  SOUTH BRUSH CREEK   \n",
        "USC00390043  1061   43.4892   -99.0631      512.1     D        ACADEMY 2NE   \n",
        "UY000001086    20  -30.6500   -56.1700       70.0   NaN         CHARQUEADA   \n",
        "UY000001084    20  -30.6500   -56.3800      190.0   NaN          GUAYUBIRA   \n",
        "\n",
        "            GSNFLAG HCNFLAG  WMOID  \n",
        "CA004035200     NaN     NaN    NaN  \n",
        "USS0006H19S     NaN     NaN    NaN  \n",
        "USC00390043     NaN     HCN    NaN  \n",
        "UY000001086     NaN     NaN    NaN  \n",
        "UY000001084     NaN     NaN    NaN  \n",
        "\n",
        "[5 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print all_record.shape[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "85284\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## using the joined table for weight-based partition ##"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# create the treenode class\n",
      "class TreeNode:\n",
      "    #ALL_RECORD_TEST = DataFrame(randn(33, 3), columns=['latitude', 'longitude', 'dummy_val'])\n",
      "    STATION_LIMIT = 100 # determine the number of stations in the leaf\n",
      "    \n",
      "    def __init__(self, _parent=None):\n",
      "        self.parent = _parent\n",
      "        self.leftChild = None\n",
      "        self.rightChild = None\n",
      "        self.stationList = None\n",
      "        self.isLeaf = False\n",
      "        self.color = None\n",
      "        \n",
      "        # pca related data\n",
      "        self.id = None\n",
      "        self.k = -1\n",
      "        self.compare = None"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "partition_data = all_record.loc[:, ['latitude','longitude','count']]\n",
      "partition_data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>latitude</th>\n",
        "      <th>longitude</th>\n",
        "      <th>count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>CA004035200</th>\n",
        "      <td> 49.1700</td>\n",
        "      <td>-104.5800</td>\n",
        "      <td>   87</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>USS0006H19S</th>\n",
        "      <td> 41.3333</td>\n",
        "      <td>-106.5000</td>\n",
        "      <td>  134</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>USC00390043</th>\n",
        "      <td> 43.4892</td>\n",
        "      <td> -99.0631</td>\n",
        "      <td> 1061</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>UY000001086</th>\n",
        "      <td>-30.6500</td>\n",
        "      <td> -56.1700</td>\n",
        "      <td>   20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>UY000001084</th>\n",
        "      <td>-30.6500</td>\n",
        "      <td> -56.3800</td>\n",
        "      <td>   20</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "             latitude  longitude count\n",
        "CA004035200   49.1700  -104.5800    87\n",
        "USS0006H19S   41.3333  -106.5000   134\n",
        "USC00390043   43.4892   -99.0631  1061\n",
        "UY000001086  -30.6500   -56.1700    20\n",
        "UY000001084  -30.6500   -56.3800    20\n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print partition_data.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(85284, 3)\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Yoav:** What is the goal of the following cell?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# # try to extract rows, and it works\n",
      "# test = stations[0:3]\n",
      "# test.shape[0]\n",
      "count = partition_data.sum(axis=0)['latitude'] # demo usage \n",
      "print dtype(count)\n",
      "\n",
      "tmp = partition_data.iloc[1]['latitude']\n",
      "print tmp\n",
      "\n",
      "print partition_data.sum(axis=0)['count']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "float64\n",
        "41.3333\n",
        "inf"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Yoav:** What does the code in the next cells do? How do you know whether it does what it is supposed to do correctly?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# this implementation might be buggy, check the logic\n",
      "def partition(df_local, leafList, direction=\"lat\", _parent=None):\n",
      "    \n",
      "    if df_local.shape[0] <= TreeNode.STATION_LIMIT:\n",
      "        stations = []\n",
      "        for index, row in df_local.iterrows():\n",
      "            stations.append(index)\n",
      "        \n",
      "        curNode = TreeNode()\n",
      "        curNode.stationList = stations\n",
      "        curNode.parent = _parent\n",
      "        curNode.isLeaf = True\n",
      "        leafList.append(curNode)\n",
      "        return curNode\n",
      "    \n",
      "    elif direction == \"lat\":\n",
      "        \n",
      "        df_local = df_local.sort(columns='latitude')\n",
      "        startRowIndex = 0\n",
      "        endRowIndex = df_local.shape[0] - 1\n",
      "\n",
      "        # the weight-based partition\n",
      "        start = startRowIndex - 1\n",
      "        end   = endRowIndex + 1\n",
      "        left = 0\n",
      "        right= 0\n",
      "        while start < end:\n",
      "            if left == right:\n",
      "                start += 1\n",
      "                end   -= 1\n",
      "                left = int(df_local.iloc[start]['count'])\n",
      "                right= int(df_local.iloc[end]['count'])\n",
      "\n",
      "            elif left < right:\n",
      "                start +=1\n",
      "                end = end\n",
      "\n",
      "                right = right - left\n",
      "                left = int(df_local.iloc[start]['count'])\n",
      "            else: # left > right\n",
      "                start = start\n",
      "                end -= 1\n",
      "\n",
      "                left  = left - right\n",
      "        right = int(df_local.iloc[end]['count'])\n",
      "        \n",
      "        midRowIndex = start\n",
      "        # make it into a binary tree\n",
      "        curNode = TreeNode()\n",
      "        curNode.leftChild  = partition(df_local[startRowIndex: midRowIndex+1], leafList, direction=\"long\", _parent = curNode)\n",
      "        curNode.rightChild = partition(df_local[midRowIndex+1: endRowIndex+1], leafList, direction=\"long\", _parent = curNode)\n",
      "        curNode.parent = _parent\n",
      "        return curNode\n",
      "        \n",
      "    else: # for 'long' direction\n",
      "        df_local = df_local.sort(columns='longitude')\n",
      "        startRowIndex = 0\n",
      "        endRowIndex = df_local.shape[0] - 1\n",
      "\n",
      "        # the weight-based partition\n",
      "        start = startRowIndex - 1\n",
      "        end   = endRowIndex + 1\n",
      "        left = 0\n",
      "        right= 0\n",
      "        while start < end:\n",
      "            if left == right:\n",
      "                start += 1\n",
      "                end   -= 1\n",
      "                left = int(df_local.iloc[start]['count'])\n",
      "                right= int(df_local.iloc[end]['count'])\n",
      "\n",
      "            elif left < right:\n",
      "                start +=1\n",
      "                end = end\n",
      "\n",
      "                right = right - left\n",
      "                left = int(df_local.iloc[start]['count'])\n",
      "            else: # left > right\n",
      "                start = start\n",
      "                end -= 1\n",
      "\n",
      "                left  = left - right\n",
      "                right = int(df_local.iloc[end]['count'])\n",
      "        \n",
      "        midRowIndex = start\n",
      "        \n",
      "        # create the partition\n",
      "        curNode = TreeNode()\n",
      "        curNode.leftChild  = partition(df_local[startRowIndex: midRowIndex+1], leafList, direction=\"lat\", _parent = curNode)\n",
      "        curNode.rightChild = partition(df_local[midRowIndex+1: endRowIndex+1], leafList, direction=\"lat\", _parent = curNode)\n",
      "        curNode.parent = _parent\n",
      "        return curNode"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print os.path.exists(\"partition_yue.pkl\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "True\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_all_leaves(root):\n",
      "    retList = []\n",
      "    \n",
      "    # using level order traversal\n",
      "    curLevel = [root]\n",
      "    nextLevel = []\n",
      "    while len(curLevel) != 0:\n",
      "        for node in curLevel:\n",
      "            if node.leftChild != None:\n",
      "                nextLevel.append(node.leftChild)\n",
      "            if node.rightChild != None:\n",
      "                nextLevel.append(node.rightChild)\n",
      "            \n",
      "            if node.leftChild == None and node.rightChild == None:\n",
      "                retList.append(node)\n",
      "        # prepare for next iteration\n",
      "        curLevel = nextLevel[:]\n",
      "        nextLevel = []\n",
      "    \n",
      "    return retList"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "leaf_list = []\n",
      "global_root = None\n",
      "\n",
      "if os.path.exists(\"partition_yue.pkl\"): # should name it with pkl extension actually\n",
      "    print \"loading partition\"\n",
      "    global_root = pickle.load(open(\"partition_yue.pkl\", \"rb\"))\n",
      "    print \"populating leaf list\"\n",
      "    leaf_list = get_all_leaves(global_root)\n",
      "    \n",
      "else:    \n",
      "    print \"regenerating partition\"\n",
      "    global_root = partition(partition_data, leaf_list)\n",
      "    # verify that the leaf_list is storing all the leaves--> done\n",
      "    partition_success = True\n",
      "    for itr in leaf_list:\n",
      "        if itr.isLeaf !=  True: \n",
      "            partition_success = False\n",
      "\n",
      "    # store the partition upon success\n",
      "    # does this writing preserve the entire tree?\n",
      "    if partition_success == True:\n",
      "        print \"writing partition to file\" \n",
      "        pickle.dump(global_root, open(\"partition_yue.pkl\",\"wb\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "loading partition\n",
        "populating leaf list"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stations.loc['AYW00090001']['latitude']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "-90.0"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## now we could draw the world map before merging ##"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Yoav:** The map below looks bad. The regions tend to be tall and narrow. Probably a bug in the code above."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from mpl_toolkits.basemap import Basemap\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "lonmin=-180;lonmax=180;latsmin=-80;latsmax=80;\n",
      "plt.figure(figsize=(15,10),dpi=300)\n",
      "m = Basemap(projection='merc',llcrnrlat=latsmin,urcrnrlat=latsmax,\\\n",
      "            llcrnrlon=lonmin,urcrnrlon=lonmax,lat_ts=20,resolution='i')\n",
      "m.drawcoastlines(linewidth=0.5)\n",
      "\n",
      "# draw parallels and meridians.\n",
      "parallels = np.arange(-80,81,10.)\n",
      "m.drawparallels(parallels,labels=[False,True,True,False])\n",
      "meridians = np.arange(10.,351.,20.)\n",
      "m.drawmeridians(meridians,labels=[True,False,False,True])\n",
      "\n",
      "lat_list = []\n",
      "log_list = []\n",
      "count_list = []\n",
      "\n",
      "for node in leaf_list:\n",
      "    for station in node.stationList:\n",
      "        try:\n",
      "            entry = stations.loc[station] # using the name\n",
      "            lat = entry['latitude']\n",
      "            log = entry['longitude']\n",
      "            log_list.append(log)\n",
      "            lat_list.append(lat)\n",
      "            count_list.append(len(node.stationList))\n",
      "        except Exception:\n",
      "            a = 1 \n",
      "\n",
      "x, y = m(log_list,lat_list)\n",
      "m.scatter(x, y, c=count_list, s=20, cmap=plt.cm.jet, edgecolors='None', alpha=0.75)\n",
      "cbar = plt.colorbar()\n",
      "plt.title('partition visual')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GDRqk0F6DBg2YPHkykyZN4u7du7k8ne96mHKWUaxdu5YyZcrQvHlz7O3tsbGx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\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zvkZhQAKExCARKQFZiUBtCSRlQUouNFUYi34VSO8C5UUQDCB++iK614epxoNJMkF2Gkgy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gtvfsH3O64SF7eemJgU3nrr8wr379y5Ars9m7S0b7Bas8jI+AazObP8+sSJn3P4cAInT37N8uUx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b3B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Ut8/ShRaunEGzw+NMhveM7rImngVmw9IpFcIt4CHX5BT665M9rLDEQEFVMTZcUz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NGj1XgpDTpo7AeMgZAxVx5swZFi1axOLFi9M8OMuQvLjW39zcnEaNGunft7e3Z968eSQk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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x99a8c90>"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##PCA ON EACH NODE##\n",
      "\n",
      "step1: extract the max-min concatenated vector for each station </br>\n",
      "\n",
      "step2: perform the pca on each node and determine k. (this step works on small sets of data locally and on cluster, but when feeding in large data it fails.\n",
      "i tried to make the mapper and reducer perform cell-wise add in a naive way to save memory, but still got the code 137 error.\n",
      ")"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# we need some serialize/de- to turn list into string, notice this is not pickle\n",
      "def serialize(inputList):\n",
      "    ret_string = \"\"\n",
      "    for i in inputList:\n",
      "        ret_string = ret_string + \",\" + str(i)\n",
      "    return ret_string[1:]\n",
      "\n",
      "def deserialize(line):\n",
      "    ret_array_string = line[1:-1].strip().split(\",\")\n",
      "    ret_array_val = [None] * len(ret_array_string)\n",
      "    \n",
      "    for i in range(0, len(ret_array_string)):\n",
      "        ret_array_val[i] = int(ret_array_string[i])\n",
      "\n",
      "    return ret_array_val"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Yoav:** Again, not enough documentation."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%writefile max_min_collector.py\n",
      "\n",
      "from mrjob.job import MRJob\n",
      "\n",
      "class max_min(MRJob):\n",
      "    # helper - linear insertion\n",
      "    # the return value is always [] + boolean, with the boolean indicating the linear insertion is successful or not\n",
      "    # in the rare case that it fails, the original list is completely useless\n",
      "    def sanitize(self, inputList, target):\n",
      "        lastIndex = -1\n",
      "        i = 0\n",
      "        while i < len(inputList):\n",
      "            if inputList[i] == target: \n",
      "                i = i + 1\n",
      "            else:\n",
      "                # set the thing in between\n",
      "                if lastIndex == -1:\n",
      "                    # set everything in between to be input[i]\n",
      "                    set_to = inputList[i]\n",
      "                    for j in range(lastIndex + 1, i):\n",
      "                        inputList[j] = set_to\n",
      "                else:\n",
      "                    slope = (inputList[i] - inputList[lastIndex]) / (i-1 - lastIndex + 1)\n",
      "                    base_val = inputList[lastIndex]\n",
      "                    for j in range(lastIndex + 1, i):\n",
      "                        inputList[j] = base_val + slope * (j - lastIndex)           \n",
      "                \n",
      "                # find the next \n",
      "                j = i + 1\n",
      "                for j in range(i + 1, len(inputList)):\n",
      "                    if inputList[j] == target: break\n",
      "                    else: j += 1\n",
      "                \n",
      "                if j == len(inputList): \n",
      "                    i = len(inputList)\n",
      "                else: \n",
      "                    lastIndex = j - 1\n",
      "                    i = j\n",
      "\n",
      "        # it is possible that last sequence is not set\n",
      "        if inputList[-1] != target:\n",
      "            return inputList\n",
      "        elif inputList[-1] == target and lastIndex != -1:\n",
      "            set_to = inputList[lastIndex]\n",
      "            for i in range(lastIndex + 1, len(inputList)):\n",
      "                inputList[i] = set_to\n",
      "            return inputList\n",
      "        else:\n",
      "            return []\n",
      "    \n",
      "    # helper function: serialize a list of numbers into a comma separated string\n",
      "    def serialize(self, inputList):\n",
      "        ret_string = \"\"\n",
      "        for i in inputList:\n",
      "            ret_string = ret_string + \",\" + str(i)\n",
      "        return ret_string[1:]\n",
      "    \n",
      "    \n",
      "    # the standard workflow for map-reduce\n",
      "    def mapper(self, _, line):\n",
      "        elements=line.split(',')\n",
      "        if len(elements) != 368:\n",
      "            dummy = 1\n",
      "        elif elements[1]=='TMAX' or elements[1]=='TMIN': # only collectingor temperatures\n",
      "            one_year = elements[3:]\n",
      "            yield elements[0], (elements[1], one_year) # yielding in the format:  station -> (max/min, reading for 365 days) \n",
      "            \n",
      "    def combiner(self, key, value):# value is a list\n",
      "        max_value = [-10000] * 365\n",
      "        min_value = [+10000] * 365\n",
      "        for value_itr in value:\n",
      "            if value_itr[0] == 'TMAX': # updating the max_value array\n",
      "                data_array = value_itr[1]\n",
      "                for i in range(0, 365):\n",
      "                    if data_array[i] == '': continue\n",
      "                    else:\n",
      "                        if int(data_array[i]) > max_value[i]: max_value[i] = int(data_array[i])\n",
      "            else: # == 'TMIN', updating the min_value array\n",
      "                data_array = value_itr[1]\n",
      "                for i in range(0, 365):\n",
      "                    if data_array[i] == '': continue\n",
      "                    else:\n",
      "                        if int(data_array[i]) < min_value[i]: min_value[i] = int(data_array[i])\n",
      "        \n",
      "        yield key, ('TMAX', max_value)\n",
      "        yield key, ('TMIN', min_value)\n",
      "\n",
      "    def reducer(self, key, value):\n",
      "        max_value = [-10000] * 365\n",
      "        min_value = [+10000] * 365\n",
      "        for value_itr in value:\n",
      "            if value_itr[0] == 'TMAX': # updating the max_value array\n",
      "                data_array = value_itr[1]\n",
      "                for i in range(0, 365):\n",
      "                    if data_array[i] == '': continue\n",
      "                    else:\n",
      "                        if int(data_array[i]) > max_value[i]: max_value[i] = int(data_array[i])\n",
      "            else: # == 'TMIN', updating the min_value array\n",
      "                data_array = value_itr[1]\n",
      "                for i in range(0, 365):\n",
      "                    if data_array[i] == '': continue\n",
      "                    else:\n",
      "                        if int(data_array[i]) < min_value[i]: min_value[i] = int(data_array[i])\n",
      "        \n",
      "        # get rid of corrupted data points, by linear inseration\n",
      "        max_value = self.sanitize(max_value, -10000)\n",
      "        min_value = self.sanitize(min_value, +10000)\n",
      "        \n",
      "        if max_value == [] or min_value == []: \n",
      "            dummy = 1\n",
      "        else:\n",
      "            # yielding to the stdout, construct a 365 * 1 * 2 list by appending min readings to max readings\n",
      "            output_string = self.serialize(max_value + min_value)\n",
      "            yield key, output_string\n",
      "        \n",
      "if __name__ == '__main__':\n",
      "    max_min.run()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overwriting max_min_collector.py\n"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#!rm mapping.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# execute the map-reduce work flow and write to a file called mapping.txt\n",
      "if os.path.exists(\"mapping.txt\") == True:\n",
      "    print \"mapping.txt already exists! Do nothing!\"\n",
      "else:\n",
      "    job_flow_id = get_job_flow_id()\n",
      "    #!python totalNumber.py -r emr --emr-job-flow-id=$job_flow_id hdfs:/weather/weather.csv > all_station_count # working reference\n",
      "    !python max_min_collector.py -r emr --emr-job-flow-id=$job_flow_id hdfs:/weather/weather.csv > mapping.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<boto.emr.emrobject.JobFlow object at 0xa3dc8d0> no_script.yoavfreund.20140517.080731.371759 j-2EFV3O64LF1U2 WAITING\n",
        "<boto.emr.emrobject.JobFlow object at 0xa33df10> no_script.yoavfreund.20140525.174308.746673 j-LTOJMJ14G840 WAITING\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "using configs in /home/ubuntu/.mrjob.conf\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "creating tmp directory /tmp/max_min_collector.ubuntu.20140525.221302.218896\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Copying non-input files into s3://yuf/scratch/max_min_collector.ubuntu.20140525.221302.218896/files/\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Adding our job to existing job flow j-LTOJMJ14G840\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 30.5s ago, status RUNNING: Running step (max_min_collector.ubuntu.20140525.221302.218896: Step 1 of 1)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 61.0s ago, status RUNNING: Running step (max_min_collector.ubuntu.20140525.221302.218896: Step 1 of 1)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 91.6s ago, status RUNNING: Running step (max_min_collector.ubuntu.20140525.221302.218896: Step 1 of 1)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 122.2s ago, status RUNNING: Running step (max_min_collector.ubuntu.20140525.221302.218896: Step 1 of 1)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 152.9s ago, status RUNNING: Running step (max_min_collector.ubuntu.20140525.221302.218896: Step 1 of 1)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 183.3s ago, status RUNNING: Running step (max_min_collector.ubuntu.20140525.221302.218896: Step 1 of 1)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job completed.\r\n",
        "Running time was 166.0s (not counting time spent waiting for the EC2 instances)\r\n",
        "ec2_key_pair_file not specified, going to S3\r\n",
        "Fetching counters from S3...\r\n",
        "Waiting 5.0s for S3 eventual consistency\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Counters may not have been uploaded to S3 yet. Try again in 5 minutes with: mrjob fetch-logs --counters j-LTOJMJ14G840\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Counters from step 1:\r\n",
        "  (no counters found)\r\n",
        "Streaming final output from s3://yuf/scratch/max_min_collector.ubuntu.20140525.221302.218896/output/\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "removing tmp directory /tmp/max_min_collector.ubuntu.20140525.221302.218896\r\n",
        "Removing all files in s3://yuf/scratch/max_min_collector.ubuntu.20140525.221302.218896/\r\n"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls -al | grep \"mapping*\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-rw-rw-r--  1 ubuntu ubuntu  83409067 May 25 22:16 mapping.txt\r\n"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!rm station_2_data.pkl"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# the suggested way of reading file: with clause handles opening and closing file, with exception being throwed propoerly\n",
      "station_2_data = {}\n",
      "\n",
      "if os.path.exists(\"station_2_data.pkl\"):\n",
      "    station_2_data = pickle.load(open(\"station_2_data.pkl\", \"rb\"))\n",
      "else:\n",
      "    with open(\"mapping.txt\") as f:\n",
      "        for line in f:\n",
      "            line = line.strip()\n",
      "            if line == '': continue\n",
      "            else:\n",
      "                key_value = line.split() # split on consequtive whitespaces\n",
      "                #print key_value[0]\n",
      "                station_2_data[key_value[0]] = deserialize(key_value[1])\n",
      "\n",
      "    # backing up the dictionary structure in a file\n",
      "    pickle.dump(station_2_data, open(\"station_2_data.pkl\", \"wb\") )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print len(station_2_data.keys())\n",
      "print station_2_data.keys()[0][1:-1]\n",
      "print global_root.id\n",
      "print len(station_2_data[station_2_data.keys()[0]])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "29088\n",
        "USC00085973\n",
        "None\n",
        "730\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Yoav: ** Why are you keeping these commented out cells around?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# # assign ids to nodes, starting from the global_root, and put the id-> node instance mapping into a dictionary\n",
      "# def assign_id(curNode):\n",
      "    \n",
      "#     id_counter = 0\n",
      "#     id_2_node = {}\n",
      "    \n",
      "#     # using level order traversal\n",
      "#     curLevel = [curNode]\n",
      "#     nextLevel = []\n",
      "#     while len(curLevel) != 0:\n",
      "#         for node in curLevel:\n",
      "#             node.id = \"NODE_\" + str(id_counter) # set the id field in the node!\n",
      "#             id_2_node[node.id] = node\n",
      "            \n",
      "#             id_counter = id_counter + 1\n",
      "#             if node.leftChild != None:\n",
      "#                 nextLevel.append(node.leftChild)\n",
      "#             if node.rightChild != None:\n",
      "#                 nextLevel.append(node.rightChild)\n",
      "        \n",
      "#         curLevel = nextLevel[:]\n",
      "#         nextLevel = []\n",
      "    \n",
      "#     return id_2_node\n",
      "\n",
      "# # now get the mapping from id to node in a dictionary\n",
      "# id_2_node = assign_id(global_root)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#leaf_list = get_all_leaves(global_root)\n",
      "print len(leaf_list)\n",
      "print leaf_list[:10]\n",
      "\n",
      "for i in range(0, len(leaf_list)):\n",
      "    node = leaf_list[i]\n",
      "    node.id = 'LEAF_' + str(i)\n",
      "print leaf_list[10].id"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1764\n",
        "[<__main__.TreeNode instance at 0x7a97cb0>, <__main__.TreeNode instance at 0x7a2c5a8>, <__main__.TreeNode instance at 0x7a2c560>, <__main__.TreeNode instance at 0x7a97c20>, <__main__.TreeNode instance at 0x7a84dd0>, <__main__.TreeNode instance at 0x7a2c440>, <__main__.TreeNode instance at 0x7a049e0>, <__main__.TreeNode instance at 0x8a13ea8>, <__main__.TreeNode instance at 0x8a13248>, <__main__.TreeNode instance at 0x6464cf8>]\n",
        "LEAF_10\n"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!heat -10 leaf_to_count"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "LEAF_10\n"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "leaf_station_count = {}\n",
      "\n",
      "for leaf in leaf_list:\n",
      "    leaf_station_count[leaf.id] = len(leaf.stationList)\n",
      "\n",
      "pickle.dump(leaf_station_count, open(\"leaf_station_count.pkl\", \"wb\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%writefile dict_wrapper_class.py\n",
      "\n",
      "class Lookup:\n",
      "    \n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# here we should overwrite the pickle for the tree structure\n",
      "# this time, with dedicated node_id for the leaves\n",
      "if not os.path.exists('partition_with_leaf_id.pkl'):\n",
      "    pickle.dump(global_root, open(\"partition_with_leaf_id.pkl\", \"wb\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls -al partition*"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-rw-rw-r-- 1 ubuntu ubuntu 28858529 May 25 23:08 partition_with_leaf_id.pkl\r\n",
        "-rw-rw-r-- 1 ubuntu ubuntu  2327396 May 25 19:46 partition_yue.pkl\r\n"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!rm all_leaf_info.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# now writing all the station info (subtracted mean from every station in each node) to a file\n",
      "# each line is a record for a station, not for a node, but the key is the node id, instead of station name\n",
      "\n",
      "if os.path.exists('all_leaf_info.txt'):\n",
      "    print \"file already there. no need to regenerate\"\n",
      "else:\n",
      "    with open(\"all_leaf_info.txt\", \"wb\") as f:\n",
      "        count = 0\n",
      "        for child in leaf_list: \n",
      "            child_station_list = child.stationList\n",
      "\n",
      "            # create the 2d array to work on\n",
      "            list_of_list = []\n",
      "            for station in child_station_list:\n",
      "                station = \"\\\"\" + station + \"\\\"\" # station names stored in nodes do not have double quotes around them\n",
      "                if station in station_2_data:\n",
      "                    li = station_2_data[station] # look-up the list from the dictionary\n",
      "                    list_of_list.append(li)\n",
      "                else:\n",
      "                    #print station\n",
      "                    count += 1\n",
      "\n",
      "            if len(list_of_list) == 0:\n",
      "                #print child.id + \" has no valid data\"\n",
      "                continue\n",
      "            else:\n",
      "                value = np.array(list_of_list)\n",
      "                # calculate the mean value, and store it in both node and file\n",
      "                MEAN = np.mean(value, axis=0)\n",
      "                child.mean = MEAN\n",
      "\n",
      "                # subtract mean from every row in value matrix\n",
      "                for i in range(0, value.shape[0]):\n",
      "                    value[i][:] = value[i][:] - MEAN\n",
      "                    value_str = serialize(value[i][:])\n",
      "                    f.write(child.id + \" \" + value_str + \"\\n\")\n",
      "\n",
      "        print \"cannot find the record for:\" + str(count)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "file already there. no need to regenerate\n"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# create a subset of data for testing locally\n",
      "!rm test_all_leaf_info.txt\n",
      "!head -3000 all_leaf_info.txt > test_all_leaf_info.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# this is the reference implementation of commputing mean, and cov for a single node.\n",
      "# # but now we need to make it work using map-reduce\n",
      "\n",
      "# M=Dout.loc[:,1:365].transpose()\n",
      "# M=M.dropna(axis=1)\n",
      "# (columns,rows)=shape(M)\n",
      "# Mean=mean(M, axis=1).values\n",
      "\n",
      "# print (columns,rows), shape(Mean)\n",
      "# C=np.zeros([columns,columns])   # Sum\n",
      "# N=np.zeros([columns,columns])   # Counter of non-nan entries\n",
      "\n",
      "# for i in range(rows):\n",
      "#     if i % 1000==0: \n",
      "#         print i\n",
      "#     row=M.iloc[:,i]-Mean;\n",
      "#     outer=np.outer(row,row)\n",
      "#     valid=isnan(outer)==False\n",
      "#     C[valid]=C[valid]+outer[valid]  # update C with the valid location in outer\n",
      "#     N[valid]=N[valid]+1\n",
      "# valid_outer=np.multiply(1-isnan(N),N>0)\n",
      "# cov=np.divide(C,N)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%writefile new_coding.py\n",
      "\n",
      "import base64,pickle,zlib,sys\n",
      "import pdb\n",
      "\n",
      "# define the new helper function\n",
      "def load(line):\n",
      "    (key,eVal)=line.split('\\t')\n",
      "    Value=pickle.loads(zlib.decompress(base64.b64decode(eVal)))\n",
      "    return(key,Value)\n",
      "    \n",
      "def dump(key,Value,out=sys.stdout):\n",
      "    out.write(\"%s\\t%s\\n\" % (key, base64.b64encode(zlib.compress(pickle.dumps(Value),9))))\n",
      "   \n",
      "def encode(Value):\n",
      "    return base64.b64encode(zlib.compress(pickle.dumps(Value),9))\n",
      "\n",
      "def decode(line):\n",
      "    return pickle.loads(zlib.decompress(base64.b64decode(line)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overwriting new_coding.py\n"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "this is the script to calculate k(top k eigen-vectors) for each node"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%writefile compute_stats.py\n",
      "from mrjob.job import MRJob\n",
      "from mrjob.protocol import JSONProtocol, JSONProtocol, PickleProtocol, RawValueProtocol, RawProtocol\n",
      "import pickle\n",
      "import numpy as np\n",
      "import os\n",
      "\n",
      "class per_node_calc(MRJob):\n",
      "\n",
      "    INPUT_PROTOCOL = RawValueProtocol\n",
      "    INTERNAL_PROTOCOL = PickleProtocol\n",
      "    OUTPUT_PROTOCOL = JSONProtocol\n",
      "        \n",
      "    # helper function\n",
      "    def deserialize(self, line):\n",
      "        arr_str = line.split(\",\")\n",
      "        arr_val = [None] * len(arr_str)\n",
      "        for i in range(0, len(arr_str)):\n",
      "            arr_val[i] = int(arr_str[i])\n",
      "        return arr_val\n",
      "    \n",
      "    # helper function, for associating id to node and put them in a dict\n",
      "    def load_id(self, root):\n",
      "        id_2_node = {}\n",
      "        \n",
      "        curLevel = [root]\n",
      "        nextLevel = []\n",
      "        while len(curLevel) != 0:\n",
      "            for node in curLevel:\n",
      "                if node.leftChild != None:\n",
      "                    nextLevel.append(node.leftChild)\n",
      "                if node.rightChild != None:\n",
      "                    nextLevel.append(node.rightChild)\n",
      "\n",
      "                if node.leftChild == None and node.rightChild == None:\n",
      "                    id_2_node[node.id] = node\n",
      "            \n",
      "            # prepare for next iteration\n",
      "            curLevel = nextLevel[:]\n",
      "            nextLevel = []\n",
      "        \n",
      "        return id_2_node\n",
      "    \n",
      "    # if we import:\n",
      "    # 1. the tree structure\n",
      "    # 2. the node.id -> node mapping\n",
      "    # we could use a multi-step function to perform svd, on each of the node we have\n",
      "    \n",
      "    # tested\n",
      "    def mapper_sum(self, _, line):\n",
      "        line = line.strip()\n",
      "        if line != '':\n",
      "            arr = line.split()       \n",
      "            try:\n",
      "                key = arr[0]\n",
      "                vec = np.multiply(self.deserialize(arr[1]), 0.01)\n",
      "                value = np.outer(vec, vec) # scaling the data so that we don't get overflow\n",
      "                yield key, value\n",
      "            except Exception:\n",
      "                self.increment_counter('group', 'mapper_exception', 1)\n",
      "\n",
      "    def combiner_sum(self, key, values):\n",
      "        local_sum = np.zeros([730,730])\n",
      "        \n",
      "        # a memory efficient way of adding\n",
      "        for mat in values:\n",
      "            for i in range(0, mat.shape[0]):\n",
      "                for j in range(0, mat.shape[1]):\n",
      "                    local_sum[i][j] += mat[i][j]\n",
      "        yield key, local_sum\n",
      "           \n",
      "    def reducer_sum(self, key, values):\n",
      "        local_sum = np.zeros([730,730])\n",
      "        \n",
      "        # a memory efficient way\n",
      "        for mat in values:\n",
      "            for i in range(0, mat.shape[0]):\n",
      "                for j in range(0, mat.shape[1]):\n",
      "                    local_sum[i][j] += mat[i][j]\n",
      "        yield key, local_sum\n",
      " \n",
      " \n",
      "    # for each key, the values only have a list, which is a singleton    \n",
      "    def reducer_svd(self, key, values):\n",
      "        \n",
      "        leaf_to_count = pickle.load(open(\"leaf_station_count.pkl\", \"rb\"))\n",
      "        \n",
      "        total_sum = np.zeros([730, 730])\n",
      "        for mat in values:\n",
      "            total_sum += mat\n",
      "        \n",
      "        if key in leaf_to_count:\n",
      "            count = leaf_to_count[key]\n",
      "            cov = total_sum / count\n",
      "\n",
      "            # now perform svd and assign the values to node\n",
      "            U, D, V = np.linalg.svd(cov)\n",
      "\n",
      "            # need to scale the D array\n",
      "            D_scale = np.multiply(D[:], 0.1)\n",
      "\n",
      "            try:\n",
      "                acc_sum = np.cumsum(D_scale[:])\n",
      "                acc = np.divide(acc_sum, sum(D_scale))\n",
      "\n",
      "                REQUIREMENT = 0.95\n",
      "                i = 0\n",
      "                for i in range(0, len(acc)):\n",
      "                    if acc[i] >= REQUIREMENT: break \n",
      "\n",
      "                yield key, i\n",
      "            except Exception:\n",
      "                yield key, -1 # -1 is the error flag\n",
      "\n",
      "        else:\n",
      "            yield \"no_such_id_\", 1\n",
      "\n",
      "    def steps(self):\n",
      "        return [self.mr(mapper=self.mapper_sum,\n",
      "                        combiner=self.combiner_sum,\n",
      "                        reducer=self.reducer_sum),\n",
      "                self.mr(reducer=self.reducer_svd)]\n",
      "\n",
      "# entry point of program\n",
      "if __name__ == '__main__':\n",
      "    \n",
      "    per_node_calc.run()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overwriting compute_stats.py\n"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## the job failed on the entire set of data ##"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# running the job \n",
      "#job_flow_id = get_job_flow_id()\n",
      "job_flow_id1= 'j-LTOJMJ14G840'\n",
      "\n",
      "!rm this_is_a_test\n",
      "#!python compute_stats.py -r emr --emr-job-flow-id $job_flow_id1 < test_all_leaf_info.txt > this_is_a_test #is submitting\n",
      "\n",
      "!python compute_stats.py -r emr --emr-job-flow-id $job_flow_id1 \\\n",
      "                         --file ./leaf_station_count.pkl  \\\n",
      "                         < all_leaf_info.txt > pca_result.txt #is submitting\n",
      "\n",
      "# !python compute_stats.py -r local \\\n",
      "#                          --file ./leaf_station_count.pkl  \\\n",
      "#                           < test_all_leaf_info.txt > this_is_a_test #is submitting"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "using configs in /home/ubuntu/.mrjob.conf\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "creating tmp directory /tmp/compute_stats.ubuntu.20140526.222143.423830\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "reading from STDIN\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Copying non-input files into s3://yuf/scratch/compute_stats.ubuntu.20140526.222143.423830/files/\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Adding our job to existing job flow j-LTOJMJ14G840\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 31.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 62.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 93.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 124.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 155.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 186.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 218.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 249.3s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 280.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 311.6s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 342.8s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 374.0s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 405.2s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 436.3s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 467.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 498.7s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 529.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 561.2s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 592.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 623.6s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 654.8s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 685.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 717.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 748.2s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 779.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 810.7s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 841.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 873.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 904.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 935.2s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 966.3s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 997.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1028.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1059.6s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1090.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1122.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1153.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1184.6s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1215.8s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1247.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1278.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1309.6s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1340.7s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1371.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1403.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1434.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1465.2s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1496.3s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1527.6s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1558.6s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1589.7s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1620.7s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1651.8s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1682.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1714.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1745.3s ago, status RUNNING: Running step (compute_stats.ubuntu.20140526.222143.423830: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job on job flow j-LTOJMJ14G840 failed with status WAITING: Waiting after step failed\r\n",
        "Logs are in s3://yoav.hadoop/log/j-LTOJMJ14G840/\r\n",
        "ec2_key_pair_file not specified, going to S3\r\n",
        "Scanning S3 logs for probable cause of failure\r\n",
        "Waiting 5.0s for S3 eventual consistency\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Attempting to terminate job...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Traceback (most recent call last):\r\n",
        "  File \"compute_stats.py\", line 118, in <module>\r\n",
        "    per_node_calc.run()\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/job.py\", line 494, in run\r\n",
        "    mr_job.execute()\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/job.py\", line 512, in execute\r\n",
        "    super(MRJob, self).execute()\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/launch.py\", line 147, in execute\r\n",
        "    self.run_job()\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/launch.py\", line 213, in run_job\r\n",
        "    self.stdout.flush()\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/runner.py\", line 614, in __exit__\r\n",
        "    self.cleanup()\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/emr.py\", line 1010, in cleanup\r\n",
        "    super(EMRJobRunner, self).cleanup(mode=mode)\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/runner.py\", line 560, in cleanup\r\n",
        "    self._cleanup_job()\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/emr.py\", line 1084, in _cleanup_job\r\n",
        "    self._opts['ec2_key_pair_file'])\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/ssh.py\", line 200, in ssh_terminate_single_job\r\n",
        "    ssh_bin, address, ec2_key_pair_file, ['hadoop', 'job', '-list']))\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/ssh.py\", line 82, in ssh_run\r\n",
        "    p = Popen(args, stdout=PIPE, stderr=PIPE, stdin=PIPE)\r\n",
        "  File \"/home/ubuntu/anaconda/lib/python2.7/subprocess.py\", line 709, in __init__\r\n",
        "    errread, errwrite)\r\n",
        "  File \"/home/ubuntu/anaconda/lib/python2.7/subprocess.py\", line 1326, in _execute_child\r\n",
        "    raise child_exception\r\n",
        "TypeError: execv() arg 2 must contain only strings\r\n"
       ]
      }
     ],
     "prompt_number": 144
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Yoav:** Did you try to figure out the problem from the stderr files?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## try half of the data ##"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# running the job \n",
      "#job_flow_id = get_job_flow_id()\n",
      "job_flow_id1= 'j-6T8VIKMY8RHX'\n",
      "\n",
      "#!python compute_stats.py -r emr --emr-job-flow-id $job_flow_id1 < test_all_leaf_info.txt > this_is_a_test #is submitting\n",
      "\n",
      "!python compute_stats.py -r emr --emr-job-flow-id $job_flow_id1 \\\n",
      "                         --file ./leaf_station_count.pkl  \\\n",
      "                         < test_all_leaf_info.txt > pca_result.txt\n",
      "\n",
      "# !python compute_stats.py -r local \\\n",
      "#                          --file ./leaf_station_count.pkl  \\\n",
      "#                           < test_all_leaf_info.txt > this_is_a_test #is submitting"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "using configs in /home/ubuntu/.mrjob.conf\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "creating tmp directory /tmp/compute_stats.ubuntu.20140527.005916.908620\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "reading from STDIN\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Copying non-input files into s3://yuf/scratch/compute_stats.ubuntu.20140527.005916.908620/files/\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Adding our job to existing job flow j-6T8VIKMY8RHX\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 30.7s ago, status RUNNING: Running step\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 61.3s ago, status RUNNING: Running step\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 92.0s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 122.6s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>8aff5612-e53a-11e3-a94d-e755ea6b183b</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 173.2s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 203.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>bb715f18-e53a-11e3-a7f3-054eed488613</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 254.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 285.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 315.6s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 346.3s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 376.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 407.6s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 438.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 468.7s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>594fa616-e53b-11e3-b43a-872b56d6dfca</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 519.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>77831fb5-e53b-11e3-b332-21d671b574dc</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 570.0s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 600.6s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 631.2s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 661.8s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 692.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 723.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 753.7s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 784.3s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 814.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 845.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 876.2s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 907.0s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 937.6s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 968.3s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 999.0s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>95613190-e53c-11e3-9077-5114ea9dc6a3</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1049.6s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>b389fc6a-e53c-11e3-aa87-172672d76e00</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1100.2s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1130.8s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1161.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1192.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1222.7s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1253.3s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1283.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1314.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1345.2s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1375.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1406.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1437.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1467.7s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1498.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1529.2s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1559.8s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1590.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1621.2s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1651.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1682.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1713.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>3f091689-e53e-11e3-9fde-05f53a330f8a</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1763.8s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>5d40af6c-e53e-11e3-9fde-05f53a330f8a</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1814.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1845.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1875.7s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1906.3s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1936.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1967.6s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 1998.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2029.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2059.7s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2090.2s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2121.0s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2151.8s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2182.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2213.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2243.8s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>7b5b05d1-e53f-11e3-af71-7d8b78bb4770</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>874baa3d-e53f-11e3-af71-7d8b78bb4770</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 30.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2324.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2355.0s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>bda8c01c-e53f-11e3-80e8-ff6cff06198c</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2405.7s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>dbe0a5f0-e53f-11e3-bea8-dbf80bac8b69</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2456.3s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2486.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2517.6s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>1e8ad7de-e540-11e3-bea8-dbf80bac8b69</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2568.3s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2598.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2629.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2660.2s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2690.8s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2721.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>9815f3f1-e540-11e3-8362-0f887c06ae20</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2772.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2802.8s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2833.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>dad0523e-e540-11e3-8362-0f887c06ae20</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2884.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2914.8s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>0b540f11-e541-11e3-8362-0f887c06ae20</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 2965.6s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>299592c3-e541-11e3-af71-7d8b78bb4770</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3016.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3047.0s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3077.7s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3108.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3139.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3169.8s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>a3494601-e541-11e3-acfe-a31808eb12d5</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>af3a3816-e541-11e3-a7f3-054eed488613</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 30.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3250.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>d360fb76-e541-11e3-a7f3-054eed488613</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3301.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3331.7s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3362.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3393.0s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3423.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>3a8eb4dc-e542-11e3-9fde-05f53a330f8a</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>46806aa4-e542-11e3-9fde-05f53a330f8a</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 30.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3504.2s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3534.9s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3565.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3596.1s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3626.8s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3657.5s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3688.0s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Got retriable error: EmrResponseError: 400 Bad Request\r\n",
        "<ErrorResponse xmlns=\"http://elasticmapreduce.amazonaws.com/doc/2009-03-31\">\r\n",
        "  <Error>\r\n",
        "    <Type>Sender</Type>\r\n",
        "    <Code>Throttling</Code>\r\n",
        "    <Message>Rate exceeded</Message>\r\n",
        "  </Error>\r\n",
        "  <RequestId>d831ee6c-e542-11e3-b635-b92af58569e9</RequestId>\r\n",
        "</ErrorResponse>\r\n",
        "\r\n",
        "Backing off for 20.0 seconds\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3738.7s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job launched 3769.4s ago, status RUNNING: Running step (compute_stats.ubuntu.20140527.005916.908620: Step 1 of 2)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job on job flow j-6T8VIKMY8RHX failed with status WAITING: Waiting after step failed\r\n",
        "Logs are in s3://yoav.hadoop/log/j-6T8VIKMY8RHX/\r\n",
        "ec2_key_pair_file not specified, going to S3\r\n",
        "Scanning S3 logs for probable cause of failure\r\n",
        "Waiting 5.0s for S3 eventual consistency\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Attempting to terminate job...\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Traceback (most recent call last):\r\n",
        "  File \"compute_stats.py\", line 126, in <module>\r\n",
        "    per_node_calc.run()\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/job.py\", line 494, in run\r\n",
        "    mr_job.execute()\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/job.py\", line 512, in execute\r\n",
        "    super(MRJob, self).execute()\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/launch.py\", line 147, in execute\r\n",
        "    self.run_job()\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/launch.py\", line 213, in run_job\r\n",
        "    self.stdout.flush()\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/runner.py\", line 614, in __exit__\r\n",
        "    self.cleanup()\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/emr.py\", line 1010, in cleanup\r\n",
        "    super(EMRJobRunner, self).cleanup(mode=mode)\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/runner.py\", line 560, in cleanup\r\n",
        "    self._cleanup_job()\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/emr.py\", line 1084, in _cleanup_job\r\n",
        "    self._opts['ec2_key_pair_file'])\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/ssh.py\", line 200, in ssh_terminate_single_job\r\n",
        "    ssh_bin, address, ec2_key_pair_file, ['hadoop', 'job', '-list']))\r\n",
        "  File \"/usr/local/lib/python2.7/dist-packages/mrjob/ssh.py\", line 82, in ssh_run\r\n",
        "    p = Popen(args, stdout=PIPE, stderr=PIPE, stdin=PIPE)\r\n",
        "  File \"/home/ubuntu/anaconda/lib/python2.7/subprocess.py\", line 709, in __init__\r\n",
        "    errread, errwrite)\r\n",
        "  File \"/home/ubuntu/anaconda/lib/python2.7/subprocess.py\", line 1326, in _execute_child\r\n",
        "    raise child_exception\r\n",
        "TypeError: execv() arg 2 must contain only strings\r\n"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls -alh all_leaf_info*\n",
      "!ls -alh leaf_station*"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-rw-rw-r-- 1 ubuntu ubuntu 70M May 26 05:34 all_leaf_info.txt\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-rw-rw-r-- 1 ubuntu ubuntu 39K May 26 20:51 leaf_station_count.pkl\r\n"
       ]
      }
     ],
     "prompt_number": 147
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!head -100 pca_result.txt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\"LEAF_0\"\t1\r\n"
       ]
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##do some basic merging on the last level, and plot##"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# function for determining merge or not\n",
      "def should_merge(child1, child2, parent):\n",
      "    parent.stationList = child1.stationList + child2.stationList\n",
      "    \n",
      "    # write the station data to file, just like mapping\n",
      "    parent_station_list = parent.stationList\n",
      "    with open(\"naive_merge.txt\", \"wb\") as f:\n",
      "        for station in parent_station_list:\n",
      "            if station not in station_2_data: continue\n",
      "            else:\n",
      "                data = station_2_data[station]\n",
      "                data_serial = serialize(data)\n",
      "                f.write('dummy_const_key' + ',' + data_serial)\n",
      "    \n",
      "    # perform pca on the file\n",
      "    #!python pca_analysis_merge.py -r local naive_merge.txt > naive_merge_result.txt\n",
      "    \n",
      "    # read from the output, and parse the k\n",
      "    new_k = -1\n",
      "    with open('naive_merge_result.txt') as f:\n",
      "        for line in f:\n",
      "            line = line.strip()\n",
      "            if line == '': continue\n",
      "            arr = line.split(',')\n",
      "            assert(len(arr) == 2)\n",
      "            if arr[0] == 'new_k': new_k = arr[1]\n",
      "    \n",
      "    # using all three nodes to return a boolean result\n",
      "    child1_metric = len(child1.stationList) * child1.k + (child1.k+1)*2*365\n",
      "    child2_metric = len(child2.stationList) * child2.k + (child2.k+1)*2*365\n",
      "    parent_metric = len(parent.stationList) * new_k    + (new_k + 1)*2*365\n",
      "    \n",
      "    if child1_metric + child2_metric > parent_metric: return False\n",
      "    else: return True"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# naive merging on neighboring pairs\n",
      "i = 0\n",
      "while i < len(leaf_list):\n",
      "    if i + 1 < len(leaf_list):\n",
      "        this_node = leaf_list[i]\n",
      "        next_node = leaf_list[i+1]\n",
      "        \n",
      "        if this_node.parent == next_node.parent:\n",
      "            if should_merge(this_node, next_node, this_node.parent) == True:\n",
      "                parent = this_node.parent\n",
      "                parent.leftChild = None\n",
      "                parent.rightChild = None\n",
      "            \n",
      "            # move on to next pair    \n",
      "            i += 2\n",
      "        else:\n",
      "            i += 1\n",
      "    else: \n",
      "        break\n",
      "\n",
      "merged_leaf_list = []\n",
      "find_all_leaves(root, merged_leaf_list)\n",
      "draw_the_map(merged_leaf_list)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Grade##\n",
      "\n",
      "The main problem with this notebook is that it contains almost only code, it does not contain enough:\n",
      "\n",
      "1. Markdown cells with explanations\n",
      "1. Comments inside the code\n",
      "1. Figures / maps\n",
      "1. Tests that prove that the code does what it is supposed to do.\n",
      "\n",
      "For the one map that does exist the results are clearly incorrect because the regions are very elongated.\n",
      "\n",
      "Overall Grade 70"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}