{
 "metadata": {
  "name": "World Bank XML"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Data from: http://data.worldbank.org/topic/agriculture-and-rural-development\n",
      "\n",
      "http://data.worldbank.org/about/world-development-indicators-data/agriculture-and-rural-development"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open(\"1_Topic_en_xml_v2.xml\")\n",
      "print f.readline()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\ufeff<?xml version=\"1.0\" encoding=\"utf-8\"?>\r\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print f.readline()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<Root xmlns:wb=\"http://www.worldbank.org\">\r\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "XML namespaces: http://www.w3schools.com/xml/xml_namespaces.asp\n",
      "\n",
      "XML schema and validation:\n",
      "\n",
      "* http://www.w3schools.com/xml/xml_dtd.asp\n",
      "* http://www.w3schools.com/xml/xml_schema.asp\n",
      "* http://en.wikipedia.org/wiki/RELAX_NG\n",
      "* http://en.wikipedia.org/wiki/XML_Schema_Language_Comparison\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(10):\n",
      "    print f.readline(),"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  <data>\r\n",
        "    <record>\r\n",
        "      <field name=\"Country or Area\" key=\"ABW\">Aruba</field>\r\n",
        "      <field name=\"Item\" key=\"AG.AGR.TRAC.NO\">Agricultural machinery, tractors</field>\r\n",
        "      <field name=\"Year\">1960</field>\r\n",
        "      <field name=\"Value\" />\r\n",
        "    </record>\r\n",
        "    <record>\r\n",
        "      <field name=\"Country or Area\" key=\"ABW\">Aruba</field>\r\n",
        "      <field name=\"Item\" key=\"AG.AGR.TRAC.NO\">Agricultural machinery, tractors</field>\r\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(40):\n",
      "    print f.readline(),"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "      <field name=\"Year\">1961</field>\r\n",
        "      <field name=\"Value\" />\r\n",
        "    </record>\r\n",
        "    <record>\r\n",
        "      <field name=\"Country or Area\" key=\"ABW\">Aruba</field>\r\n",
        "      <field name=\"Item\" key=\"AG.AGR.TRAC.NO\">Agricultural machinery, tractors</field>\r\n",
        "      <field name=\"Year\">1962</field>\r\n",
        "      <field name=\"Value\" />\r\n",
        "    </record>\r\n",
        "    <record>\r\n",
        "      <field name=\"Country or Area\" key=\"ABW\">Aruba</field>\r\n",
        "      <field name=\"Item\" key=\"AG.AGR.TRAC.NO\">Agricultural machinery, tractors</field>\r\n",
        "      <field name=\"Year\">1963</field>\r\n",
        "      <field name=\"Value\" />\r\n",
        "    </record>\r\n",
        "    <record>\r\n",
        "      <field name=\"Country or Area\" key=\"ABW\">Aruba</field>\r\n",
        "      <field name=\"Item\" key=\"AG.AGR.TRAC.NO\">Agricultural machinery, tractors</field>\r\n",
        "      <field name=\"Year\">1964</field>\r\n",
        "      <field name=\"Value\" />\r\n",
        "    </record>\r\n",
        "    <record>\r\n",
        "      <field name=\"Country or Area\" key=\"ABW\">Aruba</field>\r\n",
        "      <field name=\"Item\" key=\"AG.AGR.TRAC.NO\">Agricultural machinery, tractors</field>\r\n",
        "      <field name=\"Year\">1965</field>\r\n",
        "      <field name=\"Value\" />\r\n",
        "    </record>\r\n",
        "    <record>\r\n",
        "      <field name=\"Country or Area\" key=\"ABW\">Aruba</field>\r\n",
        "      <field name=\"Item\" key=\"AG.AGR.TRAC.NO\">Agricultural machinery, tractors</field>\r\n",
        "      <field name=\"Year\">1966</field>\r\n",
        "      <field name=\"Value\" />\r\n",
        "    </record>\r\n",
        "    <record>\r\n",
        "      <field name=\"Country or Area\" key=\"ABW\">Aruba</field>\r\n",
        "      <field name=\"Item\" key=\"AG.AGR.TRAC.NO\">Agricultural machinery, tractors</field>\r\n",
        "      <field name=\"Year\">1967</field>\r\n",
        "      <field name=\"Value\" />\r\n",
        "    </record>\r\n",
        "    <record>\r\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import xml.etree.ElementTree as ET\n",
      "tree = ET.parse('1_Topic_en_xml_v2.xml')\n",
      "root = tree.getroot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Good tutorial on ElementTree:\n",
      "http://getpython3.com/diveintopython3/xml.html#xml-parse\n",
      "\n",
      "Other options: \n",
      "\n",
      "* http://docs.python.org/2/library/markup.html\n",
      "* http://lxml.de/index.html\n",
      "* http://www.crummy.com/software/BeautifulSoup/"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "root"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<Element 'Root' at 0x2fb4b90>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for child in root:\n",
      "   print child.tag,  \":\",  child.attrib"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "data : {}\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "root[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<Element 'data' at 0x2fb4bd0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = root[0]\n",
      "records = data.getchildren()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(records)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "435456"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "records[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<Element 'record' at 0x2fb4c10>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r = records[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r.getchildren()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "[<Element 'field' at 0x2fb4c90>,\n",
        " <Element 'field' at 0x2fb4cd0>,\n",
        " <Element 'field' at 0x2fb4c50>,\n",
        " <Element 'field' at 0x2fb4d10>]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "records[0][0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<Element 'field' at 0x2fb4c90>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "records[0][0].text"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "'Aruba'"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r = records[0]\n",
      "for field in r:\n",
      "    print field.attrib"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'name': 'Country or Area', 'key': 'ABW'}\n",
        "{'name': 'Item', 'key': 'AG.AGR.TRAC.NO'}\n",
        "{'name': 'Year'}\n",
        "{'name': 'Value'}\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "records_parsed = []\n",
      "\n",
      "for record in records:\n",
      "    r = {}\n",
      "    for field in record:\n",
      "        if field.attrib['name'] == 'Country or Area':\n",
      "            r['country'] = field.text\n",
      "        if field.attrib['name'] == 'Item':\n",
      "            r['item'] = field.text\n",
      "        if field.attrib['name'] == 'Year':\n",
      "            r['year'] = field.text\n",
      "        if field.attrib['name'] == 'Value':\n",
      "            r['value'] = field.text\n",
      "    records_parsed.append(r)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "records_parsed[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "{'country': 'Aruba',\n",
        " 'item': 'Agricultural machinery, tractors',\n",
        " 'value': None,\n",
        " 'year': '1960'}"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "items = set()\n",
      "\n",
      "for r in records_parsed:\n",
      "    items.add(r['item'])\n",
      "\n",
      "items"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "set(['Arable land (hectares)',\n",
        "     'Agricultural machinery, tractors',\n",
        "     'Crop production index (2004-2006 = 100)',\n",
        "     'Food production index (2004-2006 = 100)',\n",
        "     'Arable land (% of land area)',\n",
        "     'Permanent cropland (% of land area)',\n",
        "     'Poverty headcount ratio at rural poverty line (% of rural population)',\n",
        "     'Cereal yield (kg per hectare)',\n",
        "     'Land area (sq. km)',\n",
        "     'Rural population growth (annual %)',\n",
        "     'Improved water source, rural (% of rural population with access)',\n",
        "     'Arable land (hectares per person)',\n",
        "     'Agricultural raw materials imports (% of merchandise imports)',\n",
        "     'Average precipitation in depth (mm per year)',\n",
        "     'Agricultural irrigated land (% of total agricultural land)',\n",
        "     'Fertilizer consumption (% of fertilizer production)',\n",
        "     'Forest area (% of land area)',\n",
        "     'Livestock production index (2004-2006 = 100)',\n",
        "     'Surface area (sq. km)',\n",
        "     'Agricultural machinery, tractors per 100 sq. km of arable land',\n",
        "     'Employment in agriculture (% of total employment)',\n",
        "     'Poverty gap at rural poverty line (%)',\n",
        "     'Rural population (% of total population)',\n",
        "     'Agriculture value added per worker (constant 2005 US$)',\n",
        "     'Agricultural raw materials exports (% of merchandise exports)',\n",
        "     'Fertilizer consumption (kilograms per hectare of arable land)',\n",
        "     'Forest area (sq. km)',\n",
        "     'Agricultural land (sq. km)',\n",
        "     'Agricultural land (% of land area)',\n",
        "     'Land under cereal production (hectares)',\n",
        "     'Agriculture, value added (% of GDP)',\n",
        "     'Rural population'])"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "items = set()\n",
      "\n",
      "for r in records_parsed:\n",
      "    items.add(r['country'])\n",
      "\n",
      "items"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "set(['Canada',\n",
        "     'Sao Tome and Principe',\n",
        "     'Turkmenistan',\n",
        "     'Lao PDR',\n",
        "     'Arab World',\n",
        "     'Latin America & Caribbean (all income levels)',\n",
        "     'Cambodia',\n",
        "     'Ethiopia',\n",
        "     'Aruba',\n",
        "     'Swaziland',\n",
        "     'South Asia',\n",
        "     'Argentina',\n",
        "     'Bolivia',\n",
        "     'Bahamas, The',\n",
        "     'Burkina Faso',\n",
        "     'OECD members',\n",
        "     'Ghana',\n",
        "     'Saudi Arabia',\n",
        "     'Sub-Saharan Africa (IFC classification)',\n",
        "     'Thailand',\n",
        "     'Japan',\n",
        "     'Channel Islands',\n",
        "     'American Samoa',\n",
        "     'Northern Mariana Islands',\n",
        "     'Slovenia',\n",
        "     'Guatemala',\n",
        "     'Bosnia and Herzegovina',\n",
        "     'Kuwait',\n",
        "     'Russian Federation',\n",
        "     'Jordan',\n",
        "     'St. Lucia',\n",
        "     'Congo, Rep.',\n",
        "     'Dominica',\n",
        "     'Liberia',\n",
        "     'Maldives',\n",
        "     'East Asia & Pacific (all income levels)',\n",
        "     'Virgin Islands (U.S.)',\n",
        "     'Lithuania',\n",
        "     'Tanzania',\n",
        "     'Vietnam',\n",
        "     'Cabo Verde',\n",
        "     'Greenland',\n",
        "     'Gabon',\n",
        "     'Monaco',\n",
        "     'New Zealand',\n",
        "     'European Union',\n",
        "     'Jamaica',\n",
        "     'Albania',\n",
        "     'Samoa',\n",
        "     'Slovak Republic',\n",
        "     'United Arab Emirates',\n",
        "     'Guam',\n",
        "     'Uruguay',\n",
        "     'India',\n",
        "     'Azerbaijan',\n",
        "     'Lesotho',\n",
        "     'Kenya',\n",
        "     'Latin America and the Caribbean (IFC classification)',\n",
        "     'Upper middle income',\n",
        "     'Tajikistan',\n",
        "     'Pacific island small states',\n",
        "     'Turkey',\n",
        "     'Afghanistan',\n",
        "     'Venezuela, RB',\n",
        "     'Bangladesh',\n",
        "     'Mauritania',\n",
        "     'Solomon Islands',\n",
        "     'Korea, Rep.',\n",
        "     'San Marino',\n",
        "     'Mongolia',\n",
        "     'France',\n",
        "     'Syrian Arab Republic',\n",
        "     'Bermuda',\n",
        "     'Namibia',\n",
        "     'Somalia',\n",
        "     'Peru',\n",
        "     'Vanuatu',\n",
        "     'Nigeria',\n",
        "     'South Asia (IFC classification)',\n",
        "     'Norway',\n",
        "     \"Cote d'Ivoire\",\n",
        "     'Europe & Central Asia (developing only)',\n",
        "     'Benin',\n",
        "     'Other small states',\n",
        "     'Cuba',\n",
        "     'Cameroon',\n",
        "     'Montenegro',\n",
        "     'Low & middle income',\n",
        "     'Togo',\n",
        "     'China',\n",
        "     'Sub-Saharan Africa (developing only)',\n",
        "     'Armenia',\n",
        "     'Small states',\n",
        "     'Timor-Leste',\n",
        "     'Dominican Republic',\n",
        "     'Low income',\n",
        "     'Ukraine',\n",
        "     'Bahrain',\n",
        "     'Tonga',\n",
        "     'Finland',\n",
        "     'Latin America & Caribbean (developing only)',\n",
        "     'High income',\n",
        "     'Libya',\n",
        "     'Cayman Islands',\n",
        "     'Central African Republic',\n",
        "     'Europe & Central Asia (all income levels)',\n",
        "     'Mauritius',\n",
        "     'Liechtenstein',\n",
        "     'Belarus',\n",
        "     'Mali',\n",
        "     'Micronesia, Fed. Sts.',\n",
        "     'Korea, Dem. Rep.',\n",
        "     'Bulgaria',\n",
        "     'North America',\n",
        "     'Romania',\n",
        "     'Angola',\n",
        "     'Egypt, Arab Rep.',\n",
        "     'Trinidad and Tobago',\n",
        "     'St. Vincent and the Grenadines',\n",
        "     'Cyprus',\n",
        "     'Caribbean small states',\n",
        "     'Brunei Darussalam',\n",
        "     'Qatar',\n",
        "     'Middle income',\n",
        "     'Austria',\n",
        "     'High income: OECD',\n",
        "     'Mozambique',\n",
        "     'Uganda',\n",
        "     'Kyrgyz Republic',\n",
        "     'Hungary',\n",
        "     'Niger',\n",
        "     'United States',\n",
        "     'Brazil',\n",
        "     'World',\n",
        "     'Middle East & North Africa (all income levels)',\n",
        "     'Guinea',\n",
        "     'Panama',\n",
        "     'Costa Rica',\n",
        "     'Luxembourg',\n",
        "     'Andorra',\n",
        "     'Chad',\n",
        "     'Euro area',\n",
        "     'Ireland',\n",
        "     'Pakistan',\n",
        "     'Palau',\n",
        "     'Faeroe Islands',\n",
        "     'Lower middle income',\n",
        "     'Ecuador',\n",
        "     'Czech Republic',\n",
        "     'Australia',\n",
        "     'Algeria',\n",
        "     'East Asia and the Pacific (IFC classification)',\n",
        "     'El Salvador',\n",
        "     'Tuvalu',\n",
        "     'St. Kitts and Nevis',\n",
        "     'Marshall Islands',\n",
        "     'Chile',\n",
        "     'Puerto Rico',\n",
        "     'Belgium',\n",
        "     'Europe and Central Asia (IFC classification)',\n",
        "     'Haiti',\n",
        "     'Belize',\n",
        "     'Sierra Leone',\n",
        "     'Georgia',\n",
        "     'East Asia & Pacific (developing only)',\n",
        "     'Denmark',\n",
        "     'Philippines',\n",
        "     'Moldova',\n",
        "     'Macedonia, FYR',\n",
        "     'Morocco',\n",
        "     'Croatia',\n",
        "     'French Polynesia',\n",
        "     'Guinea-Bissau',\n",
        "     'Kiribati',\n",
        "     'Switzerland',\n",
        "     'Grenada',\n",
        "     'Middle East and North Africa (IFC classification)',\n",
        "     'Yemen, Rep.',\n",
        "     'Isle of Man',\n",
        "     'Portugal',\n",
        "     'Estonia',\n",
        "     'Kosovo',\n",
        "     'Sweden',\n",
        "     'Mexico',\n",
        "     'Hong Kong SAR, China',\n",
        "     'South Africa',\n",
        "     'Uzbekistan',\n",
        "     'West Bank and Gaza',\n",
        "     'Djibouti',\n",
        "     'Rwanda',\n",
        "     'Antigua and Barbuda',\n",
        "     'Spain',\n",
        "     'Colombia',\n",
        "     'Burundi',\n",
        "     'Least developed countries: UN classification',\n",
        "     'Fiji',\n",
        "     'Barbados',\n",
        "     'Seychelles',\n",
        "     'Madagascar',\n",
        "     'Congo, Dem. Rep.',\n",
        "     'Italy',\n",
        "     'Curacao',\n",
        "     'Bhutan',\n",
        "     'Sudan',\n",
        "     'Nepal',\n",
        "     'Malta',\n",
        "     'Netherlands',\n",
        "     'Macao SAR, China',\n",
        "     'Suriname',\n",
        "     'Middle East & North Africa (developing only)',\n",
        "     'Turks and Caicos Islands',\n",
        "     'St. Martin (French part)',\n",
        "     'Iran, Islamic Rep.',\n",
        "     'Israel',\n",
        "     'Indonesia',\n",
        "     'Malaysia',\n",
        "     'Iceland',\n",
        "     'Zambia',\n",
        "     'Sub-Saharan Africa (all income levels)',\n",
        "     'Senegal',\n",
        "     'Papua New Guinea',\n",
        "     'Malawi',\n",
        "     'Zimbabwe',\n",
        "     'Germany',\n",
        "     'Oman',\n",
        "     'Kazakhstan',\n",
        "     'Poland',\n",
        "     'Sint Maarten (Dutch part)',\n",
        "     'Eritrea',\n",
        "     'Iraq',\n",
        "     'New Caledonia',\n",
        "     'Paraguay',\n",
        "     'Not classified',\n",
        "     'Latvia',\n",
        "     'South Sudan',\n",
        "     'Guyana',\n",
        "     'Honduras',\n",
        "     'Myanmar',\n",
        "     'Equatorial Guinea',\n",
        "     'Tunisia',\n",
        "     'Nicaragua',\n",
        "     'Singapore',\n",
        "     'Serbia',\n",
        "     'Botswana',\n",
        "     'United Kingdom',\n",
        "     'Gambia, The',\n",
        "     'High income: nonOECD',\n",
        "     'Greece',\n",
        "     'Sri Lanka',\n",
        "     'Lebanon',\n",
        "     'Comoros',\n",
        "     'Heavily indebted poor countries (HIPC)'])"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "forest_areas = []\n",
      "\n",
      "for r in records_parsed:\n",
      "    if r['item'] == 'Forest area (% of land area)':\n",
      "        forest_areas.append(r)\n",
      "\n",
      "forest_areas[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "[{'country': 'Aruba',\n",
        "  'item': 'Forest area (% of land area)',\n",
        "  'value': None,\n",
        "  'year': '1960'},\n",
        " {'country': 'Aruba',\n",
        "  'item': 'Forest area (% of land area)',\n",
        "  'value': None,\n",
        "  'year': '1961'},\n",
        " {'country': 'Aruba',\n",
        "  'item': 'Forest area (% of land area)',\n",
        "  'value': None,\n",
        "  'year': '1962'},\n",
        " {'country': 'Aruba',\n",
        "  'item': 'Forest area (% of land area)',\n",
        "  'value': None,\n",
        "  'year': '1963'},\n",
        " {'country': 'Aruba',\n",
        "  'item': 'Forest area (% of land area)',\n",
        "  'value': None,\n",
        "  'year': '1964'}]"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "forest_areas_parsed = {}\n",
      "for sample in forest_areas:\n",
      "    if sample['value']:\n",
      "        if not forest_areas_parsed.has_key(sample['country']):\n",
      "            forest_areas_parsed[sample['country']] =[[],[]]\n",
      "        forest_areas_parsed[sample['country']][0].append(int(sample['year']))\n",
      "        forest_areas_parsed[sample['country']][1].append(float(sample['value']))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "forest_areas_parsed['Aruba']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "[[1990,\n",
        "  1991,\n",
        "  1992,\n",
        "  1993,\n",
        "  1994,\n",
        "  1995,\n",
        "  1996,\n",
        "  1997,\n",
        "  1998,\n",
        "  1999,\n",
        "  2000,\n",
        "  2001,\n",
        "  2002,\n",
        "  2003,\n",
        "  2004,\n",
        "  2005,\n",
        "  2006,\n",
        "  2007,\n",
        "  2008,\n",
        "  2009,\n",
        "  2010,\n",
        "  2011],\n",
        " [2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.22222222222222,\n",
        "  2.33333333333333,\n",
        "  2.33333333333333]]"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aruba_data = forest_areas_parsed['Aruba']\n",
      "plot(aruba_data[0], aruba_data[1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7e072c50>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(*forest_areas_parsed['Aruba'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7e228850>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for country,areas in forest_areas_parsed.items():\n",
      "    plot(*areas, label=country)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ChotfdwmbLmHLpdpyscMdRlHzoyvLeksPT7bmP7ZuS3KttkGeE85vX3Kdoaz9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itZH4yHJXDJ4KyPtQHW+Fh8MIHLVGp9rAm6cQHGOrm1oes3IRbvkf/XW70Hj5J9V+fmnWoWnyUR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\ncONnjDGP4cbPGGMe8wY2BOGMrd+qJwAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def foo(*arg):\n",
      "    print arg\n",
      "\n",
      "foo(*'hello')\n",
      "foo('hello')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "('h', 'e', 'l', 'l', 'o')\n",
        "('hello',)\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(forest_areas_parsed)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "238"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import csnd6"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "http://nbviewer.ipython.org/gist/jacobjoaquin/5535792"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cs = csnd6.Csound()\n",
      "csPerf = csnd6.CsoundPerformanceThread(cs)\n",
      "cs.SetOption('-odac')\n",
      "cs.CompileOrc(\n",
      "'''sr = 44100\n",
      "ksmps = 128\n",
      "nchnls = 2\n",
      "0dbfs = 1.0\n",
      "''')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cs.Start()\n",
      "csPerf.Play()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cs.CompileOrc(\n",
      "'''instr 1\n",
      "idur = p3\n",
      "iamp = p4\n",
      "ifreq = p5\n",
      "\n",
      "k1 line 1, idur, 0\n",
      "a1 oscils iamp, ifreq, 1\n",
      "out a1 * k1, a1 * k1\n",
      "endin\n",
      "''')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 92,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cs.ReadScore('i 1 0 1 0.5 440')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def event(*args):\n",
      "    e = ' '.join(map(str, args))\n",
      "    cs.ReadScore(e)\n",
      "    #print e"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "event('i', 1, 0, 0.25, 0.707, 262)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for t in xrange(8):\n",
      "    event('i', 1, t, 1, 0.707, 440 + 440 * random.random())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cs.CompileOrc(\n",
      "'''instr 2\n",
      "  iplk = 0.75\n",
      "  kamp = p4\n",
      "  icps = p5\n",
      "  kpick = 0.75\n",
      "  krefl = 0.5\n",
      "\n",
      "  aenv linen 1, 0.01, p3, 0.01\n",
      "  apluck wgpluck2 iplk, kamp, icps, kpick, krefl\n",
      "\n",
      "  out apluck * aenv, apluck * aenv\n",
      "endin\n",
      "''')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 115,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for t in xrange(8):\n",
      "    event('i', 2, t*0.3, 1, 0.4, 440 + 440 * random.random())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "forest_areas_parsed['Colombia'][1]\n",
      "forest_areas_parsed['Colombia']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "[[1990,\n",
        "  1991,\n",
        "  1992,\n",
        "  1993,\n",
        "  1994,\n",
        "  1995,\n",
        "  1996,\n",
        "  1997,\n",
        "  1998,\n",
        "  1999,\n",
        "  2000,\n",
        "  2001,\n",
        "  2002,\n",
        "  2003,\n",
        "  2004,\n",
        "  2005,\n",
        "  2006,\n",
        "  2007,\n",
        "  2008,\n",
        "  2009,\n",
        "  2010,\n",
        "  2011],\n",
        " [56.3488057683641,\n",
        "  56.2577737719694,\n",
        "  56.1667417755746,\n",
        "  56.0757097791798,\n",
        "  55.984677782785,\n",
        "  55.8936457863903,\n",
        "  55.8026137899955,\n",
        "  55.7115817936007,\n",
        "  55.620549797206,\n",
        "  55.5295178008112,\n",
        "  55.4384858044164,\n",
        "  55.3474538080216,\n",
        "  55.2564218116269,\n",
        "  55.1653898152321,\n",
        "  55.0743578188373,\n",
        "  54.9833258224425,\n",
        "  54.8922938260478,\n",
        "  54.801261829653,\n",
        "  54.7102298332582,\n",
        "  54.6191978368635,\n",
        "  54.5281658404687,\n",
        "  54.4371338440739]]"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "max(forest_areas_parsed['Colombia'][1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "56.3488057683641"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "min(forest_areas_parsed['Colombia'][1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "54.4371338440739"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "score = ''\n",
      "\n",
      "base_hz = 220\n",
      "ceil_hz = 440\n",
      "\n",
      "initial_year = 1990\n",
      "dur = 0.25\n",
      "\n",
      "import itertools\n",
      "\n",
      "colombia_max = max(forest_areas_parsed['Colombia'][1])\n",
      "colombia_min = min(forest_areas_parsed['Colombia'][1])\n",
      "\n",
      "for year, value in itertools.izip(*forest_areas_parsed['Colombia']):\n",
      "    start_time = (year - initial_year) * dur\n",
      "    amp = 0.1\n",
      "    freq = base_hz + (ceil_hz - base_hz) * (value - colombia_min) / ( colombia_max - colombia_min)\n",
      "    \n",
      "    score += \"i 2 %f %f %f %f\\n\"%(start_time, dur, amp, freq)\n",
      "\n",
      "print score\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "i 2 0.000000 0.250000 0.100000 440.000000\n",
        "i 2 0.250000 0.250000 0.100000 429.523810\n",
        "i 2 0.500000 0.250000 0.100000 419.047619\n",
        "i 2 0.750000 0.250000 0.100000 408.571429\n",
        "i 2 1.000000 0.250000 0.100000 398.095238\n",
        "i 2 1.250000 0.250000 0.100000 387.619048\n",
        "i 2 1.500000 0.250000 0.100000 377.142857\n",
        "i 2 1.750000 0.250000 0.100000 366.666667\n",
        "i 2 2.000000 0.250000 0.100000 356.190476\n",
        "i 2 2.250000 0.250000 0.100000 345.714286\n",
        "i 2 2.500000 0.250000 0.100000 335.238095\n",
        "i 2 2.750000 0.250000 0.100000 324.761905\n",
        "i 2 3.000000 0.250000 0.100000 314.285714\n",
        "i 2 3.250000 0.250000 0.100000 303.809524\n",
        "i 2 3.500000 0.250000 0.100000 293.333333\n",
        "i 2 3.750000 0.250000 0.100000 282.857143\n",
        "i 2 4.000000 0.250000 0.100000 272.380952\n",
        "i 2 4.250000 0.250000 0.100000 261.904762\n",
        "i 2 4.500000 0.250000 0.100000 251.428571\n",
        "i 2 4.750000 0.250000 0.100000 240.952381\n",
        "i 2 5.000000 0.250000 0.100000 230.476190\n",
        "i 2 5.250000 0.250000 0.100000 220.000000\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cs.ReadScore(score)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(*forest_areas_parsed['Colombia'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 73,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7e50b6d0>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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F5cuXhRDuf2848lq4+73R1mtRV1cntm3bJt59990bfvFp7b5o6Vq05b5w+LER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\nASIiDWMSICLSMCYBIiINYxIgItKw/wc8bQ+/aQyP/wAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "score = ''\n",
      "\n",
      "base_hz = 220\n",
      "ceil_hz = 440\n",
      "\n",
      "initial_year = 1990\n",
      "dur = 0.25\n",
      "\n",
      "num = 20\n",
      "\n",
      "for country,areas in forest_areas_parsed.items()[:num]:\n",
      "    max_areas = max(areas[1])\n",
      "    min_areas = min(areas[1])\n",
      "    #print country\n",
      "    for year, area in zip(*areas):\n",
      "        \n",
      "        area_range = max_areas - min_areas\n",
      "        #print area, area_range\n",
      "        if area_range > 0:\n",
      "            freq = base_hz + ((area - min_areas)/(max_areas - min_areas)) * (ceil_hz - base_hz)\n",
      "            amp = 0.2\n",
      "        else:\n",
      "            freq = 110.0\n",
      "            amp = 0.0001\n",
      "        score += \"i 2 %f %f %f %f\\n\"%((year - initial_year) * dur, dur, amp/num, freq)\n",
      "\n",
      "#cs.ReadScore(score) # too big!"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cs.ReadScore(score)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 116,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for country,areas in forest_areas_parsed.items()[:20]:\n",
      "    plot(*areas)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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8S4QQwO3b2o3AviuAggLtu4B98xpXbkQ0P/kT4L/0peR6ZhTylDFnCAHcuaN9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\nQTHAExEZFAM8EZFB/X+K4FONAE5J4AAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "score = ''\n",
      "\n",
      "base_hz = 220\n",
      "ceil_hz = 880\n",
      "\n",
      "initial_year = 1990\n",
      "dur = 0.25\n",
      "\n",
      "num = 20\n",
      "\n",
      "max_areas = max(max(areas[1]) for country,areas in forest_areas_parsed.items()[:num])\n",
      "min_areas = min(min(areas[1]) for country,areas in forest_areas_parsed.items()[:num])\n",
      "\n",
      "\n",
      "counter = 0\n",
      "for country,areas in forest_areas_parsed.items()[:num]:\n",
      "    #print country\n",
      "    for year, area in zip(*areas):\n",
      "        local_max_areas = max(areas[1])\n",
      "        local_min_areas = min(areas[1])\n",
      "        area_range = local_max_areas - local_min_areas\n",
      "        #print area, area_range\n",
      "        if area_range > 0:\n",
      "            freq = base_hz + ((area - min_areas)/(max_areas - min_areas)) * (ceil_hz - base_hz)\n",
      "            amp = 0.2\n",
      "        else:\n",
      "            freq = 110.0\n",
      "            amp = 0.01\n",
      "        score += \"i 2 %f %f %f %f\\n\"%((year - initial_year) * dur + counter, dur, amp/num, freq)\n",
      "    counter += 0.5*dur/num\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 151
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cs.ReadScore(score)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 152,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 152
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By: Andr\u00e9s Cabrera mantaraya36@gmail.com\n",
      "\n",
      "For Course MAT 240F at UCSB\n",
      "\n",
      "This ipython notebook is licensed under the CC-BY-NC-SA license: http://creativecommons.org/licenses/by-nc-sa/4.0/\n",
      "\n",
      "![http://i.creativecommons.org/l/by-nc-sa/3.0/88x31.png](http://i.creativecommons.org/l/by-nc-sa/3.0/88x31.png)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}