Diana Hinuq Word Order.ipynb

{
 "metadata": {
  "name": "Diana Hinuq Word Order"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Loading the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%run helpers/diana.py\n",
      "ag = from_excel(\"data/Hinuq2.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Counting word orders"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import collections\n",
      "\n",
      "verbs = [ 'COP', 'cop', 'SAY', 'say', 'v.tr', 'v.intr', 'v.aff' ]\n",
      "others = [ 'A', 'S', 'P', 'EXP', 'STIM' ]\n",
      "search_terms = verbs + others\n",
      "\n",
      "clause_unit_nodes = ag.nodes_for_tier(\"clause_id\")\n",
      "\n",
      "word_orders = collections.defaultdict(int)\n",
      "\n",
      "for parent_node in clause_unit_nodes:\n",
      "    word_order = []\n",
      "    for word_n in parent_node.iter_children():\n",
      "        a_list = ag.annotations_for_tier(\"grammatical_relation\", word_n)\n",
      "        if len(a_list) > 0:\n",
      "            a_value = ag.annotation_value_for_annotation(a_list[0])\n",
      "            if a_value in search_terms:\n",
      "                if a_value in verbs:\n",
      "                    word_order.append('V')\n",
      "                else:\n",
      "                    word_order.append(a_value)\n",
      "    word_orders[tuple(word_order)] += 1\n",
      "\n",
      "for word_order in word_orders:\n",
      "    print(str(word_order) + \" => \" + str(word_orders[word_order]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "('A', 'P', 'V') => 112\n",
        "('V',) => 4\n",
        "('V', 'EXP', 'STIM') => 2\n",
        "('V', 'A', 'P') => 4\n",
        "('A', 'V', 'P') => 25\n",
        "('A', 'V') => 22\n",
        "('V', 'A') => 17\n",
        "('EXP', 'V', 'STIM') => 7\n",
        "('S',) => 4\n",
        "('P', 'A') => 1\n",
        "('P', 'A', 'V') => 8\n",
        "('S', 'V') => 228\n",
        "('EXP', 'STIM', 'V') => 23\n",
        "('V', 'S') => 60\n",
        "('V', 'P', 'A') => 3\n",
        "('STIM', 'EXP', 'V') => 1\n",
        "('P', 'V', 'A') => 18\n",
        "() => 6\n",
        "('STIM', 'V', 'EXP') => 5\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Positions of S, A and P"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A_values = []\n",
      "P_values = []\n",
      "S_values = []\n",
      "for parent_node in clause_unit_nodes:\n",
      "    word_order = []\n",
      "    for gramm_node in ag.nodes_for_tier(\"grammatical_relation\", parent_node):\n",
      "        a_list = ag.annotations_for_tier(\"grammatical_relation\", gramm_node)\n",
      "        if len(a_list) > 0:\n",
      "            a_value = ag.annotation_value_for_annotation(a_list[0])\n",
      "            if a_value in verbs:\n",
      "                a_value = \"V\"\n",
      "            word_order.append(a_value)\n",
      "    if \"V\" in word_order:\n",
      "        v_index = word_order.index(\"V\")\n",
      "        if \"A\" in word_order:\n",
      "            A_values.append(word_order.index(\"A\") - v_index)\n",
      "        if \"P\" in word_order:\n",
      "            P_values.append(word_order.index(\"P\") - v_index)\n",
      "        if \"S\" in word_order:\n",
      "            S_values.append(word_order.index(\"S\") - v_index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = plt.subplots(1, 3, figsize=(14,4))\n",
      "axs[0].hist(S_values, range(min(S_values), max(S_values)+2))\n",
      "axs[0].set_title(\"Positions of S\")\n",
      "axs[1].hist(A_values, range(min(A_values), max(A_values)+2))\n",
      "axs[1].set_title(\"Positions of A\")\n",
      "axs[2].hist(P_values, range(min(P_values), max(P_values)+2))\n",
      "ret = axs[2].set_title(\"Positions of P\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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AwJYCLmYaGho0bdo0DRs2TGlpafr4449VV1cnt9utlJQUZWdn+67qDQAAAADB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\nAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Box plots of positions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pylab.figure(figsize=(10,6))\n",
      "pylab.boxplot([S_values, A_values, P_values])\n",
      "pylab.title(\"Positions of S, A and P\")\n",
      "ret = pylab.xticks([1, 2, 3], [\"S\", \"A\", \"P\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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a1157Leeee26eeeaZapcG1AFhqg5s37491157bcaPH58LLrggL7/8chobG6td\nFrSbhQsX5sorrzzssauuuioLFy6sUkVQGZZi65wM8wEAFKBnCgCgAGEKAKAAYQoAoABhCgCgAGEK\nAKAAYQoAoID/B7WXVgMCL0hpAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Plots by clause type"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A_values = [[], []]\n",
      "P_values = [[], []]\n",
      "S_values = [[], []]\n",
      "clause_types = [\"m\", \"m.rs\", \"sub\", \"sub.rs\"]\n",
      "for parent_node in clause_unit_nodes:\n",
      "    word_order = []\n",
      "    clause_type = None\n",
      "    type_node = ag.nodes_for_tier(\"clause_type\", parent_node)\n",
      "    if len(type_node) > 0:\n",
      "        clause_type_ann = ag.annotations_for_tier(\"clause_type\", type_node[0])\n",
      "        clause_type = ag.annotation_value_for_annotation(clause_type_ann[0])\n",
      "    for gramm_node in ag.nodes_for_tier(\"grammatical_relation\", parent_node):\n",
      "        a_list = ag.annotations_for_tier(\"grammatical_relation\", gramm_node)\n",
      "        if len(a_list) > 0:\n",
      "            a_value = ag.annotation_value_for_annotation(a_list[0])\n",
      "            if a_value in verbs:\n",
      "                a_value = \"V\"\n",
      "            word_order.append(a_value)\n",
      "    if \"V\" in word_order and clause_type in clause_types:\n",
      "        ind = 0\n",
      "        if clause_type == \"sub\" or clause_type == \"sub.rs\":\n",
      "            ind = 1\n",
      "        v_index = word_order.index(\"V\")\n",
      "        if \"A\" in word_order:\n",
      "            A_values[ind].append(word_order.index(\"A\") - v_index)\n",
      "        if \"P\" in word_order:\n",
      "            P_values[ind].append(word_order.index(\"P\") - v_index)\n",
      "        if \"S\" in word_order:\n",
      "            S_values[ind].append(word_order.index(\"S\") - v_index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = plt.subplots(2, 3, figsize=(14,10))\n",
      "for ind in [0, 1]:\n",
      "    type_text = \"main\"\n",
      "    if ind == 1:\n",
      "        type_text = \"sub\"\n",
      "    axs[ind][0].hist(S_values[ind], range(min(S_values[ind]), max(S_values[ind])+2))\n",
      "    axs[ind][0].set_title(\"Positions of S in {0} clauses\".format(type_text))\n",
      "    axs[ind][1].hist(A_values[ind], range(min(A_values[ind]), max(A_values[ind])+2))\n",
      "    axs[ind][1].set_title(\"Positions of A {0} clauses\".format(type_text))\n",
      "    axs[ind][2].hist(P_values[ind], range(min(P_values[ind]), max(P_values[ind])+2))\n",
      "    ret = axs[ind][2].set_title(\"Positions of P {0} clauses\".format(type_text))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ffvhhdezYUX/961/ldDoVFRWlgoICJScnMy2kjWGamTX5sl8eOXJEV199tfLy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\nRKVBMJ43TrQAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Box plots by clause type"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = plt.subplots(1, 2, figsize=(14,6))\n",
      "for ind in [0, 1]:\n",
      "    type_text = \"main\"\n",
      "    if ind == 1:\n",
      "        type_text = \"sub\"\n",
      "\n",
      "    axs[ind].boxplot([S_values[ind], A_values[ind], P_values[ind]])\n",
      "    axs[ind].set_title(\"Positions of S, A and P in {0} clauses\".format(type_text))\n",
      "    ret = pylab.xticks([1, 2, 3], [\"S\", \"A\", \"P\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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CQqRz54yOouHYrT2uC8oGAIzH9MsAAAAAAgaJjB85HBcfVb0GYB49e5ZdiQkJ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\n"
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}