{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Imports"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import csv\n",
      "import codecs\n",
      "\n",
      "import poioapi.io.graf\n",
      "import poioapi.annotationgraph\n",
      "import poioapi.data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Parser class"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class ExcelParser(poioapi.io.graf.BaseParser):\n",
      "\n",
      "    def __init__(self, filepath):\n",
      "        self.word_orders = dict()\n",
      "        self.clauses = list()\n",
      "        self.clause_types = dict()\n",
      "        self.last_id = -1\n",
      "        with codecs.open(filepath, \"r\", \"utf-8\") as csvfile:\n",
      "            hinuq2 = csv.reader(csvfile, delimiter='|')\n",
      "            i = 0\n",
      "            for row in hinuq2:\n",
      "                if i == 2:\n",
      "                    clause_ids = row\n",
      "                elif i == 3:\n",
      "                    clause_types = row\n",
      "                elif i == 4:\n",
      "                    grammatical_relations = row\n",
      "                i += 1  \n",
      "                if i > 7:\n",
      "                    # now parse\n",
      "                    word_order = []\n",
      "                    c_id = None\n",
      "                    prev_c_id = None\n",
      "                    for j, clause_id in enumerate(clause_ids):\n",
      "\n",
      "                        # new clause\n",
      "                        if clause_id != \"\":\n",
      "                            # add word order to previous clause\n",
      "                            if len(word_order) > 0:\n",
      "                                self.word_orders[c_id] = word_order\n",
      "                            word_order = []\n",
      "                            \n",
      "                            # add new clause\n",
      "                            c_id = self._next_id()\n",
      "                            self.clauses.append(c_id)\n",
      "                            self.clause_types[c_id] = clause_types[j].strip()\n",
      "                        \n",
      "                        grammatical_relation = grammatical_relations[j].strip()\n",
      "                        word_order.append(grammatical_relation)\n",
      "\n",
      "                    if len(word_order) > 0:\n",
      "                        self.word_orders[c_id] = word_order\n",
      "                    i = 0\n",
      "\n",
      "    def _next_id(self):\n",
      "        self.last_id += 1\n",
      "        return self.last_id\n",
      "\n",
      "    def get_root_tiers(self):\n",
      "        return [poioapi.io.graf.Tier(\"clause_id\")]\n",
      "\n",
      "    def get_child_tiers_for_tier(self, tier):\n",
      "        if tier.name == \"clause_id\":\n",
      "            return [poioapi.io.graf.Tier(\"grammatical_relation\"),\n",
      "                    poioapi.io.graf.Tier(\"clause_type\")]\n",
      "\n",
      "        return None\n",
      "\n",
      "    def get_annotations_for_tier(self, tier, annotation_parent=None):\n",
      "        if tier.name == \"clause_id\":\n",
      "            return [poioapi.io.graf.Annotation(i, v) for i, v in enumerate(self.clauses)]\n",
      "\n",
      "        elif tier.name == \"clause_type\":\n",
      "            return [poioapi.io.graf.Annotation(self._next_id(), self.clause_types[annotation_parent.id])]\n",
      "\n",
      "        elif tier.name == \"grammatical_relation\":\n",
      "            return [poioapi.io.graf.Annotation(self._next_id(), v)  for v in self.word_orders[annotation_parent.id]]\n",
      "        \n",
      "        return []\n",
      "\n",
      "    def tier_has_regions(self, tier):\n",
      "        return False\n",
      "\n",
      "    def region_for_annotation(self, annotation):\n",
      "        pass\n",
      "\n",
      "    def get_primary_data(self):\n",
      "        pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def from_excel(filepath):\n",
      "    ag = poioapi.annotationgraph.AnnotationGraph()\n",
      "    parser = ExcelParser(filepath)\n",
      "    converter = poioapi.io.graf.GrAFConverter(parser)\n",
      "    converter.parse()\n",
      "    ag.tier_hierarchies = converter.tier_hierarchies\n",
      "    ag.structure_type_handler = poioapi.data.DataStructureType(ag.tier_hierarchies[0])\n",
      "    ag.graf = converter.graf\n",
      "    return ag"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Loading the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ag = from_excel(\"data/Hinuq2.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "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": [
        "('P', 'V', 'A') => 18\n",
        "('V',) => 4\n",
        "('S',) => 4\n",
        "('STIM', 'V', 'EXP') => 5\n",
        "('P', 'A', 'V') => 8\n",
        "('V', 'P', 'A') => 3\n",
        "('EXP', 'STIM', 'V') => 23\n",
        "('STIM', 'EXP', 'V') => 1\n",
        "('A', 'P', 'V') => 112\n",
        "() => 6\n",
        "('V', 'S') => 60\n",
        "('S', 'V') => 228\n",
        "('P', 'A') => 1\n",
        "('A', 'V', 'P') => 25\n",
        "('A', 'V') => 22\n",
        "('V', 'A', 'P') => 4\n",
        "('EXP', 'V', 'STIM') => 7\n",
        "('V', 'A') => 17\n",
        "('V', 'EXP', 'STIM') => 2\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "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": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "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": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VkpKit99+u/OiBgAAANDltVnMzJ49WyUlJc3uy8rK0p49e/Tpp58qOTlZBQUFkqS9e/dq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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6170a90>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Box plots of positions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(10,6))\n",
      "plt.boxplot([S_values, A_values, P_values])\n",
      "plt.title(\"Positions of S, A and P\")\n",
      "ret = plt.xticks([1, 2, 3], [\"S\", \"A\", \"P\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SgDogTNWBzZs35/LLL8+ECRMyceLEvPTSS2lpaal2WdBp7r777syYMWOfxy6+\n+OLcfffdVaoIKqNUKlW7BA7AMB8AQAF6pgAAChCmAAAKEKYAAAoQpgAAChCmAAAKEKYAAAoQpgAA\nChCmAAAK+H/q4AoprvJOTwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x602dd70>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "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": 11
    },
    {
     "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": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nBAcH66abbtJnn33mnA4SGRmpvLw8RURE1PqctLQ05+8Oh0MOh8MzwQKQJGVl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\nQvfcc89Vvda+ffsUGhoqy7K8FN3lud1utW3b1m9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du2VZlsLCwtSoUSPPr6HJycl6+eWX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       "text": [
        "<matplotlib.figure.Figure at 0x602d830>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "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 = plt.xticks([1, 2, 3], [\"S\", \"A\", \"P\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IAAAAALAcEhkAAAAAlkMiA9SCy2V0BAAAAPbEqmX1hC9ODEzUa2Dq2VP68kuj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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6e12790>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}