analyze_textgrids.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c3b4a8af",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import textgrids #import textgrids\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa3ab740",
   "metadata": {},
   "outputs": [],
   "source": [
    "tg_dir = \"/Users/masato/Box/cloze_experiments/filter/data_pilot/tg_corrected\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bf57c252",
   "metadata": {},
   "outputs": [],
   "source": [
    "filenames = [os.path.splitext(fn)[0] for fn in os.listdir(tg_dir) if \".TextGrid\" in fn]\n",
    "item_id = [f.split(\"_\")[1] for f in filenames]\n",
    "item_num = [i[:-1] for i in item_id]\n",
    "item_con = [i[-1] for i in item_id]\n",
    "subject_id = [f.split(\"_\")[2] for f in filenames]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "174f5243",
   "metadata": {},
   "outputs": [],
   "source": [
    "rt = []\n",
    "response = []\n",
    "notes = []\n",
    "for tg_loc in [f for f in os.listdir(tg_dir) if \".TextGrid\" in f]:\n",
    "    tg = textgrids.TextGrid(os.path.join(tg_dir, tg_loc))\n",
    "    rt.append(tg[\"words\"][1].xmin)\n",
    "    response.append(tg[\"words\"][1].text)\n",
    "    notes.append(tg[\"notes\"][0].text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9fc60be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.DataFrame({\"filenames\": filenames, \"item_id\": item_id, \"item_num\":item_num, \"item_con\":item_con, \"subject_id\": subject_id, \"response\":response, \"rt\":rt, \"notes\":notes})"
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  {
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   "execution_count": 6,
   "id": "b78a044a",
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       "      <th>0</th>\n",
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       "      <td>qulh</td>\n",
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       "      <td>qulh</td>\n",
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       "      <td>9a</td>\n",
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       "      <td>ratq</td>\n",
       "      <td>seduced</td>\n",
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       "      <td>nyca</td>\n",
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       "        filenames item_id item_num item_con subject_id        response  \\\n",
       "0    exp_10a_lbjl     10a       10        a       lbjl          caught   \n",
       "1    exp_10a_nyca     10a       10        a       nyca          killed   \n",
       "2    exp_10b_qulh     10b       10        b       qulh  NOT_RECOGNIZED   \n",
       "3    exp_10b_ratq     10b       10        b       ratq             run   \n",
       "4    exp_11a_qulh     11a       11        a       qulh  NOT_RECOGNIZED   \n",
       "..            ...     ...      ...      ...        ...             ...   \n",
       "235   exp_8b_ratq      8b        8        b       ratq         ordered   \n",
       "236   exp_9a_qulh      9a        9        a       qulh         seduced   \n",
       "237   exp_9a_ratq      9a        9        a       ratq         seduced   \n",
       "238   exp_9b_lbjl      9b        9        b       lbjl          bedded   \n",
       "239   exp_9b_nyca      9b        9        b       nyca           loved   \n",
       "\n",
       "           rt notes  \n",
       "0    0.888284        \n",
       "1    1.560463        \n",
       "2    0.100000        \n",
       "3    2.393000  form  \n",
       "4    1.483000        \n",
       "..        ...   ...  \n",
       "235  1.723310        \n",
       "236  1.382000        \n",
       "237  1.251000        \n",
       "238  1.286741        \n",
       "239  1.662000        \n",
       "\n",
       "[240 rows x 8 columns]"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "d"
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dc6199de",
   "metadata": {},
   "outputs": [],
   "source": [
    "d.to_csv(\"/Users/masato/Box/cloze_experiments/filter/data_pilot/result_pilot.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "598a0880",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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