tpanaly.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "68a0b6bb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import tgt\n",
    "import glob\n",
    "from ckiptagger import data_utils, construct_dictionary, WS, POS, NER\n",
    "import tensorflow as tf\n",
    "import os\n",
    "import re\n",
    "import textdistance\n",
    "import Levenshtein as lev\n",
    "import seaborn as sn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from scipy.spatial.distance import cityblock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b5fef886",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "gpu_available = tf.test.is_gpu_available()\n",
    "is_cuda_gpu_available = tf.test.is_gpu_available(cuda_only=True)\n",
    "is_cuda_gpu_min_3 = tf.test.is_gpu_available(True, (3,0))\n",
    "print(gpu_available)\n",
    "print(is_cuda_gpu_available)\n",
    "print(is_cuda_gpu_min_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d216dffb",
   "metadata": {},
   "outputs": [],
   "source": [
    "VOWEL=['i', 'y', 'e', 'ɛ',  'a', 'ɨ', 'ə', 'u', 'o', 'ɔ', 'ɤ','ɚ']\n",
    "CONSONENT=['p', 'pʰ', 'm', 'f', 't', 'tʰ', 'n', 'l', 's', 'ʂ', 'ʐ', 'ts', 'tsʰ', 'tʂ', 'tʂʰ', 'ɕ', 'tɕ', 'tɕʰ', 'k', 'kʰ', \n",
    "           'ŋ',  'x', 'j', 'ɥ', 'w']\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5ef97867",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   i  y  e  ɛ  a  ɨ  ə  u  o  ɔ  ɤ  ɚ\n",
      "i  0  2  1  3  4  2  6  3  4  6  2  4\n",
      "y  2  0  3  5  6  4  4  1  2  4  4  6\n",
      "e  1  3  0  2  3  3  5  4  3  5  1  3\n",
      "ɛ  3  5  2  0  1  3  3  6  5  3  3  1\n",
      "a  4  6  3  1  0  4  4  7  6  4  4  2\n",
      "ɨ  2  4  3  3  4  0  4  3  4  4  2  2\n",
      "ə  6  4  5  3  4  4  0  3  2  0  4  2\n",
      "u  3  1  4  6  7  3  3  0  1  3  3  5\n",
      "o  4  2  3  5  6  4  2  1  0  2  2  4\n",
      "ɔ  6  4  5  3  4  4  0  3  2  0  4  2\n",
      "ɤ  2  4  1  3  4  2  4  3  2  4  0  2\n",
      "ɚ  4  6  3  1  2  2  2  5  4  2  2  0\n",
      "----\n",
      "      p  pʰ   m   f   t  tʰ   n   l   s   ʂ  ...  ɕ  tɕ tɕʰ  k  kʰ   ŋ  x   j  \\\n",
      "p     0   1   4   2   2   3   6   6   4   5  ...  7   7   8  5   6   9  7   8   \n",
      "pʰ    1   0   5   3   3   2   7   7   5   6  ...  8   8   7  6   5  10  8   9   \n",
      "m     4   5   0   4   6   7   2   4   6   7  ...  9  11  12  9  10   5  9   6   \n",
      "f     2   3   4   0   4   5   6   6   2   3  ...  5   7   8  7   8   9  5   8   \n",
      "t     2   3   6   4   0   1   4   4   2   3  ...  5   5   6  5   6   9  7   8   \n",
      "tʰ    3   2   7   5   1   0   5   5   3   4  ...  6   6   5  6   5  10  8   9   \n",
      "n     6   7   2   6   4   5   0   2   4   5  ...  7   9  10  9  10   5  9   6   \n",
      "l     6   7   4   6   4   5   2   0   4   5  ...  7   9  10  9  10   7  9   6   \n",
      "s     4   5   6   2   2   3   4   4   0   1  ...  3   5   6  7   8   9  5   8   \n",
      "ʂ     5   6   7   3   3   4   5   5   1   0  ...  2   4   5  6   7   8  4   7   \n",
      "ʐ     6   7   6   4   4   5   4   4   2   1  ...  3   5   6  7   8   7  5   6   \n",
      "ts    4   5   8   4   2   3   6   6   2   3  ...  5   3   4  7   8  11  7  10   \n",
      "tsʰ   5   4   9   5   3   2   7   7   3   4  ...  6   4   3  8   7  12  8  11   \n",
      "tʂ    5   6   9   5   3   4   7   7   3   2  ...  4   2   3  6   7  10  6   9   \n",
      "tʂʰ   6   5  10   6   4   3   8   8   4   3  ...  5   3   2  7   6  11  7  10   \n",
      "ɕ     7   8   9   5   5   6   7   7   3   2  ...  0   2   3  4   5   6  2   5   \n",
      "tɕ    7   8  11   7   5   6   9   9   5   4  ...  2   0   1  4   5   8  4   7   \n",
      "tɕʰ   8   7  12   8   6   5  10  10   6   5  ...  3   1   0  5   4   9  5   8   \n",
      "k     5   6   9   7   5   6   9   9   7   6  ...  4   4   5  0   1   4  2   5   \n",
      "kʰ    6   5  10   8   6   5  10  10   8   7  ...  5   5   4  1   0   5  3   6   \n",
      "ŋ     9  10   5   9   9  10   5   7   9   8  ...  6   8   9  4   5   0  4   3   \n",
      "x     7   8   9   5   7   8   9   9   5   4  ...  2   4   5  2   3   4  0   5   \n",
      "j     8   9   6   8   8   9   6   6   8   7  ...  5   7   8  5   6   3  5   0   \n",
      "ɥ    10  11   8  10  10  11   8   8  10   9  ...  7   9  10  9  10   7  9   4   \n",
      "w     9  10   7   9  11  12   9   9  11  10  ...  8  10  11  6   7   4  6   3   \n",
      "\n",
      "      ɥ   w  \n",
      "p    10   9  \n",
      "pʰ   11  10  \n",
      "m     8   7  \n",
      "f    10   9  \n",
      "t    10  11  \n",
      "tʰ   11  12  \n",
      "n     8   9  \n",
      "l     8   9  \n",
      "s    10  11  \n",
      "ʂ     9  10  \n",
      "ʐ     8   9  \n",
      "ts   12  13  \n",
      "tsʰ  13  14  \n",
      "tʂ   11  12  \n",
      "tʂʰ  12  13  \n",
      "ɕ     7   8  \n",
      "tɕ    9  10  \n",
      "tɕʰ  10  11  \n",
      "k     9   6  \n",
      "kʰ   10   7  \n",
      "ŋ     7   4  \n",
      "x     9   6  \n",
      "j     4   3  \n",
      "ɥ     0   3  \n",
      "w     3   0  \n",
      "\n",
      "[25 rows x 25 columns]\n"
     ]
    }
   ],
   "source": [
    "class phfeat():\n",
    "    def __distance__(self):\n",
    "        for p in VOWEL:\n",
    "            for q in VOWEL:\n",
    "                pp = self.vowdf[p].to_numpy().astype(int)\n",
    "                qq = self.vowdf[q].to_numpy().astype(int)\n",
    "\n",
    "                self.vdist[p][q] = cityblock(pp,qq)\n",
    "                \n",
    "        for p in CONSONENT:\n",
    "            for q in CONSONENT:\n",
    "                pp = self.condf[p].to_numpy().astype(int)\n",
    "                qq = self.condf[q].to_numpy().astype(int)\n",
    "\n",
    "                self.cdist[p][q] = cityblock(pp,qq)\n",
    "\n",
    "    def __xor__(self):\n",
    "        for p in VOWEL:\n",
    "            for q in VOWEL:\n",
    "                pp = self.vowdf[p].to_numpy().astype(int)\n",
    "                qq = self.vowdf[q].to_numpy().astype(int)\n",
    "                xdiff = np.bitwise_xor(pp, qq)                        \n",
    "                self.vxor[p][q] = [ self.features[i] for i in range(len(xdiff)) if xdiff[i] == 1 ]\n",
    "\n",
    "        for p in CONSONENT:\n",
    "            for q in CONSONENT:\n",
    "                pp = self.condf[p].to_numpy().astype(int)\n",
    "                qq = self.condf[q].to_numpy().astype(int)\n",
    "                xdiff = np.bitwise_xor(pp, qq)                        \n",
    "                self.cxor[p][q] = [ self.features[i] for i in range(len(xdiff)) if xdiff[i] == 1 ]\n",
    "                \n",
    "        #print(self.vxor)\n",
    "        #print(self.cxor)\n",
    "\n",
    "    def __init__(self):\n",
    "        self.condf = pd.read_excel('phoneme features.xlsx', engine='openpyxl', sheet_name=\"consonent\")\n",
    "        self.vowdf = pd.read_excel('phoneme features.xlsx', engine='openpyxl', sheet_name=\"vowel\")\n",
    "    \n",
    "        del self.condf[self.condf.columns[0]]\n",
    "        self.features=self.condf[self.condf.columns[0]].tolist()[:-1]\n",
    "        #print(\"<><><>\", self.features)\n",
    "        \n",
    "        del self.condf[self.condf.columns[0]]\n",
    "        self.condf=self.condf[:-1]\n",
    "        \n",
    "        del self.vowdf[self.vowdf.columns[0]]\n",
    "        del self.vowdf[self.vowdf.columns[0]]\n",
    "        self.vowdf=self.vowdf[:-1]\n",
    "        \n",
    "        self.vdist = pd.DataFrame(index=VOWEL,columns=VOWEL)\n",
    "        self.cdist = pd.DataFrame(index=CONSONENT,columns=CONSONENT)\n",
    "        self.vxor = pd.DataFrame(index=VOWEL,columns=VOWEL)\n",
    "        self.cxor = pd.DataFrame(index=CONSONENT,columns=CONSONENT)\n",
    "        self.__distance__()\n",
    "        self.__xor__()\n",
    "        \n",
    "        #del self.condf[self.condf.rows[-1]]\n",
    "        \n",
    "        #self.condft=self.condf.transpose()\n",
    "        #self.vowft=self.vowdf.transpose()\n",
    "        #self.condfnp = self.condft.to_numpy()\n",
    "        #self.vowfnp = self.vowft.to_numpy()\n",
    "        \n",
    "        \n",
    "    def print(self):\n",
    "        #print(self.condf)\n",
    "        #print(self.vowdf)\n",
    "        print(self.vdist)\n",
    "        print(\"----\")\n",
    "        print(self.cdist)\n",
    "\n",
    "        \n",
    "pf=phfeat()\n",
    "\n",
    "pf.print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f8021001",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ckip():\n",
    "    def __init__(self):\n",
    "        if not os.path.isdir(\"./data\"):\n",
    "            data_utils.download_data_gdown(\"./\")\n",
    "            print(\"Loading CKIP data...\")\n",
    "            \n",
    "        # Load model\n",
    "        self.ws = WS(\"./data\")\n",
    "        self.pos = POS(\"./data\")\n",
    "        self.ner = NER(\"./data\")\n",
    "    \n",
    "    def pos_analysis(self, lst):\n",
    "        word_sentence_list = self.ws(lst, sentence_segmentation=True)\n",
    "        pos_sentence_list = self.pos(word_sentence_list)\n",
    "        ner_sentence_list = self.ner(word_sentence_list, pos_sentence_list)\n",
    "        return(word_sentence_list, pos_sentence_list, ner_sentence_list)\n",
    "\n",
    "    def print_pos(self, sentence_list, word_sentence_list,  pos_sentence_list, ofp):\n",
    "        for i, sentence in enumerate(sentence_list):\n",
    "            #print(sentence, word_sentence_list[i],  pos_sentence_list[i])\n",
    "            self.print_word_pos_sentence(sentence_list, word_sentence_list[i],  pos_sentence_list[i], ofp)\n",
    "\n",
    "    # Show results\n",
    "    def print_word_pos_sentence(self, sentence_list, word_sentence_list, pos_sentence_list, ofp):\n",
    "        assert len(word_sentence_list) == len(pos_sentence_list)\n",
    "        for word, pos in zip(word_sentence_list, pos_sentence_list):\n",
    "            print(f\"{word}({pos})\", end=\"\\u3000\")\n",
    "            ofp.write(f\"{word}({pos}) \")\n",
    "        print()\n",
    "        ofp.write(\"\\n\")\n",
    "\n",
    "#ca = ckip()\n",
    "#ca.pos_analysis([\"我記得今年暑假和一位墨西哥裔朋友Hellen聊天，她說關於去其他國家旅行，在她三歲後除了美國，哪裡都不能去。\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0e7c0ba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def contains_chinese(check_str):\n",
    "    \"\"\"\n",
    "    判断字符串中是否包含中文\n",
    "    :param check_str: {str} 需要检测的字符串,\n",
    "    :return: {bool} 包含返回True， 不包含返回False\n",
    "    \"\"\"\n",
    "    #print(check_str, end=\"\")\n",
    "    for ch in check_str:\n",
    "        #print(ch)\n",
    "        if not u'\\u4e00' <= ch <= u'\\u9fff':\n",
    "            #print(\"False\")\n",
    "            return False\n",
    "    #print(\"True\")\n",
    "    return True\n",
    "    \n",
    "contains_chinese(\"不一樣\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "94ae4dd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "class thai_textgrid:\n",
    "    def __init__(self, name):\n",
    "        self.tg = tgt.read_textgrid(name, encoding='utf-16')\n",
    "        #print(self.tg.get_tier_names())\n",
    "        \n",
    "    def parse_tiers(self):\n",
    "        self.twtier = self.tg.get_tier_by_name(\"IU/teacher\")\n",
    "        #self.wttier = self.tg.get_tier_by_name(\"Word/teacher\")\n",
    "        self.tptier = self.tg.get_tier_by_name(\"IU/phone\")\n",
    "        self.tsytier = self.tg.get_tier_by_name(\"IU/syllable\")\n",
    "        self.swtier = self.tg.get_tier_by_name(\"IU/student\")\n",
    "        #self.swtier = self.tg.get_tier_by_name(\"Word/student\")\n",
    "        self.sptier = self.tg.get_tier_by_name(\"student IPA\")\n",
    "        self.ssytier = self.tg.get_tier_by_name(\"EU/syllable\")\n",
    "\n",
    "        #return self.twtier, self.tptier, self.tstier, self.tttier, self.swtier, self.sptier, self.sstier, self.sttier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3cdac4c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ttiter(tier):\n",
    "    tword=[]\n",
    "    for t in tier:\n",
    "        #print(t.text, end=\" \")\n",
    "        ccstr=t.text.replace(\" \",\"\").replace(\"*\",\"\").replace(\"^\",\"\").replace(\"~\",\"\").replace(\"SIL\",\"\")\n",
    "        if contains_chinese(ccstr) and ccstr != \"\" :\n",
    "            #print(\"<--\")\n",
    "            tword.append(ccstr)\n",
    "\n",
    "    return(tword)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e8a4cf00",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sterr_tier(tier):\n",
    "    sword=[]\n",
    "    for t in tier:\n",
    "        #print(t.text, end=\" \")\n",
    "        ccstr=t.text.replace(\" \",\"\").replace(\"^\",\"\").replace(\"~\",\"\").replace(\"SIL\",\"\")\n",
    "        if \"*\" in ccstr:\n",
    "        #if contains_chinese(ccstr) and ccstr != \"\" :\n",
    "            #print(\"<--\")\n",
    "            #print(ccstr)\n",
    "            sword.append(ccstr)\n",
    "\n",
    "    return(sword)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fee08151",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tier_iter(tier):\n",
    "    tword=[]\n",
    "    for t in tier:\n",
    "        print(t.text, end=\" \")\n",
    "        \n",
    "        if contains_chinese(t.text):\n",
    "            #print(\"<--\")\n",
    "            tword.append(t.text)\n",
    "    tanswer=[]\n",
    "    for w in tword:\n",
    "        for i in range(len(w)):\n",
    "            tanswer.append(w[i])\n",
    "    return(tanswer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "74ade5e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def term_histogram(lst):\n",
    "    for i in lst:\n",
    "        xi = i.replace('SIL')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1d8e4794",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/chainwu/anaconda3/envs/thaiphone/lib/python3.6/site-packages/keras/layers/legacy_rnn/rnn_cell_impl.py:901: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n",
      "  warnings.warn(\"`tf.nn.rnn_cell.LSTMCell` is deprecated and will be \"\n",
      "/home/chainwu/anaconda3/envs/thaiphone/lib/python3.6/site-packages/keras/engine/base_layer_v1.py:1684: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.\n",
      "  warnings.warn('`layer.add_variable` is deprecated and '\n"
     ]
    }
   ],
   "source": [
    "ca = ckip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ff1ec068",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "#mport random\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def hist_plot(data): \n",
    "    # fixed bin size\n",
    "    labels, counts = np.unique(data,return_counts=True)\n",
    "#     for i in range(len(labels)):\n",
    "#         print(labels[i], \":\", counts[i])\n",
    "#     print()\n",
    "        \n",
    "    plt.rcParams['figure.figsize'] = [20, 10]\n",
    "    \n",
    "    ticks = range(len(counts))\n",
    "    plt.bar(ticks,counts, align='center')\n",
    "    plt.xticks(ticks, labels)\n",
    "#    plt.figure().set_figwidth(15)\n",
    "    #plt.figure(figsize=(20,12))\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3bca3aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "#conf=[]\n",
    "#conf=[]\n",
    "\n",
    "def matrix_update(tp, sp, vconf, cconf, pf, verrl, cerrl, vlol, clol):\n",
    "    #print(tp, sp)\n",
    "    if (tp in VOWEL) or (tp == '∅' and sp in VOWEL):\n",
    "        vconf.append([tp, sp])\n",
    "        if tp != '∅' and sp in VOWEL:\n",
    "            #print(tp, sp, pf.vdist[tp][sp])\n",
    "            verrl.append(pf.vdist[tp][sp])\n",
    "            vlol.append(pf.vxor[tp][sp])\n",
    "    elif (tp in CONSONENT) or (tp == '∅' and sp in CONSONENT):\n",
    "        cconf.append([tp, sp])\n",
    "        if tp != '∅' and sp in CONSONENT:\n",
    "            #print(tp, sp, pf.cdist[tp][sp])\n",
    "            cerrl.append(pf.cdist[tp][sp])\n",
    "            clol.append(pf.cxor[tp][sp])\n",
    "    else:\n",
    "        print(\"Something weird happened, tp=\", tp, \"sp=\", sp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0ba922cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_task(conf):\n",
    "    tvl = [z[0] for z in conf]\n",
    "    svl = [z[1] for z in conf]\n",
    "    tvlabel = set(tvl)\n",
    "    svlabel = set(svl)\n",
    "    \n",
    "    df = pd.DataFrame(np.zeros((len(tvlabel), len(svlabel)), dtype=np.int), tvlabel, svlabel)\n",
    "    \n",
    "    for i in range(len(conf)):\n",
    "        df.loc[tvl[i]][svl[i]]=df.loc[tvl[i]][svl[i]]+1\n",
    "        \n",
    "    #rint(df.shape)\n",
    "    #print(df)\n",
    "    plt.rcParams['figure.figsize'] = [25, 25]\n",
    "    sn.set(font_scale=1.4)\n",
    "    sn.heatmap(df, annot=True, cbar=False,cmap='turbo', fmt='g')\n",
    "    \n",
    "    plt.show()\n",
    "    #plot3d(df)\n",
    "\n",
    "    #ax.bar3d(df., dx=0.5, dy=0.5, dz=z, color=colors)\n",
    "    #plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1ef6e0c2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/speech-data/NL_BKK_9.5/06272018_ZOOM0008-2.TextGrid.14\n",
      "/opt/speech-data/NL_BKK_9.5/06282018_ZOOM0010.TextGrid.14\n",
      "/opt/speech-data/NL_BKK_9.5/06262018_12tiers_未翻譯_done/06262018_ZOOM0006_LR_TextGrid.14\n",
      "/opt/speech-data/NL_BKK_9.5/06262018_12tiers_未翻譯_done/06262018_ZOOM0005_LR.TextGrid.14\n",
      "Something weird happened, tp= ∅ sp= v\n",
      "Something weird happened, tp= ∅ sp= ʃ\n",
      "Something weird happened, tp= ∅ sp= ʃ\n",
      "Something weird happened, tp= ∅ sp= ʃ\n",
      "Something weird happened, tp= ∅ sp= h\n",
      "Something weird happened, tp= ∅ sp= v\n",
      "Something weird happened, tp= ∅ sp= v\n",
      "/opt/speech-data/NL_BKK_9.5/06262018_12tiers_未翻譯_done/06262018_ZOOM0007_LR.TextGrid.14\n",
      "Something weird happened, tp= ∅ sp= h\n",
      "Something weird happened, tp= ∅ sp= h\n",
      "/opt/speech-data/NL_BKK_9.5/06192018_12tiers_未翻譯_done/ZOOM0002_1.TextGrid.14\n",
      "Something weird happened, tp= ∅ sp= ɹ\n",
      "Something weird happened, tp= ∅ sp= tʃʰ\n",
      "Something weird happened, tp= ∅ sp= ʃ\n",
      "Something weird happened, tp= ∅ sp= ʃ\n",
      "Something weird happened, tp= ∅ sp= ɛ̃\n",
      "Something weird happened, tp= ∅ sp= b\n",
      "/opt/speech-data/NL_BKK_9.5/06192018_12tiers_未翻譯_done/ZOOM0003_LR.TextGrid.14\n",
      "Something weird happened, tp= ∅ sp= ã\n",
      "Something weird happened, tp= ∅ sp= d\n",
      "Something weird happened, tp= ∅ sp= h\n",
      "Something weird happened, tp= ∅ sp= h\n",
      "/opt/speech-data/NL_BKK_9.5/06192018_12tiers_未翻譯_done/ZOOM0001_LR.TextGrid.14\n",
      "Something weird happened, tp= ∅ sp= ɹ\n",
      "Something weird happened, tp= ∅ sp= tʃʰ\n",
      "Something weird happened, tp= ∅ sp= ʃ\n",
      "Something weird happened, tp= ∅ sp= ʃ\n",
      "Something weird happened, tp= ∅ sp= ɛ̃\n",
      "Something weird happened, tp= ∅ sp= b\n",
      "/opt/speech-data/NL_BKK_9.5/06192018_12tiers_未翻譯_done/ZOOM0002_LR.TextGrid.14\n",
      "Something weird happened, tp= ∅ sp= tʃ\n",
      "/opt/speech-data/NL_BKK_9.5/06192018_12tiers_未翻譯_done/ZOOM0004_LR.TextGrid.14\n",
      "Something weird happened, tp= ∅ sp= g\n",
      "Something weird happened, tp= ∅ sp= ɤ̃\n",
      "/opt/speech-data/NL_BKK_9.5/06182018_done/ZOOM0005_LR.TextGrid.14\n",
      "/opt/speech-data/NL_BKK_9.5/06182018_done/ZOOM0003_LR.TextGrid.14\n",
      "/opt/speech-data/NL_BKK_9.5/06182018_done/ZOOM0001_LR.TextGrid.14\n",
      "/opt/speech-data/NL_BKK_9.5/06182018_done/ZOOM0007_LR.TextGrid.14\n",
      "/opt/speech-data/NL_BKK_9.5/06182018_done/ZOOM0002_LR.TextGrid.14\n",
      "/opt/speech-data/NL_BKK_9.5/06182018_done/ZOOM0004_LR.TextGrid.14\n",
      "/opt/speech-data/NL_BKK_9.5/06182018_done/ZOOM0009_LR.TextGrid.14\n",
      "/opt/speech-data/NL_BKK_9.5/06152018_done/ZOOM0005_LR.TextGrid.14\n",
      "Something weird happened, tp= ∅ sp= b\n",
      "/opt/speech-data/NL_BKK_9.5/06152018_done/ZOOM0007_Jinghuang.TextGrid.14\n",
      "/opt/speech-data/NL_BKK_9.5/06152018_done/ZOOM0004_做實驗.TextGrid.14\n",
      "/opt/speech-data/NL_BKK_9.5/06152018_done/ZOOM0008_test_one_by_one.TextGrid.14\n",
      "Something weird happened, tp= ∅ sp= ʏ\n",
      "Something weird happened, tp= ∅ sp= b\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JMm3atJxwwgm5+OKLM27cuDz22GO54447cskll6Si4o1OVVlZmdNPPz2zZs1K165d079//9xxxx15/vnn6y/SDQAAAMA7UzEiVVdX56677srvf//7JMnee++d448/Pp/61KdSWVmZJBk0aFDmzp2bq666KnfeeWf22muvXHjhhZk4cWKDbU2ZMiXJGxfpfvnll9OnT5/ceOON2X///Rt7vwAAAABoRMWIdM455+Scc84pbmjkyJEZOXJkcdyUKVPqYxIAAAAA7w7bdU0kAAAAAHYtIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARSISAAAAAEUiEgAAAABFIhIAAAAARa1begIAAACwK+jQ8T1p13bX+jW8qqpDS0+hWaxZuyG1Na+39DSa3K61egEAAKCFtGvbOuPO+0FLT4MmsHDmMalt6Uk0A6ezAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFBUjEiLFi3K1KlTM3LkyBx00EEZN25c/vmf/zl1dXUNxi1evDjjx49PdXV1Ro8enVtvvXWr21uwYEFGjRqVAw88MBMmTMiDDz7YOHsCAAAAQJMpRqSbb745lZWV+fznP5/rr78+o0ePzle+8pVceeWV9WMef/zxTJ06Nf369ctNN92UCRMmZMaMGbntttsabGvBggWZNWtWJk2alBtuuCG9evXKqaeemqeeeqrx9wwAAACARtO6NOD6669P165d6z8eNmxYXnvttXznO9/Jueeem8rKysyZMyf9+/fPjBkz6se88MILmTNnTj7+8Y+noqIi69aty7x58zJ58uRMmTIlSTJkyJCMGzcu8+bNyzXXXNNEuwgAAADAjioeifTmgLRZv379snbt2qxcuTLr1q3LQw89lKOOOqrBmLFjx2b58uV58sknkySPPfZYamtrc/TRR9eP2W233TJmzJg88MAD2bRp047uCwAAAABNZLsurP2LX/winTt3Trdu3fL8889n/fr12W+//RqM6dOnT5Jk2bJlSZKlS5cmyRbjevfunddeey0vvvji9kwFAAAAgGbwtiPSkiVL8v3vfz8nnXRSdtttt6xatSpJ0rFjxwbjNn+8+f6amppUVlamXbt2DcZ16tQpSbJy5cq3PXkAAAAAmkfxmkhvtnz58px11lmprq7OKaec0lRz2mbduu3R0lOAoqqqDi09BXYy1lTz8HWGHeN7iMZmTdHYrCka266wprY5ItXW1uaUU05Ju3btMm/evLRp0ybJ/xxJVFNT02D85o8339+xY8esW7cua9euTdu2bevHbT5SqXPnzm978itWrE5d3bv/Wkq7wkLblS1fXtvsj2lN7dxaYk3taqqqOvg6NzHPUzu/5v4esqZ2fl5T0disKRrbzvD6saKi1V89YGebTmdbu3ZtTj/99KxYsSLz589Ply5d6u/r2bNn2rRpU3/to82eeeaZJMm+++6b5H+uhbT52kibLV26NLvvvnu6d+++LVMBAAAAoAUUI9KGDRty9tln5ze/+U1uuumm9OjRo8H9lZWVGTZsWBYtWtTg9rvvvjtVVVUZMGBAkmTw4MHp0KFD7rnnnvoxGzduzKJFizJixIi0atWqMfYHAAAAgCZQPJ3t8ssvz7/927/lc5/7XNasWZNf/vKX9ff17t07e+yxR6ZNm5YTTjghF198ccaNG5fHHnssd9xxRy655JJUVLzRqSorK3P66adn1qxZ6dq1a/r375877rgjzz//fGbOnNlkOwgAAADAjitGpJ/97GdJkiuvvHKL+2655ZYMHTo0gwYNyty5c3PVVVflzjvvzF577ZULL7wwEydObDB+ypQpSZJbb701L7/8cvr06ZMbb7wx+++/f2PsCwAAAABNpBiR7r///m3a0MiRIzNy5MjiuClTptTHJAAAAADeHbbpwtoAAAAA7NpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKRCQAAAAAikQkAAAAAIpEJAAAAACKWrf0BAB45+vQ8T1p13bX+pFRVdWhpafQLNas3ZDamtdbehoAALwL7Fq/EQCwXdq1bZ1x5/2gpadBE1g485jUtvQkAAB4V3A6GwAAAABFIhIAAAAARSISAAAAAEUiEgAAAABF2xSRnnvuuVxyySU55phj0r9//4wdO3ar4xYvXpzx48enuro6o0ePzq233rrVcQsWLMioUaNy4IEHZsKECXnwwQe3fw8AAAAAaHLbFJGefvrpLF68OPvss0/222+/rY55/PHHM3Xq1PTr1y833XRTJkyYkBkzZuS2225rMG7BggWZNWtWJk2alBtuuCG9evXKqaeemqeeemrH9wYAAACAJtF6WwaNGjUqo0ePTpJccMEFeeKJJ7YYM2fOnPTv3z8zZsxIkgwbNiwvvPBC5syZk49//OOpqKjIunXrMm/evEyePDlTpkxJkgwZMiTjxo3LvHnzcs011zTWfgEAAADQiLbpSKSKir8+bN26dXnooYdy1FFHNbh97NixWb58eZ588skkyWOPPZba2tocffTR9WN22223jBkzJg888EA2bdr0ducPAAAAQDNolAtrP//881m/fv0Wp7r16dMnSbJs2bIkydKlS5Nki3G9e/fOa6+9lhdffLExpgMAAABAI9um09lKVq1alSTp2LFjg9s3f7z5/pqamlRWVqZdu3YNxnXq1ClJsnLlyrz3ve/d5sft1m2P7Z4zNJeqqg4tPQV2MtYUjc2aoilYVzQ2a4rGZk3R2HaFNdUoEamlrFixOnV17/5T4HaFhbYrW768ttkf05rauVlTNDZriqbQ3OvKmtr5ea6isVlTNLaWWFONraKi1V89YKdRTmfbfCRRTU1Ng9s3f7z5/o4dO2bdunVZu3Ztg3Gbj1Tq3LlzY0wHAAAAgEbWKBGpZ8+eadOmTf21jzZ75plnkiT77rtvkv+5FtLmayNttnTp0uy+++7p3r17Y0wHAAAAgEbWKBGpsrIyw4YNy6JFixrcfvfdd6eqqioDBgxIkgwePDgdOnTIPffcUz9m48aNWbRoUUaMGJFWrVo1xnQAAAAAaGTbdE2k119/PYsXL06S/OEPf8jq1atz7733Jkmqq6vTo0ePTJs2LSeccEIuvvjijBs3Lo899ljuuOOOXHLJJamoeKNVVVZW5vTTT8+sWbPStWvX9O/fP3fccUeef/75zJw5s4l2EQAAAIAdtU0RacWKFTn77LMb3Lb54yuuuCITJkzIoEGDMnfu3Fx11VW58847s9dee+XCCy/MxIkTG3zelClTkiS33nprXn755fTp0yc33nhj9t9//8bYHwAAAACawDZFpL333ju/+c1viuNGjhyZkSNHFsdNmTKlPiYBAAAA8M7XKNdEAgAAAGDnJiIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUCQiAQAAAFAkIgEAAABQJCIBAAAAUNQiEenZZ5/NlClTMmjQoAwbNixf+tKX8vrrr7fEVAAAAADYBq2b+wFramoyefLkvO9978s111yTV155JVdccUVeeeWVzJo1q7mnAwAAAMA2aPaIdPvtt6empiZ33nlnunbtmiTZbbfd8tnPfjZTp05Nnz59mntKAAAAABQ0++lsDzzwQIYNG1YfkJLkIx/5SCorK/PAAw8093QAAAAA2AbNfiTS0qVLc9xxxzW4rbKyMj179syyZcve1rYqKlo15tRa1F5d3tPSU6CJtNQ6taZ2XtYUjc2aoim0xLqypnZunqtobNYUjW1naBSlfWi1adOmTc00lyTJgAEDcvbZZ+fUU09tcPvEiRPTrVu3zJ49uzmnAwAAAMA2aJG/zgYAAADAu0uzR6SOHTumpqZmi9tramrSqVOn5p4OAAAAANug2SPSfvvtl6VLlza4bd26dXn++eez7777Nvd0AAAAANgGzR6RDj/88Dz00EP585//XH/bT37yk6xbty4jR45s7ukAAAAAsA2a/cLaNTU1GTt2bHr06JGpU6dmxYoV+epXv5rhw4dn1qxZzTkVAAAAALZRs0ekJPntb3+bL3/5y/nFL36Rtm3b5uijj87nPve5vOc9/tQhAAAAwDtRi0QkAAAAAN5dmv2aSAAAAAC8+4hIAAAAABSJSDSK448/Pn379s3999+fJDnxxBPTt2/fv/rfiSeeuMXY/fffP4cffnjOOOOMLF26tCV3iWZywQUXZOzYsc3yWH379s2CBQua5bFoWduyrk488cScdtppTbJtdg7XXXddBg0a9LY+Z9SoUbn88suL47bn+ejhhx9O3759s2TJkrf1ebw7bM96g3ei3//+9+nbt2/uvffelp4K2+G+++7Ld77znZaeBu9grVt6Arz7/e53v8vjjz+eJFm4cGFGjRqVSy+9NKtXr64f88UvfjHt2rXL+eefX3/bHnvsUf//gwcPzvnnn5+6urosXbo011xzTU466aT88Ic/TKdOnZpvZ4BdxqWXXpqKCu+l0DK++93v5n3ve19LTwMAGrjvvvvyxBNPZNKkSS09Fd6hRCR22MKFC9OqVasMHz48999/f1avXp3evXs3GLPHHnukffv2Oeigg7a6jY4dO9bfN3jw4LRv3z7Tp0/PT3/6U+/4A03irc9T0Jz+0s9DgO2xadOmrFu3Lm3btm3pqQA7OW/BssMWLlyYwYMH59RTT82aNWty33337fA2999//yTJH//4xx3eFu8OP/3pTzNu3LhUV1dnwoQJ9Ue3JckPfvCDfOITn8jQoUPzoQ99KJ/4xCfy6KOPbrGNpUuX5owzzsiQIUMycODAfPSjH83dd9/9Fx/zT3/6U8aMGZMTTjihwZFz7DweeeSRjB8/PgMHDsyxxx6bRx55pP6+rZ3Odt9992XMmDH16/DRRx/9i6cn/bVts/NZs2ZNvvSlL+XII4/MwIEDc8QRR+Siiy7KypUrtzr+G9/4Rg4//PAMHDgwp59+el566aUG97/1dLbFixfn5JNPzvDhwzNo0KAcd9xxjfLzlHevP/7xjzn77LPzoQ99KAMHDsyJJ56YX/3qV/X3z5kzJ4cddliDzznssMNy0EEHZf369fW3jRs3Ll/5yleabd40j82nVv/sZz/L+PHjU11dnUWLFuW+++6r//iQQw7JZZddlldffbX+877//e+nb9++eeWVVxps760/EzefXvn0009n0qRJGThwYMaMGZMf/ehHW8zlxhtvrF97p512Wv70pz813Y7TpC644IL8y7/8S55++un6y41ccMEFSZJf/epXOfnkkzNo0KAMGjQoZ555ZoN/682nMd5111358pe/nCFDhtSvwbVr19aPq62tzSWXXJIRI0bkgAMOyIgRI3LmmWdmw4YN9WNeeumlnH/++Rk2bFiqq6vzD//wD1t97U/LEJHYIU888USWLVuWsWPHZujQoenevXsWLly4w9vdHI969uy5w9vinW/58uW59NJLc/LJJ2fWrFlp3bp1pkyZkhUrViRJ/vCHP+SjH/1orr766lx11VXp1atXTjrppDz11FP123j22Wfz8Y9/PM8++2y+8IUvZN68eZkwYcJfDJHPPfdcPvGJT6RHjx6ZP39+g9Mr2TksX748l19+eU466aRce+21adOmTaZNm/YXg+F//dd/5ayzzso+++yT2bNn5/jjj8/nP//51NTU7PC2efdbs2ZN1q9fn7PPPjvz58/P9OnTs2TJknz605/eYux9992XH/3oR7nkkkty2WWX5T//8z9zxhln/NXt/+EPf8jIkSPzT//0T5kzZ06GDRuWM844I//+7//eRHvEO9nq1atzwgknZMmSJfnHf/zHzJw5Mxs3bszkyZPrrxk5ZMiQLF++PM8++2yS5Le//W1efvnlbNy4MU888USS5M9//nOefvrpDBkypKV2hSb00ksv5dJLL82kSZMyf/781NTU5IwzzkjPnj0ze/bsTJs2LQsXLsy0adO2a/vr16/P9OnTM27cuMyZMyc9e/bM9OnT8/vf/75+zG233ZaZM2fmqKOOyuzZs7P//vvnc5/7XGPtIs1s6tSpGTlyZN7//vfnu9/9br773e9m6tSp+dWvfpVJkyalsrIyX//61/PVr341zz77bKZMmZKNGzc22MbVV1+dDRs25KqrrsonP/nJ/O///b8bvGlyxRVX5P7778/06dNz880358ILL0z79u1TV1eXJKmpqcnEiROzZMmSXHTRRZkzZ066d++ek08+Ob/73e+a9evB1jmdjR2ycOHCtG7dOkceeWQqKipy9NFH51vf+lZefvnl7Lnnntu8nU2bNmXDhg2pq6vLsmXLMnPmzBxwwAEZNWpUE86ed4qVK1fm6quvzvDhw5MkBx98cD784Q/nm9/8Zs4777xMnTq1fmxdXV0OOeSQPPXUU/ne976Xiy++OMkb75i1adMmt99+e30QOuSQQ7b6eL/5zW8yZcqUDB48OF//+tdTWVnZxHtIS1i1alVuueWW9O3bN0my11575dhjj81DDz2U0aNHbzH+hhtuyPve977MmTMnu+22W5Kka9euW33x/Xa3zbtf586dGxyRtmHDhvTu3TvHHntsnnzyyQwYMKD+vtWrV2f+/Pnp0KFDkqSqqipTpkzJT3/604wYMWKr2//EJz5R//91dXUZMmRInnvuudx+++358Ic/3DQ7xTvW97///fzxj3/MXXfdlb/5m79J8sbPtFGjRuWmm27KV7/61QwcODBt27bNI488kl69euWRRx5J796907Vr1zzyyCMZNGhQ/Tv3H/zgB1tyd2giq1atyvXXX5/BgwcnSb72ta+luro611xzTf2YLl265Nxzz83DDz+coUOHvq3tb45IRxxxRJJkwIABOfTQQ3Pfffflk5/8ZOrq6jJv3ryMHTs2F110UZI3joZ79dVXc+uttzbSXtKcevbsma5du+aPf/xjg9Ouv/CFL6Rfv36ZN29eWrVqlSQ54IAD8pGPfCR33313jjnmmPqxBxxwQC677LIkb6yHX/7yl/nRj35U/3r+V7/6VcaOHZvx48fXf85RRx1V///f+ta3snLlytx7772pqqqq387YsWNz/fXXO7LyHcCRSGy3jRs35oc//GEOPfTQdO3aNUny0Y9+tP72t2Px4sUZMGBAqqurc8wxx+Tll1/OnDlz/HK/i+jQoUN9QEqSTp06ZejQofnP//zPJP9zmtqhhx6afv36ZcCAAXnyySfz29/+tv5zHnrooXzkIx8pHlG0ZMmSnHjiiRkxYkRmzZplje3Eqqqq6iNPkuy3335J8hcPs1+yZEmOOOKI+oCUvPGXttq0abPD22bncOedd2b8+PEZNGhQBgwYkGOPPTZJ6o8E2Wzo0KH1ASl548XvHnvsUf+ctjV/+tOfcsEFF+Twww/PgAEDMmDAgPzkJz/ZYtvsGh599NH06dOnPiAlSfv27XPEEUfUh6HKysoceOCB+fnPf54k+fnPf54hQ4ZkyJAhDW7bHJbY+XTu3Lk+IL366qv59a9/nTFjxjQY85GPfCStW7ferlOBKioqcuihh9Z/3KVLl3Tt2jUvvvhikuSFF17Iiy++mCOPPHKLx2TnsWbNmvziF7/IUUcdlY0bN2bDhg3ZsGFDunfvng984ANb/MXQt55m27t37wavj/r3759/+Zd/yfz58/PUU09l06ZNDcb/x3/8R4YOHZouXbrUP1ZdXV2GDx/e4JReWo4jkdhuDz30UJYvX54jjjii/nSPHj16pFevXlm4cGFOOumkbd7WBz/4wVx44YXZsGFDHnvsscyaNSvnnXdevv3tb9fXbnZeW3txu+eee+YXv/hFVq9enZNPPjmdO3fO5z//+fTo0SNt27bNV77ylaxbt65+/MqVK7PXXnsVH+vBBx/M6tWr87GPfaxBLGDn89a/7Lg5GL75vPw3W758+RZrsaKiIp07d97hbfPu95Of/CTnn39+/v7v/z7nnHNOOnfunJqamnz605/e4t+9W7duW3x+t27dsnz58q1uu66uLqeffnpWrVqVM844I/vss0/at2+fG2+8MU8++WST7A/vbDU1NVs9ortbt25ZtWpV/ccHH3xw7rzzziRvhKfzzz8/Xbp0yc0335yNGzfm0UcfzcEHH9xc06aZvXmN1NbWZtOmTVusm9122y2dO3dusG62Vbt27bZ4s62ysrL+OW/zc9pbf3Zu7TmQd69Vq1Zl48aNueKKK3LFFVdscf/73//+Bh937Nixwcdt2rRp8Jr9H//xH9O5c+d885vfzJVXXpnu3btnypQp9b87vvLKK3n88ccbHOG72ZvfoKHliEhst83XPrrsssvqD1l8s2effTa9evXapm116NAh1dXVSZJBgwaloqIiX/3qV7No0aIGhzeyc3rrxR2T5OWXX05VVVV++ctf5k9/+lOuv/769OvXr/7+V199tcEv9507d97iwrVbM2XKlDz55JM57bTTcvPNN9evO6iqqtpiLdbV1f3FCyeza7n33nuz//7758tf/nL9bZuvO/NWm6/n9tbbNh+W/1bPPfdc/uu//itz5sxpcDrkmy+OzK6lU6dOWbZs2Ra3r1ixokHEHjJkSObOnZsHH3wwL7zwQoYMGZLdd989a9euzcMPP5ynnnoqp5xySnNOnWb05jdaO3TokFatWm3x/LNx48asXLmyft1s/uttb31+WblyZdq3b/+2Hn/zc9pbf3Zu7TmQd6/Na+u0007b6in7b41G27K9iy66KBdddFGeeeaZ/PM//3NmzJiRD3zgAzn88MPTqVOnHHbYYTnnnHO2+FxvAL8zOJ2N7bJmzZr8+Mc/zhFHHJFbbrmlwX9z585Nq1atdugC2yeccEL23nvv3HDDDY04a96pamtr8+CDD9Z/vGrVqjz88MMZOHBg1qxZkyQN3gl76qmn8vTTTzfYxvDhw/OjH/2oeGHj3XbbLTNnzszQoUPz6U9/usHFudm1VVdX59/+7d8aXCDy/vvv94s8Sd74uffWd+T/0s+5hx9+OLW1tfUf/+xnP8vq1aszcODArY7f/K7+m0+dfPnllxs8L7Jr+eAHP5inn346zzzzTP1tr7/+ev793/89H/rQh+pvO+igg9KmTZvMnTs3++23X7p165Z27dqluro68+bNS11dnSORdhG77757+vXrl0WLFjW4/cc//nE2bNhQv27e+973Jkn9BdqTNy7s/+ZLBGyr//W//lf22muv3HvvvQ1u39pfcOPdo02bNg2OsG3fvn0GDRqUZ555JtXV1Vv8t88++2z3Y/Xu3TsXXXRRKioq6tfkIYcckqVLl2bffffd4rH69++/w/vHjnMkEtvl/vvvz6uvvpoTTzxxqxfpGzJkSBYuXJgzzzxzu7bfpk2bfOYzn8nFF1+cxYsXZ+TIkTs6Zd7BOnfunC984Qs588wz07Fjx/p4uPmw1vbt2+eyyy7LqaeemhUrVuTaa6+tfxG02ea/YjRx4sSccsopqaqqytKlS/P6669v8S5s69atM2vWrJx55pn51Kc+lW9/+9v117Rh13XaaaflYx/7WKZNm5aJEyfmpZdeyrx589KhQ4dUVHjPZVd3yCGH5PLLL891112XD37wg/m///f/5v7779/q2D322COnnHJKTjnllNTU1OTKK69MdXX1X7yo9r777pv3vve9+frXv56NGzdmzZo1mTt3bvbcc88trhXBrmHChAn55je/mdNOOy3nnHNO2rdvn2984xtb/Ex7z3vekwMOOCA///nPM3HixPrbDz744Nxwww3p1avXXzwCjp3PGWeckWnTpmX69Ok59thj84c//CEzZ87M8OHD61+vDxw4MD169MiMGTMyffr0rFmzJjfeeONWT90uqaioyGc+85lcfvnl2XPPPTNixIg8+uij+dd//ddG3jOa03777Zfvfe97ueuuu/KBD3wgXbp0yfnnn5/JkyfnrLPOytixY9OpU6e89NJLefjhh/PhD3/4bf1RkeOPPz6jR49Onz590qZNm9x9992pqKio/yuSn/rUp/LDH/4wJ5xwQiZPnpwePXpk5cqVeeKJJ1JZWVn8a6c0Pa+K2S533XVXunfv3uBiyG82fvz4PPfcczt08bNjjz3W0Ui7iKqqqlx66aWZP39+zj777Kxfvz7z58/PnnvumT333DPXXnttVq5cmWnTpmX+/Pm58MILG1zUOEl69eqV22+/PXvvvXe++MUv5vTTT8/3vve99OjRY6uPWVlZmeuuuy79+/fPSSed5OK1pH///rn22mvz3HPPZdq0abn11lszY8aMtGrVqnjBdnZ+xx9/fE4++eTcfvvtmTZtWpYuXZprr712q2NHjx6dv/3bv81ll12WSy+9NAceeGDmzJnzF7ddWVmZ2bNnp127djn33HMza9asnHjiid5A2YXtscce+fa3v50BAwbksssuy/Tp01NRUZFbbrllizc9Nh9ptPkXsDff5iikXcvf/u3f5rrrrsuyZcsyderUXHvttRk7dmxmz55dP6Z169aZM2dOdt9995x77rm57rrrctZZZ+UDH/jAdj3mpEmTcs455+SHP/xhpk2blieeeCJXXnllY+0SLeBjH/tYxowZk6985Sv52Mc+ltmzZ+eggw7KbbfdlnXr1uULX/hCPv3pT+eaa65JXV1devfu/ba2P3jw4Nx1110555xzcuaZZ2bZsmWZO3du/TWQOnfunO9+97uprq7OVVddlZNPPjlf+tKX8t///d8ZNGhQU+wyb1OrTd7iAoCteuKJJ3Lcccfl2muv9ddmAADY5YlIAPD/XXbZZRk2bFi6dOmS3/72t7n++uvTtm3bLFy4cIvr4QAAwK7GNZEA4P+rra3Nl7/85axcuTK77757hg8fnvPPP19AAgCAOBIJAAAAgG3gwtoAAAAAFIlIAAAAABSJSAAAAAAUiUgAAAAAFIlIAAAAABSJSAAAAAAU/T8we2gkFPylzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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h3Sa1aNs07DxJKvQVKntbjqXnVqXpUQ100Ygz9M8HJ8lf5LecY8py251pynYZzZkmtPmgoceqbr00Pf/UNAUCASUkxsnlctW4VhOW3cn+rVRxcbG2bc1RSmrVlzmtWp9XboBZkn7b5tMvvxWoWaO4Co93uaSE+PDfjk3Zf+zONKFGMiO3zZukSud3kv61SPIXV5yf5JV2/WFcwOeX9hVI+UUVH+9UnaZlHiohMU5ud81OMZyq0YT3NFMy7T5ut6vGxmnSud2l5+cF38eDKb1NRnLFt2jH6nQ6MzklQZKUs6P8BT7ZO0oWtiCv8LDXaMp+7kRmJL9POpnpxPm+Kcvu9HtvqdPP6iVJ+vTDxTXKsWuMR3L2XM2OYw4pevdJJzKr3RoLFizQO+9U/CRk27Ztev311/Xrr7+G/eKVad+lrbZs/E379pS/OdbK71aXzT9SM0PJyEzX7p2hrwQ+XG6593ItWbBKX8/+3pH8SF1uu5iyXUZzpt2cqPGY3u2Vuy9P9bLSNeWzsZq9bJxmff+Mxjx4qeLiPbbUbQcT2qdUQmK80uqkqMlRDXTJtWfphJO7a+G8ZZbz0lNjtXdf+YO02FiXpj3fQR+80FFTx3fQTVc0rPaAsyn7j92ZJtRIZuS2+U3HSku3St9sCT7/+9+l4xpL53eUGiaXDEr/uVfJf99aUXt1mpZZaszDV2rOyhc0d/VLGv/maHXs0sJSjinvFaa0jwnnK3bVOOqkknssf/tL5Y9LjZfSE6R2WdLtp5ZMW7K59up0OnPpopJvqt310IVq26Gx6mWl6aSBnXXJVSfrs4+W6Petuw57jabs506I5PdJJzNDqcn5vinLXlvr87Qze+nXzdla/t2GGuU4PcZjB7uOOaTo3SedyKz2D//985//VP/+/StMv/nmm7V+/Xp9+umneuutt8IuIJTMrLrK3pZdYXrpJyD1GmQesZnBDB7WX1mNMvXSU2/akmeXEwd01/F9j9Ylg4PfeL2mInW57WTKdhnNmXZzosamzevL7Y7RYxNG6aP/fKUJT05T156tdcGVA5SekaI7Rk2ocd12MKF9So159DoNPrefJMnv92v2jIV67K6XLGUN7J2mehkeTZp28KupO/cU6Z1PsrX+lzzFuFzqdXSyhg6oq1bNEvR/D2+Qv4qLBkzZf+zONKFGMiOzzY9vIh3TWLpqWujHPPONlB4v3XScSzf977d0cn0Bjf5SWlTFbxKZsC6dyiwsLNKsGYv09Zzl2r1zn1q0aaxLrjldz0+5U9dd8LBWLw/vh4dMea8wpX1MOF+xo8bjjpJ6NpOureKUNMYlvXftwW977ckL6Lk5AS2pxu1LTWmfBfPW6KXnPtNlI/qrz8kdy6ZPfesrPfXQtIio0ZT93AmR+j7pdGYwNT3fN2XZa2N9dunRUo2a1NWr4z+tUY7TYzw1ZfcxhxS9+6QTmdUeZP7xxx/117/+tcL0lStX6sYbb9SECfYOYMTFx8nnq/g1noJ8X9n8IzXzj5q3bqI7Hr5eyxev1odvfVHjPLvEety65e7L9N7kmdr4o71XskuRu9x2M2W7jOZMuzlRY0JSvBIS4/Tem//VUw9MkSTN+XypJOmiq09R6/ZNtH5NiEv3apEJ7VPqtefe0/R3ZiszK0ODzukjd6xbXm/4V4U3bejVjZc10qr1B/TZ3INXDr3ybvkfppizcI+2/O7T1edn6eTj0jTz69D3n5PM2X/szjShRjIjr81jY0quYv5gjfRLJbtWfpH0y24p50BA8zdJHrd0djvp/v7S3z6TKvudbRPWpVOZK75brzHfHbzX4byZ32vWjEWa/MkDGnXbebrpsscPe41OMKV9TDhfqWmNsTHS9X2l6SulTTsrf63igHT7+wHFxkjNM6SB7Ut+LLA26qytTEn6bXOOVnz/s+Z8sUI5O/aq+7GtNOyiE5WX59P4Jz8+7DWasp87IRLfJ2sj84/sON83ZdlrY32W3SrjI+u3ynB6jMcOdh9zSNG7TzqRWe3bZbhcLu3dW/ErDA0aNNDixYsVG1vt8epqKcgvCHoyHxfvLZt/pGYeqm69OnrmjfuUu2+/br/mYRUXV/PmYrXg4hFnKC0jRS8+9a7t2ZG83HYzZbuM5ky7ObPcJW8En09fVG76px+W/BhC1x6tws50ggntU2rDus36dv5yfTJ1jv562YNKTIzXk6+OCSujTlqsHvjbUdqf59f9z25ScRW/x/jeZ9nyFwfUvWNyldmm7D92Z5pQI5mR1+bDO0lp8dIrSyt/3Nj+UuNU6YG50uyfpc9/kv76acl9mm8O/RtbttVpamYwW37ZrrlfLlX3Y9rJHeuOyBprypT2MeF8paY1DusupSZIrwf/3boKvttcckuNd5dKD3wiXXacdHYX5+usrcxTBnfT6PvP16P3/kfvT1mgubN+0DOPfqiJL83SxVf2U8s24d1n05TlNkUkvk/WRuah7DrfN2XZnV6fsR63BpzeXatW/KJNGyv+qGd1OTnG46SaHHNI0btPOpFZ7UHmrl276o033qiw8z/66KNasWKF8vLyNHjw4LALCCV7W44ysypemp2ZVVeStOP3ipd0HymZpZJSEjVu8lglpybppovvUfa2Kj6Wr0VJKQm66qY/6YO3ZikpJUENm2SqYZNMJSTFy+VyqWGTTNWpm2oxO3KX2wmmbJfRnGk3R5Z7e8mleTuzy38YuDOn5N8paYlhZzrBhPYJZebHX6tTtzZq3rJRtR6fmBCjh/6vuZITY3TnE79o5+4qfjVMkq8woH25fqUkV31wZMr+Y3emCTWSGVltnuSRLu8qTV9X8sN+DZJL/hI8JT+82SC55BYZDZOl45u4NO8P93ItKi65h3O7zIMH3U7UaXJmKNt+2ymPN1ZJSfFhPc+U9wpT2seE85Wa1JjklS45RvpkpZTolbJSSv4SvJJcJf+fnhD6tX/dI63fIQ1s52ydtZl57oW9tX7tVm39tXx7zJ25UjExMerSPbx7l5qy3KaItPfJ2sosZef5vinL7vT23vukjkpLT9JnNfjBPyfHeGqD1WMOKXr3SScyqz3I/Ne//lXLly/X4MGD9eKLL+qjjz7SpEmT9NJLL2nv3r36+9//rjvvvDPsAkJZu+JHNWnRSClpKeWmd+7ZsWz+kZopSd44j56eeK+atWqsWy4fq43rqvFLFLUoNS1JSckJuvz6s/TBV8+W/Q084zh54zz64Ktndffj14WdG+nL7QRTtstozrSbEzWuWVkyKlIvK73c9PoN6kiSdu3MtVCp/Uxon1BKB5eSUqsesPd4XHrgluZq0iBOdz/1izb9Vr1PgRPiY5Sa7NaevVX/irMp+4/dmSbUSGZktXlKnJTocenio1165/yDfycf5ZLXXfL/o/tIGf8bhAr2I+VulxTjcinGHfoDIBPWpVOZoTRuVk++gkLtz82L2BprwpT2MeF8pSY1JsdJiV6XLuzl0uSrDv6d1LpkH598lUu3nlL568fFSknV+FawKe1Tp26y3EE6M3dsTLn/Hs4aTdnPnRBp75O1lSnZf75vyrI7vb0PGtpLRYV+ffHxEssZTo3x1BarxxxS9O6TTmSGdSXzxIkTlZmZqWeeeUa33XabHnroIWVnZ+uFF17QRRddpL59+4ZdQChffjRHbrdbw644q2yax+vR2RedoVXfr9Fvm7YesZkxMTF65PnR6tKzvUaPfEQrlqwJO8NpO7P36raRT1b4W/z1Dyr0Fem2kU/qlWffDyvThOV2ginbZTRn2s2JGmfOKDmgOOv8E8tNP3t4H/n9xVq8YHWNaraLCe1Tp25ahWmxnlgNOa+/8vMKtHFd5fe2jnFJd41qqg6tEvXAc5u0+qeKBzoej0sJ8RXfgi85q55iYlxatGJfhXl/ZMr+Y3emCTWSGVltvitPunNmoMLfd1sDKvSX/P/EZdKWvZK/OKCBLSTXIc9PiJVObCZt2hNQ3v7QJy4mrEunMtMzUipMa9O+qfoO7K5FX6+S3x/e16BNeK+QzGkfE85XalLj7jzpnumBCn9LN5fs4/dMD2jyopJ7rCcEufdyhwZSi7rSum0V59lZZ21mbvp5h1q3bagWrbLKTT/9zB6SpLU/hPc7HaYstyki7X2ytjKdON83Zdmd3N6TkuN14smd9O3Xa2p0YZETYzxOsPuYQ4refdKJzGrfSHnMmDEaNWqUJk+erIKCAu3evVspKSlKTEzUr7/+qjFjxuiRRx4Ju4BQVi5Zpc+nzdKoO0cqPSNNmzZs0ZkXnK5GzRvq+mG3HNGZN983Qv1OP15zP1uo1DopGjysf7n5M6bOtpQ7/OqzlZKWrJS0JElSrz7dyu5XM+Xlacrdt7/aWQX5Pv3384pfxeh3Wi8d3aNN0HlVMWG5ncg0ZbuM5kwp8tt83arN+vDd+Trr/JIfqPtu4Vp17dlag846Tu9MnKVfN1n/ClakL7vdNT40/m8q9BVq+eK1yt6+S/Wy6uj0c/upectGenrsq8o7kF/p86+9qIF690jVgqV7lZrs1sDe5QetZ369RxlpsRp/f2vN+Wa3Nm8tucq5Z+cUHdctRUtW5mr+4oq/gfBHpuw/dmeaUCOZkdXmBX5p/qaK0/s2kzrVKz/v4x+ls9q59OwZAc3eWDIoNaSNVD/JpbFzKr+pugnr0qnMh8Zdr4KCQi1fsl67cvaqRZtGOufCk1VQ4NOzj06JiBpLRfp7mimZdh+316TGgiLp6w0Vp5/YUgoEDs7LSpFeuFias07atEsq9Est60qndpD2F0iTKvtlTxvqrM3MN1+ZoxP6tNe/Xh+lqW9+pZzsfep5XGsNPL2rFn61ViuX/VJ1iMM1mrKfO5EZae+TtZXpxPm+Kcvu5PY+YFA3xcV79VkNfvBPcmaMp5St52o2H3NI0btPOpHpCgQCVfwMUYn27dvrnXfeUZcuFX8RYeXKlTr//PO1enX4V8p1r9sn5DxvnFfXjx6hIcMHKTU9RT+t2ajxD7+sr2ZW8xcdIjjT7a1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\n",
      "text/plain": [
       "<Figure size 1800x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#print(vtrue, verr, ctrue, cerr)\n",
    "\n",
    "def main():\n",
    "    flist = glob.glob(\"/opt/speech-data/NL_BKK_9.5/**/*.14\", recursive=True)\n",
    "    vconf=[]\n",
    "    cconf=[]\n",
    "    verrl=[]\n",
    "    cerrl=[]\n",
    "    vlol=[]\n",
    "    clol=[]\n",
    "    \n",
    "    pf=phfeat()\n",
    "    \n",
    "    for f in flist:\n",
    "        print(f)\n",
    "        tgf = thai_textgrid(f)\n",
    "        tgf.parse_tiers()\n",
    "\n",
    "        ttier = tgf.twtier\n",
    "        tptier = tgf.tptier\n",
    "        stier = tgf.swtier\n",
    "        sptier = tgf.sptier\n",
    "        tsytier = tgf.tsytier\n",
    "        ssytier = tgf.ssytier\n",
    "        \n",
    "        #tword=ttiter(tier_list[0])\n",
    "        #print(tword)\n",
    "        \n",
    "        #w,p,n = ca.pos_analysis(tword)\n",
    "        #allw.append(w)\n",
    "        #print(w)\n",
    "        \n",
    "        for t in stier:\n",
    "            #print(t.text, end=\" \")\n",
    "            if \"*\" in t.text:\n",
    "                # Student contains error\n",
    "                ccstr=t.text.replace(\"*\",\"\").replace(\" \",\"\").replace(\"^\",\"\").replace(\"~\",\"\").replace(\"SIL\",\"\")\n",
    "                pattern = \"[0-9a-zA-Z\\s]+\"\n",
    "                # perform a regex substitution to clean the string\n",
    "                serrtxt=re.sub(pattern, \"\", ccstr)\n",
    "                #print(t.text, serrtxt)\n",
    "                pes=t.start_time\n",
    "                pee=t.end_time\n",
    "                #print(pes, pee)\n",
    "                ttl = ttier.get_annotations_with_text(serrtxt, n=1)\n",
    "\n",
    "                if len(ttl) != 0:\n",
    "                    tes= ttl[0].start_time\n",
    "                    tee= ttl[0].end_time\n",
    "                    tpl = tptier.get_annotations_between_timepoints(tes, tee, left_overlap=True, right_overlap=True)\n",
    "                    pepl = sptier.get_annotations_between_timepoints(pes, pee, left_overlap=True, right_overlap=True)\n",
    "                    tsyl = tsytier.get_annotations_between_timepoints(tes, tee, left_overlap=True, right_overlap=True)\n",
    "                    ssyl = ssytier.get_annotations_between_timepoints(pes, pee, left_overlap=True, right_overlap=True)\n",
    "                    \n",
    "                    tsystr = tsyl[0].text\n",
    "                    ssystr = ssyl[0].text\n",
    "                    \n",
    "                    sydist = lev.distance(tsystr, ssystr)\n",
    "                    syact = lev.editops(tsystr, ssystr)\n",
    "                    \n",
    "                    #print(tsystr, ssystr, sydist, syact)\n",
    "                    \n",
    "                    tp2 = [x.text for x in tpl]\n",
    "                    pl2 = [x.text for x in pepl]\n",
    "                    #print(tp2, pl2)\n",
    "                    \n",
    "                    #for x, y in zip(tp2, pl2):\n",
    "                    #    if x != y:\n",
    "                            #print(x, \"->\",y)\n",
    "                            \n",
    "                    lsn=lev.distance(tp2, pl2)\n",
    "                    led=lev.editops(tp2, pl2)\n",
    "                    #print(led)\n",
    "                    for ac in led:\n",
    "                        action = ac[0]\n",
    "                        fstidx = ac[1]\n",
    "                        sndidx = ac[2]\n",
    "                        \n",
    "                        if action == \"replace\":\n",
    "                            #print(tp2[fstidx], \"->\", pl2[sndidx])\n",
    "                            matrix_update(tp2[fstidx], pl2[sndidx], vconf, cconf, pf, verrl, cerrl, vlol, clol)\n",
    "                        elif action == \"delete\":\n",
    "                            #print(tp2[fstidx], \"->[]\")\n",
    "                            matrix_update(tp2[fstidx], \"∅\", vconf,cconf, pf, verrl, cerrl, vlol, clol)\n",
    "                        elif action == \"insert\":\n",
    "                            #print(\"[]->\", pl2[sndidx])\n",
    "                            matrix_update(\"∅\", pl2[sndidx], vconf, cconf, pf, verrl, cerrl, vlol, clol)\n",
    "                        else:\n",
    "                            print(\"No match\")\n",
    "    \n",
    "    #print(vconf)\n",
    "    #print(len(verrl), verrl)\n",
    "    \n",
    "    vflat = [item for sublist in vlol for item in sublist]\n",
    "    cflat = [item for sublist in clol for item in sublist]\n",
    "    #print(vflat)\n",
    "    #print(cflat)\n",
    "    \n",
    "    hist_plot(verrl)\n",
    "    hist_plot(vflat)\n",
    "    #print(len(cerrl), cerrl)\n",
    "    hist_plot(cerrl)\n",
    "    hist_plot(cflat)\n",
    "    \n",
    "    plot_task(vconf)\n",
    "    plot_task(cconf)\n",
    "    \n",
    "\n",
    "if __name__ == '__main__':\n",
    "    main()  # 或是任何你想執行的函式\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "28de2353",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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