misc_test.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import json\n",
    "import os\n",
    "import pandas as pd\n",
    "import wfdb\n",
    "import ast\n",
    "import re"
   ],
   "metadata": {
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     "end_time": "2023-06-15T14:54:25.081971962Z",
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  {
   "cell_type": "markdown",
   "source": [
    "# Extracting all the class characters that appear in the databases we're potentially using"
   ],
   "metadata": {
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   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "databases = ['vfdb/', 'mitdb/', 'nsrdb/', 'svdb/', 'ltdb/']\n",
    "# Path to original data directory\n",
    "databases_dir = \"data_original/\"\n",
    "preprocessed_dir = \"data_processed/\""
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-15T14:49:57.061613580Z",
     "start_time": "2023-06-15T14:49:57.015854128Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "classes_list = set()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "for database in databases:\n",
    "    database_path = os.path.join(databases_dir, database)\n",
    "\n",
    "    # LF a `RECORDS` file - contains the names of all the records in that database\n",
    "    records_file = os.path.join(database_path, \"RECORDS\")\n",
    "\n",
    "    with open(records_file, \"r\") as file:\n",
    "        record_names = file.read().splitlines()\n",
    "\n",
    "    for record_name in record_names:\n",
    "        record_path = os.path.join(database_path, record_name)\n",
    "\n",
    "        # Read the current record and its annotation\n",
    "        record = wfdb.rdrecord(record_path)\n",
    "        annotations = wfdb.rdann(record_path, \"atr\")\n",
    "\n",
    "        classes_list = classes_list.union(set(annotations.symbol))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "classes_list = list(classes_list)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# mitDB.csv research"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "def fix_retarded_string(retarded_string):\n",
    "    string_data = retarded_string[1:-1]\n",
    "    string_data = (re.sub(r'\\s+', ',', string_data.replace(\"\\n\", \"\"))).replace(\"[,\", \"[\")\n",
    "\n",
    "    # Both of these work:\n",
    "    #return json.loads(string_data)\n",
    "    return ast.literal_eval(string_data)\n"
   ],
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     "end_time": "2023-06-15T03:12:29.856245242Z",
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  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../mitDB.csv\")"
   ],
   "metadata": {
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    "ExecuteTime": {
     "end_time": "2023-06-15T03:12:36.681499582Z",
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  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "string = df['betti'][1]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-15T03:12:49.329117343Z",
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   "source": [
    "string"
   ],
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   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "fix_retarded_string(string)"
   ],
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  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "df[\"betti_H0\"] = df['betti'].apply(lambda x: fix_retarded_string(x)[0])"
   ],
   "metadata": {
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     "end_time": "2023-06-15T03:13:37.465208440Z",
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "df[\"betti_H0\"]"
   ],
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   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "df['betti_H1']"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "df[\"betti_H1\"] = df['betti'].apply(lambda x: fix_retarded_string(x)[1])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-15T03:17:08.823867996Z",
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "type(df[\"betti_H1\"][0][0])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# svDB.csv research"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data_processed/svDB_clean.csv\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
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   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "string = df['betti_H0'][0]"
   ],
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     "end_time": "2023-06-15T14:56:02.550231538Z",
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  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "'[770 641 427 332 289 272 261 247 234 212 199 182 172 156 144 123 111  94\\n  88  82  78  72  70  66  62  60  55  53  51  48  41  39  38  38  37  36\\n  36  36  35  32  31  30  28  27  26  26  26  26  24  22  22  22  20  18\\n  17  16  16  16  16  16  16  15  15  15  14  14  14  13  13  11  10  10\\n   7   7   7   7   7   7   7   7   7   7   6   6   5   4   4   4   4   4\\n   4   4   3   3   3   2   2   2   2   0]'"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "string"
   ],
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    "ExecuteTime": {
     "end_time": "2023-06-15T14:56:07.992233131Z",
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  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "def fix_retarded_string(retarded_string):\n",
    "    string_data = (re.sub(r'\\s+', ',', retarded_string.replace(\"\\n\", \"\"))).replace(\"[,\", \"[\")\n",
    "\n",
    "    # Both of these work:\n",
    "    #return json.loads(string_data)\n",
    "    return ast.literal_eval(string_data)"
   ],
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     "end_time": "2023-06-15T14:26:09.170808084Z",
     "start_time": "2023-06-15T14:26:09.136086513Z"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "fix_retarded_string(string)"
   ],
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    "collapsed": false
   }
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  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "(184045, 7)"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ],
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  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "ann\n0    161920\n1     22125\nName: count, dtype: int64"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ann'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-06-15T14:58:07.837556906Z",
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   "cell_type": "code",
   "execution_count": null,
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