88 lines (87 with data), 1.8 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import csv\n",
    "import pandas\n",
    "import sqlite3\n",
    "import random\n",
    "import json\n",
    "import itertools\n",
    "\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "db_file = 'mimic_db/mimic.db'\n",
    "model = query(db_file)\n",
    "(db_meta, db_tabs, db_head) = model._load_db(db_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "for tb in db_meta:\n",
    "    for hd in db_meta[tb]:\n",
    "        mysql = 'select distinct {} from {}'.format(hd, tb)\n",
    "        myres = model.execute_sql(mysql).fetchall()\n",
    "        myres = list({k[0]: {} for k in myres if not k[0] == None})\n",
    "        db_meta[tb][hd] = myres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(db_meta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fout = open(\"mimic_db/lookup.json\", \"w\")\n",
    "json.dump(db_meta, fout)\n",
    "fout.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}