Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import psycopg2\n",
    "conn = psycopg2.connect('host=localhost dbname=bs_db user=postgres password=postgres')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n"
     ]
    }
   ],
   "source": [
    "cur = conn.cursor()\n",
    "cur.execute('select * from bs_test')\n",
    "test_list = cur.fetchall()\n",
    "print(type(test_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bs_measure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-04 21:24:00</th>\n",
       "      <td>456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-05 14:12:00</th>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-05 12:27:00</th>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-05 09:17:00</th>\n",
       "      <td>223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-05 17:02:00</th>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    bs_measure\n",
       "datetime                      \n",
       "2020-05-04 21:24:00      456.0\n",
       "2020-05-05 14:12:00      215.0\n",
       "2020-05-05 12:27:00      206.0\n",
       "2020-05-05 09:17:00      223.0\n",
       "2020-05-05 17:02:00      285.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "test_df = pd.DataFrame(test_list)\n",
    "test_df.head()\n",
    "test_df.rename({0 : 'datetime', 1 : 'bs_measure'}, axis=1, inplace=True)\n",
    "test_df.set_index('datetime', inplace=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def test_fig():\n",
    "    sns.distplot(test_df['bs_measure'], vertical=True)\n",
    "    plt.savefig('img/test.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[01;35mtest.png\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "ls img/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],\n",
       " <a list of 14 Text xticklabel objects>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = test_df[-14:]\n",
    "plt.plot(a['datetime'], a['bs_measure'])\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_df = test_df[['datetime', 'bs_measure']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_df['datetime'] = pd.to_datetime(real_df['datetime']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_df = real_df[['datetime','bs_measure']]\n",
    "real_df.set_index('datetime', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bs_measure</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-04 21:24:00</th>\n",
       "      <td>456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-05 14:12:00</th>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-05 12:27:00</th>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-05 09:17:00</th>\n",
       "      <td>223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-05 17:02:00</th>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-05 23:37:00</th>\n",
       "      <td>343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06 03:05:00</th>\n",
       "      <td>302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06 11:04:00</th>\n",
       "      <td>209.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06 12:54:00</th>\n",
       "      <td>245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06 03:27:00</th>\n",
       "      <td>214.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06 17:02:00</th>\n",
       "      <td>227.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06 07:29:00</th>\n",
       "      <td>222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06 19:29:00</th>\n",
       "      <td>222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06 20:21:00</th>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-06 23:00:00</th>\n",
       "      <td>286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-07 00:47:00</th>\n",
       "      <td>284.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-07 06:26:00</th>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-07 08:39:00</th>\n",
       "      <td>362.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-07 08:41:00</th>\n",
       "      <td>347.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-07 08:43:00</th>\n",
       "      <td>338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-07 11:28:00</th>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-07 14:57:00</th>\n",
       "      <td>320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-07 18:40:00</th>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-07 20:59:00</th>\n",
       "      <td>294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-07 22:40:00</th>\n",
       "      <td>338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-08 09:58:00</th>\n",
       "      <td>302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-08 12:21:00</th>\n",
       "      <td>348.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-08 14:00:00</th>\n",
       "      <td>319.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-08 18:40:00</th>\n",
       "      <td>306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-08 21:27:00</th>\n",
       "      <td>273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-09 19:13:00</th>\n",
       "      <td>326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-09 02:02:00</th>\n",
       "      <td>326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-09 09:42:00</th>\n",
       "      <td>227.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-09 13:53:00</th>\n",
       "      <td>154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-09 17:21:00</th>\n",
       "      <td>164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-09 22:38:00</th>\n",
       "      <td>123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-10 02:32:00</th>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-10 10:41:00</th>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-10 13:25:00</th>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-10 19:20:00</th>\n",
       "      <td>155.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-10 20:56:00</th>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-10 21:45:00</th>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    bs_measure\n",
       "datetime                      \n",
       "2020-05-04 21:24:00      456.0\n",
       "2020-05-05 14:12:00      215.0\n",
       "2020-05-05 12:27:00      206.0\n",
       "2020-05-05 09:17:00      223.0\n",
       "2020-05-05 17:02:00      285.0\n",
       "2020-05-05 23:37:00      343.0\n",
       "2020-05-06 03:05:00      302.0\n",
       "2020-05-06 11:04:00      209.0\n",
       "2020-05-06 12:54:00      245.0\n",
       "2020-05-06 03:27:00      214.0\n",
       "2020-05-06 17:02:00      227.0\n",
       "2020-05-06 07:29:00      222.0\n",
       "2020-05-06 19:29:00      222.0\n",
       "2020-05-06 20:21:00      151.0\n",
       "2020-05-06 23:00:00      286.0\n",
       "2020-05-07 00:47:00      284.0\n",
       "2020-05-07 06:26:00       66.0\n",
       "2020-05-07 08:39:00      362.0\n",
       "2020-05-07 08:41:00      347.0\n",
       "2020-05-07 08:43:00      338.0\n",
       "2020-05-07 11:28:00      340.0\n",
       "2020-05-07 14:57:00      320.0\n",
       "2020-05-07 18:40:00      235.0\n",
       "2020-05-07 20:59:00      294.0\n",
       "2020-05-07 22:40:00      338.0\n",
       "2020-05-08 09:58:00      302.0\n",
       "2020-05-08 12:21:00      348.0\n",
       "2020-05-08 14:00:00      319.0\n",
       "2020-05-08 18:40:00      306.0\n",
       "2020-05-08 21:27:00      273.0\n",
       "2020-05-09 19:13:00      326.0\n",
       "2020-05-09 02:02:00      326.0\n",
       "2020-05-09 09:42:00      227.0\n",
       "2020-05-09 13:53:00      154.0\n",
       "2020-05-09 17:21:00      164.0\n",
       "2020-05-09 22:38:00      123.0\n",
       "2020-05-10 02:32:00      191.0\n",
       "2020-05-10 10:41:00       69.0\n",
       "2020-05-10 13:25:00      181.0\n",
       "2020-05-10 19:20:00      155.0\n",
       "2020-05-10 20:56:00      139.0\n",
       "2020-05-10 21:45:00      174.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_df['2020-05-04':'2020-05-10']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 7, 5, 22, 11, 34, 98444)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.datetime.now('%Y-%m-%d %H:%M:')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_df.to_csv('blood_sugar_dataframe.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>bs_measure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-05-04 21:24:00</td>\n",
       "      <td>456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-05-05 14:12:00</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-05-05 12:27:00</td>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-05-05 09:17:00</td>\n",
       "      <td>223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-05-05 17:02:00</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime  bs_measure\n",
       "0  2020-05-04 21:24:00       456.0\n",
       "1  2020-05-05 14:12:00       215.0\n",
       "2  2020-05-05 12:27:00       206.0\n",
       "3  2020-05-05 09:17:00       223.0\n",
       "4  2020-05-05 17:02:00       285.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_csv_load = pd.read_csv('blood_sugar_dataframe.csv')\n",
    "test_csv_load.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pytz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2020, 7, 6, 22, 5, 42, 585710, tzinfo=<DstTzInfo 'America/Chicago' CDT-1 day, 19:00:00 DST>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utc_now = pytz.utc.localize(datetime.datetime.utcnow())\n",
    "cst_now = utc_now.astimezone(pytz.timezone(\"America/Chicago\"))\n",
    "cst_now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'07_06_2205'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = cst_now.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "a = a[5:]\n",
    "b = a[6:11]\n",
    "a = a[:5]\n",
    "b = b.replace(':','')\n",
    "a = a.replace('-','_')\n",
    "a + '_' + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def test_tail(x):\n",
    "    tail_df = test_df.tail(x)    \n",
    "    tail_df = tail_df.reset_index()\n",
    "    tail_df['datetime'] = tail_df['datetime'].str.slice(start=5, stop=-3)\n",
    "    ax = sns.pointplot(x='datetime', y='bs_measure', data=tail_df)\n",
    "    ax.set_xticklabels(ax.get_xticklabels(), rotation=45, horizontalalignment='right')\n",
    "    plt.savefig('test.png')\n",
    "    # print(tail_df['datetime'].str.slice(start=5))\n",
    "\n",
    "test_tail(24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "blood_sugar_dataframe.csv  dis_test.py  pg_helper.py  requirements.txt\n",
      "bs_bot.py                  \u001b[0m\u001b[01;34mimg\u001b[0m/         Procfile      \u001b[01;35mtest.png\u001b[0m\n",
      "data_transformer.py        latest.dump  README.md     Untitled.ipynb\n"
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   "metadata": {},
   "outputs": [],
   "source": []
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