scripts/read_sql.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This file shows how to read the data from the database to a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All the imports\n",
    "import pandas as pd\n",
    "import sqlalchemy\n",
    "import os, sys\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Config imported\n",
      "   meter_name  meter_type\n",
      "0  PkFltM1Ant  WattsOnMk2\n",
      "1  PkFltM2Tel  WattsOnMk2\n",
      "2  PkFltM3Sci  WattsOnMk2\n",
      "3      PQube3       PQube\n"
     ]
    }
   ],
   "source": [
    "# Import the config\n",
    "import config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a database engine - this is used to connect to the database\n",
    "db_connection_string = \"postgresl://\" + config.DB_USER + \":\" + config.DB_PWD + \"@\" + config.DB_ADDRESS + \":\" + config.DB_PORT + \"/\" + config.DB_NAME\n",
    "# Example db_connection_string - \"postgresql://tsdb_user:husky123@localhost:5432/demand_acep\"\n",
    "db = sqlalchemy.create_engine(db_connection_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the meter_collection for the meters from our config file\n",
    "meter_collection = {}\n",
    "\n",
    "for meter_name in config.METER_CHANNEL_DICT:\n",
    "    for year in config.DATA_YEARS:\n",
    "        # Get the table name - which is of the format - \"metername_year\" and then read from the database into pandas dataframe\n",
    "        # \"*\" selects all columns, column names can be specified if not all columns are desired\n",
    "        meter_collection[meter_name + \"_\" + str(year)] = pd.read_sql('select * from public.\"' + meter_name + \"_\" + str(year) + '\"', db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['PkFltM1Ant_2017', 'PkFltM1Ant_2018', 'PkFltM1Ant_2019', 'PkFltM2Tel_2017', 'PkFltM2Tel_2018', 'PkFltM2Tel_2019', 'PkFltM3Sci_2017', 'PkFltM3Sci_2018', 'PkFltM3Sci_2019', 'PQube3_2017', 'PQube3_2018', 'PQube3_2019'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meter_collection.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87900 entries, 0 to 87899\n",
      "Data columns (total 49 columns):\n",
      "time                               87900 non-null datetime64[ns, UTC]\n",
      "PTot                               87900 non-null float64\n",
      "QTot                               87900 non-null float64\n",
      "STot                               87900 non-null float64\n",
      "VlnMean                            87900 non-null float64\n",
      "VllMean                            87900 non-null float64\n",
      "IMean                              87900 non-null float64\n",
      "PfTot                              87900 non-null float64\n",
      "F                                  87900 non-null float64\n",
      "SlidingWindowRealPowerDemand3Ph    87900 non-null float64\n",
      "Van                                87900 non-null float64\n",
      "Vbn                                87900 non-null float64\n",
      "Vcn                                87900 non-null float64\n",
      "Vab                                87900 non-null float64\n",
      "Vbc                                87900 non-null float64\n",
      "Vca                                87900 non-null float64\n",
      "Ia                                 87900 non-null float64\n",
      "Ib                                 87900 non-null float64\n",
      "Ic                                 87900 non-null float64\n",
      "Pa                                 87900 non-null float64\n",
      "Pb                                 87900 non-null float64\n",
      "Pc                                 87900 non-null float64\n",
      "Qa                                 87900 non-null float64\n",
      "Qb                                 87900 non-null float64\n",
      "Qc                                 87900 non-null float64\n",
      "Sa                                 87900 non-null float64\n",
      "Sb                                 87900 non-null float64\n",
      "Sc                                 87900 non-null float64\n",
      "Pfa                                87900 non-null float64\n",
      "Pfb                                87900 non-null float64\n",
      "Pfc                                87900 non-null float64\n",
      "EaRec                              87900 non-null float64\n",
      "EbRec                              87900 non-null float64\n",
      "EcRec                              87900 non-null float64\n",
      "ERecTot                            87900 non-null float64\n",
      "EaDel                              87900 non-null float64\n",
      "EbDel                              87900 non-null float64\n",
      "EcDel                              87900 non-null float64\n",
      "EDelTot                            87900 non-null float64\n",
      "EaNetTot                           87900 non-null float64\n",
      "EbNetTot                           87900 non-null float64\n",
      "EcNetTot                           87900 non-null float64\n",
      "ENetTot                            87900 non-null float64\n",
      "ErePosTot                          87900 non-null float64\n",
      "EreNegTot                          87900 non-null float64\n",
      "EapaTot                            87900 non-null float64\n",
      "EapbTot                            87900 non-null float64\n",
      "EapcTot                            87900 non-null float64\n",
      "EapTot                             87900 non-null float64\n",
      "dtypes: datetime64[ns, UTC](1), float64(48)\n",
      "memory usage: 32.9 MB\n"
     ]
    }
   ],
   "source": [
    "meter_collection['PkFltM1Ant_2017'].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc6a02945f8>]"
      ]
     },
     "execution_count": 8,
     "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": [
    "plt.plot(meter_collection['PkFltM1Ant_2017']['Vab'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the data as csv\n",
    "for meter in meter_collection:\n",
    "    meter_collection[meter].to_csv(meter + \".csv\")"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}