data_retrieval.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Using a Brick Model for Data Retrieval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup\n",
    "\n",
    "Import packages and load the relevant ontology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Packages\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import brickschema\n",
    "import rdflib\n",
    "from analysis import sparql_to_df, missing_values, timeseries_plot\n",
    "\n",
    "# Specify the path to the Brick model. Load the model into a graph (already contains prefixs, so don't need to specify these).\n",
    "model_path = \"./ttl_files/example_compiled_bldg.ttl\"\n",
    "graph = brickschema.Graph().load_file(model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### QUERY\n",
    "Write your SPARQL query below:\n",
    "- please refer to the id of the sensor as ?sensorid\n",
    "- please refer to the unit of the sensor as ?unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This query looks for any power sensors related to a chiller\n",
    "query = \"\"\"\n",
    "SELECT DISTINCT ?chiller ?sensor ?sensorid ?unit ?dblocation WHERE {\n",
    "    ?chiller a brick:Chiller .\n",
    "    ?chiller brick:hasPoint ?sensor .\n",
    "    ?sensor a brick:Electrical_Power_Sensor ;\n",
    "        brick:timeseries [ brick:hasTimeseriesId ?sensorid ; brick:storedAt ?db] ;\n",
    "        brick:hasUnit ?unit .\n",
    "    ?db brick:connString ?dblocation\n",
    "} \n",
    "\"\"\"\n",
    "\n",
    "# metadata and timeseries data \n",
    "metadata, timeseries = sparql_to_df(graph, query)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chiller</th>\n",
       "      <th>sensor</th>\n",
       "      <th>unit</th>\n",
       "      <th>dblocation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sensorid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CH0_PWR</th>\n",
       "      <td>Chiller_0</td>\n",
       "      <td>Chiller_0_Power_Sensor</td>\n",
       "      <td>KiloW</td>\n",
       "      <td>./csv_files/example_timeseries.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH1_PWR</th>\n",
       "      <td>Chiller_1</td>\n",
       "      <td>Chiller_1_Power_Sensor</td>\n",
       "      <td>KiloW</td>\n",
       "      <td>./csv_files/example_timeseries.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH2_PWR</th>\n",
       "      <td>Chiller_2</td>\n",
       "      <td>Chiller_2_Power_Sensor</td>\n",
       "      <td>KiloW</td>\n",
       "      <td>./csv_files/example_timeseries.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH3_PWR</th>\n",
       "      <td>Chiller_3</td>\n",
       "      <td>Chiller_3_Power_Sensor</td>\n",
       "      <td>KiloW</td>\n",
       "      <td>./csv_files/example_timeseries.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH4_PWR</th>\n",
       "      <td>Chiller_4</td>\n",
       "      <td>Chiller_4_Power_Sensor</td>\n",
       "      <td>KiloW</td>\n",
       "      <td>./csv_files/example_timeseries.csv</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            chiller                  sensor   unit  \\\n",
       "sensorid                                             \n",
       "CH0_PWR   Chiller_0  Chiller_0_Power_Sensor  KiloW   \n",
       "CH1_PWR   Chiller_1  Chiller_1_Power_Sensor  KiloW   \n",
       "CH2_PWR   Chiller_2  Chiller_2_Power_Sensor  KiloW   \n",
       "CH3_PWR   Chiller_3  Chiller_3_Power_Sensor  KiloW   \n",
       "CH4_PWR   Chiller_4  Chiller_4_Power_Sensor  KiloW   \n",
       "\n",
       "                                  dblocation  \n",
       "sensorid                                      \n",
       "CH0_PWR   ./csv_files/example_timeseries.csv  \n",
       "CH1_PWR   ./csv_files/example_timeseries.csv  \n",
       "CH2_PWR   ./csv_files/example_timeseries.csv  \n",
       "CH3_PWR   ./csv_files/example_timeseries.csv  \n",
       "CH4_PWR   ./csv_files/example_timeseries.csv  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Timeseries Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>CH0_PWR</th>\n",
       "      <th>CH1_PWR</th>\n",
       "      <th>CH2_PWR</th>\n",
       "      <th>CH3_PWR</th>\n",
       "      <th>CH4_PWR</th>\n",
       "      <th>CH5_PWR</th>\n",
       "      <th>CH6_PWR</th>\n",
       "      <th>CH7_PWR</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATETIME</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-10-10 08:00:00</th>\n",
       "      <td>634</td>\n",
       "      <td>699</td>\n",
       "      <td>647</td>\n",
       "      <td>644</td>\n",
       "      <td>679</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-10 08:01:00</th>\n",
       "      <td>642</td>\n",
       "      <td>686</td>\n",
       "      <td>625</td>\n",
       "      <td>698</td>\n",
       "      <td>688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-10 08:02:00</th>\n",
       "      <td>680</td>\n",
       "      <td>630</td>\n",
       "      <td>680</td>\n",
       "      <td>693</td>\n",
       "      <td>637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-10 08:03:00</th>\n",
       "      <td>673</td>\n",
       "      <td>667</td>\n",
       "      <td>671</td>\n",
       "      <td>697</td>\n",
       "      <td>610</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-10 08:04:00</th>\n",
       "      <td>669</td>\n",
       "      <td>666</td>\n",
       "      <td>677</td>\n",
       "      <td>655</td>\n",
       "      <td>701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     CH0_PWR  CH1_PWR  CH2_PWR  CH3_PWR  CH4_PWR  CH5_PWR  \\\n",
       "DATETIME                                                                    \n",
       "2022-10-10 08:00:00      634      699      647      644      679      NaN   \n",
       "2022-10-10 08:01:00      642      686      625      698      688      NaN   \n",
       "2022-10-10 08:02:00      680      630      680      693      637      NaN   \n",
       "2022-10-10 08:03:00      673      667      671      697      610      NaN   \n",
       "2022-10-10 08:04:00      669      666      677      655      701      NaN   \n",
       "\n",
       "                     CH6_PWR  CH7_PWR  \n",
       "DATETIME                               \n",
       "2022-10-10 08:00:00      NaN      NaN  \n",
       "2022-10-10 08:01:00      NaN      NaN  \n",
       "2022-10-10 08:02:00      NaN      NaN  \n",
       "2022-10-10 08:03:00      NaN      NaN  \n",
       "2022-10-10 08:04:00      NaN      NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeseries.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, the building data has been retrieved. Notice the query to retrieve the data did not require any knowledge of how the data was represented. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize\n",
    "\n",
    "Just as an example, we can graph the timeseries data\n",
    "\n",
    "- use function timeseries_plot(timeseries, metadata, sensors, timeslice)\n",
    "- inputs: timeseries and metadata as pandas dataframe, a list of sensors, timeslice as a tuple (YY-MM-DD HH-MM-SS, YY-MM-DD HH-MM-SS)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# timeslice = ('2021-05-11 08:04:00', '2021-05-11 9:00:00')\n",
    "sensors = ['CH0_PWR', \"CH1_PWR\"]\n",
    "\n",
    "timeseries_plot(timeseries, metadata, sensors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Validation\n",
    "\n",
    "    Is the dataset empty?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The timeseries data has 8 columns. \n",
      "3 of these have missing values.\n"
     ]
    },
    {
     "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>N. of Missing Values</th>\n",
       "      <th>% of Values Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CH5_PWR</th>\n",
       "      <td>1244</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH6_PWR</th>\n",
       "      <td>1244</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH7_PWR</th>\n",
       "      <td>1244</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         N. of Missing Values  % of Values Missing\n",
       "CH5_PWR                  1244                100.0\n",
       "CH6_PWR                  1244                100.0\n",
       "CH7_PWR                  1244                100.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values(timeseries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Possible Queries\n",
    "\n",
    "    Chiller On/Off Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = \"\"\"\n",
    "SELECT DISTINCT ?chiller ?status ?id WHERE {\n",
    "    ?chiller a brick:Chiller .\n",
    "    ?chiller brick:hasPoint ?status .\n",
    "    ?status a brick:On_Off_Status ;\n",
    "        brick:timeseries [ brick:hasTimeseriesId ?id ] .\n",
    "} \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.6 ('brick_env')",
   "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": "3.10.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
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 },
 "nbformat": 4,
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}