LMP_plot.ipynb

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   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt\n",
    "import pandas as pd\n",
    "import sqlite3\n",
    "from src.helpers import DATABASE_LOCATION\n",
    "\n",
    "# Connect to database \n",
    "conn = sqlite3.connect(DATABASE_LOCATION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interative Location Marginal Price plot for a single node \n",
    "- node: `AFPR_1_TOT_GEN-APND`\n",
    "- time range: `2019-10-01` to `2019-11-29`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "convert_day_dict = {\n",
    "    0: 'Monday',\n",
    "    1: 'Tuesday',\n",
    "    2: 'Wednesday',\n",
    "    3: 'Thursday',\n",
    "    4: 'Friday',\n",
    "    5: 'Saturday',\n",
    "    6: 'Sunday'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>node</th>\n",
       "      <th>mw</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-10-01 02:00:00+00:00</td>\n",
       "      <td>AFPR_1_TOT_GEN-APND</td>\n",
       "      <td>48.20406</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-10-01 00:00:00+00:00</td>\n",
       "      <td>AFPR_1_TOT_GEN-APND</td>\n",
       "      <td>33.55219</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-10-01 01:00:00+00:00</td>\n",
       "      <td>AFPR_1_TOT_GEN-APND</td>\n",
       "      <td>43.28855</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-10-01 03:00:00+00:00</td>\n",
       "      <td>AFPR_1_TOT_GEN-APND</td>\n",
       "      <td>40.71762</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-10-01 04:00:00+00:00</td>\n",
       "      <td>AFPR_1_TOT_GEN-APND</td>\n",
       "      <td>35.78075</td>\n",
       "      <td>Tuesday</td>\n",
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      "text/plain": [
       "                       time                 node        mw      day\n",
       "0 2019-10-01 02:00:00+00:00  AFPR_1_TOT_GEN-APND  48.20406  Tuesday\n",
       "1 2019-10-01 00:00:00+00:00  AFPR_1_TOT_GEN-APND  33.55219  Tuesday\n",
       "2 2019-10-01 01:00:00+00:00  AFPR_1_TOT_GEN-APND  43.28855  Tuesday\n",
       "3 2019-10-01 03:00:00+00:00  AFPR_1_TOT_GEN-APND  40.71762  Tuesday\n",
       "4 2019-10-01 04:00:00+00:00  AFPR_1_TOT_GEN-APND  35.78075  Tuesday"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data \n",
    "lmp_df = pd.read_sql_query(\n",
    "    \"\"\"select * from lmp\n",
    "    WHERE node == \"AFPR_1_TOT_GEN-APND\";\n",
    "    \"\"\", conn)\n",
    "\n",
    "# convert to timestamp\n",
    "lmp_df.time = pd.to_datetime(lmp_df.time)\n",
    "\n",
    "# convert to day of week \n",
    "lmp_df['day'] = lmp_df['time'].dt.dayofweek.apply(lambda x: convert_day_dict.get(x))\n",
    "lmp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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\"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 44.87084, \"day\": \"Thursday\"}, {\"time\": \"2019-11-28T14:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 39.67661, \"day\": \"Thursday\"}, {\"time\": \"2019-11-28T19:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 43.13707, \"day\": \"Thursday\"}, {\"time\": \"2019-11-28T13:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 37.21683, \"day\": \"Thursday\"}, {\"time\": \"2019-11-28T10:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 35.53378, \"day\": \"Thursday\"}, {\"time\": \"2019-11-28T16:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 41.63947, \"day\": \"Thursday\"}, {\"time\": \"2019-11-28T18:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 42.98305, \"day\": \"Thursday\"}, {\"time\": \"2019-11-28T20:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 41.8322, \"day\": \"Thursday\"}, {\"time\": \"2019-11-28T12:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 37.080009999999994, \"day\": \"Thursday\"}, {\"time\": \"2019-11-28T08:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 37.36918, \"day\": \"Thursday\"}, {\"time\": \"2019-11-28T22:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 46.567809999999994, \"day\": \"Thursday\"}, {\"time\": \"2019-11-28T09:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 35.86177, \"day\": \"Thursday\"}, {\"time\": \"2019-11-28T11:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 35.58169, \"day\": \"Thursday\"}, {\"time\": \"2019-11-28T21:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 45.029340000000005, \"day\": \"Thursday\"}, {\"time\": \"2019-11-29T06:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 38.10953, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T04:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 41.94023, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T02:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 48.39273, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T01:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 49.52511, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T00:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 45.7554, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T03:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 45.48185, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T05:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 39.36253, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T07:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 38.1866, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T10:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 35.544309999999996, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T23:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 39.266709999999996, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T20:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 28.979409999999998, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T12:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 37.02322, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T11:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 35.63916, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T22:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 30.93478, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T17:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 30.644370000000002, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T21:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 28.5767, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T09:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 36.934509999999996, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T15:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 40.043890000000005, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T08:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 37.46837, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T16:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 35.18822, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T14:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 43.55469, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T13:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 41.19555, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T19:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 28.274659999999997, \"day\": \"Friday\"}, {\"time\": \"2019-11-29T18:00:00+00:00\", \"node\": \"AFPR_1_TOT_GEN-APND\", \"mw\": 28.93836, \"day\": \"Friday\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brush = alt.selection(type='interval')\n",
    "\n",
    "# bottom plot used for selection\n",
    "base = alt.Chart().mark_rule(size=2).encode(\n",
    "    x='time:T',\n",
    "    y='mw:Q',\n",
    "    color='day:N',\n",
    "    tooltip=['day', 'time', 'node', 'mw']\n",
    ").properties(\n",
    "    width=800,\n",
    "    height=100,\n",
    "    selection=brush)\n",
    "\n",
    "# top plot used for viewing zoomed selection\n",
    "view = base.properties(\n",
    "    width=800,\n",
    "    height=300).transform_filter(brush.ref())\n",
    "\n",
    "chart = alt.vconcat(view, base, title='Location Marginal Prices for AFPR_1_TOT_GEN-APND node', data=lmp_df)\n",
    "chart\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "chart.save('LMP_chart.html')"
   ]
  }
 ],
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