{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "68fe0cfe",
   "metadata": {},
   "source": [
    "# Get data example\n",
    "\n",
    "This notebook shows how to use the ``get_data`` method to get data from the API.\n",
    "\n",
    "API-24SEA endpoint: [https://api.24sea.eu/routes/v1/datasignals/data](https://api.24sea.eu/docs/v1/#/operations/datasignals_metrics_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a3000fdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# **Package Imports**\n",
    "# - From the Python Standard Library\n",
    "import logging\n",
    "import os\n",
    "import sys\n",
    "\n",
    "# From third-party packages\n",
    "import pandas as pd\n",
    "\n",
    "# - API-24SEA\n",
    "from api_24sea.version import __version__, parse_version\n",
    "from api_24sea.datasignals.core import API\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9335e659",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Package Version(major=2, minor=1, patch=6, release=None, num=None)\n"
     ]
    }
   ],
   "source": [
    "# **Package Version**\n",
    "print(f\"Package {parse_version(__version__)}\")\n",
    "\n",
    "# **Notebook Configuration**\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.WARNING)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35363943",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\" style=\"border-radius: 0.5rem\">\n",
    "    <h3>Login Credentials</h3>\n",
    "    <svg xmlns=\"http://www.w3.org/2000/svg\" width=\"0.75rem\" height=\"0.75rem\" fill=\"#664d03\" viewBox=\"0 0 16 16\" style=\"float: left; margin-right: 1rem;\">\n",
    "        <path d=\"M6.953 1.273a2.143 2.143 0 012.874.751l.002.004 5.884 9.774a2.126 2.126 0 01-.768 2.905c-.322.188-.687.289-1.06.293H2.115a2.143 2.143 0 01-1.833-1.074 2.125 2.125 0 01.006-2.124l.006-.01 5.878-9.764.002-.004c.191-.313.46-.571.78-.75zm.502 1.53l-.001.002-5.872 9.754a.626.626 0 00.231.853.644.644 0 00.314.088h11.746a.643.643 0 00.544-.32.626.626 0 000-.62l-5.87-9.755-.002-.001A.635.635 0 008 2.5a.643.643 0 00-.545.304z\"/>\n",
    "        <path d=\"M7 11a1 1 0 011-1h.007a1 1 0 110 2H8a1 1 0 01-1-1zM8.75 5.75a.75.75 0 00-1.5 0v2.5a.75.75 0 001.5 0v-2.5z\"/>\n",
    "    </svg>\n",
    "    <p>Do not store API credentials in plain text in your notebook. Rather use the <a href=\"https://pypi.org/project/python-dotenv/\" target=\"_blank\">python-dotenv</a> package to load environment variables from a .env file.</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4a704f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# **Set Sample API Credentials**\n",
    "os.environ[\"API_24SEA_USERNAME\"] = \"Sample.User\"\n",
    "os.environ[\"API_24SEA_PASSWORD\"] = \"CheckOutSomeData!\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e7b5b41d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working Folder:  /home/pietro.dantuono@24SEA.local/Projects/API-24SEA/api-24sea-pkg/docs/project/user-manual/examples\n",
      "Python Version: 3.13.1 (main, Jan 14 2025, 22:47:38) [Clang 19.1.6 ]\n",
      "Pandas Version: 2.3.3\n",
      "Package Version(major=2, minor=1, patch=6, release=None, num=None)\n"
     ]
    }
   ],
   "source": [
    "# **Package Versions**\n",
    "print(\"Working Folder: \", os.getcwd())\n",
    "print(f\"Python Version: {sys.version}\")\n",
    "print(f\"Pandas Version: {pd.__version__}\")\n",
    "print(f\"Package {parse_version(__version__)}\")\n",
    "# **Notebook Configuration**\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.WARNING)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b4638a7",
   "metadata": {},
   "source": [
    "### Accessing the data\n",
    "\n",
    "The data can be accessed in different formats, depending on the use case. The default format is a wide format, where each metric is a column and the site and location columns are added as dimensions. The resulting DataFrame is indexed by timestamp and sorted by index.\n",
    "\n",
    "The required credentials to access the data can be set as environment variables, or passed as arguments to the `API initialization` class.\n",
    "\n",
    "API Initialization can be done in three ways:\n",
    "\n",
    "* synchrnonously via `API` class,\n",
    "* asynchronously via Pandas `datasignals` accessor\n",
    "* asynchronously via `AsyncAPI` class.\n",
    "\n",
    "The synchronous way is the most straightforward and is suitable for most use cases. The asynchronous way is more efficient when dealing with large datasets, as it allows to fetch data in parallel.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d7fa16a",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
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       "                           mean_WF_A01_pitch  mean_WF_A01_power  \\\n",
       "timestamp                                                         \n",
       "2020-03-01 00:00:00+00:00          66.398788         -41.469750   \n",
       "2020-03-01 00:10:00+00:00          66.398788         -41.730277   \n",
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       "2020-03-01 00:40:00+00:00          66.398788         -41.569560   \n",
       "...                                      ...                ...   \n",
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       "\n",
       "                           mean_WF_A01_rpm  mean_WF_A01_winddirection  \\\n",
       "timestamp                                                               \n",
       "2020-03-01 00:00:00+00:00         0.584501                  49.649211   \n",
       "2020-03-01 00:10:00+00:00         0.415421                  36.540062   \n",
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       "2020-03-01 00:30:00+00:00         0.000000                  42.564542   \n",
       "2020-03-01 00:40:00+00:00         0.000000                  45.848365   \n",
       "...                                    ...                        ...   \n",
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       "2020-05-31 23:50:00+00:00         7.014018                 258.697872   \n",
       "\n",
       "                           mean_WF_A01_windspeed  mean_WF_A01_yaw  \\\n",
       "timestamp                                                           \n",
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       "2020-03-01 00:40:00+00:00               2.258143       132.942861   \n",
       "...                                          ...              ...   \n",
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       "2020-05-31 23:50:00+00:00               8.379906        82.012861   \n",
       "\n",
       "                           mean_WF_A01_TP_INC_LAT015_DEG240_X_nr1  \\\n",
       "timestamp                                                           \n",
       "2020-03-01 00:00:00+00:00                                0.014474   \n",
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       "2020-03-01 00:30:00+00:00                                0.010997   \n",
       "2020-03-01 00:40:00+00:00                                0.010591   \n",
       "...                                                           ...   \n",
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       "\n",
       "                           mean_WF_A01_TP_INC_LAT015_DEG240_Y_nr2  \\\n",
       "timestamp                                                           \n",
       "2020-03-01 00:00:00+00:00                               -0.136504   \n",
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       "2020-03-01 00:40:00+00:00                               -0.129644   \n",
       "...                                                           ...   \n",
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       "2020-05-31 23:30:00+00:00                               -0.240203   \n",
       "2020-05-31 23:40:00+00:00                               -0.239876   \n",
       "2020-05-31 23:50:00+00:00                               -0.235032   \n",
       "\n",
       "                           mean_WF_A02_pitch  mean_WF_A02_power  \\\n",
       "timestamp                                                         \n",
       "2020-03-01 00:00:00+00:00          66.398788         -42.372609   \n",
       "2020-03-01 00:10:00+00:00          66.398788         -43.083064   \n",
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       "2020-03-01 00:40:00+00:00          66.398788         -42.344867   \n",
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       "2020-05-31 23:30:00+00:00          45.070488         284.568935   \n",
       "2020-05-31 23:40:00+00:00          45.377088         228.474125   \n",
       "2020-05-31 23:50:00+00:00          44.765888         362.100604   \n",
       "\n",
       "                           mean_WF_A02_rpm  mean_WF_A02_winddirection  \\\n",
       "timestamp                                                               \n",
       "2020-03-01 00:00:00+00:00         0.000000                  51.566748   \n",
       "2020-03-01 00:10:00+00:00         0.000000                  35.673237   \n",
       "2020-03-01 00:20:00+00:00         0.000000                  44.934812   \n",
       "2020-03-01 00:30:00+00:00         0.000000                  37.605152   \n",
       "2020-03-01 00:40:00+00:00         0.000000                  33.024645   \n",
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       "2020-05-31 23:40:00+00:00         7.020075                 241.614773   \n",
       "2020-05-31 23:50:00+00:00         6.971721                 240.347007   \n",
       "\n",
       "                           mean_WF_A02_windspeed  mean_WF_A02_yaw  \\\n",
       "timestamp                                                           \n",
       "2020-03-01 00:00:00+00:00               4.418833       155.442861   \n",
       "2020-03-01 00:10:00+00:00               3.965547       141.050661   \n",
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       "2020-03-01 00:30:00+00:00               2.492990       128.642861   \n",
       "2020-03-01 00:40:00+00:00               2.127543       128.642861   \n",
       "...                                          ...              ...   \n",
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       "2020-05-31 23:30:00+00:00               9.040361        81.942861   \n",
       "2020-05-31 23:40:00+00:00               8.750821        79.985561   \n",
       "2020-05-31 23:50:00+00:00               9.856275        84.982061   \n",
       "\n",
       "                           mean_WF_A02_TP_INC_LAT015_DEG240_X_nr1  \\\n",
       "timestamp                                                           \n",
       "2020-03-01 00:00:00+00:00                               -0.006030   \n",
       "2020-03-01 00:10:00+00:00                               -0.005865   \n",
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       "2020-03-01 00:30:00+00:00                               -0.007844   \n",
       "2020-03-01 00:40:00+00:00                               -0.008401   \n",
       "...                                                           ...   \n",
       "2020-05-31 23:10:00+00:00                                0.046481   \n",
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       "2020-05-31 23:40:00+00:00                                0.056814   \n",
       "2020-05-31 23:50:00+00:00                                0.052385   \n",
       "\n",
       "                           mean_WF_A02_TP_INC_LAT015_DEG240_Y_nr2  \n",
       "timestamp                                                          \n",
       "2020-03-01 00:00:00+00:00                               -0.017105  \n",
       "2020-03-01 00:10:00+00:00                               -0.016555  \n",
       "2020-03-01 00:20:00+00:00                               -0.016817  \n",
       "2020-03-01 00:30:00+00:00                               -0.018391  \n",
       "2020-03-01 00:40:00+00:00                               -0.018518  \n",
       "...                                                           ...  \n",
       "2020-05-31 23:10:00+00:00                               -0.023932  \n",
       "2020-05-31 23:20:00+00:00                               -0.027713  \n",
       "2020-05-31 23:30:00+00:00                               -0.029176  \n",
       "2020-05-31 23:40:00+00:00                               -0.024185  \n",
       "2020-05-31 23:50:00+00:00                               -0.030792  \n",
       "\n",
       "[13248 rows x 16 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# **API Initialization**\n",
    "# API initialization can happen either explicitly via the API class, or\n",
    "# implicitly when using the pandas `datasignals` accessor.\n",
    "# In this example we will use the Pandas accessor.\n",
    "df = pd.DataFrame()\n",
    "# The Metrics Overview is a table containing all the locations and metrics\n",
    "# available in the API, together with their metadata. It is useful to query it\n",
    "# before getting data, to check which locations and metrics are available for\n",
    "# the site you want to query.\n",
    "m_o = df.datasignals.metrics_overview\n",
    "\n",
    "# site is the wind farm name you want to query. The parameter can also be\n",
    "# a list of site names, e.g. [\"windfarm\", \"windfarm2\"]. Matching is\n",
    "# case-insensitive and passing the \"site-id\" is also accepted, e.g. \"windfarm\"\n",
    "# will match \"WindFarm\", but also \"WF\", \"wf\", etc.\n",
    "site = \"windfarm\"\n",
    "# Matching locations from Metrics Overview for the specified site\n",
    "# Also partial names of locations are accepted, e.g. \"a01\" will match\n",
    "# all locations containing \"a01\" in their name, such as \"A01\", \"a01\". Matching\n",
    "# is case-insensitive.\n",
    "locations = m_o[m_o[\"site\"].str.lower() == site][\"location\"].unique().tolist()\n",
    "# Metrics: partial matches and regexes are accepted.\n",
    "# Spaces are interpreted as .* in the name.\n",
    "# If you want to query all metrics, pass \"all\" or [\"all\"].\n",
    "metrics = [\"mean windspeed\", \"mean power\", \"mean yaw\", \"mean rpm\",\n",
    "           \"mean pitch\", \"mean winddirection\", \"mean inc\"]\n",
    "# Start and end timestamp in ISO 8601 format. The API accepts also other\n",
    "# formats, such as the one provided by\n",
    "# https://www.elastic.co/docs/reference/elasticsearch/rest-apis/common-options#date-math\n",
    "start_timestamp = \"2020-03-01T00:00:00Z\"\n",
    "end_timestamp = \"2020-06-01T00:00:00Z\"\n",
    "\n",
    "data = df.datasignals.get_data(site, locations, metrics, start_timestamp, end_timestamp)\n",
    "display(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "34204daf",
   "metadata": {},
   "outputs": [
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       "metric                     mean_WF_A01_TP_INC_LAT015_DEG240_X_nr1  \\\n",
       "timestamp                                                           \n",
       "2020-03-01 00:00:00+00:00                                0.014474   \n",
       "2020-03-01 00:10:00+00:00                                0.014559   \n",
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       "2020-03-01 00:30:00+00:00                                0.010997   \n",
       "2020-03-01 00:40:00+00:00                                0.010591   \n",
       "...                                                           ...   \n",
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       "2020-05-31 23:50:00+00:00                                0.053963   \n",
       "\n",
       "metric                     mean_WF_A01_TP_INC_LAT015_DEG240_Y_nr2  \\\n",
       "timestamp                                                           \n",
       "2020-03-01 00:00:00+00:00                               -0.136504   \n",
       "2020-03-01 00:10:00+00:00                               -0.135702   \n",
       "2020-03-01 00:20:00+00:00                               -0.130643   \n",
       "2020-03-01 00:30:00+00:00                               -0.128664   \n",
       "2020-03-01 00:40:00+00:00                               -0.129644   \n",
       "...                                                           ...   \n",
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       "2020-05-31 23:40:00+00:00                               -0.239876   \n",
       "2020-05-31 23:50:00+00:00                               -0.235032   \n",
       "\n",
       "metric                     mean_WF_A01_pitch  mean_WF_A01_power  \\\n",
       "timestamp                                                         \n",
       "2020-03-01 00:00:00+00:00          66.398788         -41.469750   \n",
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       "2020-05-31 23:50:00+00:00          45.560188         164.459883   \n",
       "\n",
       "metric                     mean_WF_A01_rpm  mean_WF_A01_winddirection  \\\n",
       "timestamp                                                               \n",
       "2020-03-01 00:00:00+00:00         0.584501                  49.649211   \n",
       "2020-03-01 00:10:00+00:00         0.415421                  36.540062   \n",
       "2020-03-01 00:20:00+00:00         0.000000                  48.983673   \n",
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       "2020-03-01 00:40:00+00:00         0.000000                  45.848365   \n",
       "...                                    ...                        ...   \n",
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       "2020-05-31 23:50:00+00:00         7.014018                 258.697872   \n",
       "\n",
       "metric                     mean_WF_A01_windspeed  mean_WF_A01_yaw  \n",
       "timestamp                                                          \n",
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       "\n",
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    {
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       "      <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>2020-03-01 00:00:00+00:00</th>\n",
       "      <td>-0.006030</td>\n",
       "      <td>-0.017105</td>\n",
       "      <td>66.398788</td>\n",
       "      <td>-42.372609</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.566748</td>\n",
       "      <td>4.418833</td>\n",
       "      <td>155.442861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01 00:10:00+00:00</th>\n",
       "      <td>-0.005865</td>\n",
       "      <td>-0.016555</td>\n",
       "      <td>66.398788</td>\n",
       "      <td>-43.083064</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>35.673237</td>\n",
       "      <td>3.965547</td>\n",
       "      <td>141.050661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01 00:20:00+00:00</th>\n",
       "      <td>-0.006446</td>\n",
       "      <td>-0.016817</td>\n",
       "      <td>66.398788</td>\n",
       "      <td>-41.678109</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.934812</td>\n",
       "      <td>2.456694</td>\n",
       "      <td>128.642861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01 00:30:00+00:00</th>\n",
       "      <td>-0.007844</td>\n",
       "      <td>-0.018391</td>\n",
       "      <td>66.398788</td>\n",
       "      <td>-41.820720</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.605152</td>\n",
       "      <td>2.492990</td>\n",
       "      <td>128.642861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01 00:40:00+00:00</th>\n",
       "      <td>-0.008401</td>\n",
       "      <td>-0.018518</td>\n",
       "      <td>66.398788</td>\n",
       "      <td>-42.344867</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.024645</td>\n",
       "      <td>2.127543</td>\n",
       "      <td>128.642861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-31 23:10:00+00:00</th>\n",
       "      <td>0.046481</td>\n",
       "      <td>-0.023932</td>\n",
       "      <td>45.233588</td>\n",
       "      <td>266.671847</td>\n",
       "      <td>7.006153</td>\n",
       "      <td>54.359444</td>\n",
       "      <td>8.840318</td>\n",
       "      <td>99.242861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-31 23:20:00+00:00</th>\n",
       "      <td>0.050933</td>\n",
       "      <td>-0.027713</td>\n",
       "      <td>45.171388</td>\n",
       "      <td>284.882482</td>\n",
       "      <td>6.990850</td>\n",
       "      <td>211.905402</td>\n",
       "      <td>9.282334</td>\n",
       "      <td>91.005861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-31 23:30:00+00:00</th>\n",
       "      <td>0.056390</td>\n",
       "      <td>-0.029176</td>\n",
       "      <td>45.070488</td>\n",
       "      <td>284.568935</td>\n",
       "      <td>6.970233</td>\n",
       "      <td>240.872527</td>\n",
       "      <td>9.040361</td>\n",
       "      <td>81.942861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-31 23:40:00+00:00</th>\n",
       "      <td>0.056814</td>\n",
       "      <td>-0.024185</td>\n",
       "      <td>45.377088</td>\n",
       "      <td>228.474125</td>\n",
       "      <td>7.020075</td>\n",
       "      <td>241.614773</td>\n",
       "      <td>8.750821</td>\n",
       "      <td>79.985561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-31 23:50:00+00:00</th>\n",
       "      <td>0.052385</td>\n",
       "      <td>-0.030792</td>\n",
       "      <td>44.765888</td>\n",
       "      <td>362.100604</td>\n",
       "      <td>6.971721</td>\n",
       "      <td>240.347007</td>\n",
       "      <td>9.856275</td>\n",
       "      <td>84.982061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13248 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "metric                     mean_WF_A02_TP_INC_LAT015_DEG240_X_nr1  \\\n",
       "timestamp                                                           \n",
       "2020-03-01 00:00:00+00:00                               -0.006030   \n",
       "2020-03-01 00:10:00+00:00                               -0.005865   \n",
       "2020-03-01 00:20:00+00:00                               -0.006446   \n",
       "2020-03-01 00:30:00+00:00                               -0.007844   \n",
       "2020-03-01 00:40:00+00:00                               -0.008401   \n",
       "...                                                           ...   \n",
       "2020-05-31 23:10:00+00:00                                0.046481   \n",
       "2020-05-31 23:20:00+00:00                                0.050933   \n",
       "2020-05-31 23:30:00+00:00                                0.056390   \n",
       "2020-05-31 23:40:00+00:00                                0.056814   \n",
       "2020-05-31 23:50:00+00:00                                0.052385   \n",
       "\n",
       "metric                     mean_WF_A02_TP_INC_LAT015_DEG240_Y_nr2  \\\n",
       "timestamp                                                           \n",
       "2020-03-01 00:00:00+00:00                               -0.017105   \n",
       "2020-03-01 00:10:00+00:00                               -0.016555   \n",
       "2020-03-01 00:20:00+00:00                               -0.016817   \n",
       "2020-03-01 00:30:00+00:00                               -0.018391   \n",
       "2020-03-01 00:40:00+00:00                               -0.018518   \n",
       "...                                                           ...   \n",
       "2020-05-31 23:10:00+00:00                               -0.023932   \n",
       "2020-05-31 23:20:00+00:00                               -0.027713   \n",
       "2020-05-31 23:30:00+00:00                               -0.029176   \n",
       "2020-05-31 23:40:00+00:00                               -0.024185   \n",
       "2020-05-31 23:50:00+00:00                               -0.030792   \n",
       "\n",
       "metric                     mean_WF_A02_pitch  mean_WF_A02_power  \\\n",
       "timestamp                                                         \n",
       "2020-03-01 00:00:00+00:00          66.398788         -42.372609   \n",
       "2020-03-01 00:10:00+00:00          66.398788         -43.083064   \n",
       "2020-03-01 00:20:00+00:00          66.398788         -41.678109   \n",
       "2020-03-01 00:30:00+00:00          66.398788         -41.820720   \n",
       "2020-03-01 00:40:00+00:00          66.398788         -42.344867   \n",
       "...                                      ...                ...   \n",
       "2020-05-31 23:10:00+00:00          45.233588         266.671847   \n",
       "2020-05-31 23:20:00+00:00          45.171388         284.882482   \n",
       "2020-05-31 23:30:00+00:00          45.070488         284.568935   \n",
       "2020-05-31 23:40:00+00:00          45.377088         228.474125   \n",
       "2020-05-31 23:50:00+00:00          44.765888         362.100604   \n",
       "\n",
       "metric                     mean_WF_A02_rpm  mean_WF_A02_winddirection  \\\n",
       "timestamp                                                               \n",
       "2020-03-01 00:00:00+00:00         0.000000                  51.566748   \n",
       "2020-03-01 00:10:00+00:00         0.000000                  35.673237   \n",
       "2020-03-01 00:20:00+00:00         0.000000                  44.934812   \n",
       "2020-03-01 00:30:00+00:00         0.000000                  37.605152   \n",
       "2020-03-01 00:40:00+00:00         0.000000                  33.024645   \n",
       "...                                    ...                        ...   \n",
       "2020-05-31 23:10:00+00:00         7.006153                  54.359444   \n",
       "2020-05-31 23:20:00+00:00         6.990850                 211.905402   \n",
       "2020-05-31 23:30:00+00:00         6.970233                 240.872527   \n",
       "2020-05-31 23:40:00+00:00         7.020075                 241.614773   \n",
       "2020-05-31 23:50:00+00:00         6.971721                 240.347007   \n",
       "\n",
       "metric                     mean_WF_A02_windspeed  mean_WF_A02_yaw  \n",
       "timestamp                                                          \n",
       "2020-03-01 00:00:00+00:00               4.418833       155.442861  \n",
       "2020-03-01 00:10:00+00:00               3.965547       141.050661  \n",
       "2020-03-01 00:20:00+00:00               2.456694       128.642861  \n",
       "2020-03-01 00:30:00+00:00               2.492990       128.642861  \n",
       "2020-03-01 00:40:00+00:00               2.127543       128.642861  \n",
       "...                                          ...              ...  \n",
       "2020-05-31 23:10:00+00:00               8.840318        99.242861  \n",
       "2020-05-31 23:20:00+00:00               9.282334        91.005861  \n",
       "2020-05-31 23:30:00+00:00               9.040361        81.942861  \n",
       "2020-05-31 23:40:00+00:00               8.750821        79.985561  \n",
       "2020-05-31 23:50:00+00:00               9.856275        84.982061  \n",
       "\n",
       "[13248 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract data for a specific location only\n",
    "display(data.datasignals.as_dict()[site][locations[0]])\n",
    "display(data.datasignals.as_dict()[site][locations[1]])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eca1d269",
   "metadata": {},
   "source": [
    "# Star Schema Format\n",
    "Data can be converted to a star schema format, where each metric's name is converted to its \"short_hand\" from Metrics Overview, and the project and location columns are added as dimensions. The resulting DataFrame is indexed by timestamp and sorted by index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a346098",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Both index and column named 'timestamp' found. Index takes precedence.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>timestamp</th>\n",
       "      <th>location</th>\n",
       "      <th>mean_TP_INC_LAT015_DEG240_X_nr1</th>\n",
       "      <th>mean_TP_INC_LAT015_DEG240_Y_nr2</th>\n",
       "      <th>mean_pitch</th>\n",
       "      <th>mean_power</th>\n",
       "      <th>mean_rpm</th>\n",
       "      <th>mean_winddirection</th>\n",
       "      <th>mean_windspeed</th>\n",
       "      <th>mean_yaw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-01 00:00:00+00:00</td>\n",
       "      <td>WFA01</td>\n",
       "      <td>0.014474</td>\n",
       "      <td>-0.136504</td>\n",
       "      <td>66.398788</td>\n",
       "      <td>-41.469750</td>\n",
       "      <td>0.584501</td>\n",
       "      <td>49.649211</td>\n",
       "      <td>4.756602</td>\n",
       "      <td>154.942861</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-01 00:10:00+00:00</td>\n",
       "      <td>WFA01</td>\n",
       "      <td>0.014559</td>\n",
       "      <td>-0.135702</td>\n",
       "      <td>66.398788</td>\n",
       "      <td>-41.730277</td>\n",
       "      <td>0.415421</td>\n",
       "      <td>36.540062</td>\n",
       "      <td>3.286199</td>\n",
       "      <td>154.679861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-01 00:20:00+00:00</td>\n",
       "      <td>WFA01</td>\n",
       "      <td>0.011589</td>\n",
       "      <td>-0.130643</td>\n",
       "      <td>66.398788</td>\n",
       "      <td>-41.638804</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>48.983673</td>\n",
       "      <td>2.772254</td>\n",
       "      <td>118.406361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-01 00:30:00+00:00</td>\n",
       "      <td>WFA01</td>\n",
       "      <td>0.010997</td>\n",
       "      <td>-0.128664</td>\n",
       "      <td>66.398788</td>\n",
       "      <td>-41.464462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.564542</td>\n",
       "      <td>3.068091</td>\n",
       "      <td>124.439561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-01 00:40:00+00:00</td>\n",
       "      <td>WFA01</td>\n",
       "      <td>0.010591</td>\n",
       "      <td>-0.129644</td>\n",
       "      <td>66.398788</td>\n",
       "      <td>-41.569560</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.848365</td>\n",
       "      <td>2.258143</td>\n",
       "      <td>132.942861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26491</th>\n",
       "      <td>2020-05-31 23:10:00+00:00</td>\n",
       "      <td>WFA02</td>\n",
       "      <td>0.046481</td>\n",
       "      <td>-0.023932</td>\n",
       "      <td>45.233588</td>\n",
       "      <td>266.671847</td>\n",
       "      <td>7.006153</td>\n",
       "      <td>54.359444</td>\n",
       "      <td>8.840318</td>\n",
       "      <td>99.242861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26492</th>\n",
       "      <td>2020-05-31 23:20:00+00:00</td>\n",
       "      <td>WFA02</td>\n",
       "      <td>0.050933</td>\n",
       "      <td>-0.027713</td>\n",
       "      <td>45.171388</td>\n",
       "      <td>284.882482</td>\n",
       "      <td>6.990850</td>\n",
       "      <td>211.905402</td>\n",
       "      <td>9.282334</td>\n",
       "      <td>91.005861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26493</th>\n",
       "      <td>2020-05-31 23:30:00+00:00</td>\n",
       "      <td>WFA02</td>\n",
       "      <td>0.056390</td>\n",
       "      <td>-0.029176</td>\n",
       "      <td>45.070488</td>\n",
       "      <td>284.568935</td>\n",
       "      <td>6.970233</td>\n",
       "      <td>240.872527</td>\n",
       "      <td>9.040361</td>\n",
       "      <td>81.942861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26494</th>\n",
       "      <td>2020-05-31 23:40:00+00:00</td>\n",
       "      <td>WFA02</td>\n",
       "      <td>0.056814</td>\n",
       "      <td>-0.024185</td>\n",
       "      <td>45.377088</td>\n",
       "      <td>228.474125</td>\n",
       "      <td>7.020075</td>\n",
       "      <td>241.614773</td>\n",
       "      <td>8.750821</td>\n",
       "      <td>79.985561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26495</th>\n",
       "      <td>2020-05-31 23:50:00+00:00</td>\n",
       "      <td>WFA02</td>\n",
       "      <td>0.052385</td>\n",
       "      <td>-0.030792</td>\n",
       "      <td>44.765888</td>\n",
       "      <td>362.100604</td>\n",
       "      <td>6.971721</td>\n",
       "      <td>240.347007</td>\n",
       "      <td>9.856275</td>\n",
       "      <td>84.982061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26496 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      timestamp location  mean_TP_INC_LAT015_DEG240_X_nr1  \\\n",
       "0     2020-03-01 00:00:00+00:00    WFA01                         0.014474   \n",
       "1     2020-03-01 00:10:00+00:00    WFA01                         0.014559   \n",
       "2     2020-03-01 00:20:00+00:00    WFA01                         0.011589   \n",
       "3     2020-03-01 00:30:00+00:00    WFA01                         0.010997   \n",
       "4     2020-03-01 00:40:00+00:00    WFA01                         0.010591   \n",
       "...                         ...      ...                              ...   \n",
       "26491 2020-05-31 23:10:00+00:00    WFA02                         0.046481   \n",
       "26492 2020-05-31 23:20:00+00:00    WFA02                         0.050933   \n",
       "26493 2020-05-31 23:30:00+00:00    WFA02                         0.056390   \n",
       "26494 2020-05-31 23:40:00+00:00    WFA02                         0.056814   \n",
       "26495 2020-05-31 23:50:00+00:00    WFA02                         0.052385   \n",
       "\n",
       "       mean_TP_INC_LAT015_DEG240_Y_nr2  mean_pitch  mean_power  mean_rpm  \\\n",
       "0                            -0.136504   66.398788  -41.469750  0.584501   \n",
       "1                            -0.135702   66.398788  -41.730277  0.415421   \n",
       "2                            -0.130643   66.398788  -41.638804  0.000000   \n",
       "3                            -0.128664   66.398788  -41.464462  0.000000   \n",
       "4                            -0.129644   66.398788  -41.569560  0.000000   \n",
       "...                                ...         ...         ...       ...   \n",
       "26491                        -0.023932   45.233588  266.671847  7.006153   \n",
       "26492                        -0.027713   45.171388  284.882482  6.990850   \n",
       "26493                        -0.029176   45.070488  284.568935  6.970233   \n",
       "26494                        -0.024185   45.377088  228.474125  7.020075   \n",
       "26495                        -0.030792   44.765888  362.100604  6.971721   \n",
       "\n",
       "       mean_winddirection  mean_windspeed    mean_yaw  \n",
       "0               49.649211        4.756602  154.942861  \n",
       "1               36.540062        3.286199  154.679861  \n",
       "2               48.983673        2.772254  118.406361  \n",
       "3               42.564542        3.068091  124.439561  \n",
       "4               45.848365        2.258143  132.942861  \n",
       "...                   ...             ...         ...  \n",
       "26491           54.359444        8.840318   99.242861  \n",
       "26492          211.905402        9.282334   91.005861  \n",
       "26493          240.872527        9.040361   81.942861  \n",
       "26494          241.614773        8.750821   79.985561  \n",
       "26495          240.347007        9.856275   84.982061  \n",
       "\n",
       "[26496 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from api_24sea.datasignals.core import to_star_schema\n",
    "start_schema = to_star_schema(df, metrics_overview=m_o)\n",
    "data_ss = start_schema[\"FactPivotData\"]\n",
    "data_ss\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d172ab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 560x560 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.colors as mcolors\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Wedge\n",
    "plt.rcParams.update({\"axes.grid\": False})\n",
    "\n",
    "# Example plotting of the inclinations in the X and Y directions, with marginal\n",
    "# histograms and alarm/warning levels.\n",
    "def CustomCmap(from_rgb,to_rgb):\n",
    "  \"\"\"Create a custom colormap from two RGB tuples.\"\"\"\n",
    "  r1, g1, b1 = from_rgb\n",
    "  r2, g2, b2 = to_rgb\n",
    "  cdict = {'red':   ((0, r1, r1), (1, r2, r2)),\n",
    "           'green': ((0, g1, g1), (1, g2, g2)),\n",
    "           'blue':  ((0, b1, b1), (1, b2, b2))}\n",
    "  cmap = mcolors.LinearSegmentedColormap('custom_cmap', cdict)\n",
    "  return cmap\n",
    "\n",
    "# # ** Parameters **\n",
    "MAIN_AXES_TITLE_FONT_SIZE = 10\n",
    "TICK_LABEL_FONT_SIZE = 9\n",
    "X_PARAMETER = \"mean_TP_INC_LAT015_DEG240_X_nr1\"\n",
    "Y_PARAMETER = \"mean_TP_INC_LAT015_DEG240_Y_nr2\"\n",
    "X_AXIS_TITLE = \"Inclination X direction, deg\"\n",
    "Y_AXIS_TITLE = \"Inclination Y direction, deg\"\n",
    "ALARM_LEVEL = 0.6\n",
    "WARNING_LEVEL = 0.4\n",
    "WARNING_WIDTH = ALARM_LEVEL - WARNING_LEVEL\n",
    "LIM = ALARM_LEVEL * 1.1\n",
    "\n",
    "# **Plotting**\n",
    "fig2 = plt.figure(figsize=(7, 7), dpi=80)\n",
    "\n",
    "# Define the grid layout\n",
    "grid = plt.GridSpec(4, 4, hspace=0.4, wspace=0.6)\n",
    "\n",
    "# Main plot (2D KDE)\n",
    "main_ax = fig2.add_subplot(grid[1:4, 0:3])\n",
    "\n",
    "# Marginal KDE plots\n",
    "x_hist = fig2.add_subplot(grid[0, 0:3], sharex=main_ax);\n",
    "y_hist = fig2.add_subplot(grid[1:4, 3], sharey=main_ax);\n",
    "\n",
    "# Color settings\n",
    "colors = [\"lightseagreen\", \"orchid\", \"teal\", \"purple\", \"skyblue\", \"coral\", \n",
    "          \"gold\", \"darkkhaki\", \"lightcoral\", \"thistle\", \"lime\", \"darkorange\", \n",
    "          \"darkgreen\", \"deeppink\", \"navy\"]\n",
    "color_cycle = iter(colors)\n",
    "\n",
    "legend_patches = []\n",
    "for i, (location, group) in enumerate(data_ss.groupby(\"location\")):\n",
    "  try:\n",
    "    color = next(color_cycle)\n",
    "  except StopIteration:\n",
    "    color_cycle = iter(colors)\n",
    "    color = next(color_cycle)\n",
    "  # 2D histogram on main_ax\n",
    "  rgb_color = mcolors.to_rgb(color)\n",
    "  light_rgb_color = tuple([c * 1 for c in rgb_color])\n",
    "  dark_rgb_color = tuple([c * 0.05 for c in rgb_color])\n",
    "  custom_cmap = CustomCmap(light_rgb_color, dark_rgb_color)\n",
    "  custom_cmap.set_under((1, 1, 1, 0))\n",
    "  h2d = main_ax.hist2d(group[X_PARAMETER], group[Y_PARAMETER], bins=120,\n",
    "                       range=[[-LIM, LIM], [-LIM, LIM]], cmap=custom_cmap,\n",
    "                       vmin=1, density=True)\n",
    "  # Marginal histogram for X axis\n",
    "  x_hist.hist(group[X_PARAMETER], bins=200, range=(-LIM, LIM), color=color,\n",
    "              alpha=0.8)\n",
    "  # Marginal histogram for Y axis (horizontal)\n",
    "  y_hist.hist(group[Y_PARAMETER], bins=200, range=(-LIM, LIM), color=color,\n",
    "                        orientation='horizontal', alpha=0.8)\n",
    "  legend_patches.append(plt.Line2D([], [], color=color, lw=4, label=location))\n",
    "\n",
    "# Draw alarm and warning levels\n",
    "main_ax.add_patch(Wedge(center=(0, 0), r=ALARM_LEVEL, theta1=0, theta2=360,\n",
    "                        width=WARNING_WIDTH, alpha=0.5, facecolor=\"goldenrod\",\n",
    "                        fill=True, linewidth=2, label='Warning Level'))\n",
    "main_ax.add_patch(plt.Circle((0, 0), ALARM_LEVEL, edgecolor='firebrick',\n",
    "                             alpha=0.9, fill=False, label=\"Alarm\"))\n",
    "x_hist.set_xlabel(\"\");\n",
    "y_hist.set_ylabel(\"\");\n",
    "\n",
    "# # Legend styling\n",
    "legend = main_ax.legend(handles=legend_patches, loc=\"upper center\",\n",
    "                        title=\"Location\", fontsize=TICK_LABEL_FONT_SIZE,\n",
    "                        title_fontsize=10, frameon=True, facecolor=\"#F5F4EF\",\n",
    "                        edgecolor='#e4e8ee', bbox_to_anchor=(1.25, 1.4),\n",
    "                        borderaxespad=0, labelspacing=0.7, handlelength=1.2,\n",
    "                        borderpad=0.5, framealpha=0.8, fancybox=True,\n",
    "                        shadow=False, ncol=6)\n",
    "\n",
    "# Set legend font color\n",
    "plt.setp(legend.get_texts(), color=\"#333333\")  # Labels in legend\n",
    "plt.setp(legend.get_title(), color=\"#333333\")  # Title in legend\n",
    "\n",
    "\n",
    "# Axis labels\n",
    "main_ax.set_xlabel(X_AXIS_TITLE, fontsize=MAIN_AXES_TITLE_FONT_SIZE)\n",
    "main_ax.set_ylabel(Y_AXIS_TITLE, fontsize=MAIN_AXES_TITLE_FONT_SIZE)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b652e951",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example plotting of the inclinations in the X and Y directions V. timestamp,\n",
    "# and yaw as well as the power curve.\n",
    "\n",
    "fig = plt.figure(figsize=(9, 7))\n",
    "\n",
    "gs = fig.add_gridspec(19, 12)\n",
    "ax1 = fig.add_subplot(gs[0:5, :])\n",
    "ax2 = fig.add_subplot(gs[7:12, 0:5])\n",
    "ax3 = fig.add_subplot(gs[7:12, 7:])\n",
    "ax4 = fig.add_subplot(gs[14:, :])\n",
    "FONT_SIZE = 9\n",
    "\n",
    "# Inclination V. Timestamp\n",
    "ax_1 = df.plot(\"timestamp\", \"mean_WF_A01_TP_INC_LAT015_DEG240_X_nr1\",\n",
    "              kind=\"scatter\", color=\"turquoise\", marker=\".\", label=\"X\", s=2,\n",
    "              ax=ax1)\n",
    "df.plot(\"timestamp\", \"mean_WF_A01_TP_INC_LAT015_DEG240_Y_nr2\",\n",
    "        kind=\"scatter\", color=\"teal\", marker=\".\", ax=ax_1, s=2,\n",
    "        label=\"Y\")\n",
    "df.plot(\"timestamp\", \"mean_WF_A02_TP_INC_LAT015_DEG240_X_nr1\",\n",
    "        kind=\"scatter\", color=\"orchid\", marker=\"+\", ax=ax_1, alpha=0.7,\n",
    "        label=\"X\")\n",
    "df.plot(\"timestamp\", \"mean_WF_A02_TP_INC_LAT015_DEG240_Y_nr2\",\n",
    "        kind=\"scatter\", color=\"purple\", marker=\"+\", ax=ax_1, alpha=0.7,\n",
    "        label=\"Y\")\n",
    "ax_1.legend(title=\"WFA01    WFA02\", ncol=2, bbox_to_anchor=(0.15, 1.05),\n",
    "            fontsize=FONT_SIZE, title_fontsize=FONT_SIZE)\n",
    "\n",
    "locator = mdates.AutoDateLocator(minticks=20, maxticks=26)\n",
    "formatter = mdates.ConciseDateFormatter(locator)\n",
    "ax1.xaxis.set_major_locator(locator)\n",
    "ax1.xaxis.set_major_formatter(formatter)\n",
    "ax1.set_xlabel(\"Timestamp\", fontsize=FONT_SIZE)\n",
    "ax1.set_ylabel(\"Inclination, °\", fontsize=FONT_SIZE)\n",
    "ax1.tick_params(labelsize=int(FONT_SIZE-1))\n",
    "\n",
    "# Inclination V. Yaw\n",
    "df.plot(\"mean_WF_A01_yaw\", \"mean_WF_A01_TP_INC_LAT015_DEG240_X_nr1\",\n",
    "        kind=\"scatter\", color=\"turquoise\", marker=\".\", ax=ax2, s=2,\n",
    "        label= \"WFA01\")\n",
    "df.plot(\"mean_WF_A02_yaw\", \"mean_WF_A02_TP_INC_LAT015_DEG240_X_nr1\",\n",
    "        kind=\"scatter\", color=\"orchid\", marker=\"+\", ax=ax2, alpha=0.5,\n",
    "        label=\"WFA02\")\n",
    "df.plot(\"mean_WF_A01_yaw\", \"mean_WF_A01_TP_INC_LAT015_DEG240_Y_nr2\",\n",
    "        kind=\"scatter\", color=\"teal\", marker=\".\", ax=ax3, s=2,\n",
    "        label=\"WFA01\")\n",
    "df.plot(\"mean_WF_A02_yaw\", \"mean_WF_A02_TP_INC_LAT015_DEG240_Y_nr2\",\n",
    "        kind=\"scatter\", color=\"purple\", marker=\"+\", ax=ax3, alpha=0.5,\n",
    "        label=\"WFA02\")\n",
    "ax2.legend(loc=\"upper left\", fontsize=FONT_SIZE)\n",
    "ax2.set_xlabel(\"Yaw, °\", fontsize=FONT_SIZE)\n",
    "ax2.set_ylabel(\"X Inclination, °\", fontsize=FONT_SIZE)\n",
    "ax2.tick_params(labelsize=int(FONT_SIZE-1))\n",
    "ax3.legend(loc=\"upper left\", fontsize=FONT_SIZE)\n",
    "ax3.set_xlabel(\"Yaw, °\", fontsize=FONT_SIZE)\n",
    "ax3.set_ylabel(\"Y Inclination, °\", fontsize=FONT_SIZE)\n",
    "ax3.tick_params(labelsize=int(FONT_SIZE-1))\n",
    "\n",
    "# Power V. Windspeed\n",
    "df.plot(\"mean_WF_A01_windspeed\", \"mean_WF_A01_power\",\n",
    "        kind=\"scatter\", color=\"teal\", marker=\".\", ax=ax4, s=2,\n",
    "        label=\"WFA01\")\n",
    "df.plot(\"mean_WF_A02_windspeed\", \"mean_WF_A02_power\",\n",
    "        kind=\"scatter\", color=\"purple\", marker=\"+\", ax=ax4, alpha=0.5,\n",
    "        label=\"WFA02\")\n",
    "ax4.legend(loc=\"upper left\", fontsize=FONT_SIZE)\n",
    "ax4.set_xlabel(\"Windspeed, m/s\", fontsize=FONT_SIZE)\n",
    "ax4.set_ylabel(\"Power, kW\", fontsize=FONT_SIZE)\n",
    "ax4.tick_params(labelsize=int(FONT_SIZE-1))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03a2df2a",
   "metadata": {},
   "source": [
    "### Category-Value Format\n",
    "Data can also be converted to a category-value format, where each metric is converted to a \"category\" column containing the metric name, and a \"value\" column containing the metric value. The site and location columns are added as dimensions. The resulting DataFrame is indexed by timestamp and sorted by index.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2fa88d9a",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Both index and column named 'timestamp' found. Index takes precedence.\n",
      "/home/pietro.dantuono@24SEA.local/Projects/API-24SEA/api-24sea-pkg/api_24sea/datasignals/core.py:1675: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  .bfill(axis=1)\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>timestamp</th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "      <th>statistic</th>\n",
       "      <th>short_hand</th>\n",
       "      <th>site_id</th>\n",
       "      <th>location_id</th>\n",
       "      <th>sub_assembly</th>\n",
       "      <th>lat</th>\n",
       "      <th>heading</th>\n",
       "      <th>site</th>\n",
       "      <th>location</th>\n",
       "      <th>metric_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-01 00:00:00+00:00</td>\n",
       "      <td>mean_WF_A01_pitch</td>\n",
       "      <td>66.398788</td>\n",
       "      <td>°</td>\n",
       "      <td>mean</td>\n",
       "      <td>pitch</td>\n",
       "      <td>WF</td>\n",
       "      <td>A01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WindFarm</td>\n",
       "      <td>WFA01</td>\n",
       "      <td>scada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-01 00:10:00+00:00</td>\n",
       "      <td>mean_WF_A01_pitch</td>\n",
       "      <td>66.398788</td>\n",
       "      <td>°</td>\n",
       "      <td>mean</td>\n",
       "      <td>pitch</td>\n",
       "      <td>WF</td>\n",
       "      <td>A01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WindFarm</td>\n",
       "      <td>WFA01</td>\n",
       "      <td>scada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-01 00:20:00+00:00</td>\n",
       "      <td>mean_WF_A01_pitch</td>\n",
       "      <td>66.398788</td>\n",
       "      <td>°</td>\n",
       "      <td>mean</td>\n",
       "      <td>pitch</td>\n",
       "      <td>WF</td>\n",
       "      <td>A01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WindFarm</td>\n",
       "      <td>WFA01</td>\n",
       "      <td>scada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-01 00:30:00+00:00</td>\n",
       "      <td>mean_WF_A01_pitch</td>\n",
       "      <td>66.398788</td>\n",
       "      <td>°</td>\n",
       "      <td>mean</td>\n",
       "      <td>pitch</td>\n",
       "      <td>WF</td>\n",
       "      <td>A01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WindFarm</td>\n",
       "      <td>WFA01</td>\n",
       "      <td>scada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-01 00:40:00+00:00</td>\n",
       "      <td>mean_WF_A01_pitch</td>\n",
       "      <td>66.398788</td>\n",
       "      <td>°</td>\n",
       "      <td>mean</td>\n",
       "      <td>pitch</td>\n",
       "      <td>WF</td>\n",
       "      <td>A01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WindFarm</td>\n",
       "      <td>WFA01</td>\n",
       "      <td>scada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211963</th>\n",
       "      <td>2020-05-31 23:10:00+00:00</td>\n",
       "      <td>mean_WF_A02_TP_INC_LAT015_DEG240_Y_nr2</td>\n",
       "      <td>-0.023932</td>\n",
       "      <td>°</td>\n",
       "      <td>mean</td>\n",
       "      <td>TP_INC_LAT015_DEG240_Y_nr2</td>\n",
       "      <td>WF</td>\n",
       "      <td>A02</td>\n",
       "      <td>TP</td>\n",
       "      <td>15.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>WindFarm</td>\n",
       "      <td>WFA02</td>\n",
       "      <td>inclination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211964</th>\n",
       "      <td>2020-05-31 23:20:00+00:00</td>\n",
       "      <td>mean_WF_A02_TP_INC_LAT015_DEG240_Y_nr2</td>\n",
       "      <td>-0.027713</td>\n",
       "      <td>°</td>\n",
       "      <td>mean</td>\n",
       "      <td>TP_INC_LAT015_DEG240_Y_nr2</td>\n",
       "      <td>WF</td>\n",
       "      <td>A02</td>\n",
       "      <td>TP</td>\n",
       "      <td>15.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>WindFarm</td>\n",
       "      <td>WFA02</td>\n",
       "      <td>inclination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211965</th>\n",
       "      <td>2020-05-31 23:30:00+00:00</td>\n",
       "      <td>mean_WF_A02_TP_INC_LAT015_DEG240_Y_nr2</td>\n",
       "      <td>-0.029176</td>\n",
       "      <td>°</td>\n",
       "      <td>mean</td>\n",
       "      <td>TP_INC_LAT015_DEG240_Y_nr2</td>\n",
       "      <td>WF</td>\n",
       "      <td>A02</td>\n",
       "      <td>TP</td>\n",
       "      <td>15.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>WindFarm</td>\n",
       "      <td>WFA02</td>\n",
       "      <td>inclination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211966</th>\n",
       "      <td>2020-05-31 23:40:00+00:00</td>\n",
       "      <td>mean_WF_A02_TP_INC_LAT015_DEG240_Y_nr2</td>\n",
       "      <td>-0.024185</td>\n",
       "      <td>°</td>\n",
       "      <td>mean</td>\n",
       "      <td>TP_INC_LAT015_DEG240_Y_nr2</td>\n",
       "      <td>WF</td>\n",
       "      <td>A02</td>\n",
       "      <td>TP</td>\n",
       "      <td>15.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>WindFarm</td>\n",
       "      <td>WFA02</td>\n",
       "      <td>inclination</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211967</th>\n",
       "      <td>2020-05-31 23:50:00+00:00</td>\n",
       "      <td>mean_WF_A02_TP_INC_LAT015_DEG240_Y_nr2</td>\n",
       "      <td>-0.030792</td>\n",
       "      <td>°</td>\n",
       "      <td>mean</td>\n",
       "      <td>TP_INC_LAT015_DEG240_Y_nr2</td>\n",
       "      <td>WF</td>\n",
       "      <td>A02</td>\n",
       "      <td>TP</td>\n",
       "      <td>15.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>WindFarm</td>\n",
       "      <td>WFA02</td>\n",
       "      <td>inclination</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211968 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       timestamp                                  metric  \\\n",
       "0      2020-03-01 00:00:00+00:00                       mean_WF_A01_pitch   \n",
       "1      2020-03-01 00:10:00+00:00                       mean_WF_A01_pitch   \n",
       "2      2020-03-01 00:20:00+00:00                       mean_WF_A01_pitch   \n",
       "3      2020-03-01 00:30:00+00:00                       mean_WF_A01_pitch   \n",
       "4      2020-03-01 00:40:00+00:00                       mean_WF_A01_pitch   \n",
       "...                          ...                                     ...   \n",
       "211963 2020-05-31 23:10:00+00:00  mean_WF_A02_TP_INC_LAT015_DEG240_Y_nr2   \n",
       "211964 2020-05-31 23:20:00+00:00  mean_WF_A02_TP_INC_LAT015_DEG240_Y_nr2   \n",
       "211965 2020-05-31 23:30:00+00:00  mean_WF_A02_TP_INC_LAT015_DEG240_Y_nr2   \n",
       "211966 2020-05-31 23:40:00+00:00  mean_WF_A02_TP_INC_LAT015_DEG240_Y_nr2   \n",
       "211967 2020-05-31 23:50:00+00:00  mean_WF_A02_TP_INC_LAT015_DEG240_Y_nr2   \n",
       "\n",
       "            value unit statistic                  short_hand site_id  \\\n",
       "0       66.398788    °      mean                       pitch      WF   \n",
       "1       66.398788    °      mean                       pitch      WF   \n",
       "2       66.398788    °      mean                       pitch      WF   \n",
       "3       66.398788    °      mean                       pitch      WF   \n",
       "4       66.398788    °      mean                       pitch      WF   \n",
       "...           ...  ...       ...                         ...     ...   \n",
       "211963  -0.023932    °      mean  TP_INC_LAT015_DEG240_Y_nr2      WF   \n",
       "211964  -0.027713    °      mean  TP_INC_LAT015_DEG240_Y_nr2      WF   \n",
       "211965  -0.029176    °      mean  TP_INC_LAT015_DEG240_Y_nr2      WF   \n",
       "211966  -0.024185    °      mean  TP_INC_LAT015_DEG240_Y_nr2      WF   \n",
       "211967  -0.030792    °      mean  TP_INC_LAT015_DEG240_Y_nr2      WF   \n",
       "\n",
       "       location_id sub_assembly   lat  heading      site location metric_group  \n",
       "0              A01          NaN   NaN      NaN  WindFarm    WFA01        scada  \n",
       "1              A01          NaN   NaN      NaN  WindFarm    WFA01        scada  \n",
       "2              A01          NaN   NaN      NaN  WindFarm    WFA01        scada  \n",
       "3              A01          NaN   NaN      NaN  WindFarm    WFA01        scada  \n",
       "4              A01          NaN   NaN      NaN  WindFarm    WFA01        scada  \n",
       "...            ...          ...   ...      ...       ...      ...          ...  \n",
       "211963         A02           TP  15.0    240.0  WindFarm    WFA02  inclination  \n",
       "211964         A02           TP  15.0    240.0  WindFarm    WFA02  inclination  \n",
       "211965         A02           TP  15.0    240.0  WindFarm    WFA02  inclination  \n",
       "211966         A02           TP  15.0    240.0  WindFarm    WFA02  inclination  \n",
       "211967         A02           TP  15.0    240.0  WindFarm    WFA02  inclination  \n",
       "\n",
       "[211968 rows x 14 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from api_24sea.datasignals.core import to_category_value\n",
    "data_cv = to_category_value(df, df.datasignals.selected_metrics.reset_index())\n",
    "data_cv\n"
   ]
  }
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