{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b10e5c25",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "rng = np.random.default_rng(42)\n",
    "\n",
    "# Generate random walks starting at 0\n",
    "counter = np.zeros(300)\n",
    "rollout = np.zeros(300)\n",
    "\n",
    "# Generate random steps between -1 and 1\n",
    "counter_steps = rng.uniform(-1, 1, 299)\n",
    "rollout_steps = rng.uniform(-1, 1, 299)\n",
    "\n",
    "print(counter_steps)\n",
    "print(rollout_steps)\n",
    "\n",
    "# Calculate cumulative sum and scale to -100 to 100 range\n",
    "for i in range(1, 300):\n",
    "    counter[i] = counter[i-1] + counter_steps[i-1]\n",
    "    rollout[i] = rollout[i-1] + rollout_steps[i-1]\n",
    "\n",
    "# normalize values between -100 and 100, lowest value is -100, highest value is 100\n",
    "counter = (counter - min(counter)) / (max(counter) - min(counter)) * 200 - 100\n",
    "rollout = (rollout - min(rollout)) / (max(rollout) - min(rollout)) * 200 - 100\n",
    "\n",
    "# Create DataFrame\n",
    "df = pd.DataFrame({\n",
    "    'Counter': counter,\n",
    "    'Rollout': rollout,\n",
    "    'CounterPlusRollout': counter + rollout\n",
    "})\n",
    "\n",
    "# add a timestamp column\n",
    "df['Timestamp'] = pd.date_range(start='2025-01-01', periods=300, freq='1s')\n",
    "\n",
    "# Save to CSV\n",
    "df.to_csv('random_walks.csv', index=False)\n",
    "\n",
    "# Display first few rows\n",
    "print(df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c61be7ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "rng = np.random.default_rng(42)\n",
    "\n",
    "size = 50\n",
    "\n",
    "# Generate random walks starting at 0\n",
    "counter = np.zeros(size)\n",
    "rollout = np.zeros(size)\n",
    "square = np.zeros(size)\n",
    "\n",
    "# Generate random steps between -1 and 1\n",
    "counter_steps = rng.uniform(-1, 1, size-1)\n",
    "rollout_steps = rng.uniform(-1, 1, size-1)\n",
    "square_steps = rng.uniform(-1, 1, size-1)\n",
    "\n",
    "print(counter_steps)\n",
    "print(rollout_steps)\n",
    "\n",
    "# Calculate cumulative sum and scale to -100 to 100 range\n",
    "for i in range(1, size):\n",
    "    counter[i] = counter[i-1] + counter_steps[i-1]\n",
    "    rollout[i] = rollout[i-1] + rollout_steps[i-1]\n",
    "    square[i] = square[i-1] + square_steps[i-1]\n",
    "\n",
    "# normalize values between -100 and 100, lowest value is -100, highest value is 100\n",
    "counter = (counter - min(counter)) / (max(counter) - min(counter)) * 200 - 100\n",
    "rollout = (rollout - min(rollout)) / (max(rollout) - min(rollout)) * 200 - 100\n",
    "square = (square - min(square)) / (max(square) - min(square)) * 200 - 100\n",
    "\n",
    "# Create DataFrame\n",
    "df = pd.DataFrame({\n",
    "    'Counter': counter,\n",
    "    'Rollout': rollout,\n",
    "    'Square': square\n",
    "})\n",
    "\n",
    "# add a timestamp column\n",
    "df['Timestamp'] = pd.date_range(start='2025-01-01', periods=size, freq='5s')\n",
    "\n",
    "# Save to CSV\n",
    "df.to_csv('random_walks_demo.csv', index=False)\n",
    "\n",
    "# Display first few rows\n",
    "print(df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7c8eeb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import DataFrame\n",
    "\n",
    "a = DataFrame({\n",
    "    \"a\": {\"2025-01-01\": 1, \"2025-01-02\": 2, \"2025-01-03\": 3},\n",
    "    \"b\": {\"2025-01-01\": 4, \"2025-01-02\": 5, \"2025-01-03\": 6},\n",
    "})\n",
    "\n",
    "a.index.name = \"timestamp\"\n",
    "\n",
    "display(a)\n",
    "\n",
    "print(\"a\" in a.columns)\n",
    "\n",
    "b = a.tail(1)\n",
    "\n",
    "display(b)\n",
    "\n",
    "print(len(a))\n",
    "print(len(b))\n",
    "print(b.size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3374174",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import DataFrame\n",
    "\n",
    "data = DataFrame({\n",
    "    \"a\": {\"2025-01-01\": 1, \"2025-01-02\": 2, \"2025-01-03\": 3},\n",
    "    \"b\": {\"2025-01-01\": 4, \"2025-01-02\": 5, \"2025-01-03\": 6},\n",
    "})\n",
    "\n",
    "index = data.index\n",
    "\n",
    "# Get type of first element of index\n",
    "index_type = type(index[0])\n",
    "\n",
    "print(index_type)\n",
    "\n",
    "# Check if all in index are of the same type\n",
    "if all(isinstance(i, index_type) for i in index):\n",
    "    print(\"All elements in index are of the same type\")\n",
    "else:\n",
    "    print(\"Elements in index are of different types\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40e72c60",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fbb3788",
   "metadata": {},
   "outputs": [],
   "source": [
    "from unittest.mock import MagicMock\n",
    "from asyncua.ua.uaerrors import BadAlreadyExists\n",
    "\n",
    "mock1 = MagicMock(\n",
    "    side_effect = Exception(\"test\")\n",
    ")\n",
    "\n",
    "mock2 = MagicMock(\n",
    "    side_effect = BadAlreadyExists(\"test\")\n",
    ")\n",
    "\n",
    "try:\n",
    "    mock1()\n",
    "except ValueError as e:\n",
    "    try:\n",
    "        mock2()\n",
    "    except BadAlreadyExists as e:\n",
    "        print(e)\n",
    "except Exception as e:\n",
    "    print(e)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "771ab4ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import DataFrame, merge\n",
    "\n",
    "retrain_dataset = DataFrame({\n",
    "    'a': {'2025-01-01': 1, '2025-01-02': 2, '2025-01-03': 3},\n",
    "    'b': {'2025-01-01': 4, '2025-01-02': 5, '2025-01-03': 6},\n",
    "    'c': {'2025-01-01': 7, '2025-01-02': 8, '2025-01-03': 9},\n",
    "})\n",
    "\n",
    "prediction_data = DataFrame({\n",
    "    'prediction': {'1': 1, '2': 2, '3': 3},\n",
    "})\n",
    "\n",
    "prediction_data.index = retrain_dataset.index\n",
    "\n",
    "prediction_data = merge(\n",
    "    retrain_dataset, prediction_data, left_index=True, right_index=True, how='left')\n",
    "\n",
    "prediction_data.rename(columns={'c': 'target'}, inplace=True)\n",
    "\n",
    "prediction_data['timestamp'] = prediction_data.index\n",
    "\n",
    "prediction_data.reset_index(drop=True, inplace=True)\n",
    "\n",
    "display(prediction_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "486b95b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import DataFrame\n",
    "\n",
    "data = DataFrame()\n",
    "\n",
    "display(data.to_dict(orient='records'))\n",
    "\n",
    "data = DataFrame({\n",
    "    \"a\": {\"2025-01-01\": 1, \"2025-01-02\": 2, \"2025-01-03\": 3},\n",
    "    \"b\": {\"2025-01-01\": 4, \"2025-01-02\": 5, \"2025-01-03\": 6},\n",
    "})\n",
    "\n",
    "display(data)\n",
    "\n",
    "data_list = data.to_dict('split')\n",
    "\n",
    "display(data_list)\n",
    "\n",
    "data_rec = DataFrame.from_dict(data_list, orient='index')\n",
    "\n",
    "display(data_rec)\n",
    "\n",
    "data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d67d551f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import mlflow\n",
    "from mlflow.tracking import MlflowClient\n",
    "from rich.console import Console\n",
    "import sys\n",
    "\n",
    "if \"src\" not in sys.path:\n",
    "    sys.path.insert(0, \"src\")\n",
    "\n",
    "console = Console()\n",
    "\n",
    "os.environ[\"MLFLOW_TRACKING_URI\"] = \"http://localhost:35785/\"\n",
    "os.environ[\"MLFLOW_TRACKING_USERNAME\"] = \"aignosi\"\n",
    "os.environ[\"MLFLOW_TRACKING_PASSWORD\"] = \"1L0FP50j3ncp123\"\n",
    "\n",
    "client = MlflowClient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98bdd6af",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import DataFrame, read_json\n",
    "\n",
    "# Load from data file (json)\n",
    "data = read_json('data.json')\n",
    "\n",
    "column_order = [\n",
    "            'CI-J3J01S1', 'CI-J3P01T1A', 'CI-J3P03S1', 'CI-W3A05F1',\n",
    "            'CI-W3A50A1', 'CI-W3A50A2', 'CI-W3A50A3', 'CI-W3A50P1', 'CI-W3A50T1',\n",
    "            'CI-W3A55P1', 'CI-W3A55T1', 'CI-W3A65_Cl', 'CI-W3A65_SO3', 'CI-W3A71P1',\n",
    "            'CI-W3A71P2', 'CI-W3A71P3', 'CI-W3E01F1', 'CI-W3K01S1', 'CI-W3K01T1',\n",
    "            'CI-W3K01T2', 'CI-W3K01T4', 'CI-W3K14P1', 'CI-W3P17S1', 'CI-W3V04P1',\n",
    "            'CI-W3V04P3', 'CI-W3V21F1', 'CI-W3V21P1', 'CI-W3V30F1', 'CI-W3V33P1',\n",
    "            'CI-W3W01G1', 'CI-W3W01P1', 'CI-W3W01P2', 'CI-W3W03I1', 'CI-W3W03S1',\n",
    "            'CI-W3_C3S', 'CI-W3_CAO', 'CI-W3_MA', 'CI-W3_MS', 'CI-W3_PL']\n",
    "\n",
    "ordered_data = data[column_order]\n",
    "\n",
    "display(ordered_data.head(3))\n",
    "display(data.head(3))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7e73c30",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlflow\n",
    "\n",
    "model_name = \"vcm-o2-vanilla-ice\"\n",
    "\n",
    "model = mlflow.pyfunc.load_model(f'models:/{model_name}/production')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e5bae06",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.predict(ordered_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd1bfaa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download pkl file from mlflow\n",
    "model_uri = f'models:/{model_name}/production'\n",
    "model_path = mlflow.artifacts.download_artifacts(model_uri, dst_path='./backup-model')\n",
    "\n",
    "print(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ef9c913",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = f'{model_path}/artifacts/xgboost_model.pkl'\n",
    "\n",
    "# Verificar os primeiros bytes do arquivo\n",
    "with open(file_path, 'rb') as f:\n",
    "    first_bytes = f.read(10)\n",
    "    print(first_bytes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8ae302f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import DataFrame, to_datetime\n",
    "from sientia_do.temporal.activities.postgres import Postgres\n",
    "from unittest.mock import MagicMock, AsyncMock\n",
    "\n",
    "postgres = Postgres(\n",
    "    host=\"localhost\",\n",
    "    port=5432,\n",
    "    dbname=\"sientia\",\n",
    "    user=\"sientia\",\n",
    "    password=\"sientia\",\n",
    "    min_connections=1,\n",
    "    max_connections=10,\n",
    "    logger=MagicMock(),\n",
    "    notification_handler=MagicMock(),\n",
    "    metrics_controller=AsyncMock()\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51dd2dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "period_hours = 1\n",
    "\n",
    "samples = 2*60*period_hours\n",
    "retrain_samples = 2*period_hours\n",
    "\n",
    "query_nox = f\"\"\"\n",
    "select p.prediction, ld.value, p.\"timestamp\" from sientia_data.predictions p\n",
    "join sientia_data.laborious_data ld on p.\"timestamp\" = ld.\"timestamp\"\n",
    "where p.model_id = '4' and variable='CI-W3W01A3' order by p.created_at desc limit {samples};\n",
    "\"\"\"\n",
    "\n",
    "retrain_query = f\"\"\"\n",
    "select \"timestamp\" from sientia_data.log_retrain lr where model_id = '4' order by lr.\"timestamp\" desc limit {retrain_samples};\n",
    "\"\"\"\n",
    "\n",
    "retrain_data_nox = DataFrame(await postgres.load_custom_query(\n",
    "    {\n",
    "        \"query\": retrain_query,\n",
    "        \"metadata\": {},\n",
    "    }\n",
    "))\n",
    "\n",
    "retrain_data_nox['timestamp'] = to_datetime(retrain_data_nox['timestamp'])\n",
    "\n",
    "display(retrain_data_nox)\n",
    "\n",
    "data_nox = DataFrame(await postgres.load_custom_query(\n",
    "    {\n",
    "        \"query\": query_nox,\n",
    "        \"metadata\": {},\n",
    "        \"datetime_columns\": [\"timestamp\"],\n",
    "    }\n",
    "))\n",
    "\n",
    "data_nox.drop_duplicates(subset=['timestamp'], inplace=True, keep='first')\n",
    "data_nox.sort_values(by='timestamp', inplace=True)\n",
    "data_nox['timestamp'] = to_datetime(data_nox['timestamp'])\n",
    "\n",
    "display(data_nox.head(3))\n",
    "\n",
    "query_o2 = f\"\"\"\n",
    "select p.prediction, ld.value, p.\"timestamp\" from sientia_data.predictions p\n",
    "join sientia_data.laborious_data ld on p.\"timestamp\" = ld.\"timestamp\"\n",
    "where p.model_id = '5' and variable='CI-W3W01A2'\n",
    "and p.\"timestamp\" >= NOW() - INTERVAL {period_hours} HOUR\n",
    "order by p.created_at;\n",
    "\"\"\"\n",
    "\n",
    "retrain_query_o2 = f\"\"\"\n",
    "select \"timestamp\" from sientia_data.log_retrain lr where model_id = '5' order by lr.\"timestamp\" desc limit {retrain_samples};\n",
    "\"\"\"\n",
    "\n",
    "retrain_data_o2 = DataFrame(await postgres.load_custom_query(\n",
    "    {\n",
    "        \"query\": retrain_query_o2,\n",
    "        \"metadata\": {},\n",
    "    }\n",
    "))\n",
    "\n",
    "retrain_data_o2['timestamp'] = to_datetime(retrain_data_o2['timestamp'])\n",
    "\n",
    "display(retrain_data_o2)\n",
    "\n",
    "data_o2 = DataFrame(await postgres.load_custom_query(\n",
    "    {\n",
    "        \"query\": query_o2,\n",
    "        \"metadata\": {},\n",
    "        \"datetime_columns\": [\"timestamp\"],\n",
    "    }\n",
    "))\n",
    "\n",
    "data_o2.drop_duplicates(subset=['timestamp'], inplace=True, keep='first')\n",
    "data_o2.sort_values(by='timestamp', inplace=True)\n",
    "data_o2['timestamp'] = to_datetime(data_o2['timestamp'])\n",
    "\n",
    "display(data_o2.head(3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b82e5a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(data_nox['timestamp'], data_nox['value'])\n",
    "plt.plot(data_nox['timestamp'], data_nox['prediction'])\n",
    "plt.vlines(\n",
    "    x=retrain_data_nox['timestamp'],\n",
    "    ymin=plt.ylim()[0],\n",
    "    ymax=plt.ylim()[1],\n",
    "    colors='k',\n",
    "    linestyles='--'\n",
    ")\n",
    "\n",
    "\n",
    "plt.legend(['real', 'prediction', 'retrain'])\n",
    "plt.title('NOx')\n",
    "plt.xlim(\n",
    "    data_nox['timestamp'].min(),\n",
    "    data_nox['timestamp'].max()\n",
    ")\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(data_o2['timestamp'], data_o2['value'])\n",
    "plt.plot(data_o2['timestamp'], data_o2['prediction'])\n",
    "plt.vlines(\n",
    "    x=retrain_data_o2['timestamp'],\n",
    "    ymin=plt.ylim()[0],\n",
    "    ymax=plt.ylim()[1],\n",
    "    colors='k',\n",
    "    linestyles='--'\n",
    ")\n",
    "\n",
    "plt.xlim(\n",
    "    data_o2['timestamp'].min(),\n",
    "    data_o2['timestamp'].max()\n",
    ")\n",
    "\n",
    "plt.legend(['real', 'prediction', 'retrain'])\n",
    "plt.title('O2')\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6bbb8cf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/grezewave/Documents/projects/sientia/sientia-dataops-laborious_temporal/venv/lib/python3.11/site-packages/sientia_do/observability/sientia_monitoring.py:80: RuntimeWarning: coroutine 'AsyncMockMixin._execute_mock_call' was never awaited\n",
      "  self.metrics_controller.start()\n",
      "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\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>prediction</th>\n",
       "      <th>value</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>265.533539</td>\n",
       "      <td>260.24646</td>\n",
       "      <td>2026-01-22 15:42:29+0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>262.758423</td>\n",
       "      <td>258.94940</td>\n",
       "      <td>2026-01-22 15:42:59+0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>263.060638</td>\n",
       "      <td>257.65370</td>\n",
       "      <td>2026-01-22 15:43:29+0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   prediction      value                 timestamp\n",
       "0  265.533539  260.24646  2026-01-22 15:42:29+0000\n",
       "1  262.758423  258.94940  2026-01-22 15:42:59+0000\n",
       "2  263.060638  257.65370  2026-01-22 15:43:29+0000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas import DataFrame, to_datetime\n",
    "\n",
    "period_hours = 1\n",
    "\n",
    "samples = 2*60*period_hours\n",
    "retrain_samples = 2*period_hours\n",
    "\n",
    "query_nox = f\"\"\"\n",
    "select p.prediction, ld.value, p.\"timestamp\" from sientia_data.predictions p\n",
    "join sientia_data.laborious_data ld on p.\"timestamp\" = ld.\"timestamp\"\n",
    "where p.model_id = '4' and variable='CI-W3W01A3'\n",
    "and ld.model_id = '4' and p.\"timestamp\" >= NOW() - INTERVAL '{period_hours} HOUR'\n",
    "order by p.created_at;\n",
    "\"\"\"\n",
    "\n",
    "data_nox = DataFrame(await postgres.load_custom_query(\n",
    "    {\n",
    "        \"query\": query_nox,\n",
    "        \"metadata\": {},\n",
    "        \"datetime_columns\": [\"timestamp\"],\n",
    "    }\n",
    "))\n",
    "\n",
    "display(data_nox.head(3))\n",
    "\n",
    "data_nox.sort_values(by='timestamp', inplace=True)\n",
    "\n",
    "data_nox.drop_duplicates(subset=['timestamp'], inplace=True, keep='first')\n",
    "\n",
    "data_nox['timestamp'] = to_datetime(data_nox['timestamp'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "46340afd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "plt.plot(data_nox['timestamp'], data_nox['value'])\n",
    "plt.plot(data_nox['timestamp'], data_nox['prediction'])\n",
    "# plt.vlines(\n",
    "#     x=retrain_data_nox['timestamp'],\n",
    "#     ymin=plt.ylim()[0],\n",
    "#     ymax=plt.ylim()[1],\n",
    "#     colors='k',\n",
    "#     linestyles='--'\n",
    "# )\n",
    "\n",
    "\n",
    "plt.legend(['real', 'prediction'])#, 'retrain'])\n",
    "plt.title('NOx')\n",
    "plt.xlim(\n",
    "    data_nox['timestamp'].min(),\n",
    "    data_nox['timestamp'].max()\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.11.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}