{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-01-13 09:41:14,117 - admix - WARNING - Initializing utilix DB failed. You cannot do database operations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DB initialization failed\n"
     ]
    }
   ],
   "source": [
    "import straxen\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial SCADA-Python\n",
    "\n",
    "In this notebook we show the basic functionalities of our SCADA interface. As a first step we have to initialize the interface. Please note that this is only possible for XENON members who are working on Midway. The straxen client will request a security token from our slowcontrol webservice. This token is only valid 3 hrs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gvolta/.local/lib/python3.11/site-packages/utilix/mongo_storage.py:378: DownloadWarning: Downloading PMTmap_SCADA.json to ./resource_cache/694d47e7cf82ecd0f80b11bed94acbb9\n",
      "  warn(f\"Downloading {config_name} to {destination_path}\", DownloadWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Received token, the token is valid for 3 hrs.\n",
      " from 13.01. 15:41:17 UTC\n",
      " till 13.01. 18:41:17 UTC\n",
      "We will automatically refresh the token for you :). Have a nice day and a fruitful analysis!\n"
     ]
    }
   ],
   "source": [
    "sc = straxen.SCADAInterface()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After getting the token we can query some parameters. Lets try to get some data for a certain time range and set of different parameters. The parameters can be specified via a dictionary which takes as values the Historian database names of the required parameter and some short name as key. Let us query as an example some different parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    \"para1\": \"XE1T.CTPC.Board06.Chan011.VMon\",\n",
    "    \"para2\": \"XE1T.CRY_TE101_TCRYOBOTT_AI.PI\",\n",
    "    \"para3\": \"XE1T.CRY_PT101_PCHAMBER_AI.PI\",\n",
    "    \"para4\": \"XE1T.GEN_CE911_SLM1_HMON.PI\",\n",
    "    \"para5\": \"XE1T.CRY_FCV104FMON.PI\",\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feel free to give in your own analysis more meaningful parameter keys. Since it can be quite annoying to look up all those parameter names we also offer some functions which can return the corresponding names. For example for our PMTs the following function returns the high-voltage and current names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PMT10_HV': 'XE1T.CTPC.BOARD03.CHAN008.VMON',\n",
       " 'PMT10_I': 'XE1T.CTPC.BOARD03.CHAN008.IMON',\n",
       " 'PMT11_HV': 'XE1T.CTPC.BOARD04.CHAN002.VMON',\n",
       " 'PMT11_I': 'XE1T.CTPC.BOARD04.CHAN002.IMON'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pmts = sc.find_pmt_names(range(10, 12), hv=True, current=True)\n",
    "pmts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beside the parameters we also have to specify the time range we would like to query. Since straxen handles time intervals in unix time nano-seconds it is  natural to do the same here (Do not worry we can also query times in a human readable way which is shown below). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9583440be2e143da87b869f58ff8159f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Load parameters:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = 1709682275000000000\n",
    "end = 1709736527000000000\n",
    "\n",
    "df = sc.get_scada_values(parameters, start=start, end=end, every_nth_value=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the name already suggest the data is returned as a simple data DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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>para1</th>\n",
       "      <th>para2</th>\n",
       "      <th>para3</th>\n",
       "      <th>para4</th>\n",
       "      <th>para5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time UTC</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>2024-03-05 23:44:35+00:00</th>\n",
       "      <td>1211.0</td>\n",
       "      <td>-94.566826</td>\n",
       "      <td>2.041337</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>3.572651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-05 23:44:36+00:00</th>\n",
       "      <td>1211.0</td>\n",
       "      <td>-94.564354</td>\n",
       "      <td>2.041424</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>3.572651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-05 23:44:37+00:00</th>\n",
       "      <td>1211.0</td>\n",
       "      <td>-94.559937</td>\n",
       "      <td>2.041635</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>3.572651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-05 23:44:38+00:00</th>\n",
       "      <td>1211.0</td>\n",
       "      <td>-94.554665</td>\n",
       "      <td>2.041380</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>3.572651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-03-05 23:44:39+00:00</th>\n",
       "      <td>1211.0</td>\n",
       "      <td>-94.551048</td>\n",
       "      <td>2.041904</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>3.572651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            para1      para2     para3  para4     para5\n",
       "time UTC                                                               \n",
       "2024-03-05 23:44:35+00:00  1211.0 -94.566826  2.041337   -1.1  3.572651\n",
       "2024-03-05 23:44:36+00:00  1211.0 -94.564354  2.041424   -1.1  3.572651\n",
       "2024-03-05 23:44:37+00:00  1211.0 -94.559937  2.041635   -1.1  3.572651\n",
       "2024-03-05 23:44:38+00:00  1211.0 -94.554665  2.041380   -1.1  3.572651\n",
       "2024-03-05 23:44:39+00:00  1211.0 -94.551048  2.041904   -1.1  3.572651"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "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": [
    "plt.plot(df[\"para2\"])\n",
    "plt.xlabel(\"Time [UTC]\")\n",
    "plt.ylabel(\"Temperature at TE101 [°C]\")\n",
    "plt.xticks(rotation=45, horizontalalignment=\"right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Querying data in a human readable way:\n",
    "\n",
    "Querying data with timestamps in nano-seconds unix times can be a bit tedious, especially, if you are not working with any straxen data. In order to allow a query in a human readable way we added some widgets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tw = straxen.TimeWidgets()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After initialization you can render the widgets via..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd2676de97244e99bbcd65e22c0fbb9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Dropdown(description='Time Zone:', options=(('CET', 0), ('UTC', 1)), value=0), H…"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tw.create_widgets()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and select any time range you would like. After specifying a time you can get the start and end time via:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1768311900000000000 1768315500000000000\n"
     ]
    }
   ],
   "source": [
    "start, end = tw.get_start_end()\n",
    "print(start, end)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to this time widget we also offer analyst to query data via run_ids. This can be quite handy if you would like to correlate your data with other detector parameters. Further, you can also query data with a coarser binning e.g. every 300 seconds. Before we can use run_ids though we have to specify the context we are working with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You specified _auto_append_rucio_local=True and you are not on dali compute nodes, so we will add the following rucio local path: /project/lgrandi/rucio/\n"
     ]
    }
   ],
   "source": [
    "st = straxen.contexts.xenonnt_online()\n",
    "sc.context = st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "39a640ac124945ecac6532c63163662a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Load parameters:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = sc.get_scada_values(parameters, run_id=\"020280\", every_nth_value=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing the time zone\n",
    "\n",
    "You can also easily change the timezone of your data if needed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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>para1</th>\n",
       "      <th>para2</th>\n",
       "      <th>para3</th>\n",
       "      <th>para4</th>\n",
       "      <th>para5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time CET</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>2021-05-24 15:03:52+02:00</th>\n",
       "      <td>1211.0</td>\n",
       "      <td>-96.117081</td>\n",
       "      <td>1.887699</td>\n",
       "      <td>-16.963255</td>\n",
       "      <td>4.632631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-24 15:04:02+02:00</th>\n",
       "      <td>1211.0</td>\n",
       "      <td>-96.118813</td>\n",
       "      <td>1.888073</td>\n",
       "      <td>-16.958317</td>\n",
       "      <td>4.983373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-24 15:04:12+02:00</th>\n",
       "      <td>1211.0</td>\n",
       "      <td>-96.130096</td>\n",
       "      <td>1.887873</td>\n",
       "      <td>-16.958286</td>\n",
       "      <td>4.983373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-24 15:04:22+02:00</th>\n",
       "      <td>1211.0</td>\n",
       "      <td>-96.134781</td>\n",
       "      <td>1.888285</td>\n",
       "      <td>-16.958317</td>\n",
       "      <td>5.216488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-24 15:04:32+02:00</th>\n",
       "      <td>1211.0</td>\n",
       "      <td>-96.126343</td>\n",
       "      <td>1.887630</td>\n",
       "      <td>-16.958317</td>\n",
       "      <td>5.216488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            para1      para2     para3      para4     para5\n",
       "time CET                                                                   \n",
       "2021-05-24 15:03:52+02:00  1211.0 -96.117081  1.887699 -16.963255  4.632631\n",
       "2021-05-24 15:04:02+02:00  1211.0 -96.118813  1.888073 -16.958317  4.983373\n",
       "2021-05-24 15:04:12+02:00  1211.0 -96.130096  1.887873 -16.958286  4.983373\n",
       "2021-05-24 15:04:22+02:00  1211.0 -96.134781  1.888285 -16.958317  5.216488\n",
       "2021-05-24 15:04:32+02:00  1211.0 -96.126343  1.887630 -16.958317  5.216488"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfcet = straxen.convert_time_zone(df, \"CET\")\n",
    "dfcet.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfcet[\"para2\"].plot()\n",
    "plt.xlabel(\"Time [CET]\")\n",
    "plt.ylabel(\"Temperature at TE101 [°C]\")\n",
    "plt.xticks(rotation=45, horizontalalignment=\"right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.6"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}