notebooks/CalculateVariable/Daytime_and_nighttime_flag.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![lop](../../images/logo_diive1_128px.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='font-size:28px; display:block;'>\n",
    "<b>\n",
    "    Calculate daytime flag, nighttime flag and potential radiation from latitude and longitude\n",
    "</b>\n",
    "</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**Notebook version**: `2` (23 Mar 2024)  \n",
    "**Author**: Lukas Hörtnagl (holukas@ethz.ch)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Description**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Calculation of two flags for each record:\n",
    "    - Daytime flag, where daytime=1 and nighttime=0\n",
    "    - Nighttime flag, where daytime=0 and nighttime=1\n",
    "- Both flags are calculated from potential radiation, whereby values below a specified threshold are nighttime\n",
    "- Potential radiation is calculated from latitude and longitude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:53.557297Z",
     "start_time": "2024-08-22T14:02:49.775813Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diive version: v0.85.0\n"
     ]
    }
   ],
   "source": [
    "import importlib.metadata\n",
    "import warnings\n",
    "from datetime import datetime\n",
    "\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from diive.configs.exampledata import load_exampledata_parquet\n",
    "from diive.core.plotting.heatmap_datetime import HeatmapDateTime\n",
    "from diive.pkgs.createvar.daynightflag import DaytimeNighttimeFlag\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "version_diive = importlib.metadata.version(\"diive\")\n",
    "print(f\"diive version: v{version_diive}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Docstring**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:53.622643Z",
     "start_time": "2024-08-22T14:02:53.563282Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# help(DaytimeNighttimeFlag)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Load example data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:54.182742Z",
     "start_time": "2024-08-22T14:02:53.810663Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded .parquet file L:\\Sync\\luhk_work\\20 - CODING\\21 - DIIVE\\diive\\diive\\configs\\exampledata\\exampledata_PARQUET_CH-DAV_FP2022.5_2013-2022_ID20230206154316_30MIN.parquet (0.061 seconds).\n",
      "    --> Detected time resolution of <30 * Minutes> / 30min \n"
     ]
    }
   ],
   "source": [
    "df = load_exampledata_parquet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:54.386235Z",
     "start_time": "2024-08-22T14:02:54.268032Z"
    },
    "tags": []
   },
   "outputs": [
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       "      <td>2.942</td>\n",
       "      <td>1.311</td>\n",
       "      <td>1.159</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.427</td>\n",
       "      <td>19.609</td>\n",
       "      <td>4.963</td>\n",
       "      <td>4.963</td>\n",
       "      <td>0.288</td>\n",
       "      <td>3.010</td>\n",
       "      <td>3.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31 23:45:00</th>\n",
       "      <td>0.328</td>\n",
       "      <td>-1.049</td>\n",
       "      <td>0.754</td>\n",
       "      <td>-0.023</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.700</td>\n",
       "      <td>14.700</td>\n",
       "      <td>244.312</td>\n",
       "      <td>2.281</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.281</td>\n",
       "      <td>...</td>\n",
       "      <td>1.859</td>\n",
       "      <td>2.258</td>\n",
       "      <td>3.298</td>\n",
       "      <td>1.426</td>\n",
       "      <td>1.286</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.667</td>\n",
       "      <td>19.631</td>\n",
       "      <td>6.490</td>\n",
       "      <td>6.490</td>\n",
       "      <td>0.288</td>\n",
       "      <td>3.905</td>\n",
       "      <td>3.905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>175296 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ET_f  GPP_CUT_16_f  GPP_CUT_84_f  GPP_CUT_REF_f  GPP_DT_CUT_16  GPP_DT_CUT_84  GPP_DT_CUT_REF    LE_f  LE_orig    LW_IN  NEE_CUT_16_f  NEE_CUT_16_orig  NEE_CUT_84_f  NEE_CUT_84_orig  NEE_CUT_REF_f  ...  Reco_CUT_84  Reco_CUT_REF  Reco_DT_CUT_16  Reco_DT_CUT_84  Reco_DT_CUT_REF  Reco_DT_CUT_REF_SD  Rg_f  Rg_orig      RH  SWC_FF0_0.15_1  Tair_f  Tair_orig  Ustar_CUT_REF_Thres  VPD_f  VPD_orig\n",
       "TIMESTAMP_MIDDLE                                                                                                                                                                                                            ...                                                                                                                                                                                                \n",
       "2013-01-01 00:15:00  0.290         0.510        -0.082          0.705            0.0            0.0             0.0  13.093      NaN  194.913         1.376              NaN         1.537              NaN          1.595  ...        1.455         2.300           1.252           1.908            1.613               0.510   0.0      0.0  31.933          34.977  -1.618     -1.618                0.288  1.339     1.339\n",
       "2013-01-01 00:45:00  0.290         0.512        -0.084          0.708            0.0            0.0             0.0  13.093      NaN  193.568         1.376              NaN         1.537              NaN          1.595  ...        1.454         2.303           1.241           1.914            1.616               0.512   0.0      0.0  32.600          34.977  -1.630     -1.630                0.288  1.337     1.337\n",
       "2013-01-01 01:15:00  0.290         0.514        -0.086          0.710            0.0            0.0             0.0  13.066      NaN  193.135         1.376              NaN         1.537              NaN          1.595  ...        1.452         2.305           1.230           1.920            1.619               0.515   0.0      0.0  38.867          34.971  -1.644     -1.644                0.288  1.335     1.335\n",
       "2013-01-01 01:45:00  0.290         1.354        -0.088          1.771            0.0            0.0             0.0  13.066      NaN  192.083         0.538            0.538         1.537              NaN          0.538  ...        1.450         2.308           1.219           1.926            1.622               0.518   0.0      0.0  37.767          34.975  -1.657     -1.657                0.288  1.332     1.332\n",
       "2013-01-01 02:15:00  0.377         0.545        -0.080          0.797            0.0            0.0             0.0  16.995      NaN  192.677         1.544              NaN         1.709              NaN          1.765  ...        1.630         2.562           1.347           2.105            1.790               0.578   0.0      0.0  39.533          34.967  -0.567     -0.567                0.288  3.545     3.545\n",
       "...                    ...           ...           ...            ...            ...            ...             ...     ...      ...      ...           ...              ...           ...              ...            ...  ...          ...           ...             ...             ...              ...                 ...   ...      ...     ...             ...     ...        ...                  ...    ...       ...\n",
       "2022-12-31 21:45:00 -0.016         0.373         0.203          0.600            0.0            0.0             0.0  -0.735   -0.735  230.499         0.629              NaN         1.253              NaN          1.224  ...        1.456         1.824           2.568           1.221            1.032               0.759   0.0      0.0  71.790          19.572   3.463      3.463                0.288  2.210     2.210\n",
       "2022-12-31 22:15:00  0.105        -2.379         0.894         -1.433            0.0            0.0             0.0   4.724    4.724  231.788         3.518            3.518         0.803              NaN          3.518  ...        1.697         2.085           2.959           1.356            1.174               0.838   0.0      0.0  62.627          19.577   5.283      5.283                0.288  3.327     3.327\n",
       "2022-12-31 22:45:00  0.220        -0.515         0.926         -0.305            0.0            0.0             0.0   9.840      NaN  237.303         1.673              NaN         0.803              NaN          2.424  ...        1.729         2.119           3.038           1.367            1.199               0.857   0.0      0.0  61.747          19.589   5.537      5.537                0.288  3.467     3.467\n",
       "2022-12-31 23:15:00  0.059         0.847         0.843          1.014            0.0            0.0             0.0   2.653    2.653  251.268         0.266              NaN         0.803              NaN          1.017  ...        1.646         2.031           2.942           1.311            1.159               0.843   0.0      0.0  65.427          19.609   4.963      4.963                0.288  3.010     3.010\n",
       "2022-12-31 23:45:00  0.328        -1.049         0.754         -0.023            0.0            0.0             0.0  14.700   14.700  244.312         2.281              NaN         1.104              NaN          2.281  ...        1.859         2.258           3.298           1.426            1.286               0.916   0.0      0.0  59.667          19.631   6.490      6.490                0.288  3.905     3.905\n",
       "\n",
       "[175296 rows x 37 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Calculation of flags from potential radiation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:54.883505Z",
     "start_time": "2024-08-22T14:02:54.558807Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dnf = DaytimeNighttimeFlag(\n",
    "    timestamp_index=df.index,\n",
    "    nighttime_threshold=1,\n",
    "    lat=47.286417,\n",
    "    lon=7.733750,\n",
    "    utc_offset=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:55.134401Z",
     "start_time": "2024-08-22T14:02:55.083023Z"
    },
    "tags": []
   },
   "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>SW_IN_POT</th>\n",
       "      <th>DAYTIME</th>\n",
       "      <th>NIGHTTIME</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMESTAMP_MIDDLE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01 00:15:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 00:45:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 01:15:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 01:45:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 02:15:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31 21:45:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31 22:15:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31 22:45:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31 23:15:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31 23:45:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>175296 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     SW_IN_POT  DAYTIME  NIGHTTIME\n",
       "TIMESTAMP_MIDDLE                                  \n",
       "2013-01-01 00:15:00        0.0      0.0        1.0\n",
       "2013-01-01 00:45:00        0.0      0.0        1.0\n",
       "2013-01-01 01:15:00        0.0      0.0        1.0\n",
       "2013-01-01 01:45:00        0.0      0.0        1.0\n",
       "2013-01-01 02:15:00        0.0      0.0        1.0\n",
       "...                        ...      ...        ...\n",
       "2022-12-31 21:45:00        0.0      0.0        1.0\n",
       "2022-12-31 22:15:00        0.0      0.0        1.0\n",
       "2022-12-31 22:45:00        0.0      0.0        1.0\n",
       "2022-12-31 23:15:00        0.0      0.0        1.0\n",
       "2022-12-31 23:45:00        0.0      0.0        1.0\n",
       "\n",
       "[175296 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = dnf.get_results()\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:55.304468Z",
     "start_time": "2024-08-22T14:02:55.272553Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TIMESTAMP_MIDDLE\n",
       "2013-01-01 00:15:00    0.0\n",
       "2013-01-01 00:45:00    0.0\n",
       "2013-01-01 01:15:00    0.0\n",
       "2013-01-01 01:45:00    0.0\n",
       "2013-01-01 02:15:00    0.0\n",
       "                      ... \n",
       "2022-12-31 21:45:00    0.0\n",
       "2022-12-31 22:15:00    0.0\n",
       "2022-12-31 22:45:00    0.0\n",
       "2022-12-31 23:15:00    0.0\n",
       "2022-12-31 23:45:00    0.0\n",
       "Freq: 30min, Name: SW_IN_POT, Length: 175296, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dnf.get_swinpot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:55.536415Z",
     "start_time": "2024-08-22T14:02:55.511966Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TIMESTAMP_MIDDLE\n",
       "2013-01-01 00:15:00    0.0\n",
       "2013-01-01 00:45:00    0.0\n",
       "2013-01-01 01:15:00    0.0\n",
       "2013-01-01 01:45:00    0.0\n",
       "2013-01-01 02:15:00    0.0\n",
       "                      ... \n",
       "2022-12-31 21:45:00    0.0\n",
       "2022-12-31 22:15:00    0.0\n",
       "2022-12-31 22:45:00    0.0\n",
       "2022-12-31 23:15:00    0.0\n",
       "2022-12-31 23:45:00    0.0\n",
       "Freq: 30min, Name: DAYTIME, Length: 175296, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dnf.get_daytime_flag()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:55.834174Z",
     "start_time": "2024-08-22T14:02:55.795759Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TIMESTAMP_MIDDLE\n",
       "2013-01-01 00:15:00    1.0\n",
       "2013-01-01 00:45:00    1.0\n",
       "2013-01-01 01:15:00    1.0\n",
       "2013-01-01 01:45:00    1.0\n",
       "2013-01-01 02:15:00    1.0\n",
       "                      ... \n",
       "2022-12-31 21:45:00    1.0\n",
       "2022-12-31 22:15:00    1.0\n",
       "2022-12-31 22:45:00    1.0\n",
       "2022-12-31 23:15:00    1.0\n",
       "2022-12-31 23:45:00    1.0\n",
       "Freq: 30min, Name: NIGHTTIME, Length: 175296, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dnf.get_nighttime_flag()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Plot some variables**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:03:08.288343Z",
     "start_time": "2024-08-22T14:02:56.443735Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x900 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(facecolor='white', figsize=(16, 9))\n",
    "gs = gridspec.GridSpec(1, 3)  # rows, cols\n",
    "gs.update(wspace=0.3, hspace=0.3, left=0.03, right=0.97, top=0.97, bottom=0.03)\n",
    "ax_sw = fig.add_subplot(gs[0, 0])\n",
    "ax_dt = fig.add_subplot(gs[0, 1])\n",
    "ax_nt = fig.add_subplot(gs[0, 2])\n",
    "HeatmapDateTime(ax=ax_sw, series=dnf.get_swinpot()).plot()\n",
    "HeatmapDateTime(ax=ax_dt, series=dnf.get_daytime_flag()).plot()\n",
    "HeatmapDateTime(ax=ax_nt, series=dnf.get_nighttime_flag()).plot()\n",
    "ax_sw.set_title(\"SW_IN_POT\", color='black')\n",
    "ax_dt.set_title(\"DAYTIME FLAG\", color='black')\n",
    "ax_nt.set_title(\"NIGHTTIME FLAG\", color='black')\n",
    "ax_dt.tick_params(left=True, right=False, top=False, bottom=True,\n",
    "                  labelleft=False, labelright=False, labeltop=False, labelbottom=True)\n",
    "ax_nt.tick_params(left=True, right=False, top=False, bottom=True,\n",
    "                  labelleft=False, labelright=False, labeltop=False, labelbottom=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# **End of notebook**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:03:08.423493Z",
     "start_time": "2024-08-22T14:03:08.413519Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished 2025-01-23 12:43:44\n"
     ]
    }
   ],
   "source": [
    "dt_string = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "print(f\"Finished {dt_string}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:03:08.579101Z",
     "start_time": "2024-08-22T14:03:08.572119Z"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
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