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",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:53.557297Z",
     "start_time": "2024-08-22T14:02:49.775813Z"
    }
   },
   "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}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diive version: v0.79.0\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Docstring**"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:53.622643Z",
     "start_time": "2024-08-22T14:02:53.563282Z"
    }
   },
   "source": [
    "help(DaytimeNighttimeFlag)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class DaytimeNighttimeFlag in module diive.pkgs.createvar.daynightflag:\n",
      "\n",
      "class DaytimeNighttimeFlag(builtins.object)\n",
      " |  DaytimeNighttimeFlag(timestamp_index: pandas.core.indexes.datetimes.DatetimeIndex, utc_offset: int, lat: float, lon: float, nighttime_threshold: float = 50)\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, timestamp_index: pandas.core.indexes.datetimes.DatetimeIndex, utc_offset: int, lat: float, lon: float, nighttime_threshold: float = 50)\n",
      " |      Calculate flags to identify daytime and nighttime data from potential radiation.\n",
      " |      \n",
      " |      Args:\n",
      " |          timestamp_index: Time series index, flags and potential radiation\n",
      " |              are calculated using this index\n",
      " |          utc_offset: UTC offset of *timestamp_index*, e.g. 1 for UTC+01:00\n",
      " |          lat: Latitude\n",
      " |          lon: Longitude\n",
      " |          nighttime_threshold: Threshold for potential radiation below which data\n",
      " |              are flagged as nighttime (W m-2)\n",
      " |      \n",
      " |      - Example notebook available in:\n",
      " |          notebooks/CalculateVariable/DaytimeNighttimeFlag.ipynb\n",
      " |  \n",
      " |  get_daytime_flag(self) -> pandas.core.series.Series\n",
      " |      Return daytime flag where 1=daytime, 0=nighttime\n",
      " |  \n",
      " |  get_nighttime_flag(self) -> pandas.core.series.Series\n",
      " |      Return nighttime flag where 0=daytime, 1=nighttime\n",
      " |  \n",
      " |  get_results(self) -> pandas.core.frame.DataFrame\n",
      " |      Return dataframe with results\n",
      " |  \n",
      " |  get_swinpot(self) -> pandas.core.series.Series\n",
      " |      Return potential radiation\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties defined here:\n",
      " |  \n",
      " |  df\n",
      " |      Get dataframe with potential radiation and daytime/nighttime flags\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  daytime_col = 'DAYTIME'\n",
      " |  \n",
      " |  nighttime_col = 'NIGHTTIME'\n",
      " |  \n",
      " |  swinpot_col = 'SW_IN_POT'\n",
      "\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Load example data**"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:54.182742Z",
     "start_time": "2024-08-22T14:02:53.810663Z"
    }
   },
   "source": [
    "df = load_exampledata_parquet()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded .parquet file F:\\Sync\\luhk_work\\20 - CODING\\21 - DIIVE\\diive\\diive\\configs\\exampledata\\exampledata_PARQUET_CH-DAV_FP2022.5_2013-2022_ID20230206154316_30MIN.parquet (0.133 seconds). Detected time resolution of <30 * Minutes> / 30min \n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:54.386235Z",
     "start_time": "2024-08-22T14:02:54.268032Z"
    }
   },
   "source": [
    "df"
   ],
   "outputs": [
    {
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       "                      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  NEE_CUT_REF_orig       PA   PPFD  PREC_TOT_T1_25+20_1  QCF_LE  QCF_NEE  Reco_CUT_16  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",
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       "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               NaN  831.680  0.007                  0.0     NaN      1.0        2.090        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",
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       "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               NaN  843.057  0.000                  0.0     0.0      2.0        1.112        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",
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       "      <th>SWC_FF0_0.15_1</th>\n",
       "      <th>Tair_f</th>\n",
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       "      <td>0.288</td>\n",
       "      <td>1.335</td>\n",
       "      <td>1.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 01:45:00</th>\n",
       "      <td>0.290</td>\n",
       "      <td>1.354</td>\n",
       "      <td>-0.088</td>\n",
       "      <td>1.771</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.066</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.083</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.538</td>\n",
       "      <td>1.537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.538</td>\n",
       "      <td>831.903</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.892</td>\n",
       "      <td>1.450</td>\n",
       "      <td>2.308</td>\n",
       "      <td>1.219</td>\n",
       "      <td>1.926</td>\n",
       "      <td>1.622</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.767</td>\n",
       "      <td>34.975</td>\n",
       "      <td>-1.657</td>\n",
       "      <td>-1.657</td>\n",
       "      <td>0.288</td>\n",
       "      <td>1.332</td>\n",
       "      <td>1.332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01 02:15:00</th>\n",
       "      <td>0.377</td>\n",
       "      <td>0.545</td>\n",
       "      <td>-0.080</td>\n",
       "      <td>0.797</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>192.677</td>\n",
       "      <td>1.544</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>831.680</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.090</td>\n",
       "      <td>1.630</td>\n",
       "      <td>2.562</td>\n",
       "      <td>1.347</td>\n",
       "      <td>2.105</td>\n",
       "      <td>1.790</td>\n",
       "      <td>0.578</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.533</td>\n",
       "      <td>34.967</td>\n",
       "      <td>-0.567</td>\n",
       "      <td>-0.567</td>\n",
       "      <td>0.288</td>\n",
       "      <td>3.545</td>\n",
       "      <td>3.545</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",
       "      <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",
       "      <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>2022-12-31 21:45:00</th>\n",
       "      <td>-0.016</td>\n",
       "      <td>0.373</td>\n",
       "      <td>0.203</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.735</td>\n",
       "      <td>-0.735</td>\n",
       "      <td>230.499</td>\n",
       "      <td>0.629</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>842.985</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.001</td>\n",
       "      <td>1.456</td>\n",
       "      <td>1.824</td>\n",
       "      <td>2.568</td>\n",
       "      <td>1.221</td>\n",
       "      <td>1.032</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.790</td>\n",
       "      <td>19.572</td>\n",
       "      <td>3.463</td>\n",
       "      <td>3.463</td>\n",
       "      <td>0.288</td>\n",
       "      <td>2.210</td>\n",
       "      <td>2.210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31 22:15:00</th>\n",
       "      <td>0.105</td>\n",
       "      <td>-2.379</td>\n",
       "      <td>0.894</td>\n",
       "      <td>-1.433</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.724</td>\n",
       "      <td>4.724</td>\n",
       "      <td>231.788</td>\n",
       "      <td>3.518</td>\n",
       "      <td>3.518</td>\n",
       "      <td>0.803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.518</td>\n",
       "      <td>3.518</td>\n",
       "      <td>842.976</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.139</td>\n",
       "      <td>1.697</td>\n",
       "      <td>2.085</td>\n",
       "      <td>2.959</td>\n",
       "      <td>1.356</td>\n",
       "      <td>1.174</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.627</td>\n",
       "      <td>19.577</td>\n",
       "      <td>5.283</td>\n",
       "      <td>5.283</td>\n",
       "      <td>0.288</td>\n",
       "      <td>3.327</td>\n",
       "      <td>3.327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31 22:45:00</th>\n",
       "      <td>0.220</td>\n",
       "      <td>-0.515</td>\n",
       "      <td>0.926</td>\n",
       "      <td>-0.305</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>237.303</td>\n",
       "      <td>1.673</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.424</td>\n",
       "      <td>NaN</td>\n",
       "      <td>842.965</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.158</td>\n",
       "      <td>1.729</td>\n",
       "      <td>2.119</td>\n",
       "      <td>3.038</td>\n",
       "      <td>1.367</td>\n",
       "      <td>1.199</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.747</td>\n",
       "      <td>19.589</td>\n",
       "      <td>5.537</td>\n",
       "      <td>5.537</td>\n",
       "      <td>0.288</td>\n",
       "      <td>3.467</td>\n",
       "      <td>3.467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-31 23:15:00</th>\n",
       "      <td>0.059</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.843</td>\n",
       "      <td>1.014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.653</td>\n",
       "      <td>2.653</td>\n",
       "      <td>251.268</td>\n",
       "      <td>0.266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>843.057</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.112</td>\n",
       "      <td>1.646</td>\n",
       "      <td>2.031</td>\n",
       "      <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>NaN</td>\n",
       "      <td>843.050</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.232</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>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "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",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:54.883505Z",
     "start_time": "2024-08-22T14:02:54.558807Z"
    }
   },
   "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",
    ")"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:55.134401Z",
     "start_time": "2024-08-22T14:02:55.083023Z"
    }
   },
   "source": [
    "results = dnf.get_results()\n",
    "results"
   ],
   "outputs": [
    {
     "data": {
      "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]"
      ],
      "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>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:55.304468Z",
     "start_time": "2024-08-22T14:02:55.272553Z"
    }
   },
   "source": [
    "dnf.get_swinpot()"
   ],
   "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"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:55.536415Z",
     "start_time": "2024-08-22T14:02:55.511966Z"
    }
   },
   "source": [
    "dnf.get_daytime_flag()"
   ],
   "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"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:02:55.834174Z",
     "start_time": "2024-08-22T14:02:55.795759Z"
    }
   },
   "source": [
    "dnf.get_nighttime_flag()"
   ],
   "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"
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Plot some variables**"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-08-22T14:03:08.288343Z",
     "start_time": "2024-08-22T14:02:56.443735Z"
    }
   },
   "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)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x900 with 6 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# **End of notebook**"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "ExecuteTime": {
     "end_time": "2024-08-22T14:03:08.423493Z",
     "start_time": "2024-08-22T14:03:08.413519Z"
    }
   },
   "source": [
    "dt_string = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "print(f\"Finished {dt_string}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished 2024-08-22 16:03:08\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T14:03:08.579101Z",
     "start_time": "2024-08-22T14:03:08.572119Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": null
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}