notebooks/Resampling/ResamplingDielCycle.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![lop](../../images/logo_diive1_128px.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='font-size:32px; display:block;'>\n",
    "<b>\n",
    "    Resampling: Diel cycle\n",
    "</b>\n",
    "</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**Notebook version**: `1` (14 May 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": [
    "- `DielCycle` is a class for plot diel cycles of time series data.\n",
    "- Uses the time info from the timestamp (datetime) index to aggregate. Aggregation is done on unique times found in the index.\n",
    "- For example, if the time part of the datetime index is hourly (e.g. 0:00, 01:00, 02:00 etc.), then hourly averages are calculated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T13:44:19.134468Z",
     "start_time": "2024-08-22T13:44:16.774481Z"
    },
    "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 diive.configs.exampledata as ed\n",
    "from diive.core.times.resampling import diel_cycle\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": "2024-08-22T13:44:19.228549Z",
     "start_time": "2024-08-22T13:44:19.221567Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function diel_cycle in module diive.core.times.resampling:\n",
      "\n",
      "diel_cycle(series: pandas.core.series.Series, mincounts: int = 1, mean: bool = True, std: bool = True, median: bool = False, each_month: bool = False) -> pandas.core.frame.DataFrame\n",
      "    Calculate diel cycles grouped by time\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(diel_cycle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Load example data**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Load CO2 and latent evaporation as example data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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     "end_time": "2024-08-22T13:44:19.480327Z",
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    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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    {
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     "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 (0.057 seconds).\n",
      "    --> Detected time resolution of <30 * Minutes> / 30min \n"
     ]
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    {
     "data": {
      "text/plain": [
       "TIMESTAMP_MIDDLE\n",
       "2013-01-01 00:15:00    1.595\n",
       "2013-01-01 00:45:00    1.595\n",
       "2013-01-01 01:15:00    1.595\n",
       "2013-01-01 01:45:00    0.538\n",
       "2013-01-01 02:15:00    1.765\n",
       "                       ...  \n",
       "2022-12-31 21:45:00    1.224\n",
       "2022-12-31 22:15:00    3.518\n",
       "2022-12-31 22:45:00    2.424\n",
       "2022-12-31 23:15:00    1.017\n",
       "2022-12-31 23:45:00    2.281\n",
       "Freq: 30min, Name: NEE_CUT_REF_f, Length: 175296, dtype: float64"
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   ],
   "source": [
    "df = ed.load_exampledata_parquet()\n",
    "co2 = df['NEE_CUT_REF_f'].copy()\n",
    "co2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Calculate diel cycle**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monthly diel cycles\n",
    "- Monthly diel cycles are stored using a MultiIndex where the month is stored as its integer number (e.g. `1` for January), followed by the aggregates grouped by time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "aggs = diel_cycle(\n",
    "    series=co2,\n",
    "    mincounts=1,\n",
    "    mean=True,\n",
    "    std=True,\n",
    "    median=True,\n",
    "    each_month=True\n",
    ")"
   ]
  },
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       "                           count      mean       std  median   mean+sd   mean-sd  median+sd  median-sd\n",
       "TIMESTAMP_MIDDLE                                                                                      \n",
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       "...                          ...       ...       ...     ...       ...       ...        ...        ...\n",
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       "\n",
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     "execution_count": 5,
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    "aggs"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show data for January:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T13:44:19.779034Z",
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       "          count      mean       std  median   mean+sd   mean-sd  median+sd  median-sd\n",
       "00:15:00    310  1.028971  1.217350  0.9235  2.246321 -0.188379   2.140850  -0.293850\n",
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       "02:15:00    310  0.979710  1.053724  0.9095  2.033434 -0.074014   1.963224  -0.144224"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggs.loc[1].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show data for June:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
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       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>median</th>\n",
       "      <th>mean+sd</th>\n",
       "      <th>mean-sd</th>\n",
       "      <th>median+sd</th>\n",
       "      <th>median-sd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>00:15:00</th>\n",
       "      <td>300</td>\n",
       "      <td>4.665877</td>\n",
       "      <td>2.219011</td>\n",
       "      <td>4.4565</td>\n",
       "      <td>6.884888</td>\n",
       "      <td>2.446866</td>\n",
       "      <td>6.675511</td>\n",
       "      <td>2.237489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:45:00</th>\n",
       "      <td>300</td>\n",
       "      <td>4.529247</td>\n",
       "      <td>2.131744</td>\n",
       "      <td>4.3415</td>\n",
       "      <td>6.660991</td>\n",
       "      <td>2.397502</td>\n",
       "      <td>6.473244</td>\n",
       "      <td>2.209756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01:15:00</th>\n",
       "      <td>300</td>\n",
       "      <td>4.587670</td>\n",
       "      <td>1.987093</td>\n",
       "      <td>4.3150</td>\n",
       "      <td>6.574763</td>\n",
       "      <td>2.600577</td>\n",
       "      <td>6.302093</td>\n",
       "      <td>2.327907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01:45:00</th>\n",
       "      <td>300</td>\n",
       "      <td>4.554280</td>\n",
       "      <td>2.077765</td>\n",
       "      <td>4.3475</td>\n",
       "      <td>6.632045</td>\n",
       "      <td>2.476515</td>\n",
       "      <td>6.425265</td>\n",
       "      <td>2.269735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02:15:00</th>\n",
       "      <td>300</td>\n",
       "      <td>4.537543</td>\n",
       "      <td>2.009197</td>\n",
       "      <td>4.3545</td>\n",
       "      <td>6.546740</td>\n",
       "      <td>2.528346</td>\n",
       "      <td>6.363697</td>\n",
       "      <td>2.345303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  median   mean+sd   mean-sd  median+sd  median-sd\n",
       "00:15:00    300  4.665877  2.219011  4.4565  6.884888  2.446866   6.675511   2.237489\n",
       "00:45:00    300  4.529247  2.131744  4.3415  6.660991  2.397502   6.473244   2.209756\n",
       "01:15:00    300  4.587670  1.987093  4.3150  6.574763  2.600577   6.302093   2.327907\n",
       "01:45:00    300  4.554280  2.077765  4.3475  6.632045  2.476515   6.425265   2.269735\n",
       "02:15:00    300  4.537543  2.009197  4.3545  6.546740  2.528346   6.363697   2.345303"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggs.loc[6].head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diel cylce across all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T13:44:20.156367Z",
     "start_time": "2024-08-22T13:44:20.064077Z"
    }
   },
   "outputs": [],
   "source": [
    "aggs = diel_cycle(\n",
    "    series=co2,\n",
    "    mincounts=1,\n",
    "    mean=True,\n",
    "    std=True,\n",
    "    median=True,\n",
    "    each_month=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T13:44:20.282047Z",
     "start_time": "2024-08-22T13:44:20.252596Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>median</th>\n",
       "      <th>mean+sd</th>\n",
       "      <th>mean-sd</th>\n",
       "      <th>median+sd</th>\n",
       "      <th>median-sd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIMESTAMP_MIDDLE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"48\" valign=\"top\">ALL_MONTHS</th>\n",
       "      <th>00:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.628153</td>\n",
       "      <td>2.292058</td>\n",
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       "      <td>4.363058</td>\n",
       "      <td>-0.221058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>00:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.660316</td>\n",
       "      <td>2.251208</td>\n",
       "      <td>2.1045</td>\n",
       "      <td>4.911524</td>\n",
       "      <td>0.409109</td>\n",
       "      <td>4.355708</td>\n",
       "      <td>-0.146708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.636666</td>\n",
       "      <td>2.313408</td>\n",
       "      <td>2.1165</td>\n",
       "      <td>4.950074</td>\n",
       "      <td>0.323259</td>\n",
       "      <td>4.429908</td>\n",
       "      <td>-0.196908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.609721</td>\n",
       "      <td>2.221190</td>\n",
       "      <td>2.0755</td>\n",
       "      <td>4.830911</td>\n",
       "      <td>0.388532</td>\n",
       "      <td>4.296690</td>\n",
       "      <td>-0.145690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.600780</td>\n",
       "      <td>2.252368</td>\n",
       "      <td>2.0600</td>\n",
       "      <td>4.853148</td>\n",
       "      <td>0.348412</td>\n",
       "      <td>4.312368</td>\n",
       "      <td>-0.192368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.628637</td>\n",
       "      <td>2.295040</td>\n",
       "      <td>2.0960</td>\n",
       "      <td>4.923677</td>\n",
       "      <td>0.333597</td>\n",
       "      <td>4.391040</td>\n",
       "      <td>-0.199040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.572976</td>\n",
       "      <td>2.241159</td>\n",
       "      <td>2.0515</td>\n",
       "      <td>4.814135</td>\n",
       "      <td>0.331816</td>\n",
       "      <td>4.292659</td>\n",
       "      <td>-0.189659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.542711</td>\n",
       "      <td>2.206123</td>\n",
       "      <td>2.0320</td>\n",
       "      <td>4.748834</td>\n",
       "      <td>0.336589</td>\n",
       "      <td>4.238123</td>\n",
       "      <td>-0.174123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.518468</td>\n",
       "      <td>2.145243</td>\n",
       "      <td>2.0070</td>\n",
       "      <td>4.663710</td>\n",
       "      <td>0.373225</td>\n",
       "      <td>4.152243</td>\n",
       "      <td>-0.138243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.490877</td>\n",
       "      <td>2.216896</td>\n",
       "      <td>1.9990</td>\n",
       "      <td>4.707773</td>\n",
       "      <td>0.273981</td>\n",
       "      <td>4.215896</td>\n",
       "      <td>-0.217896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.162079</td>\n",
       "      <td>2.357829</td>\n",
       "      <td>1.7720</td>\n",
       "      <td>4.519909</td>\n",
       "      <td>-0.195750</td>\n",
       "      <td>4.129829</td>\n",
       "      <td>-0.585829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>1.540008</td>\n",
       "      <td>2.825489</td>\n",
       "      <td>1.4505</td>\n",
       "      <td>4.365497</td>\n",
       "      <td>-1.285480</td>\n",
       "      <td>4.275989</td>\n",
       "      <td>-1.374989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>0.812312</td>\n",
       "      <td>3.235995</td>\n",
       "      <td>1.1370</td>\n",
       "      <td>4.048307</td>\n",
       "      <td>-2.423683</td>\n",
       "      <td>4.372995</td>\n",
       "      <td>-2.098995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>0.184065</td>\n",
       "      <td>3.329848</td>\n",
       "      <td>0.8400</td>\n",
       "      <td>3.513913</td>\n",
       "      <td>-3.145783</td>\n",
       "      <td>4.169848</td>\n",
       "      <td>-2.489848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-0.429212</td>\n",
       "      <td>3.634704</td>\n",
       "      <td>0.5195</td>\n",
       "      <td>3.205491</td>\n",
       "      <td>-4.063916</td>\n",
       "      <td>4.154204</td>\n",
       "      <td>-3.115204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-1.615019</td>\n",
       "      <td>4.421238</td>\n",
       "      <td>-0.1375</td>\n",
       "      <td>2.806220</td>\n",
       "      <td>-6.036257</td>\n",
       "      <td>4.283738</td>\n",
       "      <td>-4.558738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-3.062653</td>\n",
       "      <td>5.305186</td>\n",
       "      <td>-1.4945</td>\n",
       "      <td>2.242533</td>\n",
       "      <td>-8.367839</td>\n",
       "      <td>3.810686</td>\n",
       "      <td>-6.799686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-4.042698</td>\n",
       "      <td>5.514220</td>\n",
       "      <td>-3.3345</td>\n",
       "      <td>1.471523</td>\n",
       "      <td>-9.556918</td>\n",
       "      <td>2.179720</td>\n",
       "      <td>-8.848720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-4.577710</td>\n",
       "      <td>5.651760</td>\n",
       "      <td>-4.1465</td>\n",
       "      <td>1.074050</td>\n",
       "      <td>-10.229470</td>\n",
       "      <td>1.505260</td>\n",
       "      <td>-9.798260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-4.992462</td>\n",
       "      <td>5.872298</td>\n",
       "      <td>-4.7230</td>\n",
       "      <td>0.879836</td>\n",
       "      <td>-10.864760</td>\n",
       "      <td>1.149298</td>\n",
       "      <td>-10.595298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-5.340606</td>\n",
       "      <td>5.885783</td>\n",
       "      <td>-5.0780</td>\n",
       "      <td>0.545178</td>\n",
       "      <td>-11.226389</td>\n",
       "      <td>0.807783</td>\n",
       "      <td>-10.963783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-5.474241</td>\n",
       "      <td>5.918189</td>\n",
       "      <td>-5.3830</td>\n",
       "      <td>0.443948</td>\n",
       "      <td>-11.392430</td>\n",
       "      <td>0.535189</td>\n",
       "      <td>-11.301189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-5.511982</td>\n",
       "      <td>5.981464</td>\n",
       "      <td>-5.4905</td>\n",
       "      <td>0.469482</td>\n",
       "      <td>-11.493446</td>\n",
       "      <td>0.490964</td>\n",
       "      <td>-11.471964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-5.488146</td>\n",
       "      <td>5.999366</td>\n",
       "      <td>-5.3755</td>\n",
       "      <td>0.511220</td>\n",
       "      <td>-11.487513</td>\n",
       "      <td>0.623866</td>\n",
       "      <td>-11.374866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-5.415469</td>\n",
       "      <td>5.828823</td>\n",
       "      <td>-5.1750</td>\n",
       "      <td>0.413355</td>\n",
       "      <td>-11.244292</td>\n",
       "      <td>0.653823</td>\n",
       "      <td>-11.003823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-5.213165</td>\n",
       "      <td>5.753296</td>\n",
       "      <td>-5.1625</td>\n",
       "      <td>0.540131</td>\n",
       "      <td>-10.966461</td>\n",
       "      <td>0.590796</td>\n",
       "      <td>-10.915796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-5.125854</td>\n",
       "      <td>5.612403</td>\n",
       "      <td>-5.0400</td>\n",
       "      <td>0.486549</td>\n",
       "      <td>-10.738256</td>\n",
       "      <td>0.572403</td>\n",
       "      <td>-10.652403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-4.871877</td>\n",
       "      <td>5.358231</td>\n",
       "      <td>-4.5985</td>\n",
       "      <td>0.486354</td>\n",
       "      <td>-10.230108</td>\n",
       "      <td>0.759731</td>\n",
       "      <td>-9.956731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-4.576961</td>\n",
       "      <td>5.257046</td>\n",
       "      <td>-4.2625</td>\n",
       "      <td>0.680085</td>\n",
       "      <td>-9.834007</td>\n",
       "      <td>0.994546</td>\n",
       "      <td>-9.519546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-4.086386</td>\n",
       "      <td>5.077501</td>\n",
       "      <td>-3.6555</td>\n",
       "      <td>0.991115</td>\n",
       "      <td>-9.163886</td>\n",
       "      <td>1.422001</td>\n",
       "      <td>-8.733001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-3.683662</td>\n",
       "      <td>4.878181</td>\n",
       "      <td>-3.1800</td>\n",
       "      <td>1.194519</td>\n",
       "      <td>-8.561844</td>\n",
       "      <td>1.698181</td>\n",
       "      <td>-8.058181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-3.116265</td>\n",
       "      <td>4.693900</td>\n",
       "      <td>-2.6060</td>\n",
       "      <td>1.577635</td>\n",
       "      <td>-7.810164</td>\n",
       "      <td>2.087900</td>\n",
       "      <td>-7.299900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-2.441634</td>\n",
       "      <td>4.421946</td>\n",
       "      <td>-1.8065</td>\n",
       "      <td>1.980312</td>\n",
       "      <td>-6.863579</td>\n",
       "      <td>2.615446</td>\n",
       "      <td>-6.228446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-1.674700</td>\n",
       "      <td>4.368864</td>\n",
       "      <td>-0.8170</td>\n",
       "      <td>2.694165</td>\n",
       "      <td>-6.043564</td>\n",
       "      <td>3.551864</td>\n",
       "      <td>-5.185864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-0.935853</td>\n",
       "      <td>3.872267</td>\n",
       "      <td>-0.1090</td>\n",
       "      <td>2.936414</td>\n",
       "      <td>-4.808120</td>\n",
       "      <td>3.763267</td>\n",
       "      <td>-3.981267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>-0.102077</td>\n",
       "      <td>3.513286</td>\n",
       "      <td>0.5975</td>\n",
       "      <td>3.411209</td>\n",
       "      <td>-3.615363</td>\n",
       "      <td>4.110786</td>\n",
       "      <td>-2.915786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>0.609048</td>\n",
       "      <td>3.496362</td>\n",
       "      <td>1.0170</td>\n",
       "      <td>4.105410</td>\n",
       "      <td>-2.887314</td>\n",
       "      <td>4.513362</td>\n",
       "      <td>-2.479362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>1.535931</td>\n",
       "      <td>2.890533</td>\n",
       "      <td>1.3910</td>\n",
       "      <td>4.426464</td>\n",
       "      <td>-1.354602</td>\n",
       "      <td>4.281533</td>\n",
       "      <td>-1.499533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.102507</td>\n",
       "      <td>2.587564</td>\n",
       "      <td>1.7530</td>\n",
       "      <td>4.690072</td>\n",
       "      <td>-0.485057</td>\n",
       "      <td>4.340564</td>\n",
       "      <td>-0.834564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.601115</td>\n",
       "      <td>2.335173</td>\n",
       "      <td>2.0660</td>\n",
       "      <td>4.936288</td>\n",
       "      <td>0.265942</td>\n",
       "      <td>4.401173</td>\n",
       "      <td>-0.269173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.764840</td>\n",
       "      <td>2.438761</td>\n",
       "      <td>2.1500</td>\n",
       "      <td>5.203601</td>\n",
       "      <td>0.326079</td>\n",
       "      <td>4.588761</td>\n",
       "      <td>-0.288761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.751332</td>\n",
       "      <td>2.331290</td>\n",
       "      <td>2.1975</td>\n",
       "      <td>5.082623</td>\n",
       "      <td>0.420042</td>\n",
       "      <td>4.528790</td>\n",
       "      <td>-0.133790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.768296</td>\n",
       "      <td>2.382608</td>\n",
       "      <td>2.1770</td>\n",
       "      <td>5.150905</td>\n",
       "      <td>0.385688</td>\n",
       "      <td>4.559608</td>\n",
       "      <td>-0.205608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.730476</td>\n",
       "      <td>2.338114</td>\n",
       "      <td>2.1505</td>\n",
       "      <td>5.068590</td>\n",
       "      <td>0.392362</td>\n",
       "      <td>4.488614</td>\n",
       "      <td>-0.187614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.737709</td>\n",
       "      <td>2.369831</td>\n",
       "      <td>2.1610</td>\n",
       "      <td>5.107539</td>\n",
       "      <td>0.367878</td>\n",
       "      <td>4.530831</td>\n",
       "      <td>-0.208831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.717796</td>\n",
       "      <td>2.337810</td>\n",
       "      <td>2.1525</td>\n",
       "      <td>5.055605</td>\n",
       "      <td>0.379986</td>\n",
       "      <td>4.490310</td>\n",
       "      <td>-0.185310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23:15:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.682129</td>\n",
       "      <td>2.344915</td>\n",
       "      <td>2.1175</td>\n",
       "      <td>5.027044</td>\n",
       "      <td>0.337214</td>\n",
       "      <td>4.462415</td>\n",
       "      <td>-0.227415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23:45:00</th>\n",
       "      <td>3652</td>\n",
       "      <td>2.683432</td>\n",
       "      <td>2.338202</td>\n",
       "      <td>2.1140</td>\n",
       "      <td>5.021634</td>\n",
       "      <td>0.345230</td>\n",
       "      <td>4.452202</td>\n",
       "      <td>-0.224202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count      mean       std  median   mean+sd    mean-sd  median+sd  median-sd\n",
       "TIMESTAMP_MIDDLE                                                                                       \n",
       "ALL_MONTHS       00:15:00   3652  2.628153  2.292058  2.0710  4.920210   0.336095   4.363058  -0.221058\n",
       "                 00:45:00   3652  2.660316  2.251208  2.1045  4.911524   0.409109   4.355708  -0.146708\n",
       "                 01:15:00   3652  2.636666  2.313408  2.1165  4.950074   0.323259   4.429908  -0.196908\n",
       "                 01:45:00   3652  2.609721  2.221190  2.0755  4.830911   0.388532   4.296690  -0.145690\n",
       "                 02:15:00   3652  2.600780  2.252368  2.0600  4.853148   0.348412   4.312368  -0.192368\n",
       "                 02:45:00   3652  2.628637  2.295040  2.0960  4.923677   0.333597   4.391040  -0.199040\n",
       "                 03:15:00   3652  2.572976  2.241159  2.0515  4.814135   0.331816   4.292659  -0.189659\n",
       "                 03:45:00   3652  2.542711  2.206123  2.0320  4.748834   0.336589   4.238123  -0.174123\n",
       "                 04:15:00   3652  2.518468  2.145243  2.0070  4.663710   0.373225   4.152243  -0.138243\n",
       "                 04:45:00   3652  2.490877  2.216896  1.9990  4.707773   0.273981   4.215896  -0.217896\n",
       "                 05:15:00   3652  2.162079  2.357829  1.7720  4.519909  -0.195750   4.129829  -0.585829\n",
       "                 05:45:00   3652  1.540008  2.825489  1.4505  4.365497  -1.285480   4.275989  -1.374989\n",
       "                 06:15:00   3652  0.812312  3.235995  1.1370  4.048307  -2.423683   4.372995  -2.098995\n",
       "                 06:45:00   3652  0.184065  3.329848  0.8400  3.513913  -3.145783   4.169848  -2.489848\n",
       "                 07:15:00   3652 -0.429212  3.634704  0.5195  3.205491  -4.063916   4.154204  -3.115204\n",
       "                 07:45:00   3652 -1.615019  4.421238 -0.1375  2.806220  -6.036257   4.283738  -4.558738\n",
       "                 08:15:00   3652 -3.062653  5.305186 -1.4945  2.242533  -8.367839   3.810686  -6.799686\n",
       "                 08:45:00   3652 -4.042698  5.514220 -3.3345  1.471523  -9.556918   2.179720  -8.848720\n",
       "                 09:15:00   3652 -4.577710  5.651760 -4.1465  1.074050 -10.229470   1.505260  -9.798260\n",
       "                 09:45:00   3652 -4.992462  5.872298 -4.7230  0.879836 -10.864760   1.149298 -10.595298\n",
       "                 10:15:00   3652 -5.340606  5.885783 -5.0780  0.545178 -11.226389   0.807783 -10.963783\n",
       "                 10:45:00   3652 -5.474241  5.918189 -5.3830  0.443948 -11.392430   0.535189 -11.301189\n",
       "                 11:15:00   3652 -5.511982  5.981464 -5.4905  0.469482 -11.493446   0.490964 -11.471964\n",
       "                 11:45:00   3652 -5.488146  5.999366 -5.3755  0.511220 -11.487513   0.623866 -11.374866\n",
       "                 12:15:00   3652 -5.415469  5.828823 -5.1750  0.413355 -11.244292   0.653823 -11.003823\n",
       "                 12:45:00   3652 -5.213165  5.753296 -5.1625  0.540131 -10.966461   0.590796 -10.915796\n",
       "                 13:15:00   3652 -5.125854  5.612403 -5.0400  0.486549 -10.738256   0.572403 -10.652403\n",
       "                 13:45:00   3652 -4.871877  5.358231 -4.5985  0.486354 -10.230108   0.759731  -9.956731\n",
       "                 14:15:00   3652 -4.576961  5.257046 -4.2625  0.680085  -9.834007   0.994546  -9.519546\n",
       "                 14:45:00   3652 -4.086386  5.077501 -3.6555  0.991115  -9.163886   1.422001  -8.733001\n",
       "                 15:15:00   3652 -3.683662  4.878181 -3.1800  1.194519  -8.561844   1.698181  -8.058181\n",
       "                 15:45:00   3652 -3.116265  4.693900 -2.6060  1.577635  -7.810164   2.087900  -7.299900\n",
       "                 16:15:00   3652 -2.441634  4.421946 -1.8065  1.980312  -6.863579   2.615446  -6.228446\n",
       "                 16:45:00   3652 -1.674700  4.368864 -0.8170  2.694165  -6.043564   3.551864  -5.185864\n",
       "                 17:15:00   3652 -0.935853  3.872267 -0.1090  2.936414  -4.808120   3.763267  -3.981267\n",
       "                 17:45:00   3652 -0.102077  3.513286  0.5975  3.411209  -3.615363   4.110786  -2.915786\n",
       "                 18:15:00   3652  0.609048  3.496362  1.0170  4.105410  -2.887314   4.513362  -2.479362\n",
       "                 18:45:00   3652  1.535931  2.890533  1.3910  4.426464  -1.354602   4.281533  -1.499533\n",
       "                 19:15:00   3652  2.102507  2.587564  1.7530  4.690072  -0.485057   4.340564  -0.834564\n",
       "                 19:45:00   3652  2.601115  2.335173  2.0660  4.936288   0.265942   4.401173  -0.269173\n",
       "                 20:15:00   3652  2.764840  2.438761  2.1500  5.203601   0.326079   4.588761  -0.288761\n",
       "                 20:45:00   3652  2.751332  2.331290  2.1975  5.082623   0.420042   4.528790  -0.133790\n",
       "                 21:15:00   3652  2.768296  2.382608  2.1770  5.150905   0.385688   4.559608  -0.205608\n",
       "                 21:45:00   3652  2.730476  2.338114  2.1505  5.068590   0.392362   4.488614  -0.187614\n",
       "                 22:15:00   3652  2.737709  2.369831  2.1610  5.107539   0.367878   4.530831  -0.208831\n",
       "                 22:45:00   3652  2.717796  2.337810  2.1525  5.055605   0.379986   4.490310  -0.185310\n",
       "                 23:15:00   3652  2.682129  2.344915  2.1175  5.027044   0.337214   4.462415  -0.227415\n",
       "                 23:45:00   3652  2.683432  2.338202  2.1140  5.021634   0.345230   4.452202  -0.224202"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# End of notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-22T13:44:20.546771Z",
     "start_time": "2024-08-22T13:44:20.532808Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished 2025-01-25 01:48:34\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": "2024-08-22T13:44:20.688099Z",
     "start_time": "2024-08-22T13:44:20.676132Z"
    }
   },
   "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": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}