{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Res1D - Basic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open a network result file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mikeio1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio1d.Res1D>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = mikeio1d.open(\"../tests/testdata/network.res1d\")\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start time: 1994-08-07 16:35:00\n",
      "End time: 1994-08-07 18:35:00\n",
      "# Timesteps: 110\n",
      "# Catchments: 0\n",
      "# Nodes: 119\n",
      "# Reaches: 118\n",
      "# Globals: 0\n",
      "0 - Water level (m)\n",
      "1 - Discharge (m^3/s)\n"
     ]
    }
   ],
   "source": [
    "res.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['WaterLevel', 'Discharge']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.quantities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ReachWaterLevelAboveCritical',\n",
       " 'ReachFlooding',\n",
       " 'ReachWaterDepth',\n",
       " 'NodeWaterDepth',\n",
       " 'NodeFlooding',\n",
       " 'ReachFilling',\n",
       " 'ReachAbsoluteDischarge',\n",
       " 'ReachQQManning',\n",
       " 'NodeWaterLevelAboveCritical']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.derived_quantities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read all available results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1994-08-07 16:39:55.828</th>\n",
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       "</table>\n",
       "<p>5 rows × 495 columns</p>\n",
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      "text/plain": [
       "                         WaterLevel:1  WaterLevel:2  WaterLevel:3  \\\n",
       "1994-08-07 16:35:00.000    195.052994    195.821503      195.8815   \n",
       "1994-08-07 16:36:01.870    195.052994    195.821701      195.8815   \n",
       "1994-08-07 16:37:07.560    195.052994    195.821640      195.8815   \n",
       "1994-08-07 16:38:55.828    195.052994    195.821503      195.8815   \n",
       "1994-08-07 16:39:55.828    195.052994    195.821503      195.8815   \n",
       "\n",
       "                         WaterLevel:4  WaterLevel:5  WaterLevel:6  \\\n",
       "1994-08-07 16:35:00.000    193.604996    193.615005    193.625000   \n",
       "1994-08-07 16:36:01.870    193.604996    193.615005    193.625320   \n",
       "1994-08-07 16:37:07.560    193.604996    193.615005    193.625671   \n",
       "1994-08-07 16:38:55.828    193.604996    193.615005    193.626236   \n",
       "1994-08-07 16:39:55.828    193.604996    193.615005    193.626556   \n",
       "\n",
       "                         WaterLevel:7  WaterLevel:8  WaterLevel:9  \\\n",
       "1994-08-07 16:35:00.000    193.675003    193.764999    193.774994   \n",
       "1994-08-07 16:36:01.870    193.675110    193.765060    193.775116   \n",
       "1994-08-07 16:37:07.560    193.675369    193.765106    193.775513   \n",
       "1994-08-07 16:38:55.828    193.675751    193.765228    193.776077   \n",
       "1994-08-07 16:39:55.828    193.675949    193.765335    193.776352   \n",
       "\n",
       "                         WaterLevel:10  ...  Discharge:99l1:22.2508  \\\n",
       "1994-08-07 16:35:00.000     193.804993  ...                0.000002   \n",
       "1994-08-07 16:36:01.870     193.804993  ...                0.000002   \n",
       "1994-08-07 16:37:07.560     193.804993  ...                0.000002   \n",
       "1994-08-07 16:38:55.828     193.804993  ...                0.000002   \n",
       "1994-08-07 16:39:55.828     193.804993  ...                0.000002   \n",
       "\n",
       "                         WaterLevel:9l1:0  WaterLevel:9l1:10  Discharge:9l1:5  \\\n",
       "1994-08-07 16:35:00.000        193.774994         193.764999         0.000031   \n",
       "1994-08-07 16:36:01.870        193.775070         193.765060         0.000031   \n",
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       "1994-08-07 16:38:55.828        193.775894         193.765228         0.000037   \n",
       "1994-08-07 16:39:55.828        193.776154         193.765335         0.000039   \n",
       "\n",
       "                         WaterLevel:Weir:119w1:0  WaterLevel:Weir:119w1:1  \\\n",
       "1994-08-07 16:35:00.000               193.550003               188.479996   \n",
       "1994-08-07 16:36:01.870               193.550003               188.479996   \n",
       "1994-08-07 16:37:07.560               193.550034               188.479996   \n",
       "1994-08-07 16:38:55.828               193.550079               188.479996   \n",
       "1994-08-07 16:39:55.828               193.550095               188.479996   \n",
       "\n",
       "                         Discharge:Weir:119w1:0.5  WaterLevel:Pump:115p1:0  \\\n",
       "1994-08-07 16:35:00.000                       0.0               193.304993   \n",
       "1994-08-07 16:36:01.870                       0.0               193.306061   \n",
       "1994-08-07 16:37:07.560                       0.0               193.307144   \n",
       "1994-08-07 16:38:55.828                       0.0               193.308884   \n",
       "1994-08-07 16:39:55.828                       0.0               193.309860   \n",
       "\n",
       "                         WaterLevel:Pump:115p1:82.4281  \\\n",
       "1994-08-07 16:35:00.000                     195.005005   \n",
       "1994-08-07 16:36:01.870                     195.005005   \n",
       "1994-08-07 16:37:07.560                     195.005005   \n",
       "1994-08-07 16:38:55.828                     195.005005   \n",
       "1994-08-07 16:39:55.828                     195.005005   \n",
       "\n",
       "                         Discharge:Pump:115p1:41.214  \n",
       "1994-08-07 16:35:00.000                          0.0  \n",
       "1994-08-07 16:36:01.870                          0.0  \n",
       "1994-08-07 16:37:07.560                          0.0  \n",
       "1994-08-07 16:38:55.828                          0.0  \n",
       "1994-08-07 16:39:55.828                          0.0  \n",
       "\n",
       "[5 rows x 495 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = res.read()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read subsets of available results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>195.441605</td>\n",
       "      <td>193.550430</td>\n",
       "      <td>193.550369</td>\n",
       "      <td>195.801498</td>\n",
       "      <td>195.703827</td>\n",
       "      <td>197.072006</td>\n",
       "      <td>196.962128</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 376 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         WaterLevel:100l1:0  WaterLevel:100l1:47.6827  \\\n",
       "1994-08-07 16:35:00.000          195.441498                194.661499   \n",
       "1994-08-07 16:36:01.870          195.441498                194.661621   \n",
       "1994-08-07 16:37:07.560          195.441498                194.661728   \n",
       "1994-08-07 16:38:55.828          195.441498                194.661804   \n",
       "1994-08-07 16:39:55.828          195.441498                194.661972   \n",
       "\n",
       "                         WaterLevel:101l1:0  WaterLevel:101l1:66.4361  \\\n",
       "1994-08-07 16:35:00.000          195.931503                195.441498   \n",
       "1994-08-07 16:36:01.870          195.931503                195.441605   \n",
       "1994-08-07 16:37:07.560          195.931503                195.441620   \n",
       "1994-08-07 16:38:55.828          195.931503                195.441605   \n",
       "1994-08-07 16:39:55.828          195.931503                195.441605   \n",
       "\n",
       "                         WaterLevel:102l1:0  WaterLevel:102l1:10.9366  \\\n",
       "1994-08-07 16:35:00.000          193.550003                193.550003   \n",
       "1994-08-07 16:36:01.870          193.550140                193.550064   \n",
       "1994-08-07 16:37:07.560          193.550232                193.550156   \n",
       "1994-08-07 16:38:55.828          193.550369                193.550308   \n",
       "1994-08-07 16:39:55.828          193.550430                193.550369   \n",
       "\n",
       "                         WaterLevel:103l1:0  WaterLevel:103l1:26.0653  \\\n",
       "1994-08-07 16:35:00.000          195.801498                195.701508   \n",
       "1994-08-07 16:36:01.870          195.801498                195.703171   \n",
       "1994-08-07 16:37:07.560          195.801498                195.703400   \n",
       "1994-08-07 16:38:55.828          195.801498                195.703690   \n",
       "1994-08-07 16:39:55.828          195.801498                195.703827   \n",
       "\n",
       "                         WaterLevel:104l1:0  WaterLevel:104l1:34.4131  ...  \\\n",
       "1994-08-07 16:35:00.000          197.072006                196.962006  ...   \n",
       "1994-08-07 16:36:01.870          197.072006                196.962051  ...   \n",
       "1994-08-07 16:37:07.560          197.072006                196.962082  ...   \n",
       "1994-08-07 16:38:55.828          197.072006                196.962112  ...   \n",
       "1994-08-07 16:39:55.828          197.072006                196.962128  ...   \n",
       "\n",
       "                         Discharge:93l1:24.5832  Discharge:94l1:21.2852  \\\n",
       "1994-08-07 16:35:00.000                0.000004                0.000003   \n",
       "1994-08-07 16:36:01.870                0.000004                0.000003   \n",
       "1994-08-07 16:37:07.560                0.000004                0.000003   \n",
       "1994-08-07 16:38:55.828                0.000004                0.000003   \n",
       "1994-08-07 16:39:55.828                0.000004                0.000003   \n",
       "\n",
       "                         Discharge:95l1:21.9487  Discharge:96l1:14.9257  \\\n",
       "1994-08-07 16:35:00.000                0.000001                0.000005   \n",
       "1994-08-07 16:36:01.870                0.000001                0.000005   \n",
       "1994-08-07 16:37:07.560                0.000001                0.000005   \n",
       "1994-08-07 16:38:55.828                0.000001                0.000005   \n",
       "1994-08-07 16:39:55.828                0.000001                0.000005   \n",
       "\n",
       "                         Discharge:97l1:5.71207  Discharge:98l1:8.00489  \\\n",
       "1994-08-07 16:35:00.000                0.000013                0.000003   \n",
       "1994-08-07 16:36:01.870                0.000010                0.000003   \n",
       "1994-08-07 16:37:07.560                0.000010                0.000003   \n",
       "1994-08-07 16:38:55.828                0.000009                0.000003   \n",
       "1994-08-07 16:39:55.828                0.000009                0.000003   \n",
       "\n",
       "                         Discharge:99l1:22.2508  Discharge:9l1:5  \\\n",
       "1994-08-07 16:35:00.000                0.000002         0.000031   \n",
       "1994-08-07 16:36:01.870                0.000002         0.000031   \n",
       "1994-08-07 16:37:07.560                0.000002         0.000033   \n",
       "1994-08-07 16:38:55.828                0.000002         0.000037   \n",
       "1994-08-07 16:39:55.828                0.000002         0.000039   \n",
       "\n",
       "                         Discharge:Weir:119w1:0.5  Discharge:Pump:115p1:41.214  \n",
       "1994-08-07 16:35:00.000                       0.0                          0.0  \n",
       "1994-08-07 16:36:01.870                       0.0                          0.0  \n",
       "1994-08-07 16:37:07.560                       0.0                          0.0  \n",
       "1994-08-07 16:38:55.828                       0.0                          0.0  \n",
       "1994-08-07 16:39:55.828                       0.0                          0.0  \n",
       "\n",
       "[5 rows x 376 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = res.reaches.read()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Discharge:100l1:23.8414</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:35:00.000</th>\n",
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       "      <th>1994-08-07 16:36:01.870</th>\n",
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       "    <tr>\n",
       "      <th>1994-08-07 16:37:07.560</th>\n",
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       "    <tr>\n",
       "      <th>1994-08-07 16:38:55.828</th>\n",
       "      <td>195.441498</td>\n",
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       "    <tr>\n",
       "      <th>1994-08-07 16:39:55.828</th>\n",
       "      <td>195.441498</td>\n",
       "      <td>194.661972</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         WaterLevel:100l1:0  WaterLevel:100l1:47.6827  \\\n",
       "1994-08-07 16:35:00.000          195.441498                194.661499   \n",
       "1994-08-07 16:36:01.870          195.441498                194.661621   \n",
       "1994-08-07 16:37:07.560          195.441498                194.661728   \n",
       "1994-08-07 16:38:55.828          195.441498                194.661804   \n",
       "1994-08-07 16:39:55.828          195.441498                194.661972   \n",
       "\n",
       "                         Discharge:100l1:23.8414  \n",
       "1994-08-07 16:35:00.000                 0.000006  \n",
       "1994-08-07 16:36:01.870                 0.000006  \n",
       "1994-08-07 16:37:07.560                 0.000006  \n",
       "1994-08-07 16:38:55.828                 0.000006  \n",
       "1994-08-07 16:39:55.828                 0.000006  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = res.reaches[\"100l1\"].read()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>WaterLevel:100l1:47.6827</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:35:00.000</th>\n",
       "      <td>195.441498</td>\n",
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       "    <tr>\n",
       "      <th>1994-08-07 16:36:01.870</th>\n",
       "      <td>195.441498</td>\n",
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       "      <th>1994-08-07 16:37:07.560</th>\n",
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       "    <tr>\n",
       "      <th>1994-08-07 16:38:55.828</th>\n",
       "      <td>195.441498</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         WaterLevel:100l1:0  WaterLevel:100l1:47.6827\n",
       "1994-08-07 16:35:00.000          195.441498                194.661499\n",
       "1994-08-07 16:36:01.870          195.441498                194.661621\n",
       "1994-08-07 16:37:07.560          195.441498                194.661728\n",
       "1994-08-07 16:38:55.828          195.441498                194.661804\n",
       "1994-08-07 16:39:55.828          195.441498                194.661972"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = res.reaches[\"100l1\"].WaterLevel.read()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WaterLevel:100l1:0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>1994-08-07 16:36:01.870</th>\n",
       "      <td>195.441498</td>\n",
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       "      <th>1994-08-07 16:37:07.560</th>\n",
       "      <td>195.441498</td>\n",
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       "    <tr>\n",
       "      <th>1994-08-07 16:38:55.828</th>\n",
       "      <td>195.441498</td>\n",
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       "    <tr>\n",
       "      <th>1994-08-07 16:39:55.828</th>\n",
       "      <td>195.441498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         WaterLevel:100l1:0\n",
       "1994-08-07 16:35:00.000          195.441498\n",
       "1994-08-07 16:36:01.870          195.441498\n",
       "1994-08-07 16:37:07.560          195.441498\n",
       "1994-08-07 16:38:55.828          195.441498\n",
       "1994-08-07 16:39:55.828          195.441498"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index by gridpoint number\n",
    "df = res.reaches[\"100l1\"][0].read()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Discharge:100l1:23.8414</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:35:00.000</th>\n",
       "      <td>0.000006</td>\n",
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       "    <tr>\n",
       "      <th>1994-08-07 16:36:01.870</th>\n",
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       "    <tr>\n",
       "      <th>1994-08-07 16:37:07.560</th>\n",
       "      <td>0.000006</td>\n",
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       "    <tr>\n",
       "      <th>1994-08-07 16:38:55.828</th>\n",
       "      <td>0.000006</td>\n",
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       "    <tr>\n",
       "      <th>1994-08-07 16:39:55.828</th>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Discharge:100l1:23.8414\n",
       "1994-08-07 16:35:00.000                 0.000006\n",
       "1994-08-07 16:36:01.870                 0.000006\n",
       "1994-08-07 16:37:07.560                 0.000006\n",
       "1994-08-07 16:38:55.828                 0.000006\n",
       "1994-08-07 16:39:55.828                 0.000006"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Or index by gridpoint chainage\n",
    "df = res.reaches[\"100l1\"][\"23.841\"].read()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make plots by replacing read() with plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = res.nodes[\"101\"].WaterLevel.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dynamically select quantities with add() followed by read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>WaterLevel:101</th>\n",
       "      <th>WaterLevel:102</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:35:00.000</th>\n",
       "      <td>195.931503</td>\n",
       "      <td>193.550003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:36:01.870</th>\n",
       "      <td>195.931595</td>\n",
       "      <td>193.550140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:37:07.560</th>\n",
       "      <td>195.931625</td>\n",
       "      <td>193.550232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:38:55.828</th>\n",
       "      <td>195.931656</td>\n",
       "      <td>193.550369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:39:55.828</th>\n",
       "      <td>195.931656</td>\n",
       "      <td>193.550430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         WaterLevel:101  WaterLevel:102\n",
       "1994-08-07 16:35:00.000      195.931503      193.550003\n",
       "1994-08-07 16:36:01.870      195.931595      193.550140\n",
       "1994-08-07 16:37:07.560      195.931625      193.550232\n",
       "1994-08-07 16:38:55.828      195.931656      193.550369\n",
       "1994-08-07 16:39:55.828      195.931656      193.550430"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.nodes[\"101\"].WaterLevel.add()\n",
    "res.nodes[\"102\"].WaterLevel.add()\n",
    "df = res.read()\n",
    "df.head()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}