{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Res1D - Strategies for handling large files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mikeio1d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All static network data is loaded into memory by default. Dynamic result data is only loaded when required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = mikeio1d.open(\"../tests/testdata/network.res1d\")\n",
    "loaded_time_steps = res.data.NumberOfTimeSteps\n",
    "loaded_time_steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calling read() loads dynamic data for the entire network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.read()\n",
    "loaded_time_steps = res.data.NumberOfTimeSteps\n",
    "loaded_time_steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All network dynamic data is loaded into memory regardless of how read() is called. In the future, it would nice to make this work lazily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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:1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:35:00.000</th>\n",
       "      <td>195.052994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:36:01.870</th>\n",
       "      <td>195.052994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:37:07.560</th>\n",
       "      <td>195.052994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:38:55.828</th>\n",
       "      <td>195.052994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:39:55.828</th>\n",
       "      <td>195.052994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         WaterLevel:1\n",
       "1994-08-07 16:35:00.000    195.052994\n",
       "1994-08-07 16:36:01.870    195.052994\n",
       "1994-08-07 16:37:07.560    195.052994\n",
       "1994-08-07 16:38:55.828    195.052994\n",
       "1994-08-07 16:39:55.828    195.052994"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = mikeio1d.open(\"../tests/testdata/network.res1d\")\n",
    "# this still reads all network data, but only creates a DataFrame with the requested data\n",
    "res.nodes[\"1\"].WaterLevel.read().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To avoid loading the entire network's dynamic data into memory, you can specify which IDs to load when opening the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;ResultNodes&gt; (1)\n",
       "    <style>\n",
       "        ul {\n",
       "            margin: 0px;\n",
       "            padding: 0px;\n",
       "            padding-left: 2em;\n",
       "        }\n",
       "    </style>\n",
       "    <details><summary>Quantities (1)</summary><ul><li>Water level (m)</li></ul></details><details><summary>Derived Quantities (3)</summary><ul><li>NodeFlooding</li><li>NodeWaterDepth</li><li>NodeWaterLevelAboveCritical</li></ul></details>"
      ],
      "text/plain": [
       "<ResultNodes> (1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = mikeio1d.open(\"../tests/testdata/network.res1d\", nodes=[\"1\"], reaches=[\"100l1\"])\n",
    "res.nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;ResultReaches&gt; (1)\n",
       "    <style>\n",
       "        ul {\n",
       "            margin: 0px;\n",
       "            padding: 0px;\n",
       "            padding-left: 2em;\n",
       "        }\n",
       "    </style>\n",
       "    <details><summary>Quantities (2)</summary><ul><li>Water level (m)</li><li>Discharge (m^3/s)</li></ul></details><details><summary>Derived Quantities (6)</summary><ul><li>ReachAbsoluteDischarge</li><li>ReachFilling</li><li>ReachFlooding</li><li>ReachQQManning</li><li>ReachWaterDepth</li><li>ReachWaterLevelAboveCritical</li></ul></details>"
      ],
      "text/plain": [
       "<ResultReaches> (1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.reaches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WaterLevel:1</th>\n",
       "      <th>WaterLevel:100l1:0</th>\n",
       "      <th>WaterLevel:100l1:47.6827</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>195.052994</td>\n",
       "      <td>195.441498</td>\n",
       "      <td>194.661499</td>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:36:01.870</th>\n",
       "      <td>195.052994</td>\n",
       "      <td>195.441498</td>\n",
       "      <td>194.661621</td>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:37:07.560</th>\n",
       "      <td>195.052994</td>\n",
       "      <td>195.441498</td>\n",
       "      <td>194.661728</td>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:38:55.828</th>\n",
       "      <td>195.052994</td>\n",
       "      <td>195.441498</td>\n",
       "      <td>194.661804</td>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:39:55.828</th>\n",
       "      <td>195.052994</td>\n",
       "      <td>195.441498</td>\n",
       "      <td>194.661972</td>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 18:30:07.967</th>\n",
       "      <td>195.119919</td>\n",
       "      <td>195.455109</td>\n",
       "      <td>194.689072</td>\n",
       "      <td>0.000588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 18:31:07.967</th>\n",
       "      <td>195.118607</td>\n",
       "      <td>195.455063</td>\n",
       "      <td>194.688934</td>\n",
       "      <td>0.000583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 18:32:07.967</th>\n",
       "      <td>195.117310</td>\n",
       "      <td>195.455002</td>\n",
       "      <td>194.688812</td>\n",
       "      <td>0.000579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 18:33:07.967</th>\n",
       "      <td>195.115753</td>\n",
       "      <td>195.453049</td>\n",
       "      <td>194.688354</td>\n",
       "      <td>0.000526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 18:35:00.000</th>\n",
       "      <td>195.112534</td>\n",
       "      <td>195.450409</td>\n",
       "      <td>194.686172</td>\n",
       "      <td>0.000343</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         WaterLevel:1  WaterLevel:100l1:0  \\\n",
       "1994-08-07 16:35:00.000    195.052994          195.441498   \n",
       "1994-08-07 16:36:01.870    195.052994          195.441498   \n",
       "1994-08-07 16:37:07.560    195.052994          195.441498   \n",
       "1994-08-07 16:38:55.828    195.052994          195.441498   \n",
       "1994-08-07 16:39:55.828    195.052994          195.441498   \n",
       "...                               ...                 ...   \n",
       "1994-08-07 18:30:07.967    195.119919          195.455109   \n",
       "1994-08-07 18:31:07.967    195.118607          195.455063   \n",
       "1994-08-07 18:32:07.967    195.117310          195.455002   \n",
       "1994-08-07 18:33:07.967    195.115753          195.453049   \n",
       "1994-08-07 18:35:00.000    195.112534          195.450409   \n",
       "\n",
       "                         WaterLevel:100l1:47.6827  Discharge:100l1:23.8414  \n",
       "1994-08-07 16:35:00.000                194.661499                 0.000006  \n",
       "1994-08-07 16:36:01.870                194.661621                 0.000006  \n",
       "1994-08-07 16:37:07.560                194.661728                 0.000006  \n",
       "1994-08-07 16:38:55.828                194.661804                 0.000006  \n",
       "1994-08-07 16:39:55.828                194.661972                 0.000006  \n",
       "...                                           ...                      ...  \n",
       "1994-08-07 18:30:07.967                194.689072                 0.000588  \n",
       "1994-08-07 18:31:07.967                194.688934                 0.000583  \n",
       "1994-08-07 18:32:07.967                194.688812                 0.000579  \n",
       "1994-08-07 18:33:07.967                194.688354                 0.000526  \n",
       "1994-08-07 18:35:00.000                194.686172                 0.000343  \n",
       "\n",
       "[110 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, you can also specify the time range to read."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start, end = \"1994-08-07 18:30:07.967\", \"1994-08-07 18:33:07.967\"\n",
    "res = mikeio1d.open(\n",
    "    \"../tests/testdata/network.res1d\", nodes=[\"1\"], reaches=[\"100l1\"], time=(start, end)\n",
    ")\n",
    "loaded_time_steps = res.data.NumberOfTimeSteps\n",
    "loaded_time_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.read()\n",
    "loaded_time_steps = res.data.NumberOfTimeSteps\n",
    "loaded_time_steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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:1</th>\n",
       "      <th>WaterLevel:100l1:0</th>\n",
       "      <th>WaterLevel:100l1:47.6827</th>\n",
       "      <th>Discharge:100l1:23.8414</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1994-08-07 18:30:07.967</th>\n",
       "      <td>195.119919</td>\n",
       "      <td>195.455109</td>\n",
       "      <td>194.689072</td>\n",
       "      <td>0.000588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 18:31:07.967</th>\n",
       "      <td>195.118607</td>\n",
       "      <td>195.455063</td>\n",
       "      <td>194.688934</td>\n",
       "      <td>0.000583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 18:32:07.967</th>\n",
       "      <td>195.117310</td>\n",
       "      <td>195.455002</td>\n",
       "      <td>194.688812</td>\n",
       "      <td>0.000579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 18:33:07.967</th>\n",
       "      <td>195.115753</td>\n",
       "      <td>195.453049</td>\n",
       "      <td>194.688354</td>\n",
       "      <td>0.000526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         WaterLevel:1  WaterLevel:100l1:0  \\\n",
       "1994-08-07 18:30:07.967    195.119919          195.455109   \n",
       "1994-08-07 18:31:07.967    195.118607          195.455063   \n",
       "1994-08-07 18:32:07.967    195.117310          195.455002   \n",
       "1994-08-07 18:33:07.967    195.115753          195.453049   \n",
       "\n",
       "                         WaterLevel:100l1:47.6827  Discharge:100l1:23.8414  \n",
       "1994-08-07 18:30:07.967                194.689072                 0.000588  \n",
       "1994-08-07 18:31:07.967                194.688934                 0.000583  \n",
       "1994-08-07 18:32:07.967                194.688812                 0.000579  \n",
       "1994-08-07 18:33:07.967                194.688354                 0.000526  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.read().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's also possible to load every 'nth' time step with the 'step_every' parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(8984.71875), np.float64(8984.743055555555))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First plot the full time series for a comparison\n",
    "res = mikeio1d.open(\n",
    "    \"../tests/testdata/network.res1d\", reaches=[\"100l1\"]\n",
    ")\n",
    "ax = res.reaches['100l1'][0].WaterLevel.plot(linestyle='-', marker='x')\n",
    "\n",
    "# Then plot with every 5th time step\n",
    "\n",
    "res = mikeio1d.open(\n",
    "    \"../tests/testdata/network.res1d\", reaches=[\"100l1\"], step_every=5,\n",
    ")\n",
    "ax = res.reaches['100l1'][0].WaterLevel.plot(ax=ax, linestyle='--', marker='o', color='g')\n",
    "\n",
    "ax.legend([\"all steps\", \"step_every=5\"])\n",
    "ax.set_xlim(\"1994-08-07 17:15\", \"1994-08-07 17:50\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ryan/mikeio1d/mikeio1d/res1d.py:602: FutureWarning: The 'data' parameter will be deprecated in 1.0. Use 'result_data' instead.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.data.NumberOfTimeSteps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly you can also specify which quantities to include."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:45:55.828</th>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 16:58:12.888</th>\n",
       "      <td>0.001852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 17:08:12.888</th>\n",
       "      <td>0.000951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 17:18:12.888</th>\n",
       "      <td>0.000674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 17:28:31.543</th>\n",
       "      <td>0.099751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 17:38:31.543</th>\n",
       "      <td>0.056495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 17:48:52.376</th>\n",
       "      <td>0.006408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 18:00:56.369</th>\n",
       "      <td>0.001234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 18:13:18.518</th>\n",
       "      <td>0.000740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-08-07 18:25:07.967</th>\n",
       "      <td>0.000612</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 16:45:55.828                 0.000006\n",
       "1994-08-07 16:58:12.888                 0.001852\n",
       "1994-08-07 17:08:12.888                 0.000951\n",
       "1994-08-07 17:18:12.888                 0.000674\n",
       "1994-08-07 17:28:31.543                 0.099751\n",
       "1994-08-07 17:38:31.543                 0.056495\n",
       "1994-08-07 17:48:52.376                 0.006408\n",
       "1994-08-07 18:00:56.369                 0.001234\n",
       "1994-08-07 18:13:18.518                 0.000740\n",
       "1994-08-07 18:25:07.967                 0.000612"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = mikeio1d.open(\n",
    "    \"../tests/testdata/network.res1d\", nodes=[\"1\"], reaches=[\"100l1\"], step_every=10, quantities=[\"Discharge\"]\n",
    ")\n",
    "res.read()"
   ]
  }
 ],
 "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": 2
}