{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dfs0 - Relative time axis\n",
    "\n",
    "MIKE IO uses a pandas DatetimeIndex to represent the time dimension in dfs files. If the Dfs file has a relative time axis it will be converted to DatetimeIndex by using 1970-1-1 00:00:00 as start time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mikeio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "dims: (time:504)\n",
       "time: 1970-01-01 00:00:00 - 1970-01-01 00:00:56.237000 (504 non-equidistant records)\n",
       "items:\n",
       "  0:  Item 1 <Undefined> (undefined)\n",
       "  1:  Item 2 <Undefined> (undefined)\n",
       "  2:  Item 3 <Undefined> (undefined)\n",
       "  3:  Item 4 <Undefined> (undefined)\n",
       "  4:  Item 5 <Undefined> (undefined)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = mikeio.read(\"../tests/testdata/eq_relative.dfs0\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Item 1</th>\n",
       "      <th>Item 2</th>\n",
       "      <th>Item 3</th>\n",
       "      <th>Item 4</th>\n",
       "      <th>Item 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.000</th>\n",
       "      <td>-0.006862</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>0.177047</td>\n",
       "      <td>32.484425</td>\n",
       "      <td>-304.720428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.112</th>\n",
       "      <td>-0.011746</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>0.189257</td>\n",
       "      <td>32.292774</td>\n",
       "      <td>-308.553406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.224</th>\n",
       "      <td>-0.006862</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>0.189257</td>\n",
       "      <td>32.292774</td>\n",
       "      <td>-308.553406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.335</th>\n",
       "      <td>-0.001978</td>\n",
       "      <td>0.004273</td>\n",
       "      <td>0.189257</td>\n",
       "      <td>32.292774</td>\n",
       "      <td>-300.887482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970-01-01 00:00:00.447</th>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.009157</td>\n",
       "      <td>0.177047</td>\n",
       "      <td>32.292774</td>\n",
       "      <td>-300.887482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Item 1    Item 2    Item 3     Item 4      Item 5\n",
       "1970-01-01 00:00:00.000 -0.006862 -0.000611  0.177047  32.484425 -304.720428\n",
       "1970-01-01 00:00:00.112 -0.011746 -0.000611  0.189257  32.292774 -308.553406\n",
       "1970-01-01 00:00:00.224 -0.006862 -0.000611  0.189257  32.292774 -308.553406\n",
       "1970-01-01 00:00:00.335 -0.001978  0.004273  0.189257  32.292774 -300.887482\n",
       "1970-01-01 00:00:00.447  0.002906  0.009157  0.177047  32.292774 -300.887482"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = ds.to_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correcing the dataframe index by subtracting start time to get relative time axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = (df.index - df.index[0]).total_seconds()\n",
    "df.index.name = \"Relative time (s)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Item 1</th>\n",
       "      <th>Item 2</th>\n",
       "      <th>Item 3</th>\n",
       "      <th>Item 4</th>\n",
       "      <th>Item 5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relative time (s)</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>0.000</th>\n",
       "      <td>-0.006862</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>0.177047</td>\n",
       "      <td>32.484425</td>\n",
       "      <td>-304.720428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.112</th>\n",
       "      <td>-0.011746</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>0.189257</td>\n",
       "      <td>32.292774</td>\n",
       "      <td>-308.553406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.224</th>\n",
       "      <td>-0.006862</td>\n",
       "      <td>-0.000611</td>\n",
       "      <td>0.189257</td>\n",
       "      <td>32.292774</td>\n",
       "      <td>-308.553406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.335</th>\n",
       "      <td>-0.001978</td>\n",
       "      <td>0.004273</td>\n",
       "      <td>0.189257</td>\n",
       "      <td>32.292774</td>\n",
       "      <td>-300.887482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.447</th>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.009157</td>\n",
       "      <td>0.177047</td>\n",
       "      <td>32.292774</td>\n",
       "      <td>-300.887482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Item 1    Item 2    Item 3     Item 4      Item 5\n",
       "Relative time (s)                                                     \n",
       "0.000             -0.006862 -0.000611  0.177047  32.484425 -304.720428\n",
       "0.112             -0.011746 -0.000611  0.189257  32.292774 -308.553406\n",
       "0.224             -0.006862 -0.000611  0.189257  32.292774 -308.553406\n",
       "0.335             -0.001978  0.004273  0.189257  32.292774 -300.887482\n",
       "0.447              0.002906  0.009157  0.177047  32.292774 -300.887482"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Item 5'].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "767d51c1340bd893661ea55ea3124f6de3c7a262a8b4abca0554b478b1e2ff90"
  },
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "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": "3.8.10"
  },
  "orig_nbformat": 2
 },
 "nbformat": 4,
 "nbformat_minor": 2
}