{
 "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.Dfs0>\n",
       "Timeaxis: TimeAxisType.TimeEquidistant\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": [
    "dfs0 = mikeio.open(\"../tests/testdata/eq_relative.dfs0\")\n",
    "dfs0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "Dimensions: (time:504)\n",
       "Time: 1970-01-01 00:00:00 - 1970-01-01 00:00:56.237000 (504 records)\n",
       "-- Non-equidistant calendar axis --\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = dfs0.read()\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <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": 4,
     "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = (df.index - df.index[0]).total_seconds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <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>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",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "         Item 1    Item 2    Item 3     Item 4      Item 5\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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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": {
  "kernelspec": {
   "display_name": "Python 3.8.5 64-bit (conda)",
   "metadata": {
    "interpreter": {
     "hash": "1b27a185e5e38addd349bee67c436665dc7832e161e2a923b2540665280bf8fe"
    }
   },
   "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
}