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 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# Dfs0 - Relative time axis"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<mikeio.Dfs0>\n",
       "Timeaxis: TimeAxisType.EquidistantRelative\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)"
      ]
     },
     "metadata": {},
     "execution_count": 1
    }
   ],
   "source": [
    "from mikeio import Dfs0\n",
    "\n",
    "dfs0 = Dfs0(\"../tests/testdata/eq_relative.dfs0\")\n",
    "dfs0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "Dimensions: (504,)\n",
       "Time: 1970-01-01 00:00:00 - 1970-01-01 00:00:56.236909\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)"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "ds = dfs0.read()\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                              Item 1    Item 2    Item 3     Item 4  \\\n",
       "1970-01-01 00:00:00.000000 -0.006862 -0.000611  0.177047  32.484425   \n",
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       "1970-01-01 00:00:00.447212  0.002906  0.009157  0.177047  32.292774   \n",
       "\n",
       "                                Item 5  \n",
       "1970-01-01 00:00:00.000000 -304.720428  \n",
       "1970-01-01 00:00:00.111803 -308.553406  \n",
       "1970-01-01 00:00:00.223606 -308.553406  \n",
       "1970-01-01 00:00:00.335409 -300.887482  \n",
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     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "df = ds.to_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "source": [
    "Correcing the dataframe index by subtracting start time to get relative time axis."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = (df.index - df.index[0]).total_seconds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 6
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df['Item 5'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}