{
 "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",
       "geometry: GeometryUndefined()\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": [
       "<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",
       "    <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 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Item 5'].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## mikecore\n",
    "\n",
    "An alternative is to use the underlying library mikecore to read the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mikecore.DfsFileFactory import DfsFileFactory\n",
    "\n",
    "dfs = DfsFileFactory.DfsGenericOpen(\"../tests/testdata/eq_relative.dfs0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `ReadDfs0DataDouble` method you get the data as a numpy array, with the time axis or other type of as the first column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = dfs.ReadDfs0DataDouble()\n",
    "\n",
    "type(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which can be converted to a pandas dataframe. First we extract the name of items (which in this example hapeens to be not very creative)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['time', 'Item 1', 'Item 2', 'Item 3', 'Item 4', 'Item 5']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_name = \"time\"\n",
    "items = [i.Name for i in dfs.ItemInfo]\n",
    "items = [index_name] + items\n",
    "items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
       "    <tr>\n",
       "      <th>time</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>0.000000</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>0.111803</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>0.223606</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>0.335409</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>0.447212</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",
       "time                                                         \n",
       "0.000000 -0.006862 -0.000611  0.177047  32.484425 -304.720428\n",
       "0.111803 -0.011746 -0.000611  0.189257  32.292774 -308.553406\n",
       "0.223606 -0.006862 -0.000611  0.189257  32.292774 -308.553406\n",
       "0.335409 -0.001978  0.004273  0.189257  32.292774 -300.887482\n",
       "0.447212  0.002906  0.009157  0.177047  32.292774 -300.887482"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as df\n",
    "\n",
    "df = df.DataFrame(data, columns=items).set_index(index_name)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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