{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import mikeio\n",
    "from mikeio import ItemInfo, EUMType, EUMUnit\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Write a dfs0\n",
    "\n",
    "A mikeio.Dataset contains the information needed to write a dfs file. A Dataset consists of one or more mikeio.DataArrays each corresponding to an \"item\" in a dfs file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "dims: (time:10)\n",
       "time: 2000-01-01 00:00:00 - 2000-01-01 09:00:00 (10 records)\n",
       "geometry: GeometryUndefined()\n",
       "items:\n",
       "  0:  Zeros <Water Level> (meter)\n",
       "  1:  Ones <Discharge> (meter pow 3 per sec)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nt = 10\n",
    "time = pd.date_range(\"2000-1-1\", periods=nt, freq='H')\n",
    "\n",
    "d1 = np.zeros(nt)\n",
    "item = ItemInfo(\"Zeros\", EUMType.Water_Level)\n",
    "da1 = mikeio.DataArray(d1, time=time, item=item)\n",
    "\n",
    "d2 = np.ones(nt)\n",
    "item = ItemInfo(\"Ones\", EUMType.Discharge, EUMUnit.meter_pow_3_per_sec)\n",
    "da2 = mikeio.DataArray(d2, time=time, item=item)\n",
    "\n",
    "ds = mikeio.Dataset([da1, da2])\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.is_equidistant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.to_dfs(\"test.dfs0\", title=\"Zeros and ones\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read a timeseries\n",
    "\n",
    "A dfs file is easily read with mikeio.read which returns a Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "dims: (time:10)\n",
       "time: 2000-01-01 00:00:00 - 2000-01-01 09:00:00 (10 records)\n",
       "geometry: GeometryUndefined()\n",
       "items:\n",
       "  0:  Zeros <Water Level> (meter)\n",
       "  1:  Ones <Discharge> (meter pow 3 per sec)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = mikeio.read(\"test.dfs0\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From comma separated file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Decimal Date</th>\n",
       "      <th>Average</th>\n",
       "      <th>Interpolated</th>\n",
       "      <th>Trend</th>\n",
       "      <th>Number of Days</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>1958-03-01</th>\n",
       "      <td>1958.208</td>\n",
       "      <td>315.71</td>\n",
       "      <td>315.71</td>\n",
       "      <td>314.62</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-01</th>\n",
       "      <td>1958.292</td>\n",
       "      <td>317.45</td>\n",
       "      <td>317.45</td>\n",
       "      <td>315.29</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-05-01</th>\n",
       "      <td>1958.375</td>\n",
       "      <td>317.50</td>\n",
       "      <td>317.50</td>\n",
       "      <td>314.71</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-06-01</th>\n",
       "      <td>1958.458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>317.10</td>\n",
       "      <td>314.85</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-07-01</th>\n",
       "      <td>1958.542</td>\n",
       "      <td>315.86</td>\n",
       "      <td>315.86</td>\n",
       "      <td>314.98</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Decimal Date  Average  Interpolated   Trend  Number of Days\n",
       "Date                                                                   \n",
       "1958-03-01      1958.208   315.71        315.71  314.62              -1\n",
       "1958-04-01      1958.292   317.45        317.45  315.29              -1\n",
       "1958-05-01      1958.375   317.50        317.50  314.71              -1\n",
       "1958-06-01      1958.458      NaN        317.10  314.85              -1\n",
       "1958-07-01      1958.542   315.86        315.86  314.98              -1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../tests/testdata/co2-mm-mlo.csv\", parse_dates=True, index_col='Date', na_values=-99.99)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Date'>"
      ]
     },
     "execution_count": 7,
     "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": [
    "df = df.dropna()\n",
    "df = df[[\"Average\",\"Trend\"]]\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A dataframe with a datetimeindex can be used to create a dfs0 with a non-equidistant time axis by first converting it to a mikeio.Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "dims: (time:720)\n",
       "time: 1958-03-01 00:00:00 - 2018-09-01 00:00:00 (720 non-equidistant records)\n",
       "geometry: GeometryUndefined()\n",
       "items:\n",
       "  0:  Average <Undefined> (undefined)\n",
       "  1:  Trend <Undefined> (undefined)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = mikeio.from_pandas(df)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then write to a dfs0 file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.to_dfs(\"mauna_loa_co2.dfs0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a equidistant time axis first interpolate to regularly spaced values, in this case daily.\n",
    "\n",
    "*The code for this can be written in many ways, below is an example, where we avoid temporary variables.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "(\n",
    "df.resample(\"D\") # resample to daily\n",
    "  .interpolate() # interpolate linearly\n",
    "  .pipe(mikeio.from_pandas) # convert to mikeio.Dataset\n",
    "  .to_dfs(\"mauna_loa_co2_daily.dfs0\") # save to dfs0\n",
    " )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read a timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "dims: (time:10)\n",
       "time: 2000-01-01 00:00:00 - 2000-01-01 09:00:00 (10 records)\n",
       "geometry: GeometryUndefined()\n",
       "items:\n",
       "  0:  Zeros <Water Level> (meter)\n",
       "  1:  Ones <Discharge> (meter pow 3 per sec)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = mikeio.read(\"test.dfs0\")\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2000-01-01 00:00:00', '2000-01-01 01:00:00',\n",
       "               '2000-01-01 02:00:00', '2000-01-01 03:00:00',\n",
       "               '2000-01-01 04:00:00', '2000-01-01 05:00:00',\n",
       "               '2000-01-01 06:00:00', '2000-01-01 07:00:00',\n",
       "               '2000-01-01 08:00:00', '2000-01-01 09:00:00'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Or as a Pandas dataframe\n",
    "\n",
    "A mikeio.Dataset ds is converted to a pandas dataframe with ds.to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>State 1Sign. Wave Height</th>\n",
       "      <th>State 2Sign. Wave Height</th>\n",
       "      <th>Mean StateSign. Wave Height</th>\n",
       "      <th>MeasurementSign. Wave Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-10-27 00:00:00</th>\n",
       "      <td>1.749465</td>\n",
       "      <td>1.749465</td>\n",
       "      <td>1.749465</td>\n",
       "      <td>1.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27 00:10:00</th>\n",
       "      <td>1.811340</td>\n",
       "      <td>1.796895</td>\n",
       "      <td>1.807738</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27 00:20:00</th>\n",
       "      <td>1.863424</td>\n",
       "      <td>1.842759</td>\n",
       "      <td>1.853422</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27 00:30:00</th>\n",
       "      <td>1.922261</td>\n",
       "      <td>1.889839</td>\n",
       "      <td>1.897670</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-27 00:40:00</th>\n",
       "      <td>1.972455</td>\n",
       "      <td>1.934886</td>\n",
       "      <td>1.935281</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     State 1Sign. Wave Height  State 2Sign. Wave Height   \n",
       "2017-10-27 00:00:00                  1.749465                  1.749465  \\\n",
       "2017-10-27 00:10:00                  1.811340                  1.796895   \n",
       "2017-10-27 00:20:00                  1.863424                  1.842759   \n",
       "2017-10-27 00:30:00                  1.922261                  1.889839   \n",
       "2017-10-27 00:40:00                  1.972455                  1.934886   \n",
       "\n",
       "                     Mean StateSign. Wave Height  MeasurementSign. Wave Height  \n",
       "2017-10-27 00:00:00                     1.749465                          1.72  \n",
       "2017-10-27 00:10:00                     1.807738                           NaN  \n",
       "2017-10-27 00:20:00                     1.853422                           NaN  \n",
       "2017-10-27 00:30:00                     1.897670                           NaN  \n",
       "2017-10-27 00:40:00                     1.935281                           NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs0file = \"../tests/testdata/da_diagnostic.dfs0\"\n",
    "df = mikeio.read(dfs0file).to_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>VarFun01</th>\n",
       "      <th>NotFun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:00:00</th>\n",
       "      <td>0.843547</td>\n",
       "      <td>0.640486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 05:00:00</th>\n",
       "      <td>0.093729</td>\n",
       "      <td>0.653257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 10:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 15:00:00</th>\n",
       "      <td>0.305065</td>\n",
       "      <td>0.214208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01 20:00:00</th>\n",
       "      <td>0.900190</td>\n",
       "      <td>0.999157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     VarFun01    NotFun\n",
       "2017-01-01 00:00:00  0.843547  0.640486\n",
       "2017-01-01 05:00:00  0.093729  0.653257\n",
       "2017-01-01 10:00:00       NaN       NaN\n",
       "2017-01-01 15:00:00  0.305065  0.214208\n",
       "2017-01-01 20:00:00  0.900190  0.999157"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs0file = \"../tests/testdata/random.dfs0\"\n",
    "df = mikeio.read(dfs0file).to_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a timeseries with non-equidistant data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.DataArray>\n",
       "name: Random\n",
       "dims: (time:5)\n",
       "time: 2000-01-01 00:00:00 - 2000-11-29 00:00:00 (5 non-equidistant records)\n",
       "geometry: GeometryUndefined()\n",
       "values: [3.927, 0.526, ..., 2.795]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1 = np.random.uniform(low=0.0, high=5.0, size=5)\n",
    "time = pd.DatetimeIndex([\"2000-1-1\", \"2000-1-8\", \"2000-1-10\", \"2000-2-22\", \"2000-11-29\"])\n",
    "da = mikeio.DataArray(d1, time=time, item=ItemInfo(\"Random\"))\n",
    "da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.is_equidistant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "da.to_dfs(\"neq.dfs0\", title=\"Non equidistant\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a timeseries with accumulated timestep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find correct eum units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Correction of precipitation,\n",
       " Precipitation correction,\n",
       " Precipitation,\n",
       " Specific Precipitation,\n",
       " Precipitation Rate]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EUMType.search(\"prec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[mm per day,\n",
       " mm per hour,\n",
       " cm per hour,\n",
       " meter per sec,\n",
       " meter per day,\n",
       " feet per day,\n",
       " inch per hour,\n",
       " inch per min,\n",
       " inch per day,\n",
       " mm per year]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EUMType.Precipitation_Rate.units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mikecore.DfsFile import DataValueType\n",
    "\n",
    "n= 1000\n",
    "time = pd.date_range(\"2017-01-01 00:00\", freq='H', periods=n)\n",
    "\n",
    "# use default name and unit based on type\n",
    "item = ItemInfo(EUMType.Water_Level, data_value_type=DataValueType.Instantaneous)\n",
    "da1 = mikeio.DataArray(data=np.random.random([n]), time=time, item=item)\n",
    "\n",
    "# use a custom name\n",
    "item = ItemInfo(\"Nedbør\", EUMType.Precipitation_Rate, data_value_type=DataValueType.Accumulated)\n",
    "da2 = mikeio.DataArray(data=np.random.random([n]), time=time, item=item)\n",
    "\n",
    "ds = mikeio.Dataset([da1, da2])\n",
    "ds.to_dfs('accumulated.dfs0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "dims: (time:1000)\n",
       "time: 2017-01-01 00:00:00 - 2017-02-11 15:00:00 (1000 records)\n",
       "geometry: GeometryUndefined()\n",
       "items:\n",
       "  0:  Water Level <Water Level> (meter)\n",
       "  1:  Nedbør <Precipitation Rate> (mm per day) - 1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = mikeio.read(\"accumulated.dfs0\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modify an existing timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "dims: (time:10)\n",
       "time: 2000-01-01 00:00:00 - 2000-01-01 09:00:00 (10 records)\n",
       "geometry: GeometryUndefined()\n",
       "items:\n",
       "  0:  Zeros <Water Level> (meter)\n",
       "  1:  Ones <Discharge> (meter pow 3 per sec)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = mikeio.read(\"test.dfs0\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.DataArray>\n",
       "name: Ones\n",
       "dims: (time:10)\n",
       "time: 2000-01-01 00:00:00 - 2000-01-01 09:00:00 (10 records)\n",
       "geometry: GeometryUndefined()\n",
       "values: [1, 1, ..., 1]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds['Ones']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modify the data in some way..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.14159265, 3.14159265, 3.14159265, 3.14159265, 3.14159265,\n",
       "       3.14159265, 3.14159265, 3.14159265, 3.14159265, 3.14159265])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds['Ones'] = ds['Ones']*np.pi\n",
    "ds['Ones'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.to_dfs(\"modified.dfs0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.DataArray>\n",
       "name: Ones\n",
       "dims: (time:10)\n",
       "time: 2000-01-01 00:00:00 - 2000-01-01 09:00:00 (10 records)\n",
       "geometry: GeometryUndefined()\n",
       "values: [3.142, 3.142, ..., 3.142]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = mikeio.read(\"modified.dfs0\")\n",
    "res['Ones']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second item is not modified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.DataArray>\n",
       "name: Zeros\n",
       "dims: (time:10)\n",
       "time: 2000-01-01 00:00:00 - 2000-01-01 09:00:00 (10 records)\n",
       "geometry: GeometryUndefined()\n",
       "values: [0, 0, ..., 0]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res['Zeros']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert units\n",
    "\n",
    "Read a file with waterlevel i meters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mikeio.Dataset>\n",
       "dims: (time:577)\n",
       "time: 1993-12-02 00:00:00 - 1993-12-14 00:00:00 (577 records)\n",
       "geometry: GeometryUndefined()\n",
       "items:\n",
       "  0:  ST 2: WL (m) <Water Level> (meter)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = \"../tests/testdata/waterlevel_viken.dfs0\"\n",
    "# filename = r\"C:\\Program Files (x86)\\DHI\\MIKE Zero\\2021\\Examples\\MIKE_21\\FlowModel_FM\\HD\\Oresund\\Data\\1993\\Boundary_Conditions\\waterlevel_viken.dfs0\"\n",
    "ds = mikeio.read(filename)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 30,
     "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": [
    "ds.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim is to convert this timeseries to feet (1m = 3.3 ft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds[0] = ds[0]*3.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which units are acceptable?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[meter,\n",
       " kilometer,\n",
       " centimeter,\n",
       " millimeter,\n",
       " feet,\n",
       " feet US,\n",
       " inch,\n",
       " inch US,\n",
       " mile,\n",
       " mile US,\n",
       " yard,\n",
       " yard US]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.items[0].type.units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds[0].item = ItemInfo(\"Viken\", ds[0].item.type, EUMUnit.feet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.to_dfs(\"wl_feet.dfs0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![WL](https://github.com/DHI/mikeio/raw/main/images/wl_feet.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extrapolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 35,
     "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": [
    "# filename = r\"C:\\Program Files (x86)\\DHI\\MIKE Zero\\2021\\Examples\\MIKE_21\\FlowModel_FM\\HD\\Oresund\\Data\\1993\\Boundary_Conditions\\waterlevel_viken.dfs0\"\n",
    "filename = \"../tests/testdata/waterlevel_viken.dfs0\"\n",
    "ds = mikeio.read(filename)\n",
    "df = ds.to_dataframe()\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 36,
     "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": [
    "rng = pd.date_range(\"1993-12-1\",\"1994-1-1\",freq='30t')\n",
    "ix = pd.DatetimeIndex(rng)\n",
    "dfr = df.reindex(ix)\n",
    "dfr.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace NaN with constant extrapolation (forward fill + back fill)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zZw+++93voqOjAzNmzMDq1au1xugdO3bA46mqVqRRR+7zCWolMGaAhvL6jgO4/4WPAMC5ACiTARpTG8CYcAAdPREcGIhhYnMYAJBMqVj/fhfmTmlBTcA71EMREVGBHA+AAGDZsmVYtmyZ5XXr1q0b8r4PP/yw/QfkMjEpAAp4MyUwbog6pJ6IXorqiyZQF7T/fxXRAzS2NoCmsB8dPRHDUvj/WPchbv/9+7hs9kSs+psTbH9+IiI3Y+rFBbQGaK9Ha7AVGaAo5wBZkoPGrp6II8+xPxPsjMkEQIBxKfztv38fAPDfGz/hliVERDZjAOQC8hRoQVsFlmAGyMpBKRDp6o068hz7+9OP21zrx5hwIPO8egaoRprYvXVPvyPHQETkVgyAXMA8AwjgMvjhyIGIUxOaD/RnMkDhAJoyAZDIAPVG4hiU3ptPDgw4cgxERG7FAMgFxEaoxgyQaIJmCcxKr9QD1BNxJgDar/UABTEmUwITwVZHt7HsNhhjoEpEZCcGQC5glQESTdBRlsAsDUgBR8+g/QHQYCypZXjG1Poxti49zHNvX7os1m8KeAYYABER2YoBkAuY9wED5BIYM0BWBuNSBsiBAEiUuvxeBXVBH1rq0iWwPZl+o4GYcSDiYIwDEomI7MQAyAWse4BG7hygVEp1bGWWIGdcuh0IgMTE57qgD4qioLU+nQHak8kAmUtezAAREdmLAZALiDKXdQls5GWA/v6/NmH2v6zBf27Y7thzyAHHoANBojkobc2UwPZlpkOzBEZE5CwGQC4QtVgGL068sREWAHX2RPD7v6Q3133lo/2OPY+cgXEiSIxntifxZwZT1mYGLYrSl7nkZS6JERFRaRgAuYB1E3RmEOIIa4Levlefh+Nk9qpfCjicGBZpfk9qA+kAKJ5UEUuk0B9lBoiIyEkMgFxgVGWApKGETi1PB8wZIPuDj3gyPdlZbE0i7/UlrxCzOh4iIiodAyAX0LMN+klWzwCNsABImo8jz+qxWyyp/1zKUQIL+Dzwe9PblPTHElpwJwJVZoCIiOzFAMgF5L3AhOAIzQDtl7ao6HUwA5RI6ntvOREAiQBLLkuKrS8GYklt6f2ExlD6shG4Wo+IqJoxAHIBbS8w/8hfBSYvSXcyA5QwZICcWwUmsj6AsRG6ZzD92sY3pAMgzgEiIrIXAyAXiCUzy+C9I78HqMcQAMUd2yU9npIyQA40QZtLYAAQzvQB9UeTWglMywCxBEZEZCsGQC4gTuDGDNDIXAUmZ4BSava8HLvEy9QDFDAEQOkM0GA8ob3ONi0DNLLeJyKiascAyAVEv0lwlGWAAOf6gIw9QM4PQgRMGSARADEDRETkCAZALiAyQKNhErTYQkJwqg9IzgA5ESTGMgGWVQlsMJbMygBxECIRkb0YALmAlgGSlsGP1AyQefNWJzYqBcpXAjMEQJkm6PQy+HTAM64hvUWGvCyfiIhKxwDIBYaeBD2yTqxi81bxWpzIAKVSKqQeaGcCIKsSWGYZ/J7eKJKZA2itC2nH4FTDNxGRGzEAcgHRw2I5CTo5sk6sYkKy2DzUiWnQ8ZQx4EmmVMOyeDtoc4AslsF3ZIY9+r0KGsN+AICq6tOjiYiodAyAXCA6RAZIvr7aqaqqZYBEaciJDFDCItCw+2cUG2IZ/O5MANQQ8iPkl98nNkITEdmFAZAL6HuBZfcAASOnvySWTGmlqXH1zgVAcYufh90BUDyRaYK2WAXW0ZMOgBpr/IZl8iMlUCUiGgkYALmAVQ+Q4cTqwKA/J0Ri+nGOq0/3xjhSApMyQD5PukRld/bFajilmAO0u3sQAFBf44eiKCO2X4uIqJoxAHIBqwBIURRDH9BIEMkEIT6PgjG1AQDOzAFKpPRtKkKZxmS7g0SRAbKaAyRWujXWpPt/tACI+4EREdmGAZALRCyaoOXvR8qJVUxDDvm9aAilsyVO9gD5PB7Hsi/6Mni9CVosgxfEawz6R+bMJiKiasYAyAVE9kJkM4TgCMsAiRVgIb8X9Q4GQOLn4fMqjm0ZErPaCsP0/mRlgBgAERHZhgGQC4iTt7yiCJCmQY+UHqBMAFQT8KA+lA4OnBiEGNcGR3ocy75ou8HLJbCgMQBqYAmMiMgxDIBcQPSUhHzGE+xI6wHSMkA+LxoyAZAjGaCEnp0RGRrbe4Asl8EbS2B6BoglMCIiuzEAcoGIVDqS6ZmFkXFi1TNAcgnM/gyQ3DTuy/TomIcjlkqsNJNLYLUBUwYoE+QFWAIjIrIdA6BRLpFMIZEZnmNugtYzQCOjtBKRepnqRAAUdTAD5PNoGRqr4Yh2PYeQ1QRdk2mCdqgPiYjIzRgAjXIRKWsw0jNA8iowp5anA0BUKk+JVVpObYXhz6cJ2sHXSkTkVgyARjm5cTZ3BmhknFhFD1CN34OQdOyplL3ZGXmjUp8n/Txxu5/Dchm8MQBqyex3xlVgRET2YwA0ykWkk7nHoxiuG7GrwPxeLSsCOLdPV8Cr9wDZnQESAVCu6dyAvt0HS2BERPZjADTKaQ3Qvuy3WlvhNEIyQHIzt/x6IjYvDy9rD5AU9CiKMUAdE05Pu+YqMCIi+zEAGuVEcBA09ZekLxtZ82XkQYg+r0fbpyti95BCbfNY/TmcWgXm9+b+X1Bk7PT3iQEQEZFdGACNcvrKqdwZoJHSAyReS01mubhTjdAxQxO0QxkgixKYzCeVK1kCIyKyHwOgUU6bAu0bKgM0MgIgeRAioAd1TmWADHOA7F4FlsheBQYApx4xFgBwxdzJ2mUsgRER2c83/E1oJMu1DxgABLwj68QaielbYQB6YBBxKAMU8HogFn8lHFoFZm58fuCKWfjfN3djwTHjtcuYASIish8DoFFObxzOTvaJDFBspARACX0VGKAfv5NN0MlM4OPUKjC/z9j4HA74cMmsiYbLRlqmjohoJGAJbJQTQUPQogQmsg92l5CcIgYhiobukEOlIesSmH0ZIFVVLbfCyIUlMCIi+zEDNMoN1QQtdht3Ykd1JwzGjRmgUBkyQKpWArMv+JCbzv05mqBlLIEREdmPAdAoFx1iGfzY2vScmf39sbIeU7G0VWCiBKb1ANkcAMk9QFoJzL4MkJxNyi8DxEnQRER2YwA0yolJ0FarwMbWpQOgfX0jJQAy7mofcqg3Rm5QFs3Pdo4KiEuBzFBzgATuBUZEZD/2AI1yQzVBj61Nb7Wwrz9a1mMqllYCy6wC0+YA2VwaisqToD1iKwz7MkAimPJ6FHhN25NYYQmMiMh+DIBGOVE2smqCFhmg/f0x2zcUdUJ2BsihZfCGJujMIEQ7e4C0GUDDBz8AS2BERE5gADTKDZUBEntNpVTg4AhohBarwEJaD5DzTdCiRGXnKrC4NGk6HyJ4HSnjCoiIRgIGQKOcNgnaahCiz4PGzEqw/SOgDGZugtYyQHZPgjZshWH/bvAimArmsQIMkOYAMQAiIrINA6BRLjrEMnhAXwm2p7e6G6GTKVULTMyDEJ1qgjZuhmpjD1CObTByYQ8QEZH9GACNcpEhMkAAML4hBADo7ImU7ZiKIZe5QqZBiI7tBeaVeoBszADFiiyBMQNERGQfBkCjnN4Ebf1WT2hKB0C7ugfLdkzFGJQCIPFaxE7qdvfGGHuA7F8FFh9mJ3gzLQPEZfBERLZhADTKmVdOmbU31gAAdh8cGRmgoM8DT6YsJQIDOxuUAeMyeJ8n8xyVLIH59RKYqlb/aj0iopGAAdAopwcO1gFQa/3ImAUU0WYA6a9DBBDFZoA6uiN4+aN9WZfLk6B9jjRBi8fPdxl8+jWnVPt3pScicisGQKOcyGbkaoJuzjRB763yadCDMWMDNCCVwIoITlRVxYX3voRL738Zr+84YLhOy9BIy+CdKIEV2gQNsA+IiMguDIBGueFKYPIwxGpm1cxdSgZo58FBdPaks17/906H4Tp5Kwx9FZidTdCZneAL7AEC9L3diIioNGUJgO69915MnjwZoVAIc+bMwcaNG3Pe9oEHHsDpp5+OMWPGYMyYMVi4cOGQt6ehDdcErW2H0VfdJTDzEERAn6QcLyID1DOY0L7eb8p+iYAq6FAGqNAeIEVRtGCJGSAiIns4HgA99thjWL58OVauXInXX38d06dPx6JFi9DV1WV5+3Xr1uGyyy7D888/jw0bNmDixIk499xzsXPnTqcPdVQaahAiADTUpPfD7YsmqrrBVtsHTCrlBUtYBSbP1OmLJgzXGbfCKD7IyqXQEhjA7TCIiOzmeAB0xx134LrrrsPSpUtx7LHH4r777kM4HMZDDz1keftf/vKX+MpXvoIZM2Zg2rRp+OlPf4pUKoU1a9Y4faijUkQbhGgdANUF0wFQPKlW9cnVqpSnb1NR+HHLQVNvxBQAJbNXgdnZfCwPWsyXPguIJTAiIjs4GgDFYjFs2rQJCxcu1J/Q48HChQuxYcOGvB5jYGAA8Xgczc3NltdHo1H09PQY/pFOzwBZv9W1AR+UzGIkcyBQTbRVYJZN0IUHJ1FDAKTvg5ZKqdqy+oBDW2EUuhkqwFlARER2czQA2rt3L5LJJMaPH2+4fPz48ejo6MhxL6ObbroJ7e3thiBKtmrVKjQ2Nmr/Jk6cWPJxjxZJ6WQeyrEM3uNRUBfQy2DVSusBslwGX3hWJJojAySvKPNLu8HbOWuo0EnQAPcDIyKyW1WvAvvhD3+IRx99FE888QRCoZDlbVasWIHu7m7t3yeffFLmo6xe8vYRwRwZIACoC2UCoGrOAInl/L7sDFAxwYlcAuuRXrdcTjOsArOzByhR2CowcSwAS2BERHbxOfngLS0t8Hq96OzsNFze2dmJtra2Ie97++2344c//CH+8Ic/4IQTTsh5u2AwiGAwaMvxjjaG/bNyZIAAvQ9ILgVVG5EBqgnoQUOghGXwxiZo/XXLj5UugTnXA1RME7Td234QEbmVoxmgQCCAmTNnGhqYRUPz3Llzc97vtttuw6233orVq1dj1qxZTh7iqBaRNvUU20dYERmg3iougVn1AJXSBC2XkiLxlPYYenlKgcejlLTUPpdYgXuBybdlAEREZA9HM0AAsHz5clx55ZWYNWsWZs+ejbvuugv9/f1YunQpAOCKK67AIYccglWrVgEA/vVf/xXf/e538cgjj2Dy5Mlar1BdXR3q6uqcPtxRRQzNG6r8BQD1IT+AKi+BWawCKyUoMA8U7I0k0FwbMOwED8DhOUD5N0Fr/U42BmJERG7meAD0+c9/Hnv27MF3v/tddHR0YMaMGVi9erXWGL1jxw54PPoJ+j/+4z8Qi8Xwt3/7t4bHWblyJW655RanD3dUGW4JvFAf1JugU6n0cnh5z61qMGi5DD4dQBQTFJjv0xuJGwIgfya40vYCs3EStD5pOv+fMQchEhHZy/EACACWLVuGZcuWWV63bt06w/fbt293/oBcQmwfMdy8GbkHaPn/bMYf3u3Ck9fPwxHj6h0/xnwNxnPvBVZUCSxuDoDS2S95I1QA+m7wSRWqqkJR8s/a5KIHWfk/VqCEch8REWWr6lVgVJrh9gETRA9Q92AcT27ehb5oAret3uL48RXCsgSWCQpSauFzesyBhBYAJYz9OXKZKmlTI3TcFGTlgz1ARET2YgA0ikW1EthwPUDpAGjjtv3aZQcHq2tFmNYELa0Ck1dRFboUPmoKgMTjmwMgn/Qcdq0Eixe4Gap8WwZARET2YAA0imlToIdYAg8Ak5rDAIA//7Vbu+yjPX3OHVgRtGXwFiUwoPDAQMziEbQAKKsEpmeA7Co/FTUIkQEQEZGtGACNYvk2QR9p0euzty+GgVj1rArT+pmk1yIHJ4U2QpuDGdFcLO8EDxiDFLtWghW6GzwgzTwyHfeadztxy9PvMDAiIipQWZqgqTJEVmO4Juip42oN39cHfeiNJrDr4GDVNEIPWGSAFEVBwOdBLJEqOAAyBwzmEpgITrweBYoCqCoQt2klWFyaNZQvf46hj9f8/DUAQGt9ENfPP8KW4yMicgNmgKpMKqWi26b+m3yboMMBPQ6+afE0tDfVAAB2HYzYchx2GIimX0ttwBiza6ujCi2B5eoBshhS6Bc7wtuUASpmN/jhlsGvf39P6QdGROQiDICqzD8+thlzV63BW1I/TrHEJOjhBiECwE/+biaumHsYrj5tMpprAwCAAwOxko/BLqIcFw4ag7lil8KbM0bmEpgcAGmzgCpZAhvmdYoeKSIiyg8DoCrz9J93YSCWxLeefKvkx7JqHM5l0XFt+P6FxyPo86K5Lh0A7e+vjgBIVVWtBGbOAIkyUqEDArMzQMatMOQl6tqGqDaVwGKZQKqYAEgugamqHpDZuVcZEZEbMACqIvIsm34b9uUSjyHm/OSrOVxdAVAsmdJO8OYJ1cXuByYCCTEFWzRZW2WA7N4Oo5jNUK2aoOWgL8UAiIioIAyAqsievqj2dZ8NAZB4jLpAgQFQbXUFQHJ5JxywLoEVvAw+E8w01KT3QRMzk8x7gQF6CcyuZfCJIpqgrZbBy78jdmWniIjcggFQFdndrTcd99qwMWlvsRmgKguA+jMBUMDrycqa6FtEFJYBEZkUMQRyqAyQvh2GXavA0sfqKyADZLUKTM4SVvNGtkRE1YgBUBXpkAKgSDxp6PEohjgpir2+8lVtAdBA1LoBGpAyQMnCmoBFMCN+NmIVWNxqFZi2Iao9ZSaxsao8x2g4+uuUAyD9NfdEqmtyNxFRtWMAVEXkACilFrfLuUyUSOpHeAYoVwM0IGdGCswAJYwZINFPE7Vogi62zyiXUrbCkPt+5EGVkXhKm/xNRETDYwBURbp6o4bvI/HSTrhaE3TQX9D9xlRZE3R/5kRvboAGit8lPa6VwEQP0BAlMIeaoAvKAFm8zn7T0veeQZbBiIjyxQCoiuzrMwdApf1FL/qICu0Bagqng4LuwXjJZTg7DGoZoOwAyF9iE7Q5A2S9CkyUwOxqgrZnGfyAqVGeZTAiovxxK4wqss+UcSk1ANJWgRXYA9SYWRmVSKXn79QWeH87RBNJfPGnr2BaWwO8mUxJ2KIEFihyhZZeAku/1lxbYQDSHCCbMkBaD1ABq8CsAiBzBsiuCeJERG7AAKiK7M3KABWfcVBVtegeoHDAC79XQTyZ3pajEgHQa9sP4NXMP8EqyLFqDs5H1iqwzM/aapsKO0tgqqpqgVRRu8FLr3PQtFltDwMgIqK8sQRWRfb1GTNAgyVkgCLxFJKZVUuFZoAURdGyQJXKKliNAXjt4wNZl+XaJHQ4ItBp0EpgQ+wFZmMJTF5JJvYYy4flMnhzDxCXwhMR5Y0BUJVQVVXLAIm/9kspgfVG04GLomQPD8xHQ4UDoM6e7I1Yp7TUZl1mNSE5H/GsEtgQgxC1OUClZ4DkLFKpJTBzDxBLYERE+WMAVEZ/PTCAGx59A+/u7sm6rj+W1BpxDxmT3o3dnAF65aN92LqnL6/nEuWQuqAPipL/iVYQS84rtcmmuRwIAPd84cSsy0QTdLzQZfBZJbD064wO1QRtwzJ4eWJzQQFQPhkgBkBERHljAFRGl97/Mp7avAs3PrY56zqxAqzG79X24opKAVBXTwSXPvAyFvxoPbossiNm2/YOAAAmNYeLOlaxgWqpjdjFMp/Mr58/Fce1N2bdrphl8HIfjrYMfqjd4EUGyIZBiHEpgCmkBGbV6zRg7gHiKjAiorwxACqjvx4YBAC819GbdZ3oeWms8SOUCT7kDFBHTwRiRfor2/YP+1zvZbJMR4yrK+pYg35P1jGUk7mfpa2xxvJ2xTRBy6UscwYoZrFRqc/GDJDoAfJ6FHiKnAQtRhOIAZGij4lzgIiI8scAqEyG261bntkT0rIv1tsefLSnf9jn+9PWfQCAkyaNKfhYAVgeQzmZ+1nG1QctbyfKU4U0QcvZInkOUDoz5Oxu8MUMQQSAoDf9fqiqHkSJ34mWuvTPplLZOiKikYjL4MvEasaPCDIAfWZPbdCHkD+7CVre+PKjvUP3AQ3GktiUWTF1+pEtRR1vjUUWqpxECWz24c2oDXhxxpGtlrcLZAKDQjJAcrBUL03JjiZS2nVBqzlAdqwCK2IJPGAMyGKJFPxej1YCa64N4KO9/dwKg4ioAAyAymRfv7Gp968HBnDEuHrt+77Mqq36oM8y+OiX+j2GywB92NWHWDKFlroADrdYOZUPqyCsnEQ/yw0LjsSpR+QO4vy+THBSRAbIoxg3WI3GU1ovkCgBAvbOASpmCCKgZ7qAdABUG9SboMXebZXK1hERjUQsgTng5Y/24YNOY59Pn6mnZddBYyNzX6acURccvgT2QVfvkCWfnQfTDdATm8NFrQAD9AxQtGIZoPTPqyE09D5mxSyDl2f9+L0ebdJ0JJFENC4GIeqBkb/IadOWz50oLgPk83ogqmbi+AelDBDAEhgRUSEYANnsnV3duPT+l3HOnS8YLu81zWwxD/oTAVJt0Kdt+pmrBBaJp/DWzu6cx7AzE1y1N1k3DufDqhG7nEQGqKFm6CSlKA0VEpxok5gzq7DE3KVoPKUFF0GrVWA2ZoD8BfYAAdmzgMR70xRmAEREVCgGQDZ75s3d2tfyRqL9WQGQsclX9HPUBr0IZU508gyeftOS54MDuXdq358pt7VkMgPFqGQTdDyZ0lY4iYnUuejzcfIPTsRqLjFDSHutiaSW8TI0QftsnAOUCaJ8BWaAgOxsl/j9GBM2DnMkIqLhMQCy2e6Dg9rX8gnJXALrMwVE4oRfE/AiaDGDxxxAmYfgGa7LlMvqhykfDaWSGSB5BtBw23j4iyiByUvRAWgBZySuD6M0lMAyGaCEDXOARBBVaA8QAAQyx6RlgLQAKJMBYhM0EVHeGADZbI80wVhsRwFkBzzmOTciAAr7pR4gqc+nL2o8uZk3wpT1SuW0YlWyCVr8bGoD3mEzJfok6AICIJGFyQRAIuDsjya1IMe4Gap9PUAiAxQoIgMUlEpgqqpKJbDMMEdmgIiI8sYAyGY7D+gZILnPx9zzYy6BiYAmHPBaTmE2T/0dGCIDJFaU1RW4C7yskpOg+6P5B3DFNEGLPhyRARKBhfyeyKvAbJ0DVOQqMMA4DDGWTEEkpMawCZqIqGAMgGykqio6pG0q5LKXOQNkLonJJbDh5gDJt7fSr60oK3wTVKGSPUAis5HPJq4BX+HZmWTKmAESr1UevhhweA6Qr4BtMAR56GMkph+L3gPEAIiIKF8MgGzUPRg3BAxy1kcEPG0NoazrAONJP2TZA5T+emzmr31zRkgmVpzVBUdmD5AeDOaTATL2xeRDlLlEeU1kgETpzetRDKU3v7bfmH09QP5SMkCJlPa++DyK9j5HCvgZEBG5HQMgG3WYNimVSyoiA9TWmAmAouZVYHIAJDJA8s7f6fu3ZraEGDoDpK8oK1Yle4AGY/lngLSsiI0ZILn/R34OO1aBWe01li+RlYpKAVCNX/99SaZUW/qUiIjcgAGQjTq6TQGQVLYSX0/IBEC5S2A+ywyQCKBEADQ4VA9Q5rHrS8gAVXIrjMF4wnAMQylmDlDWKrBMANGTMwCyMwNUwjJ46bWK9z8kZQwBlsGIiPLFAMhGWQGQFOT0awFQTdZ1gLEJ2qr8NJApgbXW5Z8BKqUJOqRNgi5/RkHuhxqOtgy+gPJPUjQia03Q6efRAyDj8/q8hQdZuZQ2CFEv98klUzlg4ywgIqL8MACyUWePcb8vOcsjyivtTekMUK5l8DV+L0I+q60wzCUw6x6gVEpFX8yOEljlVoEVUgLTsyL5Z2fEbbMyQJmSZSBXCcyGJmh9EGIRAZC04i0ilcAURdGCIGaAiIjyw81QbWTe8FTuAerK9AdNba0DoC9VF+STvti/S0wlVlU17x6ggXgSYgD1SC2BDRQSABWVATKuxBq+B0jMGrJzEGJpc4C0Eljm2EN+L6KJFHeEJyLKEzNANtrXl96eQqzUEn07g7GklvE5Ylw6AIrEU1pJRVVVDGglDZ/egJw5mUXi+syXlmFKYO9Ie4SF/MW/vXITtLylRzno2bDh43O/NBsnX+YeIG0VWGYD1qDp52bnMvhSBiHKy+AHpAwQAMvGeSIiyo0BkI1EBmjS2DAAPQDq6k1nf2r8Xm0VGKD3AcWSKS0rURPQS2DxpIpEMmWYITS2Lh1c5WqClvciK3YneCDdXAsAKbWw4MIOkULmAEkZoHwDtaRpGGF2Bsj4vCLIsnUQYimboSZTiJj6pCpZsiQiGokYANlIZIAOa04HQKJvR5xYx4T98Hs92l/tokdIDmbC5lU9iZS+rD3g1aYjmzdHFcRzzZs6tqTXEvLJK4vKGwCJ/qZ8mqDlTEq+e3UlTD1AYsNVMcbAnJ3xe2xsgrZhFZh5GTygv19RzgIiIsoLAyAb7etPB0CTxtYC0IcXitJKQ+ZEW59ZnSWabkXJx+9V4Pd6TKt6klqwUxv0aVmRXBmgXZnNWL8wZ1JJr8XvVbQAodxZBbkhfDhyw3K+AYq5B+jotnrD9VklMBv3AitpEKJXZAb1AEgEy/KQRCIiGh4DIJskkikcGMgEQM3GEpgIdETgI/4rSmDmE77HY1zVIwKp2qAPtZnpyLl6gMQxjK0NlvR6FEUx7JJeTsUMQgTyP/knTIMQT5w0xnC9k3OA4qnit8IIWDRBi5+RnB0iIqLhMQCyyYGBOFQVUBRg4pj0rB+RuRGrwRpC6QxQc6ZJWvQM6SczvelX7unol5a1axmgeFLLZMjECbCUBmjzMZR7JVghc4C8HgWi1SnfXiWRhfFmgqe6oA+nHqGXDLN6gGycBG3XVhjaMvjMz6iYidhERG7GAMgmBzOZl4aQXyt1id4dUQITmZ9x9elG6K7M3KABaQiiIK/qEY8TDvgMQZJVYCL6dUJ5lI+GU6kNUeUVccNRFKXgpfDmDBBgzJhlzwHKZIDy7DEaSilzgMTvxGA8aVECy5THmAEiIsoLAyCbiBNSbcCLukyjsrkEJgIjMcunqzcTAMWzMx41cgZI29w0vUReZDwGotmN0FHTibEU2gl3iKnTTogU0AME6E3L+ZaokqZl8ICelQPS76HMb2cPkLYKrPD/9UT5czCW1N4T8TOShyQSEdHwGADZRN6bSazUisRTSCRTWq+PKIGJOUEia2TV8yKXn+QeIEVRhuwDEiUwcx9LMURAFinzcD0xzC/fMl6hDcDWGSA9ABIBqiCCFTuWwYvHKKYEJn4/+mMJaRWYcUd7NkETEeWHAZBN5GXJ8hYU/bGktseUKIFp+2xlTlbyRqhCUCo/ycvgAeOJUJZMqVoGwJYMkFhabVFq29Mb1eYbFer597pwx3PvW/YwAXIQl99r8Be4V5eeAdJ//ccMEQAVM2wxl3gJy+BFZrE/msjqAeIqMCKiwnArDJvIezMFfV74vQriSRX90YQ2BVqUwERmQ2Q6tI0tpaBFXoHVJy2D1/7bG83KAMnbINjRBF0TsG6C/nhfPxbf9SJiyRTWfu1MHJZZ9p+P/f0xLH34VQDAiZOaMP/ocVm30QKgQjNA+TZBD5MBEtO2BbFxqS1N0CUMQgxrAVBSe80i0GUTNBFRYZgBssmg6S/yWumvdfMy+KBps9NBiyZorfwUT2o7wYvH1DJAph4guVk53+zJUMzHKTz5xi5tFdq7u3sLeszd3YPa13/Z1WN5G5FxyreMJ28RkQ/zJGhAH4YIAJNbjAGdyDClVOTMWuVLL4EV0wOUfj8GYoms0QnMABERFYYBkE0GY8bSk+jT6YsmtBKY6AEKmjJAfZkAJxyUM0DZTdDmEliuDJA8xLAUuZqgd+wf0L7uHowV9JjyTKF3d+cIgBwugVllgOQS2GRTRksOlEpthI4ns4OvfIlVcf1yE7QogWWGJDIDRESUH5bAbKJt35AJgOqkcoVogs6VAerONEM31egnYcMyeFMJLJyjCVpbAm9D9kd+LeYmaDFtGkjPPzJLpVR09kbQWOPPWsouAkXAOgBKJFNagJJvBqjQBmB9Kwz98Y+Z0IDvfvZYTGoOZwWP/iK228j53Jn7+4tYBSZ+pwaiCe01i8uYASIiKgwDIJvIPUAAtEboPqkEJnqAzBmgg5kMUVNYL8MYBiFmMkR1Wg+QXgqxOoagDQ3QhmOQAq1IPIkNH+3TvheTp2V//1+b8Pu/dCIc8OK55WfikKYa7Tq5n2jb3n5E4klDw7acwci3B6jYJmhzH87Vpx0+5OMDmTk7JQzZLikDJN73eBJerbE+/TvDAIiIqDAsgdkkVw9QbySuzQMSJTB9dVX6ZCWyKE1hOQOkZ1/6osYeIa0UEjVngArrnRmOvgxeP6l+9b/fMNzmYL8xA/TJ/gH8/i+dANIZqte27zdcLwdAKRX4oLPPcH1U6jcyb0qaS6HbQIhG5HzLhPK0abGbe7FK2QxVlFVVFVpjvcgqBgrsgyIicjsGQDYRpR0RNIhsTVdvFGqmaqKVwER5K5MB0ktg2RmgwVhKy/RoGaCAdQbIzm0wAH0lmtwD9FwmuBEOmnqA/npg0PD9J1K/EGDMJgHA3r6o4XuRAfJ5lLyDhEL36sqVARryOWyaBSSCL38RPVohvwfmu2kBkM++HeuJiNyAAZBNBrNKYOkT08/+uB1A+gQlghpzBsi8TB6QeoASxkGIgD4vKFcGyI4ZQEB6qKP8uOJ1yMw9QObZQJ/sNwZE5iX1faaVbOJnUkgWq9CTv9YDVEAZyq5p0KXMAZKHYALpzJR5EnSUARARUV7KEgDde++9mDx5MkKhEObMmYONGzcOefvHH38c06ZNQygUwqc+9Sn87ne/K8dhliSrBygTPIgMx1Hj67TbahmguFgFZszwAMYeIHG96P3JlQGKFBE8DEUEalZ7jt32tycA0KdZC3t6jRkdc0BkfqzsLFbhfUyF7gVWTAbIZ9OO8AmLJfiFkFcK1mUmgwP6XmAsgRER5cfxAOixxx7D8uXLsXLlSrz++uuYPn06Fi1ahK6uLsvb/+lPf8Jll12Ga665Bm+88QaWLFmCJUuW4O2333b6UEsib4UB6NkaAPAowP98ea72fdDUs9IXyQ6A5L3ABkyrwHINKNS3kLC5CToTWEUTSe0EO3FMGEB2BmhvXzogmjw2fX2XKSCKZGWAzK+h8CCu0OxMwmIS9PDPYU+JSZsDVMQqMACGDJD8+8ImaCKiwjgeAN1xxx247rrrsHTpUhx77LG47777EA6H8dBDD1ne/t///d+xePFifOMb38AxxxyDW2+9FSeddBJ+/OMfW94+Go2ip6fH8K8ScpXAAGDGxCbDcnB5K4xEMqXdty4k3yb91vRGElrWISsAkvppVFXFD/73XcPjl6omYJpXFNGzNW2N6R3tzcMYD/SnA6Cj2+oBZAdA5hO0eUNXLQNURAms0CboQvbjErcttQeolFVgADC2Tm+Ur5d+XwodBklE5HaOBkCxWAybNm3CwoUL9Sf0eLBw4UJs2LDB8j4bNmww3B4AFi1alPP2q1atQmNjo/Zv4sSJ9r2AApgDIPmv86mtdYbbyif3/VIJSd5DTAQxIqMC6FtliOeQM0Abtu7LCjZKpZXAYsZSXW1A3+9sMJ6EqupBgVgWL4YJmgMk8wm6z1wC08p4+QdxBQ9C1OYAFVICy2SZSl0Flip+M1TAuIt8jTQ5PMgmaCKigjgaAO3duxfJZBLjx483XD5+/Hh0dHRY3qejo6Og269YsQLd3d3av08++cSegy+Qvjll+kcqZ4AmSHNwAOPJfX8mYxLwegyXiwBoX6aHKOT3aH0oWgAkZYD+Ig0VfGtnd4mvJnMMpt3gxUDHupBPy2ipqjHzIgIgkSGKmAIk8wnaHCAVug8YUHj5p6hVYCLIKjHDoi2DL7IE9uUzp2hf75RW3BW6HxoRkduN+EGIwWAQwWAJk+lsovUAaRkgPZgZZ95d3KvAo6Tn4OztTQcMcvlLfpx9mQBJ7v2w6gGSl58f395Q2osRx2DKAOkTrf1aEAak5/2I4xUBXXsm6Eup6ZOyCO7ECbou6ENfNJG1z1hRJbAit8IopAcoYFMTdKklsLOkzWPljJ+2FQZLYEREeXE0A9TS0gKv14vOTuPsmM7OTrS1tVnep62traDbVwuxLYXIjMgZIHMApCiKFhDs649mbm8s+YgZPCJbUWvRIC0HQGK1VV3Qh+9feHyJrybzPAFjE7S8Ws3rUbSsg7ySSzRFtzfqWS85yBEZHtG/kt3Inb7evNx+KPoyeOfmAGnbbSSzV8QVQi+BFf+/3udmHQoAuOjEQ7TL2ANERFQYRwOgQCCAmTNnYs2aNdplqVQKa9aswdy5cy3vM3fuXMPtAeC5557LeftqYV4GLzc9m3cXB/QmZ9HjUxf0m643BkSGAMiiCbqrJx1I3fa3J2Bic7i4F5HjGMVr6zXtai+vVAPSgYVYFj++IahNT45KQY4IUsRU7Kg5ACqhB8ipSdDy8UTjpQUYcWnQY7G+f+Hx+PdLZ+CWC47TLmMJjIioMI6XwJYvX44rr7wSs2bNwuzZs3HXXXehv78fS5cuBQBcccUVOOSQQ7Bq1SoAwA033IAzzzwTP/rRj/CZz3wGjz76KF577TXcf//9Th9qScxN0HIJZ4pFAJQ+oca1Hp86UwZIbnAF9Nk/gL4lhpw9Eb03Y6TtNEpVp23nkYCqqloGSARA4YAX3YNxLfvVMxiH2Cu0KRxAyOfFYDxpyADFMiWuxszQRztKYHbtBTYUff82m5bBl5ABCvm9uHDGIYbLuAyeiKgwjgdAn//857Fnzx5897vfRUdHB2bMmIHVq1drjc47duyAR+rFmDdvHh555BF8+9vfxje/+U0ceeSRePLJJ3H88faUdZygqqoWjIQyTdDHtTdg2fwjMKW11nLqrzihip4ZedUYoAcIQq3FkMRBwyal6RNfOJB/5mQ4zbXpYCqWTKE/pu9qX5djOb4IwuqCvszka086AJJ2k4/lWQIrZhl8vif/eBGrwPTZTaWWwErrAcolyAwQEVFBytIEvWzZMixbtszyunXr1mVddskll+CSSy5x+KjsE02ktP2+RAZIURR8fdHROe8jGoy1EljIGPA01wagKNAe12pIYjSRQjKlwutRsjZjtUON34ugz4NoIoUD/TGpB8hvOI6BuLFJukEqkR1A3DD8UJygG7QMUK4AqJBJ0IUNQtQyQAUEIVoJrIQMi6qq+lYYRa4Cy4VN0EREheFeYDaQT+L5DiEUGSDRBG0ugfm9HkM5K2wogenBkHhukYWpsWkIIpAO4kQW6MBATOsBqpNKYIC+wakWIGWut8pUxROiB8hnOH5B9AQ5uQw+kSo8CNEyQEX0AMWTKTy1eaeW7QOKnwOUi9/HJmgiokKM+GXw1UBkX/xeJe/eDnFC3av1AGW/FS11Ae2kWS9liOTy0GA8iXDAq5fgbAyAgHRP0e7uCPb3x7RJ0A2mAGfAtExevBaxn1dEOimLzTpz9wAVsxVGYeWfZDFN0P7iS2A3PrYZz7y5GxfOaNcuK2Yz1KGIZfqJlIpUSoWnhCZrIiI3YAbIBtoMoALKNvqgw8ycH8sASF8+L/cEeTyKtkJrMGZsMrazBwiAIQNk3rRVPJcogekZoPSxmleRAXIPkI0lsEJ3gy9qGbxxJEAhnnlzNwDgqc27tMtKWQVmRR4bwD4gIqLhMQCygT69OP+TdlCboZPZB8wiAGqV5gc11Bivl2cBDRZRgsvXmEwAtL8/jp6IscQlSnFaCUwsk8+8lpAWNMgBUPpr8XqyA6DiV4EVOgm63E3Qxr27bM4AMQAiIioIAyAbiBNvoJCmWlOgYl0Cs84AAXrwMRjTA6CAz1PQST0fzeH08x7oj2WVuMwlMHOGSDRky30zoglYK4ElUoatMrQ5QIX0ABU4pbmYvcD0AKiw4ELe6kP8XBSlsOfOR0AKqNgHREQ0PAZANhB/cRcyvdic4TBvhQEAk6SBhg0h86BEPYM0mJnEbGcDtCAyQPv6Y9ifadgeW5sOzMzziHqzmqAzJTCLZfDi9SRTqiFwKaUElu+JP6UW0QTtzw7m8rG7W9+iRJsCbfMKMCDdsM5p0ERE+WMAZINYEds3mE/wVj1AZ08bB59HQW3AixMmNhqu07epSKIvmruMVqpx9elNTTu6B7V+pZb6dFCkb8qaDnz6zBkiX/YqMBEsyk3dcoBUjkGIiTKWwHYejGhfdw+mS4R2zwASCt0TzWznwUF0Z7YyISIa7bgKzAbFBEAhU4mnyVTiAoCJzWE8vew0NIb9WiAihP36IEGfaYsKO03I7Or+fmefFjiIDJB5U1bzpGhtFZhhEnRmM9SQT5tzFIkn9a0xStgNPt/yVHFzgIorge06qGeAxGu3uwFaCPg86I8li8oA7euL4tQfrkV9yIe3blnkwNEREVUXBkA20DbwLKCx1ZwBEqutzI7NsbN7KKD33zzyyg4A9veVAMD4hnQAtDNzIh8T9msBRzhgvQy+fqgSmFQuFFtlyGWl4vYCK2wQYiJZwl5gBQYXnT2RrMvsboAWCg0EZe919AJIv4fdA3E0hrMDciKi0YQlMBsU0wNkzgCNyREA5VKj9QAl8NKHewEA7+zqKegx8tHWaMw8HS7ta2beDFUvgfktr1dVVWoY9+hL+eOllcCCBS6DL20vsMJKYHITtOBYCayE7TDk+3zQ1WvbMRERVSsGQDaIayWw/LMWcg+Mz6NoS8fzJVaBbelw9mQlZ3wAYGprnfZ1jTkDlGMStCiByc3OAZ8nK0ACpGyag8vgS+oBKrAJWvRnyezeBkMo9OcgOzigT6ne1Z2dtSIiGm0YANlAywAVUNoYI5UYmsIBKEphWQERXOyUekymH9qY6+ZFUxQFbQ16FkguydVkLYPPbJUhJkFrQUP6ejnLkM4AZfcIiWCokBVt/gKXwRc3B6i4EphVBsjubTCEQEkBkN783GVRtiMiGm0YANkgVsT2DfI+X821hfdbiAChQ/pr/f/74syCHycfcgB0/CF6kKUNQjSVwOpNGSARNMSlE3PA55GapPUsSTGbusqlHzFTKJpI4oIfv4Rlj7xumDMElHcV2EDMqgTmbA+Q2HG+EGKFGmDdt0RENNowALJBMavAmqQMkBwM5Us0IO/OBECnHdGCQ5pqCn6cfMg9K8dMkDJAUglMVdWsQYgiaIiYMkBejwKvvJ2HHAAVsamr3FQsskBbu/rx5l+78cybu/Hubr1MmErpwVBhc4CKazDus+oBcmoVmJYByi8TJhuQRhV09ERtOyYiomrFAMgGxZTAxktZlWL27wpndo8Xf7mbJ0XbKSEFDfKsIXk7jmgipQUf5h4gETTETKvlrHqAiskABS22gZDLbTv291u+lqJKYAX2APVb9AA5tQqs0HlIMrlUxwwQEbkBAyAbFNO4O6ExpO2q3lwbHObW2cyToc17hdnp5vOmYUJjCPd98STD5dok6FjSkOmoDQydARI9MCHTdOV4Ug+iis4AZd4Lefjivn69wTdpyAA5XwIz73UGOLcKzF/gajiZHACxB4iI3IBzgGxQTAlMURTcuuR4PP9eF24+b1rBz2nO+NT4nXsrT5o0BhtWLMi6XB6EKPp/wgGvllnJmQHKZFPMJbBIkZu6ipJaeluN9HPIs4f29+kBkNwfU0gGyPxa8mUVjDixFQag70VXVABkKIFFoKpqwY35REQjCQMgGxQTAAHAhTMOwYUzDinqObMCoED5k3kiAEqmVOzpS/eNyBOtszJApmbxkKkEJgIhRSmsoRxIZ5WSKVULUCKOZYAKDYBU7f7ivo5lgEQPUJ6r4WRys3YknkJPJOFoWZWIqNIYANkglszsxu5Qb4cV88kpVMAMIrvIZSqx5UOj1NAdNGeATAMjzcvgI7GU9riFZh/8Xg8i8ZRlBujAgJwBKq0HKJlSkUim8l7Jpe995kM0k4lyahVYKXOAzL1K33v6HTQwACKiMooO9JX1+RgA2aDYDFApsjNA5Q+A/F4P/F4F8aSqrUYbKgMk+nO0HqBMUCGClcEiZgDJz9ULPeAYjOlBwH6LDJDXoxQUZMl7k0USKdTlGcSIbTdqgz7szQRAfodWgZXSBG1erv+bN3backxERPlKRQfK+nwMgGxgXt1UDua/zoNFBA12qPF7EU8msFvsFSbNNDL3zURNGSBRthMNyyIAKqT/R9BO/pkl4HI/0b6+7AyQt8AMk/zeRuNJw2q4oWgr46TbO7cVRqYHqIQM0L9e/CnsPBjJmp1EROS0SH8fvn1X+Z6PAZANitkLrFQNpp3fi8ma2KEm4EVPJKFtn9BYI5XAcvQAiWAipE1XzgRAscKXwAv6MERjMAUYS2DJZOFDEAHA41EQ8HoQS6YK6gOKSRkgwekSWCkZoJMmjcHnT6639biIiPLR09ODb5fx+bgM3gaVKIH5vB7LmTzlJqZB7+7Wd4sX5AyQvBGqOFFn9QCVUALzm4YARnMFQGrhG6EKciO0qqp4e2e3VuKyoqr6qjT5vXK6BFZME7RYBRYucE86IqKRigGQDaIVKIEBxj4g8+7y5SJO7J/sTwdA8oRruW8mPSjR3ARtXQIrJgAKmLIfcgYoEk9pWaZkZhm8t4gylLwj/C9e/hifvecl3PZ/W3LePplSISpJ5cgABYqcAxRPprTgtLYCvWRERJXAAMgG5hN7uch9QJVoggaAsXXpkpeYSN0klcDklWnRRCr3MnhTCSxUxGsRQwDFc0RME5vF8YkeoOIyQPrgxn9+5l0AwP0vfGRYWi+TN2c1ZIAcXgZfaAAkb4MhMnpERKMdAyAbVKIEBgCN0vTn1rrCp0nbocX0vHIGyO9VIHqNo/HkEMvgzRmgwn+O5iGAg6YJzD0iACqyBwgwlsDkZu93d/dY3j4uDV2sC+pBXdChkQXFDkIU/T9+r1L232Eiokrhp50NKtEEDRj/WjcHIuVifl55jzNFUaRG59SwPUDFbIQqyDvCpx/TGAAdHEgHQEktA1REkCVth9Eb0ZeNv/zRPsvby6ux5BKYU9k6cx9UvsQKsEr1kRERVQIDIBtopZ0y9wDJf+lXamrv1NZa7WufR8HRbcYVRKJvJiJngHJshjpQwiow8xBAcwAkHjuRKiEDlDne3kjCUDb6oNN6eJcogfk8iiHr41SgUXwJLB3M5bu0n4hoNGAAZINKlcD+emBQ+9rj0Mqi4Zx2ZIv29dTWuqwZPnIGSGx6am6CFsFKXzSdpSnmRBzwmjNAxiBABAXJUgKgzHGbd0uP5NggVTyn3+sxbO0RdioDZOqDypfIAHEFGBG5CQMgG1QqALr4pPQ+YqdLQUi5TWiswbRM1ueLcw/Lul7OAIlAQQRJ5hKY2FG+PlR4NkvbCT1h3QMkghGxGWopAVCHKQCSd56XxbQASDGsiCtm0GM+Su0B4gowInIT/slng0r1AF17+hQcOb4e86aOLevzmt17+Ul4v6MXi49vy7rOKgMkMj/m3eB7IiIAKvzXMqiVf7InQQP6bBzRl1zKKrCunqjhcnOwJYiGa7/XYxiR4HQJLFZgAKTNAOIKMCJyEX7i2aBSc4BCfi8WHZcddJTb1NY6TG2ts7zOkAESW134zBmgTAksUnwvivnkL/b/agr7cXAgrmWGSsoA+XOUwHIEQIYSmBT0ON0EXXAGKJN5qw0yA0RE7sESmA2iFSqBjQRyBigSty6BRRMp7Dw4iPXv7wFQbAksHdDEEikkUyq6etNZmoljwgCye4BKmQQtSmBiib+530jQSmA+pSwZIH0QYoGrwJgBIiIX4hm7RPIWDwyAshkzQOYSmB4InPrDtdrXxZTAAt70Y8WSKezriyKZUuFRgAmNIe1yoMRVYKYS2ITMkv9cJbB4Qs4AOd8EbZ6GnS9mgIjIjXjGLpH813bQyxOIWVDOAGWaoEU5KJQjYCwmAPJLO6HvHxDlr4BWbhJBailzgEQGSDRrt2WCq1xN0OJ3I+D1GEYkNIUDlrcvlXkUQL6YASIiN2IAVCK54ZQZoGyWPUCZAMjn9VhuC1FMD1BQ6gHqj+q9RH5Tc3Rpc4CM7++ExhoAQ/QAZfqNfF5FW6UGGKdl28lf5Cow8fPiKjAichOesUsk/7XNACibsQcoUwKTfk4hi20hiukB0qY0x1Poy8y1qTUEQCIDVMoyeOOxivJazgBIKoHJU6GdGlrpL7oHKB0AcQ4QEbkJz9glEgGQ16MUdVId7YbKAAHWG58WUwKTN1bVM0BerWylzQEqYS+wBtNxaSWweBKqmh10xKVl8EnpeufmABVXAhsQASMzQETkIvyTr0SxCi2BHykMc4ASoglaCoAsNj4NFpFJE/0rA7Gk1qOTzgBlVofZsAqstd6475kIgFJq+vHNGaK4tPXHKVPGYvbkZhzb3lDw8+ar2GXwWgaIPUBE5CL8xCtRLJn+65nlL2vWGaChS2CKUnhwUhPIDFWM6RkguQSmNUGrxWeAzBu/HppZYg8AkVh2ACRPgvZ7Pfifv59b8HMWwhzs5Uvsa1bLEhgRuQg/8UrEGUBDEwFOJJ49BwgwNgT/w9lH4LI5k4p6nhp/+ld5MJ7UTuh1AaseoEwGyKL5ejjmDNCExhB8HgWJlIpIIolGGHt7xHP6ypQdLDoDFBUZIJbAiMg9eNYuEUtgQxOzZQZjCakJWj/Rjq3Vg4pZk5u1lVWFEsvdzSUwbThgIrMKTOsBKvz9Omxs2PB9c21AG2potRQ+IS2DL4dgkU3QMYvSJBHRaMezdonEycO8RJrSRF9JX1TeDFX/WY2t02fi1BXR/Kw/jx5oyU3Q5qXhpU2C9mL6xCYA6WBI3uLCahhiXCqBlYPIACVTqvY688EsJhG5EUtgJYolmQEaisgAHRyIQSyEkvfFGiv11ZhXWRWiRgpEDBkg0x5hpcwBAoC7Pj8DP//Tdiw58ZD084reI4sAKCbtBVYO8qyheDIFrye/jA5/h4nIjRgAlUjLAPGvZ0u1mQzQvszmpIApA1SrZ4CKmf+jPU+mgbcvkjA0QYt+avMcoGIyQABweEstbrngOO37oUpgouzmL9PvhpxpiidTeZe0onE28hOR+/ATr0TcB2xoIjDZ15feP0tRjJkGeeVRMROghTGZZur+WBIHBuLa4+WaBO2xaWaT2NbiwEAs6zqtBFam+VB+j5wByr8EJjJADOKJyE34iVcirXzAk4clEdT0RNJZmZDPa1jmLv/cSlmF1BDya2Wtv+4f0B7PPBywlB4gKy2ZHqZ9fRYBUKq8JTCPR9Fel3i9qqrih8++h/959ZOc92MWk4jciJ94JYpyFdiQwqYdxs1BztTWWu3rYub/CB6PomWBdnVHABgzQOZBiHZN7Rar2F76cG/WdeUugQHZS+Ff+nAv7lu/Ff/06zctp1UnkimIfmkG8UTkJuwBKhFLYEMzl7XM+2Ad196IOz8/He1FLn+XjQkHsFfKxNQGfVpDtB2rwKw01KRf3/r392RdFy9zE3T6uRQMxvWAb0tHr3bd3r5Y1iyjKPeyIyKX4ideifQAiDNUrJgzPo0WO6FfdOKhmDNlbMnP1Sw1VAOmOUBZq8Ds+dVfeMz49ONZZK/0rTDKt0ec+fXuOhjRrvvkwEDW7Q2b+TKLSUQuwk+8EnEJ8dDM+0s1ObQTOpAdANVJy+BFOcruDFB7UzpzZbX9RKzMk6ABZL3ejp5B7bpP9lsEQEl9M99yHicRUaXxE69ELIENzetRtKXigL5qygnZGSCv1n+jZ4D0E74dAkMMH0xIu8GXi3i9IrDZ3S1lgKwCIPawEZFL8VOvRFxBMzx5qbu5B8hOWQGQtBdY1KFVYHLgK5eTgMqUwMxN0AczIwEAYKdUDhOiCc4AIiJ34qdeibgMfnh10kowJwMgucE3HPDC41GytsIQWRm75gDlEwCVMwMUMAV8vZnxA0B6GrdZlAE8EbkUP/VKxBLC8OQ+oCaLJmi7jJMCIJENMm+FYXcGyOdRtGnTIpsixCpQAhN70onfy76ongGSs0ECS7hE5FaOfert378fl19+ORoaGtDU1IRrrrkGfX19Q97+q1/9Ko4++mjU1NRg0qRJ+Id/+Ad0d3c7dYi24EaSw5OXwjsaADWEtK8PyTQni+AjYZoEbVcPkKIoWvYkas4AJUQTdPlKYPqxJBFPphCJ68d0cJABEBGR4Nin3uWXX4533nkHzz33HJ555hm88MIL+NKXvpTz9rt27cKuXbtw++234+2338bDDz+M1atX45prrnHqEG3BHorhhctUApMzQFoAZGoKTqr2ZoCA7CyTIBquy5kdDGbGMUTiKW1PNKF7iBIYM5hE5DaODEJ89913sXr1arz66quYNWsWAOCee+7Bpz/9adx+++1ob2/Pus/xxx+PX//619r3U6dOxQ9+8AN88YtfRCKRgM9XnTMbWQIbntwEPa4+NMQtSyP3ANVldpY39wAlM5kgr43vV3oGVCKrB6giJTApAyT3/wBDZ4CCeW6cSkQ0WjjyybxhwwY0NTVpwQ8ALFy4EB6PB6+88krej9Pd3Y2GhoYhg59oNIqenh7Dv3JiCWF48magYm6OE4LSMEpfZtChCExVNd3/k7C5Byj9vEOXwMq5FYYIZKLxFPpj6QBI/AwGYkmLPqVMAMQAnohcxpFPvY6ODowbN85wmc/nQ3NzMzo6OvJ6jL179+LWW28dsmwGAKtWrUJjY6P2b+LEiUUfdzG4Cmx4B6Tm2zEO9gDJZh42BoAx+xJPppC0eQ4QoL/35gyQKIHZGWwNRw7G+jIZoLbGEMQhdJuyQAzgicitCvrUu/nmm6EoypD/3nvvvZIPqqenB5/5zGdw7LHH4pZbbhnytitWrEB3d7f275NPcu967QTOARqefNItZcPTfPzuH07Hv1z0KXz6U20AjAFQLJlyNAOUFQAl7X+ufI8lmkiiN9MD1FDj03qvuk0rwdjDRkRuVVBjzde+9jVcddVVQ95mypQpaGtrQ1dXl+HyRCKB/fv3o62tbcj79/b2YvHixaivr8cTTzwBv3/ojEEwGEQwGBzyNk5iD9Dw/mnx0fjKL1/HyvOPdfy5jm1vwLHtDdr3fmkFVjyRsn03eEDKACWN5SUt2CrjKrCQKIFJGaC6oA9N4QAODMQN2TiAATwRuVdBAVBraytaW1uHvd3cuXNx8OBBbNq0CTNnzgQArF27FqlUCnPmzMl5v56eHixatAjBYBBPP/00QiHnGmbtwhLY8OZNbcEb3znH8eyPFUVR4PMoSKRUxJOq7cvgAWkVmCkDpM8cqkATdDyFvqgIgPzoC6a/Fn1BAsc4EJFbOfKpd8wxx2Dx4sW47rrrsHHjRvzxj3/EsmXLcOmll2orwHbu3Ilp06Zh48aNANLBz7nnnov+/n48+OCD6OnpQUdHBzo6OpA0/WVdTdhDkZ9KBD+CvD2E3YMQAf29NzdB273vWD5EI3g0kdQyQPUhH0Li8rh1EzQzmETkNo6tLf/lL3+JZcuWYcGCBfB4PLj44otx9913a9fH43Fs2bIFAwPpDRpff/11bYXYEUccYXisbdu2YfLkyU4dakkimRNKDZcRVy2/V8FgPH2y10tgdi6DzxEAJctfAhOToKOJFA4Opuf+1Id8WmlMHowIMIAnIvdyLABqbm7GI488kvP6yZMnQ1X13bPPOussw/cjxWAmAAoxAKpa4uTuVAYoZxO0A8+V77FEEyl09kQBAOMbQth1cBCAHrALLIERkVtV53TBEWQglj6hhAMMgKqVvB2GIz1AmfJSdfQAiUxPUlt9N64+qM0HMgdAehM0f3+JyF34Z1+JtBIYA6Cq5Ze2qkgk7Z/NM9xWGOXtAdIzQB3d6azP+IaQdnnEPK2aGSAicil+6pUgnkwhnunzYA9Q9dK2w0iktC0x7DzhB6SVV7JK9gBF4kl8sj8dAE1sDks9QLkyQPwoICJ34adeCeSTCXuAqpe+CkzVAlY79+cKWswBUlVnym3DEau9dh4YxGA8CY+S3hhWWwVmygBpgxC5CoyIXIafeiUYzPT/eBT+BV3N5GXwokzlSAAkBRcpqZ/fX84eoEwGaGem6bm9qQYBnwchKTMk0/YC8/P3l4jchZ96JRiUlsBXcs4NDU2UwGJJuQTm7F5gov8HALzlLIGZmpkPGxsGgOGXwTMDREQuw0+9EnAJ8cggZ4C0HdptPOFbNUGL/h+gMsvghcNbagFAywCZByHyd5iI3IqfeiWIO1BOIfvJc4Cc6AGyaoJOSDWwsq4CM5WyzjgyvXWNlgFKMAAiIgIYAJXEiZMp2U/LACVUR3uAolIGKCkFQOXsARoTDhi+n3nYGAB6c3SuEhjnABGR2/DMXYIEN0IdEUQP0KBU/rGz58VqEKLoAVIUwFPGDNC4+qD2td+rYGxd+vtgriZoZoCIyKX4qVeCmAND9ch+Itsj74Tud7oJOln+bTCA9Kazl82ehPqgDz+98mTt8pxzgLgZKhG5FLfCKAFLYCODOLkPRPWTvxM9QHIAlKzADCBh1d98Cv+85HjDc+daBSbmAHEZPBG5DT/1SiBKYH6WD6qamMQsZ4Cc2AojKjUYiybocvb/yMyBV0jbCsPUBB3nJGgicid+6pVAWwXGElhVE9keMbgy4PXYOrdJZE9ihibozD5gZZwBNBSRATJv16ENQmQAREQuM2pLYGvf60Jt3WBR921vCuG49sZhb8cS2Mig9wAlM9/bG5QEvdklsHiFeoByydUDpGeAuAqMiNxl1AZA//Dfb8ATDBd9/+duPANHjq8f8jYiA1TOzS6pcKJHZyCaLoHZXbKsth4gK9pu8LmaoJkBIiKXGbUB0AmHNsJfU1vw/T7o7ENfNIGP9vbnHQBxBU11ExmfAS0D5EwAFE1kD0L0VagHyEwrgRlWqqW0QI0lMCJym1EbAD1y3SloaGgo+H5XPLQRL7y/B72RxLC3ZQlsZBDvz0CmCdrugNU6A1Rd2UER4CRSKhLJFHxejyEYYgmMiNyGZ26ThlA6JuwZjA97W5bARgbHe4AsBiGK4LhaSmAiAwToWSD5eFkCIyK34aeeSX3IDwB5ZYDEsDuWwKqbVgITPUBOlcAstsKoliZoucQlAiDxX59HqZpAjYioXHjmNmmoyWSAIsNngGLMAI0I2RkgmwMgaRWYqqYDn2rrAfJ4FO04RSO0vg9YdRwjEVE58ZPPpEHLAOVfAmMPUHUz9wA5tQoM0IPiausBAqRNW7UMUGYuEgMgInIhfvKZ6D1A+ZfAGABVN5H5iGslS7t7gKQAKBNcVFsPEAAETbOAotwJnohcjGduE60HKFpIBqh6TnKUzbzxqVMlMEAPgKqtBwiwygBxBhARuRc/+Uy0HqA8MkBcBj8ymN8fu98vj0fRgmBRAqu2HiAACPmNPUDaRqgMgIjIhfjJZyIyQPk0QevL4PljrGZOB0CAsREaqNYeIOMwRK0Exp3giciF+Mln0lDAMnh9EnT1nOQom3lMQcBn//tlngZdjT1A5gyQCNY4xoGI3IiffCa1wfRfyX2cBD1qmLMwjmSAfOYMUDX2AOXIALEJmohciGduk/pgOgMUS6YMk3KtsAQ2MpSjBGYOLkZCD1CMTdBE5GL85DMRGSAA6I8OnQVKpFgCGwmyAyDnSmBaBigTHHur6HcjOwPEJmgici9+8pn4vB7thNA3TAAUS6jafah6mXtc5H2x7H6O7FVg1RMAiQxQVKwCi4smaJbAiMh9eOa2UBdML4Xvjw0dAHES9MhgngNU40QA5DMGFyIAqqYmaHMGKJZkEzQRuRc/+SzUZgKg4RqhRQmMgxCrW1kyQD5jBkg0QfuruAdIzwBVzzESEZULP/ksaAHQMCWweIKrwEYC8X4KTmSAgqYeILFNSlX1APnNGaDMXmD8/SUiF+Inn4V6UQKLJoe8XYwlsBGhzhQAhQJlCIDEIMQqKoGFfMwAEREJ/OSzIFaC5bsKrJqm/VK2cMALRXqLHO0BMi2Dr6oeIJEBinMOEBERAyALhZbAWEKoboqioC6gZ4EcCYCytsKovvKoyFJFEsY5QFwGT0RuxE8+C9oqsOECoBRLYCOF3AdUE3BwEnTS1ANU1RkgzgEiIvfiJ58FLQOU5zJ4lsCqX11ID4CcWAWWPQm6+nqAzBmgKDNARORi/OSzkO8yeJbARo7m2oD2tdjuxE7mSdDV2AMUMmWAuBUGEbkZP/ks1OdZAmMT9MjRWhfUvpazQXbRm6DT2ZVkFW6UmzsDxCZoInKf6vl0riJ6E/Qwy+AT7AEaKcbW6Rkg87J4O5iboJkBIiKqbvzks5D/MniWwEaKeinrU+9gBqia5wBlZ4DYBE1E7sVPPguF7gXGElj1G1cf0r524oQfNK8Cq8rNUDkHiIhIYABkIZ85QKqqIl6FfR5k7ZQpY7WvFcX+oMQ8CTqpbYVRPb8bQdOwRpbAiMjN7K8FjAL5zAESf+ED1bXhJVk7uq0ev7x2jqEXyE65JkFXZwaIy+CJiBgAWchnGbwofwGA31c9JznK7dQjWhx77Fw9QNXUBG3OAEWZASIiF+MnnwW9ByiJVEpFR3cEX/ufP+P9zl7tNqL8BbAERkDAm86uZG+FUT0BkMgAxZIpJFMqm6CJyNX4yWdBXiY9EE/itv97D79+/a84984XtMvlDFA1lTmoMrTsStZWGNXzv5gc6MQSKb0E5sBkbCKialc9n85VJOT3QMQ0/dEEPtrTr12nqukTmwiA/F7FkaZaGlnMJbBkFfYAyQHQYDypN0Ezg0lELsRPPguKohhWgsnnMLEyTPyF76uiv/CpcsyToONV2APk83q0gEzubwv6+TtMRO7DT74c5JVg8nL4vX0xAPq8F84AIiB3BqiaeoAAvQ+oJxLXLmMGiIjciJ98OcgZoJ5BPQDq6I4A0E90HCJHQPYcoGrsAQL04+wZjGddRkTkJvzky6FOWgov/7X8kxe2ApADIP4IyWoSdPVthQFkZ4ACPg972IjIlXj2zkEEQN2DcQzE9E1RX922HwCHyJGReRl8okqnhOsZoHRWM1hlx0dEVC789MtBbIi6O1PyEsRsINHsyiFyBGRPgo5JqwSricgA7emLAgDCQZZwicidHDt779+/H5dffjkaGhrQ1NSEa665Bn19fXndV1VVnHfeeVAUBU8++aRThzgk0QO0u3sQAAwrweQlxMwAEaAHQMmUimRKlcYkVNfvhwjsRS9bQ8hfycMhIqoYxz6dL7/8crzzzjt47rnn8Mwzz+CFF17Al770pbzue9ddd1W8L0GUwHYdTJ8oxjeEDLOBuJM2ycxDBqt1o9xwIP173dGT/r2uD3E3HCJyJ0c+/d59912sXr0ar776KmbNmgUAuOeee/DpT38at99+O9rb23Ped/PmzfjRj36E1157DRMmTBj2uaLRKKLRqPZ9T09P6S8A2Rmgxho/+iIJ9GaWxXMnbZIFsgKg6iyBZWWAapgBIiJ3cuTsvWHDBjQ1NWnBDwAsXLgQHo8Hr7zySs77DQwM4Atf+ALuvfdetLW15fVcq1atQmNjo/Zv4sSJJR8/oGeAdh/USwWiX2IgluQ+SmTg8ygQSctoIlm1JbDsDBADICJyJ0c+nTs6OjBu3DjDZT6fD83Nzejo6Mh5vxtvvBHz5s3DhRdemPdzrVixAt3d3dq/Tz75pOjjlokAqDczBLGhxmeYDcQMEMkURdEGCkaruARWG8g0Qfems6YNLIERkUsV9Ol88803Q1GUIf+99957RR3I008/jbVr1+Kuu+4q6H7BYBANDQ2Gf3aoDRpPDA0hv2E6NJfBk5m8EqxaJ0GHTb/XzAARkVsV9Off1772NVx11VVD3mbKlCloa2tDV1eX4fJEIoH9+/fnLG2tXbsWW7duRVNTk+Hyiy++GKeffjrWrVtXyKGWrM60PLihxo9w5q/nPikAYgaIhKDPi14k0C9tneKvst+PsGnndzZBE5FbFfTp19raitbW1mFvN3fuXBw8eBCbNm3CzJkzAaQDnFQqhTlz5lje5+abb8a1115ruOxTn/oU7rzzTpx//vmFHKYtsjNAPi0DlO4B4iowMhLZQEMAVGVbYZgzQGyCJiK3cuTPv2OOOQaLFy/Gddddh/vuuw/xeBzLli3DpZdeqq0A27lzJxYsWID//M//xOzZs9HW1maZHZo0aRIOP/xwJw5zSFkBUI1fu6yfPUBkQfwuyJvnVlsJTPQACewBIiK3cuzs/ctf/hLTpk3DggUL8OlPfxqnnXYa7r//fu36eDyOLVu2YGBgwKlDKIn5xNAQ8msraNIlMK4CIyPRBN0fSwdAigJ4q2wvsOweIAZAROROjn36NTc345FHHsl5/eTJk6Gq6pCPMdz1TjpsbC0CPo+W6VGhan1BA7EkM0CUJaCVwNLBsd9TfRuNmjNATeFAhY6EiKiyePbOwe/14ObF07TvD2+pMyyDZw8QmZl7gKqt/AXoc4CEZgZARORSzH8P4erTDsfZ08bhvY4enDx5DN7860EA6ROcSE4xA0RCwBwAVeHvRq1pdWNzHQMgInInBkDDmNxSi8kttQD0v577owmtt4M9QCToTdCZEliVDUEEjM39Po+C+iA/AojInarvE7qKib+e+6PsAaJsWhO0yABVWQM0ALTWB7WvawLequtRIiIqF/75VwBtEnQsAUVJf80MEAlaBihWvSUwOeMj9isjInIjBkAFkJfBi7/2GQCRIBriB7Qm6Or73ZAzPpE4AyAicq/q+4SuYtok6CgnQVM28zJ4XxWWwIiIKI0BUAHE0LieSJw9QJRFZAN7Mxmgav3duGHBkQCAa08r/4R1IqJqwRJYAcbUppcMD8SS6InEAbAERrqsZfBVWAIDgK+efQROP7IFnzq0sdKHQkRUMQyACtAQ8sHvVRBPqtjdHQFQvX/lU/mJvrCBWPUOQgQAn9eDWZObK30YREQVxbN3ARRFwRjT5Fz2AJFQk9lmonswnR2s1gwQERExACpYc60xAGIGiATxuxFPpseEMwAiIqpe/IQu0Ng6cwaIP0JKG2sKjqu1BEZERAyACtZcGzR8zwwQCebg2McMEBFR1eIndIHMf+UzA0TCWHNwzACIiKhq8RO6QOYAiBkgEswZIJbAiIiqF8/eBZI3kwT4Vz7p6oI+Q0DMFYJERNWLZ+8CyQFQwOfhbtqkURQFLVKGUEwOJyKi6sMAqEDj6kPa10Fmf8hkbJ0eIDfU+Ct4JERENBSewQskZ4CCfpY4yGic9PvREGIARERUrRgAFUhudI0lkhU8EqpG4xr0AIglMCKi6sUAqEDydN/+GAMgMmqVSqQsgRERVS8GQCVIptRKHwJVGbkE1mJaFk9ERNWDARCRjeS94sY3hIa4JRERVRIDICIbySXS5jAzQERE1YpdmkVoqQtgb1+s0odBVej0I1swtbUWx7Y3wuPhjCgiomrFAKgID111Mv7pV2/i5vOmVfpQqMqE/F78YfmZHJBJRFTlGAAV4YRDm7D6H8+o9GFQlWLwQ0RU/dgDRERERK7DAIiIiIhchwEQERERuQ4DICIiInIdBkBERETkOgyAiIiIyHUYABEREZHrMAAiIiIi12EARERERK7DAIiIiIhchwEQERERuQ4DICIiInIdBkBERETkOgyAiIiIyHV8lT4Au6mqCgDo6emp8JEQERFRvsR5W5zHnTbqAqB9+/YBACZOnFjhIyEiIqJC7du3D42NjY4/z6gLgJqbmwEAO3bsKPoHePLJJ+PVV18t+hh4f96f9+f9eX/en/cvTHd3NyZNmqSdx5026gIgjyfd1tTY2IiGhoaiHsPr9RZ9X96f9+f9eX/en/fn/YsnzuNOYxO0heuvv5735/15f96f9+f9ef8y37+cFLVc3UZl0tPTg8bGRnR3d5cchRIREVF5lPv8PeoyQMFgECtXrkQwGKz0oRAREVGeyn3+HnUZICIiIqLhjLoMEBEREdFwGAARERGR6zAAcrF7770XkydPRigUwpw5c7Bx40bD9Rs2bMDZZ5+N2tpaNDQ04IwzzsDg4GCFjpZkL7zwAs4//3y0t7dDURQ8+eSThutvueUWTJs2DbW1tRgzZgwWLlyIV155pTIHSwarVq3CySefjPr6eowbNw5LlizBli1bDLeJRCK4/vrrMXbsWNTV1eHiiy9GZ2dnhY6YZMO9f9u3b4eiKJb/Hn/88QoeOZkxAHKpxx57DMuXL8fKlSvx+uuvY/r06Vi0aBG6uroApIOfxYsX49xzz8XGjRvx6quvYtmyZWWbz0BD6+/vx/Tp03HvvfdaXn/UUUfhxz/+Md566y289NJLmDx5Ms4991zs2bOnzEdKZuvXr8f111+Pl19+Gc899xzi8TjOPfdc9Pf3a7e58cYb8dvf/haPP/441q9fj127duFv/uZvKnjUJAz3/k2cOBG7d+82/Pve976Huro6nHfeeRU+ejJQyZVmz56tXn/99dr3yWRSbW9vV1etWqWqqqrOmTNH/fa3v12pw6MCAFCfeOKJIW/T3d2tAlD/8Ic/lOegKG9dXV0qAHX9+vWqqqrqwYMHVb/frz7++OPabd59910VgLphw4ZKHSblYH7/rMyYMUO9+uqry3hUlA/+Oe9CsVgMmzZtwsKFC7XLPB4PFi5ciA0bNqCrqwuvvPIKxo0bh3nz5mH8+PE488wz8dJLL1XwqKlYsVgM999/PxobGzF9+vRKHw6ZdHd3A9C38dm0aRPi8bjh/89p06Zh0qRJ2LBhQ0WOkXIzv39mmzZtwubNm3HNNdeU87AoDwyAXGjv3r1IJpMYP3684fLx48ejo6MDH330EYB0H8l1112H1atX46STTsKCBQvwwQcfVOKQqQjPPPMM6urqEAqFcOedd+K5555DS0tLpQ+LJKlUCv/4j/+IU089FccffzwAoKOjA4FAAE1NTYbbiv8/qXpYvX9mDz74II455hjMmzevzEdHw2EARFlSqRQA4Mtf/jKWLl2KE088EXfeeSeOPvpoPPTQQxU+OsrX/PnzsXnzZvzpT3/C4sWL8bnPfU7r8aLqcP311+Ptt9/Go48+WulDoSIM9/4NDg7ikUceYfanSjEAcqGWlhZ4vd6sVSWdnZ1oa2vDhAkTAADHHnus4fpjjjkGO3bsKNtxUmlqa2txxBFH4JRTTsGDDz4In8+HBx98sNKHRRnLli3DM888g+effx6HHnqodnlbWxtisRgOHjxouL34/5OqQ673T/arX/0KAwMDuOKKK8p8dJQPBkAuFAgEMHPmTKxZs0a7LJVKYc2aNZg7dy4mT56M9vb2rKW577//Pg477LByHy7ZJJVKIRqNVvowXE9VVSxbtgxPPPEE1q5di8MPP9xw/cyZM+H3+w3/f27ZsgU7duzA3Llzy324ZDLc+yd78MEHccEFF6C1tbWMR0j58lX6AKgyli9fjiuvvBKzZs3C7Nmzcdddd6G/vx9Lly6Foij4xje+gZUrV2L69OmYMWMGfv7zn+O9997Dr371q0ofOgHo6+vDhx9+qH2/bds2bN68Gc3NzRg7dix+8IMf4IILLsCECROwd+9e3Hvvvdi5cycuueSSCh41AemyySOPPIKnnnoK9fX1Wl9PY2Mjampq0NjYiGuuuQbLly9Hc3MzGhoa8NWvfhVz587FKaecUuGjp+HeP+HDDz/ECy+8gN/97neVOlQaTqWXoVHl3HPPPeqkSZPUQCCgzp49W3355ZcN169atUo99NBD1XA4rM6dO1d98cUXK3SkZPb888+rALL+XXnllerg4KB60UUXqe3t7WogEFAnTJigXnDBBerGjRsrfdikqpbvGwD1Zz/7mXabwcFB9Stf+Yo6ZswYNRwOqxdddJG6e/fuyh00afJ5/1RVVVesWKFOnDhRTSaTlTlQGhY3QyUiIiLXYQ8QERERuQ4DICIiInIdBkBERETkOgyAiIiIyHUYABEREZHrMAAiIiIi12EARERERK7DAIiIiIhchwEQERERuQ4DICIiInIdBkBERETkOgyAiIiIyHUYABEREZHrMAAiIiIi12EARERERK7DAIiIiIhchwEQERERuQ4DICIiInIdBkBERETkOgyAiIiIyHUYABEREZHrMAAiIiIi12EARERERK7DAIiIiIhcZ0QEQFdddRUURYGiKPD7/Rg/fjzOOeccPPTQQ0ilUpU+PCIiIhrGVVddhSVLllT6MDQjIgACgMWLF2P37t3Yvn07nn32WcyfPx833HADPvvZzyKRSFT68IiIiGgEGTEBUDAYRFtbGw455BCcdNJJ+OY3v4mnnnoKzz77LB5++GEAwMGDB3HttdeitbUVDQ0NOPvss/HnP//Z8Di//e1vcfLJJyMUCqGlpQUXXXRRBV4NERGRe61evRqnnXYampqaMHbsWHz2s5/F1q1bteu3b98ORVHwm9/8BvPnz0c4HMb06dOxYcMG245hxARAVs4++2xMnz4dv/nNbwAAl1xyCbq6uvDss89i06ZNOOmkk7BgwQLs378fAPC///u/uOiii/DpT38ab7zxBtasWYPZs2dX8iUQERG5Tn9/P5YvX47XXnsNa9asgcfjwUUXXZTV1vKtb30LX//617F582YcddRRuOyyy2yr+vhseZQKmjZtGt5880289NJL2LhxI7q6uhAMBgEAt99+O5588kn86le/wpe+9CX84Ac/wKWXXorvfe972v2nT59eqUMnIiJypYsvvtjw/UMPPYTW1lb85S9/wfHHH69d/vWvfx2f+cxnAADf+973cNxxx+HDDz/EtGnTSj6GEZ0BAgBVVaEoCv785z+jr68PY8eORV1dnfZv27ZtWlpt8+bNWLBgQYWPmIiIyN0++OADXHbZZZgyZQoaGhowefJkAMCOHTsMtzvhhBO0rydMmAAA6OrqsuUYRnwG6N1338Xhhx+Ovr4+TJgwAevWrcu6TVNTEwCgpqamvAdHREREWc4//3wcdthheOCBB9De3o5UKoXjjz8esVjMcDu/3699rSgKANi2+ntEB0Br167FW2+9hRtvvBGHHnooOjo64PP5tEjS7IQTTsCaNWuwdOnS8h4oERERAQD27duHLVu24IEHHsDpp58OAHjppZfKfhwjJgCKRqPo6OhAMplEZ2cnVq9ejVWrVuGzn/0srrjiCng8HsydOxdLlizBbbfdhqOOOgq7du3SGp9nzZqFlStXYsGCBZg6dSouvfRSJBIJ/O53v8NNN91U6ZdHRETkCmPGjMHYsWNx//33Y8KECdixYwduvvnmsh/HiOkBWr16NSZMmIDJkydj8eLFeP7553H33XfjqaeegtfrhaIo+N3vfoczzjgDS5cuxVFHHYVLL70UH3/8McaPHw8AOOuss/D444/j6aefxowZM3D22Wdj48aNFX5lREREo18qlYLP54PH48Gjjz6KTZs24fjjj8eNN96If/u3fyv78Siqqqplf1YiIiJylcWLF+OII47Aj3/840ofCoARlAEiIiKikefAgQN45plnsG7dOixcuLDSh6MZMT1ARERENPJcffXVePXVV/G1r30NF154YaUPR8MSGBEREbkOS2BERETkOgyAiIiIyHUYABEREZEtXnjhBZx//vlob2+Hoih48sknDdd3dnbiqquuQnt7O8LhMBYvXowPPvjAcJutW7fioosuQmtrKxoaGvC5z30OnZ2dls8XjUYxY8YMKIqCzZs3F3SsDICIiIjIFv39/Zg+fTruvfferOtUVcWSJUvw0Ucf4amnnsIbb7yBww47DAsXLkR/f792/3PPPReKomDt2rX44x//iFgshvPPP99yC4x/+qd/Qnt7e1HHyiZoIiIisp2iKHjiiSewZMkSAMD777+Po48+Gm+//TaOO+44AOnhiG1tbfiXf/kXXHvttfj973+P8847DwcOHEBDQwMAoLu7G2PGjMHvf/97wzL6Z599FsuXL8evf/1rHHfccXjjjTcwY8aMvI+PGSAiIiJyXDQaBQCEQiHtMo/Hg2AwqO0FFo1GoSgKgsGgdptQKASPx2PYL6yzsxPXXXcdfvGLXyAcDhd1PAyAiIiIyHHTpk3DpEmTsGLFChw4cACxWAz/+q//ir/+9a/YvXs3AOCUU05BbW0tbrrpJgwMDKC/vx9f//rXkUwmtduoqoqrrroKf//3f49Zs2YVfTwMgIiIiMhxfr8fv/nNb/D++++jubkZ4XAYzz//PM477zx4POlwpLW1FY8//jh++9vfoq6uDo2NjTh48CBOOukk7Tb33HMPent7sWLFipKOh5OgiYiIqCxmzpyJzZs3o7u7G7FYDK2trZgzZ44hk3Puuedi69at2Lt3L3w+H5qamtDW1oYpU6YAANauXYsNGzYYymQAMGvWLFx++eX4+c9/ntexMAAiIiKismpsbAQAfPDBB3jttddw6623Zt2mpaUFQDrg6erqwgUXXAAAuPvuu/HP//zP2u127dqFRYsW4bHHHsOcOXPyPgYGQERERGSLvr4+fPjhh9r327Ztw+bNm9Hc3IxJkybh8ccfR2trKyZNmoS33noLN9xwA5YsWYJzzz1Xu8/PfvYzHHPMMWhtbcWGDRtwww034MYbb8TRRx8NAJg0aZLhOevq6gAAU6dOxaGHHpr3sTIAIiIiIlu89tprmD9/vvb98uXLAQBXXnklHn74YezevRvLly9HZ2cnJkyYgCuuuALf+c53DI+xZcsWrFixAvv378fkyZPxrW99CzfeeKPtx8o5QEREROQ6XAVGRERErsMAiIiIiFyHARARERG5DgMgIiIich0GQEREROQ6DICIiIjIdRgAERERkeswACIiIiLXYQBERERErsMAiIiIiFyHARARERG5zv8PH5saMRPB/AoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfr = dfr.ffill().bfill()\n",
    "dfr.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfr.to_dfs0(\"Viken_extrapolated.dfs0\", \n",
    "            items=ds.items, \n",
    "            title=\"Caution extrapolated data!\"\n",
    "            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.remove(\"test.dfs0\")\n",
    "os.remove(\"modified.dfs0\")\n",
    "os.remove(\"neq.dfs0\")\n",
    "os.remove(\"accumulated.dfs0\")\n",
    "os.remove(\"wl_feet.dfs0\")\n",
    "os.remove(\"mauna_loa_co2_daily.dfs0\")\n",
    "os.remove(\"mauna_loa_co2.dfs0\")\n",
    "os.remove(\"Viken_extrapolated.dfs0\")"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "ce8098af3ce22f00283f2dbf9dff06733072d21f076b7f034380a2cf9868eeaa"
  },
  "kernelspec": {
   "display_name": "Python 3.8.10 ('base')",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}