notebooks/Dfs0 - Timeseries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from mikeio import Dfs0\n",
    "from mikeio.eum import ItemInfo, EUMType, EUMUnit\n",
    "from datetime import datetime, timedelta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Write a dfs0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "\n",
    "nt = 10\n",
    "d1 = np.zeros(nt)\n",
    "data.append(d1)\n",
    "d2 = np.ones(nt)\n",
    "data.append(d2)\n",
    "\n",
    "items = [ItemInfo(\"Zeros\", EUMType.Water_Level, EUMUnit.meter), \n",
    "         ItemInfo(\"Ones\", EUMType.Discharge, EUMUnit.meter_pow_3_per_sec)]\n",
    "\n",
    "dfs = Dfs0()\n",
    "\n",
    "dfs.write(filename=\"test.dfs0\", \n",
    "          data=data,\n",
    "          items=items,\n",
    "          title=\"Zeros and ones\",\n",
    "          start_time=datetime(2000,1,1),\n",
    "          dt=3600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read a timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<mikeio.DataSet>\nDimensions: (10,)\nTime: 2000-01-01 00:00:00 - 2000-01-01 09:00:00\nItems:\n  0:  Zeros <Water Level> (meter)\n  1:  Ones <Discharge> (meter pow 3 per sec)"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "dfs = Dfs0(\"test.dfs0\")\n",
    "\n",
    "ds = dfs.read()\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From comma separated file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "            Decimal Date  Average  Interpolated   Trend  Number of Days\nDate                                                                   \n1958-03-01      1958.208   315.71        315.71  314.62              -1\n1958-04-01      1958.292   317.45        317.45  315.29              -1\n1958-05-01      1958.375   317.50        317.50  314.71              -1\n1958-06-01      1958.458      NaN        317.10  314.85              -1\n1958-07-01      1958.542   315.86        315.86  314.98              -1",
      "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>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "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": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<AxesSubplot:xlabel='Date'>"
     },
     "metadata": {},
     "execution_count": 5
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "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 dfs 0 with a non-equidistant time axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_dfs0(\"mauna_loa_co2.dfs0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a equidistant time axis first interpolate to hourly values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_h = df.resample('h').interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_h.to_dfs0(\"mauna_loa_co2_eq_1hr.dfs0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read a timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<mikeio.DataSet>\nDimensions: (10,)\nTime: 2000-01-01 00:00:00 - 2000-01-01 09:00:00\nItems:\n  0:  Zeros <Water Level> (meter)\n  1:  Ones <Discharge> (meter pow 3 per sec)"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "dfs = Dfs0(\"test.dfs0\")\n",
    "res = dfs.read()\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "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='H')"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "res.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]),\n array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])]"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "res.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Or as a Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                     State 1Sign. Wave Height  State 2Sign. Wave Height  \\\n2017-10-27 00:00:00                  1.749465                  1.749465   \n2017-10-27 00:10:00                  1.811340                  1.796895   \n2017-10-27 00:20:00                  1.863424                  1.842759   \n2017-10-27 00:30:00                  1.922261                  1.889839   \n2017-10-27 00:40:00                  1.972455                  1.934886   \n\n                     Mean StateSign. Wave Height  MeasurementSign. Wave Height  \n2017-10-27 00:00:00                     1.749465                          1.72  \n2017-10-27 00:10:00                     1.807738                           NaN  \n2017-10-27 00:20:00                     1.853422                           NaN  \n2017-10-27 00:30:00                     1.897670                           NaN  \n2017-10-27 00:40:00                     1.935281                           NaN  ",
      "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>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "dfs0file = r\"../tests/testdata/da_diagnostic.dfs0\"\n",
    "\n",
    "dfs = Dfs0(dfs0file)\n",
    "df = dfs.to_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                     VarFun01    NotFun\n2017-01-01 00:00:00  0.843547  0.640486\n2017-01-01 05:00:00  0.093729  0.653257\n2017-01-01 10:00:00       NaN       NaN\n2017-01-01 15:00:00  0.305065  0.214208\n2017-01-01 20:00:00  0.900190  0.999157",
      "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>"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "dfs0file = r\"../tests/testdata/random.dfs0\"\n",
    "\n",
    "dfs = Dfs0(dfs0file)\n",
    "df = dfs.to_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a timeseries with non-equidistant data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "\n",
    "d1 = np.random.uniform(low=0.0, high=5.0, size=5)\n",
    "data.append(d1)\n",
    "\n",
    "datetimes = [\n",
    "    datetime(2000, 1, 1, 0, 0),\n",
    "    datetime(2000, 1, 8, 0, 0),\n",
    "    datetime(2000, 1, 10, 0, 0),\n",
    "    datetime(2000, 2, 22, 0, 0),\n",
    "    datetime(2000, 11, 29, 0, 0)\n",
    "]\n",
    "\n",
    "dfs = Dfs0()\n",
    "\n",
    "\n",
    "dfs.write(filename=\"neq.dfs0\",\n",
    "           datetimes=datetimes, \n",
    "           data=data,\n",
    "           title=\"Non equidistant\"\n",
    ")"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[Correction of precipitation,\n Precipitation correction,\n Precipitation,\n Specific Precipitation,\n Precipitation Rate]"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "EUMType.search(\"prec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "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]"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "source": [
    "EUMType.Precipitation_Rate.units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from DHI.Generic.MikeZero.DFS import DataValueType\n",
    "n= 1000\n",
    "d1 = np.random.random([n])\n",
    "d2 = np.random.random([n])\n",
    "data = []\n",
    "data.append(d1)\n",
    "data.append(d2)\n",
    "start_time = datetime(2017, 1, 1)\n",
    "time_vector = []\n",
    "t = start_time\n",
    "random_dt = np.random.choice([1.0,2.0],size=n)\n",
    "for i in range(n):\n",
    "    t = t + timedelta(hours=random_dt[i])\n",
    "    time_vector.append(t)\n",
    "title = 'Hello Test'\n",
    "\n",
    "items = [ItemInfo(EUMType.Water_Level), # use default name and unit based on type\n",
    "        ItemInfo(\"Nedbør\", EUMType.Precipitation_Rate)] # use a custom name\n",
    "\n",
    "data_value_type = [DataValueType.Instantaneous, DataValueType.Accumulated]\n",
    "\n",
    "dfs = Dfs0()\n",
    "dfs.write(filename='accumulated.dfs0', data=data,\n",
    "        datetimes=time_vector,\n",
    "        items=items, title=title,\n",
    "        data_value_type=data_value_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<mikeio.DataSet>\nDimensions: (1000,)\nTime: 2017-01-01 02:00:00 - 2017-03-03 09:00:00\nItems:\n  0:  Water Level <Water Level> (meter)\n  1:  Nedbør <Precipitation Rate> (mm per day)"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "dfs = Dfs0(\"accumulated.dfs0\")\n",
    "ds = dfs.read()\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modify an existing timeseries\n",
    "\n",
    "The `write` method allows to modify the data without the need of specifying names, units etc. If you need to add variables, rename, change units, you must use `create` instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<mikeio.DataSet>\nDimensions: (10,)\nTime: 2000-01-01 00:00:00 - 2000-01-01 09:00:00\nItems:\n  0:  Zeros <Water Level> (meter)\n  1:  Ones <Discharge> (meter pow 3 per sec)"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "dfs = Dfs0(\"test.dfs0\")\n",
    "res = dfs.read()\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
     },
     "metadata": {},
     "execution_count": 20
    }
   ],
   "source": [
    "res['Ones']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modify the data in some way..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "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])"
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "data = res.data\n",
    "data[1] = data[1]*np.pi\n",
    "data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shutil import copyfile\n",
    "copyfile(\"test.dfs0\",\"modified.dfs0\")\n",
    "dfs.write(\"modified.dfs0\", res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([3.14159274, 3.14159274, 3.14159274, 3.14159274, 3.14159274,\n       3.14159274, 3.14159274, 3.14159274, 3.14159274, 3.14159274])"
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "dfs = Dfs0(\"modified.dfs0\")\n",
    "res = dfs.read()\n",
    "res['Ones']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second item is modified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "res['Zeros']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert units\n",
    "\n",
    "Read a file with waterlevel i meters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<mikeio.DataSet>\nDimensions: (577,)\nTime: 1993-12-02 00:00:00 - 1993-12-14 00:00:00\nItems:\n  0:  ST 2: WL (m) <Water Level> (meter)"
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "\n",
    "filename = r\"C:\\Program Files (x86)\\DHI\\2020\\MIKE Zero\\Examples\\MIKE_21\\FlowModel_FM\\HD\\Oresund\\Data\\1993\\Boundary_Conditions\\waterlevel_viken.dfs0\"\n",
    "dfs = Dfs0(filename)\n",
    "ds = dfs.read()\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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u/BLHDWFcGAqBQD+4R8Fd9k54xV5JPuGxMyL8xOdzhDRmeQRcdiRyxl2IZ+NZvP7/PoQvPHyu5XGxTBE2C8HrsCr3uewWWKg3chR6JLKBWo/C/LyQWZhqKIjoViI6QUSniehOlcffRUSH5X+PEtHVZqxTsDaWU3kMuO1w2qztD5Y5MBbAgNuOh0+J8BM3FINahsJtM9Sj+NqTF3FiIYmP/sfRliGkuDzBsHYwEBHB6zBf76mdxPhaqHoUwlB0HSKyAvgsgNsAHADwTiI60HDYOQA3M8auAvAxAHd1d5WC9bCWHgqO1UK4eiKIo3MJg1bVP0QzBThslrqdei2SR2GcoViqyX+cWtCefRFv6MrmSMKA5oee9Kh4AqRwms1CIvRkEi8BcJoxdpYxVgDwNQC31x7AGHuUMRaTf3wcwPYur1GwDpaS+Y4rnmrZPeTFueU0GNu69eqAlKMINezUawm47UjlS4bV9UfTBWUuw4n5pOZxjYKAHK/T2gMNdyXdPAoiQtBjF8lskxgHMFXz87R8nxbvA/BfWg8S0fuJ6BARHVpaWtJpiYL1sJTKd9xDUcvuiBeZQhmLPVD6aSbRdFEz7ARIlTyMAUmDdu2xdBHXbA/CZbfgxEILQ9EgCMjx9YDUuBR60k8LK+hxiNCTSahtl1S3SET0akiG4n9pPRlj7C7G2EHG2MFIpFkuWNA91u9R9N5MaDNYTuWVgTlq8J2yUXmKlXQeQ34Hdoa9OL+c1jxOK/TkcZgfekpk9UtmA0DQbRehJ5OYBjBR8/N2ALONBxHRVQD+EcDtjDFREtPjpPMlZArlNecoAGBXRJoNcK7FxWkrsJRsnmxXC4+9G5WniGWKGPTIhmKllaHQCj2ZOw61XGFI5ku65SgAyaMQVU/m8BSAfUS0i4gcAN4B4J7aA4hoB4DvAHg3Y+ykCWsUrJHldfRQcEYDLrjsFpxbql6cHjm9jN/9+nNbxu1njGExmWsaWFTLgOJR6H8xLlcY4pkCwl4Hdg55MRXNoqySC8kVy8gWy6pS4j6nFZmCeaGnZE6/rmzOoMeudHtvRUwTtGeMlYjoQwB+AMAK4AuMsSNE9AH58c8D+GMAYQB/Jyf2Soyxg2atWdCe6gjUtc8isFgIO8NeJfSUypfw2197FsupAiwWwl+9dfNXR8cyRRTLDMMtDC2PvRvhUaxmi6gwqTTXabOiUK5gIZHDWNDddBygLrrnMbnqSU+JcY6UzN4amxU1TJ18whi7F8C9Dfd9vub2rwH4tW6vS7B+1irf0ch1k4P492dm8HcPnMZdD51FIlvE5WMBfPfZGfz+bZcivI7cRz/BO9OH/doehRJ6MmCHW9vsx5Vgl5L5JkNRVY5VT2abGXqqDi3SN5mdK1aQK5bhsmv3B61mihhQCcf1O6IzW6ArGwk9AcCbr9uObLGMv/z+CeSLFXzmndfiL958FUoVhh8dXdBzqT3JvDIru5VHwXMU+l+M+a455HW0lDRXEwTkeB025EsVlMrmzM3mnpaeHgU3iK0S2o+fXcHVH/0h/vPwnG6v2ysIQ7EJSeaK+NPvHcHx+e43ry0l87CQtk5RO66fHMQtB0awa8iLJ/7wtfi5q8Zw+VgA40E3Hjy5+cue2wkCAoDfaQMRDImZr8jy5YMeh1LirCZAGFcRBOQoUuMm5Sn0VI7l8PNsFX56bioOAPjMfad0e91eoWPfjIgGAYwByAI4zxgzZ7sgaMu9L8zhi4+cxzeemsILf/J6WCzqjVtGMLeaQ9jnhHUDr/n5X74epUpFkQAhIrx0VwgPnVoCY0yzEW0zMB3Lwm6llqEni4Xgcxoj48EvhGGfQ9lFqxmKVRVBQE51HGpJ1119pyRMMhRzspFfSOY23ee0pUdBRANE9AdE9AKkzui/B/ANABeI6Jtyf4Ogx/jJ8UUA0o7u6YuxNkfry/mVNHaFvRt6DquFmnSiDu4MYTlVwMVoZkPP3evMxLIYHXC3NbRGyXgoOlMeB1x2KwIum0boiQ8tUi+PBcwTBjQi9MSHM7Uyzhfkz2Y8U6yTQdkMtAs9fQtS9/QrGWOXMMZeITe1TQD4cwC3E9H7DF+loGMKpQoePrWM1146DAA42aKz1gjOLWewc6h5MttGuWI8AAA4Oru5taCmYxmMB7XDThxJatyAHEW6ALfdqiRsh/xOZWJhLfFMEQ6rBR4VPSpfD4SerA2qthulE6nxCysZRfH35PzmahptaSgYY7cwxv6FMRZXeexpxtiHGWP/ZNjqBGvm0IUo0oUy3npwAlYLKcnRbpDMFbGcymOX3GGtJ/tH/LBaCMc2uWjgTDyL7S3yE5yAy2aIR5HMleqkL4Z8TtXdcTxTwIDHrhpe8Zg8NzuRLSHgsuka+lEMhYZHUSpXMBXN4JbLRgCgpfRJP7KWHMVVAHbW/g5j7DsGrEmwAZ48F4WFgJv2hjHsd2Kui4bi/LLkeu8ywKNw2a3YPeTdVOqy2UIZsUxBKT3Nl8pYSORbJrI5AbcdUwaE4VL5kjJ8CJDG2aoZZ62ubMD84UWrOkqMc9x2K+xWbQXZudUcShWG6ycH8eNjCzjZQkyxH+nIUBDRFwBcBeAIAJ7EZpC6pgU9xKHzMVyyLQC/y45tA66uehTnZLkHIzwKQJpZ8dS5qCHPbQZ3fucw7n5uFk//0etwfiWNX/qHJwCgs9CTy46kAeWxqXwJvhrpi4jfiYdOqXgU2YISt2+kF3IUesp3AFJBxYDboVlpxqVOdoQ92D/i37IexY2MscZZEYIeo1Su4JmLMbzlekmNfXTA1VImWm+49MZkWH+PAgAOjAZw93Ozshjd2ju/ewnGGO5+TpI2+/qhKZxfTiNfkvZgneUojKl6SuVL8DtrQ08OJHOlpkazeKaIiZD639kvN7oZYcg6YVVnQUBO0GNXqr0amY5JFU8TIQ8u2ebHNw9NoVJhXa04NJJO+ygeUxkqJDCYdL6Ej3zreTxyurOpb8fmksgUyji4MwRA6u5Vq1ip5ekLUXzpkXMo6tAc9cLMKsaD7padqxthT0TyVC6s9H/l01Q0q9z+6cllPHBiCZdu8+ONV47iqolg298PuOxI5kuqOkwbIZUrKX0QgJSjAJpLZFuFnvhF2iy1Vb0lxjmtFGTnV3MgAob9Tuwf8SNdKCs9MZuBTt/NL0MyFvMA8pAkwpk8eU5gEH/3wGl849A0npuK44e/c3Pb41+cXQUAXLM9CEDaDSZUdoO1fOLe4zh0IYZCuYL3v2rPutd6ciGJnxxf2NBztCPSogGs33h+Og5AyiU9cloSRf7IrZcq3mA7eAw+lSvpKhkh5SjqQ08AsJwqYPtg1YOItfDq7FZpOp9ZInqJnDH9G0GPHbNx9VDuQiKHsNcJu9WC/SPShub0YkrT6+o3OvUovgDg3QBuBfDzAH5O/l9gIN9/cR4AcHIh1VHi8vxyGg6rRUmGdnJh5S7zvz5xcd0T03LFMn7rq88i5HXgfa/Yta7n6IShFpIS/cbh6TgcNgs+9dZrMBn2wGGz4Ob9nc9R4TpGelc+pfIlJXQE1HgUNe95rlhGvlRpeTEOerTj+UaTyBbh1zlHAaBljmI+kcO2Aem94sUJ84nu5QeNplNDcZExdg9j7Bxj7AL/Z+jKNjntLsqxdAFnltJ4z8smAQDfO9w0qqOJc8tp7Ah7lGat2t2gGslcEfOJHC7d5seFlQweOLm4llNQ+OHRBZxYSOLPfuHKdWs8dQJXpN0MhuL0Ygp7Iz5sG3Dhvt+9GY/e+Zo1vXfco9DzYswYa6p6UoxzzWZjpUY4sNX6tOL5RlIqV5AvVRRBQz0ZcNs15e4XEnlsk6XhuXHdDJ9TTqeG4jgRfZWI3klEv8j/GbqyTczvf+cwXv2pB1rOhuYVRDfvj2BPxIvnZR2ZVpxfSWNnTVd0uw/sGTn5/KHX7MWuIS/+9HtHOz2FOn54ZB5DPideJ9eQG4XTZsWA274pQk/nVzLYNST9rWxWi/K36hQjhhflihWUK0ypWgKgTNqr9SiiqfaGIug2Z34Db/KrzbPoRdBjR7pQVs3nLSSqM0QcNgsGPXZFCXgz0KmhcEPKTfwspJATDz8J1siZpRT+7ckpXFjJYLZF6SqvINo15MWeiA9na4b5qMEYw0wsi4lQtWKmlfonIO1qAeCy0QDe+ZIJXFjJrOvLfXQ2gRt2DnalwmPI5+h7eQTenLWRDnZlJoWO3dnJvPS399WEnriMx3KdRyHdbjWudcCk0aG8JLfW2OkFb7pr/I7kS2VE04W6YVMRv3NTeRQdvZuMsV8xeiFbhX9/Zka5fXwuoVkKeX4lDauFMBHyYM+wD/efWESpXIHNqm7bE9kS0oVy3fNxBVetHfiZpRTsVsJkyIMdctLt4koGV24f6Ph8SuUKLkYzeP0V2zr+nY0Q8TuxnOzvATIz8SxKFYbJDWhiGeFRpPPSbtzfcJEd8td3Z0c7CD0FTZoIlykYZyhqq7lqPcDFhPTebNvEhqKdKOAfEVGoxeOvISLhWayBHx9bUHSLjrfocTi3nMb2QTfsVgt2D3lRLDNMxbTL7WZXpcdGB6qGwmGzIOixa4eeFlPYGfbCZrVgR0i6aK1VdG82LnWkblQIsFO0JCX6CV42ub2DfgkteKWTnr0UKbnvwddoKHz1xpkbilZS8n6XzZQ+ipRs7HyGhJ4kw9iYe1mQk9YjAzWGYhN8TmtpZ3ZfAPA9IsoBeAbAEgAXgH0ArgHwYwB/ZuQCNxMLiRyOzydx522XYn41jwstBtefW67mG/YMS+V2Z5dSSly7kVn54jMWrJenjvi0dzZTsaziSfAmuQvR1iGupnWuGNtk18hm2KnNySWWoxswFD6HNJNCz+FFaqEnQHrPj9WIMa6kC7BZqGWvgs9pR7ZYbukFGwEPPXkMSGZzpdxYut448+qmWo9iOODCYiK/aeTG24kC3s0YuwnAByDJd1gBJAB8BcBLGGO/wxjb/NNkdOLBE9JbdfP+CCbDHs3GMcYYzi+nFaOwR5bE4LOk1eD5jsaRlRG/UzP0NBvPKsd7nTa5TnxtTUIXVqq5lG4w5HMilS8ha5IyqR7MKd6f9syJdlgsBL/OMym0PIrGzUY0VcCg19HyAshLbLut98QNReM56AEPtTXOpFCbShjxOZEvVZA0cSSsnnSaozgFYPONbeoyD55cwkjAiUu3+TEZ8uCxsyuqx03HskgXytgTkS6+Ax47hnwOnFnU3u3PxqWBN5GG6pkhn1Np7qollS9hNVusMyyjA25lt9sp55bT8DishpbF1lLbG9KvzUyzqzmEvI4Nd7AH3PrOpEgXtEJPDiTz1cbNlXShZSIbqJfx6KbcStrAHMWghqFYSOTgtFnq+kpqC0n01p0yAzEKtUuUyhX89NQSbt4fARFhR9iD+UQOuWLzzvhhWbLjxt1h5b7dER/OLmt7FHPxLEYCrqbKI61QzZxKqGo86FqT7MByKo8vPnIeE4OerrnX/Au42Mfhp9l4dkPeBCfg0ncmheJRuJpzFEC1KCKazrdMZAPm6T3xHIWesyg4XocVDqtF6SPhLCTy2DbgqvsOKJ/TRP9+TmsRhqJLHJlNIJEr4ZX7pO7bybAHjEmDahp57MwKhv1O7B2uqrDuiXiVvgc1ZuO5prATIH1gM4Vyk5LnNJ/N3OhRrEFt9iuPSz2XN+wa7Ph3NkpEQ3uon5iL5+qKDtZLwK3vTIqkRthmWA6p8BLtaLrQgaGQdtFJA2ZmtCJjYHksEWHQa0eswVDM1/RQcIaVDc3m6KVYt6Egog/ruI5Nzwszsg6TLPimlKOqVBk9Px3HtTuCdTuUPREfoulC04eUM7uaxZjKLlVL1K1W7ZIzFnRjNVvsWB76R0cXcMPOQfyfO67s6Hg9aNcb0g/Mrmabig7Wg+RR6JujsFkITlv9ZeHG3WGEvA789689i5l4tqPQk1kzKdL5EoigOnlPDwY9DkQbktkLiVxdIhuohqnMEkbUm414FL+r2yq2AEdmVxH02JXpZbwctTGhvZop4sJKBlfJwn6cS7b5AQCHZYNTS7nCML+q7VEAzRfW6WgGDpulLqfBL1482dqOiysZXD7Wec+FHvCdbL96FKl8CclcSSePQl9Dkc6X4FOZDOdx2PDJt1yFeKaIu5+bQTJXQqhFaSxgbujJ69B3ul0tIa8D0XT1s8eY9N3b1rBJC5qsoKs3GzEU/V/z1UXOLKaxb9infICHfA54HNYmQ3Farmy6VDYMnOsnB2G3Eh490yw5vpzKo1RhquWWWqGa6VgW24PuupwGNzRaCpm15IplJPOlriWxOXarBSGvo289CrXc0HoJuOy6lsfyi6war71sBDvDHvzH83MAgLCvjUfBDUWXPYpMoWSYNwEAIwEXFmryDkupPPKlStP4WpvVAr/ThrgJeldGsBFDoa8Q/iZHUpesfpiICDtCnqbQE1eJbexL8DhsuGw0gKOzzWMpZ5R8g0roya8upDcdyzSN3OQJ1k5KZPnzDbW5YBjBkK93DMVUNLOm8Apftx4GNuC2IZUvoaTDLBFAMv7uFhfZ6yYHlVG0Vzd4vE1rMylH0ShqqDcTg27MrWYVvSclhDvYXIE34DFHxsQI2nVmJ4koofIvCWCsS2vsexhjchyz/uIg9VLUJ6i5h7Fd5YM35HM2leYBNQ1cKuGMsNcJCzUbiqlYtuk1RgIuWKgzQ8E9lG57FPw1Owk9/efhOVz1Jz/Ar37pKUPW8aOjC3jlX96PD331mY5/Z6WDruZO4RdjvfIA2WIZ7hYlu1eOV8OMB8YCLZ/LabPAbqWuh57S+ZIhiWzO9pAHFVb9jvCNXaNHAUj5DC212X6jXcOdnzEWUPnnZ4wZ99fYZKxmi8iXKk2VEZNhL6Zi2TrJ8QvRNLYFXKo19kGPvakrFABm4tKHVS1HYbUQQl4nlmqkxtP5EqLpQp2AICCFdUYCLsx0EHqqehQmGIoO5BEYY/jEfx1DIlfC/ScWDUmqcu/ugRNLeGG6OXekhiJ/oYMnxqXG9SqRzRZaGwqej7ppb1iRsteCiOBz2pSS226RLpQNDT1xz4FPKOQeRaN3Dkjf17hJMzn0pp1H8WkiegsRCe9hAygt/g0Jrx0hDwqlSt2AkzNLaeyOqHc5hzwOVY/i5EIKQz6n5iCZxlCNojWk4rWMB92K4WkFn3FhhkcxJHcKa8m0lysMj51dwXQsi5+7ahSMAc9ejLV93sfPruCP734Rf3z3izg21xzia2Q+kYPDZkHAZcPf/KSzftSVVB5E0m5zo+g9vChXKsNp174kHJwcxJ++6XL83buu7+j5/C5710NPaaNDT/Lmakoua5+OZRH2OlQlQ8xS0DWCdjmK0wB+EcCjRHRenknxQSK6lohED0aH8Bb/xhI6nofgeQrGGE4vJLF/pD6RzRn0OpAplJua9E4uJJuS37VEGtQ/W7nLY0F3R0133PDoEUJZKxG/E7liRZk90Mivffkp/NI/PAEA+B8/ewkAqY+lFUvJPN79T0/gX5+4iK8+cRF/9YMTbdexkMhhb8SHX7lpF354dAHnl9vrZK2kCxj0ONruyDuh6lHoczFq51FYLIT3vnxnx2NGfU6bKeWxRoaeRgfcsFlI+Q5NxzKq3yNA9ii2SOjpbxljv8QY2wngZQC+A2APgG8CiG/0xYnoViI6QUSniehOlceJiD4jP36YiK7b6GuagaIuGWj2KACpzHQ1U8T/vucI0oVyXaNdLXwXWrtLKVcYTi4klfJZNSRp7qqhaJWAGx+UZDzKbSbwLafyCHrscNi6v19oNZCJMYbHz0YBAK/cN4SdQ16MDrhwooVSLwA8emYZxTLDt3/j5XjfK3bhwZNLbWWyeVkkH9jUSg2Ys5Jq36zWKTxHoZecd7tk9lrxu2y6VmV1QrpQNtRQWC2EsaBb+Q5Nq+T6OIPyONj1jhjuJdp+y+WL9VUAbpf/3QzJ0/jURl6YiKwAPgvgNgAHALyTiA40HHYbJKXafQDeD+BzG3lNs5hflS5oww3J7LGgG1YL4UI0jT/49xfw1Scu4pqJIF61T312csgrXRiiNU13U9EMcsUKLtHwQoCqjAcP1UxFM3DaLKoVS2MDLpQqDCttcgDLqbwp+QmgddPdSrqAbLGMj9x6Cf7hPQcBAPtH/G0NxeNno/C7bLhyfACvuXQYpQrD4xpaXBw+1YyHI9S67BuJdtCs1imK1LhO4Z12yey14neZkKPIlwyR76hlIuTGVCyDckUaFqblUQy47aiw7pcIG0G7HMWPADwO4IOQJtz9GWPsesbYrYyxP93ga78EwGnG2FnGWAHA1yAZolpuB/DPTOJxAEEiGt3g63ad+YQkAue01X+A7VYLxoNuPHhyCf/5whx+42f24LsfvAk7NCS7g4pHUTUUfBfbyqMYDbhQKFeUiptp+cOt1pQU8Utej5aWUqFUwYe/9iz+68X5JgHCbsF7ENRyKbyK7LJtAaUgYE/Eh/Mr6ZajZx8/u4KX7grBaiFcu2MQLrsFT56Lah5fqTDEMtJFf8Bth99p62iWx3I6r0siG6gO0tHLo8gWyhsWKqzF77Ir0uXdoFJhyBjsUQBSUv/Zi3G86W8fRqFcUcYANKL2fe1X2nkUZyH1S/Bd/V4iGtLptccBTNX8PC3ft9ZjAABE9H4iOkREh5aWekv5fEFFC4YzGfbgxRkpfv6ul062fB4esojWfPBOLUiGYt+I+ocVqM494GW00/GMpvKqslvX8Cj+7cmL+O5zswAAs2T2J0IeWAg4t9x8Yeb31fahjA64kCmUNXd2i4kczi2nFRFGh82C7YOelmXCyXwJFSbFoYmkSYRTHRiKTnSSOsXrsMJqIf1CT6WKrobC5+zu8CKuHOt3GWso3n2j9D3leS+t/OCgZ/N0Z7fLUfw/jLEbAdwB4AEA1wP4ChE9TURf3uBrq11mGrd8nRwj3cnYXYyxg4yxg5GIeujGLCRDob775nmK/SO+pqqoRvjM3lq9p3MraYwOuFoOahmT+yv4FLypqLa7zMXMljRUL394dB4+pw0RvxOvuXS45XqNwmmzYizoVk0en1lKwSaPkOXw93VeQ/DwxVmptPVqWYcLkEpwWynUrspffr6rHx1wYb6NUmixXEE8U9StAICIMODWZ+RoucJQKFV0DT0NyBIj3YrRpwycRVHLRMiDk//nNuXnfcPqhoJ/XzdDiWyn72geQAZAVr69HcBGt0XTACZqft4OYHYdx/Q8i8k8rtDQROIf6pv2tnfUeDI7VrNDubiSUYyNFqNcwymeRSJXxGq2qJrIBlp7FIVSBU+dj+HdN07ij954mamTu3YNeVVl188upTAZ9sBeM1WNd5zPreZUK8qOzTWH74YDTjx7Ma75+lyagYcXhgPqcz9q4aXNeoWeAMiGYuO7dl5J53boV5wQ9jlQYVJobFAnL6oVWjLpRuCwWfDIna/BkZlVzQKAAfcWCT0R0f8loicAzAH4KAA/gL8HcAljbKOSoU8B2EdEu4jIAeAdAO5pOOYeAO+RE+o3AlhljM1t8HW7SqlcwUoq35TI5rz5+u143WUj+PBr97d9LrvVAr/LVpfMvhDNtB1DGvY64HfacHophZmYdg8FALjsVvhdNtVE8UIih0Kpgv0jPtPHOx4YDeDEfBL5Un2J7JmlNPZE6sNwVY9CPZR0fD6J7YPuugEzw34nFpM5zbwGDyfw8ELE78JKutBSTqOTWdNrJaCTR5HlhkJHj4KH2FbSrT0tvdCSSTeK8aAbP3v5Ns3Ht0zoCcA5SInsCGPsNYyxP2KM3csYi2/0hRljJQAfAvADAMcAfIMxdoSIPkBEH5APuxdSnuQ0gH8A8Jsbfd1us5IuoMKqIZ1G9o/48Y/vPahUsLSjVhYgUyhhKZnHZLj1GFIiwhXjAzg8vdqyh4ITkS+SjSwojYMbVz7dKFdPBFEsMxyfq1YzlcoVXFhJY3eDoRj2u0AEzVkbZ5dSTSXJvFdDK6/Bwwk8vDDsd4KxaiOiGivyY3rlKACp6U4XQyH3pDh1NBRViXtjdtSlcgUPnFhUSrm5R2F0jqJTBjaRgmy7HMVnGGOHGGOGDCiWjc5+xtgextjH5fs+zxj7vHybMcY+KD9+JWPskBHrMBI+4WpYI5m9Vga9DqV6iVfZtAs9AdKF9dhcAi/KCbidLWZcD2tNxdNoHDSDq7ZLobzDNeGeqVgWxTJTRshyHDYLhnxO1TGvjDGcq5lPzhmWq7+0xAdXZWPNwwudDKrhfzc9hRQHdJIazxnoUUQ1ZqhslDu/8wL+2xefwkMnpeKVao6iN0aPcgVZNTWFfkN0VxsMv3BoeRRrZdjvVIzPxRV1pVk1Xr4njGKZ4UuPnMPogKtld23E79IMPQG9YSjGg24M+Rx4bqqqsXRmUcpZqJUrjg64MJdovogvJvPIFMrY3WQoWo+yjDUks/lGoNXoS96boqdHoVcyO1eUQmZ6Ggqei2nXk7MeUvkSvvX0NIBqSXQ3cxSdEvTq8/cxG2EoDIZXzujlUWwfdGM6lgFjTPEoJkOtQ08A8NLdIfidUqdsq54LQBbd0/Ao3HYrAm7zv4hEhKu2B/HcVFXD6chsAkRQ7WzfFnCp5ij4eM9GD6tdmXA8U5RmKMud6cMdzPKOpguwUDUBrgfcULTqEekEJUehY7NayMOHTOm/o/7J8UXl9kVZoK/bOYpOCLo3h4KsMBQGw3eYejWnbR/0IF0oI5Yp4sxSGgNue0f5DafNik+8+UrsHfbhbQcnWh47HHAirTJn+/xyGpNhj+mJbM7LdodxZimtaFM9eX4Fl24L1CWlOaMDLtXQ0zm5xFYr9LSo4oUAUtVT7QWfx+NbhZ6WU/rpPHEG3HaUK0xT96pTuKHQs4/CZrUg6LEbEnp65NQyAi4b9kS8ikCf4lH0kqHogoLs3c/N4EuPnDP0NTo2FET0H61+FqizmJS6svXSRJoYrMpFvDATr5sR0I6fu2oMP/7dm/GGK1s3t/PQUm3D2WfvP437ji82XVDN5NWXSv0yD5xYRDRdwFPnY3j5nrDqsdsG3EjmS00ideeWU3DYLEqvCSfgtsFhs7TIURSVRDYg5UFCXkcbjyKva9gJ0K87myezXS3UY9dD2OswpOrp6YsxvGRXCLuGvEqBRipfhEduQuwVgh6H4cns3/7ac/iT7x3V7BPSg7V8Kn69zc8CFRaTed3yE0B15/viTALH55K4ekL/mdW8vPSMPJZ1Np7FJ2U11UJJn2lqerAn4sP2QTfuP76Erzx+AYVSBW+/Qd1bGqvpJanl3HIau8LeupGwgBTaatV0F8/WGwqgPn+khp6CgBzFUGzwYmREMhsAwj6n7qGnSkUKu0p/f6kjnjGGlMHKsesh6LYbmsyuVZK+9wXjOgc6NhSN/Qv91s9gFovJvK4zG3ZHfHDbrfjGoSmUKgxXtRlJuR72DEvG6NSCZCien4orj/3KTbt0f731QkT4mUsi+PGxBXz2/tO45cCIpkT7qNKdXr/rOqtS8cQZ8lUrzBqJZwoIuusv+pL4ovaubiGpLeWyXtp5FPlSGX927zElxKZFzoAcBSB5FHqHnhaSUj/PRMiDHSEpFBtNF5DMleDvMUPBq9I2mkPSolbs8qFTxkkXtWu4CxDRJ4joX4jolxoe+zvDVrWJiGf0UwsFJJnjy8cCeE6+eF9TIzuhFx6HDeNBN07LHsWxuQQsBBz/2K14xT69pL704dWXSDIi+VIFv/ez2k2LSnd2jUdRKldwcSWDXVqDorwORDXCJvFMsSk3NOx3aXog0jjcvLIOvQi0MRQvziRw10Nn8eq/eqDl8xjRcAdIlU96Vz3VVvvx0vCpWFaal91DFU+AFMKsMGw4h6TFabnS71X7I3j87ErTrBq9aOdRfBGS3tK3AbyDiL5NRHx7fKMhK9pkJLJF+FWSqxvhF66TdBFHAk7dd6icfSM+xaM4uZDCzrBX10SnXrxsTxgHRgO487ZLcek27TnO2wakprtaj2I6lkWpwjQ9ipDXiahK2IQxJoWeGkqMhwNStZiatlEsU0RBZRzuRuHhr9Ws+q69drZ4q/CUEclsQOpCj2WKLTvW1wrv5xkLurG9RuI9lTN2ut164IUVeg2XaoRrm/3yS3cgV6zgqfPaiscbod27uocx9mb59neJ6A8B/ISI3mTIajYZjDEkciXdy0nfecMOeB02XLsjqOvz1rI34sNjZ1ZQrjDMJXKqM4F7AY/Dhnt/+5Vtj7NbLYj4nHUeBd+NNTboccJy6IkxVlfplcqXUK4w1RxFicuPN1S5zcmlue2EH9dKVSZD3VCs1Bi656bjuHm/umBmrlAGEeDUeRAV76WIZYq6hWD5/I2gPO8BkIQyU/kSdnjb9xR1E2UKYa6IMej/HTq7lMaOsAev2DcEh9WCh04u4ZUa82w2QrtPhbN25KncPX0XgIcAqJeXCBSyxTLKFaZarrkRLBbCHdeOt5Xu2Aj7RnzIlyqYjmWwlMgp5aL9zGjQXSfjwfsutDyRkNeBfKmCTEPYgFexNPZDDLeY5aE15XCjeBw2eBzWOoNQS61H0WpueLZYhstm1b30meta6Vn5xHfnfpddMdYrco6i50JP8nffKLn1i9EMdoa98DhsuGHXIB46uWzI67QzFN8D8JraOxhjXwbwewD6v4vEYBJZrj3TG5ICa2GvLJ18ciElVW5piBr2E2MDLkVqHZDkxXcNeTUrZbQkKHg+QC30BKgbCj7lUO8cBdA6D7CcymPAbcclI34lr6VGVucxqBzF49Gx8imRK8Flt8Bhs8ButSDgsikeRa8ls3k0wajQ03wip3ipt18zjpfsCuka5uO003r6CGPsxyr3f58xtk/31WwyuIvcC53Ma4V3Nz95bgWlCsOIjpVbZjE6IM0D5xUoR2cTmvLvAJQihMawjrZHwWU/miuf5lezIIKuFXCcIZ+zZegp7HPgmokgnp+Ka1bf5Ir6zqKork39PVxI5JTy67WSzBXrvPSwfP69mMzmm0S9xtXWkitK1V6jspf6toMT+NgdV8Bm1b+PWnRmG0iSG4o+9CgG3HYM+5346SnJldVLgsRMdke8yBbLuBjNIJYuYCaexeVj2gnwqkdRv1vndfHNOQrt0NN8Iochn7NuToZehL3avQoLiRyG/U4cGAsgliliQaPPI1ssw6lzsx0AJVdT6/HEMwW88TM/xWs/9SAeO9N6LrkaiWypTiF20GPH3GoO5QrrGUFATsDFPQr9Q0+8Z2fEAC+1EWEoDKQaeuqtXU6nXLLNr8zkPjCqfUHtF16yKwQAeOJcFE9fkOL1l7f0KPhFrsGj0Ag9uR3aszzmE3nDxBSHWoSeZuNZjAXdyrjOY/MJ1eOyhTI8BoSegm47LFT/Hv701LJi2P7HN59v6pZvRyJXVJLEgGTQeWHCYIdy/d3Cb2DV07zsuRoRzmykXR+FKIHdANXQU299eDvlClkeZNBj70ihttfZG/FhdMCFzz94Bp/8wQlsC7hwcOeg5vEhn0aOgkuMq1yUhjVmecyvZg0rZQ77pKa2xrLcUrmC+UQO40G30ojIRRAbyRRKhoSeLBZCyFvfuPj0hRjcdiu++usvxUw8i+8+O7Om50zkSnVe+qDHoeSN9K4q2ygOmwVuu9WQ0BOX2BntwnyYdh6FaKrbAEosu08NBfciXnfZSM8IAW4Ei4Xwv3/+cpxdSuPEQhIfu+OKln0DXB220VCspAvwOqxw2pp/d9jvapLxYIxhKprFRMiYL3TYK5XlNjbdLSTzqDCp32BA3tnHNHIZ2UIZ7hZz1ze6vlqP5/lpSaPsxl1h2K2kiDp2SjJbrPPSQzXzPXrNUABSjtKI0FMnQ8j0oj9jIn1CvGFmQb/x+su34ZNvuQpvumbM7KXoxq1XbMPn3nUdhvxO3LAz1PJYIpJF7eovrkvJvGbORm3W9nKqgGyxjMkOBkytB2XuQzpfN5uaz2kYC7phsRCCHoem7lC2WMaoQQ2VtR5FpcJwYj6Jtx2cgMVCGPa7lNLhTpF6k2pCTzVFBb0wK6WRgMtuiEcxHcsi4nd2pRG2naHYTUSNc6wVGGOi8a4FsUwBfqfNkCqEbuCwWfDWNpLk/chtbdRzawn7HHW9CIBkKLRk42tnbZcrDN99bhZcb3CHQeG72pGje4er9z96egVWCykyL60krzMG5SgA6T08Ik9WnIplkCmUlZzJcKC1kKIaiVy9R1FrHHtxUxZw2w3po5iKZRQ1aaNpZyiWAHyqGwvZjKxmiwh6e++DK+ic0QG3oi3EWUrlcZlGk96w36XM2v7cA2fwuQfOKI91MrJ2PVQnydV7Cw+fXsa1E0Hl4lk7b70RKfRkjKEY8jkVY3tMnnF+qRzW3BZw4dRi52WyuWIZhVKlvjy2xlD0Yog04LJpli9vhOlY1hCtNzXabXVTjLEHtf51ZYV9jJrCqKC/GA+66+ZyALJHodEPwZvuZuNZfOHh+mEyRnXSVz2K6s48VyzjyOwqDtaE14JuO2JpbY/CiGQ2IIWekrkS8qUyjs9L3fD7R6Q+nZHA2kJPfGdeG3riHoXeEu564XfpM9e8lnKFYTZuXN6rkXaGwtixSZsctZkFgv5iLOhCMl9SYsy5YhnJXEnTUPD7n70YR75UwS9cO648ZkQPBSB5CjYL1V1wj8wmUCyzuh1nUMOjqFQYskXjQk882Xp2KY3jc0lFcgKQDEUyV0Km0FloRqkkrAk9ce/ihhYVbGYScEsjiPVkPpFDqcKwfbA71YjtPrmfIKJt/Acieg8R3U1EnyGi1plAgSRF3YMxU0HnjAelL+JMTPIqeI+EpkchN909eU5S8Xzrwe0AgCvGjetDsVoIIwFXnY7VxaiUyN43Up0fPuixI6aiIJsr8VkUxtS28KKBJ89FcXg6jgM1TY4jsgem1QjYCN+Z14ae9g778Om3X4NPve0anVasLwGX/jMpeMXTRI8Yir+HrOlERK8C8OcA/hnAKiRxQEEL4pmC8Cj6HD4Zj4efFtsYiomQG3Yr4YdH5gFIU/ge/J8/g6+876WGr7M2RMa1pWqrgAJuO7LFMooNWkB8DKpRHsVEyIPJsAd/8f3jmF3N4SU14TDeW9Jp+Kkaeqo3andcO95zEuOcAbcdJR3mmtfCNy7dUnVuZyisjDEucP52AHcxxr7NGPt/Aew1dmn9TUWuax/09GbcVNAZ40F5Ml68waPQqHpy2qy4dFsAaTnmP+x3YjLsbdKF0pvRgXpl3IVEDn6nrU7wkIdrGitwuDquUclsAPid1+1HRpYyv2lvVXh6rYaCh576SWiTXwO0eljWw4Lc1NmtcuB2JthKRDbGWAnAawG8fw2/u6VJ5kuosN4s1xN0zpDPCYfVgpm49MVckhPGreagX7V9AC/MrGJ3xNu1KpzRoAvffzGHUrkCm9WCxWSuSfHXr0heF+sSv0ZNt6vljmvHccX4AJw2CyZqqr9GlOR/h4ZCblzrJ/00HlWIZ4qY0Clgv7CaQ8BlM9S419LOo/g3AA8S0d0AsgB+CgBEtBdS+EmgwaqGwqigv7BYCKNBl9I9vJTMw0JoGkxUy8v2SDvmfEl/uWctLhnxo1CuKLOx51dzTV3KyhCdrLpHYVToibN32FdnJADJeE2E3Dg8He/oOZJ9qMjMq7K0mh2nohk8eHJt864XEnnDJGHUaCcz/nFIsye+BOAVrJqNsQD4LWOX1t8oCqPCo+h7xgbcmI5JycPpaAbDfhesFm1P4aY90lzxt3exWfEyuS/h6JzU2DYbz2FboD5+7VdCT/UJbV5x1K3daSMHJ0P4rxfnOxrjmcgVYbWQod6P3nChQjVDwRjDaz/1IN77hSebpGJasZDM9Y6hAADG2OOMsX9njKVr7jvJGHvG2KX1N4rCqEhm9z07Qh5MRSWP4uhcApeN+lseP+h14OhHX49fe+WubiwPgLRbd9oseH5qFflSGQvJXFONfUBjNkKuyD0Kc3bpb79BMqj/eXiu7bGJbAkBl60nG+u04FGFuErF2QszqyjIxQX3vtD+/DmLiXzL8Kfe9Ke2RB8QV2YWiNBTv7Mj7MFyKo94poDTiyll994Kj6O7FzO71YKDOwfx6JllzMSyYKy5dJJ7FI01/d0KPWlx4+4wdoQ8mqGZWpINEuP9AI8qqJ3fT44vgkjqLr/n+dmOn3M1W1RVLzYKYSgMglc4CI+i/+HSG/cdW0Spwur6AHqJl+8ZwvH5pDLytDEfUM1RNIaejE9mt0Orx6ORRK7Ud/NdbFYL/E6bukcxvYr9w36866U78NT5aFNYUI1SuYJUvtTVQhlTDAURhYjoR0R0Sv6/qaWSiCaI6H4iOkZER4jot81Y63pZThVgtZAoj90EcEPxfbk3oleHON20V8qN/P2DZwEAeyL1kiG8z6CxPDbbhfLYdmh1jTeSyBb7quKJE/TaVc/v3Eoau4a8uHoiCMagDAprBfcIN72hAHAngPvkudv3yT83UgLwe4yxywDcCOCDRHSgi2vcEEvJPMJeR8ukp6A/4EObfnR0AR6H1TDNpo1y5fgA/C4bTiwkcdX2gabKLKuF4HfamnIUZoeeAO5RdBJ6KvWloRj0OJo8plK5gqloBrsiXmXS4pGZ9sWkfO7IVjAUtwP4snz7ywDuaDyAMTbHE+aMsSSAYwDGG4/rVZZS2sJxgv5iwG1Xwh3XTw72rPG3WkjJS9xy2YjqMX6XTcWjkH52qQxi6hZBjwNxDcHCWholxvsFNY9pJp5FscywK+zFSMCJsLcqx96KrWQoRhhjc4BkEAAMtzqYiHYCuBbAEy2OeT8RHSKiQ0tLa6tJNoLlVF5R9RT0N0SkXFxvvWJbm6PNhYsQ3nGt+p4q4G5WMs0WpS5yi4kGcNDjQDJfapIXaSSVK8HXh4ZCLQfDe152DkmNmQfGAh0ZisRmMhRE9GMielHl3+1rfB4fgG8D+DBjTPNdZIzdxRg7yBg7GIlENrr8DdNKilrQf7z7xkkM+51483XbzV5KS973il146g9f15TI5gRczUN0jBxa1Cm86KNxnGstjDGkCyV4TSrj3QiDKtMFz8uGYteQFMq8fGwApxaTKLRp1OTvUTervwx7xxljr9N6jIgWiGiUMTZHRKMAFjWOs0MyEv/KGPuOQUvVnUqFYVmEnjYVH739cvzJmy7v2bATx2Khlp87v8uG+QZdpWyh3JVxmq3gu+PVbFHTE8+XKqgwwOPsn2Y7TtAjGWgusQJIHoXPacOQPHhqZ9iDYplhKZVXNMbU2Eqhp3sAvFe+/V4AdzceQFIR+j8BOMYY++surm3DrGaLKJaZpnCcoP8gop43Ep2gNpazFzyKAY3S3VqUpHsfdWVzuLZWtMareH56FftGfEq/DTfwXHhSi61kKP4cwC1EdArALfLPIKIxIrpXPuYmAO8G8Boiek7+9wZzlrs2+KSxIeFRCHoMv0ul6snAoUWdEnC3Dz1xqRFPj8qJt0KZQpiUDMVSMo/np+N49SXV9GynhiKWLsBtt3bVCzTlHWeMrUBSo228fxbAG+TbDwPoyy1cOylqgcAseI6CMabsZNN58xPEA7LIX2tDYX4Z73rhRoBvIu8/sQjGgNdetg5DkSkq+lHdQnRmGwCXohY5CkGv4XfZUK4w5aIL9EYTm9I13mJkKF9zPyazuUfBjcAPjyxgdMBV17wZ9nYaeip0XRpIGAoDaDcuUyAwi+oFubpzX+0FQ+HqIEeRN1fldiMo3kIqj4srGdx/YhFvunqsTg/MYbNg0GPHUqr1bI5Ypth1aSBhKAxgKZWHw2qpGwAvEPQCfpUpd4lc0fT5Di67FU6bpaPQUz96FF6HFS67BQuJHH7ra8/C47DiPS/f2XRcxO/sIPRU6Lo0UP+9430A76HoJylkwdagceeeL5WRK1ZM9ygAqYqnlUeRNnluxkYgIlw1HsQXHzkPAPjkW65SLYEd9ruUuexarAqPYnOwlMyLiidBT9LoUfD/uylZrcWA297So+Dihd4+7KMAgA/fsg+TYQ8++Oo9mo2b7TwKxhji2e4bCuFRGMByqtCyYUYgMIvGHAXfwfeCRxFoYyiqfRT9edl6+Z4hPPg/X93yGG4oaqvSaknkSihXWNdDT8KjkKlUGI7NJXBxJbPh55JCT0JeXNB7KMOL5AsyrzIyO0cBtPcozB7Z2g0iPifypQqSefXqr9UMn5wpDIUpMAB3fPYR/Mvj5zf2PIwhms4rpW4CQS9RHYcqXYh6yaMYcNubmgFryRTKsFsJDtvmvWwNyRtMrfAT14sKdnnK3+Z9x9eI1ULYE/Hh1GJqQ8+TKZRRYehLKWTB5sdlt8Jhs1RDT7nuC8xpEXDZlB2zGplC2dQpfN2A91uspNRnc3BDMegVhsI09o34cGphY4YiLbuM3j6UGRBsDQI1MylWe8yjSOZLqFSY6uOZQmnTf694JIJ3cDfC/14i9GQieyM+zMSzysV+PaT7vDJDsPkJuKplqIls7+QoAm47GINmfD5dKG/q/ARQDT2taBiKWFqEnkxn34gPAHBmaf1eheJR9GFTkGBrIAkDyjmKXBF2K/VESIfvkrXCT9lCedN/r0IeB4iAJY3QUzTTfeVYQBiKOvYO+wEApzeQp0jJhsK3yV1kQf8iSY1Xy2MDLntPNIeG5Lj7Slp9N53Olza9R2GzWjDocWh6FNF0HkGPXZlp0S2EoahhMuyB3Uo4uYE8hchRCHodv8tWVx7bC4lsAAjJ8floWn03nS2W4d3khgIAwl6HZo4ili4qsy26iTAUNditFuwIeXBhJb3u5xA5CkGvE3DZ68pje0WTLCxfAFc0DEU6X4Jnk4eeAKnySavqaSWdV96nbiIMRQOSoVh/053wKAS9jt9lU0JPq9liz3gUg/IFMKblUfTAJL5uEPZpexTRdEF4FL3AjpAHU9EMGFMv0WuHMBSCXifgsiNXrKBQqkgqAj0yYMvrkHo8tEJP6S1iKFp5FJKh6P7fSxiKBiZCHiTzJcRaNP60IiWqngQ9TlDekS6n8lhM5jAccJm8IgkiQtjr0Aw9ZQvlvhyDulaGfA4k8yXkiuW6+ysVhlimqCT9u4kwFA1wMb+51ey6fj9TKMNlt8BqMb+KRCBQYzLkAQA8PxVHscwwEugNjwIAQl6HqkdRLFdQKFfg6YEyXqNRurMb3ofVbBHlChMeRS8wMiDtrhYSradMaZHKl0RprKCn2Rn2AgCeOBcFAGzrEY8CkAyFmkfBlWM3e3ksAIQVGY/6PEVUlu8QyeweYET+0syvth4eokU6v/llBgT9zVjQBZuFFEMx3EMeRdjrQFSlj6I6i2Lzf7eGfNXQYC3c0xLJ7B5g2O8E0fo9inS+JPITgp7GZrVg77APx+YSAKS8XK8w6HUgqpLI5dPttkoyG5Dm2tTCE9zCUPQAdqsFYa9zA4aiLHooBD3PjbvDAACHzdIzVU+A5FGkC+WmRC73KLZKHwWg7VGEfcJQ9ATbB93r7qVIbwGFS0H/c/MlEQBAoVTpCfkODk/UcjltDi873woehdthhddhbSqR5SG5bk+3A4ShUGXfsA+n1ykMmBI5CkEf8Kp9kqF445WjJq+kHh5WabxIZorco9j8hgKQEtqNHsVKugCvwwqXCZVf4oqmwt5hH7759DRWM8WmofNas2w56XwJvi3gHgv6G6uFcPxjt8LWY2XcPKzSWCKbyW+d0BNQnZ1dSzRdQMiEsBMgPApVuNz46aVk3f1ff+oibvj4j3F8PqH5u1KOYmt8mAX9jctu7boKaTu4R9FkKLZQMhuQimoW1QyFSSOWe+tT0iPsjUhy443T7r76xEUspwr4x5+eU/09xpico9gaH2aBQG9CHnVhwExha4WeRgKupoKaaLpgSg8FIAyFKuODbrjslrq5FNlCGcfmJA/jiXMrqr+XLZbB2Nao9RYIjGDAbYfVQk3CgJkt1EcBSL0tyVxJqfYCzBMEBIShUMVqIewe8uFUjaF46nwUhXIFP3tgBFPRLD7yredRbpjtGzdp+pRAsFmwWAiDHruKR1ECEeC0bY1L1rBfavxdTEpeBWMMK1vNUBBRiIh+RESn5P8HWxxrJaJnieg/urnGfSO+Oo/isbMrsFsJf/W2q3Hl+AC+cWi6aWQqL+kb9AhDIRCsl5BKd3YyV4LfaeupUl4j4fpbCwnpfUjkSiiUKlsu9HQngPsYY/sA3Cf/rMVvAzjWlVXVsDfiw0w8q9Rvn19OYyLkQcBlx0dvvxwAcLGh14J7FEET6pwFgs2CmjBgIleE37V1NmCNHsVZeVO6a8hrynrMMhS3A/iyfPvLAO5QO4iItgN4I4B/7M6yqvDKp7NL0rS7qVgGE4OS1MEOWfLgYrTeUFQ9CmEoBIL1EvY6m0JPyVwJ/h6ZxNcNGj0KHgbfN+I3ZT1mGYoRxtgcAMj/D2sc92kAHwFQafeERPR+IjpERIeWlpY2vMBJWWHzQlQyFNOxLLYPShLkIa8DXodVxVBIHoUIPQkE62fQa2/2KHpoEl83GHDb4bBZsChXPp1ZTMFhs2BCvgZ1G8MMBRH9mIheVPl3e4e//3MAFhljT3dyPGPsLsbYQcbYwUgksqG1A1WhtKloFslcEfFMUbmPiDAhT8KrJS5/uEXoSSBYPyGvE6vZIkrl6v4wmSv1zGzvbkBEdb0UpxZT2D3kNa3vxbB3njH2Oq3HiGiBiEYZY3NENApgUeWwmwC8iYjeAMAFIEBEX2GM/bJBS67D57Qh5HVgKpZRktq7a+KDk2EPzshhKU4sU1TGOQoEgvUR9jrAGBDPFhWBvGS+CL/LnLCLWYwEXMoAtVOLSVy9PWjaWsy6ot0D4L3y7fcCuLvxAMbY7zPGtjPGdgJ4B4CfdMtIcCYG3Xjk9DJ+/zsvAAD218QH+WztSk2JbCxTUAbECwSC9aHWnZ3Ibi2PApAmEV5cySBbKGM6lsW+YfMMpVmG4s8B3EJEpwDcIv8MIhojontNWlMTP3/1GC6sZHB8Xmq0q9Xt3xHyIF+qYKlGuGvFxM5JgWCzEG4QBmSMIbnFqp4AqcJpdjWHZy7GwBhwyTbzDIUpJpoxtgLgtSr3zwJ4g8r9DwB4wPCFNfCrN+3CFeMDODqbQIWxujnYEzWVT3wqXjSd7yltf4GgHwk1CAOmC2VUGLZU1RMA7I5IlZf//uwMAODaHUHT1rK13vk1YrEQbtwdVoa81KJURa1kcMPOEAAgmirgkpFAV9coEGw2uN4Tb7pL5qRqwq1U9QRUPYhvPT2N0QGXsiE1A5F1XSfjQTeIqr0UvMXejOlTAsFmYlDJUUgGIpmTml63mkexJ+LF2IBkHK6ZCJq6FmEo1onDZsHYgBsXV6TKp0yhjHypYpoWi0CwWbBbLQi4bIpHkchKBmOr5SiICK++VGox2zfsM3UtW8tE68y+ER+OygPqeTxVGAqBYOOEfdXubO5RbLWqJwD4yK2Xoliu4G03TJi6jq33zuvItRODePDkEhK5ovKhFlVPAsHGCXkdyijQRG5rehSA1KH9l2+52uxliNDTRrhuMgjGgMNTq4qbLDwKgWDj7Il4cXIhBcYYElvYo+gVhKHYAFdPBEEEPHMxptR8h00aVSgQbCau3B5ENF3ATDy7ZaueeglhKDZAwGXHvmEfnrkYq+YoRNWTQLBhrt4+AAB45mIciWwJdittmaFFvYh45zfI9ZMhPH0+hqVkHg6bBd4tMtNXIDCSy8cG4HfZ8OjpZSwl8wh5HVtmaFEvIgzFBrlxdwjJfAkPn15GyCM+zAKBHlgthJftDuOnp5ZxajFpqs6RQBiKDfOSXVJX9vH5JMaC5nVOCgSbjVfsG8JMPIvD06vYa3IfwVZHGIoNMjrgVibebR/0tDlaIBB0yk17h5Tb+02a7CaQEIZCB14qexXbBoRHIRDoRe38l5fvadZbE3QPYSh04BpZ1ZEx1vpAgUDQMUSEWw6MwGYhTIaFt24mooNFB9583XZcWMng11+52+ylCASbis+96zqUKkwUiZiMMBQ64LJb8QdvuMzsZQgEmw6b1QKbqDg3HRF6EggEAkFLhKEQCAQCQUuEoRAIBAJBS4ShEAgEAkFLhKEQCAQCQUuEoRAIBAJBS4ShEAgEAkFLhKEQCAQCQUtoM8pOENESgAtmr6MFQwCWzV6ETohz6T02y3kA4ly6ySRjLKL2wKY0FL0OER1ijB00ex16IM6l99gs5wGIc+kVROhJIBAIBC0RhkIgEAgELRGGwhzuMnsBOiLOpffYLOcBiHPpCUSOQiAQCAQtER6FQCAQCFoiDIVAIBAIWiIMBQAi+gIRLRLRizX3XU1EjxHRC0T0PSIKyPc7iOiL8v3PE9HP1PzO9+X7jhDR54lIdeQKEX2ciKaIKNVw/+8S0VEiOkxE9xHRpMbvO4no60R0moieIKKd8v2TRLRMREUiyhHRB/r1XOTHKvK5ZInonn49FyK6t+Y8npP/Nh/ut/OQHztMRCX5HN7eB3+TVxHRM/Ka39Lw2LT8GUvU3Nd350LS9/5p+bN1hOTvva4wxrb8PwCvAnAdgBdr7nsKwM3y7V8F8DH59gcBfFG+PQzgaQAW+eeA/D8B+DaAd2i83o0ARgGkGu5/NQCPfPs3AHxd4/d/E8Dn5dvv4McBcAB4rXwuRwGcBzDWj+ci/5zZJH8X5fMFIAQgKq+v387jjfL7fwOAIwAOAQj0+N9kJ4CrAPwzgLc0PPZhAL8NINEnny/Vc4H0vXfKt32Qv/dqz7Hef8KjAMAYewjSl7eWSwA8JN/+EYA3y7cPALhP/r1FAHEAB+Wf+c7EBumPp1opwBh7nDE2p3L//YyxjPzj4wC2ayz5dgBflm9/C8BriYgYYwXG2H3yuRCqHmPfnYv8cwWb4+9S+/l6C4D/ArCv385DXtvdAJbk134ewK3o4b8JY+w8Y+wwpM9S42OfBrDQcHffnYv8vc/LPzphQKRIGAptXgTwJvn2WwFMyLefB3A7EdmIaBeA62seAxH9AMAigCSkL9l6eR+kC4oa4wCmAIAxVgKwCiAsv/6E/HuXAPgLxthsv54LABeAewDsJqI75Pv69Vw47wDwb316Hs8DuA3S38UKaSc80ePnslb68lyIaIKIDkP6u/HvvW4IQ6HNrwL4IBE9DcAPoCDf/wUA05Dc7k8DeBRAif8SY+z1kNxLJ4DXrOeFieiXIe1WPql1iMp9TH79KUhf5pMA3ktEI/16LgB2QPrSTgH4NBHt6eNzAaQd55UAftCP58EY+yGAewF8B9JF8jF5bb18LmulL8+FMTbFGLsKwF5Uv/f6oWccq5//QYr/vajx2H4AT2o89iiAAyr3vxfA30LaeT0n//towzEpld97HYBjAIZr7vs4fw755x8AeJl82wZJaIwazwXAF9Ecl+3Xc/lSP5+LfB5zAO7aRJ+vrwJ4Qy+fS839TZ8f+f53oCZH0c/nUvN40/d+o/90e6J+/4cGQ8H/YJC8rn8G8Kvyzx4AXvn2LQAekm/7AIzKt20Avg7gQ21eszGpdS2AMwD2tfm9D6I+2fgN+fZ2AG75XI5C8iqu7NNzGYS0O9spf4FOQYoT99251Hy+MgBe3cefLyukENRO+e/xoryWnj2XmuO/hA4MRT+eC+Tvfc335iSAKzt5rk7/6fZE/fwPUsx4DkARknv5PkjVECflf3+O+p3hCUgXrx9DkuYFgBFIFROHIVWE/A0Am8br/aX8OhX5/z+R7/8xpOTac/K/ezR+3wXgmwBOA3gSwO6aD3BcPg8GINbH5/LyhnOJ9vG5/Buk+DVDf3++XJDyFUX5uRf64FxukH8vDWAFwJGaxxYBlOW/SwnAX/fjuUD63h+GlEc5DOD9el8jhYSHQCAQCFoiktkCgUAgaIkwFAKBQCBoiTAUAoFAIGiJMBQCgUAgaIkwFAKBQCBoiTAUAoFAIGiJMBQCgUAgaMn/D+TVvDtV0G/lAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(ds.time,ds.data[0])\n",
    "plt.ylabel(ds.items[0].name);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim is to convert this timeseries to feet (1m = 3.3 ft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ds.data\n",
    "data[0] = data[0]*3.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which units are acceptable?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "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]"
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "ds.items[0].type.units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = Dfs0()\n",
    "\n",
    "items = ds.items\n",
    "items[0].name = \"Viken\"\n",
    "items[0].unit = EUMUnit.feet\n",
    "\n",
    "dfs.write(filename='wl_feet.dfs0', data=ds.data,\n",
    "           start_time=ds.time[0],\n",
    "            dt = 1800,\n",
    "            items=items)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![WL](https://github.com/DHI/mikeio/raw/master/images/wl_feet.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extrapolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "metadata": {},
     "execution_count": 30
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "filename = r\"C:\\Program Files (x86)\\DHI\\2020\\MIKE Zero\\Examples\\MIKE_21\\FlowModel_FM\\HD\\Oresund\\Data\\1993\\Boundary_Conditions\\waterlevel_viken.dfs0\"\n",
    "dfs = Dfs0(filename)\n",
    "ds = dfs.read()\n",
    "df = ds.to_dataframe()\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "metadata": {},
     "execution_count": 31
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from datetime import date\n",
    "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": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "metadata": {},
     "execution_count": 32
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "dfr = dfr.ffill().bfill()\n",
    "dfr.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfr.to_dfs0(\"Viken_extrapolated.dfs0\", items=ds.items, title=\"Caution extraplolated data!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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_eq_1hr.dfs0\")\n",
    "os.remove(\"mauna_loa_co2.dfs0\")\n",
    "os.remove(\"Viken_extrapolated.dfs0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}