{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attempt at concatenating netcdf subdomain files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import netCDF4 as nc\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strategy: For each variable on the horizontal grid, insert the data from each subdomain into the correct position of a larger array. Also, copy all of the other  \n",
    "\n",
    "First, I need to determine how the subdomains are organized. The subdomains are divided into rows and columns. An example decomposition is:\n",
    "\n",
    "|  |  |  |  |\n",
    "|--|--|--|--|\n",
    "|08|09|10|11|\n",
    "|04|05|06|07|\n",
    "|00|01|02|03|\n",
    "\n",
    "\n",
    "For my case, I have figured this layout out \"by eye\" and stored in an array called filenames. Ideally, we would write code to determine the decomposition automatically. This is complicated by the fact that we do not always wish to recombine over the full domain.\n",
    "\n",
    "Next, I can determine the x/y start and end indces for each of my sudomains by knowing the dimensions of each subdomain. I will store these in a dictionary with a filename key.\n",
    "\n",
    "Next, I will initialize the dimensions and variables of my new file by copying the dimensions and variables of the file in filenames[0,0]. \n",
    "\n",
    "Finally, I will loop through each file and each variable and insert the data into the correct positition of my new array. I have written some functions to help with this. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def define_shapes(filenames):\n",
    "    \"\"\"Creates a dictionary object that stores the beginning and ending i, j coordinate for each \n",
    "    subdomain file stored in names.\n",
    "    Names should be orgnaized in a way that corresponds to the shape of the region you are compiling.\n",
    "    The first axis (rows) of names is along y, second axis (columns) is along x\n",
    "    names[0,0] is the bottom left subdomain\n",
    "    names[-1,0] is the top left subdomain\n",
    "    names[0,-1] is the bottom right subdomain\n",
    "    names[-1,-1] is the top right subdomain\n",
    "    \n",
    "    Beginning/ending i = iss/iee, beginning/ending j = jss/jee\n",
    "    \n",
    "    returns: a dictionary of dictionarys. First level keys are the filenames, second level are iss,iee,jss,jee\n",
    "    \"\"\"\n",
    "    \n",
    "    jss = 0\n",
    "    for j in np.arange(filenames.shape[0]):\n",
    "        iss = 0\n",
    "        for i in np.arange(filenames.shape[1]):\n",
    "            name = filenames[j,i]\n",
    "            f = nc.Dataset(name)\n",
    "            x = f.dimensions['x'].__len__()\n",
    "            y = f.dimensions['y'].__len__()\n",
    "            shapes[name] = {}\n",
    "            shapes[name]['iss'] = iss\n",
    "            shapes[name]['iee'] = iss+x\n",
    "            shapes[name]['jss'] = jss\n",
    "            shapes[name]['jee'] = jss+y\n",
    "            iss = iss+x\n",
    "        jss = jss +y\n",
    "    return shapes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that it works for my case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "shapes = {}\n",
    "filenames = np.array([['CODAR_0139.nc', 'CODAR_0140.nc', 'CODAR_0141.nc'],\n",
    "         ['CODAR_0151.nc', 'CODAR_0152.nc', 'CODAR_0153.nc'],\n",
    "         ['CODAR_0163.nc', 'CODAR_0164.nc', 'CODAR_0165.nc']])\n",
    "\n",
    "\n",
    "shapes = define_shapes(filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CODAR_0139.nc': {'iee': 25, 'iss': 0, 'jee': 22, 'jss': 0},\n",
       " 'CODAR_0140.nc': {'iee': 58, 'iss': 25, 'jee': 22, 'jss': 0},\n",
       " 'CODAR_0141.nc': {'iee': 80, 'iss': 58, 'jee': 22, 'jss': 0},\n",
       " 'CODAR_0151.nc': {'iee': 25, 'iss': 0, 'jee': 55, 'jss': 22},\n",
       " 'CODAR_0152.nc': {'iee': 58, 'iss': 25, 'jee': 55, 'jss': 22},\n",
       " 'CODAR_0153.nc': {'iee': 80, 'iss': 58, 'jee': 55, 'jss': 22},\n",
       " 'CODAR_0163.nc': {'iee': 25, 'iss': 0, 'jee': 80, 'jss': 55},\n",
       " 'CODAR_0164.nc': {'iee': 58, 'iss': 25, 'jee': 80, 'jss': 55},\n",
       " 'CODAR_0165.nc': {'iee': 80, 'iss': 58, 'jee': 80, 'jss': 55}}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shapes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new netcdf file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new = nc.Dataset('CODAR_all.nc', 'w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iniitilize dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def initialize_dimensions(newfile, oldfile):\n",
    "    \"\"\"Initialize new file to have the same dimension names as oldfile\n",
    "    Dimensions that are not associated with the horizontal grid are also given\n",
    "    the same size as oldfile\"\n",
    "    \"\"\"\n",
    "    for dimname in oldfile.dimensions:\n",
    "        dim = oldfile.dimensions[dimname]\n",
    "        if dimname=='x' or dimname=='y':\n",
    "            newdim = newfile.createDimension(dimname) \n",
    "        else:   \n",
    "            newdim = newfile.createDimension(dimname, size=dim.__len__())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "initialize_dimensions(new, nc.Dataset(filenames[0,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OrderedDict([('x', <class 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'x', size = 0\n",
      "), ('y', <class 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'y', size = 0\n",
      "), ('deptht', <class 'netCDF4._netCDF4.Dimension'>: name = 'deptht', size = 40\n",
      "), ('depthu', <class 'netCDF4._netCDF4.Dimension'>: name = 'depthu', size = 40\n",
      "), ('depthv', <class 'netCDF4._netCDF4.Dimension'>: name = 'depthv', size = 40\n",
      "), ('time_counter', <class 'netCDF4._netCDF4.Dimension'>: name = 'time_counter', size = 960\n",
      "), ('tbnds', <class 'netCDF4._netCDF4.Dimension'>: name = 'tbnds', size = 2\n",
      ")])\n"
     ]
    }
   ],
   "source": [
    "print(new.dimensions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def initialize_variables(newfile, oldfile):\n",
    "    \"\"\"Initialize new file to have the same variables as oldfile\n",
    "    \"\"\"\n",
    "    newvars = {}\n",
    "    for varname in oldfile.variables:\n",
    "        var = oldfile.variables[varname]\n",
    "        dims = var.dimensions\n",
    "        newvar = newfile.createVariable(varname, 'float32', dims)\n",
    "        newvar[:]=var[:]\n",
    "        newvars[varname] = newvar\n",
    "    return newvars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "newvars=initialize_variables(new, nc.Dataset(filenames[0,0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('x',\n",
       "              <class 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'x', size = 25),\n",
       "             ('y',\n",
       "              <class 'netCDF4._netCDF4.Dimension'> (unlimited): name = 'y', size = 22),\n",
       "             ('deptht',\n",
       "              <class 'netCDF4._netCDF4.Dimension'>: name = 'deptht', size = 40),\n",
       "             ('depthu',\n",
       "              <class 'netCDF4._netCDF4.Dimension'>: name = 'depthu', size = 40),\n",
       "             ('depthv',\n",
       "              <class 'netCDF4._netCDF4.Dimension'>: name = 'depthv', size = 40),\n",
       "             ('time_counter',\n",
       "              <class 'netCDF4._netCDF4.Dimension'>: name = 'time_counter', size = 960),\n",
       "             ('tbnds',\n",
       "              <class 'netCDF4._netCDF4.Dimension'>: name = 'tbnds', size = 2)])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('nav_lon', <class 'netCDF4._netCDF4.Variable'>\n",
       "              float32 nav_lon(y, x)\n",
       "              unlimited dimensions: y, x\n",
       "              current shape = (22, 25)\n",
       "              filling on, default _FillValue of 9.969209968386869e+36 used),\n",
       "             ('nav_lat', <class 'netCDF4._netCDF4.Variable'>\n",
       "              float32 nav_lat(y, x)\n",
       "              unlimited dimensions: y, x\n",
       "              current shape = (22, 25)\n",
       "              filling on, default _FillValue of 9.969209968386869e+36 used),\n",
       "             ('deptht', <class 'netCDF4._netCDF4.Variable'>\n",
       "              float32 deptht(deptht)\n",
       "              unlimited dimensions: \n",
       "              current shape = (40,)\n",
       "              filling on, default _FillValue of 9.969209968386869e+36 used),\n",
       "             ('depthu', <class 'netCDF4._netCDF4.Variable'>\n",
       "              float32 depthu(depthu)\n",
       "              unlimited dimensions: \n",
       "              current shape = (40,)\n",
       "              filling on, default _FillValue of 9.969209968386869e+36 used),\n",
       "             ('depthv', <class 'netCDF4._netCDF4.Variable'>\n",
       "              float32 depthv(depthv)\n",
       "              unlimited dimensions: \n",
       "              current shape = (40,)\n",
       "              filling on, default _FillValue of 9.969209968386869e+36 used),\n",
       "             ('time_counter', <class 'netCDF4._netCDF4.Variable'>\n",
       "              float32 time_counter(time_counter)\n",
       "              unlimited dimensions: \n",
       "              current shape = (960,)\n",
       "              filling on, default _FillValue of 9.969209968386869e+36 used),\n",
       "             ('time_counter_bnds', <class 'netCDF4._netCDF4.Variable'>\n",
       "              float32 time_counter_bnds(time_counter, tbnds)\n",
       "              unlimited dimensions: \n",
       "              current shape = (960, 2)\n",
       "              filling on, default _FillValue of 9.969209968386869e+36 used),\n",
       "             ('sossheig', <class 'netCDF4._netCDF4.Variable'>\n",
       "              float32 sossheig(time_counter, y, x)\n",
       "              unlimited dimensions: y, x\n",
       "              current shape = (960, 22, 25)\n",
       "              filling on, default _FillValue of 9.969209968386869e+36 used),\n",
       "             ('vosaline', <class 'netCDF4._netCDF4.Variable'>\n",
       "              float32 vosaline(time_counter, deptht, y, x)\n",
       "              unlimited dimensions: y, x\n",
       "              current shape = (960, 40, 22, 25)\n",
       "              filling on, default _FillValue of 9.969209968386869e+36 used),\n",
       "             ('vozocrtx', <class 'netCDF4._netCDF4.Variable'>\n",
       "              float32 vozocrtx(time_counter, depthu, y, x)\n",
       "              unlimited dimensions: y, x\n",
       "              current shape = (960, 40, 22, 25)\n",
       "              filling on, default _FillValue of 9.969209968386869e+36 used),\n",
       "             ('vomecrty', <class 'netCDF4._netCDF4.Variable'>\n",
       "              float32 vomecrty(time_counter, depthv, y, x)\n",
       "              unlimited dimensions: y, x\n",
       "              current shape = (960, 40, 22, 25)\n",
       "              filling on, default _FillValue of 9.969209968386869e+36 used)])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add new data from all files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def concatentate_variables(filenames, shapes, variables):\n",
    "    \"\"\"Concatentate netcdf variables listed in dictionary variables for all of the\n",
    "    files stored in filenames. shapes is a dictionary object that stores the start and \n",
    "    end index for the subdomain in each file. \"\"\"\n",
    "    for name in filenames.flatten():\n",
    "        for varname in variables.keys():\n",
    "            newvar = newvars[varname]\n",
    "            f = nc.Dataset(name)\n",
    "            oldvar = f.variables[varname]\n",
    "            x1=shapes[name]['iss']\n",
    "            x2=shapes[name]['iee']\n",
    "            y1=shapes[name]['jss']\n",
    "            y2=shapes[name]['jee']\n",
    "            if 'x' in newvar.dimensions:\n",
    "                newvar[...,y1:y2,x1:x2] = oldvar[...,:, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "concatentate_variables(filenames, shapes, newvars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that it makes sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80, 80)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newvars['nav_lat'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(960, 40, 80, 80)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newvars['vosaline'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'deptht': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 deptht(deptht)\n",
       " unlimited dimensions: \n",
       " current shape = (40,)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'depthu': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 depthu(depthu)\n",
       " unlimited dimensions: \n",
       " current shape = (40,)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'depthv': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 depthv(depthv)\n",
       " unlimited dimensions: \n",
       " current shape = (40,)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'nav_lat': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 nav_lat(y, x)\n",
       " unlimited dimensions: y, x\n",
       " current shape = (80, 80)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'nav_lon': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 nav_lon(y, x)\n",
       " unlimited dimensions: y, x\n",
       " current shape = (80, 80)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'sossheig': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 sossheig(time_counter, y, x)\n",
       " unlimited dimensions: y, x\n",
       " current shape = (960, 80, 80)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'time_counter': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 time_counter(time_counter)\n",
       " unlimited dimensions: \n",
       " current shape = (960,)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'time_counter_bnds': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 time_counter_bnds(time_counter, tbnds)\n",
       " unlimited dimensions: \n",
       " current shape = (960, 2)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'vomecrty': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 vomecrty(time_counter, depthv, y, x)\n",
       " unlimited dimensions: y, x\n",
       " current shape = (960, 40, 80, 80)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'vosaline': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 vosaline(time_counter, deptht, y, x)\n",
       " unlimited dimensions: y, x\n",
       " current shape = (960, 40, 80, 80)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used,\n",
       " 'vozocrtx': <class 'netCDF4._netCDF4.Variable'>\n",
       " float32 vozocrtx(time_counter, depthu, y, x)\n",
       " unlimited dimensions: y, x\n",
       " current shape = (960, 40, 80, 80)\n",
       " filling on, default _FillValue of 9.969209968386869e+36 used}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newvars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f5b49157b38>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vx+cK7XfbWwrtw+VD6PABklv+OUktnHrgKLXfEXd54nfoOzTq8/2JcfzmfHJ4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b4b571710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(newvars['vosaline'][10,0,:,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f5b4907cda0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7VoZUrYyh/uhj6Z8Kr895bg2BS72oPzLaTLlnagw6amPH5u6uF4ATK6s7jp8I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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b4b571ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.pcolormesh(newvars['nav_lat'][:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f5b4906b7b8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PbV3c3NNHXu8hsvGNFeo+hzQAEX2Ecol3lW17LHE7B83IhRBi5mggF0KImdO/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b49086e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(newvars['nav_lon'][:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xN0q6U9ILmz5PjXY8KGnHkM/5MklXSdq/rOzDkv5d7/f3SvrgCNqwT9K/GdI1\nuFDSlb3ft6obE/HCYV6HTBuGdh2i9rTeJ5q839u8j9u+P9u8/9q+ryTN9P7dIOmrkq5pst8k6p/Y\nPlGzHVP3OTHqz4je+Sbmc6KNb6LGKcFaK7NOUkIIt0t6Iip+raSber/fJOn1I2iDNKRrEUI4GEK4\ns/f7MUn3qZsbZmjXIdMGacj3RM+w+kQjz63N+7jt+7PN+6/t+yqEcLz36yZ1BxlPqMF+k6hfmuw+\nUcdUfU6M+jOi14aJ+ZxoYxA1LgnWgqQvmtnXzeyXRnD+JbtDCId6vx+StHtE7Xinmd1lZje2+RXp\ncma2V93/8XxNI7oOy9rw1V7R0K+DhtMn2r7f2379Gn9d2rz/2rivzGydmd3Za99tIYR7mmx3ov5G\n2l6Az4l+4/A5MYr7YM1/TrQxiBqXSPWrQwhXSXqNpLeb2ctG3aDQ/d5wFNfn45IulXSluuuc39D2\nCc1sq6TPSnp3COHo8m3Dug69Nvxxrw3HNILr0DOM13xo93sLr1/jr0ub919b91UI4UwI4UpJz5H0\nk2b2iibb7dQ/21TbS5ozpPMMwudE10jug0n4nGhjEPUdSZcse3yJuv/LGKoQwiO9fx+T9Cfqfn08\nCofM7EJJMrM9kh4ddgNCCI+GHkm/p5avhZltVLdjfCqEcHOveKjXYVkbPr3UhmFfh2Va7xNDuN9b\ne/2afl3avP+GcV+FEJ6U9GeSfqypdifqf9Ek94k6+JzoGsV9MCmfE20Mor4u6QfNbK+ZbZL085I+\n18J5ksxsxsy29X4/V9JPS9qfP6o1n5N0Xe/36yTdnNm3Fb2bcckb1OK1MDOTdKOke0MIH122aWjX\nIdWGYV6HSKt9Ykj3e2uvX5OvS5v3X5v3lZntWvqzgZltkfRqSXc00e5c/UsfWKtpeyE+J/qN9HNi\n2O+NE/U5EdqJen+NupHuD0h6fxvnGHD+S9Wd7XGnpG8Oqw2SPqNuZt4Fdf/e/xZJOyR9UdL9km6R\ntH3IbfiXkn5f0t2S7lL3ptzd4vmvkXSmd+3v6P1cO8zrkGjDa4Z5HZw2tdYnmr7f27yP274/27z/\n2ryvJF0h6e96dd8t6Vd75U1d91T9E9knap5/Kj8n2u6DNdswMZ8TJNsEAAAo0EqyTQAAgEnHIAoA\nAKAAgygAAIACDKIAAAAKMIgCAAAowCAKAACgAIMoAACAAgyiAAAACjCIAgAAKMAgCgAAoACDKAAA\ngAIMogDbk/NXAAAgAElEQVQAAAowiAIAACjAIAoAAKAAgygAAIAC2UGUmZ1jZl8zszvN7F4z+0Cv\nfM7MHjazO3o/1w6nucBo0SeAfvQJTDMLIeR3MJsJIRw3sw2Svizp30p6paSjIYSPDKGNwFihTwD9\n6BOYVgP/nBdCON77dZOk9ZKe6D22thoFjDP6BNCPPoFpNXAQZWbrzOxOSYck3RZCuKe36Z1mdpeZ\n3Whm21ttJTBG6BNAP/oEptXAP+c9s6PZ+ZK+IOl9ku6V9Fhv029K2hNCeGsrLQTGFH0C6EefwLTZ\nUHfHEMKTZvZnkl4UQugslZvZ70n6fLy/mdUbnQFDFEJo7M8L9AlMAvoEcNZK+8Og2Xm7lr6CNbMt\nkl4t6Q4zu3DZbm+QtD/RmJH+7Nu3jzbQhmd+mjDOfaLNa9z260fbR9P2Se8T43CNacPaaUOJQd9E\n7ZF0k5mtU3fA9akQwpfM7PfN7EpJQdKDkt5WdHZg7aFPAP3oE5ha2UFUCGG/pB91yv+31loEjDH6\nBNCPPoFpNtEZy2dnZ0fdBNowRm2YdG1e47ZfP9o+/LrRNQ7XmDaMTxtWqvbsvBVXbBbaqhsoYWYK\nDQbRFpyfPoGxQp8AzirpDxP9TRQAAEBbGEQBAAAUYBAFAABQoHayzRJm17vl92rOLd+SqetUonwx\nc8x8ZttK5S5Urt3npcrPzdSX2nZ+5kSpYzLn0dbMts0F9aWOOSdzTEm7c9fhgsy2cfDfoj+3X9T/\nMDjtP7Sj+oSPaHv0+FmVfY46L/C8ZqJ9tlX2OR7t4+133Lnrj9Wsq9r26mogXrvisrrHHT60s+/x\nme84N9ffV4t0T/T4m84+B52yE05Z/EblvTmddMpi3sIpicVUwl/VqG8M2EudQu/1WHI49WkgSQcS\n5blPg9ynSMrFfvGu3elD9iTK92ZO83n/MxRnhbBvpOfnmygAAIACDKIAAAAKMIgCAAAowCAKAACg\nQKuB5SmXJwLLH0yU5+RCDIclF5aY2nbqdPqYLaltmWOS23LH5BqeChIvOaZE7s7MnScXxD4O4rZH\njxec9p92LoZXNkze+Re1vlJ2UpsGlvnHVV/kOEjdDYB/qhpYfubxKJD8QGUX6S6nLA4kf8TZx+NN\n2IifztPOPg87ZUcGPPbqXmu+5ZQ9kXtnvy+zLRVAnpv+k9pWEMB+OFEuSYcTQef7b8qcB+OOb6IA\nAAAKMIgCAAAowCAKAACgAIMoAACAAtnoVDM7R9Jfqhu6uEnSfw0hvN/Mdkj6Q0nfr26E3RtDCF7I\noyuVsbwkw3hJrtkSufNszGxLZlovafiwnmzuXE0HsabuwNydmQsebznIdtV9Im579Pjk5mog9oIT\nnH06CsaOH6d4QdyD6q57vgXn4tcp8/cZfB2OL1QDgucPO+m7D0SPvUBmL6j7WPTYywxejWP378/4\nfvbe7Lw3i3g/LyC9yaUZCqy6Tzzh1XqosDWpNSIymcSLAstTFz13zPcy27BWZb+JCiGckPSKEMKV\nkn5Y0ivM7BpJ75N0awjhMklf6j0GJh59AuhHn8A0G/jnvBDC8d6vmyStV/f/Da+VtDQv8yZJr2+l\ndcAYok8A/egTmFYDB1Fmts7M7lT3+9XbQgj3SNodQlj6vvWQ8t+VAhOFPgH0o09gWg3M2BdCOCPp\nSjM7X9IXzOwV0fZgZsE/+rZlv++VdGlxQ4GV6uzv/jRtNX1i7j+d/X32x6TZ1zbfPiCl0+mo0+k0\nXu/qPifmlv0+2/sB2tdEf7AQEve1t7PZv1c3ou4XJc2GEA6a2R51/+fxgmjfcGuinosT5bmQvFTw\ndklMZUmW81zweG4kmgpZ3Jk5aNu5fvnG8zMnShyTLC/dljsmFfCda3cqDnRn5pgLMtsuSm+yV0oh\nBMscvWIr7RMhSrYcoufy+I5quusjTkTz0SiiOX4sJTJ6R/sdd+7Q+RrHeW067Lxox5x2PREde0TP\nquzj1R+XPfJd58V+wJlZEGcejx9L0kGnLJ5c4QWW73LKvNj9eOWAE84+XnB7nJz7O84+iTfBOm/t\nZjbyPiE97tTildVREFieei9ufCLP9U1XCEkh7GusrpL+kP1znpntMrPtvd+3SHq1pDskfU7Sdb3d\nrpN088qbC6w99AmgH30C02zQn/P2SLrJzNapO+D6VAjhS2Z2h6Q/MrO3qjd1td1mAmODPgH0o09g\namUHUSGE/ZJ+1Cn/nqRXtdUoYFzRJ4B+9AlMs1aXgk/FK3l/1pfyoS4lcUzDykHX6kVcLvc3+tS2\nkmOkagzHoPJSqYuXS5o5wmSbqxa1byFOtuk8AS9BZlxWJ0FmXXXqOukmAK2+mF5cVhxz5SXW9GK1\njjwVBSU95LzYD1SL9GD0+DFnH68vxNV7MVFeqI2XgDOOgfJSE3txUnuix2OYbLMdpVGoO/xi7zVZ\n4r2ug6RSSx/NHXRdovymRDnWApZ9AQAAKMAgCgAAoACDKAAAgAIMogAAAAq0GhN9YIUnTSWmzCkJ\nOM/JhTM26VTjidzGQCqOORffnLoZSoPHc8eNg+j5Lq7v/3+MH9RdvUheWXWfwQHideqRvED26nFe\ngLgXgB4Hz3uJQo8sVKN95x+OknIecBrqlcUzWbzgbO++id8MvPvOC1iu5kut3udeELl3XFzmtbPR\nVJmj4N2DufsylVBTkiXewXMLzqQCy3MfBl6SVSkfWH5gr1+++K8yB+Wkpmg9lTkm1cCSlNF/mTlm\nevBNFAAAQAEGUQAAAAUYRAEAABRgEAUAAFCg1cDyd2nOLf/dRPn3Cs5REg6HIcu9SKkg8VzweOm2\ncRAFBp/e0EyAuJfVvA6vbq+uOJC8bob0BecFmY96phdY/uRBJ3L34ejxgeoulX0k6ZhTFqsz0cML\nNPYCvevcg97L5d0Kcf1e8Pm4T6YYyLuw1Uz3Z2UCy3cmynMZy1MfFLlM5qnX+GTmmGcnyg9mot4f\nytS3mJpWlZtulUpvX7q0BfgmCgAAoACDKAAAgAIMogAAAApkAzLM7BJJvy/pAklB0n8OIfyumc1J\n+kWdXQ/9/SGEv6h70lSsVM7nEscMKzkmekoSZ5Ykx8zFeeS2tRrlt/o+EaLrd3p9nMSyXqxRdZ/q\nE/djm8rqqibprNYTJ9HsllWTbcYxUEeecoJPHnFeyDje6XB1FzeJZazuPRKHlnihJt75vPrrtMt7\naeqEujWdcXiF2vmcyES05pKLpmKfSpL35t63Thccc2GiPBd7lYrxkqR7nuOXzz+aOajEXzdc32QZ\n9HZyStJ7Qgh3mtlWSd8ws1vV7SgfCSF8pPUWAuOFPgH0o09gamUHUSGEg5IO9n4/Zmb3Sbq4t3nN\nLzYArBR9AuhHn8A0qx0TZWZ7JV0l6au9onea2V1mdqOZ5b6QBCYSfQLoR5/AtKk1iOp9RfvHkt4d\nQjgm6eOSLpV0paRHJN3QWguBMUSfAPrRJzCNBoZYmtlGSZ+V9OkQws2SFEJ4dNn235P0ef/o25b9\nvlfd/lRm0hJnbiwJgM7lPEsFOqbK21ASJN5iss3OV7s/TVtNn9j322d/f/k10o/87OCAbU+dQG8v\nQHxQPamyhShA3Asij/eRpHknaeLxqGz+sPMFxePVolpJM891yuLL4OUb9PpJXOa1yQtkPuqUxUkY\nvUDzIzXKHnH2WUEuxE6no06nU/+Amlb3OTG37PerJV0jaUf6ZLnvs1Lbcsd494wk7ckck+pa3ms4\naFvu/fGSzLbUh+LtP5o56M8z26ZPE/3BQgjpjWYm6SZJj4cQ3rOsfE8I4ZHe7++R9M9CCP9LdGxQ\nwSy8lFsL6ioZp6Rm+5VmRk9t25mpcFuiU2/MDR68TMZS+g1Cyib+TdaXOyaVePeCzDGpbRcVHDNg\nm/2AFEJYVYzGavvEqSf76ztyXv+FPuK823sZveMyb594sCJVBzp16pakx6NpQodVzSj+qHPxv+u8\nkHHZI/+4t7KPvuW8TA9Ej71szt4HVTyAqTuIinkfxHudMq+f1hlEPeaUHYge3+ns481SlJR5a3+G\nmY28T/ij08wg6lmZxuxNlKdmxkmjH0SVSrzuuj13UGoQlRuJ/12t5oxKCPsaq6ukPwwaZ1wt6c2S\n7jazO3plvybpTWZ2pbqzLx6U9LaVNhZYo+gTQD/6BKbWoNl5X5YfN8V3gphK9AmgH30C04yM5QAA\nAAVazu88noaZ5XziMqrn7phUTHRJkHjuPKXbxsDpqH1xVnEvy3idzON1A9LjuuoGlsfHeUHkXrC5\nF5dVyVB+xAlB8IKzY6ns1NWG9fPukTrn82JavBilXJbpnO84Zd+OHrtxMN9LVJiJKxorK5w2lIrX\nlNLXPhcb+uxEeS4YPRUMnmtbqovmYqVysXrVsMSu12SO+VZi44PXZw5CDt9EAQAAFGAQBQAAUIBB\nFAAAQAEGUQAAAAXGKgw3l1BzWA0dqwsySqkgyJKg7twxqQDN0ozlY/4CLq7v/39LnFXcyzLuldUJ\nSPeCv+O6vOP8bOSbo32qdR93AoS9xJ3zR6IyL7DWC6iNL0MuiHdQXTFvBkh8Pi8foZc082mnLA5s\n9tp0yinzEoNW1I2wH1dOYHnp5JHUTJ6SIPGS961c9vFar2UknhSxXCqDf64NP5Yof7Bec1DFN1EA\nAAAFGEQBAAAUYBAFAABQYKwiSJpuTEmiy9QyjMO8UIuJRmQXIC5RJ1ZkJUpiolLbSuOexuqOrjq9\nIY6BKkt+WSeWqk6cVBzr5O0jVWOg5r0kmk7gyZHjTjDKkUrG0SovJi8X67HEi1GK6/Lq8criS+rd\nW17yS+/5xJfByy/ptSF+E/MWKX58jaf09Rb69eLDltTLK9svlVBTSi9O/JzMMakYq9z7T2rB4AOZ\nY3LbUh9WXh9Ykkrs+bzMIr4PkIgzh2+iAAAACjCIAgAAKMAgCgAAoEB2EGVml5jZbWZ2j5l908ze\n1SvfYWa3mtn9ZnaLmeWycAATgz4B9KNPYJoNCsM9Jek9IYQ7zWyrpG+Y2a2S3iLp1hDCh83svZLe\n1/tZlZLwyFz8YW5bk20YC6kgw1R5qVxQZ2pbyTGFweOhJOh0ZVbVJ06vzweS1w8sr3Ocl7izfz8v\naaZXFgeSe0k0j+hZlbJjcWJNqZok0LtHvY4YJ6z0eMHZcRO8oG5PncBy73475JTFAb27nH28sji4\n2UuwmEq6ODyr+5z4WafG72TOlgqOlsoSUKaGdrlkrql78dkhcx7zy3Nty237ZqI8d31S9eU++P6n\nRND5Bwg4lwZ8ExVCOBhCuLP3+zFJ90m6WNJrJd3U2+0mSa9vs5HAuKBPAP3oE5hmtWOizGyvpKsk\nfU3S7hDC0v+3Dkna3XjLgDFHnwD60ScwbWoNonpf0X5W0rtDCEeXbwshBEmZ7zCByUOfAPrRJzCN\nBqYmNLON6naMT4UQbu4VHzKzC0MIB81sj6RH/aNvW/b7XkmXrqqxwEp0vix1/rr5elfTJz44dzZS\n75rZdfofZptvH5DS6XTU6XQar3dVnxPfmDv7+55Z6aLZxtsHeJroD9b9D0Jio5mp+7fsx0MI71lW\n/uFe2YfM7H2StocQ3hcdG6S5VTVuuVsTdQ0ry3nuPOdltqViWM/LVLglka17Sy7IMBXomAvGPT+z\nbUei/ILMMRclyr+v4JjceTLbFnemt208XwohJKI761ltn3gs9Eerxlm+/YDtauRrvJ933HEnq3i8\nn1f346pexEPRX2L+Qc+t7PPA8edVyo4dcKKl48zNuQzLy8UB6EedfZ52yurUXydjudcvTzplB2rU\ntdfZxwssj9vlZNded773pKXTuwdH4pvZyPuE7nI+g76YOWEqoFpKZwW/MnPMixLluYzlF3ovvHTB\nRd6sgrzDh9JvWmf+IfMafj1R/lDmZN49JknfzhxT7dZd/8d4BJaHkMm2vkIl/WHQGORqSW+WdLeZ\n3dEre7+kD0r6IzN7q7pvGW9cYVuBtYo+AfSjT2BqZQdRIYQvKx039armmwOMN/oE0I8+gWlGxnIA\nAIACY77m/WAluSTX/JMeV01e2Cl9kRaLlqj3LThJM+Myb5+TTvBPnRgsN7GmF48Uv7a5pIbLxZ29\n7j1SJ2mm14Y4XMzbx4uler5TFgVbrru4Gsd00e5HKmXP0wP9++i7lX126nHnhJL0q4ny8fLeH67G\n1nzoRzIHvCkTA5OKYypJaLkr/emy80I/+Gp7JtPljI77x+xOH/PQlkuS2+YPVpPbSpL2Jw+RTifK\nc287ezPbwDdRAAAAJRhEAQAAFGAQBQAAUIBBFAAAQIE1E7776kSyzVQSzqlTEmGfOyYVgNh0Gxp2\neszv6DhwvDSQvE49dcq8IPJ5J0VsJUnnmWqSTp2oBqnXCuKu+5rF99exGvtI1Yy3XiCxk8Ry03Oe\n6nu8c0c1gNsLJN7sZuAcfNwlTpbEy6IsiLudpN87kxkm10Zg+c/oC5WyD+ln0gc4t94zUkHiuckL\niW1bd6UDvneu84P500H+6fsiF4x+wXnp5J37f+4Kt/zJA87NvORUovxb6UP06UT5nkyAf/opSfPj\nkaSzKXwTBQAAUIBBFAAAQAEGUQAAAAUYRAEAABQY8zDc1WnyyW0cvMuKnMoEYW9INLwa7ltD08He\nJQHnqO20c9c2m8V8c/S4Ggx+XDOVsqNR9O3xY9V9tOgsfu7dy+eEaB/nplp0nnNcv3dv1+n0XvCx\nU7Zla3+GaS+A2wv09jJTx69h/DpI0mHtrJRt0qXVhkVygclTJxV0XhCMvvmc9ASBbTrqlu9KBvlL\nm7TglnvZ/5fsTJxHkmY2/a1bfsuFr00ek3wrSSQ/lyQ9kSh/UeaYj2W2zWe2rUF8EwUAAFCAQRQA\nAEABBlEAAAAFBkYQmNknJP2spEdDCFf0yuYk/aKkx3q7vT+E8BdtNRIjkIqlKknQ2XQi0BFbTZ+I\nY568GKgSXj116vbirfyYqP64jZPzTmJNTxz/JFVjoLyYKFfcVicGy4tvie8l77I499uTh/sDRU5v\nrx54ZKYaTOLFRMVO14xzi6/7eqehuzIJHodlNX3isHat7GTfn9mWCmPKxURtd+5RSZvW+TFMUjq+\nKRUrJaUTcX5bz08es19+Qk1Julz3+ht+IZPM8oZEgsxcWF3q2uVim/5tetPWf/12t/zYuf9XpsLx\nVeebqE9KujYqC5I+EkK4qvfDAArThD4B9KNPYCoNHESFEG6XH5/v/DcQmHz0CaAffQLTajUxUe80\ns7vM7EYzy31ZCkwL+gTQjz6BiVY6iPq4pEslXSnpEUk3NNYiYG2iTwD96BOYeEVRrSGEZzLMmdnv\nSfq8v+dty37fK9VIHLdS45AtNLUwtpRuX0nizFyCzo3V3H2D5YK3SwLLU9tyccMtBpD/5V9Jf3V7\ne/UvV7dP3DB3Nuj4J2Y36vLZ/u1ewHFp0Lgnrt9L+ugl4JyPgs3PnCy54XpqB5JH4mSb3iXwmhXv\n58V0n3DKDvZXduxgtfJjXlC0F9wet+Gc6o2/ZXs1KHnLef1B6o9qd2Wfr+ilzgmlf+WUdToddTod\nd/+m1e0T/37u7P22a/ZyPXv2cv1EqD7PJX/zlcxJ70yUJxJqSvKTxEralIxS9wP8JWlLJto6FQj+\nKn0xeczrzH9tJen+5JaMX0kEnb8hEXAuSRcmyh/OnCeOjlvm2H9MTCSwTBv+deZcq9BEfyh6Jzaz\nPSGER3oP3yBpv7/nK8paBTTg5T/Z/VnyW/+hvXPV7RO/Mtc/GCHXNIZpdnZWs7Ozzzy+/vrMTK5V\nqtsnXjj3P7bWBiCnif5QJ8XBZyS9XNIuM3tI0j5Js2Z2pbqzLx6U9LYVnxlYo+gTQD/6BKbVwEFU\nCOFNTvEnWmgLsCbQJ4B+9AlMKzKWAwAAFBiHuOw1oSR4PKfxeOqSoO6SgO+SY5rOcp5pw/oxznQu\npQNSl/iB5fWyW5fw6vYylp8+U6MNG2pkJ69rsfA5b2xon7q8zu9kad8UBY1v2To4q7nngPZWyr6r\ni4rqGhe3n3lZpez4seo9+IzcrfGcRHkmKfqmXU+55TOZIPHNiYzlub560pmwIfmrBgzdnxTExr0i\nEwj+6YI2vCSz7VsF9Q0J30QBAAAUYBAFAABQgEEUAABAgTUfE/UKzSW33ZbY1mRIxCAlITqLqYOG\nlVBTajZWaYgJOsc9JmqQ9dknflYce1E3lqr0uMXSGKU6MVEnnFiRE04CxNLXNu7wdZJhSpWEmJuc\nOKbtO6qZvrz4sTjG5/Ri9YSXzDxUKfvhKLXSF878jNPQhDXyX2Qv/ul07n7bk7kRLvHvt3WZNwbv\nNZSkGaXj1lLxTSXxi9tUTbK6JngJapfkkpumnm4uad7Bwc0ZlTXSzQAAAMYLgygAAIACDKIAAAAK\nMIgCAAAosOYDy3OGFUCei3dtsg3JgHNJG5vO+FmSOLMkSDwVnJgLWsy0wQpzOw7LhuhiDEq+WVft\nAPGozEv0Vys41gsYd8q8gN4zp6ObdbFmEHnppYqDXLdWK1q3+WSlbPOW/oSKm86p7uMFkZ88UZ0B\nEh976aYDlX1eqq9Uyh7SJX2PL1lXDT73kqN2XZwoHy8XnffdSlkuAeXpHek3u9S9e/T4tuQx2xMR\nzblJHnE/Xs0xC0UzhsbA36STkUr3pTft+lG//PCqWjMyfBMFAABQgEEUAABAAQZRAAAABQYOoszs\nE2Z2yMz2LyvbYWa3mtn9ZnaLmW1vt5nA+KBPAGfRHzDN6oQjf1LSf5T0+8vK3ifp1hDCh83svb3H\n72uhfatyTSabecqXC44Za7lg3Fw8Y1Gq9YK6UnGYuQDx0QePF/eJOPC0GmhefXJ1s5jXcTrq8vHj\nlA1R0LgXZL14qhrUu2Fjte0LlcBy54Re2anosTdrw8uUHAWSb9leTZnsPZ/4Oa9fV+912DZTrf+5\neqDv8Yv0jco+l6gaNP5dXdT3+AI9WtnnuLbUaleLVvUZsUfVwPJUELYkbclkEn9Uu93ykzN+hnEp\nHVie6xupPrlJ1ftoSWoCwBGlx5d/Gm5Pbvs5e1ly23B8OLMtETwuSYdTQf65aViHMtt+PLOtfQO/\niQoh3C7piaj4tZJu6v1+k6TXN9wuYGzRJ4Cz6A+YZqUxUbtDCEtDw0NSYvgPTA/6BHAW/QFTYdWB\n5SGEICk00BZgItAngLPoD5hkpck2D5nZhSGEg2a2R3L+UC/pdcvii17Q+8l5y6TFIw3RqUTc0cZc\n3sSSUJuSY4aV1FPqS9LZ+Zvuz5DU6hMf/vWzMRPXvHydLn9lO8k366q76nwcD7TeS7bpHVcjAeeZ\nJpMNnlP9rN60tT+Gxot/2rxpoVIWx73UjU3b7cRvXK57+x5f5MQBbXbiaWai+J+derxWG7peXSnp\ndDrqdDorqKNYrf4gSUfnfueZ3y+cvUx7Zi/L3pe5bUf0rBUfk4u/SolflyW5xJlxfNuSo0onAt2Z\nyUD5R6EaVydJb7QfSx4zPH+X2ZZKAptL3vnUKtqS1kR/KB1EfU7SdZI+1Pv3Zm8n/giOUZr9ie7P\nkus/2urpavWJ9+/r73Ir+UgEVmt2dlazs7PPPL7++uvbOlWt/iBJV839XFttALKa6A91Uhx8RtJX\nJD3fzB4ys7dI+qCkV5vZ/ZJ+qvcYmAr0CeAs+gOm2cBvokIIb0pselXDbQHWBPoEcBb9AdOMjOUA\nAAAFSmOisEpx3sDlNiRiizdkXq3ctrHWdGD5cOOyV2z9Yn8D16/PJ9+Uhh9sXsdqAsuLxbn4nHt+\n3bnVgN9N5/QHjc9sqgawblM1QWadQHLvtfGSZu5Ox1U/wwswjuva4gTfpoKc1wov2eWC0skxc7yA\nfcl/TZZ8V3vc8lyQeOreyCU+TT2nR3VB8phDmW3zieSdnw773XIpn6g0JXXvpq61JP2AvS1T4+dX\n3Ia81zRc38rwTRQAAEABBlEAAAAFGEQBAAAUYBAFAABQYK2GI7fmmkTW9K+OQTb1xUx88WIiIe/G\n3CvcdLxySZB4Si6md/zirGvbcPrMio/JrSbfBi9otpK925n94JV5Nmzsr2uh7tOL99tazU4+s80J\nGp/pDxr3ApnrBJZ7QeTecc/VA5WyOIu5d429rNpe/ZPGy9SekwtovkJ+UHUusPzn7GUrOv/42L7C\ncrSBb6IAAAAKMIgCAAAowCAKAACgAIMoAACAAmMVWP7JguDttwwp4PslmfPc1XAbqqGxPZm43VTG\n8lysb5wAetVSweC5IPFUTGkuRjlXX4MJssdFmxnLNzkvgBf0HO93elNiJkPk9JnqfnEG8YVznOd3\nwrmhz4nq2V4Nuo6DyKVqIHndwPI44HmTFir7eEHOz9f9lbI4sNxTJ6Dfy4g97IkHTbvMuV6X6sHk\n/nsygeUziXdPL2h/SfjYl9xye8dc8hhgCd9EAQAAFGAQBQAAUIBBFAAAQIFV/THdzA5IekrdSJRT\nIYQXN9GoteZHMjFR9ya2nWq6ESf84i3phcibV5Jss8ljBm0bgkF9Yn3UvtRq8INUE0EOjqfplvU3\nYIOzz4yz0nsca+LF4XhxJ+vXOc8vim1a2Fo934K3Qv2G/rq2eTFRTmxTNSbqico+z3LipLZEz9k7\n7nn6B6esmmwzbkOduDOp+vosOtd43GOiBvWJf64/qxxz4UNPpit8NHOy8xPluffBc/zi8BtzyUPs\n19PbMF1W2/uCpNkQwveaaAwwAegTQD/6BCZWE3/OswbqACYJfQLoR5/ARFrtICpI+qKZfd3MfqmJ\nBgFrHH0C6EefwMRa7Z/zrg4hPGJmz5Z0q5l9K4Rw+9LGm5ft+ILeDzAsnb/t/gxZtk/8xgfO7vjy\na6QX/OzQ24cp1ul01Ol0hn3abJ/4P+fOBnS+dHaDXjo73jFemBxN9AcLoboSelFFZvskHQsh3NB7\nHD6d2LfxoOqEYSXiLHF/pm2p2OjcW4sThitJ2nFu+pgtOzMV7kiUlxyzO3PMBYnyizLH5LZdkt5k\nV72Z2IsAACAASURBVEghhKH9WcHrE2ce79/n0I7+SNhHnYv1uHPRD2tX3+MjzsrtXllc1yHnfIec\nFyVu11Ftq+xzUpsqZQtORO/Cmf79jh+r3r2nFwcn6dw+Uw0G95Ja7lT/Rd+lw5V9vASc8XF+Ys1v\nV8r26kClLA54dwPLT1YDyzefPFMpi21IJax99uD3djMbeZ8I9zk75oLHc5NHSibSfCtRnoltt1+Z\nKzgR2hDCvsbqKukPxX/OM7MZM9vW+/1cST8taX9pfcBaR58A+tEnMOlW873pbkl/YmZL9fxBCOGW\nRloFrE30CaAffQITrXgQFUJ4UNKVDbYFWNPoE0A/+gQmHRnLAQAACoxkGsTGRHnTAef/byJ4+5fG\nIOD8soIs5yVOZRJiV9eDXyZ1XC6os+SY1LZEBvaB9aWCbMeERe2LM1KXZh6vf1x/mZcle7MWKmVb\noizmXhC5l7H8tNeGKIv5pnOqbTi9WH1rmpnpb4OXWT1up7ffViereRxELlUDyb2A8Uv0UPW4449U\nyjY/HRV497B3z8eXxru/U33l2YnyceMFkcfXa7lc/08dV/IexCRB1MA3UQAAAAUYRAEAABRgEAUA\nAFCAv/quIbk/6xfFk+UqLDmmJL4pFcOQi23KxUvkzjUOovZtOt0ff7R5ffWJe7FN1Viq6sX3y/qP\ni+uR/DipeD/vuDqxW92y/tipDRuc5+eUxfFOXkyUF88V7xcnvpT8ZJsXRIk7vWSbe05WyzZX831W\n72evr9SJd/Lu/Uzc45rgJbUsjXtM9f/cNUqdqxriB1TwTRQAAEABBlEAAAAFGEQBAAAUYBAFAABQ\ngMDyCbdYEgie25YL0EwFfOYCQVPbcsHjuW1PZbaNg6jt257sD4Q+umO+cogX6F0nQNwTH1cnaL3u\ncV47a3H+K+fVHweNb1H1WnnB5pui47zgcy/YfFeUgDMONJekcx89UynT96pFtSZxlAaWr3Xetcnd\nziUTVXL1EUCOVeCbKAAAgAIMogAAAAowiAIAAChQPIgys2vN7Ftm9vdm9t4mGwWsRfQJoB99ApOu\nKLDczNZL+pikV0n6jqT/bmafCyHc12TjVuvbkp4/6kboQUmXruiIyzXnlt+bKJfSGctPLUp/Lelq\nb1smDnhjaltJMPpJqfOYNOutKp8KEi0NLB9R4G3tPhFlZ7Zz+x/P7KgGRs84AdRxEPd6nda9ncd0\n+bKL7AVQxxnEvQBuz5OdO7Vj9opl56/WvRonOn+rc2ZfnN0nfs5eEHmc1VySnujcrYtnn5fdx6sr\nDjZ/1nEntfaTUue/S7P/bFmZdw/GfaPmagGdr0vLLrsffD6m04Nq9wnvOeWuT+6WLcje3rlfmr2s\nmbrKrfxzgjaMh9Jvol4s6YEQwoEQwilJ/0XS65prVjO+PeoGSJIOjLoB+sqoG6DuIGrCjbxP3Ns5\n3Frd3+t8s7W6pe4gqi3f6fxDa3VL3UFUa3Xvb6/uIRh5n6ij8/ejboE0Dp8TtKFM6SDqYkkPLXv8\ncK8MmFb0CaAffQITr3QQFRptBbD20SeAfvQJTDwLYeX3uZm9RNJcCOHa3uP3SzoTQvjQsn3oQBg7\nIQRro176BNYq+gRw1kr7Q+kgaoO6IUevlPRdSX8r6U3jFlgODAt9AuhHn8A0KJrXEUJYNLN3SPqC\nuknzb6RjYJrRJ4B+9AlMg6JvogAAAKZdKxnLxyHBmpkdMLO7zewOM2tv/nT/OT9hZofMbP+ysh1m\ndquZ3W9mt5jZ9hG0Yc7MHu5dizvM7NoWz3+Jmd1mZveY2TfN7F298qFdh0wbhnYdnDa12ieavN/b\nvI/bvj/bvP/avK/M7Bwz+5qZ3Wlm95rZB5pq94D6J7ZP1GzD1H1OjPozone+yfmcCCE0+qPu17YP\nSNoraaOkOyW9sOnz1GjHg5J2DPmcL5N0laT9y8o+LOnf9X5/r6QPjqAN+yT9myFdgwslXdn7fau6\nMREvHOZ1yLRhaNchak/rfaLJ+73N+7jt+7PN+6/t+0rSTO/fDZK+KumaJvtNov6J7RM12zF1nxOj\n/ozonW9iPifa+CZqnBKstTLrJCWEcLukJ6Li10q6qff7TZJeP4I2SEO6FiGEgyGEO3u/H5N0n7q5\nYYZ2HTJtkIZ8T/QMq0808tzavI/bvj/bvP/avq9CCEtp0zepO8h4Qg32m0T90mT3iTqm6nNi1J8R\nvTZMzOdEG4OocUmwFiR90cy+bma/NILzL9kdQjjU+/2QpN0jasc7zewuM7uxza9IlzOzver+j+dr\nGtF1WNaGr/aKhn4dNJw+0fb93vbr1/jr0ub918Z9ZWbrzOzOXvtuCyHc02S7E/U30vYCfE70G4fP\niVHcB2v+c6KNQdS4RKpfHUK4StJrJL3dzF426gaF7veGo7g+H1d3QaIrJT0i6Ya2T2hmWyV9VtK7\nQwh9i5AN6zr02vDHvTYc0wiuQ88wXvOh3e8tvH6Nvy5t3n9t3VchhDMhhCslPUfST5rZK5pst1P/\nbFNtL2nOkM4zCJ8TXSO5Dybhc6KNQdR3JF2y7PEl6v4vY6hCCI/0/n1M0p+o+/XxKBwyswslycz2\nSHp02A0IITwaeiT9nlq+Fma2Ud2O8akQws294qFeh2Vt+PRSG4Z9HZZpvU8M4X5v7fVr+nVp8/4b\nxn0VQnhS0p9J+rGm2p2o/0WT3Cfq4HOiaxT3waR8TrQxiPq6pB80s71mtknSz0v6XAvnSTKzGTPb\n1vv9XEk/LWlUS3l+TtJ1vd+vk3RzZt9W9G7GJW9Qi9fCzEzSjZLuDSF8dNmmoV2HVBuGeR0irfaJ\nId3vrb1+Tb4ubd5/bd5XZrZr6c8GZrZF0qsl3dFEu3P1L31grabthfic6DfSz4lhvzdO1OdEaCfq\n/TXqRro/IOn9bZxjwPkvVXe2x52SvjmsNkj6jLqZeRfU/Xv/WyTtkPRFSfdLukXS9iG34V9K+n1J\nd0u6S92bcneL579G0pnetb+j93PtMK9Dog2vGeZ1cNrUWp9o+n5v8z5u+/5s8/5r876SdIWkv+vV\nfbekX+2VN3XdU/VPZJ+oef6p/Jxouw/WbMPEfE6QbBMAAKBAK8k2AQAAJh2DKAAAgAIMogAAAAow\niAIAACjAIAoAAKAAgygAAIACDKIAAAAKMIgCAAAowCAKAACgAIMoAACAAgyiAAAACjCIAgAAKMAg\nCgAAoACDKAAAgAIMogAAAApkB1FmdomZ3WZm95jZN83sXb3yOTN72Mzu6P1cO5zmAqNFnwD60Scw\nzSyEkN5odqGkC0MId5rZVknfkPR6SW+UdDSE8JHhNBMYD/QJoB99AtNsQ25jCOGgpIO934+Z2X2S\nLu5ttpbbBowd+gTQjz6BaVY7JsrM9kq6StJXe0XvNLO7zOxGM9veQtuAsUafAPrRJzBtag2iel/R\n/rGkd4cQjkn6uKRLJV0p6RFJN7TWQmAM0SeAfvQJTKNsTJQkmdlGSX8q6c9DCB91tu+V9PkQwhVR\neb5iYARCCKv+8wJ9ApOEPgGctdL+MGh2nkm6UdK9yzuGme1ZttsbJO1PNGakP/v27aMNtOGZnyaM\nc59o8xq3/frR9tG0fdL7xDhcY9qwdtpQIhtYLulqSW+WdLeZ3dEr+zVJbzKzKyUFSQ9KelvR2YG1\nhz4B9KNPYGoNmp33ZfnfVv15O80Bxht9AuhHn8A0m+iM5bOzs6NuAm0YozZMujavcduvH20fft3o\nGodrTBvGpw0rNTCwvLhis9BW3UAJM1NoIIh2FeenT2Cs0CeAs0r6w0R/EwUAANAWBlEAAAAFGEQB\nAAAUYBAFAABQgEEUAABAAQZRAAAABRhEAQAAFGAQBQAAUIBBFAAAQAEGUQAAAAUYRAEAABRgEAUA\nAFCAQRQAAECBDW1W/gfW3OLgpwqOWWzs7NIvaa7B2pp3b6J9WzLH7D43vW3LzsSGHZkKz0+UZ86j\nkvOkjpGkCzLbxsHnoz7xff0PwyXVQx7aUX1SD6l/x0POE39cu5yy/ot32LmYj2p3peyotmUfS9JJ\nbaqULWhzpex4dFd6+3hmdLzv8QV6tLLPLh2ulO3U432PL9J3K/s8V/9QKbtCd/c9fsFj/1Rt1D86\nDX3SKTvtlMW8N6y47Oka9Sx5c1jBziP0aedzIvfmnbsGqW25T7rUthPpQ+y9c5kKMUwh7Bvp+fkm\nCgAAoACDKAAAgAIMogAAAAowiAIAACjAIAoAAKAAgygAAIACraY4mCT/dybFwS+PcfqDXGqI+ZPp\nbVtS03szxyS3nZM5JnWe3DTmXH3e9PJx8nj0OEr/YOdVD9m242ilLJ7uP6P5yj5HnRdkkxb6Hm9w\n5t5vieqWpNNaX21YZL1mKmVe/XX2WXTOF7d9vTMPPt6nW9Z/HdbXyjfgqJOCQPLTGdTJt1Knribz\ntoyLutewzrYSqfeMagaNZ4SXz7nl9pd+OSYX30QBAAAUyA6izOwSM7vNzO4xs2+a2bt65TvM7FYz\nu9/MbjGz7cNpLjBa9AmgH30C02zQN1GnJL0nhPBDkl4i6e1m9kJJ75N0awjhMklf6j0GpgF9AuhH\nn8DUyg6iQggHQwh39n4/Juk+SRdLeq2km3q73STp9W02EhgX9AmgH30C06x2TJSZ7ZV0laSvSdod\nQjjU23RIchbbAiYcfQLoR5/AtKk1iDKzrZI+K+ndIYS+qUIhhCBpjax0CTSDPgH0o09gGg1McWBm\nG9XtGJ8KIdzcKz5kZheGEA6a2R4lJoN+dtnvL5R0+WpbOwS5lAAp45D+4PLEee7PnH8+M116PpFi\nYMvWTCNS9eWmZafSIuRSKeTSGCybGd/5p+5P01bTJ+b+8Ozvs8+TZs+PdniqeszM8Wr6gi0z/WkI\nNjsXzEt7cCzab4uzz0ltrpSdrpENxUsdcFxbBu533EmN4KVUiFMVeKkRvDbUSbPgqTzncUgI47Vh\nBWkPOp2OOp1OU615xqr6xM1nf599Qfencbn3k1Q6lWOZY8bhXsCqNdEfsreCmZmkGyXdG0L46LJN\nn5N0naQP9f692Tlc/2JVTQNWZ/b7uz9Lrv/y6utcbZ+Ye83q2wCUmp2d1ezs7DOPr7/++lXXueo+\nQaQURqSJ/jBoPH21pDdLutvM7uiVvV/SByX9kZm9VdIBSW9c8ZmBtYk+AfSjT2BqZQdRIYQvKx03\n9armmwOMN/oE0I8+gWlGxnIAAIACDKIAAAAKMIgCAAAoMFYTNUvSCyDvskyKgwcz204lZoVvOZE5\nWWoace6Y6mz6rtS0Y0nO7Pd6x42DuH3xY2da9WbnOW2b6d/xiJOqwEt7EKc0mNHxyj4L2lQp81IO\nxLz0Auud+fcLWhh4nNeGeL9NUT2p88XHeefznl/chpPnVnbR5nOqZWN/D44b770ml7Yhty2VzSL3\nHpR6vXKvY6K+cMVc8hDbn96GtYtvogAAAAowiAIAACjAIAoAAKAAgygAAIACDKIAAAAKtDo7LzXb\nbmObJ12lVNsmceZg7jnNJ2afnJdbyDM1AyY1A0/KLwyakpsoNlbzTR3x7LtBs/W8YyTNPLt/Vp03\ny26LUxbP2IsX9e2WVWe9efvFvJlxXl3zNRYE9uqqs0+dRYnr1C1VF2I+PlNdTHnz5uqsyOJ7cPAE\nSL/ukj40TryXo3R2Xuo9KHeNUgsNl8yydGZwLgmvnHPL7Ut+OdYGvokCAAAowCAKAACgAIMoAACA\nAgyiAAAACjCIAgAAKMAgCgAAoMBIJoSPc7qAcW5b03KLEz+c2PZUZtrvealUBt4irUtS9ZUsMiqN\nf4qDeKp1/Ni7Hk7ZzPH+qfXbZo5W9jmqbZWyOO3BjGYq+yw4OSlKFyD2FhKuw6srttlNxTA4PYOX\nBsETt/24c62edW7NFAfx/VxnH89K0wGsBV76gdxzyqUeKFkEPXU7lLzP5N5/Eu+P4Z/PJQ+x/y+9\nDeOBb6IAAAAKMIgCAAAowCAKAACgAIMoAACAAgyiAAAACoz7XCaMyHMSs/MeX/TLJelUYtbMxtzs\nvJTcbJrczJ0dBecaprjt8fP0ruGT1aLNUdmWmepiw96ixDPqn0027+xzXNWFdjfVmGXnzcTzFvut\nM/POm2UXzxD09qmzKLF/3OA2uTMNtzo7erOw4te57oy60hl7a4nX/pIZeFLZAsQl169kdh6fthOJ\nb6IAAAAKDBxEmdknzOyQme1fVjZnZg+b2R29n2vbbSYwPugTQD/6BKZVnW+iPikpvvmDpI+EEK7q\n/fxF800DxhZ9AuhHn8BUGjiICiHcLukJZ5M13xxg/NEngH70CUyr1cREvdPM7jKzG81se2MtAtYu\n+gTQjz6BiVY6X+Djkn6j9/tvSrpB0lvjnW5e9vsLej/AsHQOSZ1Hh3a6Wn1i7qtnf599jjR70TCa\nBnR1Oh11Op1hna5en/jC2d9nnyvNPm8YTQOa6Q8WQhi8k9leSZ8PIVxRd5uZBSWmyX8ys/Btk0pm\nrqYWIP7lIbV5LTt+7pxbvuXczEHeFHFJ2fVucwOPzDb7jBRCaOTPC6V9Irwj2jlOyeC1/1Kn7Af6\nHz51aXX6/XfX76mUPardfY8PRY8l6YiqXxjEZd5ivN4ixV5agMVov7oLHsfHeQsJb1N1Ieadejz7\nWJK2O3+J2hXtd4EOVfa59Pg/Vco2/2OlSIoH83UX3Y33O+bs85RTJknvqvXePvo+8SGnslyKAyfl\nxzNSmSpyKVNS58qdp0Qq1Uvm/dE+NddwIyZPCPsaq6ukPxT9Oc/Mlr87v0HS/tS+wDSgTwD96BOY\nBgP/nGdmn5H0ckm7zOwhSfskzZrZlerOvnhQ0ttabSUwRugTQD/6BKbVwEFUCOFNTvEnWmgLsCbQ\nJ4B+9AlMKzKWAwAAFGAQBQAAUGAql0RMzcDD6sw8PeeWh3P8ckn+bCMpPWtvrRu04LA3U6hG2czT\n1UV1Z86br5TFi+96ixT7CxD3TxM76cy682bUeQv7xmXeLLt4Jl63/sFvV3UWEvZ4bahz/uMz1Wu1\n+dzqda9MQKw7O2+tLy5ch3fpc88791I1eb1yC6enzpO7RVOz8HKzlzH2+CYKAACgAIMoAACAAgyi\nAAAACjCIAgAAKMAgCgAAoACDKAAAgAIjSXFQMjt0WFJt+93MAsTvYnHiLHt8LrktXJDelpSbxuxN\nHR8ncbqCeBq1l87AWwg1Sg2xwTlu5rxq+oI4pcFRJ5fEjKpT9OejBYfnnXnmpwvTC3hpCaoJFPwU\nCtXjqqke6vBSKtQ5v5fqQVudFAfxZc4tsJtvRNXgpo+3lbY/t39JioPq+teD60q1IfchVvA6hXfP\nJbfZ7/z/7d1viFzXecfx3xOttJLWltR1gmxcgU3SBBdCFFKMIU6ZtHUqt+Cm9EXxK5GAX7VNIKFV\nDYWsoNC4YGhLaN/UCe4f0hcJNoYWbLl4GkOxXSdS7NhxFbc2OKktpa5jy0jWSqvTFzOrnTlz7p27\nz9xz7/z5fkB45u79c+bec+aevd7neYp/hubwJAoAAMCBSRQAAIADkygAAAAHJlEAAAAOTKIAAAAc\nmEQBAAA4tJJV4J6ClADfIFXAwrGza8nlYX96uaTyKu7e0PGmxGHT7415L6U/0zvR+0QahD2riRQH\ny8PLqqQzkEZTB6RSCVRJE5CywxWXnraU6BxxCoVUqoKNxFdhlZQKqe0uroyutxwvS6yTTM8x7Sk7\ncim7M5X9zJdlIy1OP1JFWZfx7A9TjydRAAAADkyiAAAAHJhEAQAAODCJAgAAcGASBQAA4DANNX9n\n3v0lUYVfJuLQxxuBN+3FWONoq/h9lUg8SXozer86usrKO1dGll3zgXND788nIvHOa8/Isms1vN16\nskTwqCrrpQoQe6X2FUf/paPzUsWFl6P3o58l9fku7xj93XR5OboWqUitVCHcuH+k+vesf4un2u+N\nzqtTWdBoURs87fYcB1ODJ1EAAAAOYydRZvZ1MztjZs8PLFs1sxNmdtrMHjOzA3mbCUwPxgSwhfGA\nRVblSdQ3JB2Jlv2xpBMhhA9L+tf+e2BRMCaALYwHLKyxk6gQwpOS3ooW3yXpwf7rByV9tuZ2AVOL\nMQFsYTxgkXn/JupgCOFM//UZSQdrag8wqxgTwBbGAxbCxH/7H0IIZhbSP31i4PVNkm6e9HBAZd23\ne/+aVjYm1l7aet15v9TZ31SrAKnb7arb7TZ6zPJ7hLT26NbrzgelzoeaaBVQz3jwTqLOmNn1IYQ3\nzOwGSWfTq306ufSvCsL+ifTEValCvFUMhIB3lqTOdVvvj782UYvGqTQm1g5FC+LPmUpxkJoIxgVs\nU0dLTNB+bmV4Zxf2plIcjC5bj+Lvq6Y4qLO4cCxVbDhVBDm1XqxK2oOqRYrXl0dzFazsjgo9p9IZ\npNIexCkOUuNiGxkiOp2OOp3O1ffHjx+vvvH2VLxHSGt3JRaWdRtPl/JsU1YwOHX9pPpTM/BLVlZ1\njAfv/857RNLR/uujkh527geYF4wJYAvjAQuhSoqDb0r6d0kfMbPXzOxzkr4q6Q4zOy3pV/rvgYXA\nmAC2MB6wyMY+YAwh3F3wo1+ruS3ATGBMAFsYD1hkZCwHAABwYBIFAADgkDXorawwb8qlkp/NanRe\n0TmgMHG5SyVFhnfmC/jKL/5ccZRdHI0lpaPz4uigfYl1EpE9y9Hxrt17bmSddHSer5Cwd19VpI6X\n2nMcQZfaLhXVF2+XandcpLi3LFGUeHk4Om8pvu69DUfF0XhlEWOzarsFiD3nwPOdURSBN+5ndfJG\nKaMxPIkCAABwYBIFAADgwCQKAADAgUkUAACAA5MoAAAAByZRAAAADrOaOeCqWY52R7ELqXDvvssl\nF33PNoqxtiJOcRC/T43I1OeN10uFzKeKl64Ovz2w/92RVc7vy5eWQBotSpwq7OvZzySWtT52nSpF\ninvLRj/PxeXh31eXdl9JNWJUHM5fMi5mVupze6vRj16O8fvzpCsoSrPgKZz8TvEm9rW1au1Ba3gS\nBQAA4MAkCgAAwIFJFAAAgAOTKAAAAAcmUQAAAA4zE53XdhReWXFkj/tKChAfozix9r+3VvizN0rO\nz6VZj85LSUUPxSM3VYD4/xLLzka7SUT1HVj52ciy9R3D0Xmpgr3VXTv0biNREDglVTjYs05VVfaV\nis5LRTKuLw9fxL1RQWJJslTEV3x92v4izGG7BYU9l9gT1ei5O5Zdn6J2jwbIYobwJAoAAMCBSRQA\nAIADkygAAAAHJlEAAAAOTKIAAAAcmEQBAAA4zEyKAw9PWoK6UxmgfmVRxBfea6wZPnE4cyo1QSz1\ngeOw8FQR0zcTy+KQ+USKg327R4vxrt8wnPagaoqDKkV7L7oqwKYLEC85Uxyk0hnEy6qmT0h95otR\n2oP13aMpDpZTRaTj/rzdYr2zIPW5y+5Mns9b9znypEwo+m76UfEmr5Skc0llMJHiBCLDPkz6nNpN\nNIkys1fV+/rekHQphHBrHY0CZhVjAhjGmMA8m/RJVJDUCSEUTYqBRcOYAIYxJjC36vibKKthH8A8\nYUwAwxgTmEuTTqKCpMfN7Fkzu6eOBgEzjjEBDGNMYG5N+r/zPhlCeN3MPiDphJm9FEJ4so6GATOK\nMQEMY0xgbk00iQohvN7/70/N7CFJt0q6OjgeHVj3g5I+NMnBatRkMMusB85Mo/MlP9s58Pqp/r8m\njRsT9w5EzN3+Puk390c7SAWAVYnOezuxTiqSK96uyjqSrlseDiu8uDq64Ubi6yS1rIpU9F8ceVc1\nWq5a4eLxkX5Vj5dqe3weLi6PFinetTwaFWlx5NqE9ZW73a663e5kO9mmcWNi7Vtb63Y+JnUOK90v\nN5VFxhV94XoCQD0ReGVf+EX7KylCfmP8/TBgT2rMj3G6IDqv7I/VbpvjiL46xoN7EmVmeyXtCCGc\nM7MVSZ+RdHxwnV+fqGnAZG7r/9v0l5mPV2VM3LszuSnQiE6no06nc/X98ePHi1euQZUxsXY0axOA\nQnWMh0meRB2U9JCZbe7nH0MIj02wP2DWMSaAYYwJzDX3JCqE8IqkwzW2BZhpjAlgGGMC846yLwAA\nAA5MogAAAByYRAEAADjMfAFiT8HgLzhCNu9vMMzzvoJjHau5DWuO/Xm28Xiq5DijpVu3THtKiXNR\nEdJ9cWxxSUjzkLiQcSqEO1UjOF4vtV2iIKxF6x1YeWtknfVE2H6qGG8V57XXtV26kPBwr6hapHiX\nhlMOVE1xUMXGjkQahMS38VK8zFerebql+ry3kHgiPYckX+qBssvt2cZhZ6o4c99qwfILJakZdhac\nh8sl5+dEwXfxHXOc+mA7eBIFAADgwCQKAADAgUkUAACAA5MoAAAAByZRAAAADkyiAAAAHLKmOKiz\n1qonlcG0h7tv1582GFI6zeeurG3T3G5JejN6vxQtOFh1R3Goe6oSfGp0x8tSIeGpkPMo1Hpl5crI\nKgcO/Syx4XhxCoIil6N0CRuJD5jaV5zSIJ0GYfyy1L6rpj2IUz2kUj8kUxwUhewPrVSpCdMrFcZf\n9pk8qQc8XwyebTwZPco+a8n1L7q/7izZ36WCz3Tp7eJt3ilY/i8l96PfqPle9del+/tKrcfaLp5E\nAQAAODCJAgAAcGASBQAA4MAkCgAAwIFJFAAAgEMrcR11HrQsgMIT0VfkyyXRAWXFiYs+qyfwoyza\n0fNZ/2QKCkiWFRou8gl3u73b1edjURu+G72/HIfvSboxtaM4Oq/qoIqjh1JROYk2jETsxYWTJR1Y\nGS0NvbE6PlxpR8Viwxei9TYqRsaNRtlVjc7zFS72urxj9HfaHcvDUZDJyzzr0XmposplX5BlXaro\nXHjOUdk2dUbhlRUdT0Xdbio6RyXtLipAvK/kfK8WtOFc8Sb6dsl37e9MwfdwnXgSBQAA4MAkCgAA\nwIFJFAAAgAOTKAAAAAcmUQAAAA5MogAAABxaCY51RGYWbjMaVL3lWEOhlN70B3UqSn9QZ5oH5jd3\nCQAABjJJREFUrziMf9A0tK9NcbqGFxPnak8iDcFqKiy8irjYayrFQSJ9wch2ieNbYtl1end04erw\n21Qx3irWk7Hx41VJZzDJvqqIiylL0sbS6DfgxahdG0ujhZ93vZc+hrla1oJUkd2iQsKSL/WAr4tt\nvw2etqUKMG8q6+KeLluwvz0lm1xXcC3OlRy/7L5clP6g7F5Qtr+2uZ9EmdkRM3vJzH5kZsfqbBQw\nixgTwDDGBOadaxJlZjskfU3SEUm/KOluM7ulzobV45W2G6CX226ApP9uuwGSnm27AZlNw5joliXm\nm3TfL+XbtyQ93S14nFKD73XL0gJO7pluvt+Tv/NvIdu+c5uGMVFF7r5dqQ3/23YLpG7Z07+GvNB2\nAxy8T6JulfRyCOHVEMIlSf8k6bfqa1ZdXm27AfqvthugaZhKSt9tuwH5tT4muucz7jv7JCrfN/jJ\nzJOo/8g4iXryO7M7idIUjIkqmET12zAFk6gX226Ag3cSdaOk1wbe/1gF1SmABcGYAIYxJjD3vJOo\nmf71CMiAMQEMY0xg7lkI2+/nZnabpLUQwpH++3slXQkh3DewDgMIUyeEkCVoiTGBWcWYALZsdzx4\nJ1FLkv5T0q9K+h9Jz0i6O4Tww23vDJgDjAlgGGMCi8CVJyqEcNnMfl/So+plv3iAgYFFxpgAhjEm\nsAhcT6IAAAAWXZayL9OQYM3MXjWz58zspJk909Axv25mZ8zs+YFlq2Z2wsxOm9ljZnaghTasmdmP\n++fipJkdyXj8Q2b2hJm9YGY/MLMv9Jc3dh5K2tDYeUi0KeuYqLO/5+zHuftnzv6Xs1+Z2W4ze9rM\nTpnZi2b2Z3W1e8z+53ZMVGzDwt0n2r5H9I83P/eJEEKt/9R7bPuypJvUq0ZyStItdR+nQjtekbTa\n8DE/Jenjkp4fWPbnkv6o//qYpK+20IavSPpSQ+fgekmH+6+vUe9vIm5p8jyUtKGx8xC1J/uYqLO/\n5+zHuftnzv6Xu19J2tv/75KkpyTdXue4Kdj/3I6Jiu1YuPtE2/eI/vHm5j6R40nUNCVYa7R8VAjh\nSUlvRYvvkvRg//WDkj7bQhukhs5FCOGNEMKp/ut3Jf1QvdwwjZ2HkjZI7ZQUa2pM1PLZcvbj3P0z\nZ//L3a9CCJvpUnepN8l4SzWOm4L9S/M9JqpYqPtE2/eIfhvm5j6RYxI1LQnWgqTHzexZM7unheNv\nOhhCONN/fUbSwZba8Qdm9n0zeyDnI9JBZnaTer/xPK2WzsNAG57qL2r8PKiZMZG7v+e+frVfl5z9\nL0e/MrP3mdmpfvueCCG8UGe7C/ZfS9sduE8Mm4b7RBv9YObvEzkmUdPyl+qfDCF8XNKdkn7PzD7V\ndoNC77lhG+fnbyTdLOmwpNcl3Z/7gGZ2jaRvS/piCGGo7kZT56Hfhm/12/CuWjgPfU1c88b6e4br\nV/t1ydn/cvWrEMKVEMJhST8v6ZfN7NN1tjux/05dbfc0p6HjjMN9oqeVfjAP94kck6ifSDo08P6Q\ner9lNCqE8Hr/vz+V9JB6j4/bcMbMrpckM7tB0tmmGxBCOBv6JP2tMp8LM9up3sD4+xDCw/3FjZ6H\ngTb8w2Ybmj4PA7KPiQb6e7brV/d1ydn/muhXIYS3Jf2zpE/U1e6C/f/SPI+JKrhP9LTRD+blPpFj\nEvWspF8ws5vMbJek35X0SIbjFDKzvWZ2bf/1iqTPSHq+fKtsHpF0tP/6qKSHS9bNot8ZN/22Mp4L\nMzNJD0h6MYTwFwM/auw8FLWhyfMQyTomGurv2a5fndclZ//L2a/M7P2b/9vAzPZIukPSyTraXbb/\nzRvWJG134j4xrNX7RNPfjXN1nwh5/ur9TvX+0v1lSffmOMaY49+sXrTHKUk/aKoNkr6pXmbedfX+\nf//nJK1KelzSaUmPSTrQcBs+L+nvJD0n6fvqdcqDGY9/u6Qr/XN/sv/vSJPnoaANdzZ5HhJtyjYm\n6u7vOftx7v6Zs//l7FeSPirpe/19PyfpD/vL6zrvRfufyzFR8fgLeZ/IPQYrtmFu7hMk2wQAAHDI\nkmwTAABg3jGJAgAAcGASBQAA4MAkCgAAwIFJFAAAgAOTKAAAAAcmUQAAAA5MogAAABz+H+T/Kds0\nwGc+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5b4908c780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3,3,figsize=(10,10))\n",
    "for ax, name in zip(axs.flatten(), filenames[::-1,:].flatten()):\n",
    "    f=nc.Dataset(name)\n",
    "    var = f.variables['vosaline'][10,0,:,:]\n",
    "    ax.pcolormesh(var,vmin=0,vmax=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Improvements\n",
    "\n",
    "Could attempt to copy over some of the metadeta..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}