dask-stratify-netcdf-demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from itertools import product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import dask.bag as db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import iris\n",
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "import stratify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "fname_cloud = '/data/nwp1/cfst/for_MarkHedley/cloudcubes_4D.nc'\n",
    "fname_temp = '/data/nwp1/cfst/for_MarkHedley/tempcubes_5D.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r-- 1 cfst users 12M Feb 28 09:35 /data/nwp1/cfst/for_MarkHedley/cloudcubes_4D.nc\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lh /data/nwp1/cfst/for_MarkHedley/cloudcubes_4D.nc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r-- 1 cfst users 4.0G Feb 28 09:29 /data/nwp1/cfst/for_MarkHedley/tempcubes_5D.nc\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lh /data/nwp1/cfst/for_MarkHedley/tempcubes_5D.nc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def func(arg):\n",
    "    cloud = iris.load_cube(fname_cloud)[arg]\n",
    "    z_target = cloud.data\n",
    "\n",
    "    # Check for naughty cloudtop values < 0\n",
    "    if ma.isMaskedArray(z_target):\n",
    "        # check for naughty cloud top values ...\n",
    "        mask = np.where(z_target < 0)\n",
    "        z_target[mask] = ma.masked\n",
    "\n",
    "    #\n",
    "    # Load the temperature data.\n",
    "    #\n",
    "    temp = iris.load_cube(fname_temp)[arg]\n",
    "\n",
    "    # Broadcast the shape of the temperature height coordinate up to\n",
    "    # the full shape of the temperature cube.\n",
    "    height = temp.coord('height')\n",
    "    z_axis, = temp.coord_dims(height)\n",
    "    reshape = [1] * temp.ndim\n",
    "    reshape[z_axis] = temp.shape[z_axis]\n",
    "    z_src = height.points.reshape(tuple(reshape))\n",
    "    z_src = np.broadcast_to(z_src, temp.shape)\n",
    "\n",
    "    # We need to promote the scalar height dimension for the cloud data\n",
    "    # in order to perform interpolation.\n",
    "    reshape = list(temp.shape)\n",
    "    reshape[z_axis] = -1\n",
    "    z_target_reshape = z_target.reshape(tuple(reshape))\n",
    "\n",
    "    # Now perform the vertical interpolation.\n",
    "    tstart = datetime.now()\n",
    "    new_data = stratify.interpolate(z_target_reshape.data,\n",
    "                                    z_src,\n",
    "                                    temp.data,\n",
    "                                    axis=z_axis,\n",
    "                                    interpolation='nearest',\n",
    "                                    extrapolation='nearest')\n",
    "    tend = datetime.now()\n",
    "    print('worker: {} seconds'.format(tend - tstart))\n",
    "\n",
    "    #\n",
    "    # Package the interpolated data within a new cube.\n",
    "    #\n",
    "    slices = [slice(None)] * temp.ndim\n",
    "    slices[z_axis] = 0\n",
    "    result = temp[tuple(slices)]\n",
    "    result.remove_coord('height')\n",
    "\n",
    "    # Now we need to demote the scalar height dimension in\n",
    "    # the interpolated data for the resultant cube and inherit\n",
    "    # the cloudtop mask.\n",
    "    data = new_data.reshape(result.shape)\n",
    "    mask = z_target.mask.reshape(result.shape)\n",
    "    result.data = ma.masked_array(data, mask=mask)\n",
    "\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Worst Convective Cloud T / (m)      (realization: 4; time: 4; projection_y_coordinate: 704; projection_x_coordinate: 548)\n",
      "     Dimension coordinates:\n",
      "          realization                           x        -                           -                             -\n",
      "          time                                  -        x                           -                             -\n",
      "          projection_y_coordinate               -        -                           x                             -\n",
      "          projection_x_coordinate               -        -                           -                             x\n",
      "     Auxiliary coordinates:\n",
      "          forecast_period                       -        x                           -                             -\n",
      "     Scalar coordinates:\n",
      "          forecast_reference_time: 2015-07-01 03:00:00\n",
      "          height: 0.0 m\n",
      "     Attributes:\n",
      "          Conventions: CF-1.5\n",
      "          experiment_number: 2\n",
      "          field_code: 88\n",
      "          source: CDP\n",
      "     Cell methods:\n",
      "          point: time\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/h05/itwl/projects/git/iris/lib/iris/fileformats/cf.py:1143: IrisDeprecation: NetCDF default loading behaviour currently does not expose variables which define reference surfaces for dimensionless vertical coordinates as independent Cubes. This behaviour is deprecated in favour of automatic promotion to Cubes. To switch to the new behaviour, set iris.FUTURE.netcdf_promote to True.\n",
      "  warn_deprecated(msg)\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Load the cloud data.\n",
    "#\n",
    "cloud = iris.load_cube(fname_cloud)\n",
    "print(cloud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3dtemperature / (degC)              (realization: 4; time: 4; height: 83; projection_y_coordinate: 704; projection_x_coordinate: 548)\n",
      "     Dimension coordinates:\n",
      "          realization                           x        -          -                            -                             -\n",
      "          time                                  -        x          -                            -                             -\n",
      "          height                                -        -          x                            -                             -\n",
      "          projection_y_coordinate               -        -          -                            x                             -\n",
      "          projection_x_coordinate               -        -          -                            -                             x\n",
      "     Auxiliary coordinates:\n",
      "          forecast_period                       -        x          -                            -                             -\n",
      "     Scalar coordinates:\n",
      "          forecast_reference_time: 2015-07-01 03:00:00\n",
      "     Attributes:\n",
      "          Conventions: CF-1.5\n",
      "          experiment_number: 0\n",
      "          field_code: 3\n",
      "          source: MOGREPS-UK\n",
      "     Cell methods:\n",
      "          point: time\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Load the temperature data.\n",
    "#\n",
    "temp = iris.load_cube(fname_temp)\n",
    "print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#\n",
    "# Calculate the upper dimension slices required to\n",
    "# farm out sub-cubes to the dask workers for interpolation.\n",
    "#\n",
    "height = temp.coord('height')\n",
    "z_axis, = temp.coord_dims(height)\n",
    "shape = temp.shape[:z_axis]\n",
    "slices = list(product(*[range(i) for i in shape], repeat=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#\n",
    "# Create the dask bag of deferred workers.\n",
    "#\n",
    "bag = db.from_sequence(slices).map(func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
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88YbtKbFWVFSk/Px8PfHEE7anIAT0pRvoS//R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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag.visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/h05/itwl/projects/git/iris/lib/iris/_lazy_data.py:107: MaskedArrayFutureWarning: setting an item on a masked array which has a shared mask will not copy the mask and also change the original mask array in the future.\n",
      "Check the NumPy 1.11 release notes for more information.\n",
      "  array[mask] = np.nan\n",
      "/home/h05/itwl/projects/git/iris/lib/iris/_lazy_data.py:107: MaskedArrayFutureWarning: setting an item on a masked array which has a shared mask will not copy the mask and also change the original mask array in the future.\n",
      "Check the NumPy 1.11 release notes for more information.\n",
      "  array[mask] = np.nan\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "worker: 0:00:07.127298 seconds\n",
      "worker: 0:00:07.413877 seconds\n",
      "worker: 0:00:06.413660 seconds\n",
      "worker: 0:00:06.639453 seconds\n",
      "worker: 0:00:05.789611 seconds\n",
      "worker: 0:00:05.649872 seconds\n",
      "worker: 0:00:05.730140 seconds\n",
      "worker: 0:00:06.082948 seconds\n",
      "worker: 0:00:05.546753 seconds\n",
      "worker: 0:00:05.870379 seconds\n",
      "worker: 0:00:04.950372 seconds\n",
      "worker: 0:00:05.040045 seconds\n",
      "worker: 0:00:04.693283 seconds\n",
      "worker: 0:00:05.405003 seconds\n",
      "worker: 0:00:05.034899 seconds\n",
      "worker: 0:00:04.997014 seconds\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Now compute get the dask worker to compute their interpolate cube.\n",
    "#\n",
    "cubes = bag.compute(num_workers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/h05/itwl/projects/git/iris/lib/iris/_lazy_data.py:107: MaskedArrayFutureWarning: setting an item on a masked array which has a shared mask will not copy the mask and also change the original mask array in the future.\n",
      "Check the NumPy 1.11 release notes for more information.\n",
      "  array[mask] = np.nan\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Merge the results from the completed dask workers.\n",
    "#\n",
    "cube = iris.cube.CubeList(cubes).merge_cube()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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InOJ12YmIampqaPny5SQSiei3v/0tXb58metIT62hoYHi4+Np0KBBNHToUNq7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\nhjFVbJ+d4Q1WdoY3WNkZ3vg/IqYzsELMdTsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube.lazy_data().visualize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3dtemperature / (degC)              (realization: 4; time: 4; projection_y_coordinate: 704; projection_x_coordinate: 548)\n",
      "     Dimension coordinates:\n",
      "          realization                           x        -                           -                             -\n",
      "          time                                  -        x                           -                             -\n",
      "          projection_y_coordinate               -        -                           x                             -\n",
      "          projection_x_coordinate               -        -                           -                             x\n",
      "     Auxiliary coordinates:\n",
      "          forecast_period                       -        x                           -                             -\n",
      "     Scalar coordinates:\n",
      "          forecast_reference_time: 2015-07-01 03:00:00\n",
      "     Attributes:\n",
      "          Conventions: CF-1.5\n",
      "          experiment_number: 0\n",
      "          field_code: 3\n",
      "          source: MOGREPS-UK\n",
      "     Cell methods:\n",
      "          point: time\n"
     ]
    }
   ],
   "source": [
    "print cube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/h05/itwl/projects/git/iris/lib/iris/fileformats/netcdf.py:2281: IrisDeprecation: NetCDF default saving behaviour currently assigns the outermost dimensions to unlimited. This behaviour is to be deprecated, in favour of no automatic assignment. To switch to the new behaviour, set iris.FUTURE.netcdf_no_unlimited to True.\n",
      "  warn_deprecated(msg)\n"
     ]
    }
   ],
   "source": [
    "iris.save(cube, 'cloudtop.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r-- 1 itwl avd 48M Mar 17 14:56 cloudtop.nc\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lh cloudtop.nc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "netcdf cloudtop {\r\n",
      "dimensions:\r\n",
      "\trealization = UNLIMITED ; // (4 currently)\r\n",
      "\ttime = 4 ;\r\n",
      "\tprojection_y_coordinate = 704 ;\r\n",
      "\tprojection_x_coordinate = 548 ;\r\n",
      "\tbnds = 2 ;\r\n",
      "variables:\r\n",
      "\tdouble \\3dtemperature(realization, time, projection_y_coordinate, projection_x_coordinate) ;\r\n",
      "\t\t\\3dtemperature:long_name = \"3dtemperature\" ;\r\n",
      "\t\t\\3dtemperature:units = \"degC\" ;\r\n",
      "\t\t\\3dtemperature:cell_methods = \"time: point\" ;\r\n",
      "\t\t\\3dtemperature:grid_mapping = \"transverse_mercator\" ;\r\n",
      "\t\t\\3dtemperature:coordinates = \"forecast_period forecast_reference_time\" ;\r\n",
      "\tint transverse_mercator ;\r\n",
      "\t\ttransverse_mercator:grid_mapping_name = \"transverse_mercator\" ;\r\n",
      "\t\ttransverse_mercator:longitude_of_prime_meridian = 0. ;\r\n",
      "\t\ttransverse_mercator:semi_major_axis = 6377563.396 ;\r\n",
      "\t\ttransverse_mercator:semi_minor_axis = 6356256.91 ;\r\n",
      "\t\ttransverse_mercator:longitude_of_central_meridian = -2. ;\r\n",
      "\t\ttransverse_mercator:latitude_of_projection_origin = 49. ;\r\n",
      "\t\ttransverse_mercator:false_easting = 400000. ;\r\n",
      "\t\ttransverse_mercator:false_northing = -100000. ;\r\n",
      "\t\ttransverse_mercator:scale_factor_at_central_meridian = 0.999601304531097 ;\r\n",
      "\tshort realization(realization) ;\r\n",
      "\t\trealization:units = \"1\" ;\r\n",
      "\t\trealization:standard_name = \"realization\" ;\r\n",
      "\tdouble time(time) ;\r\n",
      "\t\ttime:axis = \"T\" ;\r\n",
      "\t\ttime:units = \"hours since 1970-01-01 00:00:00\" ;\r\n",
      "\t\ttime:standard_name = \"time\" ;\r\n",
      "\t\ttime:calendar = \"gregorian\" ;\r\n",
      "\tdouble projection_y_coordinate(projection_y_coordinate) ;\r\n",
      "\t\tprojection_y_coordinate:axis = \"Y\" ;\r\n",
      "\t\tprojection_y_coordinate:bounds = \"projection_y_coordinate_bnds\" ;\r\n",
      "\t\tprojection_y_coordinate:units = \"m\" ;\r\n",
      "\t\tprojection_y_coordinate:standard_name = \"projection_y_coordinate\" ;\r\n",
      "\tdouble projection_y_coordinate_bnds(projection_y_coordinate, bnds) ;\r\n",
      "\tdouble projection_x_coordinate(projection_x_coordinate) ;\r\n",
      "\t\tprojection_x_coordinate:axis = \"X\" ;\r\n",
      "\t\tprojection_x_coordinate:bounds = \"projection_x_coordinate_bnds\" ;\r\n",
      "\t\tprojection_x_coordinate:units = \"m\" ;\r\n",
      "\t\tprojection_x_coordinate:standard_name = \"projection_x_coordinate\" ;\r\n",
      "\tdouble projection_x_coordinate_bnds(projection_x_coordinate, bnds) ;\r\n",
      "\tdouble forecast_period(time) ;\r\n",
      "\t\tforecast_period:units = \"hours\" ;\r\n",
      "\t\tforecast_period:standard_name = \"forecast_period\" ;\r\n",
      "\t\tforecast_period:long_name = \"forecast_period\" ;\r\n",
      "\tdouble forecast_reference_time ;\r\n",
      "\t\tforecast_reference_time:units = \"hours since 1970-01-01 00:00:00\" ;\r\n",
      "\t\tforecast_reference_time:standard_name = \"forecast_reference_time\" ;\r\n",
      "\t\tforecast_reference_time:calendar = \"gregorian\" ;\r\n",
      "\r\n",
      "// global attributes:\r\n",
      "\t\t:experiment_number = 0LL ;\r\n",
      "\t\t:field_code = 3LL ;\r\n",
      "\t\t:source = \"MOGREPS-UK\" ;\r\n",
      "\t\t:Conventions = \"CF-1.5\" ;\r\n",
      "}\r\n"
     ]
    }
   ],
   "source": [
    "!ncdump -h cloudtop.nc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from ipywidgets import interact\n",
    "from matplotlib import pyplot as plt\n",
    "import iris.plot as iplt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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gvtr7Kzf97mY8B1IJv+ZaGk65HWEp+9duejy4d++m8bgbKtx2riJpeYWY5vEV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FSxHzaxrGPnS9UUAg1VDOvUNxiWCUo+kC9p0SmmynY2jYnh4zHjKPIFuOf9L1i37+ShJf7cLDzb\nduLZtpNc4Nr/fAJAXFwcvXr14tFHHyUrK4v+/fszb9482rcPhkZNmTKFdu3asXv3btq1K+v5HSZM\nmIqptyNNt9uNZVksWrSIlJQUNm7cyN69e+varJARtvozmou4oBuFq9eUPP9m907+s2Z1HVpUe+w8\nkl3yvzfWxGem4d6ziWaXXV3s6Qw+1UJP1tCjBUa0IBADnoYCL5U7h//+dgUzV244LXsjnXbevGM0\n3VuWn24PIL5rHzo/9jLnPfg8LUbfSVLfS2g35HJumXgbF154IatWreK3337jlltuoV+/fkybNg3L\nsoiLi2Py5Mn87W9/Oy0bw4Q5l6m3pcGklMydO5epU6eyd+9eoqOjyc7OJiEhgQcffJBJkybViV1H\nuX/FpyxK23HS1x2WnaKM+pFWzyzycPC5f9Dsj4+jRkcDMKFbd569eFgdW1bz3D3vC35ICd5sSSnJ\nmPoGkb16ENP/IhxChXwJPokaKH2edJeF5hWIKpw/u3ZsBH99n648ec3Fla40YloW6/alcd/7c/EE\nQovLVBVBtNPB1IlX8/n7bzJjxgxeeuklXnrpJbKzs2nbti2pqals3ryZHTt20KFDh4p3GqbWqO3S\nYJ0fe7nihlVgy/89HC4NVhcIIRg9ejSjR48u2bZlyxYGDx5MYWGNTllXC34lgD3eTiC37oVTjYzA\nltwI/XB2iWieC6xJO1gimAC+HbswCwqI7tsbAL80IQYidbVk1BlEYjlAt0vs+ZU/dwHj2Aj14xXr\nkUieumZopYRTVRR6tWnGm3eOZspH8wkYJkX+ANYpbmJNS5Ln8XHvB3N5++776NixI/fccw/Tp08n\nOTmZQ4cOYbfbGTlyJBMnTmTlytOrpBImzLlIvZ2ePRHLsrjkkkuYPHkyjzzyCLm5uezcuZOioqI6\nsed351VcSNlyBbDZ6sdHLDQVaZQfInO28srKFaWe5y5YSNzlIxDHreW6hIZ1QjURCUgtGINZHXyy\nYgMZ+VW70evRuilLn5nEyr/ex3NjLwupT6EvwBdrtnDLLbfwxRdfcOedd7JixQpGjRrFiBEj8Pl8\nzJ07t0r2hAlzrlM/rughoCgKL7/8Mi+99BKtW7emRYsWXH755TRo0IDp06fXuj0nZnU5GbakAA57\nPfiYFRVpnluiufu4BOeWruM/eIjI7qXLeMkiq8wUrEAQWaAh8iSSsjGToaJpx/Z799tzyC44vRu8\nDsmVr1IzYMAAli9fzhtvvMHkyZMxDAOHw0GjRo1Oy5YwYc5V6sHVPHRuvvlm0tPTWbhwITk5OezZ\ns4dPP/2UTz75pK5NOykBoaPa696L1sjJQYuPr7jhWYr0+lBcTsRx8b6qEDi9pT2Ij07Tmv6gmAqU\nE6ZuQzweksI4E7VYOPdl57L14OHTeAdwXpMGTBzcs9L92rRpw4oVK9i5cyejRo0iP7/8KithwoSp\nmHq7pnkyoqKi6NixY8nzdu3asX37dkzTRK2nIRSmtKjrdU3TXYAaGwsE64Fe1aFjBT2qlz05R/hg\nwzqWpOwptV3K4HSoXVV5ZcQVVUoOEAqWrgMCKWXJ2qJDqgS8J6TPK6evQCCRITsFKZEKvngLxRfM\n5HOUeb9tY/D5rav6FhBC8OiVg3DZNeau3UpGXkHIfWNjY/n666956KGHGDBgAPPmzaN166rbEibM\nucoZNdIsjw4dOtCmTRtuu+02fvnlF3bv3s2BAwfIyMggJyeHwsJCAoEAdeUFDKDEVL7yRXUjNDUY\nVAhEaDZ6NymbuLym+Gn/Pq76ZAYfb9pAemFhySOryENOehF5aUVkprm59fPZ/JZ+qFqOuS8vl8LA\nsfR4WkI8Wkw03u07geJC4rnHfSeUo/+XL4ynEszjR6KaQ6Ew1kAKiaWWTt6/ctf+cnpXDiEED1zW\nn1kPjqddo8STtmvVsOysgqZp/Oc//2HSpEkMGDCAlJSU07YnTJhzjTNeNBVFYd68eTRu3JhJkyYx\ncuRIBg4cSPfu3WnXrh3JyclERUVht9sZPnw477zzTrV4387YvabiRsVIpW6nZy2/H3mcR2e0o/aS\ndm/ISOd387/Ef9x6aiA9g8CuPchNh7D2H0EtEGh5Au2w5OF5C8gp9ODxl80HWxEZBQVMW7GSWZs3\nMX7Op/iOc3wSQhAzZBDupcEiz05DhaLgebFFqASiT/8cSSSG38KZKZCWRPGBP0GiV8O+TyQxKoL3\nJl3PNT070aNV6dG5nmjwo28n+wtzyu07efJkHn74YW677TYsq+prtmHCnIuccdOz5REVFcWLL754\nyjYej4dvvvmGGTNm8Oyzz/LPf/6T8ePHVzp+DiDH7+GzlNAzFVnSwuEEv6/Sh6oWijZsxNm2DWpU\nMEXbEwMG19qxt2dnlQimL2Ufed8uIpCWjjOuAUe8PnyHD5HYeyjJF4/C8FrkHChi8D/eBAkPXjaQ\n/VY+aQWl687e26svFyY3xqaq5Hq9bD6cyfvrf2VjRgZ5BT7kCfcEls+He9nP+A8cxH8glUBGJiQ3\nwhWnYvcp+GMtNF2lShmcRDANn6apFEbpOJ0qhmJhSYnTpSGzDGQN/coSoyL4+40jMC2Lhet3MGXx\nfKTTQmtksDx/NzcuSeOjwRNoH9uwTN8pU6bw1Vdf8eqrr/Lwww/XjIFhwpyFnBWiGQoRERFcd911\nXHfddcyfP59Ro0YRFxfHlVdeWel93fXzxxwoyg25vYXElujHccRRJ8KpOJwINTip0LlBQ0a0bV8r\nxzUtizd/PTYiz3z3A+JHXkabO38HmcGbFdPvY8+H/yKyWRsaNumMcsgMVhjRFF5d/DP+pGOTnw5T\nQfolK7fuR/MLFEOgx0j8TosIYSOQr2OPVPEfl8kncCiN9NffxtWxPZbXi+X1kb9kKQ1uGos3ysQb\nFWzrQkNS1pO2QqRAmlAYp2O6oAijRHv9homMB4cuwA9J0TVToVxVFJq2jMbV1sRr6iXvPsfv4dZl\nM/hmxL3E2V2l+6gq06dPp2/fvowYMYLOnTvXiG1hwpxtnPHTs6EipWTp0qW8//77PPHEEzz66KNc\nccUVld7Pzxl72JKbXul+ujRREv04nbXjEGRXVFyGRqS0EREbj5EX9JjM9hWy7GDtrGX9+afv2Z9f\nuqJI5AXdELZj92qqw0nTy28i/dvPMNPdJZIlDYu4A4LIFIg8IIhMhahtkvjNELdZErXLIiLFJG4X\nRGeokGpidysIt4lZVHRsDVtVEZpGgwnjaTBhPACFq36h8LfSMwVe1UAmiCp5ygI4faWd0DS/wJED\nmhR4oy1iox28cce1Vdp3KDy5dh5es+za+RF/Ee5A+Xdqbdq04fnnn2fChAkEApWfDg9TfWRlZdW1\nCWFC5KwdaXo8Hv7617+ybNkynE4nqampOBwOLrjgAp588kluueWWKu33+7QdVU7DrksTUwsAtiru\nITQcqDiyBIbXQgKRNELPPIw0TQ4XFPHp9g0Mb1Xzo80v92wp9VzYbFiGji5MXHYFPRBcT0ts1A5f\nywvZu3Im7QfdiqrZkVJi6RYRh+HYtGlZSZMeC+ehAKn7V5C9bjl6YT5C04ho346Esddha9QQoQj0\njEzsjZNpeOvNHJ7+EdmffIZn01ZcHdrh7NAOW2IivggLp1QQoU8ilFgV0CxcUsMrglPRwgvSAkeR\niqUbTOjXg8bxVU/oXhFmOYWwQ+Guu+7i448/5rPPPuPmm2+uZqvChIJhGIwbN66uzQgTImelaLrd\nbgYOHEinTp144YUX0HWd2NhYevbsWaU1zOpEyJqvfWLpFl4NiJXYDBWKHNhjEgikpeNo3gxRR2kh\nFZsNqRsEpIWSJNAywLJA6pLm3S4nZfVnrP3sTyAlUYkt6DxicvD9GDrZ+9eRnbIWf1EujsgEXLEN\nkZaF152JNy+TmNbn0/za24lq1hSvapC75AcOvfgyCaOuwHVeRzxbt2FvnEzkhRcQuXkLlteLEuki\n9+uFWHN1mjx4H/amTfBFWmgW2PNDm4TRIyWIYHuZbyKagQMNRwB0LEyfhd2n8M7iX9ideYQxvbug\nKoLebZqftCRZVXCo5f+UBcFZh5MhhGDixInMmTMnLJp1xFNPPVVvw+XClOWsE03LspgwYQL9+vXj\nzTffrHORPIoWsBPwSfDU7CgTQLdJCIZkYjMEFEkczVvg37cfR/NmtIsp6xhSE5zXPJ5NqVkECoMC\nJOx2rOK0hz7FJM5mI+APrsApqkbb/jfRosfV7Pr5Q+KbdcFflMfhXSs4vGc1kQnNaXzeEFyxjfAX\n5eDNz0QoKkmtLsQV2xjROBo9WuBtJDFQibvyMiL7dyP9k5kYuXk4k5PR+gzFiIak60dzZO48itZt\nxN6kMRFt2xAVk0CA4ChdybewFAthhZCwXYAeJ7HiwYaCalg4cgR6oHT8p98wWbhhJws3BENexl7U\njT9de0m1CedLfUYzYelH5AaOFaUWwD96XU1yxKlHuFdffTUPPvgghYWFREVVrSZomKqxZMkSPv30\nU3799VeSkiqf8SlM7XPWieZf/vIXjhw5wmeffVYjghltc1a6j1LgwFegUNsJDjRdIHKDU5qxTVuT\nt3s3DBrAjI3reXzAoBo//vmJDdiWn45RZMeSENm9G+6lP+Fs0xqXoRLwGxz9TKSU5KRuZP+vXxIZ\n34yCrL0c2ryIpFY96TT8AVzHpS10RicRm1y6QocBmKaFpisYNjAjQIlIpslDk3EvWUrBitWoThUD\nE8XlosFNY0v62gqDIS+mYuGPNLEnKWjZoc0IaLEqOgamlJiYOAoFur/ikmKfrtpItNPOlCuq5zx0\niG3IR0Mm8PaOY/l2hyS3ZVSLrhX2TUhIoF+/fnzzzTfccMMN1WJPmNB47bXX+MMf/kBi4sljbsPU\nL84q0ZwzZw7Tp09nzZo12O01E4s4udNgpu9ajd8KLY+rcDtLRlq1jWmT6KqFsIM9sTn+VYtr9fhP\ndBvO5pwMNruzsQo1YoYM4uDfXsB/IBVHQks0BA6fQPMa7P3lMwoO78UeEYfXfZjkjgNoc9E4tBBu\nUiQgnCpgohaCmiAwix2BhE0lYcAlJIwYFqxqUg4iRiFgmYhIBbAwXRKXS0X3VrxOKBRRWl0rcaO2\n5WBmyG1DoUNsQ/7Vp2rORtdddx1z5swJi2Yts3LlSl555RV++OGHujYlTIicNaK5ceNG7rnnHhYu\nXFijyajtqkaTiBhSThI4fjzC7USvI8GE4LVcT5Sgg8PbED0vN1jpxFY7yQ0iNTvvDBjHVekfkokX\nxW4nbsRwDn/wXwqatea8PuOw8vzsWDkT3ZuPofuJb96NZt1GoJxiHe5E7JEauUpQEGWRRLMJzKhj\nSmZGEszXdxICloktRoF8iSNOxXSbQRGtYGZAdSgU6nqpX5HNVLBOI8l7XXHttdfy+OOP4/P5cDor\nP5sSpmr079+fMWPGkJ2dXXHjMPWCsybk5Nlnn2XgwIH06NGjxo/13qCbaRYRd8o2SkHdCuZRhBfs\neQJF1dDi4tGzavfHmeiK5Psb76JPcdq+6H59Sb75JmReIXsXfczGr/8P3evG78mnbb9xtOh+RYWC\nqdgUVJeKUAT2KBt+tbRI2czyP3dBMEn78TgVDUHQaUf1g5oqsecrCH/FI0bTbxGVrQazCwF2oWJ5\nTUTtJVyqNho2bEhCQgLbtm2ra1POKebMmcPzzz/Pn//857o2JUyI1P1VvZp4/fXX2bhxY60U1m0W\nGcdHQyZwd8f+Jx+L+Op+EK8gUAMCq9j3yBYbj5FbyXiKaiDSbuf9a8bQp0kzhKIQmdyWDr2uQ/cV\nEd2gDSDpevkU4hqHlkReCvBoFoomyIsx8ZxQV/tksZYSSqZtjxLwBKfZXWjYrcr/HHRpoWZKHELF\nxEJVVfxOCz1KUonBcp2zdu1aAC644II6tuTcQlEUevfuTfv2tZNwJMzpc9aIZsOGDWnRokWt3Sk3\njYzjsa7D+Eevq8uvjFHHTrsuoWHPBVuBwFYoUBQQdhWkxJAW/louSB1hs/H+NW91gugAACAASURB\nVGNItLtw5inYnIk06jAAT14a5w39HXZXxTGMElCdCppdJUKq6PbiNcXjECLoBBQqljO4XyPPxNAk\nSpSCzaGiqMF96dESK/J4oZUYruJ8shGgmAIpQUPBlJKAYuIoVDBiJd7o2p+mTS3KZVnG7pLH9rzQ\n1k3feust7r77bhSl7CVhf2FOyf6WZ+7FqsPiB2cjzzzzDBMnTqxrM8KESN0Ph6qJadOmUVBQUOWk\nBVVlTKsLEAL+sOar0uMbzYRA3Smn7jawFwUvgKoq0BuApQBS4jMM7lswj9evGIVDq72vwCtLf8ab\nFcChKCiaRfq2JTTrNgLNEZrKCaAwQmK6jnPoOeECbjgkflvlxUpqEm/csaLTqhBoKOjSQgccZrCU\nmBSA18RKFEh/WX9oyxksB6bl1/65356XybjF0/EUWBxdVpXAAFdb7u3Tl75tWpbbb/bs2cyfP5/1\n69cDsCMri39tXkyB4aVRdDQ/Z+6hQPeXtO/boCXNI+N5vNulZdLzhakcPp+PWbNmsW7dOlq2LP/8\nhKlfnBWi+csvv/Dcc8+xcuVKHA5HrR//imad+efG78nxH4uRI86PHSfHhc3VKkqMiohRwJAYGvik\ngRBKSXq5Jfv2si07i+7JjWvMBr9h8NefF9MlsRGHPUV8vH0jokDixyTnyHosw09Cs4pDIo5iqpRM\nNZ8M1SdwugW+mMoJp3FCeOLREJKj+JMkfgycARXhA+XI0Wng4pCZo7dMMQJ7QMEfMFFrMTNdhsfN\nuMXTKcqU2FMcpZyY1nCQX3el0bFFEn8dOZxOxznKzZkzhwceeIBvv/2WRo0aoZsm9/9vLqmBPKQq\nEclpqDZKzUmtztrP6qz9bM3LYPrgW8LCeRrMnz+fHj160KJFi7o2JUyInPGiuWvXLq655hree+89\n2rZtWyc2vL9zZWnBhOD6VpwPu7CDXyNg1O5UXUCaBDDh+OIdQoBVO1NrPkNn7BefsDMrm0+MTQA4\nhYoA3Ls2kbb4czpcdw8SBcMBoliftJMIjeEA0y5wWAq+iiIoa/At+uwmrjgV8iRGFAgkWqFAzzOx\nxSoYAQtfHGjppzfS9Bo6+QFv8ImA5OOmr0u9VsxTKxZQlClxpNg5cfwrEMgCyfadWUzM+YwRveN4\nou91bF+/mXvvvZfvvvuOCy64AN00eeLjb8hIK8RWfGkQ2bZgXdD2PogsHbKzNS+Df236gb/1vOq0\n3uu5zOLFi6uUAztM3XFGi+Z3333Hrbfeyj/+8Q9GjRpVJzZszU3ng12ry33NxIJYHzbTBpk1nwmo\nQoQomc60qyrxzpobIUxbs4qdWUcIGMcUTPf7yFn0FQUpO2gx+g6Uxk3xmhLVD648TrlWpligmgKf\ngwpFsSYW6gWg5QvUWBVvlIHLVBFSImMFFBavHesqRQnBlHqnE3aS4/dw4w8fcKjAXZLy0Ol3oVpB\nzyKf3YehGiBk8C8g8zQcKQ5OmUBDB2+6zjfr9jBr9fPk/+tdXnjtNbp37w7A/81fxnebdpU0t/QA\neevW4MvOIJB7mIAvB+HQUBw2tMRYHG0aM6PVHhI3HMKbcYT4+HguuugiunbtilaL0/5nMsuXL+e2\n226razPCVIIz8pstpeSFF15g6tSpzJo1iyFDhoTUz+Px4PF4qi1d1c78w0xcNoN8vYJ6X1rdx+1Z\ngQD+ffuIv/wyFCF456praRl36rCZ06FLg0alRteG282hN9/F2bgJHe58DNTgNLpUBdGGii71spd7\nAUiwOVV0DTxxpxZWKHYEclXveqJWAMISOL0Kwg/Eq+iKiZYfzJ17VKh0n4k9jUoLZpa7kMz8YGH0\nOds3szSwjYNZhdj3OFCKw2cMJAbFyeDRsKFhqha0kxBpIgKhZ5zyHYgmffZbRHe4gOezU/ngs6l8\nP/oe9h4+FnsspeTQgo8x/T6i23Yipl1n1OQ4jFaFWL4A+uE8/HsOcXjeT8xvlUXP8zqzZ88epk2b\nxv79++nRowf9+/fn5ptvpmvX0KfgzyWklOzYsYMuXbrUtSlhKsEZJ5put5uJEyeSkZHBL7/8QrNm\nzULq5/F4uOyyyzh8+DBr164lJub0K07M3PtbxYJJsAi13SYI6HXndViwfCWOVq2wN2lM14aNGNSy\nVY0e7+WVy0v+17OPkPHG20T17knciOFIbNhzQPdaSMvCl5dDUfaR4LqglEQltUDVHMWpfgS5icWf\nW0UjTAWMhgKfVnEau1BQEAivxHRCdK6KbgT3qx1RsAkVXIKAVfVjpecVsCk1gwc++JIjhcHpfeEC\nb1M/rv1OpB6sxXpSDIFjpwtvB+/J25RD7sZVmD4vCb0vRaSqZPq89HhlKiLrmOjmb/2VQH4Orcc/\niFI8apRIAo4ElJYBZMcWMKgbAM8NuoX+jVof65ufz5o1a1iyZAkjR46kdevWPPvsswwbNqxSdp7t\n5Obm4nQ6iYiohLt3mDrnjAo52bp1K3369KFx48b8+OOPIQumruuMHTuW1q1bM2zYMMaNG8ehQ4dO\ny5YiI8CMPWsqbgiYSLQGfhz2uvm4jZxc8hYtJv7yy2gVF88H14yp8WNme4MiYLjdpP/7dWKHDiF+\n5GUIISjSvWRs+pk9H73CtteeZNPXr3Nw40IObfqOXT/9l7yDW0v2o2qhjZ6sSInRoPoEE4KCZbnA\ndVhgBCywCywH+F0W/kSJKSycp3HfuT87j5umflIimADSC87dDmTZ0pjlInVw7XThyApt+l9Ki8yl\n84nr1BNRHEhqz7KVEkwz4CNz2XwaDxtTIpgQXBt1pNpQ9WPbuic0pVtCk1LHiI2N5dJLL+Xvf/87\n+/bt46GHHuLWW2/lz3/+M6ZZfefnTCctLY0mTZpU3DBMveKMEM3CwkL+9Kc/MWjQIJ544glef/11\n3n77bS688ELefvvtU/4QfT4fd955J5Zl8f777/Pqq6/SuXNnunbtyuTJk0lLS6uV9xDAQE3yYYsy\ncUUJnBECJc6PptZsaIK0LLL+N5PYoUOwN2nMeYlJxNXgWuaJ5MydT1SvC4kZ2B9pmuQvWUbqs3/H\nvWsrDUdeRcf7/0qf8X+m0/D76XTpfdicUbjikit3kGjwxUt8tpq5IKsRKrYIFdlQYDYUECeQGkiP\nRPfVvQhIHURRaN8jIRSaXzORI7/+ROq8jzCKCsq0yVqxiMgWHYho2qrssSTox/m8PdT5YqJsJ/dY\nt9ls3HDDDfz6668sW7aMESNGkJlZvTl3z1TS09Np3LjmvNfD1Az1WjQty+LDDz+kY8eO7Nu3j59+\n+gkhBMOGDePNN9/kueee47///S+9evXijTfeYNmyZXz77bd8+eWXzJo1i2nTpnH++efj8Xj47LPP\nAPjtt99ITEykXbt2TJs2jddee61KtuX6Kx9LEsBAxvjRY4ow4opQIkxq2lvfu30HltdL7LCLAbir\nR6+aPWAxj/QbiFnkwbN5C3EjhhM4lEbaq9PwbNlC4wfuodHdd6Be2BYR78ASx6Yg/UW5OKKOq/hg\nP7UYKA4FT2zNrRlLoCDeID9Bx4eJIS1MKXHmKBWXDaunRLXsQLvbH8MWHcfu6f9H3uY1JaFI+dvX\nk799HY2GlO8RKxA4dzugIDjanLxyNuuPHKzwmMnJySxatIiLLrqInj17smzZsup7Q2co6enpOGPi\nmL16ExsPpNe1OWFCpF6vaY4aNYoFCxaQlJRESkoK/fr1Y+jQodx7771cddVVOJ1OrrzyShYsWMDH\nH3/MzJkzcTqdOBwO3G43qamp9O/fn6ZNmzJ69GhWrVpFmzZtuPjii3nyyScZNGhQpZyC3AEfT/06\njxy/hwOF1ZOO7mQp36qLguWriBk0AFGc6eX8pAYV9KgeburSjVk//8yBQIADzzyHUBUSRl1J1EV9\nSkq2RThU2jdIJDUrGw2QlomUVomHr4xW8cSdPExGEeCNMFClwBS1t17sCCiYnrp37jodFJud5ItH\nEXted9K+/ZScDSuD2aKK3LQYfQe2qJOv+UtTYN/rwOwqKTT83PHTx3ww+GYuSGh6ymNqmsbf/vY3\nBgwYwNixY5kyZQqPPfZYuVmIznZyi7y8Oe8HdqflsX/O99w2uGddmxQmROq1aL744os88cQTJc87\nd+5MYmIiixcv5vHHHyc3Nxdd1wkEAgQCAZxOJzk5OezcuRObzUaHDh2w2WzExcXxwAMPMHPmTBIS\nEqpkizvg4/af/sem3Oqezi12Ea0BDLcb3569NLh1PAANIiJRa+kC5Td08qNV2vzlDxh2F4rLVaq+\nqcMhuLdXX0a1PJ8rXvwAl6mguiGhWRcO71lNkwsvpihGIk8imIZTovrAkauguMFsrOKlZlMDOk0V\npQAM00I7Q0eZJ+JKbk6bW36Pe/dmVIeTyObtEGoISXP9AuOwikg2KTT8TN+1mpf7jA6phu3ll1/O\nmjVrGDt2LGlpaVWe7TlTyS3ycufbs9mxNwVbbNWuR2Hqjnotmp07dy713DRNHnvsMebMmcM999xD\n7969sdls2O32kr+xsbF06NChyuJ4MmbsWVMDggk1GYnv3bIN13kdURwO4pxOPrluLPZQLojVgMtm\n55sb7+SKr94hM9dDwHPsnTqcgod69eeeHhexYX/wM9UdoApo0nkYO5e9T8KFA4nMt2HEK/hKeagG\ns/BofjgaYmGZQU/XmkYtAFkozxrBPIpQVWI7Vj5Ru/Ad+xy+Tt1Cm+hEJncKLfyrefPmLFiwgA4d\nOnDffffRsWNoyfrPBqYvXcvO9GyMIjcRTVrVtTlhKkm9Fs3jcbvd3HTTTXi9XtasWVPrlc5rKkm1\noRjU1Gko2rSZqB4XAtA2PoE28bV7Vxths7Hwmt/xr3VL+G77XjwBk2bxkYxp34Vbu/bE7fcx+buv\nURVBRK7EkhCZ0BTV5sTKTIeYpthNia+U2cUXalm7wnU0/CScqvzkLE7bGbJoAsTHx/Poo4/yxz/+\nkdmzZ9egZfWLo9cSo9CNFhVdQesw9Y16L5p5eXnMnDmTV155hUsvvZRXX30Vm60eZNepJmRUALtU\nCBRU77SplBL/3n00GDcWgHFdulXr/kPFqWn8qfdw/tS77Gvzd+4gzesmSqhYxyVCiG7QmrxDu3DF\nNMW0VTx9rdkV/MKq0fR50pD4Y0EYQb2WUuLSNUzvmb22eTrY820YuoFV7LW8053F2uwD9EoKPY/q\n5MmTady4MdnZ2dWWdORMQS90o0Wefrx4mNqlXq/AL1y4kPPOO4/Fixfz2muv8Z///KfOBLNbQpMy\nBYyrCyvahxIRYmBeqPssKgIBSnQwE3nvJqd20qgr1EKBzTj2NZSWRX7GLiKTWqG7IFBBdh2bXcXX\nUGJ6LWz5osbOkdRAj5AEYoJlwYwYCESeu4IJgB+s44p165bJ5tzKeYFGRETQp08fVq1aVd3W1UsC\nhsGm1ExksdOVdgqHqzD1k3ormqtXr+aqq67if//7H59++ikjR46sU3sGJ7ejW3zNCY8iqvdU6JlZ\n2Bo2DMkxo64wLQvMYPUSzRVcay08cgBFsxPToBV2XUFYEq3o5ENI0ykJYKKoAqEH61rWFsJZfz/b\nuuKIr6jSffr163fOiOYzsxexdu9BrIAPIRRUu7OuTQpTSeqtaPbo0YMRI0awaNGiujalhH/3u55W\nUTWzLuiQ1TuC1rOysDWo39NdFzZuAhICloknSUFzqfgLc3DFBEtXScPCXgiRPoHzJNdinwh6zAZs\nFoEkiV/WXrIBq/AcH2lGgOosfUPzzo4VzD+wuVK76du3L6tXl1/04GxjR1o2cHQ9MzzKPBOpt6Jp\ns9m49dZb2bt3b12bUkIjVzQfDbmV1tUsnAqiOPF39eHZvBV7cZpBp01hd86R6j1ANfDW1hVYzqDI\nBSwTd7SF0GwYgdIKafostDwLl7/syC7Cq2ErEDjE6XkF26ow0pfRAq2OUiPWB3SHiTyhGIGF5MsD\nmyq1nxYtWpCefvYH9y/atItdGUHR9GVnYI+v3ze1Ycqnzn/xgUCAgwcPEgiULaSo63q9cvr5atc2\ntmZm4TuiYbptmG4bstCGVaBVeaSoICDPiddXfapZtGkzgYwMogdchFPTmNJ3IINbtq64Yy3iNw1+\nzNiFMINiZ1MUYgoU4hp3pOhIKrmHtiJPuJPQciycVumvrBEIrmU6sqsunA5UlMOV9yLSpUWgIdjO\nUeG05apYhacfwpSYmEhOTk7FDc9wUrKOJUQp2r+LyBbt69CaMFWlzn7tGzZsYPjw4URFRdGrVy+S\nkpIYM2YM69evL2kjpSQ3t3oy75wun+/YzOM/LuDOr74g9UghsjAomKbbhlVgJ5Blx14F4TTdGgFP\n9a2NWT4fR2Z/QdKN16PYbMQ6HdzdozdaPcu6IpHBmtiRYHOo2FEwvCaqZqfNReNIWf0ZhzaXnpqX\nlkSexFvV8Fo4ssoKp1Oo2AtFUFgp/wLvx8TfoGqutz4M/A0ltggFm12lHi8h11vi4+PJyckpSeV3\nNlLkC/Du4l+OPd+/k6iWYdE8HiHEo0IISwiRcNy2fwshdgkh1gshup+kXw8hxEYhxE4hxKs1bWet\nX0nT09O56667GDFiBKNHj6aoqIiMjAz27dvH0KFDGTlyJE8//TSpqal07dqVdevW8dZbb9W2maVY\nm5bKUz8vJHCKCky6IdGz7DionHBqJ7mQV5Wc+QtwndcRV/t2AHgsH4cK3NV6jOogmLlVYCHx2I1S\n0SKOqASktGjQtk+Zfqrj5F9Zw2fhyC6dEdbKttDyBLYCgXLQIvaIhtOs3s/ch0l+gkF+Qx0zqlp3\nfU5gt9sxTRNdr14P8vpETpEHTyD4/gL5OZgBH44G4WTtRxFCNAMuBfYft+1yoK2Usj0wCXjzJN3f\nAO6SUnYAOgghRtSkrbUqmm63m169epGQkMCOHTu47777SqZfExISmDx5Mhs3bmTt2rX079+f0aNH\nExkZycKFC2vTzDLM2raZQAhOgbohQa/kR1qNQfq+lH14Nmwi4epgsu1GkVHMvX4iTaPrn8OBXdWI\nDQTtijBVdCw0h4qUFntXzaTFhVfhiIwv1cdwgF5BZLEVkKjFsuk0VRzWMYEUCHSvhT2vBoeD4ZEm\ne9zZ5PhD96LdsmULbdu2xW6316BVdcsrC34u+b9o/04iW7RDVLPH/BnOK8BjJ2y7BvgvgJRyNRAr\nhGh0fAMhRDIQLaU8Ooz/L3BtTRpaq8kNXnnlFS677DJefPHFk7Zp2LAh33zzTS1aVTFNoqI5nauh\nUuBA1W0gBaZNR8b4SkZWapQBRae/butPPcjh9z8k8YbrUCMjUIXg4+vG0jouvuLOdYUUwTVdU6AQ\njNF0Z+4BKUlqXboai2pTMBJUdE7tHeuLMrFlK6gNFIwiE8Vf9sIkatDB1hKyVlL61WcOevJYcTiF\nq5p3Can98uXL6d+/fw1bVbccHWVKKcnduIrE3kPr2KL6gxBiFJAqpdx0QohcUyD1uOeHirdlntDm\n+DI7B4u31Ri1Kppff/01/fr1Y/369XTp0gVNq/cJiQAY26kbU0N0idet0ldk4XYSKFTgaJC+X8GO\nExnjA8BUTYKnoWoXWsvv58icL/Fs2kzSTWOJ7Ba8UDk0tVjs6y+aKlB9oPtNIhUNU7c4vHsVDdtd\nVCq+VLMp+Bqq+CoQTACpgvCBPUugS4FEYrrAiJQ4bBq634QcC6eulqm/aUNBryCZQkWY0aDln9Yu\nzhiqK6ngihUrGDIk9PR7ZxoHc/LZnnYYAM/BPZg+LzHtu9axVRB1qHqcD92Zu4M3u6dACLEIOH6U\neDTV15+Ap4Dh5XUrZ9uJX7pQ2lQrtapaTz31FPPmzWP8+PEcOHCAxMREoqOjSz1iYmIYPHgwEydO\nrDclg5pGx9C3aTNWH0qlInGz8hwIhwaxfiiwoxeWfQ+BQgVV0RBRBiYWarSBWVC10aY0DDybt5QS\nTABd03H7/TSoxzcmQ9u24qOs9Rz9Gga8bvLTd9K6z/Wl2vmFhS5ESD8FrTB4fkyfhUJQNFUfIAXe\nJAMiIMKj4vWYEFu67+kKJnBOTc8a8RZKlHlan5plWSxevJinn3662uyqb9z51myyC4L1d7NX/UBS\nn6ElpfrOBmIatSOmUbuS5yc68AFIKcsTRYQQXYBWwAYRvFNuBvwmhOhDcNTY/LjmzYATq2aE0qZa\nqdUr6rXXXsu11wanm91uNzk5ORQUFJQ83G43brebxx9/HEVRmDhxYm2ad0qEKtEUBeMkpaqOYpgS\nPApaIALDOHlbxVKRxaWsbA6BWVA1u9TISBrcOp7sjz/F2aolakxwdJkYEUGsq35nG7m2WRdm5gcD\n4f1YHN69jAZteqGdUJnbJhX0QgsiT65IQgTLcKqB0m2sWPBHS+xCheLEB74GElUqFU71VgVTyuJ7\n6LNfPaXdOulo06GEdmn57bffiI6OpkOHDtVpWr1hwbrtHMoNOuJ5Mw/iy06n+eg769iq+oOUcjOQ\nfPS5ECIF6CGlzBVCfAXcD8wSQlwE5EkpM0/onyGEcBeL7BrgVuDfNWlznQ1DYmJiiIkp7aDi8/l4\n++23iYqK4rrrrqsTu3TL5N9blnKk2JEhzu7idx0GUKgHKhTM4zmVYJ6IaQ9gj1KKp3GD65O+3XuJ\nHTo4pP4R53Uk+qLeHP7oY5LvvRuhKGTpbqZtXcrDXS4J2Y7aJDOvgDs/+BzhCZb68rpzObxzNd0u\nf7RMW69LYpxCMKGkbjVSSETxtKwtQiPgKM4YdFymoEANZw1SXeoZX6S6IiQSK8Iq15PwpjY9GdYk\ntFJf8+bNY9SoUdVrXD0hYBjMWL4OCK5lZv44j6Q+l6DU49mfekDwggBIKRcIIa4QQuwGioDbjzYS\nQvwmpexR/PQ+YDrgBBZIKWvUc7TOz14gEGDVqlUsXLiQDz74gO7duzN37lyiomrfd1+3TB5Z/QUL\nD20rtf39nauQBXaoxvAQgSi5R5eAjPFhw4FeqOJe+hOeLduIGTwAoapIw8DIdyOEQI2NKbdIcMzg\ngbh/XkHBql+I6X8RAHmnipGpI1b8P3vnHWZFdf//15mZW3fv9krfBQRBpCkq2AURLNii2I1GE2ti\niTHGb35qYjSmx8TEGlSCXYQYCIqKgBQVpUnvsGyvt82dOzPn98ddFpbtsLvswn09zz7snTlz5sxl\ndt7zOedTdu9iV3UVTy5cgMMm5hwV0dn13ktkDT8bpze5wTGOoMR2gNWKXK+i1sKzkwU1viMTwqCn\n27hRjmrhNLJN1MxoAzvzmvzR/L+Rk1BaGbD66aef8thjj7X7+LoCD82Yy+pdRQBUr1uBpYdIGznu\nCI+qayOlzD/o891NtBt1wO8rgE5bJD5ioiml5MUXX+Thhx8mPz+fCRMmMH/+/AaFpzuTf2/9uoFg\nQiw1mNqui1USnA2tHZkUweWShL5bh5rgRd+2HVvXqfjgP0jTAimRlkniqJEknjQKxePBrKwksnM3\n1QsWkjBiOAknxu6drrhk8uqqb3j888/qPqcKJ9FANbtnvYqnR1/Sz5yAWWbjsBUMp40jtgwUs9zc\nrbfcNU1gJdSmJzwCFTCj0oZ0cCkKmlQwgmanj6GjcZU7MNJNSNx/H0/NH9UmwQTYtm0bAwcefUH+\nGwtLWbRhOwBmOEjx5/+h92W3IJTOKQIfp+M4IqJZUVHBddddR2lpKYsWLTqiQnkgYbNpyyTm7nB4\nSmQbUSy/Hxnwo+8tx6oOYod0tIxkHDnpICWVHyzCPbAn3uP7UvzCy2gpKWR87wo8g2NrPtGSUgJf\nf0Pp9DeQlo2WmoIjM4PcH9+FMzur9kwSp6byg+NOO6zxthfbKiuYsWY1r6xcUW97+ea1FMx8g9QR\n48g8dTxSgJ6poEtILBYIAapHIZQqoA1T47ZbEFGOrFBFpU00xcaBgqPthT+6PibIcg1RK5ouRePO\nwWe0STANw6CsrIwePXp01CiPCBsLS7n1+XeJmLHvpmThf/ENPBFvbt8jPLI47UGni2ZBQQETJ07k\n/PPPZ/bs2V0qt2xziKQoTls9pJR3oe/W4V+yjPCmzSgJCWi+RNQML1pyAorbRXjjbqJF5dihCMnj\nR5M8cQzCoeH0JeA94SQsvHV95Q8YwO6sTFInN0x6oSkCNSWKwwV3DzmTPh1UkaU5AtEIa8oKKfGH\neGvNWvxGhN011dREInVtpGlS+d+5BL9ZRa9LbiKhV379TgSEc2M5g0xpt0kwJRIzueukY1NCR3oE\nHY8mFP457mpyvG1LorFnzx5yc3O7TehZa9hSVMatz79LVSgWUhbasx3/tnUMuOVnR3hkcdqLTr1b\nN2/ezPnnn88Pf/hDfvazn3XpWo8HI5HIupjK5lseGHcQ3ryFsjffJfXiyWTecC2K243LJbDSmzc/\nstyJvPaHl/lydwHvbI3l401zennh/Ct5f8M6XvrmazaWl9U7xvIa4I4yufcJ3DrklEO4ysOjyghz\nzafT2FlRTbTcGfMkPYBoSSn+ZV8S+GoFnr59GPSDB0F4G+0rdmzbxc9KBqMjsxe0EalLzARQg7F1\n7KMFiUR6JAJIc3kZl53f4jEHs3nzZvLyulYhgcNl2ZbddYJphgLsmTODnHMvQ3V5WjgyTneh00Rz\n1apVTJo0iccee4zbb7+9s07bJib0HMSrW5ZTEWloHtgRBWlLXG6I6I0dLREeCyWiYtX6f0jLouzN\nd8mYeiXeoUMAcLkEanqk2WCHTHcir511A/m+DPKHZDB1yPB6+684fiiTBxxXZ7396atFfFq4CY/X\nxXl9+/PI8ImonZyiq8oIc/PC6ewMVKKXOdkneNK2Ca35jurPF2KWlJF48ihy7rqdBG8ODsOBkBCJ\nmO0mKA5LwRJ2A8E+UhhpsXG4XQpUyKNGODWXikzXMYmVdTNtu81FAT7++GPOOefoyYwT0CPMWbkB\nADtqsPuDf5E8eATJg4a3cGSc7kSnieZvf/tb7rrrri4rmAADkjJ5/cwbzaIsaAAAIABJREFUuGHh\n6/WE0y2dBMpVlKQohm1y8NcmkIjkKNIUWPb+h2Lgy69RfYl4hhxfty0pTeGh0ZOatbJPyujd4tSq\nx+HAUzu1/fQ5k4BJbbjSw+Pjgg18vHcjAP5IhA1F5dSYOjV6BM1wATFHr/B366icMw+EIOX88XhP\nGFLn+SuFgj8URTpjCQkchxinejAyIHGhoKfa2F1EOAF0r41bqLjDKkiJGZUx/2khiEatbiemEU8U\nRY19v1VGmFc2LeX2wa33DK2qquK1115j0aJFHTXETiWgR/jhSzNZvasIMxxkx5t/x5Pbh6wzOu/v\nMk7n0GmiOXToUKqrDy2/WDgcZv78+bz44ou89NJLZGVltXzQITIwOYvXz7yB5zd+UbdtwfYdgI1d\n0zChtJZg4fJJTMUmsteNNE0iewpq1zGXx+ImawWyV1IS/zjnEoZmZDfop7swd8867l/+fp0lp+oO\nIhX70gA6MKUkvH5DTCwti5RJE/EOG9rgJcGUNu4qQSRDokUFbqHiKDJRhMB2Cfz71iWlxFUlMVIV\nNKHEPFNbIiBxuBQino6dplUQOIRSm3mottwZoDcRB6p7LPRGxpRYqGB3nRnlFrGSLUR/A/OAXEDV\n0UanXxrFNE2uv/56pk6dyqBBrYvn7Orc8coHrNoVK6Rd8e0XeLJ702Pi1d1qCSpO6+g00RwzZgw3\n3ngjX3zxBaZp8uGHH5KZmdlo28LCQpYsWcIXX3zBn//853p19jojtd7A5Cx+P+ayus9Lcndy/cx3\nG23rToSawgKq531F6LsCokVFODIzcffPp8d99+DISK9r2y8thSHpHSf4Hc3/DhbMiBOjQgUEUkr0\nTZupnDMPW9dJnXQ+3hOHNZ8uzCvwlAuElLhLLUxTYiEhCilohKSJkSyQtRn01FIT4QAjueV7wIpa\nKB6BaguEEO2+zqkIgbdCxQ7aUCuW+0hIVAmlWE2uyDqFio3EkjYuqWFZXd/SlEiiGRbSY6NlWbU5\nk/fT2tFLKbnzzjsxTZM//OEP7T/QI8R3e2KJamzTpHLlF/S96o64YB6ldJponn322Tz33HNkZGTw\n8ssvc/nll3PdddcxYcIEampqWLJkSd2P3+9n7NixLF26FCklv/zlL7nrrrs61MI8kLlz5zJ9+nTG\njRtH3759mTRpErePOokXvvm6XjunV1K9awcFv3qV5AvGkH3lJSi5PVEaKXH0f2eew/dHjGqwvTvx\n9vZv660VKiGV8I5dhNZ8R2jNWpCSlIkTSBg1olW5NfVkG5+lkVAEZqR+iIgZMHEADhMsCaoFRpJA\ndakg7VgKoGYeSs4aBeEHRRFEHDaKD2zXIV96PRQh8FbuE8yGyIDEK1RCyfWF0yVUXKUKph4THNWh\nobssHF1cMMO9o4gUE81jx8T+oP0JmpMbBzSsfdoYTz75JCtWrGDBggXdxnO+Jcr8QSw7di8Etq3D\nlZ6NOyOnhaPidFc6TTQdDgeXXRaz3kaPHs3MmTOZN28eTzzxBKmpqYwdO5bx48fzy1/+ElVVee65\n51i9ejXffPMNvXv3bqH39mXv3r3MmDGD0tJSiouLeffdd3nllVdwqCqfbN/GlooyFJfEtEvY+9t/\nk3nbxfhOG4oTB1api0h0/8M0JyGRH550MjcN796CeSBmdZCKdz4juGwDwu3GO+wEMq69Glef3m1O\nRC0VUBGxKFgtVlTLMmPfnwCELVAscJRItASVqC4xPRLFAquF1LpSgmVJNEsgMhQac79yhhUwYhlU\n1UQFXW3eIhWAp0JpUjAhJtQyKPF4NUKO2MuAx9ZwlotYlZVarKiNyxbtkSa+Q3FWaNi5RpOJIqb0\nPZEsT/MVdUzT5Mknn2TatGksXboUn69rV+BpDVJK/jx3MTO/+g6rNiwqsGMjiflDjvDI4nQkRyRA\nyuv1ct1113HdddfV2x4IBPj1r3/Nyy+/zPe//30WLlzY6YIJcOutt7JmzRrS09N58MEHGTVqFH/7\n29944J57eOC003l86Ue8+MGrlEz/iOQJJ+M7LZacwSCKN1NhQsIQhmVnowiFqUOHkXAUFdcNrtpC\n8d9n4ht7Ar1/+iPwHZ71L4CgHUUTEHXYKFb9ZIXygBeQqL9WgIKg2OBwKQglNn0rAEsBI0UQbmRN\nUamRcFAYoTuoICr3e7SqIRCZKmGtaeFUREwQm8PGxnSDHTFxagpKAFS/TbQRdewOa5maUIjSeACQ\nW9W4of/JjR63YcMGLr74Ynw+H06nE5/Px+LFi8nJ6f5WmJSSZ/7zOa8v/rbetsCOjfQZefoRHFmc\njqbLRBUvWLCAW265hXHjxrF69Wpyc3OP6Hj69evH9u3b8Xg8zJkzh3POOYddu3ahaRofffQRnkiY\nM+++gd7jRtErIZV7hpyJKhQcikqSs2tXFzlUtn+2nOK/zyTnnivwDsvHgUp4b/241LZiJErUZIHb\nFCjh1tlcSm2zA622fTh1BTIVwrWNHB6VoCOKqot6onmwYELMKlVLwJOtEVZNnEJFrQINQRQbO0W0\nWEPS4VIJJ1sYThthgQzZaDUKljx6wk324VY1nh83lf5JGY3uLywsxLZtHnnkEWpqarj55pu7TLm/\nw2VTUVk9wQQIF+0CwBWfmj2qOeKiGQwG+fnPf87777/P888/z4UXXnikhwSAEIJoNJZWLy8vj88+\n+4zf//735Obm8utf/5qJEyceNQ+A1rBhwwaW/nEaPR65Dnd+LO1ZFAtnAhiHkSYuoln4PBqGx0aN\nHH45Ssu0cZQCuSph20IiUQyB5ZZoQsGUNp6oCpU2AoGqCixrvxDaNkRDJi6ngisgMMM2FhIFcJqC\nmhQLRxPpFDVNUJMSRTNi3sC6w8JOAFFNty4VJgyQEQVcFqoQ3H/CuWR7fAzwZTAktemX27PPPpv+\n/fuzZcsWHn744U4cccchpeTFz75i5ldrG+zzb1qDt2de3AHoKOeIimYwGOS0005j+PDhrFmzBp/P\nx/Lly9m9ezcFBQXs3bu33r9VVVWcdNJJvPnmmx2+JrJs2TLOP//8us95eXn8/e9/79BzdlWklFx1\n1VVce+vNfD0wF93a77TjTLIwgod3GxmZElBQLBD+w1/hs00bwgJcYIZtXE6VaDLo0kQVgmjYwomC\nENQ6cNR/yHmCKla1rBdSAbG+kiytwXbNoWCaFoaQeMsUbEuiCIkzWSBV0FNsFEOgBiTKYeYvPhLY\nhkTb5EYOiZDjS+TmgafgaEXicSEEjz76KA899NBRIZqNTckeSNroM9jx5nOULv+EzFPO6+TRxeks\njqho3n333WRkZHD33Xfzhz/8gWnTppGWlsbAgQPp0aMHPXv2ZOjQofTo0YOtW7fy7LPPMnfu3A6z\n8LZs2cKOHTv4/PPP+fjjj/nd737XIefpbgghuP3223n88cf55Wl/4kV1d51wWoqFy+0goh96IoGI\ntCBdRYkouAOyXojRoSIPyFcbTZd1sZNSl2gRgeWUOG21zunoQKxmaqGaRsP2Uc3GdIFixL4rQcwJ\nSauKibGmKQTSLHAIlCrZLa1OERZEDcmeUBX3LX+fP51yeauEMzk5Gb+/nTJXHGEWbdhRTzDtqEFg\nx0bsiI6s9ejuOekaCua+ARAXzqOUIyaagUCAZcuWAXDnnXdyxhlnMHfuXIYN218WTUrJW2+9xcMP\nP0wgEOD+++/nhhtuwONp3zyOkUiEX/3qV7zwwgsMGzaMfv36sXLlSnr16tWu5+nO3H333QghWDZr\nHi/84Vfc/sUb6JZJVFo40yK4yl1EIocmdgoCrVoQlTZqpopWau2vKn0ArV499alEPPuPN8MWrkQV\nAwvhFojK2vCTQxzvwYgwsbARDaTVsE/TlCRWqIQybKJZAm+NQrSV67ddCWWvE5Ef4aOCDa0SzsLC\nQm655RYmTJjQiaPsOCLm/hmWkiXzqFixEHd2b7SEpJhDmmVS9OksUkeOo2rNciAunEcjR0w0ExMT\nWb++Ye3KA3nqqad44403eOqpp5g8eXK7WZh79uzhm2++oby8nPLycl577TXy8vJYvXr1UeHZ11Fc\nfPHFPP7447z++ut8NPEuSvQAP/1yJtsDFbgyBIquYEkb/C6MxlxFm8AVUZCBWHvdYePOVHGGIGpZ\n2BoIQyLMmGesLyAwI830nagSTJb1NFfzC0zbQkkSOIICxQTFhKbTD7QOh0MhEjWxEwRaUMTKZTXR\n1jQlCTUa1WlRhNY9aypq5Sp6soqSYfNRwQbuX/4+959wLpuWfM3vf/97+vfvz0knncTxxx/P/Pnz\nef7557nrrrt45JFHjvTQ2wWHuv/5E9i+kazTJ+NMy0Iv3k3qiHGoThdRfxW7Z7+GIymNqtXLEUKQ\nMebcIzjqOO3NEXcEaooPP/yQ5557ji+//LJd6+0tWLCAq6++mpNPPpmMjAzS0tL4v//7P6688sr4\nAn4LKIpSF5Ce400ix5vEa2fdyI2fv8b2QAXUJg9wukCpdqIIkJYgrDcvoEIRHCg3usNGTwYQOBQF\nrchEtcBTLjGbETqZpBLy1RdMiUQmC4QF7kIFy2q4hlmvD1rn5appgpDbwkoGb0XrYi2lIXFUC6TR\nisZdFE+Bi0iCDR6LuVtW8fovn8G5voA//fZ3VFRUsGjRIp577jnGjh3Lhx9+yKhRR0988ikD+pDh\n81LmD5F91kXsfOsfuNKzcaSkU7FyKb0mX4u3Vx79pt7JlpeeIun4UZQt/5SkQSNwJnd+mb44HUOX\nFM3169dzyy23MHv27AaCKaXENM1WZxOxLItNmzaxatUqvvrqK15//XXeeOMNzjsvPm3SVvx+P06n\nEyll3QtGtsfH62fdyBMr/0ekdp1zT7CKraK2bJlpY++pIlxYhZaUhJqSgupLrJ8Ewdm0WHkqJXYj\ntcElEE0EHAqGT8Y2HFR3U1HAylSIOCw0P9hWy4IoHSCarkVeR8hrYTtB2KCoCrbdsmyapo3DL4g2\nW+OmayMj4Nzkocr6juKX38c7tB/JT95C7jknc12P44708DqUxRt3UOaPFXJI6JXP4Ht+jeKMvSnW\nbF7D7tnTSOh7HOHCXSBErOi0bVP02Sx6T7k5/lJ+lNDlRLO6upopU6bw5JNPcuqpp1JeXs4nn3zC\n/PnzmT9/Pjt37kRVVcaOHcsdd9zBVVddVXcz1tTUsHr1alatWsWqVatYuXIl3333Hbm5uYwYMYLh\nw4ezePFijjvu6P7j7ijy8/Px+Xy8+OKL9arVZHl8/O2079V9DptR7lzyNp9+/DF7/zETxePGmZKO\nUe3HrKwCJLn33IkzJ5a4PiRNEhLUuqQBihDIqI3mULECZqMyJwDFpWIlCcwsiUTiqVBAFZjCRpFg\nekF3xATK9IHXUDAbW0t0xqZqVUOgRPefTUqJHTVQnQ3z77n8CsIpsCMNvWw7gtZawJ1BcOs2Ct54\nmeQzR5Jx28UIh8aK8t2ce5SL5sEoB9wXSQOH4e3Rj8o1y/Hk9kEv2UvJ4jkY1RV4cnojLQtxFBXb\nPpbpcv+LRUVFeDweHnroIZ599ll27NjBmWeeyfjx47n33nsZPHgwpmkyb948fvGLX/DSSy+RlJTE\nypUrKSoqYujQoXUCedNNNzFs2DCSktpWUT5O47hcLt544w0uuugiFi5cyB//+MdG8wGbYR3Pm18g\nP/yU/731PsNOPZUfzZ3Jyr2xpNb+5V9R/OIr5PzoNhyZscB4K1Xgigqi+7xTNVHnidoU7ohCpdPG\nqLXcomnNi1fIa+EMN+xRQcE6KItQtKaSTc//Ci0xmUF3/L8Gx0gJsr0ciZRYOt1m23QRwQTw9uhL\nn8tvonTjYnb86A+kXHQaPDz2SA+rQ7Ftm1VLFlKyeC5GVXnsp7ocgcCVkY0rPZuE3gPIOOU8Kr5d\nTNXaL8kdfwUpw8agqF3uMRvnMOhy/5uDBg1i1apVFBUVsXPnTkaOHInzoDR0mqYxZcoULrjgAt5+\n+200TePXv/41AwcORIu/zXUoQ4cOZe3atTz22GMMGzaMp59+mptvjk09FRQU8Oyzz/LSSy9xySWX\nsHr1alJSUgDQlUhdH75TTkZGIuz9019x9euLO68frrx+JPfJw1PlrBNOU9q4E1TsYOPTmULEUtY1\nhwAcZQIjQ5IQ1hqdGrUNG/UAUaresJI9/3kNgLxr7m7Dt3NotKbaWVdCKCq+/sNJGjyCGq2Agmef\np+DSLTDs6Fzy0HWd8ePHE41GGTdkOF8VZ+BISceZkg62TaS8hEh5EaXL5lOxcgm5E66g9yU3sfej\ndwjv3Una6DPwZMc98Y8WRHMxcUII2R4xc3GOTr755htuv/12fD4fffv2Zfbs2dxwww385Cc/IS8v\nr17blUUF/PyT+WwsL6vbZgUC6Fu2om/bQfDblViBIP1++gCe5Ny6FHcuQ6CVWo3aWXoG6P3VOkvz\nYDRLoFbG1ihtB7iiSr3sP/uwHTK2jimh/KsFlHzxP6QZpc/lP8DXP558uzkkkoKv3seV5WbNtJkt\nJm7vjtx3333s3r2bd955ByEE90yfxWertzVoJ22L8m8WU7ZsPmkjTyd1+KlUffc1ld9+gXA4SR91\nBmkjGy/UffOZo/npxWchZecE8Qoh5CnX/r5D+l4+48FOu44jQfdLTxKnyzBq1CiWLVvGFVdcwfHH\nH8+WLVv4y1/+0kAwAUbk9OTJc+vH66mJiSSMGI6WkgwIMq69GnIy0RNs7AyB06sScUrsVKWBz2wk\nFfQ8pUnBBEATaJkaeq4EBw0EU4raNdSoAFtS9OkHVK39EmdKOuljzokLZisQCKLbitF69eD1LV8d\n6eF0CJmZmWzevJnKykoAsjITcWoNH51CUck46Szyb7wfvaSAHW/9A29uXwb+8FF6XnA1xQs/xL91\nXWcPP047E5/LjHNYaJrG3Xe3bgpTbcR7MLJzF1WfLKDnT3+CVjuVa/n9lHy1AssfILXPUMjsjwcF\ntdJGTdTwe0wi/RSiSvPzmqa065x0hFsg/fWdaUTty7BtRin47wzMcIDE/kMIF+wg+/TJrbqmYx1L\nD6MX7sKdfBMzt63mgWFHV0xiaU0AOeRUfP2+ZdiY07j6gUcRvlSMRjJJ7cOZlErvS2/Gv+U7CubM\nQHV7iQaq8eT0xuFL7sTRx+kI4qIZ54hSPnM2QlUof3cm0rIx9u7FDusknHgCjqxM9syeQWL/QeSc\nfylkOrAdFvgUom1cCIy4bDzZKmrZfovT0kMEdm6m/OvPcSSlkDb8NIoW/If863+MULtnAoLOQkob\n/5Z1FH7yPqnDx+JUvdhHyVLOzNVr2VlcRXWNzgdfryNqWcjjTsP2R/nrA3fEvGGlRHW5SRl6Mol5\ngxGKghkOUr3+G6rWfIlRVU7ykNH0uvhGEODwpeDwpRzpS4vTDsRFM06nsbWqAkXUD6fMuvE6ouXl\n2KEwCIGzRy5aWmpdHGfSGeMonzmbLf/8HVk3X4+rT+9GU+y1hpAaxaraSXjVJsI7NxEuLSIxJ4+M\n/qPx9O3N1vdepO/3fhh/uB2ElBK9eA96aSFmyE+krIjgjk2o3gR6Tb6GhD4DsZCkq913PdOybQzT\nImREeWX+12wrqqg/KyEE6SedhW/gMCJlRQhFIeqvonTJPArnv4c7qyfB3Vvw5Q8h6/RJRGsqMarL\n2T37VTRvIu6cXiQPHkVi34FH8CrjtAdx0YzTaYzO6UGCTyHot+uE05WeysD+eWyvrKrXdkyPXlTq\nYTZXlJN57dUEV66i6PmXybnzdlw9254hKlpSSumbbyP9IVL6nUC/YZPxpvRFUTWCFQVsfO8l+k66\nJu7leBDV67+ldOlHSMvE2zMfNSERb698ssZdEPMercUREFyZPqLRPmxbEjQMfO6G8a5HmspQGI+m\n8ePX/sMXm3bWbd8nmNK2CO7aQqS8GKO6nGhVBQhIzDseX/7xpJ54KuGi3ehlhfScNJVwcQGFH7+L\n4nAS9VeRMvQkajauIrBlHYl9jq041qOVuGjG6TT6pqQy+9KbmTJrGn4jlksu1e3h1SlXUhIM1pve\nG56dw9bKCibPiIV+JIwYDhKK//kSOXf/CGd2w/jQpqhZvITKOf8j9fzx5A4+i+RiBVOPORD5S3ew\nadE08k66nOy0EwgHbfSEdrzoboptmqz/88/qrPqEPgNJOeFkEvoMaLy9Bc/P+5KPv93SYF9hoIad\nnirG9erHtMu+1yUy40gpuXXmeyzatZ2cQCKVNXq9/WbQT+XqZVSuWoqW4MOd0wdnSjoJvfpjm1EC\n29ZTsnguQigobg+qy03V6uVEayrJGX85SQNOwKgso2LVEjLHnk/ykNEoWuuymMXp2sRFM06n0jcl\nlZU33ddge6+khg4SGd4EEp1OArUCmzByOHYkQtFzL5B775040pvP5ykti/KZs9A3baHHT+4hKTUX\n525ZJ5iBsp1sWvgv+p92DSk9BhMNmWgh8FgKwqtiOCVmK9LjHY2YIT+q20vO2ZeQmH88/i1rKZgz\ngwG3PdJksH5NOMLKnYWN7nPjYGl4Fze+/xavXX71ERfO3366kMUbd+Cq1Khkv2BGyospXfoR/m0b\nSBp0Ir0vu6XR2YeUIaORto0ZrMGK6NgRHduI4OnZF9XpBsCZmkHO2Zd02jXF6Rziohmny5Lh9fL6\npVdy4wfv4TdiyRF8p47BNgyKnnue3HvvQktuPNuTHQ5TMm06IOlx3z0oHg92yEbW5pWNBKvYtOhV\n8k+5ipQeg+sdq/pt8Nt4E1SCqQLrKHFwaQvOpFQG3/2rus+pJ55K9cZVVK1e3mSsYYt9lqtsEmX1\nchd3Ngu2bWXF1gLmLFmPM7jf2cuOGpQum0/lqqVknHwOueOvQHV7m+1LKEqtg09HjzpOVyIumnG6\nNMNzcnnt0iu44YN36yzO5DNPR+oRCp76HY4eOTizs3FkZeHIycKRnYWMGJRMex33gP6kXz4Foaq4\nDAV3SEVx2FhmhE0LXyF38Jmk9hra5LntoIVPqoTSFYxj1OI8kOwzL2Tnuy8Q3L0Fd3Yv3Bk5uDJy\ncSSlIoRAShs7osdSzFWWEqksxagsw6gsw4qEkVEDp8fDYy6bJ358b6ePf8HWrdz24fu4i1SktV+0\n/Vu/o/CTmXhy+9L/5gdxJMbDQuI0TYsZgU58/ykAjk/J4cXTp+JzuDtrbHHi1BExTf721TL+/tXy\num1mdQ3R4mKixSVEi0swikuIFheDlKScP56kM2IWkadGRdTE7nOnorJ35juYkQD9x17XKotHc6vo\nXlATVCwkut19q5QcLmYogH/rOiJlhehlRURKC7GM2PSmjEZRnE6cyek40zJxpmbiTM3AmZKB5klA\naA7CRduoWr+M6o1bUDrR2txSVMqkt6bhKlKhVjCN6gqKPv2ASHkxueMvJ7HfoE4bz8HEMwJ1H1q0\nNMNWbD7rm/Ld3LJoBq+ccW1cOON0Oi5N4/5TxyElPPd1TDi15CS05CQ8xzXtxu8OKHWCCRDas42K\n3as5cfKDrZ4iNHULTQcqLDQB7kwN3XFsWp6aN5HUYWPqbbMiOgiB4nAgRPNJxiw9hOEPc/U7M3jn\nqus6cqj7z2nb/HHuYtyFGtKOOQFVrVlO8cL/kj76THpdfCNKPGd1nFbSpjtlVUUBty6awctx4YzT\nAfxn5ToWfbcdh18yMr9nvX1fbtrFZ0W7MDQLpycmdqYH7GaKUrtRcUWUuiTtdtRgx5wZ9DvpMjRX\n8+tVTSJBK7XwZKiEncemcB6M6mrDs0BKFFvh25rdfFm4izG5fTpuYMDX2/bw+uJvWbh+BwBmOMje\neW9jVJXRb+pduDNyOvT8cY4+2vx6tbKiIG5xxmkXKsNhKvUwBTU1PP7ZpxTtrMFVAY4yi08WbWzQ\nXrMlTkUhUYApJeEeAiNLwXDtFy9VCBQ/KCaIgE30AFEtWfw/PNm9yMwfjhU9DMGTErXUROulYh6D\nTkKHgx01UBxOHHud/OOrZYy5pGNEM6BHWLBuG4++/VGdB3Rgx0YK5r5J8uCR9Lrohrh1GeeQOKS7\nZlVFAQ8s/4AXTp/a3uOJc4ywrbKCa957m9JQEABHUJDoV9HKzAZtNSGgJorlUdAqI4CADCfevRJ3\nDVTnC6IeiTuq4qoRRBspNB3au4Pq9Svof/NPiXhU3KVgHo5wQqw2WVw028Q+0VQCcH52+wf7FwcC\n2FGbH748k20lFbFzmlGKF/6Xmk2r6TnpGhL7xZMMxDl0DvlVqzBc3Z7jiHOMIKVkY1kpN816v04w\nAYQFimkTDVUTCVZiBCuJhCqJBKuQtolTdWJrDhTVgao5cfiT8WXm4SKF1M2CULaCMCEqbVQhcFXH\nxCyKRA8XsOc/r5Nz7mVo3kR0LEgVqCUcVmnneImgtiOjBsLhRCAYlJ3Zrn3vKqvihulvEqyIouux\nly+9tJA9H07HlZZJ/5seQPPEM1fEOTzi8xNxOpUNVcVcPOtV7FDs1rMjESo++A/6hs2Y1dWoDjcu\nbyquhFScCal4krJQVAe2ZWCZBrYZxQjX4C/dwc4VH6BoTnyZefgy80hI64WakoDL4cGoDhIJVFBT\nso3Srcvoff6VeAadWDcO3SHx9nSCJbGDFk4dkGDVVq+QNC+omkclHLcy20xw91YcSe2X2/ep+Z/x\n6pYVeHUXapkgbMQcF6W0qfhmMaVLPyb7rItJOeHkI55QIc7RwSGJpoLgB8ed1t5jiXMMMGPbCoSy\nX2yqP/scs6qa3nfcjieaQkqFEzPUupAOKSW6vxR/yXb8pdso2bIMK6pjRcOomhtXYipuXyZDJ/4Y\nT1I6hqWgq/unZEOYoAJJEK7NkeAMKoiojSPU9NSrw60SzBBY8djNVmNUV1C8YDZ6yV7yrv9xm47d\nFxYnhGDN7iJ2llbyt4+WUlwdwLQsNFQM9k/rRwPV7J37JlYkTN519+JKbV+LNs6xTatF06M6cCqx\nDBqPjpjIlL4ntnBEnDgNMW0bBQULsA2DmkVLyL3nDmRWOjKqEQIC/6JCAAAgAElEQVS8loIZaVmQ\nhBB4krLwJGWRNeCUZtvalo2jRGLkKs163BoJAAoIG0eg8TZSFfFkB7WomiDitdBqGp+sts0o5V8t\noPzrz0kbfQY9J1+L4nC22O+cnZ+zpuYtoqUX8Pp3m8jQEzgxPZelm3diWo1/93bUoGbzGoo/m03q\n8NPIPG1CvMRbN0AI8f+A24CS2k2PSCn/J4RIA94FTgb+JaVsNCOGEOJNYN9CdSpQKaUc1VHjbVE0\nbxwwBpeicueQM0nQWr7Z48RpjjTX/jWlwJdf4+7XB2dONgARh4XToxHOUNAK7MNab2wMacvYnGtr\nZlVTHWiWjRmub/UqqoIeT5tWh+UBq38YTBWrRkGrcqCFBXZEEti6jsJPP8Cd1YP8G+/Hmbw/V/Ck\n4YM4sXcuAEXhEhTTQ5bPx72fvsLy4l2YZRppaR5Kd67FUeWhGp1FpdsbnD9SWUpg+wYC29YTKtiO\nO6snvS/7Pt4e/TrrK4jTPvxRSvnHg7bpwKPACbU/jSKlrPNIFUL8Hqhqqm170KJoPjpiYkeeP84x\nQqkeoNoIU1gZQLU0DNui+rPPybx2vwe2jcRUbSQSj1PBNNrfmmutEEdsCzJUPGXUCaeiCKKZ9ad4\nm8LhUrGiFke7QWpJG80hsBwmwgNWtkF4ayWls+YQLamgx+QrSc6P5fYVCHqnpDCybw9+efl5aKrC\ntvJyblz0DBW7UxgU7cNuf4CInoS0BIW7Gzrt2FGD4J6tBLatJ7BtA3bUIDFvMCnDTqHXRTeguj2d\n/RU0iltRUaqsWHpBYlPMdrJCpI3F048hGvxpSilDwBIhRFuKkF4FnNNuo2qEuCNQnA5nVUUBNy+c\nTtiMQpUbIywIrV6DmpiIK79fvbZWCjgMBcNjoxjtOw7VpbQp+fo+4dRMBSREHYKw3TAkpjFCmomd\nKHFXKEdtVIpwgtUnAtj4HC5uGXgq/tJyfvWDmznh2ouoOGMATuHGrK0i0p8cZk+9qe74nRWVXPjW\nv1ALe6BEBJsp39dzvfM0sCYze5CYfzy9LrkJd1aPLufg41JU3KU2ph4TyH2jUwM2mk8l5JOtmuw4\nxrhLCHED8DXwgJSyzeEZQogzgCIp5dZ2H90BxEUzToeysbqYmz6fjh6QGJVuzBo/obXrqP5kAWmX\nXNjggadLCzJVMAVuW0H42+fNXHOrhDOVWOHHNhCxLSL7luvaMBRVicWOWlkCtUQeNcKpqgKcAhMb\nI0+HxNhLxNV5o7hryJnce++93PGD2/jt756hVN+/KKwJhQx3Yt1nKSUXvP0KaqEKkcbPFaksZe//\n3sKoLK21JsfQ66LrW6w+ciRxKiqeUruu/Fw9JIgaiwSpEk7mmKqeI4T4GMg+cBOxhZJfAM8BT0gp\npRDi18AfgVsP4TTXAG8c7lhbIi6acTqUT/Zsxr+tnMqF6wit+Y5oURGewYNJvegCvCc2vkyhYxFN\nF1iWwCMU3IYg2thDqAUUVUF1KUgkeroSsxzb2odTYBttf7gpfoknRSPsMHH30lBq7Q0B2LZEqwQr\n1P2m6kK9DBwZFkIQS7tUixCCwsJCpk+fzvr161GFQo6n8bJtAA8seR4raqBGGmYVk9Km4tsvKF3y\nEZmnjidt1BkIpXtExToQjQvmgfgtPP5Y2FLIJVETVPTaUJnOxLc93C79VFRvp7Km4XrzgUgpJ7Sy\nuxeB/7R1DEIIFbgc6DAHoH3ERTNOh7KhsJAd//csCaNGkjJxPJ6BAxCtSF/mdmhILMI+CCNxBRTU\n2jhKxQC1FbOkusfG2Ffl6RArkwTTLHwV2iGsrwqkJUEBXZoH70JLV/CgdD/hVCXmQV9+ljuRK/uN\n4N1pM7jsssvIzs5u4uD9fL23GGdxYoPtlhFhz+xXsfQwedfegystq92G3tUwwxbOMKgBWLe24EgP\n55BJS84jLTmv7vP2PZ+16XghRI6Usqj24+XA2saatdDNBGC9lHJvm05+CMRFM06HIaVk9epvcGZl\nknHVFW07+KA/kUjd81XBXWmjotQlImgKTagYh7l65CtTMVs4T1M4qgR6WuP7TGkTSLNR0kXd1K0i\nAASqJXCWiEavz+FSEQoY4c4vTyYcoEiFn55wHn9fPo/tz74NoQgZmT258+8fsXHjRh599NFW9XVp\n4tm8YnxTb5sZDrLrvZdwZWTT5/JbEUr3CxcxsPG6FSy99feMZdqcnNe74+cVuy7PCCFGEFsA2QH8\ncN8OIcR2wAc4hRBTgPOllBuEEC8C/5BS7ruJrqYTpmYhLppxOpCotFn77Qo8Bzn7tAZbSJQm8gvo\nqQpKDSj+FvrQDn/NyDT39xFJs3EoCqZp4wmoWGbz/WstTClK6q9r2TK21VRAZGs4S+t7ECuJCv4U\nExuJwy/ABC0Y80rtaIQTIgN0/nDGFIapXp7+1b+599prmThxIrquE4lEMAyDCRNaNwvXKzW13mcp\nJbtnvoK3Zz+yz76kyzn3tAaPoiENGw7hfUZTu8f0c0cgpbyxmX15TWy/7aDP32/vcTVFXDTjdCiR\n7UX4+jcZYtUkYWniTBBogcYfnnqywCUUHFLB8psNZEMIgUhUcToh6rBRpcAMHN5UqNNQCKfE+ohq\nElEmmxWs5jIGRfYUoHq9aGmpje7XhQlZGla1REEgVUk00ayzm6O+2G+qW4Vyu0OFUzhB7x8mrbyC\n937ze26a+QGPPfYY997baKx5qzg4QYF/02rsqEH22Rd3O8F0CxVRZaIEDASHpJlxuhFx0YzToUSL\nynGObVsaMwWBq1rBEYl5aTaGLWXteqeNx6GiVux/VDk8Kv5kie220dMlZm1snFdRoebQrU/lgIe5\n7rZxZyjQhHA6nAqRJqZmAYKrVlM9/zMSR48k+dyzcfbIbdBGx4RkaC4bg+6x8PbQwARbtxE1zQt5\na5BIzBSJVG1s08S/5SsC075ASUzl+O9/nyfXrKFnz54td9QEczZu5InPP0GrTXkvpU3xojnknnd5\ni0WsuxpuURteEjl2PGGPdeKiGafDCBkRooUVuHIyaK1voILAW6ViB2zMVq5HGikCRVMQukQqEPJJ\nzGyBrlr19CaUZOH1arGVk5CEQNsedKarfnvdbePOURG2wNZtlJpYmnfVIYhk1YpeE6RMGE9o9VqM\nomKK/vEC0rLQUlPR0lJx9uhBwvBhOHJzWmV1hRQTnIAT3I5Dtzw1TWBkgum2UNLDOJduZuuM/5LW\ntyfvvPZvJp1zXrtYgQXV1XBA/mGjqhxpmSR0s5JdLkXFXWK1KuVjnKOHuGjGaVcM2+KPaz6lLBJg\nycLFaBnJiGQPBJs/ziXUWOHoaomig6f21pQKhJpIKCCRkKzg95kkZDrqBNJUbCKy/iSZpgoEIpak\nHWIC41axDBtFE2huhWjIQjUFBCS2C9TIfoHQXCoRj9nA6NM1q64/kmKjag2K00HaxZMpnf4GvR97\nFNswsCqrMCsq0XfspOj5l1FcThJPPomkM8aiuJsu+C4tC6NgL/q2HVQUFqKFDPRAEFuPYEci2FED\n1eXBlZGDKyMHd0YurowcHMmpdZadRBL02Vgq9AxH8P/pQ1w2zJ/5H8aOHduqa2otq3YVolXud/LR\nSwpwZ/XsdtOymi3ignkMEhfNOO2GYVvct+w9Pt67ESukU/D8WySPH41wmxB0NHmc01Zw7rBxhwT4\noyhGbE1SKmA7FDwpCuGE2IPd6VUJajHhU1SBkRATrSDR/R63MuaJqnokwmGTonlJ8KiU2NWIUidG\nNCZsutsCd+yACDYkxn53KwpuS0VxCJBgYhNJs9stGF3aNv4vllI59yOSzz0bnE40txstKQlX3z4k\njBxO2qUXE9m5i5rPF7P7V0+RfPZZJJ5yEqrPh9R19B07iGzbgbVzN/qu3SRlZdFz6BAuvuJKRuXl\n89uvl1Jo6eDV8FT6sCqDRMqK0MsKqVj5BZGyIiw9hDM9G09WDmrfHFAEoa1bqCwo5PHHHuOuu+5C\n7YCE5/NKNuGS+x89emkh7swe7X6eDqd7zSTHaSfiohmn3fj3lq/4tHAzSrVkz1Nv4M7tS/LIc4mW\nN32MU6q4dth4SiUxtVMR2Niu2BNJAGqVjQeF6lxJOOlAqzN2jCvdxKEpHLgcZogoFjZPjLqQq/Nj\n8c6fFGzivoWzcFS7CDYTTK4n2egHr6W205KVUVRM2ZvvAJBz1w+pWfQFOx94GN+pY0gYPRJ3fh5C\nURBC4O7XF3e/vhhFRVTNm0/B079HGgYoCp7+PfEdn8ftD/6E+66YSupB3qhXXBEL8Zm7fQMfrFjH\nwqIdOGt6kxVWMSKxFw0rEsIMlhEIFhIu3IttWSQPHcXMtx/llOMHtc8FN4K063+ZthHBkdS4Q1RX\nJorE61EbJPWPc3QTF8047YLf0Pnbd4uw9wp2/fVVtKwsUq+4lCaqOAExC9O93cJVVn+77W74Cq9U\n2YieDUuUuFwqI7KyGJCWzlvb68f9jc3KY0rfYUAspGGg5eavfcayp7SUby2D2Xv3HLb1aOs6RkEh\napIPNcmH4nJhh8MYe4sw9u7FKCxC2jZCUYjs3IVZUUnKpPNxZGZQ/t5MVG8CPX/2AKG131Hx3gdY\nwRCpF16A75ST939POTlk3XQ9IHFIk+P6pHFG/3ym5o+mT2LzYjMpbzCT8gbzx+WLeGfDGpSQiu6P\nZYJRtAQszY2HXniAHgk+njpjAqcc16iXf4chRNO1S7syAvB7bdzh1hcCiNP9iYtmnHbhvoWzCey1\n2PXsv9DS0si46opmU5+5UHHssnGXNdmkHgIafbB6fPDy2dfgdTg4ITWXN7atAKBvYhpPjpzM0oWL\nmTVrFrNmzULXdXr27EliYiKrV69G83qJ9utFwpln4MzJOYSrBmPrBoqmvYWW5MOsrmGfuevMzcHZ\nIzfmzONwIE2ThFEj0bduo/qTz1ATE/GdOgbfuNMQioIzJ5uxU69mUNTknw/9nJRQmOOuvJzNlRVo\nbompRHG6BT84bhz3jj69nidva7j/lDO4/5QziJgmO6obr5yUk5BIcjNrpx2GENDNqn94pIqz0Gox\nVjfO0UdcNOMcNn//bhGfb99Ewd9eR03ykXHNVc0LplBxlgi8YQXZpqi2+pamKiDF58DriK2XXp0/\nismZA5k3bx6zXv2AvnNuJy8vjylTpjBr1iyGDRtW52xi2zabNm3ijj8/zaK//RNX/3zSLpxE3/75\naIpgV3VNi6NxeiDvrOEUT3+XPn+5CxFMIFppIpzORq+//P1Z1Hy+iNz77sHdr2+D/bk+H09Puhjn\nhs08++yzLPrH87xbuIXCSA29k5M5K6c/x6cemrjvw6VpDErPOKw+DpuDBV8IZDeyND1SxVlqtZiR\nqrWoboXtxRXt0lecjqdF0VxQuJnhaT1JdXXdygJxjhyBaIQ/rZ5P8bPvong8ZF43tZ5guBQVzd7n\noQkhoigVEisiUVDaJpkHPWs1N1zY6wQikQivvvoqs2bNYuHChYwdO5YpU6bw1FNP0atXr0b7UhSF\nwYMH89k/p/HVw79kzr/f4G9/+Qs/+sufKRiYy782LMe2YsatXV2DtFVcSUmopgMhFXISExmYncZz\nEy5l8hsLGLVF4fq7vk+2N1ah2pQ2f162hDmbN1EdiZXGMgr2oqWlUvrqdNIum4J32NB6HqOfbN/G\n1BtvpHzvXtasWUNOTg53H6IF3JVRGmhm95qedVTbWNH2s4wj2KwrLG23/uJ0LC2K5u1fvMmg5Cwe\nPOE8Pti1um77hB6DmNx7aIcOLk7XJmga3L5oBkV/egdF1ci44VrEAd6WLqHiKAKlwkTEAkTwpilI\nIxbPGJV26x0Q3QLp2m9pupwKo3vkcv+Is3jwwQdZvnw5d999NzNmzCA5Obn5vg7i5H75nPyLX3DJ\n5MlceeWVnHL2GdgDvNh9Myj663uE1+0AW6JqKkOHnMDQoScwZtRIThmYSyAQ4MH77ufCCy/kkQcf\nwuvZXwT5yXMncP2w4Vw/8x0qdZ3ce+4AILxxM+XvzcS/ZBnpV1yKIzNm+ZnVNcyfM4edO3aQkpLS\npmvoVjRI36R0G9H0BMEKWu26hqnpcO7gPOa1Y59xOo5WTc9urC7hti/q58Kdu3sdlrS5uM+wDhlY\nnK5N0DT4waIZfD7vY8ziKnJ/fG99wYyoOCvBWSFR/HJ/Id4QRLI0dCykW4FW1MuUgD9bEnXsf7AK\np81fz72EFStW8O9//5s1a9aQmdm2zEMHM3LkSFasWMFvfvMbVr01j/Xr1uHq34P85x9kUHouTw8Y\nT8HmraxZs4Zvln/Jqy+9zPr163E6nTz99NN4vQ1nY47PzGL65Vfxr5UrWFtSwoayUjyDBtLzofup\n/nwRe//4VxJGnIjQNEJr13Hn7bcd3YJJI/rYjaZntZqOKSDtcTs7oNc4HcEhr2naSH765SwkcElc\nOI85frPgfeb/fRpVC1eTNuUihLY/DtMdVBCVNt5qBRmwEUKgOASWEZvWcpUAWSq4QXMqLZbdCmVD\nJAeUdB2PWwEpuH/QuSQ5nNx222387ne/O2zB3EdKSgrPPPMMzzzzDN/t3UmVNNAcDgalZJPi9HBC\n/gAmTpxY196yrNj1NbOGe3xGJs+MvwB/JMIdc2bzdUEBhgYp551D4uiRBL7+BlXV+NVLL3DPpZe3\ny3V0VaSU2AfJjqD7TM829N+Oc6xxWI5ANpKHvpxFrieZkzP7tNeY4nRx3lvyKb+b+iMSTzyRjKlX\n4T5uQN0+Z1CgVAIINBOixPLEmlKyzw61ojZ2jY2RpOByK9CUaArQU0DvK1B9BrhsItLGpWiMzx/I\nkiVLALj++us75DqH9mjorHMwbQn+97lcTL/se3xZsIdbZr9PKBpFS0khc+J4/nrBRUzsP/Bwhtst\nMEwLV/FBj53utKapiH3laOIcoxy296yNZG+oCoiL5rGAZVnc+v1bSL3gHBLGnN5gv2YqHPwuLgC1\niVwCuismpgevEamaQigT9ExQvBa2y4KIgnDZXJE3gt4Jqby2aBHnndc++VA7kzE9e/Hpjbfgj0QA\n2FpZwfnHgGBCrBSaaor6d4gQyG4SctK97rQ4HUE8EVScNvH444+juJyknnNai20PzvzSGLpTYmWo\n9R6iqqYQzAQ9C0i2UVMMpCUQrtiDdXByFgCLFy/m9NMbCnd3ICshkf5p6fRPSz9mBBNgY0UpUbW+\nQHYn71npjMvmsU6Lohm/ReLs4+WXX2b69OkM//mtTVp31gHyJ5u5eWTt8aZHYiQLIlkKZrJCNFkh\nkCEJZ0tkqoWSqWMKC8UTC0659bjTmJo/GsuyWLp0KePGjWu/C4zTodREIlw9ezpODprSFgKaqT3a\nldDTFTRv++fjjdN9aFE0p+aPbna/V3UwsPbNP87Ry9y5c3nkkUeYO3curlRfk+1Mj2Sff2EkQ0V1\nNLzFZKKC6RNIj0UguAt/mkEwX1I9OPYT7CdRexnYqeG6Wpj7GJMRWwZYtmwZvXv3bjcHoDgdT01E\nBxts/SCrsht5zxq2RSgtbkocy7S4ptknoencll7VwYunX8OQlKMvADvOft59913uvPNOPvjgA4qS\nVLYHylFomG7NKVTcVfsLR+tYkK3iKheoQhAJ1hCyAgTdYBTspuyLxRgFhSScNIqMq6/AkSLxeARS\ntYg0UYsypTbJxrRp0zrMAShOx1AaCjbMUEH3mp4FMKSNmqigBrqHdRynfWlRNG8+7hQ21ZQwc+fq\nettvGHAyNw0YQ5/EZsrTx+n2/POf/+SJJ55g3rx5jBw5kpc3LiWWmqDhw09DwTRi06hSSiJlRZRs\nXk1o9zb0skKkbaH5fNjYuHvmknnp6bhG5lH49AzK3vk3mTedj56Y1ORYHh85mZHpvQiFQrz33nus\nXbu2oy47TjuyrrKQB5bNYvPeahR/I1Ob3Si5QR0pKmqEds0MFKd70KJoqkLhNyddzJCUHKqMWHWE\nLLePqfmjup3XYpzWI6XkiSee4LXXXmPRokX079+/bp+A/9/eecdZVd95//075bbpfYZBqoD0IHbF\nRjBoNJpkLdiyxmRdY3miG7Nosk+ezW7cJObZ7LPRxDXJRmWxxa4JGImCgKD0LtKGNsOUO/X2U37P\nH3eYPkxhKvzevC5z77mnfO/Muedzft/ft4DbPkoWKYmUHaTh823U79mGdGzSJkwj59zLSR99BqFR\n/mTLK4/GxLxs7j53NnvrK3nuRz4OLl7KwX94ivQrZpF1/RyMjBQA0k0ft48/h7MyC5g/cgoAr732\nGhdccAEjRgzDHoynGTtryrh1+fNEqwVGlQc92HzO+KQODTZex0OsshJ/PbheQdw79AU0Kh3I0/FU\nKuE83ehWyokuNL4x4fz+tkUxRHAchwcffJCPP/6Y1atXU9ii/mlpqAEavMQirS9s0T17KX3rXWQk\nTvqEmYy89nZ8BSOTzaSRONmCdK/B/DMnMm/8eC4dNRZPY47jNyacR/Dqe/jx8ldZ84fXOPzQk3z9\nb2/nh489RmFWDtnelKbjlJWVsXDhQp5//vmB+WUoes3e+kpuW/480RqBCJroQdnkofBJHW+lg2NJ\n8ovOpmLzcmp3bCJ71ExEjk7MNwyEUzjIPB2vEs7Tim6JZsJ1eHLnCuoSsaZlF+eNZXdZLbuqKgmZ\nDfzwvHlMVAFBw5ba2lqWLVvGkiVLWLp0KZMnT2b58uXt6rjWRCMYlonVOG/pRCIEX3md+KFDZF17\nDRlnzyJQreNEku9LJDJLcNX0iTx40YWMy2rvzs/2ppDtTWHR9d+B67/DwYMHWbhwIbdfcwPvvvsu\njEiKpmVZ3HTTTdxzzz3MnTu3n38jipNlU/AI4QYXGfaSFtZJNJbnN4TAU97cJaT+2B7sRBSPLz2Z\n0xt0CBSbRHpUzn9wiInkiFMJ5+lDl6KZcB0eXPNHPijb07RMIHh5004S4eRdY0q+w50fLeLRGfPw\nGSaG0LiiaGKPe/4pBp5XXnmFJ598ks2bN3PJJZcwf/58Fi5cyIQJzbmDISvO6or9IGF9zcGm5dG9\n+6hc9CIpM6ZRvPARNI+Jg8TKhZQ6k9fvuY3MgJ/DtXVMGVHQbZtGjx7NCy+8wOOPP86FF17Iz372\nM4LBIO+//z7p6en88Ic/7NPfgaL/cB0wI4JEtFkAjZDEbRTMeLiG/Z+8wqTL7yY1JxkZLQA34cIw\nKccaEw4iT8esBFcJ5ylPl6LZVjABnAYdN9wsiLGYS9yI88i6t5qWfX3MTH4y+zolnEOYF198kUce\neYRnnnmGK664An+LDh3HCVlxvrXqRTYGDzcvlDrV7/yV0KfryV1wI4Epk1ttc+955/HAec3FD6YE\net7YWAjBD37wA8aNG8eiRYs444wzuOiii/i7v/u7E9Z5VQwd1h09grB0PPWiVf6u39GaPBVHt/2F\n/DMvaBLM43jqJNFhlE0UFQ7RfPCGNAKWhhXpOPpbMfzpUjTbCiaA5uq0vJ/SbQOX1nXSXivZQqYn\nwD/O+OJJG6noe5YuXcpDDz3EsmXLmDZtWofrdCSYdl2I0icWY6SmU/z9h9DTWudsnpWbx23TZ/aZ\nnQsWLGDBggV9tj9F/5NwHN7fv4e3d3yOiBu4dovgH1vDDiVHndG6cmqO7mTmdQvb7cNJuPjDGtGU\ndm8NaeKpIDVBQOrY0RO7lyWgCYFuCI5U1g2MgYqTpse1Z7U6X5Nb9jiJiMAnU0h4Y2iB5hNlT71q\nrDpUeeqpp/jFL37RSjCrqqrIyMjANJMdS37/+ZpWgilth6M/fo7Uc88i5+Yr8UsfsaiNDJnk+AOM\nycjiqWuuI9uvGpYPVcoidcQdG01ojErtPAe7t7y0czNPfPIRiRhQJ0iJGlgyeU3w2RpmpdNUyODw\n1qUUTb4cw9PewwGg1br4hUZ0mJ1OCdeBXB1qHLzhjgs3uF5BLB0MUychXGSWqjI0XOixaCYFswOX\nqyvQ2rZkVwxZIpEIBQUFrF69mmeeeYbVq1dTUVGBz+fjxhtv5O677yZhtr5Trl++CT09QM4tc8n2\nBrht/DlUhSNUhiP8+Lz55AaG2bDgNOOdQ9t45NO3mlpzfeesOdw27lwgGadguw5x22lqOpJwks+L\n09IRAt7+fBdPbV6DZTtIJFMzCxlhpPPBln1IV+JKSU0girTBiGkYIYFFG8FsrEdcX7GfUNVBxl/Y\nuRdBAFqNS5qlY6drRMXQDww6TsJ1IENDS2n9uY9j2KAlIOFJ+uzyszuvsqUYWvR8pClEu844Pq8G\nORFATYIPF2bPns19991HOBzmscce4/vf/z6TJ0+mpKSEl156ifnz5/M3j/8jNPZDdhMW1a+uoPDh\nmwC496xLVBrSMOK9I7v43qdv4XFM4jGJ48CTn67l6TXrW6wlWsUgiMbiwbdNnsWxcAPvH9yDFQNP\nvcCQGutLjqLJoyTCLYJ86lvPN3s0HS3qYla3Fsw9K59j/AW3oBsnjvYRgAw5eOISma8nq0wNI2KG\nCwU6MuY2VT6SyeogOCmo5pzDkC5F84czv8S/bnmv6bWui1ZzFMm9uE2l01oySaWgDFl+/OMfU1hY\nyF133UVWVrObbty4cTz22GPMmTOHr33ta8x89G52R6po+Ggr3jEF+CeewZVFE1gw/pxBtF7RU/64\ndzMi5CFc3+wGlECi3Zqy3fPfbdwAgIYgUKPjhl2S/zrHEBoeR+CpdrGjzWFAtaW72bfmBc68+DYy\nCid22/7jzctFnj6sRpwAMc2FALQvBjIY1ihOli5F884J5+HVDT6u2E/EsljZcBhalMISSKS340ix\nkBXn37a8z+J96wAoCmTw7JzbKE7J7CPzFb3F5/Px8MMPd/r+nDlzeO6557jzzjvxFmYzauIYzv77\nbzBh3Jk8MuOLeDQ1BzOcqKhMkKjv+d9MAL56HZ+rIx3ZKnWkLV5LYESTeZiaDVbEalVBOHhoCyXr\nXmfipXeRljemx7Y4lounAmSuRkxXXi3F4NAt9+zN487m5vdjCS0AACAASURBVHFnA/CdyBss3b2/\n6T0zy8Hxddxh+MX9G1q9Phiq5o4Vi1h02R1KOIcB11xzDVVVVYNthqIPmJZdxPaynv8t/XU6NMhO\nC+gD+F0db42LFXMQgNP4gGQ5xvryPZTu+IBYKMhZV3yblOyRvfoMAI7t4q1qHHFqw2vEqTg16PGc\n5iG3Ek9AkogIPKZA+ts7eE7EkUgtLx/YyMPTruzpoRUKRTcobajnL/v2Nr2OOzbvfr67x/vx1ycF\nszN8QkfU2mih5IiyZRiglC41R3ZQuuOvOHaCEVOuJGfMLLQ+8FA4tounEsjTiGpqxKkYWHosmgWp\nqezNrEA4Jphar9zy//XZalaVJ0er35x4AZcXTiDV9PZiTwqFoiWlDfXc+torHKo/ubw/f72GqG/9\n7fZLHW+ti9YYIOTaDrbVeh3XdQiWbKR054fohpcRU+eSNXIqQvRtQQrHdjErQeZpyTlDhWKA6LFo\nAhhCR0oTzWf1Kl5WAttrygB4+JM3mJSRz3OX3kG2d5glZCkUQ4i+E0y9Q8H0VDjYdvtvvGPHaSjf\nT23ZbmqObMeXlsuYc24gvWBCv3ZCcm0XQhLSVaqbYuDolWjGGpK1ITVP90tFeYWBG9NwXNBNiWU0\nz4PurqvgGx8tUsKpUPSSkxVMXQjMhIYnpGGH288VeqqbC6xLKYnUllFXtpu6ss8JBQ+Rkl1MRtEk\nJl32TQJZA9eyzefouJpI5kUqFANAr0RTS7XB0pN5XN24yTOFgV3lJR5P3r0KJB6/iZ5hgS5JSJvd\ndRUsXPc2z1xyS29MUiiGBUdCNTy0+i3CieRNY4E3nT9cdfNJ7bOngqkhMKPNjZ9FArQQgMTuJA9S\n1zRs6VB1YD2HtyxF0w0yiiZROOkS0gvGo5s9ry/cF9gRm4DUEbk6cSWcigGg5xWBHAfTNknEdagx\nwUig+yWO2XEErQDcFoIJIBHEo0DURBfgydew9ASrK/azvGwPlxdN6HBfCsVwZmP5Eb694mXCQY1E\nYzeMz6jnV5lrWhW47ymv7drRfcEUjbmWoY4nVjQh8ESTniQpwEnXkFISqj7G3lWv4NoJJs75Bqm5\nozrcfjCwow7+KkAJp2IA6JFoSinZXVaNVe3BcSVOzAUMZBg8uWB1IpzOCby4jgSrysSTK0lgcd+a\nP7Lh+kfw6WZPTFMo+p1QIkGi8WT26AapnmQ1m4TjEErEm9bL8vkRQhC2E8Qb199w7Aj/68O3SDRo\nuLK1YP1y7cfELJtLRo3i/JGj0IQg4diE7GRkugCyOpm2SDgO644e7Zb9uhD4q5PFCdpiahpGROKN\ngh1pFh4tkuDwlmUc++xjiqfNo2DCRYgh2GWmI+EMCANx/F69TcSiq0PU7d70kk/T0Y9f2lp41lwN\nosOsQpHi5OmRaAohGGPms94pbbXckRCvMvEXCOJa6xQUCXg82gkr/tuOhLCAdLBchyd3fsT3pqsm\nw4qhw4ayo9z15uuErOT5nWp6+MMNX2NSTh53vfUaG8qavxM3TZmGzzB4p3IzDSICgAh7sOp1vOjI\nuMSolHgbktdgS5f8tuFTnklZy/mjR/Gra67jmysXs6P2WNM+H5p6BfdOvqSVTQnH4cEl77Lq8EG6\ng2lpHQqmT+j4KhzsuNsqG7O29DNK1r9BSvZIpl/9MJ5ARrtthxJ21CFQJZCmRAoQdYlOZ488pobM\n04kLByGSRdVb6qouBAKBUefit5I5qG0xhMCXpxEzVWmf04keu2cDndSKdCVIF+jgJjSR6DrGViCa\nTtoDDcGemqVQ9BttBRMgZCW4841XyfD6OBYOtVr/lZ3bATB0AX4PpmsSjUi8ro7ngEtKncBtkaph\nAP4akB6dT+xDXPH2U0RErNU+f7njQwxN49uTLgLAlZIHl7zLX/bvpTv4hYFZDXabIZdP6PgqXex4\n83c0Eanj4Ma3CVcfYcw5XyVzxFndOsZQwIraeKJdr+dYLkap23wBFODk6URNSSAEWl2zSHbsP0t6\n3oxKVwnnaUaf+lnsBgOtzb2dxzWTI0mFYhhSHY20E8zjRG27nWC2xHYkdshICqat4T3g4quSuFbH\nN5EiIcnZqRM61PH+lhzZ2fTccpxuCyYAMYndwc2rt8LBjicFQroux3avYtuSf8eXlsuMa743rATz\npJCgV7pk1epodT1IpJMSb7hb8ZCKEyCEeEAI8ZkQYpsQ4qeNy84VQmxq8bihk21/J4TY3Ph4RQjR\nrykYPRbNqN3ZfRckokCdl4Du4c253+KCrHG4NSfuYqBQDGXCCatDwewJXnS8RyTeKtl0cZXSJVx9\nhOoj23FbfqfiEv8RDc1tfxmOOVZTb8aY3f10rxPRUkj3r32Z6sNbmfLF73DGzKvRjNMsrkBKrHDn\n17fOcMIOKfX9YM9pghDicuA6YJqUcjrwi8a3tgGzpZSzgKuB/xIdV8n4rpTyC1LKLwCHgfv7094e\nuWc/LNnPutITBx0kwhq4Ao9rkmmlE4sfO+H6SSTC46qi/4phT+LYMcKbtiAMA6HrmB4fiFxSnEJs\nKag7tpu60t3UHtuN4fFj+tI4sPYVskfPZPTZX0HTTQIVUF1hIgpbi/Xe+iq+ufIFLNvl2LF4JxZ0\njJTtR0N+R0M0liexExFqju7gC9f/oNOm0IrO0cISb4ZBXKrAoF5wL/BTKaUNIKWsavzZco7CTye9\nJ6WUIQCRrKThp5/7x/RINOti3fuiJqKCqxY/1829SjwZDm4nRd8VisFkRFoaX588ldd27Wj3nghY\neGwv8RajtfqPVmPX1uIpLEQ6DnZDJdGjGzlUXYZ0XTIKzySjaBLFM67Cl5oDQDxcw66/Pk24+ihp\neWMQgOjk67C6Yj9ayItVr9ETp2Dbwjx+oWNWNDf0qz68jfTCCUowe4nrSvxVLiJXJ6aEs6dMBC4V\nQjwORIFHpJTrAYQQ5wH/DYwC7pBSdiicQoj/Bq4BdgCdt2/qA3pV3KAvCQR0EindmLlXKAYBXdP4\n6dyrMDSNvx7YB4DtOkT1GNPPKGRLWTkkdI4LWLzkIDk3fR3fmNEAeOoE+UcN4vUJkLLDdA1vShaa\n4UFrGWTXQuR0oeFxk67SWAMkQr0oet5GNGWdg9uim3ywZCMFEy9B0XvsmIOvUiI1FyOavFHRAjqR\nbIHtnrr1cavrDlBTf+CE6wgh3gcKWi4iOSL8IUkdypRSXiCEOBd4BRgHIKX8FJgmhJgEPC+EWCKl\nbDdfIqX8ZuNI81fALcCzJ/3BOmHQRVMK5ZRVDA22VZeytrIEgKmZRVxUMBZICue/zb2q1bpVkQhh\nN8a8pU9hxPzYjU4Y6boIs3ku0JQaiGS6VrvhXgtcO4HWmJusBXSk10YAOhpU+2g4yftK0XSNSuJ3\nNY5XjnasGKHgYSadLkE//Ygdd2n660twww5p0sAxBJaUJDIEjhwa1zxjX/fye7siHw/5TGp6fYAP\n260jpZzX2fZCiL8HXm9cb50QwhVC5Egpgy223y2ECAPTgI0d7UdKKYUQrwDf41QWTcfT3g9lDMHk\nacWpzcaqw9y96gXCjQUFDKHxr7OvZe6IiWR04LLMDQQIh2L4MAm1mLXwFBURP1CCtzhZf1VEJc4J\ncpSPY/rTSERq8afnUZ9qI9NsRNjECXn6JPpcaG0meuzmV+Hqo/gzCptEW9G3WJFk0JYO+BvAztAQ\nnsY/iAC8goZw7ES7ONV5E5gLfCSEmAiYUsqgEGIMcFhK6QghRpN045a03VgIMV5Kua9xpHkd8Fl/\nGjvo6mQGWl8QRgYy+f70Lw6SNYrTkbaCCWBLl4Xr3+bmD5+lMtZxWsn++qrGEVzzCDJ19hcIbdwE\ngLdakFaezAnsitScUYSCyVwT3RZoDV7sOqPP0rVa5mG2xZuaRTwURJ7CLsShhFHnolfa6FU2eqWN\nt9yhvjQ82GYNJn8AxgkhtgEvAHc2Lr8E2CKE2Ai8BtwrpawGEEL8SQhR2CiUzwkhtgBbgELgx/1p\n7KCPNBMhASnNr28dP5vilMzBM0hxWrGr9lgrwXTjCWK7D5M5ZQKJBg9H7Hqu+NN/kukN8O/nf5Xz\n8pJzlVurS/nuJ68j20wW+s+aROULL6MF6whUZOKcQKxakpoziqqSpNimhjRi1S6kgY5AQyAkOELi\n9DIw0BvWsFsGH7Yw25uSjTc1h90rfk/hWZeSUTixX1t6KVpjWy7jCrIH24xBQ0ppAXd0sPx/gP/p\nZJsvt3g5oJPxgy6ahm1i0XyH/+TOj5iZXcy5jRcnhaK/kFLywr4NhO0E0nVpWLmV4IvLsIP12N++\nnvRZc4hXacgUl4q0Br696kV+e8kCzssbzbaaUqKOhY6G12s2d/AxDFJmziC0cgNF467Conv5lKm5\nYyhZ/3pjzqZJ1l5B7WiJLygJRJMCFk6VWBkCTQgMt4WTSCYf0k1Om8YzXAxTP/4hsRwX6Z5YbKfM\nvZeqg5s4vOldDroOGYUTSETqiIeqidSWMmXe/aTljenhb1ihOPXokXv2yrHjGJ3Rt6NAKwZe2Rw1\nGHEsvrXqRdZVdq+epkLRWxwpefnARtywTsOqbZQ/+TrFDyzAzMslkDcGXIGjOWBI3KiOmxD8bOMH\nrfeBi2W2DubLvfgSgutX4zrdT6PypmSSljeWin1rkwsSksw94KsGN+riRl38lZC2V5J1RCP9mCCj\nPPlIrxCkBEGvcdBqHAJBDVnmJB/HXIxKcFrMYfoikGhTS1UzTPLHn8e0qx9m9NlfIVZfSbj6MJHa\nUkx/BinZI3v2y1UoTlF6NNJM93q5bfpMHl+1os8MsB2JaQEtou2jjsX9a17lk6/8Q58dR6HoDGnp\n6Kl5mPk5HP3VH0FoyPyRyflEQzRVtQojkHiB1hkchk9ihWSTqzZ15Bl4s/KpOrCJrOKzu21H8fSr\n2P3h78gZPQvTl9rhOoJkfdWOOP4VknV2UwSnmWIQ80uELnAjDn5L67D4uBVroPrQVmpLP6O+Yj8p\nWSPIn3ARmSMmE8gsUu5ahaKRHrtnbzhrSp+KJoAbMiG79d16opttexSK3iIE5PtSCWZGYOwYih9b\nSHz/ftB0fH4N0uLYVR6kABmw8AgDK1m0hOtHz+CdQ9vZEDzcqtkAJL2lueddwZEP3iFzxKxuC05K\nVjG5Y2dzYN1rFJ11GY4VxUnEcOw4vvR8UnNGoeldf2VbHs0K2+iNMSYaYHXQyqq+fB97P15MesGZ\n5I6dzfiLFmB4+rV8p0IxbBn0OU2ARMzFa5nYLfpxRuwEH5bt4QrVkFrRT+hCY9Fld3LHiuepzArh\neAwyLinGrfNgmTG8jUM3n1/DSrdwsZmcn6zik2J4+N2cW7n6z09TUWnTMqFDWpKUMZOoFO9SV7a7\nR0XPR07/EntWPc+hje+ge3zophfN8FCxdy3R+gpSc84gLX88KVnFCE1PFksQGqLx4UvNwfSndetY\nUkqOffYRZbuWM/7CBWQUTey2nQrF6cqQEE2JwA0ZkGW1WAYrlGgq+pmxaTlNwllBKDkOy4wxOpBF\nQyRBTLpEIg5mIhXLdvncqeGfVyTnNeeMGkNeIocjVuv6yq6QCAHFM6+kbMeHPRJNzTCZdPndHb5n\nJ6KEKkuor9hHxd61SOkiXRcpXZAurusQq6/E8AZIzR1Nau5o0nLHEMgsRGjNVYSkdGmo2M+x3StJ\nROqY+qUH8KacvtGbfYnQBLYh0U+uxr9iCDMkRBOS9WpTMk3inRXdVCj6ibFpOSy7+n7iTvOUgF83\nKQs14NUNHljyblOT6a0V5WytKAfguS2bOtxfzHBISzHwj5vJwbXvEgoeJjXnjJO20/D4ySyeTGbx\n5E7XkdIlVl9JQ1UJoaqDVOz5mHi4lpTskaTljUFKSbBkI4bHT+7Y2RRMvFgVNehDpKsE81Snx6KZ\n6jGZkJ3Dnuq+bxRt1xuQoURTMfD4dBNfG/EYnZHF3mCQLeXd6dTTGitbQtzgjFnzOLjhTabMu4+O\nuxr1LUJo+DMK8GcUkD/+fCDZwSRUdYhQVQlSuky67JsEskb0uy0KxalIj0XTZ5jcOGVanwcDQbLO\nZsswhUzVcUExiNiuy61LFrcqtl3z3vvE95dgFuRjZGcTmD4VM6e9azMmHRy/IP2ii6j4fD3le9ZQ\nOPHigTS/CcMTIHPEWadPQ2mFoh8Z9DJ6LXEsgadRx326wX1TLh1kixSnM28e3EqdG2m1rPbP7xGY\nPhUjMwOrvJzSX/wHdR9+1OH2QgPhCsaefyNHt/2FeLh2IMxWKBT9yJASzXjCxa5LiqYuNEytFy2Q\nFIo+ImTFkXZzAocbjyNMg7SLLyTjysvJvflvKHrgXuo+XN7xDnSBt8rFn55P4aQ5HNzwxoDYrVAo\n+o9eBQJdOnoM//nJGkJW3894G66BmkdXDEWcunqkKzn8v/8Fp6EBoeuknjsbp74h2RKsTXceKSXH\n+xHnn3kBW//U8YhUoVAMH3olmhNzchmZkcFnVZV9bQ+JhMREjTAVg09JKIgwm/Mvjews8r9xO56R\nIzCysnDq66lfsZKCe+7uuLl0vWjqHKLpZmNdWYVCMZzptXt2wbQZfWlHE7Yj0ewhkwmjOE2JOzYv\n7N+A9Ft40pPDxWQx9umYOTkITcPIzCT7+usITO44wEaXza5dTTdxHRs5RBoQnwoYniE1u6Q4Tej1\nWXfOiOK+tKMVibDAUb39FEMENzWOSLXQelh+taU+Ck1DaBrS7bohtaJrDK9GbY7EUgH2igFmSN6q\nWWGBHR2SpilOU4S352IntdajyuRoU7lo+wI77pJVreF6BJFcgZ5q4Ek1MUx13VD0L70+w0pqqvvS\njjYItKiHyvBp3c1cMUTwuCZarZ8uWlK2I5Ymk1XhG1Gi2bdYcQdPncRrC+oyXKozHGL5OpqhhFPR\nf/Tq7Hrzs138ev2nfW1LK6wYONKlPh7v1+MoFF1hhbRkm7AeIkwQnpaiaSjR7GMEoNe6+Bvvr2M4\nhAsFiZEmekAFFCr6nh5H3DyzYR0/Xd3/ofOW6zJv0bNIzeEfLrmI6QWFAEzLKmpX7kyhaImUss/6\nP3ock0TUALoWTVNqGGUumgNSQDRfEk6TBBrv+4QaafYbeq2LH41oSrK5uIODzNEIoONE1Dyyou/o\nkWh+dLBkQATzOOHGPNCfrl2BJ90lLhJMzxrB5MwCvjXxQsak5QyYLYqhzf7qIN98/4+U10bIsPx4\n4zogSdO9nDMyGbSWn5HKPXPPR+8gPaQt5aEQHmni1Hixna6D0gw0fPtd/FXNyzz14LQ4lKabSEf1\nie0v9FqXTMsglimI4WC5Lg0BiT/SuseoQnEy9Eg0q6PR/rLjhFhhDSei4c2TbKspZVtNKcvL9vI/\nl92hhPM0pjYW5Xcb1vPi9q3UJeIYrsCo0wiFooQaL5NBIpSU1jRtc6S6jn+58aouhfOe918ndszE\nlV0LpobA30YwATwNbdZTuZr9jh228SY0yNdBgK/Bxe6g8bZC0Vt66J4dvBwzV4IT0iEzedGpiDXw\n8Kdv8Prcbw2aTYqBx3Fdnlizin3VQQ7V1nG4shZvSCPdMrAtB9eVnGhc8faGXcwaPYIbLzhxnnFU\nxLod+KMjCEQ1ZBcXZxUINDA4lotZJhGIVsX2FYq+oFuiKaXkrd2f8Zt+Dv7pikREI5BhkmjsuRmx\nVcG904l1pUd4dvMmluz9HACfrWNUguV0LFaGpmEKrfFWL/l/wnXZWx7Esh1Mo/NAEb9Pa9yma8ee\nhkDQ9S2lCgQaOKQrkYN4k684delSNKWUPL5qBb/ftGEg7OkSJ+jFkytJYFMWqWd1+X5GpWZxRkrW\nYJum6Ec+OLCf7/zpbRKNxQF8to48UEd9eRnxYDnx6goS1RXEgxUIwyR7ytkUFs5C9+e32o+ZovPS\n+q2ETIufXHNVp8e7tHACB45tItGNrCfpSKKai7eL9TRDjTQViuFOl6K5aOvmISOYkOyE4q3y4cmN\nEXUs7lq5GIB/nnUNC8bPHmTrFH1FOJHgd5vWYzkuccdm0ZbNTYIJULtiDeXvvYO/oBhvdgFpOUX4\nzpxJiicfGQlTuWcDu7b8F7rpJ2fMLHLHnI03JQsZdvCWC5as/4x/mT8PrZMyP4/OnsuRujo+3HuQ\nWKxzF583oeM56uK3ddw27lnDq2HHm7fVdBNHeUcUimFNl6JZFmroapUBJ55w8Vb68OTHSMhkNOKP\nNv0ZQAnnMCbhOGwsK2Vd6RH+e9NG6uKxVu8LQCQs6pZ+QP2GjYy747sEsvNJqQU31EKwAmmMnPll\nimdcTUPlAYIlm9m+9D+YetUD+NJy0cIS84hDPGHj93WevvTkFV/lJ/pynt+ymePFfSzhNjn9vAkd\n7xEXb6VsJ5gAdekuGXUadiIpnGl54wiWbCBv3Ll9lhKjUCgGlmFbGT1uuXgiGrSoPfmjTX9mcmYB\nX8gZOXiGKXqMlJIHPniTz4/VsjfYcaUpsz5G3furqFq/ikDxWMbcch/e9CwCbQWzBUJopOePJz1/\nPP70fPZ+/AJT5t2HpumYruCxRUv45be/0mqbiroQq3aX4DguI1NSefEvm0gLSrx2Mtq2PgvcFIEn\npqFFJJ5g5/NmgUqwcJtmRfPGnUPZrg+pP7aHjKKJPf9FKRSKQadL0Xx792cDYUevcGoNdL+F22LC\n/0i4VonmMMB1XbZv386KFSv47buvcygSRaRnYWRnYWRloqekYFfXYFVW4ZQeI7RlG+kTpjPp1gfx\n5RSgITBqHGQngtmWgkmXUFv2GUe3/YUzZl6NHXVYvXk/n+44yHlTRwNwsKqWG37xLE7YQUhIi2ik\nxZPnlktytJgahWTIT9fHbTuWFJrOyBnzObzlz6QXTlCjTYViGDIs3bPHcaVsVwfw/2xaQlEggxnZ\nIzA1VUZrqOC6Llu3bmXFihUsX76clStXkpWVxawLzyc4Lhcz6GLV1BLd+Rl2TQ1uOIyRnYU3L59A\nahGjbp1PaiIdNyIhkgym6UlspBCC8RfcwrYl/05G4UTSC8Yjah2++/RbvPRPd5Cfkcrt/3cx3goX\nGiv42PQsXaGqZBPSscgbf16n62SPmkHpzg85uv19iqfORahzVKEYVgxb9yyArmvtwsrrrRgLlj/L\nRflj+c1FN+M3VMm9wcBxHLZs2cLy5ctZsWIFK1euJD8/n8suu4ybbrqJJ598kuLiYt7cvZMty97D\n6qDqjik0fFUCswb0oNPKo9AbTH8a486/iX1rXmT6NQ9jeAJoEZf/9+pH1AUjUG5BvHfHiNSUcnDD\nmxjeFKINlYz6wpc7XE8IjQlz7uTAJ39kx9GdjDv/JgJZI07mYykUigFkWIum60oMRIcX048rDnDv\nxy/z9MU3q1q1A4jjODzzzDP86Ec/Ijc3l8suu4xbb72Vp59+mqKionbrv7x9W4eCKQBfUMMMgx60\n+6wMWmbxZLLKpnHg01c58+I7sGMOn6zd36pUnutYHNn6Hsd2rwIkQtMRItkPUwi98aeG0HSKJl9B\n7tjZ7P14MaNmXUtW8VS2vPNTvCnZSOni2nEcK5H8aTf+tOJI6RKuOcqxz1cz7vwb++jTKRSK/mZ4\ni6YEt96E9I7D+D+uOMCPNv6Zn517/QBbdnqycuVKHnjgATIyMli2bBkzZpy46g7QYcUWDYG/VkeP\ngq/apa+rtY6a9WW2v/efVO7/lPzx57cSzFDwEPvWvIQ/o4AvXP8YhsePdF2kdBp/uk2vrWgDn698\nlkhtGYY3hdyx5yCEYPTs66kr+xzN9KAbXjTDgyeQiW560QwvuuFBMzz4Mwrw+NP7+NMpFIr+ZFiL\nJoCe8CCxOnXdlUeH7pzsqUJFRQUPPfQQH330EU888QQ333xzt4NcvEb7U9BflxxhBoKyVZ5jX6Hp\nJhMuvp1df30a3fSRM2omrmNzdPv7VOz7hDGzbyB71Mzmz9DJtKMvNYfxF9zC7uW/Z/yFC5rWzx07\nm9yxKvVJoTgVGfaiGU+4pDgmcb3j0eahcA11iSgZHn+H7ytOnkcffRSPx8OuXbtITU3t0bbfv2gO\nN7y8uOm1APTjghntv0Lb/owCJl3xbXZ/+FviDUGqSjbiS8th+tUP92j0lzniLKZedT+BbBWxrVCc\nDgx70QRIhHTI6Pi9I+Fa7lq5WBV27ydisRhvvPEG27Zt67FgAhSmpmJqGlajm9YnDXQL7Gj/t9BK\nyRrBWVfew/61LzFiypXkjJnVqzSQ1NzR/WCdQqEYinTdWHAYYIUFWkPnlT+315QNoDWnF0uWLGHm\nzJkUFxf3avv8lFR+/eWvYDYWVzergQFsGhzILGTa/O+SO/ZslTepUCi65JQQTQA3cuJB87Fo/QBZ\ncnqxdu1aLr/88pPax9yx43nw/AvxODpO1MUXVeKlUCiGJqeMaDqOi2F1nlpy54pFBGPdaFmh6BGT\nJ09m165dJ7WP77+/lF+v+wQrYiOEwLFUD0SFQjE0OWVEUyJwop1/nJJQNX8+snMALTo9+NKXvsR7\n771HONx8Q2LbNjt27ODo0aMkEl139ShMTSVq2whdJKs86WqkqVAohianRCDQcbSEiS6cps4nbXl8\ny3sUBzK4coQqlt1XFBUVcc0115Cbm0thYSFFRUXs27ePtLQ0otEolZWV5OTkcP/993Pttdcyc+bM\ndvvQteTNjhbQkEEHOyDQGlQDYYVCMfQ4ZUaakEw/0ZzOP5IjJc/t/RTLHbhAk9OBxYsXU1VVxbJl\ny/j5z3/OmjVr2Lt3L0ePHiUej7N06VJKS0uZP38+v/nNbzrdT1TaiFSNSDrI9I6TI3XzlDplFQrF\nMOOUGmkCJBp0REbnxbzXNFYJevyc6wbUrlOdlJQUxo8fT1paGvfffz+bN2/mq1/9KmPHjiUrK4sZ\nM2ZgWRb3338/U6dO5dJLL+1wP04W+BIa4TQXv8/AiTkgk39N4YDoZW1YhUIxNBFCvAQcd/9lATVS\nyrOFEKOBXcDxVltrpZTf6WD7LOBlYDRQAtwkpazrNUA+DwAAAphJREFUL3tPPdEMC7xeD46v87m0\nHbUqBaW/WLduHatXr+aFF15g9erVrF+/ntraWrKyssjOzuYnP/kJI0e2LgQgWlSWjUsHkW/grdCI\n4oIpON5ky6yXSEeJpkJxKiGlvOX4cyHEL4DaFm/vlVKe3cUuFgLLpJQ/F0L8I/Bo47J+4ZQTTWga\nmCgGgauvvpq0tDT27NnDwoUL0bSu3am3z5jJkr2fsztYBUAMG/INPFUadqw5klacZJcThUIx5LkJ\nuKLF6+5EBV4PXNb4/DlgOYMpmtPy8vvr2P2CLSXVWi1FmdmdrjM2LWcALTq90DSNV199lQULFvDo\no49y7bXX8sQTT5Cbm9vpNtn+AE/Mm89jf/1L0zIJ1HijJKosRqdkApKgVccZOarAueLUIy+r59W0\nTjWEEHOAY1LKfS0WjxFCbADqgX+SUq7qYNN8KWU5gJTymBAir1/tlCcYlgkh1K29QqFQDAwHpZRj\nBuJAQogSknOA/UG5lLKwzfHeBwpaLiJ5b/wDKeU7jev8Gtgjpfxl42sTSJVS1gghzgbeBKZIKUNt\n9l0tpcxu8Toopey3kdEJR5pSSpUwp1AoFKcYAyXOLY4370TvCyF04GtA0/yllNICahqfbxRC7CMZ\nMLSxzeblQogCKWW5EKIQqOhT49ug4vcVCoVCMdjMA3ZJKUuPLxBC5AohtMbn44Azgf0dbPs28LeN\nz78BvNWfhirRVCgUCsVgczPwYptllwJbhRCbgFeAe6SUtQBCiN82umwBfgbME0LsBr4I/LQ/DT3h\nnKZCoVAoFIpm1EhToVAoFIpuokRToVAoFIpuokRToVAoFIpuokRToVAoFIpuokRToVAoFIpuokRT\noVAoFIpuokRToVAoFIpu8v8BDmLi1avJ5bgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16900fe090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact(step=[0, len(slices)-1])\n",
    "def func(step):\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    levels = np.linspace(-75, 30, 10)\n",
    "    cf = iplt.contourf(cube[slices[step]], cmap='viridis', vmin=-75, vmax=30, levels=levels)\n",
    "    cb = plt.colorbar(cf, label=cube.units)\n",
    "    plt.gca().coastlines('50m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  },
  "widgets": {
   "state": {
    "d6925278dcd845e9b593602cf720e520": {
     "views": [
      {
       "cell_index": 21
      }
     ]
    }
   },
   "version": "1.2.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}