esgf-opendap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare esgf search connection\n",
    "\n",
    "See also http://esgf-pyclient.readthedocs.io/en/latest/examples.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyesgf.search import SearchConnection\n",
    "conn = SearchConnection('http://esgf-data.dkrz.de/esg-search', distrib=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### run a query for CORDEX datasets and show facets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hits: 2816\n",
      "Institute:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{u'AWI': 26,\n",
       " u'CLMcom': 570,\n",
       " u'DHMZ': 16,\n",
       " u'DMI': 580,\n",
       " u'GERICS': 133,\n",
       " u'HMS': 93,\n",
       " u'KNMI': 505,\n",
       " u'MPI-CSC': 665,\n",
       " u'ULg': 38,\n",
       " u'UQAM': 190}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctx = conn.new_context(project='CORDEX', query='temperature')\n",
    "print 'Hits:', ctx.hit_count\n",
    "print 'Institute:'\n",
    "ctx.facet_counts['institute']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### use facet search ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hits: 378\n",
      "Domain: {u'EUR-11': 94, u'AFR-44': 94, u'WAS-44': 48, u'WAS-44i': 48, u'EUR-44': 94}\n",
      "Ensembles: {u'r2i1p1': 94, u'r1i1p1': 237, u'r12i1p1': 47}\n",
      "Variable: {u'prc': 8, u'tas': 8, u'uas': 8, u'huss': 8, u'hfss': 8, u'ua200': 8, u'prw': 2, u'zg200': 8, u'prsn': 8, u'rsus': 8, u'hus850': 8, u'pr': 8, u'rlut': 8, u'ua500': 8, u'rlus': 8, u'rlds': 8, u'ua850': 8, u'ts': 8, u'mrso': 8, u'sic': 8, u'clt': 8, u'vas': 8, u'snd': 8, u'va200': 8, u'psl': 8, u'va850': 8, u'ta200': 8, u'snm': 8, u'mrros': 8, u'snw': 8, u'rsds': 8, u'ta500': 8, u'zg500': 8, u'wsgsmax': 8, u'rsdt': 8, u'sfcWindmax': 8, u'mrro': 8, u'evspsbl': 8, u'sfcWind': 8, u'tauv': 8, u'tasmin': 8, u'tauu': 8, u'tasmax': 8, u'va500': 8, u'hfls': 8, u'ta850': 8, u'rsut': 8, u'ps': 8}\n"
     ]
    }
   ],
   "source": [
    "ctx = conn.new_context(project='CORDEX', institute='MPI-CSC', experiment='historical', time_frequency='day')\n",
    "print 'Hits:', ctx.hit_count\n",
    "print 'Domain:', ctx.facet_counts['domain']\n",
    "print 'Ensembles:', ctx.facet_counts['ensemble']\n",
    "print 'Variable:', ctx.facet_counts['variable']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### add more constraints to select a single dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hits: 1\n"
     ]
    }
   ],
   "source": [
    "ctx = ctx.constrain(domain='EUR-11', ensemble='r1i1p1', variable='tasmax')\n",
    "print 'Hits:', ctx.hit_count\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### use aggregation context and get opendap url "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://esgf1.dkrz.de/thredds/dodsC/cordex.output.EUR-11.MPI-CSC.MPI-M-MPI-ESM-LR.historical.r1i1p1.REMO2009.v1.day.tasmax.tasmax.20160419.aggregation\n"
     ]
    }
   ],
   "source": [
    "result = ctx.search()[0]\n",
    "agg_ctx = result.aggregation_context()\n",
    "agg = agg_ctx.search()[0]\n",
    "print agg.opendap_url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### use logon manager to get proxy certificate and prepare secured opendap access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyesgf.logon import LogonManager\n",
    "lm = LogonManager()\n",
    "lm.logoff()\n",
    "lm.is_logged_on()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... logon on now with you username and password:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter myproxy username:pingutest1\n",
      "Enter password for pingutest1: ········\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lm.logon(hostname=\"esgf-data.dkrz.de\", bootstrap=True, interactive=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.is_logged_on()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this updates you proxy certificate in `~/.esg/` and prepares a `~/.dodsrc` config file for secured opendap access. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### use python netCDF4 to access opendap data\n",
    "\n",
    "see http://unidata.github.io/netcdf4-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from netCDF4 import Dataset\n",
    "\n",
    "ds = Dataset(agg.opendap_url, 'r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "show attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'historical'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.ncattrs()\n",
    "\n",
    "ds.getncattr('experiment')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "show variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'rlat',\n",
       " u'rlon',\n",
       " u'rotated_latitude_longitude',\n",
       " u'lat_vertices',\n",
       " u'lon_vertices',\n",
       " u'height',\n",
       " u'time',\n",
       " u'time_bnds',\n",
       " u'lat',\n",
       " u'lon',\n",
       " u'tasmax']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.variables.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "details about time variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<type 'netCDF4._netCDF4.Variable'>\n",
       "float64 time(time)\n",
       "    bounds: time_bnds\n",
       "    units: days since 1949-12-01T00:00:00Z\n",
       "    calendar: proleptic_gregorian\n",
       "    axis: T\n",
       "    long_name: time\n",
       "    standard_name: time\n",
       "    _ChunkSizes: 524288\n",
       "unlimited dimensions: \n",
       "current shape = (20453,)\n",
       "filling off"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.variables['time']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "show details about tasmax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<type 'netCDF4._netCDF4.Variable'>\n",
       "float32 tasmax(time, rlat, rlon)\n",
       "    standard_name: air_temperature\n",
       "    long_name: Daily Maximum Near-Surface Air Temperature\n",
       "    comment: daily-maximum near-surface (usually, 2 meter) air temperature.\n",
       "    units: K\n",
       "    cell_methods: time: maximum\n",
       "    history: 2016-02-01T11:17:01Z altered by CMOR: Treated scalar dimension: 'height'.\n",
       "    coordinates: height lat lon\n",
       "    missing_value: 1e+20\n",
       "    _FillValue: 1e+20\n",
       "    associated_files: gridspecFile: gridspec_atmos_fx_MPI-CSC-REMO2009_historical_r0i0p0.nc\n",
       "    grid_mapping: rotated_latitude_longitude\n",
       "    _ChunkSizes: [  1 412 424]\n",
       "unlimited dimensions: \n",
       "current shape = (20453, 412, 424)\n",
       "filling off"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.variables['tasmax']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a plot of the first timestep using cartopy\n",
    "\n",
    "see http://scitools.org.uk/cartopy/docs/latest/matplotlib/advanced_plotting.html?highlight=netcdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PVatWkZmZiampKbGxsdjZ2WFkZIRSqdS6CI4ePYqPjw9qtZrc3FwUCgVisVj7FGtjY4Ot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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c5d1bfd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from cartopy import config\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "timestep = 0\n",
    "\n",
    "tasmax = ds.variables['tasmax'][timestep, :, :]\n",
    "lats = ds.variables['lat'][:]\n",
    "lons = ds.variables['lon'][:]\n",
    "\n",
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.coastlines()\n",
    "ax.set_global()\n",
    "\n",
    "fig = plt.contourf(lons, lats, tasmax, 60, transform=ccrs.PlateCarree())\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clip the bounding box and plot it ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7f9c4ebfeb90>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VNm7cmB07duSgQYM4bdo0vnjxolT7XpjS/ah5AQB69+6Nv/76q4gT6w9JSEhASEgIUlNT\ncfr0aSQkJMDBwQF16tSBmpoa6tatC3Nz80+u/2PY2dkhLCwMDRo0QM+ePREWFobff/8dioqKQmA+\nkBuD+35ZbVZWFmJiYmBtbY1evXohKysLJ06cgKenJ7y8vPDHH38gIiICx44dw9GjR2FpaQmpVIqH\nDx8KIWDp6eno168f1q1bBzc3N3h7e2Pq1KlYvXo1UlNToampmee1a+HChdDR0UHDhg3h4OCAmzdv\n4ty5c1i2bBksLS2hoKCA6dOnw8nJCVWqVBHiKovC+/hhIDdGdOHChYJN+kuwcuVKzJw5E7a2tnj2\n7BkmTJiA9evXf7H2Soo3b95g3759IInMzEy8fPkSlpaWaNasGRwdHUvEcSWTyYTv3d3dHVWqVEH/\n/v0xevRoLFmypND4Vy8vL8yaNQtt27bFrl27BOcpAGhqaiI9PR0AsGTJElSsWBEksXDhQqiqqiI0\nNPSzZS8NgoOD0bBhQ4waNQqVK1fGggULoKioCENDQ0RFRcHAwAD6+voICwsDkPu7fW/Ge/v2rWCb\nTkhIQGJiIh49eoRt27ahWbNm6NixI8zNzZGWloa0tDSkp6dDV1cXlpaWaNWq1WebQU6dOoXu3bsj\nJyfn0226DRs2RP/+/TFhwoRiNR4TE4O5c+fi0qVLSExMRL169aClpYXWrVvD0tISZ8+exdOnT3H3\n7l3Ex8dDX18fGRkZsLGxgY2NDXR1dWFiYoKcnBxhGaqtrS3EYjEsLCwEBwJJ/PPPP1BTU8Pz58/h\n5+eH0NBQWFtbY9u2bWjUqBGmTZuGs2fPQkNDA1euXIGlpSW6du2K5s2bQ0dHJ1/5//jjjw8cBkCu\nI2bSpEk4evQoJBIJ2rVrh7Vr12Lnzp24efMmkpKSYGRkJEQxtG7dGnFxcTh//jyqVq0KAIL9TCKR\nwMTEBBUqVECtWrWgpKSEyMhIxMTEoGnTpmjZsiU0NTURFhaGnJwcJCcnY926dXjw4AEiIyPRu3dv\nbNq06YMY3/yQSqWoWbMmXrx4gYyMDADAli1bMHr06GJ9r0UhKCgIjRo1ynPMzs4OT548KfG2Pof0\n9HScOHECwcHBePToERQVFREcHIyOHTtCJBJBVVUVFhYWeP78OTZu3AggN963T58+MDU1/ay24+Li\ncPv2baxZswaxsbF4+PAhAGDz5s0YM2YM7t27h8jISFhYWKBChQowMzODgoIC6tevj9atW2P58uVC\nXevXr8eUKVMglUqxd+9eZGdnY9CgQSW60KS0iYuLw44dOxASEoJZs2bBxsYGiYmJEIvFiI+PR2xs\nLGrUqIH09PQiLR1/+/Ytzp07hzNnziA5ORlaWlrQ1taGhoYGkpOTcevWLdSqVQvbtm1DVFQUMjMz\nhe9fWVkZGRkZMDQ0RHBwMP766y8YGxvD1NQUmpqayMrKgp6eHmrUqIGAgAAMGTIEAD5d6Z4+fRod\nOnQo0kDdvXsXPj4+SEtLw969e9GuXTtMmzYNYrG4UK+hRCIRlpI+f/4ckZGRSE1NRWxsLNTU1JCV\nlYU7d+7g+fPniIiIQE5ODtq1awcLCwscPHgQmZmZkEqlMDMzQ9u2bWFnZ4fAwEC4uLggMTERa9eu\nRVRUlHDTGhkZISAgAPr6+qhWrRqCg4OFcCYdHR0MHz4cXbt2RXZ2NpKTkxEWFgZ/f3/ExsZCJBKh\nUaNGePLkCY4dOwYAGDFiBLZv356nTyYmJjhy5Ag2btwovCV07NgRjo6OcHZ2xtatW+Hn5wctLS0k\nJycjLS0NtWvXRs2aNWFkZITDhw8jIiICR44cQevWrT94OPzxxx8YNGgQBg0ahN27dxfp+xkzZgz2\n7duH1NRUyGQy1KlTJ8+eCyVBcnIyTpw4gVGjRgnKvX79+rhw4cIXXwxRVN68eYNNmzZhy5YtcHR0\nRIsWLVCzZk0AuQskxGLxB9esWLECixYtgouLC/7++29YWVnB1NQU5ubmcHFxQceOHWFsbCyUl0gk\nePnyJSwsLISZ6L+RSCS4c+cOzpw5g9atW6NmzZrIzs7GnTt3EBMTg0GDBgEA6tSpg+joaCgpKaFV\nq1bw8/NDbGws3Nzc4OHhAS0tLezbtw/9+vUDUPBkQU7hhISEoG/fvoiKioKVlRU0NTWRmZmJmJgY\nSCQSqKiooHbt2oJzbsaMGXj9+jXS09OhpqaGxMREPHz4EK9evUKLFi3g6+v76Y40RUVFGhsbc+nS\npYyOjv6oHQMATUxMeP78ecpkspI2lZAko6Oj+fvvv3PhwoW8detWkdqRyWR57EE5OTl88uQJjx07\nxlWrVrFXr16C/GZmZrS0tKSNjQ0rV64sHG/SpAnNzMxoYWFBS0tL1q1bl3v27OHKlSsFR5qysjIB\nUF9fn3///TdJMikpiVpaWty4cSOVlJR48OBBWltb89ixYyRzA9EBcOXKlYJ8O3fupJKSErW1taml\npcUePXrw7du3wvnWrVsLNuBhw4bR09MzT/8KwtPTkwDYokWLz3asPXv2jGPGjKGTkxMdHBwoEomo\noaHBli1bcs2aNXz8+PFn1V/SxMfHc9SoURSJRBwxYsQny5eWlsZbt27R19eX27ZtY48ePainp8fG\njRvz999/p1Qq5dGjR/M4eK5cucK///6bixcvZtu2bamrqys4nfD/CzJEIhEbNWpEFxcXqqurc82a\nNSRz792IiAhu3rxZ+N4B8MKFCyTJ2NhYmpqaEgDr1atXYuP1PVKQwy0nJ4d79uxhjx49uHXr1gKv\nT05O5qlTpz7PkSaVSnnp0iW6ubkJX3bHjh3Zr18//vXXX5RIJEKDKSkp3L17t1Bu+fLlzMzMLIGh\n+PJkZ2dz2LBh7Nq1KwcNGsQxY8ZwxowZ/Pnnn6moqMgNGzaQJCUSCc+cOcNjx45x7ty57N27NydM\nmMCJEyfS39+fly9fJgD6+/t/0IZMJuPIkSOpoaHB6dOn89mzZ3z58iX37dtHAFy/fr1QdtSoUcI4\n+vn5EQDXrVuXp76kpCQePnyY27ZtY8uWLeng4MDevXvTz8+vwD4C4Nq1az9rrFJSUvj777/T2NiY\n8+bNo5+fH2/cuME3b9588ADMysriixcvmJqaSjL3ppbJZMzOzqa/vz+vXbtWKvfInTt3KBaLOXHi\nxM92Jm7atIkrVqzg+fPnGRsbS5lMxszMTB47doy6urrcvHkzjx07RgB0cXEhAFaoUIEmJiacOnUq\njx07xvj4eJLk/fv3ee3atTy/I5KC4yg/3r59yydPnpDMvQe6d+8u3Cs///zzZ/VNTsnwWUpXU1OT\nJ06cYEBAAFetWkUA3L9/P3v27Cl80cnJyWzVqhUtLCyop6eXJyyqX79+ZdHncs2jR4+ora1NdXV1\nmpiYsF27dtyzZ4+gfKRSKR8+fMjjx4/z2rVrBMARI0bk+SGGh4cLM6R+/foxMjJSiFDw9vYusO2W\nLVvSzc3tgx95Yfj7+3Pt2rUcOnQonZycqKuryw4dOjAkJKTQ6/4dEqWurs4ePXrQyMiIJiYmNDAw\nYJ06dQRv+5d6KyLJvXv30sjIiPv27SuwzKNHjzh+/Hi2atWKo0ePFpTaf5HJZKxQoUKe0D5DQ0NW\nq1ZNOKaoqCiEEh48eJBz587lokWLihQGVVw2btxIALS3t6eXl1eJ1y/n0/gspSsWi+nq6koTExO2\natWKnp6eXLBgAStUqMC+ffvyzz//ZEpKCvX19QmAK1asoJeXF2fMmMFx48YRAI8fP14W/S6XBAUF\n0dzcnOrq6jQwMKCOjg47depET09P4dWmc+fOeV5NAfD69et56klJSWGrVq1oaGhIAFRVVaWxsTH/\n+OOPQhVYeHg469evz9WrVxdJ3gULFlAsFnPs2LHcsmULL168mMfMURi+vr6C/G3btqWnpyefPXvG\nFy9e8J9//mFWVhZ//fVXAuDJkyeLVGdxSE5O5ujRo2lra1to2N3r168JgAsWLODYsWOLNFmYN28e\nhwwZInxXVatWJQB26dKFWVlZlMlkTElJKeku5Ut2dvYXfWjJKRoeHh6cNm2asD8LPzdONyEhgd7e\n3uzWrRurVavGRYsWUUNDg/379+fRo0cplUp54cIFTp48mW3atGHTpk2FH9zQoUNLtfPljTdv3rB5\n8+ZUUVGhubm5MFM6cOAA27dvn0e5Hj58mJMmTaKlpSUdHR1Zv359WlpaUiQSUVlZWbCrDxw4kN27\nd+epU6fYvXt39unTR3iF/xgnT56knZ0d//nnn0LLPX/+nJaWlgwODv6kfstkMv72229C33r37s1p\n06Zx5syZdHZ2pra2NmvXrk0vL68SDV6Pjo7mihUraGJiwiFDhjApKSnfchkZGdy+fTutrKxoaGhI\nBwcHOjg4cN++fczIyCi0jVu3btHR0VEwt+3atYs///wzb9y4UWL9kPN18d7M8z5emiW9OOLfPyb8\nK+j+7NmzFIlEtLa2pqurK2fPns3IyMgv19NyTHp6OqdPn04VFRW6uroKP+TJkyezU6dOtLa2FoLb\n9fT02KVLF65cuZL379/nnDlz6OHhwZMnT9Lb25sHDhyghoYGNTQ0+NNPP9Ha2jqPE6Z27drFkm35\n8uU0MTEpcIeoVatW0cDAII9z71OJiorilStX6O3tzeXLl3PBggX08fEpUBl+KocPH2b9+vVpYGDw\n0cUYQUFBNDU1ZYcOHejl5UVDQ0P++eefxZ4xxsXFMTk5+XNFl1OOyMzM5IoVKzh//vwilU9LS6OP\njw/nz59PKysr6urqftkVaTKZTLhRQ0JCCIBnz54tVieTk5N55MgRwc74LbwqpaamskmTJuzRowfD\nwsIKjCzo0qUL7ezseOzYMeEJ6ebmxgYNGnD8+PF0dXVlt27d2KhRI4rFYnp4eHDBggUcOHCgsOJr\n4MCBfPr0abFl9Pf3p5GREQMDAz84N3XqVE6aNKnYdZYV9+7do4GBAX18fJidnf3R8s7Ozty+fTtJ\n8tWrV9TV1S2y2UTOt0FkZCSnTJnCzp07s169erx8+TLv3LkjbA3g6OhY4LWvX7/m/PnzWa9ePWpr\nawtbCEycOJGLFi36skr3PQ8fPmSPHj0oEok4ePBgxsTEMCIi4qPXnThxQpixPXv2jP379ycA1q1b\nl+7u7jx79iz9/Px469atIv2YygvDhg3j4MGDi/XanJGRwdu3b/PixYusU6fOBw8fX19fOjs708DA\ngHv37hWOp6amcsuWLZ8UBXDo0CFhr1k1NTVOnjyZFy5coIWFxUcdZeWJiIgIVqhQocjle/TowbZt\n2zIqKorZ2dlUUlKiu7s7Q0NDefLkSbq7u3PIkCHs27cvd+zY8U1MBOTkIpPJuHXrVhoZGXHmzJk8\ncuQIt2/fTg0NDUEXqamp0d/fn0FBQbx37x63bt3KXr168ddff+WAAQOop6dHNzc3+vv752vWK0zp\nltguY8ePH0fXrl0BAD179sTZs2eRkpKC1atXw97eHvXr14euri5ev34Nc3NzZGZmonr16oiIiAAA\ndO3aFRcvXkRqaioOHDgAAwMDXLhwAQEBASCJ+Ph4REZGwsHBARMnTkTPnj3Lzd6Z+WFkZAQfHx/U\nr1+/2Dtq+fv7o2XLlkK24P8yZcoUqKqqYsmSJcjJyUHLli0RGxuLRo0awdvb+5O2ryOJuLg4LFmy\nRNjtbP78+cWup7RgYt4xlUoBXSvgZqw+AvRcQRKBeyMhDXuOYfPMEKtiCQAQ4S0AIP5lFsY63oHb\nIlN0G2UErft7MGHCBFy5ciXf9lJSUgpcvSin7Hn48CFu3LiBuLg4xMXFAQB0dXWhpqYGX19fREdH\no1mzZpg9ezY8PDzg6emJwMBAITt1UlISnJ2dERsbK5R5+fIlRCIRZDIZXF1d4ezsjAMHDqBSpUpY\nvHgxjIyMCpSn1LZ2fF/2vZKZP38+Fi9e/EE5qVQKPz8/tG7dGtOmTYOmpiYqVqyILl26FJpyJi0t\nDZcvX8aSJUuQmJiINm3aQFdXF1paWhg0aBAsLS2LLOuXpmvXrvD398eUKVOEJJVFpUmTJlBSUoKv\nr2++qWJu3bqFvn37QkVFBampqXBxccHmzZvRrVs36OvrY+/eveX6gVQcJBIJhg0bhqNHjyItLQ23\nb99G7drkuWuJAAAgAElEQVS1gQgFUC9v2c7DFZCWClR3EiHgTCqkEkJRTQVN2mpgrLsBACAVOggK\nkGF22wdoP1AfUzdWhKKiAhrhNp49e4ZKlSqhbt262Lp1q5BKydnZudxsCC8nL2/fvsXcuXNx+PBh\ntGnTBmZmZjAxMYGioqKQDbt58+YAcpOoVq5cGaampkhNTcW5c+cQFxeHmzdvwt3dHZ06dcKMGTOE\nPVo8PDygpKT0SduQltnWjjKZjFlZWUxPT+f27dtpYWFBZ2dnmpmZ0cjISJjKr1ixotj1BgYGcu3a\ntVy6dCn79+/PJk2afHQ7vNImLCyMhoaGHDlyJMeMGUNvb+8iyZiUlMSffvqJgwcPLrCMTCbjoUOH\n8oRCZWRksHXr1nRzcysJ8csFXl5ebNKkCdu1ayfcL3fu3KEsAR98Ul6Am7YrceJURTZsrEArm9zd\n2kSGijwSWZ3n2Izn2Iw9F1Th8BkiPqAtA1ibtzJt+OOPP1JJSYnGxsbFdkrKKX1kMhl37txJU1NT\njh07lomJicWu4+zZswTAn376iWfOnCGZ6/QViURs2LDhZ8mH0rDp5kdYWBg3bNggbJkIgHXq1OGp\nU6d45MgRNm3alOvXr//sFUk5OTmcMGECzc3NSzX/VlE4efIkt2zZQg8PD3bt2pV6enqcOnUqr1y5\nUugChVOnTrFSpUrFbu/cuXPCstNvgV9//ZUODg6Mj4/n7NmzhcU4+Snd9589B5RpVw10clYgADo0\nVqN9QzV6PHKm6yB9Gpgqc19wBUHpevjYsEGDBnz8+DF//vlnXr16tay7LacQHjx4QCcnJ9avX/+T\nQ/TS0tIoEono6+ub53hKSgoHDhz4dSldqVTKPXv20NzcPE9IWc2aNblz587P6sjHGD58OGvUqFGu\nfzQvX77k1KlTWbt2bZqZmdHR0ZHOzs48ePAgSfL48eMcMmQITUxMeO7cuWLXn5mZya1bt9La2pot\nW7bkL7/8UuDqqvJMWFgYhwwZIuyJUblyZa5atYo2NjZ0c3NjRkzBSnf/MWXa11agdRUVGpsrEQCV\nlEENLUUOmmXCxfut+OtJMZccsOafD6vSpZ9xoZ5qOeUDiUTCDh06EAA3bdpUrFWV/8XHx4fNmjXL\nc0wmk3H8+PHU1dXNN6KnOJSq0n306JGgaIcPH86bN2+Wiud31qxZbNy4MYcPH05TU9OvwvP+7Nkz\nhoSEsF+/fpw3bx7T09MJ5GaZ+NwZe1paGo8cOcJZs2bR3NycAQEBJST1l2fRokU0NDTk0qVLOXDg\nQDo4ONDOzo4KCgoMCQnJ3cC7HiiNz1WyyRJVJktU+SZdhf43lbl1V66ibdsvd5WkilrujLdeK23W\naS2ihrYS67TSo13T3POuI7+O++V7JyEhgSoqKlRRUWGfPn3o4uJCR0dHHjx4kOPGjeP/+6CKxNOn\nT2lhYZFHN3Xq1Ina2tpcsmTJZ8tamNL9ZnKk9ejRA0eOHMGcOXMgFosxZ84c+Pn5oVq1amUt2kc5\nfvw4Fi9ejKCgIFy6dAlz5sxBcHBwidW/Zs0aLFmyBJcvX4aDg0OJ1ful6NChA9q3b4+JEyeid+/e\n0NHRwY4dO6CkpISQkBA4ODigXkNlVKqsgBGjFdGoqSJ2ekqxYLYUIkNFmFVQhNEP6mi+tSciw3OQ\nnZoDw8oiPNsdCGMbbah0cIGqthpEyM1llwQRVmNuGfdaTlFJSEjAli1bYGZmBktLS4wcORJRUVEA\ngAcPHqBGjRofrYMkrKyscP78ecTHx8Pd3R13795FdHR0sZIDFERhjrTSSY1ZChw4cABVqlTB0qVL\nkZ2djcjISFSvXr1Uk9F9Kp06dcLatWsxZMgQdOnSJU+q8s/l3bt3qFixIoDcG7K8K933EQoymQz7\n9+/HmzdvUKdOHQCAj4+P8P9VRyzRxvIFtCCDqQKweB5w7Loe1KuJ8QjVEQNzZAOwqJOrVHWQhNoz\nWyMJ/0vsmAQRREgSlK+crwNDQ0PMnfu/h2T9+vWRlpaG5s2b4/Hjx0VSugoKCmjXrh12796NS5cu\noUOHDvjzzz9LROF+jK93a/n/oKSkhGfPnuHdu3dQUVHBiBEjIBaL0bt3byxduhSpqallLWKBKCoq\n4uTJk9DX18f06dOxbt26Il+bnp6OJ0+e4P79+wgPD0dqaipycnIQGhqKrl27wtjYGAMGDBAyFJd3\nVFVV4e7ujvr162POnDl48OABsrKyAADt2rXDmjVr8PPPP2Pv5BcAgBO+QPuewJxftfCmWnNcQCvE\nwPz/ValIUKzv//9fkiBCNH4o1T7KKTkiIyNx+fJl6OjoIDU1VUhJVRQGDx6M5cuXo0mTJpg5c2ah\n4aolyTdjXvgvjx8/RvXq1YW/tbS0sHjxYowbN65E8lyVNTk5OTh//jwGDhyIxMREIT+WkpISSMLU\n1BRubm4ICAjAuXPnAABt2rTB8OHD0a1bt69mDCIjI4VMDo0aNYK6ujoUFBQge+eH24+AFeuUoDq0\nFxQUFBCD/+XZ+7eCTYUOdFD4Q1duXvg68fPzw9y5c6Gvrw8HBwesXbsWDx48KPLbbVJS0ientS+M\nwswL38xM979Uq1YNMTExcHd3B5D7mj1lyhRUrlwZUqm0jKX7POLi4qCqqoqOHTsiMTERlpaWkMlk\n0NHRgVQqxcKFCxETE4OAgABEREQgPDwc69evh1QqRZ8+ffDTTz+VdReKjLW1NSIjI9GmTRu8ffsW\nly9fhp+fHzq2BHbdqwa1Yb2hoKCAVGjnUaz/NhvoIFX4OxU6/5oD/+9vOV8n586dg5OTE+7evYtl\ny5ZBQ0MD3t7eRb7+Syjcj/HNznT/TVRUFFxcXPD8+XMAwPXr19G4ceMylurTePDgAezt7QHkJiQc\nOHAgTp06hZUrV+LatWvCTfTq1SuYm5vDyMgILi4uiIqKgr6+Pnx9faGgoIC0tDRoamqWZVeKTGJi\nIgwNDWFsbIzevXtj0qRJuGU7Gan437LcVGjn+RvIVbbvTQf/Vaw6SM1Tfi5Wf7J8z58/x8OHD1G3\nbl1YWFh8tPz7JKOFLSOV83G8vb0xffp0XLlyBXZ2dgByJ1caGhqftIqsJPkuZ7r/xtLSEosXL4ar\nqyt++umnIv0wyiMkBYUbGRmJCRMmQCQSoWHDhggLC4O+vj7q1auH4cOHw9zcHD4+PtiwYQO6d++O\noKAg+Pr6AkCByRLLKwYGBkhOTsbWrVtx/PhxXLhwAdXxKF+TgR3CYIHYQhXuez5mcvg3wcHBqFu3\nLgYPHoxLly7hzZs3GDBgAKpWrQpbW1t07twZly5dKrSOly9fYvjw4fjhhx8gFouhoKAAT0/PIssg\n53/cvn0bU6ZMwcWLF1GlShXh+KRJk8pc4X6MbyZ64WPY29sjIiICjx8/LmtRPhkFBQVIJBJhX4Wk\npCT88ccfePr0KbKzswEAN2/exM2bNwEAWVlZ6NOnDwAI/z569AgGBgZfldIFcjcv6d69O6pWrYpG\njRohYJQJUqGDVGgLZXSQihiYQwdpqI5HqI5HsEAsXj2Ix8B2yYiNAYZc+Am1WxkUq+0rV66gT58+\nGDt2LBYsWIA9e/ZgxowZ2Lt3r1Bm7dq1BWbhlclkmDx5Mnbu3ImRI0fi7Nmz+Ouvv3Dy5ElERkZ+\n0nh8z1y9ehU9e/bEunXrUL16dZCEnZ0dqlSpgs6dO5e1eB+noABelsAy4PKEv7//Zy/tKy/ExcVx\n586dtLCwyLPqz9jYmBYWFhw+fPhXtRgiP2QyGd+9e/fB8efPn9PKyooBrM0d7Mff6MbFnMoHtOUD\n2uYee92dUzqBq0eBV19b87dloIXp/8ZpxskG/I1ueT4F8fz5cwLgb7/9RpK8ePHiB6mUANDW1jbf\nZKRkbpYJAIyLi2NKSopwjYeHB0NDQ0tmwL4T9uzZQ2NjY54/f1449vjxYyFf3dSpUzl8+PCPZi7/\n0qCs9l4oT6xbt45DhgwpazE+m2PHjlFPT4/du3fn/v37uXv3biooKLBFixYMCgr6JvZ9DQwMZLVq\n1aisrEyRSERjY2PWqVOHtWvXpq2tLYcMGcIA1uYBduJZWVP+uk6JndqD3TqCw9qDOprgYGewQWWw\ncxMw5hHYpC74YyNw5UhQQQH8pTf4OgxczKlczKkFyjJw4MAP0k2Fh4czJCREGOtbt27R2dmZysrK\nXL58+Qe50VJSUmhnZ0cfHx/KZDIh5VVhmS3k5BIeHs7p06fTx8eHo0ePpqWlJR8+fCicv3DhAh0d\nHdmjRw+GhoYKGVV27NhRhlLLlS5JcvDgwfTw8ChrMT6bxo0bs2LFinmUa2Ji4jehbN+zbt066uvr\nUyKRMD4+nrGxsQwODubNmzcZEhLC7OxsYcewR5EqBMCD88ED00CPPmDUarCK2f/PQC3Aid3A1FNg\nw8pg97rgvM7gaBdQTwOc2hk8k9M0XzkSEhKopKSUZwerx48fc8OGDVy0aBHXr1+fZ7l2SEgIu3Tp\nwi5dunxQl7u7O5s2bcp3797x3bt3nDRpEgFw7ty5pZKC/mtDKpVyypQpVFdXp5ubGxs2bMjZs2cz\nLi4uT7k6derQ2dmZANimTRtWrlyZK1eu5MuXL8tI8lwKU7rfRfQCAGzevBl37tz5Kh0Xvr6+cHV1\nzXMsMzPzm93j9e+//8bo0aNx//79Asu8SlXF6uE5OHUdqGcLeA8FkJh7Lj4NqLUayJYCoSuAMX8A\ngc+A9rk+SPx+BehVHxhfHWi+G3h4FqjelpDJZAgLC8O5c+egpaWFOnXqoG7dugByVw1KJBLcunUL\nP/74I8zNzREVFYXjx4+jSZMmqFmzJpKSknD37l0YGBjgzJkzeeSVyWQYOnQoXr16haNHjwKAsFdy\n69atcf78+ZIdxHJOUlISTpw4gfbt2+e7KMHLywuLFy/G4cOHc/dPLgAXFxf4+/uja9eucHFxwY8/\n/lgu9tX+LpYBf4zY2FjB2fQ18e7dO0HhqqqqIjo6GsbGxuXeQ/s5qKmpITMzs9AyUzrm4GUCsKU/\n4Fz1/w8m5/7z6jUQmwooKwKvUoADfYC7McC2AGD/bWBwU2D338CBkNzygeeB6m2BBg0aCE5INTU1\nYSUcAAwZMgRZWVk4cOAAtLX/57xLSkrChQsXEBYWBrFYjLZt26JLly4fyKuoqIgdO3ZARUUFWlpa\n+Pdk5n240/fCkSNHMHbsWMTFxaFNmzbo1q0blJSU8NNPP0FVVRUzZszA/v378dtvvxWqcIHchAhz\n587Fhg0bsHfv3q9j0U9BU2B+Y+aFmJgYamholHgG2i+FVCqlWCwWnC7fU8bZnj17cu7cuQWef/v2\nLQEUuHF1cnKyMG7/fc38888/hXPLly/nqVOnqKury7CwMNrb2wubpK9YsYIACkwo+ik8fPiQOjo6\nDA8PJ0khAeKbN29KrI3yzNWrV2ltbU0zMzMeOHBA+B6GDBlCHR0dDhgwgPv37yeAAp2S/0Ymk7Fe\nvXrcvn07VVRUmJaWVgq9KBooxLzwXcTpAoC5uTnc3NzQpk2bPDOY8sqePXuE/HFHjhyBrq5uGUtU\nelhaWuKvv/7K95xEIsG2bdtgbm5e4GxfV1cX58+fh5+fH8zMzBAaGgpVVVXUqlUL/fr1AwCsWrUK\nM2fORMeOHbFs2TKMHDkSM2bMAJC7AKVy5cowNDREWlpaifXr2rVrqFatmpBr7dChQzA1NUVKSkqJ\ntVGeiYyMRGRkJKZOnYrnz5/Dzs4OAQEB2LVrF3R0dODt7Q0bGxvMmTMHkydPLvDNNDs7G1lZWZg5\ncybS0tKwb98+zJ8/P9/UVuWSgrQxv7GZLpk7e2zUqBE3btxY1qJ8lJycHGEm8L0RHR1NAKxWrRpb\nt27NyMhIrly5ku7u7vzxxx9Zu3ZtXr16lYcOHeK8efM4YsQI9urViz169KCFhQWtra1ZrVo1Yfze\nhxm9/3vRokV5HI8ZGRk0MTERzt++fZs5OTkcOHAg27Vrxw0bNjAoKIju7u48c+YM09PT6enpyfHj\nx/PSpUtF7ldiYiK7d+9OIyMjDh48mG5ubgTAu3fvfolhLHfIZDLu27ePBgYGrFSpkjDeMpmMly9f\nFhIfSCQStmrVirt27fqgjoyMDFauXJlGRkbs0qULY2NjKRaLee/evdLvUCFA7kj7H/fu3UPr1q3h\n6+sLR0fHshanUJYtW4aIiAj8/vvvZS1KqSKVSnH16lUAwLhx4/D48WMoKiqiUaNGMDc3h6qqKq5f\nvw4bGxs4OTnB3Nwcenp6ePfuHVq0aIG0tDRIJBLo6+ujcuXKwh6rUVFROHz4MNzc3D6YFf3999+Y\nPHkyKlSoAFdXV4hEIrRr1w6bNm3CrVu3cOHCBXTr1g3Xrl1DVFQUtLS0kJSUBAcHB9y5c6dY/QsN\nDYW/vz8yMzPh4OCApk2bftM2+v9y+vRpjBkzBp06dULv3r2FxJGbNm3CkiVLEBsbi3nz5kFRUREL\nFy5EaGgoTp8+jadPn+Lly5eCHbdhw4aIjo6GlZUVZDJZuRrDMktMWV7566+/WLFiRR4+fJjp6ell\nLc4HSKVSenh4UCwWc+vWrWUtTpmRmZnJqlWrsn///nR1daWTkxNtbGy4bNmyIuXGiomJoZ6eXrFm\nkvjXgodjx459cP7fM+ZZs2Zx4sSJxerT9052dja1tbXzHbdDhw5RW1ubrVu3po6ODi9fvkxPT09q\na2vTyMiIenp6rFmzJk+ePMnRo0ezf//+NDMz44QJE8qgJ4UDeZxuXqRSKZcuXcrGjRtTQ0ODR44c\nKWuRBEJCQigWi9m8eXNevHjxm4q/LQ6HDh1irVq12K9fv08eg/fOGj8/v2LVkZiYyPj4+HyvuX//\nPv/44w/Gx8fzhx9+4IULFz5Jtu+Zq1evUl9fn1FRUXmOR0VFcdy4cdy9ezfv3bvHHj16fLDyT1VV\nlUZGRlyxYgW9vLz4+PHjMupF4ciVbiH4+flRT0+vzIOpSXLjxo00NDSkl5cXpVJpWYtTZuzatYu2\ntrY8efLkZz10ZDIZN2/eTLFYTHV1dVarVo0zZszIE8GSmZnJMWPG0MzMjPb29vTw8CjSvbBr1y62\nbdv2k2X73pk1axa7du1a4H2+c+dOAmDXrl2pra0tRDkcPHiQjx49KmVpi49c6X6EsWPHcvr06Rw4\ncCBtbW25f//+UpfB29ubVapUEcKJvmdq1679SZmQC0ImkzExMZEhISHs3Lkzf/zxR54+fZrp6enc\ntWsXHR0dGRERQV9fX7Zp04ZaWlqsUaMGXV1d6eHhQT8/Pw4bNozt2rVjr169eOHChe/e9PO5ZGRk\nsH79+vk6y8jct9EKFSoQADds2PDZiVpLm8KU7nfnSMuPmJgY2NvbQywWQ01NDQ8fPkRcXNxnr/hK\nSUmBpqYmlJWV8e7dO8yZMweJiYnQ0NCAsrKykFDP1dUVQ4cOhbOzM0aMGFFCvfo6IQkdHR1ERUXB\nwKB4u4EVhaSkJKxfvx4XL17EgwcPYG5uDmdnZ2zcuFEok5ycjHv37uH169fw9fVFUFAQfvrpJ1hZ\nWWHVqlXQ19dHjRo1sHHjxiLv1padnY1Dhw4hIiICzZo1Q4sWLUq8b18TaWlp0NHRgZeXFwYPHpxv\nmUOHDiEoKAiLFi2ChoZGKUv4ecgdaUVg6dKlgt2oSpUqnDdv3ifXdf/+fTo6OlJTU5NaWlo0MTFh\n//79CYBbtmzh5s2bOWPGDKqoqLBSpUrs168f169fz969e5dgj75ODh48SENDQ7569eqLtxUREcG9\ne/cyKyuryNfo6OhwwoQJXLNmDdesWVPk60JCQgiALVu2pLq6+lc3c/sStGnThpMmTSprMb4IkJsX\nPo5UKuWYMWMExaumpsYtW7ZQIpEUuY6srCyOHDmS+vr63LVrFyUSCRMSEhgYGMj58+fzt99+y2Oj\nlMlkTE9Pp4mJCQMDA2lkZMSnT59+ie6Ve06fPs1+/fqxUqVKDAwMLGtxCuTfzh0VFZUiX5eZmUlT\nU1M+ePCAkyZNYs+ePQtcUfc9EB0dTTs7u2/WRCNXukWkRo0aBEA7OztWrlyZpqamXLNmTZGdOdu3\nb2fz5s2LvdS4WrVqPHPmDAcOHEgPDw96eXlx9erV5TKc7Uvg6+vLChUqcPXq1UxISChrcQolMzNT\nULqGhoY8e/Zska9dv349zczMGBISwh49etDAwICenp58+PAh58+fz7lz55bosuPyikwmY6tWrTh6\n9Ohvdoc1udItIq9fv+bIkSOFH9XPP/9MVVVV/v7770W63sPDgyNHjix2u0ePHqWdnR1Xr17NTp06\nCe3Pnj272HV9bfj6+tLAwKBEHWdfmp07dwohfRYWFnz79m2Rr12wYAFVVFQ4Z84c3r59m+3atcuz\nGm7t2rV89uxZvg96mUxWrDev8oqXlxfr1q37TT9g5Eq3mPj4+BAAf/jhB65Zs4YikYi+vr4fvW7+\n/PkcPXp0sduTyWQcOnSo8KPbvn0727VrV+a735cG7du3p5eXV1mLUSxycnLo6OjIHTt2cNiwYXRz\nKzjzxH+JjIwUFGx8fDyzsrLYt29fAqBYLGabNm1oYWFBPT09NmjQgHXr1qWtrS0tLS2prq5OMzOz\nr3rz89DQUBobG/PgwYO0tbUt1jLqrwm50i0mb9++ZYMGDQiANjY2vHDhAo2MjPjHH38Uep2zs3OR\nlHN+5OTkMDAwkDk5OWzXrt13sefCmjVraG1tXe5NCvlx9+5dAuDZs2epr6/P5cuXF/laLy8vAqCO\njg7Nzc3ZuXNnBgYG0tDQkDNmzODt27f55s0b+vv7MygoiGFhYXz+/DnT0tK4YcMG1qpViyEhIV+w\ndyWPTCbj7t27aWxsTE9PT1apUoUAeODAgbIW7YtQ5kpXJpN9dbYbqVTKsWPHEgDPnz/Phw8f0tzc\nnD4+PgVeU7NmTd65c6fIbaSlpXHChAnCzMfS0pJubm7cvXs3ly1bVhLdKLe8e/eOGhoajIyMLGtR\nPplKlSpx/fr1jI6OppmZGW/fvl3ka589e8bQ0FCGhYUJJoM7d+5w7ty5NDY2LtCZmJOTw/Xr19PQ\n0JBbtmwpkX6Q5O7duzl+/PgS3fo0JyeHf/75JxcsWEAnJyfWrl2bN2/eZGhoKAFQJBLlmwfvW6DM\nle7WrVvZqVMnbty48at6NcrMzOSZM2cE+9r58+cpFosLDDGqWrUqr1y5ku+5nJwcXr16lTt37uSO\nHTuYlpbGihUrfrDM8f3n/Qz7W7Dh5cfcuXPp4uJS1mJ8Fnfv3qVYLOahQ4c4cuTIEtu9rmvXrhw4\ncGChZcaNGyfsyBUfH8+YmJhit+Pt7c3Ro0dTU1OTQG5i02nTpn2SzHfv3uX06dO5c+dOBgQEcOXK\nlXRwcCAAjho1in/++adwL0ulUqqqqn4TOQsLosyVblZWFp8/f05bW1sCoIuLy1c38yVzZ2cAClzv\n7eHhwV69en1wXCqVslq1aqxTp44Qr3vkyBHeuHFD2IxFJpMxPDycK1asoLq6OhUVFQUF/Pvvv39z\nezB06NCBc+bMKWsxPpu5c+dy2LBh3Lp1K52cnD5ISlkcZDIZly5dSlNT04+GDiYnJ9PZ2Zk1atSg\nhoYGO3ToUKy2kpOTqaCgwOXLl3Pv3r28dOkSg4KCKBaLi70Z+MqVK2lsbMzZs2cL9+z48eN57ty5\nAicN7969+2YnFGQ5ULr/5unTp4I9Kzs7u8Tr/5LExsYSAG/evJnv+YSEBFaqVOmD3anCwsJoYmJC\nMne2/MMPPxQaDnbjxo0CZ8CWlpbs2bMnr127VnIdKwNu3rxJY2Pjshbjs9m8eTOHDh1KiUTCfv36\n0d7e/pMV740bN6ijo1OkrAlk7mTmypUrvHTpEmvUqFHkdjZs2EAzMzNOnjw5z3GZTMZhw4bRxMSk\nyDPnw4cP09bWli9fvqSHhwfV1NQI4KuzOZc05UrpkqStrS07d+781SldMndbSCMjI86ePfuDXZLI\n3BAofX199uvXj3PmzGGnTp0oEom4evVqymQyAmDHjh0LbeP48ePs3r07N2zYwPDwcNaqVUtQuu7u\n7sL/V69ezY4dO3LWrFlf3Uw4PT2dTZs2/erDhh4/fkwjIyM+fvyYMplMMKMVl6dPn1JfX5/t27cv\n9rWRkZE0NTX96D0gk8m4aNEiVq9enffv38+3jFQqZYMGDbhw4cIitT1r1iwhRb2NjQ0bNmxIW1vb\njz54jh8/Tjc3tzJPlf6lKHdK92snJiaGvXv3pp6eHnv16vXBJjWRkZH85ZdfOGPGDB44cICxsbEk\nKSjdwpxxZN4lyWFhYYJt7P1n6NChHDduHLds2SKEthVnKWt5QCaTUUNDg6mpqWUtymezZcsWmpmZ\nMS0tjbdv36ahoSEDAgKKdO3r16+5cOFCAuAvv/zySe3LZDKam5vz+fPnBZZJSkrigAEDaG9v/9FZ\nbGhoKE1MTCiTyZiRkVHovfX27VtaWVnxxIkTPH36NOfPn8/Q0NCPymxlZSVk9/gWkSvdL0Rqairn\nzZtHY2Nj2tvbc926dYyOjmZwcDDXr1//wRZ0Dx8+JACGh4fz5s2bvH79eoEzgqSkpA9sXunp6YIC\n/9oJCAigjY1NWYtRYnTt2pXbtm0jmes4btasWaHlX79+LWzWXa9evSKbFAprf9++ffmeCwgIYIsW\nLdi2bdsiPeSePn1KJSUlXrx4kQoKCh916l27do1GRkY8ceJEkWS9f/++YILQ0dH56t7SioJc6X5h\n3tvW+vfvTz09PVpbW3PEiBE0MTHh+vXrhXIrV64UvMRWVlasWbMmjY2N881Q8C0jk8n4yy+/cMCA\nAWUtSonh6+tLc3Nznj9/noGBgQXO4JKSkrh//342aNCA3bt3/yzH27/x9PRks2bNPtif9sSJE9TR\n0eGCBQuKvNdDeHg4bWxsaGlpyU2bNtHQ0PCj1/z99980NjYuUsjkhg0bCIB16tT5KvIVfgpypVuK\nSMotwcoAACAASURBVCQS4ckdGRlJW1tbjh8/nmfOnMnXfjljxgy2a9eutMUsU8LCwgiAV69eLWtR\nSpTz58+zYsWKnD59OgHkWfTx4sULrlmzhiYmJnR1daWnp2eJxqhKJBI2bNjwg72gt2zZwuHDh39y\nvTKZjIaGhkVaHTlnzpwiLV2PiIgQTGV2dnb87bffvrl9RgpTut9NCvbSQklJSUiQZ2VlhcDAQKio\nqGD+/PkwMTGBhYUF2rZtK5TPzMxEs2bNykrcMqFy5coYNWoU/Pz8ylqUEqV169YIDg7G0aNHAQDe\n3t6YN28eatasCXt7e4SEhODw4cPw8fHByJEjoampWWJtKykpoU+fPrh8+XKe42KxGJGRkZ9cr4KC\nAjp27AhPT0/07dsXKioqSExMzLdss2bNEBgY+NE6ra2thd9IaGgoJk2ahNWrV+P/2jvzuKjq7o9/\ngGEQZZ+BAWQXEET2RVnEpTRcc8HEfLLSbFEzNUvN0kxFs5/7GpQ89rgQWikm4gomLggIAiICLiAg\n+74OM/f8/uhxnghRhIGZoft+vfhj7nK+ZxY+93vPPd9zGIbptJ+KBCu63Qyfz8fWrVsRHx+PpKQk\nvP766zh//jxOnToFAMjPz4eOjs4L7RQUFMDd3R0+Pj6YP39+uz98RcHOzg43b96EWCyWtStSxcjI\nCOfPnwcAfPLJJ0hKSkJISAjy8/Nx5MgRWFtbY+vWrZLvX5qMHDkSJ0+eRH19vWRbfHw8bGxsumR3\nwoQJSE1NxalTpyASiWBtbY2jR4+2OY5hGHC53A7ZXLVqFQBg7dq14PP5WL16NaZNm9YlPxWG9qbA\nxIYXuo3r168TADp+/DjZ2dnRqVOnnnkcwzC0adMmSSdU/CWDoW/fvhQcHEwXL16kgoICyszMpIiI\nCFq+fDmtX7+e7ty508Pv6uWorKwkZ2dncnNze2bqnSJTU1NDAOjtt9+my5cv0549e2j69Omkq6sr\n+R67azXWW2+9RStWrJC8njVrFh08eLBLNq9du0YAyN7enhiGofj4eDI2Nm4Vv7127Ro5ODjQhg0b\nnmtLLBaTlpYWXblyhWbPnt0mD13RUwifAjamK3+4uLgQABo9ejT5+vpSdXU1ERHFxsZSXFwcMQxD\nCQkJZGRkRJcuXaKSkhJSV1cnAFRXV0d//PEHubi40LBhw0hXV5c4HA6Zm5tLfrybN2+W8Tt8MQzD\n0Pz582nEiBFSe6AkLxQUFJCnpyd5eHjQ+++/T6GhoZSbm0tERO+++y5999133TLuvXv3SCAQkEgk\nkiyy6WqJ0MjISALQqh7Ipk2byMTEhJYtW0Y7duwgY2NjOnTo0AszEd555x0C/uzQfOzYsVYrLy0s\nLHrNKjVWdOWQjIwM0tTUJFVVVfL19SUVFRUaPHgwASAVFRUKCAggIyMj+vTTTyXnvPLKK8Tlctv8\nsJ88eUIPHz4kIqJff/2VvLy8aMiQIQrRmaCpqYneeuutXr0O/68IhUISCATd2oDU1dWVQkNDJeUi\nAdAPP/zQaXtPs244HE6r39ShQ4cIANnY2HSocNHrr7/eSrxbWlpIU1OT+Hw+ubu7EwDatGlTp/2U\nJ1jRlWN27NjR6vbqaUHr+fPn0+HDh1sd29TU1KEqUFlZWTR16lQyMDCga9eudZfrUqOgoID09PSo\nuLhY1q50O4cOHaIRI0Z06xh//PEHAaC5c+eSv78/bdy4kYyMjDq9AtTT01OSafDXgv41NTW0fv36\nDt+lODk5UUhIiOT102pj+G+Bp6VLl1JgYGCnfJQ3WNGVc0QikWTW8NNPP9HevXtpzJgxXY5v/fjj\nj6SqqtpmkYY8sm7dOhIIBC9V0JxhGMrJyaHr169TQkKC3CfZt7S0kL29fadrLr8MxcXFJBaL6Y03\n3qDdu3cTh8PpdDPMiooKWrduHQEgbW1t2rZt20t/1idPniQPD49Wv8W/TjjGjh0rsd8bYEVXAdHS\n0qL79+93yYZQKKSdO3eShoYGaWpqkpqaGo0YMYL279/fJoleHkhOTiZLS0uaM2eOJG+TYRgqLCyk\nmzdvUlFRETEMQ7m5ubRs2TIyMjIiU1NT8vLyIjMzM1q6dKmM38Hz2bVrF40cObJHLw5RUVHE5XJp\n5syZLzVueXk5bdy4kQ4cOEB37twhsVhM3t7etHLlSnJ2dqb58+d32NaZM2dIX1+fJk2aRAKBQFIH\nuKWlhSorK+nEiRMS8e0tRc1Z0VVApNkZuKqqiu7evUtVVVUUGRlJfn5+9Nprr8llec2amhqaOXMm\n2dvb0xdffEHOzs7E4/HI1dWVtLW1SSAQkLa2Ni1btqxVic3Kykri8/m0ZcsWWrhwIdna2koeTsoD\n6enpxOfzZVJPujMPp9LT0wkADRs2jPh8Pl25coWWLFlCn332GdXU1JCZmRndunXrhXY2b95MZmZm\nFBsbS83NzRJx/Tv379+nq1evvrSf8gorugqGWCzuUGGcztLc3Ey+vr6dLljd3TAMQ1FRUfT555/T\nuXPnWs3K79+/3279gJiYGHrzzTdp7dq1NHToUHJ3d5eLSnYlJSVkZWWlcL3gdu/eTSYmJvTtt98S\nn8+n4OBg6t+/P5WWlpK/v/9zOyGXlZXRnj17yNTUlPLz84noz9rD3t7ekiyO3gwrugqEWCym4cOH\nk7e3d7fmLKanp5ORkVGvyYv8O08rb2VmZsrUj+zsbHJwcKAvv/xSpn50ln//+99ka2tLv/32GxkZ\nGZG7uzv5+fnR5MmTacGCBW2Ov3//PkVHR1OfPn1oxIgRrWb2H3/88QuzVBiGUcieeX+HFV0FQSQS\n0bJlywhAt9/6MwxDLi4ubQpZ9xYeP35M6urqMruoMAxDu3btIh6PR3v37pX7h3ztwTAMbd68mSws\nLOjGjRs0ffp0UldXJy6XSwBalX08e/Ys9e3bl7hcLoWFhbWx9bS62IULFygyMpI2bdpE0dHRFBMT\nQ1ZWVhQSEkIXL14kALR48eIefJfShxVdBeHAgQPUr1+/Lpf56whisZhMTExo0qRJCt0csj3i4uLI\nxsZGJmOLxWJ6++23yc3NrUO1ZRUBHx8fCg8PJ6I/V59paWm1uWAvW7aMgoOD240hb9u2jQDQjBkz\nntkVJTk5mY4cOUIAyMnJSXKePD70fRHPE1229oKc0NzcjB07duD777+Hr69vt4+npKSEoKAglJeX\nw8LCAv7+/ggODu5ScRR54tdff8WkSZNkMvaKFSvw8OFDXLlyBba2tjLxQdps3LgRH3/8MVJSUuDt\n7Y3JkycjIyPj6eQMANC/f3+kp6dDRUWlzfkikQinT59GWFgYbt68CQDw8fGR7M/OzoaLiwv8/f0B\nAGPHjkVycjLGjx8PfX19WFhYYPv27d38LnuI9tSY2Jluj5Kbm0sAKDU1tUfHbWxspMDAQDp58iR9\n9NFHxOPxaNOmTQpfD2Hx4sX04Ycf9uiYDMPQ1q1bSV9fv1fEJf/Ovn37aNSoUcQwjCS74ccff5TM\nRKuqqsjExOSZoYUtW7bQkCFDqLi4mDQ0NOi9995r1Xbqr0RHR1NhYaGk5sPAgQNp2rRpxOFwSCwW\nU0hICHl4eNCrr75Kx44d63AjzZ4M8YANL8g/paWl7abT9CTHjx8nAMTj8WjAgAE0duxYCgwMVLhG\ng2VlZWRmZtZuN4XuYO/evaSvr9+tS3xliVAoJCcnJ0nh8XPnzpG5uTktX75ccsz27dvJ2dmZFi1a\nRPv27aPo6GhqaGigBQsW0JYtW+jXX3+lAQMGSFr1vP7665JzY2JiKDg4mObMmUMAaPDgwZLiUE//\npk6dSq6urnThwgWKiIggf39/6tu3LwUEBFBERES7AhwYGEgA6PTp0937If0XVnQVgLS0NNLV1e1R\nkXgWDMNQU1MTicViSklJoVOnTtGePXvIyMiIVq9erVAPhFJSUsjQ0LBHOnMIhUIyNTWl2NjYbh9L\nlmRnZ5OhoSGdPHmSiIiOHj1KU6ZMkewvLy+nH374gWbOnEnTp08nR0dHMjU1JQB07do1STdw/LfG\nCABJb7elS5cSAEkdBgA0aNAgevPNNyWveTxemwp6DQ0N9J///EfS/ujDDz+kxsbGVsfk5+dTnz59\nCABdunSpmz8lVnQVghUrVtDChQtl7Ua7FBUVkaenJw0dOpQWLVokVwsPnsfNmzdJX19fkivaXaxb\nt47GjBnTrWPIC3FxcWRqakoLFy6k4ODg5+Z7i0QiioiIoOvXr0su2MOHD281e50wYYLk+L9m7bS0\ntFBVVRXV1tbS8ePHKTg4mLy9vUlHR4fef/99KioqajNeeXk5BQYG0rRp00goFNLVq1dp165d5O/v\nTwBIWVmZdu7cSW+88Ua3ZgixoqsA7N27lzgcTpsrtDwhFAopKiqKRo0aRdu3b5e1Ox3m3Xff7XBL\n8c7i5OTUIzUV5IXq6moaMWIEqaiokJaWFrm7u9Nvv/1G1dXVFB0dTampqe1mHSxfvpxcXV0pLy+P\nRo8e3aE7kZKSEgJABw8epKKiInr33XdpxowZzzy2qqqKhg0bRgBISUmplcBHR0dTRUUFAaChQ4d2\neal9e7CiqwAUFxfTxIkTafjw4XJ/C//zzz+3mp3IO3FxcWRgYNCtq9PmzZtHo0eP7jb78ohYLCZD\nQ0MCQEFBQa3EzcrKilxcXOjy5csdqoz3IsrLy4nL5UpWsyUnJxPQtuh5QUEB3bhxg7KzsyVlJMPC\nwmj//v2t7s42bNgg8fXzzz+nffv2SfX/jhVdBUEsFktqocozhw8fbhXHUwScnZ3p4sWL3WZ/586d\n/5iawH+FYRj6z3/+QzNmzCBvb28yNzcnCwsLmjJlSisR/v777yUNO5ctW0Y+Pj7k4+NDACg2Npay\nsrJo7dq1ZGNj06EaDCYmJgSAvLy8JM049+/fT7q6umRvb0+2trY0a9YscnZ2brWA4+++h4WFSWLJ\nTk5OUrsjYkVXgUhOTiYjIyO5DTMIhUIaNWoU7d27V9auvBRWVlZ04cKFbrN/+fJlsrKy6jWdDzrL\n03SyiIgI+vHHH+nzzz8nf39/OnjwYKuHYX8V5M8//7zV646UIj1x4gTt37+ffvnlFzIxMaH+/fsT\nn8+nAwcOEBHRnDlzaOLEieTn50cAKD4+/rn2QkJCSENDgwBIZUELK7oKxvjx42nPnj2ydqMNQqGQ\npk2bRuPGjVO4mg3z5s1rldrUHfj6+tK6deu6dQxFhmEYqq2tJU1NTQJAx44dI6FQKMlkaK9q2Ysu\nZI2NjRQTE9OqF1xzczO988471K9fP4mY37x5s10bqampknq+Pj4+XV4Fx4qugpGYmEhGRkYdTvru\nKVavXk1jxoyRy5KQL+LWrVtkbW3drWOcPn2aXn311Xb3Mwzzj+iO0RHKysokwpadnS2pn/x3nnaX\nMDY2psjISLp9+zadPHmyjSg6OTkRgDYCXVpaSufPn6eNGze2W53ur4SEhNDAgQO7/BtnRVcBmTFj\nhlxVpoqJiSE+n6+wZfm++OILmjp1areOceXKFRo6dGi7+48ePUoAWtUBZmmfiIgIUlNTIwA0adIk\n8vX1lTy4exrHJfrzIRsAMjU1ldSHkDWs6CogBQUFBKDTLVakSV1dHampqfXYah5pc+PGDTIxMXlm\nXqc0uXPnDtnb2z9zX2pqKqmpqZG5uXmPL09WFJKTk2njxo2UmJhIRUVFBICOHj3aalb7tOHmkydP\nJNvi4uLI0dGR9u/fT5MmTZKLhqzPE1224I2cYmxsDD8/P+zZs+fpBVBmVFZWgsfjYdy4cTL1o7Mc\nPXoUH3zwAQQCQbeOo62tjcePH7fZ3tjYiK+//hoCgQAHDhxAYmJit/qhiISFhcHV1RV//PEHxo4d\nC0NDQ4wfPx5BQUFQVv6fTO3btw979uyBoaGhZJuysjJUVFTg5+eHyMhIuLm5obKyUmq+TZkyBVev\nXpWaPXamK8fk5uaSl5cXDRs2jNLS0mTmR3FxMfF4PJmN31WsrKwoMTGx28cJDQ2l4cOHS14/evSI\ntm7dSvb29jRt2jSqqamh0aNHEwCFLFfYHQiFQtq2bRupqKhQcnIyEf15V7Bhw4ZnLqlevnw5CQQC\n2rp1q+Qz/O2338jf35+IiGpra8nBwUGqC1UAkIuLS4eOFYlEFB8fz4YXFBmRSCQppDJr1ix6+PBh\nj/tw7do18vDw6PFxpcHT0Eh3LjjJysqiTz75hPT19SXCQUQ0ZcoUGjp0KJ06dUrygOeVV16hcePG\ndZsvikRLS4ukLsPLFNNPS0sjHx8fcnNzo8uXL9OMGTNarZD87LPPpJoz/dtvvxEAKisre+5xT548\nIQ8Pj78WrmJFV5Gprq6mhQsXkoWFBaWkpPTo2Js2baJ33323R8eUFgzDUJ8+fV74D9NZQkJCiM/n\n08qVK1uVw2QYhjgcTqsSjwzDEJ/P77VVyF6W+Ph40tHRoblz5750Kczm5mYKDQ0lAwMDsra2bvX9\nFhYWEpfLleqF9kW2GIaRiG1YWBgrur0FhmFo/fr1PT5T8vb2pnPnzvXomNJkwYIFFBgYKFWbIpGI\nFi5cSJaWlu2KqI2NTauk/KSkJLKyspL7Zd49QX19Pbm4uNC3337bJTsikahNqEYkEpGRkREFBwe3\nm4rWHezbt49Wr15NTU1NrOj2JtLT08nOzq7HxktOTiYej9dtM8WeoLGxkXg8nlSLm6xZs4Y8PT2f\nW21t2rRp9P3330tef/rpp7Rq1Sqp+aBICIVCSkhIoM2bN5OLiwtpaWnRu+++220XoIyMDJo6dSoN\nGDCgU3eGGRkZNHz4cEpKSurU+Kzo9iIeP35MxsbGPTael5cX/fTTTz02XnexfPlymjt3rtTsbdy4\nkd54441298fGxpKqqiqVlpYS0f960v21O+4/gbq6OoqOjiYLCwuysrKiWbNm0e+//95jF/GFCxeS\nq6vrS4v705RN4M/uGC8LK7q9iIyMjB5puNjc3Exbt24lXV1dqq+v7/bxupuqqiqysLCgs2fPSsVe\nbW0tAWg393f27NmtlgTfuHGjR+9Q5IHm5maJcEVGRsrEB7FYTH379iUHB4eXztO+e/eupDTkkSNH\nXurc54kum6erYOTk5MDGxqZbbFdVVWHPnj1wc3ODpqYmvvnmG8THx6Nv377dMl5Poq2tjZkzZ0qa\nInYVDQ0NAEBtbe0z95uamqKiokLyOioqSmaNMmVFcXExAODRo0eYOHGiTHxQVlbG5cuXcefOHYSH\nh7/UuXZ2dmhqasKxY8fw2muvSc8nqVli6RGysrKkLroikQirV6+Gubk5zpw5g23btqG2thaVlZXd\nJvCyQE9PD6WlpV2209DQgNDQUAD/E9+/ExAQgMuXL4OIIBQK8csvv2DChAldHluRuHDhAkaNGgVz\nc3OZ+uHh4YH4+Hhs3LgRYWFhL3Uul8tFYGAg9PT0pOYPR2qWWHqEzMxMuLu7S9Xmxx9/jLt37yIz\nMxNGRkZStS1PeHp64tixY12yERMTg+nTp8PHxwdxcXGtVkb9FXd3d7S0tMDDwwN5eXnw8vKCn59f\nl8ZWNIyNjXHp0iUUFhbC2NhYpr54eXnhjz/+wMiRI2FnZwdvb2+Z+cKKroKRm5uLadOmSc1eRUUF\nDh06hIKCAmhpaUnNrjzi7e2Ne/fuobS0FPr6+p2ycfLkSTg4OCAyMvK5x6mrqyM5ORnnz5+HpaUl\nBg4c2KnxFJnvvvsOAJCYmCgXoRVbW1vMmTMHp0+flqnosuEFBePx48ftzq46A4fDgVAohKamptRs\nyitcLhejRo3CTz/91GkbSkpKGD16dIeOVVFRQUBAwD9ScAFIYqj29vYy9uR/aGpqoqCgQKY+sKKr\nQNy4cQNCoRCDBw+Wms2ffvoJbm5uUFJSkppNeWbTpk3YsmULLl269NLnPnnyBL///juGDRvWDZ71\nPvh8PubPn4/g4GBZuyJhypQpOHfunEx9YEVXgcjLy4Orqys4HOlEhWJiYrBp0yYcOHBAKvYUAVtb\nW+zevRuLFy+GWCzu8HkVFRXw9vbGv/71L/j7+3ejh72Lb775Bj///DPq6upk7QoAID09HXZ2djL1\ngRVdBSI/Px+6urpSsVVRUYEPPvgAe/fulavbv55gypQp0NPTw65duzp0PMMwWLBgAcaNG4c1a9b8\nY+4KpAGPx4OVldVLl7MkItTV1aG4uFiqpU1v3LiBESNGSM1eZ2BFV4EQiURSediVkZEBHx8fTJky\nRS4ecPQ0SkpKCA0NxcqVK9HU1PTcY5ubmzFr1iwUFhbi//7v/3rIw97FJ598gh07djz3GCLC9evX\nsXfvXsybNw9mZmYwMDDAwIED4enpiWXLluHhw4dd9iUuLg7Ozs5dttMVWNFVIFRVVVFfX98lG0Kh\nEPPmzcO8efPw7bffSskzxUNFReW5gltfX4/ff/8dPj4+aGlpwdmzZ3vFIhFZoKWl1e4iEuBPIbS0\ntMTs2bORkJAABwcHnD9/HvX19aioqMD69etRVlYGKysr5OXlobm5GZMmTUJ2dvZL+bFgwQLU19fL\nvhh/e0vViF0GLHe88sordPz48Q4ff+/ePbKxsaGIiAiqqKig2tpaeuONN2jixIkK181X2jwtxbdt\n2za6dOkSLV26lGxsbEhHR0dS43Xo0KF06NAhtipYF9HW1qZp06YREVFFRQV99dVXZG9vTwYGBjRo\n0CBSV1en0NDQ537ODMPQmjVryN7enlxdXQlAqxq6L+Lq1avUv39/qTV7ra+vp7Nnz7brM9jaC4rP\n3bt3SSAQvFSX0ry8PMnadxUVFeJwOBQYGNgrailIg9DQUBo0aBBZW1vT2rVrJb25MjIyKD8/X9bu\n9Rp+/vlnMjMzIzs7O+Lz+TR58mSKj4+nvLw8SklJadXv7HnU1dWRs7Oz5DcNgBobG194HsMwNGHC\nBNq7d29X34qEhIQEAkBeXl7P7PDMim4vYMmSJbRy5cqXPu/27dukra1N+fn5HWpBzcLSHTQ0NFBq\naqpUymtmZGTQypUrSUNDgzw9Pdu0Xf87iYmJZG5uLvXJxnvvvUcAyM/Pr00D2eeJrhI958mgkpIS\nPW8/S88QHx+PoUOHdqruQk1NDUxNTREVFQVfX99u8pCFpechIgwePBjbt2+XLFghIlRXV0NHRwdE\nhKSkJOzevRtcLhchISFSHZ9hGJiZmaGgoAD29vYICwtDTk4OZs6cCRUVFRDRM9Nc2GXACkB1dTXc\n3d07JLipqamoq6tDZWUlsrKy8O9//xtBQUGs4LL0OpSUlLBp0yZMnz4dDg4O8PPzw4ULF3Dr1i2E\nh4fju+++Q11dHaytrbFhwwapj6+srIxFixYhNjYW/fr1w9SpU1FYWIjJkyc/3292piv/ZGZmwtvb\nu0NtpY2MjFBUVISAgACYm5tj8uTJGD16NFRUVHrAUxaWnqeurg7x8fGIjY0Fh8OBQCBAWFgYZs+e\njfnz53dbXnVmZibs7e2xfft2+Pv7w83NDUFBQTh69CiUlJTanemyoqsAbNy4EXl5edi3b98z99fU\n1ODEiRNwdnaGn58foqKi2KWqLP9IioqK8P3332PIkCFobm5GU1MTZsyYIfVxTp8+jVOnTkEoFCIy\nMhITJ06Euro69uzZAyUlpeeKLhteUABSUlIwaNCgP4Pwz7hqh4eH44MPPgAABAUFYdCgQT3tIguL\nXHD//n18/fXXcHV1hUAgQHR0NCZNmgR1dXWpjXHv3j0EBga2yvO+du0aEhMTOzSrZme6CsDTLzIs\nLAzvvPNOm/1paWlwcnIC8OfiB1VV1Z50j4VFbiAiDB8+HPPnz0dubi5WrFiB5uZmcLlcqdgvKCjA\nxIkTMWvWLNTV1eHkyZMIDg6Gn59fq4L27ExXgfnrRW/AgAHPPObMmTMYMmQItm3bxgouyz8aJSUl\nfPnll5gyZQoaGhqwefNmqQhuYWEhdu/ejZCQEMyZMweJiYkIDw9HQUHBSxdoZ5cByzkPHjyAuro6\n7O3t4eXl1WqfSCRCaGgo9u3bh2XLlsm0MDMLi7wwZswYPHr0CFVVVfjss8+6bO/69evw8PBATU0N\n4uPjUVlZiYaGBhw+fLhTnVbY8IKc8/XXXyM9PR3Hjx9vs+/LL79ETEwMAgMDMX/+fKipqcnAQxaW\n3o2amhqmTZuG3bt34+zZs1i0aBHu3r0LPp/f7jlseEFByc/Px9q1awH8WYClX79+rfZzOBwUFxdD\nKBRKLWbFwsLSmvXr1+Py5cswMTHBoEGDEBkZ+VzBfRHsTFeOEYvFMDExwaRJk7B///42T0bFYjE+\n+OADnDlzBvn5+Wyd138QIpEISkpKbP61nPK8mS4b05Vj0tLSoKmpie+///6Zgrp+/XokJSUhPDyc\nFdx/GKqqquBwONi6dausXWF5SdjwghyjpaWFxsbGNvm5DMNg8eLFOHLkCBITE2FhYSE7J1lkgo2N\nDXJycqCjoyNrV1heEja8IMeIxWJYW1vjxx9/xKhRoyTb8/PzYWtri9zc3Ge2Es/JycGqVavQ1NSE\n0tJShIeHw8zMrCddZ2H5R8OGFxQUFRUV+Pr6Yvfu3Xj06BGAPwX1vffeQ2NjI5YvX/7M87799ltE\nREQgMjIS169fl5umgCwsLOxMV+4pKiqS5AK+8soruHr1KtTV1eHs7AyhUIjz58+3aSPT1NSE6upq\nqKiogMfjsfFeFpYehp3pKjCGhoaSTqoXL15EU1MTjh8/jvPnz8PIyAj9+vXDyJEjceXKFck5ffr0\ngUAgAJ/PZwWXhUXOYB+kKQDu7u4QCoU4fPgwQkNDMWXKFDg4OEhKPcbGxna5YSULC0vPwIYXFJDG\nxkZcv34dampq8Pb2hrIye8PCwiJPsPV0WVhYWHoQNqbLwsLCIiewosvCwsLSg7Ciy8LCwtKDsKLL\nwsLC0oOwotuLSUpKwhdffIHo6GhZu8LCwvJf2DzdXsSjR48QExODOXPmtNqelJSEgIAAGXnFlEWA\nxgAAEbFJREFUwsLyV9iZbi+gsrISS5cuxcCBAyWC+9lnn2HPnj1IS0tjZ7pyilgsRm5uLtLS0lBY\nWAiGYWTtUofIy8vDoUOHUFBQ8NzjxGIxysvLpTbuo0ePsHTpUlRVVUnNpixg83QVmNraWmzatAl7\n9+6FmZkZLCwsEBkZifT0dDg4OMjaPZZnQES4ePEivvvuO1y9ehU6OjrQ1tZGWVkZlJSUcPjwYbzy\nyis94kd2djY++eQTbNmyBYMGDXrhORUVFdi4cSPCwsLg6+uLK1euYPLkyViwYAF0dHRQX1+PvLw8\nxMXFISIiAiUlJQAAX19fBAYGwtjYGCNHjpTUCrl79y7Cw8PR0tICc3NzNDQ0oKamBvr6+ujXrx8y\nMzORn58PoVCIxsZGpKSk4PHjxwgKCsKqVasgEonQ0tKCuro6iMViMAwDDocDLS0tODg4SLXt+svC\nLo7opYwePRp9+vSBiYkJfvnlFyxYsACenp4ICAhgV6nJAUSE9PR0nDt3Dg0NDYiOjsa1a9dgaGiI\ndevWITAwsFU93LCwMKxevRo6OjpwcHBAUFAQLCwscOHCBaSkpMDR0REWFhYQCATQ1NSEm5vbC2tr\niEQi3Lx5E3v37sWNGzfA5XJRWlqKiooKaGhowMXFBbdv3waXy0VDQwM4HA5UVVVRV1cHGxsbCIVC\nNDQ0oLy8HBwOB7NmzcLy5cthbm6OyspK7Ny5E+Hh4Whubkbfvn1hamoKFxcXBAUFwdLSEurq6jhw\n4ADi4+ORk5ODpKQk6OnpQUlJCUKhELNnz4ampiby8vLQp08f6OjooLS0FDU1NbCzs4O5uTnU1NTQ\np08fGBkZwdbWFkuWLJG8Fy6XCw0NDaioqEBFRQVCoRAlJSW4e/cu0tLSMGjQIJn8L7Ci20sgIiQm\nJoJhGHh4eMDFxQXffPMNVq1aBScnJ6xYsQIuLi6ydrNXQ0QICQlBXFwcXn/9dYwfPx4qKirgcrmo\nqalBXFwcoqOjkZGRgbS0NPTr1w+vvfYaNDU14ePjgwkTJkBFRaVdsRSJREhJScGtW7dw7NgxPHny\nBB4eHvD19cW9e/eQm5uL4uJiPHz4EB9++CFWrVr1TDsMw2D58uUIDQ2Fqakp3n77bYwfPx4ikQgG\nBgbg8XggIqiqqkIsFqO0tBR9+/aVzB41NDRw584daGhoQF1dHTweDy0tLeDxeF36/Jqbm1FcXAyG\nYdC/f3+oqqp2yd6zaGlpwSeffILo6GhUVVXBxcUFlpaWMDc3h6WlJSoqKlBUVISgoCA4OTl1S1Eo\nVnR7CVu3bsXOnTvR0tICT09PlJWV4dy5c6iursaOHTtw5MgRzJ49G+vXr5e1qzKDiBAbGyuZsQ0Z\nMgQAcPv2beTm5sLCwgIlJSXIz89HY2MjVFVVIRAIYGhoCE9Pzzb/gAzD4MmTJ4iJiUFaWhqSk5NR\nVFSE+fPnIyQkBOnp6dDW1sbkyZMRFRWFAQMGYNy4cXBxcYGDgwNMTEyk/k9dXV2NtWvX4pdffkFu\nbq5k2w8//ICSkhKUlpYiOTkZurq6OHToEIyNjaU6viJRVFSEtLQ0PHz4EI8ePUJubi5UVVVhZGSE\nI0eOoKWlBf3794eZmRk++ugj+Pr6SiUswYquglNfX487d+5gxYoVeOutt+Dl5SX5u3jxouT2qby8\nHM7Ozli1ahU++ugjGXvdOZqbm1FeXg59fX2UlJTg9OnTGDJkCJycnFBRUYHCwkKIRCLY29ujrKwM\nGRkZePToEbKzs5GUlIQ7d+6Ax+OhX79+qKiogIWFBYRCIQoKCjB48GBERUXBw8MDdnZ2UFdXh1Ao\nRHFxsaSltlgsRm1tLRiGgUgkQlFRETQ0NDBs2DB4eHjAxsYG48aNa9WZOTs7G1FRUXBycsLIkSO7\n/TPy9fXFtWvXEBISgnnz5iE9PR3vvPMOLC0t4ebmBn19fQwYMAB+fn7dMpPsLRARcnNzUVRUhKSk\nJBw8eBAPHjyAv78/NDU1weVyweFwwOFwoK+vDxsbG3h5ecHKyuqFF1JWdLuJJUuWICMjAwMHDoSm\npib69++P5uZmvP/++23apXeWbdu2YenSpZLX9+/fh0gkwpIlS5Ceno5Fixbh008/lezPysqCp6cn\nKisrFSquS0Q4fvw45s+fL4nPFRYWYsyYMYiJiYGKigrU1NRgYmICsViM7Oxs6OrqwtHREZaWlrCy\nsoKHhwcGDRoEU1NTAH/eZh4+fBj9+vXDpEmToKam1qbf3FNaWlpw8eJFyYMtFRUVcDgcCAQCqX2X\n0mLx4sXYsWMHIiIi8OTJE6xbtw4rV67E4sWLFeo7l0cePXqEhIQE1NfXQygUQiwWo6WlRXJhTkhI\nQFlZGZKSkp778PF5otvr83RTUlLQp08fDBw4EEpKSigrK0Nzc7OkGwMRvXQb67S0NDg5OYHH46G8\nvBznzp1rtb+iogLvv/8+Hjx4gNLSUgwYMABVVVVYuXIl/Pz84OjoCKFQiNdeew0CgQBqamoAgIyM\nDDQ0NMDCwgJcLhdaWlqwsLCAsbExlJWVkZ+fjwEDBoDP50NTUxPKyspoampqNbatrS1sbW1x5MgR\n/Otf/+rCJ9f95Ofn45dffkFSUhJiY2Ohq6uLyMhIeHt7IzU1FeXl5fD39wfwZzeMp+JHRHjw4AEs\nLS2fKzKqqqp45513Wm1rb4aiqqqqMLnMU6ZMQUpKCrZs2QIzMzNcunQJjo6OsnarV2BhYdFuo1ci\nwldffYXQ0FDo6el1egyFnulWVlYiLS0NDMNIbicFAgH69u2L2tpaVFVV4dVXX5UcP336dFy9ehWF\nhYWSbW5ubggICICZmRkePXqEy5cvIygoCG+//TYaGxthaGjYZty6ujpoamrC2dkZ9vb2eOONN5CV\nlYV9+/ahuLgYYrEYPB4P1tbW4PF4yMnJgaqqKubOnYuioiLk5uZCLBbj7NmzqK2thbq6OqysrPDg\nwQNoa2ujrq4OQqEQTk5OMDY2RmZmJvLy8jBw4EAYGhpCIBDAzMwMvr6+GDVqlERIhEIh8vPz8cEH\nH8DR0VGu23OXlpbCwMAABgYG2LBhA/z8/CQXRhYWeaG2thaXLl3CiRMnEBcXBw0NDZw7d+6ZDWH/\nikKGF+Lj4yVT+JqaGty6dQulpaVoampCcXEx7ty5g7KyMjg6OoLD4aC+vh56enooLi5GY2MjNDQ0\noKOjg6qqKpSWlqKhoUESIK+vrwfDMNDV1cXEiRPB5/Px+PFjmJqawtPTE9988w3S0tKgpqYGIyMj\nmJqawtXVFcrKyigsLERVVRUePnyI1NRUAICpqSny8vJARGhoaICqqiq4XG6H3icRobKyEllZWbC1\ntZVcQUUiEaKjo9HQ0ABra2s4Ojo+Mz5HRHj8+DHOnz+PNWvWQEVFBQEBAfjuu++gpaUlpW9DOjzN\nDb1x4wbOnDmD8PBwrF69GmvXrpW1aywsrWAYBseOHcOXX34JU1NTvPrqq3j99ddhY2PTof9thRJd\noVCI69evY8SIEZg8eTJyc3NhaGgIZ2dnmJiYQE1NDfr6+rC3t4e1tXW3xLCICC0tLVBSUsLdu3eR\nl5eH5ORkKCsrw9DQEHp6emhoaJDMWj08PDB79myp+tDS0oKGhgaIRCJJKx4Oh4OmpiacPHkSN2/e\nxJ07d5CTkwMtLS34+flh0aJFkttxWVBbW4usrCw8fvwYhYWFqK6uRn19PYqKipCVlYXU1FRoa2tj\n6NChGDFiBAIDA184Y2BhkQVffvklfv/9dwQHB2PcuHEvfb5ciG5paSkMDQ0xd+5cqKuro6KiAtXV\n1Rg7diyUlZXx8OFDJCcnIz4+HqamptDU1MTx48f/sekub775Jo4ePSp53b9/f4jFYqiqqsLPzw9j\nx46Fvb097OzsoKGhIZUx23vI1B4NDQ3Izs7G9evXERsbi+joaFhYWMDMzAzGxsbQ1dVF3759IRAI\nJLN1VmRZFAE7OzusWbMGM2fO7NT5MhHdL774AkVFRZKlf8OGDYOamho0NDQQEBCACRMmgMPh4MyZ\nM1BXV4eFhQUcHR3h7e39j//HLCwsxJIlSxARESHZtmPHDixatEiq4zAMg4MHD+KHH37AvXv3UFtb\nC2tra0yYMAFffPEFtLW125xTX1+P9957D9euXUNxcTEsLS3h5eWF4cOHY8KECTAwMJCqjywssiAq\nKgqzZ8/GsGHDoKmpiX379r1UFotMRFdLSwu1tbXQ0tJCTU0NBAIBYmJicODAAZw6dQpNTU3Q09OD\njo4OeDwe1NXVoaGhAQcHB3h6esLFxaXDcdHexq5du7Bo0SKsWLECQ4YMAY/Hw9ChQzucc1lZWYk7\nd+7Ax8fnmeEXIkJOTg6WLFmCkpISfPXVV/Dy8oKmpiYyMjLw7bffgs/nY9++fW3O/eOPPzB8+HDM\nmjULxsbGqKqqQnV1NRoaGvDRRx916laMhUUeEIlE+OabbxAdHY3m5mbJMxsAuHbtGry9vTtsSyai\nKxaLceLECZw7dw4ikQgCgQDBwcGSfffv30dtbS2Ki4tRUVEBsViMmpoaREZG4sKFC3BwcEB6enqn\nxlZ0qqurcfDgQaxevRpWVlYwNTWFgYEBrK2tMXjwYHh5eUnuBsrLy8Hn86GhoQEejwcul4vs7GwA\nwMKFC7Fr1y6J3ZKSEgwYMAB1dXVQUVHBwoUL8dFHH6G8vBy5ubl48OABMjMzERUVhcGDB+Py5ctt\nfGtpacHmzZuRkZGBmpoaKCsrQyQSISoqCgBw+vRpVnhZFA6GYfDZZ5/h8uXL2LlzJ9TV1WFsbAwD\nA4NOZdTIRUz3KZ6enkhMTGy1rW/fvhgwYADy8/MlbcXnzp2L8ePHS3VsRUMoFCIpKQnFxcUoLi5G\ndnY2bt++jYSEBKirq8Pa2hr9+vXDjRs3UF1d3epcIyMjnDhxAl5eXpJtRUVFmDp1Km7fvg1VVVUI\nhUKoq6tDS0sLenp6MDIygpWVFdzd3eHu7o5ff/0VCQkJqKurA8MwqK6uRn5+Purq6tC/f3/w+Xzo\n6uqCz+dDX18fPB4P8+bNg0Ag6OmPioWlS9y/fx/W1tYwMTFBeHg4fHx8upS+KFei29zcjKSkJISH\nh+PcuXO4d+8eKioqcP/+fZiZmbExwQ5ARMjPz8eDBw9QVVWF8vJyFBQU4PHjx8jMzJSku7366qtY\nsmQJcnNzkZycjLS0NKSlpSE/Px8GBgawsbGBhYWFJGzR0NCA6upqFBUVoaCgAOPHj0dAQAB0dXWh\nrKwMDQ0NmJqaQl9fn82nZel1tLS04MiRI9iwYQMaGxsxfPhwDB06FB4eHhg4cCB0dXU7bKtLoltS\nUgJ9fX0UFhaCy+WCz+cjKysLubm5qK6uRmNjI1xdXTtVQu39999HaGgotm/fjoEDB2LMmDHsMkYp\n8FSUDx06hO3bt8PNzQ2enp5wdHSEk5MTLC0t/7HxchaWF0FEyMrKQlxcHG7cuIGkpCTk5ORATU0N\nS5YsgZWVFfh8Pjw9PaGlpYWqqio0NTVBIBBI9KtLoqupqQkOhwMlJSWIRCJoamoC+DOlQltbG6qq\nqkhISEBlZSVcXFxgZGQkWTFlbW0NY2NjyXp2DQ0NVFZWoqioCNXV1UhMTERCQgKam5uRmZmJBw8e\nQE9PTxKbLC4uRklJCfT09JCenv6Pz2pgYWGRDUSEW7du4ccff0R5eTmKi4tx69YtCIVC9OnTB1wu\nF7W1tbCysoK1tTUiIyM7L7oMw6CkpAQ8Hg8Mw+DBgwewtbVtMyMtKipCamqqJP6Ym5uLnJwcFBUV\nSZ5w19bWQldXF0ZGRtDW1kZDQwPq6upQV1eHiooKcDgccLncNi0+vLy8cOXKFXZ2xsLCIjfU1taC\nw+FIVrrW1dXhwYMHyM3NxaRJk+QnptseRITi4mI8efIEBgYGMDQ0fOlCNCwsLCzygFw9SGNhYWHp\n7TxPdNmnViwsLCw9CCu6LCwsLD3IC4uYs/mYLCwsLC9Nbns7nhvTZWFhYWGRLmx4gYWFhaUHYUWX\nhYWFpQdhRZeFhYWlB2FFl4WFhaUHYUWXhYWFpQf5f8/eR0eTkGgYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9c5d1bfc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
    "ax.coastlines()\n",
    "ax.set_global()\n",
    "plt.contourf(lons.clip(min=0, max=20), lats.clip(min=40, max=60), tasmax, 60, transform=ccrs.PlateCarree())\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write netcdf file with a given subset (boundingbox, time)\n",
    "\n",
    "One needs to create a new Dataset object and copy all the attributes and data needed. This is a bit lengthly.\n",
    "\n",
    "See the following example:\n",
    "\n",
    "http://schubert.atmos.colostate.edu/~cslocum/netcdf_example.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use nco to write netcdf file for a subset\n",
    "\n",
    "    ncks -O -d time,0,0 http://esgf1.dkrz.de/thredds/dodsC/cordex.output.EUR-11.MPI-CSC.MPI-M-MPI-ESM-LR.historical.r1i1p1.REMO2009.v1.day.tasmax.tasmax.20160419.aggregation -o test.nc\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use nco python wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nco import Nco\n",
    "nco = Nco()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "subset first timestamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/tmp/out.nc'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc_out = '/tmp/out.nc'\n",
    "nco.ncks(input=str(agg.opendap_url), output=nc_out, options=\"-O -d time,0,0 -d rlon,0,20 -d rlat,40,60 --netcdf4\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### show output with cdo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from cdo import Cdo\n",
    "cdo = Cdo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'File format : netCDF4',\n",
       " u'-1 : Institut Source   Ttype    Levels Num    Points Num Dtype : Parameter ID',\n",
       " u'1 : unknown  MPI-CSC-REMO2009 instant       1   1       441   1  F32  : -1',\n",
       " u'Grid coordinates :',\n",
       " u'1 : curvilinear              : points=441 (21x21)',\n",
       " u'lon : -13.1062 to -9.94999 degrees_east',\n",
       " u'lat : 26.0437 to 28.8984 degrees_north',\n",
       " u'available : cellbounds',\n",
       " u'Vertical coordinates :',\n",
       " u'1 : height                   : levels=1',\n",
       " u'height : 2 m',\n",
       " u'Time coordinate :  1 step',\n",
       " u'RefTime =  1949-12-01 00:00:00  Units = days  Calendar = proleptic_gregorian  Bounds = true',\n",
       " u'YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss  YYYY-MM-DD hh:mm:ss',\n",
       " u'1950-01-02 12:00:00']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdo.sinfo(input=nc_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'tasmax']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdo.showname(input=nc_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert to CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "client:\n",
    "\n",
    "* https://www.wakari.io/sharing/bundle/rsignell/cf_3d_to_1d\n",
    "* http://stackoverflow.com/questions/28420988/how-to-read-netcdf-file-and-write-to-csv-using-python\n",
    "\n",
    "server:\n",
    "\n",
    "* http://www.pydap.org/responses.html#excel-spreadsheet"
   ]
  }
 ],
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