examples/GCRCatalogs Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to use GCRCatalogs\n",
    "\n",
    "by Yao-Yuan Mao (last update: 7/27/2018)\n",
    "\n",
    "Links to GitHub code repos: [GCRCatalogs](https://github.com/LSSTDESC/gcr-catalogs) and [GCR](https://github.com/yymao/generic-catalog-reader)\n",
    "\n",
    "**Note: You should be running this notebook at https://jupyter-dev.nersc.gov **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Note: if you clone the gcr-catalogs repo and are running this under the `examples` folder,\n",
    "## you can also use your version of GCRCatalogs:\n",
    "#import sys\n",
    "#sys.path.insert(0, '/path/to/your/cloned/gcr-catalogs')\n",
    "\n",
    "## The following lines are to check if you're in the lsst group\n",
    "import subprocess\n",
    "assert u'lsst' in subprocess.check_output(['groups']).decode().split(), 'You need to be in the `lsst` group for this notebook to work'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The basics:\n",
    "\n",
    "- `get_available_catalogs()` lists available catlaogs; returns `dict`.\n",
    "- `load_catalog()` loads the catalog you want; returns an instance of `GCR.BaseGenericCatalog`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCRCatalogs = 0.8.0 | GCR = 0.7.2\n"
     ]
    }
   ],
   "source": [
    "import GCRCatalogs\n",
    "\n",
    "## check version\n",
    "print('GCRCatalogs =', GCRCatalogs.__version__, '|' ,'GCR =', GCRCatalogs.GCR.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buzzard\n",
      "buzzard_high-res\n",
      "buzzard_test\n",
      "dc1\n",
      "dc2_coadd_run1.1p\n",
      "dc2_coadd_run1.1p_tract4850\n",
      "focal_plane_0_test\n",
      "focal_plane_16_test\n",
      "hsc-pdr1-xmm\n",
      "protoDC2\n"
     ]
    }
   ],
   "source": [
    "## find available catlaogs, sorted by their name\n",
    "\n",
    "print('\\n'.join(sorted(GCRCatalogs.get_available_catalogs())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "buzzard\n",
      "buzzard_high-res\n",
      "buzzard_high-res_v1.1\n",
      "buzzard_test\n",
      "buzzard_v1.6\n",
      "buzzard_v1.6_1\n",
      "buzzard_v1.6_2\n",
      "buzzard_v1.6_21\n",
      "buzzard_v1.6_3\n",
      "buzzard_v1.6_5\n",
      "buzzard_v1.6_test\n",
      "cosmoDC2_v0.1\n",
      "cosmoDC2_v0.1_test\n",
      "dc1\n",
      "dc2_coadd_run1.1p\n",
      "dc2_coadd_run1.1p_tract4850\n",
      "dc2_instance_example1\n",
      "dc2_instance_example2\n",
      "dc2_reference_run1.1\n",
      "dc2_reference_run1.2\n",
      "dc2_truth_run1.1\n",
      "focal_plane_0_test\n",
      "focal_plane_16_test\n",
      "hsc-pdr1-xmm\n",
      "proto-dc2_v2.0\n",
      "proto-dc2_v2.0_redmapper\n",
      "proto-dc2_v2.0_test\n",
      "proto-dc2_v2.1\n",
      "proto-dc2_v2.1.1\n",
      "proto-dc2_v2.1.2\n",
      "proto-dc2_v2.1.2_addon_knots\n",
      "proto-dc2_v2.1.2_test\n",
      "proto-dc2_v3.0\n",
      "proto-dc2_v3.0_addon_knots\n",
      "proto-dc2_v3.0_redmapper\n",
      "proto-dc2_v3.0_test\n",
      "proto-dc2_v4.3_redmapper\n",
      "proto-dc2_v4.4\n",
      "proto-dc2_v4.4_test\n",
      "proto-dc2_v4.5\n",
      "proto-dc2_v4.5_test\n",
      "proto-dc2_v4.6.1\n",
      "proto-dc2_v4.6.1_test\n",
      "proto-dc2_v4.7_test\n",
      "proto-dc2_v5.0\n",
      "proto-dc2_v5.0_test\n",
      "protoDC2\n",
      "protoDC2_addon_tidal\n",
      "protoDC2_test\n",
      "um_v0.1\n",
      "um_v0.1_shear_test\n",
      "um_v0.1_test\n"
     ]
    }
   ],
   "source": [
    "## find *all* available catlaogs, including some older versions, sorted by their name\n",
    "\n",
    "print('\\n'.join(sorted(GCRCatalogs.get_available_catalogs(include_default_only=False))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "proto-dc2_v2.0\n",
      "proto-dc2_v2.0_redmapper\n",
      "proto-dc2_v2.0_test\n",
      "proto-dc2_v2.1\n",
      "proto-dc2_v2.1.1\n",
      "proto-dc2_v2.1.2\n",
      "proto-dc2_v2.1.2_addon_knots\n",
      "proto-dc2_v2.1.2_test\n",
      "proto-dc2_v3.0\n",
      "proto-dc2_v3.0_addon_knots\n",
      "proto-dc2_v3.0_redmapper\n",
      "proto-dc2_v3.0_test\n",
      "proto-dc2_v4.3_redmapper\n",
      "proto-dc2_v4.4\n",
      "proto-dc2_v4.4_test\n",
      "proto-dc2_v4.5\n",
      "proto-dc2_v4.5_test\n",
      "proto-dc2_v4.6.1\n",
      "proto-dc2_v4.6.1_test\n",
      "proto-dc2_v4.7_test\n",
      "proto-dc2_v5.0\n",
      "proto-dc2_v5.0_test\n"
     ]
    }
   ],
   "source": [
    "## find *all* available catlaogs whose name starts with 'proto-dc2', sorted by their name\n",
    "\n",
    "print('\\n'.join(sorted(c for c in GCRCatalogs.get_available_catalogs(include_default_only=False) if c.startswith('proto-dc2'))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/common/software/lsst/common/miniconda/current/lib/python3.6/site-packages/GCRCatalogs/alphaq.py:105: UserWarning: No md5 sum specified in the config file\n",
      "  warnings.warn('No md5 sum specified in the config file')\n"
     ]
    }
   ],
   "source": [
    "## load 'protoDC2' catalog\n",
    "\n",
    "gc = GCRCatalogs.load_catalog('protoDC2_test') # use 'protoDC2_test' to skip md5 check (which takes a while)\n",
    "#gc = GCRCatalogs.load_catalog('protoDC2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GCR interface\n",
    "\n",
    "See also the [full GCR API Documentation](https://yymao.github.io/generic-catalog-reader/index.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### quantities\n",
    "\n",
    "- `get_quantities()` loads the quantities you need; takes a `list` and returns `dict`.\n",
    "- `has_quantity()` and `has_quantities()` can check if the quantities you need exist; both return `bool`.\n",
    "- `list_all_quantities()` lists all available catlaogs; returns `list`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dec': array([-1.78091323, -2.06204367, -1.48433971, ...,  2.43747878,\n",
       "         2.49400425,  2.37360001], dtype=float32),\n",
       " 'ra': array([-0.21326847, -1.29391468,  0.68152297, ..., -2.39075041,\n",
       "        -2.46616554, -2.31633687], dtype=float32),\n",
       " 'mag_u_lsst': array([ 21.2349205 ,  21.83907318,  21.02371979, ...,  26.76194382,\n",
       "         33.03851318,  28.94227791], dtype=float32)}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.get_quantities(['mag_u_lsst', 'ra', 'dec'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.has_quantity('mag_u_lsst')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.has_quantities(['mag_u_lsst', 'ra', 'dec'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Only returns `True` if *all* quantities exist\n",
    "gc.has_quantities(['mag_u_lsst', 'ra', 'dec', 'quantitiy_that_does_not_exist'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A_v, A_v_bulge, A_v_disk, Mag_true_Y_lsst_z0, Mag_true_Y_lsst_z0_no_host_extinction, Mag_true_g_lsst_z0, Mag_true_g_lsst_z0_no_host_extinction, Mag_true_g_sdss_z0, Mag_true_g_sdss_z0_no_host_extinction, Mag_true_i_lsst_z0, Mag_true_i_lsst_z0_no_host_extinction, Mag_true_i_sdss_z0, Mag_true_i_sdss_z0_no_host_extinction, Mag_true_r_lsst_z0, Mag_true_r_lsst_z0_no_host_extinction, Mag_true_r_sdss_z0, Mag_true_r_sdss_z0_no_host_extinction, Mag_true_u_lsst_z0, Mag_true_u_lsst_z0_no_host_extinction, Mag_true_u_sdss_z0, Mag_true_u_sdss_z0_no_host_extinction, Mag_true_y_lsst_z0, Mag_true_y_lsst_z0_no_host_extinction, Mag_true_z_lsst_z0, Mag_true_z_lsst_z0_no_host_extinction, Mag_true_z_sdss_z0, Mag_true_z_sdss_z0_no_host_extinction, R_v, R_v_bulge, R_v_disk, bulge_to_total_ratio_i, convergence, dec, dec_true, ellipticity_1_bulge_true, ellipticity_1_disk_true, ellipticity_1_true, ellipticity_2_bulge_true, ellipticity_2_disk_true, ellipticity_2_true, ellipticity_bulge_true, ellipticity_disk_true, ellipticity_true, galaxy_id, halo_id, halo_mass, is_central, mag_Y_lsst, mag_Y_lsst_no_host_extinction, mag_g, mag_g_lsst, mag_g_lsst_no_host_extinction, mag_g_sdss, mag_g_sdss_no_host_extinction, mag_i, mag_i_lsst, mag_i_lsst_no_host_extinction, mag_i_sdss, mag_i_sdss_no_host_extinction, mag_r, mag_r_lsst, mag_r_lsst_no_host_extinction, mag_r_sdss, mag_r_sdss_no_host_extinction, mag_true_Y_lsst, mag_true_Y_lsst_no_host_extinction, mag_true_g, mag_true_g_lsst, mag_true_g_lsst_no_host_extinction, mag_true_g_sdss, mag_true_g_sdss_no_host_extinction, mag_true_i, mag_true_i_lsst, mag_true_i_lsst_no_host_extinction, mag_true_i_sdss, mag_true_i_sdss_no_host_extinction, mag_true_r, mag_true_r_lsst, mag_true_r_lsst_no_host_extinction, mag_true_r_sdss, mag_true_r_sdss_no_host_extinction, mag_true_u, mag_true_u_lsst, mag_true_u_lsst_no_host_extinction, mag_true_u_sdss, mag_true_u_sdss_no_host_extinction, mag_true_y, mag_true_y_lsst, mag_true_y_lsst_no_host_extinction, mag_true_z, mag_true_z_lsst, mag_true_z_lsst_no_host_extinction, mag_true_z_sdss, mag_true_z_sdss_no_host_extinction, mag_u, mag_u_lsst, mag_u_lsst_no_host_extinction, mag_u_sdss, mag_u_sdss_no_host_extinction, mag_y, mag_y_lsst, mag_y_lsst_no_host_extinction, mag_z, mag_z_lsst, mag_z_lsst_no_host_extinction, mag_z_sdss, mag_z_sdss_no_host_extinction, magnification, position_angle_true, position_x, position_y, position_z, ra, ra_true, redshift, redshift_true, sed_1000_246, sed_1000_246_bulge, sed_1000_246_bulge_no_host_extinction, sed_1000_246_disk, sed_1000_246_disk_no_host_extinction, sed_1000_246_no_host_extinction, sed_11467_1710, sed_11467_1710_bulge, sed_11467_1710_bulge_no_host_extinction, sed_11467_1710_disk, sed_11467_1710_disk_no_host_extinction, sed_11467_1710_no_host_extinction, sed_1246_306, sed_1246_306_bulge, sed_1246_306_bulge_no_host_extinction, sed_1246_306_disk, sed_1246_306_disk_no_host_extinction, sed_1246_306_no_host_extinction, sed_13177_1966, sed_13177_1966_bulge, sed_13177_1966_bulge_no_host_extinction, sed_13177_1966_disk, sed_13177_1966_disk_no_host_extinction, sed_13177_1966_no_host_extinction, sed_15143_2259, sed_15143_2259_bulge, sed_15143_2259_bulge_no_host_extinction, sed_15143_2259_disk, sed_15143_2259_disk_no_host_extinction, sed_15143_2259_no_host_extinction, sed_1552_381, sed_1552_381_bulge, sed_1552_381_bulge_no_host_extinction, sed_1552_381_disk, sed_1552_381_disk_no_host_extinction, sed_1552_381_no_host_extinction, sed_17402_2596, sed_17402_2596_bulge, sed_17402_2596_bulge_no_host_extinction, sed_17402_2596_disk, sed_17402_2596_disk_no_host_extinction, sed_17402_2596_no_host_extinction, sed_1933_474, sed_1933_474_bulge, sed_1933_474_bulge_no_host_extinction, sed_1933_474_disk, sed_1933_474_disk_no_host_extinction, sed_1933_474_no_host_extinction, sed_2407_591, sed_2407_591_bulge, sed_2407_591_bulge_no_host_extinction, sed_2407_591_disk, sed_2407_591_disk_no_host_extinction, sed_2407_591_no_host_extinction, sed_2998_186, sed_2998_186_bulge, sed_2998_186_bulge_no_host_extinction, sed_2998_186_disk, sed_2998_186_disk_no_host_extinction, sed_2998_186_no_host_extinction, sed_3184_197, sed_3184_197_bulge, sed_3184_197_bulge_no_host_extinction, sed_3184_197_disk, sed_3184_197_disk_no_host_extinction, sed_3184_197_no_host_extinction, sed_3381_209, sed_3381_209_bulge, sed_3381_209_bulge_no_host_extinction, sed_3381_209_disk, sed_3381_209_disk_no_host_extinction, sed_3381_209_no_host_extinction, sed_3590_222, sed_3590_222_bulge, sed_3590_222_bulge_no_host_extinction, sed_3590_222_disk, sed_3590_222_disk_no_host_extinction, sed_3590_222_no_host_extinction, sed_3812_236, sed_3812_236_bulge, sed_3812_236_bulge_no_host_extinction, sed_3812_236_disk, sed_3812_236_disk_no_host_extinction, sed_3812_236_no_host_extinction, sed_4048_251, sed_4048_251_bulge, sed_4048_251_bulge_no_host_extinction, sed_4048_251_disk, sed_4048_251_disk_no_host_extinction, sed_4048_251_no_host_extinction, sed_4299_266, sed_4299_266_bulge, sed_4299_266_bulge_no_host_extinction, sed_4299_266_disk, sed_4299_266_disk_no_host_extinction, sed_4299_266_no_host_extinction, sed_4565_283, sed_4565_283_bulge, sed_4565_283_bulge_no_host_extinction, sed_4565_283_disk, sed_4565_283_disk_no_host_extinction, sed_4565_283_no_host_extinction, sed_4848_300, sed_4848_300_bulge, sed_4848_300_bulge_no_host_extinction, sed_4848_300_disk, sed_4848_300_disk_no_host_extinction, sed_4848_300_no_host_extinction, sed_5148_319, sed_5148_319_bulge, sed_5148_319_bulge_no_host_extinction, sed_5148_319_disk, sed_5148_319_disk_no_host_extinction, sed_5148_319_no_host_extinction, sed_5467_339, sed_5467_339_bulge, sed_5467_339_bulge_no_host_extinction, sed_5467_339_disk, sed_5467_339_disk_no_host_extinction, sed_5467_339_no_host_extinction, sed_5806_360, sed_5806_360_bulge, sed_5806_360_bulge_no_host_extinction, sed_5806_360_disk, sed_5806_360_disk_no_host_extinction, sed_5806_360_no_host_extinction, sed_6166_382, sed_6166_382_bulge, sed_6166_382_bulge_no_host_extinction, sed_6166_382_disk, sed_6166_382_disk_no_host_extinction, sed_6166_382_no_host_extinction, sed_6548_406, sed_6548_406_bulge, sed_6548_406_bulge_no_host_extinction, sed_6548_406_disk, sed_6548_406_disk_no_host_extinction, sed_6548_406_no_host_extinction, sed_6954_431, sed_6954_431_bulge, sed_6954_431_bulge_no_host_extinction, sed_6954_431_disk, sed_6954_431_disk_no_host_extinction, sed_6954_431_no_host_extinction, sed_7385_458, sed_7385_458_bulge, sed_7385_458_bulge_no_host_extinction, sed_7385_458_disk, sed_7385_458_disk_no_host_extinction, sed_7385_458_no_host_extinction, sed_7843_486, sed_7843_486_bulge, sed_7843_486_bulge_no_host_extinction, sed_7843_486_disk, sed_7843_486_disk_no_host_extinction, sed_7843_486_no_host_extinction, sed_8329_517, sed_8329_517_bulge, sed_8329_517_bulge_no_host_extinction, sed_8329_517_disk, sed_8329_517_disk_no_host_extinction, sed_8329_517_no_host_extinction, sed_8846_549, sed_8846_549_bulge, sed_8846_549_bulge_no_host_extinction, sed_8846_549_disk, sed_8846_549_disk_no_host_extinction, sed_8846_549_no_host_extinction, sed_9395_583, sed_9395_583_bulge, sed_9395_583_bulge_no_host_extinction, sed_9395_583_disk, sed_9395_583_disk_no_host_extinction, sed_9395_583_no_host_extinction, sed_9978_1489, sed_9978_1489_bulge, sed_9978_1489_bulge_no_host_extinction, sed_9978_1489_disk, sed_9978_1489_disk_no_host_extinction, sed_9978_1489_no_host_extinction, sersic_bulge, sersic_disk, shear_1, shear_2, shear_2_phosim, shear_2_treecorr, size_bulge_true, size_disk_true, size_minor_bulge_true, size_minor_disk_true, size_minor_true, size_true, stellar_mass, stellar_mass_bulge, stellar_mass_disk, velocity_x, velocity_y, velocity_z\n"
     ]
    }
   ],
   "source": [
    "print(', '.join(sorted(gc.list_all_quantities())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### accessing native quantities\n",
    "\n",
    "Native quantities are quantities that have not yet be homogenized (to common labels/units).\n",
    "However, you can still access them as long as you know what you are doing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LSST_filters/diskLuminositiesStellar:LSST_g:observed\n",
      "LSST_filters/diskLuminositiesStellar:LSST_g:observed:dustAtlas\n",
      "LSST_filters/diskLuminositiesStellar:LSST_g:rest\n",
      "LSST_filters/diskLuminositiesStellar:LSST_g:rest:dustAtlas\n",
      "LSST_filters/diskLuminositiesStellar:LSST_i:observed\n"
     ]
    }
   ],
   "source": [
    "## print out the first 5 native quantities\n",
    "\n",
    "print('\\n'.join(sorted(gc.list_all_native_quantities())[:5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A_v\n",
      "A_v_bulge\n",
      "A_v_disk\n",
      "LSST_filters/diskLuminositiesStellar:LSST_g:observed\n",
      "LSST_filters/diskLuminositiesStellar:LSST_g:observed:dustAtlas\n"
     ]
    }
   ],
   "source": [
    "## list both native or derived quantities, print the first 5 out\n",
    "\n",
    "print('\\n'.join(sorted(gc.list_all_quantities(include_native=True))[:5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sed_2998_186',\n",
       " 'sed_13177_1966',\n",
       " 'sed_15143_2259',\n",
       " 'sed_4048_251',\n",
       " 'sed_6548_406',\n",
       " 'sed_6166_382',\n",
       " 'sed_7385_458',\n",
       " 'sed_4565_283',\n",
       " 'sed_8846_549',\n",
       " 'sed_9978_1489',\n",
       " 'sed_5806_360',\n",
       " 'sed_7843_486',\n",
       " 'sed_6954_431',\n",
       " 'sed_11467_1710',\n",
       " 'sed_4848_300',\n",
       " 'sed_1552_381',\n",
       " 'sed_4299_266',\n",
       " 'sed_1000_246',\n",
       " 'sed_5148_319',\n",
       " 'sed_3184_197',\n",
       " 'sed_9395_583',\n",
       " 'sed_3381_209',\n",
       " 'sed_1246_306',\n",
       " 'sed_2407_591',\n",
       " 'sed_5467_339',\n",
       " 'sed_3812_236',\n",
       " 'sed_1933_474',\n",
       " 'sed_17402_2596',\n",
       " 'sed_8329_517',\n",
       " 'sed_3590_222']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find all quantities that match a regular expression\n",
    "import re\n",
    "\n",
    "data = gc.get_quantities([q for q in gc.list_all_quantities() if re.match(r'sed_\\d+_\\d+$', q)])\n",
    "list(data.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LSST_filters/diskLuminositiesStellar:LSST_g:observed:dustAtlas': array([ 1035582.25      ,   580861.5       ,   980863.875     , ...,\n",
       "         3248385.5       ,    38833.45703125,   442917.21875   ], dtype=float32),\n",
       " 'LSST_filters/diskLuminositiesStellar:LSST_g:observed': array([  992498.5625   ,   590357.75     ,  1002641.3125   , ...,\n",
       "         3645767.       ,    40224.2265625,   485800.09375  ], dtype=float32)}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to retrive native quantities, you can just use `get_quantities` as usual\n",
    "gc.get_quantities(['LSST_filters/diskLuminositiesStellar:LSST_g:observed',\n",
    "                   'LSST_filters/diskLuminositiesStellar:LSST_g:observed:dustAtlas'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also rename the native quantities by using `add_quantity_modifier()`. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'balmer_alpha_6563': array([  1.76363730e+04,   4.74945703e+03,   1.73469531e+05, ...,\n",
       "          7.58312000e+07,   4.01797803e+03,   6.08336523e+03], dtype=float32)}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you can also make quantity alias\n",
    "\n",
    "gc.add_quantity_modifier('balmer_alpha_6563', 'emissionLines/diskLineLuminosity:balmerAlpha6563:rest')\n",
    "gc.get_quantities(['balmer_alpha_6563'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### more info about the catalog\n",
    "\n",
    "- `lightcone` is a `bool`\n",
    "- `cosmology` is a instance of `astropy.cosmology.FLRW`\n",
    "- `get_input_kwargs()` returns a `dict` (when no argument) from the orignal yaml config file. \n",
    "  If a argument if passed, it returns the corresponding value for key=argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.lightcone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FlatLambdaCDM(H0=71 km / (Mpc s), Om0=0.265, Tcmb0=0 K, Neff=3.04, m_nu=None, Ob0=0.0448)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.cosmology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5.0'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ProtoDC2 is a down-scaled version of the catalog to be generated for LSST-DESC DC2.\n",
      "For a description of the catalog and the methods, please see https://goo.gl/fXDQwP\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(gc.get_catalog_info('description'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'subclass_name': 'alphaq.AlphaQGalaxyCatalog', 'filename': '/global/projecta/projectdirs/lsst/groups/CS/descqa/catalog/v5.0.all.hdf5', 'lightcone': True, 'version': '5.0', 'creators': ['Andrew Benson', 'Andrew Hearin', 'Katrin Heitmann', 'Joe Hollowed', 'Danila Korytov', 'Eve Kovacs', 'Patricia Larsen'], 'description': 'ProtoDC2 is a down-scaled version of the catalog to be generated for LSST-DESC DC2.\\nFor a description of the catalog and the methods, please see https://goo.gl/fXDQwP\\n'}\n"
     ]
    }
   ],
   "source": [
    "print(gc.get_catalog_info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A_v None\n",
      "A_v_bulge None\n",
      "A_v_disk None\n",
      "Mag_true_Y_lsst_z0 {'units': 'AB magnitude'}\n",
      "Mag_true_Y_lsst_z0_no_host_extinction {'units': 'AB magnitude'}\n",
      "Mag_true_g_lsst_z0 {'units': 'AB magnitude'}\n",
      "Mag_true_g_lsst_z0_no_host_extinction {'units': 'AB magnitude'}\n",
      "Mag_true_g_sdss_z0 {'units': 'AB magnitude'}\n",
      "Mag_true_g_sdss_z0_no_host_extinction {'units': 'AB magnitude'}\n",
      "Mag_true_i_lsst_z0 {'units': 'AB magnitude'}\n",
      "Mag_true_i_lsst_z0_no_host_extinction {'units': 'AB magnitude'}\n",
      "Mag_true_i_sdss_z0 {'units': 'AB magnitude'}\n",
      "Mag_true_i_sdss_z0_no_host_extinction {'units': 'AB magnitude'}\n",
      "Mag_true_r_lsst_z0 {'units': 'AB magnitude'}\n",
      "Mag_true_r_lsst_z0_no_host_extinction {'units': 'AB magnitude'}\n",
      "Mag_true_r_sdss_z0 {'units': 'AB magnitude'}\n",
      "Mag_true_r_sdss_z0_no_host_extinction {'units': 'AB magnitude'}\n",
      "Mag_true_u_lsst_z0 {'units': 'AB magnitude'}\n",
      "Mag_true_u_lsst_z0_no_host_extinction {'units': 'AB magnitude'}\n",
      "Mag_true_u_sdss_z0 {'units': 'AB magnitude'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/common/software/lsst/common/miniconda/current/lib/python3.6/site-packages/GCRCatalogs/alphaq.py:366: UserWarning: This value is composed of a function on native quantities. So we have no idea what the units are\n",
      "  warnings.warn('This value is composed of a function on native quantities. So we have no idea what the units are')\n"
     ]
    }
   ],
   "source": [
    "for q in sorted(gc.list_all_quantities())[:20]:\n",
    "    print(q, gc.get_quantity_info(q))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### use filters\n",
    "\n",
    "You can specify `filters` in `get_quantities` to select a subset of data. \n",
    "Note that `filters` always takes a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "# note that we use a list even there is only one filter\n",
    "\n",
    "data = gc.get_quantities(['stellar_mass', 'ra', 'dec'], filters=['stellar_mass > 1e10']) \n",
    "print((data['stellar_mass'] > 1e10).all())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "570847\n",
      "570847\n"
     ]
    }
   ],
   "source": [
    "## You can use more than one filter.\n",
    "\n",
    "data = gc.get_quantities(['stellar_mass'], filters=['ra < -2', 'dec > 1'])\n",
    "print(len(data['stellar_mass']))\n",
    "\n",
    "data_check = gc.get_quantities(['ra', 'dec'])\n",
    "print(np.count_nonzero((data_check['ra'] < -2) & (data_check['dec'] > 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For more complicated filters, specify them as tuple of (callable, quantity1, quantity2, ...)\n",
    "\n",
    "data = gc.get_quantities(['stellar_mass'], filters=[(np.isfinite, 'stellar_mass')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### native filters\n",
    "\n",
    "Some catalogs (currently only buzzard and buzzard_high-res) support \"native filters\", which you can use to load only a subset of data more efficiently. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'healpix_pixel'}\n",
      "44.9959447517 56.2350032152 4.77918879056 14.4744615426\n",
      "33.7591549807 44.9914241079 4.77569650762 14.4781225874\n"
     ]
    }
   ],
   "source": [
    "gc_buzzard = GCRCatalogs.load_catalog('buzzard')\n",
    "print(gc_buzzard._native_filter_quantities)\n",
    "\n",
    "data = gc_buzzard.get_quantities(['ra', 'dec'], native_filters=['healpix_pixel == 1'])\n",
    "print(data['ra'].min(), data['ra'].max(), data['dec'].min(), data['dec'].max())\n",
    "\n",
    "data = gc_buzzard.get_quantities(['ra', 'dec'], native_filters=['healpix_pixel == 2'])\n",
    "print(data['ra'].min(), data['ra'].max(), data['dec'].min(), data['dec'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### more tips on using the quantities\n",
    "\n",
    "#### tip 1\n",
    "`get_quantities()` returns a dictionary, which you can easily turn into a `astropy.table.Table` or `pandas.DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = gc.get_quantities(['mag_u_lsst', 'ra', 'dec'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dec</th>\n",
       "      <th>mag_u_lsst</th>\n",
       "      <th>ra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.780913</td>\n",
       "      <td>21.234921</td>\n",
       "      <td>-0.213268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.062044</td>\n",
       "      <td>21.839073</td>\n",
       "      <td>-1.293915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.484340</td>\n",
       "      <td>21.023720</td>\n",
       "      <td>0.681523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.923260</td>\n",
       "      <td>17.603844</td>\n",
       "      <td>-0.304187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.874825</td>\n",
       "      <td>19.314825</td>\n",
       "      <td>-0.261973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.924046</td>\n",
       "      <td>16.110458</td>\n",
       "      <td>-1.786836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.909357</td>\n",
       "      <td>18.208616</td>\n",
       "      <td>-1.753474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.523700</td>\n",
       "      <td>17.888836</td>\n",
       "      <td>-0.262022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.700855</td>\n",
       "      <td>18.595100</td>\n",
       "      <td>0.072298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.490176</td>\n",
       "      <td>22.120970</td>\n",
       "      <td>0.063725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-1.216591</td>\n",
       "      <td>21.383211</td>\n",
       "      <td>-0.708944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.014494</td>\n",
       "      <td>21.787636</td>\n",
       "      <td>-0.402417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.625458</td>\n",
       "      <td>21.205883</td>\n",
       "      <td>-0.272478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.305759</td>\n",
       "      <td>19.265335</td>\n",
       "      <td>-0.480082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.172271</td>\n",
       "      <td>20.583281</td>\n",
       "      <td>-0.580657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.158516</td>\n",
       "      <td>16.410503</td>\n",
       "      <td>-2.263122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.485935</td>\n",
       "      <td>22.250452</td>\n",
       "      <td>-0.292392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.453527</td>\n",
       "      <td>18.530380</td>\n",
       "      <td>-0.677478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.382364</td>\n",
       "      <td>20.347807</td>\n",
       "      <td>0.334156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.688242</td>\n",
       "      <td>20.049438</td>\n",
       "      <td>0.306114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.466396</td>\n",
       "      <td>15.867884</td>\n",
       "      <td>1.843608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.355994</td>\n",
       "      <td>20.886177</td>\n",
       "      <td>1.915584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.193510</td>\n",
       "      <td>18.926830</td>\n",
       "      <td>2.335595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.128056</td>\n",
       "      <td>20.565500</td>\n",
       "      <td>1.859136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.770014</td>\n",
       "      <td>19.866911</td>\n",
       "      <td>2.293251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.928932</td>\n",
       "      <td>19.648052</td>\n",
       "      <td>1.856291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.197047</td>\n",
       "      <td>16.213640</td>\n",
       "      <td>1.365838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.145283</td>\n",
       "      <td>23.506783</td>\n",
       "      <td>1.379037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.186768</td>\n",
       "      <td>22.549137</td>\n",
       "      <td>1.419448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.579447</td>\n",
       "      <td>19.437201</td>\n",
       "      <td>2.397512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286676</th>\n",
       "      <td>2.344119</td>\n",
       "      <td>27.980766</td>\n",
       "      <td>-2.372994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286677</th>\n",
       "      <td>2.424500</td>\n",
       "      <td>27.769838</td>\n",
       "      <td>-2.445686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286678</th>\n",
       "      <td>2.328301</td>\n",
       "      <td>26.979740</td>\n",
       "      <td>-2.474418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286679</th>\n",
       "      <td>2.362773</td>\n",
       "      <td>28.483915</td>\n",
       "      <td>-2.440804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286680</th>\n",
       "      <td>2.466642</td>\n",
       "      <td>28.569492</td>\n",
       "      <td>-2.455950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286681</th>\n",
       "      <td>2.349971</td>\n",
       "      <td>27.639442</td>\n",
       "      <td>-2.430140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286682</th>\n",
       "      <td>2.386925</td>\n",
       "      <td>29.215738</td>\n",
       "      <td>-2.441397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286683</th>\n",
       "      <td>2.405529</td>\n",
       "      <td>29.051693</td>\n",
       "      <td>-2.445164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286684</th>\n",
       "      <td>2.416599</td>\n",
       "      <td>27.439442</td>\n",
       "      <td>-2.445778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286685</th>\n",
       "      <td>2.339918</td>\n",
       "      <td>26.979265</td>\n",
       "      <td>-2.393658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286686</th>\n",
       "      <td>2.387848</td>\n",
       "      <td>28.929337</td>\n",
       "      <td>-2.446784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286687</th>\n",
       "      <td>2.477501</td>\n",
       "      <td>28.996275</td>\n",
       "      <td>-2.343966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286688</th>\n",
       "      <td>2.491531</td>\n",
       "      <td>27.439096</td>\n",
       "      <td>-2.427254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286689</th>\n",
       "      <td>2.313082</td>\n",
       "      <td>31.250610</td>\n",
       "      <td>-2.341767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286690</th>\n",
       "      <td>2.474660</td>\n",
       "      <td>28.234516</td>\n",
       "      <td>-2.308086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286691</th>\n",
       "      <td>2.339188</td>\n",
       "      <td>27.606724</td>\n",
       "      <td>-2.334271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286692</th>\n",
       "      <td>2.423793</td>\n",
       "      <td>27.666565</td>\n",
       "      <td>-2.383434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286693</th>\n",
       "      <td>2.301899</td>\n",
       "      <td>28.936501</td>\n",
       "      <td>-2.395124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286694</th>\n",
       "      <td>2.349056</td>\n",
       "      <td>30.725111</td>\n",
       "      <td>-2.349983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286695</th>\n",
       "      <td>2.468506</td>\n",
       "      <td>29.804134</td>\n",
       "      <td>-2.473691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286696</th>\n",
       "      <td>2.468706</td>\n",
       "      <td>27.269358</td>\n",
       "      <td>-2.457757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286697</th>\n",
       "      <td>2.427693</td>\n",
       "      <td>31.805853</td>\n",
       "      <td>-2.362838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286698</th>\n",
       "      <td>2.420603</td>\n",
       "      <td>31.342039</td>\n",
       "      <td>-2.327386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286699</th>\n",
       "      <td>2.443260</td>\n",
       "      <td>30.946449</td>\n",
       "      <td>-2.300876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286700</th>\n",
       "      <td>2.442581</td>\n",
       "      <td>32.081322</td>\n",
       "      <td>-2.496407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286701</th>\n",
       "      <td>2.312973</td>\n",
       "      <td>28.347523</td>\n",
       "      <td>-2.426726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286702</th>\n",
       "      <td>2.447692</td>\n",
       "      <td>26.709282</td>\n",
       "      <td>-2.302419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286703</th>\n",
       "      <td>2.437479</td>\n",
       "      <td>26.761944</td>\n",
       "      <td>-2.390750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286704</th>\n",
       "      <td>2.494004</td>\n",
       "      <td>33.038513</td>\n",
       "      <td>-2.466166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18286705</th>\n",
       "      <td>2.373600</td>\n",
       "      <td>28.942278</td>\n",
       "      <td>-2.316337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18286706 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               dec  mag_u_lsst        ra\n",
       "0        -1.780913   21.234921 -0.213268\n",
       "1        -2.062044   21.839073 -1.293915\n",
       "2        -1.484340   21.023720  0.681523\n",
       "3        -0.923260   17.603844 -0.304187\n",
       "4        -0.874825   19.314825 -0.261973\n",
       "5        -0.924046   16.110458 -1.786836\n",
       "6        -0.909357   18.208616 -1.753474\n",
       "7        -1.523700   17.888836 -0.262022\n",
       "8        -1.700855   18.595100  0.072298\n",
       "9        -1.490176   22.120970  0.063725\n",
       "10       -1.216591   21.383211 -0.708944\n",
       "11       -0.014494   21.787636 -0.402417\n",
       "12        0.625458   21.205883 -0.272478\n",
       "13        0.305759   19.265335 -0.480082\n",
       "14       -0.172271   20.583281 -0.580657\n",
       "15       -0.158516   16.410503 -2.263122\n",
       "16       -0.485935   22.250452 -0.292392\n",
       "17       -0.453527   18.530380 -0.677478\n",
       "18       -0.382364   20.347807  0.334156\n",
       "19       -0.688242   20.049438  0.306114\n",
       "20        0.466396   15.867884  1.843608\n",
       "21        0.355994   20.886177  1.915584\n",
       "22        0.193510   18.926830  2.335595\n",
       "23        0.128056   20.565500  1.859136\n",
       "24        0.770014   19.866911  2.293251\n",
       "25        0.928932   19.648052  1.856291\n",
       "26        1.197047   16.213640  1.365838\n",
       "27        1.145283   23.506783  1.379037\n",
       "28        1.186768   22.549137  1.419448\n",
       "29        0.579447   19.437201  2.397512\n",
       "...            ...         ...       ...\n",
       "18286676  2.344119   27.980766 -2.372994\n",
       "18286677  2.424500   27.769838 -2.445686\n",
       "18286678  2.328301   26.979740 -2.474418\n",
       "18286679  2.362773   28.483915 -2.440804\n",
       "18286680  2.466642   28.569492 -2.455950\n",
       "18286681  2.349971   27.639442 -2.430140\n",
       "18286682  2.386925   29.215738 -2.441397\n",
       "18286683  2.405529   29.051693 -2.445164\n",
       "18286684  2.416599   27.439442 -2.445778\n",
       "18286685  2.339918   26.979265 -2.393658\n",
       "18286686  2.387848   28.929337 -2.446784\n",
       "18286687  2.477501   28.996275 -2.343966\n",
       "18286688  2.491531   27.439096 -2.427254\n",
       "18286689  2.313082   31.250610 -2.341767\n",
       "18286690  2.474660   28.234516 -2.308086\n",
       "18286691  2.339188   27.606724 -2.334271\n",
       "18286692  2.423793   27.666565 -2.383434\n",
       "18286693  2.301899   28.936501 -2.395124\n",
       "18286694  2.349056   30.725111 -2.349983\n",
       "18286695  2.468506   29.804134 -2.473691\n",
       "18286696  2.468706   27.269358 -2.457757\n",
       "18286697  2.427693   31.805853 -2.362838\n",
       "18286698  2.420603   31.342039 -2.327386\n",
       "18286699  2.443260   30.946449 -2.300876\n",
       "18286700  2.442581   32.081322 -2.496407\n",
       "18286701  2.312973   28.347523 -2.426726\n",
       "18286702  2.447692   26.709282 -2.302419\n",
       "18286703  2.437479   26.761944 -2.390750\n",
       "18286704  2.494004   33.038513 -2.466166\n",
       "18286705  2.373600   28.942278 -2.316337\n",
       "\n",
       "[18286706 rows x 3 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=18286706&gt;\n",
       "<table id=\"table47639141210320\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>mag_u_lsst</th><th>dec</th><th>ra</th></tr></thead>\n",
       "<thead><tr><th>float32</th><th>float32</th><th>float32</th></tr></thead>\n",
       "<tr><td>21.2349</td><td>-1.78091</td><td>-0.213268</td></tr>\n",
       "<tr><td>21.8391</td><td>-2.06204</td><td>-1.29391</td></tr>\n",
       "<tr><td>21.0237</td><td>-1.48434</td><td>0.681523</td></tr>\n",
       "<tr><td>17.6038</td><td>-0.92326</td><td>-0.304187</td></tr>\n",
       "<tr><td>19.3148</td><td>-0.874825</td><td>-0.261973</td></tr>\n",
       "<tr><td>16.1105</td><td>-0.924046</td><td>-1.78684</td></tr>\n",
       "<tr><td>18.2086</td><td>-0.909357</td><td>-1.75347</td></tr>\n",
       "<tr><td>17.8888</td><td>-1.5237</td><td>-0.262022</td></tr>\n",
       "<tr><td>18.5951</td><td>-1.70086</td><td>0.0722983</td></tr>\n",
       "<tr><td>22.121</td><td>-1.49018</td><td>0.063725</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>27.2694</td><td>2.46871</td><td>-2.45776</td></tr>\n",
       "<tr><td>31.8059</td><td>2.42769</td><td>-2.36284</td></tr>\n",
       "<tr><td>31.342</td><td>2.4206</td><td>-2.32739</td></tr>\n",
       "<tr><td>30.9464</td><td>2.44326</td><td>-2.30088</td></tr>\n",
       "<tr><td>32.0813</td><td>2.44258</td><td>-2.49641</td></tr>\n",
       "<tr><td>28.3475</td><td>2.31297</td><td>-2.42673</td></tr>\n",
       "<tr><td>26.7093</td><td>2.44769</td><td>-2.30242</td></tr>\n",
       "<tr><td>26.7619</td><td>2.43748</td><td>-2.39075</td></tr>\n",
       "<tr><td>33.0385</td><td>2.494</td><td>-2.46617</td></tr>\n",
       "<tr><td>28.9423</td><td>2.3736</td><td>-2.31634</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=18286706>\n",
       "mag_u_lsst    dec        ra   \n",
       " float32    float32   float32 \n",
       "---------- --------- ---------\n",
       "   21.2349  -1.78091 -0.213268\n",
       "   21.8391  -2.06204  -1.29391\n",
       "   21.0237  -1.48434  0.681523\n",
       "   17.6038  -0.92326 -0.304187\n",
       "   19.3148 -0.874825 -0.261973\n",
       "   16.1105 -0.924046  -1.78684\n",
       "   18.2086 -0.909357  -1.75347\n",
       "   17.8888   -1.5237 -0.262022\n",
       "   18.5951  -1.70086 0.0722983\n",
       "    22.121  -1.49018  0.063725\n",
       "       ...       ...       ...\n",
       "   27.2694   2.46871  -2.45776\n",
       "   31.8059   2.42769  -2.36284\n",
       "    31.342    2.4206  -2.32739\n",
       "   30.9464   2.44326  -2.30088\n",
       "   32.0813   2.44258  -2.49641\n",
       "   28.3475   2.31297  -2.42673\n",
       "   26.7093   2.44769  -2.30242\n",
       "   26.7619   2.43748  -2.39075\n",
       "   33.0385     2.494  -2.46617\n",
       "   28.9423    2.3736  -2.31634"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from astropy.table import Table\n",
    "Table(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### tip 2\n",
    "\n",
    "Sometimes you can allow slightly different quantiies (for example, lsst u band and sdss u band) when comparing different catalogs. In this case, you can use `first_available()` to get the first available of the given catalog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/common/software/lsst/common/miniconda/current/lib/python3.6/site-packages/GCR.py:216: UserWarning: mag_u_des not available; using mag_u_sdss instead\n",
      "  warnings.warn('{} not available; using {} instead'.format(quantities[0], q))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'mag_u_sdss'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.first_available('mag_u_des', 'mag_u_sdss', 'mag_u_lsst')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['mag_i', 'mag_r', 'mag_g']\n",
      "['i', 'r', 'g']\n"
     ]
    }
   ],
   "source": [
    "# use first_available to get some quantities you need, and translate to your favorite name\n",
    "\n",
    "mag_translate = {gc.first_available(*[name.format(band) for name in ('mag_{}', 'mag_{}_lsst', 'mag_{}_des', 'mag_{}_sdss')]): band for band in 'gri'}\n",
    "data = gc.get_quantities(list(mag_translate))\n",
    "print(list(data))\n",
    "\n",
    "data = {mag_translate.get(k, k): v for k, v in data.items()}\n",
    "print(list(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load coadd catalog (for a single tract)\n",
    "coadd_cat = GCRCatalogs.load_catalog('dc2_coadd_run1.1p_tract4850')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zOxflZgDw/8qrqY9EuaYoh9L2CB+0tHN5fial7RE2NrawKCudj+Rl9tr+tqZWnq6oxQKz0pJZdaSeqo4o8zJT+XhxHgfawhwKR/hoj9fQteyxr/3thmbK2sK0uC5z0pJ5o66ZLU2tYAyfLM4jJxTiYFsHra7L0ux06qMuDZEoWaEAucEAL9c0YoGbxuf12dax723Pn99uaGZ9fTPvNLYyISnExflZXHH0vRroPe9ed90O2PsKzPgIjD+DPnruc1qqOttat3OfkZQBBbNh9hVgDFgLB9/8cD/UtU/ouX/puY2e+8Xy9XDgrxDtgCV/DzMu7nx+82+g/F2YuOTD/VXPfWDFFtj0RGe29EJY+MnO/5auhfqD0FQB2M59TSAZ0vIhlAqttR/ue62FA29+uM9rqYJ3HoS2us4shaf13v+l5nS+zlAG5E6BvJkf7uNSc2DXnzpzLvxk79dbsaXz/cueBGXvfNgG4J2HoOkQTL2g83V27d+bKuHNez/8HU06B/a9BnOv+/A96ueYoPmtt2h47jmSZ5eQMvd0ml99jWhtDcH8fDIuughjHJJnzujcxF9eoXX9u7gdHeR+/OOkzJpFx569BHKyidU3YK1Lxwc7SD5tTvdy0Zoaap94go7de8AYkmfMIOtvr8JtaKR95w7atm8nmJVFICeXWEM9GR/5CB07d9K2cRM5N91I+uLFdOzZS/LMGQQLC7tzR6uqaPrLK3Ts20uwoBC3pZnw3r0kzZgBFjr27SV1/gIyzj+P8IEDYCFt8VkAdOzZi7UujS+8SORIJelLziZl7ul0fLCDYPF4oocrSF+2lJTTTiNaVdXnNbatX491XdzWNoJFhQQyMok1NxE+cAAnNY2c668jmJ9P64YNhMvLcVtaSZ49i/Cu3QTHjyPzIx/pfi1d6+96r5rWvIS1LsZxCBQV0bFtO8lzT8e2tHZnSz5tDm5jI7HGRmKNTdi2VjL/5m9oSk1m17o3ycwrYMbic7r38cfud7uOaZprayiYPIXG6iqmzl/UfVyycc1qmmtqmDT3DOZeeAkAe9evo+ZQGXkTJjFz8Tm91rV/83vsffdt2lpayJ84kakLzupuU75jG1h6HU/VVRyivaUZgJS0D49n+stWXXqQUEoKKWnp3cc5XcddZdu39HqtQK/Xe+zrryk7yLrfP01zfR35EycxftYcUtLSsQbCLS2E29uYfc55FE6d3mddrQ31vY4Zu47/uraz+eUXeO+FP5BXPJGlKz7RvY6qA/vY+Kf/ormulvScXBqqKgm3tgGWWCyKjbnkTpjI9AVnMX5WCXUVh7rfr4rdO9mzbQvNUZezLr2cuQsX9f7K7XHc1PN44vTTT+/en1dUVLB9+/bu46euYxigz76/q/3evXsZP358n+OLrmOQyspK6uvrWbRoEWlpab3ap6amUlNTgzGGvLy87uODqqoqNm7cyLhx4yguLqaioqLX8Vh6enr3cUdbWxu1tbVs27aN9PR0jDGUlJSQlpbWfTxorcUY0+u1isZIGjavC0nNzc0sW7aMwsJC/vjHP5KcnBzX+j/1qU/xxBNPkJ+fz549e8jOzu71fGVlJVdffTXvvvtu92O33HILv/zlL4f3QuKwZcsWli1bxje+8Q3uuuuuhK9/5cqVfOc73wHgzjvv5J//+Z8Tvo3BnMp7U0/Y+kfgv/47uNGh26bkQntd/89lToKmo0U7JwiXfb/zIPav90P5O/0vkwizr4JdLxzzoKHzqLmHiWfHkaOf5QDXwksVs3m//vh3HCmZmbQfMybZqZZVNJ7GIxUebLnrfTXkTZxEbXmcvf0cB1z3pCRKnXU6R0LpvR6bMWMG+/btY6hd4jXXXMPixYtpamri1Vdf7fWdCbB48WL27dtHbW1tn2ULCgo488wzSUlJ4YMPPmDXrl1A55hsp512Gtu3bx9wu0VFRRw5cqTf5+JZfrjGjx9PRcXAfy9Lly6lpKSEZ599lubm5uPaRm5uLklJSVRWVh7X8unp6TS3tGCAmDG8PnshHxRPA2BZdjq/mjeNouQQjx+q4R93lBIbYD3HfvId4Iz0FDa3tA+47fyAoSZ28g6f8gMONbHh//0HDawsmczl+Vk8UlbFM0fqONAeSWi2rvcraOBvC7L5r6oGjk3qHG0T7zsUMPCxolx+d6SOqIWgjbFy50+5ueK/OhtMPBvO+kznhYfaPVC3v7NoYQf6rQ6h5KrOIlJPE8/uLK5UbvmwmNKfUBpEWvs+bpzOQhZAcjZ0NPRtM5LkzoDCOXBkG9QfiH+5y/8NavfC+v8YuE1yNkSawT36+1lwE5z5GQ5+72e0rE/c99TJlrJkCYX/8DWq/9//o+3V10769pyCAtyams4CYoJlXHklscZG2t56KyHrs0BTcpCNU8fTnNp5LlKy9AJ2rn0j/pV0FXlHodlLzwdr2fX2m50PGMPU+Yso3bIZ1x3e91LO+Amk5+ZSvn1rXO0DoSRikXCvx6YuOBNrLQff3zisbQ/EAmbaHG645R+oq6tj06ZNgw4XUlJSQjQaZe/evUOue+rUqSxYsIB3332Xw4cP93m+67zpRCY+OpkWLFjAxz72Ma9jDEiDbY9gf/jDH3jhhWNPXE/c97//fQoKCgZtE4lE+OhHP0p9fT0vv/xynyvjA3nrrbc477zzgMGLQ3V1dZx77rndJzdpaWkcOXKE9PT0ftsfj9LSUs477zyuvvpqfvGLXyRsvT09/fTT3HRT59XBX/3qV3z5y18+KdsZyKgpJDVVwv83N74i0hgXs/DArnNpjSV5HUUSwALRjFzaJ888ruULCwupqqpKbCg5YTFj+M+lV9KWlNL92FcmFfDLsmoPU516/ZfERx/HjfHS+i8yt3XoExMZHawLFe9mU783cceV4i0LHM5OZ+M09dLwGwu0zF6IDYaGbDvWfOUrXxmxPZNO5Xmobm0bpquvvpqrr776lG/XdV0++9nPUl5ezmuvvRZ3EQk6Cytdli1bNmC73Nxcfvazn/HRj34U6LwdbsOGDVx44YXHH7yHmpoarrzySi688EJ+/vOfJ2Sd/cnJyen++VTOQDfqVO9QESlOAQP5ya20tqqQ5AcGCDbX4bS34qakDdn+WCoijUwBa8ltaepVSBprRSTwRxEJwHUCXLbkIe7eefeHPZNkVDMOjD+7gaZDKcTaA17HkQQwQHFDC02Ha9hTnD9kexk9DBBobSaalet1lBFn06ZNI7aQdCpp1rZR4qtf/Spr165lzZo1fWZbG8qOHTu6fx7qj3758uW9Br4uLy8fXtABNDU1cdVVVzF9+nQeeeQRHOfk/el1neQZY1i8ePFJ286o19r31hvpn2uhpmP4BQcZuQwQaGn0OoYkkGsMdenxX2SRkc8ah3+c/d85EsrzOookiDFQuGCE3/Ynw2KAkiP1JEV0cdJ3Yvqd9iccDg/daAxQIWkU+Pa3v83zzz/PmjVrmDBhwpDtf/vb3xKNfvjB73n3Ys/HB3L66ad3/xzvGEyDaWtr45prriE1NZVnnnmGUGjoLpJPPfXUcW/vraP3f5999tkj97aykWDr77xOMGrsbsrVbW0+YwHXaBfoK9YytcaLccDkZIo5QXamT/U6hiRQzrR2AinHOb6VjEgGyB1kXDkZnWxAPQf7M9xOHX6lo+gR7q677uKBBx7gxRdfjGsw7vfee4+f//znBIMf3rXYs4fR7t27h1xHz9vm4p1JbiCRSIQbb7yRxsZG/vCHP5CamjrkMr/4xS+6ZwAarra2Np588kmg8/5VGUTwxIuEY8WUtHqvI0iCGSDY1uJ1DEkgB7hw50ZSwzqZ8RVryQurB4ufGAeSs9TTQWQks4Dtcau4fKi0NM6JY3xOhaQR7N///d+56667WL16NQsWLBi0bUtLC7/73e9Yvnw5n/70p3s913Nk+VWrVg253S1btgAwc+ZM5s+f3+f5pqYmrr/+ejIzM7nssssGnNmna1yn3bt38+KLL5KVlTXodqurq7n77rv55je/yRe+8IXux2tra3nkkUd4+umnaWgY/GDyBz/4AdXV1Vx66aV87nOfG+qljm3j+v5upX/JAcu8rENex5AEs8OcWUVGvgCQ2+LtDImSYMZQm5Q9dDsZNayFaIfxOoYkkAvUpavo4CcGCDRrCID+bN++nSaPZ2MeCTTY9gj1+OOP8/Wvfx1jDJdccsmQ7Ts6OoDO3kQf//jHez132WWXsXz5clavXs3LL7/MCy+8wFVXXdXvev70pz+xdWvn9JMrV67st81Pf/pTnnvuOQD+/Oc/893vfpeHHnqoT7uvfe1rPPnkkwSDwe7R4wfiui6RSOcUxZdccgkzZ344m9JNN93Eyy+/DEB2djZ33303X/ziF/us4+mnn+YnP/kJCxcu5Omnnz6p4zD5Qu4UrxOMGsbAssKDbG0c+tZSGT2SmuuJRCOakcRHOk9mNE6Sr6hHku8YA8Fki0YZ8Y+WUIBwSKeVfuO0NEGBBpXuz7vvvhvXObqf6Ux7BFq1ahWf//znsdbiui4dHR1D/q/LJz7xCdLT+06r+sQTT3TP2HbTTTfx29/+tk+bv/zlL9x8880YY/jRj37EihUr+s0Xi/W+it9VAOrpO9/5Dr/61a+AznGZhsrfcx3HFolaW1u7f25oaOBLX/oSK1asYNu2bUBn98I77riDT3ziE6xYsYLXXnuNvDwNzDmkyUs7+5dLXDJDEdICOuz1EwM4HW1ex5AEUh8HH1KPJN+xFjoaVXTwk4xITINt+1DPcXalN93epkLSiLNz505uuummuAbF7k9/PXUAsrKyeO2117jzzjtJSUnhv/23/0ZJSQk333wzf/d3f8eSJUu49NJLmTVrFmvWrOE73/nOgNu4/fbbWb58OWlpaVx00UXcddddvZ5/9NFHB+zNNJTc3Nxet+IBPPDAAyxcuLDXY88++yzz5s0jFAoxbdo0tm7dygsvvMBvfvObIW+hk6Myx8H8m7xOMWo4BvKTW4duKKOGBWxAJzN+YoDxDTVex5BEsi4lLQe8TiGJpHNT3zFARkffC8syuul688B63j0zVhmrUuOYEw6HWbduHZs3b6auro6UlBSKi4tZtmxZr4G5R5pt27bxzjvvUFVVRTQaJScnh5kzZ3L22WeTk5PjdbxuZWVl3bfylZaWjtyZ45oq4adz0BFdfFwLv9p1rmZv85nWKSXE0lV89pO/lCxiR/E0r2NIgkxsPcT6dz7pdQxJsNI3cmkuG3oCFhkdLPB6ySSaUzWRi5+0Fk4kVlDsdYwR6YorruC8887zOkYfp/I8VJdix6CkpCQuuOACLrjgAq+jDMvcuXOZO3eu1zH8o3QtKiLFb3PdeBWRfEY9kkRGvvLUcRwJ5VEUqfU6iiSQE9JkB35igKSY63UMSSALuDkFXscYsWbMmOF1BM+pw5rIWFW6zusEo4Zr4a3qqV7HkAQzgIlpTAe/CQc0eLqvmAA70/X96z8a0cxPLBpPR8aW/sYkHmtUSBIZq45s8zrBqGGAVA207TsWcJN1a4WfuEBFTr7XMSSRrO2s5ouvuBGdgviJAbLbdZzkJwZwWjXF/UCqq6u9juA5fYuLjFUzxvaUlcNhDMzNqvQ6hiRYJCMXG1TvFT/ZPHEmbUkpXseQRDKGt3IWDt1ORg1roa1at4r7iQVcV7e2+Y2J6RbUgRQU6LY/FZJExqpwi9cJRpWcoGZs8xs3qF2gn1ggEgh4HUNOghZHxUE/qdudRqxdn1U/6ZwxU8eVvqNCUr9SU1PJzMz0OobndBQtMlbpXvZhyUhSl22/cXS7jK8Y4KyDO0kNt3sdRRKsJmnkzMwqJ8ZaaDyowqAfxRydVvqNienYtz9tbW1UVFR4HcNz+sSLjFURXTkajpqONK8jSIK5mrXQdwJAbovGdPAVa/l4xYtep5AEMQbSCnVy6jcWKM3P8jqGJJhN0jiSA9m7d6/XETynQpLIWFVQ4nWCUcNa2NuiAXz9JhDWyYzfxIC6dHU395uStoNeR5AEirTp9MNvDBANBb2OIQlkgVimeoMOZManH+hWAAAgAElEQVSMGV5H8Jw+8SLDMG/evCHbRCKRU5AkASYu9jrBqGEMFKc0sbu5yOsokkDB9hZMNKIBt32kPLdQg237jTGsyVvKzZWrvU4iCWAt5JW00Lgvw+sokkAWaE7WvlTGhtmzZzN+/HivY3hOlwRExqry9V4nGFWyQhp3xW8M4HS0eR1DEmhiXZXGSPKhzRnqQesXxkBKTozUQn1O/aQj4BBWjyRfMYDTqlvF+3Pttdd6HWFE0CdeZBi2bt06ZJuysjImT558CtKcoEPveZ1gVEkP6qDXbyzgJuv+fz/pGiNJvZJERi5jIGdmK21V+pz6RXLMJaOtg+bUZK+jSAI54TCat62v0tJS5s6d63UMz6lHkohIHIKOBmb2EwuE84p0W5vPuGiMJD+a37LT6wiSYG7YeB1BEsgA4xo0iYvvhNVruz+lpaVeRxgRVEgSGasmnOl1glGlvkM9V/zEANYEvI4hCWSBHUWT1BvJb1yXK2rWep1CEshaqNuT7nUMSbCoo+Kg3zhWF1H7s2PHDq8jjAgqJImMVRpse1hqw2leR5BEc1RI8hND521t4iPWck31KxRFar1OIgkUbYdwQ5LXMSTBogGdVvqN6kj9q62tpalJxxv6xIuMVbV7vE4wqoQCrtcRJMHcJJ3I+E0gFvU6giSSMawuvJgjoTyvk0gCNZWr16AfBWM6TvIbo1vbBlRdXe11BM+pkCQyVukqw7BEXPVe8RMLuGkaS8dvHKsTGb+JmQA706d6HUMSKBDSAYgfJUdUyPebQLjD6wgjVmqqhrxQIUlkrJqy1OsEIiIJldvRRmpYMyz6iWNjlLQc8DqGJFDWlA4CKZoLym8Crgr5fhNO0VhmAykvL/c6gudUSBIZqw6+5XWCUcNaKGvN9jqGJJABgvU1XseQBHPQOEm+Yi1fLv2NxkjyGWMgtSDsdQxJsPSwioN+Y9IyvI4wYmmMJBWSRMauTU96nWDUMAayQ+rl4DfBBt3f7jcWaAtp7CvfMKbzfyIyolngULYmJfETC7jBkNcxRqwpU6Z4HcFzKiSJjFXj53udYFSZlFrvdQRJsEC4HRONeB1DEsgAqRH1dPCTX028UYNt+4x1oa1aBV8/MUAsGPQ6hiSQAYJtzV7HGLGMLnKokCQyZrXqtp7hCDoaHNRvDOB0aEYSP1GPJP+xToC3s8/wOoYkkoFAsm6DEhnpYsnqZTYQa3VeoEKSyFiVO83rBKOGtbCpvtjrGJJgFnCTNeuGnxigqLHO6xiSYI0BjdPhJ8ZA+jj1HPQTF6hLT/E6hiSQBdyMLK9jjFgVFRVeR/CcCkkiY1XZeq8TjCq1Yc1c4TeucbC6/993cto0AKbfbMwo8TqCJJC10F6n26D8pCk5RDik36mfGMBpa/E6xog1Y8YMryN4ToUkkbEqTWNOxMsYmJDa4HUMSbBYsq6e+lF9aqbXESTB3s7RmH5+0jmGusYX8ZPMjghJkajXMSTBjApJA0pP1wVmFZJExqqzv+B1glFFh7z+YoFo3nivY0iCWeBIVq7XMSTB5jTv9zqCJJC1EO3QXtVPHCCjQ5NX+I0+pQPbuXOn1xE8p0KSiMgQrIW2mLps+4kBjAba9h0DFDVpjCRfsZZvlT7udQpJIGMgmKyBav3EAuGATiv9JpaU7HWEEau6utrrCJ7TJ15krPpgldcJRg1joChF3Xv9JthY63UEOQlyWhq9jiCJZC0FkXqvU0gCWQtOSLO2+YkBkmKu1zEkwUxAF1EHUlBQ4HUEz6mQJDJWhTQLTryshQMtOV7HkAQz6Iq4H9Wna5YZX3EcdqZP9TqFJJBmbfMfCzQna/IKv1HP7YGVlGgSCBWSRMaq3CleJxhV2mJJXkeQBHONdoF+Y4EjmRojyVesS0nLAa9TSAJZCy2V2qeKjHSBdvXG78+CBQvIzNTEHjqKFhmrJi/1OsGoYQzkJ7d6HUMSTV22fccAqRH1dPCTBY0fUBTRbah+YkzX/4lfGKCwUUUHv4k6Ok7qj7Xq0Q4qJImMXS1VXicYNayF1qh2pn4TS9PVJL9xgbp0/V795LyGTV5HkJMgmKap4v0mq7XD6wiSYKGIfqf9ef/992lqavI6hud0ZiQyDPPmzRuyTSQySqY/ffchrxOMGsZAblIbNWGNK+Un1tG1FD+xwI7CSbQlpXgdRRLFWjZlzPE6hZwEgZCu6IuMdPqUDuzgwYNxnRf6mY6iRcaqVt0qMBzqhO8/gaiuiPuJAdqTNO6KrxjDW7lnsi1thtdJJIGshdYj+qz6TUy3K/qKBSIFE7yOMWK1t7d7HcFz6pEkMgxbt24dsk1ZWRmTJ08+BWlO0JK/h22/9zrFqGAt1IZTvY4hCRZzVUjym4l1umXXd4zhtdzFzG3d63USSRBjIJCsvg5+k+S6XkeQBHN1q/iAUlN1XqAeSSJjVeFpXicYNYyBtKCKDn6T1K4B1P0mv7WJ1LCuEvqKtZzRtMvrFCIyCAu0BgNex5AEMoDT0eZ1jBErLy/P6wieUyFJZKw6uNbrBKNGzEJNR5rXMSTBXHXD950AkNuiATB9xRhw9Fn1E2sh3KSig58YoLBZRQc/sYDV7LYDqqmp8TqC51RIEhmrKoe+TU86D3jbYkm0xjSeg9+4oWSvI0iCadY2H7KWvHCD1ykkgYyBlLyw1zEkwaJGp5V+YgDTrO/egWiMJBWSRMas+jIVkuJhDKQHwkzK0s7UbwIRHQT4zYG8cZq1zW+MYU/aJK9TSIIlZ+l2cT+xwN7xuV7HkAQLNTR7HWHEStZQ0yokiYxV78U0C068jIE5OZrlzm9sIMPrCJJIFnZO1NhvfmQ0LrPv1Laq56CfVGUa6jI1BIDfBGMqlvTHWChys72O4TkVkkTGqB1FV+Eec3BuhzhY73re2qHbngrHZognk7VQ6WYOK7+18EZryfDCyYjnZMz1OoIkioVpsULmt6R7nUQSzFiXcxq3eB1DEsi1sK1lmtcxJEFc4LufdWhNinkdRYbLZA36tE2bd4qCnEI2jp8HWc5Yw/nR08hI0fGGyowiY1RbUgH/FP0SdwYfJGgsMQuvReeR5ESpcbPJNC1cFNhK4OgYp66Fp6MX0GGS+WP0XOrI5IbA6xRTTYlTRolzqM94qNZ2HmAETOfPxnQOXM3Rx7q4tvP7+dh2L8cW8ZK7hPnOPj4V+DOBo5elYxZ+G7uAclvErcHfEzAuMevwdmw25wZ29Fp3zyzb3MncHvkaHzCV+4P3cnXgbbrGW+4qLB07/rK1cMAWsaF9BvPYcdzvtwlMwdoYuOXHvY7eK8zDOKnYWJzrMzkEkucT69gEtvE4NxoCIse57OCc0Om4bjvE9p2U9ffZXtLpdGR2VUaPPjjYeL4929gebXv+fEz7cW4WAQIccup6tzl2Xf39PJRBtjvg8kMt09/zA2UabDvDyXQieuReFJ3GkthMAkciPD856egH2kLPMTt6fsi7vmiGtb04lrEu5/Am61gGJtB7mWOX7/r3QOsdqL2X4s0a17pcwAy+nHX5gv0l2dT1ecq1R8fwGGacY38lDLCOREvEry/e1zUQ1xpcLMHjzNHfn/NQf8b9rePOyKc4q+mDodsyvK+Oo39RJ/TVdOxjw/2q7fn4YHn6e+7Yx1zgQAFMrf7wyv9Q70l/X+dDvc4T+Yp2DTxwlUNDlmHPhDDz93s0JbrJBdsGxHHLupMLbt/vlOHr55jIZIJtZuiqRBYwwLGYUwTukRPI5RBMu4xAaAYdLX+G6LGzXhpMcDqh1PNxgoVYt4VYeC/RtleBHmOXmSyCybPAbj6xD8Wxz/f3X+j7B9nf8/18QDPdVJqctoGPo45ZflF0Ghk2mRbTwfhYDmGn8zbbLDeNDcG9HAhU97vdRdFpTLC55LjppJFM8jT1SDLWjoR+BSL+UVZWxuTJkwEoLS1l0qSRObbDG7ur+PSD6yiknllOObvdiVSR06tNIfUsdjqLJ+vdOX2e76/tQnayMLCPtbHTecK9HIBZTjl1bga5TjO73YkALHZ2kEULjaSz3p3Tb7ue2xsoy7H5u9pl0QJABq1kmHb+FDubD5jaK/NpHOCGwOtY4PexCwG4IfA6OTSSTTOZTjuPRa7gBZZyQf0hFreWYkwGxiSBScEJ5GJtG1hwY0dwAkW4sUrcWDWB4MzucyQnOBHjdF65sG4LsUg51m3A2hYcJx/jZHcvb207xoBxOtdtTCrWres+L3ajBwkklRBImgKAG60iFjmAEygiGi2F6GGcUAlOMAcba+/TvucygdBUrNtGLLwTJzjl6M6yA0wyxqQQCe+AWC2YJEIpiwkkTcGNVhENb8W6rWDSCCXPwzhpxCLlYNsxTgqxaANueDtY5+hRcToEMggEcrFYcFvBBMFGcAJ5BJJm9np/3FgNNtZONLwJYu0QzINoLdAEhMDJxwTzCCV3XimLtm/CdZuAALi1YLIIJE3BOFlYt51YePvR5VJwnHSCyXMJBrMpzCujNtDCaZEJZJPO9kAZlaaBmIlREMsizSQRskEwMC1WBMChQC3psWTqAi2k22QK3WyqnAbqTStpNokkQiQTZJybQxqdg3m30sEmZz8HA9Wk2STOis4glwzqnRZy3M7X3fPng04V1aYJC2TbNMByyNTR4LSS7aYxzRZiXcuO0GEC1qHAzcQYyLEZJLkB9gaPYK0lSIAoUVwDxW4OE918qpxGSk0V9U4rEaIECHBaZAKFTjbtbpi9wSMErEOmTcEYw7RYEakksdbZyf7AEVwg16ZxZnQ6xjE00U6jacViabNhKgMNGAwTY7kETZAQDpNjBRx26jgcqKcglkkuna8zTJQa00SNaaLFhDFApk0h1SbhGkiyAQI4gCFIgFQbImpipNkUMkkhy02jw4l0H9QBuLg8evEznJ78JgA7OI1SJlNFIRfyKpk0sYUFVDCev3Al1jhg3c4/fePg2CgLWE87aUxnDxMpp4U0OkhlIgfZTQm7mMNuSj4sFB0tWBkb5Qs8wCW8TD05lDOJDBqpYAIGSxrNfMBcUmmjgGrGc4hmsqigiD9xFS1k4BAhSgrT2c3fspoDTGc3s5nFLs5gMy9zBS9z5YcFMusyhy0cpphG8jsftzHOZD1n8S6HmMAW5hMmCYcYFUzpfM1drMuZvMN4DrOBxVQyoXcBzFrGU85MdpBFE9Xk8w7nHW0TYyp7+BvWMJPdVFJMFYXUkUsOdRigjlxSaGczC9lz9D1zbIyPsIYVPE0DWfyaz7KFBcdsN8YFvMYn+U9yqGfulgYqjhSTY9pYFyvhLyxhvTuHfBq4IvAOk80Rbgi8QfDoRYhnoxdwprOb6U4FAQMR6/BE7BLedM+gwU3nI4GN3d/71wde50vB1QQMRC08G7uAUreQM5z9YC3JJkq2aWaBs7/PxY597jimOZXdhRnXQpmbz0SnplfbqDXcF72BnXYysznIlYF3Od0pJWAgZg3vu1OZf3T9PbkWdroTeDt2GjUmh52xSZQEymi2KTTTeRvROGqY4lRzMJrPWaHdlMaKmBU4xLnOB8dkcPhu9Av8OXYmi50d/I2zgRsCrx/NAHvdcWxzp3LI5jPDqaDeprPDdu43xpl69tgJlLpF3e/dq7FF4JjuffZcs5/vBJ8kZGLELFS7Wex2iyl2apjuVB99zxx+FP0kD8Y+ymVVT3FGyzsczoO2ZEjtgKmVUFwLARc2zIKcFrh8Y+dsjF3njzEDLy2EPcWG1A5LbjPUZUBNtsOOSYar18a4+p3OZWLAzmJIjsChPJhUBQXNUJEFFYWweSos2AfTK6EhHd4+DQ4UGU4vg9YkS022Q3OyZekHlqJ6qEuH6mzAgdYgnL8d0tvAdSAcgHAy/GU+tKY5gGXHpM7P2pm7XSbWWNqDkBKF8nx4b1aArBbLOTtdWpM+zD+xyuXiLZb9RfDmvAANGYbsZstH18aYdxAO5sGsI53li6ZMqE2DyTXQHoL1JbBlmkNWOzSmwMzDlvwmy/ZJhowOKKi3TDkCSTFYO4ejsyEampMtF71vmVQN7UHI6Ohc3+Gjs5uXlENzGmyaDtktMKccdkyErdM7MzdkdP7hllSezUd2Xtl9fGGcNNxYzdFjpLKjvdnDOE4GMbcJE6shkLQAJ5hLNLwL3DCubSIQLMaYLNzoHggUEEru7D0cC+8hFqvFcTIwJhlrO7A2TDBpNk6w8Gibg0Q7tmJMamf1zaSDjeBGKzFOBqGURd3FEzdWgzGpRCP7IVZNIGne0XVsxXXD4DZAaDJJyWd0H+vFomU4TiaBpBkYJx3rthAN7wHbTCA0u9e6nUB+5/oi5b2O65xAfvcxT7R9J9H298BJI5g0rdd6Y+E9uG4zjlOA61bjOJk4wfGdx4RuO5gUAqHOY+qu13Ls+nseVw30fE/R9p3EwtsIJM0lmFJCMFjPogyHsBOlgyjJBLGu5WCwmgw3lQySCRMlYmLdRZkm2mg1YYLWIWpcgtbBGMP4WA7GMSS7ITqcSPd/u459djuHqXWaGRfNJhpwyYtlYBxDjptOHc3sCx5hQjT36LFHGxETY1qsiHwyaaWj13FUpVPfq00qSd3Pdx0vDOTYdfW7XJLDpB+cP+h6vHIqz0NVSBJJsNFSSFr9/iG+9vh7XscYNRa0O1zZrlm+/OTKrCApmlbcd8oW3k/LuPVDtusq9kykDKD75xzqT+qyJ6qeHHYyB4thDh90b7NnpoFy1JPDDk6jlTTSaaGEHb3adq3DYjnAdM5gM1M52Gcdx/N6B1uuZ+Gtmaw+bZ7eci0vHPqbQdff30WRwS6UDLXsQG2OvdjR30WOgdoOts1jL4J0XWQZLHc878dwMpyogdbX3+Oh/D+RUvTnIdeZ3WyZVGNpTIGsdijL/7BwMdQy8bSVBLFw+Y7PM7PuTK+TSALlGrgoO+R1jBGp6LYzSSoeeWNtnsrzUN3aJiIShySV3H2nJuIyMTngdQxJsHgHZs6hvlexYjhFkRNZ9kTlUM85vD1kpoGWPZe1gz7ftY4z6H9mz3i2M9zlhlrnkUj+kOuvIocqN2fIx+JddtA2bu/HX3DPjavtYNvsbz0n4ngyJHSbgz5ucZIr4lpnQ8bwi0HHs4ycIAMpsZF3Ui0nJkWHSANq310/IgtJp5IG2xYZo5ZMy/M6wuhhYV5EV2T8pvXY0eZl1LO4tOXu9jqGJJC10BLVbFD+YrBRTRXvKxZy28Z5nUISLLe/QUcFgJRZiSvCj1YqJImIDKEwBkWuvi79piWmQpLfxEwbseQGr2NIAhkDUzITNEmBjBAWJ/mQ1yEkwepSDnsdQRIsTbf/9ytQmDrmeyOBCkkiY9buI81eRxg1kqJeJ5CTIaA9oO+4aPppv7EWatp05ddfDLjqZeYrBvbkb/I6hSRYSHWkfsWq2og1hYdu6HM6jBYZo2YVqZIer/JkaI534BUZNdRl239CNoNAh6bk9RNjYEbOAa9jiMhgLMysWeh1Ckmw8sgAA6wJkSOtXkfwnApJImNUdXOH1xFGDwO1Ae1M/aZJv1LfMTik1s3yOoYkmur4PmMxSdVeh5AEy20v9jqCJFizOvn2z0CoSL0qVUgSGaOe3VDmdYTRw0JeTF+XfhPU2akvWXU08xVr4f2auV7HkIQy2HCB1yEkkQxUZO71OoUkmHan/QsWphLITPI6hud0ZiQyRtWoR5KMcSGjXaDfWFzaczRrm58YAzqd8Z9YWONe+U17QLf6+I6ut/UreqSN8GGNNaujaJExKj8j2esIo4eBjSENquc3Eat72/ymuWCjZm3zGdcaDreM9zqGJJiNZXodQRIsJaZbffymxkK7q2pSf1o2VHodwXMqJImMUR87a5LXEUaV0qCKDn7T4aqXg99EQ7pC6Ddbq2fTGM7yOoYkmBNo9DqCJJKF5Eiq1ynkJGiKqZDUL70tKiSJjFUF6pE0LJOjAa8jSIJl6FfqO8lNE7yOIAmWHFBvUD8yAd1e7ysGPihc53UKOQkyNcNtv9IXj/M6gudUSBIZo3Yf0ZX7uFnI0Ai+vpMb1C7Qb4JteV5HkASbkbOfrCT1XvEbN5bidQRJsLrUCq8jSIJlGEhxdPzbn0CGBtsOeh1AZDSZN2/ekG0ikcgpSHLictJCXkcYPQxsD8VYFNF75idhdUv2nVAsh0BHtsZJ8pGgA8XpFbq9zWfccL7XESTBcls1lpnf5Knn9oAiR1rH/MxtuhwrMkbVt46OgtdIkal7xH0nSRfZfMfgkH5kgdcxJIGirgbb9iPj6JZFX7GwqOJSr1NIgmkoyYE5aeqPo3dAZBi2bt06ZJuysjImT558CtKcGPVIGgYL86Nj+6qDH9VFLdnaC/pOMJzrdQRJoNfKlqk3ku9Yghk7vA4hCZYa1Ux8fjMuqErSQNp31ZFUnOF1DE+pR5LIGPXGriqvI4weBoz2pb7T5GomPr+xWJqLNngdQxKosUMnp/5jMNqp+ouBulRNh+43+pwOLHK4xesInlMhSWSM+uBwk9cRRg8LeTF9XfpNpgaQ9J321DLCmWVex5AEmpevniv+YyGgkzBfsYDVxRm/iVgN6zCQNM3apkKSyFj1scWTvI4wqpQFYl5HkAQbn6RRJP2mLU9FB7/RrG1+ZIg2zvU6hCSSgQO527xOIQkWQhfc+mUgdZZuo1chSWSMKhmn2wXiZmBbUtTrFJJAKWhKW1/Sr9R3Ag7Myt7jdQxJsFjbVK8jSII1heq8jiAJlh9SqaBfFmJNmjBAfx0iY9TL23Uve9wszA1rVGY/aQcaouqG7zfhtCNeR5CTIC3U5nUESShLMHOz1yEkwTScjv/ogtvAOvY3eB3BcyokiYxRe6qavY4wahgLp0VVSPKbQxEVkvzGBlVw8KPWSKrXESShDIHkeq9DSCJZmFd5vtcpJMG2t6k3/kBcvTcqJImMVUWZyV5HGDWsgSOOig5+k6oLbb4TC7V6HUESzLWwu2Gm1zEkoVycZPUe9JuU6NieCt2PdnZY2l0NuN0fJ1UXmFVIEhmjMlNCXkcYPQzsCunKg99YdHDkJxaLG9RMUH7zzuFFNIazvI4hCWVwo+leh5BEMlCWvdPrFHISNMV0rNSfYIF6yqqUNkps376dt99+m8rKSoLBIJMmTeKiiy6iuLh42Ouy1rJp0yY2bNhAVVUVxhgmT57MxRdfzIQJE05C+viEw2EeeeQRiouLufrqq4dsv3XrVtauXUtVVRX5+fmcfvrpnH/++RjdpC0ngWZA9Z/9HTA9xesUkigGQ2pdCW35H3gdRUQGZYi1zCKYorEafcPCpIYSr1PISZCk06p+RavbSCoe273wVEga4V566SXuuOMONm7c2Oc5Yww33HAD99xzD1OnDj37hbWWBx98kB//+MdUVlZy4YUXMmHCBOrq6li5ciX19fXcdNNN3HfffRQWFib0dVRXVzNlyhTa2gYfv2L8+PEcPHhw0Davvvoqt99+O+vXr2fx4sWcfvrp7Nixg3feeYc5c+awcuVKrrvuukTGFyFsVEnym7B+pb6T0jjN6wiSYEvGb+TJnY3qleQrFif5kNchJJEspEY1G7Af1cVcsoO6iUn60l/FCHb//fdz5ZVX9ltEgs7C0LPPPsuSJUtYv379oOvq6Ojg+uuv58tf/jILFy7k4MGD/PGPf+Shhx7i2Wef5eDBg3zuc5/jySefZPHixUMWc47ntQxVRAL46le/Sig08C1X999/P5dddhnvvfce//mf/8m7777LY489xrp16/j973/Pvn37uOGGG7jrrrsSGd+XDjdoUNrhyLT6uvQb/Ub9J6N6IYGObK9jSAIFHChOr/A6hiSUIdY6zesQkkgO1KWqh5kftcW8TjAy6dY2HUePWKtXr+a2224jEAiwYsUKfvKTn/Dv//7v/Ou//isXX3xxr7bV1dVce+211NXVDbi+b33rWzz//PPMmTOHp556iry8vF7Pp6en8+CDD7J06VJKS0v55Cc/iU3QvTytra38/Oc/H7Ld9OnT+cpXvjLg84899hi33norsViMf/3Xf+Xmm2/u9fx1113Hvffei7WW7373uzzxxBMnnN3P1u6p9TrCqKLOK/4zVePN+44hQFLL8G/5lpGr81BE38B+E2sbuie9jCIWUiJpXqeQk2BCksoF/QmXNnkdwXP6yxiBOjo6+Id/+AemTJnCxo0beeaZZ7jjjjv46le/yve//31eeeUVXn/9dcaPH9+9zKFDh7j77rv7XV9ZWRm//OUvAfjUpz41YI8fx3H4+te/DsCbb77J2rVrE/J6HnroIWpqanjrrbew1g74v71791JUVNTvOvbt28dXv/pVAAoKCrj99tv7bfelL32J2bNnA/DFL36R8vLyhLwGPwpHNQvZcOjWNv/JDQa8jiAJZrHEQs1ex5AEMgZKcvd6HUMSyhLKWed1CEkkA+2aNdOXkh0NktSfcLkKSSokjUCPP/44hw4dYtWqVcydO7ffNhdccAGrVq0iEPjwROipp57qt+2aNWu6excNNZj2okWLun9et+7Ed/KxWIx77rmHCy+8kKVLlx73er73ve/R0tI5G8/HP/5x0tP7n+0jEAjwuc99DujsCfWDH/zguLfpd387f5zXEUYPS8J66MnI0eGqmOo3BkMgMrYHv/Qlq8+q3zjJNV5HkESykNum40o/0qxt/Ys1R7yO4DkVkkagRx99lNtuu4358+cP2m7JkiVcf/313f/es2dPd7Glp4qKD8cWOHRo8MEN3R4nVvGMaTSUp59+mv379/Ptb3/7uNdRUVHBb37zm+5/X3vttYO27/me/Md//AeVlbpnuz+tYR2Yx83AwZDeL785rNG2fccSI5x+2OsYkmDJwbDXESShDNGmeV6HkARrC6qHhh9lBtQjqT9GPbVUSBpp2tra2LhxY9yFlyuvvLLXvxsaGvq06Xm72HPPPTfo+vbt29f987Rp0+LKMJj//b//N47j8Prrr/Pss89y5MiRYb44gQAAACAASURBVK/j97//PdFotPvf55xzzqDt586dS2Zm58wR0WiUZ599dtjbHAvSk3VbT9wszIro/fIblQb9pzl3C7HkvvtBGd0a2jUblN84QX1OfcXAvrzNXqeQBJuZZEhRwaRf6edqPEYVkkaYaDTKY489Rn5+flztJ0+e3P2zMYacnJw+bRYuXNj984YNG3j00UcHXF9Xz5+0tLQ+Rarheumll9iwYQOu67Jy5UpWrFjB+PHjWbBgAf/yL//Cnj174lrPW2+91f3z1KlT+32NPRljer3m3/3ud8f3AkS6GCh2VUjym3QdHPlOINL/bc8yutV0FHgdQRLKYkKa8MNvXF2e8Z3JGmh7QIH0gWcZHyv01zHCZGZmcs0118Tdvue4LXPmzCEtre+MCUuWLOHss8/u/vctt9zCSy+91Kfdxo0buwtJ99xzD7m5ucOJ3sdPfvKTfvO+//77/PCHP6SkpITPf/7zg842B/Qa9Hv69Olxbbtnb6r169fHF3iMOdLU4XWE0cNCXkxfl34zOVm/U78JduQN3UhGFdfC7voZXseQhDK4HYOP2SmjT3OSioN+oxEABta+u97rCJ7TUfQo13P8o8HGDvrZz37WPVtbe3s7y5cv5/777+9+fvPmzSxfvhzHcXjggQe45ZZbTihXR0cH1loWLFgw4ADfruvyyCOPcM4553DgwIF+27S3t7Nr167ufxcXx9eNsGe72traIceGGouKMjX3ebymhCHDqveKn4SA7KB2gX7TlnHQ6wiSYNWtOTSGs7yOIQllCaTv9jqEJFg0oMGH/UbjIw0skJ3kdQTPBb0OICdmw4YNQOdsZYMVf8477zwefvhhPvvZz+K6LpFIhFtvvZVVq1Zxyf/P3p3HyVXV+f9/nVt7Ve9bts4eEkiACAmBaAiSKGhAiUlENP5YvgMqP/2OMw9wY5bvfB2M4zxkvoz8cBSZYYThCw4CQzJGoNlFCSAhIAkkZCFJJ53e19qr7vn90XSnl+ru6nCb233683w8IF1Vp+59Vy91b517zudcfDG33norS5cupaamhiVLPngBxEAgQE1NTe/t+vp6fve733Hffffx3//93/2Keu/fv5+1a9fyyiuvUFbW/2pyS0tLv1FXFRX5DW+vrKzsd/vgwYMjrljXV21tbd5tB6qrmxiFXqtLB49eE7mV2nIgNU2p9CEZKVGxx+0IwmHloTaK/B3SmWQUjeUdvDiMmMA0zG8+x+0UQnxoMvUxtyO4TjqSJrjt27cDcM011zBv3vBDvzdv3oxSimuuuaa3eHVNTQ01NTWsXbuWJ554Ao9nbOrATJkyhU2bNrFp0yYOHDjA//yf/5Pf/va3vY8fOHCAa6+9lq1bt/Z73sBpb8FgMK/9DWzX0dExqrx9a0+Zqigkc3vzpqQjyTQNNiRsLUUkDZMOy5LipvFYMC1yQjqSjGKRjc3GKtrtdhDhFIWcKxmoMyvnSUPRyLw/uSY7gT311FMcPHiQ8vJytmzZktdzvvSlL7F9+/beVc16PP3001xyySXU19ePRdR+5s+fz/bt2/npT3+KZZ38Fdy2bRvPPfdcv7YDV6ELBPKbjvVBO5Img+f2jn4FvckqLCOSjHQinXU7gnCQRmPLKAfj2FpRF53qdgzhMJ3N78KgmDhSHhmhYZr2rHSWDMVOSnF5GZE0gf3d3/0dAD//+c+ZMmVK3s/bvXs3iUSC8vJymptPXr195plnWL58OVu3buWcc8Z+eOqNN96Iz+fjhhtu6L3v3nvv5eMf/3jv7b4dTdC9Ils+Bo6s6js9Lh9Hjx4dVfu+6urqWLFixSk//8PS3JVyO8KE8UYgyyr5dhmnK6NBSoUZQ6EItS4kXv6O21GEQ7SGrQculdFIBlKehNsRhMP8WSmZYJrdCZtqvyWjknLwV8nvu3QkTVD33HMPv//977n55pvZuHFjXs9JJBJcd911PPjgg9x00038/d//Pd/73vf4yU9+0tvRUltby8UXX0xNTU2/ld7GyvXXX8+vf/1rnnjiCaD/Cm0AkUj/pZyTyfxWGksk+p+gDByBNZLq6upRtZ+Y5CpDvmIWHPZkmZ0dm6mfwh0BOTESYtzb2bDU7QjCcRosGb1iFE13z68wzolUljlB6TIYyD9zdJ8tTSRT20bpr//6r1FKOf7f/v35r15x4MAB/uIv/oKNGzfyox/9KK/nxGIxPv3pT/Pggw+yefNmfvzjHxMKhbj99tt56qmn+hWibm9v54orrqCh4cOZ+vTtb3+79+uBRa5LS0v73c63I2lgu+Li4lNMZ66QTzpF8qZgtz/jdgrhMJ/0IxlHZWSImUmUgs+f9qjbMYTjFHYy/wVQxASg4HCp1LwyUVz6B3PKNMXdjuA66UiaYLq6uli/fj3Lli3j/vvvHzT1ayjXX389zz33HOXl5dx55539HluzZg07d+5k+fLlvffV1dXxt3/7t45mH8rq1at7RwwNHEk0ffr0fnWRWlpa8trmwHannXbaB0xpnjkVkZEbiV4VWel1MI78SI2TCbaP3EhMKEsq9lHklzqHZtF4IvlfQBUTQ2GydORGYsIplN6CnOy4XGCWcWqjdMEFF/D1r3/d8e2WlJSM2CadTrNhwwZCoRCPPfZY3oWnX3rpJR544AEANm3alHN0zpQpU3jyySc5//zzeffddwG47777uO222wZNL3Oa1+tl5syZ7Nmzh4qKin6PWZbF/Pnz2bOne0nnurq6vLZ5/Pjx3q9LSkqYOlUKdQ5UXSpze/OmYYpMazNOXIpIGseTkQK+prEULCg5yM6Gj7gdRThFxfAGx35xFyHEB5fQctVN5CYdSaN0+eWXc/nll3/o+7Vtm6uvvppjx47xwgsvjKrmz0MPPdT79cqVK4dsV1payu23385ll10GdE+H27lzJxdeeOGpB89TT0darqLhH/3oR3s7ko4cOZLX9voWyx7uNU9mMiJpFBQ0eG2pkWSYmCy4YZxA6zy3I4gxEPbIanxG0WEyiSnSmWSYEwXvuR1BjIEiGZGUkxWSbhT51ZggbrzxRnbs2EFNTQ3l5eWjeu7evXt7vx5pZM66deuYO3du7+1jx46NLugpamxsBOg3va7H2rVre7/ev38/sdjIBRp37drV+/Wll17qQELzvNckJ+Z50zA7LZ1IppkiRZKM40kXuB1BjIFYVi58mEWRbhn/q9uK0fHYPrcjiDEgF91y81aE3I7gOulImgC+853vsHXrVmpqavoVxR7Kww8/TCZzct6m7rOKQt/7h3LGGWf0fp3v9LkPoqmpqbfY+IYNGwY9ftlll/VOr7Ntm507dw67vUOHDvV2TFmWxfr16x1ObIbaVlkxJV8FWaiy5e3SNIE8a8yJiUNZWbcjCIdpDfvbZKSZabKp0V0UFeOchpJkpdspxBiIy2p8OdkxqZEkZ9Hj3JYtW7jrrrt44oknWLBgwYjtX3/9de6880683pPD7fqOMMpndbi+0+by2ecHdc8996C1Zv78+XziE5/Imeeqq67qvb1169Zht/df//VfvV9/5jOfYfbs2c6FNcjrR1rdjjBhWHIMNZKWkyOjaDQtc550O4ZwkNbwXsdMOlJFbkcRDlPeTrcjCCcpeGvaC8R8UhjfNEEl50q5+Kqk1qx0JI1jP/3pT9myZQvbt2/n7LPPHrZtNBrl0UcfZd26dXz5y1/u91jfUT7btm0bcb9vvfUWAPPnz+ess84a9HhnZyfr16+nsLCQtWvXcuLEiUFtfve733H33Xfzxz/+cdh9nThxgh/+8IdYlsVdd92Fz5d7WOz3vvc9wuHuP9gHHniAZDKZs53Wmn//938HwOPxcMsttwy7/8nsaEti5EYCgIzMgDJSQ1pOjkyTKJWVoEyiFMwpOsqMgg9nmr348HiD+S2eIiYO27JpDUndK9O0yEDfQQILSvAU+t2O4TrpSBqn7r//fr7xjW8Qj8e5+OKLCQaDw/5XUFDAhg0biEajfOELX+i3rbVr17Ju3ToAnn76aR5//PEh9/vkk0+ye/duAH70ox/lbHPbbbfx2GOP0dXVxTPPPMNf/dVf9Xv8xRdfZPXq1dxwww2cd955XHrppTlrLUWjUTZs2EB7ezs///nPWbNmzZC55s+fz6233gpAbW0tP/nJT3K2+4//+A/efPNNAG666SZWrJA5+EM5rUpqTuRFw/yU2yHEWEja0pFkEoWisO58t2MIhykF51a96XYM4TDlyX1BUExgGkrjgxfNERNbWHoLBkkeaCPbKR8O5FdjHNq2bRvXXnstWmts2yaZTI74X4+rrrqqt55QXw888EDv6mVXXnklDz/88KA2zz77LJs3b0YpxQ9/+EM2btyYM182279rOp1O97s9sBj2k08+yUc+8hH+9V//lXg8TiqVYvv27Zx33nkcOHCARx99lOuvv37E78tf/uVf8jd/8zcA3HLLLTz44IP9Hq+pqeFrX/saAH/2Z3/W2/EkcrvhovluR5gYFDRL/Ugj+aV+unECndVuRxBjoDUuU9uEGO9mNS8mnJa/VdMUST3JwTSkG6TWrPxmjDP79u3jyiuvzKsodi5DdcgUFRXxwgsv8IMf/IBgMMimTZtYuHAhmzdv5rrrrmP58uWsWbOGBQsWUFNTw3e/+90h93HTTTexbt06wuEwq1evZsuWLf0e/+QnP8ktt9xCKHSymn1TUxPXX389BQUFhEIhvvKVr7Bx40b27t3LZz/72bxf3/e//31+9atfUV1dzRe/+EWWLVvG1VdfzapVq7jkkksoKCjgpz/9KXffffeQ0+REt4qCsS+kborjXuiSOeLGmSU9SUbRaBIFtW7HEA7TGt5sHjzNXggxjmj4+HtfdDuFGAMFHqnvkIsV9o7cyHBKS7XRSSeVSvHKK6/w5ptv0traSjAYZNq0aaxcubJfYe4PKhaL8eyzz3L48GFaW1sJh8NUVVVxzjnncPrpp2N9gB5u27Z5+eWX2blzJ21tbRQUFHDWWWdx4YUXut6BVFtby8yZMwE4evQo1dXj8wr59j8d5/+9/3W3Y0wYX+jyMysjHQ8m+XiBh2KvXE8xSdu0F6g/69/cjiEcZNtw0wu3SsFtwwRn3IevaLfbMYRDFp/4GKsPXel2DDEGPhrxUOmTc6WBKm44i+D8ErdjDPJhfg6VrrRJyO/3s2rVKlatWjWm+wmHw1x22WVjsm3Lsli5cmXvdD0hxpSGsqwcRE3TmNEUy1HQKKlQk9sRhMMsC6ZFTkhHklE0nvBht0MIp2hYcuJjbqcQY6RQRiQNpmTVNpCpbUJMWsvnlLkdYcKYl4QCLQdS0xTJEdA42WCr2xGEwzI21EWnuh1DOEomQxhFwRMLZSSoqToyttsRxh1fdYGs2oZ0JAkhxIiiMqPNSO1Z+TBjmqxPil+a5s3GJTIayTgWdrLK7RDCQR2hJo4V7nU7hhgDdWk5VxoofbRLVm1DOpKEmLT2N3S5HWHCCMjFGCN12HJyZBKNxvZG3Y4hHLavdYHbEYTjNHjkb9UoCvZWvup2CjEGpvlkRH4uyffa3Y7gOulIEmKSKgnLqnZ50bAoI4V0TDRTikcaRaEItS50O4ZwWHXhcbcjCMcpdKrC7RDCYZ6snCuZxg9UyQq3YghyFi3EJNUWS7sdYWJQ8GYg63YK4TAfcnJkIpUOuB1BOGzl9Fcp8ne4HUMIMYKkTC02zhlB6SoYirci5HYE18lvhxCTlIxIyl+9R9Ngyfw2k6SBhExtM443W+h2BOEwj+petU2YRKO1vP+apjQ+xe0IwmFx+Tsdkh3LuB3BddKRJMQkJSOSRkHBYZ+MSjJNpxTbNopG0zHtD27HEA7LyqptBlKkO85yO4RwWFl8htsRhMP2JbVcdBuCFZapnPIdEGIUlixZMmKbdHpidNDYcpUhfxpmp2UalGn8Uj/SKBlfG/Hyd9yOIRwWz/hk1TYhxjsNXYEWt1OIMdCcsZkhpQAGkRFJMiJJiEnruXca3I4wYYRsqLLl7dI0XTIiyRgaTe3y29yOIcZAxJdmRsExt2MIhylv3O0IwkkKdszaSswn9cyMI6dKgynwVYXdTuE6GZEkxCjs3r17xDa1tbXMnDnzQ0jzwTRHU25HmDDiFnQpTYGWISxGkR+nMVKhelKFtW7HEGNAKVhctpdjXTJtxiTeoHQOmkZbmtZQPeG0jCA0iZaepEEiH5uOp9DvdgzXySV2ISapRFpq/uRNQYtHim2bptwrh0BTJMPSiWQqrWFPyyK3YwhHabyFe90OIZxmS8FtEx1KSkfSQJlGGVEJ0pEkxKTVGZ8YtZzGBQ1lWXm7NEmlRxG0ZEiSKVKRercjiDGgNbzVvFBGIxnIzkTcjiAcdnbdxTIayUBxuY46SHJvK9lOmdkhn4yEmKQqi4JuR5gwimxkWpthGrOyEolJyo98iuLa1W7HEA5TCnY3nu52DOE4RaZz5MVLxASiYVHjeW6nEGNgmszgyindEHM7guukI0mISWrF3DK3I0wYhbIwg5FOpGR6pykUHqbsuRZPstjtKMJhK6a97nYEMQYsb7vbEYSTFLSFGt1OIcbAaUEpqZyLFNuWjiQhJq21Z8g89nwd93cX2xZmicuP1CgKi3Dj2W7HEA6bXXSUIr+sBGUWjfLJUvGmSXlkhIZpQgopA5CDCnqk2DbSkSTEpFVVGCTklYNDPrQU2zbSdJ8cAk3jS5S6HUE4zGPBtMgJt2MIRyns5HS3QwiH+bMyQsM0cQ3tGTn/HUgnsqTqutyO4To5ixZiknpxfyPxjAzJyIsU2zZSQK6yGUWj6Zqy0+0YwmFZ26IuOtXtGMJRGitw3O0QwkkapnXOczuFGANHklIGIJfE/ja3I7hOPhkJMUn99k91bkeYMGanlBTbNtChhBS/Mo0vKlN2TfN87Uo6UrISlFkU2DJ6xSThZJGs2GYo6UbKzTdNVp6UjiQhJqmV88vdjjBhLMp43I4gxkCLnB0ZRaEoOv5Rt2MIhyXTUujVRDorK8eaJBbooDl0zO0YYgxEpLcgJ6XkArP8aggxSVnyBigmuek+txMIJ2k0XZW73I4hHFZdVO92BDEGlCfhdgThJAW1xfvcTiHGQDotf6uDKFm1DaQjSYhJqz0twzHydbxCehxMVOCRkWYmUSgyEVl+2iRaw0u1K9yOIRyn8YQPux1COElrrra3u51CjIFoUlbjG6iztFNWbUM6koSYtAorw0y0Uttu5NXA0YVy1cE42qbQI6Py8qX18H99fR8fqe1YsXWW9lSTK/sWztMakskQh5sXuh1FOEgD6XkF4J8kHfkuvR9+qLTmk9EYH1P7Kfe+53Ya4SStmZEYXzVV8zkfGcvzEK017TOk0DZIR5IQk1ZJxEdmSUm/zpmBX9uBwfcNbNf3seHaDvVYPtvquS8zr4DU0lJS8wrITAmSKQ/kbNebP4/9970vG/EMui+zpISShN19MjjwwJTrQJXzPpuKE69S0H4IbDt323y2PdK+BmYcbhtag+6TxbZP3u55Xr6ZPsgBe6h9jfTYqWyvh21z+r4H8Kc6PlAHyFDP7XsSo7UmnU3l3Ha+nTMD//2w9N1va7IBu8/vi9a697atsxxsf5OjXXs52rWPVxp/i63zH/GotaY+drjf9gdmGImtbV5repK2+uz7z6Pfv0Pve/h2Y/kt/6DbHqtsH9avWa631L6PtbZW8crLm8DO3Wi4Y8xkN9Sx9YNus++/o3lO39vZKUGyp5WQDpzW54E8j6V9Hxvt8SHXc3JtI983jYG3hzhHmJrJjPkbjBrw2ipSKaYmkqf+WobLl+M848bWdv6psRmloNr/xtD7srOn/pqH+5kN137gc0b6Hej3PHvAOZt9avlP5Rxm4O18z5Hy+V3r+zpG+Fso6jhIpdVzXB36nGfgfba2R3V+NdQ51MDt18cPs6v5md5zjIHt9nfsZOvRO3m27kEa40fRvecpNm3JJloTjb33jZQnlo4O2o+tbf7Y9Did8dYRtzEZSAVDISapt7oSZKsjZCuDWK0pQGOHvVixDKCwS/0Q8EBHCk99HB30YBf7sdIaO5rGdyKG7bPQJQEIebvbA1Zrsvv5YU93W63xdKbJBj14oml0wIsd8nTfVx4A6N5+xsZK2tjFfuzpYUhm8RyPQcZGF/uxq0LdeQCmdncSAWSS2e786Sz4PNhedXLbRf7u/M1JsoU+rEz36bXttbrbFPqwMnb/15vMnnwN799X+uYePvbSP5PyFXBiygoylo/CrlqKOw/TUH42rSULKep8j+LOI/jSUdpKFmBlEviycbyZBCUdBwmkOgBI+otoL5qLRvW2DSRb0RqOT19FBh/F0SNMq3+FzoIZtBfNJRKtw5tNEAtVkfaGSfiLsSyLiobXsX1Bkt5CEqFKptf9nnCikRNTVqDRFLe/R3vxXNLeCJadwpNNkQiWUtxxmKmNrwHQVjQPhaa44xAAsfAUvOkoGV8EbzpKPFRJ2hvGl4kRijfSUTiLzoJZZCwfXjtNYdcRijqP0FE4m2h4CoFkGxpFNDKdpL8Iy06DAn+inYy/gHC0Dl82SSpQRCjWgC8do7VsEaUte8n4wkTDVXgzSVBQ0nYApSAaKOfElPOw7DQKhY0m6yvAn2xD2RkyviJS3hBYHiobXsOvU1jpOO0l8/GmutCeABpNON5EKNlCccchAqkO3ilKc6TQQ1VwNiX+ShoTtUS8hRR7KykPzaAr1UpbupGMnaYwUAo2KAWtyXo6Ms14VYDZhYuJpdppTB5jWmgOCTvOwc438FshZkYWcTS6l8bkEYKeCLPCp1MWmE4s3U6WDA2JWgp8xZT6puD3hPAoD52pFgKeMB6Pj/ZkE2WBqXSkminyl5PKJphTdCaWstBao5TC1jYKUMrC1jZNiWNk7CSJTBStNaXBqXSkm1EoOtLNePFSEZpBRmfx4Sdpx8ECO5vF5/Fj21lS2QSF/lKOxQ6QJU19/DDt6UaCnggVgRkANCW7i6oW+crpSDeTyEb7vb9YSrGs4hIs5cHWWQ50vEFnupUSfwUl/imUBqp6M7/R8hz7Ol4l6IkwLTSPquBsIt5CTsTe6/6+hufixUdXppU5kaWUBCtQSnVfFUw1sq/jj9TFDxLPRomkp9O6dxGJeAEFBa10dZXi86WYOm0PJSVNWFb354L6+rm0tswknfb3tisqrqeq6j3C4U6UAq0VjY2zqKg4jGV1n1/HYmES8SICgSiRgp52kEgECAaSqPcvz3W3LaCubiElJceIRNrJZr1EIl29z4nHw4RCsT63I4SCUdT7+0ql/GSzFqFQoruNDS0tU2ltm04qWUA67ae4uIFMtvtULuCP4/MnqKo6hNUnR2vLVFBpSku7P+j13N/RUYzPFyMQSGNZ9OZobp5BWdmx3u9VPB4mHI4xsKye1vTbnlLd7ZU6ua2ef1MpH35/ul/7w4fPJpv1Mnfuzt59dXSU09lRRUPDfGKxUpSC04JNHPOUouI2ygZFd4eErQCvIl3mx1McgGQW7+EofWNqTrbv+Zc+X2cLPShbYwc83SNkvBY67AGvBxJpPI0JrM5s7zb7bscu9GJ1ZgZt3/aBle6zj6ogKp7p15Yh8vR83Td/ttiLLvSh/R5UVxqrLYmVGvy6suV+dIEPHfFhV3UXsrYa4qj6OFZHql8m2wLLHnq/A1+r9kFqeWX3MT2VxVsf7/5g5VFYTYl+2+77vcpMDaKnhCCeQWU02Smh7uMykLHKCLz/y/DJaIxPR2N0WN3PLrI1b/j93FtShFYKj9as64xSZmeZm05zUby7ZstBn482pXgiEiaoNaelUuwL+PlNQQG2UiituSga4+PxOBfFE7RYFjtCQRYlUyilKMlmafN42OPz8nikgGXxGFfE4vwhGOAPoRBRBRFbszSZJKI1MctDVSbDc+EQXZZiXjrNpdE4SnV/DfB6IMAxj0XM8vCJWIyF6Qz7fF62RsLELYuZ6e7jWNRSLEskqfV5OeTzUZHJUNjnQ3GnUjR5PIRsm7jlIWzbxDzWoHZFtuacZLL3+zEvnaYi232G1OSxeD4UZI/PzwmPRbvXQ3E6S63fx4JUiiu7onR4PL15lyUSvXnmptOclUxx2Ofr93M5J5mkxbJ4KhxmSjbDRfFE7/60htThGNWxJ4mFK7HxkQwUEkx1MftoDWXt79JSfBoHZ11KMliGJ9VFOliM1gpvJkbWG8L2+Clp2UtBvI6OonloNOWteynsqqWrsJqCztre95W6KSuIRqbhS7bhy8SxLR+2x0dp237K2vbSUTiLZKCUSNdxkqEySlv3AtBauohAvIVkqKz3377b7Tkvq2jZA9B7zlbScZCUr4DGiqVkLB/JYDnh6HECqQ6yniCxUAVpbwTt8ROKnUApi3DsBKF4c7/zwqS/hPaieWSVh1CyhUj0OApN1hsi0nWcaME0PJkkoWQL3nSMrsJqAvEWogXT+51bhuMN/c7VNIpwvIGD1ZfQWn4G3kycqsadFHceIRxvIBaq6j3fS/kKaC1d1Pu6e877tIaGyo+Q8hVQffxFStvfpfOjNxKP7udQ559638M7062UBaYS9hTSlmoi4iuiNdlAwNP93tNzjtBzztCZbmVKcBYVwZmk7QTtqUbSOoVP+fFZARoStZT6KynxV3GoazcpO87sgsWgoTlZR8RX1HsuAnAk9jZFvnK0hspgNfFsJ3Xxg73nIolslGdO/F+Cnsig85Sec5mwp5ACXymW8pG103Rl2lBofFaA2ti7vec9Rb5yktk4AU+odzuzTshYHACl3RqDLoShamtrmTlzJgBHjx6lurra5US57emMseaPUhgxH6tf+T3/+57/z+0YwmGvzZ5CfUmB2zFGJddJDQzdoeOmXCdw+Tw2kmJfJVNCs/udVPaIz5hPpqg05/N8vjjhcDuxWDHpdGjI7Q9sN9TzwuFWSkrqaGubRixWis8Xp7SsloA/RnPzLGKxwTkGbivXNgbuFQTHOwAAIABJREFUK9/cffdRVNQAQEdHVb/t5Lo/n9fc81yPNwVANuMnHi+iuOQ4BZE2mptnkM0GicWK3//etKO1pqCgtd9rGyrXcK/PBu5b+Sni/iA0JfDWx8lMCUFFjlW/ei4EpDX4VP+LBkphF3R3ulldme6vA3lMr+q5WNFzsSWtTz43me3elk/lvr/vPnouapQHIOA5+XjfPMksnoY42mNByINdGsidcaj9jvQ6+mbqextOXpDJaFTGJhvw4O1MD/297ivXBZuhsr/vytpfsbzxXi6IJ1iYzuRs0+SxBnWO5ONUnydOndbQdcJP7fMVbkcRDtsxfzotBSMfeyYVpfjaz+4lUpL7fMNNH+bnUBmRJIQQIzg8fabbEYTDNNAamXjLTyey0ZMdL+n+9483/bKO4rGRtKcbB3Ug9dDZdM77AdLpEO3tI58MD2w31PNisdJ+nUXpdIiG+tMGtRtu27m2MXBf+ebu2765eXbe9+faR67buZ6bq7MM6H1uR8e0vHIN9/osoDTa2d2RVBEkM1ynRsCDPbV/Tbvs++37difY+XQg9dvmyXz2wMfe39ZQ9/cq8pMtOlmcte/jvV8HPP3aDJsp137zfE6u231fY4/c3Ts59Hlt+eZZHG/g6o6uYdtUZG0qssl8U3zg54lTpxS0vF3odgzhMA2kPDL6ZhCtaa49Oi47kj5M8pshxCS1vand7QgTxrRGWX7aNE0hPymfXEsxjfLICoumsYHWiHxANc2MVIPbEYSDtIZMUhawMI0CIsmhL9BMZqFCOS5JR5IQk1TYkj//fH3k3T1uRxAO88isbuNoIBueWFMVxcj2TJvTPRpJmEPbnN/+ltsphIOUAm9Ajqsm8mbzXzxjMol3drodwXXySVKISWpWKOB2hAkjFpQPMcYZYiUoMTFpIFNQjPbKiCSTaKAjFHE7hnCS1vyvA/9CVbrF7STCQVpDskNG+YrJQVkW5dVS9kI6koSYpFYUy8l5vvbPnOd2BOGwYEausJlEAd6udlRGhuCbRAEXHNpDKJVwO4pwjKZoHNZ0Ex9c8eyY2xHEGMh4RlFTbpI4/aMXTfr6SCDFtoUYlSVLlozYJp2WDzKmKW+TK6emCdkafzojdZIMogBvUx3pqbPcjiIcZGl9sti2mPiUxc0Lb+aTzS/JqCSDKAVVSztpPxwmm5COB1NoIBqQkb4DxTra3I4wLsiIJCEmqVfa5YpgXrTm46+/7HYK4TAFzJSC88bxdsrP1DTdKyxKUVOTaGVRU3aB2zGEw5QFgaK81/oTE4ACimMyInQgyyMXIUFGJAkxKrt37x6xTW1tLTNnjv95sxqpEZMXpVBKViIxUVlXnANuhxCOygRk1IqJgqmkjEgyzLthGTloGm1LnSQTRRIy02Kg9954jWhb66Sf3iYjkoSYpEq9MvQ4L1qjZYUvI3XJcG2jaMAum+J2DOEwBSysP+J2DOE0Oa4aRWto3huSaW0mkr/VQbRt01x71O0YrpOOJCEmqbe6ZKhqXpTi5SXnuJ1CjIGsFJA0iur9nzCOfI4xTrO/xO0IwkFKgZ2RY6qJQmmZrphLqFCmXEtHkhCT1IyADD/OV2uBHCxMlLWk18EkGrADIbdjCIdpoL5ock8fMI7WnNEpE4tNk03LMdVEbRGZVpxLvLPT7Qiuk44kISapY0m5wpCv0q4OtyOIMeCxbbcjCIepjNRyMI0CyqLyHmwUpTgcqXY7hXCYnZaPlabRQFxWt82pvHr818Mda/IXL8QkJSOS8qQ15+/e5XYKMQaiQb/bEYSDFOCRDgcjRYNhtyMIJ2nNWR173U4hHFY6X1YDNo0CKqJSCiOXWHub2xFcJx1JQkxSe2NJtyNMDEpRVyEFfE2jgcaiiNsxhIM0kI0UuR1DOEwDDYUytc0oSlEXrHI7hXBYqDyDvzjldgzhIA00ydS2nHa/8IzbEVwnHUlCTFJT/bJiVb7emL/Q7QhiDPgzWbcjCCFGoIBQWj6cGkdWgjKOUlBYLaNXTKKAjExtE0OQjiQhJqlzi2SqQL5ai2V1GdMooKIr7nYM4SAFeDtlqLlpbKA1IgseGEdJYWYTKY9coDGJBryyaltOS1avcTuC66QjSYhJ6vWOmNsRJoxpjQ1uRxAO00BTgazwZZqsV66cmkYBwZRMxTaK1lzW9Du3U4gx4IvISDOTKGBGq6xONlBBWTmVs+e6HcN10pEkxCR1PCmrG+VFaxYeOeh2CuGw1nCArlDA7RjCQRqwpZaOcRQwr+m42zGEU7TGY2dYHJPjqonSURlpZhpvRla4HairpZloW6vbMVwnHUlCTFKXVRa7HWFiUIo/Lj7b7RTCYV1+GbliIish0xVN1OWX0YPGUIqs5eU/qj7tdhIxFpR8tDRNV0hWuM2lufao2xFcJ3/tQkxSFVJsO2/+lBR6NY1PrrAZp7tGUovbMYTDNNARkpp+RlGKe2ZscDuFGANaBrsbRQNHK+TCcy6hQqndJx1JQkxS+2Kyska+FhyTqw6msWTFIONoIFNY5nYM4TAFTGtrcjuGcFhZqtntCGJMyNQ206S8HrcjjEvxTqkdJR1JQkxSZXJgyFs4IYXJTVMVTeCXlUiMo6XYtpHSXhlBa5ozY++5HUE4TGtItst7sEkUUCA1VXMqr57pdgTXSUeSEJNUS0aWaM2XkitsxlFAZUfU7RjCQQrwtcgKiybypeWDjFG0Zn70sNsphMOUgkCJXKAxiQa6AtKRn0usvc3tCK6TjiQhJikZkZQ/jUyDMlEwLZ2pxpEOByMVpaSIulGU4uUSWcTCROmYnFuaRM5+h3b4T7vcjuA66UgSYpJ6vUOma+VLRiSZRwMtkaDbMYTDsoGA2xGEwzSwd4pMITDNrPgJtyMIh2kN0To5rprEQqa2DaVqzjy3I7hOOpKEmKSOy4EhbwVxmQJlGgUoJR2EprHkZ2qcuNdPXWmV2zGEw6anGt2OIMaAJyAjfU2iAS2Lk+TUVi+d4dKRJMQktbIk4naEicG2OevgPrdTCIfJvH8zZf0yIsk0wUyKUEpWGRVivFMKCqvlb9UkCiiLys80l64WWXlSOpKEmKTe6pKaE/lQMkPcSCkg5ZPVZUyiAR0qcDuGcJgFTG2TE3YhJgKpkWSejCUjfXM5bcVKtyO4TjqShJikXmuXGkn50JaHw1NnuB1DOMwP+NOyuoxJFKCy8jM1kT8rU7FNc9xf6XYE4TCpkWSmhF9Gbw/kD4WpnD3X7Riuk8uxQozCkiVLRmyTniCrBp1TFGZbU7vbMcY9lc0y+8Qxt2MIhym6C0i2yKgkY2hAe+TnKcRE8MeixW5HEE6TAdzG0UBUygAMkorHiLa1EikpdTuKq2REkhCT1MfLCt2OMCFcuPNlyjqkw800Gkh55BBoEhmRZC7/BLlAI/KXVNLpaxplQaBI3oNNogB/1nY7xrjUXHvU7Qiuk3dxIUZh9+7dI7apra1l5szxv1TxgXjS7QgTQiCdcjuCGAMKiCTTdIWkOLMpNGAHQm7HEGMgnJZir6ZZFD/idgThMG1DskM+WppEA14pA5BTefX4/6w31uRyrBCTVG1cOkjy8dT5F9JSVOx2DCGEmLRifqm7YhStOatTVkM1TcveCNmEFNs2iQKmtXW5HWPcCZeWTfppbSAdSUJMWg0pmSqQD+2RYtsmknn/5lGAp7PN7RhiDHQFwm5HEE5SihfKznM7hXCQ1qCRKVAm0lL8apBYawvRtla3Y7hOOpKEmKTmh+UKb160pqir0+0UwmE9U9uEWZRMRTWODZwoKXc7hnBYMCvT602iFJQviuMJZt2OIhxm2dKRlIvUSJKOJCEmrXOL5ApvXpTij4tGXq1PCOEuDWSLZKi5STSwt3IGcZnaZpwVnX9yO4JwmBTbNlNAim3nFCqURYukI0mISeo3jTIFJF8Hq2e7HUE4TFZtM5P2ynRFkyhgTku92zGE07SmOCUjfU2jNVg+GZFkmpRX6l7lEu+U9zA5ixZikvpDa9TtCBOD1ly64wW3UwiHKaAsKitBmUQBVjLudgzhsGA2w7TWBrdjCCcpxYtly9xOIRymFBTMkOOqaaZ0xvHLym2DyIgk6UgSYtL6aGnE7QgTg9bMqT/udgoxBhI+ucpmEg3YgZDbMYTDFDC/Ud6DjaI1q1peczuFGAOpLjmumsYCCqSm5CAyIkk6koSYtC6rLHE7wsRgWbJqm4E00FgknalCjHcaqC2pcDuGcJJSdPjkar6JMnGv2xGEwzTQJavcDiIjkkD+2ieIt99+m5dffpn6+nq8Xi/V1dWsXr2aadOmjXpbWmveeOMNdu7cSWNjI0opZs6cyUUXXcT06dPHIL07Dh8+zD/8wz/wL//yL25HGZde74i5HWFikFXbjBTzWqR8cgg0iQKsWCfZojK3owgHKSDlC7gdQzhsf3iW2xGEEHloDfrkfCmH1hPHqZw91+0YrpIRSePcU089xTnnnMPixYu57rrr+O53v8vNN9/MVVddxYwZM9i4cSOHDx/Oa1taa37xi1+wYMECVq1axUMPPcS+ffvYsWMH3/jGN5g5cyZf/OIXaWxsdCT7pk2bUEqN+r+FCxeOuO1vfetbI25nzpw5LF682JHXYqLjMkw1P0rRUSBXHUzTEZQPpiayElIjyTQaiPv8bscQDqtMNbsdQYwBOy0fLU3TFZT335y02wHcJ3/t49gdd9zBpZdeyq5du3I+rrXmkUceYfny5bz22vBzzZPJJOvXr+crX/kKS5cu5ciRI/z2t7/lX//1X3nkkUc4cuQI11xzDQ8++CDLli3jyJEjHyh7XV0djz322Ck99xOf+MSwj7e3t3PXXXeNuJ2ioiKuvfbaU8owGawskWk9edEareVoYZqEX66umchKJ92OIBymgFA65XYM4TAlh1XjaA3xJul0ME3Wku6CXIIFBW5HcJ2cSY9T27dv55vf/CZer5fPfe5znH/++RQUFFBfX89zzz3H888/39u2qamJz372s7z11luUlpbm3N5f/uVfsnXrVhYtWsSvfvUrfL7+c10jkQh33303b7/9Njt27OCLX/wiL774IkqpU8p/9913k8l0V/j3+XxUVFRgDfNGlEqlekdCbdy4cdht/+xnP6Ojo2PYNj6fj+985zsUyvzVIR2Oy4l5XpTi1xd/inPffdvtJMJBspyteTSQKZDab6bRgJZLv8Y5FJLagybyBLJkE3J8NUlHWEZw53Lw9VeZdeZSt2O4SjqSxqFkMsnXv/51Zs2axfbt23NOz3rxxRf5/Oc/z4kTJwA4fvw4P/7xj/nBD34wqG1tbS0/+9nPAPjSl740qBOph2VZfOMb32DHjh384Q9/YMeOHaxcuXLU+W3b5u6772bp0qXccccdrFq1asQOqZ/97GfceOONlJeXc9FFFw3ZLpVK8c///M9ceumlPP7446POJk56s1NqJOVr76x5bkcQDvNnsm5HEA5TAF45rTGNAiq62qkrrXI7inBQwpIPp6ZRCopnx2h8U0YlmcJGFiYZiuLUBluYRMaqjUP3338/x48fZ9u2bUPW+Fm1ahXbtm3D4znZ6/+rX/0qZ9uampreqTkjFdP+yEc+0vv1K6+8MtroAPzmN7/Btm1qamq48MIL8xrV9NBDDwFwxRVX4B3mg8B9991HXV0d3/72t08pmzhJru/mr7iz3e0IwmFZS04ATKMB7ZGOJNNooFY6kYwj5yCG8spP1iQKqJLFeXJavHqN2xFcJx1J49C9997LN7/5Tc4666xh2y1fvpz169f33j5w4ADRaHRQu55RS9A9cmk4tm33fh2Pn1rR0scee4xf/vKXVFZW5tW+sbGxd6recNPatNbcdtttLF++nDVr5I/3g6qUGjF5W3DsqNsRhIM0gJaOJNMowEpJjSTTdPkDtBYUux1DOEzegs2UbJVl4k2igDNrG/GnM25HGVcWXrBq0q/YBtKRNO7E43F27drFd77znbzaX3rppf1ut7cPHjlRVXXySt5IBbAPHTrU+/WcOXPyyjDQL37xi1F19DzyyCNks1mKi4uHLbS9bds23n77bSzL4o477uDVV18lm5XpKadOzuLyNb25we0IwkEKmN/cLidGJsrKz9Q0+yullo6JIlnp9DWN1hCtC7kdQzjMAkqjCbdjjCuLPnqh2xHGBelIGmcymQz33Xcf5eXlebWfOXNm79dKKUpKBhcaXbr0ZCGwnTt3cu+99w65vf/8z/8EIBwOD+qkytdoC3T37PPyyy/H7x96XvU//uM/At1T7v78z/+cFStWUFZWxuc//3l+/etf9xb3Fvk5PSL1CfLVUihXw02jkBMjE0lRZvOccfw9tyOIMXBWdJ/bEYTDWt6JSKFtQ3nlwn0/jUcOjdxoEpCOpHGmsLCQz3zmM3m377ss+aJFiwiHw4PaLF++nPPOO6/39le/+lWeeuqpQe127drV26nzT//0T0OuAOekfKe1vfTSS/z+978fdH9HRwe//vWv+fznP8/8+fPZunXrmGU1zd6YXA0UQpjF6upyO4JwWEDbzGmodTuGcJJtc0nzDrdTCAdpDbZczzVWxiMdhH11NDS6HWFckI6kCa5v/aPPfvazQ7a7/fbbe1drSyQSrFu3jjvuuKP38TfffJN169ZhWRZ33XUXX/3qV8cudB8909oikQif+tSnhmy3d+9ezj//fBYsWJCzswzgyJEjXHHFFdx6661jFdcoU/0yjz1fMxpPjNxITCgaiAbkb8A0voR0JJlGAWfXHnA7hnBQVbKZqnSL2zGEg5SCiiVRPEEZuWIaG2iNBN2OMa7MX3beyI0mAam2O8Ht3LkTAI/HM2znz0c/+lHuuecerr76amzbJp1O8+d//uds27aNiy++mFtvvZWlS5dSU1PDkiVLPqz4vau1ffrTnyYUGnpe9bXXXsu1114LdE//27lzJ08++SQ///nPqa3tf6Xyb/7mbygsLOSb3/zmKecauM3RqKurO+XnfpguqSiGfXKVNx9TW5vdjiAcpgB/1h6xnZhYUgGpz2Gi7CinzIvxrSFYwZ7wPBbHDrodRThIWRAoyhCT6W1GaQ/5Sfmky6A/OSaBdCRNeNu3bwfgmmuuYd68ecO23bx5M0oprrnmmt56QjU1NdTU1LB27VqeeOIJPB/i0MXGxkaee+45YPhpbQN5vV5WrFjBihUr+O53v8s999zDt771rX6Fxm+++WZWrlzJihUrTilb39pTpqoK+LioNMLzrYNX+hN92DZnHZRaDqaxgS4ZkWQcFZDabyY6Uj7N7QjCSUqxveJCFh+RjiSTaA2ZpHzANo6W2oMD7X7haRZe8DG3Y7hOprZNYE899RQHDx6kvLycLVu25PWcL33pS2zfvp3CwsJ+9z/99NNccskl1NfXj0XUnB599FGy2SzBYJDLLrvslLbh9Xq54YYbeOutt/rVgcpkMnz72992KqqxZgflQ9eIlOK9KdPdTiEcpIF3q0rkCpuBtF9GJJlGA7VlVSO2ExNLJBNzO4JwmFLgDUing2mKE2lZ5XaAgztfJdrW6nYM18lZ9AT2d3/3dwD8/Oc/Z8qUKXk/b/fu3SQSCcrLy2luPjll55lnnmH58uVs3bqVc845x+m4g/QU9r7kkksGdWyNVnV1NU888QQXXnghu3fvBuD555/nyJEjzJo1a9TbO3r06ClnqaurO+WRUB+2Sr+8BYxIKZ4/93zOffdtt5MIhygg45HrKKbRQLZw8MqlYmJTQCidQk7ZzVKd+vAuXIoPh9Zg+aRGkmksoCCZpkUuvp2kNc21R4mUjP3CVOOZ/EZMUPfccw+///3vufnmm/OeFpZIJLjuuut48MEHuemmm/j7v/97vve97/GTn/ykd/W32tpaLr74YmpqavqN8HHaqU5rG05paSm/+MUv+NjHPtb7enbs2HFKHUnV1dWOZBrvHqlvczvC+Kc1F+182e0UwmFFiZTbEcQYUJk02itTFk3SXej1g11sEuOM1pSmOtxOIRymFESmpOg6lntRHDExSSmAwSzLQ3m1+WVQRiKXZEfpr//6r1FKOf7f/v37885w4MAB/uIv/oKNGzfyox/9KK/nxGIxPv3pT/Pggw+yefNmfvzjHxMKhbj99tt56qmnmD795NSd9vZ2rrjiChoaGkb9/clXz7Q2n8837Gpzo7Vy5UpWrVrVe/uDFM023Z7OGIfkw/SIytpaZTSSYTTQHJZpnaZRgCcqH05NIxVXDKQUbxWe5nYK4TCtIVrvdzuGcNihymIpBTDAolUXTfrRSCAdSRNOV1cX69evZ9myZdx///1YVn4/wuuvv57nnnuO8vJy7rzzzn6PrVmzhp07d7J8+fLe++rq6vjbv/1bR7P31bNa25o1aygpcXYqQt96S4lEwtFtm2R7U/vIjQRn7n/H7QjCYQrwSxkH42jAVnJaYxoFlEY73Y4hnKQ1Z3a+63YK4TClwE7Lim0m0cCxUhkROtA7Lz4nNZKQqW2jdsEFF/D1r3/d8e3m05mSTqfZsGEDoVCIxx57jECeq9O89NJLPPDAAwBs2rSJ4uLiQW2mTJnCk08+yfnnn8+773Yf3O+77z5uu+02IpHIKF7JyJqamhyf1tZX39XrKioqHN++KWxbPknn44VlF9Dy63sp65CON1NoIOaVk13TKMAb7yKLFGY2iUxtM5BSvFR8Nqs6drmdRDhIaiSZRwH+rO12jHFH27bUSEI6kkbt8ssv5/LLL//Q92vbNldffTXHjh3jhRdeGFVx6p7RP9A99WsopaWl3H777b0jemKxGDt37uTCCy889eA5PProo2QyGTweD+vXr3d029C/U240Rcgnm7gtB4a8WBZ/mreQi3a96nYS4RAFzGzuoEGushnHDkhtDtPUFZUR9wfdjiEc9seiJW5HEA5TCgqmJ6VGkkE0kJLFSXIKfcCFokwgvxkTxI033siOHTuoqamhvLx8VM/du3dv79dTp04dtu26deuYO3du7+1jx46NLmgeelZru/DCC6msrHR8+42NjQAopVi2bJnj2zdFOM9pkUKYqCqakOVsDaMBOygfYEwzraOFUEqmqRtFa75c999upxBjINkuYxRMooDyzpjbMcaleKdMuZZPkhPAd77zHbZu3UpNTU2/othDefjhh8lkTn5A6lnBDOh3/1DOOOOM3q/znT6Xr7Ge1gbdU/kAzjvvvEmz+topUVLCNC+2zVkH97mdQjhM0b2crTCHAqyYnNiZxgKmtjW7HUMIMQKtIdEqq3uZpiQqHfm5yIgk6Uga97Zs2cJdd93FE088wYIFC0Zs//rrr3PnnXfi9Z68ItB3hFE+q8P1nTaXzz5Ho2dam1KKz33uc45uGyAej/Pggw8C8LWvfc3x7Zvk9IisWiUmL40sZ2si7ZMVg0xUkJQr4kZRintmbHA7hXCYUhCuktWATdN3QII4SUYkSUfSuPbTn/6ULVu2sH37ds4+++xh20ajUR599FHWrVvHl7/85X6Pbdhw8mC9bdu2Eff71ltvATB//nzOOuusQY93dnayfv16CgsLWbt2LSdOnMjn5QAn6zVdcMEFzJgxI6/ntLS08Mtf/pKHHnqI9vbhCx5///vfp6mpiTVr1nDNNdfknWsy2htLuh1hYrAsDk/N73dVTByHywplOVvDaMAOObs4hBgfwsm42xGEw8rSLW5HEGMgHZOFLExTlJDOwVxkRJJ0JI1b999/P9/4xjeIx+NcfPHFBIPBYf8rKChgw4YNRKNRvvCFL/Tb1tq1a1m3bh0ATz/9NI8//viQ+33yySfZvXs3AD/60Y9ytrntttt47LHH6Orq4plnnuGv/uqv8npNzc3NPPvss8DoprVdeeWVXHvttVx55ZXMnj2bu+++O2e7hx56iH/8x39k6dKlPPTQQ1hSA2hYEfn+5EdruRpjGBvYP7XM7RjCYQpQ8S63Y4gxkPZIp69pLm59ze0IwmFa9/xPmCQjHxdykhFJ0pE0Lm3bto1rr70WrTW2bZNMJkf8r8dVV11FJDL4iuwDDzzQu2LblVdeycMPPzyozbPPPsvmzZtRSvHDH/5wyM6ebLb/0p7pdH51RnqmtUH/UVIjicVODmlvb2/nhhtuYOPGjezZsweAo0eP8q1vfYurrrqKjRs38sILL1BWJh8SRyKH+jwpxf6Zc9xOIRyUARmNZChPHnUAxcTjs2VJcaNozTmd77idQjhMKSieI6MHTRPMyCrPuZRXz3Q7guukI2mc2bdvH1deeWVeRbFzuf7663PeX1RUxAsvvMAPfvADgsEgmzZtYuHChWzevJnrrruO5cuXs2bNGhYsWEBNTQ3f/e53h9zHTTfdxLp16wiHw6xevZotW7bkla1nWtu5557br27TSO666y6WLl3a775HHnmEJUuW4PP5mDNnDrt37+bxxx/nP//zPykqKsp725PZ4YRMbcuL1ix75y23UwgH+UBWbDOQBrKyTLyR2kIyhcAoSrGz6HS3U4gxkE7IR0vThLKagrh8Zujrgo1XESkpdTuG6+SS7DizcOFC4vGx6c33er3ccsst3Hzzzbzyyiu8+eabtLa2EgwG+eQnP8nKlSvz6uApKSnhN7/5zaj3/8QTT5xKbM4880x27drFnj17ePXVV2lsbCSTyVBSUsL8+fM577zzKCkpOaVtT2YhmdqWF08mw/zjR92OIRykgMqOKMfKi92OIhykAE8qgYxdMYsGjlRMdTuGcNjD5Z/gyye2ux1DOCzRIgsemEYBFV1xukKySE+PdFI61kA6kiYlv9/PqlWrWLVqldtRRmXx4sUsXrzY7RjGuHJqGT+rbXI7xriX9XrZedoZnPvu225HEQ4KpqW7wURZLUPwTSPTsM30bsEstyOIMWCn5SKlaTTQHpQOwr52bn+M8z6zYdKPSpK/diEmqQq/LH2eF6V4/tzz3U4hHKSBzoD8/pvIo+S0xjQWUBqVoqam+XjLq25HEGPAF5KeOFA+AAAgAElEQVRp46ZRgFLK7RjjirZtmmtltoKccQkxSe2LJdyOMDFozTlSI8koCiiR33/jaCAbKnA7hnBYFmiNSI0ko2jN7GS92ynEGPAXS0eSaWygSy6+9aeUFNtGOpKEmLRme6NYWqb3jEgpuRJjII8tE2ZMowCVlroFpjlUMZ24FFE3i1L8nzlX0+CTFXZNE2+WKVBCTBbSkSTEJBXJHOJT/LfbMSYEOySdDqapK5GfqYlUOuV2BOEgDbw2e5HbMcQYsJWHfZHZbscQDrI17G6Z43YM4TAL8GVlFHc/WsvUNqQjSYhJKxJewGVsxdJDDEPW8kEbAG1zenyv2ykcIT/Rk/bPlOH3JrIjlW5HmFzG8k1Fw5xMJZZPprWZyNJZFkYPj9hOTkUmBq1hS2Yzz5af43YU4bCMBQenfggLWaiJswK3sjwytQ3pSBJi0goEKplbuoT/wV14ejqT3j9jUzrLmeyC4VZA6m2bYZH+E+SaJtdzBqg1M/QRVO9+sv233addv38HPp7rttYU6RZUz/4HPDbo9lDbzNnG5nr9M6oOdnTfzP2MU5JrWzqPr4e7r+/9Ax/XQEuI3qXRB7Yb6nnDyZUxy8j7yNUmq7rn4efadr4Z8n08q+BIuafPPTJ10QSW/wwsbx4dSSP98QzXxg3jJUuON5WQ9ud+s3FgXx/JzOGTmbOZ03WKH2BM6YGYKK9jNDm15pttd1CVbhm2WUbD/8lsIKPHx8eVUb7EvO//MH/EGW3xx+wCBs7u7pvB1oMzDXf6ZGu4NbOZu7OXUR5vH/QWYDP828JIj+U6PxnunGXgc0ZzbjWabH3ZQENR7v3mew6X660017tfPq8pn+/3cN+LntsZC+6+1KItkuM8yXcGgeKv4g2tJr/zKAtP6BP4IpvwBFfji2zCF7kcb/hyAsVfJVjyP/AX/j94gqvx+Jfl3oLvNHwFm7CCF+Sxvx4KT2g1ViD3NodWguVfhi+y6f3n9rxGi9WbvzLpV2wD8LodQAjhjmSykdbWl7gYm3N4jWO6mgI66NJFzKCWEtpoo4R9ehENVNJOKQESJAkyjePM591Bbffq04kRRgMRYkTo5LCey5m8yWyO0EYJx3Q1M6gFYJ9ehEYxleN06SI0msN6LrM5RIOeyl4WkSbAEt7gPT2PFso4gz2cxZu8pc/mCLO5kOc4k92929ZodulzAbiQ5ymmg9f1uTRTTjVHOKZnUUoL83mXej2t97VpoJNCqjlCrZ7NTA7zMf0i6bo23vP7KQnBK6cBHpjaAm0FUF8CwTS0FsDUZjhvL0TScKII3lhA9zFHw+xGCCQhkIJIChqL4YllUN4Jpx2H2nKorbIIx2xW7oW9M+CdmYozaqG5UNNQrDj/HU15JxyaAkenKLoCcP47mplN3Qf8jjA0lsDxUpjfAMeLYU4TJLwQzMDzZyr2zPVQ3KWpbtZUNtucdwCawjC3CXbNhmRYcagSCpKKcEJT1tG9/Y4glHdB2gNJX/d/h6YpGorhgnc0hXF46XSIRixqy7sPtNXNmo4gFCXo3der86GxPHebnvvO2W9T3ql5u1pR1Q7n7tNMb4Pd1XCiHGbXQ1U7pL3wxlx4b4rignc0pV3d35uWInq/8bGgRV0pXPmCzXn7u6+c9JwUFemVeIIz8fhmY3krScffJJvajce/BGUpMrGaPn8tBeCdhuWdCnYabbejdRyy7/W2UIFz8frmoVQIreNYnnIA7MwxMplGdOY44MXjm0828x5k6uhOVAgcP7kd3xl4/UvIpvZhZ1vBbgW68v/D9szF45uCUhVk03vQmUPkPqUMgacalAey9aBbR962NR3s433uKAXvVCxPUff3JN0ADPxgGAR6hsQrlO9M0FF05uDJzQYvQOEnm/gTkEeOAZRvIb7g+ShvORnvy913anr//t7fNWg4MzuTmZlyjnqaiZPCh4ciHaaQINrWHPTWE9R+bDT7vHWDt9Gj7/YH/kv/+5SGCruQRqsz97l2z3OH2O6ZmWqaVRd1nrah8wzcTq77c2Xs06YyV0YN5XYB87NTaLA6eM/T2LuN6mw5n8p8hD96DrDL+17v/Qsz05ipy2mmk32e48Ss9KD9h2wfHiy6rOSg/Z2enc65mXmECaDRdPh6LjBk3/+GWicvRPRdpU+///FKWaCzrKGGvXoxx5gJSg16/OS2+tyfi9bvP9/mTN6gjuk0U9V938D9AmibFfyB40ynltndf2M92xi0bfv9HH0e6/mU/n7mEF3EdcHJ1zBwO+/fp7Td/aPteU192/d5vUpnmcUBDjO/f7aB/76fTwH6/e/ZTA5zNNdr0pqFvMWn9W95jfP4PRd1PyfH97KcBr7PLRRFk/xTegNhHecs6xCzVT3TrFY8CrJa8X+za/hJZiONlFBPObd6/w2fypJ9/9vjUd0dTRZg9flR6Pdv98TL6O5fMU+Ol5jrJdsa9trTCJJhttXYu21bw92ZdZypDnKB551++8hq2G9PZ551HJ+CtLa4I7OeM9Uh1np24VG69zXdn/kE/8v3Sy6w3und92+y59NCAV/yPINXadJa8az9Efw6zYWet3q/J0fsCkpVF0UqjvX+ft/MzuFPej5BleRznhfxDcjV871Ka/i37GXcnbmMRkqopI1l1l6KiNJBhPfsqcyxTgDwmr2I6z3/zfXe7e/vu/u1v65PA+A9eypnWweYp45zUE/nWftcGikBNIcX7uW2c2BmsyLm1zQXW+ytVhRFNavfsimKgvf/Z+/O4+Sq6vz/v86tvar3Jd2dnSSEkEBYJ4CADF8dcSECItEZlU3m6yAwqICoMDI6I4z8wAfIgF8RRx6AgwvKKoqDgEAwOOyQkLVDkk66O+m9umuve35/NF3pLd2dpKpP9enP8/GAVHXdOvdd1d3V937uWTIQyEBzJSSCsKMajt0Ms9tgdzlsmt2fO5xS/HVx/zdg+TbNnhLNnHZoL1VsaVCUJfqPTxo6IZxwCScglIa/LnZyz3lrXv/zV2x0c89b2KypjmpiXvibLdAZgi2zoKIXlm3vP8Z5Zx40zVA0VyqOeM9l8S7YU9r/a9RZAvGAIpTsP/bYVQ2vL/LQXaKY0+oO2e/ybfr94yoIJ2Dg2AS3/3hPu+9/NDqw5jA4dCcctwU6I/CbUxQ7ax0Oa3KZvVtzSGv/vv/n2P7X93evuZQkYFN9//Hetlp4+xBnxPsSSkJVb/++yhPQWgoL98A7s6Eq1r//NxaoXMZYEJorVe7YrLtE8eF1p+MrmT/kGEc5EQC8wePx+A/HzbajVAg324lSoJxK0sm16EwTHv9yfKHluY8CD3NHfj4Ajrc2d0HIGzqebHon2u1B6xRe/6G5xzy+uejAUaST6yDbhuNdiPIERx6DZduHZNXB4/fmzLTguu0opwqPt6H/uE6Dm92dOzbM5fXPzT03GKnl+DM/MWr+6UZpPVUudQhh3rJly8bdJp1Os2nTJgB27NjB7NmzCx3rgHR0/oXXX/+86RhFL56Fb+4Km44hDtJAAa2pWtEdUZz35tepjs/a5/ba7RtxAHIg20zEeO1otw83sxPXTeA4QRxvf+7hB22Od9aI5w9uW7sxsultIw6QANzMHjKpTSjlB+Xvn7jaCaKcyiEHjRPNqjV4fLP2+Zx9taPdPrLpnbjZNnS2HeWpxvHU4PHtfc37OkD0qDSLKjsJ4KXMDZN00lS4/Y91OX1UuBHCBCb8fYmRzD0PoNXpIkmaAL5c+wHXN+Tfwfsb/LUwAWIkaXW6iBInppKEdYBSQpS5YXqcGMCIdgdnHpynk142eZspcYOUEMq95h4nRpQE3aoPUNTqMmrdMpJOGu1qOjy9zMxWEcLPdmcPXSpGhQ4z160dkjFJhgBe6tyKIe9ZO1F2eTqYma2imtJR36vh7/HAc6qyJShHjXhNm51mdjvdzHArWOTWj3j+xuX/xTv1HbkLEDsZdDGC/gsdSUIcz8uU05N7vIIuAN5hGX/lJFbwF2azc8jzB99+jePYxUwq6CRMDFCU0EsdzfRSNqTNbczlfzmBKto5ltcA2MASFJrFbMht10UFO+m/SLOFQ9nFTAIkSBGkgZ25527kMPqIECbGYayniVm5zEewlm3M5R2WM4+t9FFKH+FR8w28phJ66KUs9+/w1ztw8We0bUvooYWZudeyr+dpNG9wLAo4hT8zj+2571kXFWyg/8JSH2G2M5c0AT7EHzmCtQC80PQ33LvuC0O+17V0scjZyWZ31vuFidEfA4bcPt15jXrVwR+zf0M75SxydtLpllDp9A7ZfuBro/07uIgysO+BYstoXx++j4HizPD8+3pNS9jGyZ53WJ09gvXM2+e2+9PmaLkGv1fD39PxjPX9GJ1L5NAbcbz7cfFDFD/NuMdM080Hzl3EMX83ejHMtKamJubM6R92V+jzUCkkCbEfbCokRaPr+ev/SkV9PFrDnbsDbE55xt9YTBkr117OrJ5DTccQeXZGmZfgQBcCYYVdS39KdPYLpmOIPNvUMZ//eOVrpmOIPAvNvRtvpHH8DcWU8sHNn2Hpng+YjlFULvz+yUTKJ35xarJMZiFJhrYJsR/Wrl077jaDf4GLWXvHi6YjTAlKwfGRtBSSbOJCZbzOdApRAO0Zl1l++V21ifbGTEcQBbCw8j3K/D30pMpMRxF5k8UJ7DYdQhRAV6jVdISi09kSK8pC0mQqjtnrhBCTrrfXjpXIJkNzSj4qbeLNBAin5eTFStLH2ioaTdbXZzqGKABHwaLyLaZjiLzReErfkWFtlgpLwXeEQET648jZkRDTVEPDOaYjTAlaw2txn+kYIo8yviTtoZ2mY4gCKPHIsDabKBSV2z5iOoYokOrQ2Ku2ialEkY0eiZspMR1EFEAgGzIdoeh0746bjmCcFJKEmKZKIjI/zERIBwcLKWgq32g6hSiA3uFrWospr6TtaDzJctMxRAEEPUnTEUReOWSih5kOIQogkJVFZ4ZLxtKmIxgnhSQhpqm+2GbTEaYER0GdzzUdQ+SThpq+maZTiEKQOpJ1FA7+vgbTMUSeaQ2v7TnKdAyRZ26ydvyNxNSiIZCWHkkjyQGHFJKEmKYi4UUoJeN7x6M19GVluIxV+te2N51CFIAMbbNP/zxJMu+KbdZ3LGRnrywnbhvl7TEdQeSbgl1lshKfGEmOpIWYpgKBWkpLjzQdo+gpBRGPXHWwiga09DKzUUp+Va2jUPj7ZJVF26SzsrqijRx/t+kIogBKUjK8eLhA2G86gnFSSBJimopG19PT87rpGEVPa2hNy0elVRS8U/ei6RSiAPzSIclKnkS16Qgiz5bVbKTML71X7KLxhLeZDiHyTYM/I0PbhmtYKMU1OTsSYpraveePpiNMCdLBwU7dwTbTEUQB9GblN9ZGvkSF6QgizzwONERaTMcQeSWfv1ZSsLN8k+kURSVU5iNSHjAdwzgpJAkxTQUCMlRgImSybQtpOKLlFNMpRCFIjyQrpSLNpiOIPNMaetMR0zFEXjlkY/NMhxD5pmFhu0yMP1i8J01ft6w6KYUkIaap2pr/YzrClODK0DbrOFkvS9tONh1DFECJI5Uk22hc+ma8ZTqGyDOloMTXZzqGEGICKhOycuZwnS0x0xGMk7MjIaap3Xv+ZDrClLA5qYi6cnJqk/mdy0xHEAWyK5U1HUHkWcbbTTYgE/jaRmto7qs3HUPklStzJNlIQUuprNo2nKtlKKcUkoSYpnbtetB0hKKnNVQ68ofCNmknbTqCKJAOqSNZx5spx5OUSU1tE0v76UmVmY4hhJgAORIeqXlzl+kIxkkhSYhpqqJ8hekIRU8pqPHBIr+cndpkR9U6Yj5ZLchG82TZNusoHIJdi0zHEHm2uWu+6Qgi7xzc5AzTIUS+aQimZdW24UoqZLJtKSQJMU3NnHmu6QhTglJwfER6sFhFQbN007aOH5gd8JqOIQpAyeVw67zWeozpCCLvsjiB3aZDiHxT0BbZZTpF0Zl/ZI3pCMZJIUmIaaq7+03TEaaM5pR8VNom5ZFJEm2zPCy/pzbSuMQrN5uOIfLI1fBW+5GmY4i80jjh9TjeXtNBRL5pKElWmE5RVGrmRIiUS48kOeoSYpqKxbeYjjAluBpei/tMxxBCjKMnK91WbJQOtMlk25bpS4VlfiTrKNzYEtxMiekgIt8U7CzfaDpFUWnb0Udfd9J0DOOkkCTENOU4YdMRhDAm6u80HUHk2cakJuFKMck2vmQt/uhs0zFEHpX4Y3z60IdNxxB555E5kizV65Ni/nCdLdKzXQpJQkxT6XSb6QhTgqOgzueajiHybHvFOtMRRAG0Z+R31TYKRbh9qekYIo+Ugo/Of5Yyvyx6YBdNJlZvOoQogIqEzAc0XGW9XJCXQpIQ01QyKT0yJkJr6MvKSlC2ifrk599K0iHJOhpNsnS76Rgiz5SCRRWy6IFdFJno0aZDiAJQUjIQo5CfCiGmLVmJbCKUgohHzk5tE8hETEcQBVDikaKvbRQK5NtqJy1/W23jBHeYjiAKwOPKXKHDydA2kHVyhdgPy5YtG3ebdHpqFGhKS5bS3v4n0zGKntbQmpaau20iMiGolXpdTbnpECKvNFp6mlko68Lm7oWmY4g8c/ztpiOIAsh4UqYjFB0Z2iY9koSYtjxeOZGeCDl/sVNpssp0BFEI8gtrHYUiEJ1rOobIs//Zdrqs3GahbGyO6QiiACrjdaYjFBfpJQtIjyQh9svatWvH3aapqYk5c4r/D2k8vs10hClhYLLtaNJjOorIo5KUFJJsVO2T62O20Whi1TI5vk1cDU9t+5DpGKIQsqWmE4h80zCv6wjTKYqL7h/aFikPmE5ilBxxCTFNOU7IdIQpwZWhbVZy5M+flZKudEmyTcbfSaq0yXQMkUcKKPVHTccQeafxVb1kOoTIs8V7jieclt6DgyklQ9tACklCTFs1NX9rOsKU0JuGqCt9WK2ioaFngekUogC2p7KmI4g886Yq8CRl5iubKAXHznjLdAxRAE5A5kiyze6ITKA+3NwjqqZ9bySQQpIQ01a0R4YKTERjxnQCkXcKUPLnz0a9UkeyjsLB39dgOobIs8649HCwjyITHX9RGjG1dIVbaQ/tNB2jqHh9Mt0FSCFJiGmrJ/qG6QhTgiO9keyjIZiWLsk2SrmmE4h802hSkWbTMUQeuS681X6k6RiiAJRKmI4g8k3B1irpQThYJi1XrUAKSUJMW2WlR5uOMCVUeGTOFesoSPhiplOIApjhM51ACDEWreHNtmWyYpullCdpOoIogKQTNx2hqGx7u4O+bvlZl0KSENNUadlS0xGmBLnmYCENCU+v6RSiAA4JymK0tlEoSptPMB1D5IlSsLxmHWX+HtNRRAHorMwbY6OkXHwb4b2320xHME4KSUJMU72975qOMCVkpUOSfRTsLN9kOoXIszoPBB0ZimqjQHS26QgijzyO5ox5fzIdQxRAJinzmVlHQ33PIaZTFJ3eLumRJIUkIaapkpLDTUeYEoLyKWkfDWXxKtMpRJ7V+6SIZCONpmemLClum7+b96z0SrKRK+OLraMg7U2ZTlF0FhxdazqCcXKKJMQ0pZScdE1ESD4l7aNgd+l20ylEnqWR7oO2yvplKKptPA4sKt9iOobIK42/8mXTIUS+aej1dphOUVTKZ4SonV1qOoZxcookxDSltZx0TURzxnQCUQiBdMR0BJFnfTKhmZUUinC7zOlnJbmgZR03VWk6gsg3Bdur1plOUVRmLa4wHaEoSCFJiGlK5kiamO1JmbzXOhqO2H2K6RQiz9rSphOIQtBoYtVyEmMbraE1JkND7KJId51oOoQogBnReaYjFJV1LzbLqm1IIUmIaSsQkAkRJ0Q+Je2jIZSRLsm26QO6M67pGCLPUqFWUqVNpmOIPFMKSnx9pmOIPPOEZbiijWb2LjIdoeh0tshKdnKKJMQ0lUw2m45Q9LSGxoTHdAyRbw68V/GO6RSiALYmZXybTTSa9vm/Mx1DFIDW0NxXbzqGyCtNoGa16RCiADqCLaYjFJ3K+rDpCMZJIUmIaUpWbRufUoBM4WClmL/bdARRACnpkGQVhcKTDZmOIQqgsWsWPaky0zFEnqV7FpuOIAqgrUR6hYqRpmQhafv27bzyyiumYwgxpcmqbePLamhNT8mPSTEWDUklXZJtND8gv6820WiSssKildoSM0xHEHmnSHecbDqEKIDa3tmmIxQdGdpWJIWkaDTKtddeyzHHHMPKlStpahq76pnJZPjyl7/MhRdeSEeHLEcoxIHweWVljbFoDa0piLpScLOOgm1VMtm8jWb4ZSiqTRSKkrblpmOIAmjulUKSlWSZeCvV9S0wHaHoBCKyGI/xQlJnZycnnXQSt9xyC2+++SZPPvkkV1xxxZjPWbBgAatXr8bj8bBs2TKeeeaZSUorhD3SmU7TEYqaUtDghwavjJWxUXWfTDZvo4SrTUcQeVa57Qw8yXLTMUSelQWipiOIAvCXrzcdQeSbhoq4rLA4XLIvYzqCccYLSZdccgnr1q1Da50barNp06Zxn+fz+fjpT3/Kpz71KT760Y/ywAMPFDqqEFaJhGUFhvEoBYuDMnmvjaoSM01HEAXQLqu2WUfhwS+FX+ucNnsNZf4e0zFEXmmckMylYx0Fu0u2mU5RdKRHkuFC0ssvv8zDDz+MUgqlFFprtNZceumlE27jjjvuYNGiRVx88cW88MILBUwrhF16+8Yv2E53WgPSw8E+GqpislqQleTX1TqaLKmIrDJqG4/j0hCRlaDsosjGZS4dG/XKAiUjbHl9j+kIxhktJN17770AaK2ZP38+l19+OS+//DKXXXbZhNtwHIerr76aTCbDxRdfTCYj3cyEmIjdu39vOkLRUwqWh+UzxToKegNdplOIAqj2Ge9oLfJIo8n4esgG5CTGNlpDbzpiOobIs0zPUtMRRL5piCRlhcXhmta1mY5gnNE+WatXr6aqqor777+fj33sYwfczqmnngpAY2MjDz74IF/4whfyFVGIIZYtWzbuNul0ehKSHLzKypPYteu/Tccoegnp4WAfDTV9MrTNNkEg6Mjk+DZRKLzpCkLtS4hXy9wrNlEK6kK72dk7y3QUkUdubAlupgTH22s6isgXBW0lO0HqJkOUz5BCuNFLd9u3b+eKK644qCISwNy5c3O3f/3rXx9sLCGmBb9fVm0bj9bQnJQeDtZRgJLvq20SQLfMkWQdhaKk9XjTMUQBhH1x0xFE3nlwk7Iin1U0lMWrTKcoOoedJHP3Ge2RlEgkWLDg4JcTjEb3rvzw6quvHnR7QuzL2rVrx92mqamJOXPmTEKag9M/2bYDyInXvigFh4VdnpALa3bRUBmvM51CFMCejKZc5r+0ikbTW/eK6RiiAGKZsOkIIu+yOIHdpkOIfFLQE+ownaLoOEp6QBu9JDtjxgza29sPup033ngD6J9rKR/tCTEdBAK1LFp4rekYRa91aoxUFPsp7pWlp20UVDIW1TYZf6cMa7OQ1rC56+AvJosi422VYW220bCw/SjTKYqOrNpmuJB0zDHH8Oyzzx50OwOTdgOUl5cfdHtCTBe9fXJwPhat4a2433QMkW8Kmso3mk4hCiCh5QqhTTSajKfPdAxRAFpqvnbyyAIlNgpmSkxHKDrdu2OmIxhntJD08Y9/nD/84Q+8/fbbB9zG008/zYMPPoh6v3vZ/Pnz85ROCLtFo+tpaXnYdIyiphQsDclBkXU0zO5ebDqFKIBarxSSbKJQBOOzCbUvMR1F5JnjwAl1fzUdQ+SZ8sgKi9ZR8NKcR0ynKDrJmAxZMFpIOv/886msrGTVqlU0Nzfv9/Off/55zj33XKB/WJtSipUrV+Y7phBWam75rekIU0K9R+aQslEoU2o6ghBiAmSybXvNLmsxHUHkmScoU4zYqL1kl+kIoggZLSSFQiFuuukmNmzYwIoVK3j44Yn1jujs7OTqq6/mwx/+8JCJtsPhMBdffHGh4gphlWRS/thPRHvWdAKRdwo6Q62mU4gC2JOR8TK2kcm27aQ1vLl7qekYIq80vvLXTYcQBVAbLf5FhCZbICxTXxifJeqiiy7KDU/79Kc/zaJFizjvvPM44YQTmDt3LqWlpcTjcTo6OnjzzTd5/vnneeKJJ0gmk7leSAP/fuc732HmzJmmX5IQU4LXK6ulTMT2pPGPSZFvGoJp+fm3kUy2bZ9UqEUm27aQUoAyej1b5J3Gjc+FoFyosU0oGzEdoeg0LJR5mYviDOn+++/H4/HwwAMPsHnzZm666aYxt9fvz9CnBi27d8UVV/C1r32toDmFsEkoNM90hClBJu+1kIKELwZx00FEvsnvq332LH7IdARRINWBNtMRRF45JFvOxlv6rqzcZpk+b4/pCEUnUh4wHcG4orgU4DgO9913HzfeeCORSASt9T7/g/4C0kBPpGAwyH/+539y2223GX4VQkwtoeBs0xGmhHKP9HCwjvRIslZZURzViHzRaFyvrNpmq6BPJqu1jwc3OcN0CJFnHl0UfU+Kyvb1HaYjGFdUPxXf+MY3uOiii/j+97/Po48+ytatW0dsM1BMqq6u5oILLuDKK69kzhz7x22+++67vPzyy7S2tuL1epk9ezYf/OAHaWho2O+2UqkUzz33HJs2bSIajdLQ0MCKFSs4/PDDC5B8r2g0yp///Gc2b95MJpNh/vz5nHrqqdTV1R1Qe2vXrmXNmjXs2bOH6upqDj/8cE4++eQhPdXEvnl9ZaYjTAnzAhnolXHQVlHQHdpDdXyW6SQiz+S01C4KRaRtuQxts5DW8Nru5aZjiLzL4gR2mw4h8syr5Th4uMbXdjN3SZXpGEYVVSEJoK6ujh/84Af84Ac/YP369bz77rvs3r2bjo4OwuEwNTU1HHXUURxxxBGmo06Kp59+mmuuuYY33nhjxGNKKc455xx+8IMfMG/e+MOUkskkt9xyCzfffDORSITTTjsNgH8LPBsAACAASURBVLvvvputW7dy3HHH8cMf/pAPfOADeX0NsViMG264gbvuugu/38/pp5+O1pqbbrqJ3t5e/uEf/oHbbruN8vKJjTX985//zFVXXcWrr77Kcccdx+GHH86GDRv43//9Xw477DC+//3vc9ZZZ+X1Ndiot/dd0xGmBJ8UJq20o3Q9CzqONh1D5FlPxmWW32M6hsgjJ1ViOoIokGhKVs+0juqRYW220bCsNb/nhjaYeVil6QjGFV0habAlS5awZMkS0zGMueOOO/jKV76C646+/LjWmt/+9rc8//zz/OEPf+C4447bZ1udnZ2ceeaZvPTSS5x//vn8+Mc/JhgMAuC6LrfddhvXXHMNp556KrfffjuXX355Xl5DS0sLH/nIR3j77bc55ZRT+O1vf0ttbW0u0+c+9znuvfdeXnrpJZ566inmz58/Znt33HEHX/3qV9Fa88ADD/C5z30u99ijjz7KqlWrOOecc/j3f/93vvWtb+XlNdiqqvJk0xGmhJ6sDG2zUWvZNtMRRAH0yiqL1vHFa0xHEAWgFCyqaOS13VLQt0pgj+kEIt80VCb2fwSM7dLxjOkIxk3J2QT+8Ic/7LO4Yosnn3ySK6+8Eo/Hw7nnnsvNN9/MXXfdxQ033JDrSTSgra2NT37yk3R2do7aVjab5ZOf/CQvvfQS8+bN4+67784VkaB/jqqvfe1rXHPNNbiuyxVXXMH9999/0K8hFovliki1tbU88sgjuSISQGVlJb/+9a9ZuHAhGzdu5JxzziEe3/fst/fffz///M//TDab5YYbbhhSRAI466yzuP3229Fac9111/Hggw8e9Guwmd9fbTrClBDPTsmPSTGO2V2Hmo4gCqCkqC+PiQMR6VqCJymr41hJy4Ua23hCu0xHEPnmQHNpo+kURaezRebvK7ozpB07dvD444/z05/+lJ07d454PB6P54Zy3XXXXVYWlJLJJJdddhlz587ljTfe4KGHHuKaa67h0ksv5V//9V957rnneOGFF6ivr889Z9euXdxyyy2jtnfzzTfz4osvAnDRRRcRCIw+y/y3v/1tysr658254oor2LXr4P4YXH311bz99tu529XVIwsXkUiEG264AYA33niDb37zm6O2tXXrVi699FIAampquOqqq0bd7h//8R859ND+E8RLLrlk1J8h0a+r61XTEaaEpowMk7GOhnldy0ynEAXglZGo1lE4+PvkarhttIbWuEzKbBudrB1/IzHlyJ/WkcKyalvxFJKeeOIJjj/+eObPn8/ZZ5/N//2//5cNGzaM2C4UCvHHP/6Rr33ta3zlK1/hmGOOGXW7qeznP/85u3bt4vHHH2fp0qWjbnPKKafw+OOP4/HsPcn95S9/OWK7vr4+br311tz9j370o/vcbzgc5rzzzgOgu7s7V+A5EOvXr+fuu+8G+ns8XXjhhfvcdtWqVZSU9M+B8KMf/Yht20YOObn++uvp6+uv/H7mM58hEomM2pbH4+GCCy4A+ntEffe73z3g12C7dLrbdISipzW0Z4rmY1Lki4K2iFw1tVHGlcNd22iypCLNpmOIPFMK6sIyDMo2bmwJbkbmNbOKhvroAtMpipD0qCyKM6TvfOc7nHXWWbz++utorXMrs43lq1/9Kk888QRbtmxhxYoVvPDCC5OQdHLcd999XHnllRx55JFjbnf88cdz9tln5+5v2bIlV2wZ8NRTT9He3g70F3TGa/Nv//Zvc7cffPBBursPrNjwn//5n2Sz/ZNVnHDCCcyYse+rToFAIFfgSqVSQwpf0D/P0q9+9avc/U9+8pNj7nvwe/Jf//VftLa27nf+6SCTjZqOUPSUggUBmXTFOhpmdy82nUIUwEx/URzWiDzRaHpr3iQbkAsfVpKhbRby4Calp5mwXzpp36io/WX8iOv222/nO9/5Tq54tD9Lt3/kIx/hwQcfJBqNsnLlSjZu3FiomJMmHo/zxhtvcO21105o+zPOOGPI/eGFn+effz53u6KignA4PGZ7gyfs7uvr4/e///2Ecgz30EMP5W6fcMIJ424/eJuHHnpoyJDFRx55hExm74RmK1asGLOtpUuXUlravxJIJpPht7/97YRzTyfZbMx0hClB/k5YSEMoI6sF2WamV1HuNX5YI/JIoShpO0rmSLKQDG2zVRYnsNt0CJFPCv535pOmUxSdBUfLME6jR1w7duzguuuuQymFUmrCvZEGW7lyJeeddx49PT1cdNFFBUo6eTKZDPfff/+o8wmNZs6cObnbSikqKiqGPL59+/bc7fGKSABz584dcn/NmjUTyjHY1q1bh/QCOuqoo8Z9zjHHHJO73dzcPGS/f/nLX3K3582bN+I1DqeUGrLPhx9+eEK5p5sZtR8xHWGKkKEy1nGgMyQ9FW1T4ZHeDTZSeGSOJAvJ0DYbaTyl7+B4e00HEXm2vWK96QhFpWZOhNrZckHSaCHpvvvuIxaLobUmEAjw6U9/mjvvvJPHHntsvwpKl1xyCdBf9HjmmWcKFXdSlJaWsnLlyglvP/h9Ouyww0YUi6LRvcOXxloRbUAkEsnNVwSwdu3aCWcZMLz4dMghh4z7nPnz5w+5/+qreyeCHtzeRNoa3t7gtsRepaVLqK8/x3SMohdw5OTUOhrQ0tXMNu8lTScQhaDRZH1yYmqjsFd6RttFkY0eKXMkWciflomlB5u3XFa+BsOFpCeeeAKlFKtWrWLHjh386le/4tJLL+Wkk07ar3aOP/743O3ptuR7S0tL7vZocwcNrMIG0NnZSSKRGLfNUCiUu93R0bHfmd56660h9xsaxr+SOHybgdXeEokEmzZt2q+2hm/X0dFx0CvQ2SoYnG06QlHTGhqTsmqbdWSybSvFgIQrhV/bKBT+vjrTMUQBxNKh8TcSU4wjcyRZqE/mqRsi2i5XrsBwIWnbtm0ceeSR/OIXv5jwUK7RDMyHA/DSSy/lI9qU8dprrwH9q5V96UtfGvH4oYcemrvtui7r1q0bt83BhaQDmWx7YHLvATU1NeM+JxwOD+lN1djYCPQXgQb3uppIWwC1tUPHrQ60J/ZKJvfw3nt3mI5R1JSCWV6ZbNs6Mtm2tVpS0tPMRlpGGFvHdWFz90LTMUTeuTJHkoVS/jgxX4/pGEWjrztlOkJR8JrceUdHB1dcccVBtzMwH4/Wmp07dx50e1PJk0/2T352wQUXsGDByKUZP/ShD/H9738/d//3v/89xx577JhtDp7wPBDY/66MnZ2dQ+4Hg8EJPS8YDBKL9Xdz7unpOei2Bhtob6Kampr2a/vBmpunxjLFXd2vmI4wJcwLuP1dHYQQRS8uq0BZR+OSqNhsOobIs5ZYDT2psvE3FEKY9/7ckuG0/M4C7Hy3kz1N0Wk/T5LRQlJNTc2QyaIP1Isvvpi7nUxOn65mTz/9NI2NjVRXV3PjjTeOus2HP/xhlixZwvr1/ZOk/fjHP+aaa67B7/ePur3WekgvpKqqqv3ONbwX00SLUYOLPwOFn3y0Nbi9icrHz2XRk0u8E7ItJatAWUdBU/lGquOzTCcReTbTJ7+vQkwF9ZE2yvw9UkyyTv/QNplw2zIaKuMyxHiwpvWd076QZPSIa/78+SN6nByIO+7YOzxnxozpMy73X//1X4H+4lBd3ei/3EopfvKTn+Dx9M/zsmPHDq699tp9tvmjH/2Irq6u3P2JTm49mOMM/bEa3MNpLAMZYe8k4vloa3B7Yq+KiuNMRyh6WsPOtMyRZB0Z2malkIJyrxSSbKNwZNU2CzkKFlXItAP20eDpMx1C5FlJolJ6Iw2hmb2k0nQI44z2SPrEJz7BY489dlDD22688UZeeumlXIHh8MMPz1e8ovazn/2M1atXc/XVV3PuueeOue0pp5zCgw8+yBe+8AWSySS33XYbXV1dXHfddSxatIiuri7WrFnD3XffzcaNG4c895hjjtnvbJFIZMj9ZDI5ZN6lfRk8EfjAvFejtTURwycVHzyP1kTs2LFjv7YfrLm5mRUrVhzw8ydLIFALHAG8YzpK0VIKDvUpNstQaOuEMtP7KpKNEq4m4WqCjvS2tIkmSyoyNYaMi/0kF/kspCAbGX8zMaXMTktxMEdrave8RmVgOTC9jyWNXrr7h3/4B1544QXuu+++/X5uJpPhm9/8Jv/yL/+CUirX4+Sss87Kd8whrr/+epRSef9v8+aJj//fsmULX/nKVzj33HOHzH80lvPOO4933nmHCy+8kKqqKu69914OPfRQfD4fixYt4s4772TVqlX84Ac/GPK80047bb/eH4DKyqEV2okWfwZvV15enre2Brc3UbNnzz7g/ya6spxp0WiUDRtkidbxuHF5j6yjIFvypukUIs+0UvTEZTiFbRK+PWRlxSDryGTbtpLJtm30iVQz1d73TMcwT2tm73iaI9f9F9FnnzOdxjijPZLmzZvH1VdfzRe/+EU2bNjAN77xjXF7jmSzWR577DGuu+46NmzYgNY61xupoaGB888/fzKiG9Pb28vZZ5/Ncccdx89//vMRQ7/GsmjRIn72s5+htaa9vZ2+vj7Ky8upqKjIbfPP//zPudvz588fd2Lu0QwfDtfR0TFkH6PJZDJEo9Hc/YHV5mbOnEkgEMgVhjo6OiaUYfh2g1evE/127NhBZ8dstO7veZNPhWgz3yaS0XVhe28lhGW2bZs4WvNB9TKvc6rpKKObCr9ARUi5WcLdjRA5ynQUkUfNzuumI4g80xq2Nh5LOKWRdaDM0UA+/9JoILO4DHwekAU07aE1Z8birPe/SXtmvuk0k2/gmExnWbjlEeY1PQNAcqsMzTVaSAK44YYbeO2117jpppu44447+NjHPsbSpUsBeOqpp9i6dSu9vb3s2bOHTZs28eyzz9Le3p7rgTTQG8nj8XDXXXeNGAqVbyeeeCKXXXZZ3tsdr9ACkE6n+dSnPkUoFOLRRx89oBXVoP89q6mpoaamZsjXe3t7uf/++3P3L7744gNqf/HioXOPNDc3j7qi3PBtBs9jNDBE0XEcFi5cyLp163LbTcSuXbtytysqKqivr5/Q86aTeDxOOh1i06YTWbRoDQM1yYFvw8B5bO7zc9C/oz0+cLu3t4Lt246kurqJuvqtQ86Hh7c1Gtftf2z4Pkfb32h5B76WTvvx+VKjtuO6kEz6CAbT+2zLdWHz5hPpdtvyd2I/3pt7oG2O9dx9feOGP3e0N3Z4u2Pta3D7o7Tly7qkPc7Bv96DpLTm220dHKJ9vF6MBRvXpX73X2mpWwEqD52G3SzlXZvprjx0b3uu23/2MJH2R/sAMPGejfWzSn8R6bBNv8BfZ2cPh8EXzQqxfT4cyD7He47WmubAGmr2ucVE9rH/P7LF+NEwXD5/NSfz11tr2LZtObt2LWOet5OWzP71GJ/wft7/d7K+jfsqyuzv1w9mv2O95uH704AOOJB0URPIM/D4WK8ns7iM7CGlZFpn4k9Oci/C4X8jYOwDyH0d80zklyBfx2xjHZ8Nf85Yj+9rm/Fe076yDPpXac0NbR1UZ1yaUkeNfF4+jdau6+JLdpEOVo55DDCh1zj4vnb7f2gHn/xo3X9/8DGSzjLvvd9T1b2ZcKyVQGpv6TtwyNjnttOB8UKSz+fj4Ycf5vzzz+fXv/41Dz30ENBf7LjllltGbD+4gDRw3+fz8ZOf/IRPfvKTBc975plncuaZZxZ8P8O5rsv555/Pzp07ef755/d7zp+JuPvuu3MTbZeWlnLppZceUDsf+MAHhtzfvn07J5988pjPGT4n0UknnTSkvYFC0vbt2yeUYXB7g9sSI7W2HEpH+2zKyvq7Ivf09E9YP3A/Hi/D50uitaakpJOurgbS6eCQx0Ohntxz0+n++bA6Oubz3nvHUVPbSGlpG9FoDd1dM/H5kqTTAerq11Nd3UQ2q+jrqyEarSWVLMntPxzuzu2zt7cSny81ZH/+QC9eT4b29rm5PIO/FotVEg53UlHRTG9vJUop0ukAPl+SWKycdDpEWVkz9Q0bcbMeeqJ1dHbMzu07FisnlQ6yKvIyp3Z1ss3no+f9uVdaHIc3318Z8Ohkkj5gXTBIQyZNUjnUp9M0+3xs8fvA1SxMZzg5meC0eIIOx+GxkghozfJkihafl/p0hrcCATocRUI5dHgdFIqGdIbF6RSlWhNVih1eHyioymRo93oIu3BqPM4rAT/Ph8N4tcv8TJZDU/3PKXM189JpujweFqTTdDgOT4fDNDmKxkCAU2MxlHKoy2Y4Mpni7YCf1cEgnV4PS+MJNgeCzEol6fZ46PB6OT4ep9HvB62pcDVzMxlKtEuZqznm/V6DjT4fWmteCAWJOw5LUylOiyeoybq8HPDzP5EwK+IJoh6HrT4fG3w+1oRDuQOXOckkzV4vGY9nQgeFx8XjKK14JRwceUCmNcvjCZak02g0y1LpXJYNmTq8yW4ygbL3DxZcane/hi8dY9fMk8HxDDuYGHSQAaBdana/RlXnehTQE5lFZ+USPOk4JfEWsh4/bTOO7X+um8Wf6CAVrOpvd5QDGyfdxyHbfk/9ntcIpHpY2PgoW+d9jF0NA1myzGxejcok6Ck/hECii0ismZ7yhXRWLhnZrtZUdr7L0vX3E0j1kPSX0VW2AIWmvGcr7dVHsOHQz6IdT/8BFWrQc13qWl6mpv0dwvHdZHyR/iJx6WwyniDvzfvY0P3t81+XUGw38fCMkUUr18WT7CEbKB35Xg/nuszf9iSBZCfJQCVJXynNM0/JvS9zmp5lbtOfCKR66KwoJRldR1+mBwUEvGHmlxyBGtSuq/svlTvKwdVZYpkoEW957sKUUgpXZ2mObWV3/D3KfbWEfWUovJR5Kwn4wjiD2tNa05FsZkv0DRaWHEVVcOaQY5R93e7/MR164Lt3/0Mz7urbwszIotx+tda0JXeSysZoCPd/fXD2zuRuqgL1udfk6iyO8uTup7NJvB7/kNcx0G5vppOIt2JIm8P/dXWWzkQLlcGG9zO67IptZlb40H0Whga/fle7NPVtwMHDrMjQ5wx+D15te4r2Pk1N/48B6bQXvz+T+zEb+JUcfj40+HyguXkRNTU78PuTY9bR+78foLVi27YjifVVUF7RTEPDpiHnGvu6ONHaegiJRAmRcDcoTU3Njly2IR8dY5zvDGQY/vWBfQxcZHFd2Lr1WPp6q3N/16prttLQsHnU92Nwu1pDNuvgOO7775Fi69Zj6OutprJqB7NmrWd4Z/eJXK9IpQIj3uOR7y00Nh7Hrp1L0Rp2uqWkqwPo6gCgUV1pnN4UZDVOUu+zsDG8YDK8mOIGHNILSvFkNd6NPfssgAxuRwNu2AEHnN69xZXBjw//d/Bj2bogbnUQ1ZOCrAtZUGgy9WGcjIvqy6C1xklmccsDuEEP3rYEZDSe3YkRr2VE+xU+dMgDHoX2O6gs6IgXt9yP057EiabJzAwD4H+zA5XRQ9rM1gTwtCVz7WXmhskeXgnJLE5vBtencHbG8LTFcIMeCPvRYQ/0ZfD0pEjXh3HKA6ie1JD3NNsQxJ0Rwq0MQKD/74KTiea++TWpNBlH0eX15n4IFiQSBDXMymZZnkyyOhxiTWjvcUBVJkN9JkMrDr1+L0nHGfE3szqdpjqTpdp1KdWaM/piuc+RY5JJngsGeaQ0wof7YigUG/0+TovFc9t0K8XvSiJUZTJ8IJnkyGSKLo8HrTWPlkTY4vMxK5NmUSpDzFHUZt3c8U5ZNssL4RBxpQhms7T4fdSn0ijHIZx1eT3gZ5vfx/GxOIszGdq8XmoyGWZlXTZ6PTxWWkJNJsMFPb1UaM2CdBqAP4eCrA4G2O31clQ8wYJsNnd81eE4PBYJE3cc5qTTuePCTX4/CaVYFe1lYSbDLyNhXgmHOD4W5/hkig0BP/XpDJv9fsJulj6leDMUxO+6HJ9I8kooiNKwsrePCq2pyGbp8nhy/y5Ip6nJuiRTfirefYVS79tUdG0h4wsTC1QRD9WigFB8N1lPkN5IPb5UH2l/hEhfC95skmhkJr2herL+CIFEB47OEkj1EEy04XpD+JLdhJMdhOJ76C6dR3fZAjK+CBVdG3PHRNHITJrrVqDQVLevI+MLk/KGUYAvE8ObjtFVsQgnk8CXjZPxhMh6A0R6m+msXEwsWEMo0U7dntdRCsKxVgBaao+lt2Q2DS0vE4m3EgvX5R4buD24eDRY6el/O+rXpxOli2g5q//+7//m+uuv57333st9bawDEoC/+Zu/4e677+aoo+zuyv6lL32JP/7xj6xevZqZM2fmvf2WlhaWLl2aW0Xvlltu4aqrrjrg9pYsWcKGDRsAuPbaa/mP//iPMbe/8847ufzyywGorq5m9+7duWF7v/jFL/j7v/97oL+HUjQaJRwOj9nejBkz2LNnDwC33XYbV1555QG/lv3V1NTEnDlzgP6C1uzZsydt3/vjpZde4o9//KPpGEXO5Sp+QikytK2Q2jwOjT5f7oBl4Guvv9/rcnCRqiKbZZvPl/v64O0H2uhwHNaEgpwYT7A4nRl1n43rFpB8K0HSXzbiYGHw14Aht7vLDkGjqOhp3OfBxYDhbQ/c96b7iIdq6Q03EA9V09DyV6q6N02ojbG28ab76CmdRzJQTm3bW5T27Rp1+9HaBoYUmsZ6bYP3N7jIFIh3kAxVURJtyh2oDS5iZbxBQOHLxHL7GJ6hu+wQUt7+z/eBA8TR8uzrfXl1Xh2tFUPnNQt6ItQEZuFzAqTdJG3JnQCU+arpSbeTyPZR7qulLjSPzuRulCL39dEMtBfylOFz/OyMbaI7vSf3eLmvltnhQ4llo3QkWyj19c/1F0135m4PZGgILaDMW0U820ssGyWa7iTgCdGTbh+RMeiJ0BBaQNhTStOgfQY9Ecp81SSz8dxzB7+m1vg2utN7mBVazPySZbzXu5ad8Y251xH2lFLirSRDmm296+hO7xnR5vB/B2cannHgvfYpP0FvCYnM3tc2+PUPvL8D71faTY54Dwa2Ucc2EM9UkU6HchcIBi5qhMPdpNOBIRc0YO8FgYGLG1VV7zFz5gZS6QBdXbPojdaM+xwAny9OTW0jJZEu2ttnkc0Gc8Wb4RcnBvP54rn2ACqrmgj4Y3R3z8DnS+EP9OL3x0mlQkMuolRWNREOdZN8/+sDF3MG2hkt4/B9aq0pL99NMhXOXSAZeA2trQvo6WkYkm/46y0r243H23/xJpvx5y7gDHxtgMeTGnLxZrTnjvXePrb8ZHZV1o54HUB/gaMzCen3C90DpRWfB7fSD4DTmQI0btiLE8sAqv+xgGdoOwOFklgG0rq/rcHt9GZwS7x7nzfwnBLv3sd9Ciet9/5b4oVkFk97kmx1AMr8o7+OiRh4rSjcsGdv+6Nlm6i2BN5dMdxSH+7McP/zB7+u/W1veN4x2rntnatY0Pf2iL/pw//OD4k7xuMDxwM9jsoVVkZrQxRO9w4/u1YfTN9Q+xzyyMMElywxHWOEyTwPLapCEvT3vHn88cd5/PHHWb16NZs2bcJ1935Y+Hw+jjzySE466SQ+97nPceKJJxpMOzmuvfZa7rvvPl544QUWLVo07va/+c1vOOuss/B6J97h7FOf+hQPP/wwAKeffjpPP/30fs2/NNy3v/1t/u3f/g2AD33oQzz99NNjbn/BBRfkJl3/4he/yD333JN7LBqN0tDQQF9f/wHlCy+8wCmnnLLPtrZu3ZobSuc4Do2NjcybN++AX8v+miqFpMcff5xXX33VdIyidx6Ps4yJT4YvpoYdL1bS2zT+apJi6tDAmgUNdJaOfaFBTD3xWQvJlMlSyzbRwH0nfZS4P2g6isgnN8tbaz7NjPTE5jQVU4PWsOnROrKJgyhAWmbGtddSfdGFpmOMMJnnoUZXbRuN4zicddZZ3HPPPbz77rtkMhk6Ojpoamqiu7ubZDLJK6+8wh133DEtikg33ngjd999N0899dSEikivv/46d955534Vka6//vpcEWnZsmX86le/OqgiEsAll1ySa+P555+nu3vfY6UzmQy/+93vcvcHeiYNKC0t5bOf/Wzu/mOPPTbmvh955JHc7ZUrV05qEWkqyWRG76khhlIUVa1d5IHWkIrKwZBtFFCeSI27nZh6tHwOW0e+o5Yq8rnFxIFRCkI18vd1sMCSw0xHMK7oCkmjqaioYObMmQWZF6iY3XXXXdx44408+eSTLF++fMxt+/r6ePjhh/n4xz/O5z//+Qnv46abbuJ73/se0D9M8E9/+tOISbiHi0ajnH322ZSWlvKhD32IlpaWEdvMnTuXf/zHfwT6Jwn/+c9/vs/2Hn30Udrb+7vxn3nmmRx99NEjtvnmN7+ZG8724IMP5lZxG05rzb333guAx+PhW9/61pivZTobb3igANBU0WU6hMgzpcAbkNMY22ggJScx1umfu2Z6Hf9NBw5Q2RcddzsxxSgPGyNyAVfYz+2RNSeNFpJWr17Nd77zHT7xiU9w+OGHU1lZSSAQIBwOM2PGDI4++mhWrVrFLbfcwptvvmky6qT7+c9/zuWXX048Huf0008nGAyO+V9JSQmf+tSn6Ovr4zOf+cy47UejUS644IJcoeXiiy/mz3/+M3V1deM+99Zbb+XRRx+lt7eXZ555huuuu27U7W6++eZcd7rvfe97o/ZKSiaTfPvb3wagsrKSH//4x6O2tXDhQv793/8d6O+y98Mf/nDU7R544AHeeustAK666ipWrFgx7uuZrmyfVyw/FHFk+JNttIZkj/G1JkSeKaAqNvpFBjG1OYm46QgizzTQGZECoXV0lsV920ynEHkmPblHIdcjJ3/VtkQiwY9+9CNuu+02mpqacl8fPFVTOp0mkUjQ1tbG22+/zW9+8xuuvfZali5dyhVXXMEXv/hFPB57f5gff/xxLrzwQrTWaK332ftmNJ/97GeJRCL7fDyTyfDLX/6Sr3/96+zatYsFCxZw2223sXLlygnvI5vNDrmffn+1geHK21sCBgAAIABJREFUysp49tlnOeOMM2hsbOTss8/m17/+da7HUzQa5cILL2TdunXMmDGDRx99dMyJxL/61a/S2dnJv/3bv/Gtb32LOXPmDBny9j//8z/80z/9E9A/z9JA4UmMbqyfEzFAE0JOYKwjf/ytFQ0exIS3oigpwNvdRrakzHQUIcRYtOb8nY/J/EgWGujJLYPb9gofd6zpCMZNao+kZ555hmXLlnH11VezY8eOXKFkYKnX4f8BQ7ZZu3Ytl156KcuXL2fNmjWTGX3SbNy4kVWrVh3w/DWXXHLJiK+lUilWr17NDTfcwPz587noootYunQpDzzwABs2bNivIhL09/T5+Mc/Tjgc5oMf/CA33njjPrddtGgRa9as4fOf/zwvvvgiixYt4pxzzmHVqlUsWrSIhx9+mDPPPJOXX355QnNeffe73+WXv/wls2fP5u///u857rjjOP/88znllFP4yEc+QklJCXfddRf33HMPvvdXdhKja2trMx1hClC0IxO82kY5MtbfRi7QXCk9HGzk+gKmI4g8U8jQNusoRZev3HQKUQDalZ7cgwWPOgpv7T5WnJxGJm3Vtttvv51rrrkmVyAZKBTtj8FRfT4fP/zhD/nSl76Ut4y2uvLKK9m5cyfLly9nxYoVnHLKKZSUlIz/xDxrbW3lueee47333kNrzezZs/ngBz/I3Llz97st13V5+eWXee211+jq6qKkpIQjjzySU0891XgBaaqs2tbS0sL/+3//z3SMoreKx1jKFtMxRJ7tfLmcnq3SK88WGmgtDfHagn33ahVTV2zWArJlVaZjiDySVdvs5LgZ3lhznvRKsoys2jZU6cqVzP7/bjYdY1STeR46KaXFW2+9la9//eu5nkeD7U8da/Bz0+k0X/7yl8lkMlx22WV5y2qj22+/3XQEAOrq6iY0f9NEOI7DSSedxEknnZSX9qajnTt3mo4wBchk27Zy01NirQkxQQqYEY3jT2dI+eSqqU00oKXYYKVgKimFJMu4jpeNkXnM6JJCkk0GVm3rbZJ5QwFSjY2mIxSFgh9t/e53v+PrX/86MLQQNFBUOumkkzjttNM46qijmD9/PuXl5fj9fmKxGN3d3bz33nu88847vPjii/zlL3/Bdd3c0DetNVdeeSWHHXYYH/7whwv9UoSwSjQqXcrH1z+0rZ5200FEHmkN8TaZS8c2DlDb08fOahlaYRMFqOyBDfcXxUsBi1u28/KiI01HEfnkymTbtvKG5XN4gFMuc/ZBgQtJnZ2dXHzxxUN6ImmtKS0t5ctf/jKXX345s2bNGrONk08+OXe7paWFe+65h9tuu42Ojg6UUriuy+c//3k2btxIWZl8U4WYqCVLlvDcc8+ZjlH0lMzMbB/5llormM6Ov5GYUjTgBuQquI28rpyY2qYu1SHD2izl8cnB04DQUUebjlAUCtq3/1/+5V/Ys2fPkCLSypUreffdd7npppvGLSINV19fz/XXX8+mTZv49Kc/nStQ7dmzh5tuuqkQL0EIa9XX1+P1yhCQsbnModl0CJFnyoFIQ8J0DJFnGmgtl3mvbJOqqkN7ZfEMG7WVymIWtmkLVLHbJ/OZ2UZriO2WntwDep//s+kIRaFghaRdu3Zxzz335Iagaa254YYbxl3ifSIqKyv51a9+xTe+8Y1c2z/84Q9lzhch9kNLS8sBrw44XSxmC6XETMcQBRCsTJqOIPJMAymvTARqEw1kyqtNxxAFoIHdUkiyTlZ52BiZZzqGyDOlwFciPX5z0nL+BAUsJN1zzz2kUv3LKyul+O53v8sNN9yQ133ceOONnH/++QAkEgl+9rOf5bV9IWz27rvvmo5Q9LqQ4bK20lmZbNs2DlCSTJuOIfJIAb72FtMxRAEooCLeazqGyDNHuzJHkqV8YSkkDXCq5QIHFLCQ9MADD+Ruf/azn+X6668vyH7uvPNOGhoaAHjwwQcLsg8hbCRzio1vN7VECZuOIfJMa+h+T76vttFAyiMFQtv4ejpQGSkQ2qgkIT1+bSOz6NhJa4g2yQqLAxJr1pDZs8d0DOMKcsTV2NjI5s2bUUpRV1fHXXfdVYjdABCJRPje976H1pr169ezdu3agu1LCJssXrzYdIQpwKENGetvm1SfQ6pbxvrbRgH+rGs6hsgzBTgxWWXURuGkzFVnG60cGdpmIw3ZpAwdz9Ga5JZG0ymMK8hMu4NXgvrGN75BeXlhl+K94IILuP7662lubmbNmjUsW7asoPsT09dEfrbSablyag9NDbL6iG1SUem1YiMX6A3IpMw2Uok4MtLYPl2RUtMRRL5pTVWq23QKkWfKgUBZhlhCikkAKEVg4QLTKYwryNH0m2++CUAoFOKiiy4qxC6GUEpx3nnnobXmjTfeKPj+hLDBW2+9ZTrClNCHLDttm5K6DJ6gjPW3Ta/PQ8onK1HayCM9V6yjge3V9aZjiHxTig5/YTsQiMmnXUj2yN/XAdWX/hPe2lrTMYwryE/EwCS+p512GqWlk3O14ayzzuL222+Xk2NRUBMZOtnU1MScOXMmIc3B2bZNJkMcn6KRudTTbjqIyCPlQKQhQc9WWSreFhponFFhOoYokExI5jSziQZemXcYcb/MuWId6ZFkJwWeQJas9EgCICDTgwAF6pG0a9culFKcfPLJhWh+VEcffTTQv6S5EGJ8k1Xkndo0C9huOoQoAH+ZLN1qk/4VoFKmY4gC0IBbUWM6hsgjBWhXpmW2klI0hmabTiHyTCmI1Mnf2AFtP/mJ6QhFoSCFpNbWVgCWLl1aiOZHVVFRQXl5OT09PZO2TyGmspkzZ5qOIIQxKemibZ15HVH8aSkQ2iZVVYf2ytxXtpnTJSse2Uor0wlEvmkNfa2ySMmATLvMnwoFKiTFYv3LeU72ieqMGTOkkCTEBDU3N5uOMAX0D20TdtEa3JQc6dpGASVJWezAJhpwUnIV3EZJRxY9sJLWnND9jukUogA8flkVdYB25b2AAhWSEon+SRGrqiZ32eyysrLcvoUQY5vMHoNTlwxts5F00baTBlIeOTm1iQK8vZ04iZjpKCLPKuN9piOIgpAhizZSCsoPkc/hAYEpMBfuZCjIEZfW/R8igUCgEM3vk98vXe6EmKhaWW1gHJoqOmSibQtpDT07ZJJX2yigPCYXk2yjAE9Xm+kYIs/KUgkqe2VSZusoh7+WHWE6hSiARJcMMR5Qe/llpiMUhYJeuuvt7S1k8yMkk8lJ3Z8QU1lbmxyYj03RSSVRZLUgm2gNmbgivkcKSTYqk0KSlRQyFNU2CljQtst0DFEAMkeSfbSGWOvkdhApZon3V6if7gpaSHrmmWcK2fwQnZ2dbNy4cdL2J8RUFwqFTEcoehqHNiZ3iK4oLKXAG9R4glnTUUQBZB1Zmtg2GshUVJuOIQqg1y/HIdbRmoWxJtMpRJ4pBcEqmRJgwJ5bbiWzRxYMKOiyNV/96lf53e9+R319fSF3QzQa5aWXXqK3txelpAwuxETE43HTEaYATQh5n2yjHAiUZYglpOhgEw3sqio1HUPkmevx4QalZ6htNLC7rNJ0DJFvStHhLweZTsc6gTJZFTXHdUluacQ7zacJKWghKZPJ8NRTTxVyFzkD8zIJISZGeiRNhCKOvE+20RqSPQX98ycMSXmlOGgbJ5tGZdJor8zPYRMFhNIpOk0HEfmlNVUpmfvKRum4/H3NUYrAwgWmUxhX0KFtSim01pPyn/REEmL/7Ny503SEKUB6JNkom4Ss9EayjgJKkmnTMUSeKcATm9w5N0XhaSDuk0VyrDPQI0lYJxOTC3A50oEFKHAhCfqLSZPxnxBi//T09JiOMAUodlJnOoTIM08A/OUy1t82GugNSK8VG+mUTKJuGwVUxKVAaBuPzrK4b5vpGCLPtIZsUs63B0tuaTQdwbiCFpImqzfSwH9CiImbMWOG6QhTgky2bR+loHS2nJgKMVV4MjI3h40qozIEyipasyy6kRnpDtNJRJ4pBcFK+RweTIa2FXiOpEsuuYRzzjmHuXPnEolE8t5zSGtNIpGgtbWV559/nltvvZVoNJrXfQhhq61bt5qOMCWUIldMbZSVEVDWUUBlX4LWihLTUUSeZQOy7LSNKqVHkl2U4q3SJawLL2BpTHpr2ERr6GuVoahiqIIVks444wzuvvvuQjU/xJIlSzjttNOoq6vj0ksvnZR9CjHVdXfLlcCJCCAVBxtlYjIEykbebNZ0BJFnGtAhKQ7aqDMs31frKMWTNaeydLsUkmySiStS3VJIGkxWbSvg0LbTTz+9UE3v0xlnnDHp+xRiqmpoaDAdYUoIIUOgbKM1pKIy2baNMh75vtpGAUp6rlgp7ZGCvo1kthH7eEMaT1Au1AzmqZBJ5QtWSKqvry9U0/vU0NAgcyUJMUF1dTKJ9Phc5tBsOoTIM6XAXyoHRLbRQMpT8DVEhAHeeMx0BFEAflfmXLFRnzdkOoLIM6UgUCa/r4Ol3pNJ5QsytO3ZZ59l6dKlhWh6TH6/n2effXbS9yvEVDR37lzTEYQQIm8UUNWXoLM0bDqKyDM3lTQdQeSZBhprZpqOIQqgPtlmOoLIM60h2VPQqZWnHlnErjA9kk477TRqDY0ZPO2004zsV4ipprS0lBNPPNF0jCLnsAMZAmgb7UK8Tcb62yjjyJGdjbxZuRJuGw0k/DKJuo1mpXabjiDySGvo2+Mlm5Ch44P5580zHcE46QMuxDQmK7eNL07QdAQhxARlZGiblTIB+Ry2jQPUd7WbjiHyTbuc0P2O6RQij5QCj981HaPoZLtk0SI54hJimmpsbKS1tdV0jKKXQHqu2EY5EKpJmY4hCkAm27aPBrI10jPURv6srIpqG+kTaqd4m0yMP1xg4QLTEYyTQpIQ09S6detMR5gSUlJIEmJK0EBnRHqu2Cbr9eEGZd4rK8n6ONbRymFjRIb82KavRT6DxUgya5YQ+2HZsmXjbpNOT40rbIcccgivvPKK6RhFTnM4m02HEHmmNaSi0nPFNnJOaidPJo3KpNFeuSJum5RPvqfW0ZqqlAz5sYnWA/8TgyW3NOI1NCd0sZAeSUJMU+GwXF0YTwm91CNzOPz/7N15lGR1ff//5+fe2qur9+mepXvWnoUZFgHZFAQB14BR+WLyzU8RTTyJBpd8lUi+muAxSoJBIxrDEZOvEUNcQD1KRAiQCIKMC4swA8y+dM909/Te1V37vZ/fHz1d0z29zQxVfKo/9X5wONPVdeve163urvrU+34W2ygFgbA0imzjAA3jGdMxRIkpwJW5dKyjgZ66JtMxRKkpxWCoznQKUUJKQbxVpgM4ngxtkx5JQpyU7du3L7hNV1cX7e3tr0Cal6e5udl0hIq3QXojWUn7soytEIuJm00h67bZpbOumXRIhqLaRmmfDeMHTMcQJaQ1jPfKNA9iJumRJESVSiQSXH311aZjVLQMUdMRRIlpH3p+WyfL2FpIA+NhGSpjGw0U6uTCh20KjrwGC7EYKAWBuGc6RsXJ7tlrOoJxUkgSooplMjIMZD7NDJqOIEpNQWpAig02UkDIkyWKbeMHw3g1taZjiBLTMquZlWSybTtF6hfH/K+vGNeVoW1IIUmIqpVMJnn44YdNx6ho21lvOoIoMaWgdnXKdAxRBhrIudKssU22ZYXpCKIMajPyOmwj5XsytM0yWsPIPplXdaraq66q+om2QQpJQlSt/v5+tKzCMK8BmulBJgO1jZJfeytJjyT7aMCPJUzHEGXQlB4jmpNe0VbRmvccvo+WvPTmtk3Dein8TjX6059S6OszHcM4KSQJUaVksu0ToXiJdaZDiBLSGlRAig028oExmSPJKvn6JeiA/Ext5AAr+3tMxxClpBTfW/om0ylEiSkFte0ZQnWycltRoSBzJCGFJCGq1vj4uOkIi0ICeZ5sohQ0dmRwIzJxpG2U6QCipDSQb5ChAzaryaVNRxAllnUj/HvLW0zHECWmFCRWSA/CqbSWi5JSSBKiSr344oumIywCPhvYZzqEKDHlQLhWFhO3jQKWjErh1xYKiO17geCQDB+wkQYO18nQcesoxVdW/n+mU4gy8AtyuWaq7Es7TEcwTgpJQlQpmR9pIZoVHCaBjAu3jdZQyEqDyEaRvPQ0s4kCwj0HUAVZMcg2Cplw21Z5J2A6gigDLy9lg6niF11oOoJx8hshRJWSoW0LURxiGUlkpQrbKAWBsBRSbaOBwXjEdAxRYgpwk8OmY4gyqB9Pmo4gyuD05C7TEUQZ+FJIOiYWI7Jpk+kUxslvhBBCzMmln0bTIUSJaR+yo3LF1DYKUEp6mtnIycrcHDZKhaXwa6Mzx3abjiBKTGtI94dMx6gcqZSs2oYUkoSoWsuXLzcdYRHwaEaWsbVNNuniZVzTMUSJaSDnSrPGRl44bDqCKIOxsPT4tVEqEDUdQZRY8lBI2k3HkVXbpJAkRNVasWKF6QgVL0xG5kiyUDjhyaptFlJAyJNVVGyjAT/RYDqGKDEf6KmXybZtFCvIanw20RpG9knR93hufZ3pCMZJIUmIKpVOyxv9QkJkTUcQZSCrttlJA2PhoOkYogxksm37jAcjpEMytM1KMsLYKkpB69kjpmNUnPTzz5uOYJwUkoSoUrJq28KS1Mtk2xaSVdvs1BePkAvK3Fe2UYA7OmQ6hiixmnyGhjH5cGodrdkRWWk6hSixYFwTXSJz1U2Vk6FtUkgSolr19PSYjrAIOHSyzHQIUWKyapudRqMyEaitdFB+trZRwIbeg6ZjiFJTiseazjedQpSYUlDbLoWkqULr1pqOYJwUkoSoUmvXygvgidDSR9s6smqbnTxHmjQ20oCXqDcdQ5SD9Iy20lkj201HECWmNYx2ylDUqRKXXWY6gnHS6hKiSi1dupRIRN4U5qdZyWHTIUSJjffK6iM2akjJ1VLbaKBQU4cOyNxXttHAwaalpmOIMnj7wGOmI4hS05BLyuvwpMDKlQSWLDEdwzgpJAlRxerr5SrvfNayX1Zts1C8NSertlloyViGUF4mUbeJAgJjIzLZtoUUUJuR91cbjbpx0xFEickiJdMVDh6k0NdnOoZxUkgSokolk0mZJ2kBacKmI4gyUA5Em3OmY4gSU0BNVgoOtlGAk5VVRm3ULJOoW2l/dIXpCKLEZEqAmVJPPW06gnFSSBKiSu3cudN0hIoXRYbKCLFYaGQ1ShtpwA9HTccQZVAIyAdTG/kytaR95Gc6kzwnyCu4ECdhy5YtC26Tzy+OK+Ld3d2mI1Q4zblsMx1ClIHWkEvKHEm2UUDjeIahRMx0FFFCXrRG5kiykAYONraajiHKIF6QHoS2UWqiJ/dYlxT1J8XOOcd0BOOkR5IQVSoYlIb5/BTD1JoOIcpAKQiEpeeKjQqOXCK0iQZyDS2mY4gyUEBkkVx4EyfnUET+ZoXdYq95jUy2jfRIEuKkbN++8JKmXV1dtLe3vwJpXp62tjbTESreAVbwWp4xHUOUmIz1t1cmJAVymyggMD6KV9doOoooB6n7Wuns5EumI4gSk57c07mJhOkIFUF6JAlRpfpktYEFJRgzHUGUQbI7iJeRBpFtNDAelkKSbYIj/bJqm4U0MBytMR1DlJrv87+OPGI6hSgx6ck9XfLBB2XVNqSQJETV8n3fdIQKp1lLp+kQogz8vLz12UgBcVm1zTqyapudFBDNy+qZVtGam/feQUt+0HQSUWLSk3um1NOyapu0poWoUo4jf/7zU+yl8ocoipMXCHumI4gyCXjys7WNBrQrH2Bso4F0MGQ6higl7XPNkYdNpxDilSEdtKSQJES12rRpk+kIFS+JdLu3UXxpATciBQcbFVwZsmgbBTi5rOkYosSkR5KFHJd/bv8D0ylEGSgH4ssypmNUlNi5smqbFJKEEGIOSzliOoIoA6UgXFswHUOUmAaG4hHTMUQZaE+GLNpGeiTZ6Rsr/hdHgjI5vo2CMbkAJ6aTQpIQVWrv3r2mI1S8MHK11EYy1t8+GuiLR8gF5edqI+XKJOq2UUB9Wha0sI3nBNgZX2U6higxrSHZJRdqppI5kqSQJETVWr20Xcb3LmCQetMRRBnkUsiqbZZRwJLxDKG89DSzjQa8mAwztlEoLz3NbONqjw3jB0zHECWmNXhZaTdN5Y2Mmo5gnBSShKhSjTpBwo+ajlHRQsiHUttoDV27m0zHEGWggBpZtc06hXAcHZAeSTbKBeXnahWt+eChu2TVNgs5DuSaZCjqVKmoMh3BOCkkCVGlnFiAK/NnlKdXkomeTqU+poazeLHEO12ctEU915SC3hopJNnIA8bC8sHUNo67zHSEV4ZFr7MnQgM9dfJabBWl6AhUb7vJprbS8fLa4b9qXmU0g0/lvEx6Cn7TIkNzZTKBReLFF1/kV7/6Fb29vQQCAdra2njd617HsmUn38DK5XL8/Oc/Z9euXSSTSZYtW8b555/PaaedVobkEwqFAr/+9a95/vnnGRgYoKamho6ODl73utdRU1OeLusHDhzg7//+77njjjvKsv/Fzk8VaCJBh7eU3W7PxOV8TfHfVV4zo06aITU+8T049go+tQivmXb/Jm85ER3i2cD+Gfuc8djjTd12tu00hLRLTnkzMiT8KEknPTPbfHnnydHhLWVH4a20hr+NOrq91hOFiMl/AXwNW/1NXOi8hLPAfqc+7vjvw+z3zfbY2fZzfCY1z/7myjHXtt26gWUMndD+TnbfU5/Pk3nsie57tu8/l17NpWwrzYGO3z8L/3qVe3+lzmCSPvr/Qle9NPDvlyviSUVskUxtptwVaLcRcs+bjlLRAu7ZwL6ZdyymX/SFsmpo8OIMueMztzN5nid77BPdXsOS4BrSoVOYc0UffVVQFXYtfOob2cnkW+gNfer3oDRvktOyUrI3YFcXaAoePuU4833/RJ+mub7v64lCROAk22law2/9dZzj7MFVc2fyNDzunc7F7nZcpWfdl3/06XbmOae5+BpGdZQ6lZ6Rb2oeOLX22Gz7m1TQik8X/phDNfW8haeObcuJvzxMbusBvbXQOgrucffN9bUHPPwquPcSly/e6ZHIztzmZNoKs33M0EB3HSwdmXsfk48pOPAvb3LYGE4teN62k0JShXv44Ye58cYbefbZZ2fcp5TiHe94B1/60pdYtWrhie2y2Sy33XYbX/jCF4jH41x66aUA3Hnnnezbt49zzz2Xr3zlK7zmNa8pWX6tNXfccQd/+7d/S09Pz4z7Y7EYH/rQh/jsZz9LNHriw6xuvPFGbrvttgW3+8pXvnJSeauJE5v487+ssIUzCis57A7S6NWgHEW9HydGGIABkux3jxDXYVb6SwDodYZJkiavPJZ69eSciSFgrX598XGbvTaGnXG0rxl0x1juNRIlRK8zzABjdDtDFFSBZq+WoHKp13GW+HVknTxhP0ifM8KwSqG1ZtRJE9MhNnvtNJEoZtJao5RitddCEwlSZDno9NGnRgkSYL03UWh9zt3PoDNOVIfYVFiOchRJ0qRUjpie6KqbUjkC2pm2v8dYz/nZi7jaeZzTnE4eKZzNiJMAX3Oe+xI9upH/8c+hj3qWMMzrnad5rXqOlc4Rev16RpkokmYIs02v4Tl/HWc6ezhd7aOeJAmVolO38h/elQC80f0NYzpCC0O81n2eAD67/DYe12dS44/zzuDj7PWX8R3vStqdI7xWPcdyZ5DHvDP4rX/atExNjPAO9xc0MMppzn42O124CnyteMpbxznublw10fjZ6m1gj27nNGd/sbHka3jK6+CvC3/MS6ziI+69fDTwQ1wFeQ3/7Z/NsBdjo3uYJ/MbWRvoBSCp47wj8Pi0Bpd3tNi21TuNZjWKguLz0eCMMeTX0OCMFZ/XLWovV7rPFo/V5S+h3ekjcPT2d7wr2Oav4XRnH3/kPlJsGE4er6DhZ94FvMX9NQGlp7WZf+Bdwq+Cm7mUbdMaKYpjjQcPGIlCfwL66qF9YOL/qY0O/+hjJhsk3XUwWAvDUbho50QD6fgGy2wNF6bclw1AsDDxWB/43WqIZGFT97HH+UBXEzy9FoYS8Kq9cOb+icd4Cn61EWIZOGv/sf0OxqFhfGbjaOr5w7HMUzNNNsymZveA0TDUZqefJ7N8PXVfzLLd5BXGuZ6vR09X3P16h4/f67Gxe/YGow98+3LFTy9wueypDKt74xN3OCsIRDZTSD3EnFQNgchlaD2KX+jDCazAyz4Femjux5wo1Qh69iEeTuRCQtGJ99qczuDndx3/YHDbwOuc8qAl4Pe9/FwnLAIsvNyzCp6B9rrB7y95Aid0GsHAClShCx04OmxRwxpvCXl8utzj/jCP3n/8H1qLV4unfAacsbk/+Wio82Ms0Qn2uEfQSoOGGj/MmJOd+494tuPDtF/2dq+JGGF2uIdn3X61t4QrC2fynD7ArwO7pz22zo8x4qRm/yM7/nhH89bqGIfdobmfG33cRZmpf3RH96W0ot6PMjR57FmO2+rX0euMFO9fWWhi2Bkni0fWyc/5wrDaW4LXEp+9+KI92jlAJ6tATZmTRftcwYO8k3sB2KE3cT9XsZtNs1+R0Edf2ZR73NdTP3kffSWf7ZP4tEyT+yzQwS5qSPIsrz5WLNI+l/MgS3UPp/McdYzyU301P+MqtAqgdIFV7GM/66YXmLTmfH7Jb/SFaOXiaI9X8yQp4mzTr5qWazW7uZG/5x/0J9nP+mLGMGmyRGcvBs1yfmfwLFfxIw7oNZzOcwBs02dygJU8oS87+hxNnOduNkx/zmb860/86JWDqwu8jztRDWlu5/d4nbed5QzQ4iSLD9FMFFGmvlc/4p/Nv+XfwjsCj3GNO9F2KGjFf3iX8x+FK1ntTHx22O8v5Z2BX/B+934CR9sV/1G4gk3OAc5xJtozAJ5W/EvhLZzh7OV85yUCR4/zqcIHAPhc4P8RVF4xw9TiTkE77PBXsNnpnNZm+EThgyxhmA7nEEN+zbRMk18/5W+caAsWhjm3GbV6AAAgAElEQVTX2TGxU19zReAZDvrN7GIlT/kbAYr3j/hxLnOfJUqW/boVgFY1zJgOU6OyhHWGDe5hnvRO47v+G+ijnk0c4B3uL4iQZbteQ6ffMq3t10EXfxR4hEFdwwPeBbQ7R4ptzrDKs81fTS/HegLWkGKV6i22yyb3N6YjjBFjlHjx3Na3PMBt71C0HdG0jsD+ZljfA4H8xHO5rxXSYWgYg6GjfQQax+BQk2LPMkVtBrqaFCM1iroxzcauib+x7gbFsqGJF4exMJy9V6OBZ9cqlKOKjwH4k/8T4LKnC7z5KehuhCdPgyVjiudWKUbjio1dPm1HNGt7J362IQ9G4tBbP/G5+cU2UI5iNALrun2WD8DhJsUzHU4x19m7PZYPTJxDOqyIZjWxHLzYNj3PH7WcS7VTWtvcEW9x++pXv8rHPvYxfN+fd7vm5mYeeOABzj137l/ooaEhrrrqKn75y19y3XXX8fWvf51IZOJKkO/7fPnLX+bGG28E4Pbbb+eGG2542fk9z+Pd73433/3udxfc9pxzzuGRRx6hvn7hyY1HRkZYuXIlo6PzT3JWW1tLV1cXiUTihDOXQldXF+3t7QB0dnbS1tb2ih7/RGX2DNP/DbkSPheN5iukuceSeZImG0G7/RXFwtfU23NtN98+FjrWZIFooe1fbt7JRtl+f+m0400+Bl+z2T3AE97pvMQqQqEXOSfwTUYjFBs2QLFRs6PNKTZaJrX3+py/0ycVgoE6xY42h9pxzZkHNM+tUnS2HvtwUDemaRvQxf1rX7OmD/YtgZqsIpbxSUUcdrQpNh70uHQ7PLoFfr05UHzs1IbTxLE1AwnNMx3ujGxzPWZqtolGm08sA8v7NKv64cmN0NfkFM9/ch8r+nwu2qF5cqPi0BKneC7LhiY+De5oO9YQbBvQaF9zWhcMJDR7ljms6/ZpSip+vWFiv5M5gOJ57FnmTHvuJ/ezpg/6anSxYTj1eZ16TpMNxql5AC5/7hJW9y0nEFqPE5gofGt/HN8bQKkovjeE9kfROjdtm+P5hT4KmWfxvWEmrrl54BydH6IwDBQgtIZgcB2F/F6ULqACLbiBZWidxnGbUE4c7Y/j5Q+BzqLROE4EJ7AC5cRnHM/LH8BxW0Ax7fG+NzDrbaB4Xlqn8QvD+Pm9OMG1KCdy9LNjBN87ghtchXJi054HpcAJrED7Kbz8AZSqRev+ieJaaB3KieMX+shnt0+cn1uHcupAg1fYg+M2EwhvLp7L5LkqBcppQOv0RDZ/CN/PoP0xtD+C467A9wdRgBtci/ZH8P0kbqANrTOz/nza4oMko300+jV0+MtmXOgI6gA58ow5GVYWmsk5HsNqjHodZ6W/pLh9imzxQkivGibl5Gnw4sRVuHgBYXK7YWe8eFFlgGTxYkvOKZAlT5ggIT9AjztMULuECBAmSKtfT5ocu9zDgGK9t2zaficupiTpd5LU6AineW3F+ye3Oej0Ma6y0y6S7Ha6GXTGaCjEGQqME/aDhFSA1V4LAIfdQZZ7jdOOddDpY1iliOkQCaJoX3M4MMSaQgsraCqeZ9gPknXy1PsTP8vZzj3uhTng9jHqZFjhNbDZbydGeMZzNdt5LPXqSTrpaefUddZXeaG1j22cWSy+HKKNFXRRzzDD1LODTYwTI06KjbxEPcMz/lYPsJJtnEkTfQywhFXsQ6FYQRdAcZ+TX9cwSg/LUWg2sIMRavktF9DAIOfwFCPUzshUwyhj1BazAQxTz042olFzZhumfsY5Pc7F7GU969jFa3m8+P2p202e1yO8kSQ1XMFDnM72Ged8Os+xioPF26vYR4oaxokTO/qcjVDLL5i4cHwJj7KKgzNyzpd36vkf/+/xz/Fk9u++9Ps8dPAKADZxgNe623jCO50B6uZtG5xIG2O2baa2AyaLHvNte3wGYNp2UzO/xMIX6qtFoOF/iC590HSMivGly77EG1a9wXSMGV7Jz6FSSKpQ999/P1dddRWBQIC3ve1tXHDBBdTU1NDb28vPf/5zHn300WnbL1++nG3bttHQ0DBjX57ncdlll/H444+zatUqduzYQTgcnrHdTTfdxK233grAXXfdxXve856XdQ6T+2toaODaa69ly5YtOI7D/v37uf/++3nxxenjqN/ylrdw//33L7jfW2+9lZtuumnebYLBIJ/5zGf4v//3/76sczgVi6WQ5CVzdH/+V6ZjVLS7yXAHi2ScjDhhbt0viS3/iekYotQ0XPfU3xLL15pOIkrsTbUBIguNHRaLhsZnz6V/gRceMR1FlMGvDr+KO7e933QMUWrBwyQ6ZKTHpI+f+3GuP/160zFmeCU/h8rQtgqUzWb58z//c1auXMn999/P5s2bZ2zz+OOPc+211xaHix0+fJjbbruNz3/+8zO2/cIXvsDjjz8OwPve975Zi0gAf/M3f8Mdd9zB6OgoH/7wh7niiitYvnz5KZ3D9u3bue2223jjG9/I9773vRk9jf7hH/6BO++8kw9/+MPkjy7/+rOf/YyHHnqIN7xh7upuLpfj9ttv501vehMPPPDAKWUT4kRFF80EHOLEaQLx3aZDiDJoTrZJEclCS1wlRSTrKOo7L2Og48emg4gy6B5vNR1BlIMnq7ZNtXNop+kIxlXYTHUC4O677+bw4cPcd999sxaRAC6++GLuu+8+XPfY+PHvfe97M7YbHx/ni1/8YvH2m9/85jmPG4vFuPbaa4GJ4WM333zzqZ4Ct956K2vWrOGHP/zhrMPVlFL86Z/+abEH1KTZzmGqb3/723R3d/OXf/mXp5xNTMjulyuBC+kqzh4j7KHQhZk9N8XiFynETEcQZdDnaTK+dJ63iULRtPdtuNk601FEGRwaO7WL0KKyqXC36QgVJetlTUcwTgpJFeiuu+7iox/9KGeccca827361a/m7W9/e/H2nj17GB8fn7bNgw8+yMDAAACO4yy4z8suu6z49Xe+8x1GRk6+2JBKpfjBD37A7bffTjwen3fbG264YdrKc7/73e/m3FZrzRe/+EVe/epXc/nll590LjGdn7Zj7p9yGl94E7HoaFzpkWSlZGjmHCXCDklPCkm2UbiExk9+5WFR2bSG3SPrTMcQZRCqe8l0hIryrg3vMh3BOCkkVZh0Os2zzz7LJz/5yRPa/k1vetO028cXfh577LHi1/X19cRi81+xnTph9/j4OD/72c9OKMdUTz75JFu2bOGtb33rgtsGg0Fe//rXF2/PV7i67777ePHFF3Ech69+9av85je/wfOkx8ipcqIyslVUI40TkBKhjUbiR0gF51+EQSxOCVeGttlGo4+tEiesITPv2sonkNhhOoSoMFJIqjCFQoFvf/vbNDU1LbwxFCfTgonhYscPIzt48NjqDAsVkQBWrlw57fbWrVtPKMfx+/ja1752wttPPYfGxsY5t/vCF74AwK9//Ws+8pGPcP7559PY2Mi1117LvffeS6EgPWxORni1dClfiLSHbOTgpWQVFispGIr2mk4hSmxFAJkjyUIKRTi5cuENxaLiOLAs3mM6higpDe4ATmDMdJCK8pO9smiLFJIqTCKR4Oqrrz7h7acuurdx48YZxaJkMln8Op1OL7i/eDxOTU1N8fb27dvn2Xp269ev57zzzjvh7aeew9lnnz3rNk8++SRPPPHEjO+Pjo5y7733cu2117Ju3Tp+8hP5oz5RbiJEcHVi4Q2rmPRbEWIR0RDJyzxJtklJRd9KGk0+MmA6higxrWEsP/+0FmKxUeA1U8jIJOpTra9fbzqCcVJIWuQmV20DeNvb3jbj/traYyvYDA0NkclkFtxnNBotfj04OPgyEy5soXMA2LFjBxdccAEdHR1z9qw6ePAgv//7v8/nPve5suS0jZfMkd+fXHjDKqXRPC+TbVvIx40dMB1ClIOCkWif6RSixIY8OJKT12LbKBTxvrNMxxAlphTUBOUynH0U3niH6RAV5TXLX2M6gnEyScoi9/TTTwPgui5/+qd/OuP+9euPVUt93+eFF17gnHPOmXefUwtJpzLZ9smaPId169bNmPNp0vXXX8/1118PTAz/e/rpp/mv//ovvv71r9PV1TVt27/+678mkUjw0Y9+9JQzHb/Pk9HdvThWNcgfSZmOUNHkIritstI922Lyd2un7rymRVaetk44Kat72UZ6JNlKFio53nBWFviQQtIid//99wPw3ve+l7Vr1864/4orruDWW28t3v7Zz362YCFJqWNzEYTD4RIlnd2BAwfYtm0bADfffDOOs3AnuUAgwPnnn8/555/PTTfdxDe/+U1uvPHGaUWvT3ziE1x00UWcf/75p5Rr6rxNtgq2xEAhn7zm4KBYhcOg9EqyTIT82BqCNftMBxGlpmFZcub7oFj8lgVljiQbFcLyQcw2SsGa2gMcGlthOoooMV2QoeOTHBzW1kt7Q4a2LWIPP/wwe/fupampiVtuuWXWba688ko2bdpUvP31r3+dXC435z611tMKMvNNfl0K3/jGNwC45JJLePe7333Sjw8EAnzgAx9g27Zt0+ZlKhQK/OVf/mXJctrITYQItMqbwlw8NAfwTccQJafIj8xfTBeLU226mVi+duENxaJTG5Dmqo3yESkk2Whl4uDCG4lFRpHtv9R0iIrR0dBBc7TZdAzjpEfSIvaZz3wGmCgOtbbOPgGaUopvfOMbXHbZZXieR2dnJ5/85Cf5x3/8x1m3v+OOOxgePvbGvmbNmpLnntTf388//dM/UVdXx7e+9a1pPaFOVltbGw8++CCXXHJJcYLwRx99lIMHD85Yie5EdHZ2nnKW7u7uU+4J9UrykjkKPTK8bTYazb+RZVC6a1nJDS+O4afi5IxG+0kFR6WYZKGkp2XlNgvl40dMRxBlkPPLO5pBmKELNQtvVCV2De2iP91f9cUkKSQtUt/85jd54okn+MQnPsE111wz77YXX3wx3/nOd3jPe95DNpvly1/+MsPDw3zqU5+io6OD4eFhtm7dyp133snOnTunPXauVdRK4eMf/zijo6P8+Mc/LknBqqGhgW984xu89rWvLa4Et3Xr1lMqJLW1tb3sPJVO5kiam0KxV3ojWUrj1uwxHUKUg4KhaK8UkiyUcKWIZCMdWHg1YbG4aA1Pdlf+xVRx8lRYFrOYpNHsHd5b9YUk6St8kj796U+jlCr5/7t3n/gEZnv27OFjH/sY11xzzbT5j+Zz7bXXsm3bNq6//noaGxv5t3/7N9avX08wGKSjo4Ovfe1rvOtd7+JLX/rStMddeml5ujHec8893HXXXXz5y1/m6quvLtl+L7roIi6++OLi7ZczabbtnJjUkefzDlzTEURZKHSuut/4raUhkpfhurZpdpDeSJbygnJByzZaQzKXMB1DlEGoRibbnmo4I0Nz5ZPkIjM2Nsbb3/52zj33XO6+++4Tmpx6UkdHB9/85jfRWjMwMMD4+Dh1dXXU19cXt/nIRz5S/Hr16tULTsx9KrZt28b73/9+/uqv/mra8Url937v9/jFL34BQCaTKfn+bZHrTJqOUNHOIUQjORneJsRioSATTIF0crBKY9B0AlEOGo0fkGXibeM4sCzew2hOeobaReNE5eL8VI8eepQ3rnmj6RhGSSHpJF144YX8+Z//ecn3O7WYM5d8Ps873/lOotEoP/7xj095RTWlFM3NzTQ3T78qPzY2xre//e3i7fe///2ntP/5dHZ28pa3vIV3v/vdc04Q/nJNXb3u+HMUx+RkaNu8HBQX4nI/BdNRRElpcOR330o+NKRnny9QLF6D8hJsJYUinFxJuukl01FECWkNY/m46Rii5I725o70mg5SMepDC392t50Ukk7SVVddxVVXXfWKH9f3fa677joOHTrEY489RiJR+m6jd955Z3Gi7UQiwQc/+MGS7n9gYIA3velNXHLJJXzta18r6b6nmlqUm2sScgGhlhjycXp+m6SQZCGFn10ONftMBxElJqu22anfg4wvk23bRqPJyupe1lEKaoLS08xGXqaFYO120zEqRjwkBVOZI2mR+OAHP8jWrVt56KGHaGpqKvn+e3p6+NznPle8ffPNN5e0N08ymeTNb34za9as4Vvf+tZJDck7WX19E5PBKaU499xzy3acxS7ULmPYFxKSYW0W0qjAkOkQogwmV20T9kl68lpsG4UikF5iOoYoMemRZC8v22I6QkW5cuWVpiMYJ4WkReCTn/wkP/nJT3jooYdYvnz5gtv/4Ac/oFA4uV4UH/rQhxgamvhw9frXv56/+Iu/OKWss0mn01x99dVEo1HuvfdegsGFJzz43ve+d8rHe/LJJwE477zzqmL1tVOV2iarLywkazqAKAOFLjSYDiHKwZlYtU3YZ3IlVmGXYEZei22jFGxueNF0DFEGgWin6QgVI+pE2dC4wXQM46SQVOFuueUW7rzzTh588EE6OjoW3P6ZZ57ha1/7GoHAiY9a/PSnP82PfvQjALZs2cL3v//9kvUYyufzXHvttYyOjvKf//mfRKPRBR9zxx138Lvf/e6UjpdOp/nud78LwJ/92Z+d0j6qRfpF6ZWxMPnwYh+NEz5sOoQoB1/JHEmWGpAeSdbRaMZanzYdQ5RBW22P6Qii5DQ4soDRpLSfZufgTtMxjJNCUgX753/+Z2655Rbuv/9+zjzzzHm3HR8f50c/+hFvfetbefe7333Cx/i7v/s7Pv/5zwMTPXgeeeSRBYe0JZNJ3v72t5NIJLjiiivo6Zn9DWNyXqfdu3fz4IMPUls7/9wV/f393HbbbXzsYx/jj//4j4vfHxwc5Fvf+hb33HMPIyMj8+7js5/9LP39/Vx++eW8973vnXfbaudGZYq0hayQaeQspMCXJeKto+HcQ2+UOZIsFVBSSLJNunYXuYSsAmUbreGXh88zHUOUnCI/UvqVvBezhw8+bDqCcfIpqULdfffd3HDDDSileP3rX7/g9tnsxCCcRCLBH/zBHyy4fTKZ5IYbbuCuu+4CJlZo+6d/+qcT6jH0xS9+kR//+McA/Pd//zef+tSn+Nd//dcZ233oQx/iu9/9LoFAgPb29nn36fs++XwemBhat27duuJ973rXu3jkkUcAqKur47bbbuNP/uRPZuzjnnvu4Qtf+AJnnXUW99xzT1nnYbJB/IKl5PbMX5irZhrNr8ibjiGEOBEK4llZQcVWMSXv57bJx2UYqm20hv50AzuGNpqOIspABWUkw1SxgFyUlHfmCnTfffdx/fXXo7XG932y2eyC/0/6wz/8Q+LxuSe5KxQK3H333WzatIm77rqLtWvX8pOf/IR//dd/PaEiEoDnedNuTxaAprrpppv4+te/XjzmQvmn7uP4IlEqdWxtsZGRET7wgQ9wzTXX8MILLwDQ2dnJjTfeyB/+4R9yzTXX8Nhjj9HY2HhC51LNlJIVcOajjv4nbKNRoX7TIUQZpEJSGLdVjSuvxbap7X4NbrbOdAxRQkpBY2SY2pAsemAfjRvuNh2ioqyoWWE6gnFSSKowO3fu5F3vetdJT5Y9abaeOrlcjieeeIKbb76Z1atX8773vY/Nmzfz7//+7+zYsYOrr776pI7x8Y9/nLe+9a3EYjFe97rXccstt0y7/6677uLWW289pfwNDQ28853vnPa9O++8k7POOmva9374wx+yZcsWgsEgq1evZvv27TzwwAN8//vfX3AInRAnQqPZhW86hig5hZ+WSfito2HN4PxDwMXilZORbdZRuITGl5mOIUrMdTQddXtMxxAlp/DSa02HqChnt55tOoJxMrStwmzYsIF0Ol3Sfd54440cOnSIM888k3/5l3/h4osvpqam5pT3V19fz09/+tM577/uuuu47rrrTnn/xzv99NN59tlneeGFF/jNb35DX18fhUKB+vp61q1bx3nnnUd9vQxpOFnh1XIlcD4KxTIUz5gOIkrOL8jvvlU0NI+105SWq4O2CkmHJOtofHJx6eFgo1iwtJ9jRCXQBOtkcvxJFy69kObo/HMKVwMpJFWB22+/3XSEkti8eTObN282HcMabiKE0xzB75dVGOayChc4td6BolJpAontpkOIUlLQX9NFKjgqk21bSnok2UWjGWvchheW4ag2SuVPbKoMsbgoVwqEkzY2yjxgIEPbhKhaXjInRaQFHMBbeCOx6DiBcdMRRKkpTU9ir+kUokwSMkeSVRSKmsEtMkeShXwNu0fWLbyhWGRk1bapBjODpiNUBCkkCVGl0i8OmI5Q0TSal2SOJAspvNQq0yFEGfRHD5mOIIQ4QQqXyHCH6RiixH556DxGc9Iz1EYy2fYx/WlZtAWkkCRE1cofSS28URVTKOpk1TYr+Tm5Cm6jgbgUkmw1kJeivo2UDFm0itbQn2kyHUOUhcatkUnUJ23t3irFJKSQJETVCrbETEeoaD6aA9IjyUp+Qa6W2sjxXdMRRLlITd86Go0XlGHGNlEKrl77ALWhUdNRRMkpdE4ml56k0ewdluH0UkgSokpFT5OrRvORC6UWc7KmE4gyiBZOfTVSUdmaAtJctY1CEU6uNB1DlJjrQEed9FwR9tNaPinIO7MQQszCRbFKXiKtpKR7g5VqM3K11EYJBRFH/mZto9FkEwdNxxDloOTv1T5aCifH2TG0w3QE4wKmAwixmGzZsmXBbfL5/CuQ5OXL7pdld+ej0YxIvyQLadzYPtMhRBmEPVly2kZB+UxqJYXCKcRNxxAl5mvYPbzWdAxRcorC2GmE6l4wHaRiXLjsQtMRjJPL7UIIMQuFol1eIi2k0PlG0yFEGXTWy9VBGw36MFKQ+epsJJNt2+fZI1tk1TZbuWOmE1SMV7e+mg2NG0zHME56JAlxErZv377gNl1dXbS3t78CaV6eQLNcvV9Ii+kAoiy87BLTEUQZ7G18llRwlFhePsTYpq+gqZMWq1Vksm07tcQGTEcQZeKlVpuOUDGuWnuV6QgVQS63C1GlMruGTEeoeO3IKlA2UjLZtp0cGIr2mk4hyiAiXVeso1BEh+WKvm1W1PSwouaQ6RiiHLIr8WVRCwCSuaTpCBVBCklCVKl8t1wJXIhGhlNYyQ+bTiDKQGlFQ7rVdAxRBkl5KbZSISQXtGyjFJyz5HemY4iycPGz0lcf4Lm+50xHqAhSSBKiSsXOlQ9cC8mYDiDKQ3ok2UfDpp6LZFibpaIy4bZ1NJrxFvkwZqNESBZzsZOPEz5iOkRFODB6wHSEiiCFJCGqVKhVVktZSL0MbbOTLE1sH4UUkSy2NCSvxTZycwnTEUQZLJF5kqzkxF/ACciE2wDpQtp0hIoghSQhqlR2v1wxmo9G8wB50zFEyWkCiYUnzReLT8iTIYtCLBYKRWxgs+kYosS0hl90vcZ0DFEGWqaqK+oc66Q/3W86hnFSSBKiSvnpgukIFW0MzTN4pmOIUgsMEIjIhMw2qsk1mo4gyiTpyScY22g02cRB0zFEGewe6TAdQZSBCkgvnKn2Du81HcE4KSQJUaX8jBSS5hNH0YgMgbJOoUFWHbGRhkg+ajqFKBMtl8Kto1DIW6x9lIIzm7eZjiHKQOdlKOpU9eF60xGMk0KSEFXKz0pvm/k4KC4iYDqGKDlZdcRKCnY3Pms6hSiTUVm1zToaDVIftFJDeNh0BFEGOr1eLsRNMZyV33MpJAlRpcJr6kxHqHgb5CXSQppCaqnpEKIMRiMyX4GtlgSk64ptFIpwcqXpGKLEtIan+84yHUOUhVyIm0p6JEkhSYiqpWTlqgXJ1L02UhSSrzIdQpRBXFZts1IUqAtIc9U2MkeSfbSG/nQDh8ZWmI4iykKDO246RMWQHklSSBKiasmcEwurlQkcrOREOk1HEGUQyUuXexulgYwv71e2USgiQxtMxxAlpBQ0R4dYUXPIdBRRFgq8uOkQFUGhWFu/1nQM46SQJESVynfLVYWFyBpQdgpED5uOIMrgcO1u0xFEmciqbXaq6TvTdARRYkrB5sYdpmOIspAeSZPCTpjmaLPpGMZJIUmIKuXWhUxHqHgHTAcQQpyw/kQXqeCo6RiiDBKu9A61kyz6YRut4YXBjaZjiLKQHkmTMn6GXx3+lekYxkkhSYgqlTuYNB2homk0D5A3HUOUgfZk9isrKRiK9ppOIUqsAYg4UkiyUaZOhhnbqFVehy3l44SPmA5RMR468JDpCMZJIUmIaiXt8nkpFAl5kqykdcR0BFEOGhrSraZTiBLLmg4gykKjSTW+aDqGKDGl4A2r/sd0DCHK7g2r3mA6gnFSSBKiSkU2ygxAC5FCko00bnSf6RCiDFqSq4jJym3WicrLsJUUirCs7mUlmdHMVg5+tsV0iIqwomYFFyy/wHQM46SQJESVUkpa5wvR0hyykJLffUv1JTpljiQLRV3TCUS5+IGM6QiiDDJ5GT5uJxnaNum1y19rOkJFkEKSEFXKiQVMR6h4sjaFjTRaS4HQRlr5MkeShboKkPHlb9ZG+eiA6QiiDKLBnOkIoizkItyke3fdS3+633QM46SQJESVynXKZNvz0Wg68U3HECWn8NJrTIcQZaC0kjmSLNWZldW9bKPxydTvNh1DlEHveJPpCKIsFF5qlekQFcHXPnuH95qOYZwUkoSoUvkjKdMRKppCUSdXX6yklAynsNHKwdNkjiRLjUqPJAvJ+6ut6iNyoVLYrz5cbzqCcVJIEqJKBVtipiNUtAKaA9IjyUpOaMR0BFEGrh80HUGUSWtAig62USiiQx2mY4gykLKvrXzc2AHTISrG8/3Pm45gnEySIsRJ2LJly4Lb5PP5VyDJyxc9rYlhpFv5XH5OnkFpDllIgyO98WxUk5Wrg7ZS0nvFSlp+rFbaO7TadARRBm7NcziBMdMxKkZvSuZklB5JQlQpNxEi2F5jOkbFCkkRyVIKP7vcdAhRDrIan73kR2sdjSYfkw9iNsr4UdMRRBn4+UbTESrKlSuvNB3BOOmRJMRJ2L59+4LbdHV10d7e/gqkeXm8ZI58p1xZmFNNF3QAACAASURBVIusxWArjRuXnng2qpeJtq1V40glyTYKReLQaxjY9H3TUUSJtcT6TEcQZaCzbfiFGumVBJzWeBobGjeYjmGc9EgSokpl98s8MXPRaH6LrBJkH41b8yKBiFwFt44Pq4dPN51ClElOOohaKT5wmukIogy0dCG0lCOrth21Y3AH/Wm55CyFJCGqVK5HrijMRaE4V14eLaMhsp9Y+12mg4hyUJAOyEpBtkq48sHURuNNL5iOIMrg4Eib6QiiTLQXMR2hIvj4PHPkGdMxjJNPSkJUqcKRtOkIFe10XNMRREkplJJeZtZS0FW303QKUQYhICJD26yj0SRXPGk6higxrWFfcrXpGKJMlJsxHaFijGZHTUcwTgpJQlQppy5kOkJFW0+ARumebRWdXkeq83+bjiHKQUPzuEyibqMcMFLwTccQJaZQuHlZ8MM2SkFr9IjpGKIsNCokw7km1YZrTUcwTgpJQlQpJyxz7c/HQbFKXiIto/DGzqSQkUmZraMAJX+vtjqYk96EttH45OLdpmOIcpAVNC2l8NMybHHS2S1nm45gnLS6hKhS4TV1piNUNI3mAHIV3D4Kb7zDdAhRahoaZNU2aymZbFuIRUFr6E0tMR1DlIlfkM8OAGtr19IcbTYdwzgpJAlRpZRcMVpQvQxts5DGCR82HUKUgUy2ba/2sMxZZxuFQ2RYivq2UQpqguOmY4iy0LjRfaZDVIS9o3tl1TakkCRE1XJiMrRtPgrFq2XCbQspKaLaSMG+xudMpxBl4AB1AWmu2kh6mtlHawD5wdpJ2k9TyaptUkgSomoV+mXVtvloNL9F5uWwjwZXrpbaKJaTLvc28oGMLx9MbaPRUm6wkFJw5cqfm44hykLaT1MdSh4yHcE4KSQJIYSoIgq8uOkQotQ01GWaTKcQZZL0pORgG4Wi9vBrTMcQZXBWy3ZqQ7I0un2k/TSVDG2TQpIQVSu8Wq7ez0eGttnKxwnL0sTWUdAfl7mvbKSAhCvDKazkyRB7G7kKlsV7TMcQJSc9kqaKBqKmIxgnhSQhqlSuV94M5iND24RYRDTUZOtNpxBlsCIIEUcKSTbK1u8xHUGUgedD9/hS0zFEyUmPpKnSBZkiRApJQlSp9PPSJXM+GhiWGRws5FBIbjQdQpSagkN1O02nEGWwVCbatla2VuYYsY3WcO+u32c0V2s6iig56dE9VXO02XQE4+TdWYgqFT1DXgDn46BYJS+RVvILMqzTRplAynQEUWrap7bnN6ZTiDLwtc+QPmA6hiixnp61/OLA60zHEGWgYodxAmOmY1SMFTUrTEcwTj4lCVGloh0NBFfUmI5RsQpoDuCXdJ/Sv6kSaAKJ7aZDiFLTmgv715lOIUps+eHH4Zn/h58ZMR1FlJDWmh3Dv6Fnh3wMqRRaz3/7RA0NtlHnyJAf22igsCZ4ghvrU/8FKoeXk2WOxzpac3a87dT3awl5BReiirV++GziFy9/ZQ8arsyXnalvFR6a28gwuMDyxHrKv7N9ffy2OuxMu+9E9j2f449bLvOdU7mOV7b9Ng4TiPQed4ee/WsxjSpl43CWTy3tmcyp7V9rzsxmuTxfIcNkJs9BfpdeHu2z5sDPAMh3/spwmMVJa40u8+/hxDFO/KKL1pqhbC/PDf+cbDJUpkxl2W1Zj1vuzPPt3/chnw9Me+kqFJwFMx1/v+/DyEgL+Ivnte/4pCfa5ivHsefaxnfmb4PN1RZcqF061/GO35cG/Log+RVn4jtHe3TP1R44+n78icEh3PneC49vd823jdY4nsfyVIqGfH7+qucs960oFGZsUz91P/Mcf302S+C483C15m/6B2ke65v5mCojyyUIUeUarlpH7aXtZPePUBjK4I3mCLbEcBsj5LvHCS6Lkz88Rr57nNi5rYRa4yS3HiK7ZxSnJoA/lifYVkOoZWICPicaILy6Dm8sx/jTvaAhtDKBN5Ij0lFPaFkNue4xklsPo8cLRM9sRmc8ckdSgMYfzkFA4Q1kIOygUASaIkTPWII3mCHblUQpCDRFcSIBnGgArTXZPSOE19WR7x0nu2eU8LpaEheuwBvLkd7Wj5ct4I/kCLUniKxvwE8VcGIBcp1Jnuka5u+9MWoLoPI+zwc1fbEITl7jBxVOyoOhNO5gDh1w8GuDqICD1xDGUQq/JgBZD3cgi9cUhpxPoHMc7fsoH3QsgNceh9oQjOZwD46hCppCaxRHAekCKuujww4EXAgq/ICDO5xDZ/K4gzmctI9i4g3dawyhEwG85XEIuzhjBfzxPMF9SZzMse38uIvXGgHA7UnjpCbuY8r9zrh3bPuEi5M8dnvy38KWesj7BHaNovTRbZvD5JdECAxmJz6ouApnvIAzWph2jOP3VTxWXQA/7KB8hV8bhIgLBY3yfLz6ME7aw903WjzvY5kdnHF/2v6K99U4+PVhVM7HPZKdeR5ra/A6lhLvqsXRR5cm1pqrR8do1P7RBlCO3aEQP49FeDEcBqVAa87KZHjH2DgPxGNsjUaL30ed4CTAkw2R2R53IrcnHzvb/bMd6/htZ/veLI9rKBQYCgSOPebocQNa8+n+QZJK8Y9NDfhH7z8znaYnGOTI5GPmO+aU82/O5ugPh4q3rx4b55b+QW5oaebRWHTOfFP3tzmdoc33eNfoGBdkc/y3P89w3cnH+v7EL4RywPdY3v0E0VQvw/UbGGg6HRwXtD/xC+M4M4879fGznavWLOl7mrbDv2CoroP9q996bNtiFp9wZpBspGn+n/t8pj0XRz/EK2f2fczzc1jwd2Pe+3xAzX7/gttOuX387/fR+zft/A7h3MTfab5/B+H1b8TXPgqFOrrtRJFEo44+x7Pdr5Qq/jv9adFHDzvzfl/7pHKjxEN1Mx63kKn7mu34WmsG0ofpTL1Ie81mmsLLZmy/0P78oz9zZ8rv1vGP1Vrz7OD/0Jvez+n1F7M8vg5HubMeY7bncrb8E88vxW201nSOvcQzQ4/QHF7Bsug6VidOL+bS2mcw20tDuAVHufjaZ8fwb3hu+OcAhBO5o9tN/O84E39imUycaHR83l+tqf/Csa/T6ThDQ8tZvnzXvC8jU4+pNeRyQUKh/Jx/KqMjTYyNN7Js2a7iS8NUvg/79p1LOJRixYqXUI6eeLlQM/Om0zGi0dS0l5WDB8+gJj5IU/OhBd9etIZUqpZodHTWLMef6+T+g8FMMb/vQ3//Svr7VjM62kI+H6WxcT8trfs40ruGwcHVLF/xAmvXPo1SGt9XjI4soa6+r3i7v38lS5YcKB5j9+4LKRSidLhH6PGODSGf8V7dEMQZyhffmznu/tnaDZP3eW1R/JCLM5hFZQo4GT17eyCiIBLAGc4z9WmcejytoNAeQ417oH385ih+UxhnJIcazUFOo3x/oo2SCOPHXJy8RnWPEziUPtYmawqhQw5qvIBKF3Dyx7J7NS4KhTNWmNL+CeK1RvATIdzh7MTPKV1AjRVQBR8ddifaZz4UlsehOQJZD+dIGjWUQ+U9/MYIfiKIm8xPtDsn24I5j8DhFGgoNIYIpD0KrVFIBCceP15Ah10o+Dhj+Yn2ayQABR/lgddytM04kMVzFYFkfuLxzRPfvzyT4G1Duzk7m2XQcfhJTZy0UkQ8j+FAkLeNTbwfA/zeeIq9wSD1nseBYJBdwQAvhEO05gu8MZVm1J1YGfnsbBaAZ8JhRh1FUilSjktHLkdPMMCF6Qwb8oXiz7DfdYrb1vqaWs/jqUiYVs+jLV/gqUiEcfS0PP2uw6PRCL1ugCtTKTbkC/S7DnuDQdbm8ww6DrfX17IrFOL0dIYOz5+xXb3nMey6rM3nafZ8iDbO/cdXJZQu96UKIapMV1cX7e3tAHR2dtLWJl0fK90/H+jls3u7TceYX9abaCTUBCDsntp2WQ9nKAdo/IaJhseM7SdvB9VEIW3qfk4kw2RjJ+Ph1QRxMx5exJ34NxE8Vnib7xymGs3hHsmgww5+S3RmxlQBUPgNoen7nOc8PvvSZyhknqLVK3BpOjPRIJjFzmCArdHIrI2YvcEgWmt+XBPnYDDAWekMaz2PpFL0BwI0Fya27wwGiWrN28YmVkncGo2wMZtj1HU55DqkHJdzMxlGXbfYKFqVz/PLSJinIhFaCh5/MDZGo+/zTDgMwKp8ngPBIIddlz7XodnzUCj6XJc1hTxnZHM8Hw7R67p05PL0BAMszRf4eSxKdyDA742NM+44PBcOszaXZanvU+trzs5mafb8aY0roPj15PM09f7J7+0MBng4FqPVK3BGNsew6xYbj3CsoTj1cXM9vzuDAX4SjzHkuETQNBYKKOUUn6fJ/U39uWkNv332YkZGWtAoMqE6UvFlRDKDBPws9cN7UApiqYneaKlYK7FUb7FQAZAN1Ra/DzBSuwaNIpY+QiragkJTN7pvxn2FYBytYSzRRsPQDhLjh6fts3vJuQw2bEKhaBx6gaV9TxPOjZKML6ev+SwKTpB8qJZ4sotk3RqUl6Nm/DCeG2Y8vpTYeA/R3EjxA1CokCKa7iMdXYJGUT+6t3hOgfw4qWgLhUCEvBvFD0SoH95NKtpMLlzHkv7nGKlZSc+yi2g+8gx1qUME8uPFfcXSR0hHl5APxAgePc7kuWsN/c2nE04PkEj1UD+6l/FoK91LL6BmrIvG4Z3T9pOKtpAPRAkVUsXnbbh2bfF5zAVrGGrYSMPQDgB6Ws+n4ARJjHWxZHD7tJ/NoYZ6ujeczWh+gPbYJjbWnoePxwvDW+nJ7KU5PDFXRX92olfa5O1kfoiwGyXrpWkML6Uh2AoKhnK9dKf3EnZitEZXMZQ9QtiNEHTC5P0s/dlDZLxx6oJLWFWzmZATxfMLjBWGiToxGqLLyOczJMJNeF6BjB5nvDBCQAXpy3TREGolHIjSObaDvM6Q9dIkgg3FjBnv2Kqpk8dAw0C2m/pQM3l/4u8l4ITpy3QRdiPTzmc0P1A8z8nMWsOqxGYcXHrTB+hMvTjtOBE3Tm2wiZhbR3t8I2P5YTzyZApjpLzktOcu5iYIOmGOZLpQCrJeetpxl0XXEnMTdKV2MZKfflU+4san/Twy3njx2KP5gWmZ3FfV09g2QH/fStLpemKxEVKpOvL5KMFgmtraiYl90+laahL9hEMpRkZaisWtmpohhoeXAVBf383w8DJSqYnnefLxofAY8fgQodAYvh/iSO9aPC9CKjVR6Jh6zFhsiKamg2RzMYYG2wgGM7Put6Gxi1h0BOXkicVGGRlppbdnI/l8tLjN5H4njzE1byrVMO38Jgs5Ux+rtaax8RCh8BiO4zM21ojnBQm4BQYGVhb30dDYRW2il3BkDN93SSaXkMvF8Qoh0ulagsFs8fyOzzb5vfkcv/1CtwH6Igl+tOoiyOvpF8Zmex+vmejT4AxlAVUs1hTfu7WeKLaEA/gtkdnbNfO1B7IezsEx3P4sfszFW5M4VnQ5mbbI8RZqax1/34m24SqZ1vz3b9/P5tRe00kqw3vvgzWVNx/YK/k5VApJQpSYFJIWn9v2dXPb/t6FNxRW+Myur/Jnh+81HUOU2ND+MD1bm0zHEGWwo6WePcvkZ2sTzw2S2nCW6RiixDTwP+vPYufyNaajiDK493cf4+LhZ0zHME858H9egkSr6SQzvJKfQytzshIhhHgFXVgXNx1BvIL2RaW4a6NIfd50BFEmrl/ahQ+EeY6XRxXkb9Y2BeVIEclWWtOYk4UPgGNjX6uczJEkxEnYsmXLgtvk89IwWmyGC/IhpZqknLDpCKIMCmm5Nmar8Uh5JmUW5ijASSXxamWeEZuMRSKmI4hyUYq90TYZ2gaAhv4dFdkj6ZUkrS4hRNUbmTI/i7DfcKDGdARRBn5BmjQ20kBfrfQatZFijtmkxaJVn06xbOiI6RiiTIaD0n6a4EDzRtMhjJMeSUKchO3bty+4zdSxqWJxOJTJmY4gXkG94XlW9xKLVmYoaDqCKDENDEZD5ILSXLWNBvyQ9A61jQI29nbS3dBiOooog/r8mOkIleHV11d9bySQHklCCMHuVMZ0BPEKWpU+vPBGYtEppKXYYBsFNKRzhKTXqHUUoHLy3mujgVit6QiiHLTm/NFtplNUhktvMp2gIkghSQhR9TpiMqa/mlw68pTpCEKIE+QANVmZe9BGTkp6N9hGA41jw6ZjiDLpD9abjiAqiBSShBBVb4VM5Fo9tOaNA1tNpxBl4OelSWMjHxgLy7BFG7mpcdMRRIkpYEPfIaLS28w+SvFYw7mmU1SGnQ+YTlARpNUlhBCiquyMrjQdQZSY1pBLuqZjiBLTwK6WepkjyVKe/Fyt5AIN40nTMUSpac3rhqRHNwC/+67pBBVBCklCCCGqh1J8pf1/m04hSkwpiDTKpPm2UcDSUem1YisdTZiOIMpAA+mg9PS2j6Y5L8MWATj4S0j2mk5hnBSSxP/P3p3Hx1XX+x9/fc/MZJLJTCZb06ZNuqY7lKULFFq2IlUopVCL3se9QvGC0quCCogXUbxewQs/wQUBLXBREKugbL0WENCyF6SlQAt039Ikzb7OZNbv74+QadKkWUom3+TM5/l4QGf5zjnvmSQzZz7nuwiR8rJc0pMhlZSmy0obduTOkgmZ7SirNYI3GDIdQySBRpuOIJJAARkRKezbjrLYnjnOdIqho3qb6QTGSSFJCJHyTvF7UaZDiEHjjQRMRxDJYMVNJxBJoICRDdIryY4cUSn+2pEG6jKlt5ntaA1xKf4CoCzIn2o6hXFSSBJCpLwCt4vJHrfpGGIwaM1lFWtNpxBJ4PbFTEcQSdIqvUZtKRaXQpJdpYelF6HtKMWG7BNMpxgaciaAT3q3SyFJCJHyPmwKsD0gBz0pQSmanZmmU4gBpjU07s8wHUMkgQYaPOmmY4gkcLUGTUcQSaCAorpK0zFEEnhi0qMbgKI5phMMCbJcwjDx0Ucf8dZbb3Ho0CGcTidFRUWcccYZFBYW9ntb4XCY9evXs2PHDpqamigsLGTevHlMnz49Ccn7l+t3v/sdhYWFLFmypNf2W7duZcOGDVRVVZGXl8f06dM5/fTTUUoGKYn+eayi1nQEMYg2Zk2HMtMpxEBSqv1/wm4UkBaTYYt2FHPK1xA70kBpToHpGCIJ/FEZZgxAlcyPBFJIGvJefPFFbrjhBjZv3tzlPqUUF198MXfddRfjxvU++VkoFOKnP/0pd9xxB5mZmZx55pkArF69mj179jB79mx++ctfctpppw3486iurmbs2LEEgz2ffRo1ahT79+/vsc3LL7/Mddddx8aNG5k9ezbTp09n27Zt/POf/2Tq1KncfvvtXHTRRQMZX9jce7IqUErJCTeajiCSICM/ROMe6W1mN3Gg2e0yHUMkgbLka4gd1WZ4qfP6TccQSdAgPbrblG9uW7UtxYe3ydC2Iezuu+9m8eLF3RaRALTWPPHEE8yZM4eNGzf2uK26ujrOOeccbr75ZpYtW8bu3btZs2YNa9asYefOndx55528++67LFy4kF/96ldJeS69FZEAVq1ahct19APGu+++m0WLFvHuu+/y+9//nnfeeYdHHnmEt99+m6eeeoo9e/Zw8cUXc9tttw1kfGFzB1ojpiOIQVSfJge4dpTmkflW7Kg8y0PYJQUHO9JKJu61o/3SG8m2PvJMNB1h6JBV26SQNFStW7eOa6+9FofDwfLly7njjju49957ueWWWxI9idpVV1ezdOlS6urqut1WLBZj6dKlvPHGG4wbN47Vq1eTnn54vgHLsvj2t7/NDTfcQDwe5xvf+AaPPPLIgD2XQCDAPffc02u7CRMmcPXVVx/1/kceeYRrrrmGWCzGLbfcwr/+6792uv+iiy7iF7/4BVprvve977FmzZpPnV2khpN8MrdKKikJ7DMdQSSDU76U2lHckkNVu1LONNMRRBLkBqTXr121OuRvFgDLKau2IYWkISkUCvG1r32NsWPHsnnzZv785z9zww03sGrVKn74wx+yfv16Xn31VUaNGpV4TFlZGT/96U+73d4dd9zBa6+9BsAVV1yB29396lQ/+MEPyMrKAuAb3/gGZWUDM4nIgw8+SE1NDW+++SZa66P+t3v3bgoKuj+LsWfPHlatWgVAfn4+1113XbftrrrqKiZPngzAlVdeycGDBwfkOQh7S3fIikCpZEbLHtMRRBIoLXMk2dGY+mbSItLbzG40EPXnmY4hkiCm5OulLWnN5WWy6i0AVlrKD2sDKSQNSY8++ihlZWWsXbuWGTNmdNtmwYIFrF27FkeHL8B/+tOfurRraWnhzjvvTFz/7Gc/e9T9ejweVqxYAUBDQwO33HLLsT6FhFgsxl133cXChQs59dRTj3k7N998My0tbfPYfOELXyAzs/sxug6Hg8svvxxo6wn1ox/96Jj3KVLHe02yCkWqUDrOvMYtpmOIAaY11G7zmo4hksACvCEZfmxH2ilzX9mNBvbm938hIDH0jWmtYEFj99OtpJxoALY+ZTqFcVJIGoIefvhhrr32Wo4//vge282ZM4dly5Ylru/atStRbGn3/PPPU1NTA7QNYettm2eddVbi8po1a2hoaOhn+s4ef/xx9u7dy4033njM26ioqOCxxx5LXF+6dGmP7Tu+Jv/7v//LoUOHjnnfwv4qQxG2B8OmY4hBcnLDVgoiskqf7WgI1nTf21YMbzLZtj0pwNFUbzqGGGAK8IVbTccQSZAXlr/XTt77o+kExkkhaYgJBoNs3ry5z4WXxYsXd7p+ZOHnlVdeSVzOzs7G4/H0uL3Zs2cnLre0tPDss8/2KcfR/L//9/+wLItXX32VJ554gsrKyn5v46mnniIaPdytfd68eT22nzFjBj6fD4BoNMoTTzzR732K1LE9IAc8qWRT1gwqXbmmY4gBpixwZ8nwJztSQFo0ZjqGSAIVlZM4duSMSA9CO3o/axofymTbh009+iifVCGFpCEmGo3yyCOPkJfXt3HjxcXFictKKbKzszvdv3///sTl3opIAGPHju10fcOGDX3K0Z0XX3yRTZs2EY/Huf3221m+fDmjRo1i1qxZ/OAHP2DXrl192s6bb76ZuDxu3Lguz/FISilOOOGExPUnn3zy2J6ASAlTPOm9NxK2oS0H2zPHmY4hBpjWYLmk2GBHCshv7n3VVzG8aCDmyzEdQySBV3ok2ZNSPDbyPNMpho5cKapJIWmI8fl8XHjhhX1ur/XhVWqmTp3apVjU1NSUuBwM9n4glpmZidd7eJ6JrVu39jnLke64444ut2mt+eCDD/jv//5vpkyZwsqVK4+62ly7jsWsCRMm9Gnf48ePT1zeuHFj3wKLlFTgdnFhXpbpGGKw6DhTWmTVNrtRCrxj5MuLHWmgIV1WCrIjmSPJntIiIdMRRJLUOP2mIwwdGdK73Wk6gPh0KioqEpe7mzuofRU2gLq6OlpbW0lP77kHRkZGBs3NzQDU1h7bXCKhUAitNbNmzaK6urrbFeDi8Ti/+93veP3113nxxRcZN65rL4HW1lZ27NiRuF5Y2LcJ/Dq2q62tpaysjNGjR/c5f2lpaZ/bHqm8vPyYHyvMuLx4BGtrZLna1KCodmXLPEk2FG6W1RftSNHW01jYiwKsUJCYFJNsp7ihhoxwK8E06fFtN3nRTzd3rq0c3AijjjOdwigpJA1zmzZtAtpWK/vqV7/a5f7JkycnLsfjcT788ENOPvnkHreZkZGRuHysk2273W5eeOGFxPVDhw7x6quv8sgjj/B///d/xOPxxH07d+5k0aJFvP322+Tmdq7u1tbWdup1lZ+f36f9jxgxotP13bt396uQ1HHIoLC/XKd8AU0ZSvH4yPO4Zc+vTScRAyzcKL1W7EgDYYd0oLcbDWiHfA2xIwsYVV/DnoIxpqOIgaQ1sxuOfaSK7VRvN53AOPlkHubWrVsHwOWXX87EiV3Hai5atKjT9b5Mnt3xzJ/bPTCr4IwcOZLPf/7zPP3002zfvp3Pfe5zne7ftWsXK1eu7PK4I4e99dab6mjtGhult4k4ul1B6YadSg64R/TeSAwrOg7Baikk2ZEC0mLxXtuJ4UUBVrCl13ZieEqLyYTbtqMUm/wzTacYOryjTCcwTk4FDGMvvvgiu3fvJi8vj9tuu63bNueeey7Tpk3j448/BuA3v/kNN9xwA2lp3R9wa6079UI6sofQQJg0aRLr1q3jvvvu4+tf/3qid9LatWtZv349Z511VqLtkT2i+lrY+rSFpAMHDvSrfUfl5eW9riwnhpbGiEzSm0rCliwTbzeRgCLWKj0L7UjTeT5IYR8q2Aw5Uti3o7BDhizaUcCS4YoJ6TK/qhSShrEf/vCHQFtxaOTIkd22UUpx//33c9ZZZxGLxThw4AA33ngjP/vZz7ptf99991FfX5+43tfJrY/FqlWrcLlcXHXVVYnbHn744U6FJMvq3Gmur/MkOBydv1D09yC0qKioX+2FEMPH8U07em8khhVXpsaRHpNikg0pwN8aps7X+8qzYnhRUiC0JQ3Ue7y9thPDz7jWg6YjDB0ZPa8ingpkaNsw9dBDD/H6669z/fXXs3z58h7bLliwgDVr1iR68/z85z/niiuuYOfOnQDU19fz3HPPcckll3Dvvfd2euxJJ52UnCfwiSuvvJLFixcnrndcoQ3aVpHrKBTq2xCk1tbOq/f4fL5jTChSQUNUeiSlkmnBPaYjiAGmFGQWyqptdiSrttmXlknUbUkBGZGw6RgiCZTUfg8rPtV0AuOkkNRPN998M0qpAf+vvajTF7t27eKb3/wmy5cv5/bbb+/TY1asWMGWLVtYuXIlubm5/Pa3v2Xy5Mm4XC5KSkq45557uPTSS7nrrrs6Pe7MM8/s1+tzLL7zne8kLh+5WlpOTk6n630tJB3Zzu+X5SrF0QXiMv9GyojHOKVhi+kUIgnSc2SuMzuSOZLsS1vSg9CONBB0SfHXjlqc0jMUgJxJ4Ot+NFAqkaFtw0xzczPLli1jMjfGRwAAIABJREFU9uzZPProo12GfvWkpKSEhx56CK01NTU1tLS04Pf7yc4+3DXvmmuuSVweP358ryu8DYQzzjgDn89HU1NTl55Eo0ePxu12JwpDtbV9W7L7yHYdV68T4kjn5/v56d5DpmOIQTC/7j0KIn17HxHDjfRusKv0sEzca0eOSISo6RBiwLX3SKrrtaUYVrTm/OpXTacYGup2QdOhlC8mSSGpn0499VS+9rWvDfh2OxZzjiYSiXDJJZeQkZHB008/fcwrqimlyM/PJz8/v9Ptzc3NPPLII4nrX/7yl49p+/3ldDopLi7mww8/7JLJsiwmTZrEhx9+CLRNZN0XZWVlicvZ2dmMGiUz64ujy0+TSSFTRViWm7ataKt0srYrd0TKDXYUHaCVgcXQIj2S7EqTH6nvvVmqqN4mhSTTAYabJUuWsGTJkkHfbzwe57LLLuPgwYO88sorSZnzZ/Xq1YmJtn0+H6tWrRrwfRxNeyGtu0nDTzvttEQhaf/+/X3aXsdV1+bPnz8ACYWdbQ/I3CqpYqP/eD70TGRGYLfpKGKAuTJlrjO7CrnkcNWOLBTyV2s/0iPJppTF21nHsaTmFdNJhob8qaYTGCen74aJVatWsWHDBl544QXy8vIGfPsVFRX8+Mc/Tly/5ZZbuvQOSqaqqioA5syZ0+W+RYsWJS7v3LmTQCDQ6/Y2b96cuNxxMm8hujPFI8uZpgyl+Gv+QtMpRBJYTpkF1K5apdeoLVmhoOkIIgk0UJcpi9zY0QF3avfAEZ1JIWkYuPHGG3nmmWd44YUXGD16dK/t//KXvxCN9q8b+H/8x39QV9d27uDss8/mW9/61jFlPRbV1dWJycYvueSSLvdfcMEFidXb4vE4mzZt6nF7e/bsSRSmLMti2bJlA5xYCDGcjQ5VmY4gBpjWEKyWYoMdxYG6TCn221HMnWE6gkgCKenb1yH3wHdmGLYObOi9jc1JIWmIu+2221i9ejXPP/88JSUlvbZ/9913ueeee3A6+94N/Oabb+bJJ58EYObMmTz22GP9msT702qfAHzSpEmce+65Xe73+Xx88YtfTFx/5plnetzeU089lbh84YUXMm7cuIELK2zpzxUy+XKqUDrOZ2rlw99ulAK3T1b2shsNfFyYS1iGttmTzKNjSxaQ09JkOoZIgpGhGtMRho6gzBclhaQh7N577+W2225j3bp1zJo1q8e2LS0tPPnkk5x//vn827/9W5/38ZOf/IRbb70VgLlz5/LSSy/1OqStqamJZcuW4fP5WLRoERUVFV3avPrqqzzwwAO88847PW6roqKCn/zkJ1iWxerVq3G5uj+j/J//+Z94PG1LTq5ZsyaxituRtNb89re/BcDhcHDTTTf1uH8hADbUN5uOIAaD1nx77+9k1TYb0hpaDsmXUjuq9sly03alHQ7TEUQSxFEytM2mikOywnFCeu8LZdmdFJKGqEcffZSvf/3rBINBzj77bNLT03v8z+v1cskll9DS0sIXvvCFXrff1NTE5Zdfnii0fPnLX+bll1/udrLrI9155508/fTTNDc38/e//53vfe97ne5/7bXXOOOMM7jqqquYO3cuixcv5uDBg12209LSwiWXXEJDQwO/+c1vOOecc466z0mTJiXmcCotLeWXv/xlt+1+//vf8/777wNw3XXXMW/evF6fjxCj3DIkJiUoxdr8M0ynEEmgFMQj8qXUbhQwsqHFdAyRBBqIe6TYYDca2DBxBsE0GY5qO1qjtQxcTBh7qukExkkhaQhau3YtK1euRGtNPB4nFAr1+l+7L37xi4n5hLoTjUZ59NFHmTZtGg8//DATJ07kmWee4cEHHyQjo29j1WOxzmtsRCKRTtePnAz7b3/7GyeeeCIPPvggwWCQcDjMunXrmDt3Lrt27eLJJ5/kyiuv7HW/3/rWt/j+978PwE033cQf//jHTve/8MILXH311QD8+7//e6fJw4XoydICOauQKrZ7x1PpyjUdQwwwHYdQowx/siNHVNb1sqNYmhvtlJM4QgwbSvFC/mmmU4ghRApJQ8z27du59NJL+z1ZdrvuCjLhcJjXX3+dW265hfHjx3PFFVcwY8YMfv/737Nt2zYuvPDCfu3juuuu4/zzz8fj8XDGGWdw2223dbr/M5/5DDfddFOnwlR1dTVXXnklXq+XjIwMvvKVr7B8+XK2bdvG0qVL+7zvH/3oR/zpT3+iqKiIf/mXf2H27NlcdtllLFiwgPPOOw+v18u9997LAw88cNRhckJ0oZTpBGKwKAfbM2XeNLuJhhSxVumRZEdpcZn7yo4c4RAqGum9oRhWFHDq7q1khFtNRxFJUNTadTqTlFW9zXQC45SWPmq2d+2113Lw4EFmzZrFvHnzWLBgAV6vN+n7DQQC/OMf/2Dfvn3U1dXh8XgoKCjgpJNOYtq0aZ9qQu94PM5bb73Fpk2bqK+vx+v1cvzxx7Nw4ULjBaTS0lKKi4sBOHDgAEVFRUbziN5VhiLMemOr6RhiMGjN39/5MjMCu00nEQNIa9j391yCVTKcwk40sHFsAZU5MgTKjgJjpxDLzDIdQyTBM7NOpyxnhOkYYoDdsuNXrCp73HSMoeG67eDrfUqYwTaY30OlH3gK+MUvfmFkvx6PhwsuuCAp27Ysi/nz5zN//vykbF+klu0tQdMRxGBRito0PwR6byqGD6UgZ1KLFJJsRgEu6ZFkSxrQDvkaYkca0Eg/BTtqccriBwDkTx2SRaTBJkPbhBAp7669sgpFytCaKS37TKcQSRCPyRBVO5pQ1WA6gkgCBVjh7lfgFcObAkbXyzLxtqM151e/ajrF0FC9DZrku4MUkoQQKa0yFOFNWRUohchZUrsKVLtNRxBJ4AtF8Aal4GBHulU+e+2qOb1vC/iI4USTH6k3HWLoOLDBdALjpJAkhEhp2wOtUlpIJcri7azjTKcQA0xraK2VBRbsSAH5zTL82I6cISkQ2lEc2J83ynQMMdCUxQu5suR9gnx5kEKSECK1TfGkyxthitEyAsp2lAKnW47q7EgDDelppmOIJIjJ6rq2o4ENE2cSTJP56uzofe8U0xGGBmXBWCmqyfcnIURKK3C7WJQrKwKljHicUxq2mE4hBpjWEA1JhdCOFKCU/GztSFkO0xHEAFNAU7pMyGxXGfFW0xGGhnN/JJNtI4UkIUSKqwxFeKm2yXQMMViU9FqxI6UgzRczHUMkQRxodkvPFbvRQNwjJ3HsaGLlQdMRRDJozbk1Mi8QADljTScYEmTdTSH6YebMmb22iUQig5BEDJS3GpqRxaVTiHKwPXMcBfW1ppOIAWa55C/ZjqQvkj2pxP+E3ZRUl/FGuFWGt9mNUuz1jGZB42bTScz74C8w4yLTKYyTHklCiJSm5Eg2tWhNbliWE7cjhxSSbEkB3pCcoLEbnfifsBsFjK2pMB1DJMEHmTJHEgAfPQNNh0ynME56JAnRD1u3bu21TWlpKcXFxYOQRgyEiRkyiWtKUYpNWdOYEdhtOokYYC6/FBvsSANhh5z3tBsFWKEAMW+W6SgiCUY01rOt0HQKMdBqnTIctY2G6m0pP0+SfDILIVJabVTmVUk1FWkjTEcQSeBMkx5JdqQAf0AmeLUbDcQypYhkV1rmI7SlRqfXdIShI3+q6QTGSSFJCJHatBzspBStmdosvZHsKNwkK0DZVXpECv52pJ0yibpdBV1u0xFEEqTFw6YjiCFECklCiJS2tqredAQxmJTiY+9E0ylEErg8UmywIw0c8meajiEGmAKsgKyYalcZkZDpCCIJmqRH0mHV20wnME4KSUKIlFbWKvOqpBpftMV0BJEELq8UkuxGA0GXg+YM6d1gR7LYhRDDy4H01J4TqJOMXNMJjJNCkhAipRWmyZoDqWZMuNJ0BJEErQ0yTMZuFJARieENSu8Gu9GAtmQ4ql1Ve3NMRxBJcDBjFJUuKaAAEKw1ncA4KSQJIVJaq0yRlFricU5p2GI6hRhgWkPTXo/pGCIJFJDfHDQdQwyw9lXbhP1oYH/+KNMxRDIoB9szx5lOMQQomWwbKSQJIVLcZ/JkKdOUIiMpbEkpcLilKmxHGmhITzMdQwwwWbXNvuSd2Ma0ZkrLPtMpzJu8GHwyzE8KSUKIlHZA5khKLcqSs2lCDCMKSIvFTccQSSCrttmTBeS0yETqtqM1lx18ioKIDOlix/PQdMh0CuOkkCSESGmbG2Xi5ZSiNcTlfKndaA3hJplvRYjhQgFWU53pGCIJ4kBdpvT2thWtSYuFuGPXz00nGSI0HNhgOoRxUkgSQqS00W45I5pSlGKLb7LpFGKAKQVpPlm1zY400CLv07bkCMsk6nYkI8htSCnCDjcfeiaaTjJ0yDlJKSQJIVJbQHqnpBatOa5ph+kUIgkslwx/siMF+AOtpmOIJIgr+RpiRwoZ2mZLSvHQ6KWmUwwdeZNMJzBO3sGFECmtVebeSC1KsS9jtOkUIgniETmksavM1rDpCCIJHBHpkWRHGhnaZleH0vJNRxg6anaZTmCcHHUJIVJabprTdAQxyMrcI0xHEANMawhWy8pedtUiq7bZUzRqOoFIgtoML8G0dNMxRBIcLz26D2utN53AOCkkCSFS2rl5svxwStGaC6pfNZ1CDDQZoWpbGmjwyJdSO4plZJqOIJJBy3x1dlXlzjUdYejIyDadwDgpJAkhUpuSaSFTycTAfmYEdpuOIQaYssCdJb0b7EgBmaGI6RhCiD7KbQ2SEZZ5zezo0cIlVLqkmISyoPhU0ymMk0KSECKlTZEz3SllQd0m0xFEEmgN6XlB0zGEEP3gCMnfrB0pYFRDjekYIglilpPtmeNMxzBvyc/BN9J0CuOkkCSESGkFbhf5TnkrTBUTg6WmI4gkUAqyxsrEvXbUNnGvFPztKObOMB1BJIsMN7YnrckNN5hOIYYI+fYkhEh5o9NkItdUUSErjtiS1tC43206hkiCOo+bsEsWRbAll3z22pEGWl0u0zFEMijF7owi0ynM++u3oemQ6RTGSSFJCJHyRqbLAU+qeM831XQEkQRKgSV/xraUEwiRFpH5r2wpEjadQCSBAiZVlZmOIZJEy9SiEI9C9TbTKYyTQpIQIuUtcn/U1qVB2N4JTfLBb1feQpnc1Y4U4JXJtm3JEWw2HUEkSVpYhhrbktZMCsgUAYB8bwCkr7AQ/TBz5sxe20QicsA7nIRCVTSW3Q/qv0xHEYNgfHi/6QgiSaKtcm7MjjTQ7JbuZrakHaYTiCTQwO6CMaZjiGRQito0PwRMBxkCKj6AiWeaTmGUHHUJIVJaVfXfGUMplpahE6nA7ZRVguyqNSpzJNmNBt4vzpE5kmwq7h9vOsLwNMQ7Qigg7JL3Yzty6ChTWvaZjmGcBph4ltkQQ4B8MgvRD1u3bu21TWlpKcXFxYOQRgyEQGAX2dTzZVbzoP4qWnU4Q6rjgGqbgKUnWre1OfLf3tofq6M9vrvbP+2++rP/gX7MAG9P6RgLqt8fuAxDUDJ+3ENh/335k/p7/SxmIQe4w52m7YtoDPj9OYqo9lBYaziUGHDKMYpoVgYgvbj7RYNLW0SIt/2hfMpt9WkbfW33CaUVODMH7gPB+AdbH48Fe93OJxXAjtsZqOPFZByXHXFM69BRvtZ8DwWRvr0hH7n53g6Vj3Z/f16i3i53vC2moSKewyirDscROaBrtvbbtIYnYgtZmFlCQZ9eCfuSQpIQIqVZlgeAs3mJk9jINj2NAB4yaWEK2yhlDC/p8yiliDLGgVIoHW87G6Es0DHO4W+cqjeg0ezTExjHHir1KPYwgX2MYxdTQTlAx5jHmyzjLxzShXzALP7BuWjlBB3jON5jMttpJZ0a8vkn89sKW5988qlP9jWDD3hKf54DjEtsdypbuYQ/85Y+jX9wHlpZKB1nGY9RpQt4nbPQysLSMSaynV1MTbQ5/Fy6+VRHd7rPoaPkUU6lLjr8idrepqPEp3aMErYzlr38XZ/3Sd6On+7xtn877r/jQduR29caH7Vk08gBPf7oeT/ZtoJPnneUL+vVeJz1XQ4kAByq7fKmeAknWrtxqTgx3dZtt6/HZnENpfE8xlg1iYOSuIaQdpCuYv06ENKA1ccDKDj8UpXqfIqo7vSYtueocCjd7YFVT8/zyP3HtWKPHslEVdHpoCyq4X9j5xPQ6VzjfAKrwz7oZds9PUetoUmn853wV5jiKOWLjpcoVA1dfuwb45N5Pzo5UUg68ntPP78HHfVxn/y2YnW472j/csRtFX4Y2QjWEb0Juntcb3l6ekxf7uupbcfLcWD7aJhcBo5j2EYceG88+JthXPXhbfSUpykdfnaJRWmeoiFT8ZkN7b9EOaDrjnxJgDSgfdJmhSPteBzuE4iE3kOHB6Jw3P7sbMjKh3j1MT644+vekUI5p6F1K8SbQB+xfUcRzvRTiPoy0I6PO/0CFcayiROnXrUQsmLd/wK3t+/uj+yIf/1xDy4cVFtNidu88XSardaef4n78kfZTdvieB55MS9R1fYcSh21R98P9P6H1M0+p0QLOSM2g/XOrex0VHT/mO62381+xsdGsNdR1fObpYbRsRzKHHW9tkO1FZFOj07lhPq/8oq3mT/oL4FydnrDL2YvC1jPAcbxGmd2PS7olFdTyH7KdVFbOwAdYzy7KKKUSkawnZmJ47G25+2gywdMh2OSEVRSxahOH4ajOEglo4h3yhpjAev5F/7A6yzgj/oy4p8cc4GVOF5pP87ofAxDp2ORYvbwb/yOzczmWb0ElAOlY0zmQ7brmZ3aJh6r40xlK9v0jMSx3uHnd/iy0jGW8TgHdRFvcxp8cqx3Fi/gp569TECjiZBGNnWMZT8FVDGScir0aA5QzEGKyKOaMG5CpOGjiRPZhNIKL4006yzGUEpaKMChWBZB7SBDxci3GnF8cgywJz6SD+PjqNdexjmqWBeZR6PlJYsWGslkb3wUOVYzdXFv4t/xVgWjqcKrWtkeK2KMo4aDsbwu/05xlJKr65niKGN7bDSWUmTTBEqxOTqRk5y7qdVenoudQrFVyShVyz9j0/BbLQCd9j3eqgBgY3wqVWQzgnrOtjYxUZWxW4/mH/GTyaOBKxx/ZYpVyo54Ef8buwCA0x1beD12HB8zjj9UNlPgS+/mjyx1KK1lpighBlLHHkkHDhygqEiWyRzKdu3+JXv3/qJPbevJ5iBFjKFtosH2y9nU9/lxR7bty31eGmkmq0uboz22u9uPvK275+KlkbaP9HxayWAub+GnsdsM+xjLFmZxHO/jp5FtTKMFDzXkUUMBJ/IOPlq6zaDRbGMGOdRyMhs77b99Hw1kddr+ayyglHEsYD3H0dYzcAszeYnzSCPMND7iJDYBsI1pKDRT2Nbl5/Th/un89ePzOx1IAJRYB9kZH5M4qGi/DjDb2sZoqihQ9WitmWhV0KAzadVplDjKeC82gfeY0umgZLa1LbH9KrKZzxYudr5CaXwEFeQBMIGDzHHsYE98FE/Gzkgc/PwtNpca/JRYB6mLe5ll7WKSKqNZu/GqEId0NgBe1crfYnOBzgc309jHeY5/0qzTKWNEp+fY8SCq/cBqZ3wMeTQkHtOMBy8BvKqVilgOc53b2R/PZwdjE89nGvs43bGFD2PjwFKJ1w5IHJS1P5ccmrjE+SrVcS9KKQLaDUrxz9g0sBTT47u5xPUalfEsqslBA9m04LOCPBw5j+c4tdPfxXy28BXnM4xStVToXFZHl/ImM7mw9k48jgqqsxXlOYrCOvC0xgmkWxCPM3sXHPJDbVbbt6jcRiiqgWofiS9WI5qgNBdq/BBItyjPodN2thW1ffEoqtE0pkNWK+i4ZkIV7BkBylI0prc9BjTNbphQBe+PUxwYaeFv1kwt1Xha42SEwBOGj4oUylLouOak3RpfC2gFaWHIDEN9Ztt3tLHV8Np02D/aSuy7fV+e1rbiayAdynNU4r7j9saZsR+a3bBtrCKQpjnnPfAFYWMJ/HOqlXgO00uhxqfZVWhRWNf2rXBbkaLBqxK525/T9FIIpGlq/G2vUfv+JpXHyWtSfFTU9lqU5rU9HkhsI68hTk4zlOXBrkKLSeWak7drRjbCsyfD+pM7n+MsrhzD57ZdgeUcgY63EGl9j3hkLziySUufm7g9HqvBcuShrMzEY3W8hVjkYNuqfs4x6HiASPAddLQGnFk4nIUoy49S6cRjpcRizRApA0Kg/Lg8p+FIG5vYDroVUMR1K8QDYHmwVDqg24q/VjqQTjxWCaShY5U40qZgOfOIhnehY3VgeXA4/FjOtveXUPBtiB7Eck3F4fQTjVZBtApNHKVcON0noKwMYpEdnxRmw2hcoAMQj6LRKBRYTpTyAApn2mSU5UlkjsVqIFaDI20WjrQxnV6reLSKWGQflqPgk/p9BloHUSqDeKyu82sX2ooCnO6Zidc9Gt4FuhnLUQSKbn8G8ehBtAaHa0yH+zTF2RWMI4eQFSE7nomHw8OhAoQ4ZNXTRCsB1Uq2zmRsfAQe3In72ravKXPWMTqaQ7qVho5rah3NjI7lkocvsa16qyWxj/bHh4jixklW3EPIiuCOuwhZkcQ2cmNemqxW6lUzHu0mDSfNtNJstTIy6ifqiJMb86Is1SV/+373W1XUqwDZ2sOIuD/xXIOE2eEoJ0KMEdqXuE/HNQccNUSI4dcZpOECNG5cjIxnd9pHDU2UOWo/yRmkXNURU5rp0TGkk/bJ9qP4tYcwUZqtVsZG80m30rp5LSK4cZEV91BlNSQyd3zN255LC9nay4h4Fo1WoMtr2L7dqolPUFvyTKfP/X1M4DjeZxyH5ypsv7+CAt5mPn7qiOIml2rSaWUubzGO/dSTzXamolFM5eOjHvNA5+OJI//teAz0LidTQ16nffT1mKt9P93ts/22vuRtz9LTcUtv++3pWG+gBSMuvv6POxPXOx4vtR8DpIp11y5gRqHfdIwuBvN7qBSShBhgUkgaXmpq32Dz5i+ZjiEGgdZw7+aVbKo62XQUMcCsjJ1kjn/AdAyRDHG4bNN/44lkmU4iBtgJ6Yrx6TI4wm40mr3zv0/YJ6t72U0sDte/8mMaw/J+/IerTuG0SfmmY3QxmN9DZbJtIURKUybH3YtBpRTkZXQ3NEYMbxpH5h7TIUSyWFCXcch0CpEEzTHTCURyaGJpTaZDiCRwWFCYWWE6xpBQUuA1HcE4KSQJIVKay5ljOoIYRBP8+0xHEANOEaleRDwqB3W2pCE94jGdQiSBtuvcUylOYZHWUmg6hkiCWBzKW0aZjmHcaL875edHAikkCSFSXCCw13QEMYjKm1J9jQ27soiH5GdrSwpaXQHTKUQStMZ7byOGH4227fz0qa6uNUuGtQErTx9vOsKQIIUkIURKC7bKGP5UMjFnr+kIIik0MuWjTWkOr2QkbCVbpkeyJYXC3TTWdAyRBDnpjWSlNZqOYdxbu2tNRxgSpJAkhEhpDQ2bTUcQg2hm/nY5CLIlRSwwwXQIkQwKqjPLTKcQSeB3yNcQO9JoAnkfmo4hkkDmSGrz0sdVfFjeYDqGcfIOLoRIacHg/t4bCdtwKDkIsiuZb8WmNBQ1TDGdQiRBTVT+ZoUYTto6/srfLcATm2REgxSShBApLSNDul+nkpiWiSJtK+42nUAkQX5zMXnBMaZjiCSIynBUW1IovIdONh1DJIFSMCVnt+kYQ4O8fUkhSQiR2vxZJ5qOIAZRVUueTBRpUw53lekIYqBpmL/vQtMpRJK0xEwnEMkSSa8zHUEkSV1QjqEAzpomC3xIIUkIkdIyPHKmO5XsqJN5dOxJo1wy+aXtKEDJoapdWcp0ApEMmjiBEe+bjiGSIB6H92uONx1jSGgMRkxHME4+nYUQKS3bP8d0BDFItIYXDiwyHUMkhSIeGm06hBhoGtIjHtMpRJK45FuILbVm7ifmlomIbUmKvwkNUkiSQpIQIrWFwzWmI4hBEtNwsFl6oNmTxnLLyl62o6AhQ4Ys2lW6fAuxpfSWYhwhv+kYIgksBSX+XaZjDAlNrVJIkrdwIURKq6173XQEMUgcCqbmbDMdQySFQik5VWpHTWlS7LctLX+zdqRwkNZSaDqGSBb5rAWgJSSTvEkhSQiR0rze6aYjiEGiFJxW+LbpGCIp4ljuStMhRBLs90vx167ynPKF1I40UcKZ5aZjiCSIa9hZP9F0jCFh8XGyArAUkoQQKU16MaSWpnCm6QhCiH4oy9lOwNVoOoZIgogsn21LTXmbZY4km9LyNwtAyYhMZhTK8E0pJAkhUpqWT8WUkuOWJYntySIekqV47UgrTV3GIdMxRDLIeRxbckR8piOIJHFYUJhZYTqGcefPkt5IIIUkIUSKa27+yHQEMYhyM+QsqT1pcLSYDiGSQStygiNNpxBJ4DIdQCRFyCOFX7vSGpoj0rP7QG3QdIQhQQpJQoiUlptzuukIYhDtqhtnOoJICoUO55sOIZJCeo3aVU1MfrZ2o9GkNxabjiGSRCnwuuSkTVVjq+kIQ4IUkoQQKc3nm0Zm5jTTMcQgqQjKSjJCDCsKKny7TacQSeCUIqHtKBQZgfGkNRWZjiKSIKahvEWGdck7Vxun6QBCDCczZ87stU0kEhmEJGKghEJVtLTIqkCpIsMp3ZHtKY7Ds890CJEkctBuTx6HnM+2I4XCVzafmqmPm44iBlhNSzaN4SzTMYzbXxswHWFIkHdwIURKawnsRL6mpI4xmQdNRxDJ4N6F5Ww2nUIkg4bs4AjTKUQSeC2ZbduunGGZcNuO8jPryUqTVTQP1LVS2STD26RHkhD9sHXr1l7blJaWUlws48OHi0xPCW1Lx0gxKRXkZ8iqbbYUkrmvbEtBqysA0pnQdppjGr98E7ElFZOp1O3IUlCSvZtNlSeajmLcO3trOf/40aZjGCU9koQQKc3tHoHbPcZ0DDFItBQMbcpFpHmC6RAiGTSyaptNReTt2LasqMd0BJEsWv5wARpY8bTuAAAgAElEQVSCMpWJFJKEECmtqeljQqFS0zHEIKltzTUdQSSFItJwsukQYqBpmF26GE9E5uSwI5eMbLOtqLvBdASRBLE47GyYZDqGGCKkkCSESGm1da+bjiAGUVMo03QEkSQOd7npCCIJZh5aYDqCSBLpIWpfIZ+coLOj96pmymTbIkEKSUKIlJabc7rpCGIQ5XrkLKk9xXFlv286hBhoCip8u02nEElSEzWdQCSLlhVSbSndIT/Xdv4MmQdMCklCiJTm801DKemlkip0PG46gkgCh2eHrNpmUyGHLLNsV1kO6ZFkV/KTtadpebtl1bZPzBkvUyVIIUkIkdKamj5G6xbTMcQgKfRWm44gkkCly7A2uwrJGXDbaonJJEl2JT9Ze7IUFGZWmI5hXE6GkwJfuukYxkkhSQiR0srKnzAdQQyiD6snm44gBpwmFiwyHUIkSZlvp+kIIklaZfUn24q5pCehHWkNzRHpxd8YlHG5IIUkIUSKi8flbHeqiMfhbwc+YzqGGHAKHZxEtFWWiLejZne96QgiSaTXij1pNHGn9PS2I6XA65KfbQx4bWeV6RjGOU0HEH3z0Ucf8dZbb3Ho0CGcTidFRUWcccYZFBYW9ntb4XCY9evXs2PHDpqamigsLGTevHlMnz49CcnN2bdvH//zP//DfffdZzqKGMIy0seZjiAGiVLgS2uSFUdsSRFrKcGZfsh0EDGQNOQF+n+cI4YHh+kAIikUCnfTWIJ5H5uOIgaY9Eg67ImNpSwoGWE6hlHSI2mIe/HFFznppJOYMWMGV1xxBd/97ne5/vrr+eIXv8iYMWNYvnw5+/bt69O2QqEQt956KyNGjGDlypW89tprvPfee/zXf/0XM2bMYM6cObzxxhsDlv3zn/88Sql+/zdlypRet33DDTf0up3x48czY8aMAXs+wp6cTp/pCGKQKAUnj3jPdAyRFBrLXWY6hBhoCnYUbCTgksld7SjbJX2S7EijicncZrYkPZIOy8mUVdukkDSE3X333SxevJjNmzd3e7/WmieeeII5c+awcePGHrdVV1fHOeecw80338yyZcvYvXs3a9asYc2aNezcuZM777yTd999l4ULF/KrX/3qU2cvLy/n6aefPqbHnnvuuT3e39DQwOrVq3vdTlZWFitXrjymDCJ1uNKkd0oqqQtlm44gkkJB3GM6hEgGBfuyt5hOIZIgxyFfQ+xIociol/kI7Uh6JB3mdUshSYa2DVHr1q3j2muvxel0cvHFF3PKKafg9Xo5dOgQ69ev5+WXX060ra6uZunSpWzZsoWcnJwu24rFYixdupQ33niDcePGsXr1atxud+J+y7L49re/TWVlJbfffjvf+MY38Pv9fOlLXzrm/A888ADRaNtEZC6Xi/z8fCzr6AcM4XCYqqq2sabLly/vcdu//vWvaWzs+eyky+XixhtvxOeT3iaiZ56MCaYjiEGiNexplKGMdhUP+01HEEnSnNZgOoJIgrqoxi/fRGwp7Cs1HUEkQVvP7s0cbB5jOopxi48bZTqCcfL2PQSFQiG+9rWvMXbsWNatW9ft8KzXXnuNFStWUFHRtgRjWVkZP/3pT7n11lu7tL3jjjt47bXXALjiiis6FZE6+sEPfsB9991HY2Mj3/jGN1i0aBGjR4/ud/54PM4DDzzACSecwN13382CBQtQqufuy7/+9a9ZtWoVeXl5nHnmmUdtFw6H+cUvfsHixYt57rnn+p1NiCPV1LxmOoIYJErBjJyP5ADIprSWpXhtScPE2lmmU4gkCMqqbbak0UTSa0zHEEkyNWcXa/eYTmFevrf779OpRPqUDkGPPvooZWVlrF279qhz/CxYsIC1a9ficByeqvBPf/pTl3YtLS3ceeedieuf/exnj7pfj8fDihUrgLbhY7fccssx5f/rX/9KPB7nhRdeYOHChb0WkQAef/xxAC666CKczqPXNx955BHKy8v5zne+c0zZhDhSc4tMBplKirIqTEcQSaFxZMiRre1oGFc3k7ygFH/taLRLvobYkULhq5hrOoZIkrKW1J5gut07e2tNRzBO3sGHoIcffphrr72W448/vsd2c+bMYdmyZYnru3btoqWl8wRozz//PDU1bWcFLMvqdZtnnXVW4vKaNWtoaOh/d/Knn36a3/3ud4wY0bc3mqqqqsRQvZ6GtWmtufPOO5kzZw7nnHNOv3MJ0Z38fPldShVawxtlcnBrT6pPJy3EMKPAHywwnUIkQRrgd8rXELtytuSbjiCSxG1FTEcYEhqC8jrIO/gQEwwG2bx5MzfeeGOf2i9evLjT9SMLP6+88kricnZ2Nh5Pz5ORzp49O3G5paWFZ599tk85Orr//vv7Veh54okniMVi+P3+HifaXrt2LR999BGWZXH33Xfzz3/+k1gs1u98QnTU2iorPaWS5ojXdASRFBotw2Rs6YPC9bJqmw2Fgda4/M3alSskhSS7ykqX92MAf4ZMti2FpCEmGo3yyCOPkJeX16f2xcXFictKKbKzO69ItH///sTl3opIAGPHju10fcOGDX3K0VF/zwo/9thjACxZsoS0tLSjtrvjjjsAePvtt7nmmmuYN28eubm5rFixgj//+c+Jyb2F6I/6+rdNRxCDRCmYkbvNdAyRFIpQ9dHn1xPDl7Y0dRmHTMcQSdAUk0KSXYUyZBi5XTWGZLVjgCwpJEkhaajx+XxceOGFfW7f8Qzs1KlTuxSLmpqaEpeDwWCv28vMzMTrPXzGfuvWrX3Ociz6OqztzTff5PXXX+9ye2NjI3/+859ZsWIFkyZN4plnnklaVmFX8kGQKrSG/U0y14pd6WD/F4cQw0AccoIjTacQSSC9CO0r4pdV2+yqtEk+awE+KpeeWVJIGubaV20DWLp0aZf7s7IOV43r6upobW3tdZsZGRmJy7W1yZ1IrH1YW2ZmZo8TgW/bto1TTjmFkpKSo/as2r9/PxdddBE//vGPkxVX2JDHI4WFVKEUZLp6L6iLYSpNJr60JZn6yrYOReOmI4gkac3aZzqCSJJgrPcRLqmg0C8rxR59eSwxLGzatAkAh8PBV7/61S73T548OXE5Ho/z4YcfcvLJJ/e4zY6FpGOZbLs/2ldr+9znPtdpv0dauXIlK1euBNqG/23atIm//e1v/OY3v6G0tPNZj+9///v4fD6uvfbaY8515Db7o7y8/JgfKwZfXt5ZHDjwoOkYYrDIGXDbstLqTEcQyaCgLuMQnogMp7AbpaVKaEcaTcgvq2jaVY5bPmsByht675xhd1JIGubWrVsHwOWXX87EiRO73L9o0SJuv/32xPVnn32210JSxzmO3G73ACXtqqqqivXr1wM9D2s7ktPpZN68ecybN4/vfve7PPTQQ9xwww2dil7XX3898+fPZ968eceUrePcU8LeZKWn1NFWQ5JCkj1p0nJkvjNb0pAekTPgdlTsloERdqRQpLWMJOyT4W12NDbroOkIQ8LpJTKhvLyDD2Mvvvgiu3fvJi8vj9tuu63bNueeey7Tpk1LXP/Nb35DOBw+6ja11p0KMrm5uQMX+AhPPvkksViM9PR0LrjggmPahtPp5KqrrmLLli3MnXt4We9oNMp3vvOdgYoqbCzTU4KMnUgNSsEJI5I775swQaPcpbi8cgbclhQ0ZFSZTiEGWI4Ffqd8DbEr6WxmX5Gow3QE404q9jOj0G86hnHSI2kY++EPfwi0FYdGjux+IkqlFPfffz9nnXUWsViMAwcOcOONN/Kzn/2s2/b33Xcf9fX1iesTJkwY8Nzt2ldrO++88/D5fJ9qW0VFRTz//PMsXLgwMUH4yy+/zP79+7usRNcXBw4cOOYs5eXlx9wTSgw+t3sEJSXfZefOn5iOIgZBabNMEmk/ivSRz5oOIZJI+hHazyTpjWRbmhit2TtNxxBJUhtOXieD4eKGz07rvVEKkELSMPXQQw/x+uuvc/311/c6LGzBggWsWbOGL33pS4RCIX7+859TX1/P9773PUpKSqivr2fDhg2sXr2a7du3d3rsSSedlJT8xzqsrSc5OTncf//9nH766YmVQDZs2HBMhaSioqIBySSGh3FjryQabWHv3l+ajiKSKK7hrYq5vTcUw0wMy11pOoRIlriisKnr0H0xvOW5pJBkRxpNzcRniLmTO8eqnaSljSYcLjMdo0+0hjfLU/tkuQWUFHh7bZcK5F28n26++WaUUgP+386dfa/c79q1i29+85ssX7680/xHPVmxYgVbtmxh5cqV5Obm8tvf/pbJkyfjcrkoKSnhnnvu4dJLL+Wuu+7q9LgzzzyzX69PX7UPa3O5XN2uNnes5s+fz4IFCxLXP82k2SK1TJp4LSUl/3nErVbi39Gj/4W8vHOOefte70wyM2d2c8+n6//t853IvLl/Zdq027psKydnAUVFX+7h0Yr256iUk1GjLkap9vMLFl7vcR3aWmRkTOpnOkVx8b9TUnITOTkLOt3j851EUdG/M23qbUfsBxwOPy5XQT/31bN4HB7+8Is0hnufsLc4p+tKHE5LMW1k7wcOHpfFNYsm8b3zp+G0hn7f/vF5GVy5YDynjM/p9jexOKf7RRAsBdcuKuGqhRNw9OFpWgpGZKZ9urDd0rhHPYXlbE7cMjl7MtYnv9cOHMwvnI+Dnrvij+lm9cYT8k/AUp/8faC4fPrlPHjeg1w+/fJjSlrkLeKM0Wd0uX1iVu9FknHecVw962pWTFnB5TMu5y8X/oVZ+bO6tFMoVkxZwXWzr2PVrFWsmLwi8Vr01WXTL+PyGZd3edzVs65m1axVqE9+UxSKVbNW8cP5P0zc1t5uxZQVnW7ryaz8Waw6YRUO1c3PSMOZe76QlIm2vTnJ+H0EFMy5YDwnnltMX6bgm3VuMWf92xTmLBnPpTfP5ex/m9br49yZPZ8HPm15CSee2/Ncj/4RGZy2fFKv+1IKTjy3mAu/eSKTTh7RTQM4bfmko+YeMzWbmQs79wQ9McNBej/eH93TcnCMPPqCLMfCyjO/4pJzlKfrIYDi8G39/QjJcnW5yVHoIf2kfKzc7uc8tbKP+Dvo46ilzFNGkfuv03BPy0ncptE0jXybmpKn+xj4WD8jLbKyOp/oVurT/X7k5p5Nbu4i0tIGc45URUnJf7JwwassOH0DU6fcRlZWMoo0A3MsojW8WTaXs2aeyk3nT+XKheO56fyp3PuvJ7HsxMIu7ScXeFl37QL+cNUpvP29Rdx0fu/vbYNptP/o8wAX52bQ3VuUAm675HgKfObfP4YCpbUsYdMfN998M7feeuuAb3fHjh2UlJT02q65uZn58+czYsQInn322WOaDFtrTU1NDS0tLfj9frKzsxP3XXPNNdx9990AjB8/nj17kjPnxGc+8xlefPFFFi9ezHPPPTeg27799tv57ne/C8Ctt97KTTfdNKDb701paWlisu4DBw5I76ZhJhSqoiWw85O5k0hcdrtHdLk/HK7hQOnviUbqKRh5PtFoEy0tu/FmTiQr6wQCwT2gFdnZsxOPb2r6mMqqv+F2j2REflthqr5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b50d3b5aba8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 388,
       "width": 585
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# When `return_iterator` is turned on, the method `get_quantities` will return an \n",
    "# iterator, and each element in the iterator will be the quantities we requested in \n",
    "# different chunks of the dataset. \n",
    "\n",
    "# For coadd catalogs, the different chunks happen to be different patches, \n",
    "# resulting in a different color for each patch in the scatter plot below.\n",
    "\n",
    "for coadd_data in coadd_cat.get_quantities(['ra', 'dec'], return_iterator=True):\n",
    "    plt.scatter(coadd_data['ra'], coadd_data['dec'], s=1, rasterized=True);\n",
    "\n",
    "plt.xlabel('RA');\n",
    "plt.ylabel('Dec');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### add derived quantities\n",
    "\n",
    "you can add your own derived quantities that are based on available quantities. The call signature is:\n",
    "```\n",
    "cat.add_derived_quantity(derived_quantity, func, *quantities)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "if 'gr' in coadd_cat.list_all_quantities():\n",
    "    coadd_cat.del_quantity_modifier('gr')\n",
    "coadd_cat.add_derived_quantity('gr', np.subtract, 'mag_g', 'mag_r')\n",
    "\n",
    "data = coadd_cat.get_quantities(['mag_g', 'mag_r', 'gr'], filters=[(np.isfinite, 'mag_g'), (np.isfinite, 'mag_r')])\n",
    "print((data['mag_g'] - data['mag_r'] == data['gr']).all())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use GCRQuery\n",
    "\n",
    "GCRQuery let you define cuts and filters before loading a catalog!\n",
    "\n",
    "GCRQuery objects can operate with themselves using any boolean operations (and, or, xor, not)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from GCR import GCRQuery\n",
    "\n",
    "# Let's choose a small RA and Dec range to do the matching so that it won't take too long!\n",
    "ra_min, ra_max = 55.5, 56.0\n",
    "dec_min, dec_max = -29.0, -28.5\n",
    "\n",
    "coord_cut = GCRQuery(\n",
    "    'ra >= {}'.format(ra_min),\n",
    "    'ra < {}'.format(ra_max),\n",
    "    'dec >= {}'.format(dec_min),\n",
    "    'dec < {}'.format(dec_max),\n",
    ")\n",
    "\n",
    "mag_filters = GCRQuery(\n",
    "    (np.isfinite, 'mag_i'),\n",
    "    'mag_i < 24.5',\n",
    ")\n",
    "\n",
    "data = coadd_cat.get_quantities(['ra', 'dec', 'mag_i'])\n",
    "data_subset = (coord_cut & mag_filters).filter(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Here's a full example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from astropy.table import Table\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/common/software/lsst/common/miniconda/current/lib/python3.6/site-packages/GCRCatalogs/alphaq.py:105: UserWarning: No md5 sum specified in the config file\n",
      "  warnings.warn('No md5 sum specified in the config file')\n"
     ]
    }
   ],
   "source": [
    "catalogs = ('protoDC2_test', 'buzzard_test')\n",
    "gc_all = dict(zip(catalogs, (GCRCatalogs.load_catalog(c) for c in catalogs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/common/software/lsst/common/miniconda/current/lib/python3.6/site-packages/GCR.py:216: UserWarning: mag_g_lsst not available; using mag_g_des instead\n",
      "  warnings.warn('{} not available; using {} instead'.format(quantities[0], q))\n",
      "/global/common/software/lsst/common/miniconda/current/lib/python3.6/site-packages/GCR.py:216: UserWarning: mag_r_lsst not available; using mag_r_des instead\n",
      "  warnings.warn('{} not available; using {} instead'.format(quantities[0], q))\n",
      "/global/common/software/lsst/common/miniconda/current/lib/python3.6/site-packages/GCR.py:364: RuntimeWarning: invalid value encountered in subtract\n",
      "  return func(*new_args)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b50e5889cc0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 465,
       "width": 985
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2, figsize=(12,5), dpi=100)\n",
    "\n",
    "for label, gc_this in gc_all.items():\n",
    "    mag_g = gc_this.first_available('mag_g_lsst', 'mag_g_sdss', 'mag_g_des', 'mag_true_g_lsst', 'mag_true_g_sdss', 'mag_true_g_des')\n",
    "    mag_r = gc_this.first_available('mag_r_lsst', 'mag_r_sdss', 'mag_r_des', 'mag_true_r_lsst', 'mag_true_r_sdss', 'mag_true_r_des')\n",
    "    if 'gr' not in gc.list_all_quantities():\n",
    "        gc_this.add_derived_quantity('gr', np.subtract, mag_g, mag_r)\n",
    "    quantities_needed = ['gr', 'redshift']\n",
    "    \n",
    "    data = gc_this.get_quantities(quantities_needed, ['redshift > 0.1', 'redshift < 0.3', (np.isfinite, mag_g), (np.isfinite, mag_r), mag_r + ' < 22']) \n",
    "    ax[0].hist(data['redshift'], np.linspace(0.1, 0.3, 21), normed=True, alpha=0.6, label=label);\n",
    "    ax[1].hist(data['gr'], np.linspace(-0.5, 2, 26), normed=True, alpha=0.6);\n",
    "\n",
    "ax[0].legend(frameon=False);\n",
    "ax[0].set_xlabel('$z$');\n",
    "ax[1].set_xlabel('$g-r$');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "desc-python",
   "language": "python",
   "name": "desc-python"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}