{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accessing the Coadd data using the Data Butler\n",
    "<br>Owner: **Jim Chiang** ([@jchiang87](https://github.com/LSSTDESC/DC2-analysis/issues/new?body=@jchiang87))\n",
    "<br>Last Verified to Run: **2018-10-26** (by @yymao)\n",
    "\n",
    "This notebook shows how to read in the coadd catalog data for an entire tract using the LSST Data Management (DM) data butler, and construct a Pandas dataframe with the columns needed to apply the weak lensing cuts used in recent analysis of data from Hyper Suprime Cam [Mandelbaum et al (2018)](https://arxiv.org/abs/1705.06745) using cuts given by [Francois in Slack](https://lsstc.slack.com/archives/C77DDKZHR/p1525197444000687).\n",
    "\n",
    "We have a [notebook that performs a similar analysis using GCR](https://github.com/LSSTDESC/DC2-analysis/blob/master/Notebooks/DC2%20Coadd%20Run1.1p%20GCR%20access%20--%20HSC%20selection.ipynb).  See also [the more general script](https://github.com/LSSTDESC/DC2-analysis/blob/master/scripts/merge_tract_cat.py) for extracting the coadd data from a tract into an hdf5 file.\n",
    "\n",
    "### Learning Objectives\n",
    "After completing and studying this Notebook, you should be able to\n",
    "1. Read in the coadd catalog data using the DM Butler\n",
    "2. Construct a Pandas data frame\n",
    "3. Apply the HSC weak lensing cuts to a catalog of DM-processed data.\n",
    "\n",
    "## Set Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import lsst.daf.persistence as dp\n",
    "\n",
    "from desc_dc2_dm_data import REPOS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How To Read DM Science Pipelines Coadd Catalogs into a Pandas Dataframe\n",
    "\n",
    "Here's a function to do this: pass it a butler and some information about what you want, and get a pandas df object back. In this example, we'll get the forced photometry as well as the model measurements - these are stored in different tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_tract(butler, tract, filter_='i', num_patches=None):\n",
    "    \"\"\"\n",
    "    Read in the coadd catalog data from the specified tract, looping over patches and concatenating\n",
    "    into a pandas data frame.  \n",
    "    \n",
    "    Notes\n",
    "    -----\n",
    "    The `merged` coadd catalog is supplemented with the CModel fluxes from the `forced_src` \n",
    "    catalog for the desired filter.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    butler: lsst.daf.persistence.Butler\n",
    "        Data butler that points to the data repository of interest, e.g., the output repository\n",
    "        for the Run1.1p data.\n",
    "    tract: lsst.skymap.tractInfo.TractInfo\n",
    "        The object containing the tract information as obtained from a skyMap object, e.g., via \n",
    "        `tract = skymap[tractId]` where `tractId` is the tract number.\n",
    "    filter_: str ['i']\n",
    "        Filter to use for extracting the `forced_src` fluxes.\n",
    "    num_patches: int [None]\n",
    "        Number of patches to consider.  If None, then use all of the available patches in the tract.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    pandas.DataFrame: This will contain the merged coadd data with the band-specific columns.\n",
    "    \"\"\"\n",
    "    if num_patches is None:\n",
    "        num_patches = len(tract)\n",
    "    \n",
    "    df_list = []\n",
    "    i = 0\n",
    "    print(\"Loop  Patch  Nobj\")\n",
    "    print(\"=================\")\n",
    "    for patch in tract:\n",
    "        patchId = '%d,%d' % patch.getIndex()\n",
    "        dataId = dict(filter=filter_, tract=tract.getId(), patch=patchId)\n",
    "\n",
    "        # Patches for which there is no data will raise a `NoResults` exception when requested\n",
    "        # from the butler.  We catch those exceptions, note the missing data, and \n",
    "        # continue looping over the patches.\n",
    "        try:\n",
    "            forced = butler.get('deepCoadd_forced_src', dataId)\n",
    "            calib = butler.get('deepCoadd_calexp_calib', dataId)\n",
    "            calib.setThrowOnNegativeFlux(False)\n",
    "            merged = butler.get('deepCoadd_ref', dataId)\n",
    "        except dp.NoResults as eobj:\n",
    "            print(eobj)\n",
    "            continue\n",
    "\n",
    "        # Convert the merged coadd catalog into a pandas DataFrame and add the band-specific\n",
    "        # quantities from the forced_src coadd catalog as new columns.\n",
    "        data = merged.asAstropy().to_pandas()\n",
    "        data[filter_ + '_modelfit_CModel_instFlux'] = forced['modelfit_CModel_instFlux']\n",
    "        data[filter_ + '_modelfit_CModel_instFluxErr'] = forced['modelfit_CModel_instFluxErr']\n",
    "\n",
    "        # The calib object applies the zero point to get calibrated magnitudes.\n",
    "        data[filter_ + '_mag_CModel'] = calib.getMagnitude(forced['modelfit_CModel_instFlux'])\n",
    "        data[filter_ + '_mag_err_CModel'] = calib.getMagnitude(forced['modelfit_CModel_instFluxErr'])\n",
    "        data[filter_ + '_modelfit_CModel_SNR'] \\\n",
    "            = forced['modelfit_CModel_instFlux']/forced['modelfit_CModel_instFluxErr']\n",
    "        data['ext_shapeHSM_HsmShapeRegauss_abs_e'] \\\n",
    "            = np.hypot(data['ext_shapeHSM_HsmShapeRegauss_e1'],\n",
    "                       data['ext_shapeHSM_HsmShapeRegauss_e2'])\n",
    "        df_list.append(data)\n",
    "\n",
    "        # Print the current patch and number of objects added to the final DataFrame.\n",
    "        print(\"   {}  {}   {}\".format(i, patchId, len(data)))\n",
    "        i += 1\n",
    "        if i == num_patches:\n",
    "            break\n",
    "    return pd.concat(df_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing the DM Science Pipeline processed data\n",
    "\n",
    "In order to access the DM Science Pipeline Processed outputs, including images and catalog data, we summon a data butler and provide the path to the data repository where the DM Science Pipeline tasks have written their outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "butler = dp.Butler(REPOS['1.2i'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `skyMap` object stores the information defining how we decided to divide up the sky into tracts and patches when we did the coaddition.  Here we pick a tract that is near the center of the protoDC2 simulation region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loop  Patch  Nobj\n",
      "=================\n",
      "   0  0,0   3198\n",
      "   1  1,0   5391\n",
      "   2  2,0   5661\n",
      "   3  3,0   5705\n",
      "   4  4,0   4982\n",
      "   5  5,0   5883\n",
      "   6  6,0   4743\n",
      "   7  0,1   3987\n",
      "   8  1,1   5722\n",
      "   9  2,1   5616\n",
      "   10  3,1   5098\n",
      "   11  4,1   5289\n",
      "   12  5,1   4735\n",
      "   13  6,1   5216\n",
      "   14  0,2   3149\n",
      "   15  1,2   5754\n",
      "   16  2,2   5236\n",
      "   17  3,2   6042\n",
      "   18  4,2   5138\n",
      "   19  5,2   5751\n",
      "   20  6,2   4748\n",
      "   21  0,3   2910\n",
      "   22  1,3   6241\n",
      "   23  2,3   6176\n",
      "   24  3,3   5832\n",
      "   25  4,3   5301\n",
      "   26  5,3   5484\n",
      "   27  6,3   4675\n",
      "   28  0,4   3777\n",
      "   29  1,4   6581\n",
      "   30  2,4   6376\n",
      "   31  3,4   5951\n",
      "   32  4,4   6239\n",
      "   33  5,4   6254\n",
      "   34  6,4   5508\n",
      "   35  0,5   3855\n",
      "   36  1,5   6316\n",
      "   37  2,5   6953\n",
      "   38  3,5   6004\n",
      "   39  4,5   5902\n",
      "   40  5,5   6279\n",
      "   41  6,5   5505\n",
      "   42  0,6   2745\n",
      "   43  1,6   4891\n",
      "   44  2,6   6016\n",
      "   45  3,6   5851\n",
      "   46  4,6   6383\n",
      "   47  5,6   6938\n",
      "   48  6,6   5361\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/lsst/software/stack/python/miniconda3-4.5.4/envs/lsst-scipipe-fcd27eb/lib/python3.6/site-packages/ipykernel/__main__.py:72: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of objects in the coadd catalog: 263348\n"
     ]
    }
   ],
   "source": [
    "skymap = butler.get('deepCoadd_skyMap')\n",
    "tractId = 4851\n",
    "filter_ = 'i'\n",
    "num_patches = None\n",
    "data = read_tract(butler, skymap[tractId], filter_=filter_, num_patches=num_patches)\n",
    "print(\"Number of objects in the coadd catalog:\", len(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making the Selection\n",
    "\n",
    "First off, we need to apply a baseline set of cuts to remove nan's."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of objects after baseline cuts: 137296\n"
     ]
    }
   ],
   "source": [
    "base_mask = ~(np.isnan(data['i_modelfit_CModel_instFlux'])\n",
    "         | np.isnan(data['ext_shapeHSM_HsmShapeRegauss_resolution'])\n",
    "         | np.isnan(data['ext_shapeHSM_HsmShapeRegauss_e1']))\n",
    "data = data[base_mask]\n",
    "print(\"Number of objects after baseline cuts:\", len(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we get to the weak lensing cuts.   The `detect_isPrimary` flag identifies the primary object in the overlap regions between patches, i.e., applying it resolves duplicates from the different analyses of overlapping patches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = data['detect_isPrimary']\n",
    "mask &= data['deblend_skipped'] == False\n",
    "mask &= data['base_PixelFlags_flag_edge'] == False\n",
    "mask &= data['base_PixelFlags_flag_interpolatedCenter'] == False\n",
    "mask &= data['base_PixelFlags_flag_saturatedCenter'] == False\n",
    "mask &= data['base_PixelFlags_flag_crCenter'] == False\n",
    "mask &= data['base_PixelFlags_flag_bad'] == False\n",
    "mask &= data['base_PixelFlags_flag_suspectCenter'] == False\n",
    "mask &= data['base_PixelFlags_flag_clipped'] == False\n",
    "mask &= data['ext_shapeHSM_HsmShapeRegauss_flag'] == False\n",
    "\n",
    "# Cut on measured object properties\n",
    "mask &= data['i_modelfit_CModel_SNR'] >= 10\n",
    "mask &= data['ext_shapeHSM_HsmShapeRegauss_resolution'] >= 0.3\n",
    "mask &= data['ext_shapeHSM_HsmShapeRegauss_abs_e'] < 2\n",
    "mask &= data['ext_shapeHSM_HsmShapeRegauss_sigma'] <= 0.4\n",
    "mask &= data['i_mag_CModel'] < 24.5 # !!! Doesnt have exinction correction\n",
    "mask &= data['base_Blendedness_abs_instFlux'] < 10**(-0.375)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make some plots of galaxy properties with the above cuts applied. The figure to compare with is Fig. 22 of [Mandelbaum et al (2018)](https://arxiv.org/abs/1705.06745)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/lsst/software/stack/python/miniconda3-4.5.4/envs/lsst-scipipe-fcd27eb/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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vXq24uDilpqYqJiZGe/bs0bfffqvBgwfrf//3f90WsCRp9erVmjx5sgoKClyfffzxx/r4448VGRmpsrIyjRw5Un//+9917733GniiAAAAzZMv++5WYP9dNBcUsQAAANBs1fajz5+9r0wmE/3xm6HU1FQdOHBA+fn52rt3r86dO6f+/fvrxRdf1NChQ70Wk+65554qRSBvEhISlJ2drcOHD2vPnj0qKSlRdHS00tLSNGzYMI/7UlVnMpn09ttv69VXX1VWVpb+9a9/6fr16xoxYoR+/vOfq3fv3h7vN5vN+uqrr3Tw4EHt27dPZ8+e1Y0bN9SuXTv17NlTiYmJateuneF8AABA02Gz2WS1Wl37a8JzoSpYL9H5WjyjcIZgoIgFAAAAVONt82p34+12Oyu4mrGEhAQlJCQELF7v3r29FpmMateunR566CG/7+/bt6/69u1bL7n4Iz4+3uuY2to0AgCAhuFwOBrd340rCmvuBKI40xh/LzTGnIDqKGIBAAAA1fjzA7a2H8QAAAAAgq+hCmvBLo4BTR1FLAAAAABAyCosLPQ6pqioSLGxsQHIBgAANDeNcdUZ0JRQxAIAAAAAAAAAoJ7l5ua6Vmn5sz9YcnKy617230VzRRELAAAAAAAAAOAXf/aTbU48rdIymUyyWCxur1VuRWi1WmvM01BtDCtyonCGxoIiFgAAAAAAAADAL41xzydPhbLGVJyxWCzKycnx614jbQzT0tJkt9trve5udVhFTu4KZ0AwUMQCAAAAAAAAADQZngpr3lY1eWr756k41hgLPna7PSh5eSueuVOX1WNo2ihiAQAAAAAAAACaLSOrmiTfi2PNVV2KZ74WwCh+NX0UsQAAAIAG5MuPMH6AAQAAAKHJn72+Gmp/sIqVZaFYVPO1AJabm+vx9xa/sUIfRSwAAACgAQWrhQcAAACAwGnIQomve3wZXVnWVHj6rp6KXBS4QgNFLAAAAAAAAAAAGqn6bmOYnJxc67WGWh0WTHUt6NHiMLgoYgEAAAAAAAAAmoXG1PYvGJKTk5WTkxPwuCaTSRaLRZL71WONGd01gosiFgAAANBAbDabz+OtVmuNz3mTDwAAAKgf/L06OCwWi6t45uvqsYoCGIWk5okiViMXHx/vdYzT6QxAJgAAAPBVbW8Xmkwmt9ebW+96AAAAAHXj6yqx+lpV5qnFnq8v83lTUQDz1taP31JNE0UsAAAAIMAq2mjwIwsAAABAXQRrZVkwWux5+67eCmuh1MIQ/0YRq5ErLCz0OqaoqEixsbEByAYAAAAAAAAAgPpV0Vq9Lqu4PBW5fG1h6E1Fi0OKYw3v/7F35/E1XP0fwD/3ZpE9QWwhhNQSxJoi9qVUpcRSS5USilrq6aJ9Wl2URy3Vli66oKJoUTz2h0ptVbskSFRrTSQhQRIhC9nO7w+vzO/e5O6ZuyWf9+uV12vmzplzzk3OnMyZ78wZpbUrQERERERERERERERElVfJ1Or2EhAqmeKwZJYNMh8+iUVEREREJANj5pbXlZZ38hEZh+8RJiIiIiJTlDz9ZUg6sh4GsYiIiIiIZCDXXPRyT3NBRERERERkCSVT7GlizE1/llLy9BfZNgaxiIiIiIiIyG7xPcJEREREtqFkij0yv0mTJiE+Pl5nmsePH1uoNubFIBYREREREREREREREVmEsU9l2eJTXIYyJNikqmXLlgbN8hEfH19pniJjEIuIiIiIiIiIiIiIiHRSfYdUed4TJddU7PbA2GDTyZMndQa9DA1yVSQMYhERERERERERERERkU72/A4puZ/mMufTYfb6OzYXBrGIiIiIiCogc01bQUREREREZG8sOdZRfWJN7oDUyZMnERoaWq4n4ewNg1hERERERBVQZZojnYiIiIiIyFaY+4k1bXm7u7sjODgYwJNAWk5OjtnqYEkMYhERERER2TFtT1wxgEVERERERGSbOnXqJPuYLTg4GCdOnAAAhIaGVpgxIYNYRERERER2wNzBqpJpKUrjNINERERERJWPKe98Mud7oiqSTp064cSJE3qngK8oQajyYhCLiIiIiMgOWGJ6QA6SiIiIiIgIsOw7pCorfb/jivQ0VXkwiEVEREREVImYY9oKIiIiIiKiysicT6wZm3dFfRKOQSwiIiIiokqiZNoK3tFHRERERERUfuZ8Yo1Pwz3BIBYRERERUQXUqVOnMp9V1DvziIiIiIiIqGJiEIuIiIiIyE65u7sjODi4zOctW7bUedeetmBWXFwccnJyZKsfERERERERUXkwiEVEREREZKeCg4Nx4sQJo/fTFuDiNINERERERERkS5TWrgARERERERERERERERFRaXwSi4iIiIiIiOxWixYt9KYpKCiwQE2IiIiIiEhufBKLiIiIiIiIiIiIiIiIbA6fxCIiIiIiIiK7dfHiRb1pkpOT4e/vb4HaEBERERGRnBjEIiIiIiKycXFxcdauAhEREREREZHFMYhFRERERGTjcnJyrF0FIiIiIiIiIovjO7GIiIiIiIiIiIiIiIjI5jCIRURERERERERERERERDaH0wnauBYtWuhNU1BQYIGaEBEREVFlNWnSJMTHxxuUtmXLlgBgVPqVK1eaXDciIiIiIiKquBjEIiIiIiKyISVBILnTlkd8fDxOnjxp1D7GpiciIiIiIiIqjUEsG3fx4kW9aZKTk+Hv72+B2hARERGRufGpJCIiIiIiIqIn+E4sIiIiIiIiIiIiIiIisjl8EouIiIiIiLSKi4uzdhWIiIiIiIiokmIQi4iIiIiItMrJydH4ubu7u87t5U1PRERERERExOkEiYiIiIjIaMHBwQgODjZbeiIiIiIiIiIGsYiIiIiIiIiIiIiIiMjmcDpBIiIiIiKSDd+hRURERERERHJhEIuIiIiIiAAALVu2NCptfHx8mc/5zisiIiIiIiKSC4NYREREREQEAFi5cqVR6UNDQ81UEyLDtWjRQm+agoICC9SEiIiIiIjkxndiERERERERERERERERkc3hk1hERERERERkty5evKg3TXJyMvz9/S1QGyIiIiIikhODWEREREREZBI53qFFREREREREpA2DWEREREREZBK+Q4uIiIiIiIjMya6DWNevX8fRo0eRmpoKLy8vNG7cGD179oSjo3m+1q1bt3DkyBEkJyfDxcUFgYGB6N27N1xcXMxSHhERERER2Qd7Hps8evQIf/75Jy5duoTc3FzUr18foaGhCAgIMKluFy9exMmTJ3H37l1Ur14dQUFB6NKlCxQKhUn5ERERERFR5aW0dgVMceHCBTzzzDMIDAzE4sWLERcXh40bN6Jfv35o2LAhVq1aJWt5iYmJeOGFF1CvXj3Mnj0bsbGx2LVrFwYPHox69eph4cKFEELIWiYREREREdk+ex6bFBUVYeHChfDz88OgQYOwb98+REdHY9asWQgMDMSQIUNw69Ytg+t25MgRhISEoGXLlvjuu+9w8eJFrFy5Et26dUNQUBB27Nhh6tcmIiIiIqJKyu6CWFu3bkWnTp1w4MABLFq0CBcvXsT69etx5MgRHDt2DDk5OZg0aRJeffVVWQJLf/75J9q2bYutW7dixowZuHz5Mn755Rfs378fFy5cgKenJ2bPno3w8HDk5+fL8A2JiIiIiMge2PPYJDs7G3379sXs2bNRq1Yt/PXXX9izZw9+/fVXXL16FePGjcP27dvRvn17xMTE6K3b119/jT59+iA2Nhbr16/H2bNnsW7dOpw+fRrbt29HQkIChgwZggULFpT790BERERERJWHXQWxDh06hBdffBF5eXmIiIjAv//9b7UpKUJDQ/HLL78AAH744QcsWrSoXOX99ddfCAsLQ2ZmJvr06YMvv/wSTk5O0vZmzZph165dcHR0xK5du/Daa6+VqzwiIiIiIrIP9jw2KS4uxrBhw3Do0CE4Oztj586dalMHurq6YuXKlejSpQtSU1MRHh6OO3fuaM1v/fr1mDlzJoqKijBnzhy89NJLatvDw8OxbNkyCCHw/vvvY8OGDab/IoiIiIiIqFKxmyBWVlYWxo4di4KCAjg7O2Pu3Lka0/Xv3x+9e/cGAHzwwQeIjo42qbyioiKMHj0aDx48AAAsWLBA4xzuLVu2xMsvvwwAWLFiBbZv325SeURERERElVFcXBxCQ0PL/EyaNMnaVdPK3scmX3zxBfbv3w8AmDhxIho3blwmjYODAxYuXAgASE5OxuTJkzXmlZCQgKlTpwIAfH198dZbb2lMN2nSJKmcV155BSkpKVq/LxERERERUQm7CWItWbJEGuj07dsX/v7+WtNOmDABwJM7DGfPnm1SeZGRkTh//jwAoEWLFujQoYPe8gBg9uzZKC4uNqlMIiIiIqLKJicnBydPnizzEx8fb+2qaWXPY5OMjAz85z//0Zi+tG7dukmBpx07duDEiRNl0nzwwQfIzs4GAIwcORLu7u4a83JwcMC4ceMAALm5uZg3b57WcomIiIiIiErYRRArPz8f33//vbQ+aNAgnemff/55ODo6AgD2799v0BzupX311VcGl9e5c2fUrFkTAHDp0iU+jUVEREREVEHZ+9gkMjJSeqLLz88PISEhOvMLDw+XlkuezCqRlpaGTZs2GVy3wYMHS8urV69GWlqazvRERERERER2EcQ6dOgQ0tPTpfWOHTvqTO/t7Y2mTZtK66oDK0P8888/iIuLM7g8hUKBp59+2uTyiIiIiIjIPtj72GTLli3Ssq4nujSV99tvvyErK0ta3759OwoLCw3Or3nz5vD09AQAFBYW4r///a/e8omIiIiIqHKziyCW6rQVTk5OCAoK0rtP27ZtpeVt27aZXB4AtG7d2qjy9uzZg4KCAqPKJCIiIiIi22fPY5PHjx+rPQlmbF75+fnYs2ePxro1aNAAPj4+OvNSKBRqZRr7uyAiIiIiosrH0doVMMTJkyel5bp168LZ2VnvPgEBAdLylStX8ODBA3h5eRldnoODg8457jWVl5OTg7///hvBwcEGlUdEREREVBm0bNlS4+dxcXHIyckp81loaKjGPObMmWOW+hnCnscmsbGxyM/Pl7Y1bNhQb14NGjSAQqGAEAIAEB0djdGjR5epmyF5ldTtzz//lPIiIiIiIiLSxS6CWCUvMQaAOnXqGLRP6XTx8fHo3Lmz0eXVrFkTDg4ORpcXFxfHIBYRERERkYqVK1dq/Dw0NFQtIAI8Cb6U/swW2PPYRDUvTek0cXR0hK+vL+7evSvlBQCPHj3C5cuXjcqrdLqMjAzcunULfn5+Bu0rl0GDBqFKlSoWLbM01SkiiYiIiIhIO7sIYmVkZEjLvr6+Bu1To0YNtfXr168bPFBUneO+POUZIzk52aj0qpKSkqTl27dvm5wPlfX48WONn5Xn70VERERE6jSdc+lKq3rOq/pOJkuw57GJal7G5lcSxCrJKyMjQ3o6q7x1MzSIJdeYKTY21uR8zInjDCIiIiKSS+kxlqXHTXKy+SBWTk6O2pQXLi4uBu1XOt2DBw8MLjMzM9Oi5QEwaFoQQxjycmYqn9jYWNn+XkRERERknNjYWLVz3rt376pNn2dO9j42Uc3L1PzkzEs1P0NU9HNwjjOIiIiIyFwsOW6Sm9LaFdAnKytLbd3QaR/KMzhSLdMS5RERERERkWnS0tIsVpa9j03kqL+ceanmR0RERERE5mPJcZPcbP5JLKVSPc6mUCgM2q/0XPGqU10YU6YlygPUp7cw1o0bN9C9e3cAwPHjx+3i7r3evXsDAA4ePGjlmhiG9TWf27dvS3dTnz592uD3KViTPf1+AdbXnNh+zY/1NR+2X/NjD2NCxAAAIABJREFUfc0nKSlJmo6vWbNmFivX3scmctRfzrxU8zNEecZMjx49wt9//41atWqhRo0acHS0jaGwLR53tlYnW/1/ZWu/J4B1MhTblOFYJ/3YngzHOunH9mQ41skw1ho3yc02ztx1cHd3V1s3dL78R48eqa17enoaVWZJOZYoDwDq1atnVHpt/P39ZcvLnJycnADI973NjfW1jDp16thFne3t98v6Wgbbr3mwvpbB9mserK9lGDqNnRzsfWwiR/3lzEs1P0OUt20+9dRT5drfHGzxuLPFOpWwpf9Xtvh7Yp2MxzalG+tkHLYn3Vgn47A96cY6Gc+S4ya52fx0gu7u7lIDAAwfHJVO5+3tbXCZVatWtWh5RERERERk++x9bKKal6n5yZmXan5ERERERESa2HwQS6lUokGDBtJ6RkaGQfuVTte4cWODy2zYsKFFyyMiIiIiIttn72MT1bxMza8kLz8/P7X3YHHcRERERERE5mDzQSwAaNKkibR8+/Ztg/a5deuW2roxcz6qlpeammrQPO2lywsKCjK4PCIiIiIisg/2PDZRzQswrP5CCLWXQJfkpVQqERgYaFRepevm4+OD2rVrG7QfERERERFVTnYRxCp5+RgApKSkoLi4WO8+qi/9DQoKgo+Pj0nlFRQUIDU11ajyPD090aJFC4PLIyIiIiIi+2DPY5PWrVvDzc1N2nbz5k29ed2+fRsFBQXSemhoqMa6GZJX6bqp5kVERERERKSJXQSx+vTpIy3n5eXh8uXLeveJjY2Vlp999lmjyuvdu7fa+vnz540qr0+fPnB0dDSqTCIiIiIisn32PDZxdnZGt27dTM5LqVTimWeeUcu7xNWrV5Gbm6s3v3PnzknLxv4uiIiIiIio8rGLIFaHDh0QEBAgrZ89e1Zn+ry8PMTFxUnrw4YNM6q8OnXqqA3u9JUHAGfOnDG5PCIiIiIisg/2PjYZMWKEUXmdPn1aWu7VqxeqVasmrYeFhcHd3R0AUFxcjJiYGJ153bhxA3fv3gXwJCA2ePBgveUTEREREVHlZhdBLKVSiYkTJ0rrO3fu1Jl+7969ePz4MYAnU2Z07drV6DInT55scHkxMTFITEwEANSsWRPDhw83ujwiIiIiIrJ99j42GTVqFLy8vAAA165dw19//aUzv23btknLM2bMUNvm6emJUaNGGVw31bwGDhyIBg0a6ExPRERERERkF0EsAJg+fbr00t89e/ZId/BpEhkZKS1/+OGHZbbHx8ejffv28PLywuTJk1FUVFQmzciRI9GyZUsAT+5kvHjxokHlvfPOO6hSpYr+L0RERERERHbJnscmbm5ueO+996T1NWvWaM0rOjpaeoqsdevWGDhwYJk0s2fPlt6ztWHDBilgV5oQAj/99BMAwMHBAbNnz9ZaLhERERERkUTYka1btwoAAoCYNm2axjR//PGHlGbEiBEa0/To0UNKA0CsXbtWY7rTp08LR0dHAUCEhYVpTHPlyhVRpUoVAUCEhoaKoqIi075cOSQlJUnfJSkpyeLlE5UH2y/ZM7Zfsmdsv2TPbKH92vPYpKCgQLRr104AEG5ubiIhIaFMmuLiYvHMM88IAMLJyUmcO3dOY15CCPHFF19I9f/00081plm7dq2U5p133tGaF1FptnC8U8XCNkVyYnsiObE9kdwqSpuymyexAGDo0KFYsWIFHBwc8O233+Lzzz+HEELaHhsbixdeeAEAMGDAAPz4448a8yl9d2NBQYHGdE8//TS2b98ONzc37NmzB2+88YZa2uvXr+P555/H48eP8fTTT2Pbtm1QKu3qV0pERERERCaw57GJo6Mj9u7di5CQEOTm5iIsLAwJCQnS9vz8fLz22mv4/fff4e7ujq1bt6J169ZafxdvvPEGPvroIwBPnszauHGj2vaoqCi8+uqrAICJEydi/vz5WvMiIiIiIiJSZXcRl0mTJmH//v0IDg7GrFmz0Lx5c4wZMwbPPPMMQkJC8OjRI8ybNw87d+6Eh4eHxjy+/vprtGrVCh4eHoiIiMDLL7+stbywsDD8+eef6Nq1K5YtW4bGjRtj9OjRCAsLQ/PmzZGYmIiZM2fiyJEjqFWrlrm+NhERERER2Rh7HpvUrFkThw8fxsyZM3Ht2jUEBQUhLCwMo0ePRuPGjbF8+XJ07doVx44d0ziNYGlz587Fpk2bUK9ePbz44oto3749Xn75ZXTt2hX9+vWDh4cHvv32W6xatQpOTk568yMiIiIiIgIAhVC9XdDOxMbG4vTp07h37x5cXV0RFBSEnj17wtXV1Szl/fPPPzh27BhSU1Ph7OyMp556Cr169YK3t7dZyjNUcnIy/P39AQBJSUmoV6+eVetDZAy2X7JnbL9kz9h+yZ7ZYvu157HJ/fv3cejQIVy7dg35+fmoXbs2unTpgqZNmxqdV3FxMU6dOoWYmBjcv38fHh4eCA4ORrdu3Ri8IpPY4vFO9o1tiuTE9kRyYnsiuVWUNuVo7QqUR9u2bdG2bVuLlde0aVOTBnJERERERFSx2fPYxMfHB0OGDJElL6VSidDQUISGhsqSHxERERERVW52N50gERERERERERERERERVXx2PZ0gERERERERERERERERVUx8EouIiIiIiIiIiIiIiIhsDoNYREREREREREREREREZHMYxCIiIiIiIiIiIiIiIiKbwyAWERERERERERERERER2RwGsYiIiIiIiIiIiIiIiMjmMIhFRERERERERERERERENodBLCIiIiIiIiIiIiIiIrI5DGIRERERERERERERERGRzWEQi4iIiIiIiIiIiIiIiGyOo7UrQE9cv34dR48eRWpqKry8vNC4cWP07NkTjo7y/4lycnLwxx9/4Nq1a3jw4AGqVauGVq1aoUOHDmYpj4jIllmy/9Xl0qVLOHPmDC5cuIDr169j4MCBiIiIsGgdyD5Zsg0LIXD+/HnExMTg3r17AAB/f3/06NEDfn5+spdHlUdxcTFSUlLg7+9vtjLS09Nx+PBhJCYmQqFQoGHDhujVqxe8vb3NViYRaZaZmYkjR44gMTERubm58PX1RUhICNq0aQOFQmFUXo8ePcKff/6JS5cuITc3F/Xr10doaCgCAgLMU3kiqrSKi4tx/vx5REdHIy4uDomJiZg0aRLCwsK07sM+igoLCxEdHY3z588jIyMDTk5OCAgIQM+ePVG9enWj8nr48CGOHDmCq1evorCwEAEBAejWrRtq1aplptqTNZVnjCR333Px4kWcPHkSd+/eRfXq1REUFIQuXboYfd5mMkFWdf78edGnTx8BQAQFBYmXXnpJdO/eXSgUClGvXj2xcuVK2cp6+PChePPNN4Wrq6sAUOanXr16YtWqVbKVR5XHtWvXxJo1a8SiRYvEt99+K6KiokRBQYFF6/Duu+8KAGLOnDkWLZfslyX7X21Onz4tXn31VVG7dm3h5OQkhg0bJnbs2CEyMzPNXjbZP0u24eLiYrFixQrRqFEj4e7uLvr37y8mTJgghgwZIqpVqyaUSqUYNWqUuHPnjmxlUuVQXFwsNm7cKJo1aybGjRtnljLu3bsnJk6cKBwdHUXt2rXF8OHDRVhYmHB3dxceHh7irbfeEo8fPzZL2USkLi0tTUyYMEE4OjpqHJM2bdpUbN++3aC8CgsLxYIFC0TVqlWFq6urGDBggBg+fLjw8/MTSqVSDB48WKSkpJj5G5GtGzVqlAAgIiMjDd7nwYMHYteuXWLp0qViyZIlYvPmzSI1NdV8lSSbd+DAATF27FhRrVo14ebmJsaPHy+ioqJEdna21n3YR1F+fr5YtGiRqF27tqhataoYNGiQmDBhgggLCxMeHh6iSpUqYtq0aeLhw4d688rJyRGzZs0Sbm5uwsfHRwwZMkQMHjxYVKtWTTg7O4vx48eL+/fvW+BbkSWUZ4wkd99z+PBh0b59ewFAtG/fXowZM0Y8/fTTRp+3lReDWFa0ZcsWKaC0aNEiUVxcLG07fvy4qFq1qgAgpkyZorbNFGlpaaJVq1YaBwqlfyZNmlTer0aVhC0EAYQQ4uTJk8LBwYFBLDKYJftfTa5cuSL69+8v9btjxozhAIaMYsk2/OjRIzFo0CABQAwZMkSkp6erbc/OzhYRERECgPD39xeJiYnlKo8qh+LiYrFlyxbRsmVLqS80RxDr0qVLwt/fXwAQw4YNU7tIkJSUJA3IOnXqJDIyMmQvn4j+3z///CMaNGhg0Jh0/vz5OvN6+PCh6NWrlwAgmjVrJm7cuCFty83Nlf4v1a5dW0RHR5v5m5Gt2rx5s9SmDAli8SIxlXb27FnRsWNHAUA4OjqKmTNnGnTDIfsoyszMFJ06dRIAxNSpU0VOTo7a9nv37onnnntOABCtW7fW2a5u374tgoODBQDRtWtXtRsHMzIypHyaNGmi1tbI/pR3jCR33/PVV18JBwcHoVQqxfr169W2bd++XVSpUkUoFArxySefGFxHUzGIZSUHDx4UTk5OAoCIiIjQmGbv3r1Sg12wYIHJZRUWForu3bsLAOKpp54Sb7/9tli+fLn4/PPPxaRJk0TNmjXLDBo+++wzk8ujysHaQYASubm5omnTplLbZRCL9LFk/6vJ2rVrpWOnatWqYv/+/bLmTxWfpdvw1KlTpbus8vPzNaYpKiqSBmmdO3c2a79P9m/Hjh2iTZs2Zc4/5Q5ipaamirp160o32+Tl5ZVJc/v2beHj4yMAiP79+4uioiJZ60BETzx8+FA6Z2/durV4//33xbfffiuWLFkixo4dKzw9Pcv0CZs3b9aYV1FRkejXr58AIJydncXly5fLpCksLBRdunSRZhxJS0sz91ckG5OWliZq1KhhcBCLF4mptMWLF0s3y9avX9/gYBP7KBJCiAEDBggAonfv3lrT5OTkiICAAAFAvPjii1rTlDyUUKNGDXHv3r0yabKzs0VgYKAAINq0aSNyc3Nl+x5kOeUdI8nd96xbt06qw9y5czWm+e6776Q0v/zyi0H1NBWDWFZw//59aUDt7Owsbt68qTVt7969BQChVCrF2bNnTSrvhx9+EADErFmzRGFhYZntOTk5YvLkyWoHiKenJ+9GJa2sHQRQ9frrr6u1XQaxSBdL97+lffzxx1JbrV+/vsaTCiJdLN2Gk5KShEKh0HniWmL9+vVS+z5+/LhJ5VHFt2LFCvHdd9+J5ORkcenSJeHi4mK2IFZYWJiU96ZNm7SmmzdvnpRu6dKlstaBiJ547733hEKhEMuWLdO4PT09XXrqt+QnICBA4/h1yZIlUpqpU6dqLfOPP/6Q0oWHh8v2Xcg+DBkyRK096Qpi8SIxqSoqKhITJ06U2k6bNm2Mmk6SfRQdO3ZM+tv+9NNPOtPOnz9fABAKhUIkJyeX2T59+nQpr8WLF2vNZ+3atVK6f/3rX+X+DmRZcoyR5Ox7bty4ITw8PAQA4evrq3Xq1MLCQtG4cWMBQLi5uWlsw3JhEMsK3n//famxhIWF6UyrekGoX79+RpdVXFwsAgICxIgRI/SmDQ8PVzvJW7dundHlUcVn7SCAqiNHjggHBwfRs2dPBrHIIJbsf0ubO3eulJ+Pj4+4ePFiufOkysfSbXj16tVSHvqmiI2Pj5fSartISVRayRQ9cgexoqKi1PpcbU8RCqEerPX19RUPHjyQrR5E9OT9Qu7u7uKdd97RmS4/P1+EhISojUmPHj2qliY9PV14eXlJ28+cOaMzz5ILK7zBonJZt26dcHFxEaGhoQYFsXiRmFRNmDBB+js3aNBA3Lp1y+B92UeREEJ89NFH0t81KipKZ9rdu3dLaUu/W+jvv/+WngZUKpU6n5p59OiR9FSzs7OzSEhIkOW7kHUYO0aSu+956aWXpO3Tp0/XmVdJIBaAmDx5st66mkoJsqj8/Hx8//330vqgQYN0pn/++efh6OgIANi/fz9iYmKMKu/IkSO4e/cuvvrqK71p58+fr7Z+/vx5o8qiymHJkiVISUkBAPTt2xf+/v5a006YMAEAUFxcjNmzZ8taj5ycHERERGDq1Kno0aOHrHlTxWTp/lfVxo0bMWfOHGk9MjISzZs3Nzk/qpys0YbT0tKk5Vu3bulMW1xcLC3n5eUZXRZVTi4uLmbJ98svv5SWn3vuOTg5OWlNW69ePbRv3x4AcO/ePaxcudIsdSKqrLZu3YqqVati7ty5OtM5OTmpnS8BZcekkZGRePDgAQDAz88PISEhOvMMDw+XlhcuXGhMtclO3bp1CzNnzsT8+fPRpEkTven/+ecf6fxKqVRi/PjxWtOOGDECnp6eAIDvvvsOiYmJstSZbMeiRYuwevVqAICDgwO2bNmCOnXqGLw/+ygC5BtDffPNNygqKgIAdOzYETVr1tSaT5UqVfDss88CeDJu/Pzzz42uN9kOY8dIcvY9aWlp2LRpk7Su77rD4MGDpeXVq1ertX85MYhlYYcOHUJ6erq03rFjR53pvb290bRpU2ldtREZ4sCBA5g2bRpq1aqlN23Lli3h5+cnrWdlZRlVFlV81gwClDZr1iwoFAosXrxYtjypYrN0/1vi5s2bmDJlirQeHh6u9k+eyFDWaMOqA6UdO3boTHvjxg1puWHDhkaXRSSX7Oxs7Nu3T1rXd6yUTmNqf09Emh04cADvvfeeQRdk+vTpI40fgLJj0i1btkjLHTp00Juf6rH922+/cYxbCbzyyito3rw53njjDYPS8yIxlTh79iw+/PBDaX3GjBl6LwSXxj6KAPnGUKrtydjz2S1btqgFyKhik7Pv2b59OwoLCw3Or3nz5tINHoWFhfjvf/9rcL2NwSCWhZ04cUJadnJyQlBQkN592rZtKy1v27bNqPJ69+6N119/3eD0qk/VVKtWzaiyqOKzVhCgtKioKKxcuRKRkZFwc3OTJU+q+Czd/5Z4++23pTtiAODjjz82KR8ia7ThVq1aScsxMTFYu3at1rS//vorAMDNzQ39+vUzuiwiuZw9e1Zt4NW6dWu9+6geK6dPn5aeOiei8hs2bBjGjRtnUFpXV1dUr15dWlcdkz5+/Fjtpjhjj+38/Hzs2bPHoHqQfVq1ahWOHDmCyMhIKJWGXe7iRWIqMWPGDOn8wdnZ2ejZZNhHUQnVMdS2bdtw5MgRrWlLxlD+/v7SzAAAkJCQgNTUVGnd2PZ0+/ZtnDx50qh6k32Su+9Rve7QoEED+Pj46MxLoVColWnqtTN9GMSyMNUOpG7dunB2dta7T0BAgLR85coVtYuh+vTq1Uvt6Sp9hBDSsmqDJgKsFwRQlZWVhYkTJ2LmzJno1q1bufOjysPS/S8AXLp0CZs3b5bWu3TpgjZt2hiVB1EJa7ThkJAQtTuvpkyZgt9//71MunPnzkkDsM8//xxVq1Y1qhwiOZUesBvyZKDqsQJA1qfHiSq7wYMHw93d3eD02saksbGxyM/Pl9YNObYbNGgAhUIhrUdHRxtcD7IviYmJePPNN7Fw4UI0btzYoH14kZhK7N27F6dOnZLWhw8frvOpPE3YR1GJ8PBw1K1bF8CT/2nDhg3T+MqWPXv24NixY3BwcMB3332n9iSyHOezbE+Vg9x9j2rbM3SGFdW2Z652xyCWhal2WobOq1s6XXx8vKx1UlVyAufs7Iz+/fubrRyyT9a4gFra66+/DldXV3zyySflyocqH2v0v99//73ahZjw8HDk5eVh9erVGDFiBEJCQtC+fXsMHDgQS5Ys0TtfNlVu1jqH+PLLL6X3CT169AgDBgzA119/LW2/cOECBgwYAKVSiZUrV+LVV181ugwiOZW+SFC7dm29+5Q+VuLi4mStExEZJj8/HxkZGQCeHJdPP/20tK30sW3I/0JHR0f4+vpK6zy2KyYhBCZMmIB27drhtddeM3g/XiSmEqqvTQCejNuysrLw9ddfY/DgwWjXrh06dOiAoUOH4ptvvsH9+/fL5ME+iko4OTmpvZ81PT0d3bp1U7vB9eDBgxg1ahS8vb2xbds2hIWFqeVhSnvi+WzlJGff8+jRI1y+fNmovEqny8jIMMu1LQaxLKzkhByAWmPRpUaNGmrr169fl7VOJdLT03Hz5k0AwOjRo+Ht7W2Wcsh+WTsIu3v3bqxbtw5r1qyBq6uryflQ5WSN/rf0XMAZGRlo2LAhJk6ciM2bNyM6OhoxMTHYvXs33nnnHTRu3FjtZJdIlbXOITp16oS1a9dK0/IUFBRg5syZ6NevHxYuXIjQ0FAEBAQgOjoar7zyitH5E8lNdepjT09PVKlSRe8+ljrfJiLd4uLipOm8pkyZojYlnOqxDZj2v5DHdsW0fPlynDp1CqtXr1a7s1wfXiQmAMjJycFvv/2m9tmlS5dQv359zJw5Ezt27EBsbCzOnDmDbdu24bXXXkNgYKA0C0EJ9lGkatiwYWrvcH/48CFGjBiB0aNHY/bs2ejXrx969OiBuLg4DBw4sMz+prQnNzc3tVdusD1VDnL2PRkZGWo3YttS7IJBLAvKyclRe7zPkBfbakpX3idZtCmZ/9LZ2Rnvv/++Wcog+2bNIGxGRgYmT56MN998E6GhoSblQZWXNfrf69evIzk5We2zyMhIfPTRR0hMTER+fj7S0tKwadMmNG/eHACQm5uL119/HQsWLDC4HKocrH0OMWrUKPz8889qU1xERUVh9uzZCA0NxdGjR9GiRQuT8iaSW2ZmprRsa+fbRKRbyZi0evXqmDlzpto21WMbMO345rFd8Vy9ehX//ve/8emnn6JRo0ZG7cuLxAQAp06dwuPHj9U+W7duHb755hvcunUL+fn5SE5Oxo8//ii9Rz4jIwOjRo3C+vXrpX3YR1Fp77zzDpYsWaL22YYNG7Bw4UKMHTsWu3fvltpUaWxPZCg524oceanmJycGsSwoKytLbd2Qu0IByw2qV6xYAeBJJ/vUU0+ZpQyyX9a+gDp9+nT4+Phg3rx5Ju1PlZs1+t/Sd2XWqVMHZ8+exbRp01C/fn04OTmhZs2aGDFiBKKjo9G1a1cp7UcffYQzZ84YXBZVfLZwDjFq1Cj873//g5eXl9rnBw4cQL9+/ZCWlmZy3kRyUj1ebO18m4i0KyoqwurVqwEAixcvLvN+RTn+F/LYrliKi4sxfvx4dOrUCVOnTjV6f14kJqDsuK1FixaIjo7G2LFjUadOHTg5OaFu3bqYMGECYmNj0bRpUwBPprGcOnUqkpKSALCPIs1mzZqFdevWSdOzl1izZg1GjRqF7OxsjfuxPZGh5GwrtnDdQRsGsSxIdSoEAAY/5u7g4KC2rvpYn1yioqJw7NgxtG/fHh999JHs+ZP9s2ZHtmXLFmzevBlr1qwxeGBBpMoa/W/J9Kwl3nrrLdSrV09jWhcXF6xZs0Y6sS0qKsKiRYsMLosqPls5h4iPj0deXh6qV6+u9vnBgwcREhKC2NjYcuVPJAfV48WWzreJSLfVq1cjMTERAwcOxMSJE8tsl+N/IY/tiuWLL77AhQsXjJ5GsAQvEhNQdtw2d+7cMjdtlahevbp0AzgAZGdnY9myZQDYR5FmhYWF0lS5pcdQmzZtQqdOnZCQkFBmP7YnMpScbcVWrjtowiCWBbm7u6utl35cWZtHjx6prXt6espWJwDIy8vDzJkz4evri61bt5a5O4AIsF5HdufOHUybNg3vvPMOOnToYNS+RCWs0f8+fPhQbb179+460wcGBuLZZ5+V1nfv3m1wPanis/Y5RF5eHkaOHIk333wTr732GpKSkvD666+r/S9ITk5Gr169+BQhWZ3q8WIr59tEpNudO3fw/vvv46mnnsLatWs1ppHjfyGP7Yrj0qVL+PDDD/HZZ5+hQYMGJuXBi8QEGD9u6969O1q2bCmtb9myBQD7KCorPT0dffv2xaeffoply5bh6tWrGD16tFqaixcvonv37mWmJmV7IkPJ2Vasfd1BFwaxLMjd3V0tQGRoQyidztvbW9Z6zZo1C4mJidi1a5fJJ39U8VmrI3v11VdRq1YtfPzxx0btR6TKGv1v6UFwtWrV9O6jGsTKz8/HuXPnDC6PKjZrnkPk5OSgf//++PXXX/HSSy/h888/h6urK5YuXYrff/8dfn5+UtqsrCyEh4fjzp07RpdDJBfVKchs5XybiLQTQkhPXu3duxc+Pj4a05WeXtCU45vHdsVQWFiIl19+Gd27d8fkyZNNzocXiQko/7jt5s2bSE1NZR9FatLS0tC9e3ccPnwY77//PmbOnAkfHx/8/PPP2Lhxo9rfOikpCYMGDVLrW9ieyFBythU58lLNT04MYlmQUqlUCxJlZGQYtF/pdI0bN5atTmvWrMHKlSuxefNmdOrUSbZ8qeKxxgXU9evXY9euXfjpp5/g7Oxs8H5EpVmj/y09BYXqO+W0ad68udr67du3DS6PKjZrnkNMmDABf/zxB6pXr47ly5erbevduzeio6PRvn176bPbt29zamKyqoYNG0rLDx48QGFhod59zHm+TUS6zZs3D0eOHMH//vc/ne9mVj22AdP+F/LYrhgWLVqEy5cvY9WqVeXKhxeJCZBn3Jaamso+iiTFxcUYMmQI/vrrLzRr1gxz5sxR2z5y5EicOnVKrc1cvHhRmpoSMO1/XmFhodo7ttieKgc5+x4/Pz+1qXVtIXZRgkEsC2vSpIm0bOjFyVu3bqmtN2vWTJa6REVF4dVXX8WaNWsQFhYmS55UcVn6AuqtW7cwc+ZMvPfee2jXrp3hFSXSwtL9r7+/v9p6Tk6O3n1q1Kihtp6bm2tweVTxWeMc4vjx4/j1118BAC+88ILGCzW1a9dGVFSU2oXHdevWsf2S1ageK0IIpKWl6d2n9LESFBQke72IqKw1a9Zg8eLF2LlzJ0JCQnSmVT22AcP+F5buA3hs27/z589j3rx5WLp0aZnzbWPxIjEB8o3b2EdRiQ0bNuDEiRMAgHHjxml8bUvTpk1x8OBBtfdkff/999KyKe3p9u3bKC4ultbZnioHOfuSoYg2AAAgAElEQVQepVKJwMBAo/IC1MdSPj4+qF27tkH7GYNBLAvr3LmztJySkqLWuWiTlJQkLQcFBWmdXsEYp0+fxrBhw7B06dIy87ESaWPJC6j79+9HZmYm/vOf/0ChUOj8mTt3rrTf3Llz1bZxGkIqYen+Nzg4WG299AuDNXFzc1Nb552dpMoa5xAlc/wDQGhoqNZ0VatWxZdffimt5+bmIjo62qiyiOSieqwAhvW/qseKQqHgDAVEFrBz505MnToVmzZtQs+ePfWmb926tdq5kiHH9u3bt1FQUCCt6/pfRvZh27ZtKCgowMSJE/WOE3/66Sdpv4iICLVta9as4UViAiDfuI19FJXYunWrtKzrbxoQEIB58+ZJ64mJidI5aXnPZ/WVTRWH3H2PatszJC9Ave2Zq90xiGVhffr0kZbz8vJw+fJlvfvExsZKy6rz7poqPj4ezz33HN59911MnTq13PlR5WErQVgiU1i6/23YsCHq168vrV+5ckXvPqXfIae6P5E1ziH+/vtvaVnf3VQDBgxQu6M5JSXF6PKI5NCxY0d4eHhI6+fPn9e7j+qx0rp1a9SqVcssdSOiJw4dOoRRo0Zh5cqVGDhwoEH7ODs7o1u3btK6sce2UqnEM888Y3xlqcLiRWICgA4dOsDFxUVaN3bcplAoUK9ePfZRJDFmDBUREQFXV1dpvWQMVbduXTRt2lT63Nj2VL16db1POFPFIHffo3rd4erVqwbNsKL6Pnc5YheaMIhlYR06dEBAQIC0fvbsWZ3p8/LyEBcXJ60PGzasXOVfu3YN/fr1w4QJEzB79my96dPT0/H777+Xq0yqOCx5AdXb2xtNmzY16Ef18evq1aurbfP19TW4TKrYrNH/hoeHS8tHjx7Vmz41NVVa9vLyQosWLYwukyoua59DGPJeIdW7kVXn0iayJEdHRwwZMkRa13esAE9mKShR3mOFiHQ7ffo0wsPD8emnn2LMmDF601+5cgUxMTEAgBEjRkifG3ts9+rVC9WqVTOhxmRLfH19DR4nqr7rqHbt2mrbvL29eZGYADx5/3ffvn2ldWPHbc2bN5dm0GAfRaXpG0O5urqq3QioOoYqT3saPHgwlEpe9q8s5Ox7wsLC4O7uDuDJ+91KzsG0uXHjBu7evQvgSUBs8ODBRtXdUGzNFqZUKjFx4kRpfefOnTrT7927V3ppaOvWrdG1a1eTy7516xb69u2LAQMGYMmSJQbtM23aNIOetqHKwZIXUIcMGYK///7boJ8ZM2ZI+82YMUPrNqrcrNH/Tp06FQqFAgBw+PBhPHz4UGf6CxcuSMsjRozgSSepsUYbVh1QXb16VW96T09PaVn1HVlEljZ58mRpeffu3TrPZ+/du4djx44BeHIn46RJk8xeP6LKqmRWkLffftug8/SCggJMmDBBerpy1KhRUmDi2rVr+Ouvv3Tuv23bNmmZ44KKofR4T9eP6g0NCxcu1LiNF4kJeHLtq8SePXv0XgdTHbe9+OKL0jL7KAJMH0M5ODio7fvKK69Ifcwff/yB+/fva82jsLAQe/bskdanT59udL3JfsnZ93h6emLUqFHSur7rDqp5DRw4EA0aNDC43sbgf1srmD59uvQ46Z49e6RopSaRkZHS8ocfflhme3x8PNq3bw8vLy9MnjwZRUVFGvNJT09H37590b59e/zwww866yeEwLVr1zBhwgScOnWKjzOTxJpBWCI5WLr/DQoKkoK3OTk5+PHHH3XWr+SYcnZ2xltvvaX/C1GlY+k2PHToUGl5165deusXHx8PAAgMDCzzfgEiTe7duyctazuPLS0lJQW9evWCp6cnhg0bpvEF7F27dpWeAE9LS8O+ffu05vfTTz9JZU+ePJlTCRKZyfXr19GvXz+MGzdO4/8lVUVFRYiLi8PQoUOhUCikdxe5ubnhvffek9KtWbNGax7R0dHSDXWtW7c2eNpCqlx4kZgAoH///nj66acBAAkJCTqvdQghsHv3bgBAtWrVMGXKFGkb+ygCjBtDFRYWStMP9ujRQ+0VHPXr15duriooKMAvv/yiNZ8dO3YgPT0dAPD888+jbdu2JtefrM/YMZLcfc/s2bOl92xt2LBBurZbmhBCev+kg4ODQbO+mUyQVWzdulUAEADEtGnTNKb5448/pDQjRozQmKZHjx5SGgBi7dq1ZdI8ePBAhISECADC2dlZVKlSReePg4ODlN/cuXNl/d5k/zIyMkTt2rUFAOHm5ibu3LmjNe3zzz8vtaUtW7aU2R4XFyfatWsnPD09xaRJk0RhYaFJdZozZ45Uzpw5c0zKgyoPS/a/QgiRlpYmatWqJQAIHx8fkZSUpDHd8ePHpbwWL15s2pejSsHSbXjAgAFSmr1792qt12+//aazzycqLSkpSVSpUkVqN61atRLFxcV69xs3bpxa2503b57GdAkJCcLT01MAEG3atBH5+fll0qSnp4saNWoIAKJRo0YiOzu73N+LiMpKSUkRDRs2FAD0jkerVKkilEqldIz/9NNPankVFBSIdu3aSeORhISEMuUVFxeLZ555RgAQTk5O4ty5c5b6qmRDVP9fREZGak03ZcoUKd3y5cu1ptuyZYuU7vnnnzdDjcmaLl26JFxdXQUA0bBhQ/HgwQON6X755RcBQCgUCrFx48Yy29lHUUFBgWjVqpX0P+/8+fNa0/7www8CgHB0dBSnTp0qsz0rK0vUq1dPABB+fn7i/v37ZdI8evRING/eXLrmkJKSIuv3IcsydYwkd9/zxRdfSHX49NNPNaZZu3atlOadd94x/EuagEEsK1qxYoUUMPrss8/UGmRMTIyoWbOmACAGDBggHj58qDGPrl27qg3if/zxR7XteXl5omfPnmppDP1RKpVaL7ZS5WbpC6j6MIhFxrJE/6vq2LFjUp6NGzcWFy5cUNv+999/i4CAAAFA/Pvf/5bnS1KFZsk2nJWVJUJDQwUA4enpqTFAdfDgQeHr6ysUCoVYuHChPF+SKqScnBxx5coVsW3bNmmQpfozduxYcfjwYZGQkCDy8vI05jFmzBi1fT788EOt5Z06dUoKUo0cOVLteEhLSxNdunQRAMRTTz0l/vnnH9m/LxEJce/ePenimrE/3t7eIjc3t0yeaWlp0o2aLVq0EDdu3JC2PX78WEyfPl0AEO7u7mLnzp0W/LZkSwwNYvEiMZXYvn278PDwEABEx44dy1wEPnHihKhatapQKBTi22+/1ZoP+yhKTk4WTZo0EQBE7dq1xaFDh8qk2bRpk3B1dRVOTk5i3bp1WvO6cuWKCAwMFABEz549xd27d6VtDx48EEOHDhUARM2aNcWJEyfM8XXIzOQYIwkhf9/z0UcfSUHWDRs2qG3bv3+/cHNzEwDExIkTNd4wKCcGsazswIEDIjg4WAAQzZo1Ey+99JLo06ePUCqVwsvLS8ybN0/n0ymxsbGiVatWwsPDQ0RERIiCggK17RERESYNFgCI5557ztxfn+yYpYMAujCIRaYwd/9bWlJSkhgxYoRwdnYWCoVCdO3aVYwbN070799fODk5iebNm4s9e/bI/TWpArNkGy4oKBCffPKJFAxo3LixGD16tBg/frxo3769NND//fffzfFVqQL5+uuvDT4X3bx5s8Y8EhISRJcuXYS7u7sYNGiQyMrK0lnmtWvXRFhYmFAoFKJWrVpi+PDhIjw8XHh5eQlHR0cxduxYce/ePXN8XSISQvTq1cvkMenUqVO15pudnS1mzpwpXFxchIuLixgwYIB48cUXRf369QUA0bVrVz7dUMkZGsQSgheJ6f/Fx8eL5557TiiVSuHo6Cj69Okjxo8fL3r27CmUSqXo2LGjQW2AfRRlZ2eLWbNmSTMDtGrVSowdO1aMHTtWBAUFCYVCIfr27StiYmL05nXnzh0xZswY4ejoKLy9vcXgwYPF8OHDRc2aNYVCoRDPP/+8WsCC7IscY6QScvc9mzZtkm66bteunRg7dqx0I2DNmjV1BvTlpBBCCJDVxcbG4vTp07h37x5cXV0RFBSEnj17wtXV1dpVI9Lq4MGDeP311xEXF4dmzZqhffv2SE1NxaFDh+Dh4YFZs2Zh9uzZcHBw0Lj/uXPnMG7cOFy/fh3Dhw/HihUr4OjoaHQ9Pv74Y8ydOxcAMGfOHHz88cfl+VpUyVi6/7179y6OHTuG5ORk5OTkoGbNmggJCeH7g8hklmzD+fn5OH36NC5cuIDMzEy4uLigTp06CA0NVXsJMZEtSkxMxNGjR5GcnAyFQoGAgAD06tULNWvWtHbViKgc7t+/j0OHDuHatWvIz89H7dq10aVLFzRt2tTaVSMrGz9+vPSujsjISIwfP15n+rt37+LNN9/Exo0b4e7ujl69esHJyQlHjhzB3bt3ERYWhq+//hoBAQHmrzxZXVJSEk6fPo2kpCQUFBSgdu3a6Ny5MwIDA43Kh30U5ebm4vjx4/j777+RlZUFNzc3+Pv7o3PnzvDz8zMqr7S0NBw+fBgJCQkQQqBevXro3r076tevb6bak72Ss+8pLi7GqVOnEBMTg/v378PDwwPBwcHo1q0bnJyczFD7shjEIqJyYxCWiIiIiIiIKgJeJCYiIrItDGIRERERERERERERERGRzVFauwJEREREREREREREREREpTGIRURERERERERERERERDaHQSwiIiIiIiIiIiIiIiKyOQxiERERERERERERERERkc1hEIuIiIiIiIiIiIiIiIhsDoNYREREREREREREREREZHMYxCIiIiIiIiIiIiIiIiKbwyAWERERERERERERERER2RwGsYiIiIiIiIiIiIiIiMjmMIhFRERERERERERERERENodBLCIiIiIiIiIiIiIiIrI5DGIRERERERERERERERGRzWEQi4iIiIiIiIiIiIiIiGwOg1hERERERERERERERERkcxjEIiIiIiIiIiIiIiIiIpvDIBYRERERERERERERERHZHAaxiMjicnNzERsba+1q2K3Hjx/j3Llz1q4GERERERFVQByvlQ/Ha1QiPT0dV69etXY1iIjsHoNYRJXY+vXrUa9ePfj5+eHHH380a1np6elYs2YNwsPD4evrizfeeMOs5VU0OTk52LZtG15++WXUqlULM2bMsHaVLOLGjRtYv349PvvsM3z66af48ccfsWfPHqSlpZVJu2XLFhw7dswKtSR7l5GRgXXr1mHkyJHWrorRhBB44403ULVqVQQHB+PUqVPWrhIRERHJhOM1+2Ht8VpBQQHOnj2LX375BV988QXmz5+PTz/9FN9++y3Wr1+PP//8E6mpqRr3XbVqFf755x+L1rciu337NlauXIkBAwagTp06WLNmjVXq8fjxY+zduxevvPIK4uPjzVqWEALR0dFYtWoVFi5ciKVLl2Lt2rU4fPgwsrOzy6T/7LPPkJKSYnJ5o0ePhkKhgEKhwIIFC8pTdSKyE47WrgARWcfVq1cRERGBwsJCAMCUKVPQvXt3NG7cWLYycnJysGrVKmzfvh1Hjx5FUVGRbHlXBvfu3cO6deuwb98+HDlyBI8fP5Yt76NHj6J79+5G7RMYGKjxLrL169dj7NixRuX1zDPPICoqSuv2w4cP47333sPJkycBAB4eHqhRowbS0tKQm5sLAGjUqBF69OiBdu3aISMjAwsWLMDMmTPRpUuXMvm1adMG58+f11mnKVOm4Pvvvzfqe+gzePBg7NixQ2eaFi1amH1QQeqKi4sRHR2N/fv3Y9++fThx4oTUP23atMnKtTPO2rVrsWzZMgDA/fv3MWLECCQmJlq5VkRERFReHK/ZPnOO1wwhhMCuXbsQGRmJqKgo5OTkSNtcXFxQq1YtZGZm4sGDB9LnVatWRUhICNq2bYsGDRrg5MmTWLduHX799Vc0bdoUALBs2TK9Qcxq1arh5s2bcHd3l+37bN++HUOGDNGb7syZMwgJCZGtXDn89ddf2LBhA/bt24fo6GgIIaxSj6tXryIqKgr79u3DgQMHpDYxZswYs5QnhMCqVaswf/583Lx5EwDg6+sLd3d3pKSkoLCwEA4ODggODkavXr3QpEkTnDlzBqtXr0bz5s1Rt25do8vMz8/Hnj17pPVq1apBoVBoTV+nTh3ExsaiVq1aRpUTEBBg8Ljqxo0bCAgIMCp/IjIOg1hElVRsbKw0IAKAoqIinD9/XtZBUWZmJlJTU9G8eXPExcUhPT1dtrwrgzt37iAzMxMNGjSAs7OzrIOibt26ITc3F/Hx8di8eTOWLl2q1h5KNGrUCO+++y769Omj9aRszJgxGDx4MGJiYhAZGan1TrN27dph1qxZ6N69O/z8/LTWbcGCBfjggw8ghECrVq2wdOlS9OzZE0rlk4eHb9y4gR9//BHff/89IiMjERkZKe1769YtjXl+8sknSEpKwuXLl7FlyxYkJSWVSbN27VrMnz8fvr6+WutmjISEBOzatUvjNkdHR7zwwgvo1KkTGjVqJEt5ZJjly5fjgw8+wP37961dFVmcPn1abf3mzZu4d++ebO2YiIiIrIPjNdtnzvGaPocOHcK//vUvxMXFAQCUSiXCw8MxcuRI9OvXD9WrV5fSZmRkYMeOHVi1ahWOHz+OqKioMjcU5uXlScvPPfccvLy8cOvWLRw4cACHDx8uU35GRgbWrFmD6dOny/advvrqK63b2rZti8GDB6N+/fo2OX66ceMGiouLERAQgJiYGIsHsU6ePImRI0dKgSRLyM3NxZgxY7Bt2zYAwNChQzF//nwEBQUBeNJnxcTESE8Dlp5iU9vYXZ+oqCgpMNu8eXMMHToUQghcuHABv/zyi1rQFnjyZNzIkSNx4MABODg4GFzOokWLEBcXhxMnTuDQoUNltvv6+mLQoEFo06YNatSoYdJ3ISIjCCKqlC5duiSUSqUAIAAIBwcH8c8//5itvE2bNkllARA9evQwW1kV0a+//qr2++vSpYus+b/11ltq+Ze0iYSEBKPzCg8PL5OXr6+vePjwod59v/vuO2mf4OBgnfukpKSIbt26qZXTq1cvvWVkZWWJunXrlqkjADF37lyjvqsumn6nJT8///yzbOWQcbKzs0VOTo7IzMwU7777bpm/jb1ZsWKFWv3r169v7SoRERGRDDhesy/mHq+VKCwsFG+88YZQKBRSWS1atBCnTp0yaP/Vq1cLb2/vMufA3333ndZ9XnvtNY1jmsDAQFFUVCTL94qLi9M6dnr22WdlK8cSpk2bplb/999/3+xlPn78WDx48EDk5eWJLVu2CAcHB7U6HDp0SPYyR40aJeUfERGhM+2xY8eEv7+/LGPviIgIKY8PP/xQbdv169dFo0aNNLajt99+26TyhBBi9+7dav1xmzZtRFZWlsn5EZHx+E4sokqqWbNmWL58OWrWrAk/Pz+sXLkSTZo0MVt5wcHBZsu7MmjVqpVZ83/22WfLfFYyxYSxnnvuuTKf9enTBx4eHjr3u337ttq0FZ9//rnOffz8/HDw4EG1uhsyr7aXlxeGDh2qcdvy5cvx6NEjvXnok5ubi9WrV2vc5unpaZfvXqoo3N3d4ebmBh8fH8yZMwdOTk7WrpJGhrbDiIgITJo0CV5eXggODsbmzZvNXDMiIiKyBI7X7Iu5x2vAk3cchYeHY+nSpdKTPv369UN0dDQ6dOhgUB4RERE4deoU6tSpo/a56pNYpY0fP17j59euXdM7dbqhdD2FNXHiRGlWDntgibZQmrOzMzw9PeHi4oJhw4ahc+fOZi1v27Zt2LhxI4An46svvvhCZ/rOnTvj5MmT8Pf3lz4z5Z1YhYWF2Llzp7ReelzfsGFD7N+/H1WrVi2z75IlS6SnxowVFhaGAQMGSOvLli2Dl5eXSXkRkWns578AEcnu1VdfRVpaGlJSUhAREWHWslxdXc2af0VXpUoVs+avaS5qU+d0rlevXpnPDAmGffnll9KFe1dXV/Tu3VvvPo6Ojti8ebM06DZ0SgJt07DcuXMH69evNygPXdauXYvMzEy4uLiU2fbUU08ZNY0BmY+Li4tNTv2QnZ1t0PsAgCfHwIoVK5CVlYULFy4YfAGDiIiIbB/Ha/bD3OM1IQRGjRql9i6gzp07Y9u2bUaX3bRpUxw+fFjtHUG6gli6prD8/PPPjSpbk8zMTPz8888AoHH8VPKuLnth7rZgCNVgkTksXrxYWu7cuTN8fHz07uPn54c9e/ZIN6qaMp3g4cOHpWlPGzVqhDZt2pRJExgYqDWQOH78eFy5csXocgGgZ8+eAJ7clGrs+8WJqPwYxCIiIo2DVjc3N5Py0rSfIYPi//3vf9Kyj4+PwYEeT09PrF69GgqFAtnZ2WXmwNak5AXEmp7CUb2z0VTffPMNAOCll14qs03fE2lkWbb4JNa0adNw6dIla1eDiIiIiGzEvHnzsH37dmnd1dUVP//8s8ljtiZNmmDt2rXSem5urta0JWMnoOy587Fjx8q8o9VYK1euRG5uLry9vREWFlZmO8dPxjPnGCc9PV3tb27Mu3iDg4Px8ccfAzDtSaytW7dKy9pmV9HlwYMHeOGFF3QGbbUpuTHX398fCoXC6P2JqHwYxCIiIo0nYaaemJmyX2FhIeLj46X1O3fu4OHDhwbvHxISIk1jaMgdXSXTUQwYMKDMoOivv/7Cvn37DC67tN9//x0XL14EAPzrX//SWjaRJuvXr8e6deusXQ0iIiIishExMTH4z3/+o/bZhx9+aPLMGSX69euHgQMHAtD9JJbq+EXTtOjleRqrqKgI3377LQDglVde0Riw4vjJtpw7d07tps9r164Ztf+0adPg6+tr9JNYxcXFaoHcYcOG6d2nRYsWZT77P/buOzqKqn3g+BNCGoGEFkKAEEJHEpoSiEgRBEEBRXoztCBKkfaKAr6oiArSXppAQIogShXpFlBECQENJgFpUiT0IoEECCn39wcn88vsbrYkm2Qh3885c87O7G07md3MnWfunZiYGBkyZIhNdYs8vHlWRKwadQbA/vhPAADIdzdu3NCdCKelpcnSpUttKmPgwIEiYtu0BMWLF5cBAwYYbc9JRyxjPvdWrVrxbAHY5PDhw9nqUAEAAODxNXz4cElLS9PWvby8TN4slx3/+c9/RMT8SKzMRo8ebbRtw4YNcvbs2WzVv3nzZjl37pw4OzvL8OHDs1UG8tb169d161FRURIdHW11fg8PD+nZs6dcu3ZNUlNTrc7366+/yuXLl0Xk4eMQGjVqZDHP9OnTpUGDBkbbV65cKQsXLrS6bpH/nybSEWfyAAoCglgAAIc0adIkmwJSL7zwghQpUsTmaQlGjhxpNHXhjz/+KH/++adN5YiInD59Wpun3l4dSxQM586dk44dO0pSUlJ+NwUAAAAO4rvvvpPffvtNt61Hjx7ZnkbQ0DPPPCMVK1a0enq1+vXry7PPPqvblpaWJv/73/+yVX9Gvpdeesmq5yjDMQ0ZMkTS09OtTt+1a1dJT0+XS5cuWZ0n81SCnTp1smoGGHd3d9m4caOUKlXK6L2RI0fKoUOHrK4fQP4iiAVARESuXbumPSATyGulS5c26ojdvn1b2rdvL9euXbOqDHd3d5kzZ47ZBw+bEhgYKC+//LLR9uyMxpo7d66kp6dL1apVTc7nDpgSExMjzZs3l/Pnz+e4rBMnTtjUgQQAAI8G+msFU8ZUe5n16tXLbuU7OTlJ69atrR6JJWJ6NNbSpUslISHBprpjYmJk7969IvIwoIBHQ8WKFY22RUVFycCBA60eWRUaGirvvfeeTaOaNm7cqL22ZirBDAEBAfLVV18Z3bianJwsXbp0kZs3b1pdFoD8QxALKMDu3bsnX331lbRv317KlSsne/bsyZd2XLx4Ud5++22pU6eOeHp6SrFixaRWrVoydOhQ+eOPP2wqKy0tTTZt2iR9+vSRmjVripeXl7i6uoqvr680a9ZMPvroI7l165bV5V25ckWmTZsmNWvWlP3792vbk5OTZf78+RIaGqq1OSQkRBYtWpStC8hRUVHyxhtvSO3atcXb21u8vb0lNDRUZs6caVOH4lHl7OwsTZo0MdoeHR0tTz75pERGRlpVzsCBA6Vx48Y21z9mzBijbV999ZVNI8ESExNl2bJlIiIyYsSIPJ+7/e7du7J48WIJDg7WBUN+/PFHeeaZZ8TDw0OqVq0q69evz7KMtLQ02bhxo/Tu3Vtq1KghXl5eUqxYMalevbr07dtXtm/fbnV7lFKybt06eemll6R8+fLi5uYmTk5ORkvGFCbWOH36tEybNk2ee+45CQwMlKJFi0rRokWlatWq0qlTJ1m6dKkkJiZaXZ41Tp06ZbLdTk5O0q9fP6P0H374YZbpTT23YMuWLdKoUSM5d+6cbvu5c+esqk/k4e/U//73P2nYsKHUqFHD5o7Y999/LyNHjpQGDRqIn5+fuLm5iY+Pj9SpU0eGDx8uu3fvtqm81NRU+eabb6R9+/YydOhQ3Xu//PKLdO/eXcqWLStubm4SGBgow4YNkytXrthUBwAABQH9Ncse5/7anTt3ZMeOHbptzs7O0rBhQ7vWM3jwYHnllVesTv/iiy9KzZo1ddvu3LkjERERNtWbMQqrQYMG0rRpU5vymnPr1i1ZsmSJdO3aVapXry4lSpQQd3d3qVixojz77LMybdo0+eeff7JVdlpamnzzzTfSpUsXqVq1qnh4eIiPj4+8+OKLsm7dOt0U+dlp96JFi6Rt27YSGBgoHh4eUqpUKalTp46MHj1aDh8+nO2y7alBgwba86EyW758ubRq1cqqPnThwoVl0qRJUrZsWavqjIqK0vq4pUuXtvl4ee6552TKlClG28+dOye9e/fmJkDgUaAAFChpaWnqhx9+UGFhYapYsWJKRLRl3bp1uVbvmTNndHU1b95cKaXU5s2blaenp+69zEuhQoXUm2++qVJSUizWERkZqZ544gklIsrb21uNHDlSLVmyRM2ZM0c1b95cK97BmvMAACAASURBVLNkyZLq559/zrKcO3fuqC+//FJ17NhRFS5cWMu3Z88epZRSv//+u6pevXqWbe7atatKS0uzar/cuHFD9enTR4mIKlKkiAoLC1Pz5s1TS5cuVcOHD1fe3t6qfPny6vPPP9fV0aRJE6vKt5bh30dEVFhYWLbK2rNnj1FZkyZNsphv7dq1We5TZ2dnNWHCBJWcnJytNhlatmyZ0Wds3LixUb1vv/221WXOmzdPiYjy8vJSt2/f1rYblplx7NtLXFycGjFihPL29tbqOHPmjFJKqaVLl6pChQrp6vfw8FBXrlwxKicyMlLVrVtX+fj4qLFjx6ply5aphQsXqn79+ilXV1ctf+vWrVV8fLzZNt26dUu1aNFCiYgqVaqU+s9//qOWLFmiPvvsM9WnTx/l4uKilTd06FCLn/H69etq8ODBqnDhwsrd3V29+uqrasGCBSoiIkK9+eabqkyZMlp5ZcqUUYsXL7Z6/wUEBOj2j6E7d+6oiIgI1a9fP+Xk5GTxOxIXF6ciIiLU2LFjValSpXTpAwICjNL//vvvas2aNWrNmjW6tKVLl9a2ZyyRkZFavqSkJLV69WrVtm1b5ezsrMt77do1qz773r171VNPPaVERAUGBqrx48erZcuWqdmzZxv9/jVv3lzFxMRkWVZ6err67bff1Jtvvql8fX2N9lFSUpJ69dVXs/yO+/r6qhMnTljVbgAAHmf01+ivZdi0aZNR24ODg3NcbnYYnisvXLjQqG3+/v5WHQdKPTy/d3d3VyKiVqxYoW0PCwszKjejb2NJamqqmjp1qipRooQSEdWmTRs1c+ZMtWzZMjVp0iTt2BMR5ebmpsaMGaPu3r1r9T6Ii4tTDRs2VCKi/Pz81JgxY7Q+Tvfu3ZWzs7MKDQ1VkyZN0rV/woQJFsteuXKl8vHxUbVr11ZTpkxRK1euVLNmzVLPP/+87rs2YsQIdf/+fYvlGe7HjO+GvbzxxhtZfr9KliypVq9ebdf6xo0bp5U/aNAgi+kzfk8MP3eXLl1Mtvm9996zWGbGdQ579+cBWIcgFlCAJCYmqtq1a6tatWopLy8vo3/ced0p2rBhg9EF9qyWbt26qdTU1CzL37lzp3ah3dfXV509e9YozciRI7XyihQpoo4fP26UZvny5apGjRrKw8PDqA179uxRO3fuVEWKFFEioooXL64CAgJMfob58+db3CeXL19WNWvWVCKi6tSpo/7++2+jNFeuXFHPPPOMUfmPYxArPT3d5GfNvNSoUUP98MMP2WpXZqaCWOvWrTOqr0SJEioxMdGqtmf8LUeNGqV7z7BMe530zp8/XzVq1Mjkfjpz5oz68ccfdZ36zMv333+vK2vbtm3K3d1dNW3aVN28edOorqioKK2TKSKqWrVqJgNhGdq0aaNEHgZtrl+/bvT+4cOHlY+PjxIRNWDAALOf89ixY6pKlSpKRFSVKlXUsWPHjNLcvn1bu7iQsQwcOFA9ePDAbNlKWQ5iZTZ06FCbviM7duzQpTcVxMrM2rS//PKLqlatmqpVq5YuwJixWBPE+uyzz7TjY9CgQSY7w4cOHVKVK1fWyvX09FTffvutUborV66ounXr6oJXmffRzZs3tWCZq6urqlSpksmLYY0aNVLp6ekW2w4AwOOK/hr9tczeeecdo3K7dOmS43Kzw/Bc+e7du6p06dJG7bM2ePHRRx8pEVFly5bV3aiY3SDW7du3Vbt27ZTIw5v2TJ2zpqenqxkzZuiOh5CQEHXp0iWL5e/bt08VLVpUiYjq0aOHyT7iwYMHlZ+fn1H7LQWx3n33XSUi6s033zT5HZozZ46uvM6dO5v9rimV+0Gsy5cvq5IlS5r9TWjbtq06efKkXeqrWrWqVu727dstps8qiHXnzh1dMDNjKVSokNq5c6fZMgliAfmLIBZQQN28eVM3eiGvO0X169dXfn5+6oknnlBffvmlunLlirp//746ceKEmjJliskLnJMnTzZZ9r1793SfJTw8PMt0mU+0THUA7t27p5RSKiEhQbtwnrF8+OGH2oX+/fv3a3ni4+NV69atjS5Am7sYm/nkqWzZsurixYtZpk1ISNBdSLZXpygzRwhiKfVwX5YvX95iJ7lXr17q8uXL2WqfUqaDWKmpqSowMNCorjlz5lgsb+fOndrJ7+nTp3XvGZZnr5PeuXPnqt27d6u33nrLqI6oqChVvnx5NX78eLV161YVFBSke//w4cNaOYcOHVKurq7K19fXZMApw9ixY3VltGrVymS6vXv3amnmzp2bZXnffvut9rfMysWLF1XZsmW1CxmnTp3KMm16errq27evro3W3KVnSxDL8G5YS9+R27dvG/0umGNt2sy/Lb/++qvR399SECvj+BcR9fzzz5v9rTp37pwqV66clr5w4cLqp59+0qVJSUnROtKGx0m3bt1USEiIKlmypIqIiNB+Yx88eKBmzpxpNLpt9+7dZtsOAEBBQX+N/trLL79stI+tOb/NDabOlTOCL5mXBg0aWCwrJSVF+fv7KxFR77//vu697ASxUlNTVcuWLbX0X375pdn0S5Ys0ZVfp04dlZSUlGX6P//8UxsV2bJlS7Ojzf744w+jGwnNBbEyAlStW7c2ezxm3BCWsXzwwQdmP2NuB7GUUmrXrl26GTZMLe7u7ur999/P0Ywqf/75p1aet7e3VWVlFcRSSqnjx4+bvEmgVKlS6ty5c1mWSRALyF8EsYACzHAodV52ikREvfDCC1kOhT9y5IjRnT2urq4mp5vatWuXLl2nTp2ybMcLL7ygpStSpIjZNg8ZMkRXrpOTkxo3bpzJqScSEhKM2ps5UGBo8ODBWrqlS5eabYdSSkVERNi9U5SZqb9Pt27d1KVLl2xeNmzYYFSWtUEspZQ6deqUqlatmtmTYZGH0xRknnrCFqaCWEopNXv2bKN6KleubHG6kYzjytSxZ1ievU9609PTjUbANGrUSNd5u3XrlurcubMqUqSI7qLB/fv3tc751KlTzdYTFxdn9FkMR3QppdR///tf7f1t27aZLbNRo0ZZfl/T09NV06ZNtbLGjx9vtiylHgaNDIOglo4RW4JYhgFaawK9hhdL7JU2M8O7YM0FseLi4rRRdU5OTiZHthnauHGjrvwyZcqoq1evmkwbGRlp9LtZr169LKegHDhwoC79yJEjrfvQAAAUAPTXCnZ/zTBwISJq7NixOS43O0ydK1++fFm5ubkZtdFSwCRjGnk3Nzej2R2yE8SaOHGilvbpp5+26vN07NhRV0f//v1NpktOTlZ16tRRIg9v5rLm3Ll37966srMKYh0/flwbUXjgwAGzZWZMXZ/5u3Hr1q0s0+dFEEsppbZu3aqNUDO3PPHEE7rAsi0y9y979+5tVR5zQSylHk6TangznYiohg0bZhkkI4gF5K+8feo8AIdSqlSpfKvbw8NDVq5cKW5ubibff+KJJ2T58uW6bQ8ePNAe/ppZUlKSbv3SpUtZ1lu6dGnt9d27d+Xff/+1Kq2IyNixY+WTTz6RQoWMfzq9vLzkpZde0m2LiYkxWe7+/ftl8eLFIiJSokQJ6du3b5ZtyPD0009bTGNva9euFT8/P5uXzp0756jeKlWqyKFDh+TVV181m+7mzZsSFhYm3bp1k9u3b+eozgwDBw4Ub29v3bbTp0/LN998k2WekydPag9cfvPNN+3SDls4OTlJQECAbltwcLD07NlTW/f29pb169dLUlKSduyJiKxcuVKOHj0qIiLdu3c3W0/t2rWlTJkyum2rVq0ySnf58mXttaUHfQ8cOFDu379v8r0tW7bIL7/8oq3369fPbFkiIsWKFZPJkyfrtk2cOFEePHhgMe+jzJbf8nfffVfb56GhoVKjRg2LeTp16iTNmjXT1q9evSrTp083mdbwd7NKlSry888/S/ny5U2mN/yeZ/W7CQBAQUR/rWD310z1cbL6e+QHX19f6d27t9H2mTNnms2XcYz07NnTqH9hqytXrsiMGTO0dWv6DCIis2bNEicnJ219+fLlcuTIEaN0M2fO1I6Ttm3bWnXubO2x8P7778u9e/ekUqVKEhISYjbts88+q1u/e/eubNq0yap6ctOLL74ohw4dkkaNGplNd/ToUXnmmWfko48+EqWUTXVs2LBBe53Taw0ZOnbsKBMnTjTafvDgwXzp0wOwjCAWUIAVLlw43+quW7euxU5Zhw4dpEWLFrptq1atkrS0NN22Vq1aSaVKlUTk4QX9AQMGZFlm5hNVEeMOVWbOzs669SeeeMJse4OCgnTrV69eNZku80n2iy++KC4uLmbLFREpUqSIxTSPEy8vL1mxYoX8/PPPUqdOHbNp161bJ40bN5bz58/nuN6iRYtKeHi40fbMfzNDc+fOFaWU1KtXT5o3b57jNmSHh4eHbv21116zKt+8efNE5OEFEk9PT7l+/brZxbDTtnfvXqMyS5Ysqb2ePXu2nDt3Lsv6O3bsKMnJySbf+/TTT7XXPj4+Uq1aNas+U/fu3aVYsWLa+vnz5+Xrr7+2Ku+jytrf8lOnTukCsk2aNLG6jkGDBunW58+fL/fu3TNKZ/i7Wb58efHy8sqyXGt/NwEAKIjorxXs/lpqaqrRNnvdvGcvo0ePNtq2detWOX78uMn00dHR8uuvv4qIyMiRI3Nc/7x583TnpNae31auXFkXGFJKGQXfUlJSZM6cOdr6yy+/bFXZ1hwLV65ckfXr14vIw++apX5YqVKlxNXVVVeGqb5YfqhRo4bs379fIiIixMfHJ8t0aWlpMmHCBOnatWuWfUBDx48f14KLRYoUkbZt29qlzSIi7733nrRr185o+8KFC03erAkgfxHEApAvrL2DbODAgbr1hIQEiY2N1W3z8vKS6Oho+frrryU6OtpkECKD4V0/hh2snChevLhu3VSH6/r167qLyPXr17db/fYWFhYm6uG0szYte/bssVsbmjVrJn/88YfMnTvXaP9m9tdff8kzzzwjFy5cyHGdI0aMMLpg8Ntvv0lkZKRR2jt37mh3oNqjE2YvhnelmvLPP/9odxXeuHFDfHx8LC6ZR0aJiJw9e9ZolFP79u211zdu3JCmTZtm2cHy9fWVH3/80Wh7QkKC7N+/X1u3NoAl8rBzY9gZ2blzp9X5H2c7d+7U/Qbasl87deqku6s5KSlJ9u3bl+M2WfO7CQAA8h79tYfys7/m7u5utM3Rgli1a9eW559/XrdNKSWzZs0ymT5jFFaLFi2kbt26Oa4/Y0aMDFWrVrU6b5cuXXTrhn2GrVu36kYN2vNY2LVrl9aP2rx5s8V+WNmyZY36XSdOnLBbe3LKyclJBg0aJCdOnJARI0YYBZgz27Bhg7z00ksmg7Sm0mZo166d0c2bOVGoUCFZvXq1VKlSxei91157TeLi4uxWF4Ccy7/begDACh06dBAnJyddZyYmJkbq1aunS1e8eHHp1q2byTKSk5Plm2++kc8//1x++OEH3Xu2DmU3x3DaivT0dKM0e/fu1XXEMu5IRNacnZ1l2LBh8sorr8hrr70mW7duNZnun3/+kQ4dOsiBAwesulsyK/7+/tK1a1dZs2aNbvuMGTNk3bp1um3Lli2TO3fuiK+vr/To0SPbdeaHjDsgRR5OP2hp2g9rNWnSRDp06CBbtmwRkYcjoVq0aCF9+/aVadOmia+vr1Vty/w9yTyyyhoNGjSQtWvXauuGwbeCyjCYaMt+LVq0qFSvXl2OHTumbfvll1+kdevWOWqTNb+bAADAcdFfyz2ZZzjI4GhBLJGHo7F27dql27Zy5Ur58MMPdTfXXbt2Tb766isRsc807ImJiRIdHa2tu7m5GY1WMqdBgwa69YsXL8rp06elcuXKIiJGN2fa81jI3Bfr06ePhIWF2VyGp6en3dpjL8WLF5f//e9/0qtXL+nfv7/89ddfJtPt2rVLRo4cqc0MkpXMQaxXXnnFrm0VeThd6KZNm6Rx48Zy9+5dbfvdu3elc+fOcvDgQbOzSgDIOwSxADg0b29vCQgIkLNnz2rbbty4YVXec+fOyWeffSZLly6V4OBg6d+/vxQvXlx3cTuv/fbbb7p1Wy/OF2TlypWTLVu2yJIlS2TYsGEmpyCIjo6W6dOnyzvvvJOjusaMGWMUxNq0aZOcOXNGAgMDReRhhzrjpHvIkCEONT+9NQzv3HvuuefsVvby5culWbNm2tQPSilZuXKlbN68WT744AMZOnSo2bvzDEfUpaSk2FS/4RSUmZ/TVZDZY79mDmKZe54FAAAoGOiv5Z5KlSoZjXw/depUPrUma23atJHg4GDdCLx79+7JggUL5L///a+2bdGiRZKcnCyVK1eWjh075rjeS5cu6QKROe0zZJSZEcTKzWMhc1+sRIkSdu2LOYJGjRpJdHS0jB49WhYsWGAyzYIFC6RXr15ZPkPs7Nmz2jOWXV1ddTN+2FNwcLAsWbJEevXqpdt+4sQJ6d+/vy6QBiD/MJ0gAIvOnj0rTk5OVi+GD/jNKcMpAe7fv282/dGjR6VPnz5SpUoVOXHihHz//feye/du6du3r12Hn2eH4UVfe06PUVAMGjRI9u3bJ+XKlTP5/qxZs6yeYzsrTz75pDRr1ky3LS0tTfeg6h07dsjJkyfF1dVVXn/99RzVlx9u3rypvbZ3MKJkyZLy008/GT0jLCEhQd5880158sknddMFGjK88GHrHaeGz29ISUmRO3fu2FTG48je+zXzMQQAAPIP/TX7caT+mqkgy9GjR3UjRhzFqFGjjLbNnz9f65elpqbKwoULRURk+PDhRqPissPw3DY9Pd2mqak9PDyMjjdzfSR7Hgu52RdzFG5ubjJ//nxZvny5yZlSlFIyderULPNnDh61bt06V0dE9ezZ0+QxvHHjRpk+fXqu1QvAegSxADg8w5MVU9MqiDx8PtHrr78uwcHB8t1338n27dtl48aNRlNZ5KecXkTGQ0899ZT8/PPP4u/vb/TetWvXzAZIrDVmzBijbUuXLpVbt26JiGgP+e3Zs6dVU+Q5msyd3+vXr9v9WUSlS5eWH3/8UaZMmWI0n/+ff/4pzzzzjIwePdpobncR44eYZ+xza3l7exttM/VMgYLG3vs1vy8yAQAAx0B/LXc0adLEaFtaWpr8+eef+dAa83r37i1ly5bVbbt69aqsWrVKRETWr18vFy5ckGLFismAAQPsUqfhua2Ifc9vc/NYyNwXO3funN3KdURhYWGyfv16k1M97tq1K8ugd25PJWho2rRp0qJFC6Pt77zzTpbPeAaQdwhiAXB4RYoU0a2XKVPGKM3ff/8tjRs3loULF0qpUqXkt99+kzZt2uRVE61meMfZ6dOn86kljiMqKkpWrFhhc76qVavKxo0bTU5LZ/gw6ezo0KGDVKtWTbctMTFRFi9eLMePH5fvvvtOROwzn3t+MJwOI2PqP3tydnaW8ePHy9GjR6Vr166699LT02XWrFnSrl07o45L5rnzRUTOnDlj8Y5ec7y9vXP0nLTHheF+zWqO+uyWBwAACib6a7mjcePGJvfljh078qE15rm6usrQoUONts+cOVOUUtqMFv3797fbiBpT56L2PL/NzWMhc1/sr7/+emSeC/vVV19l63nDHTt2NDnqKjk5WU6ePGm0/eLFixIZGSkiD/uUL730ku2NtVHhwoVl7dq1UqFCBd321NRU6d69O1PUA/mMIBYAiypVqiRKKauXfv362bV+w2nAQkJCdOu3bt2Stm3bytGjR0VEZOrUqUZTWjiKEiVK6NYzP4i2oLp69aoMHTo0W3e2PfXUU9KpUyej7YmJiTlul5OTk8kpBebMmSMzZswQpZQ0a9ZM6tevn+O68oPh1HCGD2O2ljXTmQQGBsratWvlwIED8swzz+je2717t0yaNEm3zTB4mJaWZlOQzfDvX7NmTavzPs4M96utd/GyXwEAcEz01+zHkfprhQoVkr59+xptX7ZsmUNOS//6668bBTSPHj0qkydPlsjISClUqJCMGDHCbvWVL1/eaGaAnJzfFi5cWHdc5uaxkLkvlpiYKL/++qvNZaSnp+foRr/sOHTokIwfPz5beYcOHWrykQCm+u6bNm0SpZSIiDRv3tyo75pbfHx8ZMOGDUbPu758+bJdj10AtiOIBcDhXblyRXtds2ZNKV++vO79iRMnag+4dXFxke7du+dp+2xh2FnbtWtXjp/f9KirXLmyJCUlybJly7KVv127dkbb7HWSGxYWZlTWhQsXJCIiQkRERo4caZd68oPhHPsrV660+Q7AAwcOGE1zMmzYMNm6davJ9CEhIbJ3716jecUXLFig+x6EhISIp6enLo0tUzj8888/uvWWLVtanfdx9uyzz+rW//rrL7l27ZrV+dmvAADAFPpruWf48OFG07DFx8fL9u3b86lFWStVqpS8+uqrRtszblhr3769VKlSxW71ubi4GN0gZ0uf4d9//9UFUBo2bChFixbV1g2Phc2bN2ezpcYM+2LZ6QvPnDlT3nrrLXs1ySqVK1eWffv2yeHDh23O6+LiIq1atTLabqrvnnkqwc6dO9tcV06EhITIvHnzjLbb0m8CYH8EsQA4tPT0dN3UcIbzZ9+/f183FV2pUqWM7v4yLC8/GV7wT0hIkDVr1uRTaxxD5cqVxcnJST7++ONsPZfJcLi/iFjsHKWkpIiImHweU2ZFihSRIUOGmHyvUqVK0rFjRytb6XgaN24sTk5O2vqpU6dk8eLFVue/ffu29O3b12RH9Ycffsgyn5OTk4wZM0bGjh2rbUtMTJSzZ89q6y4uLtK+fXtdvq+++srqtsXFxenWc6vjY+mCRk4ueKSmpmY7b1aef/553e9jWlqarF+/3ur8mfdrcHCwVK9e3a7tAwAAjx76a7krICDA5PTlo0aNsuszbQ3PnzPL6DuJWO4/jRo1StfHyCw3pmE3fFbSzp07rX4uluFn7tKli27d8Fj48ccftWBsToWGhurWV65cafZvYCg6Olreffdd6d+/v13aY62MfvbEiROzld+w7+7q6mq07fr161ow0snJSV5++WWb68k4ZrPbpxo0aJCEh4dnKy+A3EEQC4BD+/nnn7WT81KlSsnrr7+ue//vv//W3T11/fp1sycqhg9NtXQSbm/Nmzc3mtf83XfflYSEBLP5DKfosPe0AZk7Jhkyhu/ndlnu7u5Srlw5uXLlinz44Yc212fYefP09DS6I89QxjFjzbSDw4YNM/kQ2uHDh5t8Hldm9tyv9ubj4yOtW7fWbZswYYJVHbPU1FTp1auX3Llzx+TJ/bp16yxegBg2bJhu3fA7MG7cON16VFSU1R27zM8JaNOmjTz55JNW5bPEcLqS48ePm00fExOjW7f0t898nF2/ft3ux0qpUqVk0KBBum3W3vV58uRJ3XMA3nnnHbu2DQAAPJrorz2Um/21999/X2rVqqXb9vfff8uYMWNyXLZSSkaPHi3169eX+Ph4k2ky//0s9Z+qV69udDOayMORR9aM4re1/9SvXz/x9fXV1h88eCCrV6+2WI+Ivs9QsmRJGTx4sO59w2f6pqamyujRoy2Wa82x8OKLL0rx4sW19bS0NBk0aJDcu3fPYvmXLl2SV155RZ577rk8n9o+I4i1bdu2bE1Hb9h3b9asmVFQ+5tvvtGmywwNDTU5BaElGfvRmqnvszJv3jxp1KhRtvMDsC+CWEABZniClNfzKVtj/vz52utFixbphveLGJ8gpqamZvmg2927d8u+fft02y5cuJBl3bZeQLbmLh83Nzd54403dNvi4+OlS5cuWZ6wpqenGz0z6OTJk3b9exnuR5HsP1fKVFmmtmVWuXJlERH59NNPbX5Q7MGDB3Xr/fv3N5rD2tDFixdFRLLsqGVWtmxZ6dWrl25b0aJFZeDAgRbzmursWtoX2WF4rFo7R77h3ZA3b96U559/3uwDkZOSkqRr166ybds2mTp1qtF3UuTh/l23bp3ZujN3Np2dnY2m66hfv77RnYWGgS1T4uLitIcAe3h4GE1daMiwo2zue+/v769bj46OzvJ4vXr1qoSHh0vhwoW1bTdv3jRbfsmSJbXXycnJuruaRR4+TyIqKsoony2/5RMmTJCyZctq6wcPHrT4txIRbQpNkYcXd7p162YyXW78bgIAUFDRX6O/JvLwnHbjxo1G064tWrQoRzcWJSQkSMeOHWXWrFkyceJEkzNciPx/30nEuv6TqeCataOwbO0/ubu7y7Rp03TbJk+ebLHPlZycLF988YW2Pm3aNKNjt379+tKiRQvdti1btpgdhXTjxg2ZPXu2bpvhOb3Iw7+p4c2ABw4ckM6dO5sdSXb69Glp2bKlXL582Ww/x5Y+ji0CAgK0GzkHDBgg169ftym/Yd/d1KwnmacSNBxpZ61Lly6JiPnfD0tcXV1lw4YNRkFtAPlEASiw2rVrp0REW6ZOnZprdZ09e1ZXV926dS3m2bx5s5b+7bffNpnm8uXLunJFRJUvX17t3r1bS3Pjxg01ZcoUVbRoUfXUU0/p0vbv318rZ/369bqyx4wZo0u7dOlSs+2dNm2aLv348eNNprt3756qVauWUbtr1aqlNm/erFJSUrS0cXFx6oUXXlDly5c3Sh8cHKxGjRqlpk+fbnFfWrJ27Vqj8oOCgrJVluF+EBHVtm1bs3n69eunpS1ZsqSKjY21qq47d+4oX19fLW/p0qXV1atXLeZ7+umnlYgoV1dXdffuXYvpY2JidJ9n+PDhVrVv3759RvuiaNGi6sGDB1blt1adOnV0dezbt8/qvF26dDFqo7u7uxo9erSKiopSt27dUvfv31cnTpxQc+bMUQEBAUpEVO/evU2WN3ToUCUiKjAwUN2+fTvLer///nutvs6dO5tMk5SUpIKCgnRtW7duXZZlpqWlqdatW1v9nb1//74qXLiwrvzr16+bzfPEE0/o0vv7+6vffvtNe//WrVtq8eLFqmzZsqpn3yqizgAAIABJREFUz56qSZMmVv+OtGzZUpe2ZcuW2j6MiYlRdevWVfPnz9flSU9PV0WKFNHlO3DggNnPsHv3bt3nDggIMPu5jx8/rtXh6+urLly4kGXa2NhYXVuaNm1qti1Xr17VpS9XrpzZ9AAAFCT01+ivZXbw4EFVunRpo3p69OihLl++bFNZO3fuVP7+/lb1bRYvXqzVtWTJEqvKf/LJJ7U8Pj4+6t69e1blq1q1qtHnW7NmjcV8AwYM0OUZOnSo2fSTJ0/W0vbt2zfLdH/99Zfy8PAwalP79u1159xpaWnqu+++UzVq1DB5LDz//PPqrbfeUhs2bNDy3LlzRwUGBpr8fnz66afq2LFj6u7duyoxMVEdOnRIvf3228rT01OJiIqIiDD7+Zo2baor0/C7kxOVKlXSyn3qqadUQkKCVfmioqJ0bWrevLlRmn///Ve5uLhoaU6fPm1z+zL/5gwaNMjm/IZ++uknXd/JVLsB5D6CWEABdfv2beXt7a07iWjRooVKT0/PlfpSU1NVzZo1dfXNnDkzy/rWr1+vPDw8lJOTk5oyZYrZslu0aGF04iciqnjx4srPz085OTkpZ2dntXbtWrVy5UqjdGXKlFGFChVSo0aN0pXboUMHXbr33nvPbDt69uypS9+zZ88s0x45ckSVLVvWZLtdXV1VpUqVtA5K8eLF1Q8//GCUzsnJST333HNqy5YtZttlDVOBDBFRUVFRNpWTmpqqQkJCTH6mo0ePZpkvcydCRFSJEiXUt99+a7au5ORk9fLLL2t53N3ddcGErGzdulVXl6XjK0NGcMTJyUmdOHHCqjyDBw82uV8XL15sVX5rnDhxwigQM2TIEKvz37x50+higaWlY8eO6v79+ybLywhiZfymmAqOnD9/XrswUK5cOXX+/Pks23flyhVdJ7hIkSLqm2++MUr34MED9dprrykRUc7OzmrRokUWP/uSJUuMPtvq1avN5lm1apXJfeLn56f8/f2Vs7OzEhHVqlUrde/ePaMglsjDQK2pv9GsWbOM0hYuXFhVqFBBOTk5mQwG79271yjPpEmTLH72LVu26DrkISEhKj4+3ijdyZMnVfXq1ZWIqMqVK1s89jds2KBrS6VKlcym37Vrly69s7OzSkpKsth+AAAed/TX6K+ZcvLkSVWvXj2juooWLaomTJigDh8+nGXeu3fvqs2bN2v9GhcXF4sBtps3b6oaNWpo9dSrV8+qc7XVq1dreSZOnGjVZ/v1119N7u/mzZtbvAkwJSVF9e/fX5fv3XffVWlpaUZpP//8c1WoUCElImrAgAG6oKQpX3/9tVF/K2MpVqyYCgwMVMWKFVMioho2bKgWLlxolM7d3V2FhYWpmJg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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 850,
       "width": 856
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "hist_kwds = dict(bins=100, histtype='step', normed=True, linewidth=2.0, color=\"black\")\n",
    "p1 = fig.add_subplot(2, 2, 1)\n",
    "plt.hist(data['ext_shapeHSM_HsmShapeRegauss_resolution'][mask], **hist_kwds);\n",
    "plt.xlabel('{}-band HSM resolution'.format(filter_))\n",
    "plt.xlim([0.2,1])\n",
    "p2 = fig.add_subplot(2, 2, 2)\n",
    "plt.hist(data[filter_ + '_modelfit_CModel_SNR'][mask], range=(0, 100), **hist_kwds);\n",
    "plt.xlabel('{}-band CModel S/N'.format(filter_))\n",
    "plt.xlim([5,100])\n",
    "p3 = fig.add_subplot(2, 2, 3)\n",
    "plt.hist(data[filter_ + '_mag_CModel'][mask], **hist_kwds);\n",
    "plt.xlabel(' {}-band CModel mag'.format(filter_))\n",
    "plt.xlim([20,25])\n",
    "p4 = fig.add_subplot(2, 2, 4)\n",
    "plt.hist(data['ext_shapeHSM_HsmShapeRegauss_abs_e'][mask], **hist_kwds);\n",
    "plt.xlabel('HSM distortion magnitude |e|')\n",
    "plt.xlim([0,2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the published figure, for comparison: \n",
    "\n",
    "![](https://user-images.githubusercontent.com/710903/42742471-a196979e-886f-11e8-81f6-a89e425f9be3.png)"
   ]
  }
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