{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DC2 Object Catalog Run2.2i GCR tutorial -- Part II: Lensing Cuts\n",
    "\n",
    "\n",
    "Owners: **Francois Lanusse [@EiffL](https://github.com/LSSTDESC/DC2-analysis/issues/new?body=@EiffL), Javier Sanchez [@fjaviersanchez](https://github.com/LSSTDESC/DC2-analysis/issues/new?body=@fjaviersanchez)**  \n",
    "Last Verified to Run: **2020-07-02** (by @jrbogart)\n",
    "\n",
    "This notebook is the second part of the tutorial ([Part I here](object_gcr_1_intro.ipynb)). Here, we present more advanced features of the  DPDD-like object catalog, which contains the detected objects at the coadd level, through the Generic Catalog Reader [GCR](https://github.com/yymao/generic-catalog-reader) and build a lensing sample which we compare to the published [HSC Y1 sample](https://hsc-release.mtk.nao.ac.jp/doc/). The final goal is to show how the different filtering mechanisms, features of `GCR`, and the samples presented here can be useful for your science.\n",
    "\n",
    "__Learning objectives__:\n",
    "\n",
    "After going through this notebook, you should be able to:\n",
    "  1. Select a clean sample of galaxies for weak lensing.\n",
    "  2. Compute a depth map for a given SNR threshold.\n",
    "  3. Load both the DC2 Run2.2i catalog and a selection from the HSC Public Data Release XMM field.\n",
    "  4. Directly access the coadd images to investigate suspicious objects.\n",
    "\n",
    "__Logistics__: This notebook is intended to be run through the JupyterHub NERSC interface available here: https://jupyter-dev.nersc.gov. To setup your NERSC environment, please follow the instructions available here: https://confluence.slac.stanford.edu/display/LSSTDESC/Using+Jupyter-dev+at+NERSC\n",
    "\n",
    "__Other notes__: This notebook uses the [LSST DM stack](https://pipelines.lsst.io/) (see on the top right corner that we are using the `desc-stack` kernel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting a useful sample of galaxies: lensing cuts from HSC DR1\n",
    "\n",
    "In this notebook, we will build step by step a sample of galaxies from the DC2 run2.2i object catalog, and compare it to an equivalent sample built from the HSC DR1 catalog. See more info on [Aihara et al. 2018](http://adsabs.harvard.edu/abs/2018PASJ...70S...8A)\n",
    "\n",
    "### Sample selection\n",
    "\n",
    "We will start from a set of basic sanity cuts that will select extended objects and reject problematic sources, including those for which shape measurement has failed.\n",
    "\n",
    "One subtlety is that shape measurement is only run for the *reference band*, which is most of the time the i-band, but not always, we will further restrict the sample to objects for which we have i-band shapes using the `merge_measurement_i` flag."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import GCRCatalogs\n",
    "from GCR import GCRQuery\n",
    "# Load the object catalog\n",
    "run = 'run2.2i_dr3'\n",
    "tract_s = '3081'\n",
    "catalog = GCRCatalogs.load_catalog(f'dc2_object_{run}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "basic_cuts = [\n",
    "    GCRQuery('extendedness > 0'),     # Extended objects\n",
    "    GCRQuery((np.isfinite, 'mag_i')), # Select objects that have i-band magnitudes\n",
    "    GCRQuery('clean'), # The source has no flagged pixels (interpolated, saturated, edge, clipped...) \n",
    "                       # and was not skipped by the deblender\n",
    "    GCRQuery('xy_flag == 0'),                                      # Flag for bad centroid measurement\n",
    "    GCRQuery('ext_shapeHSM_HsmShapeRegauss_flag == 0'),            # Error code returned by shape measurement code\n",
    "    GCRQuery((np.isfinite, 'ext_shapeHSM_HsmShapeRegauss_sigma')), # Shape measurement uncertainty should not be NaN\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to these basic cuts, we will want to apply a set of cuts based on object properties, to ensure we are selecting well resolved and well measured galaxies. One of these properties is the measured total distortion, which is not directly defined in the schema, but can be derived from the measured $e1$, $e2$ distortion components according to $|e| = \\sqrt{e_1^2 + e_2^2 }$\n",
    "\n",
    "The GCR provides a convenience function, `add_quantity_modifier`, to add this quantity to the schema on the fly, so that we can use it afterwards to build our cuts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adds the new derived column \n",
    "catalog.add_quantity_modifier('shape_hsm_regauss_etot', \n",
    "                              (np.hypot, 'ext_shapeHSM_HsmShapeRegauss_e1', 'ext_shapeHSM_HsmShapeRegauss_e2'), \n",
    "                              overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/lsst/software/stack/python/miniconda3-4.7.10/envs/lsst-scipipe-4d7b902/lib/python3.7/site-packages/GCRCatalogs/dc2_dm_catalog.py:43: RuntimeWarning: invalid value encountered in log10\n",
      "  return -2.5 * np.log10(flux) + AB_mag_zp_wrt_nanoJansky\n",
      "/opt/lsst/software/stack/python/miniconda3-4.7.10/envs/lsst-scipipe-4d7b902/lib/python3.7/site-packages/GCRCatalogs/dc2_dm_catalog.py:43: RuntimeWarning: divide by zero encountered in log10\n",
      "  return -2.5 * np.log10(flux) + AB_mag_zp_wrt_nanoJansky\n"
     ]
    }
   ],
   "source": [
    "# Define lensing cuts on galaxy properties \n",
    "properties_cuts = [\n",
    "    GCRQuery('snr_i_cModel > 10'),                              # SNR > 10\n",
    "    GCRQuery('mag_i_cModel < 24.5'),                            # cModel imag brighter than 24.5\n",
    "    GCRQuery('ext_shapeHSM_HsmShapeRegauss_resolution >= 0.3'), # Sufficiently resolved galaxies compared to PSF\n",
    "    GCRQuery('shape_hsm_regauss_etot < 2'),                     # Total distortion in reasonable range\n",
    "    GCRQuery('ext_shapeHSM_HsmShapeRegauss_sigma <= 0.4'),      # Shape measurement errors reasonable\n",
    "]\n",
    "\n",
    "# We can now extract our lensing sample \n",
    "quantities = ['mag_i_cModel', 'snr_i_cModel', 'shape_hsm_regauss_etot', 'ext_shapeHSM_HsmShapeRegauss_resolution']\n",
    "data_basic = catalog.get_quantities(quantities, \n",
    "                                    filters=basic_cuts+properties_cuts, \n",
    "                                    native_filters=[f'tract == {tract_s}'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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HDh067n127twZI0aMiIqKili4cGGT9R05ciQ+/PDDmDhxYrz11ltx+eWXp/K2AAAAMmrDhg3xH//xH/HQQw/FggUL4je/+U0cPHgwqzXce++90aVLl5g9e3ZW8wKQfiUzq477C4D8ldHm0Kmnnhrz58+PiIjPPvus0UVFdXV1PPfccxERMX78+OMeKXfHHXfEmjVr4tNPP43FixfHsmXLjon59NNPY+TIkfH73/8+nn322ejZs2d079690V+FhYVxzjnnxNKlS2PixIlRUJDRLwcAAECT1q1bF5dffnmcffbZ8dBDD8U777wT//3f/x1XXHFFfOUrX4nHH388K3W89dZbKZ/CAAAAdDwZ74aMHTs2Fi1aFF27do1HH300Hn744Thy5Ejy9Zqamrj22msjIuKqq66KJUuWHPc+R+8USiQSDcb79u2LMWPGJJ9tdODAgdi/f3+Tvz6/Z0FBQUycODFt7xkAAKClli9fHl/72tfi5Zdfjrlz50ZtbW389Kc/jddeey3eeOON2Lt3b0yePDluvfXWBmuqdKuvr4+bbrqp0dMaAACAjq9bNpJMnjw5zj777Ljrrrti+vTp8fjjj0dFRUVs27YtVqxYESeddFI8+OCDMWvWrOjatetx7/HII4/ETTfdFBs2bIjrrrsubrzxxgavT506NV599dVW1XfllVdG//79WzUXgPRytAEA+WjFihVx/fXXRyKRiJtvvjnuueeeBq9fcsklsWzZshg1alQsXLgwBgwYEPfee29Gapk1a1b84Q9/yMi9AQCA9iErzaGIiMrKyli3bl3U1NTEqlWrYseOHTF48OCYNm1aDBs2LHr06NHk/IsuuijWrl3b6OtPPPFEPPHEE+kuGwAAIKN2794dEyZMiEQiEUVFRTFnzpzjxo0cOTIqKyvjlVdeifvuuy+uuOKKqKioSGstr7/+ejzyyCMxbNiwVn/4DgAAaP+y1hz6XHl5eZSXl2c7LQAAQLs0b9682Lx5c0REjBgxIoqLixuNnThxYrzyyitx+PDhmDVrVvz6179OWx179+6Nm2++OW677bb40pe+pDkEAACdWMafOQQAAMDxHThwIB577LHkeMyYMU3GX3311dGt218+4/fiiy/GmjVr0lbL9OnTo0uXLvHQQw+l7Z4AAED7pDkEAACQIytWrIidO3cmxxdffHGT8b17945BgwYlx0899VRa6njppZdi8eLFsXTp0jjxxBPTck8AAKD90hwCAADIkZUrVyavCwsLo7S0tNk5Xzym+5lnnmlzDbt3745JkybFnXfeGZdddlmb7wcAALR/mkMAAAA58uabbyav+/XrF0VFRc3OKSkpSV6///77sWfPnjbVcNddd0WPHj3iX/7lX9p0HwAAoOPolusCAOi8SmZW5boEAGjX1q5dm7zu27dvSnOOjlu/fn0MHTq0Vfmfe+65+K//+q+orq6OHj16tOoeAABAx6M5BAAAkCO7du1KXvfp0yelOaeffnqD8YYNG1rVHNq1a1dMmTIl7r777rjkkktaPD8VdXV1rZ67devWNFYCAAB8keYQAABADuzduzcOHDiQHHfv3j2leUfHtfZYudtvvz1OOeWUePDBB1s1PxXFxcUZuzcAANB6mkNpVlZW1mxMIpHIQiUAAEB7tnv37gbjE044IaV56WgO/fznP4+f/exn8dvf/jblphQAANB5aA4BAADkQEFBQYNxly5dUprXtWvXBuMjR460KO/HH38cU6dOjRkzZsSQIUNaNLelNm3a1Oq5W7duzXh9AACQrzSH0qy2trbZmLq6OscrAABAnuvZs2eD8f79+1Oat2/fvgbjk08+uUV5b7311jjzzDNj9uzZLZrXGv379894DgAAoOU0hwAAAHKgZ8+eUVhYmDx2OtXm0NFxvXv3TjnnT3/60/jVr34Vb731VhQVFaVeLAAA0KkUNB8CAABAuhUUFMSAAQOS4127dqU07+i4gQMHpjRvy5Ytceedd8a9994bgwcPTr1QAACg07FzCCAPlcysavS1jXNHZ7ESAMhv5557bnzwwQcR8Zdn7KRiy5YtDcbnnXdeSvNefPHF+OSTT+L73/9+fP/730+5xjlz5sScOXOS4wceeCArR9IBAACZozkEAACQI0OHDo3nn38+IiI2b94chw8fjoKCpg942LRpU/K6tLQ0TjnllIzWCEDn5YODAPlLcwgAACBHhg8fHvfdd19ERNTX18d7773X7E6gmpqa5PWVV16Zcq7evXvHoEGDUordsWNH7Ny5MyIivvSlL0WfPn2Sr33xGgAA6Jg0hwAAAHJkyJAhUVJSEhs3boyIiNWrVzfZHKqvr4933nknOR43blzKua655pq45pprUoqdPXt28ii5O+64wzFyAADQyWgOAdBmTR1FAAA0rqCgICZNmhT3339/REQ8++yz8e1vf7vR+BdeeCH2798fEREXXnhhXHrppVmpEwAA6FyaPswaAACAjLr99tvjrLPOioiIqqqq2L59e6OxS5cuTV5/3lD6ovXr10dFRUX06tUrpkyZEocOHUp/wQAAQIenOQQAAJBDp556asyfPz8iIj777LNGj3Crrq6O5557LiIixo8ff9wj5e64445Ys2ZNfPrpp7F48eJYtmxZxuoGAAA6Ls0hAACAHBs7dmwsWrQounbtGo8++mg8/PDDceTIkeTrNTU1ce2110ZExFVXXRVLliw57n2O3imUSCQyVzQAANBheeYQACnxXCEAyKzJkyfH2WefHXfddVdMnz49Hn/88aioqIht27bFihUr4qSTTooHH3wwZs2aFV27dj3uPR555JG46aabYsOGDXHdddfFjTfemOV3AQAAdASaQwAAAO1EZWVlrFu3LmpqamLVqlWxY8eOGDx4cEybNi2GDRsWPXr0aHL+RRddFGvXrm1zHbNnz270eDsAAKDj0xwCoAE7hAAg98rLy6O8vDzXZQAAAJ2UZw4BAAAAAADkEc0hAAAAAACAPKI5BAAAAAAAkEc0hwAAAAAAAPKI5hAAAAAAAEAe0RwCAAAAAADII5pDAAAAAAAAeURzCAAAAAAAII9oDgEAAAAAAOSRbrkuoLMpKytrNiaRSGShEgAAAAAAgGNpDgF0YiUzq3JdAgDAMXyoDgAAcktzKM1qa2ubjamrq4vi4uIsVAMAAAAAANCQ5hAAAABZ5UN1AACQWwW5LgAAAAAAAIDs0RwCAAAAAADII5pDAAAAAAAAecQzhwA6iJKZVY2+tnHu6CxWAgAAAAB0ZHYOAQAAAAAA5BE7hwAAAACABho7vcLJFQCdg51DAAAAAAAAeURzCAAAAAAAII9oDgEAAAAAAOQRzxwCyCBnNAMAAAAA7Y3mEEAn0FgTCgAAAADgaI6VAwAAAAAAyCOaQwAAAAAAAHlEcwgAAAAAACCPaA4BAAAAAADkEc0hAAAAAACAPKI5BAAAAAAAkEc0hwAAAAAAAPKI5hAAAAAAAEAe0RwCAAAAAADII91yXUBnU1ZW1mxMIpHIQiUAAAAAAADHsnMIAAAAAAAgj9g5lGa1tbXNxtTV1UVxcXEWqgE6opKZVbkuAQAAAADoxDSHAHJAAwgAAAAAyBXHygEAAAAAAOQRO4cAAADIqrKysmZjEolEFioBAID8ZOcQAAAAAABAHrFzCAAAgKyqra1tNqauri6Ki4uzUA0AAOQfO4cAAAAAAADyiJ1DACkqmVnV6Gsb547OYiUAAACQG9bGAJ2DnUMAAAAAAAB5RHMIAAAAAAAgjzhWDuAoTW2RBwAAAADo6OwcAgAAAAAAyCOaQwAAAAAAAHlEcwgAAAAAACCPeOYQQBp4ThEAAAAA0FHYOQQAAAAAAJBHNIcAAAAAAADyiOYQAAAAAABAHtEcAgAAAAAAyCOaQwAAAAAAAHlEcwgAAAAAACCPdMt1AQC5UDKzKtclAAAAAADkhOZQmpWVlTUbk0gkslAJAAAAAGRPYx/E3Dh3dJYrAaA5jpUDAAAAAADII3YOpVltbW2zMXV1dVFcXJyFagAAAAAAABqycwgAAAAAACCPaA4BAAAAAADkEc0hAAAAAACAPOKZQ0CnVjKzKtclAAAAAAC0K5pDAAAA0AgfNgIAoDPSHAIAACCrysrKmo1JJBJZqAQAAPKTZw4BAAAAAADkETuHAAAAyKra2tpmY+rq6qK4uDgL1QAAQP6xcwgAAAAAACCPZH3n0IYNG6K6ujq2bdsWvXr1ioEDB8awYcOiW7fslPLRRx/FgAEDspILAAAAAACgvclac2jdunVx9913x8svvxylpaUxePDg2LRpU1RXV0e/fv3igQceiFtuuSVj+X/961/H9773vejRo0e8+uqrGcsD5EbJzKpclwAAAAAA0CFkpTm0fPnymDBhQtTX18fcuXNjxowZ0aVLl4iIWLlyZYwePTomT54cq1evjgULFiRfS4dXXnkl7r///vjtb38bERHf+MY30nZvILs0gAAAAAAA2i7jzxxasWJFXH/99VFfXx8333xz3HPPPQ2aP5dcckksW7YsIiIWLlwYc+fOTUve119/PYYNGxbDhw9PNoYAAAD9a5phAAAgAElEQVQAAADyXUabQ7t3744JEyZEIpGIoqKimDNnznHjRo4cGZWVlRERcd9998Xbb7/dprxPP/10/O53v4slS5bE5s2bo2/fvm26HwAAAAAAQGeR0ebQvHnzYvPmzRERMWLEiCguLm40duLEiRERcfjw4Zg1a1ab8o4dOzamTZsWZ599dnz5y1+Or3/96226HwAAAAAAQGeRsWcOHThwIB577LHkeMyYMU3GX3311dGtW7c4ePBgvPjii7FmzZoYPHhwWmrp3r17Wu4DZIdnCwEAAAAAZE7Gdg6tWLEidu7cmRxffPHFTcb37t07Bg0alBw/9dRTmSoNAAAAAAAgb2WsObRy5crkdWFhYZSWljY7p7y8PHn9zDPPZKQuAAAAAACAfJax5tCbb76ZvO7Xr18UFRU1O6ekpCR5/f7778eePXsyURoAAAAAAEDeylhzaO3atcnrvn37pjTn6Lj169entSYAAAAAAIB81y1TN961a1fyuk+fPinNOf300xuMN2zYEEOHDk1rXelSV1fX6rlbt25NYyUAAEBnsmHDhqiuro5t27ZFr169YuDAgTFs2LDo1i39y7e9e/fG66+/Hh9++GHs2bMnTjvttPjqV78aQ4YMyUg+AACgfcjIv/b37t0bBw4cSI67d++e0ryj49rzsXLFxcW5LgEAAOhE1q1bF3fffXe8/PLLUVpaGoMHD45NmzZFdXV19OvXLx544IG45ZZb0pLrz3/+czzwwAOxYMGCqK+vP+b1/v37x+zZs2PSpElpyQcAALQvGWkO7d69u8H4hBNOSGleR2oOAQAApMvy5ctjwoQJUV9fH3Pnzo0ZM2ZEly5dIiJi5cqVMXr06Jg8eXKsXr06FixYkHytNT7++OMYMWJErFu3rtGYurq6uOWWW+Ktt96KRYsWtToXADSlZGZVo69tnDs6i5UA5J+MNIcKCho+yijVhUvXrl0bjI8cOZK2mtJt06ZNrZ67devWGDJkSBqrAQAAOqoVK1bE9ddfH4lEIm6++ea45557Grx+ySWXxLJly2LUqFGxcOHCGDBgQNx7772tynXo0KG47rrrYt26dXHOOefENddcEyUlJbFv3754991345e//GV8/PHHyfjFixfHoEGDYtq0aW16jwAAQPuSkeZQz549G4z379+f0rx9+/Y1GJ988slpqynd+vfvn+sSAACADm737t0xYcKESCQSUVRUFHPmzDlu3MiRI6OysjJeeeWVuO++++KKK66IioqKFudbsmRJvP766zF9+vSYO3fuMR/Q+7d/+7f453/+5wa7hebMmRMTJ06MU089tcX5ACCi6R1CAORGQfMhLdezZ88oLCxMjlNtDh0d17t377TWBQAA0J7MmzcvNm/eHBERI0aMaPLZphMnToyIiMOHD8esWbNanOvIkSPxwx/+MMaPHx/z5s07pjEUEXHiiSfGwoUL41vf+lby9z799NOoqvKfegAA0JlkpDlUUFAQAwYMSI537dqV0ryj4wYOHJjWugAAANqLAwcOxGOPPZYcjxkzpsn4q6++Orp1+8vhDy+++GKsWbOmRflee+212L59e/zkJz9pNvYHP/hBg/HatWtblAsAAGjfMtIciog499xzk9dbt25Nac6WLVsajM8777y01gQAANBerFixInbu3JkcX3zxxU3G9+7dOwYNGpQcP/XUUy3K9/LLL8fUqVPjzDPPbDb2/PPPjy9/+cvJ8e7du1uUCwAAaN8y8syhiIihQ4fG888/HxERmzdvjsOHD0dBQdO9qE2bNiWvS0tL45RTTslUeUCWNHau8Ma5o7NcCQBA+7Jy5crkdWFhYZSWljY7p7y8PGprayMi4plnnomHHnoo5XyVlZUNmkvNKS4uTn6A77TTTkt5HgAA0P5lbOfQ8OHDk9f19fXx3nvvNTunpqYmeX3llVdmpC4AAID24M0330xe9+vXL4qKipqdU1JSkrx+//33Y8+ePSnn++Y3v9lgN1Bzjhw5krwuLy9PeR4AAND+ZWzn0JAhQ6KkpCQ2btwYERGrV69u8pi4+vr6eOedd5LjcePGZao0IM0a2x0EAEDjvvgcn759+6Y05+i49evXx9ChQ9Na1+e2bdsWERFFRUUxcuTIjOQAAAByI2PNoYKCgpg0aVLcf//9ERHx7LPPxre//e1G41944YXYv39/RERceOGFcemll2aqNKAd0FACAPLdrl27ktd9+vRJac7pp5/eYLxhw4aMNId27twZf/rTnyIi4oYbbojevXunPQcAAJA7GWsORUTcfvvtMX/+/Ni2bVtUVVXF9u3bj1nMfG7p0qXJ688bSl+0fv36uOmmm+L999+Pf/zHf4wFCxZE165dU6pjx44dyetDhw618F0AAACk1969e+PAgQPJcffu3VOad3RcS46Va4mqqr98kKeoqCi++93vtvo+dXV1rZ67devWVs8FAACaltHm0Kmnnhrz58+PcePGxWeffRazZ8+O+fPnHxNXXV0dzz33XEREjB8//rhHyt1xxx2xZs2aiIhYvHhxXHbZZTFhwoRma/jzn/8cq1evTo7ffffd2L9/f5xwwgmtfVsAAABtsnv37gbjVNcn2WoOLVq0KCIiZsyYEeecc06r71NcXJyukgAAgDQqyHSCsWPHxqJFi6Jr167x6KOPxsMPP9zgwaY1NTVx7bXXRkTEVVddFUuWLDnufY7e8ZNIJBrNuX///vjjH/8Y//M//xPXXHNN/L//9/+Sr+3YsSPGjh0bL730Uvzxj3+MvXv3tuXtAQAAtFhBQcOlWJcuXVKad/TpCV9cW6XLSy+9FG+88UZUVFTE9773vbTfHwAAyL2M7hz63OTJk+Pss8+Ou+66K6ZPnx6PP/54VFRUxLZt22LFihVx0kknxYMPPhizZs1q9Ki4Rx55JG666abYsGFDXHfddXHjjTc2mu+ll16Kv/u7v2v09eeffz6ef/75iIiYN29eTJ8+vW1vEAAAoAV69uzZYPz581ebs2/fvgbjk08+OW01RUTU19fHnXfeGX369Inly5dHYWFhm+63adOmVs/dunVrDBkypE35AQCA48tKcygiorKyMtatWxc1NTWxatWq2LFjRwwePDimTZsWw4YNix49ejQ5/6KLLoq1a9emlOvqq6/OyCfoAAAA0qFnz55RWFiYPBEh1ebQ0XG9e/dOa13Tp0+Pjz76KF555ZUYMGBAm+/Xv3//NFQFAACkW9aaQ58rLy+P8vLybKcFAABoNwoKCmLAgAHxwQcfRETErl27Upp3dNzAgQPTVtOTTz4ZixcvjmeeeSa+9rWvpe2+AABA+5PxZw4BAABwrHPPPTd5vXXr1pTmbNmypcH4vPPOS0stL730Utx6663x5JNPxujRo9NyTwAAoP3SHAIAAMiBoUOHJq83b94chw8fbnbOF5/hU1paGqecckqb61i1alWMGzcufvzjH8cNN9zQ5vsBAADtn+YQAABADgwfPjx5XV9fH++9916zc2pqapLXV155ZZtrWL9+fYwaNSpmzpwZt912W5vvBwAAdAyaQwAAADkwZMiQKCkpSY5Xr17dZHx9fX288847yfG4cePalP/DDz+MK664IiZOnBizZs1qNn7nzp3xm9/8pk05AQCA9kFzCAAAIAcKCgpi0qRJyfGzzz7bZPwLL7wQ+/fvj4iICy+8MC699NJW596yZUuMGDEirrrqqpg3b15Kc6ZOnZrS0XcAAED7pzkEAACQI7fffnucddZZERFRVVUV27dvbzR26dKlyev777//mNfXr18fFRUV0atXr5gyZUocOnTouPfZuXNnjBgxIioqKmLhwoVN1nfkyJH48MMPY+LEifHWW2/F5ZdfnsrbAgAA2rluuS4AaLuSmVWNvrZx7ui0zQEAIL1OPfXUmD9/fowbNy4+++yzmD17dsyfP/+YuOrq6njuueciImL8+PHHPVLujjvuiDVr1kRExOLFi+Oyyy6LCRMmNIj59NNPY+TIkfH73/8+Pvjgg+jZs2eT9R08eDDZZJozZ04UFPh8IQDZ0dj/W/g/C4D08C97AACAHBo7dmwsWrQounbtGo8++mg8/PDDceTIkeTrNTU1ce2110ZExFVXXRVLliw57n2O3imUSCQajPft2xdjxoxJPtvowIEDsX///iZ/fX7PgoKCmDhxYtreMwAAkFt2DqVZWVlZszFHL9IAAID8Nnny5Dj77LPjrrvuiunTp8fjjz8eFRUVsW3btlixYkWcdNJJ8eCDD8asWbOia9eux73HI488EjfddFNs2LAhrrvuurjxxhsbvD516tR49dVXW1XflVdeGf3792/VXAAAoP3RHAJS1tRRdAAAtE1lZWWsW7cuampqYtWqVbFjx44YPHhwTJs2LYYNGxY9evRocv5FF10Ua9eubfT1J554Ip544ol0lw0AAHRAmkNpVltb22xMXV1dFBcXZ6EaAACgoykvL4/y8vJclwEAAHRinjkEAAAAAACQR+wcAo7h+DgAAACgPWrq/yw2zh2dxUoAOjY7hwAAAAAAAPKI5hAAAAAAAEAecawcdHKOiAMAAAAA4IvsHAIAAAAAAMgjdg4BAAAAAB1eY6enbJw7OsuVALR/dg4BAAAAAADkEc0hAAAAAACAPKI5BAAAAAAAkEc8cwjaIWfkAgAAAACQKZpDAAAAZFVZWVmzMYlEIguVAABAfnKsHAAAAAAAQB6xcwhypLGj49I9BwAA2pva2tpmY+rq6qK4uDgL1QAAQP6xcwgAAAAAACCPaA4BAAAAAADkEc0hAAAAAACAPKI5BAAAAAAAkEe65boAAAAAAIBMKZlZ1ehrG+eOzmIlAO2HnUMAAAAAAAB5RHMIAAAAAAAgjzhWLs3KysqajUkkElmohPaiqa3LAAAAAACQbXYOAQAAAAAA5BE7h9Kstra22Zi6urooLi7OQjVki91BAAAAAAB0FHYOAQAAAAAA5BHNIQAAAAAAgDyiOQQAAAAAAJBHPHMIAAAAAMhLjT1HeuPc0VmuBCC77BwCAAAAAADII3YOQQs09mkSAAAAADoPO4qAzk5ziLyl0QMAAAAAQD5yrBwAAAAAAEAe0RwCAAAAAADII46Vo9NzfBwAAAAAAPz/7BwCAAAAAADII3YOAQAAAACkoKkTajbOHZ3FSgDaxs4hAAAAAACAPKI5BAAAAAAAkEc0hwAAAAAAAPKIZw4BAACQVWVlZc3GJBKJLFQCAAD5SXOIDqWxh/554B8AAAAAAKRGcwgAAICsqq2tbTamrq4uiouLs1ANAADkH80hOoXGdhQBAAAAAAANaQ7RLmn2AAAAAABAZmgOkTMaQAAAAAAAkH2aQ2lWVlbWbEwikchCJQAAAAAAAMfSHCLj7BACAAAAoLNr7P/ANs4dneVKAJqnOZRmtbW1zcbU1dVFcXFxFqoBAAAAAHKpqQ9OaxwBuVKQ6wIAAAAAAADIHs0hAAAAAACAPKI5BAAAAAAAkEc0hwAAAAAAAPJIt1wXAAAAAACQj0pmVh339zfOHZ3lSoB8ozlEWjT2FxkAAAAAANC+OFYOAAAAAAAgj9g5lMea2u3T2NZVO4QAAAAAAKBj0xziuDSBAAAAAACgc9IcAgAAAABoRxr74HZjp/0AtJTmEAAAAABAB9Cax0QAHI/mEAAAAABAB2e3EdASBbkuAAAAAAAAgOzRHAIAAAAAAMgjmkMAAAAAAAB5xDOHAAAAAAA6qcaeRRTheUSQzzSHAAAAAADyUGONo8aaRhpN0Hk4Vg4AAAAAACCP2DkEAABAVpWVlTUbk0gkslAJAADkJzuHAAAAAAAA8oidQwAAAGRVbW1tszF1dXVRXFychWoAACD/aA6lmeMRAAAAAACA9kxzCAAAAACApJKZVbkuAcgwzaE0czwCAAAAAADQnmkOAQAAAACQEU3tQto4d3QWKwG+qCDXBQAAAAAAAJA9dg4BAAAAANAmnlMEHYudQwAAAAAAAHnEziEAAAAAALIuG7uNPNcIjk9zCAAAAACATqmpBpTGEfnMsXIAAAAAAAB5xM4hAAAAAABohl1IdCZ2DgEAAAAAAOSRrO8c2rBhQ1RXV8e2bduiV69eMXDgwBg2bFh065aZUrZs2RKvvfZa1NXVRffu3ePss8+OysrK6N69e0byAQAAtJV1EwAAkElZaw6tW7cu7r777nj55ZejtLQ0Bg8eHJs2bYrq6uro169fPPDAA3HLLbekLd9HH30U06ZNi6effjoGDBgQl1xySezYsSNeffXV6NWrV0ybNi1mzpwZXbp0SVtOAACAtrBuAgDInsaOiWvNEXHpvFdLc6Q7D/khK82h5cuXx4QJE6K+vj7mzp0bM2bMSC4uVq5cGaNHj47JkyfH6tWrY8GCBW1eePzv//5vjBkzJj755JP4zne+Ew8//HAUFhZGRMS7774bo0aNilmzZsXKlSvj5z//eRQVFbX5PQIAALSFdRMAQPvQVBMGOouMN4dWrFgR119/fSQSibj55pvjnnvuafD6JZdcEsuWLYtRo0bFwoULY8CAAXHvvfe2Ot/vf//7GD16dOzZsyeGDx8e//7v/95g0XTeeefFr371qygvL49f/epX8Z3vfCcWLlzY6nwAAABtZd0EANA5tabRZBcQ2VCQyZvv3r07JkyYEIlEIoqKimLOnDnHjRs5cmRUVlZGRMR9990Xb7/9dqvyHTp0KG644YbYs2dPRET867/+63E/TXf++efHjTfeGBERixYtil/84hetygcAANBW1k0AAHxRycyq4/6CdMpoc2jevHmxefPmiIgYMWJEFBcXNxo7ceLEiIg4fPhwzJo1q1X5li5dGmvXro2IiLKyshgyZEiz+SIiZs2aFYcPH25VTgAAgLawbgIAALItY8fKHThwIB577LHkeMyYMU3GX3311dGtW7c4ePBgvPjii7FmzZoYPHhwi3L+5Cc/STnf0KFD44wzzoiPP/44/u///i9+8YtfxNixY1uUDwAAoC2smwAASId0Hl/X2L0cd9e5ZKw5tGLFiti5c2dyfPHFFzcZ37t37xg0aFDU1tZGRMRTTz3VokXOH/7wh3jnnXdSztelS5f427/926iqqkrms8gBAACyyboJAICOIt3PT9KEyq2MNYdWrlyZvC4sLIzS0tJm55SXlycXOc8880w89NBDrcoXEXHhhRemlO/zRU5VVVUkEokoLCxMOScAAEBbWDcBAJAr2XiOUWtytKZppNHUchlrDr355pvJ6379+kVRUVGzc0pKSpLX77//fuzZsyd69erV4nxdu3Zt8pzu4+Xbu3dvvPvuu3HBBReklA8AAKCtrJsAACA16W5m5XtDqSBTN/78AacREX379k1pztFx69evb1W+M844I7p27drifF88XgEAACDTrJsAAIBcyNjOoV27diWv+/Tpk9Kc008/vcF4w4YNMXTo0JTmfvGc7rbkS1VdXV3KsUfbtGlT8nrr1q2tvk9bHdyzI2e5AQBypS3/jqP1vvjv3oMHD+awkvbFuqlx1k0AALRV/6n/0eI5uVozZnvNlJHm0N69e+PAgQPJcffu3VOad3Tcnj17Us75ySefZDVfKscvpGLIkCFpuQ8AAKkpXpDrCti+fXuDo8rylXVT6qybAADIlvawZszGmikjx8rt3r27wfiEE05IaV5bFh1fzJmNfAAAAG1h3QQAAORKRnYOFRQ07Dl16dIlpXlHn3d95MiRVuXMRr4vHnHQUvv27Yt33303zjzzzDj99NOjW7eG34bKysqIiHjllVdanaMpW7duTX7ybtWqVSmfbd4amX4v2crRmfJ0tu9/tvJ0lvfi+99+c2Qjj+9/+82RrTzZ+hnoTF+zzvRecv39P3jwYGzfvj0iIi644IKM5O5orJua1ty6KZOy+XdmU7L1Z5D87S9/e/gZzOevf77n9/Mnv5+//P3653v+XP/8ZXvNlJF/Xffs2bPBeP/+/SnN27dvX4PxySef3KKcn+fJRr7+/funHHs855xzTqOvFRYWpiVHKvr27ZvRPNl4L9n6enW2PBGd4/ufrTyd6b18zve/feXIZp4I3//2liObeT6XyZ+BzvQ160zv5Yty9f13lFxD1k3Na2rdlC2Z/juzKdn+s0H+9pX/c7n6Gcz1+5ffz19E/n798z3/5/z8yZ9Lufr5y+aaKSPHyvXs2TP5TYxIfdFxdFzv3r1TznnqqadmNR8AAEBbWDcBAAC5kpHmUEFBQQwYMCA53rVrV0rzjo4bOHBgyjm/8pWvZDUfAABAW1g3AQAAuZKR5lBExLnnnpu83rp1a0pztmzZ0mB83nnntSrftm3bUjoH++h8paWlKecDAABoK+smAAAgFzLWHBo6dGjyevPmzXH48OFm53zxYaWlpaVxyimntCpfIpGIbdu2tSjfySefHGVlZSnnAwAAaCvrJgAAIBcy1hwaPnx48rq+vj7ee++9ZufU1NQkr6+88soW5ausrGwwXrt2bYvyDR8+PLp169ainAAAAG1h3QQAAORCxppDQ4YMiZKSkuR49erVTcbX19fHO++8kxyPGzeuRfn69u0bl112Wcr5IiJ+97vftTofAABAW1k3AQAAuZCx5lBBQUFMmjQpOX722WebjH/hhRdi//79ERFx4YUXxqWXXtrinFOmTEk535o1a+Kjjz6KiIgzzjgjrrvuuhbnAwAAaAvrJgAAIBcy1hyKiLj99tvjrLPOioiIqqqq2L59e6OxS5cuTV7ff//9x7y+fv36qKioiF69esWUKVPi0KFDx8T8wz/8Q5x//vkR8ZdPt9XW1qaUb8aMGXHCCSc0/4YAAADSzLoJAADIti5Hjhw5kskETz/9dPLogalTp8b8+fOPiamuro6vf/3rERExfvz4eOqpp46JGTZsWLz22mvJ8X/+53/GhAkTjon73e9+F0OHDo2DBw/G6NGj47nnnjsm5oMPPojzzz///2PvvsOjqN63gd/phVQIoSSQIMQUhFAFFKWKIgoCIiJIBEEpgqAgigXFL4gFRKQoXUCKIFWUolIt1NAECQYSIJBAgIQkkP68f/BmfjvZ3WQ3md1NuT/XtdeV2T1zzpk9Oydz5pk5g6ysLLRp0wb79++Hvb1F42RlyuXLl1GnTh0A9x4uGxgYaOMakTWx/Ss3tn/lxvYn/gYqN7Z/2cZxU9nC/YVsjb9BsiX+/siW+PsjW6psvz+LH9n36tUL8+fPh4ODA+bOnYvp06dDNx4VHR2NZ599FgDw5JNPYtGiRQbzKXzFW05OjsF0LVu2xMaNG+Hu7o6tW7di7NixqrTnz5/HU089haysLLRs2RIbNmyoNAMcIiIiIiIqmzhuIiIiIiIia7LK0f3QoUOxY8cONGrUCOPGjUNERAQGDBiAzp07o0WLFsjMzMTkyZOxefNmeHh4GMzj66+/RuPGjeHh4YFBgwZh4MCBRsvr1q0b9u/fj7Zt22LmzJkICQnBCy+8gG7duiEiIgLx8fEYPXo09uzZgxo1alhqs4mIiIiIiEzGcRMREREREVmLo7UK6tixI06cOIHo6GgcPHgQycnJaNasGd588020b98ebm5uRa7fpEkTHD9+3OTymjZtin379uHs2bP4448/kJiYCGdnZwwdOhQdOnSAt7d3aTeJiIiIiIhIUxw3ERERERGRNVgtOFSgadOmaNq0qdXKCw0NRWhoqNXKIyIiIiIiKi2Om4iIiIiIyJI4aTQREREREREREREREVElYie6TzklIiIiIiIiIiIiIiKiCo13DhEREREREREREREREVUiDA4RERERERERERERERFVIgwOERERERERERERERERVSIMDhEREREREREREREREVUiDA4RERERERERERERERFVIgwOERERERERERERERERVSIMDhEREREREREREREREVUiDA4RERERERERERERERFVIgwOERERERERERERERERVSKOtq4AaePWrVvYs2cP4uPjcefOHfj5+aFFixZo0qQJ7OzszMorMzMT+/fvx5kzZ3Dnzh3UrVsXbdq0QXBwsGUqT6WmZfsXlpCQgBo1asDRkd1FWaVV+4sIjh8/jsOHDyM5ORmurq4IDg5Gu3bt4Ovra8EtoNLQcv+PjY3FH3/8gatXr8LBwQFBQUFo164d/P39LVR7Ki1L9v8AcPLkSbRo0QK1atVCXFxc6StMRGRl58+fx759+5CYmAgvLy+EhISgffv2Fju2vXLlCvbs2YPLly/D1dUV9evXR8eOHeHq6mqR8oiITJGfn4+EhATUqVPHYmXcuHEDu3fvRnx8POzs7FCvXj106NAB3t7eFiuTiKgwS4+RDSnX/Z9QuZaUlCSDBw8WR0dHAaD3Cg0NlY0bN5qUV25urkydOlV8fX3Fzc1NnnzySenTp4/Url1b7O3t5ZlnnpGEhAQLbxGZQ8v2L+zixYvy6quvipOTk1y4cEHbipMmtGz/H374QRo0aGAwHycnJxk6dKjcvHnTwltE5tCy/f/55x/p1KmTwXycnZ1lxIgRkp6ebuEtInNYsv8vkJ2dLU2bNhUAEhQUpE3FSVM3b96UDRs2yMyZM2Xq1Kkyf/58OXr0qOTn52teVk5OjmzatEm+/fZbzfMmsoTjx48r/9vCw8Olf//+8uijj4qdnZ0EBgbKggULNC0vLi5OevfuLXZ2dhIcHCz9+vWTxx57TJycnKRatWoydepUi+ybVLbFxsbK0qVLZdq0aTJ37lzZuXOn5OTk2Kw+KSkpMnz4cElOTrZZHci68vPzZfXq1RIWFiZRUVEWKSM5OVlefvllcXR0lJo1a0qfPn2kW7duUqVKFfHw8JA333xTsrKyLFI2lQ95eXly8eJFm9YhIyNDPvzwQzl+/LhN60GWY40xcmEVof9jcKgcO3v2rAQFBRn8wRd+/e9//ysyr7S0NOnQoYMAkLCwMFUw4M6dOzJo0CABIDVr1pQjR0S21noAACAASURBVI5YeMvIFFq2v66EhAQZOXKkODs7K+szOFT2aNn+77zzjkn51K9fX+Lj4620hVQULdt/+/bt4ubmJnZ2dtKsWTPp1auXPPbYY+Lr66vKp23btpKZmWmlLaSiWKr/L+z9999X8mFwqGzReuCTlZUltWvXLvb35OLiIklJSRbcMiJtrFu3Ttzc3ASATJs2TRWU+fPPP5X/ca+++qomAZt9+/YpeY4aNUqys7OVz86cOSPBwcECQJ5++ukyf4KAtGHt4KSIyE8//WTSsUHPnj01L5vKnvz8fFm3bp088MADSttbIjh05swZqVOnjgCQ3r17S1pamvLZpUuXpHnz5gJAWrduzYsNKyFrBCdFRE6ePCl2dnbF9n9Nmza1WB3Itqw1RtZVUfo/BofKqbS0NAkNDRUAEhkZKe+++67MnTtXPv/8c3nxxRfF09NT78e/du1ag3nl5eVJly5dBLh3hXhMTIxemtzcXHn44YcFgAQGBvLEgI1p2f4FEhMTZcyYMeLq6qq3LoNDZYuW7b9gwQIBIO7u7jJgwACZMWOGzJkzRyZOnCgtWrTQy6dJkyY2vdqRtG3/M2fOiLu7uzzxxBN6fX9mZqYqOFBwgo1syxL9vyGHDx9WBR4YHCo7LDHwWbx4sUn5DRo0yMJbR1R6v//+uzg5ORX5m/3ll1+U3/XUqVNLVd4///wjXl5eAkA6depkMNh08uRJpU995ZVXSlUelX3WDk4WePTRR03qy/fs2aNZmVQ2bdq0SZo0aaLX9lqfnE9MTJSAgAAlCHr37l29NFevXhUfHx8BIE888YTk5eVpWgcqm6wVnCwwcOBAk/q/7777zmJ1INux1hhZV0Xq/xgcKqfeeecdsbOzk5kzZxr8/MaNG9K9e3fVDz84OFhyc3P10n7++edKmuHDhxstc+/evUq6Hj16aLYtZD4t21/k3t1C48ePlz179khaWpoMHTqUwaEyTKv2v3btmnh7e0tkZKTRW7zXr1+v94904cKFmm8TmU7L/f/hhx8u9sRE//79lXweeOABzbaDSkbr/t+QzMxMadiwoXTs2JHBoTLGEgOf/Px8iYiIKHYwXb16dfnnn3+stKVEJZOSkqIM1J2dnYucwqagj7O3t5fDhw+XqLzc3FyJjIxU9pMDBw4YTTt48GAl3YYNG0pUHpV91g5OFjhw4ECx/bidnZ107dpVk/Ko7Jo/f77MmzdPLl++LGfOnFFd/Kn1yflu3bopea9Zs8ZousmTJyvpvvzyS03rQGWPtYKTBS5duqT0u0W9mjZtypkwKihrjJELq0j9H4ND5dDt27elSpUq8tZbbxWZLjs7W+/K/3379qnS3LhxQ7nSDYAcOnSoyDxDQkKUtH/++Wept4XMp2X7G6M7YGFwqGzRsv0/+OAD8fX1lcuXLxeZ1/r161X5dOrUqdTbQSWjZfv/+uuv0q5du2KvXtm5c6eSh6ura6m3gUrOGv2/iMj48eMlMDBQjh07xuBQGWOJgc/mzZsFgKxatcpS1SaymnfffVf57Xfr1q3ItCtWrFDSdunSpUTlFdyBDUAaNmxYZNr9+/cracPDw8vs1aNUctYOTurq1auXVK1alc+IJD2tWrWyyMl53TGCj4+PajrNwi5duqRM+eXn5ye3b9/WrB5UtlgzOFngjTfeECcnJ5s/04hsw1pjZF0Vrf+zB5U7P/74I3x9ffHRRx8Vmc7JyQmTJk1SvXf8+HHV8pIlS3D79m0AQO3atdGiRYsi8+zRo4fy9yeffGJOtUkjWra/Ma6uriWuH1mWlu2/bNkyfPzxxwgICCgyr549e6J58+ZG8yHr0bL9jxw5goULF8LevuhDgbp16yp/V61a1cwak5as0f//9ddfmDFjBhYuXAhvb+8S15W0l5aWhlmzZmH8+PF4/fXXDaapWrUq1q1bpzqei4uLw19//WU0388++wz16tVDnz59NK8zkTVlZ2fjm2++UZa7d+9eZPqnnnoKjo6OAIAdO3bg6NGjZpc5a9Ysk8t76KGH4O/vDwA4c+YMNm7caHZ5VLZ9/vnnSEhIAAA89thjqFOnjtG0gwcPBgDk5+dj4sSJpSr33Llz2LhxI0aOHIkqVaqUKi+qeCw1tv/qq6+Uv7t27QonJyejaQMDA5XxZHJyMhYsWGCROpHtDR06FMOGDUNAQADCwsIQGRlp0fJSU1OxYMECPP/880X2uVRxWWOMXFhF6/8YHCqHfvvtN7zzzjsm/ZPv1KmTMugB7nWcutatW6f8/eCDDxabX6tWrZS/t2/frpcfWZ6W7U/lj1btHxsbi+zsbLzyyismlfv4448bzIesS8v9/6233kKDBg2KzefGjRvK3w899JAZtSWtWbr/v3v3LqKiojBo0CDVPk9lgyUGPgcOHMD+/fvx5ptvwsHBQbO6EtnCrl27VP+zdMcthnh7eyM0NFRZXrNmjVnlnT17FidPnjS5PDs7O7Rs2bLE5VHZZovgZIHp06fDxcUFo0aNKnEeROZIT0/Htm3blOXi+r/Cadj/VR6WvvB43rx5SE9Px1tvvWXRcqjssvY50orY/zE4VA717t0bUVFRJqV1c3NDtWrVlGXdq76zsrJUB6GmRPSbNm2q/J2dnY2tW7eaVA/SjlbtT+WTVu3v7u6O5cuXF3mFgy7dq3D4O7IdW+z/x44dAwA4ODjgnXfeKVEepA1Lt/+ECROQnZ2NGTNmlLiOZDmWGPh8+umnAIBTp05h9erVuHTpkjaVJbIB3TvknJycEB4eXuw6umObDRs2lLg8wPyx1NatW5GTk2NWmVR2WTs4WeDatWv47rvv4OvriwULFuC3337DnTt3SpQXkakOHz6M3NxcZdnc/u/gwYPKXXZEJZWVlYVZs2bBw8MDa9euxdatW5GSkmLrapGVWfscSUXs/xgcKoeeeeYZs24XFxHlb90fZHR0NLKzs5XlevXqFZtXUFAQ7OzslOUjR46YXA/ShlbtT+WTVu1fq1YtdOzYsdT5kHVZe//Pz8/Ht99+CwD45ptv0KxZM7PzIO1Ysv13796NOXPmYNGiRfD09CxxHclytB74xMTEYNOmTQDu7d/9+vVD3bp1ERISgnHjxuHEiRPaVJzISv7++2/l74CAADg7Oxe7TnBwsPL3uXPnlOm2zS3PwcHBpOlsdMvLyMjAv//+a3J5VLZZOzhZYNasWcjMzMSVK1fw7rvvonPnzvDx8UGHDh0we/Zsnigli9Dt/wDTziXp9n8ASnW3HBEALF++HFevXkVaWhomT56Mp556CtWqVUOrVq3w6aef4urVq7auIlmBtc+RVMT+j8GhCi47Oxs3b94EcO9ksO5UBoWnGKlVq1ax+Tk6OsLPz09Z1p1KgcqeotqfKj4t2z8xMVH5u7hpMqhs0KL933vvPVy6dAnr16/HkCFDtK4iWZA57Z+eno5Bgwbh1VdfRadOnaxVRTKT1gOfL774Avn5+Xrv//fff5g+fToiIyPRvXt3XL58uWQVJrIy3bGNKeMaQ+lOnTpVovL8/f1NmpqxcHkcS1Uc1g5OAvcCjPPmzdN7PycnB7t378aoUaMQGBiIadOmGezviUqq8LmkmjVrFrsO+z/Skohg+vTpeu/n5+fj4MGDePvttxEUFIQ333wTmZmZNqghlUVanCOpiP0fg0MV3MmTJ5Xb3V599VXVg8d1b3sHoAr6FKV69erK3+fPn9eglmQpRbU/VXxatn/BlQ1eXl7o37+/JvUjyypN+2dmZmL48OH45JNPEBwcDB8fH0tVkyzEnPZ/4403YGdnh88++8xa1SMLM2Xgk5qaiiZNmqBOnTqqu8J1bdmyBc2bNy9zV7cRGVLwmwdKNq4BzBvb6I6lrFEelW3WDk4C9+4AjYiIQHh4OHx9fQ2mycjIwDvvvIPu3bsjKyvLrPyJjNHt/zw9PeHi4lLsOuz/SEtXrlxBzZo10bBhQ73fVoGcnBzMmDEDjz76KG7dumXlGlJZpMU5sorY//FMcQVX8EygatWqYfTo0arPCneOpj4oTjeduVc3kXUV1f5U8WnV/nfv3sWuXbsAAOPGjYOXl5cm9SPLKkn73717FwsXLkR4eLjyUOWjR4+iY8eOiIqKwt27dy1WX9KWqe2/fft2LFq0CIsXL4aHh4e1qkcWZsrAZ82aNYiOjsbFixeRnJyMTZs2oX///nrPort27Rq6dOmC2NhYq9SdqCQyMjJU02WXZFwDmDe20R1LWaM8KtusHZwE7t0Vum/fPpw+fRo3b97EmTNnMG/ePIN3i27duhX9+vVT3VVKVFLs/8jWAgICsGvXLpw6dQrXrl3DhQsX8N1336Fdu3Z6aQ8dOoSuXbsyQE6anCOriP0fg0MVWF5eHhYvXgzg3gOHC19NVPjhxKZEOwEGh8qL4tqfKjYt2//7779HRkaG8hwKKvtK0v6rV69GUFAQhg4diri4OL3Ply1bhmeffZYnFcoBU9s/JSUFL7/8MkaOHIn27dtbsYZkaeYOfKpWrYru3btjxYoViIuLwwsvvKD6/MaNG+jTpw/y8vIsUl+i0tJiXAOYN7bRLdMa5VHZZYvgpCFhYWEYNmwYjh49ip07d6JBgwaqzzds2IAvv/yyVGUQAez/qOwJDg7GwIEDsXv3bhw+fBjNmzdXfX7gwAGMHz/eRrWjskCrc2QVsf9jcKgCW7x4MeLj4/H000/j5Zdf1vu88FWkxqYUKUx3Pm2eJCy7imt/qti0av/s7GxMnToVTk5OWL58Odzc3DSsJVlKSdr/+eefx7Vr15CZmYmYmBgsXLgQjRs3VqX5+eefsWzZMktUmTRkavuPHj0abm5umDZtmhVrR5ZW2oFP7dq18f3332Pt2rWqgUx0dDSWL1+uaV2JtKLFuAYwb2yjW6Y1yqOyyxbByeJ07twZx48fx8CBA1Xvf/jhh2XupBSVP+z/qCxr3rw5/v77b7z99tuq9+fOncs74Ssxrc6RVcT+j8GhCuratWt499130aBBA6Mn8go/1NjUWyx1H+bm6elZ8kqSxZjS/lRxadn+U6ZMwYULF/Dll1+iVatWGtWQLKm07e/i4oKQkBC8/PLLOH78OGbNmqU66Jk1a5aW1SWNmdr+mzZtwvfff48lS5bA3d3dijUkS9Nq4PPss89i/fr1qgEQg0NUVmkxrgHMG9volmmN8qjsskVw0hTu7u5YunSp6m7QtLQ0bNq0SdNyqPJh/0dlnaOjIz755BNVgCgvLw8rV660Ya3IVrQ8R1YR+z8GhyogEVFOBvzyyy9GHyRe+EpSU3/Uuum8vb1LWEuyFFPbnyomLdv/r7/+wtSpUzFmzBiMHDlSqyqSBVli/x81ahQ++ugjZTk6OpoP9CyjTG3/Gzdu4NVXX8Xrr7+Otm3bWrOKZGFaXxzStWtXDBkyRFk+ePAg8vPzS50vkdaqVKmiel5WScY1gHljG92xlDXKo7LLFsFJU9nZ2WHu3Lnw9/dX3vv77781L4cqF/Z/VF58/PHHaNiwobLM/q/y0focSUXs/xgcqoAmT56MPXv24Oeff9abZ1hXvXr1VMu6D9Esim66kJCQklWSLMbU9qeKSav2v3r1Kp599ln07dsXM2bM0LCGZEmW2v/HjBmjHESJCOLj4zXLm7RjavuPGDEC3t7emDJlihVrR5ZmqYtDJkyYoPydnp6OlJQUTfIl0pK9vT2CgoKU5ZKMawDzxja6YylrlEdlly2Ck+bw9vbGq6++qixfunTJIuVQ5aHb/92+fRu5ubnFrsP+j2zB0dERb7zxhrLM/q/y0focSUXs/xgcqmCWLl2KTz/9FJs3b0aLFi2KTHv//ferlq9evVps/iKCpKQkZTk8PLxkFSWLMKf9qeLRqv1TU1PRtWtXNGvWDEuXLjV5agyyLUvu/56ennj00UeV5Tt37miaP5WeOe3/ww8/ICYmBu7u7rCzszP60j3wjY+PV30WHBxs4S0ic1kqOHzfffchLCxMWS58pTtRWaE7tjFlXAMAV65cUS3r/tbNKS8xMdGkKcEKl8exVMVgi+Ckubp166b8zX6cSku3/yt8jsgY9n9kK+z/Ki9LnCOpiP0fg0MVyObNmzF8+HCsWbMG7du3LzZ9ZGSk6jkDFy9eLHadq1evIicnR1lu06ZNiepK2jO3/ali0ar97969ix49esDHxwdr166Fo6OjdpUki7HG/q970sPcB9yTZbH/J0tfHHLfffcpf/v5+WmeP5EWHnroIeXvhIQEk6ZA1L2CODw83Kw77nTLy8nJQWJiolnleXp6qqa6ofLN2sFJc7EfJy3p9n+AaeeSdPs/Ozs7tG7dWvN6ERlSo0YN5dwn+7/Kw1Jj5IrY/zE4VEHs2rULzz//PBYsWICnn37apHWcnZ3xyCOPKMvHjx8vdp3o6Gjlb3t7e3Tu3Nn8ypLmStL+VHFo1f45OTno06cP0tPTsWXLFri6umpYS7IUa+3/BQfUbm5unLKyDClJ+4eGhpr00j2R5OjoqPqsfv36ltokMpM1goMFJ8x9fHzg7OxskTKISqtTp07K33fv3kVMTEyx6+iObR5//HGzyuvYsaNq2dyxVKdOnXgRTgVi7eCkuXTzrlGjhsXKocqhVatW8PDwUJbN7f8iIyP5OySrKugD+burHCx5jqQi9n8MDlUABw8eRI8ePfDZZ59hwIABxaY/d+4cjh49CgB47rnnlPcPHz5sUlkFOnTogKpVq5agxqSl0rQ/lX9atX9+fj4GDhyI2NhYbNu2zaQH4q5evbpEdSbtWHP/j4uLA3DvZJbuvPpkOyVt/3///dek12+//aasGxAQYPQzsh1rBYevX78OAJyylsq0Bx98UDXlZXFjm7t37+LkyZPKcu/evc0qr1atWqoL7UwZSx06dKjE5VHZZu3gpLkK+nGAfTmVnqOjI3r27Kksm3suif0fWVN+fr4yjSf7v4rP0udIKmL/x+BQOXfq1Cl07doV48ePx2uvvVZs+pycHAwePFiJcj7//PPw8vICAMTGxuL06dNFrr9hwwblb1PKI8sqbftT+aZl+48YMQJ//vkndu7cadKt1j///DO2bNlSonqTNqy5/+fk5GD37t0AgNGjR5u9PmmP/T9ZKzicl5ennNDu1auX2esTWYu9vT1efvllZXnz5s1Fpv/ll1+QlZUF4N5VnG3btjW7zFdeecXk8o4ePYr4+HgAgL+/P/r06WN2eVR2WTs4aa6//voLwL3ZQ5566imLlkWVg27/99NPPxV5t1xycjL++OMPAPd+g0OHDrV4/YgKREdHK88a4rFsxWatMXJF6/8YHCrHzp8/jy5duiAqKgrvv/9+kWnz8vJw8uRJ9OrVC3Z2dsqcyO7u7njnnXeUdEuXLjWax5EjR5QD2MjISE5fZmNatL8xycnJeutT2aJl+0+YMAEbNmzAr7/+isDAwCLzSk1NxeLFi/H888+rTsCQdWnV/gkJCbh7926x5S1atAhJSUno3bs3HnvssVLXn0rHkv0/lQ/WDA6uX78eKSkp8PHxQd++fUtSXSKrGTlyJGrWrAkA2Lp1q+puicKWLFmi/G2oLz116hSaN28OLy8vvPLKKwaPh/v27YsHHngAwL27gv755x+Tynvrrbfg4uJS/AZRuWGL4KQ5Fi9eDODexaHe3t4WLYvKFt2xvanj+oSEBHTo0AGenp7o3bs3MjIy9NK0bdtWueMtKSkJ27ZtM5rfd999p5T9yiuvlLkplahiK+j/Hn30UYSHh9u4NmQpWp4jqXT9n1C5lJCQIPXq1RMA4uLiUuzL3t5eAAgA+e6771R55eTkSLNmzQSAuLu7S1xcnF55+fn50rlzZwEgTk5OcuzYMWttKhmgZfsbMmLECCU9AFm3bp0VtopMpWX7T506VQCIg4NDsfk4Ozsr+dSvX1/y8/Nt9A1Ublq1/9ixYwWAVKlSRd577z25e/euwfKOHDkinp6e0qRJE0lJSbHWZpIRlu7/dV24cEFZNygoyDIbRGaLjY2VWrVqydixY4tNm5ubKydOnJCnnnpKHnnkEeX9I0eOyIIFC2TPnj1F9uUZGRlSv359ASBLly7VpP5Elvbjjz8qfdeIESMMptm7d6+S5rnnnjOYpl27dqrj4WXLlhlMd/DgQXF0dBQA0q1bN4Npzp07Jy4uLgJA2rRpI3l5eSXbOCrTbt68KTVr1lTG1deuXTOa9qmnnipyrHXy5Elp1qyZeHp6ytChQyU3N1f1eU5Ojqxfv16WLl1qcPyua/v27QJAatSoIYmJiSXbOCqXLl26pPQ9AKRx48YmjeGioqJU/d/kyZMNpouLixNPT08BIE2aNJHs7Gy9NDdu3JDq1asLALnvvvskPT291NtF5UfDhg2V39GAAQNMWufy5cvSvn178fDwkF69eun9ZvLz8+WXX36RRYsWyenTp4vM6+TJk+Ls7Czu7u7yzz//lHg7qGzTcoxcGfs/BofKoeTkZImIiFD9WE19eXt7y507d/TyTEpKkhYtWggAadiwoVy4cEH5LCsrS0aOHKmcRNy8ebMVt5YKs0T7i9zrTA8dOiQffPCBqqMEIDVr1pQlS5bIqVOnJCkpycpbTLq0bP+5c+eWKB8AMmXKFBt+C5WXlu3/wAMPqD6///77ZeXKlZKRkSEiIikpKTJr1izx8vKSAQMGyO3bt2212fT/War/N4bBobJHi4HPhQsXlBPZAKRFixYGB9Y5OTnyzDPPCAB5//33rb2pRKUyf/58cXBwEADyxRdfqE6GHj16VPz9/QWAPPnkk5KWlmYwj7Zt26r60UWLFhkt76effhJ3d3cBIGPGjFGdIIiNjZXQ0FABIC1btuTJ+QrOWsHJjz/+WPnMwcFBXn/9dYMX+pw5c0b8/PzEx8dHDh8+XPoNpDIvIyNDzp07Jxs2bFAuAtZ9vfjii7J7926Ji4szenHYgAEDVOsUdRxw4MAB5eRn3759VX1qUlKSPPzwwwJAGjRoIGfPntV8e6nsslRwcvny5arPX3jhBbl165ZePomJidKgQQNxcXGRrVu3arZdVLZoPUaujP0fg0PlUIcOHUp8Qnf48OFG801PT5fRo0eLq6uruLq6ypNPPin9+vWTunXrCgBp27Yt7xgqAyzV/tWqVTMpj4YNG1pxa6kwrdp/165dekFAU18ODg6SkJBgw2+h8tJy/1+1apW4uroabF9fX18JCAiQQYMGyaFDh2y0tVSYpfp/YxgcKlu0GvicPXtW73N3d3f54osvJCUlRXJycmTv3r3Spk0b8fT0lPnz59t4y4lK5rfffpNGjRoJAAkLC5P+/ftLp06dxN7eXry8vGTy5Ml6d2Poio6OlsaNG4uHh4cMGjRIcnJyiizv6NGjSkApKChI+vXrJ08++aS4uLiIq6urjB492uwgPZVP1ghOvvPOO3p9eePGjWXnzp2Sl5cnqampMm/ePPHx8ZEmTZrIv//+a9FtprLj66+/Nvn4YO3atQbziIuLk4cffliqVKki3bt3l9TU1CLLjI2NlW7duomdnZ3UqFFD+vTpIz169BAvLy9xdHSUF198UZKTky2xuVTGWCM4+e233+rlGxwcLOvWrZPs7Gy5c+eOrFq1SgICAqRevXry559/WmPTyUa0HiNXxv7PTkQERDpSUlKwa9cuxMbGIjs7GzVr1sTDDz+M0NBQW1eNiIg0lJycjF27diEuLg5ZWVmoWrUq/P39ERYWpjxDgYjKho4dO2LXrl0lWnf48OGYO3eusjx79my89957SE1NVaWzs7ODvb09fHx80L9/f0yYMAG1a9cuVb2JbC06OhoHDx5EcnIy3NzcEB4ejvbt28PNzc0i5Z09exZ//PEHEhMT4ezsjAYNGqBDhw58zksl8/vvv2PMmDE4efIkwsLC0Lx5cyQmJmLXrl3w8PDAuHHjMHHiRDg4OBhc/9ixY4iKisL58+fRp08fzJ8/H46OjsrnqampiIqKwpYtW/QehO3o6Ijc3Fw0adIEw4cPx8svv2y0HCItxcfHY9++fbh8+TLs7OwQHByMDh06wN/f39ZVIyuZPXs2Ro0aZVLatWvX4tlnn9V7Pz4+Hv3798exY8fQqVMnLF++HF5eXsrn2dnZGDFiBL777jvk5uaq1nVwcEBeXh5CQkIwdOhQjBo1Cq6urqXbKCITlOf+j8EhIiIiIqJKJjs7G3v37kVsbCxu3LgBFxcX+Pn5oXHjxmjUqJHqJCQREZWMpYOTSUlJ2L17NxITE5Geng4vLy8EBgaiZcuWCAwM1KQMIqKyKCUlBb///juuXLmC1NRUeHh4oFatWmjevDnq169v6+oRlRsMDhEREREREREREREREVUi9rauABEREREREREREREREVkPg0NERERERERERERERESVCINDRERERERERERERERElQiDQ0RERERERERERERERJUIg0NERERERERERERERESVCINDRERERERERERERERElQiDQ0RERERERERERERERJUIg0NERERERERERERERESVCINDRERERERERERERERElQiDQ0RERERERERERERERJUIg0NERERERERERERERESVCINDRERERERERERERERElQiDQ0RERERERERERERERJUIg0NERERERERERERERESVCINDRERERERERERERERElQiDQ0RERERERERERERERJUIg0NEVK7cuXMH0dHRtq5GuZWVlYVjx47Zuhqkgby8PBw+fNjW1SAiIiKqtG7evInly5ejb9++FlknOTkZTz31FDw9PfHEE08gKSmpNNW1CRHB2LFj4evri0aNGuHAgQM2qcf58+fxyCOPwMvLC/369UNGRoZN6kEElJ39ojILDg6GnZ0d7OzssHv3bquV2759e6XcpUuXWq1cImMYHCKiElmxYgUCAwNRu3ZtLFq0yKJl3bhxA0uXLkWPHj3g5+eHsWPHWrS8iiYjIwMbNmzAwIEDLHSlkgAAIABJREFUUaNGDbz22mu2rhKVUFZWFrZv345hw4YhICAAzzzzjM3qcurUKXz88ceYOXOmzepAREREldPMmTOVk2uWeu3YsUOv3Pz8fBw6dAhTpkzBI488An9/fwwcOBA//PCD0bqWZJ0C48ePx9atW5Geno7t27fjrbfeKtX3ZgvLli3DzJkzkZKSglOnTuG5556zST1efvll7N+/H2lpaVi9ejU+++wzm9SDCCg7+wURkaOtK0BE5c9///2HQYMGITc3FwDw6quv4tFHH0VISIhmZWRkZGDhwoXYuHEj9u3bh7y8PM3yrgySk5OxfPlybNu2DXv27EFWVpatq0QllJGRobTlb7/9hvT0dOWzgIAAq9Xj9u3b2L17N7Zt24ZffvkFcXFxAICoqCir1YGIiIgIALp27QoPDw/ExsZizZo1uHDhguZlFD5+njNnDt577z2kpKSYnEdJ1tF18OBB1fKRI0dKlI8tFd6GixcvIjk5GX5+fjatR3n8LqniKCv7BRER7xwiIrNFR0crgSHg3vRWx48f17SMW7duITExEREREfDx8dE078rg2rVruHXrFoKCguDs7Gzr6lAp3L59G1euXEFgYCCqVKlikzp07NgRVatWRY8ePTBv3jwlMERERERkC6GhoRgyZAg++eQTnDhxAoGBgXpp2rdvjwMHDuDMmTMGX6dOncLhw4exbds2zJ07F8899xwcHf/v+tnCwaGXXnoJCQkJuHXrFt5++22T6lmSdXQ1adJEtdysWTOz87C1wttQt25dm5wArwjfJVUcZWW/ICLinUNEZLZGjRrB3t4e+fn5AAAHBwc0btxY0zICAwPxySefAADatWtn1jzeBERERGDy5MkAgMcee4y3qZdjtWrVUtoyKioKDz74oNXrsH79elSpUgVxcXHo06eP5sFgIiIiopLy8PBAz5498fXXX6vef+6558w6bho+fDiOHDmC9u3bIz09HdnZ2arPCy7ScXd3x6RJkzB9+nTk5OQUmWdJ1tH1+eefIykpCQcOHEDbtm3x+eefm7yupWVmZsLV1bXYdIMGDcKhQ4ewZs0aBAUFYeHChVaonb6FCxciKioK//77L7p164YJEyaYtb6p20tkitLuF/w9EpFWeOcQEZktLCwMc+bMgb+/P2rXro0FCxbg/vvvt1h5jRo1sljelYHWgTuyHVu1pY+PD5ycnBASEoLRo0fbpA5ERERExjRo0EDvPTc3N7Pzad68OcaNGwdA/84hXa6urqhevbpZeZdkndq1a+PXX39FWloafvnlF9SoUcOs9S0lPT0dPXv2NCmto6Mj5s+fj9TUVJw4ccImFzoBQHh4OA4ePIjbt29j1apVZt2Rv23bNsyYMcOCtaPKpjT7hTn7HxFRcRgcIqISGTZsGJKSkpCQkIBBgwZZtKySDOzo/7i4uNi6CqSRstCWderUsXUViIiIiFQ8PDw0y+ull14CAL07hwpzcnIyO++SrFMWjRgxAmfOnLF1NawiMTERAwcOLPb3QGQtlWn/IyLLY3CIiIiITFZRTmoQERFRxWFvr92pjaCgIAQHBxd551BltmLFCixfvtzW1bAKEcHAgQNx/fp1W1eFCEDl2v+IyDr4zCEiIiIiIiIiov/v+++/LzNTuJUlx44dw7Bhw2xdDat59913sXPnTltXgwhA5dv/iMg6GBwiIiIiIiIiIvr/HnroIVtXocyJj49H9+7dkZGRYeuqWMXixYvxySef2LoaRAAq3/5HRNbDaeWIiIiIiIiIiMowEUFMTIxNyj5x4gTatWuHS5culTqvmJgY5Ofna1CrksvNzcV///1n9POZM2di6NChVqwRVXZF7Rda7n9ERIUxOEREpXb9+nXcuHHD1tUgIiIiIiIqkblz56J9+/a2roae06dPY+LEiQgODkavXr1MWufYsWMYOXIkmjdvrnp/9+7deOGFFxASEgI3NzfUrl0bXbt2xapVqyAiBvPasmULWrVqhfj4eNX78fHxsLOzU71eeuklg3kkJSXhq6++QsuWLREaGoqbN2+atB0F9u7di+HDhyMiIgJeXl5wdXVFnTp10L17d3z33XcmPx/q0KFDGD16NGrXro0xY8YYTNO/f3+MHTtW70T9Rx99pLe9u3fvBgC99w29jE0H9swzzxhM/+GHH5r8/RQlJiYG48aNQ2BgoOr9xMRETJs2Da1atUL16tXh6uqKxo0bY/bs2UaDFHv27MHAgQMRHBwMFxcX+Pv7o1evXjhy5IhZdYqLi8PkyZPRvn171KhRA87OzvDy8kJISAiioqLw22+/lWhbz58/j3HjxiEyMhI+Pj5wdnaGn58fIiMj0b9/f3zxxRfYuXMnYmNjcf36dSQnJysvY9v8zz//YOzYsXrf37Vr1zBx4kSEh4fDxcUFfn5+6Nq1q1l1N2W/KMn+FxgYaPA3FRwcrJf//v37i/zdxsXFFbsd+/btQ58+fTBt2jTV9zN48GBUq1YNVatWxSuvvILs7GyjeZw8eRITJ05E8+bNUaNGDbi4uCAwMBAdOnTArFmzcOvWrWLrYQ07duzA0KFDERERAV9fX7i7u6N+/fro3bs31qxZg7y8PFtXkch8QkRUAnfu3JFVq1ZJt27dxNHRUdauXWuxsi5cuCAAlFe7du2UzxISEmTChAnSqFEjcXd3Fw8PDwkLC5MRI0bIkSNHzConNzdX1q9fL/3795fQ0FDx9PQUJycn8ff3l0ceeUSmTJkit27dMjm/xMRE+fTTTyU0NFT+/PNP5f3MzEyZPXu2tG7dWqlzy5Yt5ZtvvpG8vDyz6iwicuDAARk+fLhERESIl5eXeHl5SevWrWX69OmSkZGh9/09/PDDZpdhqujoaBk7dqxERkaKj4+PODs7S82aNaVLly4ye/ZsSU1NNSmf5ORkmT59uoSFham+u7t378qiRYukS5cuEhAQIM7OzhIUFCRvvPGGpKSkGMwrJiZG3njjDWnYsKG4urqKl5eXtG3bVtasWVOibdyxY4e8/vrr0rRpU6lZs6Y4OzuLn5+fNGrUSF577TX57bffSpSviMg///wj48ePl8jISPH19ZUqVapIkyZN5MMPP5QbN26IiKjaMiAgwOS8t2/fLkOGDJHw8HDx8fERNzc3ue+++6RXr16yevVqyc3NNSmfXbt2qeoQFRVVkk0tUlZWlqxatUrat28v06ZNU332yy+/yAsvvCD33XefuLq6ip+fn/Tt21f+/fdfg3ndunVLPv30U2nTpo14eXmJq6urREREyNSpUyU7O9vkOmndP+jmu3z5cunevbvUrl1bnJ2dxcPDQ+rUqSOdO3eWsWPHyooVK+To0aNy+fJluX79uvJKS0szuzwiIqKKaMmSJarjEwCyZMkSs/IYOnSoapxRnKCgIFV5Wq5z5coVmT59ujRt2lSVvmHDhkbXSUhIkJkzZ0qLFi2U9EFBQSIicvPmTenVq5fed1R4jJWUlKSX75EjR2TVqlWyatUqVXo/Pz/l/YLX33//rayXkZEh33//vTzxxBPi4OCgWvf69esmfV/nzp2Tdu3aCQAJDw+X9957TxYvXixff/21PPjgg0p+wcHB8vvvvxvMIzY2ViZPniz333+/qg7dunUzmP7HH3+UVatWybBhw1Tpe/furbe9Bd/Xli1b5LHHHjP4vT755JMyZ84cOXDggMHyfv/9d/nggw/Ew8NDWeeFF16Qf/75x6TvyJCUlBRZtGiRtG/f3uDvbcmSJaryCr/69eunyu/y5cvStWtXo+mdnJxk69atxdYrKytL3nzzTXFychIA8sgjj8j06dNl6dKlMm7cOPH29lZ9B1lZWSZtb15ennzwwQfi6OgoVapUkbFjx8rSpUvl888/19uHjL0uXLig5FewL7Vq1crg9/f999+Ll5eX0by++uoro3U1d78oyf63du1aefvtt/XqWNAf6Lp+/bosXbpUJk+eLBEREUV+L7pSUlJkzpw50qhRIyXtpEmTREQkLi5Or68DILNmzTKYz9ChQ8XBwUF69+4tc+fOlaVLl8p7770nwcHByro1a9aUTZs2Gf1eS0K3jrt27Soy7b///ivt2rUTDw8PGTlypCxatEgWLFggI0eOFE9PTyWf5s2bF7vvFvRpJfk/RWQJDA4Rkcny8vLk119/laioKNU/QAA2CQ5t2rRJqlSpYvSgzN7eXl5//XXJyckptoy///5bORjy9vaWMWPGyMKFC2XWrFmqf95Vq1aVPXv2GM0nLS1NVq5cKd27dxdHR0e9g40jR47oDUx0X3369DE5QHTjxg0ZMGCAABB3d3eJioqS2bNny6JFi2TUqFHi7e0tAQEBsnjxYlUZlggOJSYmSs+ePZWDzvHjx8uCBQtk3rx58vjjjytlV69eXX744QeDeWRnZ8tPP/0kzz//vLi4uOh9dwcPHpR69eoZ/e7Cw8Pl5s2bSn6ZmZnyxhtv6B10677effddk7dx7969ymC7Xr16MnHiRFmyZInMnDlTr73btWsnJ06cMDnvO3fuyBtvvCH29vbi5OQkzz77rHz11VeyZMkSeeutt6RmzZri4+Mja9asUdXflOCQlgeyIpYNDh08eFDGjBkjfn5+eoOMxMRE6dSpk9G29PDwkEOHDqnyW7x4sfj4+Bhdp0OHDiYFiLTqHwo7ffq0NGzYUABImzZtZNasWbJo0SIZNmyYuLm5FTuItURgjoiIqDwqbXAoPz9fgoKCykRwaMaMGdKgQQOpX7++2NnZqdIbCg7duXNH2rVrJ/b29nrfQVBQkFy9elUaNGggAMTR0VFq1aolzs7OBo8tGjduXOTFLsWdaC6wb98+CQkJkfDwcINlmRIc2rx5s3h4eIiDg4PMmjVL8vPzVZ9nZGSovk9HR0e9AMWbb74pDRo0MHii2lhwqEDh31TBMakx+fn5qnFPQZ1MvQArKipKAEiXLl1MSm/M4MGDVWOpwr+3Dz74QFl2dXWVoKAgg4GiZcuWici94/MaNWoIAHFzc5Pg4GDx9fXVS1+tWjW5ffu20Xrl5ORI586dlfRvv/22Xppz586pxgFDhgwpdnvz8/PlxRdfFADi4OAgBw8eVH2em5srL730kl59J06cKDt37lRed+/eFRGR1157TerVq2dwDCkiMnnyZAEgdnZ2UrNmTalZs6ZeOgcHBzl+/LheXUu7X5i6/xVYu3atWevcuHFDbwxSODj0999/S//+/Q2OVSZNmiTp6enywAMPGPz99e/fX5VXcnKyNG3aVDw9PQ0Gd+/cuSNt27ZVfa/r1q0rdrtNZWpw6NChQ+Lr6yvh4eFy8eJFvc//++8/qV69upKXv7+/xMTEGM2PwSEqaxgcIiKTpKenS8OGDSU8PNzgVTLWDg79+OOPBgdAhl7PPfdckQfl27ZtUw7OatSoIXFxcXppxowZo+Tn7u4uZ8+e1UuzdOlSCQ0NNXigtGvXLtm2bZu4u7sLAPHx8ZGgoCCD2zBnzpxiv5PExEQJCwtTBnGxsbF6aZKSklQHUwUvrYNDf//9t3IwNHHiRL2T7Xl5edK6dWtVHb799ltVmrlz50q1atUMtt+uXbtk69at4urqqhwUBgQEqAYOBa/BgweLyL0roAquJnR0dJTAwECpVauWwfz/+OOPYrdx3rx5SvBnyJAhkpmZqZfm8OHDct999yn5VqlSRTZv3lxs3mlpaUo71a1bV44ePWowTUHwTfdVXHBI6wNZEcsEh/744w8JCQkx2D6TJk2S2NhY5coxOzs78fPzkzp16uilvf/++yUnJ0dyc3NlyJAhSnp/f38JCgpSBfAKXlOnTi2yblr1D4UdPXpUCc4988wzekHho0eP6vW1gYGBqkFsaa4oJSIiqkhKGxz6/vvvlXGGqSwVHNINgkyZMkWV3lBwKDc3V06ePCkHDhxQTpLrHttFRkZK/fr1ZcWKFZKRkSEi947Pt27dKqGhoXrf20svvWS0/rrpijrRrLsNf/zxh14ZxZ0E3759uxLgWLRokdF0H374oSrf2rVrq8YiuvUYOnSoKq3WwSGRexcCFt7WkydPFrueiCgn1Iu7g6E4Bw4ckLNnz8q0adP06jJlyhRxcnKSIUOGyL59+5QxcmZmpup4FoCEhYXJgQMHxNPTUzp16iR79+5VysjPz5dff/1VNY4AIF9//bXRek2fPl2VNiEhwWC6zz77zKzvTzffAQMGGExz9+5dqV+/virfQYMGGU0rInLixAm9sfoXX3yhjAd1gyYHDx7U27cHDhyol3dp9wtT978Ct27dMnudZs2aqdYpHBzasmWL/PLLL/LDDz/ojccnTZokgwYNkvbt28vWrVtl+PDhqs/HjBmj5JOTk6PcmbVixQqj9Tl58qQqDxcXFzl37lyx22EKU4JDFy9eFB8fH3F1dS2y3NmzZ+vtP8bufGNwiMoaBoeIyGw3b94Uf39/1T8/awaHmjZtKrVq1ZKIiAhZuXKlJCUlSWZmpsTExMiUKVMM3k308ccfG8z77t27qm0ZOnSo0XRVq1ZV0j377LMG04iIpKam6h18/u9//xNXV1d55JFH5K+//lLWuXz5st4UBEFBQXpXxulKS0tT7mKoWbOmXLlyxWja1NRUVcAC0DY4FB0drdyZ8f777xtNt3TpUlUd3NzcVAe/p0+fltOnTxu8G+yDDz4QFxcX6dy5s2zatEnS09OV9VauXKm6qqvgKq2IiAipV6+e/PDDD6pAztmzZ1VTUAD3pogoiu7A8PHHHy+ybeLj46V27dpKekdHR9m9e7fR9Lm5udKhQwcB7gUVTp06ZTRtdna2XpCtqOCQJQ5kRSwTHEpISJCjR4/K4cOHJTw8XJX/0KFDJTg4WMLCwmT+/PmSmJiorBcdHa36vgHIypUrpV+/flKlShWZPn26JCcnK+lv3rwpgwcPVqX39/c3ereelv2Drtu3b0tAQIAA94JXxgJyCxYs0OvLDAWniIiIKjtDwaGvv/5aNR2r7uvatWty4cIF+f3332XcuHFKIKIsBId0nTp1SpW+qGnlRO6NLQp/Dz169FCCQoWlpqZKZGSk3jqF774oYO6J5gKFTyIXdRI8Li5OuaDuySefLDLfw4cP69Xd0IVWIiI//fSTKp0lgkMioro7pvAJcWMK2vm+++4zqQxTFT5OrlWrltHvJz8/X7mjveDl4eEhw4cPN3qsvHr1alX6xx9/3Ghd2rRpo0pr6M4akXuBFt10n376qdE8U1NTVVPRFRVInD9/virf2rVrG01boOBizIKXu7u70TtX9u3bp0rr4+NTbP7m7BciJdv/zF1HN3ABGJ9WTkRk/PjxqrQPPvigPPXUU6qZW+bMmSPVqlWTBx54QJXXxx9/LMC92T+KGl+LiGqqTED/DqSSMiU4VLA/Dx8+vMi8UlJS9O42W7BggcG0DA5RWcPgEBGVyLPPPqv6x2fN4FDBQMHQ3Rsi957bonuiFoA4OzsbPAG7fft2VbqePXsarceTTz6pOjAsSuE5qu3s7GTChAkGD6xTU1P16nvs2DGjeb/yyismHQAXKHyCWavgUGpqqnI3TuPGjYu8OyspKUnvyqv169cbTPvCCy+o0jk5Ocny5cuN5l34iiQPDw958MEHjT6D6OLFi8o81wXpjU09eOrUKeWOJTs7O6PPtdG1fv16VX38/f3l2rVrBtNOnTpVSVdUcK3Azp07VXkXFRyyxIGsiOWfOVR44AZARo4cafT3VXiqPQ8PD6latarRQFteXp7efNq6AVtdluofdK9yrVOnjtF0OTk5UrduXVUd5s+fX2TeRERElZGh4FBJXmUtOHT16lVV+uKCQ3l5ear07u7uxT6jMCYmRnVsDBi/e8jcE80FCt+hVNRJ8D59+ijp9u3bV2zeugENR0dHuXz5ssF0f/31l6oOlgoOFT5e9/LyKnK6NRGRd999V4B7F8VpqXBARvfuH0MmTZqkSv/QQw8VOcbLzMxUxkrAvQsXjWncuLEq723bthlMd/78eVW60aNHG81z5cqVqrQ7duwwmjY1NVU13Z6dnV2xzzQqPAtHcc9VKjwFuqGZG3SZs1+IlL3g0Ny5c1Vp/fz8THoG661bt5QLQj/88MNi0xcOQrm6usqdO3eKXa84xQWHduzYoXxe1AWfBVq2bGnS/xMGh6issQcRUQlUq1bNZmW7ublh2bJlcHFxMfh5REQEli5dqnovOzsbX331lV7ajIwM1fLVq1eNluvn56f8fefOHdy6dcuktAAwbtw4TJs2Dfb2+t2ul5cXevTooXrvxIkTBvP966+/MH/+fACAr68vXnzxRaN1KPDQQw8Vm6Yk/ve//ynf1/jx4+Hg4GA0rb+/P5544gnVe7Vr1zaYNigoSLX8/vvvY8CAAUbzfu6551TL9vb22Lx5M7y9vQ2mr1OnDh5++GFlOT09HbGxsQbTvv/++8jMzAQAtGnTBqGhoUbrUaBnz5549NFHleVr167hiy++0EsXHx+PDz/8EADg4OCA1157rdi8TW3LnTt34tdffwUA9O3bt8i03t7eaNasmeq9FStWmFSOJRRu/44dO2L27NlGf1/du3dX9QXp6en4/vvv0bBhQ4Pp7e3t9X4zx48fN5jWUv3DypUrlb/r1KljNJ2joyNeeOEF1Xvx8fFG0xMREdH/GT9+PHbu3GnwtX37dmzYsAFff/01evfubfAYvSxwdHQ0K33h7ahevTo8PDyKXCckJARRUVGq9zZt2gQRMavsopi6HSdPnsTatWsB3BsrtG3btth1Vq1ahYcffhhNmjTBqlWrEBAQUKo6lFbnzp1Vx9a3b9/GokWLilxn9erVAFDkmKcknJ2dVctFHXcC0Dt+DgkJKXKM5+Ligvvuu09ZTkxMxN27dw2mHTJkiPJ3WFiYajymy87OTrVc+HhcV3R0tGo5JyfHaFovLy+0bNlSWRYR3Lhxw2h6AHrbHhERUWT6Bx54QLV87dq1ItNb6zdpKW5ubqrlvn37wsfHp9j1lixZorRraGgokpOTi3zVq1dPtX5mZiYOHTqk3YYYMXv2bAD3fpP16tUrtp7333+/av2///4b2dnZFq8nUWmV756IiGzGlgcykZGRxQannn76abRv3x67d+9W3luxYgW++uor1UFep06dEBwcjLi4ONjZ2WHw4MFG8zR0oOrr62swraUOJKdPn6783a1bNzg5ORWZLwC4u7sXm8ZcycnJSrDN0dERvXr1Knadb775BkOHDsXly5cxfPhwtGrVymC60g5ifH19UaNGjSLXiYiIUP02Lly4oBf4+e+//7Bx40Zl2dgAxpAhQ4Zg7969yvKcOXPw4Ycfqg6gv/76a+VgsU2bNvD39y82X1Pb0tCBbFHuv/9+1QF2wYFs4bawBnPb39XVFfXr18fp06eV98LCwopcp/D+eOHCBYPpLNE/3L17FzExMcpyUYNY4N4JhmnTpinL169fLzI9ERER3RMREYHOnTsXm+61117DkiVLivw/X9ENGDAACxcuVJZv3bqFc+fO6Z3stLQNGzYofz/44IMmrdOoUSPs37/fUlUqkQkTJqgu0Jo1axZGjRplMNBy4MABxMbGok2bNggJCbFmNfUYG9uas056erpe0AAARo0ahZYtWyIhIQFdu3Y1Oq4pHJTMy8szWvbt27dVy0lJSUXWtWHDhqrfiimBDHMUzq+owFZFVPgCWWO2bNmi/N2vX78SlRUTE6O6IFNr2dnZ2LFjB4B7v8nCFzCaIisrCxcvXkSDBg20rh6RphgcIqJyx9gdQ4W9/PLLqgBAamoqTp48iSZNmijveXl5ITo6Gjt27EBoaCgiIyON5mfOgaq5TDmQTE5OVgUrmjZtqln55vr555+VwMYDDzxgUtCiTp062LZtm+Z10WIQk5aWppdm27ZtqjY3Z7DWs2dP2NvbIz8/H8C99ty/fz8ee+wxAEB+fj4WL16spNeyLSvjgay5vwFT2h+wTP+QmpqqWjZlEKtL60EsERERAVFRUfjoo49sXQ2badu2Ldzc3FR3fcTFxVk9OLR161bl77p161q1bC317t0b9evXV2YnuHDhAjZu3IjevXvrpV21ahUAmDQjhKWV5ALQwuvcuXPHaNrWrVsb/SwmJgaLFy/GsmXLVO8XdQdb4bvEoqOjMWjQIKPpq1atqvxds2ZNg0Gs0ih8517BWJD+T25uLg4ePKgsL1u2DLVq1TI7H0sHUo8cOaLMIFK9enXVzA/mMOUCUCJbY3CIiCqsp59+GnZ2dqoDyhMnTqiCQ8C9k62Fp5kqkJWVhY0bN2Lx4sXKNF0FtJxqwZQDyb1796pOOAcHB2tWvrnK0sDNUoMY3Tt/AMDT09Pk/D08PHD//ffj33//Vd7bt2+fEhw6duyYatoxLduyMh7ImvsbMGcQq3X/4O/vDycnJ+WOoYsXL+LmzZuqwaquwu/rTt1BRERE2rC3t0eXLl1Ud/dWJg4ODggLC1NN01XUFLmWcubMGeVvc469yxoHBweMGzcOw4cPV96bMWOGXnAoPz8fP/zwA5ydnYudCrq8MGeMnJeXh02bNmH27Nk4deoUXnzxRcyfPx9PP/20SesXnhp7y5Yt+Oqrr/Tu6C+g+5vu2rWryfUk7SQkJKguhG3YsKFeO5YFuv8LMjMzTboLlai8YnCIiCosb29vBAUFIS4uTnmvuHmFC8THx2PevHlYtGgRGjVqhEGDBsHHxwc//PCDhWpbvD///FO1bMsBU0UZuBUwNIhJSEhQLRc3/VdhjRs3VgWHdJ9XY8m25IGs+cwN9Jamf7C3t0dkZCQOHz6svLdp0yajVzkWPjFT+NldREREpI0mTZpU2uAQcO8uDN3gkLWfw5SVlaW6m9vYs2vKi5deegmTJk1Spgv/888/cfDgQdV0ebt378bVq1fxzDPPGL1QqCLKzs7GkiVL8Omnn+LOnTt4//33sXXrVri5uanG7sXp2rUrQkNDcfbsWQCqlqEJAAAfKklEQVT37nZbvXq10anKCp4z6uDggDfeeKPU20Hmu3nzpmq5qGe62pJuPdPS0pCRkYEqVarYsEZEllM2n7pIRBVGwbM6TH0tXbpU0/ILT4tVcEeFMadPn8aAAQNQv359xMTEYOfOnfj999/x4osvan7bubkKHzhpOa2duXSfiVTeB27GFA4kFp7TujiFn4ule4BpybY0dCBL2tCqfxg7dqxq+YsvvjD6GygYxAL3HvJakmkCiYiIqHgjRoxQTUld2RS+WKm4Z7xqrfCxd+GTyOWNq6srRo8erXpvxowZquWyNKWctezYsQMNGzbEsGHD0LZtW8TExGDkyJElGmvb29vju+++U/12R48erUznp+vXX3/FX3/9BQCYMmWK3jN/yToKz9gQHx9vo5oUrbzUk0gLDA4RUYXm5eWlWjZ2RVZaWhqGDx+ORo0aYceOHfj555+xfv16vSnobKm0wQot6Q7WyvvAzZjCU4+lpKSYtb63t7dqWXfAY8m25IGs9rTuH/r166eaOuT06dMYP368XjoRwf/+9z8A96aT+/LLL0u2AURERETFKDytdUmeA1IahZ9hqnuBTHk1YsQIeHh4KMs//vgjLl26BODe3TM//vgjfH190a1bN1tV0aqmTp2KJ554Av/99x/GjBmDZcuW6Y3XzdWqVSv88ccfaNSoEYB7z+lt1aoV5syZg//++w/x8fFYsGABnnvuObi4uOCrr77ChAkTtNgcKoHCQehTp07ZqCZFKy/1JNICg0NEVKEVHmQYeo5KbGwsWrdujW+++QbVqlXDn3/+iS5duliriiYrPLXD+fPnbVQT9fd64sQJm9XDkvz8/FTLulPplTY/S7YlD2S1ZYn+wc7ODitXrsQHH3ygBCG//PJL9OzZE3v37sWVK1dw4MAB9OzZE/v27UPr1q2xa9cuq5+kISIiospDd0o3Hx8fhIWFWbV8b29v1cVZJ0+eNPvirLLG19cXr7zyirKcm5uLWbNmAQC2bduGW7duoW/fvnBxcbFVFa1m9uzZePfddyEiqF+/Pj777DPN8m7UqBGOHj2KgQMHArh3Id5rr72GkJAQBAcHY/To0Xj88cdx7Ngxvbu5yLoK35G4ffv2EuVT1DNjtVBe6kmkBQaHiMiigoODISImv1566SVNy9cd5ABQzfEM3Lsb5IknnsDp06cBAJ9++qneVHRlha+vr2pZd05wa9MNdNy8ebNCBiBCQkJUy+ZevZienq5a1h1gW7IteSCrHUv2D/b29vjoo49w+PBh5a6yjRs3ol27dggICEDr1q1x+vRpzJs3D/v27UPdunU1KZeIiIhM9+KLL6JPnz62roZV6E7F9cgjj8DOzs6q5dvZ2aFx48bKcm5uLlasWGFWHiKCXbt2aV21Uhk7diycnJyU5QULFiA9Pb1STSl35coVvPnmm8py3759Vd+JFn777Tds2rQJ3377LZKTk3Hq1Cns3r0bhw4dwo0bN7Bq1SqrBzxJX0BAgGq8ev78eezdu9esPHJyctC2bVscPXpU6+opdPsiAFi3bp3euaXixMXFISIiwuxnFxNZG4NDRFShJSUlKX+HhYUhICBA9fl7772H//77DwDg5OSkmuqprCl8Unr79u3IysqySV0iIyNVyyV5VtTvv/+uUW0so0OHDqrlM2fO4Pr16yavf/HiRdVyx44dlb8Lt+Xhw4dx5cqVEtRSHw9ktWPp/iErKwsTJkxAmzZtcObMGcTHx+PAgQPYs2cPzp8/j5iYGAwbNkxvikMiIiKyvAsXLmDVqlVo3bq1raticWlpacoxDwAMGTKk2HVyc3M1r0e7du1Uy5999pneBVdF+fLLL7Fu3TpN6lI4OFbS7Q0MDET//9fenUdFcWV/AL/d7MIAgiJGBSFGJUCQ0XGNQKKo4zJG5aiIJy6MKKJDUKPGDcElIzEyxigCorZGnSgjoyjGQRCVcc+4MWeMuOAguIEiCoJN9/394Y8eqqu76WYRI9/POXUO1f3eq1tLN6/qvqoOClLNP3v2jDZs2ECpqank6upKffv2rVecvwZbt26lV69eqeZ1DXpSf7yhPjIyMmjUqFG0a9cuCgkJIXt7e3J3dydfX1/q0aOH6Gki74K6HI/6XDt4E9cX1L9TFyxYQMysd/0lS5aQQqGg3/72tw0dmoq7u7vgkYelpaW0atUqvetXVVXRxIkTaeTIkQ2eCAVoaEgOAcA7S6lU0rVr11TzU6dOFbxfUVFBMplMNW9vb6+z41iXjmpD6tevn2D+2bNnqhFnb5r6iVtCQgIVFBToXX///v307bffNnRYDWrw4MGC40GhUBh0slnzbipPT0/q3Lmzal59XyqVSkpISKhHtP+DjmzDaOzvB7lcTqNHj6by8nJKS0ujrl27kpOTE/Xs2ZN8fHzIxcWlzrEDAABA/X311VekUCjI39+/qUNpdIcOHSKFQkFEr++eHz58uMZypqamqr+LiooMuqCrjylTpgjm8/Pz6YsvvtCrbmZmJq1Zs4aioqIaJJaa60pEBg0SUzd//nxBsmnZsmVUVlbWLO4aIhI/geHBgwday6r/XmrNpJImP//8M40cOZICAgLe+d9uqsvnz9zcXPX3gwcP6NmzZ1rLVlZW0o0bNwSvNfRnnIgEyVIiorNnz9Jf/vIXveru3r2bvvnmG1q2bFmDx1WTVCql8ePHC16LjY2ls2fP6lU/LCyM/vWvf9G8efMaIzyABoXkEAC8s06cOEFlZWVE9PrCbmhoqOD9W7duCUaiFRUV6RyBY2hHtaH5+vqKfjNp6dKlOjt4ROJH61VUVNQ7lsDAQEFH8/nz5xQcHKzXCKacnBwKCQl565ND9vb2olGT27Zt06tubm6u4HeEvvrqK8H7nTt3Ft19tXbtWtHdRurUR05q2pfoyDaMxv5++PzzzyktLY22bdvWLJ4zDwAA0Jga+m7ntLQ0+vHHH8nR0VF0V7a2ZepzEdXQOi9fvhTMG9qP1/fugvXr1xPR67tl4uPjRb+PWc3Ozk71d2VlpWAgHtHrR/KeP39eVE/f9fD09BRd4E9KSqLw8HCd+zg9PZ1GjhxJa9euFf1uqKExVKu5rkREFy5cEJU5c+YMlZaW6myHiMjNzY1GjBihmq/eLxMnTqy1bn0YOnhJ/XjU5/jRZxnq56OHDh3SeOzL5XL6+uuvBa/pGoBYVVVFgYGBVFZWRh07dqw1DkMZmhgx9G6e+hyT+n7+2rdvL5jfsGGDxrblcjlNmzZNdDwXFRVpjUd9+1QnmGszZswYUVzz5s2jzZs366wnk8lo8uTJ5OfnR2PGjNFrWfUxa9YsMjIyUs2/evWKhg8fTqdPn9Zap6qqisLCwighIYEWLlxIHTp0aPQ4AeoLySEAqJP6nqi8CRs3blT9HR8fT1ZWVoL31TupVVVVdOTIEY1tZWZmUnZ2tuA1XR3VxuhImpmZ0cyZMwWv3bt3jwICAkT7o5pSqaTIyEjBa7m5ufXeX61bt6bg4GDBa0ePHqUJEybofPTDpUuXyN/fn8LDw7U+8/ltOYkhIlq8eDE5Ojqq5i9cuED79u2rtV5iYqLqb19fXxo7dqyoTEREhGC+rKyMhg8fTk+ePNEZT03FxcUaH0fXmB3ZulwIMURd7tAz9Bioy0lsQ34/7Nixg/7617+SpaWlKOELAAAAhtM0WKqujwG7ePEiTZgwgYiIBg4cqLFMZWUlPXr0SPCarj5cXevcv39fMP/gwQOD+l4FBQWUkZGhs0xSUhKdO3eOiIjmzp0rerRyTR9++KFgPiIiQtVnunbtGvn5+dHFixcFZZhZdKeIrscpb968WfT7nN999x15e3tTYmIi5eXlUWVlJRUWFlJGRgYFBATQkCFDaPTo0TrvxFHflrU90ll9XS9dukRxcXHEzKRQKCgpKYkCAgL0fnxzzd/cISLq06dPo//WbXFxsWBe2zljteqBldVqPqJd32WUlJSIyri6ugrmL168SKGhoYJExIkTJ8jPz0+0Pc+ePUs3b94kZqbk5GTBHVwZGRmUm5tLREQrVqygjz/+mGbNmkWLFy+m5cuXa5xiYmJo7969org1Ud8etZ1DPH36VDCvK6Fp6OeCqG6fv0GDBgnmV65cSTKZTLUucrmc0tLSqE+fPnT+/HnRgNr4+Hitg97U97WuO8JqMjU1FSWClEolhYaGkr+/P6WkpFBhYSHJ5XJ6/Pgxpaam0rBhw2jy5MnUsmVLg3+LrK48PT1F5+zFxcXk4+ND06ZNo1OnTlFxcTHJ5XK6e/cubd26lby9vWnTpk3k4+MjOncHeGsxAEAd/P73v2ciUk1r1qxptGXl5eUJluXl5VVrnQMHDqjKL1y4UGOZBw8eCNolIm7Xrh1nZmaqyhQXF/OqVavYysqKe/ToISg7ZcoUVTvJycmCtufOnSsom5SUpDPemJgYQflFixZpLPfy5Ut2c3MTxe3m5sYHDhxguVyuKpuTk8NDhw7ldu3aicp7enpyREQEr127ttZtqU1paSm7uLiI2nZ2duZ169bxL7/8wuXl5fzw4UPOzs7mqVOnsqmpKX/66aeCONXNnDlT0N6mTZt0xvH8+XNBeTMzM66qqtJZR30ZsbGxWstmZmaysbGxYP2Kioq0lv/ll1+4RYsWTETcpk0bLigo0FhOqVTyp59+qvEY3LlzJ5eXl6vK3rlzhz///HN+7733ROVdXFx49uzZvHjxYkH78+bNE5U1MjLiP/7xj3zy5EkuKiriV69ecV5eHiclJbGHhwcTEfv4+OjcfklJSYI2hw8frrVsXezdu1fQ/tixY2ut0717d0Gd7Oxsg5YxcuRIUZnG/H7o3bu3qpyDgwOPHz+e582bx8uWLePIyEjRFB0dzd9//z2fO3dOn00IAADQ7EydOlX0fzsoKMigNoqLi3nlypVsbm6uamPHjh0ay27ZskW0vF27dulsvy51oqOjRXWOHz+us456eSsrK05KSmKlUikqm5yczGZmZkxEHBISorFMTbGxsaL2jY2NuX379iyRSHjIkCGiOidPnhTViYyM1LmcrKwstrKyEtXTNg0bNowrKip0tql+jEilUr5165bOOl5eXqJl2dracsuWLZmIeM+ePTrr62ovLi7OoLqGun37NkulUkHs27dv11lH/XiztLTkx48fay2fl5fHRkZGgjrr168XlcvKytK436RSKTs5OanOnfr27cvPnz9nV1dX0fldy5Yt2czMjB89eqRq98iRI3ofI+qTubk5L126VOt5j0KhYFtbW0GdrKwsrdtCqVSKztXi4+O1lq/L56Iun78bN26whYWFxuPYxcVF9X3Xvn17vn37Ni9evFhUtkWLFty3b1/B+WlVVZXoXNbR0ZGfPn2qcx1qWrFihUH7zMHBgS9fvqx3+7Vxdnau9Xu1oqKChw0bZlCcvXv31rkdfH19VWW3bdvWYOsDUFdIDgGAwUpLS9nGxkbwD9DPz6/Wk4m6qqqq4q5duwqWt27dOq3LS05OZgsLC5ZIJLxq1Sqdbfv5+Wn8h25ra8tt27ZliUTCRkZGvHfvXt6xY4fGDopUKuWIiAhBuyNGjBCUW758uc44AgMDBeUDAwO1lv33v//Njo6OGuM2NTXljh07cqtWrVTrcezYMVE5iUTCAwcO5NTUVJ1x1SYnJ0drLJqm3/3ud/zkyROt7cnlcu7cubOgzuTJk3XGoKljnZaWpnMZ6sfTqFGjdC4jNTVV0Knu2bMn37t3T1QuNzdXFb+rqyvfuHFDZ7uFhYXcpUsXjdvK2NiYnZycuG3btqp9e+bMGY1le/XqxTKZTNB2Y3Rkq6qqRJ8ZOzs7fv78uc71NMSUKVME7X/wwQc6k4nl5eWiiwfz58/XuYzg4GDRiYymCwqN9f3Qq1cvg/ZLzalbt2585cqVum1cAACAd9Dt27dVF+vVpwEDBnB0dDQnJibyrl27eM+ePbxnzx6WyWQcHx/Pq1ev5pCQEO7Zs6foIjcRcWFhoWo5N2/e5ISEBB43bpzGspaWljxr1ixOSEjgq1ev1rlOeno6b9y4kSdNmiQYoFQ9WVtbc1hYGO/cuVPjhfuaZdu2batKiri5ufGyZctYJpNxbGws+/v7M9Hr5NGGDRv0Opd78eKF6MJ99TRkyBAuKytjZuarV69yYmIiz5kzh+3t7UVlpVIpjx07ljdv3sz/+c9/NC7rwoUL7O7urrNfZGJiwpGRkVr7iikpKbx+/XoeM2aMxvpt2rThL7/8kvfs2aOKvabDhw+zRCLRGH9iYmKt20vd119/rerXFxcXG1y/Nkqlknfs2MGRkZHs5OSksQ+7YMEC3rZtm+pc5vr165yUlMRz5sxhS0tLUR1XV1eOiooSJEpTUlI4IiKC27RpIypvamrK06dP57i4OM7Ly1PVmT17ttb9KJFIODw8XJV8iIqK0tjuvn37BOurUCj4s88+q3O/mog4LCxM43b885//LCo7aNAgfvbsmcby1fu25uTp6Sk4Z6zv50Lfz5+6vXv3sqmpqdZt0KdPH87Pz2dmFiSHOnbsyHPmzOGTJ0+yQqFgZuYTJ07wwoUL+YMPPtDYlrOzMy9YsIC3bNnCOTk5mg/UGhISEkRJOE3T4MGD+e7du7W2Zwh9kkPMzJWVlTx79myN3+E1JyMjI8FxrA2SQ/C2QXIIAPTy8OFD3r59O0dFRWm8c4Xo9UiftWvX8okTJxp8+Y8fPxadIDk7O3NERAR///33vH37dl61ahX36dOHiYg/+ugjTk9Pr7Xd69evc+vWrbX+g3///fdVdyE8ffqU7ezsRGX69+8v6IhlZWWJOl/t2rXjmzdvaowhMzNTNWKverK0tOTTp09rjTs3N1dw94GmydXVlS9fvsx37txRvWZjY8N/+tOf+Pr16wbuAe1yc3O5X79+OmORSCQcGhqqtcN69OhR/vbbbzWuk0Qi4eDgYI6Pj+eff/6ZmV/vC5lMxtHR0dyxY0dRHWtra/7iiy84KSmJHz58yMyvTzCXL1/Onp6eGmMcMWIEx8bG8tmzZzXGeOnSJe7bt69gGdOnT+f4+HjevHkzBwcHs6WlperuHF2j7Gp6+PAhDx06VOf2a926NWdkZDDz/074LSwseMqUKXzx4kWtbTdURzY9PZ1Xr16tcfQkEbG7uzuvXLmSZTIZv3jxQq/1runKlSu8adMmHjt2rMb2Bw4cyOvXrxck/fbu3cvr1q3TeOxJJBKeMGECx8XFqS603Lt3j9euXctDhgzRuAxvb29etWoV79+/X7WMxvh+YGY+fvw4W1tb13oSpG2ytbWtNfEIAADwLsvMzOSpU6eyn5+fzoue9Zk8PT0Fy9R08VfbVH0HQF3q1LxwWNt05MgR0bap+b6zszMzM//jH//gYcOGcevWrdnExIQdHR25b9++HBMTw/fv3zdo29+9e5cDAgLYxsaGbWxsuHfv3iyTyQR3YERGRuq9DgsWLNC6LLlczjt37uQ//OEP7OTkxGZmZmxjY8Pdu3fnRYsW8X//+1+dsda8+FvbpC1JtX//fvb29mYzMzPu0KEDjxs3rs4DdVavXs1EtQ9Mqyu5XK73+qakpDCz5rtRtE3V1O/a1zap31m1e/du/vjjj9nKyootLS3Zzc2NZ82aJdqeFRUVPHPmTG7VqhXb2dnxqFGj+NKlSxrXWalU8saNG1kqlaoGSBoySSQSwfKjo6NVg/M0Tebm5tyzZ09V4isuLk7j0x2qJ2NjY+7evTs/efKkQT4X+nz+NMnJyeFJkyZxhw4d2MTEhG1tbdnf3593796tSvwwv36iyfjx4/nYsWMaE8aDBw/Wex1WrFihM6Zqjx494piYGPbx8eG2bduyiYkJ29jYsKenJ4eGhuq8LlIf+iaHquXk5PD8+fO5W7dubG9vz8bGxtyqVSvu168fL1myhO/cuaPXcpEcgrcNkkMAoJfjx4/r3Qno1atXo8Xx6NEjlslkPH78ePby8mJHR0c2MTHhVq1asaenJ8+YMYPT0tIEHZza3Lt3j2fMmMFOTk5sYmLC7733Hg8YMIC3bt0qulh+8uRJ9vLyYgsLC+7atSuvWbNGNVLt0KFDWu8CIXo9EsjDw4MPHDjAzMxnzpzhDz/8UOe27Ny5s9aOilKp5L/97W88btw4dnFxYQsLC7azs+OePXtybGysalRTfn4+e3l5cXx8fJ0u3OtDqVTy3//+d0EsVlZW7OHhweHh4VpPtqrVllyqnsLDw5n5daJG3+OxevupP+pP2zR9+nSdsf700088bdo0dnNzY1tbW9WJYv/+/XnFihV1Trylp6fzlClTuFOnTmxpack2NjbcrVs3joqKUiW4mJm7dOnC33zzjUGjDevbkdV1XKtP+naKa9L0+AJNU81HSqrfvahtqr7Qkpqaqlf5Ll26CGJrqO8Hdfn5+ezt7c12dnYaRwXXNo0ePdrg7QwAAADvvpr9herkELwdqh/DVXMwEtTPtWvXuFOnTrx69WpmZi4oKODs7Gz+8ccfOS4ujmNiYnjlypUcGRnJixYt4vDwcB4/frzgrrTo6OgmXgsAgKYjYW7gX5IGAAAAAK2USiUtWbKEdu3aRRcuXCBra2u6efMm5efn06NHj6ikpITKy8tJLpdTVVUVvXr1ip4+fUp5eXl09uxZKi0tJXNzcyotLSUTE5OmXh0AAAB4i0gkEtXfzs7OlJeX13TBgEphYSE5OTmRnZ0dFRQUoA/XAI4dO0ajR48mX19fSk1NNbj+mTNnaNCgQRQUFESbN29uhAgBAN5+xk0dAAAAAEBzUVlZSYGBgXTw4EHKzs4mBwcHIiLy8PAgDw+PWuu/fPmSQkJC6IcffqDi4mJydHRs7JABAAAAoJ62bNlCCoWCJk2ahMRQA8jOzqYRI0aQQqGoc2KnT58+NHjwYLK3t2/g6AAAfj2kTR0AAAAAQHOgVCpp7NixlJKSQtOnT6fevXsb3IaFhQUtWLCAJBIJ2dnZNUKUAAAAANCQiouLKTY2lqRSKU2fPr2pw/nVe/nyJQUGBlJFRQV5eXlRu3bt6tzW/fv3ydfXtwGjAwD4dUFyCAAAAOAN2LhxIx08eJCIiIYOHVrndu7fv0+9e/cmU1PThgoNAAAAABrJjBkzqKSkhAICAqhTp05NHc6vXkpKCt27d4+IXveLFQpFndpJT0+nFy9e0MCBAxsyPACAXxUkhwAAAADegA0bNqj+rj6hNZRcLqfVq1fT3LlzGyosAAAAeEeo/6S0Uqlsokian/j4eOrQoQN16tSJgoOD6fz581RaWkqzZ8+m5ORkMjIyoqVLlzZ1mO+E/Px81d8FBQU0b948gxJESqWSEhISaOLEibRjxw6SSnFpFACaLwmr9x4AAAAAoMFZWFhQRUUFERE5OjrSsWPHyN3dXe/6d+7coZCQEGrTpg398MMPjRUmAAAA/Eo9efJE8PspLVq0oOfPn+PidyMrLCwkJycnnQmK2bNn03ffffcGo3p3nThxgvz8/ASveXh4UFhYGPn7+5OrqytJJBLB+yUlJXT16lXKyMignTt3UlFRER0+fJj69+//BiMHAHj7IDkEAAAA8AZ88sknlJWVpZo3NTWlyZMn07hx46hXr15kaWkpKK9QKCgvL4/++c9/0oEDB+jgwYP02Wef0e7du/FDxgAAACCyZ88emjBhguC19PR0PDarkV2+fJm8vb21vu/p6Unnzp0jCwuLNxjVuy0kJIQSExM1vmdubk4ODg5kZmZGlZWVVFpaSiUlJar3fX19adu2beTi4vKmwgUAeGshOQQAAADwBuTk5JCPjw89ffpU4/sODg70m9/8hoiIysvL6fHjx1RVVUVEr0f+xsTE0MyZM0UjIQEAAKD5evLkCR0+fJhOnz5NMpmMXr58KXjf2tqagoKCyMPDg2xtbUXJI6g/uVxO7u7ulJubK3rvo48+op9++onatm3bBJG9u5iZNm3aRJGRkVRcXKxXHW9vb5ozZw4FBQWhPw0A8P+QHAIAAAB4Q+7cuUOhoaF09OhRvcq3aNGCJk2aRF9++SVGNwIAAIBIVlYWffLJJ3qVNTIyUg08gYZ169Ytmjt3Lp06dYoqKyupa9euFBQURGFhYWRqatrU4b2zysrKKDk5mdLS0ujKlStUWFhI5eXlZG1tTfb29vT++++Tj48PDRgwgHr16tXU4QIAvHWQHAIAAAB4wy5dukT79u2jU6dO0Y0bN6ikpISkUinZ29uTg4MD9ejRg3x9fWno0KHUsmXLpg4XAAAAAAAAAN4xSA4BAAAAAAAAAAAAAAA0I9KmDgAAAAAAAAAAAAAAAADeHCSHAAAAAAAAAAAAAAAAmhEkhwAAAAAAAAAAAAAAAJoRJIcAAAAAAAAAAAAAAACaESSHAAAAAAAAAAAAAAAAmhEkhwAAAAAAAAAAAAAAAJoRJIcAAAAAAAAAAAAAAACaESSHAAAAAAAAAAAAAAAAmhEkhwAAAAAAAAAAAAAAAJoRJIcAAAAAAAAAAAAAAACaESSHAAAAAAAAAAAAAAAAmhEkhwAAAAAAAAAAAAAAAJoRJIcAAAAAAAAAAAAAAACaESSHAAAAAAAAAAAAAAAAmhEkhwAAAAAAAAAAAAAAAJoRJIcAAAAAAAAAAAAAAACaESSHAAAAAAAAAAAAAAAAmhEkhwAAAAAAAAAAAAAAAJqR/wMeypkORgDw9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 850,
       "width": 835
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(221)\n",
    "plt.hist(data_basic['ext_shapeHSM_HsmShapeRegauss_resolution'], 100, range=[0,1], density=True);\n",
    "plt.xlabel('i-band resolution')\n",
    "plt.xlim()\n",
    "plt.subplot(222)\n",
    "plt.hist(data_basic['snr_i_cModel'], 100, range=[0,100], density=True)\n",
    "plt.xlabel('i-band cmodel S/N')\n",
    "plt.subplot(223)\n",
    "plt.hist(data_basic['mag_i_cModel'], 100, range=[20,25], density=True);\n",
    "plt.xlabel('i-band cmodel mag')\n",
    "plt.subplot(224)\n",
    "plt.hist(data_basic['shape_hsm_regauss_etot'],100, density=True);\n",
    "plt.xlabel('Ellipticity magniture |e|');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can compare this plot to Fig 12. in [Mandelbaum et al. 2018](http://adsabs.harvard.edu/abs/2018PASJ...70S..25M):\n",
    "<img src=\"https://github.com/LSSTDESC/DC2-analysis/raw/master/tutorials/assets/fig12_mandelbaum2017.png\">\n",
    "\n",
    "A quick visual comparison will highlight two things:\n",
    "\n",
    "  - We are missing a lot of galaxies between about 24.2 and 24.5 mag\n",
    "  - We have a bump near resolution of 1\n",
    "\n",
    "We are going to investigate to understand these differences below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at the depth of the survey"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One obvious reason why we would be missing some faint galaxies is if DC2 run2.2i is shallower than HSC. We will test this here by measuring the depths of both Run2.2i and the HSC DR1 XMM field. More generally, it is also a concern for most science analysis to have spatially uniform sampled data, which can be checked by looking at the depth of the sample.  \n",
    "\n",
    "There are several ways to do this, in this case, we are going to check what's the magnitude which has a median SNR closest to 10, the SNR cut of our lensing sample.\n",
    "\n",
    "Code to do this calculation for the HSC DR1 XMM field is commented out below because that catalog is not currently available, but we know the answer from previous runs. That is printed out for comparison with the calculated number for DC2 run2.2i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import an efficient alternative to binned_statistic_2d, defined in utils/cic.py\n",
    "from utils.cic import binned_statistic\n",
    "    \n",
    "import healpy as hp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def depth_map_snr (ra, dec, mags, snr,snr_threshold=10,nside=2048):\n",
    "    \"\"\"\n",
    "    Constructs a depth map on a healpix grid for a given SNR threshold.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    ra, dec: Array of coordinates on the sky (in deg.)\n",
    "    mags, snr : measured magnitude and snr for the sample\n",
    "    snr_threshold: SNR\n",
    "    \"\"\"\n",
    "    # Remove potentially problematic entries\n",
    "    good = np.logical_or(np.logical_not(np.isnan(ra)),np.logical_not(np.isnan(dec)))\n",
    "    # Create array of healpix pixel indices corresponding to coordinates \n",
    "    pix_nums = hp.ang2pix(nside,np.pi/2.-dec[good]*np.pi/180,ra[good]*np.pi/180)\n",
    "    \n",
    "    # Create output map\n",
    "    map_out = np.zeros(12*nside**2)\n",
    "    \n",
    "    # Bins in magnitudes\n",
    "    bin_centers = np.linspace(22+6/30.,28-6/30.,30.)\n",
    "    \n",
    "    # For each healpix pixel\n",
    "    for px in np.unique(pix_nums):\n",
    "        # Select all objects within this pixel\n",
    "        mask = px==pix_nums\n",
    "        if np.count_nonzero(mask)>0:\n",
    "            # Compute median snr in bins of magnitude\n",
    "            median_snr = binned_statistic(mags[mask],snr[mask],np.nanmedian,nbins=30,range=(22,28))\n",
    "            mask2 = np.isnan(median_snr)==False\n",
    "            # Find magnitude corresponding to snr threshold\n",
    "            if np.count_nonzero(mask2)>0:\n",
    "                depth = bin_centers[mask2][np.argmin(np.fabs(median_snr[mask2] - snr_threshold))]\n",
    "                map_out[px]=depth\n",
    "            else:\n",
    "                map_out[px]=0\n",
    "        else:\n",
    "            map_out[px]=0.\n",
    "    return map_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/lsst/software/stack/python/miniconda3-4.7.10/envs/lsst-scipipe-4d7b902/lib/python3.7/site-packages/GCRCatalogs/dc2_dm_catalog.py:43: RuntimeWarning: invalid value encountered in log10\n",
      "  return -2.5 * np.log10(flux) + AB_mag_zp_wrt_nanoJansky\n",
      "/opt/lsst/software/stack/python/miniconda3-4.7.10/envs/lsst-scipipe-4d7b902/lib/python3.7/site-packages/GCRCatalogs/dc2_dm_catalog.py:43: RuntimeWarning: divide by zero encountered in log10\n",
      "  return -2.5 * np.log10(flux) + AB_mag_zp_wrt_nanoJansky\n"
     ]
    }
   ],
   "source": [
    "quantities = ['ra', 'dec', 'mag_i_cModel', 'snr_i_cModel', 'ext_shapeHSM_HsmShapeRegauss_resolution']\n",
    "\n",
    "# Data from DC2 run2.2i\n",
    "data_dc2 = catalog.get_quantities(quantities, \n",
    "                                  filters=basic_cuts, # Note the only apply the basic_cuts\n",
    "                                  native_filters=[f'tract == {tract_s}'])\n",
    "\n",
    "# Data from HSC DR1 XMM field \n",
    "# cat_hsc = GCRCatalogs.load_catalog('hsc-pdr1-xmm')\n",
    "# data_hsc = cat_hsc.get_quantities(quantities,\n",
    "#                                  filters=basic_cuts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Note__: Runtime warning will arise due to objects with incorrect fluxes but the results are unaffected by them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/lsst/software/stack/python/miniconda3-4.7.10/envs/lsst-scipipe-4d7b902/lib/python3.7/site-packages/ipykernel/__main__.py:20: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.\n",
      "/opt/lsst/software/stack/python/miniconda3-4.7.10/envs/lsst-scipipe-4d7b902/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1076: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis, out=out, keepdims=keepdims)\n",
      "/global/u2/y/yymao/.beavis/DC2-analysis/tutorials/utils/cic.py:11: RuntimeWarning: invalid value encountered in greater\n",
      "  mask = (x > r0) &  (x < r1)\n",
      "/global/u2/y/yymao/.beavis/DC2-analysis/tutorials/utils/cic.py:11: RuntimeWarning: invalid value encountered in less\n",
      "  mask = (x > r0) &  (x < r1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run2.2i_dr3 median i-band 10-sigma depth  24.710344827586205\n",
      "HSC XMM median i-band 10-sigma depth  25.28965517224138\n"
     ]
    }
   ],
   "source": [
    "# Compute i-band depth maps for Run2.2i, print median\n",
    "m10map_dc2 = depth_map_snr(data_dc2['ra'], data_dc2['dec'], data_dc2['mag_i_cModel'], data_dc2['snr_i_cModel'])\n",
    "print(f\"{run} median i-band 10-sigma depth \", np.median(m10map_dc2[m10map_dc2 > 0.0]))\n",
    "\n",
    "# Print the corresponding value for the HSC data, computed previously\n",
    "print(\"HSC XMM median i-band 10-sigma depth \", 25.2896551722413795)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import gzip\n",
    "def save_map(outfile, depth_map):\n",
    "    f = gzip.open(outfile, 'wb')\n",
    "    pickle.dump(depth_map, f)\n",
    "    f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment the following to save the depth map.  It's used in the next tutorial\n",
    "# There is no need to save unless you've changed run or tract\n",
    "# map_dir = 'your_choice_goes_here'\n",
    "# save_map(f'{map_dir}/m10map_{run}_{tract_s}.pklz', m10map_dc2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we see the difference between the two samples, HSC is deeper. (In the case of Run 1.1p, this is partly due to the fact that it was interrupted mid-run so it doesn't reach the full depth of DC2.  For Run2.2i_dr3, about 500 visits in the center of the field were omitted.  If we had picked a tract in this deficit area the plots and the depth measurement would look considerably worse.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impact of blendedness\n",
    " \n",
    "The second discrepancy between our sample and the Mandelbaum et al. plot is an excess of galaxies appearing very well resolved compared to the PSF (resolution > 0.9). To understand this difference, we are going to select a few of these objects and extract postage stamps from the DM stack for visual inspection.\n",
    "\n",
    "We begin by selecting galaxies in our lensing sample which are near perfectly resolved by adding a cut on resolution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_cut = basic_cuts + properties_cuts + ['ext_shapeHSM_HsmShapeRegauss_resolution >= 0.98']\n",
    "\n",
    "data = catalog.get_quantities(['ra', 'dec', 'mag_i_cModel', 'ext_shapeHSM_HsmShapeRegauss_resolution'], \n",
    "                              filters=sample_cut,\n",
    "                              native_filters=['tract == 3081'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will extract a few postage stamps at these coordinates, to do so we will reuse some of the code from the [DC2 Postage Stamps tutorial](/DC2%20Postage%20Stamps.ipynb). Please have a look at that tutorial to fully understand the function we will be using here, but in a nustshell we are going to query the DM data Butler to retrieve cutouts of the Deep Coadd exposures of these objects in the i-band."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from desc_dc2_dm_data import REPOS\n",
    "\n",
    "import lsst.daf.persistence as dafPersist\n",
    "import lsst.geom as afwGeom\n",
    "import lsst.afw.coord as afwCoord\n",
    "import lsst.afw.image as afwImage\n",
    "import lsst.afw.display as afwDisplay\n",
    "\n",
    "from astropy.table import Table\n",
    "from astropy.visualization import ZScaleInterval\n",
    "\n",
    "# Please check the DC2 Postage Stamps tutorial for all the details of how this works\n",
    "def cutout_coadd_ra_dec(butler, ra, dec, filt='i', datasetType='deepCoadd', \n",
    "                        skymap=None, cutoutSideLength=50, **kwargs):\n",
    "    \"\"\"Produce a cutout from a coadd at the given ra,dec coordinates\n",
    "    \n",
    "\n",
    "    Parameters\n",
    "    --\n",
    "    butler - lsst.daf.persistence.Butler of the data repository\n",
    "    ra, dec - coordinates of the center of the cutout (in degrees).\n",
    "    filter - Filter of the image to load\n",
    "    datasetType - 'deepCoadd'  Which type of coadd to load.  Doesn't support 'calexp'\n",
    "    \n",
    "    skymap - [optional] Pass in to avoid the Butler read.  Useful if you have lots of them.\n",
    "    cutoutSideLength - [optional] Side of the cutout region in pixels.\n",
    "    \n",
    "    Returns\n",
    "    --\n",
    "    MaskedImage\n",
    "    \"\"\"\n",
    "    # Create a lsst.afw.geom.SpherePoint coordinates object\n",
    "    radec = afwGeom.SpherePoint(ra, dec, afwGeom.degrees)\n",
    "    cutoutSize = afwGeom.ExtentI(cutoutSideLength, cutoutSideLength)\n",
    "\n",
    "    if skymap is None:\n",
    "        skymap = butler.get(\"deepCoadd_skyMap\")\n",
    "    \n",
    "    # Retrieves the tract, patch info for these coordinates from the skymap\n",
    "    tractInfo = skymap.findTract(radec)\n",
    "    patchInfo = tractInfo.findPatch(radec)\n",
    "    \n",
    "    # Get pixel coordinates on the tract\n",
    "    xy = afwGeom.PointI(tractInfo.getWcs().skyToPixel(radec))\n",
    "    bbox = afwGeom.BoxI(xy - cutoutSize//2, cutoutSize)\n",
    "\n",
    "    coaddId = {'tract': tractInfo.getId(), 'patch': \"%d,%d\" % patchInfo.getIndex(), 'filter': filt}\n",
    "    \n",
    "    cutout_image = butler.get(datasetType+'_sub', bbox=bbox, immediate=True, dataId=coaddId)\n",
    "    \n",
    "    return cutout_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this tool to extract cutouts in hand, let's have a look at a few examples in our sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an instance of the data butler for our run data repository\n",
    "repo = REPOS[f'{run}'[3:]]\n",
    "butler = dafPersist.Butler(repo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1500x1500 with 16 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1209,
       "width": 1216
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(4, 4, figsize=(15,15))\n",
    "\n",
    "for ra, dec, ax_this in zip(data['ra'], data['dec'], ax.flat):\n",
    "    # Extract the cutout using the data butler\n",
    "    cutout_image = cutout_coadd_ra_dec(butler, ra, dec, filt='i')\n",
    "    \n",
    "    # Plot the postage stamp on the same scales, with some arcsinh range compression \n",
    "    ax_this.imshow(np.arcsinh(cutout_image.image.array), vmax=4, cmap='binary');\n",
    "    \n",
    "    # Let's add a crosshair to guide the eye\n",
    "    ax_this.axhline(25, color='k', alpha=0.5);\n",
    "    ax_this.axvline(25, color='k', alpha=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### So, one thing may jump to eye, these objects are supposed to be large, extremely well resolved, yet they often look fairly small... but they have a lot of sometimes very bright neighbours. This is very suspicious and may indicate a problem with the DM deblender.\n",
    "\n",
    "Looking at the [SCHEMA.md](https://github.com/LSSTDESC/gcr-catalogs/blob/master/GCRCatalogs/SCHEMA.md#schema-for-dc2-coadd-catalogs) one may notice a field named `blendedness`. This is a metric produced by the DM stack defined  for deblended objects and \"measures the fraction of the total flux in the neighborhood of a source that belongs to its neighbors\" [(Bosch et. 2018)](http://adsabs.harvard.edu/abs/2018PASJ...70S...5B).\n",
    "\n",
    "Let's have a look at the `blendedness` values of the examples in our sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/lsst/software/stack/python/miniconda3-4.7.10/envs/lsst-scipipe-4d7b902/lib/python3.7/site-packages/GCRCatalogs/dc2_dm_catalog.py:43: RuntimeWarning: invalid value encountered in log10\n",
      "  return -2.5 * np.log10(flux) + AB_mag_zp_wrt_nanoJansky\n",
      "/opt/lsst/software/stack/python/miniconda3-4.7.10/envs/lsst-scipipe-4d7b902/lib/python3.7/site-packages/GCRCatalogs/dc2_dm_catalog.py:43: RuntimeWarning: divide by zero encountered in log10\n",
      "  return -2.5 * np.log10(flux) + AB_mag_zp_wrt_nanoJansky\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.77196875, 0.67613115, 0.92295569, 0.89298037, 0.92297811,\n",
       "       0.92413947, 0.99069868, 0.74097552, 0.88860161, 0.8866249 ,\n",
       "       0.98923703, 0.99385025, 0.86873228, 0.84240061, 0.94371736,\n",
       "       0.32133813])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.update(catalog.get_quantities(['blendedness'], \n",
    "                                   filters=sample_cut,\n",
    "                                   native_filters=['tract == 3081']))\n",
    "\n",
    "data['blendedness'][:16]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As one would expect, they mostly have large values, close to 1, indicating that these objects belong to very blended neighborhoods. Because the deblender seems to be failing for these objects, we can use this metric to try to exclude them from our sample. \n",
    "\n",
    "As a matter of fact, this is what was done in the ([Mandelbaum et al., 2018](http://adsabs.harvard.edu/abs/2018PASJ...70S..25M)) paper where an additional cut on blendedness was introduced to guard against deblender failures. \n",
    "\n",
    "Let's try to rebuild our sample following the same approach:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "properties_cuts = [\n",
    "    GCRQuery('snr_i_cModel > 10'),                              # SNR > 10\n",
    "    GCRQuery('mag_i_cModel < 24.5'),                            # cModel imag brighter than 24.5\n",
    "    GCRQuery('ext_shapeHSM_HsmShapeRegauss_resolution >= 0.3'), # Sufficiently resolved galaxies compared to PSF\n",
    "    GCRQuery('shape_hsm_regauss_etot < 2'),                     # Total distortion in reasonable range\n",
    "    GCRQuery('ext_shapeHSM_HsmShapeRegauss_sigma <= 0.4'),      # Shape measurement errors reasonable\n",
    "    # New cut on blendedness:\n",
    "    GCRQuery('blendedness < 10**(-0.375)')                      # Avoid spurious detections and those contaminated by blends\n",
    "]\n",
    "\n",
    "quantities = ['mag_i_cModel', 'snr_i_cModel', 'shape_hsm_regauss_etot', 'ext_shapeHSM_HsmShapeRegauss_resolution']\n",
    "data = catalog.get_quantities(quantities, \n",
    "                              filters=basic_cuts + properties_cuts, \n",
    "                              native_filters=['tract == 3081'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/lsst/software/stack/python/miniconda3-4.7.10/envs/lsst-scipipe-4d7b902/lib/python3.7/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: \n",
      "Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  \"Adding an axes using the same arguments as a previous axes \"\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 850,
       "width": 835
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(221)\n",
    "plt.hist(data['ext_shapeHSM_HsmShapeRegauss_resolution'], 100, range=[0,1], density=True, histtype='step', label='New cuts');\n",
    "plt.xlabel('i-band resolution')\n",
    "plt.xlim()\n",
    "plt.subplot(222)\n",
    "plt.hist(data['snr_i_cModel'], 100, range=[0,100], density=True, histtype='step', label='New cuts')\n",
    "plt.xlabel('i-band cmodel S/N')\n",
    "plt.subplot(223)\n",
    "plt.hist(data['mag_i_cModel'], 100, range=[20,25], density=True, histtype='step', label='New cuts');\n",
    "plt.xlabel('i-band cmodel mag')\n",
    "plt.subplot(224)\n",
    "plt.hist(data['shape_hsm_regauss_etot'],100, density=True, histtype='step', label='New cuts');\n",
    "plt.xlabel('Ellipticity magniture |e|');\n",
    "plt.subplot(221)\n",
    "plt.hist(data_basic['ext_shapeHSM_HsmShapeRegauss_resolution'], 100, range=[0,1], density=True, histtype='step', label='Original cuts');\n",
    "plt.xlabel('i-band resolution')\n",
    "plt.legend(loc='best');\n",
    "plt.xlim()\n",
    "plt.subplot(222)\n",
    "plt.hist(data_basic['snr_i_cModel'], 100, range=[0,100], density=True, histtype='step', label='Original cuts')\n",
    "plt.xlabel('i-band cmodel S/N')\n",
    "plt.subplot(223)\n",
    "plt.hist(data_basic['mag_i_cModel'], 100, range=[20,25], density=True, histtype='step', label='Original cuts');\n",
    "plt.xlabel('i-band cmodel mag')\n",
    "plt.subplot(224)\n",
    "plt.hist(data_basic['shape_hsm_regauss_etot'],100, density=True, histtype='step', label='Original cuts');\n",
    "plt.xlabel('Ellipticity magniture |e|');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\o/ The excess of high resolution objects has disappeared, mystery solved!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__If you want, you can check out [Part III: Challenges](object_gcr_3_challenges.ipynb)!__"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "desc-stack",
   "language": "python",
   "name": "desc-stack"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}