diff --git a/catalyst/curate/poloniex.py b/catalyst/curate/poloniex.py index d09afcc6..824b5e58 100644 --- a/catalyst/curate/poloniex.py +++ b/catalyst/curate/poloniex.py @@ -261,7 +261,7 @@ class PoloniexCurator(object): vol = df['total'].to_frame('volume') # set Vol aside df.drop('total', axis=1, inplace=True) # Drop volume data ohlc = df.resample('T').ohlc() # Resample OHLC 1min - ohlc.cols = ohlc.cols.map(lambda t: t[1]) # Raname cols + ohlc.columns = ohlc.columns.map(lambda t: t[1]) # Rename cols closes = ohlc['close'].fillna(method='pad') # Pad fwd missing close ohlc = ohlc.apply(lambda x: x.fillna(closes)) # Fill NA w/ last close vol = vol.resample('T').sum().fillna(0) # Add volumes by bin diff --git a/catalyst/examples/running_catalyst_in_jupyter_notebook.ipynb b/catalyst/examples/running_catalyst_in_jupyter_notebook.ipynb new file mode 100644 index 00000000..1d1b3d90 --- /dev/null +++ b/catalyst/examples/running_catalyst_in_jupyter_notebook.ipynb @@ -0,0 +1,2493 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Running Catalyst in Jupyter Notebook\n", + "The [Jupyter Notebook](https://jupyter.org/) is a very powerful browser-based interface to a Python interpreter. As it is already the de-facto interface for most quantitative researchers, `catalyst` provides an easy way to run your algorithm inside the Notebook without requiring you to use the CLI.\n", + "\n", + "To use it you have to write your algorithm in a cell and let `catalyst` know that it is supposed to run this algorithm. This is done via the `%%catalyst` IPython magic command that is available after you import `catalyst` from within the Notebook. This magic takes the same arguments as the command line interface. Thus, to run the algorithm just supply the same parameters as the CLI but without the -f and -o arguments. We just have to execute the following cell after importing `catalyst` to register the magic.\n", + "\n", + "Please remember to ingest the data that you need from the console since that functionality is not supported from within the Notebook. For the example below, you first need to run the following from the CLI:\n", + "\n", + "`catalyst ingest-exchange -x bitfinex -i btc_usd`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Register the catalyst magic\n", + "%load_ext catalyst" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup matplotlib to display graphs inline in this Notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note below that we do not have to specify an input file (-f) since the magic will use the contents of the cell and look for your algorithm functions." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2017-12-12 23:24:22.056836] INFO: run_algo: running algo in backtest mode\n", + "[2017-12-12 23:24:22.138743] INFO: exchange_bundle: pricing data for [u'btc_usdt'] not found in range 2015-03-01 00:00:00+00:00 to 2017-06-27 00:00:00+00:00, updating the bundles.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ingesting daily price data for btc_usdt on poloniex\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2017-12-12 23:24:23.713668] INFO: exchange_algorithm: initialized trading algorithm in backtest mode\n", + "[2017-12-12 23:24:27.865042] INFO: Performance: Simulated 851 trading days out of 851.\n", + "[2017-12-12 23:24:27.865826] INFO: Performance: first open: 2015-03-01 00:00:00+00:00\n", + "[2017-12-12 23:24:27.866537] INFO: Performance: last close: 2017-06-28 23:59:00+00:00\n" + ] + }, + { + "data": { + "image/png": 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ovO22CBzMnQv9+0dHvaAA2rTJXu+iRTEFpE8f+Prr6Kg/+mjcr1evuHbffSMw\n8fTTsYpJmjCzU6cIbkyZEvkx9tknggNVBSaWLYP//jembvTsGe+lpCSCOOkUkcbklVdihZNDD139\n2NlnwxNPwI9/DBtsENOGcnPXfxtl7SnQsQYU6BARERERkep8+in8v/8XQYmiohjJsOGGEfQ477yY\nvlEb48bBX/4S+SA22CASa06ZAkccEfkoKlu4EPbbL4Iq330XoypKSyPx5TbbRODjmWciwLLhhnDw\nwbHSR5s2EbRYtCgCJP/zP1UHUpor99jqczqNNIy1DnSYWWdgJLB3UvQicKW7L1jDBm0C3Ad0B8qA\nu939ZjPLAx4C8oEpwE/SOszsZmAwsAQ4zd3fTcpPBS4jlon9nbvfl5QPAu4F2gBPufsvkvIq66jU\nRgU6RERERER+QNxhyZLoBLdqFUGBoqIYZTF3boyagJjG8cgj8Prr8L//G0k367vjPGMGbLddJPF8\n4okYUdG3byTLnDYtppXcckucW1UOidLSGFEi0hzVR6DjUeBDYExSdDIw0N2PWcMGdSeWkX3XzDoA\nbwFHAz8F5rn79WZ2MZDn7iPMbDBwrrsfbma7ATe5++5J0OJNYBCxpOxbwCB3X2BmrwPnufskM3sq\nueYZM7suWx1Z2qhAh4iIiDR67jBiRAxD3377+CV3xowYtj5yZPyaK7K+zJoF//d/kcvhvPOyd/7d\nYyRCt27ZO+HLl0diy7lzI6AwdSrk5cFBB8UKHp9/Hvfo3h06d47OfPfuMXqipCQCFUuXxlKf//43\nvPhirNbRtm3kkhg4MEZEFBXFVlwcr0tKImCQBjNKSiqu2WAD2GijeD/u0fZ99omVP9q2XXef59VX\nRx6Jk06KPBPffBPt2WST+CvyQ1YfgY533X2HmsrWooGPAbck277uPicJhrzg7luZ2R3J/kPJ+Z8A\nBcB+yflnJ+W3A4XEiJPn3X3rpHxoep6ZfVqpjkJ3H5ClTQp0iIiISKNTXg4PPBArCOy0U6x4MHky\n/O53sb/55hHcGD06hq2/8AJ06BDXLlgQnbQuXeJ1unqBO7zzTnQsx46FzTaDoUPhk09iv7gYdt89\nOqXuq3ZOFyyIfAMtW0YHdf78mBs/aVLkK2jfPjqjPXrEfrqyRHl51K3VDBo393ieHTpUBAHefTcS\nOb7/PnzwQUyzyM+P5/zWW3DkkfDVV3Ht8uUxTWLFighIlJZGUCE3N+4L8d3o1SsCC99/H8GSnXaK\nlTE6d46yJrytAAAgAElEQVTv4OzZkcRz440jf0XLlpFzYsGCyCsxa1YEAdq0iba2aRP/Ley3XwT9\nli+PbebM+F536RKBgry82Lp0iREcZWXxHdX3UqTxqyrQUZdBTN+b2V7u/kpywz2B7+upcZsCOwCv\nARu7+xwAd59tZhslp/UCvsm4bHpSVrl8Rkb59Cznk6UO/c4hIiIiTcKSJXDqqfEL94UXwocfwh57\nRBLDbt1WPXennSJ3wODBsSzk3/4WwRGIpSOnTo2O4aBB0RGdOzfm7A8eHIn8br89VoqYMiV+sZ43\nLzqKS5bE0o0tWkSn9IsvolOYdlw7d45O5i67xPHvv4/gx6xZcW2fPvFre7qcY48e0bHMnBZgFr+6\nn3NO3Kdjx0h4WN0KDg0x33769BgRkK4+sWBBTCmYMSOSPUKs3tC9e0VQJ91yclZ93bZtPMNsHWz3\n+PzTz7lVqwgYFBXFs+vUKc4pKYlnVFpaEaBYtCj2W7SIfBKvvhrtbd06ngvE515SEoGozG3Filh9\n46OP4p5dusQ98/Nh771jBNGwYRFQmDYtAhg77BDvt6QkRmVstlkEFFq2jC03N+6dlxdtLSuLz3HO\nnGhTmzYRqOveffXPYcRqY7DrbrPNYK+91v4+ItJ41SXQcTYwJsnVYUARcNraNiCZtjIeuMDdF5tZ\nVUMoKv8v34icHNlirdWV18nIkSNX7hcUFFBQUFDXW4iIiIjUaNky+Mc/4lfr5csjCDBvXgyXX7gw\nOorLl8P48RWBiJpWOjCLlSB+9auYy//jH1f84j16dAQcdt45fpEvK4NDDqkIJFx88er3e/PN6FBv\nsEFMHYDouG63XXRqV6yIzmt1AYcVK+LX9E6doiNbXh7vddGi6NymI0bKy2OViJtuivYtXRqfURoI\nqcw9pi1sskl0pLt3j5UhevSIbenS+Dxbtoz2LV8ewYjvv4+O+5IlEUxJt5ycCDrk5cXnv3BhnNe6\ndRwvKYHnn4/PYeON435t20aQp2fP+GynTIn23ndf1J0mQEy3dCnLdFu8OIIJ7drFe1i4MNrXqlWc\n265dPNPly+NzTNvYoUN8fmlOicyAUOb7KS2Nz+eII+J5L1sW7U2DLq1bx+eQueXmwlVXxXejtDQC\nI2kQpLJNNln1devW8b2rTqtW8bd//9hERKpTWFhIYWFhjefVedUVM+sE4O4L16hlq96rJfBP4F/u\nflNS9glQUIupK58C+xJTVwrc/X+S8juAF4ipKy+4+1ZJeebUlax1ZGmfpq6IiIhIvSkuhssvj2BD\n+iu7e8UogJ13jqH6rVpFp3+DDWI1hc6do6OcmxvD8HfcsaHfSePjHtN25syJQMDs2TFFYdas2Nq1\ni8+zrCy23NwISHToEAGKjh0j0JAGNcrL4zOfPz8+/44d49ySknh2rVrFyIXDD6/fRI8LF0YAYvny\nCAa1bRtBjXTEh4iIVFjjHB1mdpK7/93MLsx23N3/tBaNug/4zt0vzCi7Dihy9+vMbATQJUlGehhw\nTpKMdHfgxizJSHOS/Z3cfX6ajBR4A3gSuNndn65Uh5KRiojUs5deio7EbrvFP9Dfey9+md1gg+go\npInf8vJi3nT37hWdjM8+i3ndeXlwzDFVr2tfVhZ/06HfqfRX8aKiqCvtxLRuHZ3INC9BU7JoUXS2\nysric+ncueJYaWl0vNJfa5csic5Ru3YN1971obw8AgDTp8eWfgfmzYvyefPiOzZoUCy5+N138d3o\n0SN+IU+HzC9ZEp3UkpIY0l9SEh1miM8xPz8+9/QX9HSOv3uMimjbNj77gQMjMFFcHG1LO9NLlsTz\nW7gwEoTefXf8Mn7QQavmAOjSJUYFVP5FXERERKq2NoGOs9z9TjO7Ittxdx+1hg3aE3gJ+ICYUuLA\npcAk4GGgNzANON7d5yfX3AIcSiwv+1N3fzspP42K5WWvzlhedidWXV72gqS8a1V1VGqjAh0i0qDc\nYcKEGKLeuXP8crjlljGPfcMNK+ZqVxUMqK82wOpzxtOAxKJF8PbbMYS7Xbvo3M2YER3Jjz6K4dMD\nBsRQ+0WLYvh4ly4x3Hr27MiEn84xb98+5nn37x/DwdP7pNn5FyyI69Ls+OkQ9nTed8uWUf/ee8c9\nFi+ObenS2N54I4IBubkwZAjcc08Moe/QIY537AgPPRTvZZttIg9CTk4kVGzZMjqiy5bFe0qDJmm9\nmZ+PeySGLCqKjmuvXqsez0zKN316dIDffrtiPvyMGVE+d268/uKLqKtFi4p7fp9kyZo9O8rLy+Nv\n+/bx2Wy8cQSWevSI/AD5+auuMLBoUcXnU1YWx9Ph/CUlFfP83WMpw1atoi0rVsSxdKh/Tk7F+583\nL9retm20o127igSVaQLC0tJVExKWlVUM3y8vX3U//VtWVjG9IH2f5eURiEg/39QGG8R3pFu3OP7y\ny/Hd2WCDeA+zZ0f7iovjdYcOcf/0e5oOxzeLc775piKnQJoXITc3ji9bFltpaXxHFi2K6SUtWsTn\nkj6PdOpAr14xZeDAA+v3v1EREZEfqrVedeWHSIEOEVnfFi6Mznz37pEs7tJLo/N04onRQXv66Ria\nveGGFfO9u3eH666Dww6LDPjFxdG56ts3Osdffx2dxOXLI2jQs2d01hYvjg5fu3bxy3R6/1deiY7h\nt99GR3vu3FhOb7PNKjp46S/m220XHbh+/aL+srIIGBQUrP2IAvcITKQrQqRz5YuKojPZpUt0JtOO\ncNpxhooVHSorL4/P4fvvI3neq6/Ge0vnxM+fH8kbzzwzOq5jx8ZIkJ12qhjG3qZNdG7feqtiPn2n\nTnGfRYsq5rS3bh2f9dSpcd90VYt0tYrWraP9vXtHkGHHHSvm4vfqFR34DTeMc7bdtmLI+rJlEUTp\n2DHu1aNHtKny+/zqq/guTJ8eySq/+SaCZekKA507x/ckXUVh6tS4X+vWFXP8W7eOsq+/rggQpe+t\nQ4eKZ7xiRfzt3Dnez7Jl8T1I5/in12YGpNLgQU7OqltmgsbM8jRo0qJF3K9Fi5rzU4iIiEjztjYj\nOm6u7ri7n7+WbWu0zMyfeMJZsCD+4ZbO50yzXBcXxzJsixdXDGXt1Ck6A6Wl8Q/WhQvjH6e77Rbr\nbKf/0IX4x/qyZfGP98r/SJWmK9uv3+4xP3jhwugcpMuhzZ4d5dOnx3dp2rTInN+jR8VSZ+k/7NNO\nwcKF8X1p0aLiF9f019fMX2FLSio6A5U7F9m2zTePTms6zDpNuJYOgU/r11Jr1SspieeYmxsd8fQX\n6xUr4r/5b7+NTu/SpfE5f/ddBBImT4aPP45zOnWKsgEDKhIIZltlIF2W8d//hlGjIsix227x/Vm8\nOEYBLFgQz7Zdu/h/V1FRzFlfvjw6ymmHdOnS6KjvuWeMvOjePV5vtFH8Ej5jRnSU01/fO3aMKQH1\nOS99fVu2LIIBW2219t/rNCCU/veSTpFI75uOvkhXVmjVSv8tiYiIiKyttQl0nFrdcXcfs5Zta7TM\nzA87zOnYMf6Bn5tbMRc6Nzc6rIMGVay5nZsbnYipUyt+DevUKToQ//pXdGZXrIh/AOfkVKwrv3hx\n/FqY+ctk2pmobv/77+PXxXbt4le0ylunTtGm/PzYWraMTk86bDwd+pvOc/7qq7hXZsbxjh3jvG+/\njc8kJyc6PEuWVPyqmW7t2sUvheXl8XnNmbPqsPrKW8uWq3bCzSreY1lZdAbTIeLpuZnnZw4jbtcu\ntrZtVx0inf7Km/lrb3VlxcWRS6C8vOLXzPTZZi4Bl7mfJjxLh5TPmBHfkc6d4zvy/ffRoe3YMQIX\nS5bE92CjjSqywffqFcnn+vSJX5Hnzo22FBXF80o7yitWxHNNs85ntjHdr9zuzPeZbdh4+vqjj2Kq\nQPrc04RrCxdGG9Jzc3Pj2Z9yChx3XMxpnzYt6ktHBwwYEO/vq68qvh8lJRVDvKuyfHl8Vum2eHF8\n1m3bRp1t2sR7a9u24lmnv5DDqpnrM19nO/b99/G9b9ky7ltWlj1YlOY+SINL6TKJaXCg8j5EYDTN\nTJ+bW/G97dYtfqHv0qViOHs6zL5//4rvQE5OPJO6TkcpKdEv3CIiIiLyw1FvU1fMrCPg7r64vhrX\nWNXn1BX36LimCfHS0SEQwYq33opOc9qZT+f2pn+z7bdpE52kpUsjgFF5W7gwOmtffRWd79LS6Hy3\nbRsdsHSec05OlPfvHx3RNGla2tksLY1OaxqI6NGjorOdJmJr3TraUVQU5/XuHb8Iu1d00CtvlQMO\naXvSLW1rGvioHJxI75MOQ0+HSZeXrxpEyQyOVC6rfKxjx0gol5tbMUonXS6v8jJw6X5ubnwmpaUR\n6OnVKz6XBQuio54Ot24KiQHTKQLVHV++PL7Lv/1t5DbYaqsIpK1YUfEc3nknzunXL95769YVQYrq\nOu+5uasGWjp0iDrT4EIaeEhfp8ko06BT5n7l15WPtW4dQYfS0oqAWrZgUevW8azTIE0aUMsMrqX7\n6XKAIiIiIiKy7q11oMPMtgXuB7oCBswFTnH3j+qzoeuLmR0K3Eis1DLa3a/Lco5ydFShsLCQgoKC\nhm6G1IGeWdOi59X06Jk1DXpOTY+eWdOjZ9b06Jk1fnpG2VUV6Mipwz3uAi5093x37wP8Eri7vhq4\nPplZDnALcAiwDTDMzAY0bKualsLCwoZugtSRnlnToufV9OiZNQ16Tk2PnlnTo2fW9OiZNX56RnVT\nl0BHe3d/IX3h7oVA+3pv0fqxKzDZ3ae6+wpgHHB0A7dpndB/EM2TnmvzpOfaPOm5Nk96rs2Tnmvz\npWfbPOm5Nk/18VzrEuj4ysx+a2abJttvgK/XugUNoxfwTcbr6UlZs6P/+JsnPdfmSc+1edJzbZ70\nXJsnPdfmS8+2edJzbZ7q47nWJUdHHjAK2CspegkY5e7Fa92K9czMjgMOdvf/l7w+CdjF3S+odJ4S\ndIiIiIiIiIg0UtlydLSs6SIzu9/dTyYSj56/Tlq2/k0H+mS83gSYWfmkbB+YiIiIiIiIiDRetZm6\nspOZ9QRON7M8M+uaua3rBq4jbwD9zCzfzFoBQ4HHG7hNIiIiIiIiIrKWahzRAdwB/BvYDHiLWFo2\n5Ul5k+LuZWZ2LjCRiuVlP2ngZomIiIiIiIjIWqpLjo7b3f3sddweEREREREREZE1VpdVVzpULjCz\n++uxLSIiIiIiIiIia6UugY5tzKy9mbUAMLOWwE7rplkiIiIiIiIiInVXbaDDzHLMbJyZlQI7AouA\nFWZWluy/VFMFZtbazF43s3fM7AMzuyIp39TMXjOzz8zswSRwgpm1SuqcbGavmlmfjHtdkpR/YmYH\nZ5QfamafmtnnZnZxRnmd6xARERERERGRpqumER0vAB8RIzf+5u457p4DbAicBGxoZidVdwN3LwH2\nc/cdgR2AwWa2G3AdcIO7bwnMB85ILjkDKHL3/sCNwPUAZrY18BNgK2AwcJuFHOAW4BBgG2CYmQ1I\n7lWnOkRERERERESkaasp0HGgu1/l7u8Bu6SF7l7k7o+6+7HAQzVV4u5Lk93WxEovDuwHPJqUjwGG\nJPtHJ68BxgP7J/tHAePcvdTdpwCTgV2TbbK7T3X3FcC45B4k19amjgNqeg8iIiIiIiIi0vhVG+hI\nAgeY2XbAPDMbYWbbZjunOskUmHeA2cCzwJfAfHcvT06ZDvRK9nsB3yT3LgMWmFnXzPLEjKSscvl0\noJeZdQOKa1nH/KQOEREREREREWnCWlZ30Mw6AxOA3sBGwD7AVWZWAkwFytx9+5oqSYINO5pZJ+D/\niOknq52WVlvFsarKswVr0vMrX1NVHZZxrKLQrHZr74qIiIiIiIjIeufuq8UKapq6chXwJtAf2Bbo\nm+z/HXgDOLKODVgIvAjsDnRJ8msAbALMTPanE4EVkhVeOrt7cWZ5pWumA30ql7v7d3Woo1NSR7Y2\nN+ntiiuuaFL31bbuPn89s8a/ZT4jPa+mt1X1zPQsG9dWX89Dz7XpPbPGVldz3hrj59gY29SYtqb6\n+TTVdv8Q3+v06c5xx9X/e61KjTk6gBHuXu7uU4EuwBHAx8A+SVm1zGyDZGQIZtY2uefHRKLT45PT\nTiVGjgA8nrwmOf58RvnQZMWUvkA/YBIRcOlnZvlm1goYmnGv5+tYR7NTUFDQ0E2QdUDPtXnSc22e\n9FybJz3X5knPtfnSs22e9Fwbp2zxh5tvhvHjoaio5uvr47nWFOhY7u6lAGZ2AfAAMYVlA6CHmZ1X\nizp6AC+Y2bvA68Az7v4UMAK40Mw+B7oCo5PzRwMbmNlk4BfJebj7x8DDRJDkKeDnHsqAc4GJxAox\n49z90+RedaqjOdJ//M2TnmvzpOfaPOm5Nk96rs2TnmvzpWfbPOm5Nj6LFsG228L8+fF68mR49FEY\nPRry8+Hjj2u+R30812pzdABtzGxHIofFOcBpwLLk9U+AnwF/qe4G7v4BMChL+dfAblnKS5J7Z7vX\n74HfZyl/GtiyPuqQ2tH/VJoePbOmRc+r6dEzaxr0nJoePbOmR8+s6dEza/yayjN67rkIZjz9NHz6\nKdx2G/zoR/DLX8IXX8SxvfZa9+2w6ua1mFkhFUk6dwHeAtJVTHKAru6+3bpsYEMyM6/u8xERERER\nERGRcMYZ8OabsPHG8ffjj6F79zh2ww0wbRrcdFP19/j44xgRssceNddnZnhdk5G6e4G77+fu+wGX\nEzk6Xky2zlRMBamu4k3M7Hkz+9jMPkinu5jZFWY23czeTrZDM665xMwmm9knZnZwRvmhZvapmX1u\nZhdnlG9qZq+Z2Wdm9qCZtUzKW5nZuORer5pZn5rqqI1NN90UM9NWzbbpppvW5SMVERERERGRJqy8\nHJ58Em69FZ59Fo45piLIAbDNNhHEuPji6qewnHMOHHUUTJwYf488EoqzLh1StWoDHWa2i5l1B3D3\nPwHjgWOA/YAL3P3GWtRRClzo7lsDPwLONbMBybE/ufugZHs6qXMrYlrJVsBg4DYLOcAtwCHANsCw\njPtcB9zg7lsC84EzkvIzgCJ37w/cCFyf1LF1tjpq8V4AmDp1aoNnrW3s29SpNeapFRERERERkWbi\nv/+Frl1jaso558BFF616fOut4aWX4Prr4YknKsrdYezYCJRMmgRffRUjQ4YMgUMPhdJSePjhurWl\npmSkdwJmZr8ws38AvySWnH0JqE0iUtx9tru/m+wvBj4BeiWHswUXjiYSipa6+xRgMrBrsk1296nu\nvgIYl5wLsD/waLI/BhiSca8xyf745DyAo6qoQ0RERERERETq6I474Gc/i/1bboEtK2XR7N0b2rSB\n44+H116rKH/zTTjxRHjkEfjNbyKfxzXXwNdfw89/Dv/zP/DAA3VrS02BjhbESIidiVVLZgN7uvtv\nieVd68TMNgV2IFZfATjHzN41s79asgQtEQT5JuOyGUlZ5fLpQC8z6wYUu3t5ZnnleyWrsywws67V\n1CEiIiIiIiIidfDttzFt5bTTqj7HDD75JEZ0vPZaxTK0f/87FBTEKI7iYjj7bGjRIvJ8AAweHFNd\nnnoKysoiqelee8E++1RdV02rrrQAtnb37czsU+Bs4IZaXlvpTVkHYlTFBe6+2MxuA650dzezq5P7\nnkn2UR5O9qCMJ+dXvibNIFrVvaoqX83IkSNX7hcUFDSZbLciIiIiIiIi69ro0XDZZRGgyMur/tye\nPSsCHNOmQa9e8NBD8MorsULLz34GubmrXtOqVYwQufhi+MtfCpk1q5A2bWDAAHj55ez11BSseBC4\nxMwmAN8DhQBm1g9YUMO1KyXJQccD97v7BAB3n5txyt1AOktnOtA749gmwEwiONGncrm7f2dmXcws\nJxnVkZ6fea+ZZtYC6OzuxWZWVR2ryQx0iIiIiIiIiDRnxcUwc2YkD63JNdfAvffGsrLbblu7+5vB\nbrvB+PEweTIMHAj9+sGf/lT1NUOHwhFHwA47FDBvXgFffRVBlTFjRmU9v6ZVV34HtAUOAjYHFgLb\nA+8CO5jZwtq9Fe4BPnb3lQvJpElOE8cAHyb7jwNDkxVT+hJTZCYBbwD9zCzfzFoBQ4EJyTXPA8cn\n+6dmlD+evCY5/nwNdfzgvPLKK2y11VYN3QwRERERERFpBEaNgsMPhxUrqj/vtdfgL3+JURW1DXKk\nzj03RnJMnRoBj9ro0CGSkt52W80jR8w964yNOBj5LDI5MN+ru2j1e+xJJC/9ILnegUuB4US+jnJg\nCnCWu89JrrmEWDFlBTHVZWJSfihwExGgGe3u1yblfYnkpHnAO8BJ7r7CzFoD9wM7AvOAoUny0Srr\nqNT2rG/VYq3e2n4EP0j6jERERERERJqWxYshPz+288+vOudGaSnssANcfjn85CfrtYmrSPqdq6Wm\nqCnQ8TWr57voALwHnJkGDZqr5h7oKCsro0WLFuvk3s3lMxIREREREfmhuOMOePbZGHFx9tmRBDQn\nyzyQMWMiN8eLL8ZUlIZSVaCjpqkrfd19s2Trm2wbArcBd9Sy4k3M7Hkz+9jMPjCz85PyPDObaGaf\nmdkzGauuYGY3m9nkZEWWHTLKTzWzz5NrTskoH2Rm7yfHbswor3MdzUHfvn259tpr2WabbejWrRtn\nnHEGy5cv58UXX6R3795cf/319OjRg9NPP31lWWr69Okce+yxbLTRRmy44Yacf/75K4/dc889bL31\n1nTr1o3Bgwczbdq0hnh7IiIiIiIisg789a9w1lmxCkrbthH0SE2YAFdeGTk8rrwSrr66YYMc1alp\nedms3P0fwEa1PL0UuNDdtwZ+RCwpOwAYATzn7lsSuTMuATCzwcDm7t4fOIskoGJmecDlwC7AbsAV\nGYGL24kRJlsAW5jZIUl5nepoTsaOHcuzzz7Ll19+yWeffcbVV18NwOzZs5k/fz7Tpk3jrrvuAiIK\nBlBeXs4RRxxB3759mTZtGjNmzGDo0KEAPPbYY1x77bU89thjzJ07l7333pthw4Y1zJsTERERERGR\nevXBBzBnDhxwQAQwzjsPbr45jrnDyJFw993Qty8MGVL98q4NbY0CHclSsbW61t1nu/u7yf5i4BNi\nlZOjgTHJaWOS1yR/70vOfx3obGYbA4cAE919gbvPByYChyZJTTu6e5pM9D5gSMa96lJHvTFb+21t\nnHfeefTs2ZMuXbpw2WWX8eCDDwLQokULRo0aRW5uLq1bt17lmtdff51Zs2Zx/fXX06ZNG1q1asUe\ne+wBwF133cUll1zCFltsQU5ODiNGjODdd9/lm2++WbuGioiIiIiISIO76y445RRIsxsMGwZvvBEr\no0yaBIsWwfvvwz//CTfc0LBtrUm1y8ua2YVZivOAo4Bb6lqZmW1KJCB9Ddg4TT7q7rPNLB0h0gvI\n7D1PT8oql8/IKJ+e5XzqUEd6rzl1fU9Vaej0FJtsssnK/fz8fGbOjNVzN9xwQ3IrL0ycmD59Ovn5\n+eRkmYQ1depULrjgAn75y18C4O6YGTNmzFhl6ouIiIiIiIg0DR9+CMcdB3vsAc8/D//5T8Wxtm3h\njDPgllvgyy9jSkteHuy1V8O1t7aqDXQAHSu9dmA2sarJB3WpKBkFMp5Y4WSxmVUVCqg8lsGSerON\ncaiuvNrmrME1TUrmSIupU6fSs2dPoGKaSja9e/dm2rRplJeXrxbs6NOnD7/5zW80XUVERERERKQZ\nWLECfvpTOPZYWLIECguhV69Vzzn7bOjXD3beOVZhaSqqDXS4+6j6qMTMWhJBjvvdfUJSPMfMNnb3\nOcn0k2+T8ulA5hCBTYCZSXlBpfIXqjkfYHYd61jNyJEjV+4XFBRQUFCQ7bRG59Zbb+Xwww+nbdu2\n/P73v1+Za6O6lVB23XVXevTowYgRIxg5ciQtWrTgrbfeYo899uCss87it7/9LQMHDmTrrbdmwYIF\nPPvssxx33HHr6y2JiIiIiIhIPZgwAc45B370o+qTivbpE3k5Dj4YKmU+aBCFhYUUFhbWeF5NU1fu\nAm529w+zHGsPnACUuPsDNdRzD/Cxu9+UUfY4cBpwXfJ3Qkb5OcBDZrY7MD8JVDwD/C5JQJoDHASM\ncPf5ZrbQzHYF3gBOAW5ekzqyNTwz0NGUDB8+nIMPPphZs2YxZMgQLrvsMl5//fVqR3Tk5OTwxBNP\ncN5559GnTx9ycnIYPnw4e+yxB0OGDGHJkiUMHTqUadOm0blzZw466CAFOkRERERERJqQV1+FM8+E\nxx6DPfes+fxTT133baqtyoMPRo3KPjbDqvuFP1l29VJgO+BDYC7QBugPdCICGHe4e0k199gTeAn4\ngJge4sk9JwEPEyMrpgHHJ0lGMbNbgEOBJcBP3f3tpPw04LLkHle7+31J+U7AvUnbnnL3C5LyrnWt\no1LbPdvnk6zVW+Xn1tD69u3L6NGj2X///RusDY39MxIREREREWmOpk+HI4+E3/wmVlB59FHIzYWT\nT4bvvoMdd4Q77oAjjmjolq69pN+52q/51QY6Mi7uAOwM9AC+Bz5x98/qvZWNjAIda66xf0YiIiIi\nIiLNzaOPwgUXwP77w2uvweabx0IZc+bEsrDTpkXw47rrGrql9aOqQEdNyUiBlcvCFtZ3o2TdqG56\nioiIiIiIiDQv7nDJJfCPf8ADD8A++0SwY/58eOklKCmBG2+MFVaOP76hW7vu1WpEx1pVYDYaOAKY\n4+7bJ2VXAD+jIjnope7+dHLsEuB0oJRYoWViUn4ocCORn2O0u1+XlG8KjCOWvX0bONndS82sFXAf\nsBPwHXCCu0+rro4sbW+SIzoaA31GIiIiIiIi68f118NDD8Ezz8AGG0TZ3LnQsmUsCdtcVTWiIyfb\nyfXsb8AhWcr/5O6Dki0NcmwF/ATYChgM3GYhB7gluc82wDAzG5Dc5zrgBnffEpgPnJGUnwEUuXt/\nIkByfVLH1tnqqO83LSIiIiIiIlKfiovhlltilAbAsmVw331www3wf/9XEeQA2HDD5h3kqM46D3S4\n+10Aj1sAACAASURBVCtAcZZD2YILRwPj3L3U3acAk4Fdk22yu0919xXECI6jk2v2Bx5N9scAQzLu\nNSbZH5+cB3BUFXWIiIiIiIiINEppfo1nnomVUH71K9hhB/jb32D8+FgKVkJNy8s+QaxwkpW7H7UW\ndZ9jZicDbwK/dPcFQC/g1YxzZiRlBnyTUT4d2NXMugHF7l6eUd4r2e+VXuPuZWa2IFmFpao6RERE\nRERERBqd666L6SkXXgiXXgqzZ8OPfxyJR88+u6Fb1/jUlIz0j8nfY4DuwN+T18OAOWtR723Ale7u\nZnY1cANwJtlHeTjZR554cn7la9LATFX3qqo8q5EjR67cT9fszc/PV8LPGuTn5zd0E0RERERERJok\nd0i7nB9+CH/8I3zwAfTsGWU9esSqKj80hYWFFBYW1nhetYEOd38RwMxucPedMw49YWZvrmnj3H1u\nxsu7gSeS/elA74xjmwAzieBEn8rl7v6dmXUxs5xkVEd6fua9ZppZC6CzuxebWVV1ZJUZ6EhNmTKl\nprcoIiIiIiIiUifTp8MvfgETJ8Ihh8TIjQ8+gD/8oSLI8UOWDj5IjRo1Kut5tc3R0d7MNktfmFlf\noH0d2rPKyAsz655x7Bjgw2T/cWCombVK6ugHTALeAPqZWX6ymspQYEJyzfNAukDOqRnljyevSY4/\nX0MdIiIiIiIiIuvFokXw9NPw17/CY4/BySfD9tvDllvCu+/CEUfAlVfC/2fvzOPsKKrF/627zr5m\nksm+ECAJCMiSsBNQkbDIrsATlYcP3BBFUeSnIj6fy3vuKIiKQJ4PQVRQFgEFBpEtSNhCEpIQksm+\nTGafu9/z++PcvvfO5E5mJpnJZIbz/Xzq093V1VXVVX379jl16tTy5fAf/zHctR1Z9DV1xePzQINz\nbnXmeBpwRX8udM7dBcwHap1zjcANwMnOucOANLAGuBJARJY6534PLAUSwKcy67umnHOfAR4jt7zs\n8kwR1wF3O+f+E3gZuC0Tfxvwv865lUATqhzZVRmGYRiGYRiGYRjGMJJKQUsL1NYOd02GjkcegZ/8\nBJ55Bo48EqZOhY0b1dHoTTdBVZWmmzFj1/kYveP6K+M758KAt6TrchGJDVmt9hGcc6YDMQzDMAzD\nMAzDGGIiEbj3XnW4uXo17LefCv/RKFxxhTrjDIV2vq6pCV57DVatgmQSPvGJnG+LvUk6Ddu26ZKu\nvh7zJkTg8cfhzjvViuOll/Q+zzgDKir2fl1HE845RGSnHu+XosM5dyHwiIi0O+e+ChwOfEtEFvfj\n2tuAM4EtInJIJq4auAeYilp0fDCz6grOuZ8CC4BO4GMi8kom/qPA/0Mdh/6XiCzMxB8O3AEUAQ+L\nyOd2t4wCdTdFh2EYhmEYhmEYxiCwbh1s3QqzZkFpKWzaBM89pwqK667TKRtXXw0nnwyvvqqWDiJw\n+eVQWQkLF8KaNTBzpioMPvxhePJJOPRQ2H9/ePFF+NSnhnYVEhF44gn1nVFTAw89BIsWqaLFOb2X\nr31Nt4EAJBLwf/+n137yk6rYeP/7YezYoavjO4k9VXS8JiKHOOeOB/4TXY3l6yIyrx/XHg90AAvz\nFB3fA5pE5L+dc18GqkXkOufcAuAzInKGc24e8BMROTqjtPgXqmBxwEvA4SLS6px7AbhKRBY55x7O\nXPPoQMvope6m6DAMwzAMwzAMY8hYswYmTwa/f7hrMnS0tqqfibPOUoeaW7bAnDmweDEce6wqAz75\nSV0utRCRCBx9NLS1qYLht7+F//ovnfbx859DMKjp3nwTjjtOVyOZOXPw6v/QQ7BiBYwZAw8+qM5B\nDz1U7+PYY9WXxrRpUF+vdfjyl2HiRFV8+Hxw3nlw0knDY2ky2tlTRcfLIvJu59x3gNdF5C4vrp+F\nTwUeyFN0LAdOEpEtGcekT4rIbOfcLzL792TSLUP9e5ycSf/JTPwtQAPwFPCEiMzJxF/kpRtoGSKy\n03K5pugwDMMwDMMwDGOo2LRJrRh+8IPR6Wxy61a11Lj7brVuuPNOVWa8+qoqBD7wASgq6l9eq1fD\nCy+oZcdZZ6mTzuuv31l58P3v64oljz6aO7dpk05/+fnPYcqUnfPuiYhah7zwgub15puwYIFOkxk3\nTpUs/a23MbT0pujorzPSDc65W4H3At/L+Ovo74othRjrKRZEZLNzzjPcmQisy0u3PhPXM35DXvz6\nAukBxvWzDC+vnRQdhmEYhmEYhmEYQ0FbG3z2s3DUUfDDH+r0jJ6+HYaDaFStJqZNU0uMX/4SGhvh\n9NNV2O+NSERXDnnkEfWt4ferL4qPflTzy/dFceihGgbCjBk555xbtqh1RSGuvlotPn73O7jkEtiw\nAS7MrNF53nnw7W/DiSd2V1Tcd5/W2efTcP/9OhXllFPgnHPgoot0qo0xcuivouODwGnA90WkxTk3\nHrh2COrTUxPjUJ8chYx8dhU/kDL6c41hGIZhGIZhGMaAENHpGdXVMH26xj30kFoWPPOM+qL4y19U\n8H7gATj77L1fx1RK6/j44/D3v6sVQzisioQNG7RORxwBl10Gn/+8Ot1ct06tJcJhWLkSli5Va4dj\njlHFwPTpmu/Mmeo7Y7DpTckBOo3l1lu1Hs89p/4xPv5x+O531QrkuuvUR8j//R90dsJdd6mj09NO\n0zqLwKmnar94U2KMkUd/FR1jUB8ZOOc8Y5/lvSfvky3OuXF500q2ZuLXA5Pz0k0CNmbi5/eIf3IX\n6QE2D7CMgnzjG9/I7s+fP5/58+f3ltQwDMMwDMMwjHcoIt2nUWzcCJ/5jFo1pNNqKeGcOqG8/nq4\n556clcM3vwnXXKMWE4VWFhkIzc2qdKivh7Ky7ue8egSDGp59Vq0Vysvhve9Va4iTTlLrhcWLdVqN\nV8eTToIf/Uh9bMyZo/cSicC8eXD44arM2ZUCYm8ybx5ceqn6P1m1Sp2GAnzjG/ClL8G7360WJStW\naL2fegoOOGA4a2z0l4aGBhoaGvpM118fHa+Ts6AoAqYDb4rIQf2pjHNuGuqj412Z4+8BO0Tke865\n64CqjKPQ04FPZxyFHg38uIAzUl9m/4iMdckLwFXAi8BDwE9F5JGBltFLvc1Hh2EYhmEYhmEYu+Sx\nx+BDH9JpKJWVOlXjjTfUL8SNN6rlw/r1Op2jvr6wU8ozzlBfEq2tOjVj9mxVevzgB+qIsxD33gt/\n+pNaUKxapRYJTzyhgv2WLWqJceqpOvXkz39WnxnhsKYLhXT6xp13atnvJJYs0f45/3z1HWKMXPbI\nGWmBzA4HPiUiH+9H2rtQa4xa1A/GDcD9wL2oZUUjcKGItGTS/wydJtMJXOYtYeuc+xi55WW/lbe8\n7BF0X1726kx8DfD7gZRRoO6m6DAMwzAMwzAMoyDbtqnzy9tv16kQra2qRBg3Dg4+eGAWDjt26LSR\n/faDujpdpeSpp9TnRUOD+vRYvFing+zYAa+8Al/8ItxwgyowDjxQlzRdsECvj0RUAfOPf+iqLmee\nqXk7p3Xs7FRLDlsJxBjJDKqiI5Ph656FxmjFFB2GYRiGYYwUEgno6tLR5FQKtm9XE3kTYox3Osmk\nTh8pLYWSEp2qUF2tU02amlSB0NmpUzoSCQ3JZG4/kVAFxvr1aqmRTmu+W7fCk0/CxRer34fJk3dd\nj92t+4EH6jSLJ59Ufxdr16oCZepUuPZa9fNhGO9U9nR52WvyDn3oFJJaEXn/4FVx38MUHYZhGIZh\njARiMXjf++Bf/9KVEtas0fn306bBwoUDX93AMAbKjh1QVaUrVjz5JPzmNxCPw69+pVYDkQi0t6ui\nobRUlQbLlsH48Tp9or4+5wsinVZh/gc/UGXCtm3w9ts6HePMM7WspUtVUTF+vJabSsHEiSr8x2LQ\n0aHKiyVL4G9/g0mTtD6xmF5fW6tKi3BYry8t1Xp4visCgdx+MKh1mzxZLTX8fi17zBg4/ngtdyh5\n7DG13vjIR7SdDMPIsaeKjhvyDpPAGuCPIhLdw0qtAVqBNJAQkbkZfxz3AFMz5XxQRFoz6X8KLECn\nnHxMRF7JxH+U3LSW/8qb1nI43ae1fC4T32sZPepnig7DMAzDMPY5YjH47/+GX/9a5+6vXg1TpsBt\nt6mSY+ZMFSjvuEOdHC5apNYdoPP4vVFib9S6pEQFxYcfVuHv9tt1Gcd//3cVFA84QEe+Tz9dhb5E\nQkfEQQW+1av1uKxMBdMdO1So/ec/VUAsL1dLk0mTdOutPtHSogJleflwtKLRX1KpnKKhqkoVCA0N\naiWxdi289ppOs/D7tb/DYfjCFzT+wQe1n1Mp7efOTlU4jBmjPig2b9bjTZv0ulhMLSuqq3WljMMO\n0/3991flyMMPq7A/Z46Wt2lT7jlav16f/+JiLauoSJ/z971PHWh6xGKabuLEnZ11GoYxshj0qSs9\nMr9JRK7ajetWo05Fm/Pivgc0ich/O+e+DFRnnIguAD6TcSI6D/hJAUelDngJOFxEWj1HpSKyyDn3\ncOaaR3sro0D9TNFhGIZhGMY+RUcHnHeeKhxuvBFefVUFv/e+VwXFntxwgy5b+dOfws036xKSwaDO\n1W9sVMXE7Nmab1WVCpQf+IA6NHzuOXWuuHq1KkNeeCFn1u+NbHd16XFHhwqodXUqEBcXw7HH6vmu\nLlV+bNyoU2q8aTY+nwrAnoC8ZUtuWoBz6kjxS1/SJSsrKjRPv3/vtndfrF+v9xQO6z01N6sQ3dio\n7QlqUTNxot6Tc3rfhfZLStTSoNB0I2+aRSCg1gfJpE6naGvTdq+s1DSe1YKnxNq6VdvUK+e11+Dp\np1UJEA5rfUH7IZHQOldX6zadzik1PCuJtja9bt48tWaYOlWVCccco8qt1lZVKoRCWp8XXlBFmbfq\nhYiW03NlEW96SHGx5m8OIg3D6A9DrehYLCKH78Z1bwNHikhTXtxy4KS8ZWGfFJHZzrlfZPbvyaRb\nhjo5PTmT/pOZ+FuABuAp4AkRmZOJv8hLV6CMBhGZVaB+pugwDMMwDGOvkE6r08FNm1So9AToceN0\nxHrMGI1fuBDOOgtuuql/wqAIfOUrcN99uozkl7+sQub//q9aVhx1lFptJJOqmNiVT48VK3SkvKZG\nhXnQOsyYkVNQ9KWIENF7q6xU5YVITmAfN07zE9H87rtPp0C8/LIK3JGInuuN6dNV8A4GVbEwaZKG\niRP1+m3bNH+fTxUC69apcqK9XUNFhdarslLT1NZqu7e3a/1aWlQIr6jQujzyiLbdhAnaN57/h0mT\ndNpQa6vex0svaV+K5O7N288/bm/X/dJS7aOWFi0nFNL4oqKcE0m/X5URngVFa6vWORzW9IGApvHa\nNJ3WMHUqvP/9uh+JaH09hYtXZnOzBr9f63LQQarI8JxY7mvKJsMw3rnsq4qO1cAOdMrJrSLya+dc\ns4hU56VpEpFa59wDwHdE5NlM/N+AL6OKjrCIfDsT/1WgC1V0fEdETs3EHw98SUQ+0FsZBepnig7D\nMAzDMAaNZBJ+/nM1929tzQm6zc1q+l9WpqP/4bAKqGPH6oh8VZUK6aGQCqnz5g33nex7pNPw5ps5\nRdHGjapQ8UJpqbZpKqUhGFSfC2Vleq6iQpUZra05BcXWrdo35eV6vqpK825tVaXD4YfDhRdqXoOB\niCoaolEtp7JSlSeJhJ73plmImJNZwzAM6F3RMdxGYceKyGbnXB3wmHPuTVTpUYielXeZtIVe87uK\nHxDHzz+e95z0HpxzzJ8/n/nz5w80C8MwjHccbW06glhSoseRiDqnGztWhYXt29WMvaYG5s7tPiod\niagQWFmpTuZ2h3XrcsJJebkKMZ5jOWP00dGhQp+IKgO80NSkCgFvKcZoVJ+paFSfCW9EOxjUuCVL\ndOuNcXi+JNJpHf3PD+m0Pt/FxToSPn68bguRTqtgvHQp/Od/qrLj9NN1uog3naC6WvOYN88E2N3F\n59M2nT17uGuy+3jPQk96TkmyZ8QwjHcqDQ0NNDQ09JlusBQdu/W6FZHNme0259z9wFxgi3NuXN60\nkq2Z5OuB/EWbJgEbM/Hze8Q/uYv0AJt7KWMnXpPX+PyRn+f8s87fnVs0DMPYY9raVECqqoJZO02y\nG35E4MUX1au9N/L4/e+rMHjIIXq8fDm8610q7HV16Yd8XR1s2KDm72PHqlDZ3q5KigkTVEi96CLd\nT6d17n5zs17X1JQzAw8Euof2dnW8OH58zhy9s1PTHn64+jIIheDcczX/nixbBk89pXX3TLVbW1UY\n9hQ3g00ioUJ2ebnuNzZq2LJF2/Gpp1Rw9/wiHHOMtqOIOghsb1chL5HQe62q0vYeN07b793vVoG9\ntVXbralJ9732SaXUX4PPl3MEGItpENFzRUU5IT8aza1o4Jy2u3Oad2OjCv9lZbkQCOT8BfTcplI5\nk3rPfD//OJ3Omep3dOh+IKDXrlunSjFPOeGcPh9eqKxU/w5NTTnz/s2b9V68vINBrUsgAAcfnBsx\n9+5n7VpNEwp1D86pksTzT+GcPsdNTbk6p1J6PhbTfGfMgPPP12Uozf+AYRiGYQycnsYHN954Y8F0\ngzV15WMicscArykBfCLS4ZwrBR4DbgTeA+wQke85564DqjLOSE8HPp1xRno08OMCzkh9mf0jRKTF\nc0YKvAg8BPxURB7JOCP1ytilM1JugHlvzOO53z+HM/W5YRh7ERGdi3/ddTrfe9MmFdYOOkgF4Pp6\nFa4OPBCuv16Ftz2lsxMWL1ZhcMuW3La+Xp3JBYMqDG/ZovP7V65UQXnqVFUcJJMq2F1xha4AsXSp\nCnSzZuk890LkC95lZSqQFxdr/A9/mJs7XlurYfv23Lx5v1/LzBeenYMFC3LLFHpEo/D88zqy39mp\nAvC55+ZWhejo0O2yZXDGGeqEMRTSMt56S4XW4mLNx+fTlS5qa/X+6utVsdLWpvkEg2rB0tSk7XDg\ngTmLg3Q654wxmVRBeu1aTReJqDA+ZYqGceP0uuOO0zb2+1WxsXhxbpWKKVPUMsabN19eruW+9pre\n69q16t9g7VpVMHntWFWlacvKtIzVqzW/cFiD56hQJLdKRyik9xYO5yxlIGdWX1Wlfgk8RYinDEkk\nui/XmL/1+zV4lg0+X/fgxfVUmvj9Ou1gyhR9Fnr7i/YUKZ41j+eUsasrt2zkYPDWW9r/3jPh82kd\n860+DMMwDMMYXPZ0edm/AReKSEvmuBq4W0TevwcVmg7ch04nCQD/JyLfdc7VAL9HrTEae5T7M+A0\ndHnZy0RkcSb+Y+SWl/1W3vKyR9B9edmrM/G9ltGjjsI3oGRNCQvPW9irVUcymfNwXVJS2OO5YewK\nER3NffttmD9/ZwHNGL1487GXLYPXX88Jp5Mnq6AMcMstcOSRKqAtXqzOAOvrVQnhHPzxj+ro7owz\ndOR/xw4VCGfOVAF45UoVoGMxFcImT1aBta1N31mlpSqIbdigZR5wgDruq6/P+QhYt04tL5JJrUdF\nBVxwgU47qagYmVNCXnxRnRxeeqneV1mZWozMmqX3n0qpsqOoSEf6fT5tM0/Z8dxzKsgnEjr/3/Oj\nUFqq7X3ssSqEr16tigLIraxQW6v/FX6/KjCmTbPlNQ3DMAzDMAbKnio6XhaRd/cVN9rwFB0IVP1x\nHvMmPkc47AiH9aO4qUnNo70lskIhVXhMmKBxkUhumazjjlNnVZ7Jrzci2tWlo5fve58KCp5Tsqam\nnCMyTxhJJnPmxNFoLsRieu2ECZreG6EbTlIpvQefLzdi5o2MDtQwJp3OtfFQG9VEIipo+nwqrJSU\nFC63vFzvpa2te2htVWEnGlUBs6JC+7itTYXN1la9NhTS/VRKBdc33lChcvp0ePZZHQ30Rl3LyrQs\nrw1bWlTwCgS6m5fnPxtenNduPUdoITfq7J074ABdxs/zLN/Wpuk9wc3rx3BYn+kzz+x9hH4wiMe1\nDt6IqOeFfzDNvVOp3IgxdF+WL789/X5ti2Aw17Y92z0W05FrT6gNhfQZyLc22L5dlRPNzdr+3jSC\ncFhH/A85RKcb1NSo+f8RR8Cpp/bPu/0LL8Bjj+m7ZNIk7cfly/V5OfDAXB82NalQHovlVg3wln4c\nNw5OOEGFfMMwDMMwDMPY19lTRcdLwLki0pg5ngrctzsrrYwksooOIPxWCdfMWMjcw87PKhYqKnRJ\ntsrK3DXRqAoooZAKRpWVKvzcf7+OlJaU5JYGGztWhd5XX1XTaW9+seeIKhrVOdmeMOIJmUVF3UMo\npELLhg0qNHnCoSfATZum84KDQRV6OjtVkPL7cya927bpKHFJSW65N2/rKWU88+G1a1VIKy7WekUi\nOcdutbWa3/r1udHJeFyFPG8utmf67N2rN7/bWzfdUwZ5QmQikTNV9trVE87z22PcuFxbeObrzuXK\n9kJ+fXrGxePqxMzv13bq7NS4fLzl31KpXBt5obxc+7WoSIVZT0lVXq59UFur18bjuefmkEN0/ny+\nJ/W2NhVIm5q0Dt7a9t510ai2c08FRn7beMoQT3DPF8xFtE6e8iwaVdP2ZctyfV9erum9Z8Zrq1hM\nn7PnnlPP/0uW6Eh/UZH2YUmJjn7X1+csCbwyIhEtszdiMb13r33Ly7WuXV1atnPax573+3zloPcq\nyz/eVVwkou3rXO436f22e7ZnKqXpE4md2zz/uKREf28zZ+p9Njdrfn6/9kVdnbZLdbX2d3m5xhUX\n7947yjAMwzAMwzDeyeypouM04Jfokq0AJwJXisgjg1rLvUjmnn6M+vW4TUS+VyBNVtGBjBxfHZ4g\nLqIC2ltvqfIlkVABq6RETdu9+dw+n8bPmqXCqLeWvbdNJnO+AJJJHS1+440G3v3u+VnBtqhIlR+e\n4DhlSmF/AZ51RjzeXej0LGBisZwywxMkPYuKdHrn0XRPcI9EVBnjKUO80X+RnCVCvmVJzzgv3lO2\njEYaGhoGddWgf/xD/S+8612qxEkkVCHR0aFTAnbs0GfKmxbh9emupjgEgznlUTi8syWNiPa1Z3Xi\nKU3ylWY9j3uLC4dVyeCtuhAIaNy+Mo9+sPvLGHqsz0YG1k8jD+uzkYf12cjD+mzfx/qoMHu0vGzG\ngefhwNHoCiufF5Htg1zHvYZzzgf8DHV8uhF40Tn3ZxFZ3vtF8HrZ6/zpwT/t8yuwONfdx0NNjVqe\n9JdJk/pOc889DVxwwfxucUVFfU9l8PlyI+ADxedTgdlGv3ePwX45nniihkIceeSgFdMN51SxVlKi\nFjyDgc+3b/pGsD+zkYf12cjA+mnkYX028rA+G3lYn+37WB8NjH6NXTrnHheR7SLyoIg8ICLbnXOP\nD3XlhpC5wEoRWSsiCeBu4Oy+Luqa2sX/LPwfBmOlmr1Ff9YYNkYe1q+jE+vX0Yn16+jE+nV0Yv06\nerG+HZ1Yv45OBqNfd6nocM4VZVYoGeOcq3bO1WTCNGDCHpc+fEwE1uUdr8/E7Zo8q46Rgv34RyfW\nr6MT69fRifXr6MT6dXRi/Tp6sb4dnVi/jk4Go1936aPDOXc18DlUqbEx71Qb8CsR+dke12AYcM5d\nAJwqIldkjj8MHOUtP5uXbuSYbhiGYRiGYRiGYRjGO4wB++gQkZ8AP3HOXSUiNw1ZzfY+64EpeceT\n6K7IAQo3mGEYhmEYhmEYhmEY+y79XXWlGPgkcDwgwNPAL0QkOrTVGxqcc37gTdQZ6SZgEXCxiCwb\n1ooZhmEYhmEYhmEYhrFH9FfR8XugHfhtJupioFpELhzCug0pmeVlf0Juednv9vfa4uLizdFodJDW\nfDAGSlFR0ZZIJFI/3PUwDMMwDMMwDMMw9j36q+hYKiJz+op7p+Cck5G08spoo7e1kg3DMAzDMAzD\nMAyjX8vLAoudc0d7B865ecC/hqZKhmEYhmEYhmEYhmEYu8cunZE6515HfXIEgWedc42Z46nA8qGv\nnmEYhmEYhmEYhmEYRv/ZpaIDOHOv1GIQcc5NAhYC9UAKXQb3pz3SnAT8GVidifqTiHxrr1bUMAzD\nMAzDMAzDMIxBp6/lZdfurYoMIkngGhF5xTlXBrzknHtMRHpaoPxDRD4wDPUbcqZPn85tt93GKaec\nMtxVMQzDMAzDMAzDMIy9Sn99dIwYRGSziLyS2e8AlgETCyQ1Z5aGYRiGYRiGYRiGMcoYdYqOfJxz\n04DDgBcKnD7aOfeyc+4h59w7cvWYwSKdTg93FQzDMAzDMAzDMAwDGMWKjsy0lT8AV2csO/J5CZgq\nIu8Gfgbcv7frtzcQEb773e8yc+ZM6urquOiii2hpaQFgwYIF3Hzzzd3SH3bYYdx/vzbF8uXLOfXU\nU6mtrWX27Nnce++92XSXXXYZn/rUpzjjjDMoLy+noaGBhx9+mMMPP5zKykqmTp3KjTfe2C3vhQsX\nMm3aNOrq6vjWt77F9OnTeeKJJ/qsp2EYhmEYhmEYhmEMCBEZdQH1PfIIquToT/q3gZoC8dJb2JeZ\nNm2aPP744/KjH/1IjjnmGNm4caPE43H5xCc+IRdffLGIiCxcuFCOO+647DVvvPGGVFdXSyKRkM7O\nTpk8ebLceeedkk6n5eWXX5YxY8bI0qVLRUTkYx/7mFRVVclzzz0nIiKxWEyeeuopWbJkiYiIvP76\n61JfXy9//vOfs3mXlZXJs88+K4lEQr74xS9KKBSSxx9/XERkl/UsxK76xYIFCxYsWLBgwYIFCxYs\nvHOCFJLxC0WO9ICuuvLDXZwfl7c/F1jTSzqJJCJS8Z0KSafT3QTtvoA9D7uLp+iYPXu2PPHEE9n4\njRs3SjAYlFQqJe3t7VJWViaNjY0iIvL//t//k8svv1xERO655x458cQTu+V55ZVXyje/+U0RUUXH\nRz/60V3W4XOf+5xcc801IiLyzW9+Uy655JLsua6urm6Kjl3VsxD9af/Rzg033LBXrjH2Lvl9ZP01\n8uitz6wv9y0Gqz+sX/cee7OtrV8Hh32xHffFOu1LjNT2Gan13h3sXgvTm6Kjr+VlRxzOueOAVJFf\negAAIABJREFUfwNed869jGp5rgemoo3wS+AC59wngQQQAT7UW35diS7aYm20x9upCFf0ux6qJxle\n1q5dy7nnnovPpzOURIRgMMiWLVsYP348p59+OnfffTfXXnstd999N7/+9a+z1z3//PPU1NRkr0ul\nUnzkIx/J5j158uRuZS1atIjrrruOJUuWEI/HicfjXHjhhQBs3LixW/ri4mJqa2v7XU9jZ+bPnz/c\nVTCGAOvX0Yn16+jE+nV0Yv06erG+HZ1Yv45OBqNfR52iA1gLPAXUAyngVyLySH4CEfm5c+5AYAG6\n+kqst8xiST3V1NU0IEXHcOKcLigzZcoUfvOb33DMMccUTHfxxRdz4403csIJJxCNRrMP1OTJk5k/\nfz6PPvpon2V4XHLJJXz2s5/l0UcfJRgM8vnPf56mpiYAxo8fz4oVK7JpI5FI9lx/6mnsjL3URyfW\nr6MT69fRifXr6MT6dfRifTs6sX4dnQxGv45GZ6RJ4BoRmQMcA3zaOTcrP4FzbgGwn4jsD1wJ/KK3\nzKLJKABNkabekuxzSMac5BOf+ATXX389jY2NAGzbto2//OUv2XSnn346a9eu5etf/zof+lDOqOXM\nM89kxYoV/Pa3vyWZTJJIJPjXv/7Fm2++2WuZHR0dVFdXEwwGWbRoEXfddVf23AUXXMADDzzA888/\nTyKR4IYbbuh27ZVXXrnLehqDg/0RjCysv0Ye1mcjA+unkYf12cjD+mzkYX2272N9NDBGnaJDRDaL\nyCuZ/Q5gGTCxR7KzUT8eiMgLQKVzblyh/DxFx47IjqGq8qDjWVtcffXVfOADH+DUU0+lsrKSY489\nlkWLFmXThUIhzjvvPB5//HEuueSSbHxZWRmPPfYYd999NxMmTGDChAlcd911xGK9Gr5w880387Wv\nfY3Kykq+9a1vdVOczJkzh5tuuokPfehDTJgwgcrKSsaOHUs4HM7W8+yzz+61nsbgYC/HkYX118jD\n+mxkYP008rA+G3lYn408rM/2fayPBoaTfcGZxBDhnJsGNAAHS94Ss865B4DviMizmeO/A18SkcU9\nrpeXN73Mu299N3eddxcXv+tiL57R3G5DTWdnJ1VVVaxatYqpU6cO+Hprf8MwDMMwDMMwDCMjG7qe\n8aPOosPDOVcG/AFdYraj5+kClxSUnEeiRce+yIMPPkgkEqGzs5MvfOELHHLIIbul5DAMwzAMwzAM\nwzCMXTEanZHinAugSo7/FZE/F0iyHshfNmQSsLFQXr/4/i/gFfjD23/goK6DzGRoN/nzn//MpZde\nCsCRRx7J3XffPcw1MgzDMAzDMAzDMEYSDQ0NNDQ09JluWKauZPxhfBuYICILnHNzgGNE5LZByn8h\nsF1Erunl/OnAp0XkDOfc0cCPReToAunkkZWPcNr/ncbV867mx6f92Iu3qRPDiLW/YRiGYRiGYRiG\nsa9NXbkDeBSYkDleAXxuMDJ2zh0H/BtwinPuZefcYufcac65K51zVwCIyMPA2865VcCtwKd6y28k\nrrpiGIZhGIZhGIZhGO9UhmvqyhgR+b1z7isAIpJ0zqUGI2MReQbw9yPdZ/qTXzQZpSRYQlOXKToM\nwzAMwzAMwzAMY19nuCw6Op1ztWQcgGamj7QORsbOuducc1ucc6/1cv4k51xLxtJjsXPuq7vKL5aK\nMaF8gjkjNQzDMAzDMAzDMIwRwHBZdFwD/AXYzzn3DFAHXDBIed8O3AQs3EWaf4jIB/qTWTQZZUL5\nBDa2F/RVahiGYRiGYRiGYRjGPsSwKDpEZLFz7iTgQHSp1zdFJDFIef/TOdfXuqWFlpctSDQZZWL5\nRJZsXbKHNTMMwzAMwzAMwzAMY6gZlqkrzrnzgA+gio4DgLOcc+9xzo3dS1U4OuOo9KHMii+9EkvG\nqC+rpzXaSio9KG5Eho0777yTE044YdDTGoZhGIZhGIZhGMa+wnBNXbkcOAZ4MnM8H3gJmO6c+6aI\n/O8Qlv0SMFVEupxzC4D7UWVLQR7+1cOkJEVwQ5AHH3uQs087ewirNvQ4129jlgGlNQzDMAzDMAzD\nMIyhpKGhgYaGhj7TDZeiIwDMFpEtAM65cahPjXnAP4AhU3SISEfe/l+dczc752pEpKC30WMvPZbi\nYDEbX9nInKN2afxhGIZhGIZhGIZhGMYQMX/+fObPn589vvHGGwumG65VVyZ7So4MWzNxO4DB8NXh\n6MUPR0ap4u3PBVxvSg7QVVeKAkVUF1fTHG0ehKoNPd/73veYOXMmFRUVHHzwwdx///0F0/l8Pm66\n6Sb2228/xo4dy5e+9KVu50WEa6+9lpqaGvbbbz8eeeSR7Lk77riDOXPmUFFRwcyZM/nlL385pPdk\nGIZhGIZhGIZhGP1huCw6GpxzDwL3Zo7Pz8SVAi17krFz7i50Kkytc64RuAEIASIivwQucM59ElWo\nRIAP7Sq/aDJKUaCIqqIqWqJ7VLW9xsyZM3nmmWcYN24c9957L5deeimrVq0qmPb+++9n8eLFtLe3\n8573vIdZs2bx7//+7wC88MILXHbZZTQ1NXHrrbdy+eWXs2HDBgDGjRvHww8/zLRp03j66ac57bTT\nmDt3Locddtheu0/DMAzDMAzDMAzD6MlwKTo+jSo3jkMtLxYCfxQRAU7ew7wjgB9dyeWQnidF5OfO\nuQOBBZmyY7vKLJqMEvaHqS6qpjnSf4sOd+Oe+7eQG2S3rjv//POz+xdeeCHf/va3WbRoUcG01113\nHZWVlVRWVvK5z32O3/3ud1lFx7Rp07L7H/3oR/n0pz/N1q1bGTt2LAsWLMjmccIJJ3Dqqafy9NNP\nm6LDMAzDMAzDMAzDGFaGa3lZAf6QCYPN7cBNqPJkJzIOSPcTkf2dc/OAXwBH95aZN3VloBYdu6uk\nGAwWLlzIj370I9asWQNAZ2cn27dvx+fbeabSpEmTsvtTp05l48aN2eP6+vrsfnFxMSJCR0cHY8eO\n5a9//Svf/OY3WbFiBel0mkgkwiGH7KRXMgzDMAzDMAzDMIy9ynAtL3u0c+5F51yHcy7unEs559oG\nI28R+SewK9OLs8koQUTkBaAy329HT7ypK9VF1SNi6kpjYyNXXHEFN998M83NzTQ3N3PQQQehuqWd\nWbduXbdrJ0yY0GcZ8XicCy64gC996Uts27aN5uZmFixY0GsZhmEYhmEYhmEYhrG3GC5npD8DLgZW\nAsXAx4Gf76WyJwLr8o43ZOIKEk1GCQfCVBVVjQhnpJ2dnfh8PsaMGUM6neb2229nyZIlvab/n//5\nH1paWli3bh0/+clPuOiii/osIx6PE4/HGTNmDD6fj7/+9a889thjg3kbhmEYhmEYhmEYhrFbDJeP\nDkRklXPOLyIp4Hbn3MvAV/ZC0YWcZ/RqirD090u597l7aY4245vug/cOYc0GgdmzZ/OFL3yBo48+\nGr/fz0c+8hGOP/74XtOfffbZHHHEEbS1tXHZZZdlfXIUwjlturKyMn76059y4YUXEo/HOeusszj7\n7LMH/V4MwzAMw8ghAqkUJJO5kE5rXG8hnYaqKqirg0Cge15eHl46EQ0ezmnw9ocqzuidnsayAzne\nk2v7k1fPZ8879p4j75o93e+Lnml3Z+vdT8/9gW57/qa8bf5+z20yCR0dEApBWZmG4uLuv42aGqio\nyOXtbfP3vW1++xcK+ffZs6/62/eF+mtfOgYIBiEc1hAM7nze59N4v3/nZ7hQ//Xn+ehv3HCn39Vz\n0Ffw2jC/Tfq79YLfryEQ0G2+d4Xenr38/UikgWi0gb5wwzHdwDn3D1Rl8GtgM7AJ+JiIHDpI+U8F\nHijkjNQ59wvgSRG5J3O8HDipx3K3Xlo54Tcn8K1TvsXG9o3ct/w+7rngHpxzjIZpGj6fj1WrVjFj\nxozhrsqAGC3tbxiF8P4QfD7Yvh3WrYO33tKPoKIi/cMOBPTPORDQP+HSUjjgABg7FtraNM7ng5IS\nTReNQiRS+IOrt+NCIf9nl0rBtm3Q1aX7iYTux2KabssW3S8u1jrs6oMsFMoFEc0rkdCPvPp6vb5n\nPfKPPbyPQhFobtYyvD/TQmHSJG237dv1Oi++qUnjAoHCwWt7L+SXm9+PPfd7O59O6/3G4xry9z1B\ndiAfIN6HQ8+Ph57t1Nvx7qbx7qvn/Rb6IN2X9vd2mbEYbNigv8tEorvSolDIT5NK5T4O8/vZue7H\n+cHng5YWfa7D4Vye6XQuH58vF/Kf577aaU/jetKbQsQLXv28bf4z2DPPwTzem3n3h75+n/nHA0k7\n0Lx8vsLPm/feKdSPu7vfF70p0Pq7zX+mej5n/d06lxOc89sjv116xnnpSktzCo+ODv0/9RDR/6TO\nzu7CYf5+z/dBbyG/roVCoTYt1Ac9r9nXjkHfc7GYBu+bIj99/rdIb/2S3299PR/9jRvu9P15DvoK\nkGuT3srtLR5yCj6vD9Lp3p+3/uxPm+YQkZ3eFMNl0XEpOm3mM8DngcnoKiyDhcuEQvwFXfXlHufc\n0UBLISWHx+vLolz7QJhgeTWr6lo4/3eDWEtjt7k3szCxcznBwwuhUHchsNDoVm8jX5EIbNqkaXr+\ncXg/aK9cn0//fGIx/WD1XqbJZO7DxUuXTOYEmPzgnH6Abt2qQmNnJ1RXawgGd35RFDpOp7XeXV3d\nhT6P3j5E9iQuEIBjj4WDDtL22rFDz3l9MHEi1Nbqh3VLS06g7UubnEzm2jEa1TbzPhy8j4d8AdOr\nT88P6UL591cbXuh8T01+fvAE/J7BORUq/P6c0BqLdd/mh/Z2aG1VRYX3/JWXw+TJMGMGVFbm2iVf\nAPL79ZqVK/X6oqKcYsFTQoRCqnDoKdDk/4nnHzu387lCHxV1dfpx5vV9SYneM6jSpbg414+hUO8f\nZJ5gH4t17+90GjZvziluevstONf9GXAOpkzprmDpGZJJePJJuO02rSvk4qurYdy4nUfOvTZPJHLn\nEonuv7e+/pB7i/MUPd47LP89NpAPEsgpSLzfXT59He9Jmp4fnD3vcV/bH67yvXdkScmuFWiFFGz5\n/0MDxXsneOXsSV6DSX8UIrt6P/f2mxvs4+HM2zAMwxg4e13R4ZzzA98WkX8DosCNg5z/XcB8oNY5\n1wjcAIQAEZFfisjDzrnTnXOrgE7gsl3lF0vG+NiHi2jpqOK2Tc1ccgz86U+DWePhw43gf9J77tEP\nAU8jmK888BQKnnDU2whXofhwGCZM6G7Glm8e6OF9ZEFulN0L+eaG3odZIWVMIKDnKitV0Bo7Vj98\nW1pyo9G7GsX2jp3T64qLtd75eGn6o+gpFN9bXCwGP/yhjkqOH69KDW9UPx6Hxka9jzFj1GQ6X4DO\n1+r2FNICAW1Dr01DIb2P/D7N1wDna4F7ChOFNNr5+/09X0jQzw+egF9ersKx1xeg7eQpGrznw9v3\nTCq956G8XM1SKyo0Lh7PKQ36SyzW/Rqv/3s+F4ZhvPPwlKf7GoWUgIZhGIaxpwzX1JV/AqeISHyI\n8j8N+DFqNXKbiHyvx/mPAv8DrM9E/UxEflMgHym//gBe/PxfADjrd2ex4qoV2NSJ4cXa3zAMwzAM\nwzAMw8jIhjupy4dr6spq4Bnn3F9QqwoAROSHe5qxc86HruryHmAj8KJz7s8isrxH0rtF5LN9ZujX\nVVdKgiUjYtUVwzAMwzAMwzAM4x1Kf0y6+2PKPdTXeI5UepqxFzJl31V8LwyXouOtTPABg21IORdY\nKSJrAZxzdwNnAz0VHf0ykhR/jKJAEVVFVbREW8ySwDAMwzAMwzCMocNzAufNgd68WR1xFXIWtrvB\nK2dv57UnTtN6xhVayqanMzRvbntfnp/z50T35jSoL6dC/XE6NNjXFFImeA6M+pq739fxYKXp7RrP\nKVkgoNveHLLtKt45uP32gj+jYVF0iMiNAM65UhHp7Cv9AJkIrMs7Xo8qP3pynnPuBGAFcI2IrC+Q\nhpSLUhQoIuQPEfKH6EwMdnUNwzAMwzD2kHRavY22t+s2/0O4pUU9XnvLMuU7nyq0HFKhNSx3R7gZ\nzPOhkHpmLi3NOb4qtKxQoQGp/sbt6fW7m2df+wNJO1j7g5Wf50wtX4jsq5zdqc++Etfzt9Vfwb+z\nU3+7XujoyHmjBnXkVl3df8/U/fVevbfz2xOnaT338wVoz1uzz5dziJbv2dvzwtyX12dPCPfuJf+e\nCu3vzvnBvqaQMiF/Ca13Ah//eMHoYVF0OOeOAW4DyoApzrlDgStF5FODkX2BuJ7/MH8B7hKRhHPu\nSuBOdKrLTiSJEvardz/PqsMwDMMYBXR1wZIlumyRN3LmnAqF27cXFgR7+4AttCwPDE6cx0D3d+ea\nfaWMkVz3XZWRP+q2qyXAduc8qDfkioqcV2zvQ7eqSpdJqqzceVSt0HJI+XHe8lG7K9wM1vloFFav\n1mXK8tfbLfQxvydxe3r97ubZ1/5A0g7W/p5cly+EeUsM5QuRfZWzO/XZF+IKLdfXV/D5VIFXXp4L\nZWWal2EYu81w/YJ+DLwfVTggIq86504cpLzXA1PyjiehvjqyiEi+s41fAd2cleaTeCLGd1PfxTlH\nqCVEc8T8dBiGMcLYlTDeM8TjqgCIRguvs+tt0+nc3EpvSZz85XBaW3V9YW/d4/4GkZwQV8gsdU/2\ne8atWQP77acflaGQfnCKqDA4ZkxhAdALwWDhEab+CHG7E+cx0P3duWZfKWMk1723Mpzr3xJgu3O+\nkHWDYRiGYYwyGhoaaGho6DPdcK268oKIzHPOvSwi787EvSoihw5C3n7gTdRCYxOwCLhYRJblpakX\nkc2Z/XOBa0Xk2AJ5SfCbIeJfiwFw/G+O5zvv+Q4nTjuRfdlXx/Tp07nttts45ZRThrsqQ4KturKP\nsGGDCrH779+/9PlzBt9pJnW7g6d0WLoUFi/W+bmFRnTTae2LZct0pLOpSRUPPa0B+jOq5JwK/CUl\nusZvzzmR3tbbzzcL7TkKXFkJNTU6SuUJYX2F/Pzzr+lrfyBp8/cnTVKFhmEYhmEYhjEi2ddWXVnn\nnDsWEOdcCPgssKyPa/qFiKScc58BHiO3vOwy59yNwIsi8iDwWefcB4AEsAP4WG/5FQXC2f2qoqpR\nvfLKaFeQGH3Q3g7r1/ft+ElE53p//OM6cj9jhsblO37q6Qgqf1pAIQG854j43jze1ZzZ/sytHYrr\nPHw+mDULjjhChXJv5NYzJfcE9oMOgmuugXHjVHDPtzboaRFgGIZhGIZhGKOc4VJ0fAL4Ceo4dD2q\nlPj0IJcheQERuSHv3DeAGcARQDQTClIUKMruTyifwPq2gj5LDWP3SKXg1Vdh40Z4+234xz909N4b\nkYed5+z3dzvQtKkUTJ7cfU7prhxA/exncOqp8Npr3Z0+eaGnMygvX4++FAR767i3+bL9tYAYjOt6\nu9YwDMMwDMMwjAEzXIoOJyL/NiQZO+cDfoZOXdkIvOic+7OI5C8vezmwQ0T2d859CPhv4KJC+YXz\nLDr2r9mflU0rh6Lag86iRYu46qqr2Lx5M+eccw633HILoVCIBx98kK997WusWbOGgw46iFtuuYV3\nvetdfOQjH6GxsZGzzjoLv9/P17/+db74xS/ywQ9+kKeffppoNMqhhx7KzTffzJw5c4b79mDixO7C\nYF/7/U0HanbveXbvzT9Bb9toVKdzJBLd6+tZQbS27uwvYNYsLW/8eDj7bPiv/1JHcl699ta2rKy7\nk7D+ctxxA7/GK9OE+X6xvm099y27j+JgMR877GMEfPu+g7JXN79KIp3g8PGH43M+tnVu4+nGp6ku\nqqY0VEprtJX2eDvVRdXMnTiX0lBp9tpYMsaG9g3UFNdQVVQ1jHdhGPsmIsKaljUs2bqEt1vepq6k\nDuccr215jeZIM3WldaQlTVrSiAinzTyNE6eeiBvAO9ebIjqQawzD6I6IEE1GaY4243M+Ar4AAV+A\nSCJCU6SJ7V3baepqoiXawqH1h2b/M0cz+dPP898vIkIinaAz3klXootYKobP+fA5H8WBYkqCJZQE\nS97x7yQRIZlOEk/FSaaTlIXKSEuarkTXTiGSjFBVVEVRoIhIIkIinSDoCxL0B4klY3QluuhMdGbb\nPJlOkpIUOyI72NKxhY54B+XhcmbWzGS/6v3wOV+39IX2OxN6HE/FqS+rx+d8RJPRbPA5H0dOOJKg\nL8jEiomMKx1HWtIk0gnaYm2Uh8pxzpFIJUimk9k6AdSV1FESLMm2RV/PwnD56FgJvA3cA/xRRAZt\nKRPn3NHADSKyIHN8HSAi8r28NI9k0ryQ8emxWUTqCuQlM386k5VXqXLj/uX38+vFv+ahf3ton/YR\nMX36dMrLy3nkkUcoKSnhzDPP5JRTTuHcc8/ltNNO46GHHuKII47gt7/9LV//+tdZsWIFwWCQ6dOn\n85vf/IaTTz45m9cdd9zBBz/4QYLBIF/+8pd58sknefnll4fx7jLzsNat6916oed+f9N5+/G4enaP\nRAr7Jyi09faLitS/QSiUq7BnNTBmjHq+L+REDtgR2cFrW15j/rT5Q9d4xojj76v/zof/9GHO2P8M\n1rSuYV3rOs4+8Gz9MwiXE/QFmTdpHufMOmdIyk9LmngqTtgf3umD5OGVD9MaayWVTvHsumd5dv2z\nTK6YTGVRJY+vfpyqoirebnmb4kAxaUlzwtQTaI+1E0lGKA+VU1VUxeaOzbyy+RXGl48n5A8RT8XZ\n0rGF2pJaWqItXHrIpdQW1xIOhNnYvpHtXduZVjWNjngHrbFW/M5PcaAYn/MRT8Wzf4ZPNz5NR7yD\nslAZZaGyrHXe3AlzqSutw+d8vGf6e5g3ad6QtNtg0xHvIJKI4JyjLFRGyB9ie9d2xpSMweFoj7eT\nljQlwRJiyRilodJR/7HcGyJCPBUnkowQTUYZUzJmQMrBoRTwU+kUTzc+za0v3Up9aT1jSsYQ8uv/\nRSwVoz3WztLtS0mmk4T8IaLJKO2xdooCRRQHiykKFBFNRlm8aTE+5+OQcYcwo2oG2yPbSUua2WNm\nM7Z0LNu7tuN3fvw+P4lUgruW3EU8Feew+sOIJqO8tPEl/D4/s8bMyj5b48rGkUgl2NSxiVQ6xeaO\nzZSHy5lQPoGOeAfVRdUk00kmVUzC7/Pjcz7KQmXEkjGiySixVIyKcAVBX5BYKhOXjBFLxQj4AoT9\nYUL+ECF/KHu9V0cfPoqDxZSFykikEsRTceKpOH6fn8pwJZMqJtEeb+f2V25ncsVkpldNpyxUhs/5\nKAoUUV9Wn+3jmuIaKsIVOOfwOz/hQJiiQBF+58/2qcPhnMPhSKS1vEQqQUpS2Trm19f76E6kEyRS\niawSyXs/NkebaY22kpZ0t2cnP11RoIjiQDFBfxBXcIFAxRPsvOCc2ynO53w4NF4QYskYiXSi2/me\n5WfrhiMlqazQkd8e3r5HwBcg6A8CkEwns4JHIp0gloxlf2ORRCSbXzYusw35Q7xr7LvwOV+2DUVE\n+9/nz5bn1TElKd2mU9njVDpFSlIk00kcjtqS2mydkukkaUnnnqW85yot6WyZWYEpnUJ2WoyRnfpE\nkG5lF9p6AlgqnSKeitOZ6KQj3kFnvJNIMtLt+a8prskKqIl0guJAMbUltYwpGUNtcS3l4XIWbVhE\nS7SFw+oPy9bHe07nTpzLwWMPpjXaSke8I/t8pyTFlo4t7IjsyN6/w2UFYSD7HAf9QdKS7tZuPudD\nRGiLt9EabSWSjGR/f157+5wve58lwRLqy+qzfeO1qdcWvR3HU3Ha4+20x9rpTHRmn8eefeD3+SkN\nllISLCEcCGfbLJqM0pnoxOd8jCkZQ1rS1JfVk0glsoJ1Z7yTaDKK3+dn/5r9mVM3h9ljZlMRrqAl\n2kJrrJXOeCf1ZfXZd1tdSR2RZESVAgndJtKJnfrba7N84Tv/ufL2U+lUt99PwBfo9nv02jaeimff\nPdnjzLtFkNzvIa8ekWQkW0fQwXi/89MR78DnfFlFUGmoNLtfFCiiOdJMLBXLvn+8ZyPsD2fTlgZL\nKQ4WE/QF8Ts/1cXV1JfVUxYqozXayqodq3ir+S2AbBmlwUzIy8OLLwmWEPQH2diu64EUB4qz/2Ox\nZIwXN76IiLCubR1NkSYcjqA/SEW4gvZYO4JkFTLe7xlgS8cWYin1nen9VwvCs5c/SyEfHcOi6ABw\nzs1FrSjOAZYCd4vIbwch3/OB94vIFZnjDwNzReSzeWlez6TZmDleCcwTkR098pKDbz6Y1z/5OgBv\nbH2D835/HiuuWtG3omMwPpB2s2+mT5/O9ddfz3/8x38A8Ne//pWrrrqKU089lbq6Om688cZs2lmz\nZvGrX/2KE044oU8fHS0tLdTU1NDa2kp5eflu1W0wGI3OSJ9pfIZL77uU5mgz3z7l23ziyE8MeOSt\nPd7Oqh2raOpqIpKMUF9Wz1ETjnrHa75HCsl0kreb3+aZdc+wunk1M6pn8OCKB3lhwwvcec6dnDL9\nFESE59c/z99X/53aklo64h0kUglue/k2zjrgLG6YfwMvrH+BzR2bGVc2jjl1cygPlbNyx0rWtKwh\n7A+zf+3+HFh7YFYrXxGuyNZhXes67lt+H8+tf46WaAtbO7eybNsykukkNcU1HDHhCOpK6miLtbG6\neTWCMGvMLPzOz6HjDuXk6SezqX0T69vWc/6c86kvq6cr0UU0GaW6qLrXZzGeirO2ZS3JdJKgP8i4\n0nGUh8vZ3LGZm164KSus1ZbUMq50HI2tjVSEK6gIV5CWNNFklJSkCPvD+JyPZDrJMZOPoa6kjvZ4\nOx3xDqLJKMl0kufXP09rtJV4Ks4dr97BVXOv4tl1z9Iaa6W6qJrxZeN5betr/Gvjv6gpruGSgy+h\nuriaVza/gnOO8WXj8TkfsWSM/Wv3pzJcSTgQZkzJGCrCFdnRufyP3JriGmbWzOwmwMVSMSrDlYT8\nIZZvX87y7ctZ1bwqO/qxqX0Tmzo2sb1rOwCd8U5KQ6WkJU1nvDMr4MVSMRKpRFaRE0nZ+aiGAAAg\nAElEQVRGCPvDxFIx9qveD7/PT8gfYmK5jpoEfAEa2xpJS5r2WDvRZDSrDCoJliBI9sPN+6gThMpw\nZbePXe8jNi1pqoqqsoomL3gfxOXhcvzO321UJj//3t7lPZ8VEckKzyJCwBdAENpibfoBG23NCniR\nZEQVYMFiwv4wHfEOKsIV3T66U5LKKok8YS8lqezHriDdBKbetvn3PbVyKsXB4qzA632siuQ+WndE\ndjC5cjKfOvJTdCW6aIm2EE/FSUuacCBMabCUOXVzKAoUEU/FCflDVIQrugmOYX+Y2XWzmVkzs9/v\nFxFh2fZlLNu2jIAvwFETjyItaZZsXUJFuILSYCnburYR8AUYXzYev8/PuNJx2fdAWaiM5mgzQV+Q\nxtbG7P10JjopChRRFCgi5A/RFmvLPo+egiHkD2U/rGPJWPZ+84UIbzSyM97ZTShLpVM0R5vZ0LaB\nRDrB5e++nJZoC42tjXQluhCErkQXmzs2ZwXjHdEd2Q/lVDqVfW48AUQQRCS7DfqDWp4vqMJ4D+HD\nq6/30R30BbMCus/5CPlDVBdXUxmuxO/zd/v49ruc4BlLxbJC1K76KV/IybfK6RnnPWMAYX+YoD/Y\nLV1++Z7CxCvD53xZxZkX59U5vy6eAOdwWaHNEzw8xU2+Eq5QXFeiize2vgGQbb/8d4lXrvd78xQg\nPY/9zp99JvIFo4AvkFXe9BQMfc6XLdOrv5d3z3bfqS/Q90xvv/2e50L+EKWhUspCZVmhMewPEw6E\nB6RofWvHW7zZ9Ga3eiXSCZ54+wkaWxupLq6mNFiKiCpifM7H2NKx1BbXdnt2vOcV2El56LWFw2X7\nvCJcQWW4kuJgcfY3GPAFss+kZ43SHmtna+fWgm3htW+h46AvSHm4PPt/47VJ/rMnIlmBtjfaYm3s\niKi4tqVjS7bdPUG7KFBEIp1gRdMKlm1bxtJtS+lMdFJVVEVVURXFgWI2d2ymI97BurZ17IjsoCRY\nQnFQLUaKA8XZ33h+/3rPmncv+b8Hb9+7b09OSUkq+x8KqsgJB8IEfcFsG+e/77zfhqfc7Pn8d6tj\npm9BFSj5ysp3Ir05Ix02RUe2As6NAX4I/JuI7Ibd/E75XQCc2kPRcZSIXJ2XZkkmjafoWJVJ09wj\nL7n4q7O56z+XAhBNRqn6bhWxr8X2aUF7+vTp3HzzzSxYsACApUuXcuSRR3LKKafw5JNPEg7rdBwR\nIZFIcNttt/GhD31oJ0VHOp3m+uuv5w9/+APbt2/HOUdbWxurVq1i+vTpw3Z/I1XR4X2A5L/EY8kY\nVzx4BQ1rGvj++77P4eMP57zfn0cilWBixUROnnYy8ybOI5KMMKliEvvX7M9Lm15iRvUMRITtXdt5\nbv1zXPu3a3E49qvZL2vWtWz7MqLJKIlUgoPGHsQxk46hvqye0qAKSy9ufJFpVdOYPWY2G9o3cOSE\nI5k7cW63+r6TX5pDRSQRYdn2ZTR1NRH0B3l9y+usbl7N3W/cTVGgiCPGH8EBtQfwVvNbnDDlBC47\n7LJu0zoKsSOyg8898jnuev0ujpl8DDOqZ7CpfRNLty2lPd7OAbUHMK1qGrFkjDeb3qSxtTH7oRfw\nBbKjbkWBIhbMXMCp+51KbXEtNcU1qiwJl7OmZQ2vbXmNpq4mKosqqS2u5fgpx/f5UbIv8+TbT3LH\nq3dwzoHnMK5sHDsiO9jUvompVVM5ZfopNLY2cs+Se4gmoxw89mD8Pj9bOrZkR31XNq2kPd5OLBVj\na+dW2mPt1JXWUVtcS3GgmHAgTNgfZmvnVt5ueVtHiQO5UWJvlGVW7Sxm181mv+r92NyxOatQGV8+\nnjElYxCRrBUKqFVAV6KL8nA57bH2bL75xFNxVjatRFCz6Y3tG9nSsYVEOsGUyin4nZ/ycDlFgaKs\nqamnQMn/aAv4AjjnaIm2ZD9C8z9iAVpjrYhIt482n/PRlejKWprkX7MrgQMKCx1AVnD2lFkAleFK\nKosqqQxXZgXb4mBxN8GiM95JW6ytW7kOl1UiFQWKsh+W3kdkviC2q21a0oT9Ksisbl5NPBXPKn7y\nP1i9kUrvY9swDMMwjMFhn1p1xTlXAZyLWnTsB9wHzN3lRf1nPTAl73gS6qsjn3XAZGBjZupKRU8l\nh8d+P3ibb6Suh1CI+fPnM65sHI00DlJVh45169Zl9xsbG5k4cSKTJ0/mq1/9Kl/5ylcKXtNTqL3r\nrrt44IEHeOKJJ5gyZQqtra1UV1ePSCXD3mR713a6El1Mqcw9hhvaNnDBvRewZOsSjppwFFMqp7B4\n02Jaoi3MnTiX5Z9eTnFQ/XK8cuUrvLDhBZojzfxh6R/42+q/URwoZm3rWlY0reCQcYewpmUNfudn\nfPl4KsOVvHzlyxxQe0C3eqQlzaodqygJlvDaltd4fv3zLN++XEfc0gmOnng069rW8YuXfkFdSR03\nNNzAV47/Cs2RZja2b+SPy/5IXWkdh4w7hJJgiSpfyicyuXIyh9UfxvFTjh8RviKGGhEdVU5Lmo54\nB4++9Si3vnQrNcU1HDvpWDZ3bGZ7ZDuRRISVO1bS2NrI/jX7U1daRzQZ5ZCxhzClcgp/v/TvHDT2\noN2qQ01xDQvPXcgtZ9zSp1IEVNmSkhSlwVJaoi0UB4uJJqNUhit7VW5Nq5rGtKppu1W/fZWTp5/M\nydNP7vX8jOoZfOWEwu/L4cTvUyUFkN32JOQP7fbzNNooDZUW/F301nYeARcY0DvOM6c3DMMwDGPo\naGhooKGhoc90w+Wj423gfuD3IvLcIOftB95EnZFuAhYBF4vIsrw0nwIOFpFPOecuAs4RkZ2ckTrn\nRC68EObNgy98AYD3Lnwvj3/08X1a2J8+fToVFRU8/PDDFBcXc84553DSSSdxzjnncO655/KHP/yB\nuXPn0tnZyVNPPcVJJ51EaWkpxxxzDJdffjkf//jHAbjlllv41a9+xVNPPYXP5+Paa6/l1ltvZeXK\nlcyYMWPY7m+oLToeXPEg75n+nqzioT9s6dhCVVEVP3zuh3z/ue8DcPK0k7Mj84+uepQvH/dlrjji\nChZtWMSaljUcNfEowv4wB409qN9z6VPpVHbe6WCbqTWsaeCOV+5getV0qours34glm9fTleii6A/\nyIa2DaxpWcPTjU8zsWIiv7/g930KCwOlLdbG0m1LqSup45+N/2RC+QSOmHAE7bF2Nnds5ogJRwxI\n+PBGvHdEdmSVEZ457pqWNUytnIogtMfas86TYqkYbbE2Fm1YxLPrniXkDzG9ejo+56Mt1pYNaUmz\nqX1Tdp5rOBDmxKknctlhl7EjsoPl25dTX1bP2NKxhP1hZtbM5IDaA7qZHBqGYRiGYRiGsXvsU1NX\nnHNORMQ5VyoinUOQ/2no8rU+4DYR+a77/+zdeXhV1fX/8fcKhEkShgABJAmTIiiIqKiAGkVFRcVZ\nRAaHOotWv1VQ+UnQOoBah9ZqrSjgPLUKCoJaoLRqURQVZcYQZpEpDEJMsn5/nJMYMgEZyL3x83qe\n83DvPvvss8/ZhT53ufdeZqOAz939PTOrDbwIHAFsAPq7e3ox7bh//TX06QNLl0J6Os9N+D1Xj/4w\nogMdbdu25dprr2XChAmsWbOGc889l7/+9a/UqVOHadOmMWLECJYsWULdunXp1asXzz//PAcccAAT\nJ05k6NChbN26lREjRnDdddcxYMAA/vWvf5GQkMB9993HkCFDqnWg4+NlH3PWq2fRpmEbeib15KiW\nR9GhSQcWbVhEi/ot+HTlp7Rt1JYTU04koV4Cyzcv5/XvXufPs/9Mdm42qa1Tebrv0zSq04h3F77L\nii0rSGqQxFkHn0WTek0qpc9VITs3m+vfu545a+bw+2N/z4fLPqRDQof8fQpOaXsKCXUT+G79d2z8\neSPzfpzHt+u+pVHdRny++nM+X/U5hzQ5hIMTDibHc+jcrDM7ftnBxz98zLwf53FQ44NYt30dxxx4\nDCszV7JowyLqxdbLf+dxteOIsRgOiD2ARnUbsTN7J0s3LuWcDucw/6f5rNm6hgZ1GpCVk8Xqravz\n95ioYTU4oNYBxMbEYma0btiajC0Z1IypSVytOOrF1stfBnBA7AF0a9GNHkk9yM7NZsWWFeR6brCO\ntU4D4mvHE2MxNK3XlKYHFNnLWEREREREKlmkBTqOA8YC9d092cwOB6519xv2e2dKEcZj4PzzoUsX\n+Nvf+KnrwTT94N8RHeio7ior0JGVk8Wxzx3L8F7DOSD2AFZmrmTyksms3rqaQ5seysrMlRxz4DGk\nb0lnZvpMtmVtI6VhCocnHs4jpz1CzZiapW64WN24Ow/95yGmLp3KxYdeTMaWDDb9vIl129cxZ80c\nkhsks2brGg6MP5BOTTrRObEzG3/eSEqDFM486EwWbVjEko1LiLEY5qyZQ/1a9endpjfHJR2Xv0Fa\ncTb+vDF/6cX2rO35KdtaxrXknQXvcHji4bRv3J5NOzdhGJ2advrNjImIiIiIyG9JpAU6/gdcCEx0\n9yPCsnnuflg5221EkLI2BUgHLnb3LcXUywG+BgxY7u7F5mXMD3R89RV06wbXXw9//WvUboZZXZgZ\ns1fOZvXW1TSq24heyb32eunH9qztZOdm06BOA3Zm7+TTFZ/y9bqvWfjTQiYvmcwxBx7D6xe+rh/G\n5TR58WRWZa7iqm5X/WZTXIqIiIiISOWKuECHux9jZl8VCHR87e6Hl7Pd0cAGdx9jZsOARu4+vJh6\nme4eX7SFIvU8//288AKcdx40bKhARxUzM7o+05Wk+CTSN6ez45cdXHvktQw9Zih1atbB3Vm/Yz1L\nNy5l2aZlLN+ynC6JXXj+q+eZsmQKNawG9WvVZ/POzRzR4giObHEkHRI60DO5J91adKvqxxMRERER\nEZG9EGmBjrcIUsr+BTgWuBk4qrgNQfex3QXAie6+zsyaAzPc/ZBi6m119z3uoLhboGP3cgU6qlDB\n9+/uzF41m/tn3c/abWtpXLcxn6z4hJoxNWnXuB3tGrWjVXwrPl35KUe3PJrRp4ymZkxNVm9dTWL9\nRGrVqFXFTyMiIiIiIiJlEWmBjiYEm4WeQrB8ZBpws7tvLGe7G929cYHvG9y9SL43M8sC5gLZwGh3\nf7eE9hToiEDFvX93Z+xXY6lbsy5nHHQGjes2LuFqERERERERqQ5KCnTsfY7GCuTuPwGXFSwzs98D\nj+/pWjP7EEgsWAQ4MGIfupDs7mvNrA3wLzP7xt1/KK5iWlpa/ufU1FRSU1P34Tayv5gZv+v2u6ru\nhoiIiIiIiFSSGTNmMGPGjD3Wq5IZHcUxswx3Ty5nG/OB1AJLV6a7e8c9XPMCMMnd/1HMOc3oiEB6\n/yIiIiIiIhJRMzpKUBFpLiYClwOjgSFAkSUpZtYQ2OHuWeESmh5h/b2WkpKirBxVKCUlpaq7ICIi\nIiIiIhEqkvI+VsR/oh8NnGpmCwn2/3gIwMyONLNnwzodgS/M7CvgY+BBd1+wLzdJT0/H3ffpeOS/\nj2BpxoiPRxQ5d/7r5/PGvDdwd3Jyc3B3Oj3ViY5/6Yi7syNrBzuydtD5r51587s3S73PXR/dRevH\nW9Pr+V60frw1Hf7cgb/P+Xup12zcsZG4B+LYumvrbuW7sndxx7Q7OO6549j5y859fubKOtLT0yvg\nfyoiIiIiIiJSHe3XQIeZbTWzzGKOrUDLCrjFyUBzoD0wzN03A7j7HHe/Jvz8KXAHUDc8Ektoq0LF\n1Y7Dcc46+Kwi55Ljk8nYkkFWThYd/tKBCV9P4MftP/Lj9h95bd5rNBrdiBPGnUCnpp24oOMFpd7n\njyf/kTcufINbjrmFhTct5I6ed/Dhsg/zz3+//nt6jO3B/PXz+Xbdt8xIn8EV715B34P7Ur9W/d3a\nqlWjFqNPHc0nV31C7Zq1dzu3N+uiJLJozKKLxiv6aMyig8Yp+mjMoo/GLPpozCKfxmjf7NdAh7vH\nuXt8MUecu1fEMppvgfOAmSVVMLMYgrS2fYBDgUvNrEgK2ooWXzueZgc04+gDjy5yLrlBEOj4x/x/\nsGXnFq6edDWntTuNk9uczO8m/o6RJ47k7IPP5rlzntvjkhkz4+gDj+bCThdSq0YtTm17Kh8v+5hc\nz+WnHT9x9qtn06FJB4557hjOePkMRvxrBO0bt2dcv3H79Dz6ixZ9NGbRReMVfTRm0UHjFH00ZtFH\nYxZ9NGaRT2O0byJp6Uq5uftCd19M6ft9dAcWu/tyd/8FeA3oV9l9O6rlUdx/8v3EWNFXntwgme/W\nf8fjnz3O032f5pgDj+G8Q86jT7s+JNZP5A89/sA9J95TZMbF3lj61VKa1GvCx8s+5vzXz+fiThfz\nQr8X+OKaL1hy8xL+c+V/eOS0R4rM2JDIpn/oqieNa/Wkca2eNK7Vk8a1+tLYVk8a1+qpIsa1WgU6\n9tKBwIoC31eGZZWqfeP2JaY/7ZzYmRWZK0hqkES/Q/ox4/IZXNjpQi7vejmfXvUpsTViy3zfGTNm\n8Ltuv2PQPweR3CCZ+3vfD8DBCQdTp2adMrcrVUv/qFdPGtfqSeNaPWlcqyeNa/Wlsa2eNK7VU0WM\na8Skl91bZvYhu++rYQQbmd7t7pPCOtOB/3P3L4u5/kLgtLw9O8xsIHC0u99STN3oejkiIiIiIiIi\nvyGRnl52r7j7qeVsYiWQXOB7K2B1CfdSDlkRERERERGRKFKdl66UFKT4HGhvZilmVgvoD0zcf90S\nERERERERkcpSrQIdZnauma0AjgXeM7MpYXkLM3sPwN1zgJuAacB3wGvuPr+q+iwiIiIiIiIiFSfq\n9ugQERERERERESlJtZrRISIiIiIiIiK/bQp0iIiIiIiIiEi1UaWBDjMba2brzOybAmWNzGyamS00\ns6lm1qDAuSfNbLGZzTWzrgXKh5jZovCawQXKu5nZN+G5x/fmHiIiIiIiIiISvap6RscLQJ9CZcOB\nj9y9A/Av4E4AMzsDaOfuBwHXAs+E5Y2Ae4CjgWOAkQUCF08Dv3P3g4GDzaxPafcQERERERERkehW\npYEOd/8PsKlQcT9gfPh5fPg9r3xCeN3/gAZmlkgQKJnm7lvcfTNBNpXTzaw5EOfus8PrJwDnlnCP\nvHIRERERERERiWJVPaOjOM3cfR2Au68FmoXlBwIrCtRbGZYVLl9VoHxlMfUBEgvdo2kFP4OIiIiI\niIiIVIFIDHSUxIr57sWUs4dyEREREREREammalZ1B4qxzswS3X1duPzkx7B8JZBUoF4rYHVYnlqo\nfHop9QHWlnCP3ZiZAiMiIiIiIiIiEcrdi0xyiIQZHcbusy8mApeHny8H3i1QPhjAzI4FNofLT6YC\np5pZg3Bj0lOBqeGSlEwz625mFl77bjH3GFKgvAh3j+pj5MiRUdWujsp7/xqzyD8KjpHGK/qOksZM\nYxlZR0WNh8Y1+sYs0u5VnY9IfI+R2KdIOqL1/URrv/WsFfesJamwQIeZHWBmNfbxmleATwgyomSY\n2RXAQ8CVZrYLuJtfNyv9FOhsZlkEG47eAeDum4BlwHpgDfCCB5uSAkwGZgG7gBru/kFYPgUYEbZ1\nQ3jPaik1NbWquyCVQONaPWlcqyeNa/Wkca2eNK7Vl8a2etK4Vk8VMa5lDnSYWYyZDTCz983sR2AB\nsMbMvjOzh83soD214e4D3L2lu9d292R3fwFoSRCYiAcaAr3NrD1BSti/uHst4H7gzLAfZwBZYfmJ\nwFlheSOCWRzNwqN9gbSzo4He4TXfEaSlrZb0l7960rhWTxrX6knjWj1pXKsnjWv1pbGtnjSu1VOV\nBjoI9sFoB9wJNHf3JHdvBhwPfAY8ZGYDy9BuR+Azd9/l7jnAv4HzgHOo/LSzspf0j0r00ZhFF41X\n9NGYRQeNU/TRmEUfjVn00ZhFPo3RvrHS1rWUeqFZrLv/Ut46xVxzCPAOcBzBzI6PgC+Age7euEC9\nDe6eYGaTgAfd/ZOw/ENgGHASUNvdHwjLRwA7gJlh/dPC8l7AHe5+TjF98bK+HxERERERERGpPGaG\nF7MZaZmzruQFMMysM3BIWDzf3ecVrrOP7S4ws9EEAY6twFwgu5RLKjXtbFpaWv7n1NRURdJERERE\nRESqUOvWrVm+fHlVd0P2o5SUFNLT05kxYwYzZszYY/3yzOhoQJCtJAn4hiCA0BnIAPq5e2aZGi56\nn/uBFcAtQKr/mhJ2urt3NLNnws+vh/UXEOzVcVJY/7qw/BmC5TYz864Ny/sDJ7r79cXcWzM6RERE\nREREIkj4X/GruhuyH5U05iXN6ChPoONJIItg2UduWBZDkMGkrrsPLVPD5C8z6Q/UINictB1wH9AX\n+Dk8prr7MDM7B3gG2A7sBH5x927hZqRLgM0EM0LigE7uvjkMhtQJ+58L/L5ARpaC/VCgQ0RERERE\nJIIo0PHbs6+BjjIvXQFOAbrkBTkA3D3XzO4Cvi1ro2bWkiCt7DKCwMUcggBHc4KgRW0ggSCVLMCB\nwAagLkEwY2FY3gLYRhDIiCUImmwJgzEHEARGaoRtpZe1vyIiIiIiIiISOcqTdSXL3YvsnRGW7SpH\nuwA/Ar2AowmCFauBnkBXd+8AXESQVQWCrCu/c/f2BDM/jgjLzwH+6u4HuXtb4Euge3jMc/dD3P0g\n4FF+zeAiIiIiIiIiIlGsPIGOOmZ2hJl1K3QcSTDrokzcfTVB8CEDWAVsIQhSbC4we2QlwUwOwj9X\nhNfmEMzaaFywPLQqLCtcXrAtERERERERkajw4IMPcs0111R1N/bJqFGjGDRoUKXeozxLV9YCfyrl\nXJmYWUOCGRYpBEGON4Eziqmat0BnX7OrFBfc0QIvERERERERqRSjRo1i6dKlTJgwocxtzJw5k4ED\nB7Jixa//3f7OO++siO7td2bF/VyvOOVJL5tagf0o6BRgmbtvBDCzfwI9gIZmFhPO6mhFsJwFghkZ\nScBqM6sBNHD3TWaWV54n7xoDkospL5bSy4qIiIiIiEhVysnJwd0rPUAQ6fY2vWyZl66Y2dFhmte8\n74PN7F0zezJcOlJWGcCxZlbHglHsDXxHkBr2orDOEILUtgATw++E5/9VoLy/mdUyszZAe2A28DnQ\n3sxSzKwWQXaXiSV1Ji0tLf9QkENERERERERKM3r0aFq1akV8fDwdO3Zk8uTJPPDAA7z++uvExcVx\nxBHBtpLjxo2jU6dOxMfH0759e5599tn8NmbOnElSUhJjxoyhRYsWDBgwgDPPPJPVq1cTFxdHfHw8\na9eu3W0ZyPLly4mJiWHChAmkpKTQrFkzHnjggfw2d+7cyZAhQ2jcuDGHHnooDz/8MElJSezJypUr\nueCCC2jWrBlNmzbl5ptvBmDZsmX07t2bJk2a0KxZMwYOHEhmZmaJ72H69On553bt2sWQIUOIj4+n\nc+fOfPnll3v1blNTU3f7jV6S8uzR8TeC9KyY2QkEaWUnECw3ebaU60rl7rMJghqbgR3A2cADwHzg\ndjPbDlwGnG9mDYCxQBMz2wy8AHQxs67u/j3wBsF+HAsI9g0ZFO7jcRMwE9hKsD/HtWXtr4iIiIiI\niAjAokWLeOqpp5gzZw6ZmZlMnTqVjh07ctddd3HJJZewdetWvvrqKwASExOZPHkymZmZvPDCC9x6\n663MnTs3v621a9eyefNmMjIymDBhAlOmTKFly5Zs3bqVzMxMmjcP5h0UnuXx3//+l8WLF/PRRx9x\n7733snBhkJg0LS2NjIwM0tPT+fDDD3nppZf2OEMkNzeXs846izZt2pCRkcGqVavo378/AO7OXXfd\nxdq1a5k/fz4rV67MDz4U9x5at26d3+6kSZMYMGAAW7Zs4eyzz+bGG28s13svrDyBjhp5y0uAS4Bn\n3f1td/9/BLMnyszdb3b3Ou5eF2hKkAp2LPAxMMrd48PPd7r7LoIAx3/D+lcAz4RNPUOQtaUZ0BkY\naWYN3P0DYB1wvLs3BQ42sz6IiIiIiIhI1DOrmGNf1ahRg6ysLObNm0d2djbJycm0adOm2LpnnHFG\n/o//448/ntNOO41Zs2bt1taoUaOIjY2ldu29y/dhZqSlpVGrVi26dOnC4Ycfztdffw3Am2++yd13\n3018fDwtW7bMn5lRmtmzZ7NmzRrGjBlDnTp1qFWrFj169ACgXbt29O7dm5o1a5KQkMCtt97KzJkz\n9+o99OrViz59+mBmDBo0iG+++Wavnm9vlSvQYWZ5e3z05tclI1C+TU4LOwVY6u4rCDYpHR+Wj+fX\ntLD9CGaT4O7/AxqYWSJBCtpp7r7F3TcD04DTwyU3ceHsEcJrzy3u5u7ap1RERERERCSauFfMsa/a\ntWvH448/TlpaGs2aNWPAgAGsWbOm2LpTpkzhuOOOIyEhgUaNGjFlyhR++umn/PNNmzYlNjZ2n/uQ\nmJiY/7levXps27YNgNWrV9OqVav8c3uzbGXFihWkpKQQE1M0dLB+/XouvfRSWrVqRcOGDRk4cGB+\n/wu+h8TERAYMGMDatb/mLMmbjZLXx507d5Kbm1vkHmVVnkDHq8BMM3sX+BmYBWBm7QmWr1SUS4BX\nws+J7r4OwN3XEszUgJJTxpaWYnZlMfWLyPGccnZfREREREREfiv69+/PrFmzyMjIAGDYsGFFlohk\nZWVx4YUXcscdd7B+/Xo2bdrEGWecsdt/aC98TXk3Im3RogUrV/76Mzivf6VJSkoiIyOj2CDEnXfe\nSUxMDPPmzWPz5s289NJLu/U/7z0sX74cCN7D/lKerCv3m9nHQAuCWRN5TxQDDK2IzplZLHAOkPdG\nSoqpFR5xo/QUsyWVFzFy5EhiawRRNGVdERERERERkZIsWrSIVatW0bNnT2rVqkXdunVxd5o3b85H\nH32UnzklKyuLrKwsmjRpQkxMDFOmTGHatGl07ty5xLYTExPZsGEDmZmZxMfHF1untBUJF198MQ8+\n+CBHHXUU27dv56mnntrj83Tv3p0WLVowfPhw0tLSqFGjBnPmzKFHjx5s3bqVhtKN3XkAACAASURB\nVA0bEh8fz6pVq3j44Yf3+B5KsrcrKfY260qZAx1hZpVF4VE7zGCy2d0XlbXNYpwBzHH3vPk768ws\n0d3XhctPfgzLS0oluxJILVQ+vZT6Rdxx9x00qNOgvM8hIiIiIiIi1dyuXbsYPnw4CxYsIDY2lh49\nevDss89Sq1YtXnzxRRISEmjbti1ffPEFTzzxBBdddBFZWVmcffbZ9OvXr9S2O3TowKWXXkrbtm3J\nzc3l+++/L1KntFkg99xzD9dddx1t2rShZcuWXHbZZbzwwgul3jMmJoZJkyYxdOhQkpOTiYmJYcCA\nAfTo0YORI0cyePBgGjZsSPv27Rk0aBCPPfZYqe+hJHs7W6Xw5INRo0YV315Z96Awsx/4dRZEXq/q\nA18Dv3P39DI1HLTdAHgOOI0gM8oFBAGVL8N7fAl8AtR19+Hh8plTgMXAo8CN7n6smV0PPAEsD8tv\nB44E2gL/AX4C/gEcDDwZblJasB++fvt6mtRrUtZHERERERERkQpkZtpLsQI888wzvP7667ulfY1U\nJY15WF4kSlLmPTrcvY27tw2PNuHRFPgrv2Y9KasngA8J0tceRpAedjgwjiCQ0g24HHjIzM4gmJny\nApAQ3vsGM2sE/AG4heA5nwTGhJuSPg1cR7CXyNXArsJBjjxZOVnlfBQRERERERGRqrV27Vo++eQT\n3J2FCxfy6KOPcv7551d1typFeTYjLZa7/4NfNwndZ2YWR5D29Vl3b+rum919C0FmlWfc/RSCVLE7\nw6BFP2CCu9/k7klABsGmo3kZV55293YE6Wk3F8i4MsHdOwNXAWuL9iSgQIeIiIiIiIhEu6ysLK69\n9lri4+M55ZRTOO+887j++utZsWIFcXFxxMfH5x953wtuXhpNKjINLABmVp/yBVDaAj+Z2QvA4cAX\nwO8plHHFzCo94wrALzm/lONRRERERERERKpecnIy3377bZHypKQktm7dWgU9qjzl2Yz0tmKKGxFk\nSflLmXsU9KkbwT4bX5jZYwTLVvZ7xhWAxx56jGYHBDEVZV0RERERERERqRqVnnUFiCv03QmWgAx0\n96Jhor23Eljh7l+E398mCHTs94wrANf83zV0bd617E8jIiIiIiIiIuW2t1lXyhzocPfiWyynMJCx\nwsxWAhsI9vtw4EXgejPrARwBbA2zs0wEbjSznsB5QGOgBTAVuN/MriPYlDQZmBsue8k0s0EEWVja\nAdNK6o/26BARERERERGJHmXeS8PMnjWzw0o4d4CZXWlml5Wx+ZuBJmH/PgU6AqMJMq10BeYC44E7\n3X0ykEOwqehG4HqCTUs3EaSUfbJAm7eHwZEbgL8BDQnS2NY2sz7FdUR7dIiIiIiIiIhEj/IsXfkr\ncI+ZdQbmAeuBOsBBQDzwPPByWRp296/NbA2Q6u4b8srNbCdwTIHlK9MJlrWsBq5099fDeneaWSJB\n4GOsu18flh8OnA7MBNLdvVNY3h84l2AWyG40o0NEREREREQkepRn6cpc4OIwy8pRBMtFfgbmu/vC\nCuibA1PNzIG/uftzVEHmFQU6REREREREpCqMGjWKJUuW8OKLL1Z1V6JKudPLuvs2YEb5u1JEjzCY\n0RSYZmYLqYLMK7/kaumKiIiIiIiIVA2z4n6+SmnKHeioLO6+NvxzvZm9A3SnCjKvvPjEi3zRNEgA\no/SyIiIiIiIiIlVjb9PLlnkz0spkZvXCJTGY2QHAacC3BBlWLg+rXQ68G36eCAwO6x8LbA6XuEwF\nTjWzBmbWCDgVmBoGUTLNrLsF4bHBBdrazfnXn09aWhppaWkKcoiIiIiIiEipVq5cyQUXXECzZs1o\n2rQpN998M8uWLaN37940adKEZs2aMXDgQDIzM/OvGT16NK1atSI+Pp6OHTsyffr0/HO7du1iyJAh\nxMfH07lzZ7788suqeKyIkJqamv/7PC0trcR6ERnoABKB/5jZDmANMAlYBJxMsAFqJnAK8JCZ1SII\nVPQ0s58JNkG9IWznOuAAgo1SvwVGuftmMzudIG3trPDcYnf/oLiOaI8OERERERER2Ru5ubmcddZZ\ntGnThoyMDFatWkX//v0BuOuuu1i7di3z589n5cqV+T/UFy1axFNPPcWcOXPIzMxk6tSptG7dOr/N\nSZMmMWDAALZs2cLZZ5/NjTfeWAVPFl3KvXTFzA4GbgdSCrbn7ieXtU13/8HMxgNHAvHu/pCZvQ6M\ncfc3zexpYG4YtLge2OjuDc3sEuA8d//SzDoBFxMsS2kFfAS8aGYxwF8IlrSsBj4Hni6pL9qjQ0RE\nREREJLrYqIrZ18JHlrRNZPFmz57NmjVrGDNmDDExwbyCHj16ANC2bVsAEhISuPXWW7n33nsBqFGj\nBllZWcybN4+EhASSk5N3a7NXr1706dMHgEGDBvHEE0+U65l+Cypij443gWeAvwM5FdAeZtYKOBO4\nH7gtLD4ZuDT8PB4YCfwN6Bd+BngL+HP4+RzgNXfPBtLNbDHBPh9GMINjeXiv18I2FhTXF83oEBER\nERERiS77GqCoKCtWrCAlJSU/yJFn/fr13HzzzcyaNYtt27aRk5ND48aNAWjXrh2PP/44aWlpfP/9\n9/Tp04c//elPNG/eHCD/T4B69eqxc+dOcnNzi9xDflURbybb3Z9299nuPifvKGebjxHMEnEAM0sA\nNrl7bni+YDrY/BSy7p4DbDGzxpSeWra4VLTFUqBDRERERERE9kZSUhIZGRnk5ubuVn7nnXcSExPD\nvHnz2Lx5My+99BLuvwZj+vfvz6xZs1i+fDkAw4YN26/9rm7KPKMjDCYATDKzG4B/Arvyzrv7xjK2\n2xdY5+5zzSw1r5iiKWG9wLnCSkshW1xwp8Rw33vPvsfGKcGjKOuKiIiIiIiIlKR79+60aNGC4cOH\nk5aWRo0aNZgzZw7btm2jQYMGxMfHs2rVKh5++OH8axYtWsSqVavo2bMntWrVom7dursFQQor7Vx1\nt7dZV8qzdGUOuwcUbi9wzoG2ZWy3J3COmZ0J1AXigMeBBmYWE87qKJgONi9V7GozqwE0cPdNZlZS\nClkDkospL9ZJl5/EncffWcZHERERERERkd+KmJgYJk2axNChQ0lOTiYmJoYBAwYwcuRIBg0aRMOG\nDWnfvj2DBg3iscceA4KsKsOHD2fBggXExsbSo0cPnn322RLvESQO/W0qPPlg1KhRxdazSIwGmVlt\n4N9AY4IMLI8CnQiypAwEDgEWA8cB1wBdgIbAiUAs0A2oD7wMvA1cRbA85SzgY2AhcC9wF9Aa+Iu7\n/6GYfvioGaO458R7KulJRUREREREZF+Y2W96VsNvUUljHpYXifyUe48OM7vIzOLCzyPM7B9mdkR5\n2nT3XcBJwO+AGcAZwKvAKILZGFOBLwkCGGOBo4HTgeVAGkF2lu/Da/8fsDOs+1cgFxgKPAfUJgh4\nnGJmhxTXF+3RISIiIiIiIhI9KmIz0v/n7lvNrBdwCkHg4ZnyNuruO9x9JtCfYInNaoKsLq3c/RLg\nBeDcMCiyHujj7scSpIrNS227DrjH3Tu6+4sEs0C6A5uAj929jbs/AORlXinilxyllxURERERERGJ\nFhUR6MhLKdsXeNbd3wdqlbdRM4sxs6+AtcCHwFJg8/7OvKIZHSIiIiIiIiLRoyICHavM7G/AxcDk\ncH+Ncrfr7rnufgTBZqHdgY7FVQv/3NfMKyWVF6FAh4iIiIiIiEj0KE/WlTwXE+yP8Yi7bzazFuye\ngaVc3D3TzGYCxwIN93fmlc9e+oy0z9MApZcVERERERERqSp7m162wrKumFkzoE7ed3fPKEdbTYBf\n3H2LmdUl2Hz0IWAI8A93f93Mnga+dvdnzOwG4DB3v8HM+hPs3dHfzDoRZF45hmBpyofAQQQzThYC\nvYE1wGzgUnefX6gfPuSfQxh37riyPoqIiIiIiIhUIGVd+e3Z16wr5Z7RYWbnEKR/bQn8SDBTYgFw\naBnba0WQErZLmB94I0G2lE+BO4FxZvY8QfBjbHjZocAgM7uKIIBxTlh+NMFsjW3AT8Dl7u5mdjhB\nsGMxsB0YXTjIkeeXXG1GKiIiIiIiEilSUlIIfyvKb0RKSso+1a+IpSv3ESwr+cjdjzCzk4CB5Wgv\nG7jW3eeaWX1gDkHgYzgwyd2PN7NhQCN3/8XMzgBau3ucmR0DPOHu6WbWCLgHaE+wVGUO8L/wHk8D\n/d19tplNBr4qqTPao0NERERERCRypKenV3UXJMJVxGakv7j7BiAm3D9jOnBUWRtz97XuPjf8vA2Y\nTzArox8wPqw2nl/TwfYDJoT1/wc0MLNEoA8wzd23uPtmYBpwupk1B+LcfXZ4/QTg3BIfTullRURE\nRERERKJGRczo2BzOvPg38LKZ/UiwHKTczKw10BX4DEh093UQBEPCPUGg5FSxpaWWXVlM/WJpRoeI\niIiIiIhI9KiIQEc/4GfgVuAyoAFwb3kbDYMnbwG3uPs2Mytpt5nCi7OMCkotC7DgrQWkLU4DlHVF\nREREREREpKrsbdaVMgc6zKw9wSyL/4ZFucB4M+sFNAQ2lKPtmgRBjhfd/d2weJ2ZJbr7unD5yY9h\neUkpZFcCqYXKp5dSv1hJ/ZJIuzytrI8iIiIiIiIiIhWg8OSDUaNGFVuvPDM6HifIglLYlvDc2eVo\nez5BFpeWwBNh2VRgVjizw4D3w/KJwFNm9keCmRk7w2DIVOAJMzslLI8Hhrv7ZjPLNrPFYXkscH1J\nHVm2aRn3ziz3BJVq54evfqDNEW2quhuyDzRm0UXjFX00ZtFB4xR9NGbRR2MWfTRmkU9jtG/KE+hI\ndPdvCxe6+7fh3hplYmY9gbbAEuAgM/sSuIsguFHcshQvcK5Ic8XULXiuuPLdXN3tarJzs/flEX4T\nln21jKTDk/ZcUSKGxiy6aLyij8YsOmicoo/GLPpozKKPxizyaYz2kbuX6QAWl3JuSVnbLdBGCvBN\nge8LCIIrAM2B+eHnZ4BLCtSbDyQC/YGnC5Q/DVwSXvt9gfLd6hXqg0e76dOnV0q7I0eOrJR2Ze+U\nZVw1ZpGv4LhqvKJPSWNWWf8OS9lU1N8tjev+sz//PdS4VoxI/P8wjW3pInHM9sZvaVyjdYzKYl/G\nNfzNXuS3fHnSy35hZlcXLjSzq4A55Wi3JM28QNYVYL9kXYl2e7NRi0QfjWv1pHGtnjSu1ZPGtXrS\nuFZfGtvqSeNaPVXEuFoQBCnDhWaJwD+BLH4NbBwF1ALOC4MRZe+YWQowyd27hN83unvjAuc3uHuC\nmb0HPODun4TlHwG3A72BWu7+QFg+giDt7ayw/mlheS/gdnfvV0wfyvZyRERERERERKTSuXuRbSzK\nvEdHOLuih5mdBBwWFr/v7v8qa5t7sN+zrhT3wkREREREREQkcpVn6QoA7j7d3f8cHhUZ5Ci8kehE\n4PLw8+XAuwXKBwOY2bHA5jAIMxU41cwamFkj4FRgajjTJNPMupuZhde+i4iIiIiIiIhEvfJkXak0\nZvYKwdKTpmaWBVxLENAYb2ZXAhnARQDuPtnMzjSzJQRLU64IyzeZ2X3AFwRZVUa5++bwFjcA44A6\nwGR3/2C/PZyIiIiIiIiIVJoy79FR2cxsJsFeG39z9yPCsnnufljpV4qIiIiIiIjIb1W5l65Uonru\nPrtQWXaV9EREREREREREokIkBzp+MrN2BMtOMLMLgTVV2yURERERERERiWSRvHSlLfAs0APYBPwA\nDHT39H1oYyxwFrCuQJraMcDZwC5gKXCFu2dWbO9FREREREREpCpEbKAjj5kdAMS4+9YyXNsL2AZM\nKBDoOAX4l7vnmtlDgLv7nRXaaRERERERERGpEhGZdQXAzG4r9B1gCzDH3efuTRvu/h8zSylU9lGB\nr58BF5SzqyIiIiIiIiISISJ5j46jgOuAA8PjWuB04O9mdkcF3eNKYEoFtSUiIiIiIiIiVSxiZ3QA\nrYBu7r4NwMxGAu8DJwBzgDHladzM7gZ+cfdXyttREREREREREYkMkRzoaAZkFfj+C5Do7j+b2a7y\nNGxmQ4AzgZP3UC+yNzARERERERER+Q1zdytcFslLV14GPjOzkeFsjv8Cr4Sbk36/D+1YeARfzE4H\n7gDOcfc9BkzcPaqPkSNHRlW7Oirv/WvMIv8oOEYar+g7ShozjWVkHRU1HhrX6BuzSLtXdT4i8T1G\nYp8i6YjW9xOt/dazVtyzliRiZ3S4+31mNgXoSRCouM7dvwhPX7Y3bZjZK0AqkGBmGcBI4C6gFvBh\nuMHpZ+5+QwV3P2KkpqZWdRekEmhcqyeNa/Wkca2eNK7Vk8a1+tLYVk8a1+qpIsY1YgMdoa+A1YT9\nNLNkd8/Y24vdfUAxxS9UUN+igv7yV08a1+pJ41o9aVyrJ41r9aRxrb40ttWTxrV6qohxjdilK2Y2\nFFgHfAi8R7AR6XtlaGesma0zs28KlDUys2lmttDMpppZgwrr+G+E/lGJPhqz6KLxij4as+igcYo+\nGrPoozGLPhqzyBf1Y+QOy5btt9tZaetaqpKZLQGOcfcN5WynF7ANmODuXcKy0cAGdx9jZsOARu4+\nvJhrPVLfj4iIiIiIiEhUeOMNGDIEMjMhNrbCmjUzvJjNSCN56coKYEt5G3H3/5hZSqHifsCJ4efx\nwAygSKBDREREREREol/r1q1Zvnx5VXdDatUq02UpKSmkp6fvdf1IDnQsA2aY2ftAfnYUd/9TBbTd\nzN3Xhe2tNbOmFdCmiIiIiIiIRKDly5eXmqVDIluYSGSvRXKgIyM8aoVHlUhLS8v/nJqaGv1ro0RE\nRERERESi0IwZM5gxY8Ye60XsHh0VKVy6MqnAHh3zgVR3X2dmzYHp7t6xmOu0R4eIiIiIiEiUC/dy\nqOpuSBmVNH5Rt0dHuJzkDuBQoE5eubufXJbmwiPPROByYDQwBHi3zB0VERERERERkYgRsYEO4GXg\ndeAs4DqCgMT6fW3EzF4BUoEEM8sARgIPAW+a2ZUEy2MuqqA+i4iIiIiIiEhF27IFGjSAnBz4+GPY\ntavEqhG7dMXM5rj7kWb2TYElJ5+7+9EV1P6twFVALvAtcIW7ZxWqo6UrIiIiIiIiUS7Sl660adOG\nsWPHcvLJZVnAUP2ZGR4XB2lpQaDj73+HDh2w996LrqUrwC/hn2vMrC+wGmhcEQ2bWUtgKHCIu2eZ\n2etAf2BCRbQvIiIiIiIiUhFycnKoUaNGVXej6vvx3XfQowds3w6ffw7t2kEJ2Vhi9nPX9sUfzawB\n8H/AH4DngFsrsP0awAFmVhOoRxBIEREREREREdlvBg8eTEZGBmeddRbx8fE8/PDDxMTE8Pzzz5OS\nkkLv3r0B+Oyzz+jZsyeNGjXiiCOOYObMmfltjBs3jk6dOhEfH0/79u159tln889t2LCBs88+m0aN\nGpGQkMCJJ56Yfy4mJoZly5blf7/iiiu45557AJg5cyZJSUmMGTOGFi1acOWVV9K5c2fef//9/PrZ\n2dk0bdqUb775ptLeT76kJHj/fRg7NghylCJiZ3S4+3vhxy3ASRXc9moze5Rgf44dwDR3/6gi7yEi\nIiIiIiKyJxMmTGDWrFk8//zznHTSSSxfvpxhw4bx73//mwULFhATE8Pq1as566yzePnll+nTpw8f\nf/wxF1xwAQsXLiQhIYHExEQmT55M69atmTVrFqeffjrdu3ena9euPProoyQlJbFhwwbcnc8++yz/\n3lbCjIg8a9euZfPmzWRkZJCbm8uf//xnXnzxRfr27QvA+++/T8uWLenSpUulvqN8XboExx5EbKAj\nzLpyNdCaAv109ysroO2GQD8ghSCQ8paZDXD3VwrXTUtLy/+cmppKampqeW8vIiIiIiIiEWYPv/n3\nWlm3Aim4h4iZMWrUKOrWrQvASy+9RN++fenTpw8AvXv35qijjmLy5MkMGjSIM844I//a448/ntNO\nO41Zs2bRtWtXYmNjWbNmDT/88APt2rWjZ8+exd6zODVq1GDUqFHExsYCcNlll3Hfffexbds26tev\nz0svvcSgQYPK9sBlMGPGDGbMmLHHehEb6CBI+ToL+AjIqeC2TwGWuftGADP7B9ADKDXQISIiIiIi\nItVTpO1V2qpVq/zPy5cv54033mDSpElAEKDIzs7O37x0ypQp3HvvvSxatIjc3Fx+/vnn/FkWt99+\nO2lpaZx22mmYGVdffTXDhg3bqz40bdo0P8gB0KJFC3r27Mnbb7/Nueeey5QpU3jyyScr6pH3qPDk\ng1GjRhVbL5IDHfXcfe/e/r7LAI41szrALqA38Hkl3UtERERERESkRMUtISlYlpSUxODBg/nb3/5W\npF5WVhYXXnghL730Ev369SMmJobzzjsvf7ZG/fr1eeSRR3jkkUeYP38+qampdO/enZNOOol69eqx\nY8eO/LbWrl1LUlJSqf0aPHgwzz33HL/88gs9evSgRYsW5Xr2yhDJm5G+Z2ZnVkbD7j4beAv4Cvga\nMODZUi8SERERERERqQTNmzfP3xTU3YssKRk4cCCTJk1i2rRp5ObmsnPnTmbOnMnq1avJysoiKyuL\nJk2aEBMTw5QpU5g2bVr+te+//z5Lly4FgqBHzZo187OndO3alVdeeYXc3Fw++OCD3TY4Lcm5557L\nl19+yZNPPsngwYMr6hVUqIgLdJjZVjPLBG4hCHb8bGaZBcoryuPAPCAWOAroVoFti4iIiIiIiOyV\n4cOHc99999G4cWPefvvtIjMpWrVqxbvvvssDDzxA06ZNSUlJ4ZFHHiE3N5f69evz5JNPctFFF9G4\ncWNee+01+vXrl3/t4sWLOeWUU4iLi6Nnz57ceOONnHDCCQA88cQTTJw4kUaNGvHqq69y3nnn7bGv\nderU4YILLuCHH37g/PPPr9gXUYotO7cA8PMvPzNu7jjGzx1fYl3b0+Yj1ZWZjQNmuvsLeSlm3T2z\nUB3/rb4fERERERGR6sLM9rjxpuy9++67j8WLFzNhwoT9cj8zo9799RjcZTDZudl8t/47Dko4iAnn\nTcDdi6yvibhAh5n1AeLc/a1C5RcAme7+YQXcIw6Y6+6lJt9VoENERERERCT6KdBRcTZu3Ei3bt14\n+eWXd8vgUpnMjMydmfR7rR8rMlfw5TVfElc7Lm9ciwQ6Im7pCnAPUNzCoJnAvRV0j7bAT2b2gpl9\naWbPmlndCmpbREREREREpNp57rnnSE5Opm/fvvstyJEnrnYcHwz8gM+v/py42nGl1o3EGR1fuPtR\nJZz7xt27VMA9jgQ+A45z9y/M7HFgi7uPLFRPMzpERERERESinGZ0RLeSxq+kGR2RmF423sxqunt2\nwUIziwUqatbFSmCFu38Rfn8LKDaVbVpaWv7nwjl7RURERERERGT/mDFjBjNmzNhjvUic0fEQkAjc\n5O7bw7IDgCeBn9y92IBEGe4zE7ja3ReZ2UiCzUiHFaqjGR0iIiIiIiJRTjM6otu+zuiIxEBHTeCP\nwO+A5WFxMjAW+H/u/ksF3edw4DmC9LLLgCvcfUuhOgp0iIiIiIiIRDkFOqJb1Ac68oSbg7YPvy5x\n958r4R4xwBfASnc/p5jzCnSIiIiIiIhEOQU6olt12KMDgDCw8W0l3+YW4HsgvpLvIyIiIiIiIiL7\nQSSml90vzKwVcCbB8hURERERERGRiDJ+/HiOP/74Sml7xYoVxMfHlzrTJSYmhmXLllXK/SvTbzbQ\nATwG3A5o/pKIiIiIiEgk2LwZvvwSsrMhMxO2bq3qHlU5syIrMypEUlISmZmZ+e2fdNJJPP/88/vl\n3pUtYgMdZvbx3pSVse2+wDp3nwtYeIiIiIiIiEhV+OwzGDAAWrcO/qxbF1q2hGbN4MwzYfnyotes\nWQPvvAN/+hPccANccQXs2LHfux6NcnJy9qpetO5rEnGBDjOrY2aNgSZm1sjMGodHa6BlBd2mJ3CO\nmS0DXgVOMrMJxVVMS0vLP/YmX6+IiIiIiIgUkpsLY8bA4MHwxBPwyScwYgRceCFccAGcfz706AFL\nl8KCBbBtW3Bs2QLHHgunnAJvvw1Dh8KuXTB+PHTpAs89FwRBDjkkqH/ttRCFP85Hjx5N+/btiY+P\n57DDDuOdd94ptt60adM45JBDaNSoETfeeCOpqan5szDcnT/+8Y+0bt2a5s2bc/nll5OZmQnA8uXL\niYmJ4fnnnyclJYXevXvnl+Xm5jJixAhmzZrFTTfdRHx8PDfffHP+PT/88EMOPvhgEhISuOmmm/LL\nx48fT69evbjtttto1KgR7du359NPP2X8+PEkJyfTvHlzJkwo9md2mc2YMWO33+glcveIOgg2CP0B\n2EWQ9vWH8PgauKkS7nciMLGEcy4iIiIiIlIpsrPdr7/e/fvvq7onleff/3a/+mr3E09079XL/YUX\n3M8/371TJ/e773Z/9VX3sWPd168vvZ3bb3dPSnI/+WT3jh3dDz7Y/Ztvdq+zfbt7ly7uTz21e3l2\ntkf6b7u33nrL165d6+7ub7zxhtevX9/Xrl3r48aN8+OPP97d3devX+/x8fH+zjvveE5Ojj/xxBNe\nq1YtHzt2rLu7jx071g866CBPT0/37du3+/nnn++DBg1yd/f09HQ3Mx8yZIjv2LHDd+7c6enp6R4T\nE+M5OTnu7p6amprfVh4z87PPPtszMzM9IyPDmzZt6lOnTnV393HjxnlsbKyPHz/ec3NzfcSIEZ6c\nnOw33XSTZ2Vl+bRp0zwuLs63b99e7vdT0viF5UV+y0dc1hV3fwJ4wsyGuvufq7o/IiIiIiIiFc4d\nHnoIXnsNVq2Cd9+t6h4F3OHnn6FevWAWxr/+BT/8EMyq6Ny59OvmzoU5c2DTJti5E6ZNC5aX3HAD\nnHhiMHujdm24/PJ979fo0fDgg5CVBY8+CtdfDwkJu9epVy+Y9dGjBxx9dHDk5MAtt+zVLWxUxexo\n4CP3fUbJBRdckP/5oosu4oEHHmD27Nm71ZkyZQqHHXYY/fr1A+Dmm2/mOHLkfwAAIABJREFUkUce\nyT//yiuvcNttt5GSkgLAgw8+yGGHHca4ceOAYL+NUaNGUbdu3X3q25133klcXBxxcXGcdNJJzJ07\nl9NOOw2ANm3aMHjwYAAuueQSHnjgAUaOHElsbCynnnoqtWrVYsmSJXTp0mXfXkg5RVygI4+7/9nM\negCtKdBPd6/QuS/uPhOYWZFtioiIiIiI4A7ffBP8AG/bFmJiYOpUePHFIIAQHw//+x/07g3//S/0\n7Ln/+7hrVxCc+Oc/4auvYP58+OmnIKixdi00aQLdusFdd8GwYcEzrV8PNWsG+2hkZARLTZYvhzp1\n4LjjoGnT4NzQocGSlJp7/tn5w6YfeOv7t3hv8Xss2biEjk06smnnJrJysjij/RkM6zmMhHoJQbsj\nRgCwdddW5v80n5WZK1mxZQXZudncfMzNxD71FAwcCFdeGQRFDjlkr15FWQIUFWXChAk89thjpKen\nA7B9+3Z++uknYmJ+3W1i9erVJCUl7XZdq1atdjufF+QASElJITs7m3Xr1hVbf28lJibmf65Xrx7b\ntm0r9lxeAKVJkya7lRWsv79EbKDDzF4E2gFzgbydUhwod6AjTC07AWgetv13d3+yvO2KiIiIiMhv\nVHo6JCVBjRrB92++CQID338fBDh++ikIErRtG8xwuO8+aNuW7Nxsaj7yCFx1VZBtJCcHfvwRUlKC\n62L2YVtFd/j0U1i3DhITg2BFXFxw7scfgwDFzp1BRhOA774L+tiqVbDh5x13QPv20KIFfPFF8Ge7\ndmAGn38Of/lLEPho2jSY7bFjR7BPxsCBwf06dAjqFiNzVyYjp49k+ZbldG7WmUOaHML/Vv2Pr9d9\nTU5uDt+v/56LOl3EsJ7D6JDQgcUbF9OkXhMM4+9f/p3uz3XnxqNvZMnGJaSlpvHmd28yauYokhsk\nk9QgiVZxrfhu/Xd8uvJTXr3gVWInTw6CSrNmldqvSJCRkcE111zD9OnTOe644wA44ogjimwE2qJF\nCyZOnLhb2cqVK/M/t2zZkuUFNm1dvnw5sbGxJCYmsmLFCqD0LCrRmmGlOBEb6ACOAjp54dGtGNnA\nbe4+18zqA3PMbJq7L6iEe4mIiIiISHX1888wahQ89VQwcyM+PthAs2ZNuO02mDgRYmOD5RwxMcH5\n8Afl0o1LOWHcCdx/8v1cfvTRQbAgJycIJqxbFwQkxo4tfqmHO/zhD0HWkQMPDJaXuEP9+sEMhjVr\nYOFC6N49+LxyZZDFpE6dIBjjDg0awAcfwJFHFm3/+ON3/3700cEGoHuQnZtNdm427s6cNXN47svn\neG/Re+zM3smlh13KgM4D+GTFJ7zx/Rsce+Cx9D2oL7meS4+kHsTVjstvp13jdvmfj2x5JOPnjueD\npR/QoHYDWv2pFb2Se/Hx4I/pnPjrcppd2bu46M2LuPiti3n9udepVaMWAPPXz99jv6vS9u3biYmJ\noUmTJuTm5jJ+/HjmzZtXpF7fvn0ZOnQoEydOpG/fvjz99NO7zda49NJLGTNmDKeffjpNmjTh7rvv\npn///vmzQor7aV2wLDExkWXLlpXrWSrn5/u+i+RAxzyCGRdrKrphd18LrA0/bzOz+cCBgAIdIiIi\nIiJSVG5uEMDYtAk2boQNG4IZAxMmBEGBZctg+/ZgKUiDBkGwomZNNv28iVpei3oNG2JmZGzJYOqS\nqXyx+gveW/weAw4bwO0f3s6Jj31KmzFjoHnzIBCyaxfMmwd9+8Khh8L06cEsi6ZNg/58/31wvzff\nDPpz0EHwyy+/zsCAYBbJp58GMzO6di1xCcmmnzfx3frvmPfjPOb9OI8ft/9I1+ZdydiSwZQlU1i/\nfT09k3ty30n3kZWTxYYdGzAzYmNiWbd9HSu2rGDzzs38b9X/+GrtV/k/dts1bsdVR1zFH0/+I/Vi\n69G4bmMALux04T6//iFdhzCk6xAARpwwggPjDiwyA6F2zdq8dfFbXPzmxVz69qVc3e1qXvrmJaYu\nnbrP99ufOnbsyP/93/9x7LHHUqNGDQYPHkyvXr2K1EtISODNN99k6NChDBkyhMsuu4yjjjqK2rVr\nA3DllVeyZs0aTjjhBHbt2sXpp5/Ok0/+unChuBkbBctuueUWhgwZwtNPP82gQYN4/PHH93mWR+H6\nVTVLxCIl4lKYmU0HugKzCTKwAODu51Twff4/e3ceZldVJfz/u86dax6SVFKpTGRgJsyigEQGAR9F\nW38gIAQRpxexGfr3vi8OLaHpttUWtUVAW5DJBlQcQCbDFFBpiIEwSUiAJCSVylTzfIdz1vvHPrfq\nplIZq0INrA/Pfurefc/ZZ997LpU666y990xgCXCIqnYOeG0fJZQYY4wxxgyzX/8annkGZs1yExs2\nNcG558IZZ4zqlG0zDnV0wF/+4rIXTj99x9up7vi7mR+CsXWru4hvaHCZEMcc49qvr3eTWk6c6LIl\nolH3s/BxJuOWKn3qKTd8IZ9JUVTkhnR0dbnARWtr/88gcK+vW+ce54eOZDJum5ISqKqCykr38+ij\n4QtfQGfN4tn1zzK7ajbJaJK23jaWb1rOLS/ewpK1Swg0IONnKImXEPWinDn3TI6tPZYPTPsAR9Ue\nxQ3P38DNy27m/nPvpyvbRVtvG7WltcQiMWb8339HHnoIzjrLTbLZ0uLex9Sp+Cd/iK6kR2m8lPZ0\nO4ry13V/ZWv3ViaXTOaoKUcxsXgi6Vyalza91LdNoAGqyprWNfzwuR+yqXMTB088mEMnHcohkw6h\nKlXFy5tfZlrZNE7d71RmVszk5mU3c/tLt1OZqqQqVYUgZPwM1UXVzCyfSWmilGNqj+GYqcdQEi/Z\nN9+t3ZTOpTnnvnNY3bKarxzzFT4y9yPMqJgxarINhouqUldXx913381JJ5000t3Zp0Rk0PMX1m/3\ni2Q0BzoGPVPh5KHDdYwSXJDjOlXdbppjEdFrrrmm7/mCBQtYsGDBcB3eGGOMMWbvvfgiPP44HHSQ\nm0jw5z+Hyy5zkwfW1bn09BtvdBeGP/+5uygEdwHp+4Pf2X3lFZfi/tBD7u7wuefCqlXugq693U1I\nKOL2z7cHbpx+PO7a9H13Abl1q7v7XFzsSmWlu9Nc2p+aTi6353MQmJGRv+gH9x1atcp979atg/Xr\n4bnn3LCLRMIF3ObPd68ddpj77rS3u/OdL52dLiBXWuraramBefPcRJMtLW5f33dDMMrKYPp0F7z7\n29/cd6muzgUfGhtdFkM269rNP85mXbBjxgy32scHP+i+p+ACGm+84dotL3elosL9FHF9mzHD7R8E\nrsRibpsB/9/0ZHv409t/4j+f/0/Wt62nqaeJQAPKE+XMq57HOQefw8L5C0lGk+SCHO3pdkrjpcQi\nse0+4m888Q1uWX4Lk0smU5YoY0P7BtrT7VxyxCV899TvkA1yrG9fT01xDZ2ZTjZ0bOB/PfS/eHnT\ny4gIUS9KoAHHTj2WaWXTqG+v58WNLyIidGe7OWjiQVSnqvHEQ0TwxKMsUcZXj/0qH5j2ATwZ3/8f\n7uhCeaxZvHgx73vf+0gmk/zHf/wHN998M6tXr+7L6hiv8udvyZIlLFmypK/+2muvHVuBDgARmQHM\nVdXHRaQIiKhqxzC1HQUeBB4Jl7QdbBvL6DDGGGPM6KIK//zPbqz8xz7mxuDPns3Ki8/i5bJuZlbM\nZFPnJpq6mziibH/m/+/vI729sGiRW33gwQddSvwZZ7g71Bs2uMkRe3vdBet++7kLwyefdMGUgw5y\n28Vibi6C7m73fPp0F0wJAnjrLXdRGom4dvKBjaOOcsfq6nJ35Vevdq9Nn+4CIRs3unYnTnT75y9W\nczl3wTl/vls1YcECt89g8hfgvu9+vpvZK/kL/u7u/ovrzk73Ptevd0tfRiIue6Cmpv+iWbX/4jmR\n6J+8MhJxF99lZf0ZCd3d/fsNLPnPtqfHve/C918YVCgsr73mAlBVVe55b68r6bT7DIuK+ksq5QJY\nf/sbPP2062tJidu+stItNzpzpntv73+/O8e9vXD00TROKiHTtJXa+x51E0GGwzj6Sirl5pXo7HSf\nR329+9zybU+cSPtBs+nK9dCebmdr91ZK4iUcMukQ0rk0Gzo2EGhAZbKS4ngxfuBTmijtu1jPD6/Y\n2r2VjR0beWXzK8QjcRLRBGWJMmZVzKIr20VHuoP2dDsdmQ66s92kc2nSfrrvZ8bPbPN8m/pcmjca\n3+DIKUdy/qHn89nDP0vUG96ZARq7GznyZ0cyp2oOyxqWUZ4sp7G7kdJ4KdVF1Vx8+MX80/v/ie5s\n9zbzW+QFGtDU3URFsmLQ4Mp7yXgJdFx77bXccMMNZLNZDjroIG644QaOPvroke7WPveuZ3SIyNHA\niUAt0IObW+NxVW0eYrtfAL4IVKnqbBGZC/xUVU8ZUof7278TaFTVq3ayjQU6jDHGGPPu6Ohwkwqu\nXu1WR9i82V18Vla6C0jPcxeab76JivDwTy7n4da/Mbd6Lps7N3PL8ls4ru44GjoamFIyhYpkBX9d\n/1c+UHMMd9zTQ/Sv/wPf+hYbzzoZP5lg6sN/JqiqJDdvDon1DQRdnSw/aiotfidL1i6htmQKH6w7\nnnc6NzCldAqdmU6OeSeHVFaRrZtC6cYmSKdR32fjjCpKE2XEVGjULhp7m2nsbqS5p5myRBnJaJLi\nWDGTi2uo6QiIN2xGJ06kY1I5XjpDcUsXEo9DLEYQ8dBIBFFFHn8Cue8+WLLEDc3ZtMkNQ+jtdZ/H\nihUuGJPPMonHYdIkF2DITwqZDzI0NbngCrjtVV3mQC7nPvv2dhe4SSbdRXjh43i8Pzih2l+WLXPn\nqqrKbROLuUDAtGkuANDS4oIfy5e7LIJ89oqIK9msO8dB4PpV2JdIxNWnUu5xft/CEou5wFEq5drL\nB0CgfwjHwDJjhlvCtKOjP9CSTLqfntcfzMr/zGYJZs5g48nHUuwlKMlArxfwRnYjrzeuYG3rWta0\nrqGho4Ga4hqKYkU8u/5Z1rWtwxOPTx74SdJ+mu5sN37g46uPH/j05Hpo6GigMllJLBKjMumGQ6T9\nNL25Xlp6Wli+aTmpaIrieDGTSybT0tPC2y1v44nH1NKpeOLR2ttKV7aLiEToyfW4NnJpurJdVKeq\nmVA0gZqSGg6ZeAi++mT9LC29LbzT9g6l8VJKE6WUJcoojZdSFCsiEUmQiCZIRBJ9gZGd1c2unM3E\n4on79FfDqqZVvLblNU6acZJbXtXslfES6HivetcCHSLyWeAfgTXAC8AWIAnMA47HBTz+WVXX7WX7\nLwHHAs+r6hFh3auqeujO99ytto8HngFexS1Zq8DXVfXRAdtZoMMYY4wxw0fV3Rn/8Y/hpZfchW48\n7i46u7rg1FNdqn9NDUyahMZiaHMzflUFvp8j6OnmxehW/qn1XiQW51MHfoq1rWspT5Zz0fyL2H/C\n/tscrifbw1ce/gp/XfsMF837NA9vfJoVjSuISIQJRRNo6mmirbeNmpIaMn6GymQlk4oncezUY1nR\nuILXt77O7MrZbO7aTDKaZFXTKrJ+FkVJRVN44pHxM8QiMbqz3eSCHBOKJvSVymQlbek2Mn6GrkwX\nGzs3sqVrC8WxYrqyXSSjSQINyPpZiuPF9GR7SPtpBEFR9qvcj4/O/SgL3spx1j/9F5uPmMv6k48m\nV1KEl/PpmDaJxsPmABDEokyOlDO1J0aqvZtEJiDR0U3FynV4zc0uGFFTU3AqlJ7iBJkoUFSElpcj\n6QzS24uk00hvGulxj8mkEYmEWRMuSCHisbEqzoP7ZWnLdNCZ6XQl635m/ay7iI6X9V1MJyLbppZ7\n4hHxIkQkQsSLEPWi7rF4xDMBJBNEIjEiXqTvzjxA1IsS9aL46tORdpkI+fYAFPf3az6g0JPtQVE8\n8djQsYHXt75OzIsRj8QJNCAX5AjUBUjyQxoKy5qWNShKb66XjnQHyWiSudVzOXjiwcyqmMWsylnU\nltaypWsLXZkuDq05lBOmn8CWri3ctvw2JpdMpiRess17TUVTTCmdQmtvK7kgR1N3E629rSSjSZLR\nJEWxou1W4QDozfWSiCQGneAw62dp7G4kEU1QkawY90MxzJ6xQMfY9m4GOr4C/EJVe3bw+uFAtao+\nsZftP6+q7xOR5ap6RDjU5EVVPWyvOrx9+2cAPwI84FZV/e4g21igwxhjdlc67VKQm5rcUnolJe5O\nanMzvPqquyM7aZK789jT4+7IJpMuRT6fEt/T4y74WlvdBcUXv+iWwsvfXSy805gv5eXujncu5+5k\nZjJwzz2uLxMmuONls+7O7v77u4udVAoOPNDtO5Cqa2O0jHXt7YXnn3d3jdNpd6G2YIH7zLq6XNq8\niHve1OTed3m5e59Tp7r3vTO+795vKrX9a/m71jubPyG/fybj+pfJuLqKiv6J/4Kg/5yn0/13n3O5\n/vbzd6wHjrH3fVdyuf7Hvu/a27zZ3eHftKn/bntzc39pbXVDJ2bNctv29Lg77bFY//vL6+x0Kf25\nnLvrnf9+zpu37bwE+VK4fyoFc+a4z7zwuzmwtLWhy/5GR9tWXvj0B3n7iBn0iI9m0nQmhA108MD6\nx2juacYPfHJBDl99PPH6L4C9CPNr5vOVY77CuYecu9uz2T+w8gH+uu6vHD/9eD4y9yNEJMKz65+l\npqSGGeUzqG+v7wss7MyWri2koimKYkU09biL7ohE+lZSgF3PsB9oQGtvKyXxkr6lH3tzvXRnu/vu\nqOf/oH1p00s8vvpx1rauJUj3ko4o2SA7+BKJKA0dDTR2N/YNL8hfmFelqujOdtOebu/ro6qSiqVI\nRVN9gYF8u7v7fGLRRE7b7zQmFE2gJF5CcbyYkngJJfESYl6Mzkwn7el22tPttKXbyPrZbfobaLBN\nloOv/ed9YL0nHtWpagRxy3dqDg+P0oTLRBAEX30E6XuPnnikoilSMReUCjRgQtEEDqs5jEAD0rl0\nX4AlHxTIT1IZaNBXJpdMZlblrL7PYKRWUTBmKCzQMbaNp8lIvwe0AguBrwKXAq+r6jeGoW0PWAWc\nAjQAfwPOVdU3BmxngQ5jzMhaswZ+8hM3KduUKW7JucpKN/b7gAPcNjNnuknWhksm44INiQQsXerG\n0E+e7C4W8xeTmQy8+aa7MGxoQF9+uW/SQS0t4c2jZhHp6WXS5k6yZSVsrKvgz/t5VPd6xH3oiUJv\nFEq6cxz5VhfxaAIpKiITj9AZh5YkJNu6OPrBF/HSGYJkAj+VIEgm8FLF+Mk4fjJOLh4j0t5OpL0L\njUXdP3a+z/LjZrClKkF5R5ZkVvHiCVKdvUzd1E2ys5dIVw/Jjm68G37iAgVz57qJ6aJR9Fe/ghdf\nRI46Cj7yETfR3apVLj184kR3wep5bqb90lL3mVRXu4BMfgWBCRPg/vvd3Aep1PYlP1Fjb6+7wG9q\ncscpKNra2jdmvuPguXRNrsSPxSir30rpytUEpaVIRwfpmdNQDSCXI1tZjl+UItrZTaqtk+jGLRCL\nIQcc4NLV33jDHTubdWn93d1uXHx+XgDPQ7PZvsCFZLNuCMGcOf0TSOYn6nvnHdd/VUgk0HgcjccI\nolHwPLz2diSd6U/PD4cBaDyG9Pa6y8RIBBDQgjkHvAgai6Ixl2avkQhEPIKIR+B5aBgUCRIxuqpL\naatI0VwWc/UitBR7tCShMRnQEs1y/DvKxB6hpSxOOiZMak4T5LL9d7LVR0TIxiKsrImRiQnRAHKx\nCMm0z4SNbe6yUTw8Eddfz93Nl/B5ojfL9C1pint8sokIuUQMTaboiSqdEZ8OL0ubZGiMZ3k6vpF3\n5s/kkMmHkYqlSEQSxCIxYl6M0kQpp88+nbqyum3u7ttF5dB0Zbpo7mkmFUtRntg2uPlen7PAmPcS\nC3SMbSMxR8dE4AvATKBv9h1V/dwQ2/WAS4APAwL8CbhlOCIPInIccI2qnhk+vxrQgVkdFugw76rG\nRncxe/jhNvv8e00u536uWeOW0tuyBebMwX/qSXJ33s4DJ0zgqYpW6rqjHBZMZFI6xtaKGNPrOwgi\nHvut2ETyQ6cRO+lD6BNPQHs74vvuDvPs2S4g0dPjLl6Litzd/qKi/jvokYjLvujuJvvwH4kse5Fc\nKkGkJ83mA6bRPLGEqq2dlDd39l1M+p7wzoQoK6fE2VgR4YVUKw9M7aQ34pbPO3POmVQkK1jbupaI\nF6GurI5jao+hK9OFr37f2OaebA+vbnm1bxK4ZDRJVaqKqqS7+3rfivsINKAsUUZZogxVpS3dRiKS\n6LtIzE/8lk+/VpQTp5/I5JLJ9GR76Mn19N3dXdm0ks5MJxk/w8ef2sjlrxTx+sxiJm/poqG2lKhE\neaqyletnbODjW6q4YHMNrZUpXijtoCQDkzJxuqNKIqcc3QDJHCAeFe0ZSlu7CcSdz5LGdl47bDKr\npxVTHiSo0ASxTJZoOkc0naU7Bm2lMdIxjyCX4Z1IF29FWukuL6KxCN722qj3OokWFZOLesyZuD8T\niibgiUdjdyMNm95kYiZGR1UxiXgRyWiSWCSGIIi4u7317fV09LZT1N7DBzuqOKC7iBUThc2RHtqC\nbmI9GXojylsVATkCJnQGaBCQjoAfFXJRDz/i4fkBR7QXUxJECAKfQAO6IgFrKpSOiE9W3B1pRSmO\nFTOpeFLfOHjSabJBli6y+AQuTT/MSsg/zt91zve98Gd+ZQBB+tLZPfHw1SciEaqL3Pj7/F1uRSlP\nlPd9X4piRfx9699p6WmhLFFG1IvS0ttCIpKgM9tJzIuRjCbxA7/ve5oPgAy8q52/+z7Y3e5ckGNl\n00ra0m0ko0miXpSsn6UiWUFVqorqVDXVRdVUp6qZWTGTE6afYMELY4x5l1mgY2wbiUDHs8CfcfN0\n+Pl6Vf3tENstBnpV1Q+fR4CEqnYPpd2wrU8Bp6vqF8PnFwDHquo/DthOnzn1k0R8n6rGTTRMn0tr\n9WQiuRxRP4tke9GOdRz+yst0pxI0l5XiSYSoJAElECVQHxEPv3w6kUlHoeqjmkPFIyM5cp4SVM6m\nZ8ZxRLK95OIpiloa8HIZcvEiBCXW1UoQT5KLpfD8DNHeLrwgh+9FyUQCvEgKokmCSIyyjSuJ9bSz\n5oQLSZdOACDVuJ5McSW51PYzMe+IBpr/EAa8oCQ6m1z/iyt3OLO5l8tQse4VestriPZ2uruEXgQJ\nfLKpMiSXJeJnIAgQ+tOAg0iMrurpaCSK52eJpLuJpt377SmfTBCJ9R9zL/9IlFyWaLYHL5chXVxF\nJJcm2ttJEEvgRxOIBlTUv+b6DeQSxaRLJyB+zvV5YHuBT7S3k1hvB9GedqSnCa+nhQhCumwKEaIQ\npEm0rCPa1YxPjkBzBPhE/BwT1iynrKmeVE83TSUJqrpzdBWXkI2nCKJxgkgMPxLB9zwCL4JogAQB\noASRCKgSz6SJZdLEM2nimQyxrHssCs1VE8hGo/lE1m0/t/BxR1kl7TVzkNLpRHpayXlK1oMgEkWj\nSbzwv1yqkiBZTiKTZcP7zqOnYsqeffi6g+9V4efp54j1tJNs34KKhx9L4qkPQUAuVUomWUYQSwz6\n3Yz2dhJEYoO/HgREsz0o4tr0sxS1bEACn8FIeIdZtL/0fV8H1EeyvXhdTdC9Ga9rC153I8nuDjwv\njkbjFDWtp7e0mmT7ZiI9beCnkVwaL/CJp3uYsqmBWC7HpvIkL0yLs6UY5jQLSyeneeCE+Rw89TL2\nT32Qbr+VNelltPkbSXnldAetCEJT2/Mc+5c/cdzmYv64Xwf1xTk8Ihy9tYgZ7VH+Xp2hOdpFOgLl\nfpKJ6RQluWjfd8kLfErSPp3RgL/VCa8dcjiJWDVFUk4qUkGRV06n30RHsJVAfQICElLEnOT7mRid\nRUxSVEanUhs7oO8fobFwEbe860GWdf2WOcn342uOmCRJaxdTYvtzcOoUNmffZkXvU0SIUhObixLQ\nE7QTlTi+ZtmUXUVWewnw6fJb6A7aSHhFRCVBTjNMie1PSaSadn8L7f7mbY6dlFISXjGCR1xSTIzN\nojo6g6z2ECFGSWQCxV4FnkSG/D5zmqEpt54OfysJr5iklJL0SohKAg8PwcOTCBI+zgca8lSVzqAJ\nX3N4EsEj0v+z4LHgjYnzbowx5r1p4UILdIxlIsKdd25//sLzuk8CHS+p6uFDamTwdp8DTlXVzvB5\nCbBYVT8wDG3/f8CHBwQ6jlHVywdspwtmVBIItCeifFDgfbEI6YiQiQg5L0ouMpFnp88j7kN1dxpf\nesl6re6PPo0Q0TiQpSizigldDQQSAdxM4vEgTjSIUNfeyYFbhZ6YkMoqm0o8emKQyrpz05aEuK+k\nskomAl1xyHlKJFDivhANAmK+EA8iNJTGyHkxPv5GB0U5JesJLck4qVzAuooKStK9FGWzdMdidMfj\noBD3fRJ+lrifIZFTErmApB/QkozxdlUZW4tTTOxKU9PZw6SuHnpiHl4QUJx1F4gSfoXSUaE7FqE7\nFqE0naOhNEJlr09nPIKnEFHwRShN+2QjHtmIhx/+UayAipDwAyZ3pIkGiu8JvVGPrlgE3xMmdmWI\nB9t/X8O5xVEBRVCBdDRKUyqF4D6nhO+TzOVI5HKIQm80Qi4ipLI+CnTHI8T8gLgf4Kny+kSP1qRL\nqS5NK9U9SjYiZD2hvweu74EonXGhI6G0J3J0xIWueJRAAiZ25whw72VzSYyWZAxPIwgxRCOowJsT\nprKp9BDS8cNRqSCaXUYi10zcb8fTDiJ+lqgqMR+iqgR4+J4gKkTU9aM3GqEnFqUnFqMnGqU3EqU3\nFkUQatu7iQcajif2UYL8J477xAJqOnuY2raBokwLXfEE0SBGwo8QDZRo4KP4BOJTksn1lf2bilg6\n7WDOev1lKnoz4efjkYlEWFdRTXNRFZM6G6ns6aQ1laCs132HmlMJ3qksYVJnDxFVPFX3Hcz5pHI+\nMT+gMx5ha1EUJCCZVQLxCEQoyfiUZnw8hc54lGigJHLue+gpZCKmSQ2dAAAgAElEQVQeEVWiQUBP\nNEJ3LEouIpSkcxRnc2QiLlMmkQvIecLG0hi+J33nkvy3UcH3IJD+oug2z/uKp6QjPq3JgPZEnM54\ngs54irZkBNE0scBnY0kxlb0ZmlNFdMdLUUpQr4hAYmQiSdZVHoAwiyJ/GsW5OiKaIh1poiQ7i5Q/\nebd+r2W8Ntrir1ORPpSoFhGQpTe6hazXTtKfSNx3d7yzXjtprwnf60E0ihDB05grxIj7VXgM79J4\nxhhjjDEj7Ze/HN2BjlmzZnHrrbdy8sknvyvH+9CHPsSFF17I5z43pIEYe21P36+IcMEFyqZNS9i8\neUlf/auvXjtooGM4/pp9UEQ+oqoPD0NbhZL5IAeAqnaKSNEwtV0PFC7GXoebq2M7T63d9Sq5l+9y\ni53LpxgnwjTd2eEM14PNel1YYl6MWCSGqvZN4jUt20VXpovXs110pzvx0720aDftG1aTWFuPVFcT\nLa1Au7ugvd1lncTjxIvLmFQ9HT8RIxPzSHtKpLGZ1NvvUNrYTGNlCW9PKKF7QjkVVbWUJcogm0WB\naDTmxhFnckh3N15XD5uLiimaNosW2G4yrS0Dng/0ds5HPUG8be9krmLABGeqBIFLj07nesnmMmRy\nbl1z7ewg2dxBEBECEbLxCBQVES0uJZYs6p/dPO1mu49E40S9KOXJcmZXzmaaF2FKkHOTfQU5sn4W\nP8iRH1ASaNDX93gkzuRInOmROBXJCopi235N9/zu9sj8stkTOT/L2ws/xvs2NfDn+75N8az9CXIZ\nct1dZLs76Hl5GcGWLWyc8UESk6dS1NFLtrSY3ppqire0ULKpmTdqqt2Ef+FyiUEygVdUTLyknFSY\nip+KpuhFSYfnNRtkyfpZcj1d+O2t+BGPIBF3WQT5yRgBzw+I9KaJ9mTwsjlypUXkilNIxP3KEyAm\nUcpSFW62/8BNDpf/XhT+7J8R3xs0vT4VTfWlyI/s3exy4P0FzxPAtEG2qwiLMcYYY8x7xy9/OdI9\nMEN1110AC8LiiFw76LbDEei4HPi6iKSBLO4aQlV1F9O871KXiBypqi8CiMhRwKArvOyFvwFzRGQG\nsBE4FzhvmNreY1EvysyKmXu9v4i4WcNjKarZwdraB+5l4yfudbdMaDymckcjMfb/b7ca88GDbfCh\nd7U7xhhjjDHGmDHA930ikaEPzd2VIc94qKqlquqpakpVy8LnQw1ygAug/EZE/iwifwZ+BVw2DO0S\nzvtxGbAY+Dtwr6quGI62jTHGGGOMMcaYPbV06VIOPvhgqqurueSSS8hk3PyADz74IEcccQSVlZWc\ncMIJvPrqq337zJo1i+uvv5758+dTWVnJeeed17cfwP33388RRxxBeXk5c+fOZfHixX2vrV27lhNO\nOIGysjLOOOMMmpvdaIZ33nkHz/O4/fbbmT59OtXV1fzsZz9j2bJlzJ8/n6qqKr761a/2tbN69WpO\nOeUUJkyYwKRJk7jgggtob2/fpo/f+973mD9/PiUlJfj+tln9b7zxBvvttx+//vWvh+2z3OtAh4gc\nEP48crAylE6FK67EgQOA/4VbWvZAVX1hKO2GbX9PRFYA3wFeA45S1e8Mtd33miVLlox0F8wesnM2\nttj5GnvsnI0Ndp7GHjtnY4+ds7HHztnocPfdd/PYY4/x9ttvs3LlSv71X/+V5cuXc8kll/Dzn/+c\n5uZmvvSlL3HWWWeRzWb79vvNb37D4sWLWbNmDS+//DK333474AInF110Eddffz1tbW0888wzzJw5\ns2+/e+65hzvuuIOtW7eSTqf5/ve/v01/li5dyltvvcWvfvUrrrjiCr797W/z5JNP8tprr/HrX/+a\nP//5z4Abqv/1r3+dTZs2sWLFCurr61m0aNE2bd1777088sgjtLa2bpPR8eKLL3L66adz4403cs45\n5wzbZzmUjI6rwp/XD1K+v6OddoeqBsCNqppV1ddU9VVVze5yx92zGDg4nED1TeBrw9Tue4r9Mhx7\n7JyNLXa+xh47Z2ODnaexx87Z2GPnbOyxcxYSGZ6yl7761a9SW1tLRUUF3/jGN7j77rv5+c9/zpe/\n/GWOPvpoRIQLL7yQRCLBc88917ff5ZdfTk1NDRUVFXzsYx/jpZdeAuAXv/gFl1xySd+En1OmTGHe\nvHl9+1188cXMnj2bRCLBOeec07ef+yiEb33rW8TjcU499VSKi4s577zzqK6upra2lhNPPJHly5cD\nMHv2bE455RSi0SjV1dVceeWVPP3009u8t8svv5za2loSiURf3TPPPMPHP/5x7rrrLs4888y9/twG\ns9eBjvyKJar6oUHKcEwV+4SIfEqGeYIDVX08DKQAPIebiHTcsl9a45Od1/HJzuv4ZOd1fLLzOj7Z\neR2/7NyOT8N+XlWHp+ylurr+S9MZM2bQ0NDAunXr+P73v09VVRVVVVVUVlZSX19PQ0P/Who1NTV9\nj4uKiujsdGt6rF+/ntmzZ+/weJMn96/uV7hf3qRJk/oep1KpbY6TSqX6tt+6dSvnnXcedXV1VFRU\ncMEFF9DY2LjD95b3s5/9jOOPP54PfvCDO+zj3hryHB0iEhGRs0TkH0XkqnwZhr59CfgNkBGRdhHp\nEJH2Xe20hz4HPDLMbY4q9kt9fLLzOj7ZeR2f7LyOT3Zexyc7r+OXndvxabyd1/Xr1/c9XrduHVOn\nTmXatGl885vfpLm5mebmZlpaWujs7OTTn/70LtubNm0ab7/99r7sMgBf+9rX8DyP1157jdbWVn75\ny19ut5TvYPkLP/3pT1m3bh1XXTUc4YMBVHVIBXgY+B1wLXBNvgy13SH26THglYLyavjzYwXbfAP4\n7S7aUStWrFixYsWKFStWrFixMvbLaDZz5kw97LDDtL6+XpuamvTEE0/Ub37zm7ps2TKdNm2aPv/8\n86qq2tnZqQ899JB2dnb27ffEE0/0tbNo0SK98MILVVV16dKlWllZqU8++aQGQaAbNmzQlStXqqrq\nggUL9NZbb+3b7/bbb9cTTzxRVVXXrl2rIqK+7/e9XldXp08//XTf8wsuuED/7d/+TVVVzznnHP3i\nF7+ovu9rfX29Hn/88Tpt2rRt3lthHwvr2tra9KijjtKrr756p5/PLs7rdtfyw7G8bJ2qHjYM7Wwj\nHLLyGWCWql4nItOAKaq6dFf7quppu2j7IuAjwE6H2Kjq+FsX1BhjjDHGGGPeY0RER7oPOyMinH/+\n+Xz4wx9m48aNfOITn+Ab3/gGyWSSW265hcsuu4y33nqLVCrFCSecwEknndS3344cc8wx3HbbbVxx\nxRWsWbOGyZMnc+ONNzJv3ryd7jdYuzt7fs0117Bw4UIqKiqYM2cOF154IT/84Q93uG9hXVlZGY89\n9hgnn3wy8Xica6+9dod92pPrc9EhjCEKO/hd4AlVXbzLjfes3ZuBADhZVQ8UkUpgsaoeM8R2z8BN\nmPpBVW0ahq4aY4wxxhhjjBnFRESHeu1rRo6I7FGgYzgyOp4Dfh8uCZsFBJc+UjbEdt+nqkeKyHJc\ngy0iEh9imwA34JaufSyMIj2nqpcOQ7vGGGOMMcYYY4wZYcMR6PgB8H7g1WEOkWVFJIIbd4OITMRl\neAyJqs4dahvGGGOMMcYYY4wZnYa86gqwHnhtH+QB/Rj4PTBJRP4N+Avw7WE+hjHGGGOMMcYYY8aR\n4Zij43ZgP9wyrel8var+YEgNu7YPAE7BDYd5QlVXDLVNY4wxxhhjjDHvLTZHx9g2EnN0rAlLPCxD\nIiJJ4MvAHNyysD9T1dxQ2zXGGGOMMcYYY8z4N+SMjr0+sEgCeAYXHIkC96nqtSLyR+A4wAdywG9V\n9fJwItI7gaOARuDTqroubOtrwOfC7S/PrwATrrDyI9wQnVtV9bth/UzgXqASeBG40IIpxhhjjDHG\nGDM+WUbH2LanGR17PUeHiBxW8DgmIt8UkQdE5NsiUrSr/VU1DXxIVY8ADgfOFJH3AScCl6rqZOBB\n4B/CXS4BmsPJRH8EfC889kHAOcCBwJnATeJ4wE+A04GDgfPCoTAA3wWuV9X9gdawbWOMMcYYY4wx\n41AymdwsIlgZmyWZTG7ek/O91xkdIvKiqh4ZPr4eqAZuAz4BVKvqwj1oqwiX3XEp8FcgoaqBiBwH\n/ElVy0XkUeAaVX1e3GosG1V1kohcjVvONp+t8QiwCDevxzWqemZY37ediGwFagqOsUhVz9irD8IY\nY4wxxhhjjDGjxlDm6ChMGzkFOEZVsyLyDPDybjXgsi5eAGYDNwJvh31qFRFwGSfFItIOFAPHA6Wq\n6otIm4hUAVOB/ylodkNYJ7gVYfLqgWNFpBpoUdWgoL5299+2McYYY4wxxhhjRquhBDrKReQfcMGI\nhKpmwaVMiMhupYmEwYYjRKQMt5TsgcCbqjoPQETqgIdUdb6IvAZ8eGATbBtwKawfbFhOfvuB+wza\n3919H8YYY4wxxhhjjHn3DTZ3x17P0QE8DZwFfBR4TkRqAERkMm6y0D3pWHvY3nFARZjpAVAHNISP\n64Fp4TEiQLmqthTWD9inHpg+sF5VG3dyjMH6NqbLNddcM6batbLvPn87Z6O/FJ4jO19jr+zonNm5\nHF1luM6Hndexd85G27HGcxmNn+No7NNoKmP18xmr/bb3OnzvdUeGEuj4uqpeXFA2h4GBTap6yq52\nFpEJIlIePk4BpwKvA08BZ4ebXQTcHz5+IHxO+PqTBfXnikhcRGbhlqVdCvwNmCMiM8St2HJuQVtP\n7uAY486CBQtGugtmH7DzOj7ZeR2f7LyOT3Zexyc7r+OXndvxyc7r+DQc53UoQ1d+ISKVwBLgUeAv\numdLtE4B7ggzKzzgV6r6sIisAO4VkeuA5cCt4fa3AneJyJtAEy5wgaq+LiK/xgVJsrgVWxTwReQy\nYDH9y8u+EbZ19Q6OMe7Y//zjk53X8cnO6/hk53V8svM6Ptl5Hb/s3I5Pdl7HpxENdKjqmSKSBBbg\nloD9voiswwU9HlXVdbvY/1XgyEHq1wDvG6Q+jVtGdrC2/h3490HqHwX2391jmN1nv1TGHjtnY8vA\n8/XWW/Dqq/AP/zD49mbk2f9jY4Odp7HHztnYY+ds7LFzNvrZOdoze7287KCNuaEjZwJnAJNV9did\nbFsH3AlMBnzgv1T1BhG5BvgCsCXc9OthwAIR+RrwOSAHXK6qi8P6M4Af0Z+5kV9qdiZwL1AJvAhc\nqKq5cCjLncBRuPlEPj1YYEZEdDg/H2OM2Vu33gp//CP84Q8j3RNjjDHGGGNGBxFBh3ky0nzDxQUT\ne8Zwk4B+CjhhF7vmgKtU9SDg/cBlInJA+NoPVPXIsOSDHAfiMjoOxAVTbhLHA34CnA4cDJxX0M53\ngetVdX+gFbgkrL8EaFbVubgAyfeG8BEYY8w+19YGHR0j3QtjjDHGGGNGvyEHOoBngKSITMXNh3Eh\ncJuqZna2Uzhp6Uvh405gBTA1fHmwJWM/DtyrqjlVXQu8CRwbljdV9R11S9zeG24LcDLw2/DxHcAn\nCtq6I3x8H7DLyVONMWYktbZCe/tI98IYY4wxxpjRbzgCHaKq3cAngZtU9WzgkD1qwA0xORx4Pqz6\nioi8JCK35FdmwQVB1hfstiGsG1hfD0wVkWqgRVWDwvqBbamqD7SKSNWe9NkYY95NltFhjDHGGGPM\n7hmWQIeIvB/4DPBQWBfZg51LcFkVl4eZHTcBs1X1cGATcH1+00F2113UD3wtP+HGwHopeM0YY0Yd\ny+gwxhhjjDFm9wxledm8y4GvAb9X1b+LyH7AU7uzo4hEcUGOu1T1fgBV3Vqwyc+BP4aP64FpBa/V\nAQ24IMX0gfWq2igiFSLihVkd+e0L22oQkQhQpqotg/Vx0aJFfY8XLFhgs90aY0aEZXQYY4wxxpj3\nuiVLlrBkyZJdbjesq67sKRG5E2hU1asK6iar6qbw8ZXAMap6vogcBPw3blnYqcBjwFxcVspK3Dwb\nG4GlwLmq+oaI/Ar4nar+SkRuBl5W1Z+KyKXAIap6qYicC3xCVc8dpH+26ooxZlRYsACefhp8H7zh\nyMUzxhhjjDFmjNvRqitDzugQkXnA/w/MLGxPVU/exX7H44a7vCoiy3FDR74OnC8ihwMBsBb4Utje\n6yLya+B1IAtcGkYhfBG5DDcRan552TfCw1wN3Csi1wHLgVvD+luBu0TkTaAJ2C7IYYwxo0lbm/vZ\n2QllZSPbF2OMMcYYY0azIWd0iMjLwE+BFwA/X6+qLwytayPPMjqMMaPFrFmwdi2sXw91dSPdG2OM\nMcYYY0bePsvoAHKqevMwtGOMMWYH2tpg4kSbp8MYY4wxxphdGY6R3n8UkUtFZIqIVOXLrnYSkToR\neVJEXheRV0XkH8P6ShFZLCIrReRPBcvLIiI/FpE3w6VnDy+ov0hEVoX7LCyoP1JEXglf+1FB/Q6P\nYYwxo42qW3Glrs5WXjHGGGOMMWZXhiPQcRHwv4FnccNXXgCW7cZ+OeAqVT0IeD/wFRE5ADevxuOq\nuj/wJG5FF0TkTNyys3Nx83b8NKyvBL4FHIObqPSagsDFzcDnVXUeME9ETg/rBz2GMcaMRp2dkExC\nVZUFOowxxhhjjNmVIQc6VHXWIGW/3dhvk6q+FD7uBFbgloD9OHBHuNkd4XPCn3eG2z8PlItIDXA6\nsFhV21S1FTcp6RkiMhkoVdWl4f53Ap8oaKvwGPl6Y4wZdVpboaLCTUJqQ1eMMcYYY4zZub2eo0NE\nTlbVJ0Xkk4O9rqq/24O2ZgKHA88BNaq6OWxjk4hMCjebCqwv2K0+rBtYv6Ggvn6Q7RnkGBN3t6/G\nGPNua2uD8nIoLbWMDmOMMcYYY3ZlKJORnoQb9vGxQV5TYLcCHSJSAtwHXK6qnSKyo2VOBs6kKuFx\ntpthdRf1e2TRokV9jxcsWMCCBQv2tAljjBkSy+gwxhhjjDEGlixZwpIlS3a53V4HOlT1mvDnxXvb\nhohEcUGOu1T1/rB6s4jUqOrmcPjJlrC+HphWsHsd0BDWLxhQ/9ROtgfYtINjbKcw0GGMMSOhtdUy\nOowxxhhjjBmYfHDttdcOut2Q5+gQkXIR+YGILAvL9XuwiskvgNdV9T8L6h4APhs+/ixwf0H9wvCY\nxwGt4fCTPwGnhf2oBE4D/qSqm4B2ETlWRCTc9/5BjnFRQb0xxow6bW2W0WGMMcYYY8zuGo5VV34B\ndADnhKUduG1XO4nI8cBngJNFZLmIvCgiZwDfxQUuVgKnAN8BUNWHgTUi8hbwM+DSsL4FuA630svz\nwLXhpKSE29wKrALeVNVHw/rCY5yaP4YxxoxG+aErltFhjDHGGGPMronqHk9bsW0DIi+p6uG7qhuL\nRESH+vkYY8xQffvbLpPjoINg8WK4666R7pExxhhjjDEjT0RQ1e3m5xyOjI4eETmh4EDHAz270aFb\nRWSziLxSUHeNiNSH2R35DI/8a18TkTdFZIWIfLig/gwReUNEVonI/y2onykiz4nIShG5J5wPBBGJ\ni8i9YVv/IyLTh+EzMMaYfcYyOowxxhhjjNl9wxHo+DJwo4isFZG1wE/Cul25DTh9kPofqOqRYXkU\nQEQOxA2LORA4E7hJHC883unAwcB5InJA2M53getVdX+gFbgkrL8EaFbVucCPgO/t8Ts2xph3UX55\n2bIyC3QYY4wxxhizK0MOdKjqy6o6HzgMOExVj1DVl3djv78ALYO8NNiysB8H7lXVnKquBd4Ejg3L\nm6r6jqpmgXvDbQFOBn4bPr4D+ERBW3eEj+/DzQNijDGjVmFGh01GaowxxhhjzM7tdaBDRK4SkXyW\nBKrarqrtInKJiFwxhD59RUReEpFbClZvmQqsL9hmQ1g3sL4emCoi1UCLqgaF9QPbUlUfaBWRqiH0\n1xhj9ql8RkdVFTQ2jnRvjDHGGGOMGd2GktHxGeDOQervAj63l23eBMwOJzLdBFwf1g+W5aG7qB/4\nWn5W0YH1UvCaMcaMOvmMjro62LgRfH+ke2SMMcYYY8zoFR3KvuFwkW2oakZEBgtA7JKqbi14+nPg\nj+HjemBawWt1QAMuSDF9YL2qNopIhYh4YVZHfvvCthpEJAKUhUvUDmrRokV9jxcsWMCCBQuYOXMm\n77zzzt68RbMbZsyYwdq1a0e6G8aMGvmMjkQCqqtdsKOubqR7ZYwxxhhjzLtryZIlLFmyZJfb7fXy\nsiLyKnCqqm4eUF8DPK6qh+5GGzOBP+a3FZHJqropfHwlcIyqni8iBwH/DbwPN/TkMWAuLiNlJW6e\njY3AUuBcVX1DRH4F/E5VfyUiNwMvq+pPReRS4BBVvVREzgU+oarn7qB/gy4vGy5hs6u3Z/aSfb7G\nbGvKFHjhBaitheOOg+uvh+OPH+leGWOMMcYYM7L2xfKy/wE8JCIniUhpWBbgsjC+vxsduht4Fpgn\nIutE5GLgeyLyioi8BJwEXAmgqq8DvwZeBx4GLlXHBy4DFgN/x01Y+kZ4iKuBq0RkFVAF3BrW3wpM\nEJE3gSvC7YwxZtTKZ3QAzJgBllBmjDHGGGPMju11RgeAiJyJCxQcEla9BnxHVR8Zhr6NOMvoGBn2\n+RrTL5OB4mL3UwT+z/9xk5JebSFaY4wxxhjzHrcvMjpQ1UdU9SRVrQ7LSeMlyGGMMaNBPpsjP/PR\n9OmW0TFc1q6FH/94pHthzPB56il46KEdv97eDvfd9+71xxhjjBkpQwp0AIjILBH5gYj8TkQeyJfd\n3PdWEdksIq8U1FWKyGIRWSkifypYYhYR+bGIvBkuP3t4Qf1FIrIq3GdhQf2R4VCYVSLyo905xnvV\ntddey4UXXjjS3TDGDJBfcSVvR0NXVOEvf3n3+jUevPwy3HLLSPfCmL2Ty8Fvfws33dRf99hj8NOf\n7nifP/wBvvIV9/vCGGOMGc+GHOgA/gCsBW7ALQebL7vjNuD0AXVX4yYz3R94Evga9A2Tma2qc4Ev\nAT8N6yuBbwHH4CYrvaYgcHEz8HlVnYebC+T0nR3jvW4vF8sxxuxDhfNzgAt0rFu3/XZ//zucdppd\nwOyJ9nbLjjFj17JlLmhx5ZXQ2enqmpvh2WchCAbfZ/Fi2LIF6uvfvX4aY4wxI2E4Ah29qvpjVX1K\nVZ/Ol93ZUVX/Agxc2vXjwB3h4zvC5/n6O8P9ngfKwxVeTgcWq2qbqrbiJiY9Q0QmA6WqujTc/07g\nEzs4Rr7eGGNGlYEZHfmhKwMDGs89B7290LLDxbLNQG1tLtjR1jbSPTFmzzU1wVFHwcyZ/cHP5mZX\n3nhj++2DwAU6DjnEreJkjDFjmd3YMbsyHIGO/xSRa0Tk/eFQkSNF5MghtDcpv2RtuNTspLB+KrC+\nYLv6sG5g/YaC+vpBtgeoGXCMiUPo76jy3e9+l7PPPnubussvv5wrrriCjRs3ctZZZ1FdXc28efO4\nZQc5208//TTTpk3bpm7WrFk8+eSTgBvmcs4553DhhRdSVlbG/PnzefPNN/nOd75DTU0NM2bM4PHH\nH+/bt729nc9//vPU1tYybdo0/vmf/9kmGzVmNw3M6KiocP+4d3Rsu93zz7ufDQ3vXt925cYb4Zpr\nRroXO9be7n5aVocZi5qb3cTEhVleLS1uOeqBw9iefRYWLYLKSvjkJ102iDHGjFWqUFfnbvAYsyPR\nYWjjUOBC4GQgnyyp4fPhNHBchYTHGWy8xc7q98iiRYv6Hi9YsIAFCxbsaRPvqvPOO4/rrruOzs5O\nSkpKCIKA3/zmN/zhD3/gvPPO49BDD+W3v/0tr7/+OqeddhqzZ8/mQx/60Hbt7GoYy4MPPsgDDzzA\nHXfcwcUXX8zpp5/OF77wBRoaGrjtttv44he/yOrVqwFYuHAhtbW1rF69ms7OTj760Y8yffp0vvCF\nL+yTz8CY8WRgRgfApEmwdSuUlfXXPf88VFe7QMchhzAqPPkkJJNDa+PRR+HDHwZvOMLyA+QDHevW\nwWGHDX/7xuxL+UBHIrFtRsdHP+oCHV/8Yv+2P/sZrFgBX/2qywq7+eaR6bMxxgyH9nb3987WrTDg\n3qx5D1iyZAlLlizZ5XbDEeg4G9hPVTPD0BbAZhGpUdXN4fCTLWF9PVD4Va4DGsL6BQPqn9rJ9gCb\ndnCM7RQGOsaC6dOnc+SRR/KHP/yBCy64gCeeeILi4mJqa2v5y1/+wsMPP0wsFmP+/Pl8/vOf5667\n7ho00LErJ554IqeeeioAZ599Nr///e+5+uqrERHOPfdcvvSlL9He3k5PTw+PPvoobW1tJBIJkskk\nV1xxBf/1X/9lgY5Qby9Eo64YM9DAjA5wgY4tW2D2bPe8owPefhvOOgs2bHj3+7gjS5fC/vsPrY1z\nz3V3n+fMGZ4+FWpvh0jEMjrM2NTc7DI0qqu3zeg480z4+te33ba+Hr79bTj1VHdxsGyZuyNqU3MZ\nM3q88gpMmAC1tSPdk9GvsdH93LLFAh3vRQOTD6699tpBtxuOe2SvARW73GrHhG2zLx4APhs+/ixw\nf0H9QgAROQ5oDYef/Ak4TUTKw4lJTwP+FA5JaReRY8WlJywc0Fb+GBcV1A8bkaGXvXXeeedxzz33\nAHDPPfdw/vnn09DQQHV1NUVFRX3bzZgxgw17eVVUU1PT9ziVSjFhwoS+LJBUKoWq0tnZybp168hm\ns0yZMoWqqioqKyv58pe/TGP+N5ThyivhtttGuhd77je/sRUr3g07yujYUhCefeklOPRQN1Z/tAxd\naWhwF1dbdhhG3rVs1gV6Nm8evn4VamtzgZjBJnc1ZrQrHLqSD9Y1N8MJJ8D69f0ZS+Ce19W5x1Om\nuCDHvvr/yhizdxYtgl/8YqR7MTY0NbmfW7fufLu774b7h/0qz4wVwxHoqADeCJdp3dPlZe8GnsWt\niLJORC4GvoMLXKwETgmfo6oPA2tE5C3gZ8ClYX0LcB2wDHgeuDaclJRwm1uBVcCbqvpoWP/dgmOc\nmj/GYHwf7rprxzOY74jq0MveOvvss1myZAkbNmzg97//Pa9IEBcAACAASURBVJ/5zGeora2lubmZ\nrq6uvu3WrVvH1KlTt9u/uLiY7u7uvue+77N1V79JdmDatGkkk0mamppobm6mpaWF1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Fmz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\nFh0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2017-05-30 23:59:00+00:000.4272005.6968560.1570897.5258960.5538730.6364330.0000015203.8611715203.86117654481.707339...100000.0672987.804904672987.804904822[]0.022121448.6334950.02215.6968567.525896
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851 rows × 45 columns

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0.556554 \n", + "2017-06-10 23:59:00+00:00 10.202471 0.556226 \n", + "2017-06-11 23:59:00+00:00 10.701594 0.556349 \n", + "2017-06-12 23:59:00+00:00 9.243228 0.560459 \n", + "2017-06-13 23:59:00+00:00 9.664441 0.560505 \n", + "2017-06-14 23:59:00+00:00 8.509086 0.563520 \n", + "2017-06-15 23:59:00+00:00 8.460040 0.563202 \n", + "2017-06-16 23:59:00+00:00 8.701195 0.562997 \n", + "2017-06-17 23:59:00+00:00 9.387043 0.563869 \n", + "2017-06-18 23:59:00+00:00 8.929482 0.564133 \n", + "2017-06-19 23:59:00+00:00 9.291024 0.564087 \n", + "2017-06-20 23:59:00+00:00 9.629482 0.563982 \n", + "2017-06-21 23:59:00+00:00 9.438247 0.563769 \n", + "2017-06-22 23:59:00+00:00 9.650902 0.563511 \n", + "2017-06-23 23:59:00+00:00 9.637450 0.563183 \n", + "2017-06-24 23:59:00+00:00 8.974032 0.563994 \n", + "2017-06-25 23:59:00+00:00 8.887696 0.563699 \n", + "2017-06-26 23:59:00+00:00 8.542984 0.563751 \n", + "2017-06-27 23:59:00+00:00 9.000000 0.563942 \n", + "2017-06-28 23:59:00+00:00 9.000000 0.563613 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88719.512199 10 \n", + "2015-03-11 23:59:00+00:00 89213.414638 89213.414638 11 \n", + "2015-03-12 23:59:00+00:00 90426.829272 90426.829272 12 \n", + "2015-03-13 23:59:00+00:00 90106.707321 90106.707321 13 \n", + "2015-03-14 23:59:00+00:00 86335.365857 86335.365857 14 \n", + "2015-03-15 23:59:00+00:00 86158.536589 86158.536589 15 \n", + "2015-03-16 23:59:00+00:00 87469.512195 87469.512195 16 \n", + "2015-03-17 23:59:00+00:00 88786.585369 88786.585369 17 \n", + "2015-03-18 23:59:00+00:00 86966.463418 86966.463418 18 \n", + "2015-03-19 23:59:00+00:00 78417.682927 78417.682927 19 \n", + "2015-03-20 23:59:00+00:00 79871.951223 79871.951223 20 \n", + "2015-03-21 23:59:00+00:00 79935.975613 79935.975613 21 \n", + "2015-03-22 23:59:00+00:00 79411.585369 79411.585369 22 \n", + "2015-03-23 23:59:00+00:00 81957.317076 81957.317076 23 \n", + "2015-03-24 23:59:00+00:00 81472.560979 81472.560979 24 \n", + "2015-03-25 23:59:00+00:00 75000.000003 75000.000003 25 \n", + "2015-03-26 23:59:00+00:00 75088.414637 75088.414637 26 \n", + "2015-03-27 23:59:00+00:00 76015.243905 76015.243905 27 \n", + "2015-03-28 23:59:00+00:00 75152.439027 75152.439027 28 \n", + "2015-03-29 23:59:00+00:00 77118.902442 77118.902442 29 \n", + "2015-03-30 23:59:00+00:00 74006.097564 74006.097564 30 \n", + "... ... ... ... \n", + "2017-05-30 23:59:00+00:00 672987.804904 672987.804904 822 \n", + "2017-05-31 23:59:00+00:00 654481.707339 654481.707339 823 \n", + "2017-06-01 23:59:00+00:00 668231.707343 668231.707343 824 \n", + "2017-06-02 23:59:00+00:00 704878.048807 704878.048807 825 \n", + "2017-06-03 23:59:00+00:00 733506.097589 733506.097589 826 \n", + "2017-06-04 23:59:00+00:00 750304.878077 750304.878077 827 \n", + "2017-06-05 23:59:00+00:00 758597.561001 758597.561001 828 \n", + "2017-06-06 23:59:00+00:00 803932.926860 803932.926860 829 \n", + "2017-06-07 23:59:00+00:00 867256.097594 867256.097594 830 \n", + "2017-06-08 23:59:00+00:00 806097.561006 806097.561006 831 \n", + "2017-06-09 23:59:00+00:00 848018.292715 848018.292715 832 \n", + "2017-06-10 23:59:00+00:00 856402.439057 856402.439057 833 \n", + "2017-06-11 23:59:00+00:00 855487.804911 855487.804911 834 \n", + "2017-06-12 23:59:00+00:00 896890.243937 896890.243937 835 \n", + "2017-06-13 23:59:00+00:00 783414.634176 783414.634176 836 \n", + "2017-06-14 23:59:00+00:00 816189.024421 816189.024421 837 \n", + "2017-06-15 23:59:00+00:00 729969.512223 729969.512223 838 \n", + "2017-06-16 23:59:00+00:00 724847.561003 724847.561003 839 \n", + "2017-06-17 23:59:00+00:00 743140.243931 743140.243931 840 \n", + "2017-06-18 23:59:00+00:00 795762.195152 795762.195152 841 \n", + "2017-06-19 23:59:00+00:00 759573.170761 759573.170761 842 \n", + "2017-06-20 23:59:00+00:00 787195.121981 787195.121981 843 \n", + "2017-06-21 23:59:00+00:00 827591.463446 827591.463446 844 \n", + "2017-06-22 23:59:00+00:00 800121.951250 800121.951250 845 \n", + "2017-06-23 23:59:00+00:00 814878.048812 814878.048812 846 \n", + "2017-06-24 23:59:00+00:00 815518.292714 815518.292714 847 \n", + "2017-06-25 23:59:00+00:00 762987.804907 762987.804907 848 \n", + "2017-06-26 23:59:00+00:00 757103.658565 757103.658565 849 \n", + "2017-06-27 23:59:00+00:00 729756.097589 729756.097589 850 \n", + "2017-06-28 23:59:00+00:00 768658.536612 768658.536612 851 \n", + "\n", + " transactions \\\n", + "2015-03-01 23:59:00+00:00 [] \n", + "2015-03-02 23:59:00+00:00 [{u'commission': None, u'amount': 304.87804879... \n", + "2015-03-03 23:59:00+00:00 [] \n", + "2015-03-04 23:59:00+00:00 [] \n", + "2015-03-05 23:59:00+00:00 [] \n", + "2015-03-06 23:59:00+00:00 [] \n", + "2015-03-07 23:59:00+00:00 [] \n", + "2015-03-08 23:59:00+00:00 [] \n", + "2015-03-09 23:59:00+00:00 [] \n", + "2015-03-10 23:59:00+00:00 [] \n", + "2015-03-11 23:59:00+00:00 [] \n", + "2015-03-12 23:59:00+00:00 [] \n", + "2015-03-13 23:59:00+00:00 [] \n", + "2015-03-14 23:59:00+00:00 [] \n", + "2015-03-15 23:59:00+00:00 [] \n", + "2015-03-16 23:59:00+00:00 [] \n", + "2015-03-17 23:59:00+00:00 [] \n", + "2015-03-18 23:59:00+00:00 [] \n", + "2015-03-19 23:59:00+00:00 [] \n", + "2015-03-20 23:59:00+00:00 [] \n", + "2015-03-21 23:59:00+00:00 [] \n", + "2015-03-22 23:59:00+00:00 [] \n", + "2015-03-23 23:59:00+00:00 [] \n", + "2015-03-24 23:59:00+00:00 [] \n", + "2015-03-25 23:59:00+00:00 [] \n", + "2015-03-26 23:59:00+00:00 [] \n", + "2015-03-27 23:59:00+00:00 [] \n", + "2015-03-28 23:59:00+00:00 [] \n", + "2015-03-29 23:59:00+00:00 [] \n", + "2015-03-30 23:59:00+00:00 [] \n", + "... ... \n", + "2017-05-30 23:59:00+00:00 [] \n", + "2017-05-31 23:59:00+00:00 [] \n", + "2017-06-01 23:59:00+00:00 [] \n", + "2017-06-02 23:59:00+00:00 [] \n", + "2017-06-03 23:59:00+00:00 [] \n", + "2017-06-04 23:59:00+00:00 [] \n", + "2017-06-05 23:59:00+00:00 [] \n", + "2017-06-06 23:59:00+00:00 [] \n", + "2017-06-07 23:59:00+00:00 [] \n", + "2017-06-08 23:59:00+00:00 [] \n", + "2017-06-09 23:59:00+00:00 [] \n", + "2017-06-10 23:59:00+00:00 [] \n", + "2017-06-11 23:59:00+00:00 [] \n", + "2017-06-12 23:59:00+00:00 [] \n", + "2017-06-13 23:59:00+00:00 [] \n", + "2017-06-14 23:59:00+00:00 [] \n", + "2017-06-15 23:59:00+00:00 [] \n", + "2017-06-16 23:59:00+00:00 [] \n", + "2017-06-17 23:59:00+00:00 [] \n", + "2017-06-18 23:59:00+00:00 [] \n", + "2017-06-19 23:59:00+00:00 [] \n", + "2017-06-20 23:59:00+00:00 [] \n", + "2017-06-21 23:59:00+00:00 [] \n", + "2017-06-22 23:59:00+00:00 [] \n", + "2017-06-23 23:59:00+00:00 [] \n", + "2017-06-24 23:59:00+00:00 [] \n", + "2017-06-25 23:59:00+00:00 [] \n", + "2017-06-26 23:59:00+00:00 [] \n", + "2017-06-27 23:59:00+00:00 [] \n", + "2017-06-28 23:59:00+00:00 [] \n", + "\n", + " treasury_period_return volume treasury \\\n", + "2015-03-01 23:59:00+00:00 0.0200 47597.424668 0.0200 \n", + "2015-03-02 23:59:00+00:00 0.0208 67168.440498 0.0208 \n", + "2015-03-03 23:59:00+00:00 0.0212 81226.398297 0.0212 \n", + "2015-03-04 23:59:00+00:00 0.0212 71521.587766 0.0212 \n", + "2015-03-05 23:59:00+00:00 0.0211 66108.884634 0.0211 \n", + "2015-03-06 23:59:00+00:00 0.0224 40276.571970 0.0224 \n", + "2015-03-07 23:59:00+00:00 0.0224 22856.945604 0.0224 \n", + "2015-03-08 23:59:00+00:00 0.0224 13853.794890 0.0224 \n", + "2015-03-09 23:59:00+00:00 0.0220 62908.377680 0.0220 \n", + "2015-03-10 23:59:00+00:00 0.0214 67389.782195 0.0214 \n", + "2015-03-11 23:59:00+00:00 0.0211 25811.471790 0.0211 \n", + "2015-03-12 23:59:00+00:00 0.0210 27275.117559 0.0210 \n", + "2015-03-13 23:59:00+00:00 0.0213 46670.674822 0.0213 \n", + "2015-03-14 23:59:00+00:00 0.0213 23011.197461 0.0213 \n", + "2015-03-15 23:59:00+00:00 0.0213 13427.567949 0.0213 \n", + "2015-03-16 23:59:00+00:00 0.0210 22313.424619 0.0210 \n", + "2015-03-17 23:59:00+00:00 0.0206 22235.369855 0.0206 \n", + "2015-03-18 23:59:00+00:00 0.0193 89923.415728 0.0193 \n", + "2015-03-19 23:59:00+00:00 0.0198 78451.537176 0.0198 \n", + "2015-03-20 23:59:00+00:00 0.0193 16197.477968 0.0193 \n", + "2015-03-21 23:59:00+00:00 0.0193 15082.649219 0.0193 \n", + "2015-03-22 23:59:00+00:00 0.0193 18263.490755 0.0193 \n", + "2015-03-23 23:59:00+00:00 0.0192 23532.384930 0.0192 \n", + "2015-03-24 23:59:00+00:00 0.0188 64117.869871 0.0188 \n", + "2015-03-25 23:59:00+00:00 0.0193 53254.531782 0.0193 \n", + "2015-03-26 23:59:00+00:00 0.0201 33260.968413 0.0201 \n", + "2015-03-27 23:59:00+00:00 0.0195 22731.111936 0.0195 \n", + "2015-03-28 23:59:00+00:00 0.0195 17724.687732 0.0195 \n", + "2015-03-29 23:59:00+00:00 0.0195 31825.768349 0.0195 \n", + "2015-03-30 23:59:00+00:00 0.0196 30725.359920 0.0196 \n", + "... ... ... ... \n", + "2017-05-30 23:59:00+00:00 0.0221 21448.633495 0.0221 \n", + "2017-05-31 23:59:00+00:00 0.0221 21235.805383 0.0221 \n", + "2017-06-01 23:59:00+00:00 0.0221 18959.512934 0.0221 \n", + "2017-06-02 23:59:00+00:00 0.0215 12549.643232 0.0215 \n", + "2017-06-03 23:59:00+00:00 0.0215 10767.981906 0.0215 \n", + "2017-06-04 23:59:00+00:00 0.0215 11880.383420 0.0215 \n", + "2017-06-05 23:59:00+00:00 0.0218 15390.903185 0.0218 \n", + "2017-06-06 23:59:00+00:00 0.0214 42687.668790 0.0214 \n", + "2017-06-07 23:59:00+00:00 0.0218 22094.398527 0.0218 \n", + "2017-06-08 23:59:00+00:00 0.0219 16977.816089 0.0219 \n", + "2017-06-09 23:59:00+00:00 0.0221 9521.116767 0.0221 \n", + "2017-06-10 23:59:00+00:00 0.0221 16167.315725 0.0221 \n", + "2017-06-11 23:59:00+00:00 0.0221 19034.136815 0.0221 \n", + "2017-06-12 23:59:00+00:00 0.0221 48340.947688 0.0221 \n", + "2017-06-13 23:59:00+00:00 0.0221 23172.058884 0.0221 \n", + "2017-06-14 23:59:00+00:00 0.0215 34717.773840 0.0215 \n", + "2017-06-15 23:59:00+00:00 0.0216 55839.127994 0.0216 \n", + "2017-06-16 23:59:00+00:00 0.0216 22402.387929 0.0216 \n", + "2017-06-17 23:59:00+00:00 0.0216 16637.617543 0.0216 \n", + "2017-06-18 23:59:00+00:00 0.0216 19988.156662 0.0216 \n", + "2017-06-19 23:59:00+00:00 0.0219 13719.302504 0.0219 \n", + "2017-06-20 23:59:00+00:00 0.0216 20548.879169 0.0216 \n", + "2017-06-21 23:59:00+00:00 0.0216 24534.642260 0.0216 \n", + "2017-06-22 23:59:00+00:00 0.0215 15181.549108 0.0215 \n", + "2017-06-23 23:59:00+00:00 0.0215 8139.412746 0.0215 \n", + "2017-06-24 23:59:00+00:00 0.0215 16770.845452 0.0215 \n", + "2017-06-25 23:59:00+00:00 0.0215 18137.882004 0.0215 \n", + "2017-06-26 23:59:00+00:00 0.0214 36367.695758 0.0214 \n", + "2017-06-27 23:59:00+00:00 0.0221 33486.182337 0.0221 \n", + "2017-06-28 23:59:00+00:00 0.0221 21827.313647 0.0221 \n", + "\n", + " algorithm benchmark \n", + "2015-03-01 23:59:00+00:00 0.000000 0.000000 \n", + "2015-03-02 23:59:00+00:00 -0.002199 0.071713 \n", + "2015-03-03 23:59:00+00:00 0.017130 0.064622 \n", + "2015-03-04 23:59:00+00:00 -0.014638 0.115538 \n", + "2015-03-05 23:59:00+00:00 -0.001346 0.023905 \n", + "2015-03-06 23:59:00+00:00 -0.014547 0.035857 \n", + "2015-03-07 23:59:00+00:00 -0.003541 0.035857 \n", + "2015-03-08 23:59:00+00:00 -0.007748 0.035857 \n", + "2015-03-09 23:59:00+00:00 0.039234 0.051793 \n", + "2015-03-10 23:59:00+00:00 0.044173 0.159363 \n", + "2015-03-11 23:59:00+00:00 0.056307 0.099602 \n", + "2015-03-12 23:59:00+00:00 0.053106 0.099602 \n", + "2015-03-13 23:59:00+00:00 0.015392 0.096056 \n", + "2015-03-14 23:59:00+00:00 0.013624 0.096016 \n", + "2015-03-15 23:59:00+00:00 0.026734 0.123936 \n", + "2015-03-16 23:59:00+00:00 0.039904 0.095618 \n", + "2015-03-17 23:59:00+00:00 0.021703 0.095618 \n", + "2015-03-18 23:59:00+00:00 -0.063785 0.044183 \n", + "2015-03-19 23:59:00+00:00 -0.049242 0.055165 \n", + "2015-03-20 23:59:00+00:00 -0.048602 0.050043 \n", + "2015-03-21 23:59:00+00:00 -0.053846 0.048578 \n", + "2015-03-22 23:59:00+00:00 -0.028388 0.065804 \n", + "2015-03-23 23:59:00+00:00 -0.033236 0.038540 \n", + "2015-03-24 23:59:00+00:00 -0.097961 -0.033763 \n", + "2015-03-25 23:59:00+00:00 -0.097077 -0.014042 \n", + "2015-03-26 23:59:00+00:00 -0.087809 -0.033763 \n", + "2015-03-27 23:59:00+00:00 -0.096437 0.009373 \n", + "2015-03-28 23:59:00+00:00 -0.076772 -0.009050 \n", + "2015-03-29 23:59:00+00:00 -0.107900 -0.012074 \n", + "2015-03-30 23:59:00+00:00 -0.092077 0.013358 \n", + "... ... ... \n", + "2017-05-30 23:59:00+00:00 5.696856 7.525896 \n", + "2017-05-31 23:59:00+00:00 5.834356 7.752988 \n", + "2017-06-01 23:59:00+00:00 6.200819 8.221495 \n", + "2017-06-02 23:59:00+00:00 6.487100 8.593625 \n", + "2017-06-03 23:59:00+00:00 6.655087 8.782075 \n", + "2017-06-04 23:59:00+00:00 6.738014 8.894622 \n", + "2017-06-05 23:59:00+00:00 7.191368 9.499428 \n", + "2017-06-06 23:59:00+00:00 7.824600 10.338645 \n", + "2017-06-07 23:59:00+00:00 7.213014 9.539858 \n", + "2017-06-08 23:59:00+00:00 7.632222 10.107570 \n", + "2017-06-09 23:59:00+00:00 7.716063 10.219521 \n", + "2017-06-10 23:59:00+00:00 7.706917 10.202471 \n", + "2017-06-11 23:59:00+00:00 8.120941 10.701594 \n", + "2017-06-12 23:59:00+00:00 6.986185 9.243228 \n", + "2017-06-13 23:59:00+00:00 7.313929 9.664441 \n", + "2017-06-14 23:59:00+00:00 6.451734 8.509086 \n", + "2017-06-15 23:59:00+00:00 6.400514 8.460040 \n", + "2017-06-16 23:59:00+00:00 6.583441 8.701195 \n", + "2017-06-17 23:59:00+00:00 7.109661 9.387043 \n", + "2017-06-18 23:59:00+00:00 6.747770 8.929482 \n", + "2017-06-19 23:59:00+00:00 7.023990 9.291024 \n", + "2017-06-20 23:59:00+00:00 7.427953 9.629482 \n", + "2017-06-21 23:59:00+00:00 7.153258 9.438247 \n", + "2017-06-22 23:59:00+00:00 7.300819 9.650902 \n", + "2017-06-23 23:59:00+00:00 7.307222 9.637450 \n", + "2017-06-24 23:59:00+00:00 6.781917 8.974032 \n", + "2017-06-25 23:59:00+00:00 6.723075 8.887696 \n", + "2017-06-26 23:59:00+00:00 6.449600 8.542984 \n", + "2017-06-27 23:59:00+00:00 6.838624 9.000000 \n", + "2017-06-28 23:59:00+00:00 6.829478 9.000000 \n", + "\n", + "[851 rows x 45 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%catalyst --start 2015-3-1 --end 2017-6-28 --capital-base 100000 -x bitfinex -c usd\n", + "\n", + "from catalyst.finance.slippage import VolumeShareSlippage\n", + "\n", + "from catalyst.api import (\n", + " order_target_value,\n", + " symbol,\n", + " record,\n", + " cancel_order,\n", + " get_open_orders,\n", + ")\n", + "\n", + "def initialize(context):\n", + " context.ASSET_NAME = 'btc_usd'\n", + " context.TARGET_HODL_RATIO = 0.8\n", + " context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO\n", + "\n", + " # For all trading pairs in the poloniex bundle, the default denomination\n", + " # currently supported by Catalyst is 1/1000th of a full coin. Use this\n", + " # constant to scale the price of up to that of a full coin if desired.\n", + " context.TICK_SIZE = 1000.0\n", + "\n", + " context.is_buying = True\n", + " context.asset = symbol(context.ASSET_NAME)\n", + "\n", + " context.i = 0\n", + "\n", + "def handle_data(context, data):\n", + " context.i += 1\n", + "\n", + " starting_cash = context.portfolio.starting_cash\n", + " target_hodl_value = context.TARGET_HODL_RATIO * starting_cash\n", + " reserve_value = context.RESERVE_RATIO * starting_cash\n", + "\n", + " # Cancel any outstanding orders\n", + " orders = get_open_orders(context.asset) or []\n", + " for order in orders:\n", + " cancel_order(order)\n", + " \n", + " # Stop buying after passing the reserve threshold\n", + " cash = context.portfolio.cash\n", + " if cash <= reserve_value:\n", + " context.is_buying = False\n", + "\n", + " # Retrieve current asset price from pricing data\n", + " price = data.current(context.asset,'price')\n", + "\n", + " # Check if still buying and could (approximately) afford another purchase\n", + " if context.is_buying and cash > price:\n", + " # Place order to make position in asset equal to target_hodl_value\n", + " order_target_value(\n", + " context.asset,\n", + " target_hodl_value,\n", + " limit_price=price*1.1,\n", + " )\n", + "\n", + " record(\n", + " price=price,\n", + " volume=data.current(context.asset,'volume'),\n", + " cash=cash,\n", + " starting_cash=context.portfolio.starting_cash,\n", + " leverage=context.account.leverage,\n", + " )\n", + "\n", + "def analyze(context=None, results=None):\n", + " import matplotlib.pyplot as plt\n", + "\n", + " # Plot the portfolio and asset data.\n", + " ax1 = plt.subplot(611)\n", + " results[['portfolio_value']].plot(ax=ax1)\n", + " ax1.set_ylabel('Portfolio Value (USD)')\n", + "\n", + " ax2 = plt.subplot(612, sharex=ax1)\n", + " ax2.set_ylabel('{asset} (USD)'.format(asset=context.ASSET_NAME))\n", + " (context.TICK_SIZE * results[['price']]).plot(ax=ax2)\n", + "\n", + " trans = results.ix[[t != [] for t in results.transactions]]\n", + " buys = trans.ix[\n", + " [t[0]['amount'] > 0 for t in trans.transactions]\n", + " ]\n", + " ax2.plot(\n", + " buys.index,\n", + " context.TICK_SIZE * results.price[buys.index],\n", + " '^',\n", + " markersize=10,\n", + " color='g',\n", + " )\n", + "\n", + " ax3 = plt.subplot(613, sharex=ax1)\n", + " results[['leverage', 'alpha', 'beta']].plot(ax=ax3)\n", + " ax3.set_ylabel('Leverage ')\n", + "\n", + " ax4 = plt.subplot(614, sharex=ax1)\n", + " results[['starting_cash', 'cash']].plot(ax=ax4)\n", + " ax4.set_ylabel('Cash (USD)')\n", + "\n", + " results[[\n", + " 'treasury',\n", + " 'algorithm',\n", + " 'benchmark',\n", + " ]] = results[[\n", + " 'treasury_period_return',\n", + " 'algorithm_period_return',\n", + " 'benchmark_period_return',\n", + " ]]\n", + "\n", + " ax5 = plt.subplot(615, sharex=ax1)\n", + " results[[\n", + " 'treasury',\n", + " 'algorithm',\n", + " 'benchmark',\n", + " ]].plot(ax=ax5)\n", + " ax5.set_ylabel('Percent Change')\n", + "\n", + " ax6 = plt.subplot(616, sharex=ax1)\n", + " results[['volume']].plot(ax=ax6)\n", + " ax6.set_ylabel('Volume (mCoins/5min)')\n", + "\n", + " plt.legend(loc=3)\n", + "\n", + " # Show the plot.\n", + " plt.gcf().set_size_inches(18, 8)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also, instead of defining an output file we are accessing it via the \"_\" variable that will be created in the name space and contain the performance DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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algo_volatilityalgorithm_period_returnalphabenchmark_period_returnbenchmark_volatilitybetacapital_usedcashending_cashending_exposure...starting_cashstarting_exposurestarting_valuetrading_daystransactionstreasury_period_returnvolumetreasuryalgorithmbenchmark
2015-03-01 23:59:00+00:00NaN0.000000NaN0.045833NaNNaN0.000000100000.000000100000.0000000.000...100000.00.0000.0001[]0.02003170.02000.0000000.045833
2015-03-02 23:59:00+00:000.000278-0.0000250.0110450.1208330.290503-0.000956-85544.47495514455.52504514455.52504585542.000...100000.00.0000.0002[{u'commission': None, u'amount': 318, u'sid':...0.0208980630.0208-0.0000250.120833
2015-03-03 23:59:00+00:000.051796-0.005688-1.1975440.1134160.6335380.0772390.00000014455.52504514455.52504584975.642...100000.085542.00085542.0003[]0.02124429830.0212-0.0056880.113416
2015-03-04 23:59:00+00:000.3421180.0349550.4018610.1666660.5244000.1814680.00000014455.52504514455.52504589040.000...100000.084975.64284975.6424[]0.02122458890.02120.0349550.166666
2015-03-05 23:59:00+00:000.637226-0.038185-3.9140030.0708340.9768960.5505200.00000014455.52504514455.52504581726.000...100000.089040.00089040.0005[]0.02111174400.0211-0.0381850.070834
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5 rows × 45 columns

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" + ], + "text/plain": [ + " algo_volatility algorithm_period_return alpha \\\n", + "2015-03-01 23:59:00+00:00 NaN 0.000000 NaN \n", + "2015-03-02 23:59:00+00:00 0.000278 -0.000025 0.011045 \n", + "2015-03-03 23:59:00+00:00 0.051796 -0.005688 -1.197544 \n", + "2015-03-04 23:59:00+00:00 0.342118 0.034955 0.401861 \n", + "2015-03-05 23:59:00+00:00 0.637226 -0.038185 -3.914003 \n", + "\n", + " benchmark_period_return benchmark_volatility \\\n", + "2015-03-01 23:59:00+00:00 0.045833 NaN \n", + "2015-03-02 23:59:00+00:00 0.120833 0.290503 \n", + "2015-03-03 23:59:00+00:00 0.113416 0.633538 \n", + "2015-03-04 23:59:00+00:00 0.166666 0.524400 \n", + "2015-03-05 23:59:00+00:00 0.070834 0.976896 \n", + "\n", + " beta capital_used cash \\\n", + "2015-03-01 23:59:00+00:00 NaN 0.000000 100000.000000 \n", + "2015-03-02 23:59:00+00:00 -0.000956 -85544.474955 14455.525045 \n", + "2015-03-03 23:59:00+00:00 0.077239 0.000000 14455.525045 \n", + "2015-03-04 23:59:00+00:00 0.181468 0.000000 14455.525045 \n", + "2015-03-05 23:59:00+00:00 0.550520 0.000000 14455.525045 \n", + "\n", + " ending_cash ending_exposure ... \\\n", + "2015-03-01 23:59:00+00:00 100000.000000 0.000 ... \n", + "2015-03-02 23:59:00+00:00 14455.525045 85542.000 ... \n", + "2015-03-03 23:59:00+00:00 14455.525045 84975.642 ... \n", + "2015-03-04 23:59:00+00:00 14455.525045 89040.000 ... \n", + "2015-03-05 23:59:00+00:00 14455.525045 81726.000 ... \n", + "\n", + " starting_cash starting_exposure starting_value \\\n", + "2015-03-01 23:59:00+00:00 100000.0 0.000 0.000 \n", + "2015-03-02 23:59:00+00:00 100000.0 0.000 0.000 \n", + "2015-03-03 23:59:00+00:00 100000.0 85542.000 85542.000 \n", + "2015-03-04 23:59:00+00:00 100000.0 84975.642 84975.642 \n", + "2015-03-05 23:59:00+00:00 100000.0 89040.000 89040.000 \n", + "\n", + " trading_days \\\n", + "2015-03-01 23:59:00+00:00 1 \n", + "2015-03-02 23:59:00+00:00 2 \n", + "2015-03-03 23:59:00+00:00 3 \n", + "2015-03-04 23:59:00+00:00 4 \n", + "2015-03-05 23:59:00+00:00 5 \n", + "\n", + " transactions \\\n", + "2015-03-01 23:59:00+00:00 [] \n", + "2015-03-02 23:59:00+00:00 [{u'commission': None, u'amount': 318, u'sid':... \n", + "2015-03-03 23:59:00+00:00 [] \n", + "2015-03-04 23:59:00+00:00 [] \n", + "2015-03-05 23:59:00+00:00 [] \n", + "\n", + " treasury_period_return volume treasury \\\n", + "2015-03-01 23:59:00+00:00 0.0200 317 0.0200 \n", + "2015-03-02 23:59:00+00:00 0.0208 98063 0.0208 \n", + "2015-03-03 23:59:00+00:00 0.0212 442983 0.0212 \n", + "2015-03-04 23:59:00+00:00 0.0212 245889 0.0212 \n", + "2015-03-05 23:59:00+00:00 0.0211 117440 0.0211 \n", + "\n", + " algorithm benchmark \n", + "2015-03-01 23:59:00+00:00 0.000000 0.045833 \n", + "2015-03-02 23:59:00+00:00 -0.000025 0.120833 \n", + "2015-03-03 23:59:00+00:00 -0.005688 0.113416 \n", + "2015-03-04 23:59:00+00:00 0.034955 0.166666 \n", + "2015-03-05 23:59:00+00:00 -0.038185 0.070834 \n", + "\n", + "[5 rows x 45 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "_.head()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/source/beginner-tutorial.rst b/docs/source/beginner-tutorial.rst index 2b34bba1..12792ca4 100644 --- a/docs/source/beginner-tutorial.rst +++ b/docs/source/beginner-tutorial.rst @@ -928,7 +928,6 @@ functions. context.asset, target_hodl_value, limit_price=price*1.1, - stop_price=price*0.9, ) record( diff --git a/docs/source/example-algos.rst b/docs/source/example-algos.rst index ff5d5ea7..217acf06 100644 --- a/docs/source/example-algos.rst +++ b/docs/source/example-algos.rst @@ -184,7 +184,6 @@ one day prior to the current date. context.asset, target_hodl_value, limit_price=price * 1.1, - stop_price=price * 0.9, ) record(