mirror of
https://github.com/saymrwulf/prophet.git
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* update docstring with mdape in list for python perf metric func * re-run python notebook cell to generate mdape in results table * remove comment Co-authored-by: Ben Letham <bletham@gmail.com>
902 lines
278 KiB
Text
902 lines
278 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"block_hidden": true
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Importing plotly failed. Interactive plots will not work.\n"
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]
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}
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],
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"source": [
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"%load_ext rpy2.ipython\n",
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"%matplotlib inline\n",
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"from fbprophet import Prophet\n",
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"import pandas as pd\n",
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"from matplotlib import pyplot as plt\n",
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"import logging\n",
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"logging.getLogger('fbprophet').setLevel(logging.ERROR)\n",
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"import warnings\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"df = pd.read_csv('../examples/example_wp_log_peyton_manning.csv')\n",
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"m = Prophet()\n",
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"m.fit(df)\n",
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"future = m.make_future_dataframe(periods=366)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"block_hidden": true
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading required package: Rcpp\n",
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"\n",
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"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading required package: rlang\n",
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"\n",
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"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\n",
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"\n"
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]
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}
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],
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"source": [
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"%%R\n",
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"library(prophet)\n",
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"df <- read.csv('../examples/example_wp_log_peyton_manning.csv')\n",
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"m <- prophet(df)\n",
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"future <- make_future_dataframe(m, periods=366)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Prophet includes functionality for time series cross validation to measure forecast error using historical data. This is done by selecting cutoff points in the history, and for each of them fitting the model using data only up to that cutoff point. We can then compare the forecasted values to the actual values. This figure illustrates a simulated historical forecast on the Peyton Manning dataset, where the model was fit to a initial history of 5 years, and a forecast was made on a one year horizon."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"input_hidden": true
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "a1e4d1b9644e4792b4bcbb0e24c1f0a1",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n"
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]
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},
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{
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"data": {
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"image/png": 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Ws2bNMv1imSxeBKEjHVZOZJUjOordbkdZWVlWxjll87VnMulitSbhRRA60iHeJ53iGTKFTIz7KygoyFrRkc3XTqQWEl4EYYDZV07pYJXLJMjCmHn06tUr1U0gshQSXgSRhqSDVS6TIAtj5jFw4MBUN4HIUkh4EUSaYnarXCZBFsbMIhPdxkT6QMKLIAgiAmRhzBwywW1MwjG9IeFFEAQRBWRhzAzS3W2cCcIx26E8XgRBEETWwN3Goiimpds4XfIMEqEhixdBRIDM+gSROaS725jiDdMfEl4EEQYy6xN6SIinP3a7HW63G263W7EYpYuASXfhSJDwIoiwpHs8CBFfSIgTZoDiDdMbivEiiDCkezwIEV8ovoYgiI5CFi+CCAOZ9Qk1DocDVqsVsizDarWSECcIImZIeBFEBMisn11EiuFijGn+lyAIIhYS5mqcPHky8vPzMWTIEOW1119/HYMHD4bFYkFDQ0OiTk0QBNEueAzXY489hrFjx8LlcmnedzqdkCQJjDFIkkSuxhTjcrlQWVkZdJ8IwswkTHjdcccdWLt2rea1IUOGYNmyZRgzZkyiTksQHYYG8+wlUgwXxfyZh0gimSDMSsJcjWPGjEFjY6PmtQsvvDBRpyOIuEC71rKbSDmSKObPPNCOYyJdoRgvglBBg3l2E42wopg/c0CJRIl0xbTCq7q6GtXV1QCAQ4cOpbg1RLZAgzlBwio9IOsjka6YVniVl5ejvLwcAFBSUpLi1hDZAg3mBEDZ6dMFEslEOmJa4WUmaBDOLrJxMKc+3gbF+REEkUgSJrxuvfVWOJ1OHD58GH369MHMmTNxzjnn4IEHHsChQ4dw3XXXoaioCOvWrUtUE+ICDcJEpkN9XAvF+ZmH9iwIaBFBmJ2ECa+XX37Z8PWbbropUadMCDQIE5kO9XEt6Rbnl6lCoz0LAlpEEOkAuRojkG6DMEHECvVxLekU55fJQkO/IKipqYl4T2JZRHTt2jWRzSeIkJDwikA6DcIE0R6ojwcTLs7PTBamTLZWqhcEoihiyZIl8Pl8YQVmLIsI2rRFpAoSXlGQjcHWRHZBfTw6zGZhymRrpXpB0NTUhIULF0YUmO1dRJhJTBOZDwkvgiCIKDGbhSnTrZV8QeByubB06dKoBGasiwiziWki8yHhRRAEESVmtDBlg7UykQLTbGKaQ1a4zIWEF0EQRBTwibCqqgotLS00ISaZeApMtagxo5hOpBWOBF3qIeFFEAQRAXJHmYN4iAaje2k2d22irHDUj80BCa8YoJUCQWQnZnVHZRPxEg38XhYVFcFisWD9+vW45pprMGPGjAS0un0kygpH/dgckPCKElopEET2YkZ3VLYRL9HA72W3bt0giiL69euHEydOJKDF7SdRMW3Uj80BCa8ooZUCQWQukazZZtk9mM1W93iJBn4v33nnHfTv3x8FBQXxbWicSMSmCbP042yHhFeU0EohvcnmCYsIT7TW7FTvHsx2q3s8RYPdbofb7Y5j69KHVPdjgoRX1NBKIX3J9gmLCE+6WLMT0c69e/eisbHR1JYfNfEWDerrJ4hkQcIrBmilkJ6ky8RKpIZ0sWbHu50ulws1NTWQJAmiKKKsrCw+DU0T9u7dq7n+888/n8YFIimQ8CIynnSZWInUkC7W7Hi30+l0QpIkMMYgSRIaGxvj09A0obGxUXP9tCAjkgUJLyLj6eiERfFhmU+6WLPjWbzb4XBgw4YNisUn29xt/fv3hyiKkCQJgiAgLy8v1U0isgQSXkRW0N6JleLDiHSgPf3UbrejrKxMiXE6r08fyDKDxSIkqdXRk4jFT0FBAa655hqsXr0ajDFMnz4dhYWF9HwTCceS6gYQhJkxig8jCLPR3n5aUFCAyy67DAUFBTh8woPPDpornxXQJiofe+wxjB07Fi6XK27HPnXqFBhjYIzR800kDRJeBBEGHh8miiLFhxGmJR791CvLOO2V4t+4DtIeUelyuVBZWRlRpPXv3x8Wi38atFqt9HwTSYFcjQQRhnQJvCbiQ7rG83W0n7ac9AAAZMbg9knItYqJaGa7iHVzTHvDAxhjcWoxQYSHhBdBRCBdAq+JjuFyueBwOOD1emGz2YJ2uZldlHWkn7p9EhgDZAbs3n8cwwu6x7l17cdut6Oqqgq1tbUoLS2NeI2xpI9pbGyELMsAQDsbswCzPMMkvAiCIADU1NTA4/FbfjweD2pqapTBOdM3WahtPbLJLD8ulwvTp0+Hx+PBpk2bIgbAx2IhU+9spFCCzMZMzzDFeBEZRbSxHck+FmF+mpubQ77ndDrhdrshSRLcbnfGBWFzrcWtXmYi1hgv7nadNWtWxMm1oKAAZWVluOKKKzJOTEdDNo1xZtooRRYvImOI54qGH8vtdsNisWD+/PkoLy+Pc4sTh5FJ3Sxm9njR0etRf3/Xrl1YuXKl8p7NZtNkcs/Ly1NcUrIsZ2TOJwa/tcsCc6WTMLJgRVPUPFKfUFu3+PH4d7MBM1mAkoGZEmmT8CIyhniWBuIWDlmWIcsypk2bljY5fowGVAAZNch2dNJQf5+7miSpbUdfcXGx5vMtLS2wWCyQZRkWiwUtLS1xuxYzwAL/j4svM6HfOADEty9nmwDhZFspNTNtlCJXI5ExxDP1g8PhULaZA22Bt+mA0YBqJjN7POjo9ai/7/V6NaILAD788EPFsgL4+0Nubi4EwW8NOnr0aFyuI9Xs3bsXmzZtwsF93wXivBgYzCW8AP+kOWPGDNjt9rj35Ux7NqIlG1PlqPtRKiHhRaQ9PE4BQNSxHZGw2+2YP38+bDYbLBYLcnNz02ZgMhpQ8/LyYLFYYLFYMmKQNbrGWOJV1N+32WwaUQVASag5Z84cAP7+8MADD4AxBlmWMWfOHFRXVyfs+pIBL5K9ceNGrHnjVRzc/50pY7zUuFwuNDU1wWq1xk0wZKMAAWKLhSPii8DSIHlJSUkJGhoaUt2MrCDd4oAS7SZIt9+Do243gLSOVwuF0TXG0g/0MV7Tpk2D1+vVfEYURWzatAl2ux3jx4/Hv//9b+W9cePGYd26dXG/rmTgcrlQUVEBr9cLxhgEQcAw+2W4ccpvIDOGy39ybqqbGITePTx58mSUlZXF7GJWP88+Scbmujq8+tI/ASDm4xFEKMLpForxCkO6TrrtJR1jHRIdp7Br1y44nU7k5eWZ/rdQow4urqyshMfjgSzLEAQhY+KTjK4xln6g/r7T6VSC59UwxpRj9ejRQ/Oe/u90gT/nra2tuPzyyyEIAiyiiB+dVwDG2nY4mg31sw4Affv2bXdcHx/fvjlyCnf8fCJ8Xv9r6g0VBJEoSHiFIB1FSEdJx2DLRO5Uqa6uxj333AMAiqUjHS1FZtrNkyg6eo3cFcsYU/6XMaZxMR86dEjzHf3f6QLfOKJ2dlx08XA0f9uE3dvqMaioJIWtC01H77F+fNuwYQOOnvbix/37QZZlfPXVV2kx5hHpDwmvEKSjCOko6ThBJ3KnSm1tbdDfZhdeRlZaM+3m6SihrNAduUaeoFOSJMUVW1hYGHSsoqIijauxqKgofheWRPjGEW7hY4xh9/YPAcYwf+6TeHJpLa68YEKKWxlMR/uxfnzr27cvup324rw+fSBLEvbu3WuqMS/bPC7ZRMKE1+TJk7Fq1Srk5+dj9+7dAIAjR47glltuQWNjI/r374/XXnsNZ599dqKa0CHSUYR0lHSdoBNV0qe0tFQz0ZaWlsb9HPEknJU2E8oeRbJCt+caeawTTx3CXbFGx+revXvYv9MFvnFk2rRpEAQBgiD4U0gwBq/Xix0f1AE3m094AR3rx/rx7dTpVthOumG//Ersb/wKv/zlL03zjGSjxyWbSNiuxjvuuANr167VvPbEE09g7Nix+OKLLzB27Fg88cQTiTp9h8nWHR9m2W5rBsrLy7FgwQKMGzcOCxYsML21K9O3xcf7+vjk9s477yj5ucItsvRJU5OZRDXeGcbLy8vx7rvv4oorroDdblcEmCiKOLjv24zNZK4e3w4cd2P/d9/B9e4G/Oc//8H06dNNc92Z/ixnOwkTXmPGjME555yjeW358uW4/fbbAQC333473nzzzUSdniDiQmFhIRwOBwoLC1PdlIi0d1t8upQNife2fz65cdF11VVXhV1kbd++XUk5kcwkqlwgPvbYYxg7dmzc7pPdbkf//v3xwQcfgAVySMgyw+rXXozrecwKA8MXn+6G5POBMWaqUlDZmuIiW0hqjNeBAwfQq1cvAEDPnj1x4MCBZJ4+JsjUS6RbH2iPqzidrjHernB9OEFFRUXIY7pcLixevFgJSLfZbEmbDOMRbxoqXqixsTGwS5CBMUCSfEAgh1mmx7Ue2PcdPtu9U/nbTKWgeF+vqanRvE5xX5lByoLruWk7FNXV1UqCwlTsHsrG4Pp0JhEDUjr2gVhjYNLtGuMZqxaLkHM6nZrs9hdffHFc2hANHY03DSeu+/fvr5RMEgQLbDYbJEkytZUlXs/6/m/3gulSiJgt1crSpUvh8XiwdOlSVFVVYfr06WmxSCLCk1Th9aMf/Qj79+9Hr169sH//fuTn54f8bHl5uRJTU1KS/O3N2Rhcn64kymrjcDhgtVohyzKsVmtG9AH9pJXt/TyUkAv1O/Eg/IaGBowdOzYpk19HLX3hxHVBQQHKysrw8edfoXNeL1xR9ht89dEHKP/F9aac1DvyrOvvac8+BbCIIuSAoDZbdQr9fautrU2rRRIRmqQKrxtuuAFLly7FI488gqVLl+LGG29M5uljIl13+GUjibTacNdSGhR4iEioSYv6eRsulwtz5szBypUrlTxe6t+poqJCCcZP5uRnJBCjsfyoS+wAMBTXBQUFyOneA4dOeNBj0HCMGDUa9kGhF8WppL3PulHfz+95Hq4r/SW++PRjdM21Yvbs2abq//pFUWlpKTZt2tThRRK5K1NPwoTXrbfeCqfTicOHD6NPnz6YOXMmHnnkEdx8881YtGgR+vXrh9deey1Rp48LmbAFPxtIlNXG6XTCFwi89fl8plphtmfwDDVpZXM/15cdcjgc8Hg8yvs84Jr/RhUVFXGZ/OLR7kiWH32JnbvvvjtkSZxTHgnvftWCq/t70b2zDdu+PYphfcyXLqO9z7pR3z9vkN9d3PXMbhg66HzTPQNGiyJ1fjnAX7EhljEgnWI6M5mECa+XX37Z8PX169cn6pRElpIoq01eXp6SZNJMgbftHTyz2a3IBVZeXh5aWlqUa1f/juPHj9eILiD4vqfKQqgX2jU1NWhtbVWKeRstCtRiAwhfYmfX/uM4fMqD9V8cxp0j+kIycaVsvjM+lrqKRn3/rXecqHO+A8YYtn+wBXV1daar1ahfFPG/2zsGpFtMZ6ZCmeuJjCARVpuWlhYlw3cy0wdEor2DZ7a6FfkkxeOzLBYLcnNzcfvttyu/o9vtxsqVK4O+a1TbMtkWQr3lasKECXjrrbcU97fFYjEU0bEIbb7PSWb+NAtm0l1q0awOLo9UV1EvVtV9H4BfdAUWVpIkYcGCBVi6dGlaWIHaOwZk8+LLTJDwIrKacC47h8OB3Nxc0w1SkQbPcNcUi2jIlFgQPkmprZfcssV/R0EQNEWyLRZ/ikMzBFyrJ1lJkoLyHxoV9wbaJ7Ql2S+6ZJPENKpFJwQBTJajiq8LZRHin6+srFRyl3HCWQ/NRnsFVLYuvswGCS8ia6mursa0adMgSZImiFotOMw4SIUbPKON/YkmKDtTYkH0OxJ5hvqysjKUlZUZWlOqqqoUl2Sqr5u3n7sW9TDGQoqFaIT2x83Hse27HwAAeWfYIDNmGuGlFp0WiwWiKEIQhIhiI5JFyOFwYP2GDf40GoCStd9MC6xwdFRANTU1KTnCUt2/sxESXkTa0x7LjMvlwv333w+fzwcAmqzVesExY8aMRDW93YSaUCNNONEKKrPGglRXV6O2thalpaVhSziFcjOpY7z49XCxzWOHiouLTSO6AG0yzSVLlsDn80EQBDDGlJ2XHRELP7R6lX8/X78X/c7ujMvPPzcOLe84asuO1ZaDuXPn4uj3RyLem3AWoa17j2L06NEYedmV+OKTXejStSsKLxyIq666yjT3PBra4/J2uVyaDSRLlizBxo0b0+aaM3Yw3m8AACAASURBVAUSXgZkioslG+hIkKnaRSOKIhwOh2kFR7REckGor6+1tRU1NTWG12fGWJDq6mrcc889AKAULzcSX0Z9glNYWBhx958gCPD5fKay9PFJllvo+P3Qj1PtGbtEXSLrv21uxGU/McdGErvdjjdXr8PyNW+jaNSluOPGq2ETI1e6s9vtmP/Sm9jq2ozrx1+l+S1kxuDctBnP/fX/4PP6ReeqnBysXLnSFPdaTbznIqfTCa+3TWibeYzL5HmYhJeOcBN5tKttInl0JMg0NzcXbrcbFosFzzzzjPI9LjhEUURTUxNcLlfaPPiRXBAOh0PJVM4Yw8KFC1FcXBzUn80YC1JbWxv0t9FzqO8TNTU1SgZwIzGlj6Hi1iQzTkpGu9w43Jrh9Xphs9kwb948tLS0GFr51IiW4AoiJvE0AgBGjhqNs348BJLM/LnGuuZEJb4Kh4/AkGEj0O/sM5TXGPPHsL3+1tv4/sj3APwXKgiC6e51Itz9DocDNptNE+NohkWVnkwKdTCChJeOUBN5tKttIrnEO8hU79ZZuHAhFi9ejMmTJ5tuq3kowrkg7HY7Jk+ejOeeew6AfzfXtGnTDC1BZsvvVVpaqjx7/G8j9H0CQMR4Hy5GAf/kzOPAzDgphaKmpkaZUD0eD+677z6/0FDt5DSKXdMLLwZzJQwW4BeCDAxrN76LL7fXY+I1V4Xtm1xg7Wyox5s76xWrF2P+Y5151tngoguAKStTJML6brfb4XQ6lfgus45p6e55iAhLA4YPH560c9XV1bHOnTszURRZ586dWV1dHWOMsXHjxjEExiQAbNy4cUlrExGeuro6Nnv2bOVexYPZs2czURSV+y0IgqY/pDN1dXXMarUq12axWNjs2bNT3SyFcPdzwYIFbNy4cWzBggVRHyPUM61m6tSpTBAE5fcYN26c6e51pH4+depUzRjFr0f9t81m0/wOGzduZH97aTlzzHiOOWY8x/DQCnbuY2vZO58fZLIsJ/kKjTl6ysO2/KeF/f31NSy3U/B9PN7qZYdPuJnXJynfkWWZLaxdy3I7dWYW1ed9ksy2/KeF3f27R5lgsSi/y9SpU1N1eSGJpt9mKrFceyLG/3gQTreQxUtHKEtItKttIvkkwjKj30nGTOp6ag92ux3z58/X7Og0y2o/kouhsLAQLS0tKCwsDHscfZ+I5DYtLi6G1WpVfo+KigpT3Wd1LjJBEDBx4kQ8/PDDmjaWlZVhyZIl/kB0qxUWiwUej0dT9opXYlD3ZX2M16kTx/Hxtg9xxU+uhRDshUwJDMD29zfD6/FAlrVWkM8OHgcAFHQ/A//ZvQ1OpxNjxlyOrbrPr9+wET8dOhwMDENGXAKr1Qqf1wurLXJOsFSg3xTCN/90JJ4vXYg21CFdXZIkvBDcgY0mcu5WpBiv7MBoJ1m6uZ7CUV5erik/YpbBKpyLoSODbDhx7nK5MH36dCW+a/z48XG7nnjhdDqVdBgA8Oabb2LNmjWaHWl2ux0bN27UBN9XVFTg7bffBmMMgiAoRd95X3a73UGuxlPHf8DDd9yGYX3W42eXXpLU6zRCEPyi8eJRl8KWkwOf13//L/nZZdi9/5iS7LXhg/fxixuuVfrHA//vL7Dl2ODzAlZbDs4bXIKvj5yEzKDLzG8et6oefm+NNouko+CIhWgW1Onqksx64RXLYF5eXk6CK0MxWj0a7SRLh4c6WkINbKlcSYeL2TMaZPnrHWmrXtQsX74c69atM9Vk5nA4lCoKHKOJRn9P9bUl9TFeTqfTILhegNfrxbtOpymEF+DPqH9hUQn++uIb+GanP8Zr0MXD8VXLSWUjQN3m9zT944fvj2DuC29gV30dRlzyMwwZNgKS7N/VuPPDOuT36KHEv23YsME091pPqH6fjoIj3phx93U0ZL3wSlfFTMSPSOLbbEHmodALJn0B6GgESqpN9+FcDOpBVhRF1NfXY+bMmVGnfgglKNU1OQFzZjDn7uF7771XaStjTKkjGeraonHZ6F2NEACbzYYxl1+euAuKERYIlh9cXIIxP7sEfc7qDJ8sK1n2BQi45GdjNJPwxaMuxUXFJSgqGYkc0QKf7E8MyxgwdMQl2LurHrIkwSKK6Nu3b6ovMSShxEU6Co72EKkSh9l2X0dD1guvUJ06k/3nhJZ0Fd96YaUWTFVVVUom9mhzU7lcLlRUVCjWn1T9FqGELh9k58yZg5UrV2L58uVK/FIsJWREUdTsUlXX5AQQVWb0VFBeXo7t27crO1J5/dCOLhz0Fq8zunbDnxf/C6NGp/4ZaPVKOHLKG8imD0iM4aTHh2++P4XzzuoExvzWMIvA0HPgxXjpjdX4qH4L+haOwIDCYcr7ALBraz0aXJsxeIQdFxaNQOsNv8B3e5vw4x/3R0FBQSovMyyhxEU6Co5YiWYhmC4LYzVZL7yMOnWqV/1EctFbUtIhd5e+j6oLPns8HtTW1ip/qy0koQSKUSFpM4oPAFi1apWS+gEIL5S4OG1qatLk6lIXRHaoanLqRZnZKCsr0+Qkc7Qz6a/L5UJNTQ26du2KXhcM0bzXuUtXDBxaBGaC2Ke9R0/jh1YvZACyzMCYoAgwr8QtWAwyBMiM4fyhw3D5ZZfgi8MnccojBUofMXy09QNM+/VN8Ho8sOXYMHvxv3DOj3qjS96P4P3hEDZt2oTc3FxT3nPAWFyko+CIlXRdFEci64UXENyB9Te7pqYm41cW2YxR7i4+KZv1fuv7KKB1PZSWliqxPXqLl5FA4cfjouuqq64y3c4+wLjiwN13320olNS1OK1WqxJYznep8jJRDodDKRdkVsHFCWX9COV2MrLcq8vGOBwOWPZ8DQy5WnMemZkniSpvi4yAyJIBn8Swar0T9Vs24+JRl6Jw2AjITIDbJ+PQSU+g2DdTvrvVtUXZ4ej1Ajvr6zBi1GgcObAfm1fXQpYkVFZWmvqZD0Ume2fSNYYrEiS8DNBbQNS72tLxwSQiwwONfT5fWghu/YCkLvjM26vetQiEj/HSH8+MogsIrjgwf/78kGWD1LU4fT4fysvL0dzcjDfffBMAIMsyjh49qrEcmi2tQLhNH5xQYiyUe9WpKxsj+7yac4oWARYBpiiU7a8iEBBeMoMMfyLVj7bWY+otN8Ln9cBqs+Gv/1yO4pJRAIBjrV4llktm/j2Lw0ZfCluODV6vP36tcOQl2Pfdt9j63Q+QuuQBPxxIG4tKuBCDTJuf0jWGKxIkvAxQ3+ympiYsXLgw40ydmUhHV37pJLjVVjr1a+Fie8LFPzmdTsOs5mYj2oHYyDLGRceKFSsUy96OHTsUy6Hb7UZFRQUqKiqUY6TytwhVc9KoXUZuJ7VVVO1eraqq0pSNEQUBErRYRYtpLF4sEEAvBcQUY8Bb/3oFXo8bAOD1eLB22SsoKhkJIOCKlLmlzP+dwuEjUfXCG/jQtRlDSuyQGPD+xrWQh14D2DqZNq5PT6QQg0ycnzLRpUrCywD9ikIfU0GYj1gmqVCko+DmfbO9rlHujvP5fBBFEfPnzzfldaqJZiDOy8uDKIpgjEEURU0tTh7PpXbJ8ti2t99+G+vXr1eC7Y1Ed7JcO0YhD+FqTurhCwl9EuCWlhY4A2VjunbtiouGDEXNnlOa7wowT3YrjfWK8VT8RmWO/NYwOZAyYve2er9L8ZKfYfjI0Rg8bATOHzocksxQ8/e5bRY9JqN3796mW1wZoe8Tzc3NsFgsYIxl3fyUzi5WEl46jCbwTDR1ZhodnaQ4fFJ3uVxYvHgxZFmGKIqmHNA6Gnhq5I679957AZi3DmmkwZYHjS9ZsgSSJClikl+PkcWssLBQk2iUW4iA4N2Sydx4o3f/8vZEe7/1u0BlWYYgCMjLy1P6uTOQEwp79mi+y4WMGWAATvskVeJThgn/dQtWvf7PQOZ5G8ZNullps8wYdm2rxyOTfw6fx4uXn3saf/vnmxh0cQlkBvhkhouG27H/68/94pIxNDc3p/Qao0VvlV+zZg0kSYLFYkFVVVXWzE/pvgGOhJcOo8lsxowZaXVTs5GOTFKhJnMhkN9IMEvdFB0Oh0MJGG9PkV+9Ow7wxz2FKpqdaiINtvx9buEB/PeupaVFcxwjF2xFRQU2bNigiFCOXnR3VOzGgl4kAu2zvq9bt07ZVCBJEqZPnw4AaGlpQZ8+fQxTKQhg+PzQCVzcuxusoiVelxQz/gLZDB6fDNEiwC1JaNy6DTvr6/Dg/zyBz3Z/BIBb5xgYBDAw7PigDj6PVwmm3+bajIEXDw/kA2M4f+hwfLXDha8BIFBI3KxWbTVGVnkuqPX9PJNJ5nOYCEh46Qi3iyKdTZuZTqyTFL+XeXl5Sr4r9WTOA5AZY/B6vaZ8sHft2qXU3vP5fNi1a1dMbeSB6mqhAgCSJJnyeiMNtvx9tejKycnB0aNHMX78+LClvuz2tvqV/DcVBCFIdCd7l5VeJMZqfdf/JowxtLa2Ytq0aZBlGVdccUXQhoKDJzz410fNKLf3hU9msIr+190+Cbv3H8fwgu7xu8Ao4FW+ZQZ8vL0Bfy6/GV6PF2KgYZJPwttvvoqqF97AsJGjIDOgcMQlsAbKBdlsNhSNuhQA8PH2D7H9/S04v3gUCvr/BF8fkCAIwQLbzKit8tkaBhPrc2i2uZuEl45odgiZybQZ7w5ltg4aC9FOUuo0AzyWR58wVJ3NXJZlJUO4WaiurtZkMZckKSpLlf7+8t/o6NGjmDt3rumKZquJNNjq3TCTJ09Gt27dMGfOHABQityHEl+8fmVFRQXeeecdyLIMn8+nEXjhgvuT8ezEGmjMf5PTp08rr6mLZUuShHfffRfIH6H53vP1TbhrdF9NkWyeNyvZyCrR+PGHdfAGLFnMq8pP55ax9o1XUTxiFGTGcGFRCWYufA27PnShZPSlGDxsBHZurceMgPvR0ncIfnar360+8KLBuOSn/vqclZWVaTP2xWPHX7qO97Fce7jkySmDpQHDhw9PdRPY7NmzmSiKDAATRZHNnj071U1idXV1rHPnzkwURda5c2dWV1dnquOZkbq6OuU+AmCCIDCbzRZ0zbNnz2YWi4UBYBaLxRT3m1NXV8esVqtyDfy/SO2MdH/r6urY7NmzTX3fI7VR//64ceM0v9G4ceMiHqc9z0Gyn51I7Ve/t2DBAiYIguZ3EEWRCYLAHA6H/78ZzzHHjOcYHlqh/LflPy2s1etjjDHm9Ums+Vgre7/xSEKvS813R0+zhqbv2cYvDrGXtu1lL2/7ls36x3KW26kTs4gis+XkMFH1HNhyctmC2rXM+cUhtvqTZla7cx97fcd3bPUnzcz55SE25aFHmcUSePZvmKFc89wX32Q1NTUZP/bpyYbxnjHGJk2apOn7giAk5XrD6RayeEVJsl0M0RBvP7f6eK2traipqUmrVVA01NTUaLKeWywWPPPMM5o0Ci6XC01NTcquOJvNZor7zTGKzbJYLBEtVTU1NYpbMZoCy2bBaFXOg8L17dVfQ2lpqWLpAoCioiLce++9YVOFtMeSkMyYk1DWd/XGAvW1bd++XeNKNuTbT4A+F2leUmeu//aHVhw+6U6axetYqxf7jp2GEMhID/gtXxcMHY7HF72OL7Z/gMIRdqxe9irWvPYCwBgkyYdtrs346dDhOO2TwWSmBNPv3vYhDuz7FharCH/ujDZTngCgsbExLWKG4mmhSvc4qWiorq5W8vZxQo1/yYSEV5SYMZFbvMWgw+GAKIqQJAmMMSxZsiT1Jtk4o9+9VFhYqBFd6vQKLBDnE3HSSjI8NosnEX3ooYfQvXv3iDv9Fi1apFyLPhjfrC4HvchQ16CMxuXP3Yq1tbUoKirCvHnzNDFtoQbg9rr0krEwM5owAQRtLFC/p4cH23Ms3+1G1/4XAe62z0iydmcjL0itJxGxX58fOgGZAbu3fYAtmzbhrEEjUPDTIejZLRfnFw5H89dfYOm8J9HlLH/aEFmWlVguFgjGt1r8z+6qV2uwqPLRwA5lK66c9CscG34JcPxb/7ULAvr372+6hTUnUjxqezGjMSHe1NbWav62WCymyNlGwisGzGYRiLcYtNvtmDx5MhYsWKDEgaTTKiga8dCzZ0/N3zt37sSOHTsgiiJ+97vf4emnn9bsbGOBOBgz/Q7tiW/QB9Bfe+21mvjFK664QhmAN27caJprVYsMt9uNJ598Usm5pU54Gkl8lZeXo7KyMij4PlxtzljEaDIXZkYTpj6IHoCSOsLhcGDx4sVKwlSLxQKr1apsHhAEAT+78mqc0ScfK3a2nUeSZU0uL3XBaTUeH4trXcevW06BMWBnQ71SX1Ge/gbw2Sd48VfF+Mfcv2D5kvma74hWK+579HFcVDwCXln25/NiAvZ8tBWLZj8KSQqkTGFeHPyuCZ1LgD1ev1A8s1dfjB2Yb7qFNaBdeAiCYBiP2l7MaEyIlUjPqN7i/fvf/z7sIjVZkPBKc+ItBo2K8JrVGqIm2s0PZWVlWLx4MbxerzKQAf7g9CeffDLo82YtFm1037llC2irOWg0IQPAmjVrFLFRU1MDt9tv6nC73aZyMXORwcXWl19+CQDKvXvnnXewadOmqFb/+uD7CRMmYM2aNYa1OdsjRtW7zeIVpB2qZFC4eo3cSivLMqZPn658lltNuIUXAJYtWwYAOOfcHrDkiLhx8I+w/OMDANoyvwN+d5wcSMUgywwWi3a3p5Egay9+lyaw9f0t8Hr99RU562tfxIp/PBv0HcYYjrQc8Qfgb/sQWza9hyEll+DjhjqtW54x7PpgE4S8EZAHjgEAdD2nB3r37o3evXubpt9znE6n0vcFQVAEc6QxKdox22zGhFiIZsxXW7zD7WpONiS8iCDUBYOB9KgFFk28AhcmEyZMQM+ePTV1+wAo8Vx8tyN34eXl5YWMKUoFRoNqdXU17rvvPiV+bdGiRZgyZQqKi4uVCRloczF5PB7MmTMHI0eONHXySC4ypk+fjvr6euX18847D/v27TNc/YeadPSCxel0YuXKlYZ9Ri9G58yZgzfeeCNie+O5+zncsfQTpvra6uvrsXz5ck0siz4Xocvlwpw5c/DDDz8AALbv2IHLritFl3PPUz4jqcSUEKjdKMv+0j0Wfeb4OAovLuKKR1/qdyOqYjKXzvl/YEwO+o4oihhcYseubfX4wx2l8Hm8eNNmw3//rgK2nBx4vZ5Asi/m/77UZtW2mDRPHwDN7mrGGH77299GtNiYdQd+vIk2Ro1bvAHzhFSQ8CIU9A8sr22XDgGYDlUyUYvFEuQ+crlccDgcigDJzc3Fgw8+qKST4K/97W9/Cwq0N9MgFqo00v3336/ZNOD1erFgwQJ06tRJqcGYl5eHBx54QLGAvfnmm1i+fDlsNpsS22e1Wk1XKNput2PYsGEa4TVgwAC0tLQov0NeXh4qKyvDxsEYDbrRxrisXLnS0B2pJ57PS7THUls6i4uLsXr16pCxfPzz/Fng78mShEP7v0OnXqOVz7FAqR6OzAAZwM59xzC0dzfYVIlV4yW8Trp9SumfIcUjcP0vfoU3/vmPtjYMvBzi5+/5+zpj4EHyPGP9dlXiVJ8POH70CGY8+wq+2P4+zuh2Nmr+73/h83kBoa0kkhiw3pllUlbT0tKijFEWi0URXeqFoL7dkfqNGa+zPcQao2amsZyEF6Fg9MCmUwAmC9Sj46JjyZIliovIGUiIynG73Zg7dy4A/2p54sSJePjhh4MeRLMJT33MU0VFBQYMGBC0yxFoS5a5fft2PPus3z2zfft2PPfcc5rPeDweJehUFP1JKVM5OBudu6ysDIsWLVLu4QcffKCIZLXYUsfB6IteGwlWtXVXfZ1lZWVKrU7A/ztFc+/j+bxEcyz9goLvxAX87tg777zTsE+rnwX+2R69+uBblfVHYv7ILY9Phk8O5PDiBatlBpvY9v14xHgda/Xi80MnIDGm1Iq85qZf4k3PBW1Hv2Y67rxpPPZ+sRtv176kWMN8Pi/Wr3gd1/3XL2G12eDzAVarDePGXolOfS/CRcUjwBjDjwYMwqHPtuGzHiPwXrP/mREFwVSTshpHYCONeoERbrNJVVUVmpqaYLX6p3b1ooT3HzNeZ3uINUbNTGN5ROE1b948/Pd//zfOPvvsuJ30r3/9KxYuXAjGGO6++26lhAWROPSFv406q9FAny4BmE6nU2PxYYxp4pUcDgdsNpsmwJjvXBRFESNHjjS8NrMJT33M0zvvvAOr1QqbzabErV166aVwuVxK5n317tSysjI8//zzQaVxuHDzer3trnMZD0JNgHa7HVOmTNFs/GhpacGMGTOUoHnuIubiUR0Ddvvtt2sGXf016q18drsdv/vd7/B///d/ABB1Utl4Pi+RjuVyuZRJlyNJEmw2mxIHZGS9zMvLC4r5u3j4SOT9qBe+Vb3GLV67m4/5rVCsLYO83jsnM+DQCTfO7ZLT7hJbbYWwWcCQJWDwsBFgdR7N56646Vd4q+bvkCVt7JZz+au49qZb8Ohzr2L1ts+xLWcg7t7iQcOIM/Hl4ZOQwTDnMwu80nD8V498IOBiFy2CqSZlPerFgb6dtbW1yligrkggiiLuvvtuFBcXa4SZ/jkw03XqiWbxF0uMmpnG8ojC68CBAxgxYgSGDRuGyZMnY/z48R2qXbd7924sXLgQ9fX1yMnJwTXXXIPrr78e559/fruPSYRHn7lXEATDHEahBvp0CMDkD5U6Q7cabvWqqalBc3Mz3nrrLWXVb7Vag1aF6iD1eAvPjliT+D3SZ1cvKSnBsGHDFIF10003KfFr6t2pdru2NI56gwEARbSkanAONwEWFxfDarUqxa+5O9kRSIPCa1bOmzcPtbW1yu/DhYm6rmWka3S5XJg3bx4AxFyAOJ7PS6hj6S1dHCN3uZ7t27cHvZbbKTdoXJdkvyVLkhksghAIrg/lVmRoPHIK3TpZkWsVjT4QFbLsF18WAILADM/lZTK6ntUd0FnZJEnC1ve34Lo77sMHu9p8iTu31mPd+g0YNMwOr+R3j1oEAWcK/t9Ocp/C6NGjTTMpc/i4zdPGFBcXB4mHoqIiZdceX5BwUd23b1/FHc/7OaB1r6vHPTON8YmwQJrJiBBReD3++OOYNWsW/v3vf2PJkiWYNm0abr75ZkyZMgU/+clPYj7hp59+ilGjRuGMM84AAFx++eVYtmwZHn744dhbT0SFejJTB2qmUxLNSPCHiuer8vl8sNlsKCsr0widZ599FpWVlVi5ciUAv4vl2muvVVaF3FXDRRl3V86YMSMu7YzHgGK3+4s6b9q0SVntNjQ0YNeuXcr1rl69Wvm8Ps6Hl8ZxBkoF8ZI6APDb3/4WkyZNwtKlS+F2u5WUBIlEfX9CrUq5dUeSJGXX3sKFC7F48WJMmDBB414rLCxUrs/r9UIURRQXF2Px4sUA/H1fvenAaKLlz4xRAeJEu2HVeZv08Yb8vEbuwpEjR8YkEDmCIODEsWNoad4HoXdf5XWZMTQfdwesXCxg9fIH1+sFkRywVglo/6JcCeAPxG5ZYLxb0icxfP3Jbv8fZ3QHBAtw6ntYrTZcOGx0UNum3noTfB43rDYbcP9rAIA9u3ZgeI7fQn7wq08wIK+LaSZljlO1o5EXr3/33XeDNoiocw0yxoJ2Yqv7eVlZmWI5i3desHiSKAukWea3qGK8BEFAz5490bNnT1itVnz//ff4+c9/jquvvlozaEfDkCFD8Oijj6KlpQWdO3fG6tWrUVJS0q7Gp4J0DExUT2Z6i5cZVnbxgj9UfGAJFdOgn9x79uypEaZqN0y8LT7xGlCMLF/qhJnc7Roqzof/VpWVlcrALQgCjh07BqfTiQceeECp3Th9+vSINSDbi5EQNZoA9UKI3ydJkjQ7U7l1z+FwKBYcQRCwfft2JTGwJEloaWlRzmO0azWcAExkjIzaysEDqq1Wq5L6gj+3VVVVGtd5bm5u1KKruLhY2UzB2f3Rdlh270TXCb3AA9Yl5o+74jHs3NrFWFtE12mvhMMnPUpwe0divQQIgfxbDIIAMBgnL/ZJqvxiU/2W6cEbZuIXDz6GCy4ugVd1XQDg6VcC7HkPau/6LvYjOLAPQNuuRrNMyhyHw6HZ/MPzCep3qIqiqIQOCIKAq666SpPbLpQXQ+2iN5vb0UxuwUQQUXj99a9/RU1NDc4991zcddddePLJJ2Gz2SDLMi644IKYhdeFF16IP/7xjxg3bhy6dOmCoqIixb2hprq6GtXV1QCAQ4cOxXSOeKEXWWYNwIyE3sQKGMd4ZQrqAdRocJkxY0bQ78HjffQWL6NdYR0h3gPKgAEDYLPZgoQ0d6uFivPhqON9GGN4/vnnFREWz2SNoTASoka/idHiwSg/Gb9fTqdTcbv4fD40NzfDYrGAMaaJX+TH9nq9sNlsynWqLaiR2hvP30UtMAEov79aXHo8HrS0tMAZcJ0DwZsDQsEth2r3Mv8NZUnCF7u2AecMD5ybKRYntbBSW6H2H2vFkVP++xDaDRkZWWbwSHLAvdm2YVEyFF4MY67/OZwrXgXXUqOnzsL5hYVgjKFLjm5au+73wKmjYGcaW27NmkxCHRYQqni90Wf0CYV5f1bnlwMQFIRvJnFjJrdgIogovI4cOYJly5ahX79+mtctFgtWrVrVrpNOmTIFU6ZMAQD86U9/Qp8+fYI+o869kQqLmFFFcyB1sS8dRb+aS5d2hyMa66Ne6KhjGvgE7XA4NNaPNWvWYMWKFYoAiWe74jWgqC0jgiBodmW6XC6NmNK3S+3C0sf78JUzD1JPdHkNo/sTKrheL5Zramo0Ow/11j21UOPWIkEQ8MADDyifqampUaxGPOiev7dr1y4sWrQIkiQpCVYTuRLnNUKtVquSAFWP/n707ds3ZD8KVePSSLBy1K/683bx87a9z3cPAwjUUvS/zkVTe2g6ehqHT7qVgH4GBsYE+IJ/Asx68wPcNbgLrv7La1gTSEG3Npdu2wAAIABJREFU6HMfrh4p4LOPGvD0TjcgdNZ+qXM3sGt+a3hui1mVF7RhAaHuczSfCRXny4PwzVgazmwWyHgSUXjNnDkz5HsXXnhhu0568OBB5Ofno6mpCcuWLcP777/fruMkEvXKVpIkLFiwADabzbQrhGwjFusj3xWk3uFjtMnA4XAEldeJtVxQNO2Kx4DiVMV/AMBbb72lxEk6Azs81eWOABi6sCSdWwbwT+7cdRUuSDse6AVVOIuS0eKhuLgY9913n1Krr7i4WBHW/LhNTU2orq5WBMPcuXMxadIk2O32oOSx/G+Xy4X7779fEaJut1tjLdVbwjqKfmK84YYbNBtA+P2aPHlyVImNQ/VDLhzVfYdjsYi4YOhwfBbY2ti9s63N7ccC6SUCMVia/F5yWw3H460+5IgWJTdWtPgC1jUewC8IgAUMHt3uWwA4gK6orJoH+eoHNK+fbNyN2VN/CU8gjkvD9aFjiA8daEbB2QNiaq/ZiDSm6OczdVxY3759M1bgGGGGcKGU5PEqLS1FS0sLbDYb5s+fj+7d41dcNV7wAYpPwnwSu/vuu8OuMonkEMnlw5NKLlmyRBFXQJvF0miTAX9fHagdq8BOpCtKH4RuFP9ht9uVYHielysvLw8VFRWayVa9209NTk6OMrknq3/rJ432WpQYY/jNb34Dr9cLi8WC+fPnY8aMGXC5XHj++eeVa+dxYACwb98+zTF4LU+n0xm021PdFu6a1pcaai/qfgMAp06dUs5vsViC4naMXOj8OOEErFro5uXl4cCBAzjjjDPQ9O0+nPT4sOdgMwD/b8AC/8etWsdbfTgjR4SMNssWD4hnDJDB0HT0FHwyQ++zOsX8G6jTSTDmF26nPAYmL0CTzZ7zoasOXoM+HYn9+/YCg8wpvMIt5GIREI7Azl91XjpegiiTDQhmDRdKifDatGlTKk4bM7fffjs++eQTbNmyRYkNMaNJNhsJ5/IxKgyt30odapOB+v32CJBEuaKMBgx9bEdeXh7uvfdexT0G+AUZFyM8MJ3vfAKgERcXXXQRnn/++ZT271hcsdwqxa9VvctPlmXce++9APyumFtuuQX//Oc/Afgnnddeew0zZ85UvqPPe+Vw+BNX8q38zzzzjMZdpy7UbST6Y11R6/tNaWkpNm3apPytj9vJy8vTxKypXbS8DiV3Wep3pqqF7gsvvICdO3fi410fQZJkbN6/HbjuDwCAT7c3oGTUaH8y00BA/UmPhBzRoli4AC7QACi5vmJ3OLbVgvT/LTP/8TySsfDCNcG5H4/0HQVBtMR89vPOK4jxG8nB5XJpFkytra1Kma9odySq++LkyZOVPHiA1mWc7hg9c0ZjZiIXxrFAmetVqGNgpk+frilOarFYMH78+FQ3sV3EMhGYwQwbDeEmaKM4FkEQUFxcHLTjUf/9jsZfJSoo1GjAmDFjhhLbwfusWmwCCNqpOWLECDgcDuzYsQOtra147733lM+OGTPGFPc8Wles3iqlh2/BB4BXX31V896OHTuUfxtZlMLdR3X9PFmWNaKmvStqo/OFittRp9bgOcbU+ZokScLy5cuVxQUvlq3fmcqtwppkuns2AWPvBQQBOz/chWEjR8EiCIEs9nzCBr44dBL5XXPR6pWUVBIWoWMpJdpcjQwWACc8Em57eUfE73Fe+1pClwdfw8kYtcT3hw8DA81l8dLvbgWglPlasWKFYu0Ot/lF3xerqqrQqVOnDoVSmJFQz5zRmJnIGM1YIOEVQH3z1Du6AChuxuXLl2PdunVps5sRiG0iMHpQEx3j0xGMJmh1gDIAxcLDJ5/169drcnIZme25q4a/H6sYTURQaKgBg5+Lu570K1heCohbhbZv346PPvpIEzuktuaq6/6Z3brLrVJ6salGkiTU1tYaxrIBbfFseosSEPo+6uvnqXN8dWRFrT6fkYtEHVemzzFm5EpS/9vtdmP69OmaJLtOp9P4d/lkAzB4LIaOuASyzACLvzi2LPtdi1JAHB040RpwQwZivyB0qGZjW2C9n8Yjp2I+xkkW+5T22cc7ccWlI0216FRbVfXw1wRBUMIJjASEvi/yFCqPPPIINm/eDMDcscrR3o9Qz5zRmGmW3ZIkvAKob56+7AifvEMlHTUzsUwE6s+63W6l/EQ6pM7Qx3Tx3ToAsHDhwqCVob6EUqjM/vpaaKn6HYx29akzTqsHGVEUMXr0aMVFDrQJUHVma44oivjb3/4GwC9muFtWXesyVYRKJgoEJ83VJxQFAJvNhh49ehgKM5vNhuuuu06J64oWh8NfkN3r9QbFyMRjRW20AOLFzQFoxifuZnQ6nZgwYYIm7QQPyOcirb6+HvX19cp9dTgc2LBhgzLmDRg0BBdfcT3ePtoVnwhd0O3HQyAFfjaJ+xLRlsHeAiiB9yzwfnvcjAeOu3H0tDdQhJsF3JoCfEbZUxNAly5dsHfvXtxzzz0pf845aqsq4O+r3HrN7yu3ZPt8PuzatSuovUZ9cdeuXRort3qHb0eJp3CNxWAQblFq1kosJLwC6G8et/bk5eVh+/btSjb0dAtG1E/IvMyKUcdTfxaAMkmbXWwaxXQBbVvt1Tm6+A63UPXL1Dt+PB5/LbRUxwSoBzQeLG40KOl3B27ZskUZqC0Wi3Jt+jJBsiwreaHU4iVZ1xtqwDZKJpqbmxtU5spu9yfNfeSRRzSTCgCMGjUKL730kua1kSNHYtiwYcouV7fbjeeffx7z589XUthEQp2cVU08VtT6BdCTTz6p2QjBF4P6WnyiKCI3N1ezueCrr77Ck08+qTm+2lVdVlaGnTt3wuOT0fv8QRg1/ibsq2vEJx/tx12v78Tau0eBMX/urBNu/47FLjliIJieuxcZZDBYmGBYxzESe4+e8idPZf7yRJLM4BMkeEN7keOGsH0Vhv/XTWhsbOzQcx5va1lLS4smHnPKlCno27evsgCpr69XRLYsy7jvvvsMkxyrd3Q7nU6NMAe0LveOEO+g9XAGA/1vHe6ZM4PIMoKEV4BwN8/lcmnKjcRCqs3XaqvAkiVLsHDhwpA7sfQWBO6GMLvY5A+pfjdiXl4eampqlNi8NWvWYOHChUHJQQEEuWl42Q19kLPD4UjqPY0lQFQd/J2Xl6ekDQDaMtkbuS7Uq2Eu0NSvJ/v61NdhlExUPylya2dra2vQ8fVCTBRFJcN7ZWWlpiSLfvIKZW1zOrXJWfXt6ehgzxdAvG1ffvll0GcYY0G1+AAoVl7OU089ZWjhVN/XHTt2QJIkfLx7J7r9qAC5Yr7ynleSwSCi1Sdhau1u9O7WCc/81xDIjEEUhEC+LeC0V0Ynq3Gm+UjIDBADAs4rB9yNMkPjZx8j0elNx193A3r27oNunWzttlQmYqecOrGxLMvo1q2bJkSCbxzhyLKMmpqaiFZ8/UKhtLQ0LuNZLJ6VaNAbQnj+xVCbCvh/6iSxZhRcHBJeKkINmE6DvEjR3FSzbF1VTxbh0i/wh69v376aOAKjkjNmQm/VmzBhAgBg2rRpigWHZ6TnlhN1ctDi4mKcf/75+OSTT5RjDho0SNnhpw5yBsLnT4on+l1NkQJE9f3tgQcewFNPPRUyvgloq+8HAL/5zW+Uz1588cV49tlnE37fww3YDocjaKKwWCyaSTFUsehQTJw4UTm+enID/OKUJ1ANZ22LhzsxHHwBpC4HZUReXh4KCws1fb+5uVmTLNbo3vNJGgAaGxuVsU2WJHz0wRZ0HvFz5bO+gBDyBnyO+461BjLa+y1cPAWET5Lhs1gMayuGwyfJfqElMMgAvD4ZokXAZzsasODPvwdufSq2A8ZIj169AQAFBQXttlTGW3QAWosXAMydOxc/+clPlAVAcXGxJp2M1WrVpM5RW/HVscqiKGLSpEk4deoUSktLUVhYGJfxLN7PhNoQohZb4SpqmGW+jQYSXlHgCASuyrIcMpDRiEQ8kO0l3INhFFOi/my4kjPJJNTKzOghNdrdZ7PZAPgn79/+9rfo3r07jh49qhFonE8//VSJm1AL8mTVNzOa+CMFiOr7244dO0JO2lxIcOvPvffeq1jHAOCjjz4yjBuJN+H6pd1uxwUXXIBPP/1Uee3888/XtMnpDC4WzTcM6F2qFotFSTLLdwaGstCEs7YZlZziq/F4bEbh/ZxbW402DzDGlM0i69evx5w5c7By5UosX748otVJlmUsWLAAS5cuxaxZs9rc0BYLho3+GT6xtJVwkxjg9UmY9fbnbd9XBcAz+FM+MOX12JTXR/uOKcf8eNuHqNu8CYUldnzc4IIUZsdqImivpTIRQpzPOXzHqc/nU1LH8F32gH9BOXHiRPTs2VOp4hAudY4oiujZsyeKi4vR0tKiVG6IV+3YeHkC9AXh9fHXRjkWudU7HcJjSHhFCQ+uj8WUnuiVcSyEezD0E7a6gLBZTLaRVjORdvfl5ubiwQcfVAo/z5s3D1VVVfif//kf7Xb6AIwxTJs2LShuIln3VD3xh0p3ECmYtqioCOvXr9dYPQRBwK9+9SsMHjw44r1dtGhRu4RELK6LSAP2wIEDNcJr4MCBQdesLhbNxbXP51MGaR7zdMMNNyjfcwZ2jamx2WyaPF76QtIAlNQRateGWiDzCSHaTQmREjxWVVWhtrYW//73vzXf4zsVKyoqUFpailWrVoW1bHLUG4Xcbrcmcz9jDGvfeAVd7aWa1/Yfd2NX83HlNZ7ygZf14bm2PD7ZvwsySr48fFJJvrpraz0evqMUXo8Xb9hsuO33FRBtOQh+MuPHPukMdD0nH3ldc9CrW+wJXzmJ2Clnt2trMFosFs3GGH6vRVHEyJEj4VDFsvLFsj51Dg83qa6u1gTpx6saS7ziqSIZAox22/NwIP77mD08hoSXDqNJo6amRllVe71e3HzzzXjsscciBuIm4oHsCKEeDCOLntmCEkNZD/X3Sy0+uNuxZ8+eyiCkNlPX1tYGWUTUfxu5lZN1T/UiSi26eExTc3Ozcm36IFNu+ePxavn5+Th48CAAYNmyZbj//vs1bS8rK9MkVwSAbdu2YevWrTGZ7dtj7g/X1x5++GGldI7NZsPDDz8cdM+dTqcm1cLChQsVcXHXXXcB8O/QXLlyJdatW4eqqio0NTVp3JiCIGDKlCkaYavfJShJUlA+LL1ljAsadc3HWH4ro0VQRUWF8rq6vbIs45133lF2JnJEUUSvXr3w7bffas5nsVg0QlaWZZw4caKt7bKMN19aCvHDPcD1MwLXg6AwK4mnfQhYvjw+OWDpEuCVgX0/tMInMRScrauXqOPoaY/imtz6/mZ4PV7IsgSfD/jmBw98N/9/Yb/fUa4deC7O6d0PffO6YMC5XToU6xSP8VJ/fj6/LFq0CJ06dcKWLVuCFlGRrOD6BbbP59NYcc1YjaU9hgCnKjVKOoTHkPBSEe2k8e233+Kee+4BgKjEl5k7ACfULi2zYGRpirS7j7t++Hb7vLw8TX3CoqIivPvuu3C73RBFEWPHjtVYFkK5lZNxT/XX4XQ6Nb+FehJWp31QW/7UeYDUlg2j2Ig5c+YEWQm5JTAasz2fNJqamuLqirXb7XjmmWdQW1uL0lK/JSZUEW3eDvXKv7i4GLW1tUrm/tbWViUwmVvEGGPIzc0NcqkbpZkwikPLycnB6dOnY742o8WEvp/zez9v3jyloDm/JnX8F+/XfDcjAGWM4thstiALIgCNS4sxBt9prXULTDsmdMkRAxYvv2tRYkwpHSTJMmTGcOS0J6LwklWZ7otH/QxWmw0+H2C12nA4fyhwNOafNCaeu78UP17yOn5ydeixJFkYnR9AUBoR7ka3WCyYOHEiHn744bBWcDW8b6mts3y8N4voAowD6yMJYv13zBIeEwoSXiqMBkKOkdth0aJFUW8/TyQd3ZXCV0LcNx7Naj3ZGK3oQsVb8barU0wIgqDkwgH81ou5c+cCaEtE+Pbbb2vOWVxcnNLfQX0d6tQX+pgmdV/lv48+DxCHJ1zkaUX48UMJh2jqVeoLPMezkDyPxfJ4PNi0aRNuv/32sOV6gOCi6OqJRh8uUF5eHnK1X1ZWhiVLliguSXWcHYf3S74T2OfzaVyW4Qg1wVRVVWH79u1obm7Gb37zGyWo+Pe//z0mTZoEZyD+yxmIb7NarZg3b56yyOBumEmTJikWO24F0NO1a1dcc801eOutt9p+l9a2xKUMwCmv1uHXySq25e1iAg4cd6NLjoguOaJS7NonMZx0++CTGc7qbDO8fsbaimsPHlaC//fcq3hv6050+XEhVu8XgXbkBIsFn9eDj+rrMGncFSEt6ski1NyjL4Ol/ve6detw7bXXRj326/uq1+uFz+dDdXV13GqOxgP1WH/06FHcd999kGUZNpst5H2J5IlIdXYBPSS8VBgNhHrrgprevXsnuYXBxGOlpo5nYYxhyZIlpsxarl/RhYu3qqmp0YgJLirVqAc1fu1qpkyZEucriB39gNzc3BzUTqNafUVFRZpdUZzLLrsMH3zwAaqrq7Fw4UIMHDgwKNYJaBOjgwYNwoMPPhi2L6jbCKDDrotQgbX8+qMt1wO0ZXi3WCwYMGCAJjWDKIph+7ndbsfGjRs11lN1LJZ6ILfb7ZqYmmiu28g1rA6GVscqMsYwZ84czJ07VwkJUKdPKSwsxK5duzS1O6uqqrBu3bogKwCfdG02G4YOHYrGxkZtP5FVkz1jcPu0fejT7R/iwuIRATckw0MrPkHXXBEv/3oYpIDvkIHh04N+y1lJwdmG18+tXR9va8DqZa/ghFvC+n6/AL7zH0FDwxtAyU0Rf9NYsNpyUDjiEghIfTyuPkSiqakJxcXFmvhFNdylHY0oUcNd83oRZ7ZgdN6Oyy67TBlXIhkFQln8Um3NNMKS0rObBJ77A/DX6ps1a9b/z963x0dRXu8/M7Obi2iMpmiKEimI3IwkEIILGmJRFBVNSbWt2lC5hASRhoup1tJitaBBJXJPIFDSKmgbDSCmitEQIMs9YAooCMVYMf1qJEaEJLs78/tjc968887MXpKQpL/yfD5Rdnfu817Oe85znoPS0lJUVlZaGl2KorAMqc6ELy+dL9A9k5jqxIkTmdvZ7XajsLCQ/d5V4XA4kJubi9GjR7PsPECvuxYoxLqO2dnZXcKbSQMylf4BwDKaAK8cxIcffqjTc2pqasKePXuYp48gyzLCwsJY2M3j8eDIkSM6om1iYiIefvhhxnc7cuQIZsyY4bMdJCd7ldwlSYLNZkNaWhqeeuqpVhtdo0ePxty5czF69GgWHqZjR0dHs/v3V64H8BqlJCw6fvx43blmzpwJAD7bOfEGRaOLv0ba1+FwBH3ftA///lwul2mCCOBdLND9uVwupiVWWFiIadOm6UKqlZWVuvHM4XCgqqpKV38WgNHw5iZlTdPgFjynM0tPo+rA3ubfvd+dbfRg2c5T8KBFyZ5qOH7XoPeYaZqGb8+7oGoaPtq/F9Mfug8bX/szSov+Yv2g6v8P+Ljc+vcgEfvZO3hu9Rvo0T8OstxiBPPPqiNB558yZQo0TUNeXh5mzJiBJUuWICMjAykpKQgNDWXvjPo1LRjJKPEHvqQa3486O/nLDKKB2JbjtGaOvJD4n/d4mVnDJFRn1ZBJNbqzrWbAeqXmy7Vqds9paWk6hXdeE6ar1mwUw1CxsbEAgHnz5plmKgYCSZIwdepUvPDChSX2BgoyLsmT8c4778But7NMPfIKifw1Qv/+/XHy5En2LlNTU/HBBx8YBjTKmgTACjATKHvOrJ4hAFRVVek4Qm2BOEhWVlbqvD7x8fEIDQ1FY2MjZFnWebzMeB68F6qsrIx5ASVJQn19vd+VcDACtrR9a0IaoseD0v8B6N6F3W43lH3SNA0bNmzQbUee6/j4eN29PPbYY2w7l8uFU6dO6fh/AAC15TihiszCgQyXR+Pg7p1IHH6zTj7iH598hcnDY5oLB7XUbfzkq+9w0w8vR4jNO9HXN7hx/Ouz0ABU7toJN3mer73R+gFJMvDOi0D/JOttAsV/TuDYlnU4e89dcHlUyJL3ujqbj+twOHSJXI2NjSgpKcFbb70FoCWpxqo8lhnE0mjJyclwuVxQFAXp6elMVqKrje2AecayGMI362++Eq66ioH5P294+RpE09LSmDYKQZIkhIWFdRnynlls259r1eyeeW2i6upqdt9duWajeB+FhYVYt26djtOjKAruvffegPSNAO/75ScrQmdwBHjCOmVjejwejBw5Ejt27IDb7UZOTg4j3ZqtDkeNGoWCggLdtZ84cQI5OTlsG7vdzowuq9D6+++/j+3btxvevziZk/eltc9KHCSBllW9y+VCZWUlHn/8cbz44ouGLEOzhITk5GS2kKqqqtIZcUeOHPGr+2PWV3wtdlob0hD7MZ27rq4OCxcuZOKXS5cuxbPPPqvLWFRVFXV1Ria6y+XS9d0JEybo2oiiKOjVqxcaGxtx4sSJlh05wys6IgzLKj4zHHtggoOp1vMgL5estXjDzPS9vMR6DXHDR8Bmt8PV1AQ8ON/6AXHaYm2GLMPV1IivP96LK0OB/bXdcCy0a8oPbN68mUUlKEzIV9ggWBklfHu88847Wd8mo74rePWtIGYsi7SA/Px8XWidEhKsEq4C8Qh2FP7nDS+zQZRWFoBX6ZombUmScMcdd1iu/DsL4krNlzEJ+C4q6nA4kJ+fr8ueIfXjrsYDoPtobGyEJEmoqanRZfLRZEX/5kGGChlnNLmTMKVYOqajOQIiYZ0H1WAkkFEmgjL1xPYRGRmpM9TI0KR2w6Nbt244d+6c5fsXwwGSJOm8pcE+K9F4Kikp0RlLVMqKztnQ0KBTmydj5Q9/+APT8qLsr9raWt1754uIW+n+iG0sKirKksjrr98Fcu/i9rfeeqtOu6mkpMToobIAKdfTu6upqWHcMDLiAGDXrl36HTnD60/vH0fZiVqI0JolJTyaaFBR8oLU4ilTjZnSFIocEJeAxa9uxJaiDXjb181w4fU2Q/IuVLr/sKf3Y4C7dcTiS1zsa5rGQmPUJ8wWWWbZ6GVlZbpElGPHjul+P3DggGXd3q5ARufnYTOjKzMzkz0HSrQBYNkHKaLTFRIJ/ucNL9Earqqq0qXwktsfaPEMdBXDwwr+XKu+MkAofEep6bNmzcKSJUu6hJvWjNDMh+Hefvttw4Dkdrtx+vRpw7Fo8n3iiSeQkpKiK88idtjWTKjtkWlK51RV1SCc6Au8mnVVVZXhOpKTk1m4TlVV7Nu3j/HkxEH9+++/B2DNA+GPJcsy7r33XmzevLnNxYajoqJ0/ZAghlg0TcOqVasQERGBJUuW6Axvel7FxcUoKSnB4sWLERoayrIE/ZXFomsZP3481q9fr/OwmSGQkIaZYKpVOxGNWgA4ffq0bhHRt29fHD9+3HAeSZIwcuRIxj9TFEVXSmjWrFlIT0/Hs88+a2hTsuQtgA14w4dm+GP6z/BiYRH6Dx5m+I2XiaDPX9Y34ocRoQi1Kc3feQOSKoAbhwxD35uG4u28XYZjEXrfOBS9Y7rhfcstTFB9CIgZbPha+uZzZD+7ED+I7sGSAYDg6RkXYh5wOByYPXs280iTN5NfhI0YMQI7duzQtQ0zTzOf2ayqKj755BPdufbu3YukpCQsW7as08qiWcHpdCIpKYl55latWoXly5cjPT3dYHQBeukfsz7Y1kVRe+N/3vAiUIhKTDcXeRP/DfCXWkvbmMXEqYGSYRIZGdklRGCtBr7a2lrm8SGvpFgsOzk5GXv27DEcU9M0vPjii+jTpw9SU1NRWlrKSNz8pBksR6C9Mk3pnGKb5EmxI0aM0BWClmUZs2fPNhghdrsdS5cuZVpQpIjOG5uVlZVMSJeHmXI+wSxExmfSBWOoU91F4q8FSqz1eDx48cUXAZgXAQda+DKi1ISV7o+oSM8fh0La4vsNJKVdVOQmuQvS36LQz29+8xuDoK3dbsekSZNQVVWly36jheHgwYNx+PBhVhpm9+7dbAF19913Y9OmTawtLVq0CCkpKejVq5dOKkeWZdycNAoVfp652+3Cwd0VuG7gEN33KporfEBj4qiypOGrs40IUWT0uJwMrxYSvgagvtE3J/PXj/4c9Sf/ifc/8nNhhH8fBt7OAaa9qvv6eqkWs3/1YwxLHI7dO7eDHEWff/45pk6dGhQ9Q1ywtmWM5PePjIzUcREPHjzIzu3xeLB9+3Zdgg2BhIMpo7WgoED3u2hgU2LGtGnTYLPZTOs8dpaRUlhYqOPpejweZGZm4sSJE3j55ZcNwtczZ85kz8+MekMJBUD7yNy0FRcNL+hdsr7gdrs73VIOFIESRf2VZ+C9S50Fq2LRRJzkVfdJDFJRFEycOJGRq60mclVVMW3aNBZSpe94BGLI8miP1RV/zrq6Oh0na86cOYiMjNStTikUNnLkSNP27HK5kJGRwSby0NBQLF68GNu3b9fxqcyekT9Pr9g+WmuoU904AKwmnQjKxhMnEUqpt7oHACguLmbZmxMnTvSZNMIvQMTzAy3hjPPnzyMnJwdvvfWWzltHYQ9fk3dRURF7T6qqshJVxcXFuvcNAL169cJTTz2F9PR05p0gLiZRAugd0jOg8LkkSSwblO6Hr8owYcIEfPTRRzh79iycTiecH24F+hs1v3jYbHb0H3Iz5mw+ovuetLno/xIkeDRAbbYTTn/bgC/rvbw62k7jinCbYd3PB+Ozwwfxx/QHgMf/7vO6GD7ZDjR8B+X1bHh+1vIsfzIqEcPiWmSApOa/U6dOtYqeAbR9oSXuP378eF14PS4uTlezk/5Il5CMNOoTDQ0NmDZtWkDecQA6rzqfDdwVohw8VFXFiy++aFiEzpkzRxeVoQQ5p9OJzMxMrF27Fi6XC5IkGURnOwsXDS94O5XZxDxgwAB8+umnLLzRlRohwWqlFegKTJwMulqdRl/Fogk0GSqKgsWLF5tOphRiAowrP5qkCC6XyzAJ97M5AAAgAElEQVTwBmN8BushswKd0+l0YtGiRUx7KSUlBUAL52PChAk4cuQIduzYofN+ieAHLPJwkQeIPD7r1q0ziKkOHz68U3ggmqYhKSkJV155JQuT0eKgsrKS1ZwjUOFzvmLBK6+8giNHWowDMtzz8vIQFhZmOUmSQS+2ldjYWKSlpWH16tXsu+LiYjzyyCN44403WMYhFSHnjy+2CzHDlIyhN99803A9p06dMnAPCwsLdRxM3qvLv2uS+IiPj9eRkZOTk9HY2IiePXuiZ8+e2L59u/f6Jf+e/d+u3ICw6wbh+IF/Gt5Zk0dDiI3I9RRu1PBlfQP7t4YWb5e/GriyJKH87b/D7WoCXn8K+NkC9ltqbDTO/esjlJy9StjJa+lpNcchQ4XarJykO4/UfHmS17BtLT2jrQstfv/GxkasX7++5RKbow65ubnIzMxk168oCu655x5WXSE/P1+3jy8ngtnihTfexWzgzpgDRPFi/jr5Kg2zZs3CwYMHDYty0rQTM4C3bNnSJWSgLhpe8HaqZcuW6VYJiqKwgc6K4NfZEAnY5OEBAo/R85OBoijYs2cPqquru8y98p4Hs5BXWVmL6r7b7UZlZSViYmJ0xxC9Ry+99JIhU1Uc+HmZAkKghkawHjJ/4FPMPR6PIXszUPD3KcuyjgRP77u0tBQPPvigLmuuoaHBcCyrVX5bVv9mWcQ7d+7ElClTDAb1ggULDO8sMjKSZTASKisrdYYXwVcmI+B9hxMnTsTKlSt133/0kTfWde211+LUqVPs+9dee013PTQJiLwbs3YhGkOih5PAaxCZhUGtQIYznY/KL1HWGIGMj0aPCn9HvT52KL753pj9euzQfnQbNhzdQpTmkGPz82j2gMmSxP5NoUi3qmLt3s8tz6XIEgsJ4ovDQOM5IPQSAECPy8MwYXIqSnK363eS5GbagB2aJLNzhYe0JKpIaFm09ezZk4Xf6dmIsFp8mS20glmU8PtTQgSBaA+FhYUGTtemTZtYWJHnWc6aNYst1Mxw3XXXIS4uTleHFIBhbO3M8d/hcGDx4sWYPn06uw/yVlP9XbPKFJIkoa6uDr///e9NJYW6StTqouHVDHLh5+TkYPPmzdA0DY899hiGDBmCSZMmdXrarVlH5ldKHo8HeXl5WLduXUAxev54fBkJ6ox8/b/OhGgY9u7d2+fvVLYlJCSEXT+f7bZo0SJTrgMPSZJ0wpxA8OGE9grPOp1OrFq1in1WVRXl5eV+J13KXuO3Ee+TxDb5NuJwODB37lxdnb+wsDBD9hMfzuRL97Rm9c9nL/385z/XGTEejwcrV65EaGiorj0ePnw4IGM5LS0Na9asYdpF9957r8575ssbaSYroqoqcnJyUF1drfveLGtW1MMT60oC0IUO6+rqMG/ePKSmpiIpKcngvaRJ2CoMSoiMjNTJS+zcuRNOp1OnbM/r3hF69uyJ0tJSvFpcgmV+ZfA003TA52dOwh+W/hm3jBjRXMvRC6+hJUGW9NwuTdNwvPYc3rUg8QNej1fyfQ+gbNPrcLtcOmmKS+wK/l1nUu5KktEvLhHX9O6LiGslvPW5hlvkalx3PgRH/9MN/a+61LtZ89/nn39u0AQMtP+a8RyDXXyQ5zkiIgKLFi1idImlS5fC4XAYpBDIS9jY2Ija2lqd0RgbG4uXXnqJbUu6f4RTp04Z2m97JI61twec+LuAtz8lJCTg0KFD2Lx5s6EyBRldqqri5ZdftlxYa5qGw4cPt/na2oqLhhcHh8OBxMREbNq0ifEu9uzZw1z4nWV8WU36ycnJOtFM6oiA7xi92fFiYmJ0K4SukPkB6LNO165di1WrVunSgfnfy8vLmXejsbERWVlZmDRpkm5VxMOKL2T2zHJyclgIriOfTWFhoeH6zDw4PHr16oWYmBjs3r2bkfNFkKSAWS3G9PR0nDhxgulHlZeX47bbbtMZPmLGFBk9wYZZiVBvph3Gg0jtDocDv/nNb/Dqq3rSNBnLZoP/xIkTAbR4rAOdIGpra029oWKWrNiOZFlmYwV58Px516qqqvDb3/4WAPDee+8hOzsbFRUVrE+KmZd8sWMedrsd3333ne47TdNQWFiI1atXs+NZ1bl0OBwIjxmISytP44UPP4UVLrEr+BrGd9Y0+c+o2luBkSMcLJwItPC+vKR7jZOdaNnGCrLklZ3405oiVO7aieLwbjjvAaYMj8HdA67G53Xn9DtUfwT8cyuOexpx7NA+KJvfgB0SKjxu7F0agieXr0e/0c1CrJL3P/44Xv7AG9RWNWTNIEYtyONFGcJkHJstAgDvAouXT/nggw8waNAg3ZjhcDhw+vRpXbkssd3cc889bTa62jsTUhxLhgwZgn379rHFHtAyzwHQZX9Tf6QMb9679+qrryIpKalTnSntKI7y/wesiguLGSIdCTMvAkGcFFRVRXx8vM/yF/zxSAcpKipKdyzSLOoKcDgczDC0Kvuwbt06g0GyZ88eZGRk4Pz584Z3qigKpk6dinHjxum+HzhwoMHTl5+fr+u4pBjfFWBGQD916hTKy8uhqiruv/9+Rjrn97HZbEyM06yNREZG6j6Lz5w0sQB96R4yhPnj8uWpRJSVlQWswk26Q2YcKAoxUCmfpKQkDBw4EKNGjWLGOsHhCKy0T3JysuHZ0fdUvsVms+Ghhx4y6KoBXkOPShb5M0KLiop0n998803MmjULdrudlXriMy8nTJiA9PR0jBkzhrUBm82Ge+65x7St89dF31ldjyQBNj96pePX7Uejy9zjdknElV5jShOzFzXd/0nnq9HtmwSuyBJkeI2v1MmPI33EjwAAPxv8Q1SfOQdZljCxN3ctm/4E2d0A1eOBqnpLMLlcTVA9HrhdTfj4gBNNbhUSWiZACrNSaa62jH1kMPh679QncnJy0NDQoCsVRZzTjRs3spJUlI3Mw2azYebMmXj55Zd15aMOHTqk2+6rr77CE088oftOHDdOnz7dpvJwvuao1kIcS+Lj43WLPZrnpkyZwnQngZb2Dnjv84YbbjAcW+xvHY2LHi8BVqvcyspK1jA7mnRo5UUoKyszeENoEvQV6uIHFSotQvvy4qMimbczQcKBmqYZeBTV1dWWHhOzMKKiKCx13+l04p133mHE9dWrVxvuVzS6r7/++g57JmlpaT5LhNx6663Yvn27qVfL7XYjMTER2dnZKCwsRE1NDbZs2cK0nHy9W3re1L7ECYSMD54bmJmZaRBs9bcSJuPGn8cLANMbGz9+vG71Dnjf80svvaRb9R49epT93lovRllZGbKyspjXW5ZlncRKVFQUHnvsMcPzr6mpCYrrl5qaivfee499PnHiBHJzcxmBmvfW0fPkywoB3skoOjpa9zwpXBUbG4t169ahoaEBkiRh5syZBo4X4H1f6zf9Aw3XJfp9Pg0WhtdfXpmPuJsGYUDcMMbzIkV78U8zaNoboUgSIDUXbpcljOx1BX5yY7Q3a1KSIENCTDcATeeBkHBIioJhN4/C7vIPvAfQNCg2O6CpsNlDMGjoCHxG4UnJa2gSx4tCsW0Z+/y9dyupEpI7oL7O12Dctm2b7hgDBgzAqFGjUF9f77c82qefforY2Fjk5eWhoKAAPXr0wNixYzFjxgzmOdq3bx+Sk5MZTzjY+w7W0+0PvFeaeJtlXHa6JEkoKipCbGwsYmJidM/xmmuuYfxL4nSJSE1NbdP1tRUXDa9m8KngZhMBT2oOxJ3anvFuq46cnJxsWLnY7XZmlJglBZBAqkjUBLyZf3zKclcJN1JpCFIiv/POO1FVVcU4GYqiwGazsdT5kSNHYufOnQajNCUlBYmJibpnSJOPr3fVo0cP3ed+/fpduJsV4HA4sG3bNhQWFmLXrl04ePCg7vcrr7wSNpvN1DALCQlh0gaUpUT8RV8kU2ojFI40S8HmQ7wiN5AnwvvjfNHzLywsxIEDB7B3716D5/WSSy7RqecPGjQIDz/8sI4LxqfTmyHQOqZmz58KsYsSK4C3LqjZeTdu3Ij8/Hykp6frzmF1bgp7LFy4ECdOnGD9b+PGjTpvV5kf6RvKSDPr+7xhsWTJEpYdS+C1rHDNQOCnf/L5bP6y/9+m37tVDQd3V6Df4GFwqypkSYKmtaQQkrfLo+r1vqzg0TTYZRmy5PVQnfeoqPmuEVddGgJZBj79aD/+8uI84JvzQOyd0BrPYc+OMt0xbrv/5+jXpxduHDYCV15/E2yKhHp4+WMEXhOwrWOfr4WvFUdv0KBBqKqqYp+JJyguvBRFwcmTJ3Hs2DFTj7cIl8uFrKws5ObmYvfu3ez72NhYg3A08YTbUnGirXOe1WItKiqKcVc1TcP777+PsrIy3H333bpFoshf69GjB8LDw9miY86cOZ3O2b5oeMH4opcsWYKCggJdeja5LwOJ3VtlG7alMVp1ZHGSevTRR1FVVaXL0OSJ8tTpRZFRfsAmgrqvcERHgWoB8gWDi4uLdWKQgLe005YtW+DxeLB3717Mnj1bl71ot9st9Vv8EeGzs7OxZcsW5hXryHRkmqgjIiIMRpeiKIiOjtYpsN9///0sxVwUCeU12kh806xkCD8xyLKMc+cEDk0zqD3xK26xtqeZLpzZccy8OaRDRer55K2kY5jx88yQkpLC3n1r+qbZpMJ7Law4dJmZmUGVnoqNjcXtt9+Ozz77zOD1oLHGigoBePXdfGWk+TMseJ4Tzp7x+1w/rTVvF7LdjhsTHNA0DeddKsLtcot8hNYyZqmaBreq4XyT73dYU9+I664MhwRvdqMstxgbxw8dwLPpD3ilJgDgy08gyTI04RnF9BuEux/+Fc6cb4IsAaGK9xh8EfD29tpYgc4jyrYcOnRINy7ffvvt6N27N/Ly8nTb9evXD5988oml1p0Z9uzZY+BpOhwOzJs3z6AR1hbtwfZYpBcWFupqqBYWFjKOL3/P1I43btxoeA6k7RgSEoLs7GxkZ2d3eKTKFy4aXjDXsqJVLqXo8u56f2nD/PH4bMP2Lr1QVlZmEJOrqakxiOeJgqP85Gs28RQUFPjV1ukolJUZy6YA0BkbNpuNGSDUGevr6zFy5EgWhjNTeg4U5HXqqI4rZmGKWjSE2bNnIyUlRdcmeeNSJPmSRptVogKB2gh5VqwKZNO2JFpLoOsNVheON3D27NljSHcnr1ZVVZXhGnnY7XbMnDkTBw8eRGpqqm5129q+KU4q/jxPgLeN8hOYL+8feXVFz6WoW8fXm5RlGffddx/OnTtnuE+zccnMsOB1kngtK8l9Hn4TGy1w850/QZ/Yocyw8qgUVmwh17tVFR4NcH52Bi9vO+nzeN1CvaTzowf3onL3TvSNuxk/ujEeX9Y34uh+JzxuwduraZAVBWrzGChJEk59XIV1y1/GwIQRuHFIoneilijjzbsbeTd9SUpYIVgvKu8tJk++GHakwvX5+fm6/v+DH/wA//rXv0x1CcXKHaJ2n5nHOTc3VzdnWNUt7Qg4nU6sWbNG58kW9fqIW0njjDhX2e12Sz1HCjt2tvF10fCC9UqHF5ekFyXWdeQ9Cny2YUhIiG4VYZVF1NbrJv0Wyqyigt48ZFnW8brM7ovAa0a5XC6DKndHrxjEezSTSHC5XIiIiNAZlKIelJkoajAIZjXXlmdlxf8wQ319vW4QB6CrzWjWrnkvFQk2zps3z6DfU1pa6rN+Jf9c7r33Xp2RRKndral8QNvNnTvX9HePx4Pp06dj27Zt7Bp5bhTgFVJ94YUXTPc365utWeGLniczXiiF/cVzi+OM6NUVkZubqzOeiFsnGtoEK8+ameeOJiKgRU6isLAQX3/fCFEj/o6+P8DW41/7fTYJP74bLo+KhubbUdnk2MztAtDg9rq/Dn1R7/NYd37+N5QUVuDqH0Qh7/m5cDW5YLPb8eTy9bhhcAIGJTig2O1wc9QQTdPQ76YEHK/a31zRwoZtG1+Hqnqg2Ox4ds3fETd0OOxhlyA81I5LuoXg0jAbC7GbSUqY9WmenmI2D/gCvQ+iANAx+IU+HeO+++7T9a+BAwfi+eefZ/vxbYd4sDzXkSDOAwReOseqbml7wd/YWCbwls0y0Um7bMWKFTovIX/9tAihJIbWvKMLiYuGF1qsfiIeigYVn01UVVWFgoIClvJr5rqnAS4nJ4d1GD7lvj0xYcIE1NTU4PTp09i3b58pmVxVVWRlZQGA6X3xnUHE5s2bkZ+fjxkzZrD9OlLfS5wsSIuI9wxQKYn77rsP0dHRqKmpMXhL2jNsGkgxXbP6e4GAD/MFA15QlVdNt+IG+vNo8WEIf6EXCm0SVFWFzWbTGQzBwExCgzdsSOH9qaeewrx58/DBBx/oJh4xI5MHb6jyGlvBtg0xy4y8qr/4xS9w/Phx9OjRw5IXZ+Yh9/W++YkxEC6NL89aIEYw6Z5h5k903x977Xlg2GSf+wJAwtChTCRVg9frJcpHuDwqFEmC5McR/cHG1+FxeyDJ3nFMU1W43cDHB5zoH5+A/nEJmPLkc1jxR334v2fvG/CrWU/j8D4nPv6oEns//AcAwO1qQtmmvyE+YTiu6jMIV10aipgrwvHDiDBLGQgzQxaAaVg82AW2+D7I20aJP2VlZRg7dqwuAUhMYKmsrGR1Pc0MLgLNA2RQEg+4pqZGV8dQrFvaXghEciJZkEgScf/99yM7OxvFxcU66gVRgUJCQhAfH28wttryji4ELhpe8DaIxx9/nLluN27cCMDIrxBXplS+wEwLyeHQa4LxKfftdc28Z4Qvqsp3PPp3Q0MDFi5cyFb61PgAvdhfbm6uzu2tqioWLFjAQhK8nlJHgR9kHA4HysvLDTpOqqoyIvKdd96p+02SJN0KsjUIdHXLh6D4+nuBnlv0yPgCafuIxhq/EDCTTQjUoxXIJA+Yl/fQNC3o9k7P2Eyn7NZbb8XOnTtZTUbqaw6Ht+oEn3xBCxwrA1n0NrSXF1dVVVx22WXIzc1lvBQ6n3huHsnNmZ1ieRTA6DWzOoZ4vNZylfh6mSI+O3YEGOb/GGF2pSVjsdnLdc6lIrz5eyLV13zXiIpTvrlkbpcbmqYCmpffpzWr0Q8YOgIyvJ7Vs9+eQUv9H0BWFCTf/wAGxg/DjUOGYfEf9EaZ1Kz8apMlKJzhZ/XczAxZoIXvy4+5rV1gU6iZxG0B/SJ5yZIlpqEzyrSn6/Y1ZvDzGd0vvWu73Y4pU6YgPj6e/d7eY7yvBQEPK6NLkiS8++67yM7Oxmuvvab77YorrkDv3r3R1NSEadOmsYUQjcPt8Y7aE51ieC1atAirV69mKe1r165FWFhYZ1wKAKOWEC/CJnZAMQ6/dOlSy0K7fJjMys0bDPiJRJxsqaH169cPx44dM6x8NE0zCOhRxhuvQF5ZWcmIibQfXxoF8C/geSGRn59vMLoINLBER0czDgDgfU+iSncwEEnfvJdTLAmTnKyv+8kXIw4EvFG0detWZkxfccUV+Oabb3TbkmEjerDM6lmanSdQj1Z1dbWpEcEfa/HixTqeiKZpQbV3/hmboWfPnqxdipMLeRR5KQAAfkMLbSEDp6WlMS8Dj/z8fB0npaCgAJMmTfJJ4K+qqrIMM7Ym7BOowWyGmpqaoM5lhue3VGLmnYMBeKUaNK94l1f+QQOaPN5n84d3j+GcH2K9pqn0D/xs4jR0uywCvW4ahl43DmkOaQMRkVcCnCjF3Y+kY0BcAuRmcdTk+x/AB5teh8flDVOOvv8BAECIIqNPVDdcHubVarN6blYGmVjmh8ZgKyFfK4gL+sbGRhQUFOgI5rW1tbrCz4A+eSYQkj3xYWn+4Oc8OveFDMcFsiAQQ408eLL9l19+qfvtzJkzumQ4AGyRRtQHekcAUFJS0qmZjR1ueH3xxRdYvHgxjhw5gvDwcDz44IPYsGEDfvWrX3X0pTDQipMf9CmrhOe+iIbU0qVLfb48CmG2hzaM6KZ9/PHH2QRPHd5ms+HkyZPss8iH4kFK33V1dTpPyZEjR/yGub76yrq8x4WGL+E7PkMTAJsYPR6PgccUDHjjlAYvyrAzKwmzbNkyZGZmsu2tsgetQEYRDY52ux2TJ0/W1e8TvT68rpSZ2KLVefzpDfGrYl9lpGpraw3cjBkzZgTc3vnVsNkksnv3bjZwmhmzYsZeUVFRQKvr1sLhcGDlypW60kqAkZPicrmwcuVKrF692jTsnJ+fj4yMDFNPBYWVWnt97e6VDjD8vb9WxYcnvsZtfaKgaRI0qUVIFRpw3qWi7rwLZ84HJpwLeBfBl0VE4JHMLPzf901odHuYvMR3355hmYySLOPSyy5nRpcsAf0GJ+D3eX/Dx5W7cNMwBwbED/OOFTYZ3UJsuixJ8blROO7OO+/U6akB0NV/pb6pqioT8g3UgBGNDVVVsX//ftYmyFgS+yNfFkyMdCiKgtmzZ6OsrIyV/OKTdMQ5jy/BYxZqbQ/PcCALArO5WJZllsFMIVEekZGR+Pbbb03POXPmTNTX16OmpgabN29mz7m4uJjJvXQGWp/q1Qa43W6cP38ebrcb586dM+gkdTSIZJqSkgJFUZhStDhRU8N57rnnUF5eHtBLM0vhbg34iamxsZFlu0mShIcffhjPPfccJk6cCLfbzc53yy23MItfhM1mQ1RUFF5++WXd9zt27IDNZmNZgGb7dqSOFY/8/Hx8/rl1Md0BAwawQS4tLQ1hYWHMOH3//feZCnSw4InUmqZh5syZePbZZ9nz5knqdHze45WXl9eqc9NgSivTvLw8JCYmIiUlBdu2bTO0zaeeeopl3q5atSqgc9J+VlwhflXsq/3ytQED2V4ErYYVRdEpTxPGjx/vUw2c3z8kJASpqakBq8a3FrGxsabXaga3243p06fr3gd5L0Sjq1evXqbvuCMg8vUIg84fx1VBBCWa3GpztqCGTw7tw5sFS/DR/j0so3Fa0T+Dui5ZUTDEcUuzYCqapSW8Hq+bEkfAHhICWVFgt4dgUIIDsuQ1uqTmwGL/ZtX7AXHDQHaWTbb2EtG7GTVqFFauXIni4mImNE2gvhMZGamr4nDw4EHT0KQVkpszg3mQgcCT3cX+SCVxzPoM7b97927cddddbIHOL1rKysqQkZGBjIwMfPjhh6aVFmjBP3fu3FaPn2bPzKpd89eVlJTEjEue3hMfH8+qDADesccqxFpfX49169Zh8+bNhkVRZ6rXd7jH65prrsGcOXMQExOD8PBwjBkzBmPGjDFsRy57oGM8LA6HQ5e9R2E4+o3fLpjBsC18C6vjSJKk0/l54403mLLxmjVrmGdg9+7dWLp0KUpKSnTZjpIk4Z577kFlZaWhMaqqirFjxyI6OhpHjhxBQ0MD+vbti/Xr1zPXbUfqWBHy8/MN3gURSUlJOi5YoJl5/sCn8EuShPr6erzwwgtwOp06UjuR1Hv16qXb34wr6GvVR9lVxPnRNA05OTnIy8vTCSCaIVAeRSAQy0j5SlAQNcYA85qXIsRi7dT3srKydIKHL7zwAlJSUiyfm9lqmopPtxeHS4Q4ofbq1QtffvmlJdfG7XbrPK8idYEQFxeHxET/yvEXAlY1AY+vfRpps5/BumVT4Jq4ynQbHmfOufD8B5/iGuk7lM39OdwuF95cnYsX1hahe9+bAruY9S3jjOrxeEv8SC2FrWXJq2o/KG4Ypjz5LHa8+zYSfjwW/ZvDjKT7RdvK9H9JQqhNgtxsuIkgY0PkWVr1J4qE0BifmpoaUAifQF5ys/GNDA06D+8NCg0NZbIJ1dXVhtB3Tk4O+vTpYzkHmc1lYh8Kpu5kW8GPBStWrMCCBQuwc+dOg0gyyT0tXLjQUMFCRE1NjaUnvTPV6zvc8Dpz5gw2btyIf/3rX4iMjMQDDzyAv/71r3jkkUd026WnpzOPUkJCQodeY3V1NZ555hlDCIlHoO7XtvAtxOOQxkz37t11PCdaxURFReGqq67Cv//tVZR2u92orKzE22+/beB7bdq0CTabzZLUu2bNGtbBDx06hBUrVlhy2azQnhIU/lYnZmGZYHhMvpDcnGlDE2pBQQELOZgZd2ZeQnEFaRWG8FU0uqioyK+Xtb0MfQAGcrwv4q5Y8iYpKQnPP/+8z/du9iyoPIiZ0eRv0SP+fkHCbRz4Z60oCu666y5ERERg0aJFppUENE3De++9h9LSUowbNw5jx4416DcBwKZNm7Bp0yaWmdqRXi+rhIimxkZ89vE/8fvcfMz9yP9x3j76fwCAw5CA++YCf3sabhdQuXsn7rjeD9+y5hjwQb73/81QNQ2Vu3bipqFeDS5Z8nqyZNmr77Xq+blwN7lw9MBu9O43EHEJiZAkb51HCYBE/5ck2BUJNllG9SdVwH/CoTR7vhISEuB0OjF58mSDuCngjRKY0QbEUD8ZBv7GS766SHx8vGlbEOuglpWVWVYk4cdsQkFBAXJzc1n2O+/R5MfnqqoqllFJXDLKDOwIUVmzsYD6F0/zkCQJhw8fxhtvvGHgRcqyjISEBFb9QpZlREdH667/8ccfN9X362h0uOH1/vvv40c/+hG6d+8OwBtCqKioMBheHQ3qBGvXrtWtWM2s/GDVr9tjAuA1ZkRQhfrf/va37DsiWANG3gl953a7kZCQgB49erAafna7HdHR0bqJo7GxEUVFRUFxpNq7Wr04sYuYNGkSAK9oqDhZt9XwdTgcuPvuu5lEhcvlYpmdZsbdr3/9ayZ5oSgKpkyZwtqHvxWkGE7gERcXF9C1tlfpDpEcr2ma5Yo3NjYWdrudcdL8GV2A0TvHZwKmpaUxI6yrgp41L0hLyRciRDmM4uJilJSUoH///oZkFdq/M9LeaZFhIPtrGt5/61X865MqYMwfjTtuXQbc8Zj5QXvGQlYU2Gx29IsfjoOnfWh3bf8zsNdYBN1uD8FQx0gAQKhNhsvjLXJtk2VU7XXC3eSCqnrgdgNH9jkxdNhwyJIEhWo8ahogNXvJZG8Ysun89zhr8zDDy+l0IikpyXDvcZQA/TQAACAASURBVHFx6NWrF0pKSixFh4HAFuwEK76W8b71Wa1WcwkZZQ899JAuGSosLEyX/S7LMtatW4fc3FzdfEKhyffeew8nTpzAkiVLdJnuwS66g4XZWBATE8POffjwYaxfvx5ut9uQXMUXiZ80aRKqqqrY3AygQ64/WHS44RUTE4Ndu3bh3LlzCA8PR2lpaYd7tERYuZbNZCKAjlOmtzonDyqCK3qEunXrhtGjRyM+Pl5Xg5GXnVBVFXv37oWiKJg1axYiIyPZvYqrJyv1cj40yzfu9gx5Ad6JPSkpCTt27DBMbIqiID4+3tLQaw/DV+S+8NlfwYS5/HmkiO9hluXmS5+KR3t5esSC8ZIkWa54+bCZ2+0OSHJE9BjxNel8Efm7EqitE9dPlmXT8iVmmVpNTU244YYbLLOEO6Nkl8NhFMQlaKqKT/95EBCYIQ/0knEyCtjv47g/zZiN2GEj0Sd2KJ7+6wHrDaUWrpKsKIi/9XZcddXVSP35QxickAhNA8LtCr53eQAJuCxUQdzwEXgtxA6XC7DZ7LhxmAN2RWo2sCQosgeaxxt6pNqMXo+Z/j3RexRx8OBB9OrVi71jcTwzmz/8jXniAov4WqIKfTBZrQ6HA6+99hpGjRrFFkADBw5kMixAi9QMn3wi4s0339SN3XzdVTpPe4MMfjIO+aSl3NxcvP7666YLGuJkU+JAZWUlcnNzUVJSwjQoO8Nz7A8dbngNHz4cP/3pTzFkyBDYbDbEx8d3esHKsuasNdHoIrE2s5h+oOrXVsWqg4V4TkK/fv2YVALvEfruu+/Yqpp4AGQcRUVFoaioiMkVuN1uLFq0SEfmpXqVZ8+exccff8zkJniOiqglxgt38h2prSUoeI0bIlTyJYOWL1+O2traC8pFELkvW7Zs0YUceGOHf+ci/HmkfE18Ha09IxqBvsou0bbEL+TDsVbgnwVxVAgXmk/SXnA6naiurtYJUA4bNgzl5eVsm9jYWFMOnKZpGDt2LMaOHWtKIZg5c2bn3v/GPwH3P+1/u1MHcUXUVT43Gfer6bApMqyD1c2QKalHxsQn5+PHqQ/hB5eEIjLchogwO75rcDOeFxlPNw4ZhoXrirBn5w70H3IzBgxOwCV2GzyaCgkSwuwKvlc9LPRIxPurLwtl3i7A24bNKhAAwCeffGJYMFE/P3DggGFclmXZ55gn8rXsdjv69u2rM8JlWQ46q9Xh0Jc3A8BqHHqfqzcbm3hoZnqB48eP13m8oqKi2jV6YQVeZ4sMKYq2WGXajxw5kvHAXC4X8vLyYLfbmVME6BzPsT90io7XM888g2eeeaYzTm2KZEF7CfA2gi1btpgSyfkQA79KFydG0Z1cUFDQ6kwlq3MeOXIEI0eOxIABA/Dwww9j9+7dOsIhrwHDIzY2Vqf4zXspRHI3QVQ5J6+WmXBncnIyM0rdbjeqqqqCum/ek8brQ5lxIMjwtCrH0h5hN9H743a7Dateul5ejNfMc+PPI2WVWRaoTER7gh+UfWmSmYVjs7Ky/KrX07MQOSoXkk/SXnA6nbjtttvQ1NQEm83GQsqFhYU6w8sqE5f4O5SNOm/ePNbG/CnwXyg4nU6888473g8nfCdyEP6+Lg9K/X+Axx+w3OYP7x7DH++6QSfdYIp6byKVBg2vL8/BjpI38fiT85A4/Gbc0P1SqKoG52dnGFFekrx6XPEJw9HgUnFw907YFRnRt46EpHmNrBBFRoPsgaYBdkViBHvF5FpsNptpqL9fv34oKCjQGTRWXEzA21f8ad8RX6umpgYlJSU4evSobptAC2CbHZsfl0R+L+ln0nzCl1eTJAl9+vTRLQ7bO3rBg8bN6upqZmyJ0hpxcXGmRqIkSbjyyit1SvdkgInbRUVFMc5aVwg7doqcRFeDw+HArFmzDN9TfT8R+fn5mDdvHiIiIth3Ho8HM2bM0KXbiu5k4ga15TpXrFjB+EwETdNw5MgRvPrqqxg+fDjjdgHWExjdM3VuTdOwdu1a1hF49XXe2BGNKz6tlxfu5N32VF/PXyoypXD/5Cc/wW233Ya5c+caCn6L4HlHpaWlePbZZ9mKjDxyv/vd75CUlMSyZFsD8ujw562rq2PXzF8vPxiT0n8wSEtLM9WraQ9xy2BQVlZmGLT37NnD3iMRcOmzaDDu2bMn4BR0h8OBJUuWMLmM/4YwY2FhIfOUUz93OBy6cQEwJ6xTzbmoqCgmW/Dee+8xoys0NLRTDE8Dx/DMab/7aB433GKhagEnvzkHl1v1LQX2/nLgyAfNB9Xw3ZlafHxgNx7/+b34Z6VXHFNuJslz/4EsSThcuQ+/m/wANixbiLlTHkDV/j0sa5FI9ZIkIcymQIKE+GsuN713M8+KLMuGBbgvLibgHScDkZGJiYkBANNMWFrotAVm4VO+bm1aWpqhj2dkZKCwsJAZJzTOt7c0Cy9VsXr1astnHxkZidLSUvTv31/3m6ZpBpkIWZZht9tht9t1286YMQNPP/00pk6diqeffrpdpDHagoslg+BtAEuWLDF8b1aug5c1EMneYiiOPGm+DIfWQBzYeaxfvx4rVqxg3hEK94hcrKioKMM984ahr9UWcU/Msnn4lUQwCu5W2Xz+Vn38BCV6kkQDMtjyPTwcDgcmT56MlStXsu8WLlzI9NR8ZfytXbs2qDBzcXGxKdekpKQkKCHWtiK5OU2eD6kXFxdj06ZNmDNnji4cUVpairS0NJ03FgjMzU8hm4KCAsbr6AzJkvaCWVhRhCRJGDZsGKuByrefhISEVte5bCsM4ba/ZgF2fwJepI7qG90vC8O9BXusNzhlzv1SVQ/e/vsG/Op+L7lMliSE221QVY19rty1o4Vg7wIO7N6J2KHezEZJkmCXZa9yfrOXzMzzRvQIcbyeM2cOAGNpNVHoU4Q/CgpP0zBDexg5ZtEcnjsoLq7oukXecnsl7PDgPWlm47y4ADl27JhhG4/Hw+5NlmXcfvvtSE1NRUFBActu5MOOdH+dHX686PFCywTNIykpyTQs6E/WgBfqdDgcWL58ORRFMaiqtwX+Bvba2lqsWLECK1asgMPhQH5+PkaNGsUs/t/97nd47LHHDLw2VVWxevVqrFrlW6eH38fh8Aripaen64TxHA6vNo3dbg9oBW+2gpQkCXa73VKkMiIiwiffgAYdQltXkN99953us5lb2wwUlgwETqcTL774YpuP0x6gAfeOO+7Qfa+qKhYuXIjGxkZD+EH0xlrVRSNvWX5+PkaPHo28vDz2LFvjJewMkOCk2LcD0QdSVRXl5eWGPggAQ4YM6bQJweFwMEMDAOBqAM4ZxXF5XHNtTEDHnvp3PzoUASrjSxIQbpe9UhHNhtRQxy2wh9ibRVTtGHrzSI4L5s2EvDTUxrhhVhDfBXlcxHBbbW0tyjjRbRE2m82nh0ikaYhITExsF68vPw5T1Q2+bi1xzUSIdR1pnBcTqzIzM5GZmdkq7xHvSRMNL6ocQ+O7lTcS8L4jRVEQGhqK1NRUZGVlYd++fT7H5c5IXOFx0eMF46pAlmXcddddpo3eTNaAxA737dtnEOqkxAHSSGmPATUuLs5SWkGSJF04SIzhA9AVDhVBnhZfniaR32SFYO7djGx6zz33MCFXnjNDyMjI8MsfogLKHo+nTeEbp9OJ9evXW/5us9lw7733oqSkBG63mw0m5MEJ9LxmK1C73Q5VVTuF9+RwOEzbPIXExMklLS0Nq1ev1rUjMdRmVvtSvOeODqu2BjQhkMeXJqn09HS8+uqrpm3WF2ih0R6Ls7bghRdewNlGN5a/8rL/jQGc/nczh+3AJmDIfZbbffFtg+8DaeaRAZvdjunpE9lnqVkWIsQmsc83DU3EK399CxU7yuEYmYSbhiYyA8srOwHd/mawWvxFRUUhNjbWwCF1OFpEt/naqrIsY/LkyYiJidG1CzpHcnIyoy6I3jVapLbV4ylGOKxqClMbnjx5so7Yz2f0m/FkgyknZgVa2OXk5JgmE8XFxbHjiWLOBEVRWHIVz0cTjTQKNWuaxpQAOpPKcNHwQnATdGxsrMF1m5ycjPr6ehw6dEg30TqdTuTk5GDz5s3QNA3bt29vdaiLYBUWJc8OaQRt3rxZJ/ppBqvvaQIQFfJp+0ANAF57zN+982RTAEyI0iqMl5SUhBdeeMHvNaSnp7eLgrmvFRfgfWbZ2dksQy01NbVV501OTkZYWJhOtd2XYntHwEpUc9y4cUhMTNRdF3EHqXadWbFs3ntAxpv4bK0SDLoa6L7FMNSuXbuCOo4kSbjjjjtaXU+0vRFxuZEDZQXWO8tW+zS8/B/I2M8Tf3wX5v32SSTfegv7jlTp7TInCCEBsUMS0WvQEESE2WCXZdzUIwKyLGH3Z2cgQ4IGb6jRKrXSbHKnGrulpaVMwJpfRObn56OoqAhxcXHYtm0bmpqadMYzr/fIL8RKS0sxceJEndr8gAEDMGrUqFZlv/PGEZ3XLNvciui/evVqJkNBGf033HADsrKyWIUTPqNRNFLJSx3sdTscDpw7d87wPVXr6NOnD9LT0w3JTYB3zlu+fLlBFUE0aHljtisQ64GLhhdDoBO02QT80ksvAYBOLBMwZr20R1zZLCxqs9kwYsQI3QqbYt9idocv9ysZXBMnTkR8fDwqKyuxa9cufPTRR2ylMG7cOFOJDcCYQVhYWMj4QYFkw/AZbmZChgRZlvH8889bHsfquG0B8T+srsnlcuHJJ59kxWg/+OADLFu2jK3C6DoCuVaRNwegU8VErWQsbrjhBkRFRWHevHlMCdrpdOLgwYO67Lza2lpd26AQAxWbf/DBB7FhwwY2WHYFr08wEMNQRUVFuoH/0ksvxdmzZw378f0xJCSkyxhdAHDFlcZ3PuL2sbi2Vx+8UTAVmNQs/dF4DjhrbpgHC0k2yk2c+b//GL7zlgLiJmBJgqppkCUJYXaZlQoiHpcsASoAaN7P4XZz6oLZ5A4ADQ0NyMnJwbvvvqtbRFZVVen4vrQvvXu+XfC1XokL3L17d935Pv74Y5w6dSroti+KVd955526DMBgy6URaV30xvHHECMUQPBcVoIvcWyq1iGejzxdNObw8w5v0BLvqyv1LeCi4aVDIBO0mYuYN3BiYmLgcHgVykW3tdnqP1BQ46qrq9MZfikpKRg7diymTZtm2EeSJOZRuPvuu7FlyxafmTi04gZgKiiraRoLq4oK8WLnz83NxZo1a9j+/nRtePjzLs2ZM6fDOxF5RX1lWfKGr9vtxtSpUyHLMjRNQ1hYWED6N3yIgLyFF1I7JxBYyVgsXLiQvV9e8Zp4S9T+6urqDDpAjz/+OBYuXAi3240NGzboRFonTZp0we+1PctZkSFpVqsPgF+jCwDGjh3bpSaGM9984zWEVBWQJNwx/hHMW5iLcy4P6r+txz9ow2U/b7dzSpJkMLKO/7MSqePG4gOu/Q/tGYndn50B4C103e+qS3H0P2ehShqU5kxGTXdcMM+YXZFx4w/Nk5PI2yyWC9I0DRs3bmT/JgNEDI/xUjM5OTmsJJSmabDZbCyCoKqqqaGhaRoaGhqCXpzzBl5jYyOLsBD4bHNfx+AXlWZjnHiMiRMnory8HEePHmWyQa1xLKSnp+PEiRN49dVX8cUXX+h+owo3YkSEDDxe45G8emlpaVi3bh3rj13N6AIuGl5Bw+FwYPbs2WzSURSFeUL4hllXZySkapqGGTNmBB1u5PWCRO7V6dOnUVJSYtpRFEVhPClALzp63XXX6UpLKIqC3r17A2jpyGbZLnv27DEti2G26uc7si9DSkRyczadWb20lJSUgEKMFwLp6emorKzUZTb6A933+fPn/brizbhPbSnufaEhtg9SvOb5gx6PBy+++KJOaJh4h3wJHb7sx4X2drV3OSuzrK/Y2FgUFhbqRGF5+EvI6GzcM2Y0FuUsgMvVBJvNjuT7fgpFlhBqk9FnYCzwnXEfxWZDq/O331sC9Ttzz5nLpP1LkjcyKUkSLgmxYWjPSOytPmPKTe0T1Q0nar+HCt/Jl744R/zCQJIk1NXVGZJteGzcuJGNy7IsY8mSJUyrjeq6mqE1i3Pe8OdLVlHIUKQDWB2D9yiJC4OUlBQW6RBL5oWEhATNZeVB9JmGBiMH8KuvvjIUzybk5+cjMzPTUGLrqaeeuiBZmO2Ji1mNQcLpdCI3N1fnyVm8eLFOPyo/Px85OTmmgyufKRIoeL0gscPu3bsXmzdvNt3P5XJh06ZNyMvLQ0FBAStnoiiKqajjqlWrMHr0aFYY1az0ycaNGw3ZbIA3HCXLMltdpaam6jIKSW8rEDgc3oLgYrZQaGhop8sMiAr2waCgoMBn9o8YmlAUpd21c1qDtLQ0y8xSHuPHj2dZSrTSpzZLfYH0ycR2TL+7XC5UVVW18x3oIS4Sgu2PZhCzvhwOB9NoCgTffPNNm6+hPXHLyBH4y5ub8YvHsjHhiWdwdP8u/PPAHoQoMv71scn7kSSMGf+L1p/wn1tbDiXLuCE2HvaQEG+Wokn7l5sJ9vwQS4r0olLEFZeE4PofXGr43gxEmM/Ly8OAAQMMv5NnJycnBx9//LHlcSikSIun2tpaOBzeuq6hoaE+k5eCFUomg/HZZ5/FsmXLEBoaCkVREBYWhuzsbEM2otUxyrgsTX7ustvtOnoJ3388Hg8effRR3fwXLHJycnD+/HnT+TIuLo5pffHaW06nE4899phBJoOEUgEwmkd+fr5Ob7Ar4KLHK0iIpEKXy4VXX30V27ZtY9/5kpwIJuQWCGhiI8I/GVei0rvL5dKV2xEnPtqeUqVpxVBXV4eDBw/ikksuMbiwqRQQkehpdZebm8vcx0SyVlXV1AtoBZHQfe2112Lu3LmdvnqxIpoHAl4Owmw1JoasuhQZ1ETfiAdNJBMmTAAAy0zUwYMHIz4+3rIeJeA1UC9kGTHxOV8oo9YqdGWGnTt3dqhGWyAYOuxmnKg9hz9MfhBulwt/y8/FzN//CVvf2gBM/6luW5tiQ5+BNwE+6l8HCpvNhqlPPQuXR8W/Du3B2DtGG55L926hOO/y4LKwlilsyLWXY091nWnWYmS4HYOiIxCA7QWn04nKykqcPHnS53YUTvfnveQlVXjvaF1dHcrKyrBnjw9tswDBG0WtHTccDgcSExOxadMm3feiBqNIt2lL2b/8/HzTjEZJkvDEE08gMjLSVDVfpKNIkgSHw4EZM2bossoptOsvwaCjcdHw4hAI78Ms86W8vBw/+tGPmJ6VSBakUKQkSZg9e3bQLz4tLc0gCcGDPAuSJOGWW27BlVdeiY0bN5qGCvnsRBF8CrHId6NSInxpCToOrYAoxETGiVjyZNGiRUhJSQno/smDRuf74osvkJWV1eas0LbCjOMXKGRZ9ln3jAZl4jF09r0SCgsLfQpFAi1ZSLIsw2azWbaxvXv34sCBA7j66qsNfA5Cjx492nzNvmAWGrxQIEOUklWsDFK+AkNXwWVhNhzd74TbRcKkTfjwH5vhcXuA3X8DrhvMtnW7XXhnw5+Buxe0+bwJt46GBODwPieSkkZhyLDhhm2uu/ISw3dk/EeGh+Day42iryKp3szgNit4bQU++5uv0sFfC59gIiIlJQX19fU6w6s19Rn566aElWXLlrWqLZnNb5IkGZ4VTxNoDX2GUFBQYPlbZGSk5SKJ6CiNjY2MS7p9+3ZdQoGoUdmVKBsXDa9mBMr7qK2tNchJAMCpU6cwdepUlJSUYOzYsUhJScHp06eZoOT06dPhdrvx8ssvsxTZQOFweIVYMzIydCFOWZYRHR2Nf//73wC8naG8vBx2ux0hISHM2qfwoqIolvISdrsdkyZNMmSl8GRv3j1O2Ts0eZFHhDxb+fn5Bn4LXw9SPD5NgKRiTtXp+fN1ttow0MLxI09eoKDU50CKea9duxZNTU2t0sZpbzidTqxevTpgThINcFagUI2V0SVJEsaOHduqaw0G7ZHp6gvieJKWlobY2FgkJSWZbt/Zgo5msMkS4hJHYoPdDpdLAyQJNwyMxb6K7cDOv3j/OJz8+DBwd9vPu2fbVuzbXgqP24M38nKxofgd9LwzOaB9ZQkIs8kIs8hc9Iey5qzxQNr71VdfjZtvvhk33HADDh48iLi4OB1XiUJ2Yu1Y3kAaMWKE7pj33Xdfq9olXbdZlY5AE0ny8/Mxbdo0w72PGzdOt59IxG+LQWO1yNI0b0k2q0US/311dbWOM0rzHX+NgSQYdCQuGl7NMON9mDUkX8RvwFvupbi4mK38KV7v8XjYpOOvdI1ZRzlx4oQhw1CSJJw+baylRhl1QIsQZXR0NOLj41FSUmLq2p00aZKOuEjXIZK9RZALnRo5eT7MoGka8vPzERERgfr6elYclsKgs2bNwiuvvGKQyyB0lcmpNcWLY2JiUFlZifj4eJ9hLuLzAa3XxmlPFBYWWoYELwQ0TcNjjz0GABc03HihwU+EDQ0NKCwsRExMjKWndObMmV1iJc5DkSXEDk3Eo9nPYPWCp6GqKt748yp0v/qHqPnCvPB3a9EvLhGfHNoLaBpUKvGiaXC7mrB/1w6kBGh4hdhkdAtpndEFeMczsfafFRG+pqaGjaXkcbn11ltZtMPtdqNXr15MCiYzMxMHDhxg3jRVVbFjxw7Y7fY2l8pKFkTA+SodgTgUnE6naca2zWZDdHS0LgxOXn8aF2w2G6qrq1sVKs/OzrbMtqcKLVaLJF5+iLIYFUXBxIlesd38/HzmeOhqkhIXDa9mBMr74C3tw4cP4+2338a3335r2I5W/nl5ecwdTXC5XMjKyjJVJ7byvL355pu67ciIIwOMvgO8g0BERARL7afGx5PdeSiKgrS0NIPBxxujxB3j4fF4kJmZif79+wccelNV1dQwozI0VivNrqA2TCAPXzAGyalTp7By5UrY7XZLFWkAOvXo/1UEsjjp6uAncE3TUFBQgKVLl5py2yRJapUxf6EhSxIUCfiu7gw0VYOmqnA1NeI/X5p7KwEArkbAHhrUeQYeeQ0/ffwpPP/YL+B2uVgih8fjQUhICO4eMzrgYw3uEbjwqxn4iIa/OrE8VFVFY2Mjtm7dqvv+1KlTmD59OgCYGheqqqJfv36tFk4lkNyNKAIeqEOhrKzMMIb36tULX375JVatWqWr2whA1441TTPdhmAW1eA/b9u2zbTCCl96y5fXTvSKAd4FI2/QdiWjCwCg/Rdg6NChHXKeiooKbf78+VpFRUXA+8yfP1+TvGp+ln+KomhxcXGG7+12u+Fc8+fP1xRFYfvNnz9f0zRNe/jhh3X7SpKk2e12TVEULTw8XMvLy9NSUlI0RVE0WZY1m82mybLs87roOHl5eVpFRYVmt9t111VRUaGFh4ezc6SkpPg93oX6S0lJadd33Vbk5eWx92T2R+/A6l7M2lleXp5h27y8vE66Qy8qKip83qev+w9kO0VRNEmSNJvNpjuPLMus7f83QhwXJEnS5s+fb9puQkNDgxpz2hMffvih7o/H2QaXtu3Tr7X5f9mkhYZ5xwGbzabBaryTJA3hl2uYtSmoP1tIiPboUwu0Ox/4pXbPzyZoK/5Woq34W4k2dc7vtA/Lt3fo8+DHPKv+azWOBrO9+BceHt4ubUCcw8Qx3OocZv08MTGR9WO+P86fP9+0f8uyrI0ZM0Z3DvH8eXl5WmhoqCZJkq7dZ2Rk6PoLP94Heg/itiEhIVpGRkan9S1fdstFw6uNEDuqWYNUFMVy8srIyLA8HjUy+o7v5LIsGxoWb7TJsqzZ7XafE6CiKGxiF40qavh8RzYzDNrrj+7H6jl15uRkhYyMDMv7SUpKshyI6f2Jg8iYMWMM23YF4yMvL4+1pZCQEL9Glc1mMywU/BleoaGhWnZ2NjtPe01EnYWKigotJCTEtP3yk4wkSYYxoCPhy/D6vtGlbT/xtVb80WltVdE/tDm/m6c9v2ipJot9VJI0hTfIphQEbnhl/MX7HGRZk2VFCw0L01b+/R/ajpO12s6TtVqDy93hz4TGvOzsbL+Lahq7UlJStLy8PN04bfVHC2bxGBeqrwfqUOAXBYqiGPowzRW+FmNkgNK2oiMhMTHRMM9kZGSwRb/ZWG/ljDBDMNteaPiyWy7qeLUSTqeT6YXk5uZi9OjRGDFihCkfgPROAgGvySKKkxK0Zn6Ax+NhSvlAS+ydqtDPnDkTt99+O8aMGaM7R1JSEjIyMrB9+3bGoxG5YmbcsZKSEsvrDsYtb7bv7bffjrKyMowbN850G16KoavAStNLURQMHDjQZxYqcX/mzZvH9GV41zrgTXjoCpy29PR0bNu2Dc8995xpnVAe119/PcrLyzFo0CDL0DYP4j66XC5ERkay83SVtO/WgvptRkYGMjIydEkSaWlpCAsLY1pLXbU8EomF2hQJgxMSkZk1Bw//aiJuGX2nbruBN8XjiT/mwG63e78IVBxW9QBrvOOPpqpQVQ9cLhcO7t7RrMfV+jElEHzyySe6P4LD4S2J46+tEzRNw7vvvovY2FiUlpYiIyOj5VlwGDBgADIyMrB06VLDbxeSvypqzFkhPT0dy5cvh91uh6ZpeP3119m4LmZm8uO93W5HYmIiy3QnqgAV0iZtv5CQEISF6bNNSWeSwrCSJOHRRx81ldkJRNMwmG07Exc5Xq2AqNwrSZIlIdwfaNAVY9hmDU9McTbTBOP5Xq+88oqBTyLLMu666y5G+OTPy6c1T5o0ycA3u/rqqy3v46GHHsL69euDUqgnaJrGjI4tW7borpW4BF2FWM9DzHC97LLLEB8fz+pIrl692icPTNM0vP/++9i+fTvT3snOzkZZWRl69OhhWROzM0BtkleKNsMTTzwBh8OBqqoqltWlBTARq6qKPXv2IDk5uVPrUrYnfJGCu7qyNuAtsyMB6BZiY9pYkeF2/HLqDDjL3oerqQmSLKP71dHo028gVmzYhOI31qNECVDB/vx3QJM+ScluD8HQm29hHD12tAAAIABJREFU9RYvpMD/l19+qfvcr18/9m9a7GoBaHVpXMZ1cnIyTp48abro6t69O1asWIEFCxYYauh2leSKyspKZgRpzVIYqqrqFoGFhYW6++vbty8mTZqEAwcO6GQ1ykxU5EV+r6jFZbYQ4WV2ampqmNxOIH0LMJa36wq4aHi1AjxhUdQLCRak0u0r84Qa04MPPsikIwCvd8EszZc8CGbgO5BoWGVnZ+PgwYOs4PGCBQt0xEwrSJKEN954w/I5JCUl4auvvsLRo0ctj1FZWanr9ACY8B2do6uB15JRVRXff/899u7dC8D7ziZPnuyzvBBlijY2NmL69OlQVbXT6zL6gtPpxJo1ayx/T0lJYUVrs7KyWFsMFBs3bsS7777bZe//fw1Ss9fJJksIsymIuiQEl4ba8OOkWzDlyT9h5Z+ehOrxYNt776CirBQrX9+I2c++iLJXSmCsTmmCot/rPjpGj8XEab/GTQmJ0LRmZfoLcmf+QTqCRLL3145JNZ2kIswWJ+Xl5cjPz2fjBr+QXrJkScAahxcKJB1DoGuj+YTmqoKCAt3zOHLkCKZPn45Zs2Zh0aJFOnK/CCpfx4PkH4YMGeKzTuuaNWvYPORLaofPdmzP0mDtiYuhxlaAPFBUFsUf7Ha7ZcmVoqIiQ+ZJYWEhMjMzkZmZycJQDocDCQkJun35FRpdl6/SLuTGBbyrABLGpPNGRkbi3XffZeFH3m2rKIql9hJ1TLNnYbfbsXv3bhw7dszyugCvd2jXrl2G72ny7oqhRjKIb7/9djZI8yVo0tLSWIkcEWPGjGHhJhKKpSK3fPixK0HMfOrevTtr2+Hh4SwVnvcWBALeS9teJXy6MmhCEMugdEVIkvf99LryElwe7g2f2RUJZ7/9xltAuxmupkb8483XcaRyHwb/305IzvXAB3lA6QrzA+98Ffj6FPs4YMhwPLtiHWKHJrKyP3xx646EuHAQvTIDBw7UbS/LMstUJiFpWZZxzTXXGI5NVU0mTJiAYcOGsTmkK7R7s8xGvgJKZmYmcnJyTBf1LpcLb7/9NpYuXaqjCohtPT4+3jAmDh48GIqiYP/+/cjKyjLtD2VCxRh/z8vpdGLevHloaGhg42pnP18eFw2vVkCccK0gSRIyMjKwbds2LF++3HTbuLg4g4GzatUqrFy5EitXrsRtt93GGqIoLCl+djgcTMOEBx03LCwM8fHxrCOsWbMGNpvNMh7O880mTpzYqjCiw+GA2+32y3Fzu91Mt8UM7V1qqb3A118Tn6PD4cCsWbMs983NzdXVVyPjbevWrUhuLsXUlcAvOABvqFWSJEyZMkW3mhS38wWbzYb777/f9Pn9/wqzFP+uCsmk9uHX37sQN3wkFJuex1S84a/IeiQFzr+vhrKvCP2kr4FDFrxQoW1c2/sGhNlkSBIQZlMgSxJujI5otRBqW+Br4aBpGo4fP876q81mw4oVK5Cenq4bx0NDQzFs2DDD/t27d8eoUaOQl5eH/fv3d5l6rEBLoWyC2H9VVTWtukAgz1d1dTX7rrCwEOfPn4fH40FDQwNqa2tRXl6OuLg4ts3BgwdZ/V+rhad4bb6eFxl7W7du1SnZB1t8/ELiYqixlaAJd/v27ZauZU3TEBERgbJm5XczvPTSS+jTpw+LYZeXl+u0nHhLXSyvYFZMNS0tTeeStdvtcDgcaGhowKRJk1BZWalzcU+ZMgUxMTGWMXArkbpAEwYaGhp0pX9ai7bufyHhi7NTX29evG7r1q3Yvn07M1hiY2MxadIkHD16lK2AO1s8VQTd57x587B161aoqgqXy2WoaedweIucFxUVIS4uDseOHcOmTZssE0+io6Px4YcfdnnOU3uBJugLXSuyrZDgLTqtSZKOaxUZbseQxOF4ZvXfsHbhH/DpPw8B0HShZVVTfXurpJZJXbHZkHzfA7DJMiRIuPGHETjX5O4UowtoeT80rov1bz0eDyZNmoSYmBhERUWhtraWiYfy40BVVZWu3UuShA0bNuiOo6oq7r///i7B56SEEOJQRUREGDhZ/oq5u1wurFy5EmvXrsXixYuxatUq9pvWrEYPAB999JHp/qqqMt6rWE6NvzZfmmdmhrNV2aZOQ1tTJjsCXV1OYv78+VpSUpJlqrE/TRhZlrW+ffta/p6dna2Fh4cbUput0tArKiq0jIwMLSUlRZema7fbfabt+kNeXp42ZswYLS8vz6+OFX/t/HaSJGkDBgzQSRIEkrJNOkj/bfAlOQGA6d5UVFQYZBo6U2bAF0RZEUmSdPIPZto9GRkZWlJSkqkURVeUCrnQaI1m4IWALzkJVVW1E1+f1Xad+karP+9i35PMRNFHp7UFf9mkhfnTvZqw1CgjcVVvDYAW+YOrtD8VbtLe/Oi0tvuzb7S9n53pEvdO7ycvL49JS/D3RNqH4eHhTLOPl1vIyMjQQkND/Y5rNC53djsQUVFRYSpvI/5FRERY/iZKR9B4N3/+fL/HbYsUBP9eaH7tDHkaX3bLRY9XG0Eeoerqasvit7RqsiqurKoqjh8/bnkOMws+NDTUMg2drmnBggXYuHEj+97lcjE+jVnari8Q76GpqYll4fXs2ROnTp2y3Ofhhx9GfX297p5tNhuysrIYmTwQ4irg27XclZGWloaCggLLZIf33nsPpaWl6Nmzp8EjZCVX0dkQszk1jqMiVjzgEwcAmHq9uqJ370LjQteKbA9IkoTeUd1Q+30TNI7m7pWZ8PJUYocMw1/efBvHK3fj5KlTWJ2/CgZK/LrpwKxNLZ/zfgV87/Wc/CC6B/oNToCMzuFzWUF8PwsWLGBtnrwnZSb1EQEgKysroCLbBMoA7CrtgWQgfCVUEc6etU6jCAsLM1T4aGhowBtvvOH3uDabrdXjPe95JI9kV/OkX+R4tRPS0tIMMfHo6GhdzJ8KXaekpPgkwYsICwvTaZOIukBWEOPiVDy7NfpB4mQ6bdo0n0YXABw/flyXBUdlf8SUZX9ISUnp9GLRrYXD4WCF0q3g8XhMn2WXco1zMOOC8IYxz3XhEwd86ZqtXbu2y3HaLsILWZIQorSMbV6ZCa/xFaIoSBx+M5566ik8/MgvYQ8x6lcZwFlYd4x/iBH4JQmA1Fl5jL5BmYg0licnJxvoIx6PB0VFRUEllgBdpwYtgZKuCNddd51lVrkv3u/u3buxbNkypKSkYMCAAVAUBeXl5T65vIRgnAJmIC22rmh0ARcNr3aDw+HALbfcovvuP//5DzRNY+Tj9PR0rFixAm+99RaWL18esPE1cOBARnIvKyvDihUrAmpIoojjtm3b8OGHH+rEWQOFOJmadTixc549e5atdoiEHWzhY1mWkZiY2OU6TjDwld1oBat07K4CrVnfyGazIT093cDHoPZKiQOKovh8Bl0xa/UivBjaM9LAt5IlCRIkhNha+F/Db3Zg9rznITePa7Ki4JrefQ0kfNlmR48f9UXij+9Cr74DIMFbkBvwGnRdEdSmp0yZggkTJqC4uFjnyZVlGaGhoUhNTWWJJYEkl0iS1GVq0FqhW7durdqvqakJtbW1eOutt/DLX/7Sp5HGzx2+ojlmIDFzfuHW1TOHL4Ya2wn5+fnYuXOn7jtN0wzq8k6n07QgqAhFUZhwXVuUrc1CGq3p5Lz7tq6uDi+99JLh+mVZxrhx45gSNCUJUPYP4L3/tLQ0VjneF2gw68oGSCCgAra8+KgkSRg8eDAOHTqkK27er18/9OvXr0uQba1Aaedaszgq374J9LmsrAy5ubmorKzEgQMHdCK9ZIxRIdv/9vf8vwRJAhQZBk/Yt3VnWKRR9Xjwxb8+hc1mR+TJctT1TvJ+/30d/nO2FjWfncTBijI8s+oNDPt/7d17WFV12jfw71r7gAWoiSKKpY9nUNAED1gxmKOTUxoe3jfNLipN1DTT5sqmmq6hqYlKLTtYVziNYwdz3ndq1PShNJRE22UeSrNRJw9PouggqKgB+/R7/oC13Huzt4Is1tqwvp/r6orDdvG7YR/u/Tvc99BhsFlkdGlznf7BNMDKlSvrHKZSOm/4NmKeO3fuFYsnAzWPd+VEZDgJ3B5x8ODBa6pVKYRQZwVjYmJCXkOSJMycOdPv5zdkC0ywWl2+KzRVVVVht5WBiZcGHA4H5syZ45eIKFW7JUlS73x5eXnqA/Jqd2Sv14uZM2eqSVc4FIJTfubIkSMhhIDFYsEtt9yCb775Rn3xHDNmDD799FO/J6Y+ffrgyJEjagf7Rx555Io/Jz09HVOnTg3baeJrkZ2djcOHD2PRokVqRejAgrJerxcHDx7EsWPH1JpY4ag+p/J8nxCVQrG+9wmr1Yply5YhKSnJNKcZWxJleVCWJL8dXSnDboHNboOzurawtBDwej1ItZxG0Vv/t2afKSR4vbWtopwC+7914LZbbkHM9XbERNoNi+lqlBfzwDeMsiz7JV1lZWV1CmtbrVbExcXhxIkT6utCdnZ22CVdwOXtEe+8806dOmYKi8WCPn36qKewg/E9SaiUngl2W7vd3qBky1ew0izKMqOyp1rZynCtP6MpcKlRA4WFhX53TpvNht/97nfqEeRHHnkETzzxBGbPnh2y0Ggg5TaB2bvRdX98n3yEEKiqqsKYMWPU5dSysrI6M2HR0dFwuVzqu4/FixdfcbZr+/btSEpKqld/sebC4XDglVdeUf+uyt8y8L6gVLIP52W3YP1EA/neZ91ud52/92OPPYbs7Ox695Gj8KEUNrXKck25CZ+78MDUoVi08mOMnHAfbHY7ZIsFVpsNvxo7CS/89R+4d85CjJk643JVdK8X0W1vqHOdcKS84QjcUjF27Fi/+2+o2R0l6QIuJxvhyrefqM1mq7NsOnnyZPz0009XfC1Tqvnn5uYiJiamztaaIUOGYNasWXj99ddRWFh4TcuBvltgAmsoTps2Tf1bhdtWBs54aUDZeFldXa1WMd6zZ4+agDidTnWmQ6HMFin1tQD4TbcGXj9c6v4E1rhRlo5kWcapU6fQu3dvv9srG0eV2ynLU77fD3x3GG6nfLRQWNvOqT7CrdhfMFc7lafcT0Kd7qrPBlsKT5bama4IqwSLLKHNdTUvI61sFvRoH4lLKUMR13sgfp35f7B/pwP9UtPQIykFVS4PftMrGetXvgVJkiGEF5Is4+L5s03eEFsLvj0D3333XXWW33d2Wjn9HfhGI/Cxfy3FqPWSl5eHjz/+GBMmTEBpaSkGDhyIJUuWqN+XJAmlpaXq65skSbjtttuwbds2v7hcLhdmz54NSZJgt9txzz334MMPP1S/n5GRgczMzEat5lyphmJWVpZae9Lo181AuideBw8exD333KN+fuTIEfzpT3/C/Pnz9R6KZnwLRip9DmfPnu13G98XH1mW8dZbbwWdZn744Yfh8XhgsVjUcgLh1FQ3sIimEpfX68WaNWvUpuHKdPqMGTPQtm1bvw35SpsMi8WC4cOHBy3DEe6JR0P5Tn1fTdgV+7sGgS9SgeU0lKbo1PxYLTJujm+Dr//nLPrGRuF6++WXkTatrGrLn5oN+MoJSAmyLMHrFeiXkgZbhB1ulwtWmw0Dht5S0xDbsM6M9ae84cjKygr6fKyUmLgaZQYm3N5c5uXl+U0ASJKEzZs3+z1vCSHQoUMHdTLAYrGgXbt26ptoX8rn1dXV+Oabb/y+9+qrr6KioiLoUmFDNMtm9I2oD9ZobrdbdOzYURw7duyKtwvnAqpC+Bdss1gsIjMzU7zzzjsiIiJCSJJUp9Do6NGj1UKkgRYuXChkWa5TlDJc+BYWtNvtIYvfSZKkFsYMLGiH2qJ2U6dODXoNWZabZbHUq7laMVUEKUbaEigFJdPT08WQIUOC3u/JGFcqIno1jqPl4mKVq87Xtx05I154f52wt2olZNki7K1aiRfeXydW7y4W73/7s/h/e06Il97/VDyw4CmxZNV6UXDoP+Lbn8vF/5Rf0iiq+qmoqPD7TwuBxYUTExNFZmZmnSLR4Vo0OFjRVFmW6xQ+ttlsamFk5XXuas9twa6rFPk2qshpUwrbAqoFBQXo0aMHunbtauQwGs23kB4ArFmzBvn5+Xj99ddRVlaGn3/+2e8U38aNG9X/Hz58GJmZmSgsLMT+/fuxatUqdRZJ2esTLpl64AmSN954A/n5+X5tMZT6Tr7Lh8o7j/nz56tLjl6vF6tWrfK7vrKPoCWcZAxGmfoOtfwmyzKys7PDahOoFppDsVBquJpZqiBfh4Qfv3XA7XTB6/XA7QJ+/NaB7v0G1RRelSX0G5SKAalDANT0ZoywWtCmVT1qgGkoOjpa82v6FheWZRn33XcfMjIykJ+fr25FGTduXNieWp44caL6+gRcPln+yCOP+O3N9Xg8KCsrw0033eR3WMy34f2VKKVo8vPz4fF4IMsyli5dGpa/k6ZgaOK1evVqTJkyJej38vLykJeXBwAoLS3Vc1gNlpGRUae2lVLD5Mknn/TrcygCToksWrQIr732WtCN1pIkhVUCErjJX6nRopTIUCxfvlwtpeGbOO7evdvveiJgr9fYsWMRFxfX4hIPhe/y24oVK+osSYwbNw5vv/22QaMjaphQ27JkSUL/wWmw2m1wuwCrzYb+g9Nq93FJtU23JVhlSV2S7NU+0rDejFpS9vsG7isSPnXvwjXpAqBuf1H6rLZt21ZdpuvRowfmzp0Lj8fj9+bYd8lx2rRpyMrKwu9///srNtSWJAnDhg1DUVGR+rtp7tsrGqSpp9tCqa6uFjExMeLUqVNXvW24LzUKIer0LgycSlaW6AJ7fl1pijbcengF9uALNrZQt3nhhReuOh3dEqebQ1GW35T7jMViEbNmzTJF7BQ+GrPU+O3PwZcavz5WLtb9UCIWr1ovHlzwtHhl1Xqx7ocS8f+/OyFW7T4u1uw9Kf77x1Niy79LRdHhM+LrY+XiF6dbo4iMF9iH84UXXlAf57Isq/1Zm6NgPUZ9e/gqt8nMzAzal9X3de9Kr5ctQVguNebn52PQoEHo2LGjUUPQVHZ2NpKSkkJ2Tw9cblGmbZWWKsGE2+m++mxWDHWbKxXQU3i93mveYNncKPGtWLFCbaeTl5eHlStXGlanjaghkjq1RoS17ixVTSshGYkDU5E6ZCiqXF64PF5IAGRlo72y8V6SaktJhP/G+voKfK4PPAn+xRdfoKioqNk8zn1XNLKysvDkk0+q31NqU3o8HhQVFQEA5syZU+cUp1LJX9SeavddIWpo3+CWwLDE66OPPgq5zNhc1XcvS2ZmJpYuXRq0GF/79u1x5swZAOFZVqA+MQa7TWBjZUDpzyb5PRjD7dhvUyosLPQ77WemxJOav2BJF1BzovE6mwWVLg/sFhkuj4DbK9WuTYqafV6S0iLocjHWlkp5M/rQQw/hxx9/bFaP88CG2StWrFD75iqFw5Ukq7q6Wi2zEUgIgcceewwVFRUAgNatW+PVV18F0PAWQS2BIYnXpUuXsGnTJrzzzjtG/HjD+b7g+r7Ti4iIwK233oo1a9YAaBllBRQZGRmwWq1+zVftdrt6ACFcu8g3pZiYGL9E1GyJJ7VMUu0+rqgIC+TaGS0lr1JmuqIiLHB7BWRJQmLHaFxnwP6ukydP+n3euXPnJvtZ+/btU1uoAeG3fzeUwDeHvgljYOFwi8US8ncohMDixYshhIDValVXesy2qV5hSOIVGRnZYhKKaxFq2W3MmDFYv369+rnNZmsWD876UCoJK20olOllZTOnw+EIq8rCegicBezevTsef/xx0z0JUcuScmNbbD9aDktttmWVZVgkZabrcqshufZrvnXA9HTo0CG/z5sy8fr444/9Pq9PPb9wEBMT47cdRpZlnDt3Tq1GH1g4PCkpST3BGUh5nvNN5IQQ2LNnjz7BhBFWrjdAqL5V5eXlftO0Y8aMaVEvwoGVhJXp5VCNTlsyh8OBn3/+GTabDS6XC16vF0eOHMH8+fORlJTU4uOnlk2Z5ZIkwCpLsFokyC7AK9ecZgSUwqoGD1QngWUaJEkK+6VGpQq/sg1E1J5Uf/nll9UyE0uXLvVbsQCALVu2ICcnxy/eUIQQePfdd1vsSfZQ2KvRABkZGWjVqpXfvgZZllFVVeV3u7i4OL2H1qRC9fgLp16UelASTaXsRmpqqjrzZYb4qeXzTapkSYK1dolRBi4nXrUzYGaQnZ2NhQsXqpvMm0OtQt++vIFt3ZTnKmV7yPz58/HMM89g5MiRAICcnBzY7cEbngfu5wu3Pop6YOJlACUBmTlzJiIiImCxWBAREYHp06cjIiICkiS12A2HwZoih2p02lL5JpoejweDBg1S7wdmiJ9aPqWRtrJ5Xpal2sM0lxMvWZJ0L5pqpJdeegnbtm3D888/3yxm9X2flwMbXCv9FzMyMtQC4h6Px6/od2FhIWbNmoWEhAS/f3vbbbf5JWVmfM7jUqNBQvX8SkpKCs/eUk0orHtqNYHApudZWVkhe78RNUdKYVQBUTPTVbvsKENCK5tFnQ3r1SHK0HHqrTl1cfB9Xo6JicG8efPgdDphtVoxffp0dXlw37596v4tr9eLHTt2wOFwqLEqJyNdLhcsFgsSExMxdepUdW+X2ZYZASZehgt8IDanB6aWzBR3qETTLPFTy3e5XETNJnpZkhBpt6DS5b08G2aSZcbmzPd5OVSdysBDQmvXrsXnn3/uN6s3bdo0nDp1Cvn5+Vi+fLlp9vKGwsSLyABmSjTJfPrFRcNukeH2CpRUVOHUhWrYamt6KflWh8gIQ8dIDaccjvIt9Ky0SVJ60AohUF1djZycHEycOBHz58+H0+mEJEnqfrHmUsesqXCPF5FBHA4HcnNz4XA4jB4KkaZa2SyQZQl2q4wOUXZ1I73NUlu1XgK6trseAB8HzUWoQ1CBe5aV2a9NmzZh9uzZqKqqgsfjgdfrVfeLmXFfly/OeBEZwIwlNMicLLIEiyTBBUmtdq/MdvFx0HwE7k31TZx89yzn5ORg06ZN6uwXUFu7TZaxYMECv8bbZsUZLyIDmK2ERrjhLIt+IqwWDOjcunbDPdAxOkKd7eLjoPkIVQ4o8DY5OTlBT0F6vV688cYbpk+6AM54ERniSu8eqWlxlkV/Vous1vbyrRvNx0HzUt9evcuWLVObZytLj9zbdRkTLyIDmK2ERjgJNsvC33/TUyrZC1zOvPg4aJmys7PV0kgxMTHqBnsm1zWYeBEZhCcbjcFZFmMotbwC29TycdDyOBwOtfRERkYGk+sATLyIyFQ4y2IMSQLatLKhQ1TwVjLUMigFU51OJwBgxYoV2LJli1rlHmDNQiZeRGQ6nGXR33+1i0S76211evUZpXfv3kYPoUUqLCyEy+VSP3c6nXj55ZexYcMGeDweREREmH5fJRMvIiJqcjGR4TXT1blzZ6OH0CJlZGTAZrOpM16SJGHdunVqZXvffo5mxXISREREpAmlQXZmZiYsFguEEGrSBQAWi8X0+yqZeBEREZFm0tLSMGTIEABQi6gCgNVqxZtvvmnq2S6AS41ERESkMd/TwxaLBdOmTfNrrm1mTLyIiIhIUzw9HBoTLyIDOBwOPiERGejChQt+n0dHRxs0kpaLp4eDY+JFpDO2rCEy3q5du/w+N/uGb9IPN9cT6YyNgYmIzIszXkQ6Y8saCheffvop1q9fDwC466676twXU1NT1Y937typ6c8+efIkxo0bp/7snJwcTa9PFK6YeBHpjJtOiYjMi4kXkQG46ZTMzOl0onPnzprPohE1B0y8iIio3iorK/Hee++hoKAAxcXFkCQJXbt2xZ133onJkyfDYrEAqLuUmJycjA8//BDFxcX4wx/+gJSUlKBLjb7Lm4F8b3fu3Dn89a9/xdatW3H69GnYbDb06NED48ePV68L1GyinzlzJgBgxowZiIyMxD/+8Q/89NNP6NixI8aPH4/+/ftr/WsiComJF5HBWFqCmovKykrMmDEDBw4c8Pv6oUOHcOjQIXzzzTdYunQpZNn/3FZRUZG6l6wxlAbbZWVleOCBB1BSUqJ+z+VyYd++fdi3bx9++OEHPPXUU3X+/erVq9UyEi6XC8XFxXjrrbfw7LPPNnpsRPXFU41EBlJKSzzzzDMYOXIkHA6H0UMik1q/fj1SU1P9/gv00UcfqUlXWloaPv/8c6xbtw59+/YFAHz11VfYuHFjnX93/vx5PPDAAygoKMCmTZswbNiwkOPYuXOn+t8nn3yCdu3aAQCioqIwZcoUAMDbb7+tJl1jx47F5s2b8dFHH6FTp04AgE8++QR79+6tc+3KykosWrQIhYWFGDp0KADA4/FwyZN0xcSLyEAsLUHNybZt29SP586di5iYGHTu3BkzZsxQv759+/Y6/65r166YM2cO2rRpgxtuuAEdOnS46s8qLS3F3LlzUV5eDrvdjldeeQW9e/euM44FCxagdevW6NWrF+69996gY1Wkp6djxIgRiIqK8kssy8rKrjoeIq1wqZHIQCwtQeHirrvuwpIlS/y+FjjrdfbsWfXjuLg49WNlpgkAysvL61y7d+/e6jJhfVRUVGDOnDk4efIkZFlGbm4uBg0aVGcc119/PVq3bh10HL5jVXTr1k39OCIiQv3Y7XbXe2xEjWXIjNe5c+cwadIk9O3bFwkJCVxeIdNSSks899xzrGBPYe+GG25QPz516lTQj5WlQV++Sc7VVFZW4tFHH8WRI0cAAE8//TR+9atf+d1G+Rm//PILKioqgo7Dd6wKq5VzDWQ8QxKvRx99FHfccQcOHDiA77//HgkJCUYMgygspKWl4cknn2TSRWHv1ltvVT9etmwZysvLcfLkSSxfvjzobRrK7XZj4cKF2LdvH4Ca5cy77777iuNYunQpKioqcPjwYaxatUqTcRA1Jd3T//Pnz2Pr1q3429/+BgCw2+2w2+16D4OIiBpoypQp2Lx5Mw4cOICvvvoKo0eP9vv+8OHDMWrUqGu+/t69e/1WQN42vHx4AAANRUlEQVR88028+eab6udKOYlZs2bh66+/RklJCdatW4d169b5XWfChAlITk6+5nEQNSXdE6+jR4+iQ4cOePDBB/H9998jJSUFr732GiIjI/1ul5eXh7y8PAA1myyJiMhY1113HZYvX4733nsPX3zxBU6cOAGgZu+UUscrsJREQwgh6nW7mJgYvP/++/Wq40UUbiRR33u6Rnbu3Ilhw4Zh+/btGDp0KB599FG0bt0azz33XMh/k5qayuO+REQaCzxFa6bDHQcPHvT7vE+fPgaNhFqiK+Utus94denSBV26dFFrqEyaNAkvvvii3sMgIiITY6JFRtF9c31cXBxuvPFG9d1GQUEBEhMT9R4GERERke4MOVv7xhtvYOrUqXA6nejevTtWrFhhxDCIiIiIdGVI4jVw4EDu2SIiIiLTYcsgIiIiIp0w8SIiIiLSCfsnEBGR6Zi5lAYZizNeRERERDph4kVERESkEyZeRERERDph4kVERESkEyZeRERERDph4kVERESkEyZeRERERDph4kVERESkEyZeRERERDph4kVERESkEyZeRERERDph4kVkAIfDgdzcXDgcDqOHQkREOmKTbCKdORwOjBw5Ek6nE3a7HQUFBUhLSzN6WEREpAPOeBHprLCwEE6nEx6PB06nE4WFhUYPich0oqKi/P4j0gtnvIh0lpGRAbvdrs54ZWRkGD0kItNJTU01eghkUky8iHSWlpaGgoICFBYWIiMjg8uMREQmwqVGIiIiIp1wxotIZ9xcT0RkXpzxItJZYWEhqqur4fF4UF1dzc31REQmwsSLSGcxMTHwer0AAK/Xi5iYGINHREREeuFSI5HOysrKIMsyvF4vZFlGWVmZ0UMiMp2dO3f6fc5TjqQXJl5EOsvIyEBERATLSRAZ6OLFi0YPgUyKiReRzlhOgojIvJh4ERkgLS2NCRcRkQlxcz0RERGRTph4EREREemEiRcRERGRTgzZ49WtWzdER0fDYrHAarXWOdZLRERE1BIZtrl+y5YtaN++vVE/noiIiEh3XGokIiIi0okhiZckSRg9ejRSUlKQl5cX9DZ5eXlITU1FamoqSktLdR4hERERkfYMWWrctm0b4uPj8Z///AejRo1C3759kZ6e7neb7OxsZGdnA2ArByIiImoZDJnxio+PBwDExsZi/Pjx2LFjhxHDICIiItKV7onXpUuXcOHCBfXjjRs3on///noPg4iIiEh3ui81nj59GuPHjwcAuN1u3Hvvvbjjjjv0HgYRERGR7iQhhDB6EFfTvn17dOvWTdefWVpaig4dOuj6M8OFWWM3a9yAeWM3a9yAeWM3a9yAeWM3Iu5jx47hzJkzQb/XLBIvI6Smppq2sKtZYzdr3IB5Yzdr3IB5Yzdr3IB5Yw+3uFnHi4iIiEgnTLyIiIiIdGLJycnJMXoQ4SolJcXoIRjGrLGbNW7AvLGbNW7AvLGbNW7AvLGHU9zc40VERESkEy41EhEREenENInX8ePHMWLECCQmJqJfv3547bXXAADl5eUYNWoUevXqhVGjRuHs2bMAACEE5s2bh549eyI5ORm7d+9Wr7Vw4UL069cPCQkJmDdvHsJ90lDL2J944gn0798f/fv3x9///ndD4qmvhsZ94MABpKWlISIiAosXL/a71meffYY+ffqgZ8+eePHFF3WPpaG0jH3atGmIjY1tFoWOtYo71HXCmVaxV1VVYciQIRgwYAD69euHP/7xj4bEU19a3tcBwOPx4Oabb8Zdd92laxzXQsvYu3XrhqSkJAwcODDs2/RpGfe5c+cwadIk9O3bFwkJCXA4HE0fgDCJkydPil27dgkhhKioqBC9evUS+/fvF48//rjIzc0VQgiRm5srFi5cKIQQYsOGDeKOO+4QXq9XOBwOMWTIECGEENu3bxfDhw8XbrdbuN1uMWzYMLFlyxZDYqovrWJfv369+PWvfy1cLpe4ePGiSE1NFefPnzcmqHpoaNynT58WO3bsEE899ZRYtGiReh232y26d+8uDh8+LKqrq0VycrLYv3+//gE1gFaxCyHEl19+KXbt2iX69eunbxDXQKu4Q10nnGkVu9frFRcuXBBCCOF0OsWQIUOEw+HQOZr60/K+LoQQS5YsEVOmTBF33nmnfkFcIy1j79q1qygtLdU3gGukZdxZWVli+fLlQgghqqurxdmzZ5t8/KaZ8erUqRMGDRoEAIiOjkZCQgJOnDiBtWvX4v777wcA3H///VizZg0AYO3atcjKyoIkSRg2bBjOnTuHkpISSJKEqqoqOJ1OVFdXw+VyoWPHjobFVR9axf7jjz8iPT0dVqsVkZGRSE5OxmeffWZYXFfT0LhjY2MxePBg2Gw2v+vs2LEDPXv2RPfu3WG32zF58mSsXbtW32AaSKvYASA9PR3t2rXTb/CNoFXcoa4TzrSKXZIkREVFAQBcLhdcLhckSdIxkobR8r5eXFyMDRs24KGHHtIvgEbQMvbmRKu4z58/j61bt2L69OkAALvdjrZt2zb5+E2TePk6duwY9uzZg6FDh+L06dPo1KkTACAuLg6nT58GAJw4cQI33nij+m+6dOmCEydOIC0tDSNGjECnTp3QqVMn/OY3v0FCQoIhcVyLxsQ+YMAAfPbZZ/jll19w5swZbNmyBcePHzckjoaqT9yhhPp9NBeNib050ypu3+s0F42N3ePxYODAgYiNjcWoUaOaTeyNjXv+/Pl4+eWXIcvN76WxsbFLkoTRo0cjJSUFeXl5TT1czTQm7qNHj6JDhw548MEHcfPNN+Ohhx7CpUuXmnzMze/e1UgXL17ExIkTsXTpUrRu3drve5IkXfWd3U8//YR//etfKC4uxokTJ7B582YUFRU15ZA109jYR48ejd/+9rcYPnw4pkyZgrS0NFgslqYcsiYaG3dzZtbYtYr7StcJV1rEbrFY8N1336G4uBg7duzADz/80FTD1Uxj416/fj1iY2PDquxAfWnxN9+2bRt2796N/Px8LFu2DFu3bm2q4WqmsXG73W7s3r0bs2fPxp49exAZGanLHl5TJV4ulwsTJ07E1KlTMWHCBABAx44dUVJSAgAoKSlBbGwsACA+Pt5vNqe4uBjx8fH45z//iWHDhiEqKgpRUVEYM2aMPpvxGkmL2AHg6aefxnfffYdNmzZBCIHevXvrHEnDNCTuUK70+whnWsTeHGkVd7DrhDut/+Zt27bFiBEjwnpLAaBN3Nu3b8e6devQrVs3TJ48GZs3b8Z9993X5GNvLK3+5spzWmxsLMaPH48dO3Y03aA1oEXcXbp0QZcuXdQZ3UmTJvkdJmsqpkm8hBCYPn06EhIS8Nhjj6lfHzduHFauXAkAWLlyJe6++2716++99x6EEPj666/Rpk0bdOrUCTfddBO+/PJLuN1uuFwufPnll2G/1KhV7B6PB2VlZQCAvXv3Yu/evRg9erT+AdVTQ+MOZfDgwfj3v/+No0ePwul0YvXq1Rg3blyTjr2xtIq9udEq7lDXCWdaxV5aWopz584BACorK7Fp0yb07du36QbeSFrFnZubi+LiYhw7dgyrV6/G7bffjg8++KBJx95YWsV+6dIlXLhwQf1448aNYX2KWau44+LicOONN+LgwYMAgIKCAiQmJjbdwBVNvn0/TBQVFQkAIikpSQwYMEAMGDBAbNiwQZw5c0bcfvvtomfPnmLkyJGirKxMCFFzsufhhx8W3bt3F/379xfffvutEKLmhFt2drbo27evSEhIEAsWLDAyrHrRKvbKykqRkJAgEhISxNChQ8WePXuMDOuqGhp3SUmJiI+PF9HR0aJNmzYiPj5ePbW5YcMG0atXL9G9e3fx/PPPGxlWvWgZ++TJk0VcXJywWq0iPj5e/OUvfzEytCvSKu5Q1wlnWsX+/fffi4EDB4qkpCTRr18/8eyzzxoc2ZVpeV9XbNmypVmcatQq9sOHD4vk5GSRnJwsEhMTw/45Tsu/+Z49e0RKSopISkoSd999tygvL2/y8bNyPREREZFOTLPUSERERGQ0Jl5EREREOmHiRURERKQTJl5EREREOmHiRURERKQTJl5EZEo5OTlYvHix0cMgIpNh4kVERESkEyZeRGQaf/7zn9G7d2/ceuutarXq119/HYmJiUhOTsbkyZMNHiERtXRWowdARKSHXbt2YfXq1fjuu+/gdrsxaNAgpKSk4MUXX8TRo0cRERGhtsohImoqnPEiIlMoKirC+PHjcf3116N169Zqv83k5GRMnToVH3zwAaxWvhcloqbFxIuITG3Dhg2YM2cOdu/ejcGDB8Ptdhs9JCJqwZh4EZEppKenY82aNaisrMSFCxfw6aefwuv14vjx4xgxYgReeuklnD9/HhcvXjR6qETUgnFenYhMYdCgQbjnnnswYMAAxMbGYvDgwZAkCffddx/Onz8PIQTmzZuHtm3bGj1UImrBJCGEMHoQRERERGbApUYiIiIinTDxIiIiItIJEy8iIiIinTDxIiIiItIJEy8iIiIinTDxIiIiItIJEy8iIiIinTDxIiIiItLJ/wLIvg75AnNoIgAAAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<Figure size 720x432 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"from fbprophet.diagnostics import cross_validation\n",
|
|
"df_cv = cross_validation(\n",
|
|
" m, '365 days', initial='1825 days', period='365 days')\n",
|
|
"cutoff = df_cv['cutoff'].unique()[0]\n",
|
|
"df_cv = df_cv[df_cv['cutoff'].values == cutoff]\n",
|
|
"\n",
|
|
"fig = plt.figure(facecolor='w', figsize=(10, 6))\n",
|
|
"ax = fig.add_subplot(111)\n",
|
|
"ax.plot(m.history['ds'].values, m.history['y'], 'k.')\n",
|
|
"ax.plot(df_cv['ds'].values, df_cv['yhat'], ls='-', c='#0072B2')\n",
|
|
"ax.fill_between(df_cv['ds'].values, df_cv['yhat_lower'],\n",
|
|
" df_cv['yhat_upper'], color='#0072B2',\n",
|
|
" alpha=0.2)\n",
|
|
"ax.axvline(x=pd.to_datetime(cutoff), c='gray', lw=4, alpha=0.5)\n",
|
|
"ax.set_ylabel('y')\n",
|
|
"ax.set_xlabel('ds')\n",
|
|
"ax.text(x=pd.to_datetime('2010-01-01'),y=12, s='Initial', color='black',\n",
|
|
" fontsize=16, fontweight='bold', alpha=0.8)\n",
|
|
"ax.text(x=pd.to_datetime('2012-08-01'),y=12, s='Cutoff', color='black',\n",
|
|
" fontsize=16, fontweight='bold', alpha=0.8)\n",
|
|
"ax.axvline(x=pd.to_datetime(cutoff) + pd.Timedelta('365 days'), c='gray', lw=4,\n",
|
|
" alpha=0.5, ls='--')\n",
|
|
"ax.text(x=pd.to_datetime('2013-01-01'),y=6, s='Horizon', color='black',\n",
|
|
" fontsize=16, fontweight='bold', alpha=0.8);"
|
|
]
|
|
},
|
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{
|
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"cell_type": "markdown",
|
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"metadata": {},
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"source": [
|
|
"[The Prophet paper](https://peerj.com/preprints/3190.pdf) gives further description of simulated historical forecasts.\n",
|
|
"\n",
|
|
"This cross validation procedure can be done automatically for a range of historical cutoffs using the `cross_validation` function. We specify the forecast horizon (`horizon`), and then optionally the size of the initial training period (`initial`) and the spacing between cutoff dates (`period`). By default, the initial training period is set to three times the horizon, and cutoffs are made every half a horizon.\n",
|
|
"\n",
|
|
"The output of `cross_validation` is a dataframe with the true values `y` and the out-of-sample forecast values `yhat`, at each simulated forecast date and for each cutoff date. In particular, a forecast is made for every observed point between `cutoff` and `cutoff + horizon`. This dataframe can then be used to compute error measures of `yhat` vs. `y`.\n",
|
|
"\n",
|
|
"Here we do cross-validation to assess prediction performance on a horizon of 365 days, starting with 730 days of training data in the first cutoff and then making predictions every 180 days. On this 8 year time series, this corresponds to 11 total forecasts."
|
|
]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"output_hidden": true
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
|
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"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Making 11 forecasts with cutoffs between 2010-02-15 and 2015-01-20\n",
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"\n"
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]
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},
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{
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"data": {
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"text/plain": [
|
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" ds y yhat yhat_lower yhat_upper cutoff\n",
|
|
"1 2010-02-16 8.242493 8.954992 8.423614 9.496403 2010-02-15\n",
|
|
"2 2010-02-17 8.008033 8.721365 8.226481 9.219106 2010-02-15\n",
|
|
"3 2010-02-18 8.045268 8.605072 8.103985 9.104483 2010-02-15\n",
|
|
"4 2010-02-19 7.928766 8.526855 8.023088 9.042035 2010-02-15\n",
|
|
"5 2010-02-20 7.745003 8.268741 7.757920 8.779416 2010-02-15\n",
|
|
"6 2010-02-21 7.866339 8.599935 8.084956 9.060284 2010-02-15\n"
|
|
]
|
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},
|
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"metadata": {},
|
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"output_type": "display_data"
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}
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],
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"source": [
|
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"%%R\n",
|
|
"df.cv <- cross_validation(m, initial = 730, period = 180, horizon = 365, units = 'days')\n",
|
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"head(df.cv)"
|
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"output_type": "display_data"
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},
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n"
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},
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"data": {
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>ds</th>\n",
|
|
" <th>yhat</th>\n",
|
|
" <th>yhat_lower</th>\n",
|
|
" <th>yhat_upper</th>\n",
|
|
" <th>y</th>\n",
|
|
" <th>cutoff</th>\n",
|
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
|
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" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>2010-02-16</td>\n",
|
|
" <td>8.957284</td>\n",
|
|
" <td>8.471820</td>\n",
|
|
" <td>9.464242</td>\n",
|
|
" <td>8.242493</td>\n",
|
|
" <td>2010-02-15</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>2010-02-17</td>\n",
|
|
" <td>8.723736</td>\n",
|
|
" <td>8.215131</td>\n",
|
|
" <td>9.236662</td>\n",
|
|
" <td>8.008033</td>\n",
|
|
" <td>2010-02-15</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>2010-02-18</td>\n",
|
|
" <td>8.607496</td>\n",
|
|
" <td>8.059417</td>\n",
|
|
" <td>9.089188</td>\n",
|
|
" <td>8.045268</td>\n",
|
|
" <td>2010-02-15</td>\n",
|
|
" </tr>\n",
|
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" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>2010-02-19</td>\n",
|
|
" <td>8.529364</td>\n",
|
|
" <td>8.019238</td>\n",
|
|
" <td>9.034076</td>\n",
|
|
" <td>7.928766</td>\n",
|
|
" <td>2010-02-15</td>\n",
|
|
" </tr>\n",
|
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" <tr>\n",
|
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" <th>4</th>\n",
|
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" <td>2010-02-20</td>\n",
|
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" <td>8.271329</td>\n",
|
|
" <td>7.779745</td>\n",
|
|
" <td>8.760580</td>\n",
|
|
" <td>7.745003</td>\n",
|
|
" <td>2010-02-15</td>\n",
|
|
" </tr>\n",
|
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" </tbody>\n",
|
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"</table>\n",
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"</div>"
|
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],
|
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"text/plain": [
|
|
" ds yhat yhat_lower yhat_upper y cutoff\n",
|
|
"0 2010-02-16 8.957284 8.471820 9.464242 8.242493 2010-02-15\n",
|
|
"1 2010-02-17 8.723736 8.215131 9.236662 8.008033 2010-02-15\n",
|
|
"2 2010-02-18 8.607496 8.059417 9.089188 8.045268 2010-02-15\n",
|
|
"3 2010-02-19 8.529364 8.019238 9.034076 7.928766 2010-02-15\n",
|
|
"4 2010-02-20 8.271329 7.779745 8.760580 7.745003 2010-02-15"
|
|
]
|
|
},
|
|
"execution_count": 3,
|
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"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from fbprophet.diagnostics import cross_validation\n",
|
|
"df_cv = cross_validation(m, initial='730 days', period='180 days', horizon = '365 days')\n",
|
|
"df_cv.head()"
|
|
]
|
|
},
|
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{
|
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"cell_type": "markdown",
|
|
"metadata": {},
|
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"source": [
|
|
"In R, the argument `units` must be a type accepted by `as.difftime`, which is weeks or shorter. In Python, the string for `initial`, `period`, and `horizon` should be in the format used by Pandas Timedelta, which accepts units of days or shorter.\n",
|
|
"\n",
|
|
"Custom cutoffs can also be supplied as a list of dates to to the `cutoffs` keyword in the `cross_validation` function in Python and R. For example, three cutoffs six months apart, would need to be passed to the `cutoffs` argument in a date format like below:\n",
|
|
"```python\n",
|
|
"#Python\n",
|
|
"cutoffs = [pd.Timestamp('2013-02-15'), pd.Timestamp('2013-08-15'), pd.Timestamp('2014-02-15')]\n",
|
|
"```\n",
|
|
"```r\n",
|
|
"#R\n",
|
|
"cutoffs = c(as.Date('2013-02-15'), as.Date('2013-08-15'), as.Date('2014-02-15')\n",
|
|
"```"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Cross-Validation in Parallel "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Cross-validation can also be run in parallel mode in Python, by setting specifying the `parallel` keyword. Three modes are supported\n",
|
|
"\n",
|
|
"* `parallel=\"processes\"`\n",
|
|
"* `parallel=\"threads\"`\n",
|
|
"* `parallel=\"dask\"`\n",
|
|
"\n",
|
|
"For problems that aren't too big, we recommend using `parallel=\"processes\"`. It will achieve the highest performance when the parallel cross validation can be done on a single machine. For large problems, a [Dask](https://dask.org) cluster can be used to do the cross validation on many machines. You will need to [install Dask](https://docs.dask.org/en/latest/install.html) separately, as it will not be installed with `fbprophet`.\n",
|
|
"\n",
|
|
"\n",
|
|
"```python\n",
|
|
"from dask.distributed import Client\n",
|
|
"\n",
|
|
"client = Client() # connect to the cluster\n",
|
|
"df_cv = cross_validation(m, initial='730 days', period='180 days', horizon='365 days',\n",
|
|
" parallel=\"dask\")\n",
|
|
"```\n",
|
|
"\n",
|
|
"The `performance_metrics` utility can be used to compute some useful statistics of the prediction performance (`yhat`, `yhat_lower`, and `yhat_upper` compared to `y`), as a function of the distance from the cutoff (how far into the future the prediction was). The statistics computed are mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), median absolute percent error (MDAPE) and coverage of the `yhat_lower` and `yhat_upper` estimates. These are computed on a rolling window of the predictions in `df_cv` after sorting by horizon (`ds` minus `cutoff`). By default 10% of the predictions will be included in each window, but this can be changed with the `rolling_window` argument."
|
|
]
|
|
},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 6,
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"metadata": {
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"output_hidden": true
|
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},
|
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"outputs": [
|
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{
|
|
"data": {
|
|
"text/plain": [
|
|
" horizon mse rmse mae mape coverage\n",
|
|
"1 37 days 0.4971086 0.7050593 0.5075009 0.05882459 0.6765646\n",
|
|
"2 38 days 0.5029463 0.7091870 0.5125229 0.05940706 0.6765646\n",
|
|
"3 39 days 0.5252677 0.7247535 0.5186555 0.06001158 0.6751942\n",
|
|
"4 40 days 0.5326181 0.7298069 0.5215775 0.06032500 0.6788488\n",
|
|
"5 41 days 0.5401377 0.7349406 0.5226521 0.06041353 0.6838739\n",
|
|
"6 42 days 0.5438937 0.7374915 0.5230473 0.06043453 0.6891275\n"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"%%R\n",
|
|
"df.p <- performance_metrics(df.cv)\n",
|
|
"head(df.p)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
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"metadata": {},
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"outputs": [
|
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{
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|
|
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|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>horizon</th>\n",
|
|
" <th>mse</th>\n",
|
|
" <th>rmse</th>\n",
|
|
" <th>mae</th>\n",
|
|
" <th>mape</th>\n",
|
|
" <th>mdape</th>\n",
|
|
" <th>coverage</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>37 days</td>\n",
|
|
" <td>0.493403</td>\n",
|
|
" <td>0.702427</td>\n",
|
|
" <td>0.504660</td>\n",
|
|
" <td>0.058481</td>\n",
|
|
" <td>0.050009</td>\n",
|
|
" <td>0.684102</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>38 days</td>\n",
|
|
" <td>0.499306</td>\n",
|
|
" <td>0.706616</td>\n",
|
|
" <td>0.509672</td>\n",
|
|
" <td>0.059061</td>\n",
|
|
" <td>0.049334</td>\n",
|
|
" <td>0.684102</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>39 days</td>\n",
|
|
" <td>0.521533</td>\n",
|
|
" <td>0.722172</td>\n",
|
|
" <td>0.515789</td>\n",
|
|
" <td>0.059662</td>\n",
|
|
" <td>0.049502</td>\n",
|
|
" <td>0.682732</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>40 days</td>\n",
|
|
" <td>0.528744</td>\n",
|
|
" <td>0.727148</td>\n",
|
|
" <td>0.518585</td>\n",
|
|
" <td>0.059961</td>\n",
|
|
" <td>0.049254</td>\n",
|
|
" <td>0.683874</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>41 days</td>\n",
|
|
" <td>0.536127</td>\n",
|
|
" <td>0.732207</td>\n",
|
|
" <td>0.519498</td>\n",
|
|
" <td>0.060030</td>\n",
|
|
" <td>0.049334</td>\n",
|
|
" <td>0.691412</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" horizon mse rmse mae mape mdape coverage\n",
|
|
"0 37 days 0.493403 0.702427 0.504660 0.058481 0.050009 0.684102\n",
|
|
"1 38 days 0.499306 0.706616 0.509672 0.059061 0.049334 0.684102\n",
|
|
"2 39 days 0.521533 0.722172 0.515789 0.059662 0.049502 0.682732\n",
|
|
"3 40 days 0.528744 0.727148 0.518585 0.059961 0.049254 0.683874\n",
|
|
"4 41 days 0.536127 0.732207 0.519498 0.060030 0.049334 0.691412"
|
|
]
|
|
},
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from fbprophet.diagnostics import performance_metrics\n",
|
|
"df_p = performance_metrics(df_cv)\n",
|
|
"df_p.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Cross validation performance metrics can be visualized with `plot_cross_validation_metric`, here shown for MAPE. Dots show the absolute percent error for each prediction in `df_cv`. The blue line shows the MAPE, where the mean is taken over a rolling window of the dots. We see for this forecast that errors around 5% are typical for predictions one month into the future, and that errors increase up to around 11% for predictions that are a year out."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {
|
|
"output_hidden": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"image/png": 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\n"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"%%R -w 10 -h 6 -u in\n",
|
|
"plot_cross_validation_metric(df.cv, metric = 'mape')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
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"text/plain": [
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"<Figure size 720x432 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from fbprophet.plot import plot_cross_validation_metric\n",
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"fig = plot_cross_validation_metric(df_cv, metric='mape')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The size of the rolling window in the figure can be changed with the optional argument `rolling_window`, which specifies the proportion of forecasts to use in each rolling window. The default is 0.1, corresponding to 10% of rows from `df_cv` included in each window; increasing this will lead to a smoother average curve in the figure. The `initial` period should be long enough to capture all of the components of the model, in particular seasonalities and extra regressors: at least a year for yearly seasonality, at least a week for weekly seasonality, etc.\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Hyperparameter Optimisation\n",
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"\n",
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"Auto parameter tuning can also be carried out by evaluating the parameter combinations in serial and using the in-built parallelization over cutoffs. An example implementation with multi-processing in Python is shown below, with a grid of six combinations of `changepoint_prior_scale` and `changepoint_range` parameters. The function `create_param_combinaitons` creates a dataframe of parameter combinations, which can be evaluated serially to call `single_cv_run` with the `parallel` keyword to parallelze over cutoffs. The best parameter combination is selected based on the best `rmse` score but can be switched to another performance metric depending on the use case.\n",
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"\n",
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"As an alternative for creating parameter combinations, one could also use the `ParameterGrid` class in `sklearn.model_selection`. This would need to be installed and imported separately if required, as it is not included with Prophet."
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]
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},
|
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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" The best param combination is {'changepoint_prior_scale': 0.05, 'changepoint_range': 0.8}\n"
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]
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},
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{
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"data": {
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>horizon</th>\n",
|
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" <th>rmse</th>\n",
|
|
" <th>mape</th>\n",
|
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" <th>params</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>200 days</td>\n",
|
|
" <td>0.450030</td>\n",
|
|
" <td>0.034958</td>\n",
|
|
" <td>{'changepoint_prior_scale': 0.05, 'changepoint_range': 0.8}</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>200 days</td>\n",
|
|
" <td>0.453755</td>\n",
|
|
" <td>0.035471</td>\n",
|
|
" <td>{'changepoint_prior_scale': 0.05, 'changepoint_range': 0.9}</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>200 days</td>\n",
|
|
" <td>0.456887</td>\n",
|
|
" <td>0.035469</td>\n",
|
|
" <td>{'changepoint_prior_scale': 0.5, 'changepoint_range': 0.8}</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>200 days</td>\n",
|
|
" <td>0.490453</td>\n",
|
|
" <td>0.039134</td>\n",
|
|
" <td>{'changepoint_prior_scale': 0.5, 'changepoint_range': 0.9}</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>200 days</td>\n",
|
|
" <td>0.463969</td>\n",
|
|
" <td>0.036428</td>\n",
|
|
" <td>{'changepoint_prior_scale': 5.0, 'changepoint_range': 0.8}</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>200 days</td>\n",
|
|
" <td>0.512077</td>\n",
|
|
" <td>0.040488</td>\n",
|
|
" <td>{'changepoint_prior_scale': 5.0, 'changepoint_range': 0.9}</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
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"</div>"
|
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],
|
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"text/plain": [
|
|
" horizon rmse mape \\\n",
|
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"0 200 days 0.450030 0.034958 \n",
|
|
"1 200 days 0.453755 0.035471 \n",
|
|
"2 200 days 0.456887 0.035469 \n",
|
|
"3 200 days 0.490453 0.039134 \n",
|
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"4 200 days 0.463969 0.036428 \n",
|
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"5 200 days 0.512077 0.040488 \n",
|
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"\n",
|
|
" params \n",
|
|
"0 {'changepoint_prior_scale': 0.05, 'changepoint_range': 0.8} \n",
|
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"1 {'changepoint_prior_scale': 0.05, 'changepoint_range': 0.9} \n",
|
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"2 {'changepoint_prior_scale': 0.5, 'changepoint_range': 0.8} \n",
|
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"3 {'changepoint_prior_scale': 0.5, 'changepoint_range': 0.9} \n",
|
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"4 {'changepoint_prior_scale': 5.0, 'changepoint_range': 0.8} \n",
|
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"5 {'changepoint_prior_scale': 5.0, 'changepoint_range': 0.9} "
|
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]
|
|
},
|
|
"execution_count": 2,
|
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"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
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"source": [
|
|
"from fbprophet.diagnostics import cross_validation, performance_metrics\n",
|
|
"import itertools\n",
|
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"\n",
|
|
"def create_param_combinations(**param_dict):\n",
|
|
" param_iter = itertools.product(*param_dict.values())\n",
|
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" params =[]\n",
|
|
" for param in param_iter:\n",
|
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" params.append(param) \n",
|
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" params_df = pd.DataFrame(params, columns=list(param_dict.keys()))\n",
|
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" return params_df\n",
|
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"\n",
|
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"def single_cv_run(history_df, metrics, param_dict, parallel):\n",
|
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" m = Prophet(**param_dict)\n",
|
|
" m.fit(history_df)\n",
|
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" df_cv = cross_validation(m, initial='2600 days', period='100 days', horizon = '200 days', parallel=parallel)\n",
|
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" df_p = performance_metrics(df_cv, rolling_window=1)\n",
|
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" df_p['params'] = str(param_dict)\n",
|
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" df_p = df_p.loc[:, metrics]\n",
|
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" return df_p\n",
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"\n",
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"\n",
|
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"pd.set_option('display.max_colwidth', None)\n",
|
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"param_grid = { \n",
|
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" 'changepoint_prior_scale': [0.05, 0.5, 5],\n",
|
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" 'changepoint_range': [0.8, 0.9],\n",
|
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" }\n",
|
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"metrics = ['horizon', 'rmse', 'mape', 'params'] \n",
|
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"results = []\n",
|
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"\n",
|
|
"\n",
|
|
"params_df = create_param_combinations(**param_grid)\n",
|
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"for param in params_df.values:\n",
|
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" param_dict = dict(zip(params_df.keys(), param))\n",
|
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" cv_df = single_cv_run(df, metrics, param_dict, parallel=\"processes\")\n",
|
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" results.append(cv_df)\n",
|
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"results_df = pd.concat(results).reset_index(drop=True)\n",
|
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"best_param = results_df.loc[results_df['rmse'] == min(results_df['rmse']), ['params']]\n",
|
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"print(f'\\n The best param combination is {best_param.values[0][0]}')\n",
|
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"results_df"
|
|
]
|
|
},
|
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{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Alternatively, in some cases one could benefit from parallelizing over parameter values instead, when the number of parameter combinations are large and the user has access to a large number of cores or a cluster. In the example below, parameter combinations are evaluated in parallel using `dask.distributed.Client`. The helper function `parallelize_param_combinations` parallelizes the calls to `single_cv_run` for each parameter combination. The cutoffs in `cross_validation` are then evaluated serially. To switch to other parallel modes in this example, import the `concurrent.futures` module and set `pool=concurrent.futures.ThreadPoolExecutor()`for execution in threads or `pool=concurrent.futures.ProcessPoolExecutor()` for multi-processing."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\n",
|
|
" The best param combination is {'changepoint_prior_scale': 0.05, 'changepoint_range': 0.8}\n"
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]
|
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>horizon</th>\n",
|
|
" <th>rmse</th>\n",
|
|
" <th>mape</th>\n",
|
|
" <th>params</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>200 days</td>\n",
|
|
" <td>0.450030</td>\n",
|
|
" <td>0.034958</td>\n",
|
|
" <td>{'changepoint_prior_scale': 0.05, 'changepoint_range': 0.8}</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>200 days</td>\n",
|
|
" <td>0.453755</td>\n",
|
|
" <td>0.035471</td>\n",
|
|
" <td>{'changepoint_prior_scale': 0.05, 'changepoint_range': 0.9}</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>200 days</td>\n",
|
|
" <td>0.456887</td>\n",
|
|
" <td>0.035469</td>\n",
|
|
" <td>{'changepoint_prior_scale': 0.5, 'changepoint_range': 0.8}</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>200 days</td>\n",
|
|
" <td>0.490453</td>\n",
|
|
" <td>0.039134</td>\n",
|
|
" <td>{'changepoint_prior_scale': 0.5, 'changepoint_range': 0.9}</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>200 days</td>\n",
|
|
" <td>0.463969</td>\n",
|
|
" <td>0.036428</td>\n",
|
|
" <td>{'changepoint_prior_scale': 5.0, 'changepoint_range': 0.8}</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5</th>\n",
|
|
" <td>200 days</td>\n",
|
|
" <td>0.512077</td>\n",
|
|
" <td>0.040488</td>\n",
|
|
" <td>{'changepoint_prior_scale': 5.0, 'changepoint_range': 0.9}</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" horizon rmse mape \\\n",
|
|
"0 200 days 0.450030 0.034958 \n",
|
|
"1 200 days 0.453755 0.035471 \n",
|
|
"2 200 days 0.456887 0.035469 \n",
|
|
"3 200 days 0.490453 0.039134 \n",
|
|
"4 200 days 0.463969 0.036428 \n",
|
|
"5 200 days 0.512077 0.040488 \n",
|
|
"\n",
|
|
" params \n",
|
|
"0 {'changepoint_prior_scale': 0.05, 'changepoint_range': 0.8} \n",
|
|
"1 {'changepoint_prior_scale': 0.05, 'changepoint_range': 0.9} \n",
|
|
"2 {'changepoint_prior_scale': 0.5, 'changepoint_range': 0.8} \n",
|
|
"3 {'changepoint_prior_scale': 0.5, 'changepoint_range': 0.9} \n",
|
|
"4 {'changepoint_prior_scale': 5.0, 'changepoint_range': 0.8} \n",
|
|
"5 {'changepoint_prior_scale': 5.0, 'changepoint_range': 0.9} "
|
|
]
|
|
},
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from dask.distributed import Client\n",
|
|
"import functools\n",
|
|
"from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor\n",
|
|
"\n",
|
|
"\n",
|
|
"def parallelize_param_combinations(history_df, params_df, single_cv_callable, pool):\n",
|
|
" results = []\n",
|
|
" for param in params_df.values:\n",
|
|
" param_dict = dict(zip(params_df.keys(), param))\n",
|
|
" if isinstance(pool,(ThreadPoolExecutor, ProcessPoolExecutor)):\n",
|
|
" future = pool.submit(single_cv_callable, history_df, param_dict=param_dict)\n",
|
|
" results.append(future.result())\n",
|
|
" elif isinstance(pool, Client):\n",
|
|
" remote_df = pool.scatter(history_df)\n",
|
|
" future = pool.submit(single_cv_callable, remote_df, param_dict=param_dict)\n",
|
|
" results.append(future)\n",
|
|
" if isinstance(pool, Client):\n",
|
|
" results = pool.gather(results)\n",
|
|
" results_df = pd.concat(results).reset_index(drop=True)\n",
|
|
" \n",
|
|
" return results_df\n",
|
|
"\n",
|
|
"\n",
|
|
"single_cv_callable = functools.partial(single_cv_run, metrics=metrics, parallel=None)\n",
|
|
"\n",
|
|
"pool = Client()\n",
|
|
"results_df = parallelize_param_combinations(df, params_df, single_cv_callable, pool=pool)\n",
|
|
"best_param = results_df.loc[results_df['rmse'] == min(results_df['rmse']), ['params']]\n",
|
|
"print(f'\\n The best param combination is {best_param.values[0][0]}')\n",
|
|
"results_df"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Recommended Hyperparameter Ranges"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"In the examples above, we have used recommended initial settings for `changepoint_prior_scale:[0.05, 0.5, 5]` and \n",
|
|
"`changepoint_range: [0.8, 0.9]`. We could alternatively also use a random search to carry out a sweep between a range of values e.g. `np.random.uniform(0.05, 5, 3)`. Other parameters like the `seasonality_prior_scale`, `holidays_prior_scale` and `seasonality_mode` could also be optimised for. For the seasonality and holiday prior scales, recommended values to start with are `[0.1,1,10]`, and it is better to set these values on a log scale in the grid e.g. `np.random.uniform(-1, 1, 5)`."
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python (prophet)",
|
|
"language": "python",
|
|
"name": "prophet"
|
|
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|
|
"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
|
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"nbconvert_exporter": "python",
|
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"pygments_lexer": "ipython3",
|
|
"version": "3.8.3"
|
|
}
|
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|
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"nbformat": 4,
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"nbformat_minor": 1
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|