{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import networkx\n",
"import math\n",
"import scipy.optimize\n",
"import numpy\n",
"from lib.time_graph import *\n",
"from lib.time_graph_signal_proc import *\n",
"from lib.vis import *\n",
"from lib.syn import *\n",
"from lib.io import *\n",
"from lib.datasets import *\n",
"from lib.time_graph import *\n",
"from lib.experiments import *\n",
"from IPython.display import Image"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime, date, time, timedelta\n",
"import statistics\n",
"\n",
"def check_time_range_traffic(file_id, days, hours):\n",
" start_time = datetime.strptime(\"1/04/11 00:00\", \"%d/%m/%y %H:%M\")\n",
" file_time = start_time + timedelta(minutes=int(file_id)*5)\n",
" \n",
" check_day = False\n",
" \n",
" for d in days:\n",
" if file_time.weekday() == d:\n",
" check_day = True\n",
" break\n",
" \n",
" if check_day is False:\n",
" return False\n",
" \n",
" check_hour = False\n",
" \n",
" for h in hours:\n",
" if file_time.hour == h:\n",
" check_hour = True\n",
" break\n",
" \n",
" return check_hour"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"friday_snaps = []\n",
"days = [4]\n",
"hours=range(24)\n",
"\n",
"for i in range(0, 8640):\n",
" if check_time_range_traffic(i, days, hours):\n",
" friday_snaps.append(i)\n",
" \n",
"friday_snaps = numpy.array(friday_snaps)\n",
"\n",
"friday_snaps = friday_snaps[12*12:12*24]\n",
"\n",
"friday_snaps = friday_snaps[0::36]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"sG = read_graph(small_traffic[\"path\"] + \"traffic.graph\", small_traffic[\"path\"] + \"traffic.data\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"G = TimeGraph(0.)\n",
"\n",
"for i in range(len(friday_snaps)):\n",
" for e in sG.edges():\n",
" G.add_edge(e[0], e[1], i)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"F = read_time_values(small_traffic[\"path\"]+\"traffic\", G, friday_snaps)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'cut': array([ 1., 1., 1., 1., 1., -1., -1., -1., -1., -1., -1., -1., -1.,\n",
" 1., 1., 1., 1., 1., -1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., -1., -1., -1., -1., -1., 1., 1., 1., 1.,\n",
" 1., -1., -1., -1., -1., 1., 1., 1., 1., 1., 1., 1., -1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., -1., -1., -1., -1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., -1., -1., -1., -1., -1.,\n",
" 1., 1., 1., 1., 1., 1., 1., -1., -1., 1., 1., 1., 1.,\n",
" 1., -1., -1., -1., -1., -1., -1., -1., -1., 1., 1., 1., 1.,\n",
" 1., -1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" -1., -1., -1., -1., -1., 1., 1., 1., 1., 1., -1., -1., -1.,\n",
" -1., 1., 1., 1., 1., 1., 1., 1., -1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., -1., -1.,\n",
" -1., -1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., -1., -1., -1., -1., -1., 1., 1., 1., 1.,\n",
" 1., 1., 1., -1., -1., 1., 1., 1., 1., 1., -1., -1., -1.,\n",
" -1., -1., -1., -1., -1., 1., 1., 1., 1., 1., -1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., -1., -1., -1., -1.,\n",
" -1., 1., 1., 1., 1., 1., -1., -1., -1., -1., 1., 1., 1.,\n",
" 1., 1., 1., 1., -1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., -1., -1., -1., -1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" -1., -1., -1., -1., -1., 1., 1., 1., 1., 1., 1., 1., -1.,\n",
" -1., 1., 1., 1., 1., 1., -1., -1., -1., -1., -1., -1., -1.,\n",
" -1., 1., 1., 1., 1., 1., -1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., -1., -1., -1., -1., -1., 1., 1., 1.,\n",
" 1., 1., -1., -1., -1., -1., 1., 1., 1., 1., 1., 1., 1.,\n",
" -1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., -1., -1., -1., -1., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 1., 1., 1., 1., -1., -1., -1., -1.,\n",
" -1., 1., 1., 1., 1., 1., 1., 1., -1., -1.]), 'score': 0.0004761904761904762, 'edges': 4.0, 'swaps': 0.0}\n"
]
}
],
"source": [
"tc = TemporalCuts(\"name\", \"diff-sparse\")\n",
"c = tc.cut(G)\n",
"print(c)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/networkx/drawing/nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n",
" if cb.is_numlike(alpha):\n"
]
},
{
"data": {
"image/png": 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MKpZOLYrPaC5UVyIyDbilH45qc51dO+4HPCwP5C0if3gZjt0ajsT03pKQRL4oeKSuunJ3kfnqlgtNcy+MgueaoajJkc+1ez80X0qhT1KydGp5UEZTIiIdu8OlT8DT+HR8XlXdS1X13Fq2sy5Jnpfjm2grIpsBn1PVq8rcwsYmTER3KLpyEj5GSqF/FfQ+Drv8p+or1Wu0P2agjKZERN4LoKp/CHh/GvBWVf11VRvWKCRRNiiUKipI6v0pTLkHfvx91fsq2tZmY2R+2rCZ1klwwU1wAnCGqvbXUhLJDJTRlIjIBcAVquobnisi5wA3qeqa6rasQRCZuAGeaodNW/yX+oYrFvgk9faCtsOApEx+p2EImWmJyE47wuxnYNsMHEqNJJHMB2U0Kx1Bxsljghmn0hFYuwPc9qwTLw32iQwZpxFCp16URRtuJvYwIhYUUU7cLMhXIkphdR98KAdbZIaHsOdtRlXuiRkoo+kQke2Al0Le3wao+fp7nXPSUrhKVJfEKCwYmtRLyuR3moRbPPHeoPyqqtwTW+Izmg4RORH4g6o+F/D+F4C7VfX5qjasQRCRHYEPq+qVMXYuS0CFUUZSdE8sD8poRnYIMk4ebzHjFBORLkRmIXIWIieud53bycANMY9QjqReo7yk5p7YEp/RVIhIGyHJiiIyjvhCpc1LgFJ5B9z0Ijy0HYzH5ZhFMfqkXqPcpOaemIEymo39gAdC3n8frty7EURw9dbWFmA72If4DvTyJPUa5SQ198SW+Ixm4z+BP4W8vy/wYJXaUq8kCWqIwpTL00dq7okZKKPZGKuqvkt4IpIBUNVyFjVsLEarVF6MKZenjxTdEzNQRtMgIlsBrxa9OOjkvxcuPhCW1KRx9UMlHOgzFZZnIRfwvimXV5+44rMVvSfmgzKaicOAuwBfJ/9+kLsXFJFjMOWCIMrvQFdduYfIt+6D920O7yYsqdeoDkXis/0grdAipiRhGGWkQEfsajjwi3BXkJO/YM3KlAuCKbsDXUR2AHRz1ffGSOo1qoV79mcg0vVjuPooWNLlEtyrdk8sUddoTIpmSAod/Z62G7Aa2JxgJz/4CZkaZU/i9FTjrwXOVtU4YelGDRCR83Hq/lX1z5oPymg8hmZIR+A60k6B1nZnkMYwMjzaj3hO/maj/A7044DbzTilnpZaBA/ZEp/RiESFQccl7+T3FdRsYmYSUlOIIAd6UdmGb8OfgT1V9ZcVba1Rt9gMymgs4odBx8GUC/zwyrF3wz1ZZ8TDy7GLTPTqQy0FrgEuAa79PDyxAfbyZrxGGvGiXH8M+yNyYrVXFMwHZTQWIrNwnWBnGY62DpiNqs2gfBCR066Au89y6hz+QQ3BqhN5hteFMtJBkQ93AMa2OAmwqkZV2hKf0WgkCYOOwpQLAvCCG7Y8S/UJ4ImQXa2URr3hM6jwZCXyg76qRbnaEp/RaOTDoEeLKReE8w7gH6F7lFt1wqgW5ZSyGhVmoIxGI4mOWBCmXBDN0UTPLlNTtsGIScoGFWagjMYiWRj0MqCnH/p0yNk/0slvDENEOgBUNWqmmpqyDUZsUjWoMB+U0YjEDYOeBrR+G749C/7eASsx5YI4HAncGWO/1JRtMGKTqkGFzaCMxsMLg8bNhHr6XAi0fxi06rKT4dEO1ctR/Z4ZJx+KqubuCocQryRJaso2GLFJ4sOt+KDCZlBGY+LpiM0V2fEVuPLLrkMN0naT6jewDvAR1FXoewzaW2FbRMJDjVWXIXJ3Do7IRMtKWUBKOpgHXB9z34oPKiwPymhoRGQPYDdV/VXIPnNU9YIqNiv9lCl/6Vci278PntwU2iVqudV8funAJVVPJzxQoipalbbEZzQ6k3EKBmHYKG0kow41FhE5Dk66CvYVb7mVKNUJIw3MBJZrjWtBgS3xGY3PDsDfI/axJb5CSgk19l+e+yzw+8tUn8Ir24CV0kg/Xi2op+H/psDUjIvWq0l9LjNQRqMzERed54uIdOJG88YQpYQaD5ODEpG9gU1V9YeDLzojZrJR9YDqyl1F7lY4nBoOKsxAGQ2PhjtaJ+GWK4whRhVqLCIbAycAs8vcLqNKiMhuwMJaDyrMB2U0O124hF1jiJJDjT2NvguAi1U1V4G2GdXhw8Bvat0Im0EZqUWEnYGPJPjIr1V5JuFpuoBnE36m0UkSatwBbI/IibiQ4xnAb9SCHuoWEWkDOlS1u9ZtMQNlpJmdcLWD4kTZCS4setBAicgmwJsRn5sE/LXUBjYkXv4S0aHG4K772UBvDm58ChbtCu+teBuNSnI4cFetGwG2xGekm7twBicTY1uMiy4qZDLwfMQ5tgBWlKvBDcRMnG/ON9S4YMTQghvodmagfSpMxZVisCKE9csBwP21bgSYgTJSjCoKnA5ELTV0A6d7+xcymegcqBZVjRux1jwUyEX1w4AO5S8NQHBcvlSxFINRJgqkrF4UOXUv6IkILKoatsRnpJ35wCvAlJB9XsZfvHQH4E+VaFRToLpSRI7bHS59Ap4GtsMt50Xp60XlRxlpwEfKqgtyj4AisidVynUKw2ZQRqqJMYsKmj0BbKKq/mKW3qjxx7A/IidasbxADn8SfuuVvX+J6DImeay+U5oZkrI6AhiDq5bb2u4kqToYqppb06Vam0EZ9cB83CxpF5/3XiJe6QdH0ajxEzAW+A/gBi8woOajxlKoYMTjfwDnen+nqhSDMSqSSFlVVG8vDDNQRupRRUU4HfgFMH7ondx6yATNnkbiI4DqrVV1envkR431qA03qohH351ExgEbCvwRVt+pESiflFXFMQNl1At3MmIWtXI9bO0Vwy1ApCsHHxhcvoM7vB9YRUaNVcrXiiIf8Rjmq8vzNCMjHv0oLkyYqlIMRsmMWsqqWpiBMuoCn1lUN2z5WRjYTERmA9cpbI63fCeQ+4RbW88v390LHExlRo1ln70kpeD6/Jxhs8wRhPnsitmHQjWBiPyoZ9iJX/MRcmQGXmPL577JyZ+LkOGthKE2oqmbpVozUEY9UTiLegn4vaqqiLxzR7jxSaZ+6HaO3SJDrjDKrBNAkUMBkYJ++SP8mp1Z4neepKPGSsxeSmE0EY/DEJGNgLU+4cYzgYezsHVrUTTfEnbiPC5B0JYcmV2Ai8NOQQUMtRGL+lmqVVXbbKubDfQoUAU9svD1dbBgHtMHhAHNkI3chAFdwGGq3sGKtj6FMxO262jQtf6HG9zWgh5V4esT1o7Y5wc+Cezh996tsP0jsEhhg0K3d726B5AN2/Bi1DXIb4tApdbPU1Nu0OXduzg3qkehq1ZttYq6Rl0hggAfBG53vzXyTt+lCmOmsojFvsF+w9mFRSxk16AVqHXAbFxodZJ2LSJ89vI0sOtguyOPKa3ARsDGRdsmBX+3MixvVhSWnQRbbj7yiMtXwlY3gwpuqW8FrhTJSmDl9yBzAhzUAptcDQedBifgs8wpIl8G7vaSd4eVYhD0XcRbZjxelflxrkOtSIlvsTKkqGpuGGagjPpHZBZwDdB5B0fxcX5ONxMCdx/PWn7B8RwVvNLVC0z265zDm8HRBHbO2Q3w3W/AVxYzZGA6cH6rYjuZf20AWAOsxmkKFm6rgTWqOiIvSYSjGBHxSDfwMVXu9BTHx+FqZU3cH3b8EZw1GfbKgbZASx9oK+hT8OyHYd4SWO+d8w3gKOBKhozbOvU6kkoY6jhUwpiIMB23HBrXt3iEajo07CLxIlpz0JXxDxrKV82taUSrGSij/hE5Cxek0KpA1CwqYvY0OGoUkTG42cEmAf8W/bAFWPYV/9nL2n/DZsdCdtDIqGpPkq8ZF89ILGR43tgiYLcRRsEn9L6IPmB5N0yb4Hxzn8YZu9fwDBwjDPIZU+DiY6HDZ3Su3SBlnz1VwpjENLZ5ym50K47IxEfgL3vAW9ughxpVzQ3DgiSMRmDQ6SvAVZweOIsaz1qu5Iyc+KioZGGgG3reCYuXiFyI+9Gu9o6/GjdbWOL9/83C2YublegxcMn9cO2h0DK24NDdMOFLqv3/KN9XDkbVL+IxMHIvVuj9eLffB4G3qOppYef3OvZp+CZWr+iBrneI6DtwhqIXeBF4wdteUdXsyM9FUvZAlYLrWM7IyNQgzhD94WC4+I8uYT26aq5bTv+At+8bDKVwVKaNNoMy6h7PB4ULK0eBXVnI00wdsesUFulD7Pan8eh+OeeMactAz2J2br2Rk577BcfftpIt1sc4669VecZbLnsvbtlrHug9xJ29VJCiWVTQ7GnYdYug9wj4+ALoU9XI2U/UMuPQfjIWp/H3Fm/bmuHRgQO4yMS8AXsxaOYZvsQ6rA2xZ3DlWLJMqy9LRM7GGZoLImfzPrp9uMFFRWdbZqCMVFHyj7nI6TufIzmeXwybRXWylhv5r799Vn+0HyJdD8CXumHy++H+vXi0+zH2+hkJlohA3gQ+CvwRmF/ghynsnEd0ytXCa8cdwFG+5y/w3cU43Lqr4C9nwHSNUSk30TJj6HGkFWe0tscZsO0ZaVBXAi9A2wuw4TfQslPIIQONiTfYaMfNJsbi/HRj4TeHwjGXQOvY4s8Qw+ClxpdVMPtZA2v3g52fckoh50Z8LtYyMBXwV5mBMkZFuUeHpf6YTxLZ6wJ4YBMY0+ZC2UbMonbimezDTNtqgq5d6c4luwJ7quqtyfwNG56HzttAHwJuLe6wY81eqoBvxOPwHQZ9d1HHUsjOg9uPUf1ogvNX3FB7RmUigzOw/zcdvvYpf/9Xbx+c91u4Kv/8FQao5K9PH7DB29a7f1s2wOqbYfx2I4+5YgVsPQOyD/kFrLg21tiX5TP76YdcBuQF+NeOMD3UsNQw4s98UE1EhZYayq2ikNCX0P6aSP+dwLgn4OP3wmeBwwaAr3NG2yf4Rcs6xjOGDQOtZD+dN04efXg/uvj+ht5+uHYe6Nmq2u+3R8Gx7qCGvgnvvLeF7BI7YbMfeB/ck7AJIxKrE34+Em/WusLbHhLhNuA9+Pq/ulfBt05R1deSnkecGfNZslx7EmQ3Ac4WkXacYXsA+JuqrndtrKEvK2D2k/9jB9iTMP3JGuv22QwqxaRldhLRxpJGhxHfbSpwHKE/imwPfG4R/OQV4DxVfaSgUV33wX/1IVt9lJXTV7FZFz4zGRHZDjhUVb8f/7vkFkNmalQnEjl7SQMJfFDeVOMu4FNJlnEilxkrQLD/68GvwL5b4qoo/xWnROI7yPA5ZqwlSxHpBPbFSWyNw9n2f8DGf4HVD1Ll8PtRz35EZilcIzGXgUmYPxiFGagUU26DUqmlhlKc0zG+W0StspfWwlvfodq72L9Nsgswzakn+HeQIjIJOFZVb475XeoiwTQR8TqwPIl9DbUw1FHGxFsW3A9XCykL/FZVHw84VuFAahfgYwyFY/8S9zspZNgg0ZtVvQPYH057B1w6w3/5sQLPVoIByAD0vxvOfNAZorxOn/4I9v8kHNwSr3ZgP3AeqleW3OZiai67YVvg5oLM9OlySsdUQpInZjuHtS/hdyvaBtZFtQ/nWJ/lnedDftcGF8F0ysjvklsUcO6FDSfPAxMVXhxwckVxLn6vwtyatztiw0li5Z/ztRRJYxU8A+Nwsk5fB74CbF50nOmgOdABbyu8HANFWw708PDfSRWfLZilTooq8r5mYcOTrlpyR6nH8PY7sZzfwSrqphj3fIRWk82TZO06LygaRixB0TxD7dSAdmZ74Nq5IJ8RkS+LyOkg58GZ/3SO66RkXozRvjXARt5zflvAtRn0QQ0hk+Hkp53ywzDqMtclEjcTmrbYSUXFYcjXkG7y/i8I8X+p6npV/amqnoX7bXxeRK4QkSO9CMK8TzTDyFlEpmhbTEh+lXt2xOf33NcHq8+uwLMVW7W8BVp2g5yq9ha9NY8iUeAQyl5ixYIk0k/ZFKrB/UjCHba5dXDPNfC+d4oM6r7lpXny+3suicK/BVjeA1v4HPO1HjhtArA3zin/b2AhXDcXLn8nLtAiuMV5FzXgghQu/yPM6YDQ3I11/t9vGL14BsrrjL4EdMLc4+CbjzJ8iagiTv40IPD61fDcVNgWN6OIoqY1guJQ8JzHDlRR1ReAK0Ukgwu0mAPSC3dfB4deSXkCHHzqmum/YbP3iuhyVf1bVDsTsIqRMlo2uP+DAAAfCklEQVRBZPBTLY8osVJAH3AXZU7aNQNVIcoV4BBtULQbXj0PtukSYSMY3CYU/T0G8j8eAZb1wpY+x1u1Fr7dCX/6MnT2uG1CD2y8Acb1BSxFe0mrPISvc3rrT6rm7oRBQ9AFbAv9u8BF98K5b4EOv/yKbpDVbt88Hc/C5dcDF4jIv4EfaEGS4dB1V+BnB4gQkuMxALx3SxHZG/g88F31fBEJlBgagX32cLlEcfxQUD/l3O8EjiXhwEJd2sB9wH0iMh6mfxhe6XOCG4HEGiQW/J4Lnq2Or4L+HjhBRA4FrlIvAnCUPED82U8LEGQcZ+IiASfh/4zkfZMzkzYwCguSqBDlDHAQ+WgGfvwsjJ088t3XVsJWV0JuDW5Zaw2wtuDvNbi6PsOm7v6RTj39cNepMOPZUtoeJ9JJRDbBFcKbBmzkPr5iFmy+5chDL38dvvF/cPkx0DaGolwaLxDiBOB54Ieq2jv8uucEMiHfQQU+/i/45XeA76nqYJXRtOQyVQMRuXANrJjg/DA1idZKOy54JvdLyPjNMEtRp/B9tkRkW9yy/u2q+qdRDXRdBN7NEs9IDQBfCrynI3OpqqPbV2tnZqNuCYMARgQ44ORfPgdcClwM117upOKGfS7Q+RuzfUUO29wiyMwEuQz6lpTS9uHO6Vw3/PhMYLb7DlyM+/EdBGzk/5nh3w0+koH1S91ry1aCXAx8GJhYcK2mAlcAs+AtHfGv+6tvgFwbco18a0810uZ1MJdpHdUIqs118vu95Lf+xUkDHMKeLdyo7RPu9/LihxgepBG2DQvSeAUuzkIu5j2NVwPNPScnKpzp/Tupote91je+kTcSRMzhluKOAS7CJb5+AXhrwbH8fiCjivwhINIJ2BjO+gn09sZpu/eZMcC+kPmKqzukCq+9Bi2HUhQZ5dOO0O9W+GP2OtS3A6d41+kiXBjzZsBuwNfh0m9CLuK6922A7FHAhRHt8o0AbJQNOAQ4TFVRmKsuSi/swtVFFF9lrpXfQGqgG876oTdAejfeqlSMY0U+W0AXyNVOCT/slgxug4NFYLPL4Z4crItpoMoegVeOzZb4Kki8vKMVK2HSd0DXAPcC/9SCpaai45VVOiZ8qQGBvmehfYfgI7yxDCbegvtID/Av4J+g+5AwOTPsu4Xl0ohI3mAdiDNSA5B5DV6+BLo2CTnl08CuIBeo6oVx2tiIiMilOLHQbF51QKFLUlwjqFaELWGDtOIEg98NPAv8XFXX+Bwj8ZIdXP85+OJpAX7aPIPLjCIyAfj6f8O1n4LHiCkGTAk10CpOrS1ko2/hs6j+DfDqsQmOVTjTKEveBKFLDWFt79sAD5yAz4ixlJlHub4bLvBnX/jaD2Bdcd6K38zvwlo/I7V7NukALhr2Okx8Ah7PuWW8wXLu6pb/5mrB0mozbkGrDkXXdSfgfNwMf++izxfnVUUu2bnfRn/Ukvsitx9jgBuBLbz7Wdez4po3oNE3ypycF2ZQSm9fUCJr227w2opytb2a3819r/xSY0Dboet6uEPhLHXr6U3mV+EYYL+i11qAr2uVfQ31siUZSHnG4hPAN3AakWMp0TcdPlgcdBO0AdcB2w62Ayb2w8v9kA0xTi+mdeBR8wY0+gZ0wUm/g+w6n4cqcUdcyuwkYXs3BmbhIro+Cy9/2OeHUXJwRjW/G5z+85Ftz62bylPHeyPLDb1uhtDfjLME4PLiGbBntA6oddvSvJUykALehgsSmgP/+8UQY+M3yx8DLQeHDxY/kvEM4U5F522ZDLf0wzzv+a6rWbH5oCqIiBwOHAibXwYrH6ICRezKkW/lJSYeDLwPF6J+q6o+6x2/LHV9aoFI5kIY+BjIYNsnjFvzxhvrN93QSm5Lms3PUlAP6HVYdxBs+7jq2cN3kauAM9Q6hkBGoy/o8qoyH4NXroRJIblk616Ejb4FufE4/9ADsHxj2PJHDEsN6euD7Ieh813Ar7RIU9CptrBAVZ/07v/RxKmcmxIsUTcGyY3A83Nhh2OBxeoVA6tg4mfJ5S5EZAec+OVGuFIK52lRgIZqovLh6UGk62bYYwVn/+NiLp6cpb0D6L5h/cmLW8jtQ0SJc1zOR1lr29QMn3pAG8HAv0AQmYqXw+LVx1poxikc79kPK2MS8lntBm4RGVgOA7dCi08RxN4++MkPIXeTqq7Nv7pOOru249/rVrFZgYHKvQzjzwUu9zFO+wNvquqT3smXkWL1D19qPYWrh41Ejs1cDr7wO2CHomOUPcCh4LgJ1rS36AQ+jVvC+yKwacxzlL3tFdmc+OlchQ190JtF+nfm6QFQ3Y7nuweQ5sr18cRgNVgMdtAHgQvZH1fzNjfBVvSb8lmyK/iNFTzTc/nAhk7WKKh2slav5iNL3+PyAi8CZgwdn02Aa4kZ9p7WreYNqIctmRF4bQXs1hZwnIokfhIr3yq7Hk69FbgQeFsJ50h/0mpAZ3wHRyqo3sGRWY2fuJjKvJASrkmsKK4ep/l4fs3b20QbvnlVvb2w4eiC+zfsmc6B7sJCBdWpPKV9SH/BAGOGZ6jGAFcRkX9YD5v5oGISr+ZRdgO0flQDJE8qVRsnXr7Vmpdhnx1VnylBPbxuCvD51jZS4HY+yAe5PbZyJpWobVNtEtQDykL2dNj/OtUHK98wA4L8uz1LYdx80Dmq+rrfMz2fIzma+cznSI50MoODBQdFZHvg+8BPVPW/K1RFu2qYgYpJzGJ/5a+IGZOmK7RXTILOOCb1rzcnMgu4hhj6er3Q3wFfqevvW4f4JaiD3A/MOR7m/9wlvA97pgMGXL3AZHH+5I/gFOeXQfY1aClrFe1qYkESMVGNUhWPHzxQ/lLu0gYtA/BykOLySySo71SnfAAXAFAuyl7bpgbErgfU5mTq60GhvNEoLL/xEq4MvYrIqQfCrf0gxdE8AnyI20ccaAN8BtgeV4SzX0QOhrYjoP9ZaAkpaTPI04TUs6oFZqCSMZ8RtVwGSWIESo68G3zRhYbvDxzqXhn4H9jss4w0oPURdTd6YnfGUSj0q8j/ZHK5dMm+JGcVbmQd+Tv3FK+PR+QHNFp4fYopGPgOq1ulqjlEHsq5ZfU4dLTDZavhjxu7XMaVqnqPiDwBp+4C12zjHzE4SCr7iaY1UKVpYslqmP0YfH0ytBV2hklvbr5KZ9hyYZ7BUY2ICLAXbrYwBrgfJ1XT795HGGlAG7bQXhGxO2PcwGAgYN8+hRXfPPnklavnzNn2ggsueGnwnYI8IuAN4A7Spl02nHnA9XF29JaKdgceRqTxcsDSTVDdqlUZp3HZCfAMO/Hr8C4rkyNzcB/tS74lK25cyRY/VtVnRC48Bt5cBpuFGahEVbSrRdP6oJLVa1KBC26Ei1fBRtfBmw8wysTVeEEXzncEsgj3AG8KPALMV9V1Acctq6Bs3ZDMB9WLy/s6CBjIQUcOsp61uhuYOefCC9fhlks2uuDCC5dSlEfkHaOytXDKQUDgSAiDDvfKNcqIRdEzvYDDOZI7kRjdjCIomel5f5Lrb4LyrtLro25mAxUn6MHj1VXwlkNU+x71Zl7n4RJc2yE7AK0/w810/PD1H8U7/+uvwRY/BF0C3Kaqb0S1tJkK7Y0gXmc81AF7mfV9sPnNsP8pztAMZtbPmTNHJr722tmzvvvdU9qy2U2pR+UJT6Gc4GqofqRT2boJWS+yoA3e3wYtCkxlEYt9PQzDmcLT+jDTtu7UdcsARDq74OWHYZOtfHZPbT/RtAYK4s5i+nsh82HVlvneZ/IzL29VRAFy+AcwR1TKDTt/Xx88fqLqPv8d8+sUHvcoEpa7aAiiO+NAYyIil6mn+lF0zLkK0yWu0UsjIhM3wH1jYNeYYfb1H8HYAIjIJpPh6mfh0AxsCbTfwVF8nJ/TzYTAz41nLT/lk729zPvhcbAS1w8tgztzMP3rIAX9TW8/3D1b9QM3DT93SsLTa52IVcstZgJuccXYUVXKHX7+SeNg7YuxssmTf6+GLrQXuBVk3XvJtn09zq8UKowJfJ6CApHesRqmyuxP4O6cE8WN813iVVe1rWIbMA64Cdi04Jnuz4FOYVHo7duFhdqPZB91yuaZoWP6qVcMLILMZ4Crcf7VFm/fxGVBKnEdMmW3eHWEKoorQd4dsMuI4IcYnwn8rIhsJCLTReR8EbkEls+GX38bRviTRhVR493b20r9fF2juhI3k9kBV2r+vB/A3Jtgb1RnELwMdxduebCQJKHrWZwQZ+oQkfYVMCBu6S4OfbigE6MGiEg7rkLvxaq6quCZPkeg7ypOZzxrfT87nrVcxem0oj17wZOqmsu/59N3dUPmNNWBH3uvrwYuF5GTYLO/4AK5MjG2xVQqPL3WI4Vab/6jisHNdxYTf+Y1YTNcJ3chLqz8LOA9QEfE+dOtd1dnGzANiCwMCVw27DVXJ6ruZx3AcR+FwxplNtjIGy7w5iqKtDxVdXBGXyh3VLxN5SnNhdxDYuhqAju6PuuMX8BAcZmg4m2wLEgltqaeQUHojChwFhPyGY/ePjjtUVh7ArAMuFRVz1PVr6vq/araG3KsVOYj1DmP4srCR7FBRAqjnPKh65Foumcd+9yqejdulBslddUH3IUFSFQdL41kDvBDVV06Ygd3T+4egAG/WVR+9iQh97Cgv4HA/k2fU9UL4ZYvwOqolaLKhqfXesSQhq2UWUz4zCsX6nsqZVRj22jvMZfG2OcAYPrgawl8UH2Q/Q/4CtBe6+9a9J2mkBe99YRH67W6aqNvwDnAu8L22Rc+0Q0rB5De4lnUVJ7SASTyHpLARw16NORCK/lW8po09QxKhJ1FOBf3YPydwdGl9gEPAueIcG7BtnPBp9vgsrtdFMwwukFOU40/A/L2DR3VGKPmBRGZHLHPA8B+g//zRqzEmHW0wfy/wV+BOSJymohsFtkikS5EZiFyFiInemHv5eZjwC8BUF05HWa+4HLpenDRev3evz3AAtIaLt/gOL8PD2qIWK+I7PR32KkTpmbQBVdyZl+nt/DSSTdXcmZfBo28h17fH9dHPR/k5YD3Ki+hVutRQ21HLCMiVfIzICUgUgUXbnwicCW0va1c/qMkoxrbSrnX7Ah8PsZ+l1FYQ2eo3IFvyYp+yPbAssIRK7AN8DVc6YOdR5xnZKRhRUrO4xI8Lyl67Ru4nJouhRMVzvT+nVTre9Twm7vms9T5Nk/M+4hwGnozwj6Li+q7ERgs5dPNuK5Nef1VUN2U11/tZlxF7iG+ZUF0LVUovVP7m1bDjUQh4wNPQ+YLuEJ/uwfcvKrcNNtKvd/DO+uAfT4LTBn2uk/oet6g5GDuDnAzsKXPsTqBL3lG4UBANEEBwTJ83+OBaQX/3z2OkbatzFvIgOQ5+Pte8OWI+yhev7P1yPcqX6etFBdI2c5d85tX441Yxf56e+CU3wC7Rdw88x+leMNFUmYi9ukCTvZ9P2DWAUzwRretfqNkXGTWMcDVS+ChXIwCggpzy/B9ryz6/+UURJDaVoUtYkDS74xVhM+IzwMH+b9XnZWXWg3Em1pJAuJKDvUshTFvdc+b7zGaU7mhzhCR44CFqvpY2H67i1z3JDxFAlHYT4nsezr8aC+YTJBeH7Tm4IVMPMmh5HJDBWK2D0P7F6H7QdVr3VsyGThGVWOJxxplIqn81oiPyzuA/Wt932omoVbzEUYKtohZVGSkivmP6mMDtgBmB+7jLcX0u4z9dRrXN+SNkrPBOVP5ZbtTveP57VO8xS8577OE1At9OZcLM1ddOfA5wIRa34Om2kapRAJsDlxLoU+0hls1lhOLt6aO4isgX+fJj8hIFe9aNqdyQx2hqitwnfVIhnT8jmiFVnFO6VacH2kMbhT8sLdfMbcAk1qCS3204/QBP0/MmlXqFNajCwgWtNtrZyfQ2g5t4s41fQAe3cV1cv7yA0alKFmJxKv3dj5woXrWKgUElQWpGA1ZD6q0Wk/f/Tl85jzoKFSstqTZxqNfRNpVtTh0/BacEfFTLIchI3MLMLQU45bVDiN62a4dt4zcR4zfXRYGboQDTxXZCLfM+ADwsBYkecdtt0LXI/COqHMaZSdJEc12hg9ITgW+r6pvlr1VJeL1g7dV85wNaaBIVLFWBU7bBa5fALOeozmL/TUN74dFZ8CliKwk719yxDUyhyPSxZBvKMkouY9gQzKMNmiZDefMVn1cRDbH5Wed5em09QOPXAPPzo7R7lZoaYVDitptVJ6kRTT3QKRLYG/gVY3wlTYFtV7XrNBaaYLw8VdWQftOBWusFjLeiJvnp8lBT6/zCRX6lx4t2TeUQK8v5yK5ntDoKD5Vp/bg6/vCGaR3fQdu7Q0OV/c7XrD/zbZKPHNdnh8wzv1RhXU56HkEni6+5826NaQPyj0bcRTHe/tg7AmqvUu8F+5kyBdls6dGocBPI9DR7jr4Qv/SHt7fcSheiomt19cPfA9e6YHVGq1O0UKA70tV+1T1wVnwcHtAIbKA450f4EMzKoDAlIfhpRj3Os84gQ4vEjTI39lUNKSB8pgPvBK+S8dS2OR3+f8VGDYw31MjEeWnaUlwrGJR2HlxP98OuffA594LJz8Kz/dDNudkSyI+Nuj7Kia2cfQYH3Aco4yISEZETgV23gX+Q1yRzLhGCgm/501FQ+dBRVTM7QaOV2V+0WcE+CBwuxmoBsAFMSzFzUbKwcj8pBJzXZ4WeefO8EAmnoHzO28p383KuVcQEZkEnAfcoqr/8l6ciDM2hwE5XIRoHJr+XjXyDApKCB/3lj4tZLxxSBLEEEVQGYOZhI+S86XmZxa+uAu8PeNEWiPpB26FC0XkCBE5SETeKbDZGvizJvt+qS2sWO+IyCHAacA5g8YJYHgRzd8SfzbV9PeqoQ1UiC/KwsebhyShvhDQ2Q9Adj28SZGRAVwH5IoiLiCZSnjstrVCyx7ueK/j/E6TgD0OgDu8QIm4FPvQjFEiIi0iciawPXCWqvr7vt3A5kni97tNf68aNcy8kHzgg4WPNydJQn3X48pXT8UZqnbcaLelBe7eGxY9A1sojCxl4IzPDG/Z7Whcx7IKmIfq8tG2TaB3d1ikqn8f+aa0AVcSb6kwzYUV00eBfBQ+slcishVOuf7bqvpEjCMmeR6b/l41tA8qj6eV9wucL6ob+JiaZl5zkMxP49b8HSOMjIi0AtcAF6jq6DuOUtrm548o13GMIYb7jXy1FcVVaX4vcLGqrot5XLtXCWgWA1UboUMjHcQIYshBfwZ+j49g5/BDyaY4XbvZqjp639YoxUTLfhyjMC2hC5/IT4W+NbDhIDj7EdVvJz38BpHft8JhbeFLfXavaHAfVB4LH296QoMYFPrehJ7POfWRULyZ07eAc6vRNgICLCp4HCMiLUGgfQKMewQOT3pgEdloN3i1xaXA2L2KoCkMlEfVhQ6NlBARxCCw4AWY8kP4tIhsEX04XQg8IiIfH3yx1PLtpQdYhB6nF7L9OOOLiwZ7Cjg88jjNTkxtxYwzXofHvs+AiIwFLnsezsy45cHR3fMmoCmW+AxjkJAgBhEZj6tcemYcn4KInDIdnvw9fIUQX0XsjiZZgEXYcXZ7Gf4wCSYJZFtc1F9pbWo2RGbh/IxxlEXWAbNR/V70YaUduAq4QlVfKXijPPe8QTEDZRgFeImW5wKnqWo2bN9ukYn98NwmMEb8l4PySzXVGw17/pMB2Cqg/Ef121RPiJyFW+qNE2XXD5yH6pXhh5QW3MDnZlV9bvSNbB6aaYnPMCJRN3q9CbhIREJ17sbDLZtAR4BxgtpI1sStTdX0MjoBrEqgnRcZBu49QxcDPzDjlBwzUIZRhKouxmnsnR64k+erkCQlOipNstpU1WlTHSEiY6fBxGz8xO5Whsq1+B1PcDlSt6nqU+VoY7NhBsowfFDVvwELReSEwRcLAyFcYmwu5uGqJVlTcgXXZkZExojIl4H/9wj8qs1JpEXNooJkrwqZDfxZVR8qV1ubjWZQkjCMklDV+SLyuXeLHP9XOI7hgRA5omcqeeJL1kQoF0QwmgqujUPMaygiHcAJuOTsH3gzZxCZicuDmoT/PR4KAw84l4h8Aaf88afyfrnmwgyUYYSgMO9NWJKDzkyJv5ccZDNRkjXBygU3IBIZeSciY+fCttNB41hNhX5pNBmdmNfQi6j7DK7y9o+0ONlWdSUi04qONSh7hYuEPDPoXEtEntoHbnpI1VJaRolF8RlGGCJzFabH8DUFMgDZt8G3FzoNyNsHR+pD5whVLiAg8s6LODwS19H2HAMP3w6/kxhL9znQt8H1T8GvgL9p3I5gdDO8yhHjGios3xuuegy2A34cSzvPLwzcGaTAcw240P5XsUjJUWMGyjCCKE8tqUHJGhHZDJgBTMF17rer6jNxZYoUFmTgHJxR2gx4DbhTVZ8paO/LxPMt5x6FyW+HdwD7AiuAXwzL0SkkhjZdTTvjGNcwCwOr4b6JqodU+lyYVFFZMANlGEEkS9r0IzDnSEQ2B2bsCns/Bl9sjbF82A8DB8JX/wa/VNXXA9p7LfEK4q0HTsknmXqzsY8BWwMPAfNUtdc7bkkzvNiMdlZWTQFWE3utKhbFZxjBJK0l1UdMyRpVfV1Vv/8UvNASMxqwDXoegD5f4wTkYFONvxTZRkGQhKouV9XrgbNxHfDXROQiEdlHI7TpKDW3SmSiNxtZihsIXIIzsEsRmesZxjhUM3rRIiWriAVJGEYwsWv39EPfOpi3CfyDOJI1Q0tmR0r83+GIyDtv2fD9wJ7XwNtPgoG2UdQa8nxRDwEPiciY98CnsnBUW3StqaHcqjgzhuBZWb7t04GHEQmclYlIBpjyO3jfkTA2TjEsRh+9aJGSVcQMlGEEMw+4Ps6OLcB74Z8Pw8a4Jba9EblPVTeM2Hl455zkN9iXhdVtIu90p2MjnJH5H+BXs90sZmmcA+WgLROSZAqgqj24ZNMe4i1z5mcMkdp0JJuVzQCnBA68C3gn7horsHhH+FcGjojZxtEWAVyl0BtzUNH0BQdHixkowwhCdZkXnhzpEM/Agn+qXgEgrsLtfsBpnoL1SuB/gSe9GUpU5+xLP4zZG3bFBTTcrKprinaJ1V6FvsWwcFf4KiLfVtUXQk4be8YwAGN/Ch/9rPOvvYwrKfEy8MqwtiZQvBiAI/cUueYpt2y6FngQuGGYmK873nlx2kiE+kMU0+GNeTAm5o0b1bkMC5IwjHCGZjtRSZthS1Fb4Jbh9tgNOh+DL7UkNE45l7f0e4mKCkvQXnEzoy/iZh43qV/7S1D3FvgZLtgiv20DTMjvdB3s8yU4rD3GNcjBBuCrmSjF8ApH1onIdsBXgQdy8CmxKL6qYAbKMKIYGWJdnLQZO8Q6JzJL4boWGJugBcmi5BK2V9z+X8YZg2+panfBscoftSZylsIlMZfJYimGl2Mg4X9Y6cCVU+kgP3Or0LmMkdgSn2FE4TqZGeWo3ZNxn00ye8riogHj5xklbK83c7pIRLbHRe+9gJP+6UuyzEm0Nl2eVRIz+IS4fpx46g+JcrVE5DBc1dybB3PNKnQuwx+bQRlGNUm2ZNYHnIPqNZVt1HBEZHecRt0/gVvVJQWHzRjAzb72plglw/8Elc0lCjPMMXKuROQtwMnAfbh8sOBO0goOVhQzUIZRTeoo0VNE3g18FFjQC0vb4VGClyb7gWXEX4asrhpDDCUMgW7gJO+1G3wjMI2qYgbKMKpNHUnleDWNjvgH3DANto8QzI3f5mr6cWIoYayHN/eA3yyFq1X12VGdzygbZqAMo9rUm5NdpEvheYkXbh5/1lfG4JOI80QOCDyB1ztrPSAwhmMGyjBqQbU653JQQqg5UWHhw49f7Md5AHg35VBMr6MlVWMkFsVnGLWgjJGBVSB2sq5CRxY2T5Tk5QzC9wqM9g2UUBMrgFK08+IbV6OimIEyjFqS75zTTWxNwiwM3Az7nSJyESA4FY1/Af9S1eBw8TJo87nDyDic2sYewA4/gvd8sno6fUaZsSU+wzDCGcUymaeisae3bYozWv3AIpzhWqKqA0kDR7yquFNwhmgKQ4ZsA/AU8ATwvMLnqeTypFFRzEAZhhFNGSMPPeOyC7AXsNPuMOER+GoMxXSyMLA3XP+km9UtxhmiZ1S1P+Bk5oOqY8xAGYYRTSUjDysfhFE3Yf3GcKxgoWEY0TijMw0nu9SDMxSxijPGoNI1lmbijGdfwPt54zoz4XGNCmNBEoZhxKNykYexgzAopcaSaefVLbbEZxhGbammn6g+wvoNDzNQhmHUHvMTGT6YgTIMo/bUm/yTURUsSMIwjNpT2SAMo06xGZRhGOnC/ESGhxkowzAMI5XYEp9hGIaRSsxAGYZhGKnEDJRhGIaRSsxAGYZhGKnEDJRhGIaRSsxAGYZhGKnEDJRhGIaRSsxAGYZhGKnEDJRhGIaRSsxAGYZhGKnEDJRhGIaRSsxAGYZhGKnEDJRhGIaRSsxAGYZhGKnEDJRhGIaRSsxAGYZhGKnEDJRhGIaRSsxAGYZhGKnEDJRhGIaRSsxAGYZhGKnEDJRhGIaRSsxAGYZhGKnEDJRhGIaRSsxAGYZhGKnEDJRhGIaRSsxAGYZhGKnEDJRhGIaRSsxAGYZhGKnEDJRhGIaRSsxAGYZhGKnEDJRhGIaRSv4/B1gxU6fs4eoAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"draw_time_graph_cut(G, \"primary_school_td.svg\", c[\"cut\"])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"c = temporal_graph_transform(G, F, 2, 0.)\n",
"#c = temporal_graph_transform(G, numpy.ones(Fshape[0]), .0001)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/networkx/drawing/nx_pylab.py:611: MatplotlibDeprecationWarning: isinstance(..., numbers.Number)\n",
" if cb.is_numlike(alpha):\n"
]
},
{
"data": {
"text/plain": [
"{'0': array([ 0.18250847, -0.15794278]),\n",
" '86': array([ 0.30310487, -0.20965114]),\n",
" '32': array([ 0.06987987, -0.0838195 ]),\n",
" '25': array([ 0.4225651 , -0.23740256]),\n",
" '15': array([-0.03598401, 0.01582715]),\n",
" '1': array([-0.67924587, 0.32749639]),\n",
" '12': array([-0.75949408, 0.37252969]),\n",
" '38': array([-0.63020302, 0.35149615]),\n",
" '58': array([-0.5970037 , 0.27466767]),\n",
" '87': array([-0.82077516, 0.42929772]),\n",
" '17': array([-0.70000208, 0.43649227]),\n",
" '48': array([-0.54186503, 0.26780126]),\n",
" '22': array([-0.52596056, 0.23192355]),\n",
" '2': array([-0.2243784 , 0.13293801]),\n",
" '72': array([-0.32404565, 0.20205026]),\n",
" '62': array([-0.12201514, 0.03567913]),\n",
" '90': array([-0.18259888, 0.1502331 ]),\n",
" '7': array([-0.25037719, 0.21747988]),\n",
" '36': array([-0.44670351, 0.22944968]),\n",
" '43': array([-0.00229913, 0.10767679]),\n",
" '81': array([-0.16954768, -0.09943115]),\n",
" '68': array([-0.10839213, 0.05334948]),\n",
" '3': array([-0.24898908, -0.80134115]),\n",
" '84': array([-0.30572616, -0.83581881]),\n",
" '49': array([-0.20588472, -0.88959924]),\n",
" '14': array([-0.28161772, -0.69364204]),\n",
" '4': array([-0.33353714, -0.90994779]),\n",
" '21': array([-0.31168058, -0.74988836]),\n",
" '28': array([-0.18775438, -0.96405745]),\n",
" '53': array([-0.30919261, -0.5669643 ]),\n",
" '5': array([-0.91628616, 0.54044631]),\n",
" '89': array([-0.9373539 , 0.58321715]),\n",
" '34': array([-0.86983564, 0.49531281]),\n",
" '69': array([-0.86241501, 0.54832194]),\n",
" '79': array([-1. , 0.61287164]),\n",
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" '70': array([-0.04916571, 0.0920583 ]),\n",
" '55': array([-0.04488249, 0.1302277 ]),\n",
" '73': array([0.06722174, 0.0752563 ]),\n",
" '51': array([-0.01931539, 0.19806565]),\n",
" '8': array([0.39341628, 0.5494325 ]),\n",
" '97': array([0.35205617, 0.47532322]),\n",
" '67': array([0.299742 , 0.44779107]),\n",
" '91': array([0.31050551, 0.38817362]),\n",
" '9': array([0.76207623, 0.20153364]),\n",
" '23': array([0.72224938, 0.22783425]),\n",
" '99': array([0.81929201, 0.24897942]),\n",
" '45': array([0.69356972, 0.14808059]),\n",
" '11': array([0.79191568, 0.29209617]),\n",
" '42': array([0.63776714, 0.15868927]),\n",
" '24': array([0.60806907, 0.09556875]),\n",
" '10': array([-0.7782612 , 0.50493499]),\n",
" '66': array([0.84667124, 0.3480361 ]),\n",
" '13': array([0.40555411, 0.05765848]),\n",
" '16': array([0.29922913, 0.05629079]),\n",
" '18': array([0.51337064, 0.06452052]),\n",
" '44': array([0.40988923, 0.0340194 ]),\n",
" '30': array([0.19648692, 0.08110081]),\n",
" '65': array([0.28887517, 0.01837441]),\n",
" '31': array([0.52556496, 0.09309966]),\n",
" '46': array([-0.30739898, -0.44271557]),\n",
" '20': array([-0.31633865, -0.66615772]),\n",
" '85': array([-0.18030854, -0.07099775]),\n",
" '19': array([0.1738795 , 0.03667884]),\n",
" '60': array([0.10659391, 0.1377561 ]),\n",
" '71': array([-0.28791866, -0.5762822 ]),\n",
" '94': array([-0.26839832, -0.46705179]),\n",
" '98': array([-0.49156488, 0.18242478]),\n",
" '40': array([-0.56610687, 0.14669971]),\n",
" '95': array([-0.36782521, 0.14225357]),\n",
" '83': array([-0.42534436, 0.17265405]),\n",
" '56': array([ 0.53529357, -0.24233811]),\n",
" '27': array([ 0.60131454, -0.31310825]),\n",
" '52': array([ 0.57258205, -0.1638762 ]),\n",
" '26': array([-0.2341893, -0.1942139]),\n",
" '37': array([-0.21300749, -0.22632152]),\n",
" '33': array([-0.27702058, -0.31858063]),\n",
" '47': array([-0.24537887, -0.34643236]),\n",
" '39': array([ 0.60301813, -0.40214144]),\n",
" '74': array([ 0.60485702, -0.26165828]),\n",
" '29': array([ 0.50662661, -0.40174664]),\n",
" '88': array([ 0.66870426, -0.46005252]),\n",
" '50': array([ 0.57136335, -0.34435066]),\n",
" '78': array([ 0.3894977 , -0.43449696]),\n",
" '80': array([ 0.27711547, -0.45692074]),\n",
" '76': array([0.06253763, 0.23321054]),\n",
" '35': array([0.19725101, 0.22547405]),\n",
" '61': array([0.26974763, 0.3014139 ]),\n",
" '63': array([0.09328786, 0.18542861]),\n",
" '93': array([0.17460215, 0.27755079]),\n",
" '82': array([-0.0573306 , 0.12219849]),\n",
" '41': array([ 0.1817284 , -0.47988771]),\n",
" '64': array([ 0.1172176 , -0.51001667]),\n",
" '96': array([0.16284315, 0.1393929 ]),\n",
" '57': array([0.00482533, 0.27491996]),\n",
" '75': array([0.31537127, 0.13355094]),\n",
" '92': array([ 0.52789467, -0.03963841]),\n",
" '77': array([0.44309983, 0.07362373]),\n",
" '54': array([-0.21919693, 0.11622148]),\n",
" '59': array([0.2513154 , 0.36562967])}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"draw_time_graph_cut_values(G, c[\"assign\"], F, \"primary_school_td.svg\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"draw_time_graph_partitions(G, \"primary_school_td.svg\", c[\"assign\"], False)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.19050809628051743"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L2_error(F, c['transform'])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"K = [5, 10, 20, 40]\n",
"\n",
"results = compression_experiments(G, F, K, 5)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.87305588, 0.70768239, 0.59836889, 0.46158191],\n",
" [ 0.19189918, 0.17817595, 0.17081546, 0.15817729],\n",
" [ 0.22343697, 0.21890282, 0.20816496, 0.18720717],\n",
" [ 0.28669265, 0.27703713, 0.25879688, 0.23989775]])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"K = [5, 10, 20, 40]\n",
"\n",
"results = numpy.array([[ 0.87305588, 0.70768239, 0.59836889, 0.46158191],\n",
" [ 0.1925559 , 0.17970188, 0.16977256, 0.16329276],\n",
" [ 0.23102427, 0.2132812 , 0.19909818, 0.18990198],\n",
" [ 0.28271556, 0.27284988, 0.26597022, 0.26199616]])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
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8GVTZs2FdS20tPTE5s2bUp69IKNBFfyCayMfS64An9T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\n/PDD0dLSUvE5SUfiDwAAAABGlKsFRftJOg0NDUV3d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ee0+//PKLJOm5557TlClTXI67rLVr16p37952B+RNJpOKiorcuq3yCgsL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\nVKuvhm7UqJGOHTtmt956pbI7nD592uH6xo0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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_file_name = \"figs/compression_traffic.png\"\n",
"plot_compression_experiments(results, K, output_file_name, .9, 9, 0.1, 1.)\n",
"Image(filename=output_file_name)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"G = read_time_graph(primary_school[\"graph\"], 0.05)\n",
"\n",
"F = generate_dyn_heat(G, 1, 15.)\n",
"F = F.flatten()\n",
"\n",
"F = F - numpy.mean(F)\n",
"F = F / numpy.linalg.norm(F)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"K = [5, 10, 20, 40]\n",
"\n",
"results = compression_experiments(G, F, K, 5)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
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Al1OzJ1cOTIkYqgrXdy244dO3L+/Pm67tXU1JT58+enra0t7e3tFXHbzTffXLepbdfKFDbT1wAA\nAAAAalOEjkLABnXS0dGRVatWpbOzM8mrXx36sY99LF/+8pcbccxhrwgPXgDq6Nix8qir3klta9cm\nhw7Vvt7NN1dePbpyZTJxYv3Oy7B2+nTyxBOVUdv27bWvN2JEcvvtlVHb0qUGCxZRV1dXdu3aVXVq\n24svvlj3/SZOnJglS5ZcMrVtyZIlmVBD9XgtTGEzfQ0AAAAAoDZF6CgEbFAHJ06cyJo1a7J169Yk\n1eO13s+3tbXl8ccfH5LrfrhUER68AAyiUinZtavy6tHHH0/OnattvebmciHU/+rRW28tfx7q4MiR\n8u24vUHbQBvM8ePLgwV7g7Y1a5J58wwWLLJjx45l69atl0xu27ZtW86ePVv3/ebOnXshaOs/ue2W\nW25J82WebUWfwmb6GgAAAABA7YrQUQjYYIC6u7vzzne+M3//93//mleHjhkzJj/72c+yfPnyRhyV\nFOPBC8AQO3u2fHdj/6tHd+yofb1Jk5JVqyqvHr3xxvqdl2GtVEr27i2/TXujtg0bklOnal9z1qzK\nKW2rViXTp9fvzAyO7u7u7N27t+rUtsEIycaNG5fW1tZLpra1tbVl8uTJhZ7CZvoaAAAAAEDtitBR\nCNhggO6///587WtfqxqvJZVXh371q1/NRz/60Qackl5FePACUACHD5fLoN6gbd268nWktZo/vzJo\nW748MW2VOunqSp55pvLq0aeeSnp6al+zpaUyarv7bm/Za8nJkycvmdrW+zo1kNrxMm666aa0trZm\n48aN6ezsrPv6A2H6GgAAAADAwBShoxCwwQB8/vOfz2c+85kritc+8IEP5C//8i8bcEr6K8KDF4AC\n6ulJtm7tu3p07dry1Lbu7trWGzUqufPOvqDtnnvKxZB7HKmTzs7y7bj9J7Xt3l37eiNHlt+y/aO2\n9na35V5renp6sn///qph2969e6v+N8u1zvQ1AAAAAICBKUJHIWCDGv3FX/xFPvrRj15RvPbmN785\nP/7xjzN69OgGnJT+ivDgBeAacepUsnFj5dWjzz1X+3rTp5eroN5JbatXu8eRujp4MFm/vnJS20sv\n1b7epEnJypWVUdvcufU7L0Pr9OnT2bZtW9W47cSJE40+Xk1MXwMAAAAAGLgidBQCNqjBd7/73fzL\nf/kvL4RrF/8y6h+1LV++PD/96U8zadKkIT8nlyrCgxeAa9gLL1QGbevXl0dh1aq1tfLq0TvuSATv\n1EmplOzcWQ7Zeie1bdyYnD1b+5o331wZtK1cmUyZUr8zM/RKpVJefPHFdHR0ZMuWLRVh265du9Iz\nkLtqB5npawAAAAAAA1eEjkLAViBdXV155plncvDgwRw7dizd3d2ZMmVK5s2bl7a2towYMaLue27a\ntCnd3d1pb2/PuHHj6r7+9eh//s//mfe+973pfuVKsWrxWu/n29vb88gjj2TmzJlDfk6qq/bg3bx5\nc9X/jSZMmPCa613J9wHgOtbdnTzzTF/QtnZtsnlzuRyqxZgxyYoVfUHbmjXJ/PmuHqVuzp9Pnnqq\nckrbM8/U/pZNyleN9o/a7rxTh3m9OHv27P/P3p1Hx3XX9/9/zUijfbEtJ17lVZJjO7IseZGlc0jI\nl0CaliWUrVBOkpISDjst58spZTlAoLSctjSBhCWBpAco4QekBdK0X5ZD0xDZkmzJkmPHkSzHtmzL\nu7VvM5r7++N6RrrWSB7duTNzZ+b5OEcn0mj8uZ9x5Otr3Zdeb/X09FhCbS+//LJaWlqSPo7U5/Op\nsbFRixcvVmlpafht0aJF836cl5eX1H0DAAAAAAAAQLyMRFG6EOk5ly5d0tatWy2PEWDLMGfOnNEP\nf/hD/fu//7s6Ozs1MUcdQk5Ojl7zmtfonnvu0Xvf+16VlJQ4cvyPfvSjeuyxx+TxeFReXq5bbrlF\nmzdvtryVlZU5cqx08Lvf/U5vetObwv+f5guvrVu3Tn/4wx+0cuXKhO8Tc4sUYIsFp1AAwCyDg9L+\n/dOBtn37pPPn7a+3bNl0mG3PHmnXLnO2I+CQoSHpwAFrU9vp0/bXy8mRamutobaKCsnrdW7PSK5H\nH31UH/nIR5K9DVtycnJuGHK7URDO5/Ml+2UAAAAAAAAAwCweBwsRCLBliNOnT+uzn/2sfvSjHykY\nDEYVggl9oRUUFOgDH/iAPv/5z8ccZPvoRz+qRx99dNYxZiorK4sYbFuzZk1Mx041L774ov7oj/5I\no6OjkuYPr61YsUIvvPCCNmzYkPB9Yn4E2AAACWcY0qlT1tGjbW3S+Li99TweacsW6+jRrVulOLT1\nInOdPWtOyA21tLW2SgMD9tdbtMjMXs4MtS1f7tx+kVjj4+OqqKjQmTNnkr2VpMjPz4+q7W2u55SU\nlMSlYR0AAAAAAABAZiPAhgX59re/rU9+8pMaHx+3hF9u9IV0/XOXLl2qv/u7v9MDDzxgey/Hjh1T\nU1OTjhw5okOHDqmtrU3nIzSERNpbQUGBhoaGbB87lbS2tur1r399+PXOF14rKyvT888/ry1btiR8\nn7gxRogCAFzB75c6O62jR7u67K9XWGimg2aOHqUFFg4KBqXubmtL28GD5peyXWvWWANtO3ZIRUXO\n7RnxlcotbG5QVFQU9djTSI8VFxfLS60hAAAAAAAAgBkYIYqoBAIB3XfffXr66afDAajrg2Fz/e+Y\n63kej0d79uzRT3/6U8dGVZ46dUrPP/+8fvGLX+hXv/qVAoFAxH15PB5NTU05ckw3O3jwoF73utep\nv79f0vzhtdLSUv3ud79TXV1dwveJ6EQKsCX6xAsAQERXrkyng0JvV67YX6+83Dp6tK5OKihwbr/I\neBMTUkfHdEtbS4v0yiv21/N6zXLBmaG2W2+VmNboTslqYVu6dKkee+wxjY6OamBgQP39/RoYGAi/\nRfp4YmIioXtMBI/Ho5KSkqjHnkb6uLCw0NGfyAQAAAAAAACQmtyQoyDAliB+v19vf/vb9eyzz8ow\nDMs3iRf6vyDSr12+fLl+/vOfq6GhwZkNX/PCCy/o9ttvjxigy4QA2+HDh3XHHXfo8uXLkuYPrxUW\nFurXv/614/8P4Cw3nHgBAIiKYUjHjllHjx48KAUC9tbLypK2bZsOtNXXS1VVZmoIcEh/v7R//3Sg\nrblZOnfO/np5eWb2sr5+OtS2fr05SRfJl4wWtkcffVQf+tCHFvRrJiYmbhhym/lxpMf8sdQNulRW\nVtYNm95uFITLy8sjBAcAAAAAAACkODfkKAiwJch9992nH/zgBzEF1653/Vo5OTn61re+pb/4i7+I\nad2ZBgYGtHjxYnk8HkvrWyYE2Lq6uvTa1742PFJ1vvBaXl6e/vM//1N33HFHwveJhXHDiRcAANvG\nx6X2duvo0RMn7K+3aJGZCJo5ejTCWG3ALsOQTp+2trTt3y8ND9tfs6zM2tK2a5fEpVxyJLqFbdWq\nVerp6VFubm5CjhdiGIbGxsZuGHK7URAuGAwmdN+J4PP5bI9BDX2ck5OT7JcBAEBGGB8fT+vv50cj\nKytLeXl5yd4GAAAA4DpuyFEQYEuA73//+/rLv/xLS+BppoX+tPJ8QSqPx6OHH37YsZ+C9/v9ys3N\nzbgAW09Pj2677Tadu1YXMd/veU5Ojp555hn98R//ccL3iYVzw4kXAABHnT9vHTva0iINDdlfb+NG\n6+jRmhopwWERpLepKenoUWuorbPTfrmgJG3YYA211dYyMTdREtnCZqd9zS0Mw9DIyIit9rfQx4OD\ng8l+GXGRl5cX9djTuR7Lzs5O9ssAAMD1HnvsMX34wx9O9jaS6rHHHtMHP/jBZG8DAAAAcB035CgI\nsMXZhQsXVFlZqeFrFQMzQ2AzLeR/Q6QWt+tDbI888ohj/xj1er0ZFWA7ceKEbr/9dp0+fVrS/OG1\n7Oxs/fjHP9bb3va2hO8T9rjhxAsAQFyF0kGhQNu+fdJLL0l2m39ycsw00MzRo8xwhMPGxsxywZmh\ntp4e++tlZUnV1dZQ25Yt5uNwVqJa2JLVvuYmwWBQQ0NDUbW/zRWMGxkZSfbLiIvCwsKox55Geqyk\npEReRmoDANLcxMSENm7cmLD2XLdZvXq1jh07ltHXkwAAAMBc3JCjIMAWZw8++KCeeOKJWQEwaToY\ntXHjRt111116zWteo02bNmnNmjUqLi6Wx+PR8PCwzpw5o+7ubrW0tOjXv/612traLOuE1ro+xPbd\n735XDzzwQMyvIZMCbKdPn9Ztt92mkydPSpo/vOb1evXUU0/pve99b8L3CfvccOIFACDhhoelAwem\nA23NzdLZs/bXu+kmMxEUCrTt3i2Vljq3X0DS5ctSa6sZZguVC166ZH+9wkJpx47pL9ndu6XycrKY\nTkhEC1sqt6+5SSAQ0ODg4ILb32a+jY2NJftlxEVJSUnUY08jPVZUVLTghn0AABItk1vYaF8DAAAA\n5uaGHAUBtjg6ffq01q9fr+B1bReGYSgrK0vvfOc79fGPf1y7d+9e0LonTpzQY489pu9///u6cuVK\nxG+Qho7x9NNPx9wOlikBtr6+Pt1+++3quVb1EOmPxszX/u1vf1vvf//7E71NxMgNJ14AAFzh9Onp\nMFtzs7R/v1mDZdfmzdOjR+vrzforRrrBQYYhnThhbWk7cCC2L9tly6wtbbt2SYsXO7bljBHvFjba\n19xlcnLS9hjU0Nvk5GSyX4bjvF6vSkpKohp7Otdz8vPzCcEBAOIqU1vYaF8DAAAA5ueGHAUBtjj6\n3Oc+p6985SuzmtJ2796t733ve9q6dWtM64+Pj+upp57SF7/4RZ0/fz7iWNLc3Fw999xzuuOOO2wf\nJxMCbBcuXNDtt9+urq4uSTcOr33961/Xxz72sURvEw5ww4kXAABX8vvNUaMzR48ePWp/vYKC6bqr\n0PjR1aud2y8gKRCQDh+2htpimZgrSZWVZpgt1NRWUyPl5Tm353QVzxY22tfSi2EYGh8ft9X+NvOx\ndPl+xEzZ2dm2x6CG3vI4YQEAbiATW9hoXwMAAADm54YcBQG2OCovL9fZa6OZQr/NDzzwgL797W8r\nKyvLseMMDw/ry1/+sh5++GFNTEzMCsyVlpbqhRde0K233mpr/XQPsF25ckW33367Dh8+bHmdM818\nzV/5ylf0N3/zN0nYKZzghhMvAAApo7/fnOE4c/RoLDMcV66cDrPV10s7d5pzHQEHDQ9LbW3WUNvJ\nk/bX8/nMENvMprZNmySv17k9p4N4tbDRvoZIDMPQ6Oio7TGo/f39GhwcjPjv/1SXm5trewxq6M3n\n8yX7ZQAA4ijTWthoXwMAAABuzA05CgJscdLZ2ant27dbgk/ve9/79Pjjj8ftmN3d3brvvvu0b9++\nWSG21atXa9++fVq5cuWC103nANvAwIDuuOMOHTx4MKrw2mc/+1l98YtfTMJO4RQ3nHgBAEhZhiG9\n+qp19Gh7u2R3FJzXK91663Sgrb7eHEVKMggOO3/ezGLODLVdvWp/vZISM38ZamnbvdvMZ2a6eLSw\n0b6GeAkGgxoeHrY9BrW/v1/Dw8PJfhlxUVBQYHsMamlpqUpKShz9wU0AgPMyqYWN9jUAAADgxtyQ\noyDAFif/+I//qE996lPhINmOHTu0d+/euH8DLxgM6mtf+5q+8IUvyO/3hx83DEPbtm3TCy+8oOLi\n4gWtma4BtqGhId15551qbW2NKrz2yU9+Ul/72teSsFM4yQ0nXgAA0srEhHTw4HSgrblZ6umxv15J\nibRr13Sgrb5eWrbMuf0CMrOYx45ZA23t7eaXs12rVllb2nbuNL+cM4nTLWy0r8HtpqamNDg4aHsM\n6sDAgEZHR5P9MuKiqKgoqra3uZ5TVFQkL4F2AIibTGlho30NAAAAiI4bchQE2OLk3nvv1Q9/+ENJ\nZgiqpaVFO3bsSNjxW1pa9La3vU1nzpyxhLDuvPNOPffccwsK0qVjgG10dFRveMMb1NTUFFV47UMf\n+pC+8Y1vJGGncJobTrwAAKS9ixfNRFBo9GhLizQwYH+9deuso0dra6W8PMe2C3fgsa0AACAASURB\nVEhmkeChQ9ZQ28svm2E3Ozwe6ZZbzDBbqKmtulrKyXF2327jZAsb7WvIBH6/3/YY1ND7E7Gkb13K\n4/GopKQk6rGnkZ5TWFhomVAAALDKhBY22tcAAACA6LghR0GALU527Nih9vZ2eTweveY1r9H//M//\nJHwPly5d0jve8Q49//zzljDWvffeqyeffDLqddItwDY+Pq67777b8vtyvUSOfkViueHECwBAxgkG\npa6u6UBbc7PU2SnZvZb0+aSaGuvo0YoKMzEEOGhgQDpwwBpqi6WkIjfXzF/ObGpLty9dp1rYaF8D\nojc+Pn7DENyNgnGBQCDZL8NxWVlZUY89nevjvLw8QnAA0la6t7DRvgYAAABEzw05CgJscbJq1Sr1\n9fXJ4/Hom9/8ZtJ+yicQCOgDH/iAnnzySUso62//9m/10EMPRbVGOgXYJicn9aY3vUm/+c1vogqv\nvec979EPfvCDJOwU8eKGEy8AAJA0Omomg2aOHu3ttb9eWdl01VWo7mrJEuf2C1xz5ozU2jodaGtt\nlQYH7a+3eLE5NTf05btrV+pPzXWihY32NSBxDMPQ2NiY7TGo/f39GhwcVDAYTPZLcZzP57thyO1G\nwbicdK/eBJDS0rmFjfY1AAAAIHpuyFEQYIuT4uJijYyMyOPxaP/+/aqtrU3qfr7whS/oS1/6kiWc\n9cgjj0T1j9N0CbAFAgH96Z/+qZ599tmowmtve9vb9PTTT8vr9SZht4gXN5x4AQDAHM6etQbaWlul\nkRH761VVWUePbttmtrcBDgoGpVdesba0dXRIfr/9Ndeutba01dVJRUXO7TneYm1ho30NSD2GYWh4\neDjqMaiRgnGDsaSBXSwvL8/2GNRFixappKRE2dnZyX4ZANJUuraw0b4GAAAALIwbchQE2OLE5/Np\nampKHo9HFy9e1BIXtD888cQT+uAHP6hgMCjDMOT1evXEE0/o/vvvn/fXpUOALRgM6s/+7M/0s5/9\nLKrw2hvf+EY988wzysrKSsJuEU9uOPECAIAoBQLSkSPW0aNHjkh2/wmTl2cmgWaOHl2zJr3mN8IV\nxsfNENvMUFtXl/31vF5p69bpQFt9vfmxm/MMH/3oR/XNb37T9q995JFHHN4RALebmprS0NCQ7TGo\nAwMDGokl+O5ihYWFtseglpaWqqSkhB/QBDCndGxho30NAAAAWBg35CgIsMVJaWmphoaG5PF45Pf7\nXfNNol/84hd697vfrYmJCRmGoaysLP3oRz/SO9/5zjl/TaoH2AzD0H333acf/vCHUYXX3vCGN+iX\nv/ylfLRzpCU3nHgBAEAMBgel/funA23NzdL58/bXW7bMGmjbtUsqLnZuv8A1V69aR482N0sXLthf\nLz9f2rHD2tS2bt3C8piGYWh0dNT+Jubx4Q9/WP/6r/9q69fef//9tsNvN1JQUCAPoVUgbfn9fg0O\nDi64/W3mY+Pj48l+GY7zeDwqLi6OeuxppI+Lioo4fwJpKt1a2GhfAwAAABbODTkKAmxxcsstt6ir\nq0sej0eXL1/WokWLkr2lsOeff15vectbNDQ0JMMw5PP59NOf/lRvfvObIz4/1QNsDz74oJ544omo\nwmu33367nnvuOeXl5SVhp0gEN5x4AQCAgwxDOnnSOnr0wAFpYsLeeh6PWW0VCrTt2SNt2SLRzAuH\nGYbU22ttadu/P7apuUuXWgNtu3dLZWVzP7+zs1M1NTX2D5iCOjs7VV1dnextAHCxiYmJWQG3hQbh\n/LHMkXYpr9drewxq6P38/HxCcIBLpVMLG+1rAAAAwMK5IUdBgC1O3vzmN+vZZ5+Vx+PRSy+9pM2b\nNyd7SxYdHR26++67df78eRmGodzcXP3yl7/U61//+lnPTeUA2yc+8Qk98sgjUYXXGhsb9etf/1r5\n+flJ2CkSxQ0nXgAAEGeTk1Jnp3X0aHe3/fWKiqSdO6cDbfX10ooVzu0XuGZqypySOzPUduiQ+bhd\nGzdaA221tWZ7myQ99NBD+vznP+/M5iN6s6QDkqJt81glqU7Sr+K2o4ceekif/exn47Y+ABiGofHx\ncVvtbzM/DgaDyX4pjsvOzrY9BjX0GI1KQHykSwsb7WsAAACAPW7IURBgi5OvfvWr+sxnPiOPx6PH\nH39c73vf+5K9pVleffVV3XXXXerp6ZFhGCooKNBzzz2n2267zfK8VA2wfeYzn9FXv/rVqMJrO3bs\n0O9+9zsVMy4q7bnhxAsAAJLgyhUzDTRz9OjVq/bXKy+3jh6tq5MKCpzbL3DN6KjU3m4NtR0/bn+9\n7GyputoMs916a7+effZB/b//99Nrn32tpCclOXVtXCDpMUkfifL5j0r6oCSnxppelHS/pOclSe94\nxzv0+OOPq7S01KH1ASA+DMPQyMiI7TGoAwMDGhwcjPj9sFSXm5trewxq6M3n8yX7ZQCulA4tbLSv\nAQAAAPa4IUdBgC1O2tvbtWPHDnk8Ht1zzz36+c9/nuwtRXTp0iXdfffdOnDggCSpuLhYv/nNb7R7\n9+7wc1I1wHb9vt3uqaee0r333pvsbaQ9N5x4AQCACxiGdOyYNdB28KAUCNhbLytLqqmZDrTV10tV\nVZLX6+y+AUmXLlkDbS0t0uXLdlczlJv7uPz+jysYHJe0QtKPJN3h0G7HJVXoxi1sqyT1SHKqLeP3\nkv5cUp/y8vL08MMP6/3vfz+j6wBkjGAwqKGhIdtjUAcGBjQ8PJzslxEXBQUFUY89jfRxSUmJshgv\njzSU6i1stK8BAAAA9rkhR0GALY42b96sV155RdnZ2erp6VF5eXmytxTRyMiI3vrWt+q3v/2tJGnR\nokX6/e9/r5qaGkmZEWBzw02MJ598kgBbArjhxAsAAFxqbMysugoF2vbtk06etL/eokVmzVVo9Oju\n3dLSpc7tF7jGMKRXX7UG2g4ckMbHF7LKIUnvkvSyJI+kz117y3Zgh4/qxi1sj0r6kAPHCkj6kqQv\nSzK0efNm/eQnP1F1dbUDawNAZgkEAhocHLQ9BnVgYEBjY2PJfhlxUVxcHPXY00gfFxUVycsPOsCF\nUrmFjfY1AAAAwD435CgIsMXRt771LX34wx+Wx+PRu971Lv3bv/3bnM8dHx/XQw89pB//+Mfq6+vT\nmjVrdP/99+tTn/pUQn6iz+/3695779VPfvITSdLSpUv1v//7v7rlllsyIsCWLDN/LwmwJYYbTrwA\nACCFnD8/HWZrbpZaW6WhIfvrbdxoHT26fbuUk+PcfoFr/H7p8GHzyzYUajt82Ay7zW1E0sclfe/a\nx7fJbGNbHeNubtTC5lT72mlJ75H0giTpgQce0MMPP6zCwsIY1wUA2DU5OanBwUFbY1BDH09OTib7\nZTjO4/GopKQkqrGncz2noKDAFT+Ui9RhGIZGR+cf1T4xMaHq6mqdPXs2QbtyxqpVq9TZ2XnD9jX+\n3AAAAACRuSFHQYAtjiYmJrR582adOHFCHo9HP//5z3XPPffMep7f79frXvc6vfjii5awlcfj0d13\n361f/epXCftH1Sc+8Qk98sgjkqSVK1fq+eefV2VlJQG2OCHAlnhuOPECAIAUNjUlHT1qHT360ktS\nMGhvvZwcqa5uOtC2Z4+0bp3ETRXEwdCQ1NZmbWo7dSrSM38s6UFJw5LKJD0l6Y0xHn2+FjYn2td+\nJel+SVdUXFys73znO3r3u98d45oAADcYHx+PegzqXME4N38P066srKyox57OFYzLy8tL9stAAo2M\njKioqCjZ20iq4eFhfrgBAAAAiMANOQoCbHH2i1/8Qm9961slSYWFhXr++edVV1dnec4//MM/6NOf\n/vSskFoo2PT1r39dH/vYxxK2569+9av6zGc+I4/Ho9WrV6u3t5cAW5wQYEs8N5x4AQBAmhkelvbv\nt44e7euzv95NN1kDbbt2SaWlzu0XmOHcObNYsKVlumSwv1+SjskcKdp27ZmfkPT3st+SNlcLW6zt\naxOS/kbSv0iSduzYoaeffloVFRU21wMApJtQ65TdMaihNzd/j9GunJwc22NQQ285tAmnDAJsBNgA\nAACAubghR0GALQEefPBBPfHEE5Kk0tJS/fSnP9Wdd94Z/vytt96qI0eORGxZMwxD1dXV6ujoSNh+\nJenJJ5/UBz7wgXBA7fpmuFQKsLkdAbbEcsOJFwAApDnDkE6ftgbaDhyQxsbsrefxSLfcMh1oq6+X\nbr1Vys52dt+AzDLBY8fMQNvevRN65pm/0blz/3LtszskPS0ziGZHpBa2WNrXrCG7v/qrv9Lf//3f\ncyMdAOC4YDCo4eFhW+1vofeHYhlD72L5+fm2x6CWlpaqpKRE2VzXJsTMANt5SfPFuL4r6a8TsSkH\nfF3S++f5/IikZdfeJ8AGAAAAROaGHAUBtgTw+/2688479cILL0iSsrOz9bGPfUwPPfSQ8vPzlZeX\nJ7/fL0mWlrPQx3l5eRodHU34vp999lm9613v0vj4+Ky9pUqALVUQYEscN5x4AQBABvL7zVGjM0eP\nHj1qf72CAmnnzummtvp6afVq5/YLzPDMM7/SX/zF/RocvCKpWNJ3JNkZz3l9C1ss7Wv/JukDkoZV\nVlamp556Sm98Y6xjTgEAiJ+pqSkNDg7aan8LPZaM7xEnQmFhYdRjTyN9XFxcnFLfC06WmQG2Yc0f\nYJuQtFGzu3PdZrXMH2mY72pyRFKod44AGwAAABCZG3IUBNgSZHh4WHfffbdefPHFcDitrKxM73//\n+/VP//RP8wbYlixZokuXLiVl301NTXrTm96kfnOGTDi4lgoBtqysrGRvYUEIsCWGG068AAAAksw5\njaG5jaGmtsuX7a+3apV19OiOHRI3Z+CQ06dP6z3veU/4B7Ok90l6RPPf+oxkZgubnfa1EUkfk/R9\nSdJtt92mH/3oR1pNgBMAkAH8fr8GBwcX3P428+OJiYlkvwzHeTweFRcXRz32NNJzCgsLU2KaRywW\nEmCTpMckfTjem4rRY5I+eIPnEGADAAAAbswNOQoCbAk0MTGhBx98UD/4wQ8sITDJOqJTsracveUt\nb9EzzzyTjC1Lko4cOaK77rpLZ8+eDT+WCgE2IBI3nHgBAAAiMgzp+HFroO3gQWly0t56WVnmqNGZ\no0dvuUWinQI2BQIBfelLX9KXv/zla/+G3SzpJ5KqF7DKuMzgmyR9XAtrXzskc2Toy5I8uueez+n/\n/t/PafPmbC1evIBlAADIYBMTE7bHoIY+DgQCyX4ZjsvKylJJSUlUY0/neiw/P9/VIbiFBtjc3sIW\nTfuaRIANAAAAiIYbchQE2JLgZz/7mf76r/9ap0+flqQ5/1FrGIZycnLU1NSkurq6RG5xlt7eXt11\n1106evRoyjSwAZG44cQLAAAQtYkJM8QWCrQ1N5shN7tKSqRdu6YDbfX10nXXRsCN/P73v9ef//mf\nq6+vT1KepH+R9KCkeN2wNSR9V9InZAbgVkj6kaQ7ws9YulSqqpIqK83/ht6vqKCIEAAAJxmGobGx\nMdtjUENvwWAw2S/FcT6fL+qxp3M9lptrZ7R6dBYaYJPc3cIWTfuaRIANAAAAiIYbchQE2JJkcnJS\n3//+9/XEE0+ora0t4nMKCgr01FNP6e1vf3uCdxfZ1atX9Sd/8ifat2+fJBFgQ0pyw4kXAAAgJhcv\nTre0NTebY0gHBuyvt379dJitvl6qrZXy8pzbL9LShQsXdN999+m///u/rz3yDpkhs0UOH6lfZjju\np9c+vlvSv0qK/vp99erZwbaqKvNLPyfH4e0CAIAbMgxDw8PDtsegDgwMaHBwMNkvIy5yc3Ntj0EN\nvZ+dnR1xbTsBNre2sEXbviYRYAMAAACi4YYcBQE2Fzh79qz+8Ic/6OWXX9aFCxcUCARUUVGh9773\nvVqxYkWyt2cxNjamL33pSzp37pwk6cknn0zyjoCFccOJFwAAwFHBoPTKK9bRo4cOSXZ/2MTnk7Zv\nnw607dkjbdwouXgcEpIjGAzqn//5n/XpT3/62iixP5L0Xw4f5W5J/y0pW9LfS/orSc6Mwc3Kktat\nmx1sq6qSysuZtgsAgJsFg0ENDQ3ZHoM6MDCgkZGRZL+MuCgoKIgYcissLAx/P/8fJd0s80cPSme8\nLZJULClrxnpubGGLtn1NIsAGAAAARMMNOQoCbAAyihtOvAAAAHE3MiK1tVlHj54+bX+9sjJp9+7p\n0aO7d0uLFzu3X6S0xx9/XA8++KDM0Z5nHV59paQ+ffWr39XOne9XV5fU1SV1d5v/ffVV+1nN+eTm\nmuNHI40lXbaMPCcAAOkgEAhocHAwqjGocwXjxsfHk/0y4qJY06G2EkntMge5u8FC2tckAmwAAABA\nNNyQo4jcJQ0AAAAASF2FhdJrXmO+hZw5Yx09un+/GXSLxuXL0n/9l/kWUlU1HWirr5e2bTPb2+B6\nhmFodHTUsfWampquvfdHMm8ROukuSU+pq2uvPvrR96ihwfpZv186cUI6dswMtfX0TL9/dt4sXYGk\nuVNoExPS4cPm2/WKiyMH26qqpEVOT1AFAABxk52drSVLlmjJkiW215icnLQ9BjX0sd/vd/BVOWPo\n2lsMPwITN3+r6MNrAAAAAFIHDWwAMoobksMAAACuEAiY6ZyZobYjRyS7/0TMy5N27LCOHi0vp6rK\nhUZGRlRUVHTjJ6a1YUnOt2/cdFPkcFtFhVRQ4PjhAABAijMMQ+Pj47ba32Z+PBWPSloXWmj7mkQD\nGwAAABANN+QoCLAByChuOPECAAC41uCg1NpqHT164YL99ZYvnw6z1ddLO3ea9VVIKgJsUm/vsM6e\nLQyPIp05lnRoKD7HXL16dmNbVZW0fj3lhQAAwD7DMDQyMnLDkNvFixf1+OOPS5IaZcb5+yUNSBqU\nlAo3ih6V9KEF/hoCbAAAAMCNuSFHQYANQEZxw4kXAAAgZRiGdPKkNdDW1mbOV7TD65W2bLGOHt2y\nRcrKcnbfmNfMANt5xaOHzJ1GJC279v5cNy8NQzp/XhGDbceO2f/Sn09WlhliixRuW73a/GMDAAAQ\nq5nXgNd30QZlDbSF3mZ+fFnSt+X8wPho3SSzfa1kgb+OABsAAABwY27IURBgA5BR3HDiBQAASGmT\nk1JHh3X0aHe3/fWKiqRdu6YDbfX10ooVzu0Xs8x38zKdxXrzcmpKOn16drCtq0s6ccL8vNPy8szx\no5HGkt58MxN6AQBA9Jy4BnxU0kec3NQC5UraKalBZotcg6TlN/g1BNgAAACAG3NDjoIAG4CM4oYT\nLwAAQNq5fFlqabGG2q5etb/emjXTYbY9e6S6Oik/37n9ZjgCbM7fvJyclF59dXawrbvbDL3FQ0lJ\n5GBbVZVUWhqfYwIAgNTlxDXguKQKSWcc3Fes1ssaaNsmKXvG5wmwAQAAADfmhhwFATYAGcUNJ14A\nAIC0Zxhmcmfm6NGODikQsLdedra0bZt19GhlJbMVbSLAltiblyMj5vjRmcG2ULjt0qX4HPPmmyMH\n2yoqyIICAJCpnLoGTHYL240UStqt6VBbtaS11z5HgA0AAACIzA05CgJsADKKG068AAAAGWlsTGpv\nnw60NTdLJ0/aX2/RIuvY0fp6qazMuf2mMQJs7rl5eeWKGWS7vrWtq0saHo7PMcvLZwfbqqqkdesk\nny8+xwQAAMnn1DVgolvYCiStkXTUgbW+8Y1v6I477tDmzZvl5YdhAAAAgDA35CgIsAHIKG448QIA\nAOCac+esY0dbWmJL7VRUWEeP1tRIOTnO7TdNEGBzT4BtLoZh/vGIFGw7dswcWeq07Gxp/frIY0lX\nr6bwEACAVOfkNWAiW9gelfQhSVclNUtquvbWLPN12FFaWqo9e/aooaFBjY2Nqq+vV0lJiTMbBgAA\nAFKQG3IUBNjSwJIlS274HI/Ho8uXLydgN4C7ueHECwAAgDlMTUkvv2wdPXr4sBQM2lsvN1eqrZ0O\ntNXXmzVTHo+j2041BNjcH2Cbz9SU1Ns7O9jW1SWdOGH/j8t88vLMIFuksaQ33ZTxf6QAAEgJTl4D\nJqqFbZWkHkm5ET43JeklSXtlBtr2Sjpm8zgej0e33nqrGhsbw6G2iooKebjIAQAAQIZwQ46CAFsa\n8Hq98ng8mu9/pcfj0dTUVAJ3BbiTG068AAAAWIChIenAAevo0b4+++vddNN0mK2+Xtq1SyotdW6/\nKYAAW2oH2OYzOSkdPz472NbdLZ2J0x3m0tLIwbbKyoz7owUAgKs5fQ2YiBa2UPtatC5I2qfplrZW\nmWE7O5YuXaqGhoZwoG3Xrl0qKCiwuRoAAADgbm7IURBgSwOhANtcDMMgwAZc44YTLwAAAGJgGNLp\n09ZA2/790rjNW1Mej7R5s3X06Nat5jzFNEWALX0DbPMZHjbHj0YaSxqvwvabb54Ots0Mt23cKOXn\nx+eYAAAgMqevAePdwjZf+1q0JmUG2m6/9nF5ebl6e3ttrZWdna2amhpLS9uaNWtoaQMAAEBacEOO\nggBbGiDABkTPDSdeAAAAOMzvlw4dmg607dsnvfKK/fUKCqSdO62jR1etcm6/SUaALTMDbPO5ciVy\nsK2rSxoZcf54Ho9UXj472FZVZU75TeP8KAAASROPa8B4trAttH1tLtdfA169elV79+7V3r171dTU\npLa2Nvn9fltrr1ixwhJoq6urU25uLJE7AAAAIDnckKMgwJYG5hshGnqcABtgcsOJFwAAAAlw9arU\n2jodaGtujq1matUq6+jRHTukFA1AEWAjwBYtw5DOnYscbOvpMUeWOi07W9qwYXawrbLS/GPo9Tp/\nTAAAMkE8rgHj1cLmRPtayI2uAcfHx3XgwIFwoK2pqUnnz5+3daycnBzt2LHDEmpbsWJFbC8AAAAA\nSAA35CgIsKUBAmxA9Nxw4gUAAEASGIZ0/Lh19Gh7u9neZkdWllRdbR09umlTSqRrCLARYHPC1JR0\n6tTsYFtXl3TypBQMOn/M/HwzyHZ9sK2qSlq61Gx2AwAAkcXrGjAeLWxOta9JC78GNAxDJ06cUFNT\nUzjU1tnZafv+ytq1a9XY2BgOtW3btk0+n8/WWgAAAEC8uCFHQYAtDRBgA6LnhhMvAAAAXGJ8XDp4\n0Dp69NVX7a9XWirt2mUdPerC60wCbATY4m1iwsyLXh9u6+6Wzp6NzzEXLYocbKuslEpK4nNMAABS\nSbyuAZ1uYXOyfU1y5hpweHhYra2t4UDb3r17deXKFVv7KSgo0K5du8KBtoaGBi1dutTWWgAAAIBT\n3JCjIMCWBgiwAdFzw4kXAAAALnbhgtTSMh1oa2mRBgftr7d+vTXQVlsr5Tp1O84eAmwE2JJpeNgM\nsl0fbOvqkmzeB76hZctmB9uqqqSNG6W8vPgcEwAAt4nnNaCTLWxOtq9J8bkGNAxDXV1dlpa2I0eO\nRLxHE42qqqrwyNGGhgZt2bJFWVlZMe8TAAAAiJYbchQE2NIAATYgem448QIAACCFBIPSK69YR48e\nOmTOT7TD55O2b58OtNXXmymaBM4+JMBGgM2tLl+OHGzr6pJGR50/nscjrVkzO9hWWSmtWydlZzt/\nTAAAkiWe14BOtbA53b4mJe4asL+/X83NzeFQ2759+zQ0NGRrrZKSEtXX14dHj9bX16u0tNThHQMA\nAADT3JCjIMCWBgiwAdFzw4kXAAAAKW5kRDpwwDp69EwMt+vKyqbDbPX10u7d0uLFzu33OgTYCLCl\nGsOQ+voiB9t6eiS/3/lj+nzShg2Rx5KuXCl5vc4fEwCAeIr3NaATLWxOt69JybsGnJqa0pEjRywt\nbd3d3bbW8ng82rp1q6WlraqqSp4E/hAMAAAA0psbchQE2NIAATYgem448QIAACANnTljDbTt3x9b\nZdSmTdbRo9XVZqLGAQTYCLClk0BAOnVqdrCtu1s6ccIMvzmtoECqqJgOts0Mt5WVJbRQEQCAqMX7\nGjDWFrZ4tK9J7roGvHjxovbt2xcOtbW0tGhsbMzWWmVlZdqzZ0+4pW3Xrl1c3wIAAMA2N+QoCLCl\nAQJsQPTccOIFAABABggEpMOHpwNtzc3Syy/bT9Pk50t1ddbRo+XltpIyBNiSf/MSiTE+Lh0/Hnks\naV9ffI65eHHk1rbKSqm4OD7HBABkgGDQvL4MBMzq0fnen+OxkeFhFb3jHZLidw0YSwtbPNrXJHdf\nA/r9fnV2dqqpqSkcajt58qSttbKyslRTU2NpaVu3bh0tbQAAAIiKG3IUBNjSAAE2IHpuOPECAAAg\nQw0MSK2t001tzc3ShQv211uxwjp6dOfOqBIyBNjcd/MSiTc0JB07Fnks6dWr8Tnm8uWzg21VVeao\n0ry8+BwTANKaYUhTUwsPc0UR9nJ8zVg/HwzG/NtluRZSfK4B7bawxat9TUq9a8CzZ8+GR47u3btX\nBw4c0OTkpK21li9fHg6zNTY2qq6uTnlcdAAAACACN+QoCLClAQJsQPTccOIFAAAAJJk3XU+csI4e\nbW+XJibsref1Slu3Tgfa9uyRNm+WsrIsTyPAlho3L5E8ly9HDrZ1d8c2GXguHo+0du3sYFtlpfl4\ndrbzxwSQ5gwjvcNcM99H1BIRYJPstbDFq31NSv1rwPHxcbW3t4db2pqamnTu3Dlba+Xk5Kiurs7S\n0rZq1SqHdwwAAIBU5IYcBQG2NECADYieG068AAAAwJwmJ6WODuvo0WPH7K9XVCTt2mUZPTpSXEyA\nLQVvXiL5DEM6ezZyuO34cTNL4TSfT9q4MfJY0pUrbU0RBjKXYZgtVvEMViU7zOVgWxfST6ICbAtt\nYYtn+5qUfteAhmHo5MmTlpa2gwcP2r7/s2bNGktLW01NjXw+n8O7BgAAgNu5IUdBgC0NEGADoueG\nEy8AAACwIJcvSy0t04G2lpaYZhyOlJerqLdXEgE2wCmBgHTy5OzGtq4u8/F4fPetsFCqqIg8lrSs\nzPnjIY3N1daVLmEu2rqQaXw+s74z9N9r7494vQm7BlxIC1s829ekzLgGhTmWdwAAIABJREFUHBkZ\nUWtrqyXUdvnyZVtr5efna9euXeFQW0NDA987BwAAyABuyFEQYEsDBNiA6LnhxAsAAADEJBg0kzGh\n0aPNzWZrW5Q35hPVvuE2mXDzEu40Pm42tIWCbTPDbTYngN3Q4sWRg22VlWYxI6JwfVtXsoNX8Twm\nbV3IBFlZljBXpICXKz4f65rXjY6fKZFj5KNtYYt3+5qUmdeAhmGou7vbEmh76aWXIt5DikZFRYWl\npW3r1q3KmudrDQAAAKnHDTkKAmxpgAAbED03nHgBAAAAx42NSW1t1tGjp05FfCoBtsy5eQn3Gxw0\npwRHGkva3x+fY65YETnYtnGjlBtNgsAwkh/mSlTYC8gEbg5zORX2ysqSvN5k/04nXSIDbFJ0LWzx\nbl+TrrsG/MQnVLhnj1RXZ/7Fl0FfFwMDA2pubg6H2vbt26fBwUFbaxUXF6u+vj4catuzZ48WLVrk\n8I4BAACQSG7IURBgSwME2IDoueHECwAAACTEuXPWQFtrqzQ8TIBNBNgQB/O1ddkIThn+gIav+nXu\ndEAXzgZ0qc+vS+cCunLer6sXAzICAfnkV7YCytb0+9E+NtfnC7L9yvMFlJsVUK7XL58noKygX95g\nQB7aupBJIrV1JTusFY/mLq9X8niS/buNBEl0gO1GLWyJaF+T5vnhjeJiqbbWDLOF3jZtMv9sZIBg\nMKgjR46EA21NTU3q6uqyvd6WLVssLW1VVVXyZlBAEAAAINW5IUdBgC0NEGADoueGEy8AAACQFFNT\n0pEjGvnf/1XRR8w+DAJsiLvr27qS3cwVz+YuIBO4JawVzzGMtHUhTSU6wCbN38KWiPY1aYHtw/n5\nUk2NNdS2dauUkxP3fbrBpUuXtG/fvnCoraWlRaOjo7bWWrJkifbs2RMOte3evTv89QcAAAD3cUOO\nggBbGiDABkTPDSdeAAAAIJmScfPSDVwVYAu1dSU7rJWIsBdtXcgEXq+7w1xOhb1o6wJSWjKuAedq\nYUtU+5q0wABbJD6fVF1tDbVt22aG3dJcIBBQZ2enpaXtxIkTttbyer3atm2bpaVt/fr18vD3CgAA\ngCu4IUdBgC0NEGADoueGEy8AAACQTATYpOGvfEWFWVnJbe4CMsCkfAp6s2VkZcvI9smbky1vTray\n833y+lI8zEVbF4AUk6xrwEgtbIlqX5Ouuwa8804VdnRIFy/GtmhWlrR5sxlmC40h3b5dKimJdbuu\n19fXp71794ZDbQcOHNDExISttZYtWxYOszU0NGjHjh3Kz4BgIAAAgBu5IUdBgC0NEGADoueGEy8A\nAACQTATYMut1w4VmtnUlO6x17f1Rf7bOXfLpzPlsnTmfrVN9Pp06m60TZ3zqH8lWQNnyy6eArO9H\neiz0flBeSXO3qqxcKVVVSZWV5n9D72/YIOUmopIHADJMsq4Br29hS2T7mhShhbegQDp7Vmprm35r\nb5d6e2M/WGWltamttlYqK4t9XRebmJhQe3u7mpqawqG2s2fP2lrL5/OptrZWjY2N4VDb6tWrHd4x\nAAAAInFDjoIAWxogwAZEzw0nXgAAACCZCLBl1utOKS4Jc8W1uSvF2roMQ7p0Serqkrq7zf+G3u/u\nlsbGnD+m1yutWzc72FZVJa1ZY/4WAgAWLpnXgDNb2BLZviYtYIz8xYtmkG1msK2nJ/YNrF1rDbXV\n1UnLl8e+rksZhqHe3l5LoK29vd32/any8nJLS9v27duVk5Pj8K4BAADghhwFAbY0QIANiJ4bTrwA\nAABAMhFgS7HXPVdbV7LDWk6PYfR6Jc/cbV1wn2BQOnNmdrCtq0s6ftycluu0nBxp48bZwbaqKjML\nwJcQAMwtmdeA45Ieufb+x5W49jVpAQG2SPr7pYMHraG2o0fNhHcsVqyYHWorL0/bv8hGR0e1f/9+\nS6jt0qVLttbKy8vTrl27LKG267/fDwAAgIVzQ46CAFsaIMAGRM8NJ14AAAAgmQiwScP/5/+oMC/P\nvWGu0PvZ2SnV1gWE+P3SyZPTwbaZ4bZTp+JzzKKi6UDb9e1tS5bE55gAkEq4BrQRYIu44IjU0WEN\ntR0+HHtyu6xseuxoKNS2cWNaXgsahqGenh41NTWFQ22HDh2KeI8rGhs3bgwH2hobG3Xrrbcqi8pW\nAACABXFDjoIAWxogwAZEzw0nXgAAACCZuHnp0M1LALaMjZkT2a4PtnV1SRcuxOeYZWWRg22VlRKn\nAgCZgmvAOF4Djo+bIbaZobaODmliIrZ1i4utgba6OmnTJvOHHNLM4OCgWlpawoG2vXv3amBgwNZa\nRUVFqq+vD4fa9uzZo8WLFzu8YwAAgPTihhwFAbY0QIANiJ4bTrwAAABAMnHzkgAb4FYDA9OBtpnB\ntq4uaXAwPsdctWp2sK2qStqwwRxZCgDpgmvABF8D+v3muNGZobb2drPBLRb5+VJNjTXUtnVr2v2l\nFQwGdfToUUtL29GjR22vt3nz5vDI0cbGRm3atEneNGy3AwAAsMsNOQoCbGmAABsQPTeceAEAAIBk\n4uYlATYg1RiGdPHi7GBbd7f5Nj7u/DG9Xmn9+sjhtvJyiclkAFIN14AuuAacmpKOHbOG2trapP7+\n2Nb1+aTqamuobds2M+yWRq5cuaJ9+/aFA23Nzc0asRkIXLRokRoaGsKBtt27d6u4uNjhHQMAAKQO\nN+QoCLClAQJsQPTccOIFAAAAkombly64eQnAMcGgdPr07GBbV5d0/LiZFXBabq60cePsYFtVlbRs\nmeTxOH9MAIgV14AuvQY0DOnEidmhtljnamdlSZs3m2G20BjS7dulkhJHtu0GgUBAhw4d0t69e8Oh\ntuPHj9tay+v1qrq62tLStmHDBnn4Sx0AAGQIN+QoCLClAQJsQPTccOIFAAAAkombly69eQnAcX6/\nmQm4PtjW1SX19sbnmEVFkYNtlZXS4sXxOSYARINrwBS6BjQMqa9vdqjNib+8KiutTW21tVJZWezr\nusS5c+csLW2tra2amJiwtdZNN91kCbTt3LlT+WnWagcAABDihhwFAbY0QIANiJ4bTrwAAABAMnHz\nMoVuXgKIm9FRqacn8ljSWEtv5rJ0aeRgW0WFxCkJQLxxDZgG14AXL0rt7dZQW09P7OuuXWsNtdXV\nScuXx76uC0xOTqq9vT3c0tbU1KQzZ87YWis7O1u1tbWWUFt5ebnDOwYAAEgON+QoCLClAQJsQPTc\ncOIFAAAAkombl2lw8xJAXPX3m0G2SGNJBwfjc8zVq2cH26qqpPXrpZyc+BwTQGbhGjBNrwH7+6WD\nB62htqNHzRa3WKxYMTvUVl6eFnOye3t7LYG29vZ2BQIBW2utWrXKEmirra1VDn9xAwCAFOSGHAUB\ntjRAgA2InhtOvAAAAEAycfMyTW9eAog7wzDb2WYG20Lhtu5uyeaEsnllZUnr1kUeS1peLnm9zh8T\nQHriGjCDrgFHRqSODmuo7fBhyWZIK6ysbHrsaCjUtnFjyv9lNDY2pv3794dDbXv37tUFm3Wsubm5\n2rlzZzjU1tDQoOVp0mYHAADSmxtyFATY0gABNiB6bjjxAgAAAMnEzcsMunkJIGGCQam3N3Jr26uv\nSvH4tlxurjl+NNJY0mXL0qIkB4CDuAbM8GvA8XEzxDYz1NbREXv6urjYGmirq5M2bZKys53ZdxIY\nhqHjx4+Hw2xNTU06dOiQgsGgrfU2bNgQbmhraGhQdXW1slP49wcAAKQnN+QoCLClAQJsQPTccOIF\nAAAAkomblxl+8xJAwvn9Zojt+mBbV5d0+nR8jllcHDnYVlUlLVoUn2MCcDeuAbkGnMXvN8eNzgy1\ntbebDW6xyM+XamqsobatW1N6JvbQ0JBaWlosLW39/f221iosLNTu3bvV2NioxsZG7dmzR0uWLHF4\nxwAAAAvjhhwFAbY0QIANiJ4bTrwAAABAMnHzkpuXANxjdFQ6dmx2sK27W7p4MT7HvOmmyOG2igqp\noCA+xwSQfFwDcg0Ylakp8y+mmaG2tjbJZlgrzOeTqqutobZt28ywWwoKBoN65ZVXLC1tL7/8su31\nbrnlFktL2+bNm+VN8dGsAAAgtbghR0GALQ0QYAOi54YTLwAAAJBM3Lzk5iWA1HD1qhlkizSWdGgo\nPsdcvXp2Y1tVlbR+vZk9AJC6uAbkGtA2w5BOnJgdartwIbZ1s7KkzZvNMFtoDOn27VJJiSPbTrQr\nV66oubk5HGprbm7W8PCwrbVKS0u1Z8+ecEvb7t27VZKivy8AACA1uCFHQYAtDRBgA6LnhhMvAAAA\nkEzcvOTmJYDUZhjS+fORg23HjkkTE84fMyvLDLFFCretXi1REgO4H9eAXAM6yjCkvr7Zobbe3tjX\nrqy0NrXV1kplZbGvm2BTU1N66aWXLC1tPT09ttbyeDyqrq62tLRVVFTI4/E4vGsAAJCp3JCjIMCW\nBgiwAdFzw4kXAAAASCZuXnLzEkD6mpqSTp+eHWzr6jLLc+Lx7cG8PHP8aKSxpDffLHFvHXAHrgG5\nBkyIixel9nZrqM1maMti7VprqK2uTlq+PPZ1E+zChQvhMNvevXvV2tqq8fFxW2stXbo0HGhrbGzU\nzp07VcAscAAAYJMbchQE2NIAATYgem448QIAAADJxM1Lbl4CyEyTk9Krr84OtnV3m6G3eCgpiRxs\nq6qSSkvjc0wAkXENyDVg0vT3SwcPWkNtR4+aLW6xWLFidqitvDylktOTk5Pq6OiwtLT12myxy87O\n1vbt2y0tbWvWrKGlDQAARMUNOQoCbGmAABsQPTeceAEAAIBk4uYlNy8B4HojI+b40UhjSS9dis8x\nb745crCtokLKz4/PMYFMxjUg14CuMjIidXRYQ22HD0uBQGzrlpVNjx0Nhdo2bkypWdenT5+2tLS1\ntbXJ7/fbWmvlypXhMFtjY6Nqa2uVm5vr8I4BAEA6cEOOggBbGiDABkTPDSdeAAAAIJm4ecnNSwBY\niKtXIwfburqk4eH4HLO8fHawrapKWrdO8vnic0wg3c28BjyvzLoGXHbtfa4BXW583AyxzQy1dXRI\nExOxrVtcbA201dVJmzZJ2dnO7DvOxsbG1NbWZmlpO3/+vK21cnNztWPHDktL24oVKxzeMQAASEVu\nyFEQYEsDBNiA6LnhxAsAAAAkEwE2bl4CgBMMQzp3LnK47dgxc2Sp07KzpfXrI48lXb06pQp2gISb\neQ2YqbgGTEF+vzludGaorb3dbHCLRX6+VFNjDbVt3Srl5Diz7zgyDEOvvvqqpaWto6NDwWDQ1nrr\n1q2ztLRt27ZN2SkS7gMAAM5xQ44i6QG2973vfck8fFp46qmnCLABUXLDiRcAAABIJgJs3LwEgHib\nmpJ6eyO3tp04Idm8xz6vvDwzyBZpLOlNN0kej/PHBFIJATauAdPG1JSZlJ4Zamtrk/r7Y1vX55Oq\nq6cDbbW10rZtUkGBM/uOo+HhYbW2tqqpqSkcart69aqttQoKCrR79+5wqK2hoUFlZWUO7xgAALiN\nG3IUSQ+whdrDYN98/wsJsAFWbjjxAgAAAMlEgI2blwCQTJOT0vHjs4Nt3d3SmTPxOWZpaeRgW2Wl\n+TkgExiGodHR0WRvI6kKCgq4H5WuDMNMSM9saTtwQLpwIbZ1vV5p82ZrU9v27VJJiSPbjpdgMKiu\nri5LS9vhw4dtr1dVVWVpaduyZYu81J4CAJBW3JCjcE2AjUmm8UGADbByw4kXAAAASCYCbATYAMCt\nhofNUp1IY0kvX47PMW++eTrYNjPctnGjOWEOAJCiDEPq65vd1NbbG/valZXWUFttreTylrL+/n41\nNzeHW9qam5s1NDRka62SkhLt2bMnHGqrr69XKYlwAABSmhtyFK4JsME+GtiA6LnhxAsAAAAkEwE2\nAmwAkIquXIkcbOvqkkZGnD+exyOVl88OtlVVSevWSdnZzh8TAJAAFy+aDW0zQ209PbGvu3atNdRW\nVyctXx77unEyNTWlw4cPh1vampqadOzYMVtreTwebd261dLSVllZyf1fAABSiBtyFK4JsNHAFh8E\n2AArN5x4AQAAgGQiwEaADQDSiWFI585FDrb19JgjS52WnS1t2DA72FZZKa1aZU6cAzAtEAjoP/7j\nPyRJ99xzj7JJgMJt+vulgwetobajR82/ZGKxYoW1pa2uTlqzxkxJu9DFixe1d+/ecKittbVVY2Nj\nttYqKysLh9kaGhq0a9cu/g0GAICLuSFHQYAtzRFgA6zccOIFAAAAkokAGwE2AMgUU1PSqVOzg23d\n3dKJE1Iw6Pwx8/PNINv1wbaqKmnpUtdmFoC4+u1vf6vXv/71kqTf/OY3uvPOO5O8IyAKIyNSZ6c1\n1PbSS1IgENu6S5bMbmrbuNGV6We/36+Ojg5LS9upU6dsrZWVlaWamhpLS9vatWtpaQMAwCXckKMg\nwJbmCLABVm448QIAAADJRICNABsAQJqYkI4fjzyW9OzZ+Bxz0aLIwbbKSqmkJD7HBNzgwQcf1OOP\nPx5+/zvf+U6SdwTYNDFhhthmhto6OszHY1FcPN3QFnrbtMmV86rPnDljaWlra2vTpM260xUrVlha\n2urq6pSXl+fwjgEAQDTckKMgwJbmCLABVm448QIAAADJRICNABsAYH7Dw2aYLVK47cqV+Bxz2bLZ\nwbaqKrOUh3v5SGWBQEArVqzQpUuXJElLly5VX18fY0SRPvx+c9zozFBbe7vZ4BaL/HyppsYaatu6\nVcrJcWbfDhkfH1dbW5uamprCobZz587ZWisnJ0d1dXVqbGwMh9pWrlzp8I4BAEAkbshREGBLcwTY\nACs3nHgBAACAZCLARoANAGDf5cvTYbbrA26xZhUi8XikNWtmB9sqK6V161xZzgNYTI8PXXrtkUuM\nEUX6CwbNvxhmhtra2qT+/tjW9fmk6urpQFttrbRtm1RQ4My+HWAYhk6ePGkJtHV0dNi+R7l27VpL\nS1tNTY18Pp/DuwYAAG7IURBgS3ME2AArN5x4AQAAgGSaGWA7r8wKsC279j4BNgCA0wxD6uub3djW\n1SX19JgFPU7z+aQNGyKPJV25UvJ6nT8msFDT40MflGRIepwxoshMhiGdOGG2s4UCbQcOSBcuxLau\n1ytt3mxtatu+3VWzqUdGRtTa2moJtV2xWWman5+v3bt3W0JtS5cuvfEvBAAA83JDjsI1ATbEDwE2\nYJobTrwAAABAMs0MsGUqAmwAgEQKBKRTpyK3tp04YWYanFZQIFVUTAfbZobbysrMZjcg3qzjQ39z\n7dHXM0YUCAmln69vauvtjX3tykprqK221vwLwAUMw1B3d7cl0Hb48GHbZSeVlZXhQFtjY6O2bNmi\nrKwsh3cNAEB6c0OOwjUBNhrY4osAG2Byw4kXAAAASCYCbATYAADuMT4uHT8+O9jW1WVmGuJh8eLI\nrW2VlVJxcXyOicxkHR8a+oJeIcaIAjdw8aK1qa2tzazzjNXatdZQW12dtHx57Os6YGBgQM3NzeFQ\n2759+zQ4OGhrreLiYu3Zsyccaquvr9eiRYsc3jEAAOnFDTmKpAfYXvva19LAliC///3vk70FIOnc\ncOIFAAAAkskwDI2OjiZ7G0lVUFDA9yIAAK43NCQdOxZ5LOnVq/E55vLls4NtVVXmqNK8vPgcE+nL\nOj40NDL0QTFGFLChv186eNAaajt6NPYazxUrrC1tdXXSmjVJr+qcmprSyy+/rKampnCoraury9Za\nHo9HW7ZsCY8cbWxsVFVVFf8mBABgBjfkKJIeYAOARHLDiRcAAAAAAACIxeXLkYNt3d1SPHLqHo9Z\n3HN9sK2y0nycSZC43uzxoaG2td+KMaKAQ0ZGpM5Oa6jtpZfM2dWxWLJkdlPbxo2S1+vMvm26dOmS\n9u3bFw60tbS02P7hrCVLlqihoSEcaNu1a1fGN5UDADKbG3IUBNgAZBQ3nHgBAAAAAACAeDAM6ezZ\nyMG2nh7J73f+mD6fmWuINJZ05cqkl/ggSWaPDw0F1QJijCgQRxMTZohtZqito8N8PBbFxdMNbaG3\nTZuSmmD2+/3q7OzU3r17w6G2EydO2ForKytL27Zts7S0rVu3jpY2AEDGcEOOggAbgIzihhMvAAAA\nAAAAkGiBgHTy5OxgW1eX+Xg87hQUFkoVFZHHkpaVOX88uEfk8aHhz4oxokAC+f3muNGZobb2drPB\nLRb5+VJNjTXUtnWrlJPjzL5t6OvrCwfampqadODAAU1OTtpaa9myZZZA244dO5THPG0AQJpyQ46C\nABuAjOKGEy8AAAAAAADgJuPj0vHjkceSnjsXn2MuXhw52FZZKTHFLbXNPT40hDGiQNIFg+bJfmao\nra1N6u+PbV2fT6qung601dZK27ZJBQXO7HuBJiYm1NbWZgm19fX12VrL5/Oprq7OEmpbtWqVwzsG\nACA53JCjIMAGIKO44cQLAAAAAAAApIrBQenYscjhtlhzDnNZsSJysG3jRik3Nz7HhHPmHh8awhhR\nwJUMQzpxwmxnCwXaDhyQLlyIbV2vV9q82drUtn27VFLiyLYXwjAMnTp1KjxytKmpSQcPHtTU1JSt\n9crLy9XY2BgOtW3fvl0+n8/hXQMAEH9uyFEQYAOQUdxw4gUAAAAAAABSnWFIly9HDrZ1d0tjY84f\n0+uV1q61BttC4ba1a6WsLOePiYWbf3xo+FlijCiQAgxD6uub3dTW2xv72pWV1lBbbW1S5kuPjIxo\n//794UDb3r17rzVILlx+fr527twZDrQ1NDTMuicFAIAbuSFHQYANQEZxw4kXAAAAAAAASGfBoHT2\nbORgW0+PFAg4f8ycHLOh7frWtqoqs9HN43H+mJjtxuNDQxgjCqS0ixetTW1tbeYJPlZr11pDbXV1\n0vLlsa+7AIZh6NixY5aWtpdeekl2b6lXVFSER442NDTo1ltvVRaJawCAy7ghR0GADUBGccOJFwAA\nAAAAAMhUgYA5oe76YFtXl3TqlFn247TCwsjBtqoqackS54+XyW48PjSEMaJA2unvlw4etIbajh6N\n/cS+YoW1pa2uTlqzJqHJ5MHBQTU3N4cDbfv27dPAwICttYqKilRfXx8ePVpfX6/Fixc7vGMAABbG\nDTkKAmwAMoobTrwAAAAAAAAAZhsfNwt8Io0lPX8+PsdcsiRysK2iQioqis8x01l040PDzxZjRIE0\nNzIidXZaQ20vvRR7FeeSJbOb2jZuNGdNJ0AwGNTLL79saWl75ZVXbK+3ZcsWS0vbpk2b5E3QawEA\nQHJHjoIAG4CM4oYTLwAAAAAAAICFGRycDrTNDLZ1dUk2S3BuaOXK2cG2ykppwwYpNzc+x0xl0Y8P\nDWGMKJCRJibMENvMUFtHh/l4LIqLpxvaQm+bNkkJOrdcvnxZ+/btCwfampubNTo6amutxYsXa8+e\nPeGWtt27d6uIVDUAII7ckKMgwAYgo7jhxAsAAAAAAADAGYYhXbo0O9jW3W2+jY05f0yvV1q3LvJY\n0jVrpKws54+ZCqIfHxrCGFEA1/j95rjRmaG29nazwS0W+flSTY011LZ1q5ST48y+5xEIBHTo0CFL\nS9urr75qay2v16tt27ZZWto2bNggTwLHqAIA0psbchQE2ABkFDeceAEAAAAAAADEXzAonTkzO9jW\n1SUdPx77BLtIcnLMKXaRxpIuXy4lO2tgGIbtRqAb+fjHP67vfe97im58aIg5RvSBBx7Qww8/HJd9\nFRQUEPIAUlEwaJ60Zwba2tqkq1djW9fnk6qrpwNttbXStm1SQYEz+57HuXPnwmG2vXv3av/+/Zqw\n2Tx38803h8NsjY2N2rFjh/Lz8x3eMQAgU7ghR0GADUBGccOJFwAAAAAAAEBy+f3SyZOzg21dXVJv\nr9ns5rSioulA2/XtbUuWOH+8SDo7O1VTUxPno0QzPjTEHCMaT52dnaquro7rMQAkiGGYJ++ZTW0H\nDkgXLsS2rtcrbd5sbWrbvl0qKXFm33OYmJjQwYMHw4G2F198UWfPnrW1ls/nU21traWlrby83OEd\nAwDSlRtyFATYAGQUN5x4AQAAAAAAALjX2JjU0zM72NbVFXtGYi5lZZGDbZWVUmGhc8d56KGH9PnP\nf965BWd5q6T/TzceHxoSkPQOSf8Rtx099NBD+uxnPxu39QEkmWFIfX3WUFtbm5lGjlVlpTXUVltr\nnrDjxDAM9fb2Wlra2tvbFbBZGbp69WpLS9v27duVk4DxqQCA1OOGHAUBNgAZxQ0nXgDA/8/evcdn\nXdf/H39c2zgfBRQQB3KQg4zTOG2UpZnlgbJSS82ziRYJAl9P9fProfp6KEC0LCXNNL9qVlbfTCvT\nLNsAGYeBgAhyGEoO5TwYbOz6/fGBwQXXxri2Xfts1+N+u1032cVr789r/7znzevp6yVJkiRJUuO0\nbdvBUNvh4bbt2+vnmT16HBls698f+vQJVpYei61btzJhwgReeOGF/e+cDvwCqKv/PtoaONZ1nVGg\nrtaabgKuAt4A4KKLLmL27Nl06NChjs6X1Ghs2nRw7eiB1+rVtT+3V6/YUFt2drAjup7s2rWLgoIC\n8vLyKkNtmzZtSuisli1bMmrUqMpQW25uLl27dq3jjiVJjVEYchQG2CSllDBcvJIkSZIkSZKalmg0\nyEocHmx7993gVVpa989MS4PeveOH2zIzIT29ql6jzJ49m8mTJ1NaWgp0B54Bzqj7JpPqdeDrwEZa\ntmzJrFmzuO6664hEjjVQJ6nJ2roVFi2KDbatWAEVFbU7t3v32Clt2dnQsyfUw/0TjUZZvXp1zJS2\nJUuWUJHgz9CnT5+YKW1ZWVlkZNR0iqYkqakIQ47CAJuklBKGi1eSJEmSJElS6qiogA0bjgy2rVwJ\n770H+/bV/TNbtIC+fY8MtvXvD127BpmKJUuW8LWvfY3ly5cTTE27Y/+rsQUXyoF7gO8DUQYNGsTz\nzz/PkCFDGrgvSY1CSQkUFsZOalu6FBJc21mpU6cjJ7X17Rukj+tGexMaAAAgAElEQVTYjh07mDdv\nXuWUtjlz5rB169aEzmrTpg1jx46tDLXl5OTQqVOnOu5YkhQ2YchRGGCTlFLCcPFKkiRJkiRJEkBZ\nGaxde2SwbeVKKCqqn2e2bXswzNarVwlz5kzmjTce3/+3nyKYxnZS/Ty8zm0ALgX+BcC1117LrFmz\naNOmTYN2JamR27MnCLEdGmpbvDh4vzbatTs4oe3Aa8AAqOOJZxUVFaxYsSJmSlsQVk7MwIEDY6a0\nDRw4kLR6COJJkhpOGHIUBtgkpZQwXLySJEmSJEmSdDS7dsHq1fHXkhYX1/XTngUmADuBzsCTwPi6\nfkgd+z/gKmAz7dq149FHH+WSSy5p4J4kNVllZcG60UNDbQsXBhPcaqNVKxg2LDbUNngwNG9eN33v\nt3nzZubOnVs5pW3u3LmUJNh7x44dycnJqQy1jR07lnbt2tVpv5Kk5ApDjsIAm6SUEoaLV5IkSZIk\nSZJqY+vWIMgWby3p9u2JnroK+BqwYP/XNwH3AS3qoOO6tAe4DXgQgJEjR/Lcc8/Rr1+/Bu1KUgqq\nqAgu3wNhtgPBti1bandus2aQlRUbahs6FFq3rpu+gfLycpYuXRozpW316tUJnZWWlkZWVlbMlLa+\nffsSiUTqrF9JUv0KQ47CAJuklBKGi1eSJEmSJEmS6kM0Gkxnixdse/fdmmy/iw2HwUjgOSAs4bDY\nkN2UKVO47777aF7Hk4okKWHRKKxbFzupraCg9qMz09Jg0KDYUNvw4dC+fd30DXz44Yfk5+dXhtrm\nz59PaWlpQmcdf/zxlWG23NxcRo0aRes6DOBJkupWGHIUBtgkpZQwXLySJEmSJEmSlGwVFbBhw5HB\ntpUrYc0a2Lfv0OqD6zmhHfAo0NDrOf8XuB7YSefOnXnyyScZPz7sa04liSDUtnFjbKhtwQIoKqr9\n2aecEhtqGzECOneu/bnA3r17WbRoUWWgLS8vjw0bNiR0VkZGBsOHD2fcuHGVobbMzEyntElSSIQh\nR2GATVJKCcPFK0mSJEmSJElhUlYWhNgODbcVFm5g/vxL2bv3X/urrgEeAtokubsSYBLwBACf+tSn\neOaZZzjppJOS3Ick1bFNm2JXjy5YAAmu8YzRq9eRobbu3Wt/LlBUVBQzpW3hwoWUlZUldFaPHj1i\nprSNGDGCFi3CtrZaklJDGHIUBtgkpZQwXLySJEmSJEmS1BiUl5dzxx33cP/93yf4OGkQ8DwwJEkd\nLCFYGbqcSCTCHXfcwR133EFGRkaSni9JSbZ1KyxaFBtsW7EiGKNZG926xYbasrOhZ0+o5QS03bt3\nU1BQQF5eXmWorTjBdaktWrRg1KhRMaG2bt261ao/SVLNhCFHYYBNUkoJw8UrSZIkSZIkSY3J66+/\nzte//nU2btwItAQeBCYA9bX6LQo8BtwElNK9e3eeeeYZzjjjjHp6niSFWEkJFBbGTmpbuhTKy2t3\nbqdOR4ba+vaFtLSEj4xGo6xZsyYm0FZYWEhFggG83r17xwTahg4daohZkupBGHIUBtgkpZQwXLyS\nJEmSJEmS1NgUFxdz5ZVX8sorr+x/5yKCkFnHOn7SVoJw3AsAnHPOOfzyl7/0v+FK0qH27AlCbIeG\n2hYvDt6vjXbtgpWjh4baBgyAWoTGduzYwVtvvVUZasvPz2fLli0JndWmTRvGjBlTGWrLycmhc+fO\nCfcmSQqEIUdhgE1SSgnDxStJkiRJkiRJjVFFRQUzZszg9ttvp7y8HDgbeLmOn3IO8Arp6Rncf/99\nTJkyhbRaTAOSpJRRVhasGz001LZwYTDBrTZatYJhw2JDbYMHQ/PmCR1XUVHBypUrY6a0LVu2LOH2\nBgwYUBloGzduHIMGDfL3hiQdozDkKAywSUopYbh4JUmSJEmSJKkxmz17NhMmTAC6Ax/U8eknAhuB\nxzjnnOuYOhXOPBMi9bWtVJKasooKePfdg2G2A8G2BCegVWrWDLKyYkNtQ4dC69YJHbdlyxbmzp1b\nGWqbM2cOO3fuTOisDh06kJOTUxlqGzt2LO3bt0/oLElKFWHIURhgk5RSwnDxSpIkSZIkSVJjNmHC\nBGbPnk2w6vPRuj4diD172DCYOhUuvjjhgT+SpAOiUVi3LnZSW0EBFBfX7ty0NBg0KDbUNnw4JBAe\n27dvH2+//TZ5eXmVobZVq1Yl1FYkEiErK4tx48ZVhtr69etHxGS0JFUKQ47CAJuklBKGi1eSJEmS\nJEmSGqvy8nK6d+/ORx99BPwN+GwdP+FvwOeALgST2DIq/6Z7d7jxRrj+eujUqY4fK0mpLBqFjRtj\nQ20LFkBRUe3PPuWU2FDbiBHQufMxH1NcXMycOXMqA23z5s2jtLQ0oZa6dOlCbm5uZaBt9OjRtE5w\nepwkNQVhyFEYYJOUUsJw8UqSJEmSJElSY/Xqq69y1llnES9gVjfKgW7Ax8CrwJlHVLRuDddcAzfd\nBH371vHjJUkHbdoUu3p0wQJYvbr25/bqdWSorXv3Yzpi7969LF68mPz8/MpQ2/r16xNqJyMjg2HD\nhsVMaevZs6dT2iSljDDkKAywSUopYbh4JUmSJEmSJKmxqt/1oZVPAWbTvv0Etm+v+hmRCHzpSzBt\nGowbF3wtSapnW7fCokWxwbYVK6CionbndusWG2rLzoaePY/pcn///fdjAm0FBQWUlZUl1E737t1j\nAm3Z2dm0aNEiobMkKezCkKMwwCYppYTh4pUkSZIkSZKkxqj+14ceEKwR7dKlC489tpEHH8zgn/+s\n/jvGjoWpU+ErX4GMuh4KJ0mqXkkJFBbGTmpbuhTKy2t3bqdOR4ba+vaFtLQafXtpaSkFBQWVoba8\nvDw+/PDDhFpp3rw5I0eOjAm1dT/GqXGSFFZhyFEYYJOUUsJw8UqSJEmSJElSY1T/60MPOLhG9NVX\nX+XMM89k/nyYMQN+/WvYt6/q7zz5ZJg8Ga69Ftq1q6f2JElHt2dPEGI7NNS2eHHwfm20axesHD00\n1DZgQI3Sy9FolLVr18YE2goLC9lX3S+WavTq1Ytx48ZVhtqGDh1Ks2bNEjpLkhpSGHIUBtgkpZQw\nXLySJEmSJEmS1Bgltj40H5i8/88PATk1fRowmwkTJvDooweftX49PPQQzJ4N27dX/d3t28OECTBp\nEmRm1vCRkqT6VVYWrBs9EGhbuDB47dxZu3NbtYJhw2JDbYMHQ/PmR/3WnTt38tZbb8WsHt28eXNC\nbbRu3ZrRo0dXBtpyc3Pp0qVLQmdJUjKFIUdhgE1SSgnDxStJkiRJkiRJjc2xrw/dB9wL3LX/zwDp\nwN3Abfv/XJ2Da0Q3btxIxmGTdbZvh8cfhwcfDEJtVcnIgK9+FaZNC/IMkqSQqaiAVatiJ7UtWABb\nttTu3GbNICsrNtQ2dCi0bl3tt0WjUVauXBkzpW3ZsmUkGqvo379/5crR3NxcTj31VNLTj/Y7UJKS\nKww5CgNsklJKGC5eSZIkSZIkSWpsjm19aBFwOfAGAJdccgkAzz777P6/Px14GjipmjOOXCMat6oc\nfvtbmD4d3nqr+p/h058OgmznnQdpadXXSpIaUDQK69bFBtoKCqC4uHbnpqXBoEGxobbhw4OxndXY\nunUrc+fOrQy1zZkzhx07diTUQvv27Rk7dmzl6tGxY8fSoUOHhM6SpLoShhyFATZJKSUMF68kSZIk\nSZIkNTY1Xx/6InAtsIW2bdvyk5/8hMsvvxyAp556iokTJ1JSUgIcBzwOfLm6pxJvjWg80Sj8+99B\nkO0Pfwi+rkr//jBlClxxxVEH8UiSwiIahY0bj5zUVlRU+7NPOSU21DZiBHTuXGX5vn37WLZsWeXK\n0by8PN59992EHh2JRBg8eHDMlLb+/fsTiUQS/Wkk6ZiFIUdhgE1SSgnDxStJkiRJkiRJjUnN1ofu\nAqZyINw2atQonn32Wfr16xdT9e6773LppZcyf/78/e9cD8wA4iXJql8jWpV334VZs+AXv4Bdu6qu\n69wZvvUtmDgRunat0dGSpLDZtAkWLgzCbAf+uWpV7c/t1evIUFv37tW0sYk5c+ZUBtrmzZvH7t27\nE3p0586dycnJqZzSNnr0aNq0aZPoTyJJRxWGHIUBNkkpJQwXryRJkiRJkiQ1JkdfH7oYuARYDsAt\nt9zC9773PZo3bx73vL1793LHHXfwwAMP7H/nVOBZYOhhlTVbI1qVzZvhZz+Dhx+G//yn6roWLeCy\ny4KpbIMHH9MjJElhtG0bLFoUO6ltxQqoqKjdud26xYbasrOhZ0+IMy2trKyMwsLCmClt69atS+ix\n6enpDBs2LGZK28knn+yUNkl1Jgw5CgNsklJKGC5eSZIkSZIkSWpMql4fGgUeBm4B9tC9e3eeeuop\nPvvZeBPajvTqq69y+eWX85///AdoAfwQ+DZw6AfyNV8jWpU9e+C554L1okuWVF979tkwbRqceWbc\nPIIkqbEqKYHCwthQ29KlUF5eu3M7dToy1Na3L6SlHVH6wQcfVIbZ8vPzKSgoYO/evQk9tlu3bpVh\ntnHjxpGdnU3Lli1r97NISllhyFEYYJOUUsJw8UqSJEmSJElSY1H1+tBNwNXASwCMHz+eJ5544pj/\nW+umTZu4+uqreemll/a/Mx54AjhwTmJrROOJRuHVV2HGDHjlleprhw6FqVPhkkugikFykqTGbs+e\nIMR2aKht8eLg/dpo1y5YOXpoqG3AADjsd9iePXtYsGBBzJS2jRs3JvTI5s2bk52dHTOlrUePHrX7\nOSSljDDkKAywSUopYbh4JUmSJEmSJKmxiL8+9G/AFcB/aNGiBT/60Y+YOHFiwqvMotEoP/7xj7n5\n5pvZs2cP0B14iiAsV7s1olV5++0gyParX0F1w2+6d4cbb4Trrw+G7EiSmriysmDd6IFA28KFwWvn\nztqd26oVDBsWG2obPDgmJR2NRlm/fj15eXmVobZFixaxb9++hB7Zs2fPmCltw4YNo1mzZrX7OSQ1\nSWHIURhgk5RSwnDxSpIkSZIkSVJjEbs+9GHg/xGs+oRTTz2VZ599lqFDh9bJswoLC7n44otZvnz5\n/nduBr5PsFa0dmtEq/Kf/8AjjwSvjz+uuq51a7j6arjpJujXr05bkCSFXUUFrFoVO6ltwQLYsqV2\n5zZrBllZsaG2oUODXzr7lZSUMH/+/JgpbR9X9wurGq1atWL06NGVobbc3Fw/I5UEhCNHYYBNUkoJ\nw8UrSZIkSZIkSY1B7PrQR4DHgQIAbrjhBqZPn07rQz5krwu7du1i2rRp/OxnP9v/zijgGuBbdbJG\ntOrnwlNPwcyZsHJl1XWRCHzpS8F60U98IvhakpSColFYty420FZQAMXFtTs3LQ0GDYoNtQ0fDu3b\n739slFWrVsVMaVu6dCmJxj769esXM6Vt8ODBpKen1+5nkNTohCFHYYBNUkoJw8UrSZIkSZIkSY3B\nwfWhAC2BUjp16sTjjz/Ol770pXp99osvvsi1117Lli1bKp99oKe6WiMaT0UFvPQSTJ8Ob7xRfe2Y\nMTBtGnzlK1APmTpJUmMTjcLGjUdOaisqqv3Zp5wSG2obMQI6dwZg27ZtzJs3rzLQlp+fz/bt2xN6\nTLt27Rg7dmxlqC0nJ4eOHTvWvn9JoRaGHIUBNkkpJQwXryRJkiRJkiQ1BgfXhwZOP/10nn76aU46\n6aSkPH/Dhg1cdtllvHFIkqw+1ohWZf78YCLb88/Dvn1V1/XqBZMnw7XXVg7IkSTpoE2bYOHCIMx2\n4J+rVtX+3F69DobZDgTbunenoqKCZcuWVa4czc/P55133kn4MaeeemrMlLb+/fuTlpZW+/4lhUYY\nchQG2CSllDBcvJIkSZIkSZLUGAwcOJB33nmH9PR07rnnHm699dakrxXbt28f9913H3feeSf79u1j\n4MCBLF++PKk9FBXBQw/BY49BdQNt2reHCRNg0iTIzExef5KkRmjbNli0KHZS24oVwSjQ2ujWLXZS\nW3Y29OzJx5s3M2fOnMrVo/PmzWPXrl0JPaJTp07k5ORUhtrGjBlD27Zta9e3pAYVhhyFATZJKSUM\nF68kSZIkSZIkNQYbNmzg5ptvZvLkyeTk5DRoL/n5+Tz00EP88Ic/TNoEuMNt3w5PPAEPPgjr1lVd\nl54OX/1qsF505Mjk9SdJauRKSqCwMDbUtnQplJfX7txOnY4ItZX36kXh0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K1vwbvvJrdP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w/QsuXQb4Cl/rwkb3NeOQy5QPuYx7FbUs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\n1K1b165Q8eeffy7TGAUnKeLi4nT11Ve7JDYAqAjuuOMOrV27Vr/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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output_file_name = \"figs/compression_heat.png\"\n",
"plot_compression_experiments(results, K, output_file_name, .9, 9, 0.05,1.)\n",
"Image(filename=output_file_name)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}