Commit c26ea990 authored by Andreas Schmidt's avatar Andreas Schmidt
Browse files

Add a sample notebook. Refactor regression out.

parent 034268b7
......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 6,
"metadata": {
"collapsed": false
},
......@@ -10,12 +10,14 @@
"source": [
"from xlap.parse import evaluate, parse_config\n",
"import xlap.analyse.jitter as jitter\n",
"from xlap.analyse.regress import linear as linear_regression"
"from xlap.analyse.regress import linear as linear_regression\n",
"from xlap.analyse.trace import trace\n",
"from xlap.analyse.common import extract_durations"
]
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 7,
"metadata": {
"collapsed": false
},
......@@ -23,13 +25,42 @@
"source": [
"config = parse_config()\n",
"data_files = config[\"data_files\"]\n",
"original = evaluate(data_files[\"sender\"], data_files[\"receiver\"], config=config, kind=0)\n",
"df = jitter.prep(original, config=config)"
"original = evaluate(data_files[\"sender\"], data_files[\"receiver\"], config=config, kind=0)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/matplotlib/figure.py:1742: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
" warnings.warn(\"This figure includes Axes that are not \"\n"
]
},
{
"data": {
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9t5tq7dVqvsmYbMVmvqTlwPreKkfECmAesAr4L2BZmTqbgZOAr0laBawEDi2tV+L/AvcB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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f00fc374cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"trace(original.iloc[47],config)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
......@@ -51,9 +82,9 @@
},
{
"data": {
"image/png": 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RM8/u8Y8GdoX7EeAagreFbwcuDcvrCd4c3t3v9cCSIWKxgOBlm9vCPh8dKt4L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\nX382s9PDuu8aYI7vAx4BTgK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XHeecc248KKc7OFMkbc05/rKZ3RLuP2tmp0v6FHAFwVu+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\n4vw/JtbopwQfVTUAAAAASUVORK5CYII=\n",
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LMnf9PvCBD3Duueeyb98++vr66OvrY/78+VxwwQXs27ePffv28Y53vIPW1laWLVtGa2sr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\nejqAN1efJo0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efc61bd5668>"
"<matplotlib.figure.Figure at 0x7f00fa2c4b70>"
]
},
"metadata": {},
......@@ -61,12 +92,13 @@
}
],
"source": [
"df = jitter.prep(original, config=config)\n",
"jitter.trace_jitter(df)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 10,
"metadata": {
"collapsed": false
},
......@@ -77,6 +109,24 @@
"text": [
"R-Score: 0.0444517602497\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/matplotlib/figure.py:1742: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n",
" warnings.warn(\"This figure includes Axes that are not \"\n"
]
},
{
"data": {
"image/png": 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H8rzn6BHwTNhR7Cwc5sYQ45vEcWH6IgD75bDXqKm6p1YxSpbDqjiPy+f5C68N\njeDl0BDwJGiaApU+jaFp2m721qQayUep+CUAB+uw130r31Nnxqg9oi23U7FPilEh63keC/bN1Drs\n5zkIeBIzbU+Xul4kp2272VuTvHkexVFxHF46RzC96qYvHaYRLaP31HLbLGL7KNny9er0w/fFC68N\nG9tyR+0aV96HRWA2yXbuUdunha6fu+/+qTufhyurcer85ancv31DxKN6zLBJwJOYaUuduxbBnbbt\nZm813TzhqBJ0w/RaWh5GFhFVCyZPa9233XbiqkbJnn74vrjw0pvx1MWr2/bHcGU1fuvbP9ixf4pV\nuApH7RpX1b4tTKKde1juG32P1a6fe5z901SLqsvv73d40jdEbNsnwp/DzypaiZm2VYXqGj3l16dt\nu9lbdcdFFmF1Do6suqD72W+8HqfOX46Hzl2KU+cvO0fgAFx46c3KcCeLmMq6b0UnbriyuqsVsBZP\nLsQr556IPzz/mTh75kS88Npwq/Nb3h9V+ydis8P5zIvX4rlvvl77MG+3lpaHW9fJN//4R1NznWxr\nx+62ndv0gDQV4xyrXT/3OPunajWtrr/f9llGj9NJ3c+7hIhtP1/83KSuG0w3AU9iugYq+6Xr0vDT\ntt3srbNnTkRW8XoekVSjJCV70ahgsuoa+iuraxpbcMDqzs88pnOkxG46/nX3i6aOZJvVtfV49/pa\n5b8NV1Z3dU0rd0pvrG+MdZ3ci/tkWzt2t+3cw/CAdJxjtW0aVfHdjbN/Fk8uxPNPPhILDd9N3e9P\nOjzpckz2DRGb9sk0BIbaq3tPwJOYroHKfhm9SGYRsTA/V1mFftq2m721eHKh9klfSo2SVHgik4au\nDf3Uns7CYVB3fjZ1Ag/SuB3/Ly5di6cvXt12v3j64tV48NylTtOoqx7edLGbe9IkOqVV98mnL16N\nLy5dG2ubCk2jQSbRzj0MD0ibpkPVuXtu0Pj3iuOp6/4phwoREa+ceyLma96n7v2b6nQ1jdKt2pYH\nz13acS5WnSdt33UesS0oadon43wXTfqGNXXt1ZXV6nCY8ajBk5hpXFWoSxHcadxu9tZCzY0kpUZJ\nKtS4SkPXJYojBKGw36rOz2l+EFXXWWtbAauqjk7dA5myuhpFhfm5QXx4c6PyGrebe1KXMKutrkjV\nfTKPiN/69g/isU/c22u7yu/1uU8vxMtvvD3RVbRGi+qW9/tgJovrN27GQ+cuJdGenq1ZrW02q48L\nG/4pIm44KbCyAAAgAElEQVQfT13O26qaNE9fvBqvfv+d2vcZfX30u6hz//xc4yjdpeXhVv2b0W2p\nq2n19MWrW99tl7bDaJ2ds2dOxNnf+W6srd/+64PZLM6eORG/8vXv9v4uyuqOzS71i+raq//+PQHP\nJAl4EpTqqkKpbjfjSa2xnLLDMIT7KKgKuq/fuFk5rUEQCvsrtQdR49xj6+oMddX0u3OD2Xj25z4Z\nERFPXbxa+TN9AplRdWFWMcqiqahsRDR2zoup412Lz1a91wuvDStHr4+rKgQoOtLzc4P4YOS+sZui\ny/tVbLcqUGh6PSJipWa636jiO33+yUfi2W+8vjUK5M7B9gkqdeHeb377B63vX7XSXFlRp6vpOCuO\nsS7TIMtFlJ9/8pF4/slHdqyiVVaEQ1X/trae156Xo+/ZpktA1XQ+1bVLb6xvdHr/um1K5bq9XwQ8\nwJ5IrbGcsnGe5HIwykF32zLFwP5J6UHUOPfYSYf+s1kWG3m+9d4RsWM6yqjintR35aOzZ07E2d/+\nbqxtbO9OfnDj5lbnrmpUwNMXr3YKtIoaQW0h0VsrqzFT0bme1GpZTe+Rx+3pguXpLOO8/36uzlU3\nortq+mOxH7oGkc+8eC0+9+mF+PDm7YDg3etr2z7LOMd9cax2CWRG63S1hZt9t6X4bl8598S27+Wh\nc5cq91HXoKas61TULvvjrYbzaf74oPKh1h2z/arG7GYU0VEg4AH2TEqN5ZQZLZWuFIJQT8dgOvW9\nxzbV4BjHRp7HVz7/aOWy6mWjq5GNM634Zs2ohNGRG2Vdu7qzWVa7Tc+8+HsRke1YorpsN+FZ3TLY\nfd6jCKm6Hg/jfAfj3gu6tlG6jJYpW11brxyJM/pZ+h73o1PguhxDRTiyeHIhnvvm642jcsc5B6u+\n90mey33ai12O8/vn52qPr48cm4m5wey2fxvMbAbFXacc9hlFdFQJeI4wjXY4HFIICag3zUHofj7l\nBXYap61W9ztnz5zoPKqlcM/xzWlQVZ3Wu+cGjR2tUaOjHNqmFY9u/923piTVDUyYRHHW9Tyv3abV\ntW5TR3YzYrbr6mXFe9R17Ptcm/tO7a6rY/PUxasxPzeILNuc1lR1jHZtozTth/m5Qe/vuthPvY/7\nrPp4r1IOR7702U82hllV9XHaVB1bfWr6tSmmF9ZdN9pGl40qPuvTNSOZ3ltd2wqFR8/vmxv5tqLL\nhfK0tIVbU9u7jCI6ygQ8R5RGOxwu0xwSkC4FvOHgjNNWa/udV7//zo5Cy6P1Xao66nVTSbMsOncw\nR6eA1I0+yCPi0ee+FR/cuLnVAd6P1XUWWoKTNrsdMdulMzr6HnUd+z7X5r5Tu+vq2ERs/47qjtEu\nbZS6/ZBFxNUv/dU4df5y7++oGNVUddxXmc2yzuHLPccH8aXPfrJXmPXq99/pFe7UHVvl92kLXuos\nzM9VnuPF9/jq99+JF14bto4ui7h9/Xj64tXa7bn/1vsV23/q/OXKKYfPfuP1bQXbR+sSdXHUSxQI\neI4ojXYYn9FvHBUKeMPBGaet1rRU8+hT86YRF2V1nda6p/Rl5U5q0+iD/V4ueXTbmorQllXVHzp1\n/vJY7YK26TazWbajiHPdtg5XVjttR9+p3X2u+atr6/HUxatbq1x1HXFWFx3cPz8XS8vD+ODDm523\nofDcN1+PxZML8eXFR+KxT9zb+B2Xpw61+XHN6K66MKtYxa6rPiux/Yk7j20LRrsY/b7rrhtf/c4P\nOwVH5RX0qn6n6vhqWnlsXEoUCHiOrL1stOv8cpgZ/cZRooA3HJxx2mpNHaai07SyuhZzg9n4yucf\n7Xzfquq0ti0dHVE9yiEi4iPHZnY1vWQ2y+In5o51nkpTZzQ4qaufUpZFxK/9/Ke2fm+37YK26TYb\neb5jlEjdvs/i9iiHpu1oGmlS1Y4fp+bLOCPOyuYGs3H64fvGno40+n027bfZLIvPfXqh0yifQl3Y\nWtcP6lI8ejbL4hd/+oH48uIjjX+vPCJpZXUtZiJiJovYyDf/zp2DmfjgRvU+W5ifi9MP3xcXXnqz\ncfpa11FBdYFMOQgt76tJ1wXrE4odZgKeI2qvGu06vxx2Rr9xlJx++L7KApanH77vALYGjpZx2mpd\nO0x97ltNNX2aOt7zc4NY/vt/dcff2m3tkJksi1/7+U9FRP10pe0/v9npLSumpxSq6qdUGa0nFLH7\ndkHxM3VLXHetwVJV5LppO6pCu7p2/Oc+vbBtqk5X44w4KxSd9a41irqoG7lULEPed5JTOVBt6ge1\nPUSfzbL43vN/bevv1K0SVbWiXETERtz+wfU8jxs3N2Iwu33KWfFZI7qdO7u1kefxh+c/U/lvdaOy\nmoq1F+YGM/HjtY1tP1eM3NEWj+i3Jhm1lpaHcer85Xjo3KU4df5yLC0PD3qTGp09cyLmBrPbXqsb\n0tbnszXd5OAwMGWFo+TlN97u9ToclNTaYV30aas1/U6dLvetosM6XFndKoL61MWrcfJXvxURsdVZ\nrPJe6an+0vIwfuXr3x27U5nFZqf/nuODrZEHHzk2s1UIejbLtn5u1GyWxWB2+6tV+3Hx5EI8/+Qj\nrUtGl/+9bj8W06W6HJOLJxfi137+U52/79FtLfZLXae4T/ukrh3/8htvb9s35X3cpNgPVZ+/qe7O\nK+ee2Pr9cc3PDbb9d9V+K0ZxjdOOm8mybZ+raYpk20P0ItwbPecidoYdVeFOlbWNPD56x7HKz9on\nNCufO2Vzg9mtc7CsvH8KxWcsj/y55/igQ7gzG3cOZmvDzPL7HLb7QhdG8ExAiqNWula07/vZdH45\n7ExZ6c+0zf03qX1e17B2TWeapNgO62KcFRKrfuf6jZuNSzc3qesIvnt9LZ558dpWZ7ntvlh8R+MU\ngo3Y7Jy+cu6JWFoexvD3X4vhymb9k2K62f98a7pZVSHetY085ucG8dGPHGvdj6OjWr64dG3HlJ1i\nytBonZv544PK/dt1utToe0d0/77LI3DqihD3aZ80teNH36+80tmNm+txvWHFsbrP36Xg9rgGM1k8\n+3Of3PF6XY2ccaYLref5ts/VNEXyZz/18cZRULNZFg+duzR2weQq762uxdUvbY6iK76zvivpffSO\nzbigCGOOD2biI4PZbTW8IqpHBJX3T6HuunL8jmNx/I5jtd9DMaqrrv7X6P4/rPeFLgQ8E5DqlI0u\nFe37fjadXw67voUJUzTJQOYo32APStM+j+jeeVhaHtYOla67pk9ySef9cFTDx8P2uVNth3UxzgqJ\n5d+pWwWry32rqcNbjEyoUv77bSMGilodxbLJ5Wklo8Vgf+GB7Vel0e+6roM92tHtqijMO3qunH74\nvm2d9OHKagxmsh1TYZqmSxWfo+r8282KmG3tky7nfdd2fDnsGb3H1Kk6JydZcLtLiNe0D9qmHM7c\nGshSHkAz+rmaQqJiFNSz33i98rMVoc6kwp2I29/bbqZGFiFq4fraRlxf24h7jg927OOqaYZV33tT\nkPiVzz9aO41uNARtO04P832hjYBnAg7zqJW+n+0odH452sZ5opqCpvneuwlkUr7BNjUEp7mD3DRE\nfHSVi7bvtq4eQRZRO5130ks676WDDh/7HkNVPx/R/1q0X597P8+Rw9wOm4Td3LdmW0YTVHVUqwor\nt30XPzF3bOt36o6dpeXhZqfugZ2/X/z9ST9oLAcuJ3/1Wzuur1UjhOo6+cX5thfnX1vh5C7vO047\nvs90n/Jx0NZh7+PZn9tZzHtU2z4ob8toW6g4pttGjpw9c6J2pa7RUVCjx/gkR+yUNa2S1dVsljWO\n4ou4vf+6jKyJaD5Pu1yvmo7T0fZsl205jAQ8E3CYR630/WzFyTeaTt85SK/U0zR33jh4u3nCdlDa\nworRG2WfIo1tUu14tY2C6dJQXloebrsW1q0mM2l9lh1t+m7r/k65wGhhr5Z0nvQ1uKnx1+VYn8T3\n2jdkqfr5s7/93YgstkYNdO0o7kfout/h2WFuhzVpCkLKo05efuPtsc6ncTqex+841nlkSKGqsziq\nbZTIzK3pLXfPDSoLy07iQePS8rB2la3yCKG66VJVneVJnn917ZO68/5Xvv7drd8b/f992vF97udV\n52SxzQ+du9S7yHFhfm4wkVkJbe27tpEjiycXaldjm8myePDcpa3QdGF+Lr7y+UdrQ5HdGsxEpylZ\nWUTtNLu2pePL+69pyt2p85cbC7SPnqdd2tmjK/HNZLfbDm3LxR/2+0KEgGciqg7SwUwW12/cjIfO\nXTqQgGBSAcW4I3I+vHn7AlG+aU+DPp1dU0pIXdsx3eXJznBltfP1rMuTqf26wY57LWwrGN/WSFxa\nHu5Y6eLd62tx9ne2N6bL29olOGgbydH3aWBd47yuoVZXgLSp0Gjf9x5d0nmca3Cxj37hgR/F3xtp\nVHYZpl4UBK0b3t/3e63SpaPRdh5VFdrs0lHci9C1fExev3FzTzqxfVZz6tJWSflhTt11/dXvv7Nj\nCtHoSnh9z6e6+jpNqo6ltukvEf3PgVHFv62srsVgJot7jg+21QjZ7fdaFIiuUxSTbZvuU/cZ9vqh\nR93fr6uR0qcdX3evKE9Tazsnx10ye24wW1lrp6zLta/tmtDlWlO3Glt5ClZxLtbVcNqttY32AtVF\nbatC1edvG101uv+azvOqa0/xXnfMzmybgtWk6j5e3A7bpvUdlVklAp4JKB+kxfzh4mRtupnuReNi\nkgHFOMN6m57ITkPDaZzObipTSqBK2zHdtWFZrKDSZ6RBVWO26QY7DfV/tqYBVOjayLnw0puVHfC1\n9bzyWtI1OCgX/awaydH3ifvoHP2mWhMRzd9dUyN/tOPT5XfK+lyDt33vD2z/3rsOU687Vrp+r23H\ncVtHo8t5VKetszLp0S5V51mXbSv/jUks+tB3ylvKD3Pqrutf/c4PW4+XPudTUdC0z1WlXFx5tH38\n4c31yiXLC5M4B9Y28jh+x7EdS7SPq0uB6HJQUjUSpslePPT44tK1sY6Hvu3gs2dOxNnf+e62UROD\n2Sw+/5ce6DVyrCocmIlby3+PGMxkcdedx3oHeG3XvrqRks998/Vt71UsqT56ryxGylT9TFM4ubq2\nHh85NtM6UmYv1K0kV7Uv66adRWwPNxdPLsSr339nR3HyQl0dqo/dvb6rByRdLCQW4u+GgGdCRk+I\nU+cv77iYV10Y96pxMemAou90lKYnsuVG/l4/PatLosfp7DZ1glN+Csjh13ZM931qNnq+jE53aarV\nUBTQbDo/qsKLroFM1fk3zrWwa7HIKqMN9KbrRVUHvG7/jwYHS8vDykZT3ZKpxT5v6xYVc9bL96MX\nXhvG5z690LmBXtcRzGOzcXjhpTc7PQ2t0zSyZlTT997nKfnq2vqO7e7yvXa5t7d1NJ775utjN/ab\nOivPvHgt/uJP3R1vraz2erJepe3Ybdq28t+pG4kyeuzVjQoqT+f7yq3VlNqk/jCnaUTGbn6/rK3D\nVlYu6jv63fYJOnZTMyRisiNium5LecpTcR86iFEFX1y6tm3kVpvR/TXWKL+KC/9jn7g3vrz4SOt7\nl0PAOwczO1ZoGm1nFAFe36mxbaNvqq67axv5jgf2zz/5yNaol7rr1+jPPHTuUuN2vbe6Fl/5/KPb\n9kHTMTM3mIl7P/qR24MKPlyLhgXMdsgievVVmqadRewMN19+4+3Ga0VVHarhu+u1D4LKxjm3yyOV\nDjsBzx7oemGsa1zUNYTrlDs3kygqtZvAomkbyk849/LpWd3fr7tJdynQVzc1IuWngHtB4DVd2jqT\n4zyhfWtltdcT1o08jz88/5naf68LL6pqBJR/b3Tky9aIlhivkTpup6JceLjpOth3VYvhrX194aXq\nosd11vM8/t35z9TWgoi4Xbfg1PnLlfejl994u3OjaPHkQuNTvqanoU2/V/4bbdfXpu99nCkAo+/Z\n5XvtEhy0FYgcd7j+YDZrLKq5urYe/+J772w7jrKI+Nynuz3IqSvG3kVdJ7ZuO8thb51xp/PtZqra\nNNzj6o7FtqLIo7/fVbGaVNt5Wp5W2vd6OnqMdPke5gazcedgZuwl4Lvqc80od3abfrdvR7uPr37n\nh71+fnR/9R3lVzWycW2jerRqWVUIODeYrQxqd9vWbhrp1/W6O85op7b7TlFYuGmgwKg7R86TZ168\ntiPcuef4ICKi8vOMG3TUTTsr9BkVXlWHaiNvPl52U5T6qEzLGpVe9dsE1F0Ay683nQDFhWtpedj4\nXsWFcXjraVzbBaSLqr/ZZVsKTSdReQpDU42L3ar7+3VGO7ujywFGbF4cTj98X+V+qUr8J/k5xrG0\nPIxT5y/HQ+cuxanzlzt/d5N6790cP8XfOMjtP6j33it1x/RoMbu+hQ3vn5/r1Xhvu/40hRdFg7nq\nu3j2G69XNiyf/cbrna/Fo8Z96lsuPHz2zIkYFGuqjmjrgNd56uLVsWoT/OlnLsWDf3KudluKugWT\nqstSV5+nUDwNLU/3KxqkXRTX17pztel7rzoXIjY7Wm3v+dTFq/HBhzejYldu+1677MvFkwvx/JOP\nxML8XGSxud+K+gNN947ZrHlLj81kW8dhU5Hs8n+//MbbjX83Yvu1vervlBWfq/z5yrpuZ1dt99/i\nuKn7+23Xqmm5x1Udy1lE/MyfvqfyGC+7fuNmr/ddPLnQen6Xiyv3uX7cc3yw7Rip+x6Kc6A4pr70\n2U823t8moe28KyseTHxx6VrttWVhfi7+8Pxn4pVzT+xJONjUAW7bX21thrLd3D+69gUm1WdYPLkQ\nr5x7Yse+7/N3+o52qrvvRFTv17b9VtREqhvpWYxumuR5sXhyIT736YXGc2H0QXmdwUx9OFM8OCxf\nG8vX3LZwp6jB1XbvOcyM4JmgpidbVSdVW6LbZahw1w5Cn5N6t8OWG4fyZbdrMYx7Q+i6akSfzlC5\nsxuxM+Gv2y9tI4L2216NjOr6xLJuPzWNwpj09o/7dHUaazJM4klx3TEdsfmk6K2V+ulV83ODbUtr\nR9w+X/qs/NB2/Wk7X+quQXVPuYrX+66mMu5T8XLHp6r+wkx2e8pVxP5cIzbyiFe+906c+jP3xutv\n/Wjb/rrrI7ebAJOqy9JnylXEyKoXH97s9T5NSw03jY4pppuUpy50DRNWVtd2PBn76B2z8Q/+y+0d\n0y77sm76c9Nx8Ys//UDjVJnVtY2te2yf+2D5PbtOb26SR3S6Xo1bWLVJMZ2vfL1rq4fSpa202zbS\npO4zVVOn8oj43R+8t2Nq5emH74t/+t0/2vbZi05ieSpc2zTMLiOhC3XfbXlJ8dHQu6hjUlcD7Pkn\nH4n59/4g/uYXHt/2N/dyRNU4K4mt53ntuVoe8dlXl3ZB3T1rNst21JAp/37fmlZ1RYK73D+69gUm\n9RCi73ZU6TLaqVyXJmL7NLNiFa2q/drlmtjW/+j6HZanx2VZVNY2WloexguvDRvPhdEH5VXXieOD\nmdop5RGbK3lVXRvvHMxUftZiGnrTdh9VAp4JKd+w87hdPb7uBD798H2tc5qLNLPuBO1yQepbVKpL\n8ce2C8aXPvvJHQVDIyLyPLaKho7ToeizakTX4eNV+6eq4d13GcO9KJjXRVOR63EbQEvLw20F9IYr\nq7WrxjTVBehSbLxq6OV+BUTTUJOhfLMdXe5xtwXTm2qA1RVDLkZ41NW46doxe+6brzdud5cGTXFs\ndS0cWXRmPnrHbFy/sT52kce5wWx87tMLOzobo/9e1Vgv9nndMblXK2dUeeV77+x4bXRllHFXISor\nN2S7qOtwz99qtFXto7qlhp/9xutbSxVvBmk/2nGN7zJapUm51EG5vbrbfdnUKW6rbRBxeyp0n7Dt\n7rnbI6j6Tm9u0uV6VbWdXe7fTdMQIm6PaK4qRF6la1tptx3Nce8zVW2vquOhbmrly2+8XVkfsk/d\ns+K1X/n6dzutjlh3Ljz7c59svB+11QC7cuUPdmzXXt6j61YSm58bxI9+fLP2XlR3tJVHfPbRtY3z\niz/9QGUNnmLJ87ZpOl336dLyMN7/8c6AfnRU4+jPlleK7BoO7eYhRJd+S9eguWq0U92KWeWi212/\n89MP39erflLZ3XODbQF3XV2yphpZXRagGdX1QXndPs4ii//vx2s77qdNQVbb1P+jTMCzS5snx+/F\nakV1qyLcqVp+rmsAkUdsq40xesJFRKd5iMOV1W1DD5suckvLw9q/effcIB597lu1F4Dy377j2Eys\n3dh5Uq6t57WNg7ZGcF3DqOpCOBqy1cmi/SZXaGp0141wqFO1Wk3XJ2jjrs6ym2WHn/vm6zsaxmvr\neTz3zZ0rozV1WqsaseXCunXHc1NANGo3Ic1ePyEa1aWeU1XHd1KBU93Nuq4YctX79elAti2z2uVv\n3T8/17twZETE9RvrnQuvNj31euwT93Z+AjeqfrrouJNQJqc4norrYPm6NPpEvU9BxiLYKq+s0kex\n3HFZeVRW+XdO/uq34jP/8ce3RgUNV1b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+f9eunwBQk0R8qR9zLk2yhjzHsZ4ffGdNGZmt\n6rojliPYgo1bWXS8UwjKYT0DtFvArw9rli7omOAzg+NUtYm6aW9kmSL8fLPNLKK6hcaNsqvWdKCZ\nBNatDTKYOohRO/vIvue7jrI8zqRMzUbMWgbVXvXm1nVGx1DBJPATApGyqFhWMoOOvJUJ4/NjZ83S\nBU0EP3uW1P4xGVTILBWTkmDV0q/rotUK7H7jFNlFDbWfm+xXad/DxXP7Gn5mMndkBBD/fK8g1qTz\nQZfHJIPnLwD8nwD+O/ezTQC+H4bhVsdxNtX+/2+Tv7zuwrmspdJDDz2cO+DXqXr3nAiOYZxNMO27\npPByHL0ECryzoSuZ4Z3siWKJ7LwA0DX6JlEgXfSpFeLrMk0DEaZlOXw5WxSdlkoYSo1+24h2Et1p\nbEgidi7VGFK1KleBd2JPjRdx1+pFxi2UqagwUCXjpkoV5TVFIaTagW6Lj12aSUfOlGoFTk8EXUXu\nAHKCPoqNTL170yi7bC2hSq4eeOZgwxxhDiMTkhXXhk8umks+d9neKI6ZAd/FZKnS5JSb3Bul75JJ\n+xjsTxnp08gQdy2l3rGJ4LsJTDPm8oUAQw++gME+z7gsuByGDSTkgO9q9aTYe5aRDd/ccxQDvot5\nA37TO6D2DyqwZdtpVxY8OZ4vNAjqtwtx129ms2TO/KTh57quWCYEUNyMtZkMLcEThuH/dBzncuHH\nvw7gxtq//xLALpwHBE+7mdEeeuihh6hg7U+j6qx4joOPzp0VidRO+x5ch9ZjKQTlelexVqTJ6kq2\neCebAjOWVAaXigAzMT6SiiLxETQTEWvT8/LlbFFKVSgjylRMNe53xO8D5o6Nbr9/+Lb4ItWFoIyd\nh05qdQdMIrv5QoC7a2SRLEsqKiF1voG1BbZxtroJqmi6DK4jzwpgx8mk/QZ9DmD6Ga3Y8kKDWO66\nay+xLllUQbVO5QtBw/mB5gw12XdEsDnIz2dGhO75GV2YIK5tsuw6lUaRbg2mfn+mEGBs883K7+pg\nspZSJHzcDCAdbMqtT09U9aCiCJcD1ffjQD1nXMdRZqZNBBWEcPDI+hUNz1SW+ad6TrbBHpZtmssX\nGs4z05IQfNepB6RYti2DSouICuKJ5Gmrx2s3I2qb9IvDMDxR+/c7AC5O6Hq6GsND2YaWwj300EMP\n3QomMBhVRHf1lfMwPqUXCRThOFXhVF1U7fREgI1P7o/cMl3VlnN4aLrlblSo9IP4z1BQRZ903/cc\nB3evXmR0nTLjVDyPCJvoFa8ZI7aaZWKrMnSjETU8ZN7CWLXfs85Za5YuiC16qRtjfCteHZ7al8PG\ntUvw1tZ1eGT9iqaW00lF2c8l8C2C2XPaeejkjCR3ADtyJ5P28Y07VtTHhVdrU5zNpPHI+hV4a+s6\njG2+Gdu+uLzhGd2+MovtLx9r6o64/ZVjuH1l1nic6eaOKtshd7pAEjaeYzcrWbkZW+NYAEBVjsSv\nbSOjOdz7uF2GrG4N1rWCt23fbgNGVrH236yTGsvmNG1nHwXs+KYIKiH6PKehzbcNQqjnjEn5NL/f\njozmsGLLC/imQO44gFK02TbY42CazOm2DEgbBJUQGx4fk45fag7w9y4D/yxbPV67GU5oMHhrGTzf\n5TR48mEYZrjfnw7DcB7x3d8D8HsAcPHFF6/89re/ncBldw75QoBiYQI/n1kkaQ899KDBxWn05nUC\ncBwHngOUDI3dPs/FxXNn4ednJlEsV+r/lxlq+UJQ/5wI13GQnZdu+t7hdz6Ufl4FdizqXLLrF897\nIHeG/M6y7FwAVUPk/fGi9NyZtI+Dxz9ARbJHM1Knz3OV18fOIyJfCJA7XZAeW0Sf52LJR+fUv0e9\nJ9XvTBD3+xTOnj2L2bNnR/qu7Dmx9wPA+BnqoLrfKOOXOp7Ne08CruPAtVgLosB2veFBrRmquXuu\ngLp3E6jGJFsvZGPNgQPPrb6rPs/FnFrbbuka5zi4jLi+w+98iHl9FeV+7dZKWEzArtl0rnmug6sv\nuQBA9Dm1cP6A8tnL9gYAuHCwDwP9KXJdSmLNfO3EB9KMGP6+Ww3bOZhyHZQqodV7TxoL5w8oxwK/\nl4qIss6fS3AcB5fNdpC5YPr5RJ1b7RynncCaNWv2hWG4Sve5qF20fu44ziVhGJ5wHOcSAO9SHwzD\n8E8A/AkArFq1KrzxxhsjnrJ78H889tf4+oFeA7IeejiXcO+yUm9edwhpv4xC4GI6qbSIeQNhg5YM\n6yATgv9cIzLpEGObb2z42Zc27ah9xw6eE2D1lRfh5TdPG0Rmm6/3K0QKcTaTxsKrP17TaqhA3Ibv\nXr0I/+vnlnHX3gwH04KWVKpyNpPGv7nrRvKK7x85oM3uYho1N8aMduk0d0ZGc7jv+wcaxoCDIkIU\njTrqqLBr1y4wuyNKNxvqO9Xn7im/a4O0X8bDt10NoLGMLJenx7vJ8VTd4JiGh223QxVEPQoAsUrb\nB/s8TBTLTVoTsvc39OALynvp8xwM9tNi3kD1+Xzje/LuZjMVKuHuKFCtqdW16UYA5t0JZVpHLoC5\nAyHyE+NN3/3Sph34/WUVcr+WCaerIv6Prl+BG4eyRnuFuCZGXQfe2vpryt9Xj9vs8Gcz/QAgPWc2\n4yk7i5ninypKkt66M/7xTbBBM5e7DdWxVgRAjwV+bojIW2rw6OAADZ3tbEs3TZC0Lu0fLi9j+NYb\nG34WRQvN9xxsW/rx8yJLR4Wo3swzAH4bwNba33+d2BXNAMhaufXQQw899BANMqPm9ESAe7aP4ctP\nvwpArWfAkC8EuHzTjgZSIGqb43IYYvcbp9CfctWtK4Tr3XvkFB4aXoY1SxdICZRcvkC2xwaAHa+e\nqHf6MBEIjFJjbtJu2YZYUTlyJh1tZPfIawqoNIVk5+aP+wfLyvinm3Y0aYmwEsYtzx7EumsvIZ1f\nSq8i6S4chaCM+55+FZNBpeHeKcNc1eGHHU8m5CrTIgKSI3kG+lIY/eq0Rkic0pG7Vy9q6Hqjw+Zb\nrpE6SSbC4wymAuQzCUmXJKjWVH5tEscaK2Phr4UJ0ooOXAXTY1JcA6rn+JC8Pr7Eiq2F1DqTSfta\nQVZe0FZcE6PsLSYlbNT6olp3zqXOQJtvuQYbn9yvFEDuJpiUcqnK8tiYSkrYPUSj5lTSBI8DKOdV\nFJQrYb0MkGF4KIsHnjlodRxeO/B8hkmb9G+hKqh8keM4bwPYjCqx87jjOP8MwBEAd7TyIrsG774L\nvPYaPvH2G7j2RIiSm0LRS6Hkegg8H4HrIfBSKHkpFN3q32XHrYpS9NBDDz30YA0TYkcEI1H2Hjll\nJdoow1TJ7vyM1Nn+8jHyMypDi3e0TcgbW/FgXYt0VWcp/hh8FohInGx8Yj++/PSr5LtjZAZg1tWJ\nIitk5JGYLRLW7pQymk9PBA1EnGkbY1vikDmJfFRVREHyvGTviWVk6ARtqU4/tu2X+c49OidBPGfU\nri6ZtG9F7gDJdFs7l5xkYFovKklsXLuE7Mo4wXXeM20RbvLM+TVg49olyL2+r+kzMsFo9j1qLX3g\n1msa7sum0x6gJ1pFiOs3RY7ryP1WdgaaN+BLCd92apAOD2UjN4joRpho0rFgAj8m4ABJVJwlXfwV\nAvV5pQpY2UK2z0chvM61dTwKTLpo3Un86jMJX0v342//FrjjDvwKgF8x/EoFDgKvSgCVagRQ4Kaq\nRFADQdT48+r/a8QR9++S61W/w30+cFMoeQLJVD82fyzu55LvF4VrPB+IqWxNSFVldPfQgwoDvhuJ\nhDhX0YpU4CgIUSVbVi2ej9tXZpWkRtL41kvHrIx+CqYOq02HKV2GwixCNJlhulSuCtm6GVRCbVmb\njMxQIZcv4ONf3oFyaJRQFQsyQkl0xNYsXWDVMejrdywHgIbspSgY8F30+y4e23MUc9M+ZvkumX3D\nO3z3jxwgxyVrvww0v0/mBLNnoeooI54TiG5oRzU/4nZbi5rx143wPaclQueqbAO+i5+uzTGD6TNn\nY2l4KIuRd15DNuM1rIsbiFJAJqLMrolaS6MQhDbrvJgVqSLAdOR+KzsDybJnfK/a7ahdGBnN4al9\nyQlHA9Xnv2bpAnx3/4m22vs2GYRA4xo2MppLlEBJEmxeJdldOiliZm4CWlQzHT3BCRsE9guCixD9\n5RL6y/bdaDqNwPWkBFEp5aPo1Iggz0Pg+lWCqfZZ+H2YdFxMudPkVZjyUXA8Muupgbxi5+TJJ3YO\nkiCbPlbFNa+H5tvTqrQsgPa2H3QdYFaqO4gDxwEeuWMFnth7FLvfOBXpGFHaWCZd39sKZNI+Hri1\n+1KJbaOKSSHThUQpa8fezqcR59k7DnDFph0NzkWSEXidAaVqs67L/mk12rkc8s9JJLVy+QKe2pdr\n0jWZKJakZEvad+u6PXE1FiaCSn1fyBcCpH0PN1w1X7o25/IFLSHDcKYQ4M2t67S6KVR0H5A7mVEJ\nk3yH9DfiZvx1Ewb7Ui0rU2DrksxuYiSOaZmR6TPnycNM2m/Sm6HKRdj3TNZS2/XW1E7JZtIN9ia7\nXooAY59VzcU4mWoixHm//rqFiWk2RYGubTjT5hIzSEU4AO4SSj13HjoptVM8x8HX71ieGKESVz8O\nmM5k6uS+S4HNqyTtTVkWmiqjbPMt10izCce5TMLzFT2CxwYXXQT8yq/gzPvvo1Lx8M57H8Avl5Cq\nlOGXS/Arpdr/S+grV//tdt2UNIdfKcOvlJEuTXX6UqxQzZoiCCKOPApTPvCD/wj4PtDXhycnytj/\n7gSm4NVJrdDvw/W/cDEqvo/vv3EaBXjWWVPKz/DH4rKmLphVTYmfIOrBbRbTOBkVfHoynzqayxca\nBAwpx4aBJ3eqEWgP+YkALnEvnuPUN0bWgjTOBjLY52nbdtvCdx08cOs12Pb84UTJnbTvwXUQ+Xrv\nXr0IqxbP74iT0m3kDpCsgGw7wIa5aamQLUwcbjHKzs/78wVz075S4LEQlOvlA65TfV+ZtE+UiVSM\niRZbFIIy9vzsdOzjpGuZWzoHl9LGoKLUUQmTpMpNbMGuP+6e0w04o1iPowiOy6AicUw0xIDmzBmZ\n026SoRJFjywuTMY3dQ3Us8vlC7hh64vYuHZJEynEkCTxL8skempfrqPtpHWBiEoIPLJ+RVNJExOO\nVwmpU8euhGFdE4ra69K+q80+NSlztsFDw8uwavH8rtqD+TFtu046irIz2TxRZZQND2Wx5dmDTXZe\nT4enR/DY4eabgZtvxuiuXfjKnorRRHNrJAkjfhqIoHIZfZUAqXL1932VEr79pZVAEOClQyfw7L6j\nOPvBOPoqZXgcccSOM9938DufylYzi4rFpr8PHnkP75z8YJqAqp27r1KCV/t/f1jGfB9Io4KgMIXS\nVBGpcgC/MnOjV9WsqQD9ZQPH7u3X6/+8pPanCbVS748ncXEaNBNBzSRS4KVQ9lL1DClVyd2sdD9O\nl0CW//FZU05/Hz71CxfjhyfG8U6hjLlzZ+POG67Eje57wGtngL4+DM/3Mfzbn6iTYujrA3wfV3zl\ne8b3GGJ6YRYNC4ZyGDY4t7IIhu9WqStxr037LqZKFVTCKlF05/ULtYKyppCJLSaZnsoiPgDIKJJK\ndPXO6xc2RKpkBvwVRGemHroPlPZMHFDizyKYEUzN0XMdZwqBcWYeI3Q6RXAmQURMBBWjiKfMGWcO\nFa+3w689t6/MWmlptKq0yBTDQ1my3EeFJMtT+QwF1bhSZZBQJJmpNo4JVCSODeEiEhZRCKgkNJhs\nITunrtsbg4psT5Lg1z1L01K6dkIXiOCvz5bs0hGPKi0mimRRCXEnAT7IKiM7+jw30SCm2HlPRZzZ\nZNvr9OMoIXb2O9kYprI9z3cdnh7BY4mR0Rx+/s6HtdalelRcD1Ouh6lUn/az2Uwa+LUqW3/954Hr\nuXNSiw0Ui8g1AH5isUn6tT8AgDDEr37tBbx76mydkGIk1cLBFL71pZVSUon9/dUnR3H2w4kaiVVC\nqvb9OV6IAVQwOTGJ2U4FYVCEV2omr6qkWAC/XIbPvl/7XV+ljIvTLs5+WIBbChpIs3MlawroXNbU\nP+H/81/MvvNTx20usauTSp6QrZSC++0+4OMXY7ivD9eNl7D3+DiCpu97yO9O4/VfuBjzf3wKvxu6\ndRHzwE3hul/4CL74S1dNk02Kvw/9Qx5vny01ZU1VPB+mW6LvOdj2xeVNRqiNUe+7jlKbhI/WUcRR\niGntKNWcpoyec0lj4nxALl9oKtkCaKNdlmWXSfsolspWJZ/M2NWlyp+rCIGuKrtsB0wdOt7ZEImC\njU/sBxw0iG4/tueocRalrV5Fq2CzTlbbH68jy7xtINtnVOXjuzfdRNqIFEmWpEOvInHiEC5RM1SS\nLmlt5Tl12T9JkCxRuxgCnXWQTTKjol6fjnhUjVuK+K2EId7cuq7hZ0llyfGgCEVVUwcV+DWEz1ad\n5btYtXi+kdC97l3JyC8+I4m3oSliUzXHTDMFzzf0CB4LsIXyXy2tANATPGnfUwogip+VbcZsgSgE\n5YaSmFZvknAcbFj3i7jv6QP4QFgE/+C2ZcAvqo/5/74yi8w+4BdB3hkRHeU6iYXmhfaqoSz+P4r4\n+vWrMXzNApJ8asXf3/vhUQSTU3Uyqq9WutdfKWEWKkCxiLRTwQVuiMmJSaTKAVKVchOpNVPhhRV4\n5YpZ1hTDT6t/ZWt/SOwEPiH7+fcA/GezUz2p+F0llcKk48HzPdwJv541xWdQhakULlkwFxf9YLCB\nPEr/+H38p9AlSu58BJ6Hsuej6HoYmJ3GTcuy+M5rJ3GmjKYMqv6BWcA/zKsf/9PFd5EbLzXpTi2Y\nPwc7N/4q4JlrTfGQbcZ2JBXwkQt6JFE7EaLZMOejeKx71B8+uR9FjpRgmR22mSW+62CiWOple51n\nsHWYZESBjMAOUS05VXXqAqKRO6JjQh3D1tmyWSeZI6F6fnxDBxYJnysQrzZlbqLN2J9y67/XPcco\nLbgp6EicThAu3QCT8cY/u1aRLBSZ98AzBzFVqigJlE46yCbPJur1mQpu2wTIxGtJMktOdv0iASxb\nd5lGpMrH4tcQvmOoSotPdj2AXHSdKldj9zD04AtNPrItsdmJ0syZgB7BYwGTSCZLq2ULBtCsdi+C\nImzEBaIchk2RkVaClcawzhue4+D2lWabtU3ttS4izT4nuz5AsUin27c5/UvCERIJLUDejWXnoZM4\nfnoCi+b4+IObrsQtn7ioRhwdwX998TBOnR7HpYMefudTl2HNlRn83cHj+POdh1EpBvXMqkFU8Fuf\nvAQrLxlsIqFeP/o+Xj78DgoTk+ivlfxlUiFWfHQQi2anlOTVmTPjOH1mAk5QRH9YQSYVYlZYbvzc\nDNYqcEslDKAEBEC/6oOShg5rbU/2DDCk+v1fTP/zr1Sf+yoA1zXKXhL/Hu7rw3UTZRx8t4AzZQd9\n6X4su+IivJI7K5TzyTOywlQKd/7yx/CnPz2GIkdsNXX/E8oK+eOeDx36WoFCUFaWBBYTyDjJ1PQv\nbLWLZNozPcws2DpMts7nYF8Kg/0p0mGzcSqA6l4qCmyenqiW1vHHMMliEG0IKlIulhbwjgRl94gC\nu6IN8Ecam05l68iydyY1mXpJR7zF65OVWZxPsHHu2VijsrTikizUHNWR/t3gIFOZgkD860sy60p2\nLRSxdu/jjWtTEqDe8ZlCYOxjRcnqE4/3wK3X1I9lQqSPjOZIO8Nmb+lEaeZMQI/gsYAuOqMaUDbM\nJkOn62JZm0IWBS6HIZ7al8OqxfO154/CqEZZcLslOmRjMNlc82c/+xF89rPXNf18064Xkbvs2qaf\n7/HT2P27zaJ8nwCRBaOBqjyw4R7K04TPH42M4TsvHxGyk6Z1p2Y7FfyLTy/EDYsuqBNEe3/8Dp5+\n6S0gKNbL+QacCj6/5ELsOfwOJscnm4/FZUDNqpTglkq1DKoy/EqAvkoZlw2mcIEX0hlYpZnX3a6O\nSgWYmqr+sURT1tQrwJU2B/jr6RLSKChywuJ86R6fNdVMMBE/F7KmqppU0xlUjcduPhZPPtVJKOL7\noWNWmjtTkfY9OFx5jQ1mpVyEcM7Lcq5zAeL+TAYiOAPattzzTCHA2OablZlhNjbOtucPSyPXosAm\nZUvd9/SrmAwqZHmAbK9mpQUyR8LE7oka2afshih2YtIR71ZmK8xEUO/knu1j2Pb8Yamv0KoshCgl\n2Ul0f0oS3eTAm14L5S+K+pJJIEpAXYRtBhk15x++bRkpDC6C12wTYUtsdosv2E3oETwWMI3OiOBZ\naJsFKsk02iiIQzB104LcDrQ7RbBdY8N4DHheNWMqncaO4yWcnD1ferxsJo1/snYJbhCNZgBvS+bH\nJ4ayOBxD5FU3N1GpVEmeYhF/v3MnfvlTnzIuzfuX/22PIJ5e1Y1K1XSj/HIJaZTxmSsz+Pj8WcmW\nBRaL1s+im9BXE3vvpNZUFJQdV0k2lVwPFb8Pk3AFwijFkVo14ojIbqJ0rIqez2laCeRT7W/q2kyz\npgpBOTJBMxFU8Oj6FWTXKQDo8xxpltFgn4ev/cYyPLH3qLTd90xEkq1jW415Az7WXXsJtj1/I1xO\ngQAAIABJREFUGBu2jzV1McrlCw0iybl8ARu2j+HTV83HqfFiw5hR6ow5VcdA53Tq9jGTrm65fKEu\nGk0dT9YNh+1vAJ3ZQ9kxukwb6prjBO6i2AJJ22edDkZ2G1TPXqUxAiRvM1O2KSUfobWZOgQbBz6O\n9o1paZ3ueKo1Lum5Edf/uH/kAPk7imhJYs6r5kkSvlMrNJBmEnoEjwXYJAKmo/467Rwq7dcEnRaO\nikoiyGriz/WJ1W5Cq11jI8oY0C3aKuNYVYoXpW2tlvBy3Xo3sNKcOcDFFxsf+9XXZkvfgVMrdGZj\n4OOKMRB5AwrDatZUwnpSB4+8h+/vfxthEMgzsGpZU3O8EL96+Vy8+/6HePvdM6hMFaeF0msZVA0C\n6TwBFpoL/XYbvLACr1TELADAzNIgKgpkkUz8vEooiZ381FlP9c95HoY/+hMMX+Rj9J1xPHfoPbw7\nFWLOnAF8YdVirF7yUaCvD3+25xieO/x+Q9aU19+H9DsX4rFbrsCnth3DeMWpk1jtyJpKsvsRoO4U\n0p9ysWpxpquIrPxE0EDgmGg2hQB2v3EKd69e1JDZs2bpAvzVS0el5XphCNz7xH6svmKekpxR7WM2\nXd2YI50Z8K1KDpkTHiUjRbaPmVxz1OBMVFsgSYdZ1QnKBkk5ZJ127Gw6QPFoRRYCZZsCzfIRSQQl\nO/3s42STxf2urpyTR5IahnH8j5HRHNll1gFNtCQRZKbmSSbtK6/dRHetl1XYI3iswAbFzw//EA5A\nTqKkBlanhaOiGA6mNfGtQic3l3amCLZrbEQZAyrjJmrUgn3HNpOnlWTomqULmlq368oueVAdaLY8\ne1DairIBjgOkUtU/CWpN/d7WF5FLqzdovoPfRwB8pPZzSkMgk/Yxtvnm+tw8cXoci+b4KBamMH62\nIJBHVfFxvzxNLPGi5X45qBFFFPlEfJ+V7im+PzcFBIWp6Z83HLuE/vIMLucDnzXVQuyq/jUEQWuK\nE5T63dqfJvxx9a+XhR+XHJfIUqIznXgS6mN/5+GjH/g1Mqs566n6b6osMGWUNVWqiai/NxXiovlz\n8L99dil+/brFWLV4flNG01SpgpffPA3PdVDuEtGiOFex49UTGP3qzfX/37D1RaUWU7kSasmtiWIJ\n948cwM5DJxu6wWUzaUwUS8Z7gE6zioLnNJcaxom6m+g3Rt2rbGwB3j4S2x5Trb1N7FlVttoNW19s\n2sdkdhqAROzmbnDsWtkBKgpUtmmS9nLcZ5+E/W6TWSKeT7a2mMx72X0/tS+H21dmG4hzHkmrEEb1\nP7Y9f5hc/0PQ703nG5i8y41rl0hbvjMtHxlMfcxeVmGP4LHG8FAWu878BG9uvZH8TFIDq9NlTlFI\nBNOa+FagGzb2dqFdYyPKGNi4dglpVMcxamwzeVpJhjJ9Kv4qHMBYhBygO9CwSHMnxq/u/ahq86mx\nwjZr0QBh8/UMYQQ7AB5ZvwL3Pf2qtJSCKveJmo2RrTk5lEHmAXAqZSyancLv33gFvlATQhczoYa/\n8SK8pg550yRUA8lUJ5+ELCdJhz2fJ6844ulcz5pKhRWkSkXMQvSyxKUJXo8RNlf/GvZ93Ox41ewp\nkSxyvaYMKFH3SUdeNWZaNZNQ9Swpy+/bZE2JmTFJOK6nhYwitt63q3Mftb9EvTfd9+LsVaa2gGgf\n8aSjrASP7T0m9qxqPxYDF7Lyv/uePoBZvpuI3dwNjh3/TpLuAJUkkg5Kxnn2Sdnv1FzjSzap89ke\nk4G6752HTpLfaSe1ryJbdNqyFFS+gdW7FB+E5sGY+pidljjpBvQInhYg6RaUnSInopAIUUt3kkA3\nbOztRDvGRpQxMDyUxZZnD0pT4uMaNaaZPFHa7dpANtZCQLmhizCZD+0ev6rsKwdQdoLZuHYJHr5t\nmfFY0RF2l2bS9TF+/8iBhm5+d16/EA8NLyOvA4B15P54vlA/ZpzMrJNXnyK12jauXVK/1nYaeE5Y\ngV8u1wkjuvxOngFV/Uwj+TTHC1GanCK/P+hU6t0AeRJs7GcnEdZK+ngSqh8VeEFQ//m5kDWFIMAA\nAgx0+josYZM1FXgp4If/e71L35+9kccHZcjJI3YMhW6UknzSfL/suLE79A34LuYN9idaAq1aV5MQ\ntDWxBUyyiHjosp/4/SurKUniAxey8j+V9pet3dgtjh17J7YdoDpd4hQHcZ69qp27zfNQzTWeaHQt\nNNJ0875bxpwMMrLlnu1j2PLsQWy+5RryeanKswC1b3DD1heNfDEZWRNU1MkApj5mpyVOugE9gqcF\nOJcGlm2dtmrRbPX9d/MiK8NM2cijEEmbb7mmZSVk4sYippq34zm2sv44zjHjYuPaJdiwfUxKPvDz\nN2oHBdmY//ody7Vj5aHhZXXyRQQ1Pr/ynQMYL9qX8z00vEzZKUcHVWSLv9ahB1+wbkUeFaHjophy\nUYSf2DF1Th0AlNevaHpubyk68zVFvMMQXliBX652xUuVS7jQdxBMTgJBUCeIZjsV/Ot/tBhuUMRf\n7PoJKsVinaC69dIp/I8jobTML40KPnNVBj/86buYKkzWSCw1+bXqkkEzTavyzO0mZp01dXRapPMz\nLbomU0ypSuwMsqZWXPURVFIp/ODYh5iEWxc3R8rHTddeBmzbVyezTP/+91dW8J92HseHoVsnsVL9\n/fjKbyzHLasWVz/bYiS9j/D7gUlJUhLnMQGltzQ3ncwzpuw2ne6myX7SbVnotjZqHN9H1c6dkYKy\n52GjfcMTjabkjondqrrviWJJOh7nDbR+zgM0sXt6IsB9Tx/A7Suz2P7KsabOmXetXhRJbwxQ28f8\n+6LegGqtUtnN4poklnL5rtM2iZNuQI/gaQE6rZ3TCbCNiVo0fa/1EyuqZlAnSJZu28iTRpTMH5t3\n0cnMNiAZEtfUKBaJlVaO1+GhLPYeOSXNYOHnb5RsOSqSNG/Ax+0rs1L9h6gYGc2hWGouS/I9B+uv\nW9hkAKZ9D2uWLsANW1+MfQ2mY3+GNFiSwnMc46iseN+659MwJxwHZcfDBbNnNWTksXlwpPb931u7\nBL9S+9171zXOkU8vncJ39zVrqrAsv18YyuK10Ry2GMxFqw4zlQoQBPjuK2/hoZFXUZ6c1ndKo1pK\nh6BUJ45mo4Lf/aXL8OmFFwBBgL0/fgdP7nkTKBbrWVN+pdqlj8rAmuuF+NySC42F1QvjBYyPT8Iv\nTWdetVynqcXoL9cyv6Jyp69X/7pc9ru/i3bIm2p/mvB17t+eZ00c2fz9h2NH8V4xJLOmjMr3OHLq\n3/7qkmoHSs8zKknSIZP2MVWqxFqXR0ZzODspH7/jxVJDiU4UUHbb3iOnGvYU0Z4ztVdanYVuYz9E\nsVHj+D6mAS/+eUTRvtEhk/Yx2J+ysgN09y3Tmdl8C60zkyRU+3QhKOO7+080lUX5roNVi+WdcE1A\nvcu5ad/a5hUhI24AwscUkzmTFj7qcjhhG63MVatWhXv37m3b+VqFXbt24cYbb1R+ZqZkZyQFSmAV\naH25DAOVCkuVVth+PklQz6tbW1S2Gp18Fwwm85ohqesVRS95bQLxmO18Rrr164pNO6TRFwfAm1vX\nSY+pWiOSvg9TwWdV1K/V4496hhSS7vQUB6xzks4gV40HCknvnbt27UJ+7se1Ufe5aR8fTAakQHCc\n8UCVEZropjCnWff+o16f2JEEYYhUpTyt76TSjRJK96T6UEbf58irBg0pefkfdWxvBmtNzVg4TgOZ\nNOmmcLoY1sXKpwmiRuKoobQulcInP/YRVFI+9ubOIl8C+gdmYeHFF2D/zwsowK1/3+nrw/CnLkc6\neB9XL1/eQGL9q8cP4MREuUl3qpqd5eGi+XPwvY2fAfr6MPKjd7HthR9L5x+1BtlmXersOfE8qvJo\nah01XS9l9oPvOpg9KyXNfo5qo0Zdv22647HnobpGIJpm17wBP1I2uOq+W+EPmh5TZXepEMcXoWzV\nWb6rnD/3Livhjw/1a/cxky5a57KP5TjOvjAMV2k/1yN47GHjCJ4viOLwtQI2C2knF4BueV7dgm5Y\njHXzWkYKJJl1IjtHEgZXKxDlWnSERpL3YTu/VISQbTRPBtl7tYl2q7rUtBtp38Xr//5zRgZ5NxhT\n1LymHB44aEpXtw1StMOgT2r9aSJ4ZjDcShl+rZSvTh7ViCOeMKJEz3/nU1msunS2WQmezd+6zwQz\n/9nPRASuVyebSp6H9GAaYcrHu1Mhio5X77xX9nzMywzi7bMljjyiySs+62nTrcukGVUvHfsA/+2V\n4xgPnfr3mTC6mDV10fw5eO4Pbpr+vucBjmMV9DFx9F0AcKDsggdU1/VWBK95Qlt3/t2bblLu83et\nXhQ5i4eh3UFGU9i8dxvijEdcX0S2B1Ll/+x8m1ZUcPGST8bK9NeNoXPBxzIleHolWj0kAlVKXhJl\nD6awKd3ppGbPuaTTpIKpo9Pt+klUKnDSm79q/HbTM4qSiq1LwU7yPmzXI9P6f+M29hyoVHfTNHJS\nn6YGz3FQCcO608/EqFuFyVpHM7EsQ8ww6fayZKqLXVxSr1Xlt/zakBSBJGs5O5NRcT1MuR6mUn2R\nvv/HX07G8Ld+P2FYLXdKiFR6ZMePMD5e4LKcqqTWvBSwfsVH65879vM8fvz26Wr5oGHW1FwfKBWm\n4NU+P5OzpvwaIZguTVV/MPEBAKI8723gyign+Vv5j6+v/THGQ9y/a1lTN8PDr9ayk4quX9OUSgF/\n5gPZ+Q2k0kNv5jnyqblMr+gJnfdqxxW1q9jvPlb7/l8deAV/6aaQyQzikx9bgH84eha58RLmz5uN\nf/GZJfjcysWN5JZCBJ2tcbpsX7avqOxom4YXFFTt1XUZJK2ETSkf+7+MxFdl1STREEW8FsqGYYTd\nrl27cKOG3FHtrSZk1rnmY6nQI3h6SAQyh893HYwXS0qBtE5BJQjdjgWglTpN3VIeaOPodDvh1Q0d\n2rrtGfWnptvamhg4Os0h8T7ijGPb9ci0/j9KG3tVC9V5hCAoT9qQ+jSQR+1WLZ6vdNqzmTROj09h\nQtJ63gT8e2oF6dAuUKTemUKAsc03Rz5uK9cKWbZNnH2VajnbQ3REIvhYmVNCYsv/+S06s2F9LXpd\nv85rzSP7Mp2cQQ94+JaluPXqBXWS6fnRY/jGjh+hNFVEXy2DarZTwaBbweT4JF2+V/t5GhV8fumF\n2HPoHUxOFKoZWCz7qva5ZXMCvHGqUtejmu1UcGG/gzNnJmrk03THv5SQvcVKAWcswhAoFjEA0B36\n3nmz4b9rWn1NNfwb/j9bJR9IpaQ6UWdDFycnK5iAh/8nlcKkW+uUx5FPTl8frl50IRYezwC+j/9+\npoj/+eYZFBy3IYMqNau/1smPI6+4rn+BSIopMrDePzlVFcz3PAByUvz0RICNT+4HEM+3iRsQpWwY\ntk9TpcLt0oxds3SBVt9RBd3equsU2A4t2G5Cj+CZQehmA1omnClTj2+HU2ySwkcJQrcr6hxFhJhB\nV+sbNXqc9PiycXS6XZi8G7JnOvmMdFpBkwRZII6p21dm8d39J6SRJP4+4mZB2K5HUbvAmKxnqrHz\nyPoVxqnWpmvG8FAWW549KCWOWKRMZqQyPQZVjbxqvHVa+NwWSRKmNp1BWqFRYbqvmmp+nI9IqqvN\nTAkG2LZNByDVqBovA/9h1xHcuvqq+s8e/MvXkZtzMTCn8bMOgJTnNJVAyvCdTBobf5del+9dVsLX\nDzS6MGnfqwv2q8a2AyCsaU2JpXupWmc+nnjyywH6KxV45WBa3Fwgi8QMqM8vvQhL5vdLM6xy757B\nwSPvV0ko/vsNJJZc08ovl+B2jRpbBJRK1T+Fxvczu/ZHix9N//Oq2p+W4xuokrB9fbg5dKVZU4GX\nQunPUzi14ALMnzdbL34u/Ozge5P48esn8WvwUPKq5NaeH/jI/tKVuO7jFzd8/nOnf4LjhUpTV8Cy\nm8Jz39+Pz39y0fTnuawp1R7dat9yZDSHp/blGkauA+D2lclVXejs8cG+1IyyUeKiR/DMEMyErkvi\n4nHFph3Sz7XSKTZ5TpRh4zlOW+ttozhEuvujjMsHnjmI4aEs7h85UC/h8BwHd16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TAAAg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\nKr3iDYqR0RzGp0rkd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"text/plain": [
"<matplotlib.figure.Figure at 0x7f00fc374e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
......
%% Cell type:code id: tags:
``` python
from xlap.parse import evaluate, parse_config
import xlap.analyse.jitter as jitter
from xlap.analyse.regress import linear as linear_regression
from xlap.analyse.trace import trace
from xlap.analyse.common import extract_durations
```
%% Cell type:code id: tags:
``` python
config = parse_config()
data_files = config["data_files"]
original = evaluate(data_files["sender"], data_files["receiver"], config=config, kind=0)
df = jitter.prep(original, config=config)
```
%% Cell type:code id: tags:
``` python
trace(original.iloc[47],config)
```
%%%% Output: stream
/usr/local/lib/python3.5/dist-packages/matplotlib/figure.py:1742: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.
warnings.warn("This figure includes Axes that are not "
%%%% Output: display_data
[Hidden Image Output]
%% Cell type:code id: tags:
``` python
df = jitter.prep(original, config=config)
jitter.trace_jitter(df)
```
%%%% Output: stream
4018 / 4095 are no outliers.
%%%% Output: stream
/usr/local/lib/python3.5/dist-packages/matplotlib/figure.py:1742: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.
warnings.warn("This figure includes Axes that are not "
%%%% Output: display_data
[Hidden Image Output]
%% Cell type:code id: tags:
``` python
linear_regression(original,"Feedback_D")
```
%%%% Output: stream
R-Score: 0.0444517602497
%%%% Output: stream
/usr/local/lib/python3.5/dist-packages/matplotlib/figure.py:1742: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.
warnings.warn("This figure includes Axes that are not "
%%%% Output: display_data
[Hidden Image Output]
%% Cell type:code id: tags:
``` python
```
......
......@@ -112,10 +112,6 @@ durations:
Start: PrrtSendEnd
Stop: LinkTransmitStart
Source: sender
Encoding:
Start: PrrtEncodeStart
Stop: PrrtEncodeEnd
Source: sender
ReceiverIPC:
Start: PrrtReturnPackage
......
import math
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (16, 9)
......@@ -10,54 +10,21 @@ plt.rcParams.update({'figure.autolayout': True})
def trace(df, title, export=False):
fig, ax = plt.subplots(figsize=(8, 4.5))
plt.grid()
base = df["PrrtSendStart_T"]
sender_color = "#AAAAAA"
receiver_color = "#888888"
series = np.transpose(np.array([
["PrrtSendStart_T", "PrrtDeliver_T", "black", "EndToEnd"],
["PrrtSendStart_T", "LinkTransmitEnd_T", sender_color, "SenderTotal"],
["PrrtSendStart_T", "PrrtSendEnd_T", sender_color, "Send"],
["PrrtSendStart_T", "PrrtSubmitPackage_T", sender_color, "Submit"],
["PrrtSubmitPackage_T", "PrrtTransmitStart_T", sender_color, "SenderIPC"],
["PrrtSubmitPackage_T", "PrrtSendEnd_T", sender_color, "Enqueue"],
["PrrtSendEnd_T", "LinkTransmitStart_T", sender_color, "SenderEnqueued"],
["PrrtTransmitStart_T", "PrrtTransmitEnd_T", sender_color, "PrrtTransmit"],
["LinkTransmitStart_T", "LinkTransmitEnd_T", sender_color, "LinkTransmit"],
["LinkReceive_T", "PrrtDeliver_T", receiver_color, "ReceiverTotal"],
# ["DecodeStart_T", "DecodeEnd_T", receiver_color, "Decoding"],
["HandlePacketStart_T", "HandlePacketEnd_T", receiver_color, "HandlePacket"],
["PrrtReturnPackage_T", "PrrtReceivePackage_T", receiver_color, "ReceiverIPC"],
["SendFeedbackStart_T", "SendFeedbackEnd_T", receiver_color, "Feedback"],
]))
n = series.shape[1]
starts = df[series[0]] - base
ends = df[series[1]] - base
plt.hlines(range(n), starts, ends, series[2], linewidths=[5])
plt.xlabel("Time [us]")
fig.canvas.draw()
ax.set_yticklabels(series[3])
ax.yaxis.set_ticks(np.arange(0, n, 1))
if export:
plt.savefig(title)
plt.show()
def box(df_data, export=False, title=None):
ax = df_data.plot.box(vert=False, grid=True)
def box(data_frame, export=False, file_name=None):
"""
Display a boxplot for the durations contained in data_frame.
:param data_frame:
:param export:
:param file_name:
:return:
"""
ax = data_frame.plot.box(vert=False, grid=True)
fig = ax.get_figure()
ax.set_yticklabels(list(map(lambda x: x.get_text().replace("_D", ""), ax.get_yticklabels())))
plt.xlabel("Time [us]")
fig.set_size_inches(8, 4.5, forward=True)
if export and title is not None:
fig.savefig(title)
if export and file_name is not None:
fig.savefig(file_name)
def describe_table(df):
......@@ -87,7 +54,7 @@ def correlation(df_data, title="Correlation.pdf"):
i = 0
for column in columns:
ax = df_data.plot.scatter(ax=axes[i // cols, i % cols], y="EndToEndTime", x=column, grid=True, marker="+",
ax = df_data.plot.scatter(ax=axes[i // cols, i % cols], y="EndToEnd_D", x=column, grid=True, marker="+",
color="black")
ax.set_ylabel("EndToEnd [us]")
ax.margins(0.1, 0.1)
......
......@@ -18,7 +18,7 @@ def _filter(x, durations, source):
def extract_durations(config):
durations = config["durations"]
durations_send = [_dn(x) for x in durations if _filter(x, durations, "sender")]
durations_recv = [_dn(x) for x in durations if _filter(x, durations, "receiver")]
durations_send = [x for x in durations if _filter(x, durations, "sender")]
durations_recv = [x for x in durations if _filter(x, durations, "receiver")]
return ["EndToEndTime", "Sender_D"] + durations_send + ["Receiver_D"] + durations_recv
return ["EndToEnd", "Sender"] + durations_send + ["Receiver"] + durations_recv
......@@ -5,9 +5,9 @@ from xlap.analyse import box
def jitter_causes(df, durations, export=False, file_name=None):
stats = df["EndToEndTime"].describe()
stats = df["EndToEnd_D"].describe()
threshold = get_outlier_threshold(stats)
outliers = df[df["EndToEndTime"] > threshold]
outliers = df[df["EndToEnd_D"] > threshold]
reasons = [d + "_D" for d in durations.keys()]
......@@ -33,8 +33,8 @@ def trace_jitter(data_frame, export=False, file_name=None):
"""
Displays (and saves) a stacked boxplot of durations.
"""
thresh = get_outlier_threshold(data_frame["EndToEndTime"].describe())
df_no_outliers = data_frame[data_frame["EndToEndTime"] <= thresh]
thresh = get_outlier_threshold(data_frame["EndToEnd_D"].describe())
df_no_outliers = data_frame[data_frame["EndToEnd_D"] <= thresh]
box(df_no_outliers, export, file_name)
print("{} / {} are no outliers.".format(len(df_no_outliers), len(data_frame)))
fig = plt.gcf()
......@@ -45,4 +45,4 @@ def trace_jitter(data_frame, export=False, file_name=None):
def prep(df, config):
plt.rcParams["figure.figsize"] = (16, 9)
plt.rcParams.update({'figure.autolayout': True})
return df[extract_durations(config)]
return df[[x + "_D" for x in extract_durations(config)]]
import numpy as np
import matplotlib.pyplot as plt
from xlap.analyse.common import extract_durations
def _create_line(config):
tr = config["time_reference"]
color = {
"sender": "#AAAAAA",
"receiver": "#888888",
"e2e": "black"
}
def _creator(duration_name):
if duration_name == "EndToEnd":
return [tr["sender"]["Start"] + "_T", tr["receiver"]["Stop"] + "_T", color["e2e"], "EndToEnd"]
elif duration_name == "Sender":
return [tr["sender"]["Start"] + "_T", tr["sender"]["Stop"] + "_T", color["sender"], "Sender"]
elif duration_name == "Receiver":
return [tr["receiver"]["Start"] + "_T", tr["receiver"]["Stop"] + "_T", color["receiver"], "Receiver"]
else:
duration = config["durations"][duration_name]
return [duration["Start"] + "_T", duration["Stop"] + "_T", color[duration["Source"]], duration_name]
return _creator
def trace(data_frame, config, export=False, file_name="TraceJitter.pdf"):
"""
:param data_frame:
:param config:
:param export:
:param file_name:
:return:
"""
fig, ax = plt.subplots(figsize=(8, 4.5))
plt.grid()
line_creator = _create_line(config)
durations = [line_creator(x) for x in extract_durations(config)]
series = np.transpose(np.array(durations))
n = series.shape[1]
# Starts and Ends
tr = config["time_reference"]
base = data_frame[tr["sender"]["Start"] + "_T"]
starts = data_frame[series[0]] - base
ends = data_frame[series[1]] - base
plt.hlines(range(n), starts, ends, series[2], linewidths=[5])
plt.xlabel("Time [us]")
fig.canvas.draw()
ax.set_yticklabels(series[3])
ax.yaxis.set_ticks(np.arange(0, n, 1))
if export:
plt.savefig(file_name)
plt.show()
......@@ -76,7 +76,7 @@ def evaluate(sender_file, receiver_file, config, kind=0):
df[name + "Cycles"] = diff
df[name + "_D"] = diff * df[duration["Source"].capitalize() + "Cycle_D"]
df["EndToEndTime"] = df["Sender_D"] + df["Receiver_D"]
df["EndToEnd_D"] = df["Sender_D"] + df["Receiver_D"]
return df
......
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