| { |
| "metadata": { |
| "name": "" |
| }, |
| "nbformat": 3, |
| "nbformat_minor": 0, |
| "worksheets": [ |
| { |
| "cells": [ |
| { |
| "cell_type": "code", |
| "collapsed": false, |
| "input": [ |
| "from scapy.all import *" |
| ], |
| "language": "python", |
| "metadata": {}, |
| "outputs": [ |
| { |
| "output_type": "stream", |
| "stream": "stderr", |
| "text": [ |
| "WARNING: No route found for IPv6 destination :: (no default route?)\n" |
| ] |
| }, |
| { |
| "output_type": "stream", |
| "stream": "stderr", |
| "text": [ |
| "WARNING:scapy.runtime:No route found for IPv6 destination :: (no default route?)\n" |
| ] |
| } |
| ], |
| "prompt_number": 1 |
| }, |
| { |
| "cell_type": "code", |
| "collapsed": false, |
| "input": [ |
| "ans, unans = srloop(IP(dst=[\"8.8.8.8\", \"8.8.4.4\"])/ICMP(), inter=.1, timeout=.1, count=100, verbose=False)" |
| ], |
| "language": "python", |
| "metadata": {}, |
| "outputs": [ |
| { |
| "output_type": "stream", |
| "stream": "stdout", |
| "text": [ |
| "\r", |
| "send...\r", |
| "\r", |
| "send...\r" |
| ] |
| }, |
| { |
| "output_type": "stream", |
| "stream": "stdout", |
| "text": [ |
| "\r", |
| "send...\r", |
| "\r", |
| "send...\r" |
| ] |
| }, |
| { |
| "output_type": "stream", |
| "stream": "stdout", |
| "text": [ |
| "\r", |
| "send...\r", |
| "\r", |
| "send...\r" |
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| }, |
| { |
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| "stream": "stdout", |
| "text": [ |
| "\r", |
| "send...\r", |
| "\r", |
| "send...\r" |
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| }, |
| { |
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| "stream": "stdout", |
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| "\r", |
| "send...\r", |
| "\r", |
| "send...\r" |
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| { |
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| ] |
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| { |
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| "stream": "stdout", |
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| { |
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| { |
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| "\r", |
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| ] |
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| { |
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| "stream": "stdout", |
| "text": [ |
| "\r", |
| "send...\r", |
| "\r", |
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| ] |
| } |
| ], |
| "prompt_number": 5 |
| }, |
| { |
| "cell_type": "code", |
| "collapsed": false, |
| "input": [ |
| "%matplotlib inline\n", |
| "ans.multiplot(lambda (p, q): (q[IP].src, (q.time, q[IP].id)), plot_xy=True)" |
| ], |
| "language": "python", |
| "metadata": {}, |
| "outputs": [ |
| { |
| "metadata": {}, |
| "output_type": "display_data", |
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iDsUR6hPq6KZINfXJJ9Cli0KHiFR+Ch6lsO63dbS8oSXetYteeSNS\nnoyBKVOsoRYRkcpOwaMUPtz2oZbRisOsXQu5uZrXISJVg4JHKcQkxih4iMPk9XZocr2IVAUKHsWw\nJdoAaxntyfSTdLi1g2MbJNXSV1/B8uXQt6+jWyIiUja0nLYAW6Itf4vix756jIRTCWxP3k76+XRG\nB48GrC2My2IrZJHSCA21KpNOm+bolkhloeW04uy0nJYLgcOWaONWr1v5ZPsnfLvnWx5r9RjfPvot\nU36cQmRIpKObKdWAzXZhLkdmprXT7OTJjmyRiEjZUvAAVias5MDJA3yw6QPejnublje05Gz2WW72\nvJkpP04h8XSio5so1YTNBsePQ1SUFTrOnoWFC61HXtl0EZHKrNoPtYxcOZK317/NbV63ceDUAV4N\nehVXF1cSTycy58E5wMVDMCLlZdcuax+Wo0etTeCeeQY+/1y7zcqV0VCLOLtq2+NhS7RhS7Sx58Qe\nsnKyeLz149gSbXT26UxI4xAibZH55yp0SHmy2WDRIvjwQzhzBl58EWrXhpQUR7dMRKTsVdvgUXCS\naKQtksiQSCJtkfnHFDakorRta/Vu/Oc/cPKkejhEpGrTctoCCoYNBQ+pCCtWQO/eVjn0oUMvfV5z\nOkSkqlHwAPVySIWz2axS6P/+N1xzDbzzjnVcQUNEqrpqP7lUxBFGjYKcHPjoI9i3D667ztEtkqpC\n901xdtV2jodIeSpYj6Pg53kbvk2aBA0aQFISvP229ZyWy4pIdaDgIVIGCoaLwl/bbHDrrTB+PHz7\nLZw/b9XneP55WL1agUNEqhfN8RC5SjbbpZ9nZcGBA/Drr/DJJzBuHMycCQEB1nDKd9/BqVMwejSM\nGaPQISLVj3o8RK5STAx4e1s1OD76CN5/3woV110HaWmwdCm4u8ORI/Dqq+DqapVBL7jLrEKHiFQ3\nmlwqUgoFh0527bKCxrRp4OYGzZrBhg3w7LPg5QWdO1vn59XjiIy8tDZH4aEZkbKi+6Y4O/V4iBSj\nYDhYsQIOHYI337SGUdq1s+ZpjBpl9WA0bXrxZm4Fh2GKotAhItWVgodIIXmBw2aD22+H996DGTMg\nOBjGjoW//tXq6SjYk1G4R6NgsFDIEBG5QJNLRQqx2WDrVpgzB1q2hM2bIT0d7rkHfvoJ1q279DWF\nw4WCh4hI0TTHQ+R/bDZrIujQodYk0DNn4KWXrAmiiYlWECl8vkKFOBvdN8XZldjjkZOTw5NPPklA\nQACBgYHs3LmTlJQUevbsSXBwMEFBQSQmJgIwc+ZM/Pz86NixI4sXLwYgPT2dhx9+mKCgIHr06MHx\n48cB2LBhAx06dCAgIICxY8eW3xWKlEJMDAwcCAMGwIkTVvgIDoawMGsYpXHjS1+j0CEicuVKnOPx\n/fff4+rqSmxsLKtXr+bll1+mXr169O3bl969e2Oz2dixYwe1a9cmKiqKLVu2kJ6eTkBAAF27dmXq\n1Km0adOGUaNGsWDBAsaNG8fkyZMZNGgQCxcuxMfHhx49ehAfH0/btm0r4pqlmiipR+LUKYiLg9hY\nWLkSUlNh0yb48ssL8zfyXq+QISJSNkrs8ejZsyfTp08HIDExkbp167Ju3ToOHTpE165d+fTTT+nc\nuTObNm3C398fNzc3vLy88PX1Zfv27axbt47w8HAAwsPDiY6Oxm63k5WVhY+PDwBhYWFER0eX42VK\ndVRwZYkxcPAgfPopDB4MrVpZE0dHjYL1661VKcnJVuiw2S4NLQoeIiJlo1SrWmrUqEH//v1ZtGgR\nX3zxBfPmzaNevXqsWLGC1157jQkTJnDnnXdSp06d/Nd4enqSmppKWloaXl5exR7LO56QkFDGlybV\nSeGgkJtr7YPywQdWj0ZsLGRnWxVEAwLgqaegTRtrdUqeJk2sXg7N3RARKT+lXk47Z84ckpOTad++\nPXXr1uWBBx4A4P7772fkyJH85S9/wW63559vt9vx9vbGy8sr/3hRxwDS0tLw9vYu8vtGFlinGBIS\nQoj+RZAi5IWFjz+2lr7+9BPY7Va9jdtvhzfegMceu7hqaHH0KyaVic1mw1ZS4RgRJ1Ji8Pj44485\nfPgwI0aMwMPDgxo1ahAUFMTixYt5/PHHWb16NS1btqR9+/aMHDmSzMxMMjIy2L17Ny1btsTf358l\nS5bg5+fH0qVLCQoKwtPTk1q1apGQkICPjw/Lly+/KGAUVNxxkbywkZpqLXnt2NEq7tWnj9XT8fXX\nl9bXuBwFDqmMCv+HbMyYMY5rjEgplLicNj09nf79+5OUlER2djYjRoygTZs2DBgwgLNnz+Lt7c28\nefOoU6cOs2bNYsaMGeTm5jJy5Egeeugh0tPTiYiI4OjRo7i7uzNv3jxuuOEGNm7cyLBhw8jJySEs\nLIzXXnvt0sZpWZhcRr9+cPy4tcPruXPw6KPg62uVLA8JKbpUuUhVp/umODvV8ZBKJycHliyBJ5+0\n5mlMmlR074bmakh1pPumODtVLhWnVNSQ9fbt8Le/WTvCDhxo9Xb4+1uh43+lZC6i0CEi4nwUPMSp\n5AWOvI9Hj8LEidC2rbVHSpMmVq2NI0dg9GgYM8bq6ejf3zHtFRGRK6PgIQ5XsHfDZrPma/z8M4SH\nQ4sWsHMnvPOO1asxfjw0b37pe6h3Q0SkclDwEIez2awCX1FRMG0a1KtnDZ9cey0MGWJNIg0NBddC\nv60KGyIilY8ml0q5utwEz+xsiI+35mscPGgFi+PH4bnnYMuWi0uWi0jp6L4pzq7UBcRELqe4gFHw\n+OnTVnnydevg++9h1y6oWxdSUqwVKrfeagWQiRMVOkREqioNtUiZKDhPIyfHmhS6ZYu1EmXwYGjd\nGm67Dd580zrn9detwJGcbE0S/fBDa6Jo3i6wCh0iIlWTejzkD4uOhgULrNoaR45Ye6S4u4OnpxUs\nunWDe+6x6m3ce+/l30u7wYqIVG2a4yFX7YsvrCGR33+3ypY/9ZQVNrp3h65drXNKUz1Uhb5Eyo7u\nm+LsNNQipZY3nJKdDW+9ZQ2hPPzwhZoas2ZZy17zQkdpKXSIiFQfGmqRUssLHs88Y83XWL8e/vSn\ny79GoUJERArSUIuUONRx6hRs2wbDhlkrUyZPhoceuniLeQ2XiDgH3TfF2anHQy4KDUlJsHWrFTS2\nbrWWvp48CTfdBIcOwYgR1kqVevUuDhoKHSIiUhrq8agGiuuNOHcOYmJgwgRrUui2bZCZCe3awV13\nWY927azhFFdXbTMvUhnovinOTj0eVUjhgJH3dcHjR45Yxbtmz7Z6NBo2tIp2/b//Zz169rTKk4uI\niJQHrWqphApvGV94R9c8MTGQlmaFjTFj4C9/gZYtreP//Kc1rJKYaK1IWbDAmrtxudCh4RQREfmj\n1ONRCRXVsxEYaE0C/fhjWLsWli2z5mS8/rq1/LVDB/jzn+GNN0ou4lUcBQ8REfmjNMfDiRUOGNnZ\n1jyM//zHChF791rDJYcOWWXKjYHbbwc3N2vr+O+/t3ozbLbL732iFSkiVUd1v2+K81OPhxPKCwKr\nVsE111hDI199BT/9ZG2qlpxszcvIyrICyK+/wquvwpo1FweMvMmgJW24ptAhIiIVRXM8KlDBORjF\nzdMA+PJLqyrom2/CgAHWHI0RI6yN15KSrF6M+Hhrd9fvvrO+HjvWChBFhQgFCxERcRYKHhWouOBh\njBU23nnHWsI6fboVKjIzoVcvq5ejbl2rdsblFA4Y2nBNREScjYZaKkBmplVefO1aq/rniROwYQMs\nXgzHjlkPgGbN4M47rXkcoaFWZdCiejFKGzAUOERExNlocmk5yMmxwsPKldYOrtu3w/XXW0Mmd9xh\nTf7cs8faxfX4cfD1hXnzrCETsJa4zpmjgl0icuUq631Tqg/1eFyB4lZ/GGMFiZUrrYfNZpUY79IF\nXnnFeo2398VBonCo+NOfLn4O1GMhIiJVT4lzPHJycnjyyScJCAggMDCQnTt35j83b948OnXqlP/1\nzJkz8fPzo2PHjixevBiA9PR0Hn74YYKCgujRowfHjx8HYMOGDXTo0IGAgADGjh1b1tf1h5SmQNfh\nwzB3LvTrB7feCmFhsGWLtU38jh3WHI2oKHjwQSt0XAnNzRARkaqqxODx/fff4+rqSmxsLOPGjWPk\nyJEAbNu2jY8++ij/vKSkJKKiooiLi2PZsmWMGDGCrKwspk6dSps2bVizZg39+vVj3LhxAAwaNIj5\n8+cTGxvLxo0biY+PL6dLLL3iKoDabGC3W2FiyBBo2hTatrXmaPj7w+rV1vDIRx9Bnz5w881Fv//l\nNlXThmsiIlIdlBg8evbsyfTp0wFITEykbt26nDhxgpEjRzJ58uT8scRNmzbh7++Pm5sbXl5e+Pr6\nsn37dtatW0d4eDgA4eHhREdHY7fbycrKwsfHB4CwsDCio6PL6xovq/BKk9xcOHPGqpOxdy9MnQoz\nZ8INN1jzNfbvtyZ+fv659Rg40JqjUXCL+OKUNniIiIhUVaWa41GjRg369+/PokWL+Pzzz3nqqaeY\nNGkStWvXzj8nLS2NOnXq5H/t6elJamoqaWlpeHl5FXss73hCQkJZXdMVyZu3sXQpfPopvPeeVXp8\n6lQrhNSuDadPw7/+ZVUJffllhQQREZGrVerJpXPmzCE5OZnGjRtz8803M3jwYDIyMti1axfPPfcc\noaGh2O32/PPtdjve3t54eXnlHy/qGFihxbuYiRCRBWZghoSEEFKG/+rHxVm9FrNmWdVAz5+HJ56A\nAwesTdWutAKoiEhFs9ls2AqPD4s4sRKDx8cff8zhw4cZMWIEHh4eNGzYkF27duHu7s7Bgwf5+9//\nzqRJk0hKSmLkyJFkZmaSkZHB7t27admyJf7+/ixZsgQ/Pz+WLl1KUFAQnp6e1KpVi4SEBHx8fFi+\nfPlFAaOg4o7/ETab9fjlF9i9GyIirHkZR45Y8zSKCxgKHSLibAr/h2zMmDGOa4xIKZQYPHr37k3/\n/v0JDg4mOzubd999F3d3dwCMMbj8b3LDTTfdxNChQwkMDCQ3N5fx48fj7u7O4MGDiYiIIDAwEHd3\nd+bNmwfAtGnT6NOnDzk5OYSFheHn51eOl3mxgkW5mjUreRmrVpmIiIiUjWpfQKxgPQ3t0ioilZ0K\niImzq/Z7tWgZq4iISMWp9j0eIiJVie6b4uyqfY+HiIiIVBwFDxEREakwCh4iIiJSYRQ8REREpMIo\neIiIiEiFUfAQERGRCqPgISIiIhVGwUNEREQqjIKHiIiIVBgFDxEREakwCh4iIiJSYRQ8REREpMIo\neIiIiEiFUfAQERGRCqPgISIiIhVGwUNEREQqjIKHiIiIVBgFDxEREakwCh4iIiJSYRQ8REREpMIo\neIiIiEiFKTF45OTk8OSTTxIQEEBgYCA7d+4kPj6eoKAgQkNDCQ8PJyUlBYCZM2fi5+dHx44dWbx4\nMQDp6ek8/PDDBAUF0aNHD44fPw7Ahg0b6NChAwEBAYwdO7YcL1FEREScRYnB4/vvv8fV1ZXY2FjG\njRvHyy+/zLBhw3j//feJiYmhV69eTJgwgeTkZKKiooiLi2PZsmWMGDGCrKwspk6dSps2bVizZg39\n+vVj3LhxAAwaNIj58+cTGxvLxo0biY+PL/eLrUg2m83RTfhD1H7HUvsdq7K3X8SZlRg8evbsyfTp\n0wFITEykXr16LFiwgNatWwOQnZ2Nh4cHmzZtwt/fHzc3N7y8vPD19WX79u2sW7eO8PBwAMLDw4mO\njsZut5OVlYWPjw8AYWFhREdHl9c1OkRlv3Gp/Y6l9jtWZW+/iDOrWZqTatSoQf/+/Vm4cCFffvkl\nN954IwBxcXF88MEHrF27lh9++IE6derkv8bT05PU1FTS0tLw8vIq9lje8YSEhLK8LhEREXFCpZ5c\nOmfOHPbu3cvTTz/NuXPnWLBgAYMHD2bJkiXUr18fLy8v7HZ7/vl2ux1vb++Ljhd1DCAtLQ1vb+8y\nvCwRERFxSqYE//3vf8348eONMcakpqYaHx8f89///tcEBgaakydP5p+XlJRkWrVqZTIyMszp06dN\ns2bNTEZGhpk4caKJjIw0xhgzf/58M2TIEGOMMW3btjUHDhwwubm5pnv37mbTpk2XfO8mTZoYQA89\n9NBDj1I+mjRpUtJtXcShXIwxhstIT0+nf//+JCUlkZ2dzUsvvcQTTzxBo0aN8odWQkJCGD16NLNm\nzWLGjBnk5uYycuRIHnroIdLT04mIiODo0aO4u7szb948brjhBjZu3MiwYcPIyckhLCyM11577XLN\nEBERkSqgxOAhIiIiUlZUQExEREQqjFMGj9dff51OnTrh5+fH3LlzHd2cK5Kbm5tfcC0oKIg9e/Y4\nukmlsnHjRkJDQwHYv39/fvuHDBlCZegUK9j+4grcObOC7c8zb948OnXq5KAWXZmC7U9JSaFnz54E\nBwcTFBREYmKiYxtXCgXb/8svv+QXTHzqqaec+vc/Ozubvn37EhQUxD333MN3331XKf9+pXpxuuBh\ns9lYv349cXFx2Gy2SrfMdvny5Zw9e5bY2FhGjRrFyJEjHd2kEr355ps8/fTTZGZmAvDcc88xfvx4\n1qxZgzGGb775xsEtvLzC7S+qwJ0zK9x+gG3btvHRRx85sFWlV7j9//73v+nbty+rV69m7Nix7Nix\nw8EtvLzC7Y+MjOSVV15h7dq1ZGZm5ldhdkaffvop119/PWvWrOGHH37gmWee4fnnn69Uf79S/Thd\n8Fi+fDmtWrXiwQcf5P777+eBBx5wdJOuiIeHB6mpqRhjSE1NpVatWo5uUol8fX35+uuv8/9ntHXr\nVoKCggC47777nL64W+H2f/bZZ5cUuHNmhdt/4sQJRo4cyeTJkyvF/1YLtz8uLo5Dhw7RtWtXPv30\nUzp37uzgFl5e4fZ7eHhw4sQJjDHY7Xan/ht+5JFH8recyM3Nxc3NrdL9/Ur143TB49ixY2zZsoUv\nv/ySadOm0adPH0c36Yr4+/uTkZFBs2bNGDhwIP/85z8d3aQS9erVi5o1L9SSK/iP3XXXXUdqaqoj\nmlVqhdt/0003ARcK3P3rX/9yVNNKpWD7c3Nzeeqpp5g0aRLXXXedg1tWOoV//nkVjlesWMHtt9/u\n9D1Ohdv/z3/+k2effZYWLVqQkpJCcHCwA1t3eddeey3XXXcddrudRx55hHHjxpGbm5v/fGX4+5Xq\nx+mCR4MGDejWrRs1a9bkzjvvpHbt2vkby1UGb775Jv7+/uzZs4f4+HgiIiLIyspydLOuiKvrhV+L\nvKJvlU3hAneVxZYtW9i/fz+DBw/m0UcfZdeuXTz33HOObtYVqV+/fn5P5f3338/mzZsd3KIr8/jj\nj2fprKkAAAZuSURBVLN27Vp2795N3759ef755x3dpMs6dOgQnTt3pl+/fjz66KNV4u9XqjanCx4B\nAQH88MMPABw5coSzZ89Wqn84zp49m18Ovm7dumRnZ5OTk+PgVl2Zdu3asXr1agCWLl2a321bWXzy\nySd88MEH2Gw2Gjdu7OjmXBE/Pz927NhBTEwMn332GS1atGDSpEmObtYVCQgIyJ8XsXr1alq2bOng\nFl2Zc+fO4enpCUDDhg05ffq0g1tUvOTkZLp168abb75J//79gcr/9ytVX6n2aqlIPXr0YM2aNbRv\n357c3FymTJmCi4uLo5tVasOHD+eJJ54gMDCQ7OxsXn/9daefY5An7+c8ceJEnn76abKysmjRogW9\ne/d2cMtKx8XFhdzcXJ599lkaNWpEr169AAgODiYyMtKxjSuFwr/nxphK9btf8PdnwIABTJ06FW9v\nb+bNm+fglpVOXvtnzZpF7969qV27Nu7u7sycOdPBLSve+PHjSU1NZezYsflzPd59912GDh1a6f5+\npfpQATERERGpME431CIiIiJVl4KHiIiIVBgFDxEREakwCh4iIiJSYRQ8RKRKWrhw4WULEObm5nLf\nffcxffp0wFpFdMsttxAaGkpoaCgvv/zyReePHz+eRx99NP/r4cOH06lTJ9q3b8+sWbMA+Ne//pX/\n+mbNmtGxY0cAvvvuO9q3b0+nTp3yz50zZ07+uR06dMDDw4O0tLQr2uvm4MGDhIaGEhQURK9evZx6\n6a9IPiMiUsUMHTrUNGvWzDz66KPFnjNixAjToUMHM336dGOMMfv27TP3339/kecuWbLE+Pv757/f\nqlWrTK9evYwxxmRmZhpfX19z+vTp/POzs7PNPffcY3bs2GGysrLyn8/KyjJ+fn4mOTn5ovd/5pln\nzMyZM40xxkRERJgvvvjCGGNMTEyM+e6774q9ht69e5v58+cbY4yZNWuWefbZZy/7cxFxBurxEJFK\nLzIyMr/nAqytC6ZOnVrsXjdffvklNWrUIDw8PP+cLVu28Pvvv9O5c2d69OjB3r17AWu35hkzZjBm\nzJj8czt16sSHH36Y/345OTm4ubnlf/3ee+8RFhbGn//8Z3bv3o2vry916tTBzc2NgIAA1qxZk3/u\n5s2b2blzJwMGDACK3+smKiqKTp064e/vT1RUFAC7du3ivvvu+//t3b9LI10YxfGvaBQUGRWbKPgD\nRQMqWAoqEgIGBBvB2AxYCFpoaaLGQrRIISS2MY2KhZBKLBQlCAb8AwSxGAW1CVglGIKiIFssDptd\nl31h3x1c9nzKJPcm3OowmXmO/ZveB4eJfGYKHiLy10omk3i9XnZ2dojFYni9XlKpFIFA4KdrLi8v\n2dvbY21trSiYNDQ0EA6HOT09JRwOY5omhUKB2dlZNjc3KS0ttT9bUVFBTU0Nr6+vTE5OMjMzQ2Vl\nJQAvLy8kEgnm5+cBeHx8xDAMe211dXVRf0okEikacPdR183V1RXJZJLz83PS6TT7+/tYlkVvb6/d\nPntwcEChUPi9AxVxwKebXCoi8l8FAgECgQCrq6u43W6mp6d/uWZ3d9e+snF3d0d5eTmtra0MDg7a\nZXH9/f1kMhmOj495eHhgYmKCXC5HJpNhfX2dUChENptlfHwcr9fLwsKCvX8qlWJoaMgeu24YBvl8\n3n4/n89TW1sLQC6Xw7KsoiK677tulpeX6e7u5v7+3r76kcvluLm5IRqNMjc3x9bWFiMjI9TX1//m\niYr8eQoeIvJP+bYt9z2wDA8Ps7S0RF1dHcFgkIuLC5qamhgbG7NH75+dnRGPxwmFQjw9PeHz+QgG\ng0U3nMLX4PH+9weAx+Ph+vqabDZLVVUV6XSaYDAIQDqdxufzFa1/77oxTdPuuuns7KSrq4ujoyMA\nYrEYPT09nJycEIlE6OjoIBqN4vf7/8iZifyfFDxE5K+3srLyw2slJSVFXTcbGxu0t7czOjr64R6L\ni4uYpsnh4SFlZWVsb29/uCdAPB7n9vaWRCJBIpEAvj6l0tzcjGVZdmEbgMvlIhaL4ff7eXt7Y2pq\nCrfbDYBlWbS1tRV9x0ddN4Zh4PP5GBgY4Pn5mb6+PhobG/F4PJimicvloqWlxX5iRuQzU1eLiIiI\nOEY3l4qIiIhjFDxERETEMQoeIiIi4hgFDxEREXGMgoeIiIg4RsFDREREHKPgISIiIo5R8BARERHH\nfAF06XGfj0wwrAAAAABJRU5ErkJggg==\n", |
| "text": [ |
| "<matplotlib.figure.Figure at 0x7f126c1f6410>" |
| ] |
| }, |
| { |
| "metadata": {}, |
| "output_type": "pyout", |
| "prompt_number": 6, |
| "text": [ |
| "[[<matplotlib.lines.Line2D at 0x7f126c35ac90>],\n", |
| " [<matplotlib.lines.Line2D at 0x7f126c35af10>]]" |
| ] |
| } |
| ], |
| "prompt_number": 6 |
| }, |
| { |
| "cell_type": "code", |
| "collapsed": false, |
| "input": [ |
| "ans.multiplot(lambda (p, q): (q[IP].src, q[IP].id))" |
| ], |
| "language": "python", |
| "metadata": {}, |
| "outputs": [ |
| { |
| "metadata": {}, |
| "output_type": "display_data", |
| "png": 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uHai2fbu1M6WoHSeaHhFncvbs2Uv+zpPSu9I/jq8YPBYsWIDNZiMiIoK4uDiG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\nbCZvdagTstlsFOxeUSXNRUTkV4U/N0W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|
| "text": [ |
| "<matplotlib.figure.Figure at 0x7f126c0fd290>" |
| ] |
| }, |
| { |
| "metadata": {}, |
| "output_type": "pyout", |
| "prompt_number": 7, |
| "text": [ |
| "[[<matplotlib.lines.Line2D at 0x7f126c3d8f10>],\n", |
| " [<matplotlib.lines.Line2D at 0x7f126c3d8f90>]]" |
| ] |
| } |
| ], |
| "prompt_number": 7 |
| } |
| ], |
| "metadata": {} |
| } |
| ] |
| } |