compute_edge_xing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from deepgd._dependencies import *\n",
    "from deepgd.tools import *\n",
    "from deepgd.utils import *\n",
    "from deepgd.modules import *\n",
    "from deepgd.data import *\n",
    "from deepgd.xing import *\n",
    "from deepgd.ipyjobs import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load from 'cache/G_list.pickle'\n"
     ]
    }
   ],
   "source": [
    "G_list = load_G_list(data_path='data/rome', index_file='data_index.txt', cache='G_list', cache_prefix='cache/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load from 'cache/generate_data_list(list,sparse=False,pivot_mode=None,init_mode=pmds,edge_index=full_edge_index,edge_attr=full_edge_attr,pmds_list=ndarray,gviz_list=ndarray,noisy_layout=True,device=cpu).pickle'\n"
     ]
    }
   ],
   "source": [
    "data_list = generate_data_list(G_list, \n",
    "                               sparse=False, \n",
    "                               pivot_mode=None,\n",
    "                               init_mode='pmds',\n",
    "                               edge_index='full_edge_index',\n",
    "                               edge_attr='full_edge_attr',\n",
    "                               pmds_list=np.load('layouts/rome/pmds.npy', allow_pickle=True),\n",
    "                               gviz_list=np.load('layouts/rome/gviz.npy', allow_pickle=True),\n",
    "                               noisy_layout=True,\n",
    "                               device='cpu', \n",
    "                               cache=True,\n",
    "                               cache_prefix='cache/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(data_list, open('cache/generate_data_list(list,sparse=False,pivot_mode=None,init_mode=pmds,edge_index=full_edge_index,edge_attr=full_edge_attr,pmds_list=ndarray,gviz_list=ndarray,noisy_layout=True,device=cpu).pickle', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data(edge_attr=[240, 2], edge_index=[2, 240], edge_pair_index=[4, 210], full_edge_attr=[240, 2], full_edge_index=[2, 240], gt_pos=[16, 2], m=21, n=16, pos=[16, 2], raw_edge_index=[2, 42], x=[16])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "05642bbb3f94457da9e9a6608d60b81c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/11531 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for d in tqdm(data_list):\n",
    "    del d.flip_edges_r\n",
    "    del d.flip_nodes_r\n",
    "    del d.movlsq_r\n",
    "    del d.perturb_r\n",
    "    del d.phantom_r\n",
    "    del d.random_normal_r\n",
    "    del d.random_uniform_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fDB482NJLMQmnTp1Cjx49cOvWrSwRvsGDB8PHxwfjx4836v6rV6+iadOmOHDgAKpWrWri1ZkP+U8r8M2jvIizd4RUYoegnn7Z203L6A1bfEyuZ+bMmZg5cyYSckmwQlZaezExMdiyZYvRruHXr1+jS5cuWLhwYY4WPQA4WsYPcfZUn9Qay+DlZlj4mFxPgwYNULFiRfz111+WXkqmOX36NEJCQjBo0KAsGX/2v7vg2fV/uPbS8K8GIQQGDRqE5s2bZ0leoblxx0sgkX4sZdtoVMYoWPgYq2DGjBmYNWsWlEqlpZdiNPLQKHy57CC6jZ6RJdae/EQY1t5zxEuXqkZ1uJg/fz4ePnyIn376yeRrswT3TuzCpw530L+eB7s5cxksfIxVULt2bdSsWRNLly619FKMQh4ahW/WnMNLl6rY/dI1S4ItFDuPQmNLifCGuvYOHz6MBQsWYOPGjQa3L8qOCCGwefNmjO7aFDM6VGbRy2Ww8DFWw4wZMzB37lzExMRYeikGo7j1DHFqOo5LFFmy31Tv2nFIVXEADHPtPXr0CL1798aKFStQunRpk6/LEly+fBlCCFSvXt3SS2GyABY+xmqoVq0a/P398csvv1h6KQbj71UUUk0iAEBqI9IVJXloFAK3XzPMKkxMRIv96xG0fR76Vyuqt2tPpVKhe/fuGD58OFq0aKH/fNmcTZs2oUuXLjkvFYPRC05nYKyK0NBQNG7cGOHh4cifP7+ll2MQ8q8DoQh7Cv9W9SAbOzD1a0KjMHLdRShVakgltgjqWUM/N92lS4CfH57nyweXN2/0XtOYMWMQFhaGnTt3wtY29/yOrlixIv755x/Uq1fP0kthsoDc818qw+iBr68vWrZsicWLF1t6KQYjy5uAGfuXQhb/KM1rqBM9+USVKo3+LtGTJwEAz7289F7Phg0bsG3bNqxcuTJXiV5oaCjevXuHOnXqWHopTBaRe/5rZRg9mTp1KhYvXoyXL19aeimGUagQPaezbn+vopBqQ/AT4+FfpqB+Y78XPk3dunpdfv36dYwYMQKbNm1C4cKF9Zsjh7B582Z07tw5V4k5kxL+l2WsjvLly6Njx45YsGCBpZdiGNpWPq9epXmJzNcNQQlX0P/cTgRtmwuZYrt+Y78XvgKtWmV46bt379ClSxf88MMPqFGjhn7j5yA2b96MLl26WHoZTBbCe3xMtkEeGmW2Cv737t1DjRo1cOPGDRQtmkMSkzdsAHr0oJY5mzalfd1vv1FncwBwdQVu3wby5k37+mfPAFdXxAJwVCphl05zWCEEevXqBWdn51xREOBDwsPD0bBhQzx8+DBXdvRgCLb4mGyBNihjxal7GLnuQpYXBfbw8ECPHj0wb968LJ3HpOjh6gRAPeUAErunT4FFi9K//tQpAECos3O6ogcAP//8M27evJkjI2P1YfPmzejUqROLXi6HhY/JFnwUlHHjSZbPOWXKFPz999948iTr5zIJhgqfNhR/3jzg+fO0r3/v5nxQsmS6wx4/fhyzZs3Cpk2bIJVK9VlxjoPdnNYBCx+TLfD3Kgo7DbUOkqri4L/pTyA+PkvnLFGiBPr37485c+Zk6TwmQ1/hK1YMyJ8fePsWaNqUnmfPTvv698IXk05R6aioKPTo0QN///03ypYta+jKcwT379/HnTt38Omnn1p6KUwWw8LHZAtkvm4QJ5ajg4sKQQeXQLb2V9rLymLxmzhxIlauXInIyMgsncck6BHcAoAsPa3V16sXPf/6K3D//sfXJiYCZ84AABwbN051uMTERPTs2RODBg1CmzZtDF52TmHLli1o3749JBKJpZfCZDEsfEy2IDIyEm9vHMdP37aH7O/5QOHCwO7dWS5+bm5uGDZsGGbNmpVlc5gMrfC9fg1oNOlfqxU+jYbELyEBmDr14+uuXgViYxHp4ADPNPLWvvvuO0gkEkybNs3opecENm3ahK5du1p6GYwZYOFjsgWHDh1C48aNKXeqenXg4EGd+HXuDMTFZdnc48ePx6ZNmxAREZFlc5gEOztyYQpB4pceFSvS840bwMyZgL09sGIFEBKS8rr3bs7jajW8vb0/Gmbbtm1Yu3Yt1qxZk6sDPh4/foyQkBA0a9bM0kthzAALH5MtOHToEJo0aaI7Ua0aiV+RIsB//5Hll0XiV6RIEXz11VeYMWNGloxvUgwNcLl+HShXDhg2jKy/KVNSXvde+K7kzftRCbdbt25h6NCh2LBhA1xcXEyx+mzL1q1b0aZNm1zRWYLJGBY+Jltw+PDhlMIHkPgdOKATvyy0/MaMGYNdu3bh5s2bWTK+ydB3n08rfDdu0HNAAJAnD7B9O3DihO6696kMLz4oVRYbG4suXbpg+vTpqKtnNZecDEdzWhcsfIzFuXv3LpRKJXy0X9bJ0Vp+Li7Anj1Ap05ZIn4FCxbE6NGjMX36dJONKQ99YniXhIzQ1+IrV47cm/fuAbGxgLs7MGYMvTdxIrlLnz0DwsOhcnCApGbNpFuFEPjyyy9RrVo1DB8+3HRrz6Y8e/YM586dQ8uWLS29FMZM2Ft6AQyj3d9LswVM1aokfk2bAnv3kvht3QpkkGxtKCNHjkT58uVx7do1VK5cOVNjyY9cw8jgu1BqbLDx+C0ERZ+CzDMf7b35+ADlywPGuNX0FT6JhOa4cQO4eZP2TceNA5YsARQK+hGhprzJCBcXeGn3BAEsXboUFy9exKlTpyzalsdclXy2b9+Oli1bIk+ePFk2B5O9YOFjLM5H+3upUaVKSvHr2BHYts2k4pcvXz6MHz8e06ZNw6b0SoLpgWLLASjzlAcAKO0coLj3GrK/klWJsbMDypYlEdSKofZY685MDX2FD6DxbtygR/XqQIECwOTJwNixwKRJwGefAQDOSyRJ1vaZM2cQGBiI48ePw9nZ2YhPbjgajQaRkZG4efMmbt68ibCwMJx/osJDDxlg74iN5x/o3R/QGDZv3oyBAwdmydhM9oSFj7EoQggcOnQI3333XcYXJxe/ffuyRPy++uorLFy4EBcvXoSfn59xg9y6Bf+967Gx3VgoJU6Q2gH+7RoC1fORCF2/DkREALdu0WPnzpT3u7npxDC5KJYqpRO+jPb4ALp32zbdPh99QCphduUKoKKCAcFv32JqhQp4/vw5unXrhqVLl8LLgPZE+hIdHZ0kbMmfw8PDUbhwYXh7e8Pb2xsVKlTAq/LeePiY7lOq1FDcepYlwvfq1SscP34cGzZsMPnYTPaFhY+xKLdv34ZGo9H/i7ZKFeDQIZ34dehAX+4mKqGVJ08eTJo0CYGBgdj5oSDpy8yZkN08iaDYi1C07JnMVddXd01cHImeVgi1ltmNG0BUFD0OH045rrMzpTMAwK5dOosxLbdp8shOLU5OwPTpwOefJwniwdhY/FGiBNq2bYsePXqgU6dOxn1uAHFxcQgPD08hbNpjlUqVJGze3t7o1q0bvL294eXlhbwfFNGWh0bhbFJDXbt0O85nhp07d6JJkybIly9flozPZE+4OwNjUf78808cOXIEq1atMuzGa9dI/J49A2QyilY0kfjFxcXBu0lXtB70Lbo2rGyYpXHjBlCpEmBrS8Lm6WnY5BoNEBmpE8Pkovj0aer3pOU2jY0FmjenPdLLl3XXq9WAlxcQEQFVgQKo5eGBDh064OjRo9i/fz/s7dP/Pax1TX4obDdv3sTjx49RpkyZFAKnPXZ1dTVoz9Ace3wdO3ZEly5d0K9fvywZn8mesPAxFqV3795o1qwZBg8ebPjNISFAkyaZF7+YGBKYkBAgJATyyBgML9EMiRJHSDUqBPWrC1nlYvqN1bs3sHYt8MUXwO+/G76W9HjxAvj5Z7LYKlQg8dK6TdOr5GJrCwwZQoKsFcVZs4A//sA+n/qYUEuGF7fO4Oy2v+Hu7p5suhcfCVty1+SHwubt7Q1PT88MhTO78PbtW5QoUQL3799HwfT2VZlcBwsfYzGEEChevDiOHz9ufOHjkBCy/J4+zVj8lEqynN4LXNIjIoLC+98T2PwLrKjVLul1/xuHMGPBV2RVZbSWKlUoovLWLaB0aeM+U3rs2UNBKS1akKsXSN9tqlSmPo6tLeRla2Fkh/9BKXGCPdRoJo1EQsS5JJFTqVRJgpZc5FJzTeZE1q9fj+XLl2PPnj2WXgpjZnLGTzMmVxIWFgYHBweUKVPG+EEqVaI9vyZNALkcaN+eGrbev/+xwN25k7plJJEA3t40VqVK8C9RFRsjbKBUC+oUcU0BVFhMqQCDB+va/XzI9OkkoEOHZo3oASkT2F+/JsGPiqLn589p/kKFSKTz5gXOnk0971GjgcLTD0oJBQYlwg47z95CvpsnULx4cXTo0AGenp5wc3ODm5sbXF1dk47TivY0ZyNhU8BJ69YLW3yMxViyZAlOnz6N5cuXGzdAQgLlqIWEUCDIP/+kX9Dazi6FwCU9vLxI/JKR9CX+5DpkI3ol5byhXTvg778poT45V65Qsr2jI3U8L1EizWWkKRCJiSReWiHTPic/vnePPq+NTQorNUNsbChatEAB4MkT4PVryMvXSbL4nNQJGN+4OMpKYxEVFYWoqCg8ffo0xbP2YWtr+5EgOtu7Yne+WkiwlUAqscvS9ANTEBsbi+LFiyM8PDzXl2NjPoYtPsZiHDp0SL82NyoVufI+tOBu3SKxSI08eYBmzUiMtALn7a130rjM1+39F3dloGYZGuvNG0o98PUFVq4Eklf60HYu+PLLDEVv5NoLUCZqsPHEbQTd2glZ2EkSthcv9FobABK9vHkBV1d6uLmlfNYey+XUi69vXxLroKAkEZfZvkJQt6pQ/LAU/tcUkBXpDEyYkMG0Au/evUsSxFc3bqDM339jbR47JNSkHw9ZmX5gKvbt24eaNWuy6FkpLHyMRRBC4PDhw/jxxx91JxMTyVr6UODCwpJyzlJgY0OlubTCVrky7e8NH05CEhNDidqZrchRqxZFRTZvTut79gxo1Qr4+mtg7lzaS9u6leaeODHdoRT/HYcykcRXaWsPxVt7yEJDdZ/HxSV1IdM+FywINGpE1uubN2m7XbUkJJDwrVtHf0NbW8DPD7h4EejcGbK6XpANbwG0XAAEXgLatKG/YxrY2NggX758yCeRoPzmzRQk8/Yt/CtosLFGayht7LI0/cBUsJvTyhEMYwGW7TwhPNp/K4KnBQnRq5cQVasK4egoBNkyHz/KlBGibVshJkwQYsUKIc6fFyImJvXBQ0OFcHOj+5o2Tfs6Q4mOFsLfP+W6fHyEaNSIjseOTf/+o0dFsE8D4TN2k/CYuEv4TNopgtfsFeLKFSGePBEiMVG/dUilNN/bt+lfd+GCEDVr6tZavz6d+/RTer19u+7aL76gc35+QiQkpD2mRiPEpk3076Edt107IcLCRHDIExGw7aoIDnmi3+ewEHFxcaJgwYLi0aNHll4KYyFY+BizExzyRJSftIO+/MduEsHl6+i+REuXFqJ1ayHGjRPin3+EOHMm4y/41Lh+XQh3dxqzSRMh3r0zzeLj4oTo2fNjYZZIhEjvizQsTIjChYUARPCo6ZkTiOLFac7791N//8ULIYYPF8LWlq6zsaHnp0+FUKmEyJOHXkdF6e5580YnZlOnpj5uctEEhKhUSYjgYOM+gwXZvXu3aNiwoaWXwVgQFj7G7ARsuyo8Ju5KegTM2SDEqVP05WtKskr81GqyPD8Uv4YNhbhz5+Prnz0Tonx5uqZtW/0tu7SoVInGunw55fnERCGWLhWiSBF6385OiDFjyIoDhFAoyFIGhChX7uNxDx+m9+zthTh3Tnf+yRMhhgzRCWiRIkL89huJaA7k888/Fz/99JOll8FYEG5LxJgdf6+isBMUlCKV2MG/fSOgbl3A1GWjfHwo1cHdnZ7btqV9v8xiawv88AMVe9bi5AQcO0bBNMuX6yIu4+Kopmh4OO2trV1L+3OZIbVC1adPA/XqUeL8ixeU3nH5MrBgAe1/ArQX+b7xLD755ONxP/0UGD2a9loHDKB0iblzKep12TJa97ffUlDR8OHU9iiHoVKpsH37dnTu3NnSS2EsCAsfY3Zkvm7wiT6JOgWVWR/27uNDqQ7FitGzqcQPoBQGgAQgLo6CT96+BQYNArp2pSCYQYOA48eBkiWpvqYpEr+TC9/Tp5RbWK8ecO4czbN+PTXw1QqetuXQ9evpCx9AgTDe3hRU5OlJwTpv39LfLSQEWLhQN38O5MiRIyhbtixKZ1WeJZMjYOFjLIJ91HX0rmBvnpD3ChXI4tOKX5s2mRc/hYJSBfLnp2oqrq4kQqVKUTHpLVuAMmUomjJfPmD3bqB4cZN8nKQk9k2bSKT+/pvyECdNInHr3j1ltGfybuwZCV9YmE6cX72iz7BvH6VxeHubZv0WhKM5GYCFj7EQb968QX5tpwEDkIdGGdfVvEIFneV35AiV/Xr3zuD5k5g6lZ6//ZbSHE6eJGGIjCQxLFVKJ66NG5O70FTExtLz6tXkjmzViop2z56dukWpFT5t9RpnZyqtlpyoKKo4U6MGcOGCruybnR3QsKHp1m5B1Go1tm7dysLHsPAxluH169cGC588NAoj113EilP3MGLFaWw4cSPjm5Lj7U3iV7w4cPQoWX7GiN+hQ/QoWJD2xAAqEXbiBNCgAfD4MQkgQPuBO3cCNWuSoGSGhw+BPn2AzZvpdcGCVJv0v//St8bKlycBu3+fXteurdufi48H5s1LuY83ejQJZJUqtDc5aVLm1p0dUKnw55L1yN9kCCISuAWRtcPCx1iEN2/eoECBAgbdo7j1DEoVVR1JsLHDnFm/omrVqhg7diz27t2LWK0llB4fip+hlp8QQGAgHY8dm7JbepEiVBlFKyo2NmQZVqxILsi6dYE5c3Tlz/QlIYHEqUIFYM0a3fg9e1Jt0oyS2B0cKNFfG3DzySd0vHUrVaGZMEG3j3ftGvDTTxQQ9O+/NFdQEAl9TiQuDvjtN8gbd8aiu05I8PwEI9ddNNxjwOQqWPgYi2CMq9PfqyikEvpPVqqKw9z7V7Fm1CgUKlQIc+bMgZubG5o0aYLZs2fj7NmzUKclMF5eJH4lStBenSHit38/RW8WLgyMHJnyvSdPgE6dKCrS25vEZepUoFs3qvKSmAhMnkyuz4gI/eYLDqZ+ehMmkOu0UyeKKAUME2ytuxMgUWvaFOjcmSy7SpV0+3gVKuiu8/PTifygQVQpJqfw7h0wfz7tUY4YAUWeEoiTvK+Y876kGmPFWDqfgrFOHBwchFKpNPi+4JAnImDrFRH8+TjKKStYUIiLF4UQQrx9+1bs2rVLjBo1Svj6+orChQuLrl27it9//13cvn3748Fu3hSiRAldDl5GeYQajRD16tH1c+akfC8mRohatei9unWFiI0VIihIl/s2ZIgQu3YJUawYvc6XT4jly2nM1Lh7V4jOnXU5gt7eQuzdS+9t307n2rTR/w83bpwILl9HBDT/QgR71dXl4/36a/r5eAkJuuovQ4fqP5+liI4WYvr0pGIB2mo0wcu2CJ+A/6hoQsCebF9dhslauDsDY3bi4+ORL18+xMfHG9SROwUqFdCjB7nrXFwoYMXXN8Uljx49wv79+yGXy7F//37kyZMHMpkMMpkMTZs2RaFChWgPq3Fj2j9r2JD2y9LKJ9T2wnNxIYtNG0iiVlP6wrZtZGGcOkVRngCd69WLXG6tWlFro3HjdPt0XboAS5eSm1SjAaKjyVJZtIj236RSoF8/oHVrcnnGxlJ+3qJFFEDTowedi4n5+DnZsbyIF0a2GQOlxAlSVRyCwndDNrQL5e5llGIREkJ7lPHx9Pdp3dq4f7Os5OlTctH++iu5bQGgfn1gyhRar41NjmubxGQdLHyM2Xn69CkqVaqEZ88y6W6KjyfX35495L47ejTN6EkhBK5du5YkhMeOHYOPjw8Joa8vPpkwAY4PH0LebgAU/UfB37d4yi9HIYA6dShXLjAQ6N9fJyzz55OQOTuTS7JgwZQCFBFBghEfT6Lq60tRlPfvk9jZ2NBeWmqFuE3ER811z+3EjP1LKfimYkUKeNE+qlb9uIvFjz8C//sf7Y1eu5Z9cvkePKC1/fmnrulu8+YkeJ9+mvH+J2OVsPAxZic8PBwtW7bE7du3Mz+YUknRmYcOkQWkUAAeHhneFh8fj5MnT0Iul0Mul+NGaCiqefjhcevRSJQ4QZqYgKCzKyELP0MC9vZt2i2QTI2dHVmVhQuTmObJQ8/aYyGoLVLevMB336V+zQfP8odKjFx3EUp7R0ihRtDbc5Cd2AVcvfrx55JIqAJNcjH09qZqMCdOUIujlSvN87dIi9u3qarM8uW6Hwzt29Meat26Fl0ak/1h4WPMzvnz5zF06FBcyGx4v5Z376g33okTlFZw9Gi6PfFSIzo6Gt8E7cLx+CJJ55KsouQ4OJC1kycPWWv37tH56tVJHFITHu2xWk3WyY0bgLMz5DN+geJBDPx3r4bs5vvEcjc3SlFI78s7JoZEz8lJZ+XogXz0DChCHsG/jhdks96XW1MqyXV69qzuERb2cZNbZ2eyDC9epM/x669UtszcFlVICEXGrl2rs5a7dyfBq1rVvGthciwsfIzZOXToEKZPn47Dhw+bbtDXr6lZ7PnzFMF45Ihun01P5LOWYORLV9oHswOCPi0GmU9RsiYHDKDk99u3ad/t4kXA359EKDAQmD5dv0mUSqBPH8ivPkzqfi5VxSHo8WHIzuylPUd7e2DGDHItplbXUwhyRapUNJ6Tk35zBwfTD4SqVUns0uLNG/o7JhdDrcAnp2DBlFZh7doG/+DQm/Pnqfff1q302t6e9j4nTswVFWUYM2OhoBrGitm6dato37696Qd+/lyIKlUokq9qVWrPoy+JiUJ4elLk4+Jduqg/tVo35s8/07nISF1roL59047MTGeugM7jUnao2HaVIkO//jrjbg9CCOHqStcY0lMuLk6IvHnpvrt3DVvz06dC7N5NLYu03R9SexQrJkT79kLMnElRqIb8G6SGQiFEq1a68R0dhfjqK8PXzzDJYOFjzM6///4r+vbtmzWDP3kiRIUK9CVZq5YQr17pd9/OnbqGt8nbBm3YQOdLlhRCqRTi9WsSVYAa0MbFGb5GhUIEl68jfMZuTj28fu/ejNMetJ8xJMSwubt0SSnixnD3rk5AR48WYuJEIZo1E6JAgdTFsGxZIXr0EGL+fCGOHMm4v6JGQ33+kvf+c3amHo3cPJYxASx8jNn5+eefxYgRI7JuggcP6MsWEKJBA/368Gmtih9/1J1LTBTC15fOL1lC+W7a67y9jbNmNJqkLu7BUxak3ZD2+fOUeXxdutA5LXXf5+IdP27Y/MuX030tWhi+9uQsW0bjFCokxMOHdE6tpoa7q1YJMWoUdXzXdotP/rC1pZ6CAwdSHuGZM/QDQq0WYts2IWrX1l1bsKAQgYEpPzvDZBIWPsbsfP/992LSpElZO0lEBFlpgBBNm1JCeVrcvEnXOTmlFLM1a3Rd4ePihPjyS3rt4iJEeLhx69q7VycYGVmjGg11oc+XT+dG1CaxawV41y7D5n/6lITHwSFzjX81GiE++0yXSJ+Wu1elEuLSJSH+/FOIYcOoKa69/cdiaG+fUiQLFRJi1iyysBnGxLDwMWbnf//7n/jhhx+yfqKbN3Ud2D/7TIj4+NSv+/ZbumbQIN05lYqsOoC+tH/8UbfHZKiVpUWjEaJGDRpn7lz977tzhyxXrSh8840Q3bvT8cqVhq9DO9amTYbfm5yHD0mgACH++kv/+2JjhTh5UoiFC4X45BMhJJLUXaTOzmQdjxkjxNq19GPD0P1UhkkFFj7G7HzxxRfit99+M89k167pgjE6d/64PNe7d+ROA4Q4f153fsUK3f7UunW6L+MNG4xfy6ZNOsstJsawexMThZg9W2ctFSpEgTgzVhtefuuHH2iM/v0Nuy81Vq/W7UXqG3ASG0t7jKVK6f6unp4kcLNmCdG1qxAeHqmLYeHCQrRoIYJ//CttNzHDZACnMzBmp3fv3mjbti169+5tngkvXqTk69evgd69gRUrdGkCf/4JDBtGHcy1TVoTEykl4vZtShCfP59Kjv3wA1VmMQa1GqhcmXL4fvuNcuCM4cIFoFcvyDUFdekQahWCXp2CrDAonaB4cd2zuzvlHiYnNJQKU7u4UGHt1FIm9EUIKte2ZQsVvpbLqRpMarx9C/z+O7BgAVWuAaiKzeTJVHpN23VCy7NnKVMqzp4Fnj6FvHwdfNVpElR2EtioVaj67hzqlJCiTJkyKFOmDDw9PeHu7m58OTwm12Of8SUMY1qM6cWXKfz8gL17AZmM2vrkyUP1MW1sKBEboO4JWlauJNHz9KQv6rg4YMgQyqszlpUrSfTKlAEGDzZuDLWaRPz1a2ys0gRKCeXvKe0kUES8guzPpanf5+pKIqgVxGLFSPSeP6cfAW3a0Ou0BCs9bGyo/qhCARw8SMcjRqS8Jjoa+PlnYPFi4OVLOlejBpUV69gx7XmLFqXaqJ99Rq+FAB48gGLTBaie0leXsJPAvmQVvH1zEdu3b8fdu3cRERGBd+/ewcPDI0kItaKofV24cGEWRiuGLT7G7Pj7+2PWrFlo1KiReSc+coQKFiuVwDffULugRo3oCzYyUpcU7u0N3L1LAvH4MdCiBbBrF5XyMob4eGr3c+8eCU2/foaPsX8/9f+7cgUA0KBaEzxu8Q00dg6Q2gJB7q8ge3OXim0/ekSPhw/JotNoMh7f3p4+r9ZSTG41Jj/Onz/1ai1btlDB7Tx5KDm+fHmy6hYuJAtX20KpYUMSvJYtja76om1IrFSpIZXYIain30dFp9+9e5ckgtrn5A8hRAoh/FAY86VVqJzJFbDwMWanWrVqWLFiBapVq2b+yYODgXbtqNOBjw9ZYZMnU1UQQOf6zJOHanRWrkz99wxsmpuCX34hofX1JeEyxLV4/Towfjyweze9Ll0a54cNQ4clSzD517WYt3In/pgxBi183VO/X62mzgUfCuK5c2QFOzpS+bMXL/Rbj7Nz2sK4bBkgl0PeshcUlRrCf+9ayEKP0X0yGbmNTfRjJ7OdFl6+fJkkgh8K4927d5EnT540hdHDwwNO+lbLYbIlLHyM2fH09MShQ4dQpkwZyyxg505qwpqYSFbHvXtU4Do+nqy9+/fpOnd34PRpoHRp4+eKiaHu51FRZBV16qTffc+eAdOmkUtWraauDpMnA6NGoWu/fmjYsCFGjRoFHx8frFq1CrVr1zZsXSoVWbqvX1OZtBIlyLrVCmNykUx+HBOT7rDy8nXwTYf/IU5biu3JEchG9aNyZjkEIQSePn2apjBGRkaiSJEiaQpjyZIlIZFIuA1SNoaFjzE7hQsXRnh4OAoXLmy5RXTvDmzcSMfz5pFVtWQJ8NVXdE4qpX2rmjUzN88PPwCTJgG1agFnzmTs3ouLo/2w77+nmpm2tsDQoVQL1M0N169fR+PGjXHnzh04OzsjICAAcXFx+PHHHw1fW8+ewPr11Mdu9OiMrxeCAlTSEcZAt0+womKzpFv61/PAjA6VDV9bNkatVuPRo0dpWotPnjxBsVotYNNgMISdJE13LGM5WPgYsyKEgEQigVKphMTYPbPMolJR4MqjR7pzP/1EhaG1wRfbt1Obm8zw6hUFs7x6RS5WmSzta4UgIZ4wgfYXAWpc++OP5G59z4ABA+Dt7Y0pU6YAAK5evYq2bdvi7t27hgdrrF5NLYaaNgUOHDDs3jSQh0Zh+KozSBS2VvuFn5CQgPHrz2J76Kukc7nxB0BOxogwLoYxntjYWDg6OlpO9ADqiv7oEe3xzZ8PAJD/uhaBNbtDXr4OiWBmRQ+gsV+9olSK5s3Tvu7UKaBBAwrpv3uXUg327qUGu8lELyIiArt27cKIZFGTlStXRp48eXDmzBnD19e6Ne03Hj1KLk8TIPN1w7wOPoi7sg8/dKhgdaIHAA4ODmhbszykEtrLlUrs4O9V1MKrYpLDFh9jVh49eoSaNWvi8ePHWTuRUpnSHad9PHpEgvLmDUUyJiZCXr5OyhZB0vuQfd6RrDVjiYqivb2YGOoT+MknH19z9y65Qdeto9eursDMmcDnn3+c0wbgq6++QsGCBTF79uwU56dNm4Y3b95g4cKFhq/z009J+NatI+E1ER06dECnTp0wcOBAk42Z0+A9vuwL5/ExZuXNmzcokJkISY2GohRTE7Tkx9HRGY/1vvO4wtNPlxMncYLiVBhk35clS+3zzykQJk8ew9Y5Zw6JXrt2H4ve69f0/qJFFFDj5ASMGUNuzjTyGx8/fox169bhxo0bH73XrVs3tGrVCvPnz4etobl47duT8O3YYVLh69u3L5YuXWrVwifzdWPBy6awxceYldOnT+Obb75J3TX39m3Ggvb4cZJgpYtEkjLUvmhRssLkcppHS/36kPf6GiMfOkMJO7L4Ti6H7LycRAkgMerZExg0iDqjZ7SXdv8+4OVFKROXL+s6gycmUsh/YCBFbQJAnz7A7NkZRo6OHz8eCQkJWLx4carvV65cGX/88Qfq16+f8d8mOTdvUo5hoUL0gyIVS9MY4uLiULx4cVy9ehUlsqo5LcMYCQsfY1Z+XLMPiu2H8U3ZwpA9vJpS3JILUnoUKUJipn1oxS35sbYSyfnzwB9/UMUWbRI1QIIzcWLSHpp861EoflsD/3uXILt1miq62NsDf/9NKQ1aKlYkAezXj9IdUmPwYLqvVy+aVwhyr44fT+XCANrTW7gQqFMnw4/74sULeHl54fLlyyhVqlSq18yYMQPR0dFYtGiRPn/BlFSoQAJ4+DC5Pk3EkCFDUKFCBYwfP95kYzKMKWDhY8yGPDQKX686i3hhQ5bV9nmQhSez/JycMha0YsXouvR484YE588/qballnLlqBRZo0ZUxSU5KhVZPdo8NWdn4NIlqkASGgr88w+VHdPWmLSzo1JagwZRyS9tPcywMEpUt7Gh5PO4OKq4IpfT+2XLUvpE5856Vy6ZNm0aHjx4gGXLlqV5zY0bN9CsWTNERkYa7u4cN47qZ44dmxTsYwoOHz6MUaNG4fLlyyYbk2FMghkLYjNWTsC2q8Jj4q6kR8CcDdRf7upVIaKjM9dyRqMR4vRpIQYPFiJPnpTV/L/9ljqVa7uWp9WOp0ULev+TT+i5dm0hEhJ07yckCLFjhxAdO6bsKefiQnNcuaJrF9SnjxBDh1LvO4C6ky9YYHDH9jdv3ggXFxdx8+bNDK+tWrWqOHr0qEHjCyGEOHxY11zXhKjValGqVClx+fJlk47LMJmF0xkYs+HvVRRSqAEAUqjh374R1WysXJmsLWNqN75+TbUg/fxo/+2vv6jUWOPGZPU9fEguxUePyBorWRLo0CH1sRo3pmcfH8DDg7oBTJ2qe18ioWCVrVtp3AULaO3Pn1MKRNWqwIYN5GLdsoUsTltbKlcWHk4BLI6OBn2833//Hc2aNYOXl1eG13bv3h0bNmwwaHwA5HYtVIjcnWFhht+fBra2tujTpw9WrVplsjEZxiRYWnkZ6yJ4yXoR0PwLEdx3pPGDaDRCnDghxMCBKbt2u7gIMW6cEDdufHxPhw50zcyZaY974gRd4+MjhEJB1pqNjRAHD6a/lrNnqTu7nV3K3nElSgixbBn10jMCpVIp3N3d9baYwsLChLu7u0g0Zr4+fWjNP/5o+L3pcO3aNVGiRAnj1sQwWQQLH2NerlyhL9jy5Q2/NzpaiMWLhahcOaXANGtGzWLTciPeu0ciJpEI8SSdxqUJCTo36ePHQgQG6gTs+fO07zt6VOdG1XYOt7HRvS5ZUojvvqMO4gbw66+/irZt2xp0T/Xq1cWhQ4cMukcIoWu226iR4fdmgJ+fnzhw4IDJx2UYY2HhY8xLfDztj9nY6NeFXKMh66tfPyGcnHRi4uoqxIQJQty6lfEYkybRPb17Z3ytTEbXrltH3dq1+32dO3+8B3nrFp1PLsLt25OFd++eEDNmUAf35O83aiTE8uXU+T0dEhIShIeHhzh58mTGa07GnDlzxPDhww26RwghxKtX9O9iZyfEixeG358OCxYsEAMHDjTpmAyTGVj4GPPj60sicO5c2tc8fy7ETz8JUbFiSuFo0YKCU+Lj9ZtLqSQXKCDE8eMZXz9rFl2rFY87d4TIn5/O/fEHnYuOpmAWiYTOOzrqAmlevUo5nlotxKFDQvTvnzLoJm9eCsQ5dizVoJ7ly5eLJk2a6PcZkxEeHi5cXV2FSqUy+F7RtCmtbdUqw+9Nh4cPH4qCBQuKGH1+6DCMGWDhY8yPNvLxn39SntdoKMKwd2+dmABCuLsLMXmyELdvGz7XihU0hp+fflGjx4/T9RUr6s6tXi0EIIIr+YuAiX+IYL9mdI2NDe0zVqlCr+fNS3/s16+F+PNPIerXTynm3t5CzJkjxMOHQgghEhMTRYUKFcT+/fsN/7xCiJo1axrnWvzpJ1pP9+5GzZseMplMrFu3zuTjMowxcB4fY36+/x4ICADGjoX88/FQXI2E/+3zkK34SRdVaGND3QmGDaM8OWOLWtetS+2Ali2jxPKMSEigCMfYWOpe7kYlp+RdhmJkmVa6ep539kIWOIKiNbt1o/zC8HD9S5vduAEsX04d2bV1S21tgZYtMb1yI6x7rMTiScPTbjCbDvPmzcPt27exdOlSw268fZvyFvPnp8oy2txEE7By5Ups2LABO3fuNNmYDGMsnM7AmB9ttZT7MRi57iJWXIrCyGgXyNUFKFE9IACIiAD++w/o2NF40Tt3jkSvUCGqoqIPDg6AtuzX0aPAnTtAhw5QvLFLWc9zwCj6HAEBdG1AgGH1PH18qFff/fvArl1Aly6AnR3kt17gX+ENZck6GLXuEuShUQZ8YKJbt27YsmULEvUp7ZaccuUo+f7NG+pFaEI6deoEhUKBZ9pSbQxjQVj4GPPzXvgUKmcoVZTXp5Q4Yf/Q8dQNfcYMyqPLLL/+Ss+ff26YKGnz+ebOJSHYsQP+T25AKkhIpKo4+Mc8BFatIsutTBn9rMnUsLcni3bTJuDRIyj6jYTGnnL9lCo1FLcMF4oyZcqgbNmyOHTokOHradeOnk1smeXNmxdt2rTB+vXrTTouwxgDCx9jfsqUAaRS+F9TQGpP/wnaQY01W//BqXPnTDPH8+fA2rXkMh0+XP/7hADUJMY4f54KVffrB1nwWgT1r4f+9s+o1Nq4QTprb/p007gFXVzg37V5Uh83JMbj9vHdUGvXYwBGJ7NrhW/HDvpbmJB+/fpxMjuTPbD0JiNjpdSqRQEjGw+IgG1XRXDIE7Fjxw5RtGhR8dtvvwlNZsqXCSHE3LkUqPHZZ/rfc/myEJ9+mjLwZNeulNckJgrh769739fX6AT1tAgOeSICtl0Vm0/dFM2aNRMymUy8MDDF4O7du6JIkSIiIXnJNX1ITNRFwV67Zti9GaBSqYSbm5sICwsz6bgMYyhs8TGW4b27U/YsDDM6VIbM1w3t2rXDiRMn8Ntvv2HQoEFQKpXGja1WUxkzAEjWrTxNoqOprJifHxWvdnGhPTiAGtomx86Ouj1oy6s1bEjnTIjM1w0zOlRG57pe2Lt3L6pWrYratWvj6tWreo/h4eEBLy8vHDhwwLDJtcW3AZO7O+3t7dGzZ0+sXr3apOMyjKGw8DGW4b3w4dq1FKfLly+PU6dOISEhAQ0aNEBERIThY//3H+0Vli1LkaFpoVaTiHl7A7/8QmL2zTdUs7JPH7rm8OGP79u2TecGXLECCAkxfI16Ym9vj/nz52PmzJlo2rQpNm7cqPe9PXr0MM7d2b49PWdBBKbW3Sk4mJyxJJY2ORkrZe9ecqf5+6f6tkajEYsWLRKurq5i7969ho3dsiWNPX9+2tccO0a5fVqXZePGVE5Ni0JB5ytVSnnfy5dCFCxI72nnqVKFEuWzmPPnzwsPDw8xceJEvWpfRkZGisKFC4t4fZP9tbx5I4SDgwj2qisC1p4RwSHplHkzEI1GI3x8fMRxfYoJMEwWwcLHWIaHD0k0ChVKN7H86NGjonjx4mLmzJlCrVZnPG5YGI3r5JR66a2HD4Xo21cneKVKCbFhw8driIvTFcB++lR3fsoUOtekCQmElxe9HjVKv8+dSZ4+fSoaN24sWrVqJaKjozO8vkGDBmLXh/uUehDcdZjwGbtJeEzcJXwC9phU/L7//nvjyqoxjIlgVydjGYoVo/y6ly91Cdyp4O/vj7Nnz2LPnj3o2LEjXr16lf64S5bQc+/eQOHCuvPx8dQAtkIFSkNwdKSozOvXKQH9w5ZIjo7AJ5/Q8dGj9BwVBWg7nM+aBeTLB6xeTSkJixdTl/UspmjRoggODkaFChVQp04dhGTgZjU2ulNRwEOXt2hkWkVa9OnTBxs2bEBCQoLJxmQYQ2DhYyyDjU2a+3wfUrx4cRw6dAgeHh6oXbs2rqV1fUwMdUoHUga17NkDVKkCTJgAvHtHSfGhoZQv6Oyc9sTafD7tPt/s2TRHu3Y6UaxdmyrRAMDAgboO7VmIRCLBokWLEBAQgMaNG2PLli1pXtu1a1fs2LED8fHx+k9w5gz8j+6AVBUHAJBK7ODvVTSzy07C09MTvr6+2GOGHwoMkyqWNjkZK2b48Iz34j5g5cqVwsXFRaxdu/bjN5cu1XVQF4K6J7Rtq3NrVqggxL59+q/v6FG6r3JlIe7eFcLBgV5/2B9PrSbXJyBE69aZ6yRvIGfPnhWlSpUSU6ZMSdMV3KhRI7Fjxw79BkxMFKJGDUo1+d8PSakmpmbp0qWia9euJh+XYfSBhY+xHL/9RmJhYMuaS5cuibJly4rRo0fr8tQ0GiGqVqXx/vqLWhFphSpfPhJXQ4M84uJ0rZB696bnXr1SvzYykrozANQz0Iw8efJE+Pv7izZt2ohXH3aHEEL88ssvok+fPvoNFhREn6F06QxbJ2WG6OhokT9/fvHy5cssm4Nh0oKFj7EcWouqVi2Db42OjhafffaZaNSokXj8+LFurPz5qXGs1sobOJCayhqL1pKztaVeden1/9uyha51cPjYKsxiEhISxNdffy28vb1FaGhoivceP34sChQoIGJjY9Mf5NEjXQumbduycLVE586dxbJly7J8Hob5EN7jYyxHpUr0HBICaDQG3VqoUCHs3LkTTZo0QfMaNfDPjF8Q2PwLyF19gIcPgVq1gJMnac/P3fAOB0k0bgx5+ToIbDoU8i8nU/eCtOjUibpJJCRQUWxjE/CNQCKR4Oeff8aECRPQqFEj7NixI+k9d3d3+Pn5Yd++fekPMmYMFahu21aXy5eF9O3bFytXrszyeRjmQ7gtEWNZSpQAHj0Cbt1KW1QSE4HISOqUcPt2yuc7dyB38cY3Hf6HOIkTpKp4BJWOhezr3tTmJ5PINx3E16deIV7iCKm9DYJ61YTM1y3tG2JigJo1qb3SV1/pCmWbkdOnT6Nr164YPHgwAgMDYWtri99//x1Hjx7FmjVrUr9p/35AJgOkUgr88fTM8nXGx8ejePHiuHjxIkqXLp3l8zGMFntLL4CxcqpUIeE7e5YiLj8QNdy+TVVY0mmxc/ST2ohLahnkCIWHN2QmED0AUEhcES8hy02ZKKC49Sx94XN2puLYdetS2bSWLc1iPSWnbt26OHv2LLp06YKLFy9i5cqV6Ny5MyZOnIjY2Fjk+bBTRXy8Lgo2IMAsogcAjo6O6NatG9asWYOJEyeaZU6GAVj4GHOh0ZDAfShqV67Q+717p39/iRJUgqxcuRTPcSVKYH3/0ZDYCKiEDZzsbU0aeu/vVRRrTkUgEbb6h/X7+QFz5gDjxlFLpCtXqM+gGXF3d8ehQ4cwatQo1K1bF9u2bUOtWrWwZ88edOnSJeXF8+ZRmTYfH2DsWLOus2/fvvjiiy8wYcIE2HyYS8kwWQS7OhnTERtLDWRTc0lGRJBlkRZ2dpRcnoq4oUwZwMkp1dsmTpyI8PBwDJvxCwJ+WQ1/LxfM/3aAST/WpF/W4nDYE8wY3jt9ay85Gg3VCZXLgebNgX37TOJ6NYY///wTU6ZMQffu3XEzxhH1Og6Cv1dR+iy3b9Nea3w8cPAg0KSJWdem0WhQrlw5bNmyBX5+fmadm7FeWPiYNJGHRkFx65nuS1II4OnTj0VN+5xOBRYAgKvrx8KWkEABIRUr0t6SAZw+fRodOnTA5cuX4ebmhh07dmDBggU4cuRIJj71x+zfvx+zZ8/GwYMHDbvx8WOgalXg+XPIZ/0ORaUGur+lmRBCICEhAUeOHMGAyQsgaTwMthKn9/uVNSAbOwjYuxfo2xewUKBJQEAAYmNjsWDBAovMz1gfLHxMqshDozBy3UUoVWpINSoEnV8D2cndZNWlhURC+0OpWW1lywJ58358T2wsnbezoz0+R0e91hcXFwc/Pz9MmzYNPXr0AAAkJCSgWLFiuHTpEkqVKmXEp06d69evo2PHjggLCzP85p07IR/zPUZ2+B+UEic42dsioFkJ1HCTIC4uLumhVCpTvE7tnKGvtQ97e3tIpVI4+w+EQ5UWSUvrH3EcM9bPAQoUoGAcN/MJcnJu3LiBJk2aIDIyEvb2vPvCZD38X5kVEBcXh5cvXxr0eO3VEnY+TQEASlsJFDaFIYuNpfqaqQlbuXJAyZKG96bLk4fuDQ+nfaYqVfS6berUqahUqRK6d++edM7BwQFdunTBunXrMH78eMPWkQ4lSpTAw4cPIYQwfB+qXTsoDt9PqnsZl6jBlJ9XwilkJ5ycnCCVSuHk5JTi8eE5qVSK/Pnzp/t+WmM4OjrC7v2/iXy9HCPPvoFS4gSpKg7+59/36nv7Fhg8GOjZE+jQgWqQmhEfHx+ULFkSBw8eRIsWLTK+gWEyCVt82ZCPXIwAlEqlweKlfajVahQqVMigR8grW0yXRyIuUQOpjUDQJ4Uga+hLwmdqOnWiHndr1lD+WwZoXZxXrlyBq6trivcOHz6Mb7/9FhcvXjTpEvPly4cHDx6gQIECBt8rD32CkesukfUssUNQTz+zujuTOHgQ8i8mQeHpB//IK5CFnQTy5ydLW5tHKZVSHl/PntSQNo29VVMTFBSEc+fOYcWKFWaZj7FuWPiyGcldjEhMgOrIUry4chhCCIPFS/vIkyePURFzqQlwlhAQQIWeJ02iQtDpoHVxTp8+PYW1p0WtVsPDwwPBwcHw9fU12RJ9fHywZcsWo8c0298y3UXIgRYtgIIFgVevqFD42bNAqVLApk2UhnHsmO76/PnpR0nPnkCzZuTKziKioqJQoUIFPHz4EM7pFQ5nGBPAwpfNCNx+DStO3Ut63aVKEczsWBVSqTT3hntv2AD06EFdD5JVHEmNCRMm4M6dO+l2Ih83bhycnJzwvbZrgglo1qwZJk6cCJlMZrIxzc7evUDr1uTKfPuWhO/WLXI1a7l/n/491q4FLlzQnXdxofZNPXsCDRtmSYSqf8+vULxmMwxp09ByPw4Yq4BLlmUz/L2KQiqhPRmpxA6tqpcx2mLLMejZnujUqVP4999/8WsG1VD69OmDNWvWwJS/6UqWLImHDx+abDyLoHVnvn1LLk0hgO++S3lN6dKUf3j+PAW8TJ9O+X3Pn1Ovw08/pWvGjgXOnaMxTIA8NApPyn6G09FOGLnuIuShWd/eibFeWPiyGTJfNwT19EP/eh6W2wsyN15e5EaLiKD9plSIi4vDoEGDEBQU9NG+3odUr14djo6OOH36tMmWqA1wydG8fKk7njuXImjXrUtp2SXH2xsIDKQ0k0uXqJ+hhwfVQl24kHoRenuTq9rAVJQPUdx6BpWgH3embnzLMB/CwpcNkfm6YUaHytYhegCJno8PHafRUTwwMBCVK1dOdV/vQ2xsbNC7d++061IaQa4QPm2TXhcX4Ouv6QEAGZULs7EBqlUDfviBfpycOAF88w2lP4SH0/5spUp0zZw5dI2B+HsW1DW+tdGYtPoOw3yEudtBMEyqaPvdpdKm5uTJk8LNzU1ERUXpPdytW7eEq6urUKlUJlne1q1bRfv27U0ylkU4dkzXqql5czr3/LmuDdH+/YaPqVLRfYMHC1GwoG58QIi6dYVYtIhaHenDzp0iuHwdEdD8CxEsP2/4WhjGANjiY7IHaezzKZVKDBw4ED///HOGLs7klC9fHp6enoZXW0mDEiVK4MGDByYZy+yoVMCXX+pea1MyihTRWXsTJhjcGgr29hTtuWwZEBVFgUm9elFu5unTwOjRVGO1aVPgzz+BFy/SHmv9esjCz2DGoWWQfapfLifDGAsLH5M9SEP4pk6diqpVq6Jbt24GD2lKd2eOdnUGBdHfVfvDIXmRgVGjgGLFKJhl0ybj53BwoKjcNWuorN26dZQML5EAhw5RWTp3d8oRXL2aAmy0qFTA9u107OGRpWkTDANwOgOTXYiIoAowbm7AkycAKIqzY8eOqSaq68Pjx4/h6+uLR48eQSqVZmp5arUaUqkUMTExkOSkL+bISKqDGhMDjB8P/PgjWWXJfxD88QfwxRfUDzE01LTC8+oVsHUrpUccOPBxonyvXiSabdvS+Y4d6XqGyULY4mOyBx4eVLMzKgp49sxoF2dyihUrhlq1amH37t2ZXp6dnR1cXV3xOKNC3NmN0aNJ9Dp31pWD+zAH7/PPKTozPJzclqakYEFg0CAgOJiKdv/yC9CgAXWn37iR1tWpk+56Ly/Tzs8wqcDCx2QPbG0pMhAAQkIQGBhotIszOb1798bq1atNsMAc6O787z9gyxZqjrtoEaBW0/kP66na2+sq5kyfnmZKSaZxdaWGt8eOUXPhefOod6FKpbtmyRLqXH/0KOQhjxG4/Rrn9DEmh4WPyT683+eL2LkTq1atyjBRXR86d+6MgwcP4mXyHDYjyVHCp1RSygFAYlaqlM7NmFrVlc6dgTp1yOJetCjr11e6NLlef/qJXmvF+N07YMkSyAePx8jlJ7Hi1D1OaGdMDgsfk32oXBny8nUwOUyFL2f+iqJFM5/LVaBAATRv3hxbtmzJ9Fg5Svhmz6YeiVWqACNH0rm0LD6AcvXmzqXjefOoUos52LyZnrVifOQI8L//QVGlEZT21KKKE9oZU8PCx2Qb5O6++KbDBJys0hLr7ktN9ivfVNGdOUb4wsJ0IrZkiS5YRWvxpdU6qnFj6hr/9m2GxcJNgkajEz6VilyhjRoBc+fCf+YYSN8vUyqx44R2xqSw8DHZBoW0OOIk2l/5GpP9yv/ss89w4cIFPHr0KFPj5AjhE4L2yFQqClpp0ED3ntbiS6/A9Jw59Pzrr7QPl5WcPg08egRoLfsKFZLeklUqhqDetayrdB9jNlj4mGyDv1dR3a98VRz830WaZFypVIqOHTti/fr1mRonRwjfunXAwYNA4cI6q09Leq5OLdWrA336AAkJVKczK9Fae9qgJm3ZuvdYXek+xmyw8DHZBpmvG/3KlzxH0PZ5kH07gCwCE2AKd2e2F77Xr4ExY+h43jyqyZmcjFydWmbOJPfoypXAlSumXydAlqlW+AoXpucPhI9hsgoWPiZbIfN1w4xp/SDzzA88ewb07auzVDJB06ZN8eDBA9y8edPoMbTCl21rPnz3HSX/169PuXMfoo+rEwDKlAGGDydxmjzZ9OsEqCPE3btUNeb1azqXzNXJMFkJCx+T/bC1JWvD1ZXKXc2alekh7ezs0KNHD6xdu9boMfLmzQuJRIJXr15lej0m5/x54LffyJpbsiR1cdPH1allyhQqKLB7N3D0qGnXCuisvU6dKBgHYIuPMRssfEz2xN2dajra2FAe2pEjmR5S6+7MjMWWLd2dajVZaBoN1d6sWjX16/R1dQL0o2P8eDqeMMFkDWcBpHRzfvYZ8OABlS3z9DTdHAyTDix8TPaleXNg0iT6wu7dm1yfmaB27dpQq9U4f/680WNkS+H74w/g7FnqhDBtWtrX6evq1DJmDAngqVO6ItKmICQEuHmTukNoIzq9vPQTZIYxASx8TPZm+nQKyX/0CBgwwPDWOckwRYPabCd8UVH04wAAFi8G8uVL+1pDXJ0AuToDAuh40iQgMdH4dSZH2wWiUyeqDwrw/h5jVlj4mOyNvT1V9i9cGNizB1i4MFPD9erVC+vWrYPayICZbCd848ZRcEirVlR2LD0McXVqGTaMumbcuAH8+6/x60yO1s3ZpQvv7zEWgYWPyf6UKgUsX07HkyZR4rORVKxYEe7u7jhi5J5hyZIls4/wHToErFoFODlR1wMbm/SvN9TVCdDe2/ff0/HUqVQDNDPcvEm9AQsUoAa1N27QeRY+xozYW3oBDKMX7dpRi51Fi4CePYGLF6nljRFo3Z1NmzY1+N4SJUrgv//+M2pek5KQAHngIiiafwF/j/yQhYSkbOL7YTCKENRrDyCxSV67NPm1qd1nawt4ekJu7wrF3I3w797S+KRyrbXXvj2Jqlb42NXJmBFuRMvkHBISaL/v3Dlyk23cmLGVkwoPHjxA1apV8fjxYzg6Ohp07/nz5zF06FBcuHDB4HlNQVxcHI4fP47lu0/hpF1FJNo7QqqKo4T/8DNZNq+8fB2M7PA/KCVOkErsjC8jVrMm5fBt307NZ52dgbg4alhboIDJ180wqcEWH5NzcHCgklx+fmQ5aHu3GUjJkiVR/tNOGLxkHwa0qGvQF7i59/g0Gg0uX76M/fv3Qy6X4+TJk6hSpQryNR6MRPG+rqnECYomnSGr6Jbyh8CHPwpCQiiYxNf3YwsrvftsbKBwqQulxInme98twWDhi4gg0cubF2jRArh/n0TP3Z1FjzErLHxMzqJcOeDPP8ndOWYMVSmpXt2gIeShUYiu2BHHomxxbuVp/NyrBmRVS+p1r6urK16+fIn4+HiDrUV9uX//PuRyOfbv348DBw6gUKFCkMlkGDFiBDZu3IgCBQpAHhqFkesuQqlSU/eC0QMB3wnpDzxuHLBgAVV1GTfOoDX5h0ZhY/L5jOmWoHWvtmlD+5Ic2MJYCBY+JufRowcVYv7jDzo+f56sCD1R3HqGREEBHnGww6zJ81C2QxWUGzIkQ9epra0t3N3d8fjxY3iaKOH61atXOHz4cJLYvXz5Es2aNYNMJsPcuXNRunTpj+6R+bohqKcfFLeewd+rqH7WlzHBLZmZ70OSR3MCvL/HWAwWPiZnsmgRcOIEBXQMHw6sWKH3fp+/V1FsPP8ASpUajonxKB12GvWG/YwG332HMfPnw79vX9ikM5bW3Wms8CUkJODUqVNJ7str166hfv36kMlkWL9+PapWrQpbPcRJ5utmmAAZmseX2fmS8+ABcPIkIJUCrVvTObb4GAvBwsfkTKRSYP16oHZtCulv1gwYOFCvW1NYL2ULQVa8J2KmXseKp08xtH9/5B03Dt/OnInuAwfCwcHho/sN3ecTQiA0NDTJolMoFPDy8oJMJsOsWbNQv359ODk56T2e0RiTx2cqtm6l51atdNY5pzIwFoLz+Jici68v5a8BwIgRwPXret+a1OutSgng22/hHB6O4UOH4jqA6U+f4p+vv0YZNzfMmTUL0dHRKe7VR/gePXqElStXon///ihRogTatWuH69evY8CAAbhz5w7OnTuHOXPmoGnTpuYRPSBTrs5M86GbE2BXJ2MxWPiYnM3AgdS6KDaW9vuMTbB2dQX++AO258+jbcOGOKBS4b9XrxC2cCHKeXhg+PDhCHvvmktN+N69e4fdu3fj22+/ReXKlVG5cmVs374dDRo0gEKhwJ07d7B06VJ069YNRYoUyeSHNpJMujqNJioKUCiox1/btnTuzRvg8WMKckllD5NhshJ2dTI5Gxsbasdz+jRw9Srw7bfA778bP16NGtSGZ/16VBs/HssfPMATAL8pFPDfuBF16tWDi29DXI8thl+2HsXLa0cgl8tx4cIF1K5dGzKZDP/88w9q1KgBu+xWdNlSrs5t22ju1q11aQva/T0uTs1YALb4mJxPvnzAhg2AoyOwdCkdZwYbG0qXuHEDCAyEu5MTZoSE4F5sLD7VFMQpjReiCvhg/okXCH1tj8mTJyMqKgqHDh3C5MmTUbt27ewneoDlXJ2puTk5sIWxICx8TO6genXKUQOAoUOBO3cyP6azM3WHuH4d6NIFUqUSb1V5Efc+kRv2DvDyb4dWrVrB2dk58/NlNZZwdUZHU01ROzsqU6aF9/cYC8LCx+QevvqKOhS8eUP7fQkJphm3aNEksfC/exFSVRwAQKpWwd8hxjRzmAG5QzEENv8C8hgzBdMAwI4d1M6oSRPqv6eFLT7GgnCtTiZ38eoVWX/37tF+XybbGCE8nPrGaQtAOzpCXqoaFGVrwP/OBcjunAO+/BKYMSPlF3s2Qx4ahZH/noLS1h5SG4GgvrWNz8kzZN5eX0PxPBH+n30C2bcDdG9UqUJ/03PnqH4nw5gRtviY3EXBglTP094e+OknYOdO48f67z/KE9SKnr09MGwYZOFnMMMtBrLWdXXBNV5elFphqmatJmbf5XtQ2lIsm1LYQHErc93s0yMxMREKhQJ9J8zFlyWbYkWtdhgZ7Qp5aBRdoFYDt27Rsbd3lq2DYdKChY/JfdSrB8yeTccDBwKRkYbdr9EAM2dS6P2rV7rzy5dTUWWArJSgIODSJUqef/kS+OYbKqB98GDmP4MJOXr0KNYvngY70B6f0bU20yE6Ohpr1qxB79694ebmhlGjRuGZnQvU9u8LaSdqdGJ77x4QHw+UKJF+x3iGySJY+JjcydixFD4fHQ307q2/Jfb6Nbk2AwPptf37jJ/vvwf69KGUCYBcdQBQuTIgl1NlkjJlyDps1oz2GiMiTPuZDESj0WDevHno3r07lk0fhd/71UX/eh7GtxRKhhACISEhmDt3Lvz9/VGmTBmsX78eTZo0wZUrV3DhwgWM69sWUgntjaYQWw5sYSyNYJjcytOnQhQvLgQgxJQpGV8fEiKEtzddnz8/PQAhhgwRQqMRQq0WIm9eOvf06cf3K5VCzJolhLMzXePoSPO+e2f6z5YB0dHRol27dqJevXri/v37JhlTqVSKPXv2iBEjRggPDw/h4eEhRowYIfbs2SOUSmWq9wSHPBEB266K4JAnupMLF9Lf56uvTLIuhjEUFj4md3PokBC2tkLY2Aghl6d93aZNOlHz9RXC05OOW7YUIiGBromIoHNubunP+eCBEH370rWAECVKCLFqFYmnGTh37pwoU6aMGDVqlIiPj8/UWA8ePBB//PGHaN++vcifP79o2LCh+OGHH8S1a9eExtjPM2wY/V0WL87U2hjGWFj4mNzPtGk6wXryJOV7iYlCTJyoE6nu3YX45BM6rlpViNevddfu2EHnmzfXb94TJ4SoVUs3dv36Qpw7Z7rP9QEajUYsWbJEuLi4iA0bNhg1hlqtFqdPnxYBAQHCz89PFCpUSPTq1UusXr1aPH/+3DQL/fRT+nvs22ea8RjGQFj4mNxPYqIQjRvrREutpvPPnwvRogWdt7MTYsECEj6tlRYZmXKcWbPovdGj9Z9brRbi779JdAGyPAcP/liAM8nbt29Fnz59RJUqVURYWJhB975+/Vps2rRJDBw4ULi6ugpfX1/xv//9Txw9elSoVCqTrlMIoftb3Ltn+rEZRg84uIXJ/djZAatXAy4uwP79wA8/UDRmrVpAcDAlqMvlwNOnVO4sXz5KZSj5QVf2DwNb9MHWljqe37wJjB9PwTJ//UVh/AsWmCTJ/vr166hTpw4kEglOnToFbz1SBMLDw7Fo0SI0b94cJUqUwJ9//omaNWvi1KlTKYJW7O1NXM731SsqWi2Vfvz3ZRhzYWnlZRizsWePzupydKTjWrWEuH9fiCVL6LW9fdouuEqV6JozZ4xfQ1iYEG3a6Nyf3t5C7N5t9HCrV68WLi4u4q+//kr3uoSEBHHw4EExZswY4e3tLYoVKyaGDBkitm3bJt6+fWv0/AZz6hR97urVzTcnw3wACx9jPSQkCFGzpk50evemSMxduygABiC3ZGrEx5Mo2tgIEROT+bX8958QFSro1vLZZySKehIXFyeGDx8uypcvLy5evJjqNU+fPhX//vuv6NatmyhYsKCoXbu2mD59ujh37pxQa9295mb5cvq8PXpYZn6GEezqZKyFqChAJgPOn6dqKwDV9Lx2jep6ajRAQAC5JVMjLIxyAcuVA/Lkyfx6WrcGrlwhd2f+/ORarVwZGDeOcgnT4e7du2jYsCGioqJw7tw5VK9eHQDl1l2+fBmzZs3CJ598gvLly2P79u1o1aoVrl+/jjNnziAwMBA1a9aErSWa0QJco5PJFrDwMbmfM2eo0sqRI0CxYsDGjUDBgpDfeIrAqf9CXqwS0K8fdWJIC+3+XuXKpluXgwMwZgyV7xoyhIR1wQLa//v7b13/vGTs2rULdevWRe/evbFp0yZIJBLs2rULX375JUqXLo3OnTvj2bNnmDFjBp4+fYrNmzfj888/h7u7u+nWnQnkkbFUKNu9kqWXwlgx3IiWyd0sWwaMGEFBJA0akOgVKwb5KzuMvK6GUuKEjRWbIKimM2TpjWNMYIu+uLoCf/5Jxa5HjQKOHwcGD6YaoEFBQP36SExMREBAAFatWoUlS5YgKioKbdu2xdGjR1GrVi20adMGcrkcFSpUgI3Wos1m/LX3DH4o9ilUJR2w8YENgkKjzFIom2E+hC0+JncSHw988QX15ktIIPE7eJAsPgAKl/JQvu+rp5Q4QvH3VoryXLUq9UjLrBQ+LTVrAgoFsGYN1bE8fx5o0ADr+o1GpX7TsPZoCPLly4dhw4bh5MmT6N+/PyIjI3Ho0CGMGzcOPj4+2U70hBA4cOAAWrdujam/r4PKzgEAoFSLLC2UzTDpwW2JmNzHw4dA167AqVO6ruwDBqS4RB4ahZHrLkKpUkMKNYLkv0B2Xk5vFi8OfP01CWfhwnTOwwO4f5+a0ppjfyomBpg7F/JNB/FFmzHQ2DvCTqgxuk5+fNXBP3t2eE+GSqXCxo0bMX/+fMTFxWHcuHFwrynD2M0h9DeX2JmkZijDGAMLH5O7UCiAbt0omKV0aWDLljT7vclDo6C49Qz+XkUhK1uAcv0WLgRCQ+mCPHlIMIcMoTEcHYF373SFq81A4KoTWBHyMul1/3oemNHBhPuMJubNmzdYtmwZFi1ahHLlymH8+PFo1apVUjBNir85ix5jIVj4mNyBENQPb8wYChJp2pT68hU1sP2OEJTMvnAhsG9fyvfKl6dEdDO6E1NYptnYSnrw4AGCgoLw119/oUWLFhg7dixq1apl6WUxTKqw8DE5H6WS3JIrV9LrceOAOXMyb5mFhACLFlEfPm1bo2rVqLN7z55kAZqB7GwlXb58GQsWLMCuXbswYMAAjBo1Cp6enpZeFsOkCwsfk7O5e5d63128SK7Jv/+mvDxTMngwjevsTHtvAODuTgEzX35JpdCsCCEE9u/fj/nz5+PatWsYOXIkhg0bhkKFCll6aQyjFyx8TM5l/36yvF68oMTyrVuzJury00+Bo0eBHTtoroULdVGeTk5A//7A6NFAxYqmnzsbkZCQgPXr12P+/PlQq9UYN24cevXqBUczWb4MYypY+JichxDAjz8CkyZRkvdnn1EaQlZYHEIARYoAL18CDx5QmoEQlBqxcCFVXNHSujW5QZs3N+s+YFbz+vVr/PHHH1i8eDF8fHwwbtw4tGzZMtulTjCMvrDwMTmLd++Azz+nRHSAyoxNm0ZdELKCR49I7AoVImvvwy/7GzdoH3DFCtprBMjqHD0a6N2bLMIcSmRkJBYvXox//vkHrVu3xtixY+Hn52fpZTFMpuEEdibHID9wEYGDZ0N+8R61Dtq2DZgxI+tED0hZqiw1C8fHB/j9dyAyEpg1ixLkr16lfUEPDyqD9vRp1q0vC7h48SL69u2LatWqQQiBixcvYtWqVSx6TK6BhY/JEchDozDy4EOsKNsAIztOhHzzYaBDh6yfWN+KLUWKAJMnU7DNihVA9eokeNOmUT7hkCEUJZpNEUJg7969aN68Odq1a4dq1arhzp07WLBgAUqXLm3p5TGMSWHhY3IEilvPoNSQxaW0d4Ai1sE8ExtaqszBgQpeX7gAHDoEtGtHJdD++ousxpYtgb17aZ8wG5CQkIB///0XVatWxYQJEzBgwADcuXMH48ePR8GCBS29PIbJElj4mByBv1dRSCVUpksqsYO/l4GJ6cZibI1OGxugcWOKBL1xA/jqK0q3CA6mIJjKlakwtXZf0My8evUKc+fORZkyZbB69WosWLAAly5dQr9+/eDgYKYfFQxjITi4hckxmD2ROzERyJuXCl6/egUUKJC58aKjSex+/pnqiQKUAzh8OAmjGVoH3bt3D4sWLcK///6Ltm3bYuzYsahWrVqWz8sw2QkWPoZJi7AwCl4pVYoKVJsKlYqiUhcupA4MALlIe/emdIiqVU0313vOnz+P+fPnIzg4GIMHD8bIkSNRsmRJk8/DMDkBdnUyTFpkVSsiiYRE7uxZSozv2JHEcPlyKonWvDnlB6bSiNYQNBoN/vvvPzRt2hSdOnVC7dq1ERERgXnz5rHoMVYNN6JlmLTI6h58NjaAvz89bt8GFi+m0mgHDtCjQgXKB+zfn/YH9SQ+Pj5p387BwQHjxo1D9+7dIZFIsuZzMEwOgy0+hkkLczSf1VKuHHVbf/CAqtKUKkWu1uHD6XjKFEqmT4fo6GjMnj0bZcqUwcaNGxEUFIQLFy6gT58+LHoMkwwWPoZJC3MKn5aCBam7xO3bwNq1QJ06FBQzezbg6UnW38WLKW6JiIjAqFGjUL58edy6dQv79u3Dnj170KxZMy4rxjCpwMEtDJMasbEU0WlrSx0ZLFWIWQjg5EkKhNm6NWnfT95pMDY1bIeIq0dxYee/GDp0KL755hsUL17cMutkmBwECx/DpMa5c0Dt2oCvb/apuBIRAfz8M+T7L+KbFt8gTuIIe2iwsGsVtK/paenVMUyOgV2dDJMalnBzZkSZMsDChVAELkCchCzQRNji3IN3Fl4Yw+QsWPgYJhXkIY8R2PwLyCs2sPRSPsLft4RlqtgwTC6BXZ0M8wHy0CiMXHkaSthBaisQ1Ke2eSrFGIDZq9gwTC6C8/gY5gMUt55BCbKolBobKG49y3biIvN1y3ZrYpicArs6GeYDLFYQm2EYs8CuToZJBXYlMkzuhYWPYRiGsSrY1ckwDMNYFSx8DMMwjFXBwscwDMNYFSx8DMMwjFXBwscwDMNYFSx8DMMwjFXBwscwDMNYFSx8DMMwjFXBwscwDMNYFSx8DMMwjFXBwscwDMNYFSx8DMMwjFXBwscwDMNYFSx8DMMwjFXBwscwDMNYFSx8DMMwjFXBwscwDMNYFSx8DMMwjFXBwscwDMNYFSx8DMMwjFXBwscwDMNYFSx8DMMwjFXBwscwDMNYFSx8DMMwjFXBwscwDMNYFSx8DMMwjFXBwscwDMNYFSx8DMMwjFXxf+EuKMKrt0oMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_vis(G_list[idx], data_list[idx].gt_pos, \n",
    "          highlight_edge=bentley_ottmann_xing(G_list[idx], data_list[idx].gt_pos), \n",
    "          with_labels=False,\n",
    "          node_size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([  3.,  14.,  24.,   8.,  13.,  49.,   4.,   0.,  48.,   2.,   4.,  10.,\n",
       "          7.,   0.,  82.,  15., 105.,   0.,   0.,  28.,  29.,  46.,   7.,  82.,\n",
       "        120.,   0.,   2.,  15.,  82.,  13.,   0.,   5.,  48.,  20.,  61.,   2.,\n",
       "         56.,   3.,  29.,  49.,   2.,  85.,  67.,  57.,  15.,  18.,  12.,  11.,\n",
       "          8.,   1.,   3.,  77.,  15.,  65.,   1.,  63.,  81.,  22.,  77.,  75.,\n",
       "         21., 143.,  24.,  32.,   0.,  55.,   3.,  38.,   0.,  26., 123.,  29.,\n",
       "         84., 132.,  47.,  25.,   9.,  23.,  18.,  16.,   0.,  70.,  19.,   6.,\n",
       "         27.,  11.,  41.,   1., 153.,  66.,  46.,  60.,   1.,  64.,  51.,   0.,\n",
       "         18.,  20.,  72.,  60.,  19.,  93.,  82.,  32.,   9.,  64.,  30.,   7.,\n",
       "         78.,  17.,  16.,  54.,   3.,   5., 122.,  79.,  36.,  22.,  41.,   5.,\n",
       "         49.,  58.,  13.,   8.,  62.,  11.,  13.,   7.,  26.,   0.,  14.,  80.,\n",
       "          4., 122.,  95.,   9.,   3.,  19., 124., 118.,   5.,   2.,  43.,  10.,\n",
       "          1.,   0.,  11.,  48.,   2.,  41.,  10.,   8.,   8.,  45.,   0.,  15.,\n",
       "         86.,   1.,  38.,  26.,  29.,  15.,  39.,   0.,   9.,  49.,  66.,   5.,\n",
       "         70.,  31.,   8.,   0.,  48.,  53.,  40., 114.,  46.,  15.,  28., 111.,\n",
       "          0.,   0.,   1.,  28.,   0., 117.,  26.,   0.,   8.,   1.,  16., 109.,\n",
       "         25.,  10.,   2.,   2.,  60.,   0.,   0.,   3.,  20.,   3.,   0.,  71.,\n",
       "         69., 112.,   0.,   8.,   5.,  12.,  52., 109.,  10.,   7.,  23.,  47.,\n",
       "         26.,   0.,   8.,   3.,  95., 177.,  23.,  32.,  12.,  33.,  28.,  25.,\n",
       "          5.,  25., 109.,  32.,   2.,  85.,  11.,  18.,  79.,  18.,  20.,  93.,\n",
       "          2.,  74.,  57., 120.,  55.,   6.,   7.,   4.,  49., 110.,  57.,  49.,\n",
       "          7.,  24.,  80.,  23.,  50.,  58.,  10.,   5.,  64.,  92.,  18.,   5.,\n",
       "         10.,  29.,  84.,   4.,  31.,  39.,  20.,  27.,   1.,   3.,  17.,  26.,\n",
       "         38.,  38.,  32.,  92.,  20.,   7., 121.,   7.,  49.,  73.,   0.,  14.,\n",
       "        124.,  88.,  66.,   7.,  42.,  24.,  93.,  45.,   0.,  91.,  16., 138.,\n",
       "         12.,   6.,   5.,  26.,   6.,   0.,   9.,  68.,   2.,  42., 124.,   0.,\n",
       "         26.,  49.,   0.,   4.,   3., 122.,  36.,  78.,   5.,  15.,   5.,   8.,\n",
       "         58.,  55.,   9.,  58.,  11.,   1.,  40.,   2.,  38.,   4.,  26.,   0.,\n",
       "         15.,  26.,   3.,   2.,   2.,  17.,  69.,  30.,   8.,   5., 173.,  22.,\n",
       "         61.,  16.,  25.,  18.,  69.,  91.,   3.,   9., 103.,  82.,   4.,  31.,\n",
       "          6.,   3.,  14.,  75.,  72.,   6.,  19.,  15.,  54.,  42.,  78.,   3.,\n",
       "         28.,   0.,  57.,  95.,   3.,   3.,  28.,   5.,  59.,  38.,  63.,  76.,\n",
       "         62., 111.,  26., 105.,  13.,   0.,  27.,   5.,   1.,  34.,  25.,  75.,\n",
       "          6., 107.,  38.,   5.,  13.,  20.,  49.,  33.,   1.,   0., 126.,  85.,\n",
       "          7.,   8.,  84.,  21.,  29., 117.,   1., 139.,  24.,  33.,  18.,   0.,\n",
       "         29.,   2.,   0., 143.,  80.,  19.,  36.,   6.,  26.,   1.,  17.,   0.,\n",
       "         26.,  16.,   8.,   3.,  46.,  39.,  14.,   0.,   0.,   0.,  43.,   2.,\n",
       "          0.,  55.,   0., 122.,  10.,   3.,   3.,  25.,  67., 105.,   3., 104.,\n",
       "         45.,  49.,   4.,  14.,   0.,   3.,  38., 108., 121.,   0., 128.,   9.,\n",
       "         13.,  63.,   2.,  90.,  10.,   4., 126.,   3.,  10.,   0., 120.,   3.,\n",
       "          2.,   8.,  48.,  11.,  47.,  11.,  57.,   3.,  21.,   3., 121.,   0.,\n",
       "         35.,   7.,  28.,  34.,  11.,   0.,   0.,  40.,  11.,  67.,  62.,  10.,\n",
       "         40.,  44.,  96.,  26.,   0.,   4.,  35.,   3.,  19.,   5.,  43.,  39.,\n",
       "          7.,  50.,   4.,   0.,  44.,   0.,  37.,   6.,   8.,   5.,   5.,  12.,\n",
       "         30.,   0.,   2.,  98.,  10.,   2.,  95.,  56.,  40., 116.,  39.,   4.,\n",
       "         19.,  53.,   0.,   9.,   1., 158.,   5.,   1.,  47., 104.,  13.,   3.,\n",
       "        141.,  33., 106.,   8., 152.,   8.,  32.,   4.,   0.,   6.,  45., 107.,\n",
       "         12.,   6.,  25.,  27.,   0.,  63.,  40.,  43.,  80.,  35.,  71.,  51.,\n",
       "          0.,  26.,   1.,   1.,  12.,  10.,   2.,  10.,  19.,  10.,  24.,   4.,\n",
       "          0.,   0.,  62.,   1.,  15.,  28.,  47.,   2.,  37.,   4.,  26.,  28.,\n",
       "         99.,   2.,  73.,  22.,  12.,  84., 107.,  14.,   2.,  59.,   0.,   3.,\n",
       "        109.,   7.,   6.,   3.,   6.,  49.,  20.,  97.,  60.,  67.,   5., 129.,\n",
       "          0.,   5.,  10.,  29.,  21.,   4.,   1.,   2.,  14.,  20.,   8.,  10.,\n",
       "         24.,  27.,   2.,  99.,   6.,  23.,   2.,  20.,   2.,   3., 114.,   2.,\n",
       "        152.,  16.,  52., 112.,   4.,  27.,  48.,  37.,  19.,  46.,   3.,  12.,\n",
       "         32.,   2.,  17.,  28.,  19.,  28.,  86.,   0.,  23.,  90.,   0.,  65.,\n",
       "         39.,   1.,  46.,  32.,  15., 147.,   9.,  26.,  85.,  16.,   3.,  90.,\n",
       "          0.,  62.,  42.,   1.,  32.,   0.,  72.,  11.,  87., 104.,  15.,   3.,\n",
       "          0.,   8., 154.,   0.,  24.,   1.,   3.,   3., 109.,  98.,   0.,   0.,\n",
       "          4.,   9.,   5.,  50.,   0.,   0.,  74.,  18.,  50.,   0.,   8.,   7.,\n",
       "        170.,   0.,   6.,  11.,  68.,   5.,  55.,  15.,   0.,  76.,  65.,   7.,\n",
       "         70.,  72.,  28.,  12.,  40.,   3.,   7.,  61.,  16.,  35.,  94.,  19.,\n",
       "         41.,  22.,   4.,  39.,  41.,  77.,   2.,   9.,  37.,   1.,   6.,  89.,\n",
       "         40.,   8.,   1.,   2., 107.,  27.,  68.,  16.,  29.,   2.,  66.,   5.,\n",
       "         62.,  17.,  24.,   2.,  47.,   3.,  41.,   0.,  25.,   3.,  11.,   0.,\n",
       "         32.,  19.,  33.,  13., 146.,  19.,   8.,   1.,  17.,  21.,   0.,  78.,\n",
       "         10.,  22.,   3., 106.,  55.,   2.,  44.,  10.,  13.,   1.,  11.,   2.,\n",
       "         31.,  12.,   1.,   6., 120.,   1.,  52., 148.,  62.,  42.,  14.,  91.,\n",
       "          9.,   0.,  33.,   8.,   0.,  42.,   0.,   0.,   0.,   0., 103.,  28.,\n",
       "          0.,   0.,  12.,   5.,  11., 109.,  36.,  34.,  28.,   1.,  11.,  37.,\n",
       "         22.,   2.,   4.,  94.,  32.,  53.,  14.,  12.,  10.,   8.,  47.,  82.,\n",
       "         31.,   7.,  40.,   9.,  84.,  38., 119.,   3.,  89.,  46.,  53.,   0.,\n",
       "         13.,   4.,  99.,  64.,   9.,  50.,  82.,  35.,  34.,  34.,  19.,   9.,\n",
       "          0.,  63.,   0.,  82.,  90.,   7.,  64.,  52.,   5.,  62.,  37.,   2.,\n",
       "         31.,  10.,   3.,  21.,   4.,  98.,   0.,  38., 106.,   1.,   6.,  16.,\n",
       "         33., 102.,   3.,   3.,  21.,  16.,   1.,  33.,  19.,  50.,   0.,   2.,\n",
       "          3.,   7.,  21.,  64.,  10.,   1.,  25.,   0.,   2.,  10.,   2.,   8.,\n",
       "         57.,  65.,  99.,  25.,   0.,  55.,   0.,  12.,   9.,  88.,  33.,  34.,\n",
       "         20.,  53.,  85.,   1.,   0.,  31.,   0.,  65.,  27.,   0.,   7.,   4.,\n",
       "          9.,   8.,   9.,   9.,  16.,   2.,  78.,  14.,   2.,  98.,  13.,   1.,\n",
       "          0.,  22.,   2.,  52.,  45.,  14.,  37., 101.,   2.,   5.,   4.,  29.,\n",
       "          1.,   9.,   4.,  13.,   0.,  59.,   2.,  52.,   6.,  25.,  54.,  42.,\n",
       "         81.,  20., 139.,   0.,  20.,   0.,  17.,  43.,  26.,   0., 133.,   0.,\n",
       "          2.,  52.,   0.,  53.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch = Batch.from_data_list(data_list[:1000])\n",
    "Xing(reduce=None)(batch.gt_pos, batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e278daa07db4db288dfc519d37596d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 3 3\n"
     ]
    }
   ],
   "source": [
    "for idx in tqdm(range(1)):\n",
    "    batch = Batch.from_data_list(data_list[idx:idx+1])\n",
    "    print(idx, get_num_xing(batch, get_bf_xing(batch, batch.gt_pos, eps=1e-5)).item(), get_n_xing(G_list[idx], data_list[idx].gt_pos))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vis_xing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "edges = data_list[0].raw_edge_index.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0,  5],\n",
       "        [ 0,  6],\n",
       "        [ 0,  8],\n",
       "        [ 0, 15],\n",
       "        [ 1, 12],\n",
       "        [ 2,  6],\n",
       "        [ 2, 15],\n",
       "        [ 3,  8],\n",
       "        [ 3, 10],\n",
       "        [ 3, 14],\n",
       "        [ 3, 15],\n",
       "        [ 4,  8],\n",
       "        [ 4, 11],\n",
       "        [ 7, 14],\n",
       "        [ 9, 11],\n",
       "        [ 9, 12],\n",
       "        [ 9, 15],\n",
       "        [10, 13],\n",
       "        [11, 13],\n",
       "        [12, 13],\n",
       "        [14, 15]])"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.unique(edges.sort(dim=1).values, dim=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = 0\n",
    "intersections = get_xing(G_list[idx], data_list[idx].gt_pos)#fast_batch_xing([get_seg_list(G_list[idx], data_list[idx].gt_pos)])[0]\n",
    "vis_xing(G_list[idx], data_list[idx].gt_pos, intersections)\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layouts = [\"gt_pos\", \"perturb\", \"phantom\", \"random_normal\", \"random_uniform\", \"flip_edges\", \"flip_nodes\", \"movlsq\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a3910b839457490aa236c0902c6598d8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/11531 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "TypeError",
     "evalue": "get_n_xing() takes 2 positional arguments but 3 were given",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/8r/3cqw5w7105xfsj57ks8fl5t40000gn/T/ipykernel_96896/1722950221.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn_xing\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_n_xing\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mG_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlayouts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.9/site-packages/tqdm/notebook.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    255\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtqdm_notebook\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    258\u001b[0m                 \u001b[0;31m# return super(tqdm...) will not catch exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.9/site-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1184\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1185\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1186\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1187\u001b[0m                 \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: get_n_xing() takes 2 positional arguments but 3 were given"
     ]
    }
   ],
   "source": [
    "n_xing = list(tqdm(map(get_n_xing, G_list, data_list, [layouts[0]] * len(G_list)), total=len(G_list)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4f87d927845496e8409307146b66007",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/11531 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c4039d9867942069094e669d06b0986",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/11531 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "executor = concurrent.futures.ProcessPoolExecutor(16)\n",
    "for layout in layouts[6:]:\n",
    "    pos_list = map(lambda d: d[layout], data_list)\n",
    "    n_xing = list(tqdm(executor.map(get_n_xing, G_list, pos_list), total=len(G_list)))\n",
    "    np.save(f\"num_xing_{layout}.npy\", np.array(n_xing))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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