{
 "cells": [
  {
   "attachments": {
    "image.png": {
     "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAHsCAYAAADGj0QFAAAABHNCSVQICAgIfAhkiAAAABl0RVh0U29mdHdhcmUAZ25vbWUtc2NyZWVuc2hvdO8Dvz4AAAAldEVYdENyZWF0aW9uIFRpbWUAU2EgMTcgSnVuIDIwMjMgMDc6MjY6MDHih7S+AAAgAElEQVR4nOzdd3RUVdvw4d+0JDOZ9JBCICQCIdTQm48BIShVaa8FREA+C6AiUgQUhfiIBZCmKM2CgAIqRZESegvSOyGBQAIkpNdJJtP29wdv5iUmQVRwCOxrLZbLOfucuc+eyblnn12OQgghkCRJkiSpylI6OgBJkiRJkv4ZmcwlSZIkqYqTyVySJEmSqjiZzCVJkiSpipPJXJIkSZKqOJnMJUmSJKmKk8lckiRJkqo4mcwlSZIkqYqTyVz6R86ePUvLli0ZOnQo+fn59tczMzPp0qULI0aMICUlxYERSpJU1ZhMJhYtWkTLli3ZsmULcm2zPyeTufSPGAwGjhw5wvnz57FarfbXzWYzJ06cICEhAZPJ5MAIJam8Xr164eHhwYIFCxwdyn3h4MGDKBQKIiMjyc3N/cfHE0KQmprKkSNHyMnJuQMR3v9kMpckSZKkKk4mc0mSJEmq4tSODkC6d1gsFg4fPszBgwdJSkoiKysLFxcX6tSpQ9euXalfvz4qlequxpCZmcmuXbs4c+YM169fp6ioCB8fH9q0aUPnzp3x8fGpcL9Dhw6xZcsWkpKSMJvNuLu7U716ddq1a0eLFi1wdXW1l01NTWXz5s0cO3aMnJwcdDodvr6+NGjQgE6dOhEQEGAvazAY2L9/P8eOHePatWvk5+ej0+moX78+PXr0IDQ01F7WbDbzyy+/8NNPPzFgwAC6d++OQqEoF+vp06eZPn06jRs3ZsSIEeh0ulvWyY8//sjatWvp0qULTzzxBFu2bGH//v3k5+dTo0YNevToQYsWLdBoNGX2s1qtnDhxggMHDnDp0iWys7NRKpWEhIQQFRVFixYtUKvLXgJOnDjB4sWLUSqVTJ8+nSNHjrB582aSk5NRKpUsXrwYgOvXr7Nv3z5Onz5Neno6xcXFeHl50axZM/st7JsZDAa+//57duzYQXR0NN7e3qxevZrjx49jtVoJCwvjmWeeISgoCICLFy+yZs0aLl68iBCC5s2b88wzz+Du7l5hHRUWFrJ371727NlDamoqarWaOnXq0L17dxo0aIBSeaPdcvbsWRYtWsSxY8coLi7mq6++Yvfu3fbj9O/fnz59+pSpw9jYWHbs2EFSUhJCCGrWrEmXLl1o3bp1uTpfsGABu3fvZsSIEYSHh/PLL79w/PhxMjMzGThwIN26dbN/BzZt2sSFCxcwGo3o9Xr8/f1p0aIFDz/8cLn6q0hcXByLFi1Cq9Xy/vvvc/ToUTZs2EBycjJ6vZ6OHTvSrVs3nJ2dsVgs7Nixg61bt5KZmYm3tzc9e/akQ4cO5Y5rsVg4dOgQBw8eJDk52X4dqFu3Ll27diU8PLzMdeCLL75g3bp19phefPFFnJycAPD29uaDDz4o87mlp6fz22+/cfLkSbKzs9Hr9dSqVYvIyEgiIiJwcXEpF5PJZOKnn37i999/p7CwEH9/f7p27UpkZOSf1tMDQ0jS/1q3bp3w9/cXer1eaDQaoVAohEqlEq6urqJu3bpizZo1wmazldnn4MGDAhDt2rUT2dnZ9tdTUlJEtWrVRFRUlLh06dJtxxAVFSW8vLyEi4uLUKlUQqFQCI1GI7y8vET//v3FtWvXyu0zZ84cERAQILRarVAqlUKhUAi1Wi10Op1o06aN2L9/v73sxYsXRZcuXYSHh4dQq9X2c3R2dhaenp5ixowZZY49ZswYUa1aNaHT6cqU1+v1omXLliImJsZe1mazibVr1woPDw/Ro0cPYTKZysVqsVhEdHS0UCgUYtq0aaKkpORP6+Tdd98VCoVCPPPMM2LEiBHCy8vLHouzs7OoWbOm+Pbbb4XFYimz3/r160VAQIBwc3MTTk5OQqlUCqVSKbRarQgNDRWfffaZKC4uLrPPb7/9JmrUqCFq1aolFi1aJIKCgoSLi4tQKpXCw8NDCCFERkaG6N+/f4Wfk4eHh3j88cfFhQsXyhw3OztbvPjiiwIQv/zyi3j44YeFm5ubUKlU9u9Yhw4dREJCgtixY4do2rSp0Ol09u1ubm6if//+wmq1lqufjIwMMWTIEOHr6yucnZ3t56nT6URISIhYtWqVvez27dtF7dq1BVDhv6lTp9rLmkwm8dprrwk/Pz/7d0upVAoXFxdRs2ZNMX36dFFYWFgmliFDhghAzJ8/Xzz++OP27xkgPv30U2Gz2cTq1atFgwYN7OdX+n3VarUiICBArFu37k+/E0IIsXv3bhEWFiZatGghVq9eXeazUqvVws/PT7z33nsiLy9PvPPOO8Lf39/+PXBychJ+fn7i559/LnfcNWvWCH9/f+Hq6lrhdWD9+vVlyj///POV1mfNmjVFenq6vWxsbKxo1KiR8PDwsB+79G+1WbNm4uDBg0IIIYxGo5g6daoAxJdffim6d+8uPD09hVqtFkqlUjg7O4vAwECxePHi26qrB4FM5pLd+vXrxdChQ8XPP/8szp07JzIzM8Xx48fF5MmTRWBgoAgICBAZGRll9rnTyfzZZ58VH374odi7d6+4evWquHbtmti0aZPo3r270Gg0YsyYMWXKHz9+XAQEBAgXFxfxxhtviBMnToi0tDRx+vRpsWLFCvHaa6+JAwcO2Mu/9dZbQqlUig4dOoiNGzeKlJQUceXKFbFt2zYxevRoMW/evDLHnzx5snjzzTdFTEyMuHDhgkhNTRWHDh0SI0aMEDqdTkRFRZVJXPHx8aJTp05CrVaLxMTEcudXVFQkGjVqJGrUqCF27tx5W3VSmsyrVasmQkNDxdy5c0VKSoqIj48Xo0aNEnq9Xuj1enHy5Mky+/3yyy/i6aefFitXrhSnTp0SaWlpIi4uTsycOVMEBweLoKAgsXv37jL7lCZzNzc34e/vL0aOHClOnz4t0tLSxObNm4UQQqSnp4tRo0aJDz/8UOzfv18kJSWJK1euiI0bN4ouXboIJycnMWzYMFFQUGA/7s3JvEaNGqJFixZi48aNIi0tTezatUvUr19faDQa0bt3b9G5c2fRt29fcfjwYZGeni5WrlwpwsPDhUKhEF999VWZeC0Wi3juueeEWq0WkZGRYs2aNSIjI0MkJSWJjz/+2J7gjhw5Yi9vMBhE165dhbu7u5g7d64oLCy0/yv9AWY2m8Xw4cOFQqEQDRo0EF999ZVITk4WaWlpYunSpSIsLEzo9XqxZs2aMvGUJvOAgADRoEEDsWLFCvtndfr0aXHhwgURFRUlVCqVeP7558Xhw4dFamqqiI+PF2vXrhUDBw4slywrU5rMfXx8hLu7u3j11VfF2bNnxdWrV8WsWbOEk5OTqF27thg0aJAICwsTM2bMEElJSeLixYvijTfeEK6ursLDw0NkZmaWOe66devEsGHDyl0HJk2aZL8O5Obm2ssbjUaxc+dOAYj27duLq1ev2uvTYDDYGwCHDx8WWq1WODs7ix49eoiYmBiRnp4urly5IlauXCleeeUVcejQIfsxS5N5YGCgCAkJEStWrBDXrl0T586dE4MHDxZKpVI0atRInD179rbq634nk7n0pywWi5g1a5Zwd3cXH374YZltdzqZVyY5OVl069ZNeHt7l2kNLV26VHh4eIgmTZqUa5lWpGPHjiIgIEBs3LjxH8VjNptFdHS0cHV1LXPHwmQyiXfeeUc4OzuLN998s9x+MTExQqFQiP79+4uUlJTbeq93331XAMLHx0csXry4zHnm5uaKN954Qzg7O4tu3bqVu3NSEZvNJn755Rfh7e0tJk2aVObuQGkyB8Trr79+W3cObmaxWERUVJRo2LBhmTsiNyfz8PBwkZqaWma/48ePC41GI5RKpXjmmWfKtOaEEGLmzJlCp9OJDh06lDnH9evXC5VKJbp27VruboDVahVffPGFcHNzE4899liZeuvZs6dwd3cXX375ZYXnsXHjRvuPp1OnTpXZZrPZxObNm0VISIjo0KFDmbsFpcnc19e3zI/IUvv27RN169YVDRs2/Mt1+0elyRwQb731Vrm7FiNGjBAKhUJ4enqK7777rsz2nJwc0b59e6FQKMSyZctu6/0sFouYMWOGcHNzE5988kmZbb///rsAxCOPPCJycnLK7VtQUCB69eolFAqFePrpp8tcK0rZbDb7Z3tzMg8JCREJCQllyhYXF4smTZoIb29v2Tr/X7LPXCojPz+fQ4cOER8fT25urn1aWXx8PADbtm1jwoQJd+39bTYbly5d4siRI1y9ehWDwYDNZkMIQXp6OtnZ2Zw8eZJ27doB4O7ujkqlwmAwcPr0aRo2bFiuH/hmnp6elJSUEBcXx3/+8x/0ev2fxpSVlcX+/ftJSkoiLy8Pq9WKEIKEhAQMBgPx8fGYzWacnJzQaDR07NiRZcuWsXz5ciZOnIivry9wY7rN7Nmz0ev1tG/fHn9//79UN3Xq1OGJJ54o01/p4eFBjx49WL9+Pdu2beP69esEBgbat5vNZmJjYzl//jxZWVmYTCaEEFy/fh2VSsWxY8fssf/R8OHDK3y9VEJCAkeOHCElJYWioiL71MSSkhKuXr1a6foCL730UplxCQAREREEBgaSl5dHz5497XVWqlWrVmi1WhISEjAajWi1WgA+//xzdDodXbp0oWbNmuWmQXbv3p0PPviAw4cPc+3aNYKDgys9n1I2m43NmzeTm5vL0KFDCQsLK3fc8PBwmjZtysaNG8nKyqJatWpltnfu3Jk2bdqUO7azszNarZbc3FwOHz5M69atb/l9vR3u7u6MHz/ePi6gVLdu3Zg/fz7NmjWjXbt2ZbZ7enrStGlTYmNjOXfuXLlj5uXlcejQIRISEspcB86fPw9ATEwM48aNu+0YT548ydmzZwGYMGECXl5e5cpUNL4E4KmnniozNgXAxcWFJ554go8//phr167ddhz3M5nMJbu0tDTGjBnDgQMHSE9PL3OBLpWcnHxXY/jpp5/49NNPuXTpkv0iIv6wYERycrI9mbdv354GDRoQGxvL4MGDadasGS1atKBVq1Y0b9683AClp59+mq1btzJz5ky2bNlCq1at7OUDAgLKXVBiY2N59913OX/+PNnZ2RQXF2Oz2cqUycjIKFNPbdq0ITw8nM2bN/PLL78wdOhQ4MbgoG3bthEaGkrHjh3LXXxvRaFQEBoaWi5pANStWxcfHx8SExOJi4uzJ/OMjAzGjh3LgQMHSEtLw2AwYLFYyuybnp5e7jOGGz8S6tatW2EsVquV5cuX8+WXX5KYmFjh56RSqSgsLKxw/2bNmlX4uq+vLyqVCn9//3Kfg5eXFyqVCovFgsFgQKvVYjKZOHLkCEajkaVLl7J58+Zyx7TZbGRlZaHRaLh69eptJfOCggIuX76M2Wxmw4YNHD9+vFwZi8VCXFwcJSUlJCUllftc2rdvX+Gxa9euzcMPP8ySJUt48cUXadSoEa1atbL/+7PBkBWpUaMG3t7e5V738/Ozb69oQF3pYNKbF3uCGwNES783pYMb/+l14PLlyxQUFFC9enUaN278l/atX79+hYk+KCjI/n2QZDKXbvLKK6/w66+/EhERwdixY2nZsiWenp5oNBp2797NhAkTKCkpuWvvf+rUKYYOHYpSqaRv377079+f2rVro9VqUSgUvPPOOyxbtgyj0Wjfp1q1aqxevZro6Gi+//57Tp8+zQ8//ICTkxOhoaFMnTqVXr162RPnk08+iRCCDz/8kC1btrBt2zY0Gg2enp4899xzvPfee/ZW35UrV+w/biIjI5k2bRoNGzbE09MTpVLJ3r17ee6558q12vR6Pb1792br1q0sW7aMQYMGoVarWbFiBSUlJURERNCkSZO/XD8VtWZK38/Z2RmA7Oxs4EYSmzBhAsuXLyc4OJh3332Xdu3a4evri5OTE3FxcYwePbrSBX08PDwqnbmwY8cOPvjgA+Lj4xk6dCh9+vQhNDQUvV6PQqFg9uzZzJ49u8IfCYC9fv9IqVSiVqsrbKnefDEv/TFVUFCA0Wi0L1B0K2q1muLi4luWKVVUVGQve+bMGc6cOXPL8n9MhkCFyRVutIinTJmCn58fc+fOJS4ujnXr1uHk5IS/vz8TJkzghRdeqLSVWpFb1SeARqOp8LNUqVQIIcp9TsOHD2fDhg00a9aM8ePH06JFC/t1YOfOnUyYMKHM3+DtKCgowGQyERQU9JdnxJT+/VcW/x9/7D+oZDKXAEhMTGTt2rU0atSIxYsX07Rp0zLbdTrdXf/DWbBgASUlJbz00kvMmTOnzEW9uLi40tuRAQEBzJ8/n08++YQjR46wc+dO1q9fz7lz5xg+fDhms5l+/fqhUCjQarU8++yzPP300yQkJLB//342bdrE7t27mT59OsnJySxbtgylUsmOHTtISEigQYMGbN682Z4w4cYt81vdgu7Xrx+TJk0iISGBAwcO0LBhQ9avX49Go+Gpp576W1P8KmvpFhcXYzabAexTgOLi4ti7dy8KhYKffvqpXGs4IyMDs9lcaZ1WlkwsFgt79+7lwoULvPXWW3z00UflyvzxzsXdotVqUalU+Pn58fXXX9O9e/c7clxnZ2f7HZ2ff/65zFS123WrZOzn58eUKVOYPHkyR44cYffu3WzYsIEjR47wyiuvkJmZyfjx4/9SQr9TLl68yLp164iIiGDx4sXlfnRqtdq/dQ3Q6XSo1Wpyc3MRQjjk3O53ctEYCbjR/wk3bnWGh4eX2WaxWIiPjycvL++uxnD+/HmcnJxo1qxZuSSTkpLClStXbrm/Xq+nQ4cOvPfee+zfv58hQ4aQmprKnj17yiVCpVJJvXr1GDp0KCtWrGDevHkEBQWxcuVKe19vamoqRUVFdOrUqUwih/+bk18ZX19fBg0aRGZmJlu2bGHTpk2kpKQQHBxMVFTUX6kW4MaPh8TExAo/g0uXLpGTk4NarbbfGk9JSaG4uBh3d/dyidxms3HlyhVSU1P/chxFRUWkpaVhs9no0qVLue0Wi4WDBw/+5eP+HTqdjnr16mEwGDhx4kSldwIqcqtkUrpGgUqluqvrgqtUKlq3bs3YsWPZuHEjr732GhaLhXXr1t2RJVH/jtKxMX5+foSFhZXZVnodKCgoKLffzfVZUX0FBwfj5uZGUlISFy9evMNRSyCTufS/SgeCFRYWcv369TLbEhIS2Lx5M0VFRXc1Bjc3NywWC8nJyWVadyaTie3bt1fYd5mfn1/hRdzZ2ZkWLVoAN1qupX3Fpbehb6ZSqQgNDcXT0xObzWZP/C4uLqhUKs6dO1fuPc6dO8ePP/54y/MZMWIECoWC7du3s3TpUnJzcxk2bBhubm5/UhMVS0xMJCYmpkzdGAwGYmJiuHr1Km3btrX3CZe2WouKirh69WqZ4+Tm5rJ06dK/1deoUqnsi3qUDmi62cqVKzl69OhfPu7f9corr1BUVMSGDRs4c+ZMpYk3MzOzzP+7u7tjtVorrAO1Wk3nzp3x8PBg3bp17N69u9Lj/tV1w0tKSir8O3JxcSEiIgK4MWjxr97GvlNKv5sFBQWkpaWV2Xb+/Hm2bNlSYfyl14/KYm/SpAlhYWHYbDZmz55d4Q8Cecv8n5HJXAJu/LEFBgZy/vx5pk+fzqlTp8jJyWHr1q2MGTOG+Pj4CldmupO6du2K2Wxm9erVLFu2jLS0NK5cucJnn33G3Llz7beSb/bDDz8wdOhQli1bRlxcHLm5uaSkpPDzzz8za9YsnJ2dCQ8Pt19shg0bRnR0NHv37iU9PZ3c3FyOHDnC/PnzSUxMpHbt2oSEhNjrxMfHh3379vHBBx9w6dIlUlNTWbVqFUOHDv3THzd169YlKirKfutfp9Pxwgsv/O36MZlMfPTRR/zwww9kZmaSnJzMnDlzWLJkCQDvv/++vZ+0fv36+Pv7YzQaGTduHEeOHCEzM5P9+/fz4osvsmvXrttaZeyPtFot4eHheHh4MGPGDL7//ntSU1O5dOkS06dPZ9y4ceVGot9NTz31FL169eLw4cO8+uqrLF++3L5SX1JSEhs2bOC1115j/PjxZfZr3LgxRqORffv22Qe7lc5SgBuj4Hv27ElaWhqvv/46n332GRcuXKCgoIDU1FT7rI6JEyf+pW6FQ4cO8f/+3//jyy+/5NSpU2RnZ5Oens6mTZuYPXs2SqWSsLCwf7UObxYREUFAQABnzpxhxowZnDlzhpycHGJiYhgzZgwXLlwod5cKoFatWuj1eq5evcqOHTswGAxYrVb7j2AvLy/GjRuHVqtl2bJljBs3jgMHDpCbm0t6ejoxMTF88MEHFf5AlG7TvzgNTrrH/fLLL/YVtNRqtdBoNEKlUol69eqJCRMmiODgYBEcHFxmnzs5z7x0jjIgVCqVUKvVQq1WC2dnZzF06FDxzDPPCEB888039n2io6OFVqu1l9doNEKtVttXDuvbt2+ZVeMCAwOFUqksV16hUAg/Pz8RGxtrL2u1WsXYsWOFQqGw10npsevWrSsWL15sn49dVFRU4TmtXbvWvhrW888/f1v18Eeli8b069dP9OvXr0zdlK6GNXv2bGE2m8vst3HjxjIrjJX+c3NzE1OnThURERGicePGIi8vz77PzSvAVSY9PV306tWrTD2WxtKvXz8xefJkAYglS5bY97l5nnlF86+FEKJly5aibt26YseOHeW2nT59Wvj5+Qk/Pz+RlpZWZltqaqro27dvhZ9r6fl37ty53Dl4e3vb60aj0QiNRiP++9//2ssYjUYxbNiwMp//zcctXXyoonnmlc3d3rBhg/Dw8KjweAqFQoSFhYnLly9XWvc3u3kFuIocOnRIAOKFF16ocF73lClTBCBGjBhR5vW1a9faV4q7+TpQv359MWHCBBEUFFTh92PcuHH2v93S+gwNDbWvGWCz2cTWrVuFh4dHmXovPf9atWqJPXv22Ou+dJ75Dz/8UOH6CQsXLhSAGDdu3G3V1/1ODoCT7Lp3786WLVv46quvSEhIQKfT0apVK55//nlycnI4efJkuX10Oh3NmzcnLCyszKAujUZDkyZNqFOnzi0Hit1MpVKxdu1aFixYQExMDAUFBfj7+9O7d2/69evH3LlziY+PL7M++2uvvUbLli3ZuXMn8fHxZGRkoFAoCAkJoXv37jz55JNlpvvExMSwbds2+zz2wsJCqlWrRtu2bRk4cKC9VQ43+tU//vhjmjdvzo8//sj169fx8vIiMjKSl19+mbi4OJo3b06NGjUq7YNt1KgRYWFhJCUlMXDgwNuqh8oEBgYyefJkIiMj2bJlC1lZWdSpU4dBgwbRsWPHcuMMunbtyuHDh5k/fz7nzp3DycmJJk2aMGzYMJydnTl37hw2m63cvPVGjRrdcoBetWrV+Pbbb1myZAlbtmzBYDBQs2ZNevbsyYABA1i8eDHNmzcv8zmp1WqCg4Np3rx5mXXybxYeHk5hYWGF3RBarZYmTZoghCh3ngEBAXz33Xds376d9evXEx8fT3FxMd7e3jRo0IDHH3+cRx99tNw57N69mwULFnDq1CkKCgoQQtinc8GNrpqFCxcyZMgQfvzxR86cOUNeXh5ubm6Eh4fTuXNnOnfuXGaKYa1atWjevHmlo9k7duzI+vXr2b59O2fOnCE9PR2LxULNmjV59NFHef755ysdnf5Her2ehg0bVjqdrfRvs1atWhV+ngEBATRv3pyaNWuWeb1nz55s3ryZr7/+mgsXLqDT6WjTpg2DBg0iKyuL48ePV3i8//73v9SuXZtNmzaRmpqK2WzG39/f/nkpFAo6d+7MqVOnWLp0KbGxsWRlZeHl5UW9evXo1auXvWtMoVAQGBhI8+bNK53F4evrS/Pmze3r+T/oFELITgpJuluOHz9ufyDLDz/8QI0aNf7yMd577z3ef/99Ro4cybx58+5ClJIkVXWyz1yS7qJffvmF9PR0unXr5rB+UEmS7n/yNrsk3WE2m42SkhJ27tzJ3LlzqVu3Lh06dLjrAwglSXpwyZa5JN1BBoOB4cOHo9Pp6NGjB8XFxfTp04e2bds6OjRJku5jsmUuSXeQSqWiXr16PProo3h7e9OlSxcGDBjwjx6mERISwqOPPlrpWumSJElyANzfUFJSIpcklCRJku4ak8lkf97B7ZAt87/IZDKxaNEiNm/eLJO5JEmSdFdYLBZWrFiBp6fnbZWXyfwvUigUxMfH07NnTyIjI2VClyRJku6ooqIi2rVr95ceyCST+d+gUCgIDg6mfv36jg5FkiRJus8YDAYUCkWZBYn+jBzNLkmSJElVnEzmkiRJklTFyWQuSZIkSVWcTOaSJEmSVMXJZC5JkiRJVZxM5pIkSZJUxclkLkmSJElVnEzmkiRJklTFyWQuSZIkSVVclV4BTghBYWEhubm55OXlUVhYiEajoXbt2uXWszWbzWRlZZGdnY3BYMBms6HVavH398ff399BZyBJkiRJ/1yVTuYGg4ElS5awceNGEhISSEpKonr16ixatIiuXbvay1ksFrZs2cI333zDyZMnKSgowGaz4eLiQuvWrRk2bBiPPfaYXGddkiRJqpKqdDIvKSnh5MmTCCF44okniI2NJSUlpVw5k8nE3r17ycnJYfjw4Tz00EMoFAqOHj3K/PnzOXz4MMuXL6ddu3YOOAtJkiRJ+meqdDL39PTkk08+QQiBVqtl5MiRFSZzZ2dnXnvtNZRKJT4+Pmg0GgCioqKwWCxMmzaNxYsXy2QuSZIkVUlVegCcSqXC19eXatWqodfrK33CjEqlonr16gQEBNgTOYBWqyUqKgqFQsG5c+f+rbAlSZIk6Y6q0sn8Trh+/ToAvr6+Do5EkiRJqqrMZjNCCIe9/wOdzLOysli1ahU2m43evXtXWs5ms1FSUkJJSQkmkwmr1fovRilJkiTdyzIyMli0aBFHjhxxWAxVus/8nygpKWHp0qVs376drl270q9fv0rL7t69mzlz5lBcXIzNZuP8+fP06NHjX4xWkiRJuhddvnyZadOm8eOPP9KzZ0++/fZbh8yMeiCTuZDXC4oAACAASURBVM1mY9OmTcyYMYPg4GC++eYbPDw8Ki3fsmVLZs2ahc1msw+YkyRJkh5s58+fZ9CgQRw/fpxu3boxbdo0h01xfuCSuRCC33//ndGjR+Pt7c3XX3+Nn5/fLffR6/Xo9XrgRr/IrRK/JEmS9GB46623uHjxIq+//joTJ07Ex8fHYbE8UMlcCMH+/fsZNmwYrq6ufPzxx0RERDg6LEmSJKkKsNls5OXl4eTkhKurK7Nnz2bnzp38z//8D66urg6N7YEaALdz505GjBhBcXEx0dHRdOnSBZVK5eiwJEmSpCrg4MGDjBs3jl9//RWz2UxISAhDhgxxeCKHB6hl/vvvv/P222+TnJzMsmXLeOyxx8rMOZckSZKkiggh+P7774mOjiY9PZ2IiAhsNpujwyqjSidzo9HInDlzmDNnDgC5ubmYTCYGDhyIs7MzAIcOHUKv1/P1119z4MABAgMDmT59OjNmzChzrLp167Jw4cJ//RwkSZKke1dxcTHvvvsus2bNwtPTk48++oghQ4bg5OTk6NDKqNLJXKFQoNfrCQgIALD/92YqlQqFQkFgYCBNmzYFID8/v1y5wsLCuxusJEmSVKWkp6czYcIEVq1aRYMGDZg9ezadOnVydFgVUghHLllTBZnNZsaOHUvXrl3p1q2bo8ORJEmS7pK0tDSmT5+OwWBg4sSJBAcH/yvvazAY8PHxISsr67b746t0y1ySJEmS7qTs7GxWrlzJkCFD8Pf3Z8yYMbi6uuLu7u7o0G5JJnNJkiRJAuLj43n77beJiYnBaDQyevRoAgMDHR3WbZHJXJIkSXqg2Ww2Dh48yIgRIzh58iSdOnXi+eefd3RYf8kDNc9ckiRJkm5WWFjIsmXLeOqpp7h+/Tpvv/0269evd+hqbn+HbJlLkiRJD6z09HTGjx9PjRo1GDNmDE888QQuLi6ODusvk8lckiRJeqCUPjTLycmJmjVr8vHHH9OwYUMiIiKq7GJi8ja7JEmS9MAQQrBixQpeffVVkpOT0Wg0DBw4kJYtW1bZRA4ymUuSJEkPACEEubm5TJw4kaFDh7Jz504SExMBUKur/k1qmcwlSZKk+5rJZOLgwYM899xzzJgxg06dOrFgwQIiIyMdHdodU/V/jkiSJElSJWw2G3v27GH8+PFcvnyZUaNGMXz4cOrUqePo0O4omcwlSZKk+5ZCoUCpVBIYGMioUaPo06cPbm5ujg7rjpPJXJIkSbrvJCcnY7VaCQ0NJTIykjZt2uDs7IxKpXJ0aHeF7DOXJEmS7htGo5GNGzfSrVs3+vXrR3FxMSqVCp1Od98mcpAtc0mSJOk+kZmZyTfffMO8efPQaDQMGDDA0SH9a2QylyRJkqq8c+fO8eGHH7JhwwYaNWrEmDFj6N69+30x7ex2PBhnKUmSJN23hBB8+umnrFq1ir59+zJ58mTq1q37wCRykMlckiRJquIUCgVTpkyhdu3ajBs37r7uG6+MTOaSJElSlZOcnMz3339PrVq16NevH0FBQUyYMMHRYTmMHM0uSZIkVRlms5lt27bx8ssv8/7773PixAmKi4sdHRa5ubkOjUMmc0mSJKlKyM/PZ+HChYwcOZJdu3YxZswYXn755XtiERilUsnevXsRQjjk/eVtdkmSJOmel5uby/vvv8+SJUvQ6/X8/PPPREZGotPpHBaTxWLhwoUL1KlTB3d3d1q2bIlCoXBILLJlLkmSJN3zFAoFZrOZpk2bsm3bNrp27eqwRC6EoKCggLFjx9KlSxc2bNgAgJeXl0PiAdkylyRJku5R+fn5nDt3joiICDw8PHjvvfcA8PHxcVhMJSUlHDt2jMmTJ7Nr1y6ioqLw8/NDCOGwVjnIlrkkSZJ0D7p69SozZ85kwIABbNq0CbiRxB2ZyAsLC1mxYgUvvfQSR48e5fXXX2fevHm0a9fOoYkcZMtckiRJusfExcUxadIktm3bRtu2be+Jx5UaDAbee+89vv/+ezw8PJg7dy5PPvkker3e0aEBMplLkiRJ95Bt27YxZMgQsrKyGDRoEJMnTyYoKMihMV28eJFnn32WkydP0rp1az799FOaNWt2Ty1OI5O5JEmSdE+IjY21Pxzl7bffZvTo0Q4drW4ymYiJieH1118nNTWVPn36MGPGDIf/uKiITOaSJEmSwwghMBgM6PV6WrRowbBhw2jZsiW9e/dGqXTssK5r167x+eefo1KpePfdd3n99dcr/XFhsQksVhsalRKV8t/vP5fJXJIkSXIIIQS//fYbe/bs4ZVXXiEkJITo6GiHPyAlPT0dPz8/atSoweuvv45Go6FTp04VDnKz2gTX84tJyjZQZLLg6qSmlrcrAR5alP/ioDiZzCVJkqR/XVFREbNnz+bLL79EoVDQoUMHQkJCHJrIhRCsW7eOTz75hC+++IIGjRrT8uEOOKlVFSZyIQTnr+ex9fx1lArs5VLyimkb6kuQ57/XRSCTuSRJkvSvSktL46233uKHH35Ap9OxfPlyHn/8cUeHhUKh4Nq1axw9epTftu0m3upJXrEZgCBPHY+G+eOiuTHozWq18cXeeH47k0JOkRlnJXhmnKNb9+5kGUq4nFVINb0LTup/p6tAJnNJkiTpX5OYmMjw4cPZv38/jzzyCAsXLiQ0NNShMaWkpCCEICgoiJdffpnGzVtwWelHTpHJXuZKjoGdCWk8Wtcfo8XK57vj2Xcxg6wCI8VZqVzY/j1Zx3ehspl4vFcfcovNmKw2mcwlSZKk+09qaioGg4GRI0cyevRo/P39HRaLyWRi9+7dzJw5Ez8/P+bMmYOnpyf6GmEYk7LKlc8sLOHY1WzOXs9j+/k08oqMpJ09RPa+NRhTLuLVsD35LtUA0GpU/+pAOJnMJUmSpLsqMTERgNDQUFq0aMHChQupXr06np6eDospNzeXpUuX8tlnn5GWlsY777xj7xcvNJor3KfIZOFUSi6p+cWYSoyk7VtH+oHfsJQY8YvsR0Dbbmj8/HDRqKjhpcP5X2qVg0zmkiRJ0l0ihODYsWNMmjSJoqIifvvtN/R6PQ0aNHBoXGlpaUydOtW+mtvKlSt55JFHcHV1BaC6p464tHx7+SKzlet5xRSZLLg4qdEU53L5pzmkHN+Hxs2LoF4v4x7WHI3OlRBfPa1q+RDqo5ej2SVJkqSqzWq1smPHDoYOHUpaWhqvvPIKLi4uDo1JCEFqaipPPfUUsbGxdOjQgW+++Ybg4OAy5Wr76olL05KaV0xesZnz6QUYzRa8tBriTh4ndumnZF86h676Q9QeOAFnn+r4uDoT4q3jvz0i8NY7/+vnJpO5JEmSdMeUJsylS5fy/vvvU716dWbNmsWQIUMcOu0sLy/PvpqbyWRi5MiRvPXWWxWu5qZQKIiqF8jJlFx2nL+Oq5OKQK2Cq8f3sn/JPAwFeUR07U/U4FFcMVgRAiLrVqNfRLBDEjnIZC5JkiTdQUIIvvvuO6ZOncp//vMfRo8eTVRUFE5OTg6LJzk5mUWLFvHll1/i7e3N6NGjGTBgAB4eHpXu56JRUc/PjbT8Yqx56ez5eSV7f12Nu7cvLZ4YSa22XQgLqkYrrYZ6/u6E+3v8ayPXKyKTuSRJknTHlC4AM378eAYMGEBYWJhDHw8qhODQoUMsWbKEtm3bMnHiRNq0aVPmLoEACoxm8o1mFIBNCHQaNRolXD59lJWfzeb077up17wtPQaPIKBOI7zdXekY5o/eSY2nzgmNyrFLz8pkLkmSJP0jVquVmTNnEhgYyKBBg2jdujVNmzZ1eB85gFKppF27dixcuJDmzZuXu60uhOD3y1nEp+dzJcdAWkEJXjonQr20XDi4kxWffULSpUQ69BlIzyGv4urhiVKp5OHa1Qj1uTcefwoymUuSJEl/k81m4+rVq4wePZq1a9fSqVMnunTpQkBAgEMTudFoZNasWdhsNiZOnEhQUFClTzo7cS2XPRfSOXApg6NXc3B30VDNBbYe28KRnxbjptczasonRHTpgxAClVJBeID7PZXIQSZzSZIk6W8wGo3s2rWL//73v5w4cYInnniC999/n4CAAEeHRkpKCrt376agoIBXX3210r7xErOV305fYc7O8+QUmbFZLYjsayiOb0Ak/I5f3cYMGTGK8UOfAqWKghIz7s4aPLROOLDnoEIymUuSJEl/2VdffcXcuXMxGo2MHz+e4cOH4+Pj47B4bDYbhw4donnz5jz00ENMmTIFb2/vyhO5xcrui+ksjk0ky2BCmE3Yko7Dsd8gMwld445E9B6ItnYzLCippnPCS1f5ID6bzebQR7bKZC5JkiT9ZdeuXUOv1/PRRx/x+OOPo9VqHRZLYWEhM2fO5Oeff2bixIk888wztGnTptLyJRYr+y5msPT3RDIKjAiLCXFiI5zeBjYbikcGoWrQHmf/EISAG0Pkbu369evk5uY6bEEcmcwlSZKk25KXl4dWq8XJyYmJEyfy6quv4u/v79AW6dWrVxk3bhxr1qzBx8eH6tWr37J8jqGET7ae4eiVHNILjJiLDdg2fwFJJ8ArEB4ZjCIwDJXOGZ2TmuqeWpxUqj+NIyAggAsXLiCEcMjofceOpZckSZLueYWFhSxfvpyHH36YDRs2AKDX6wkMDHRYIrdYLMTGxjJw4EDWr1/PI488QmxsLJGRkRWWN1qsrD95hcjZMSw7dJmjl9NIPHUU849TIfkkiuAmKB57FWVQOCq1khBvV1rX8qFxdU90TuWTuclk4vDhw4waNYqEhASUSiWRkZEOm4YnW+aSJElSpZKSkli0aBELFiwgMDAQm83m6JDIzc1lzZo1TJs2DaPRyBtvvMHYsWPx8vKqsHyxycLGsyl8tOUMqXnFCGMhxnP7MB7+FWExoW3ZHRHRDZuLG85qJQ0CPHi6RQjNanhTz9+j3Bzy9PR0fvrpJ6Kjo9HpdHTs2JE6deo4dD69TOaSJElSOTabjT179jBz5ky2bt1K586dGTduHG3btnVoXPHx8cydO5elS5cSEhLClClT6NWrF+7u7hWWT0jP57cz1/jtzDXSCoqxFWZTFPsTpvjfUXr4oW3RE98m7fF1d6W2rxv/qV2NtiG+BHho8XF1KdMqF0IQFxfH3LlzWbFiBaGhoUyaNImoqCiHJnKQyVySJEmqwPHjx3nnnXfYv38/o0ePZtSoUQQFBTnstrrNZuPAgQO8++677N27l0ceeYSZM2cSHh5e6VKxi/dfYMXhSyRlFZFlMFJyJQ7zru+wZV9FExKBtl1fNP4h1PB2Z1Cbh+gSHkhNL12lq7n9/vvvjB8/nkOHDvHEE08QHR1NnTp1UN1Gn/rdJpO5JEmSVE7jxo1p27YtY8aMoXfv3o4Oh5SUFMaPH09sbCxvvvkmn3zyyS1bwwcSM5ix9RxGs5VikwnTmT2U7FqKKC7EudljuEYOROWsxV2rZnDbhxjarg5qZeXHW7t2LYMGDcJisTB+/HjeeustdDrd3TjVv0Umc0mSJAmr1cqFCxdYvXo1r7zyCr6+vn+aMO82IQQFBQW4ubkREBDAxIkTycnJoX///hXGJYD8YjNJ2YV8vjue/BIzFGZTfGQzxiObUDjrULZ7BtfmUbg6a/DVO/M/zYJ5oV0dVBUkcpvNxrVr15g3bx7Tp08nPDycsWPHMnDgwHtiqdqbyWQuSZL0gCssLGTz5s1MmzaNxMREmjZtSs+ePR2eyI8ePcry5ct56aWXCA8Pp0ePHrfcp8BoZldCGtfzi7meX0RRchzFh37FfPkEmqB6OLd+EqqHE+qr5+GH/HisfiBdwgMrTOQAaWlpTJ06lZUrV9K9e3fGjh1Lhw4dHDoVrzIymUuSJD3AsrOz+fzzz1m8eDEWi4UPPviA1q1bOzosABISElizZg2PPPII4eHhlZYrsVi5mFHAgcuZnE7Jw0VhpeD4dgxbvseak4Y2Igq3ll1RefmjUiqZ9HhD/lPbH19XZ5S3uLXu4uJCjRo1GDt2LEOHDiU4OPhunOYdIZO5JEnSAyonJ4eXXnqJzZs3U79+fWbNmkXr1q3RaDQOi8lisWAwGPDw8KBHjx6EhYURFhZWaXmj2cqO+OskZhRyIbOAa2kZnN/yA/E71iNMRnRRL6Ct1xYnVz1qlZLBrUPp1bhmpYPcCgsLWbRoEa+++ipeXl6MGTMGtVrt0BXubodM5pIkSQ+opKQktm/fzuOPP86MGTMICQlxWCyl/dMvvfQSSqWSNWvW4ObmRvPmzSvdJzGjgC/3JXDgUiYlZisU5XL11wWkHtuLR/UQRkXPIlXpTXxGITW9tAxtW5tuDSt+ehrcuLX/zjvv8MUXX2CxWBg3bhxubm5343TvOJnMJUmSHhBCCLKzszEajQQEBBAREcGvv/5KWFgYvr6+DouruLiYHTt2MGXKFOLi4ujevTtZWVkEBgZWuk9hiZkFexOIS8unpLiYvEtnSN6wBFNOGsFtuhDReygufsFEerkyPLIeLYN9UKsqvqVuNpsxm83odDqGDx+O0Wjk2WefvVune1fIZC5JkvSASExMZPbs2eTm5vLhhx9So0YN2rdv79CYCgoKWLhwIV9++SVWq5UJEyYwfPjwSldzKzJZyCws4VBSFolZhZjys0nb/xspe9YACoI6P01EpycICAggyFNHk+pehAe4V5rIs7Ky+Oabb9BqtbzwwgvUq1eP+fPn35OD3G5FJnNJkqQHwJ49e3j77bc5ceIEffr0qXShlX/TpUuXmDRpElu2bKFu3bq8/fbbPPbYYzg7O1dYPr3AyPGr2WQWlrD/UgZJly6SsvV70k7tx9mnOjW6DsGrdmNqBvnTNtSXLuGB+Lg6o66kf/z06dNER0ezceNGoqKi6N+/Py4uLlUukYNM5pIkSfc1i8XC4sWLmTx5MsXFxbz22muMGTPGoc8eB9i1axevvfYa586d47HHHmPWrFnUrl270tXUjGYrp1JySco23HgsaWYyp7+KxphxjZotOxDUbShWrRd6rRONg7zo1jAIH9eKfxTYbDa2b9/OuHHjOHXqFD169GDevHkO7Wr4p2QylyRJuk+ZzWY+++wzoqOjcXJyYv78+Tz33HMObXkaDAZWrVrFu+++S0FBASNHjuTDDz+scLS4TQjyi80Uma3kG80kpOdjKi7iyI6NrJg5BbWLlhqP9ie867P4+njjpFLSLtSHQW1C0WoqTm8Gg4HVq1czceJESkpKmDJlCpMmTaqSrfGbyWQuSZJ0nxFCIIRAo9HQpEkT+vfvz0svvUSrVq0cHRqHDx9m8uTJ+Pr68uGHHzJw4MAKF6cxmq3sS0zniz0JHLyciUoB1SmAU1s5suVnAoIfon2f5/Bo0gG9zoUanjrCAzyIrONXYSIXQpCQkMCCBQtYuHAh9erVY/z48fTu3bvKJ3KQyVySJOmuEUKQU2QircCIEFDNzRkfV2eUd3FltaKiIn744Qc8PT3p27cvHTp0oG3btg5dR7z0salKpZK6devy5ptv0qZNG9q0aVNhIjdbbWw4fY3ojSc5n16AAoG4Fkfy4fWI1ASaP9yBLk+/QEj9JjirVbQJ9eEhXzec1RXfojeZTOzYsYNPP/2UmJgYnnrqKd544w1atmyJWn1/pMH74ywkSZLuMTYhuJRZyMlrOeQbzQjA1UlN4+qe1PVzr3QJ0X8iLS2NGTNm8O2331KrVi06deqEp6enQxNWUVERy5cvx2QyMXz4cAICAhg5ciQajabSFvHGsynM3RnH2ev5CJsVcWYH4thvCFMR6mZdaTvgJULrPYRKqaB2NTdCfCpP5FarlV9//ZUJEyaQmprKtGnTeP755wkMDHT4Y0vvJJnMJUmS7oJ8o5lTKblkFJbYXzNZTBy/moOfmwvelQzO+ruSkpJ49dVX2bhxI2FhYSxfvhwPD487+h5/R1paGj/99BPHjh1j4MCBeHp6VjpaHeB6XjFrj18hKduAzWhA7F+JOLcLnLVoIgfhFN4OLz9/GgR6EOLtSqCH7pY/jFQqFdWqVSM4OJg5c+bw+OOP3xe31f9IJnNJkqS7oNB4Yz70H+UbzWQZTHc0me/fv5+XX36Z5ORknnvuOT755BP8/Pzu2PH/KovFwvXr1wkMDCQ0NJTo6Gg8PDzw9PSssHyJxUq2wUSJxcrBy5nkF5fglHMFsfVbxJUz4FMTVcehKAJro3PW0KthEK0fqlbp+5vNZs6dO4fVaqVZs2a0b9+eDRs24OTkdF+1xm9WpZO5zWYjIyODK1eukJKSQkZGBlqtlsjISGrUqFHhPhkZGRw+fJiMjAzUajWhoaG0atXqvuk3kSTp3iAQf2vb3/HNN99gMBh45513eOGFFxw67aygoIBNmzYxb9483nvvPTp37nzLB7cYzRZWHUlmc1wq6QXFGIuNZJ+NJX3HT5BxHUX9R1C07A2unmhUSrrUDySk2q2XWE1PT2fMmDEYDAa2bduGVqutdMrb/aJKZ7DCwkLmzJnDpk2bSElJIT09naCgIBYtWlRhMj9//jzR0dHs3bsXq9WKzWbDx8eH/v37M2nSJIc+XECSpPuL3lmDt86JTEPZ1rmbiwYv7T9bsMVms5GTk4NGo8Hd3Z3JkyfTv39//vOf/zh0oFtaWhpffPEFS5YsAcBoNFZa1mK1kZpvZM3xJL47dBmrTSAMuVzbvYaso9vQanWE9RxMYXBLcm0a3JzVPN0ihJGRYVTTVz5/XKlU4uXlRd++fXF1db1vW+J/VKU7DiwWCzk5OYSFhTFu3Dg6d+5cadmCggI++OADVq1axZAhQ4iJieHnn3/G39+fTz75hKVLl/6LkUuSdL9zd9HQqLonOqf/azO5aFQ0qu6J1z+8xb5nzx769+/PqlWrMJvN1KxZky5dujg0kaekpDB8+HBmzpxJrVq1+PHHH3nssccqLGu22jiUlMU3By4ya8d5EjMLuXLhPBdWzyFr/3o0ei8C+46ifsdetA2vRf9mwSwd/DBTezShrp97hQn64sWLDB48mMuXL6PT6RgyZAgDBw7ExcXlbp/6PaFKt8y9vb354osv7P9/+vRp4uLiKiy7Z88edu/eTYcOHZg6dar99Tlz5tCsWTM+//xz+vfvf08MGJEk6d5mE+JGSxJQAGqlolyCUSkVhPm7E+Sp41J2IUJALW9X3F3+7w6gEALb/95xVyr401akxWLh22+/ZeTIkbi4uNC9e3csFgsajcZhLVCbzcbBgwfp27cvmZmZ9O/fn+nTpxMUVPHTySxWGxczCvnhSBKHkrLIKizGfDUO07Yl2HJS8WrUnoeeepMgXy8Gtn6IJkGe1PZ1Q1XJ2upWq5WYmBiGDBlCfn6+vY/+Xn9k6Z1WpZP57bLZbJw6dYrU1FQ+/vjjMtsaNmxIq1atSEpK4tChQ0RFRTkoSkmSqoL8YjPrT15h/amrZBhKCHTX8kzzWnQKD0TvXP6S6uqsplFg+YFfecVmTl7L4UpOEQoFBHvpaBzkVSbZl7JarVy+fJmZM2eyaNEiGjZsyLhx4/if//kfh62xLoQgKyuLFStWEB0djbOzMxMnTuSNN96o9CEpFzIKiN5wkg1nU8grNqExG1BcPEjJvtWg1uD08NP4duyL2llLqK+ejmH++FZySx1u9I0vXbqUmTNn4uTkxMSJExk7duzdOuV72gORzA0GA8nJyVgsFlq0aFFu+8MPP8zZs2dJTEx0QHSSJFUl605eYeG+C5isNxZCuZhZyOd74lEqFfRoFHRbLeRik5Wd8ddJK/i/PuUzqXlkF5l4rH5guTnT169fZ+rUqaxbt47evXszduxYWrdu7dD+4PPnzzNnzhxWrVpFeHg4o0aNok+fPhWOPbJYbVzIyOfl7w9yMCkLq82GLSMJ46mtELcHpX8omhY9UYc2xYQKPzcXujWofstEfvbsWT799FPWrFlDo0aNGDt2LD179nxg+sj/6IFI5sXFxeTm5qLT6Sq8jV6zZk1MJhNZWVkV7m+1WjGZTMCNKQ8Wi+WuxitJ0r1r1dEkeyIvlWc0sz8xg/YPVbutKWdXcg2kF5YfHJaWb+RabjEP+erLvO7q6kp4eDiNGjVi0KBBt3zO979l5syZrF69mieffJLRo0fTtGnTCstZbILYxEwWxyYQeykTm9WCuHIKceRXSLsAdduibd0T3+A62BQKOtULYERkGE2qV9y6t9lsrF+/no8++oizZ8/Sr18/3nzzTRo2bOjQRH7u3DlCQkIcdnv/gUjmFouFkpKSSh9tp9frsVqtlY683LBhA2+99RYGgwGA3NxcevbseVdjliTp3pRlMJV7TQjIKTZRWGK5rWReYDT/6bacnByWLFnC6NGj8fT05I033kClUt1ywZV/04ABAwgPD2fw4MGVToUzmq18f/gy3x1M5Oz1vBvjDM7uRBz8GSwmaPcMivodUHu4U8NLTz1/d8ZFNSA8oOKxSwaDgc8//5zZs2eTnp7OBx98wMsvv4yHh4fDErnVamXjxo288847dO7cmZkzZzokjgcimatUKjQaDWazGSHKz+8sKSlBqVRWOjWte/fudOrUCSEEFouFSZMm3e2QJUm6RwV767iUZSjzmkIBge66Sh+5+Uc+rs4obqw4Xu44Hi4qTpw4wejRo9m3bx9arZaRI0c6dKQ63FijY+3atTz66KPUqVOHRx99lEcffbTCskJAsdnCfzedZmfCda7lFmErLsC24zs4txM8AqDDUJShzVCqVPjrnWn7UDX6RtSkrp97hcfMz88nOjqa+fPnU61aNTZv3nzLGUz/BoPBwLJly5g8eTImk4mAgACEEA75YVGlp6bdLmdnZ9zc3CgsLKSoqKjc9pSUFDQaTaWrE6nVavR6PW5ubuj1eocNOJEkyfEGtX4IH1cnbr5c1/TUEVmnGq4VDICrSA1PHcHeZZchVSsV+OtUxG7byODBgzl9+jSDBw/mqaeeusNn8NfZbDb27t3LtGnT2LRp0y3Lmq024tLymLH1DX0kzQAAIABJREFUDFv+P3vnGRhFubbha7bXZDedhJBG751QVJSDICAHGyAWRBGxogiC5RxBjgpKUUFQP1FRLEeODRHBhiACghTpEAgQ0vv2PvP92BBBEkVMEZ3rl252Z97ZhLnnfcr9HCjA5vRgy95H5ccL4NB3CM06IQyYgDK9Gxq1iowYM/8a3IFHB7Wjd3psrdaskiSh0WgYNmwYn332WaMLOcCiRYt48MEHSU1NZdmyZUyePLnRIgR/i525yWQiKSlcmLJ3716aNWt2xs+3bduG0WgkNTW1cRYoIyNzwfCP1gmolQKbj5VS7PDRzGqgd3osfdNizvkYKqWCfhlxZBU7KKsylQm5Kln77lLeXf4mFouFmTNnctNNN2EymX7jaPWHy+XC5/MRFRVF3759mT9/fo1FxKeodPv5NquI1fvy2Z5TRpnNgf/Ad7g2fUrIZcPY40o0nQYimaIxaJRc06kpg9s1ZUj7mtvYKioq2LBhA4MGDSIyMpIHHngASZKIja3dyrUhSU5OZtSoUUycOJGuXbs2qsvc30LMVSoV7dq1IzY2luXLlzNkyJDqnx0/fpzNmzeTnJxMly5dGnGVMjIyFwJalZKBrZvQMzUGly+IRa855x356Rg0KjomWfAGw8V0Odk2Xn15CZ06dWLmzJlkZmY2quFJXl4ezz77LGVlZSxdupS4uDhGjBhR686z0u3n1U1ZrDlQQLHDh8tho3DNmwQOb0FljMDwj3Fom/cgwmykZVwE13dL4coOSVgMtbu5ffDBBzz55JNMmzaNiRMnNrqIO51OvvjiC1q1akW7du247rrr+Mc//tGoPvin+FuIOUD//v1p164dK1as4PLLL2fs2LHYbDYeffRR7HY71113HYmJiY29TBkZmQsAQRCw6DVY/qAtK4BWKVTP+X7vvffo0aMHUVFRjVqZffjwYe677z6+/vpr2rdvj81mIzY2ttY1uXxBPtmdy2d787H7Atjzj1P44QJ8RcdRNWlO4pUTiWrWnBACnZOsPDq4Pekx5l+ddqZQKOjXrx///Oc/GTFiRH1d6jkTDAaZOnUqy5YtY9y4ccyePRuz2fynEHIAQaqpIuwCwePxMH/+fObOnQuE5+YGg0EMBkP14JQ9e/ZU+7Rv2bKF8ePHk5OTQ3R0ND6fD6/Xy8UXX8x77713Tk/BgUCAKVOmMHjwYK644or6uzgZGZm/NDabjXfeeac6F52SktLYS8Lr9bJlyxbuvPNO8vLyGDlyJPPnzyciouaiNH9IpNDm4fN9+ezOL2fj4XzcWTs4ufo1/I4KDC17YB4wlsioGBIj9XRvFsU9/VvTzGqs8Xgul4v169dTXl7O6NGj/xQDsAKBAAcPHuS2225j9+7dXHLJJTzzzDN07Nix3h64XC4X0dHRlJWVYTTW/F39ksb/pv4ASqWSVq1a/Wqb2Ok9f5mZmaxcuZIVK1Zw9OhRNBoNPXr0YPTo0X+adg8ZGZm/NqIosnfvXhYtWsTy5cvp3bs3paWljS7mubm5vPPOOzz99NNER0czY8YMbr311lqFvMzlY1N2CXvyKtlXUIm9opTcbz/Avu1zVDoD8ZeNwdTjCqLMBro3i2ZQmyYM69AURQ0CKEkSJ0+e5PXXX2fevHm0b9+eQYMGNXpY3eFw8MknnzBr1iyKi4u54447mDx5cqP/rmrighZzjUbDtddey7XXXnvOn0lPT2fatGn1uCoZmYbD4w8SkiSMGlWDh2UDITHsS678azbF1Mf1eb1e1qxZw/z58/n++++5+eabue++++jYsWOdnQMgJIa949VKBb/1ZyGKIrt27WLBggV88MEHdO/enenTp3PppZfWaoCy62Q5n+7J5ceccoKiSFH2QQq+XYFt3zY0ic1JuuQaIluE2856psRwa+8MOiRaahXynTt3Mnv2bNasWUOfPn2YMmVKrd1FDYndbud///sfarWa5557jquvvhqz+dfHrzYWF7SYy8j8XfH6g7z0fRZ78ioJSRItYs3ceXFLomopJqpLXP4gBwttYU9xoFmUkVbxEWdMB7uQcXgD7CuwUWj3AJAeY6JlXAQ69R+rVA4Gg6xcuZKpU6ficDh44YUXGDlyZJ3uPoOiSHapk8PFDvzBEDFGHe0SI3+1//3LL7/kkUce4cCBA4wdO5aHHnqIlJSUGg227J4Az607wAe7TlLk8CCFQmhydlDx3f9wF+XQtOc/SBl4PUpLHGqVmp6p0dyamUFqtKnW/PiqVat4+OGHOXnyJLfeeiuTJk0iLS2tzr6T88HhcGA2m4mPj+eRRx5Bp9PRvn37Gr+TPwt/jX99MjJ/I0RRZMpHO9iWU1792oFCO9tzyvnvuH6o/6Do/BreQIhtx8s4XGyvfq3I4cXpC9IzNQat6s97szsX3P4gm4+VcrzMWf1aqdOH2x+kW7No1H9gl65SqWjatGn1kJT+/fvXaTRFlCQOFNrZdryUYNUotlKnjzKXl/4tE7Aazi7WW79+PVdeeSV6vZ6nn36aiRMn1ppytHv8PP7Zbl7/IRuPPwhBP8E9XyH+8BEEvWT88w5SLrmKJhF6Oja10jc9lj7ptRfNBYNBnn76aR5//HEsFgtPPPEEd9xxR6NW8EuSxKpVqxg/fjzr1q2jbdu29OzZs9HW83uQxVxG5gJj49ESDhY7zno9z+bhw90nGdUttd7OXe72c6Lcddbrx8uctIqPIM58Yc+OLnf5OFlx5vWJksSJchcZsWZiTb/v+rxeLzt37kSr1dK1a1d69erFxx9/XC8jS12+IMfLnNVCfooSp4/8Sg8WvQZBCNuPQrjmKDMzkxtvvJErrriCESNG1OiCKUkSJU4fH+zKYeWeXALBEJK9lND2lYgHNyJYEoi7fCxNu/ejdUIkfdNjGdAqgfiImkP0oVCIrKwsnnrqKT744APatWvHU0891ahDUiRJori4mKVLl7JgwQJ0Oh1btmyhbdu2jbKe80EWcxmZC4zsMif+YKjGnx0otNXbeSUJnN4AvhrO7QmEcPkv/AFE5W4/IfHsBh+HN4g3INbwidqprKzk/fffZ/78+SQlJfH555+j0WjqzVjEHxKx1+L5Xu72hWewBwKsX7+eyspKrrjiCkwmE4sXL651N+zw+PnqUCEbjhTz3dFiiirtBE7sI7htJVLJMRTp3VF2HUooMQOTVkXX5CguaRH/qw91Bw4c4L777uOnn37i6quv5tFHH6V169Z18h2cL3v27GHBggV88skndOrUiWnTpjF48OBGXdPvRRZzGZkLjHizDpVCgY+zxSW5lpafukAQQK9RolYqCPxiaphGqUCnajz3q7rCrA0XEv6yY1evVqL5HSH2kpISZs2axfvvv09CQgLjx4+v912nSiFgUCtx+c5+qApfVzhS8Mknn7B161a6dOlC8+bNaxXybSdKeWVjFmv2F+D0Bwm4HHj3rkPc/RWS24ayxwgUrfuhNEfTJNLADT3SuLxNE6KMWn7tSgOBACqVimnTpnHLLbc0ep/2gQMHmDBhAgcPHmTkyJFMmjTpgtqRn0IWcxmZC4x+6XH813qCA0X2M163GjSM6FizLWZdYTVoaRKpJ+cXofYki4FIfc2Dii4kok064s266uI3AAFIsujP+fr27NnD+PHj2bt3L5dccgmzZs2ic+fO9W71adSqSI4ynhVdiNSraRKpRyEImEwmxo8fz8iRI0lOTq71WPsLbEz9aCdbjpfiD4pIXgfC5v8iHfkB9BGoh09BmdAcQaPFolPzzIguXNoq4aw57Kc4efIkELY/bd++PW+99RYRERGNNi70dJKTkzGZTPz73/9m3LhxWCyWC3ImuizmMjIXGGa9mrlXd2Pm6t3sL7AhAWnRRv41uD3Rpvq9OZq0KnqlxqAUBPJs4Wr2ZKuRbs2iGr2aXSKc+xTgvG/GETo1fTNi2Xq8lCJHeCRyeoyJrsnR51TNHgwGmTZtGrt372bMmDHMmTOHmJhz92w/F34OGkgEQiKVnvBI1iijlvZNLEhSON0SEiUitEo0tjzGXX83c599lrZt29Y6dxzCBY5ZxQ4e+N9WNh4tQRRFJHsJrF2IVHYSmrZDc9mtqKzxGNQqmlr0zBrakcvbJKI4rVpdlMLz4JSCwPHjx7nrrrsoKiri+++/R6fTER8fX6ffye+loKCA3NxcOnXqhMlkYu3atY3qq14XyGIuI3MBEmPS8vx13SmyewmKIgkR+j9Uaf17sBo0/KN1Ai5/EEEQMP4JRNzhDXC81Em+zY1RqyItxkxCRDgd8XuJNmoZ3DYRly+IQqHAoPntm3xlZSVKpRKz2czzzz/P6tWrufPOO+t0wmJIlLB7A5Q6ffiCITyBENmlDuzeAJIEcWYd3ZpF0a1ZFB2SLFTaHHy1ZjX/fupJSktLWbNmTa3h41BIYn9hJU98/hMf/5RHUAJ8Lji+Cza9C2IIOgxE6DKU6CZNyEyNYUi7JK5ol0iS5efRrIGQSJHDy9ESB3a3h/S4SKIjrAwYMIDy8vIaR1A3JH6/n+3bt/PYY49RUlLC0qVL6dGjxwUv5CCLuYzMBYtCEGgS2ThhSkEQMGn/HGF1pzfABztz2HK8tDpfnBZt4qrOyXRuaq3RqOS3EAQBk+7crm/Hjh0sXbqUTp06cfPNN9OiRQsmTZr0u8/5a4iSRG6lix05FZQ6vXgCIbJKHJi0KpKtRlQKgWKHl125FZh1alzlJSxevJjXXnuN2NhYZs2axdixY2s8tj8o8tWBfB5euYs9VZEeHCWw92s4sB70EdBxEEKL3ii0elrFRTBlQFt6p8ecEQERJYm9+ZV8sDOH/YcOk73lK9pfNpyB3dsyeuxtxFtMjWrPWlZWxnvvvcezzz6Lz+djwoQJJCXVb1qqIZHFXEZG5oJmd14F3x0pxn9aUd7xcifrDhWSbDX87naycyUQCLBixQqeeeYZsrOzSUhIqLedp8sX5EChnVKnNxyJ8AWodPuxewNE6tXVZkElDi9rvtnAW4sXsG7dOvr27ctjjz1GZmZmjf3jEjDv6/0s3XyUo6VVvfWlObDlv1BwGJLaQNcrIT4DQaGgeayJqf9oS2ZazFmpjEBIZNWek2zdsol9n71F4f7tqPRGjNZY0qONJEZH1st3cy4cO3aMZ555hv/+979YrVYWLVrEwIEDa7WqvRCRxVxGRuaCZsfJijOEHMJ55ZxKN6VOX72IucPhYMGCBSxcuJCKigpeeOEFxo4dW2+GJ05/kDKXj1OPCiExnJMOiRIVbj9RBi1iKMTW9V+x6tUFFOQcZ9y4cdU+67U5l037aAeLNhzGEwiFv7SjW2HDMgj6oPUl0O1KMFpQCQq6Jlt4cngXLm2ZcEZ+vHqNHj9fr/mM7e+9iLu8mNaDRpHRdwguf5DsUifdm0Wjb4SUTDAY5JtvvmH58uX069ePV199lYSEhL9EaP10ZDGXkZG5oJAkiYAYLnRTKxWoarEJVQpCvVQlOxwOHn74YZYuXUpaWhqrVq2iV69edXoOUZIInnaNAnD6ZZp16uoWQUEQ8LicfL9qBauXLcao1/Liiy9yxx131Hj9gZDIyQoXj6/azbvbjxOSJPA6YfcXsGs1qHXQ8zro8A9QqrDolLxwXQ/G9EhDWcNDgSRJ2O12Fi9cxMaXZqEzW+g5dippfQajUCoRAKWg4DdN4usYjyfckaDX6xk2bBjx8fH07dsXq9XaoOtoKGQxl5GRuWCwewMcKLRxosyJRqWkeayZzslWdpwsD+8uq1AIAmnRJuLrwZFOEASsVisjR47k4YcfrnPDE5vHz/5CGznlLjRKBa3iI0mI0BFn0uH0OZEk0KmUNLUYKHX5iDZoWPvWIr5ZsYzu3bvx6COPMHDgwBqF3BMI8f724yz45gB7CmyIEuGw+q7VcGwnxKRA96ugWQcAoo0apgxow029MmpcayAQYM+ePcyZM4cPPviA5p16kHTpdcS36Y6iaudr1qlpEW9CX482w6cjSRInTpxg6dKliKLIv//9b+Lj4391uuZfAVnMZWRkLgj8wRD//fE4W0+UVQt3pF5N/xZxXN66CRuzSyh3+1ApBNomRDKwdQKWOup9LykpYcOGDQwePBiTycTkyZMJBoN1PqIzGBJZsSOHH46XVl+j1VDE0HZJtG0SQUCUyKt0IwjQJiGCeLOOCL0Gw8W9SDEJTJgwgU4dO9YaVl/07UGe/foAJU5fuEI9ezvsXgMV+dAiEzpdAdYmALSINXNf/5ZM6NeyxmN5PB5WrlzJ/PnzOXDgABMnTuSKkTex12vkULEDf1AkxqTlkhZxtElomAlooiiydetW5s6dy9q1axk4cGC1fe1fHVnMZWRkLgi255SzMbvkDEMUmyfA5mNl3N4ngx6p0ZS7/OjVSmLMWqKN2joJs584cYJZs2axevVqZs2axW233VZvodo9+RV8n11yhsNehdvPd0eLaRFnpl9GHHavn0qbg3VffE6LLh3p1qoLncfegP2qK4mOjq7xuFnFdl5Yd5C3fzxOhScAPjfs+gz2rwdJDO/GW/YBvRkB6JFi5dkR3eiZGlOr8938+fNZvHgxSqWSBQsWcM0112COiKSX20+Rw4M/ECLKpCPWpG0wD4IPP/yQGTNmkJeXx8SJE7nnnnswGAy//cG/ALKYy8jIXBDszK2oxTc9QL7dw4BWTUitWcvOmyNHjjBq1Ch27drFkCFDGD58eN2e4Bf8eKLiLKtcgDKXj0K7l4xYMyatir0/bOTBeyZy22230bL5U0RGRtYq5IeKbDz44XY2ZRdT4QmBswy+fxeyfwSNHq64H5q0AEGBArioeSwvXNeDDknWX7VlLS8vJzY2lkWLFtG7d+/qgrIYk5YYU/2P4j0dSZJ49NFHmTt3LlarldmzZ3PzzTf/KRzmGgpZzGVkZC4IzNqab1dKhYBefW63MlGScPtDFNo9OH1Boo0aYk06tGrlGcLlcrlYtWoVd955J0qlkgcffJCHHnqozt3cfom5lt52lUKBFPLj8XjQ6/X07NmTJUuW0KNHDyIja275KnN5+d+OHGZ9vocCuxekEBQehc3vQemJcF780tvAYEGtVNApMZKHL2/P5W2a1Nhj7/V6ycvLIz4+HpPJxKxZs5g8efKfoldbEAQSEhJo27Ytc+bM4fLLL78gLVn/CLKYy8jIXBD0y4hl07FSHL+YDJYYaaBNwm/3MEtAkd3LD8dLKXGE+7VVCgVpMUZ6psRgPO1h4ciRI0yfPp2kpCQmTZrE9ddfj9FYf0NsTnFx81g2ZhdT6fZXvyYIYMbLtyv/x3cBN5MmTcJisTBu3Lhaj7P+cCGvbz7CWz+eCBe5eR1wZBvsWBkOq3ceAh0uB70ZnUrBwNZNePLKzrRPtNRYdC5JEj/88ANTp07lgQce4Prrr8dgMDRqCDsYDLJ582aioqJo27Ytd911F8OHDyc1NbXR1tSYyGIuIyNzQZAabeLG7qmsPVhAToULlaCgVbyZ4R2aYjX8tm2qLxDiUJGNkirjFYCgKHK0xEm8WU/bJpG4XC6MRiMtW7bkscceo3Xr1mRmZjZYT3JCpJ7R3VL4Yn/VNSoVREsudn6wjB3frqkWrdoIhkTe3JrNnC/2cbikygSmIh9+WgOHN0FELHT7J6R0QtDoiTJouLV3Brf1yaBV/K8/EEVGRtKnT1+Uxkh2niwnKEpEGTU0tRhqHbBSX9jtdhYuXMjbb7/NwIEDeeKJJ4iMjPzbCjnIYi4jI3OBIAgCvdNjaZUQiScQREDApFVhqRLyApuHo6UOTBoVHRKtKJVnbjHd/iBlbj+/NGk7ZZX607erWbFiBfPnzyc1NZWbb74ZtbphLWsVgkBmWiyt48PXuHPnLqY9MJWTRw9zyaWXcd2E+9lZ5KYVavwhEY1KSZxJh1Ih4PEHeWXjYf6zdh+lLn/YBKboSNjNrfAING0PfcegtCZg0mvp3iyK567pTrMoIxG1hPfXrVuHzWZjxIgRtG/fnlvum8LBMh87T5YjSqBRKciIMdEjJQaNqmFmA2RnZzNt2jQ+//xz0tLSuPLKKxskavJnp17EPBQKnbetoVKp/NvlOmRkZM4NhUKoKq76ucDKHxT54kA+H+/OxVvVztU81sz4Phk0PX2+u0CNBV0+j5sls+fx8Zsvk5iYyLZt20hNTW1wIT+FSiFg1av48tMPufPOO/H6fAy8ahTNh93K12VK7OsOYfMEaBMfgdWgwR0I0S4hkje3ZrMxuzR8EEmCE7vg65ch4AuH1XteDQoVOo2KgS0TeO667mcMSTkdv9/PO++8w/Tp03E4HBw8eBBrXAIFHrD7fm718gZC7C+0kxptqvVYdYUkSWzdupW77rqLXbt20bt3b95//30SExPr9bwXCvUi5rNnz+bDDz88r88uX76cNm3a1PGKZGRk/qpkFdv46KeT+II/V4EfLXWwck8eN/VMqy4qM2nUNInQU+7yI0oSYihEXvZhPn/rJfb/sIGuXbvyzDPPMGDAgMa6FCRJIi8vj1deeYXFixcTHR3NxHvuw9/qEg4Uu/AEQhQ7vARFic3HS0mqGrTz9rZsSl2BsIi7ysNDUnZ+BpHx0HtIuO1MocKsVTF1QBumDGxXq4lLbm4uL730EkuWLCE2Npa5c+fStGlTCu1eKj2Bs94vSRInylz1LuYlJSXMmTOHgoICpk2bxowZM+p0Kt2FTr2IeVxcHC1atDivz9Y0DEBGRkamNrYcLztDyCGsacdKnRTaPdVirlEpaBkfgS8oklvpJmv/Qd6dP5PKwhxuuvEGHnnkEVJSUhrjEqrZtm0b8+fPZ+3atfTu3ZuHHnqIVl168ujKnwBw+oKIUtiStcTpw+ULUuryhUVWDEHR0XD/+IndkNwBOg0OD0tRKDFplDwysB339G9Zo5CHQiE2bdrEggUL+Oqrr+jfvz8PPfQQffr0QRAEBOFMS9nT+WVKoy6pqKjAYrEQFRXF3XffzZgxYxg6dKgs5L+gXsR89OjRjBgx4rw++1f1zZWRkakf3P6aHb4CIZHgL/rSo41aeqZG08JtJiFQwtamcQyZcAtjxoyptU+7odixYwcTJkzg+PHj3HLLLdx9991kZGSQZ/MQEMMPK2JV+rLC7afM6aUgFB64giRB1pawLaujFDoNgnaXQUQcAO0SIpgxpCNXdWpWo/C6XC7effddnn/+eY4cOcK9997LHXfcQVpaWrWbXIROTbRRi90T4PRvVaNU0Mxa9znrUCjE+vXrefXVV3n44Yfp0KEDl112GYCciq2BehFzs9mM2Wyuj0PLyMjUM4V2Dz/mlKFTKemaHEWU8exomdMX5GSFC4UAyVZjgzl81UTXZCs/HC896/UmkXpifrH27OxslEolzZo1IyGzG12WL8dkMv0pIoItW7YkIiKCuXPnMmrUKEwmE4IgEKnXkBRpILfSjUGj5GS5g3yb5+cHlVAQtrwP+78FjQ763QgZPUCtQyHAmK4p/GtIR1rERdTYdlZZWcns2bN55ZVXAHjrrbe44oorzioq02tUdGpqxeELUuLwAqBVKenS1EpsHXvgBwIB3n//ff7973+Tm5vL5ZdfTocOHWQR/xUa9F+g1+vFZrOxd+9ecnJyUCqVNG/enDZt2mA0GuWwiYxMIxAUJYIhkUBQ5NsjRazcnVvtC75ydy4390yna0oUCkFAlCSOFDvO8A43alVkpsaQFmNC0Qg32w5JVro3i2Z3XkV40pgAVr2Gfhmx1SITDAb54YcfmDRpEmq1mrVr1xIREdHou/G8vDyysrLo3bs3JpOJb7/99ixfdaNGxZD2Sby97RgHi2xklbrDO3RJBHsJbHgTTu6G6GTodxMktYaqSv/xvTNYcG33Ws/vdDq55ZZb+Oyzz+jatSuvvvoqHTp0qPG9AhBr0jGiY1PybR58wRCxZh1mbd0VCkqShNPpZN68eTz99NPEx8ezbNkyRo4cWWfn+KvSYGKenZ3Nyy+/zBtvvIHNZkOv1yNJEm63m9TUVO68805uuOEG4uLiGmpJMjJ/eyrcPvbkV5Jd6uRkuYttOWUoFApMVTvtcrefD3efJDXGSIxJR4XLz+68ijMmlLl8QX7KqyDapMWib/gH8gidmlt6p7P1eBnHy53o1Ep6NouhVXw4OlheXs6HH37IrFmzEEWR2267rdEq1X/J7Nmz2fDdd8x/YTEtO3RCFCVMOjWROjWqKk/08AQ4I1a9mqOlzrCQB/1wci/8+DHYCiGjJ2SOhMh41EoFXZIsjOudzsSLWv3q+bOzs8nKymLs2LFMnz6d5s2b/+aaBUGot2K3oqIi7r77bj799FP69+/Pww8/zMUXX1zr4BiZn2kQMc/Ly2PGjBl8/PHHDBgwgF69ehEdHY0oihQXF7Nu3TqefvppXC4X999/PyaTqSGWJSPzt8YbCLEjp5zsMieiKFHu9lPs9KFSKNBGKlBXiUlupZtih5dokw6bx19jRXO5y4/dE2gUMQew6DVc3qbJGa9JksS+fft48cUXefPNN2nTpg1Tpkxh2LBhjerZ7ff7USqVKJVKRl9/Pca4ppwI6Dh+oICQKBFt1NKmSSQtYs2olQqyiu3MXL2blXvzcPqC4PfAni/DYXVBCJvAtB8Aah1qpcCYbik8PKhdrSYwhYWFOJ1O0tLS6NixI6+99hppaWl/io2Ux+PB4XAwceJE7rrrrjofL/tXpkHEfNOmTXz55Zc8+OCDjB8/niZNmlQ/aYmiyNVXX83s2bN55513GDlyJC1b1jxyT0ZGpu4oc/kocfqQpLDVqUS4jioQEnH4AkQZwnlk6bQiMpGfi7BOR+L8fCXqmnKXj2KHD61aQYxexTvvvMPrr7/O0KFDeeyxx2jXrl2j7colSeLIkSMsXryYa6+9lj59+tCpa3eKdU3Ic/ix2T04vUFOlLsocniJ1KvZdqyUp7/Yx/aTFYQkCSoLYPN/IXc/WBOhxwhIagcqNTqlgms6JzMMcWT8AAAgAElEQVRzWEdSomreEFVWVvLUU09x9OhRFi5cSHp6Or169Wrgb+JMvF4vGzZs4NJLLyUtLY0XX3yR2NhYLJaGGZv6V6FBxDw/Px+VSsW4cePOMuVXKBS0bduWESNGsHbtWpxOZ0MsSUbmb4//tGpvhSAQqVNj1Khw+YMEQj+Lc3yEjlizDgGI1GuI0Kux/2J3btFrah0S0hCERIndeRXsza/EHxIRgCijlkEjriUtLY0bbrih0Sdo7dmzh7Fjx7J37160Wi2ZmZm4AiJZZW6OlDg4WeHG7g2gEASijVre/fEYe/IrsXmD4aesvAPw3VtQkQvNOsHFt4A5GgSBBJOWd8ddRK/0GHS19I8DGAwGWrRogc/nIyIiouEuvhaKioq4/vrryc7OZt68eVxzzTXn3db8d6dBxNxkMmE2m3+1ElEQBKKjo+UiOBmZBiJSp8agUeL2BwGIM+tIjTKyI7ecIkcIpzdAcpSBy9skYq3apUcZNLRvYmF3XkW125peo6J9ooVIfeOJeV6lm735FRSXlPL1+28Qn5xKr0EjMMUlcNMtt6JtIKvRmnA4HHz66afcddddqNVqpk+fzoMPPohSqcQX8HKywk2h3Uu5248nEMTjD3GwyEa52x8ekuL3wJGtsO2DsJtbl2HQ8xpQqhAEMKqVTB3Ylv6t4s86tyRJ2O12tmzZQq9evbBYLNx9991IklSj37wnEOJkuYt8mweTVkVqtJEoo7bOCxu9Xi/fffcd9957Lzk5OQwdOpQuXbrU6Tn+bjSImHfp0gWr1cpHH33E2LFjzxrZl5ubyzfffEP37t3/FHkbGZm/AxaDhuaxZjyBEC5fkGBIIiCKaJQKRCn8gC0h4PQFCIkiKoUSpUKgTUIkJq2KQ8V2FAi0iDOjVys5UuJArVSQFGloMJ9uCO/KCysc7N3xI2uWv8zeLevJHHwVXS4ZRG6lCn8w1ChiLooiR48e5aWXXmLx4sXEJadx4/iJ3HnbzdX3QIUAoijh9AWodPuwe4PYvH68ATGcuKgsgn1fw4Fvwz3jfYZC814ghGsarHo1TS0GCu3eGs+flZXFwoULWb58OS+88AI333xzrcVkdm+AjUeKya10V792sMhOv/RYUmN+DtsHQxI2b9hFz6BRYVCramx5q43S0lKWL1/OggULEASBhx56iAceeKDWUa4y50a9iPm+ffs4efJk9f8Hg0G6du3KvHnz2LFjB7169SI+Pp5QKERBQQHr1q0jKyuLMWPGyDtzGZkGQiEItE2IJFKvodDmYWduOfk2DxqVAgEBlVLAGwjyzeFCMtNiSIw0IElQ4vRyvNyF0xtElCS+PFhAgc2DwxtEo1LQPtHCZa0SiK/j3uPa8Hg8fPXpR7z74kLKivIZOPo2+g0fic5gRBQbJ5cviiLrN2zg2Wee5bvvviOuQx96Dh+Dv3knVuwu5LJWEu0TraiUCppE6tlXYKPE6cPpCxIQpbCbW+4+2PU55B+A1K5hf/W4NBAUKAUBq16N1aBBr1GeNX9ckiTWrVvHnDlz2LZtG0OHDqVjx461rleS4EixgwK754zX3f4gP5woo2mUEZVCwO4NcKDARr7NQ0iSiNCpaRUXQXKU4Zx374cOHWLevHmkpaUxefJkrrzyygabSvdXpl7EfPny5bzzzjtnDFs5Vbn+1ltv8cknn5zRmuZwONDr9SxZsoRhw4bJhQ8yMg2ESqkgJcpIrEnLnvxK3P5QVYGbhD8UHmKiUigotntJjDTg8AXYebKcvEoPoiRR4vTy9aFCBARizVpUCkX4Ri9KXNul2a/mb+sCSZKYOXMGb7zxBoJaz+j7/0Xni/6BVh9unYqP0FdX5TcUDk+A+UteYdmL8ykuLSO5/9W0uWwEWmssBXYfhQ4fuRVuZg41Y9SoUCsVHCl1UOkNhCe6Bf1waCPsrHJz6z4c2l4KBgsIAjqVQEqUCbVSgUqpICRKtE34Of/t9/tZsmQJCxYswOl0cv/99zNhwgQSEhJqXbMoSZS7fYRqePixefw4vQEMGhV78ys5UGirfl+5y0e5y0ekPrF6et1v0aJFC2bPnk2nTp1o06aNLOR1RL2Ied++fc/bUSkmJqaOVyMjI/NbSECZy3vWtMOgKOHxhzBowjfcCpevWsghHIZ1+UMIAkSG1KgUCgIhkb35lWSmxdA8tn6dIAVBwO/zkdQ0mXHTZ2FIbFFdV2/UqOiQaEFbTw8UvmAIXzCESaNGUWVabnd7ueWOu/n47dcxW6PpdcP9BDMycanVnOrMliQosHtYl1VE6/gIln6fRZ6takccCsHWD+Gnz0FrhKEPhIvdoKoAMTws5vRiw6RIA9HGcBSkoqKC6dOn88Ybb6DT6Xj11Ve55pprfrNPWyGASqFAgLP6EgRBQKNSUunxk1fpPkvw7d4AR0uddGsWVeOx/X4/S5cuJRAIcM899xAXF8cNN9zwm9+vzO+jXsR82LBhDBs2rD4OLSMjU0+YdBqijVrKXL7qG7ogQGqUkSijDkkCX1A8ozXtlC+6JIF42qwTbzCEN1izZ/ofxe12c+LECRITE4mMjGTOnDlUVFQQG59AboWLPJsHvUpJWowRi15b49jTP4InEGJ3XgWHiux4AyH0aiUdk6y0Toggp9KLMS6JlNYduPbuaUhNWvHDiTJs3gBWvabaCMYbCLF8yxE+2J33c1i9Ih82vRuuWk9sDZnXQVwGCgHMOjUdEy20jIugRayZ7FIn3mCoumjR4fXx448/MmvWLL755ht69OjB4sWLfzW0fjqCIJAabSSv0o2rqiASwqmYlvFmDBollR4fgZBY4+fdp33mdHJycli0aBGvvPIKzZo1Y9y4cbLVdz3ReIbKMjIyfxpUCoGMGBOdmlo5VGSnwu1HoRCINWoZ0j6RSL0aQQCTVoVOrayuZI8368itdKNSCKhOG6kVZdRirQcDmdLSUpYuXcrbb7/N9OnTGT16NDqdjiZNwoYxqdEmUqPr13TqUKGNPXmV1Q81To+Ptz9cyYAurTAmptP3yutp3+9yrLHx5Ns86FRK/EERX0hEpVRQ7vJxsNDG8Qp3+KHJ74HjO2H7ynBYve0l0HEQWMLXFKFT0b9FHNd2ScEXCBEUpbN2wTvWreHxN5ZQUFDAbbfdxtSpU89qA/4tmlmNuJKDHCt14vQFUSsVxEfo6JocPpdercKoVYWNa05DEATiTGfWR4RCITZu3Mj8+fNZt24dAwYMYPr06bIhWD3SoGJeXFzMjh07KCkpwe/31/ief/7zn3KoXUamgdEoFbSOj6Dc5SPaoMHuDaBUCGTEmslMi63OO0cZtWTEmDlYFM6bpseYyLd58AZC1RXspiqv9rougDt48CAzZ85k7dq1dOzYkeTk5Do9/rmyv9B2RnSioriAZXNnUDRoEDffNw2NVo01NtwmFmvSkhJlZH+BDbVCoMLtZfvJCkpORT88dvhpLRzcAEp12Fs9vRvowqKXYNYRZ9aRHm2mf4t4bJ4AW46VnDENLiFCx5H8bBwOB//5z3+4+uqriYqqOeT9aygU4YLIphYDLn8ItTI85EWj/HlqWvNYM5XuAL6qqIsgQFOLgabWn+1dQ6EQy5YtY+7cueTm5nLPPfdw++23k5qaKg9KqUcaRMxFUeS9995jzpw5FBcX4/f7EcWzwzWCINCtWzdZzGVkGhhBEEi2GhnQSklWsQObN0CcWUeLWDMRp/WP69RKujWLIsak5XhZ2OCpX0Ysx8tcHClxYNKquKxVAq3iI6pDyn8USZL4/PPPmTZtGllZWVx11VXMnDmTjIyMRvHs9gTOvHdZYuLpP2IM3ftk0jIxhrLj5VR6wpsVtVJB5yQrXZtaWXsgnw1HSnAFQuFCN1clfPMK5B8KV6n3GQ2x6VCVu06PMdHUYsCi1xAQRUpdPto1sWA1qDlc5KDMZiPRYqBtcjwXTZ/GTTfeQMuWLf/QBLhTU9oia/DXUSoEWsZFEKlXc6jIQSAk0iRCT3qMCYP2ZylRKBT4/X4cDgevv/46gwYNknfkDUCDiPl3333HE088QWFhIaNGjaJz58619hQ2a9asIZYkIyNzGsGQSEgCq1FLnwzdr7YZ6dRKWsVH0Cr+5wrqzLTYelvbxo0bmTJlCseOHWPatGk89thjjTooJdGs4atvN1Cce4zulw1FbzQxcNQ42idG0jTKxEC9lk3ZJRQ7vKiUAhmxJl7bdISVe/PDBxBDYQFfuxAC3vC40t6jwRTeTetUCjpU5cdP4QuKOLwBVAqBJpEGCo4eZPLE27n99tvpPn48KqsVq9Va79euVipoajHS1HLmeFRJkigrLycQCJCQkMDtt9/OyJEjzytCIHN+NIiY79+/n6KiIlasWMHAgQMb4pQyMjLngChJHC2x883hYnbnVaBVKemVEk3vjFiaWY2/fYB6pLKyEovFQrdu3bjlllto3bo1w4cPByC71MmxMicS4UrsZlZjvVfOQ3gC275vPuH1J59EbzSR1rYzKS1aE2/WkREbdrm0GjQMaZ9EbrmLvfmVzP/6AGsOFAISeBzhtrMdq0Clhg4DofMVoNEjAF2aWkmL0qP7hd+GWasiyqDFHxRx+oL4hHAoXxRFAoEAKlXjlT85HA6+/fZbpk6dSt++fXnxxRfR6XSykDcwDfIXIAgCMTExtGvXriFOJyMjc44cLXHw0sYjfH+0hBPlLhy+AEs3H+HSFvFMv7w9mWkNn/IKBAKsW7eOjz76iEceeYTk5GSmTJlSHVI/XOzgrv/+wLrDRYhSOPzbLy2GBdd2p0ty/QiIJEns3buXRYsW8fbbb9OhQwduGH8X7Xt0whJhJMliDM96L3GgUSoIiCJPrN7Nih05eIJVYfmKgnDvePaPEJ0EnYZAWpcqExi4sUca0we25aPduewvtFe3CaqVCjolRlJ87CBbN5dhTGlLIGjgtkeepktGEhptw5jz1ERubi6vvfYaL774Inq9nq5duxIM1lzZLlO/NIiYt2zZEqPRSE5ODomJiQ1xShkZmXNg49ES9uZVsregsrrtKBCCNfvzUSgE/u/6XkSbGk4sHA4Hy5YtY+HChZw8eZKrr76a5OTkaiGvdPu5/e3NbDhaUv2ZkCix/mgJ//p0FwtH9iAtpm536IFAgK+++oo5c+awceNGRo4cyUMPPUTbdu1QKFWoFAI/5pRxrNQZtsb1+Hlr+wk2HyutOoIEJ36CbR+H28/Su0PnwRAdTikqBYFkq54h7ZNo1cTCTXoNW46VcrjIXpVzj6Ro/1amzX2GgrJK7l/wBiZLFCgM/FToJM5iJsp4/nny8+XAgQPMmDGDNWvW0LVrV2bMmEFmZuYfytnLnD8NIub9+vXj6quv5p577mHevHlccsklDXFaGRmZGiixe/j8QD65FW525JafIeSnkICdJ8tZe6CAMT3SGmRdFRUVzJgxg9deew2TycTKlSu56KKLzniPJxjiu9OE/HTWHizkyTX7mD2iCzGmuhGUYDDIsmXLePzxx6moqGDOnDlMmDDhjF7pE2VOfsqtICRK5JTbeeOHE6eZwARg9xewcxUE/NDtynBoXRuu/lYpBGJNWhIiDBwusiMQrg7/Z8em1b+T/3tpCU/MnIEvGOLKWyehN/2cS7d5ApwodzWKmD/wwAOsX7+eG2+8kccff5ykpCS5Wr0RaRAxV6vVTJo0iaysLAYMGEBCQgKJiYk1FrEsXbpUHkgvI1NPHC1x8MxX+3FV9QrnVXqo8PiRJM4alhEKiVR6a24hrWvKy8sZNWoUGzZsoE+fPixevJg2bdrU+N7a3NaDokS+3cP/fZ/F9Mvb/SFhkSSJ8vJynnzySRYtWkRqaiqLFi1i+PDh1fajoiQhSrC3wIY/JHKo0M6KnTnk2z3hVTorYMen4bYzjREGjoe0riAICIBGKZAQaUCjVIQ91k9zqlMKUFpWwiOPPMKbb75JSkYLht12P20z+6NQhvvWixxeylw+Kj1+DBol6THmerWulSSpOj+vUCh49NFHGThw4FkPNzKNQ4OIeWFhIY899hgff/wxrVq1wmKx1FqNKj/ZycjUD4GQyIqdJ6qFHCDGpMWkUVPu9qGs+rcnEM7TWo062iY0zCSrY8eOUVRUxIQJE5g8eTJpaTVHAwRAr1biCZztLmfUhkPeh4rtOH0BzLrzN63Jycnhrrvu4ssvv2Tw4MFMmzaN3r17o1AoECWJCpefArsHly/IsTIn23PKWLU3n1KXL2yFV3QEdqwMV60ntQ27uVWF1Y0aFTqVArVKAUjEGLUkmLXV+X6fz8emTZv4z3/+ww8//MDQoUO558FpeCzNKHJ4ESWJrBIHZS4fCiFc6b4hq5gyl58eKdH1IuiiKHLo0CFWrVrFwIED6dy5MxdddNFZkROZxqPBWtNWr17NuHHjGDlyJPHx8Wi12hqFOz7+7Jm8MjIyfxy7J0CB7cxRmZE6NR2TLOzKrcDlDyJUeXTrVAqGd2hK73psOcvPz8dut9OiRQu6devGq6++SvPmzX+1CjpCp+a23hm8uOHwGTt0hQDNY0wYNSqUgvCHNwWhUAhBEJg8eTITJkwgPT29+mclDh+bssPiafcGWLU3l20nynD6Q2E3t8ObwkYwPkc4pN7uMjCHCwljTFr6N4/DEwhR5vKjEMBq0NC5aRSdm1rx+XzVBWUVFRU8+OCDTJw4kbj4BA4V2bH7AuRWuilz+RCE8Hx5i16NBJwod5EabSQx0lDLVZ0/X375JXPnzmX79u3ExsbSuXPnOj+HzB+jQcS8qKgItVrNfffdR/PmzRvilDIyMr9Aq1KgVv5S5AQSIvQM72AkPcbEuqwiInVqbu+dTo+0OLSq+hlScvjwYaZOnYrP5+OFF16gZcuW9OzZ8zc/Z9ComDG0I8mROuavO0SR04dWpaBzkoWmFiNKhUCnplaMmt9/a/N4PHzxxRcMHTqU9PR0Fi5cSGxs7FmGJ7vzKihx+tiTX8E3h4s4WurEFxTBWR5uOcvaBAoVXHQzpHQCTVhczRolV3VMpkOiBV8wRJHDiy8QwqxT0y7RgtdRyZRHHubTTz8lISGB5557jqFDh2IwhD/fPM5MpF7N6n15xEfoMGvVRBk0aKp+R/6giN0bJLEOgymnfj+LFy/G4/EwZcoUBg8eXHcnkKkzGkTMIyMjMZlM1X+UMjIyDY9Jp6Zz0yjW7A+bl0gSOP0BbO4AvdKiuaJdIvf2b4VeHR7LWV8Zr2+++YYxY8ZQWVnJLbfcQnR09O/6fLRRy4MD23PnJa1YtP4wu/MqUSjCeeiMWBOdmli5870fKHJ4aZMQycS+LWj2G37tkiQxYsQI9u3bh9/v57rrrqsO9Tu8AfbkV1Jg82DWqVl7IJ9F6w8jStLPtqq2Ivj2tfCQlLg0GHRf2ASm6ksc1CqW+df25IfjZQRCIlqVArO2KtUohCvas48d56233qJnz568/PLLtG3b9gyHO41SQZLFwEUZceGhNgJnDJFRKYUz8u5/lJKSEqZMmcJ7772HxWLh5ZdfZtiwYY3a0y5TOw3yW8nMzCQlJYUVK1Zw8803Y7FY5Ny4jEwjMLJrMxy+APsLbORVunH7Q6REG0mLNrHjZDk2T4DeabGcx8b2VzlVULZ06VJmzpyJxWLh8ccf59577z0vq0+lQsCs0/DwoPYcKLCRXeakSYSerBI70z/dVe2dnm/zsj2nnCWjepJeZSojSeGpbv5gCDEUQqdWotNqmDlzJi+99BKZmZnV53F4A6zZn0+FO1wIuD2njPnfHPx5IUEfFGSFhdzrDM8d73Ut6MPFaMkWPa9c35PLWifiC4Y4WuqkoKrSXRDA7/MS8LgxJ1vp3KsnH3/8Mb179/7VVEOLODN78iuprCpcPHWsOJOOJhE1+LCeJ6WlpeTm5p7xcCHz56VBxFySJFq3bs2TTz7Jrl276Nu3LyaTqUZf5YEDBzaILaGMzN8RjUrJHf1a8FNuBav35aEUBKwGLYIQFrnjZU6ax5ppFlW37m/BYJAVK1bw7LPP0qVLF6ZMmcLw4cPrxFu9TZNI2jSJpNTh5bl1B88YggJQ6QmwZGMWz17VFVGSyK1wc7DQxuETuezbsp4u7dtw1aDLyMzMpHv37mfsPPcV2Khw+/EFQxwqcvDe9mM/H9hRCge/C7edGazQ6xpo1Q+FzkiETs2oLs248+KWdGoaFmaNUkGruAjsnkD1mNHNn39I7v6d9HlqFsqUGIYOHfqb16tUKLi0ZTx7823YPH4EQSDKqKFjkqV62M354nK5+PHHH+nTpw9t2rTh2WefJS0tTb4nXwA0iJh/8cUXvPbaa7jdbpYtW8by5cvR6XQ1/kP+9ttv5T8cGZl6RCEIxJl1JEToCYREHL4AkiRh0KgABRUeP82oGzE/5WKmUqno0qULU6dOZejQofXiBnm8wlU94OSXZJXYAShz+dieU85Pe/bw2RuLObB9EzkjrichvQ3Du2WcFUIusHvwBUNsyi5h9f4C7N5A+Kmn9Djs/AyO7YT4DOh6JSS1QalS0SnJwuTL2jC0fRIWw8/93wpBIDXGhEal4GSFG08gSG60kaBZS6Tu93nNx5h09EnX4PQHEYBIvfpX/fTPhRMnTvD888+zZs0a5s6dy5AhQ+jatesfOqZMw9EgYj5s2DAyMjLO6b2nV43KyMjUDyqlgpAocbDIjtMbREJCp1aSbDVgqKO8q8fjYd68eTRv3pzRo0fTrVs3OnXqVG8OYTFGba0701Oz1fMr3Hy9ZhUfvjSP0sI82l00iM4DrqTQHaTc5SfKeGY7m06l5L87TlRV+1e1w53cA9+/C/ZiaHMJdB0GJisgcEXbJvznys50TLLWmEoUxBClxw6S0bQZ1uh4Mu+ZiNt1I7Gxv79rQKNSEKX64zPjJUlix44dPPbYY6xbt45u3brJhcoXIA0i5mlpabX2jcrIyDQ8GqUCdyBEqdNX/ZovKGJQ+6t26H+MwsJCpk+fzrvvvkt0dDRDhgwhIiKiXounUqNNdEy0kFvh/kXbmsDIrim43W4+Wf4qrz/9FB6Xg05X306bwWPIEVWEyt24/UF0agU7TpZzuMjOnoJKXvruEN5TbfliCH5aA5vfA60x3Dve7jKoEtSL0mN59qputK6lN9/j8bBkyRJmzpzJ5MmT+de//oXebCLC3HjjQSVJ4uuvv2bChAmcOHGCW2+9lcWLFzfqVDqZ86NByxIlSaK4uBibzUYgECAlJQWTyYQoijidTrRarezrKyNzjrh8QdyBIJIUHktq1qrPuQLdHxQxqJXER+hweANIUtiMJdasPcNU5vcSCoXYsmULTzzxBJs3b+ayyy5j4cKFRERE/PaH64AHB7RBIpzr9gZCWPRqBrZuQkudl2nTprFs2TJ0UQn0Gv8vEjuGC90kSaLY7qHE6WX7yTIOF9v5+mAB32aV4AsRNoGxFcL2TyFrM8SkQLd/Qlo3qEoVtowzc0X7pBqFXBRFjh49yjPPPMPbb79Nx44d6dOnz5+iCHjv3r2MGTMGk8nEc889x/jx42Uhv0BpMDGvqKjg008/Zc2aNWRlZeFwOFi6dCl9+/at/u/U1FSGDh0qC7qMzG+QV+lmT14FBXYvLl8QjUpBRqyZFnFm4s263+wPD4oiKoVAi1gzbn8ISZLQq1WolALe4NnuaueCKIosW7aMZ599FofDwb333st9993XoEZQRq2ax4d05HCxnVKnjySzhn0/buKOR+exdetWLrv8CuIvvgZPZBKnusrUVS1fG48W4wuKfLI7bALjD0lhIc/ZDbtWh/PkzXtCl2EQE3ZzizZo6JIcxU09Urmo+dnX6fF4+PLLL5k9eza7du1i1KhRTJo0iY4dOzaamIuiiCRJKJVK0tPTufXWW+nXrx8DBw6U770XMA0i5m63m0WLFvH888/Ttm1bUlJS+Oqrr/B4wi0aSqWS3Nxc1q1bR+/evWnSpElDLEtG5oLE5Quy9UQZxQ4vlW4/J8pduPxBfjheRp/0GDokWuiQZP3VnmOdWkmETk25249J+/NtQKUUiDKcXx5WEAT27t2LJEk888wzDB06lMjIhrGDPcWB/FLaJMbQMi6CNGuAV199lXnz5lFcXMy0adMYft1odlcKHCtzYfcGUCgEInRqLHoNeZUu1uzL46d8OyFJCg9J+Wkt7P8WQv5wkVubS0AfjjIkW3QMbZdEl+RoEASC4pnDaiorK/m///s/lixZgs1m4z//+Q+jR49u1MmRfr+f1atXU1RUxPXXX09EREQ43K/X10lngUzj0SBivnXrVt555x2uvfZannjiCX766Se2bt1a/fP/Z++846Oo0z/+nu0t2WzKpocACSGA9A6CIuVQ7Ng9FSynYj0UT89+VjwROdDfKXpWzoqKiiiCiggIhCIQEmp6T3azvc78/pi4EBIQlAQ89/0HL5jZmZ0dduf5fp/v83w+RqORgQMHsnTpUux2ezSYR4lyBKocXpo9AURRotLukSusgbAYZn+jC7VSQaJJR7fEw6/FmvVquifF4KiwEWpx5xIE6JpgQq1UsL3KjkohkGkxYtQe/jEhSRJOpxOFQoHJZOKhhx7ipptuIjs7u9PStYFAgFe/28YzP1TgC4TQaVSkmHQ8dmYfJElCrVbz4YcfMnr0aDRaHTXFNbgDYRKMWkKiRIPbz2tr97K7wYXDF5Bn7G47rH4LSjeD0QJjroHMPqDSoBBgWJcELuiXhUkn3xutSkHSQVaxDoeD22+/ncWLF5OVlcXrr7/O0KFDT+jMV5Ik5s2bx+zZszGbzZx66qn06tULo/H4tiFGOTF0SjAvKiqisbGRv//971itVgwGQ6sUkyAIxMXF4ff78fv9RzhTlChRQmERCTlVbjukFSskSviCYepdPrIsBlSHMd1QKRT0TbcQZ1Czs9pBSJToEm8kGBb5orCKQEgO8CatmlHdEsmKN7ZJCwcCAb755hv++te/ctNNN3HjjVTp63oAACAASURBVDdiNps7fTa+oaKZmz5pEXKRZFnVfQoFs3UC/5p6CX/+859buXr1z7TQ4PbT7A1QZnPz2bYKiuucctGcJEJ9CXz/FtTvk9fHx98IcfIEI1an5tIBGQzvlky4JU+vUgj0SY0jriWjsWPHDi6++GL27NnDhAkTmDNnDj169OjEO9IaURSpr6/ntttu44MPPqBXr17Mnj2bvLy8E3ZNUY4/nRLMA4EAJpMpYh3YHqIoEgwGI32pUf5YhMIiCkFAoTjxRUG/hnqXD7NGjUbTMVrmB2M16dCqFLj8cqV2uOU3oxDkFLnQ8vdfqoZTKgS6JsTQNUEOdNXNXlbtqY0EcgCXP8imchsJJl0kHS9JEhUVFbz55ps88cQTpKeno9frCYVCx1StHgqLeIJhJAmMWiWqX5nmPXXOVwf+4WyAj5+AjN4s001j4wgXlw7q0ur18UYtA9ItvP7jPj7bVsHu+pZAHvLD3o1Q8An4PdD7DFnNTaNHKQgMyrTw7AUDGNEtmTKbm5pmL0qFQFa8kZQW5bVwOMycOXOor6/nlltu4a677jqhmUZRFPnxxx+ZNWsWmzZt4pxzzuHBBx9kwIABJ+yaonQMnRLMLRYLPp+PsrKydteLAoEAu3btIiYmJqrf/gej0eXj+731VNo9qBUK8lJiGdktqUN9mY8n6/Y3sLyoml21zaQY9fRIi+WcUzJIPo6ymocSb9RwSlocm8ptxOk1NLr9smWpQY0oQSAsYtarUR7DwEiSwO6VXcDEgwYHIAutuHxBTFoVoVCIjRs3MnfuXBYvXsxpp53GX//6V8aNG4dGc/Rr7Q5vgMKaZhpcASQkEoxa8pJjSTAeexpaivwpyC1jmadASnd5ln0INk+A7/fW8db6/azcVUOjuyWz4aiH7ctlRTeDRW47yxkOai1alYI/5afy5Nn9yU2OpaHlfvdJi8PU0kFgt9sBiIuL495772Xs2LFMnTr1hD/PJElix44dOBwOZs2axY033hh1pvwfpVOCeb9+/cjIyODhhx/mpZdearN/1apVvPrqq4wePRqr1doZlxTlJCAsSsxZuZN1JY24/bL9ZpJJR6Xdw6WDsk/6WfrWShvXL1pHYY0jEgDNOjXlTW5mju+FWf/bBT3aQxAEeqXGkWjS0TXByJYKG6U2Ny5fiNKgmyyLgb31LpJjdMdwDRL+YJjSJjcObxAEgUSjliSTFg4aV5WWlvKPf/yDVatWccsttzBjxgy6det2TJXZkiSxel89VXZv5L5VN3uxewKM7ZF87I5njeVQtRNyR4LOBCMuAY0eFK2zJHZPgPc3lbJg1S62VTfLWUBJgupdUPAxVBRCej4MvQCSuoJSjVmn4qbRufTLiKewzsHyXTVolArMOjV6jYpeKWZU7gbuvPNOxo4dy4wZM8jJyaF79+4ntPWstrYWSZJISUnh/PPPZ/DgwfTs2ROdTvfLB0f5XdIpwbxPnz5cd9113HPPPQwbNozs7GxsNhvz5s3jySefZMOGDXTp0iViwhLlj8GKompW7qqLPNAlCWqdPpbuqGJAZjz5hxHfOBkIhkUW/rCL7dXNrbY3+4LM+66Ys07JYFh2Yoe9v1IhkGrWkxyrI8Ni5MvCKprcfmJ0ajQqBRV2NxtKBcb3PLoUbyAssqfBRZ3Th6dF6czhDeALhRiQEY+pRW40LS2NyZMnc+mll3LhhRf+qplnhd1D5SHCLiAvVVTYPOQlH31P+pdffon+i2fwBsPyjLr7EDmgA1lmPSmmA4OZ/Y1OXlmzhx3VdtmgRBRhf4EsAuOogz7jYdhFoNUDAga1gq33nsXqvXU4fEGKapupd/lRKxR0TTSSHKOnyRNgQvc49u3bR35+PsFgEK1We0IDeUFBAdOnTyczM5PFixeTkJBwzM50UX5/dEowVygU3HDDDaSkpPDYY49RVlaGTqdj3bp1aDQaRo4cycMPP9zKrSjK/z5fFVe3McUAOaDvb3Sd1MHcHxJ5e0Npu/scvpDsb90JCECTx49KKZAUc2DWJZumuPEGQuiPYqbb4PLT4PKTHKOjqtlLMCwhAW5fEIWrnn8v+JC/3HADJpOJW265pd1ziJJEszfI7jonLn+Q5BgdXRJMrVrfQB7wtFcZExaliAHJkZAkCZvNxgsvvMCjjz5KXEIi2WecS3XWYJrlpAI5STFcN6IbY3ukIklQVNvMzMWb2FjeJFer+91Q+B1s/EiexY+dDr1PB8CgUZIZZ+CrW86g2uHFHQjj9AVx+eX1/UAoTElFNZjVJCanst8RYsWKFVgslhMquOJ2u1myZAn33XcfNpuNYcOG4ff7j2n5I8rvl05VgDvnnHOYNGkShYWFVFdXIwgC6enp9OrVK+qR+wfkcOYSaqUC7RGKJU8GFILsq23zBtvsO4rasyNSXNOMVq0k+xc8uAFECUJhifbqRkVJIhASOVymXZIknP4QkiRR6/QhIJFo0mHQqHAHQoQDARqKN/PQ069SvqeIU/r0YeLEiW3ew+4JYNSqWgro6nC3mH/sqXeyt8HFuLyUVgE90ahFIQhtBnKqlvT1ET+vKLJt2zbmzJnDxx9/zJAhQ7j33ns588wzeaeghC8Kq1EL8Ni5A2n2Bthc3sjuOid3fFhAjdMnS7I2lMGWL2DvekjtAQPOktvOgDi9mtvG5nHr2DwSY3QUlDcCEJakSHFuU+kuir56F5XXzlWzHsNrNWO1nrjecYDKykpeeukl/v3vf5OYmMi9997LddddF+0d/wPR6RFUq9UyYMCAaDVlFC7om8nKXbWRtC7IM82uCUZ6pnSO/OevRdtiJTrrk81tAmnvFDMJxmOfDX23u5alO6r4cX89Oo2SoV0SuXhgF/qkHX7pSaEQMOvVaFWKNtkAs15z2B7xBpefz7dX8FOlnZAooVTI6W9vUEStELDqldSv/ZTVn72HXiFx3333MWjQIOyeAI2eAEpBDtbrSxupsnuwGLUILUVonkAIpSBg1msQJYntVTaGdz1gJGKN0dE9ycSeemere5dm1pMed/i0vSiKfPzxx8yZM4eioiIuu+wy7rjjDnr27AnA5UO6cengrmwqt/Htrlo2ljWyZl89myttcoV+KAD7N8HWL6ChFHqdBn3OgPiMyOjr3om9uXlMD0xaeVARq9NQ7/SjUytREmb/2q8o/voDvPWVDBl/Nlq9gSzLie/TfuaZZ3j11VcZM2YMd911F6NHj44G8j8Yna7NvmvXLiorKw/bTz5y5MgO7VO12+0sXLiQNWvW0NTUhNlsZvjw4UyfPj1a5dnJ5KfGMePUHry6bh9Nbj8Sssb19SNzSDV3XDX48UCpELhqaDd+qrDxdkFJRBo0JUbHHaf3JCfx2AYjG0sbueODjWyrao60mn2zq471JQ0svHIEGYcJcgLQJd5Ihc1DSaMrkr7WqBQMyLC022fuC4V5/tsiPt5ajjsQQhQlOfUtQYxWheC24V75Gt79W8nt3p1HH32EiRMmUOYIsrWoGn8wTHGtg6WFldg8AUCeZYuSRJ41lpRYPQJQ7/KTFTZSYfe0en+FIDAsO5Ekk46yJjeiBFnxRronmtAdRrXO7XYze/ZsFi5ciCiK3H///Vx55ZUkJrauS6h1+PipoonddQ5e/3EfTZ6WavVQUA7i276Wq9zHXgNdB0fW1wGePLcfM8bmtSrA651qpqzJjcthp/DT19i8bDFSOMz4q2/h9Mnn0is7je6JMZxozjrrLNRqNTNmzCAzM/OIbcBR/jfptGD+ww8/MGPGDHbs2HHE161bt45BgwZ1yDVUVlYyYcIEiouLMZvNpKSksHXrVj799FPee+893nvvPXJzczvkvaO0RaEQuHBgF8bnp7KrtplYnZoeyebf7MvcWVhjdbx29UhuHpPL898UcUp6HPdMPAWFIBxTml2SJPbU2thaaW+1lhwIiywvrqHB5T9sMAcwaFSc3iOZvQ0G9je6MWiU9Ew2y5Xo7dDslKu6f57JuwIhPC2pcZ1agdBcR9P2H8jqNZAnX3iZySMGYPP4WbW7FrluTOL7fXWUN3kQFAIGtZJgWMTlD7G1ykZKrB6p5fpLm9wMyopv95r7pMXRuyXrcKTbVVVVxR133MFHH31EYmIib731FuPGjWu3yKy0yc31/12PxEFLDwEvfP0ilGwGSzqcNh1SckEQEIBhXeKZMTaPywZ1Ralsfc7kGD19YkXufPgR1n69jNiERG5+ciFqaybPryujccVe0mMNvH/dqfTNsBzhUxxfPB4PX375Jd26daNfv35MmDCB8ePHnxTmLVFODJ0SzDdv3sxtt91GdXU1EydOpGfPnsTExLT7xesogQVJknj22WfZs2cPN998M4899hhmsxmXy8UDDzzAggULeOmll3jqqaeio9rjRDAsUt3sZW+DE7c/TEqsju5JMZj16kjAFoA4vYah2cfu53wyoBAEhnezMrzbr2+pdPhCvLWxrN2iMFGCb4qq6P8LgUKlVJCXbCYv+ZezWl8WV7VKyQdCIgR9iH4vWlMyiXn9MF39IBm9BxE0JSIIsPtnhTSg0ePH7mkpYhMlQqIcOAVBwBsI4w+F0KoOCMwcbiDyc8Gbze0nJEokmLSYtKo24jF2ux2Xy8WECRNYsGDBYe2Uy5rcTH973YG1+FAAavfC6jfBXgPZA+Vq9YQMQF4fH9UtkSsGd2VktyT2NzrRa1RYDBr0ahXBYICNGzcyc+ZMduzYwbnnnc9zc5/jx8ogVyxaS6glHbOrwcmE+St4fuogLh3csVbPoihSUlLCww8/zPvvv89dd91FTk4ORmNbhb4ofyw6JZgXFBRQUlLC7NmzmTZt2glZywkGgxQVFREMBvnb3/4WSeWbTCYef/xxXnzxRcrLy3E6ndH2uOOAKEnsrXeyobRRbhsCqh0eqpq9jOqe9KvEQf5X0SgFelhNfF7Y/v50y/EVHsmMM6I8SDkOZwPhzV8h2asR/nQ1SnM34nsNR2/QEtdSPRc8yEREp1KiVbX+DQuCLGuqVaow6zUICGhVCpJj9e2moUVJotzmZlN5E03uAKIkEatTc0p6HD2ssbidDtatW8f48eMj8qNZWVntWqnaPAEWbdjPOwWl7KxpaRX0uWDXGtl/PByE/pPhlIlgMCMAGXEGeqbE0jUhBn9Y5NvddfhDYZQKgUyLgS56kS+XLOapp55CpVJx991385e//AWr1Ur/ee9FAvnP1Ln8fLClnHF5qVhjOqaX2+12s3z5ch5//HGKioq4/PLLueSSS064ME2Uk4NOCeY+nw+z2czkyZNPWFGGUqkkNjYWhUKBw+EgPT09ss/hcCAIAmazOSqqcJwIhEWKah2RQA5yu1SdU+4ljtNrjkmh7H8ZvUbFGXmpLFy7H+chXuJpsTpOSW+bpv61+IJhcqwx9LDGsLOmGV/pdvyrFxMuL0TVbSDeEDh8Qcx6Dd0STfRryQhkxBkobOmpN+nUZCcYKW1yI0oSSoWAQoKQQmBwZjy9UuIIixIalYJeqWZi2qlQD4RENpc3UePw4fAF8ATCaJQKgqKIRa9h4bx5vPbaayxYsIDJkyfTp0+fw36mJz9cxzM/lh/Y4LbB+sWwfyPEWuVq9eyBoJQfd4My4+mTZibeqEOjFKhz+iKDlrAoUdrkpsZRw7NzniMpKYmHH36Y8ePHR/Tdm9vpYADYXN7Ejmo71piUY/+POQJOXxCbrYm3X/8P//6//8PlcvGPf/yDSy65JGpKFSVCpwTztLQ0NBrNCTVRUSqVXHLJJXz++efceeedPP744+Tm5rJ3717uvfde4uPjOe+886LB/DghilJLcdQh2yUJhy9IWJSiwfwgTstL5fbTejJn5U48LQMgq0nDI2f1pXvSby+wCoREdtY0s6vOQTAsMiwrjoofv6Jy6VsEm6rQDjqTwCmTaVTFYWt0o1Z4eHBS70iQy7QY6ZMWx/YqOwIwqpsVX1A+Z0iU0GtUTOmTzPCuifiCYdRKBdkJRgZlJbRbP2D3BKhq9lLa5KbB5ScsSQiCQL3LT4+kGIYPH8EPP/xA7969D/uZPIEQ4+Z+zbaKxgMba3bDt69Ccy1k9pXV3BIyQJAnEafnJjOlTzpqpQIJSZbCPWiCEQ6HUCpVqOJTeXL2PxkxZCBZWVmtl94EaG9NxKhVtSqe8wbDePwhYnQqNL/gL98edk+AgvIm6pw+bHXVfLRsJXFxcSxatIhBgwZFvcejtKJDgrkoiq0MU8aMGUPfvn155JFHeOGFF1Cr1YedoSsUig5b+zn//PNZtGgR99xzD0OGDEEQBCRJolevXvz3v//l1FNPbfc4r9eL3W5HkiSCwSAul6tDru9/CUEQMGiUNHvbiqfo1MqTXqq1szFqVPzj7H784+x+PP9NIamxei4edHzWX0VRYmdNMxvLGgmLEmI4zL4fvmTXB//CqNMx5b6necsu90mHJQiHRYJheHjZdib0TkejVKBUCIzomkTXBBMljS40KgXTR3RHq1JSWG0nw2IgscUC1OUPolIoDluZDiAi0eQOUN3sRQKkcJjGkmJqFQIl3RI5b9wZTJw4od1jPYEQXxVWMfOjzexrbPkthoOwex2sWQQBn9x2NvxiWRAGubf9plE5ZCYYW7XDJRl1ER+ArT+sYMV7r3PNfU9hzu7CWeeei1aloKGl08Ji0KBRKemRGENxvbPVNSkFgfF5qQzNTiQsivxY0khhdTNNHj97G5xkxRm444xeaFVKVIf57su6AGEaXAFESaSgrJGy2iZ0BiOq2ETOuXEWvbLTGTa0Z3QgHKUNHRLMv/nmG7Zt29ZqW9euXXn55ZcpKChg8uTJWK3WdoViLrvssg5rESsvL2flypVIksSkSZNISUmhtraWgoIC5s+fj9VqjfSsHszatWt55pln8Hq9SJLE3r17mTp1aodc4/8KGqWCHtZYtlTYCP7slw3EGTSkxxkO+0CLAref3uu4ns8dDFFmcxMMhRAEBQqlkozcfAaPO5Prp0/j0S0+wNvmuJ01zTy3opB7JsopbkGAVLO+Tdtg/8zWywA/92gfiVidpqXiXMJjq2fv959T+MUi8kZNoOnUwQTC6W3W5UFeH3/ws63MX7XrwEZnI+xYATtWHtBm7zkmMhsf2S2JL288jbAAhdXNNLj9KBUC2fEmRElifWkjwVCYPVsL8Hvc1JTsYVS/fGqavWyqaMLekmGyGDQMzU7kqxljufS1dWyvtuMNhtFrlEzqmcr0Ed2QJNhY2sRPlTZ+3N/AFzurcLUsnbyybi8zx/XimuHd2/T/B0Ii26vt/NTSE19ZU8f6b79k05K3uO7h54jvkks41sq2xiB96p1kJ5rQ/E7MiKJ0Dh0SzFesWMGiRYvabI+NjcXpdPLee++1e5wgCJxxxhkdEszdbjdPP/00Cxcu5LnnnuOqq67CaDTi9XpZtGgRd9xxB3FxcTz99NNYLK0rh8eOHcuoUaMAIgV0UY6MUiFENLYr7R78IdnJq3tSDMkdVCAUpX1CYYkmu4NN3y7HEGOm15BRZOTkcemdD5KZnkjzj6vbPc4XEqlz+XH6gtS75Ipzi15NvFH7m2eGaqVA93gDmzb8yObPFlFesIou/YczYsLZWGLbV74TJal1IJckOa2+5Quo2AFpefL6eJo8IDco4exTMnjh8hGYWrzGh3dNlEVyBAFRDLN5eyGmYBi/1szZ02+ltuRPjBoxjOQW9beD18dtngDf76njvH4ZvDN9NEu2ldPoDpBg1HL1sG7E6NS4AyFKmtw0uv18XliF5yB52gZXgFfX7qVXqpnTe7ReV9/f6GJbpY1AKExdRRkr3n2djcuXEJOURklVDXW6ZLwt4kpLfirntB4p9E23oGlnwBPlj0mHBPPp06dz5pln/qpjD9d28lspKSlh7dq1JCUlMW3atMjauF6v5+qrr2bevHls3bqVsrKyNsFcqVRG1swUCkW0de0oMWhU9E230C0xhpAoolcr0WtUR+wpjnL88Tqb+eb9/7Dk3bdISEknb8AwlCoVSqUKi1FLaozugBXoQRg1KnRKBav31tHo9hMWwaRVkZccS15y7G+zqRXDNPz0PevfnENjVTnjLpnGyCkXk5qZTUZCbBs5339+XcgP++r55KcKeUMoCLt+kKvVnQ3Q63Q4Zbxc8NbCqO5Wnjh3APGGA2p8giCgVsrLa2+99RYvvvgip0+YxDV/mYGgSUQ3oBvxRi2lTe5WyoQ/4wmEKLd5yE8xc8vYtlm8QEgkLIpsKm9qFchBbtOrbPayp97F6T0ObA+FJaqbvQRCIvsLf+Ljl+ZQUrSNlN7DyJt0KcrM/EggVyoEVEoFRbXNJJp0ZCecePW5KCcHHRLMc3JyyMnJ6YhT/2ocDgderxej0dimyE2lUhETE0NzczNeb9t048nOOxv2s7nSxtS+mQzKTjyp1qOVLXKjUU4Mzc3NzLjpL3yx9AuSs7tz0S1/iwxG4w0aMi1G3rxqJIOeWcYh3VZkWQwMzU6k4iCHM38ozNYKG4kmLSm/0rNdFEUef+wx5s59HoVGyxUzH2Lg2D+h0elIjtWTn2JuNeN88fvdPPj5Twc6I/xu2PSZnFYPB+G0a2W3NJUctAUBzu+bSZbFgNjO0NHj8TBr1ixef/114uPjGdTvFHJSE1AqVZFiPVGS2u37ByLLRu0Rq1Nj0KraHQj8rEcfPsRnXUIiLEoUb1nP//19BuFQiFHnXkb2uItoFDUEQmJEyc9q0qFTK/EFRWqd3mgwjxKhU6rZQ6EQzc3NqNXqdvtERVGkubkZhUJBTExMh7SvJSUlERsbS0FBAYWFhfTs2ROFQoEoipSXl7N9+3b69+/fZlZ+MrOtysakBStpdMldAvO+LWZSfipvXTO6jVNVlD8WkiSxfv16Lr30Umpqapgy5SxuuPsBXFoL3mAYq0lH/wwLiSYtiSYti68dw8yPC3D5wygESIrRsviqU/m+vKFNUHMHQtQ4vFhjdMek1hcOhykrK+PWW29l2bJl9O/fnyefepq8gcOod/kx6zV0iTdGZvzNngBjZ73P1gMfSl4fX/tf2brUkg7jb4SETEA26InVKvlTL7ntNCzKRjMHv39xcTF33XUXy7/+mrye+bz4wgJOHT26zbWmxOrRqRS4DgncKoVAl/jDB1ClQmBQZgITejr5saSBwEH1IiqFQIJBQ+pBgyBJklAgYY3RkdunH+nd8xg64RxGnXkhIQRqnV5qmr0IgkCSSUeaWR9RGFRGRWKiHESnPPG3bdvGzJkzmTp1KjfffHOb/S6Xi0cffRSHw8FTTz1FUtLxVwPLzs5mwoQJ/PTTT0ybNo3bbrsNq9VKQ0MDc+fOJRwOc8YZZ5CdnX3c37sjaHL7ueXdDZFADvJsYllhNQ9+uoU5UwefwKuLcqIJBoMsXLgQv9/PXXfdxW233UZSUhKSJM8CD9VsP7d/JlNOSaO62UuMRonZpJeruMvaP794jA6v4XCY1atXM2vWLIqKirjooot44IEH6NVLLvbLim+9Tl5p9/DPr3ccCOQhP1TuhHXvg7MeeoyCgedA3IH6ml7JsfRKPaCAlxSjI64lK+Tz+Vi6dClPPvUURbv2MOSMs7jprvvIys8nJIptVOeSTFp6pZopqnFECthMWhX5KWbMh7OhayHTYuCSQdnsqXexrLAKmzeAQhBIMGq4aGAWY3IOLAVs2rSJsrIyho8eS15aErPm/oewQo1KKZBs0HJev0zW7qunye1v1eVj1KiOKPEb5Y9HpwTznTt3snv3bsaOHdvufqPRSO/evXn22WdpbGzskGCuUqmYMWMGKpWKJUuWcOeddyIIAqIokpmZyaxZs7j22mt/N72b3+2uPdCWcxASEku2V0aD+R+U+vp61Go1cXFxzJo1i0mTJnH22WdHvteCIKBStj+jUyqVZBwUVHUqJUkmLVXNrZeetColCSbNMbWQSpLEnj17kCSJO+/+G5OnXk4oJpb9DS5SzXp0aiUOX5DSRhfrSxv4Ykc1H25tEYJx26FoFWxbLv978HlyMDfIgbtrvIHhXZNw+kORTIFZr2ZUtySSYnQ0NTUxf/58Xnn1VfyiwJlX3cyoKVPxao2sK2lgtNLaxq1NEAT6pltIMulodMsD5gSj9qgNgNLjDDx+Tn/G90yhpNGFgNyrf3qPFCwGLX6/n/fff5958+YRDAZ56aWXGNZ/IJnxBhzeIFqVkpRYHRaDllHdk9hSbqPW6SMsSiQYteSnxLbyr48SpVOCeWNjIzqdjvj49pWslEolVqsVl8uFz+frsOtISUlh1qxZXHzxxdhsNoLBIGq1GovFQpcuXX5Xsog2T7BVCvFgAuG263VR/vdZvXo1Dz30EBMnTuTOO+8kNzf3NxkHGTRKeqfG4fKHcPjkqm5ZA/6AM9ovUVZWhl6vJykpifPPP5+8voOoFWIpbg4TampEo1KSZtaTn2xmZXEl760r5vtKD3ZvS0Fecy2s/1A2STEnw6grILl7ZH38gQk9OX9gV1LNejaXN7G3wYVBo2REdiLZiTHsKtrJ/fffz/Lly0nuksP5191Bbt/BqFsGNw5fkEq7h+QYXZtshUIQSI8zHNGW9UhYDBou6J9FWJQQBCIDjfr6embPns0bb7yBUqnkkUceITc3F71aSffEmBad+wPnSY01EJOjxu0PIbb8v8Tq1L8bQ6IonUOnBHOFQoHf70c8Qm4uFAoRCoUOu/94odfryc/P7/D36Wgm5acyf1XxgYfeQXRLaL+1J8r/JuFwmFdeeYW7776bUCjEiBEjqba78EkqEk0aEoxHN4Nz+YPUOHzoVAoyLEYUgkB2gglrrI6SRhe+oEhmnIEEk4YGl2y2kmjSkGBq//xbtmzhyiuvJDMzk08//ZQ4i4VAbICm+gOmLaFAiD31TjaVN/H3T7cSEsUDhXhNFbBsnmySkjMURl8JhgO+CR9fP5az+qRFgvCkXmmIkrw+rVQI+P1+t6GlXAAAIABJREFUnn32WT777DNOO2MCZ854AENcQquMgiTJSm1hSeqwh+HBbXz79u3jzjvv5IsvviAtLY2PPvqIvn37tuqQOTRGCwLE6NTtyuJGifIznRLM09PTkSSJpUuXcs0116BWt/5S2mw2Vq1aRVJSEiZTNBAdDekWA1MHZFFl99DsCyJKEiqFgqx4Ay9fPvREX16UTkCSJCorK3n++ed5+eWXiY2N5b4HH6bHqZNZvrsp4h7WLdHEiK5JGLUqQmExErzULSptggA7qmSFuJ8rtWN1asblpWCN0WFQq8hNikVCwhsM882uWkoaZV12hSCQY41hSJeEiJSpKIooFAq6dOlCbm4u2dnZBINBvAGRZm+gVUGdxx+kyunjuZVFB8xLAh7Ysx5WvQEaHQw6RzZK0cqFZzE6Nc+eP5Bz+2W0uh8KQUAhgNPpRKVSodfrufvuuxk6dChXT7+Wr4pqqHX42hwTq1O3WTPvCEKhEN999x0bNmzgggsu4IUXXjhstjJKlGOlU4L5oEGDGDx4MA8++CBer5ehQ4disViQJIm6ujq+/PJL3n33XaZNm9Zh6m//i9w3qQ+ZcXrWlzYRDIvE6tRcMSSbXGvU9e2PgNfr5bHHHuPdd99l9OjRPPTQQyhTurOxtLHV60qb3Jj1GvKssRTXOdjX4MQbDBOn15BrjSFWp2Z9aQPhg3rTHL4g6/bXMyYnmSZPgCq7B39YpMHlp8bhRd8i1SpKEvvqnVgMGvqkxlFWWsK6deuYPHkyFouFhQsXEh8fjyAI+DyBVtNOhy/I5z+VsqakiZCIPE22V8P2r2HnKohPg75/gpxhoFKjEGB01yQuHNiF60e13/paW1vLk08+SW5uLtdffz35+fnk5+cjShL5KWZc/hDuloI2QRBIidWRaTF2qDxqTU0NarWahIQEzjzzTNLT0xk8eHA0kEc5rnRKMM/IyGDmzJn8/e9/Z9asWfTo0YOEhAREUaSmpoaqqirOO+88rrnmmogzUZSj48/DuvPnYd1P9GVEOQEolUp69uzJzTffzHXXXUd2djYfbm5bfh4WJeqcPlz+EPsanJGgXef00ewNYtAoWwXyn7F7g/xY0kCd0yfP5kWJ7VV2NCoFXRNMEe31kChRa/dQuvVHFjz/HIWFhRiNRs455xwSEhIi54vRqYg3aCi3uam2e1i5q5qfqhwEfl59qyqCgiVQuweyB8iz8aSuIAhkWQxcPDCLa4d3Jy+ltWe7yxfE4Qti0KrwBwIsWbKEs88+m/BBtSMKQaBrgiyBWmH34AuGsRg0ZMUbSTAeuTr91yJJEtu3b2fu3LloNBrmzp1LcnIyEydO7JD3i/LHplOCuSAIjBw5kjfffJMVK1bw9ddfU15ejlKp5NRTT+Wss85i1KhRHVLFHiXKseAPhamwe1AKAsmx+sgM9GShrq6Or776irPPPhuz2cwNN9xAOByODIIjHuWH4PQFafYG2gRtfyhMtcNDvKFtF4c3EGJvg7NVCjokijg9IRJMQbRqJQIQ9Pv48L8fsPydV9m/dw/33XcfI0aMaHM+lUJBSqyexQX7eHtzBTZPQF4fl0RZAKbgU7kFrd9k6D0OjHKGaVwPK4+c2Zf+mfEYNerI5F6SJDZX2CiqbKSpsYFEawpZCUY++ugjUlNT24hDqZUKuiSYSDMb5GUGpaLDPAIkSWLVqlXce++9FBQUcOWVV/5m5UhRkihrclNc6yAQFkkzG8hLjo1qSkQBOimYg1wEl5WVxbRp05g2bVpnvW2UKEfNnnonq/fWRboEYnRqTstNPup2pI6mtLSUW265haVLlzJ37lxuvfXWNh0Y2fEmtlbaWm1TCAJGrQpfsP0uB8vh+qYFgYMtxhQCWAxa3HYPbn+IRKNEwOflizf/j6/f/Q9Ggz4yK26PYFhk1sebWLKt8sBGvwfWvQs7vpFNUk79M+SOiJikjOqWxJIbT29lLfozxbUONuyr4/0FT1G8+UeuvPsfOHr1Y1BWBlZrQpvXg1wc19F65qIo8t5773H99dcjSRIPPfQQM2fObNdY6miRJIkdVXY2ljVF6hqqm73UOX2MzbViaOf+RPlj0enfALvdTlNTE36/LIKg1+tJSEiIFr5FOaHUOX1sLm9q1e7n9AX5YV895/bN+G065L8Rj8fDmjVrmDlzJuXl5Vx99dVcfPHF7b62d6oZmydAnctHMCy2zIZ19E23sK3Kht0TaKPoNiAznpAosb/BhT8URhAEjBoVOdYYdlY34w4cWGNOjtXJTmEqger9u1nyyvMUb1jNqaNH8cQTTzB0aPvFlzZ3gPs+3XIgkEsi1O2Hgk+golBuNxt9BSQfWAsfnp3IG1eNbDeQe3w+Vq5ex7wnH2bv9k3kDRiGomXmu6O6mf7p8SgP00/fkYiiyOOPP85DDz1Enz59mDVrFhdddNFv1q9o9gXZ3+huIyVbbnNT3ew9Lp73UX7fdFowd7vdfP311yxbtozCwkIaGxtRKBRYrVYGDhzIlClTGDZs2O9GtCXK/xb1Lh9Of9vWyCa3nwaXj1TzidEgqK6u5vXXX+eZZ57BbDZz//33c9VVV5GYmNju6006NafmWKls9sj94d4A26rtlNncJBq1xOjUkZ5xRUsBWH6KGb1GSUacgSZPAJVCIM2sx6hV4Q2EKaptjqTnjRoVuXEqdq77jk9ffxFbbRXTpk3n9ttvIzc3t1XbV6Xdw7YqO7XNLtbss/HS2j3yjnAQ9q6HLcvA1Qj5Y2Dg2WA8IKV85ZCuzJ06iARj2+dBc3MzHyxezNynn6GisoKx513O6RdcSVJ6FiCbnQRF8YQYIgmCQEZGBueddx5//etfGTly5HGRp/YGwjh9wXb31Tl90WAepXOCudfr5cUXX2TevHnYbDZGjBjBkCFDCIVClJeXM3/+fL788kseffRRzjrrrN+UjooS5dcQFmVv7fYIhg9nudHxNDQ08Oqrr5KTk8MjjzzCmDFjflHcyKhV0cMay3/W7uG/BaWs2VePKEnkWWMZkhXPTWPy8AbDxOrUZMQZMOnUCEBWvJFMixEJueVMAvqkmVErBUoa3QTCIn6nnS8WvcK6zz+g2Rckdfw1WMddQkJ6l0ggF0WJ7/fU8uLq3Wwoa6LB6cHhb5lRepph8+ewaw2otTBsqlytrjUiAKd1T2T6yBym9M0krp30f1NTE0899RRvv/02Co2eS+94kH6jz0BvPJDZSzTpOtUaVJIkFi9eTHp6OsOHD+fyyy9n4sSJpKenH7f3UCkFNCoF7ZjbtZu5iPLHo1O+BevWreOVV14hNTWVTz75hMzMTNRqNZIkEQgE2LhxIzNnzuRf//oXAwcOJDMzszMuK0qUCPEGLQaNqs3sR6+WZTU7m0AggEajoWfPnsyfP59TTjmF5OTko57lbSlv5NmVReyobj6wrdJOUa2T8/tlMD4/HaVCiKiIiZJEaZObHVXNuPxBEk06BmZaiDdqGZgZT++0OIJhkX8vq+D7dRsJaMwknXM9TcYU5qypYH2tj/kXD6VrYgxlNjf3f7qFgnIb3oNVCpsqYM07svd4XIrsdpaUDUr5MXTFwEzmXDSURJO2XanYkpISrrnmGtavX8/w4cO5/5HHqFRb8YYODLa0KiWjuiV2mjqa0+nkr3/9K4sXL2bKlCnk5uaSkJBwXAM5gFmnITlWj90bOLiMAZNWRYbl96NcGaXj6DRt9pqaGj744AN69+7dZv+kSZOoqqrinnvuobGxMRrMo3Q6aXF6uifFUFhtJ9QyE1erFAzpkoBG1XnpWq/Xy6JFi3jppZf48MMPycjIOOZWJn9IZP73u1sF8p/xhcLc+eEmih5q/Rvb3+Dim921iC3pdIcvSIPbzxk9konTKfE5HWhNsXxR4sVwxjQESUWtQocUBsIhlhbWkPfop2y6YxJFzS5W7z+o110MQ1UxfP+GLM+aPQDGXQ+aA4WFsTo1b04f0+7nCYVCLF++nOuuuw6bzcYFF1zA7NmzSUtLwxsMsa3STq3TF+l1jzN0TKvZwfzswHbzzTezevVqBgwYwC233NKqFe94olEpGJgZTygsUm5zI0lyBmZolwQsnfB5o5z8dFp+xmq1HlGbPTk5OdpjHuWEoRAEBmfGkxqrk1vTFAoy4wxYO8nMIhwOs3fvXubOnctLL73E4MGD2bVrFxkZGb988CF8/FM5b6/ff9j97kO8tiVJYlN5UySQ/4zLF2RPvYN9677miccf59U336bB7SdkiMfrDyEdUowVFCVGLFiOy3/Q+b0O2P0jFHwMSjUMvRD6Toxoq6fE6kgy6Xj58mGHXJNstRoMixTt2M4DDz6IRqPh/vvv5/bbb8dolNXg9GoVQ7Pbrx8AuYLeHQiBBAatCs1xKGQMBAJ8/vnnPProo5SXl3P55Zfz5JNPHvfZ+KGYtCrG5aVg8wQIhkXMejXaThxoRjm56ZRg3q1bN9RqNTt37iQ1NbXNfr/fz65du8jOziYuLqpeFuXEoFAIZFrkdePOJBQK8c033/DPf/6TtWvXMnXqVO68887DVob/Ev6QiO8wJjwA/dLNNLj9ePwhTFoVRq0Km6ftYqwoSTh9QUIi5OTk4HK5sBi0lNk8hA/jsxAJ5JIEDWWw7SvY9QMkdYMBZ0JWX1BpUCkE+qeamNA7i9NyrZySduB3HwyL7Kl3UlxeAxoDgiKWSRddycg+uZw5efJRu7XZPH521TmpdfiQJImkGB251hgSjbo2+ufHgtfr5aOPPsLv9/PQQw9x/fXXt+lp70iiM/Eo7dEpwXzkyJFccMEF3H///dx4441MmTIlMkuvqanhrbfe4rPPPuO6664jLS2tMy4pSpSThm+++YYZM2bgcDi45557uPbaa0lJSTmmc/iDYcrtHvyhMA2uwzsPCsD5/bJYvKUMhzdInEFDrxQzBo0KT+BANX/RpnVotVp6jz+NC88/j9PHjCYlLYNT7T9RXNeMy3+EixHDULYNNi2B2n1y3/iAs8CSBi1r/kaNihlje3JqbirJsfpWfdK7ax0sXPQeXy9exOUzHyU+OZXc0ZM5pWf6UQdyTyDElgob+xpckUr8epcflz/IiG5JxGiP3bTE6XRiMpkwmUzMnDkTl8vFoEGDOjWQR4lyODolmH/yySd8+OGH7N+/n5tvvpmZM2diNpsJBoO43W68Xi86nY7Zs2czZ86cyHGCIPDEE08wadKkzrjMKFFOCD169KBLly785S9/aeU9frTUO32s3luPzeNHAoprmlEK0F4R/rypg9lSacfhC0asNrdX2hneTU5Vh0Mhvv3obZYsnEtOz15ccvoQTEYrJqNs5Tk2x8reeiefbKvAf7jZ/87vYN37cgvaqMug1+mRtPrPGNQKLCY9STG6Vgpmbn+IUpubHZs2ULjhB/ZsK2Bo8hQCosCO6may4o8ua+LwBSlpdLdSvBMliXKbhzy3/5iCuSiKLFu2jMcee4yXX36Z3r1707dvX4Bj8nSPEqUj6ZRg7vP5cLvdWK3WyLZQKIQgCJGRLoDD4Wh1nCAIBALt9GJEifI7JhQKUVhYiEqlIi8vj6ysLJYuXdrGTfBo8AXDbKuyU3/QbLxPahxDshJYd4jhSrxBzbqSekAgEApTbnNT0uQhJIrsbXBweb6Z/7wwj1WffUCCJY6bZ9xC35wukeMVCoH8FDPWWB1xeg11Lt+BympJAm8zbPhIVnOzpMLQqdB1IChar+saNCoePLMvHn+YZm+A2BZrz6amJpz+MIGQyNnTb6PP8DHkDRweOa65HbvfI92XQwVWQG5BdPtD2DwB9tQ7sXsDJJm0dEuMiVzHwTgcDt5++20efvhhwuEwBQUF9O7dOxrEo5x0dEowv/LKKzn//PN/1bHRorgoJyOVdg+76hxIEnSJN9I10XRU7VA+n4/FixfzzDPPkJ6ezptvvonFYvlVgRzA5Q/R6PYfsi2Iw9c28DV5gny4pZwzeqSwt9FFSaNLtkmVJJZ/8x3fz/8SKos4Y9zpPPbYYwzo37/V8QJyINYqFTj9wQOBPByEmt2w8RPZLCV7AAycIqu5HXJPVILAuB5WFIKAUiGgUigIBoOsWbOG559/nu55+Zx60XS0Ol2rQA6QdAzFiAaNCr1GifeQYj+tSoHdG2RTeWVkWWF/g4t99S7G56dGArokSezcuZMXXniBN954g969e3PPPfcwZcqUo76GKFE6k04J5nq9Hr3+5NC3jhLlWAmERMKihFatQCEIfLGjkjd+3MeWChuSBD1TzFwxJJuLBnY54nmqq6uZO3cub731FhaLhXPOOec3/y4kpIhv+c/sbXRTXO9q9/W+kMiGskac/qB8XNBPuOh7xJ++Juh1cNU103jknjvJyspiZXE1WqXAwKwE9Bo1IVHi459KeW9zGZ6fg2TID0Wr5UI3rxMGngO9ToOYw7RoCbCzxsGU3hIJRi1CwMP/vbGI559/HrvdzujRo8mON+AM+SIysgBmvZo+qUdfHGvWq+meGENRTXPEJ12pEEgx66lxeFvVBwA0uP1sLm9ibK5swbxjxw5mzpzJypUrueyyy7j99tvp169fVNAqyklL9JsZ5bjh8YdYuGYPX+ysQpJgQl4yN4zKJeZwRh4nOYFQmJ21DvbVu5CQiDdo0asVPL18B9urDvRwr95bR3GtgwEZFnKsse2eq6CggPvvv59vv/2WIUOG8PjjjzN06NDfLF9s1KiIN2j5dlcta/bXt7Rihdu1NP2ZepcftVJActsIb/gEcdcaBJUWw+lX0WfKlayqDvLvD76i3O5GKcjSri9eMIR/ry3mw21VVDtaUvoBL6z5L+zdAIZYGHsNdOnfZn38YEKixN4GF6t21zIyWcWjDz3LO+/8F4PBwMKFCxk3bhwGUwyJcT521Tlw+oNYDBpykmJIijn6e6VVKemfYSFOr6GkyYUkQZbFgFmvYX1JQ7vHlNs8kb9brVaUSiVPPfUUV155JVarNZpaj3JSEw3mUY4LTl+QBz/byvxVuyKBZEVxDYXVzTx9/kASTb+vit+wKNtrbq+yRz5Pg8vPt7trKa51tnl9vcvHc9/uZMHFw9rsc7vdLFiwgOXLl3PhhRfy8ssvExvbftA/VgwaFZsrmnjtx31HbEc7FMFRS2jlfxDLt6OwpBFzwT1o4lPYVuflhXXl7Gt0R167r9FN/znLECXpQGrdVg1fzYfGMjmdfto0SMg66vf/ZO1Wyv5zHz/88AOjR4/mvffeIzk5ORIw0+L0pMbpQQIEOcV/rBg0Knqlmslv8T8XBPl7qjyMip5OJbBs2TImTZqE1Wrl008/RaFQRIN4lN8F0WAe5biwobSBf/+wp9WMMCxKvLWxlIsGZTOpV9qveiCfKOzeAJV2T5sZrssfiijEHYrN3VoKtqmpCZ1Oh9FoZObMmUyaNImLL774uAaHjaWN/Gft3mMK5CoB8sK17LBVoz1lHIYxV6LUy+v+22sc7G9ytzkmch+CPij9CX58DzwO6DEKRlwa8R6P0ykxG3RUN3sJtFOARtAHFYV4Vr3GDrXEjBkzuP/++0lOTm7zUiHyx2/j4Nsdo1OTaTFg8/gj6fdQMEj1/l28+MKT2BpqWbhwIePHjz8hRi1RovxaosE8ym9GFCV2Vtnb9csOhkW+La5mfM9UVIrfTzj3BcMH1oUPIjlGh0olEAy1DuiCAGNykgC5lWnr1q0sWLCAfv36ceONN9K7d+92pYx/K/UuP/sb3W1iXrvDDVcjqLQM7dmFf0y4luezsygKWwirDeQlx3D1sG58t6eOgnJbe0fLJik7VsjV6hoDDLsQeo0DlVw0Nik3gfP7Z5CZYOHF74tZu7+RxogYjQSOBrlt7acvSUiy8vC9d3PFFVd0ulBUn7Q4REmiqtlLY1MTP65Yxhdv/huvw8a1115LTk7OL58kSpSTjGgwj/LbETiifrlOrfxdzcpB1sLWq5VtCqV6p5qpd/lYva8+EjEFAUZ3t3LpoK74/X6WLl3Ks88+y9q1a5k5c+Zh3diOB8JhUtA/b4u8s6MONn6CQqnmn/e+ydCuyXTLSKW4zklIFOmeaKKHNZY1+xsQaGcwUF8CGz8+4D0+4CxIz4+0nZ3dJ5055/UlJyWeOqePy71dyUmK5Z2CUmqcPhBFKN0C25ajTOvBSwtmM2XC6b+6iv+3oFMrGdwlgeL95Tz1wst89tH7pCQl8swT/+Ccc86JdtBE+V0SDeZRfjMKQaBHihmDRtlmNqtVKRidk4zydzQrB4jTa0gzyy5VB6faMy1G5lw4mO931/L6+n2ERYkL+mcydUAXdAqRF154kWeffRaXy8X8+fO55JJL0Gg6rgAwNVZPz5RYtlba2+ybfV5/CsoaeH9zBaLWgIYg00/rx9CuVhQKgS4JJrokmFodk2tWE6tR0BxoSZFLolzgtmEx2GsgbxQMPl+uVhfktefTcpN5/qLBZMfL50o0aZnUK41cayxDsxP5aHMpi38qR0zLo+cFf+GVv93AiL55HXZPjgaFIFC+p4iP33mTkSNHyq14AwYcF+/xKFFOBB0ezCVJIhwOI0kSSqXysD8WURQJh8OoVKpowUkHICGnvOX1XgmlQoH2OHo+D+2SwMzT83hmZTH+kBzQNUoFt47NY9gRjDBOVtRKBYO7JKBRKdhe3YwkSiSbdQzJSiTRpKVboomrR3SPvD4cDnPTTTfxyiuvkJOTwxtvvMHpp5/e4d/lfhkW/jI6lwc++ymiAKdSCIzITuTcPmkM19l47oLzCEkCRtWfCUhQZvMQb9SiUgiU29zsrHEQEiW2VNh4/MvtB04e9MH2lVDwCYQC8tp4vz+1WoSO06u5ZGAWKoVASBRRK+X2vXijFpNa4Mu3/o8Lunfn3XlXdMi9ECWJQFiU1eyQ/9+ONHD8WaxKqVQyYsQI3nnnHQYNGtRK0Op4IUkS/pBIuCUzoxAEdCrlb9KFjxLlcAhSB+YAm5ubWbVqFatXr8btdpOfn8/YsWPp0aNHm9nKtm3bePXVV7n99tvJzs7uqEv6zQSDQe666y7+9Kc/MXny5BN9OUfNhtJGnv9mJ9/urkOUJAZnJfDomX3ol5lwXB+y728q5cMtZUhInN07ncsGZ//uC4lCokRYFNEoD/8gliSJv/3tb+zatYsHHniAAQMGHPN9lSQJT8tavUKQ2860R/nw31DSwPxVxbj8YYZkxjHGKvHy/Hm8/tp/uPWZhWT3H0G100uMVk2iScueOgdfFFZRXOdEkuQMSqSITpLktPzmpbJJiiUNhk2VTVIOg1al4IaROfxtYm/S4mR/7c2bN3PNNddgsVhYunQpBsPx9d12+YMs21HFv1YVUd7kxRqj5c9DuvHn4V2J1bV+vkiSRE1NDa+99hoKhYLbb7+9QzXVRUni8+2VPPbFNjZV2JAkibzkGBZcNJRROUmof+e/iSgdi9vtJiEhgcbGxohD4C/RYcHc6/XywAMP8PLLLyNJEjqdDpvNRn5+PrfeeisXXXRRq8KXZcuWce2117JkyRIGDRrUEZd0XPg9BnO7J8DE+SsorGntb31KWhwvXTaMU9ItJ+jKft/Y7XY2bNhAnz59SE1Nxe12Y7fbf5UVpihJVNo9FNY0U2HzoFIKZMeb6JViJtF09C5fgUCAFStW8M9//pN1P66nz4jTGHfptYTjs6h2eFEKArnWGOZ9W0xde24p4aBskrL5c2gogW5DoN9kSMxqo+bWHhf3z+CR0cn06NEDQRD49NNPyc3NJT8//9huyFHwn/9n77wDoyrTtv87Z2pmJjPpPSQkgdBr6EjTZbGAqIhlcdXVfV1WRRTdZUERxRd0d139FGyvhbWi2EVRrFioSi+hJCGNtEkmmd7OOd8fJwRDgiKQILvz+49McvLMZJjrPPdz39e1vogFH25v5fKm1QjMGpvPvEl9W33vtm3b+Ne//sUHH6yi/9ARvPHaKyTFdVzjXaPHR/8lqylr9LX6eq/kaJ6+ahij89p270eIcISTEfMOOyBavXo1y5YtY8iQITz//PO8+uqrPPLIIwQCAe655x6WLVtGY2Pbc74Ip5/n1h1k7zFCDnCgzsXqPYfPwIrOfurr67n77ruZOXMm7733HqFQCLPZfNKZ1o3eIF8frOXzwmp+KGtg06F6Pi2sYlOpHX+4bVd9e0iSxBNPPMFtt93G9l27Of/3M5k+az4pOT1oag5WCcsKXx2oaV/IAx7YsQa+fUlteBtyCYy4AhKzTkjICQV489mlzJo1i3Xr1iEIAlOmTOkQIW/0BvlgZ0Ubu9awpPDc+mIam7voJUnijTfe4IYbbuCtd96l39jfMukPt7Gp0t3GBvd0suST3W2EHGBfrZvvjzctECHCKdBhZ+ZvvvkmoiiybNkyunXrhiiKjBs3jgsuuIBbb72VJUuW4Pf7WbBgwRnpaP1vYnNZfZvu5HBYwhGSWPzJbp5fV8SVg7NYeFH/M7K+s409e/YwY8YM9u7dy7hx4xg3btwp2XwqCpQ7PGwurafeE2gxZnH5w0iynUGZ8UTZ2tq+vr21jE8LqwjLCn3itHzzwj9Y/eEqMjIyuGfJw1i6DUIS1GhTWVGoc/spc3goa2eOHL9bdXM7sAFMNpjyV0jKAY36vAyAohEIHmfGHnc9fPsK8qGtFHfN6vBegVqXj4bjiLE7EKLC4cGkhX/+85888sgjNLlcTP3jHEZfNA2jyczhJh8bSuyc3zvthDz1fymr91a3+3VJUVrm2yNEOJ10mJiXlJSQl5dHfv7RrlWtVktOTg5PP/00d911F//4xz8IBAIsWLCgo5YRARjWJY4Pd1W2CHo4LBNoPh5t8odo8oe47+NdvLm1lG/vnETMWWq/2tEEAgE++OADZs2ahdPpZMaMGSxevJjExMRTuq6Cgt0TpNblx+EN4Q6EEASwGXUggCsQIpWjYh6WFZZ8sotVuypRZAlvZRGvvf8UnooDJOf05Kp5ixl37ijXpSZnAAAgAElEQVT21zpxeINoRIEGj58NJXbcgTCtrFxkCZpq4LOnVDe3jN4w+hqIUfPUzQYtfxvfndt/0wefX2L2O5tZV2ynsslPQJLVn68tVm8EaksgsSsfffgB3bt3P6XX5OfIjDE1uwq2rTiZ9Vq6xpvR6XTYbDai4xL5/YJHyB/Y2p3P4Q1idwdI+gUBLifKBb3S2FnVdm0aQTir/BYinD10mJgbDAY8nnZ2AEBGRgYPPvggJpOJxx9/HLfbzYBjEpoinD5uHZfPWzsq2FHpQFYgdJyNwd5aNze9sp6nrx5BjCki6Meyd+9e/vGPf2C1WvnrX//Kn/70p1P2Vj+CLCvYPQGcvlDLTVedJ4AgCnBMW8uWsnq+KapFkcI07PiGw1+swOdqQuw9gcTJ17HOGYV7UzGTeqUBcKjezd4qJ85A65l5/G44tEXNHpfD0Pc36vm4JQ5Q3dIWXdiP28b3AMBk0PHSdeewo6KBa19az7aSKij+Xu12DwWg/2+hYCrRyRmn5TX5KaIMOi7qk8bWigaafEed94yiQi+lipKD++nTpw833HAD8f3PwUnnBj0tuLAf7+6sYF/tUetfAchPjmZARqRHJcLpp8PEPD8/nxUrVnD48GHS0tLaPJ6VlcWiRYuw2Wy88MILfPPNN7hcbT2vI5w6Wq2Wx6YV8MrmYtYerGNPtRNfO25tsqKwrqSerw7UMLV/5hlY6a+ThoYG4uLi6NatG7NnzyY1NZVx48adtusLgFEnoteoPuBKq1EmEa2mdWtLYY0Tf0hClsI49m7CL4Ew/HLkbqPY0yhzyFtHRaOXIVnxjOuejKyoSWqtcNbBzk9h3zcQZYUBF0LuENCrotcnJZoJ+Wn8bkjXNuutbPLhqCqHje+qPx+dAAVTIacAdEY+2lXB1AFZxJtPz43O8fjd0K5oNRo+2XOYA3VObEKQ6s2fsWvjKh46tI6nnnoKY5SJzNRUNh6yE6XXoPlRST3aqMPWQVUoo07Dk1cO5ZnvDvJpYRWhsMIFvVO5ZlgO47pFmt8inH46TMzHjRvH888/z5tvvsmsWbPa/Z60tDTuvfdeEhISWLx4MT5f24aRCKeHQV3iyUuMprzRy7UvrjuuZWedJ8D+Wmcnr+7XSTgc5vXXX+e5557jpZdeIj09nenTp592YxEFiNJpyIozoxUFPEEJURAw67V0iTWjPeb36aUAAgoanZ64MdNpqq5HissEjRYZ8IUkiuwuvthXxYiuiXyxv6q1T7q9HNa9AtUH1bJ6wdTmJjcRrQDTBnahf3oMY7unEHtMhUaWZYLle1HWLodd30NGLxh2uRqyotEiChCQFHZXNTIsOwHDTzgDnio6jYYZQ7tybn4KP+zex5OP/pOST1YRHxfLlVdeiTuksKmylqomL02+IBWNYdJsUViNOqxGHcOy40+r18KPEQWB8d1T6JUaQ7HdhSxDXqKFZGskCjpCx9BhYn7RRRcxbdo0HA4Hfr//uDOdNpuN2bNnk5SUxMsvv4zFYmn3+yKcOtYoPb2j9IzOSWJLuaNd/+5gWKbeEyAkqQYg/6243W4efvhhHn30UZxOJ5s3byY9Pf20zMzLikK9O0C9J0Cc2UBitJFYk4GeKTYsei3VLr8aPRoTRXacGbNB/W8qSRLvv/8+f//fxWgm3IiYmIUmPh1FOprAduQvJskKn+2r5uXvy1pMfFAUtSz+xf+pZfVe46HgYnVn3kyXeAv13iCNvhAZMaY2Biw/bN/JnDtup3zfHrUkP/RS0B7dgY/PS0IritS6/DT5QiRFa3hwzS4+2n2YQFiioEs8903qR4Lt9J1TN1WVsuSum9m4cSMTJkzglVdeISo6hvUldg7Vu1GAVFsU1kAYnUZkWHYCPVNt6Dvh/Z0cbSS5A87kI0Q4lg4T8+joaF577bUT+l6TycSNN97IjTfe2FHLifAjHr28gGfWHWy31C4KUGx3Ue7w0DUhul3fb0lWCMuy2szzHyL4ChCWZCRZprz0EA8uWcKKFSvIzc3l6aefZsSIEafl9wTCEt8V1VFkd7UchWfGmumfHkNuQjRGrYbcxOiWufNvi+tYV1JHbJSBc7sn43B5UKQwV+Tq2WWysrfaiSgKyLKCwNEJMlmB4vrmfG5FAZ8Ldn0K2z4CgxkGnA99zgPN0UkSjSiQZDGQZDFS0ejjsa/2cdPobuQmWPD5fGj1RpbvcFGUWABpo6H7iBZvdlCPC7LiownLMu/vqOCGlze0nNMLgNWgpdrpZ2dlI+/+zznEWU59l6ooCrfccguFhYXMnj2befPmERcXR7XTR0Wjt+WGVRQEoo06NKJATJSuU4Q8QoTOpFO82YuLi1sMY063C1SEk+Pqgi48v76kze7catSRZImi3OGlS5ylVeetokCty09Fo4cmXxCzQUd6jIl0m+mstqgMSzJVTh8VDi9FBw6w9L6/ULhnF5dddhl/+9vfTuuc9O6qRg7Wte4NqWz0YjVqGZgRi9mgxeEJUObwsKfaiQYF9+FiarVamvwhxucVsPKtt8nukklYFnh3RzlL1uyisMaJoqid8ZJ8TFCKvRS2rYZDWyExBwZPgS59ODaixWbUEfejsnqty8+G4lrqSwr56MNV/O4Pf+SJzaXQ59x2584V4NXvS0i1Glm5taxVLKsCOANhDDot5Y1enttQzF3nnVyKnKIouN1ujEYjOp2O+++/n7179zJ9+vSWkJRAWCLUTgSrJCv4Q5Jq/3oWv2cjRDiWThHzTz75hGeffZYBAwYwduxYxo0bR5cuXTrjV0c4Dg9cNIBdh5vYVNrQ8sGvFQSGdImjR4qVYFhubsQ6+olX4/TxTVEtjpZYSyi2uxmVk0iXuBNzKfq1oTR7lW88ZMflD1FV56TO6WHytTOZc/P/0LN72wawU2FfTdsmT1lRqPcE6JFsIyXaiMsfoqTeTSgYoGb3BvateR2NzsDY2x6iqMnMuF5d0Gq1aIGL+2Xi8AX5+6d7qGryEvqxkCsyFG2CbR9DU3NISr+JEJPaZg1aUSA3wUKs6WjJXJIV6twBijZ+wZNPPsnA4aPUB35CBf1hmS/3V7ebr64A/kCIKJ3I9oqTM4ySZZlNmzbx8ssvc+WVVzJq1ChGjhzJiBEjWs22m3RaTHptm9Q7g1aDxaiLCHmE/zg6RcwLCgpYu3Ytb7zxBu+//z4ZGRmMGTOGGTNmMHjw4EhS0RkgxWbi2auHsfjj7azcdhhFgSgNVDl9lDs8jDkm6UxRFHZXN7UScgCXP8T3ZfVkxJoQBYHDjV7WHqhBVhTOyUv+1Yu8JMts2H2AwpIK0nO6k5qVw40LH8EaG0/IYCUsK23mghVFobDGSbHdjaIodIk10yvVdkJHDpLcVuQAAmGZbRUOqpxefCGJ/ZV2dq9ZSfV37yMHAwy64mY0Oj0uX6hVMl2UXsOMIV1ZubWM0gbPUSEP+mDLB7DnK0CBIZdC95FgVHtS9BoRvUbAF5Tom24jP9nWagJOlmWQwtii9Fx6ze8YNXQwPfoNhA8//tnnuOvw8YXaF5KIUSDtJM/M33nnHebPn09NTQ0DBgxgxIgRaDSaNiY1tig9uYkWdh9uQm5+YoIA2fFmTHotW8sdlDs86LQiuQkWusZb/qt7RCKc/XSKmA8ZMoQVK1bQ0NDAM888w7PPPsuyZctYunQpgwYN4rbbbmPq1KmYzeZIYlonoShw17tbWVNYzRFDKldYYWeVk701TvLizfRJs7XswvxhGacv2O61Gr1BvIEwXx+o5bG1+3A0f9/yjSXcMqYbF/XNQPMrvGFTFIWSkmLm3TQDBA3X/m0J6TndiU9RLVmbfEFCYRmtXtPqZ74pqqWw+mjH/+EmH9UuPxN7qjveI5rY3ju5e5KNbRUNrb4mCKoRTGWTF39IIhT0U/j6IxxatwZTUiZjbn6AxLw+AMSYdETpRIrtLho8QeLNBp7+7gBf7q85ekF3A3z7CpR8D4YoOP92SG0dORqUZMKyOp6VHG3kwl7pfFJYBYDf1ch3T91LZm4+t577CFmZSWRlZuDyh0iNNlDl+mkb1Ev6ZfLv7w+1+5heUENZ/jzml0WgBoNB7rvvPhYvXkxSUhIPPfQQv//974/bkKjXihR0iScmSs+eqiYkRaFHspXsOAvrSuoo/9GNT4XDiycQZmBm3C9aU4QIvyY6Nc88Li6OuXPncuutt7J27Vpef/11fvjhB2677Tbmz5/PJZdcwsUXX0xubi4pKSkdmgP9387/+2IPnxRWH+tHAkBYhtvf3cZ5vdLJSVDPIPUaEYNOC7T9IDfotJQ2eHhhYxH13gCCICAATn+I5RtL6J5so2eKrWOf0C/E4XDw4YcfMnv2bMIKDD//MqxxR6NaBSBKr0WjaS3J1U4/ZQ3eNtc7VO+m2K6W0KuafAiCQJdYE0nRUeh/NP7UPyMGhzdArctPSJLRakRiTXriTHq2HarhUOFOXv9/D1BdVkLaoDEkjLsaS5Zq2mI16shNiGZLeQM1Lj9lDW7WF9Xw+cF69eKyBFUHYONK1c0teyCMuRbM7ZuUyIoq6j+UOzDoyhiQbGLj+nVsWvEE/roKxg3Ip5v16NqjjTrevWkcM1dsZF+tG88xJWwB6JEUTW6ylUSLgbpj/N/jTHpGdk1g1tgeZMef+NRKVVUVc+fO5Y033qCgoIBFixbx29/+9mdv/HUakZ4prd97B2qd1Lr8rXoKFEVhR6WD3qk29B04ShchQkfSqWJ+BLPZzAUXXMC5557L+++/z2OPPcb69et5/PHHef755xk+fDiTJk3ioosuolu3bpHdegfwv5/ublfIj+ALSnywo5zbJvQC1E7n7kkWap2+VjPLWlEgK9bExkN2dlepJU2tRiRKq0EUBaqdPorrXPRItv4q/o6KonDw4EGefvppnnnmGbKzs7n8uv8hccBYRP3R0q/FqCMjxtSm69nhDRJup7FKVhQ+L6xGFAWk5lLHnuomBqTHMiAztmVW3KDVMKZbMpWNXtyBEGa9jjRbFHsPVfDdh2/y6esvEPB5OW/69fQ871Icgpn0WBOJFgO9UmxoRYGDdW7WFdexamcFdm+z+5nfBfvXq93qigIDLlC71aOif/L18IUktcN+zyG8m3ax45OVWLUCt82dy6233EJcXOvd6tDsBD780zhe+b6UiiYf64pq2XG4kXizgbzEaK4cnIUoCFw/PJfPCqvYV+MkrCj8tkcKk/tlMm1Al1/sLujxeAiFQsyYMYO77rrrlKxinf4Q/namOAJhGbsnSFo7HvgRIpwNnBExb2xs5Msvv2TNmjVs27aNoqIiBg0axIUXXojNZuOzzz7jgQceYOvWrdx///107Xp6m5D+23lu/UHqPe2XzI+gAPZjgiyy4y3IChysc9HoDWExaEmPiaK0wcPuqia1Sxj1g1GSFaKNuubdn0LrVrozRygU4pNPPuG5555j4sSJ3HXXXfTuN4DiBi/FdnW3GWcy0D0pmtR2PtiNOvUmhWP0wOkPEQjLJEcbm89oBZAV9tc6SY8xtbpWlE5DXuJRkT18+DBP/nMxq958i9jkVKbdPJfeQ89BbzSSZDEyIDOWWJMBjSjw5f5q1hRW8cHOiqNNZo7Dqogf2KC6sQ29DDL7tLi5pduMVDb5j/uayE47DZve4suy7SRmZDH/nnlcf+W044bHpMSYmXOeepO3vaKBLeUNHG70EWvSt4SW5CZYGDKxd8sffUR2AumxJ94/4XK5+PDDD5k+fTq5ubncf//9JCQktIpNPhkMWg1ajdjmhkwUBSz6M/JxGCHCaaFT370HDx7kpZde4r333qOyspJgMMikSZNYsGABAwcOJCYmBo1Gw4wZM3jrrbdYsGABV1xxRUTMTzMrvi/9yV05qJ/BF/VpbemqFUW6JUbTJdZMWFbQiAIldhd2dwCrUYfFqMPlV3eKwbBEKKwh2WokN9HSIclUJ4NOp2P8+PE888wzjB49mtRU9Zy7d6qevESrWlkQBfRasd01pzc7iP14d6cA3qCELUpHcb0bhycIAsSbDeQmWGjwBtq9MTjCjh07eP21VxkyagyTrp9FTHpXRFEk0WJkWHYCKdYoBEFNVlu+4SAf7q4iEJabZwWL4LvXoKYIMnvDOb+H6ERorgTcPrY7PdNiuOudLTT5w21/eeVe+OZFlMZq9HkDyZt2E5kDRqHVavEHwyioNzDHq6r0z4ijd1oM+2uc/FDegK/ZvS49Joqh2QnEmQ2/+CaupKSEO+64g2+//RZBELjiiivIy8v7hVdpn9Tmv9+xiWtdYs1EGyPpjRHOXjpFzDds2MD999/PV199hSiKpKWlcfPNN3PjjTeSkdE2lCEhIYHRo0cTExOD19v2fDLCqeENhRGgXQc4UBuyEkwG7J62uzlBEDDqjp4rNjaHXCRGGxicGcuGQ/UEwzKCAAadyCX9M+mWaG1znc5CURSamppYsmQJBQUFTJs2jd69e9O7d+sZZ40oYNL//HmpQafhvPwUvjpYS53Lj8MbpM7tR5ZlvimqRa/VENNsTuIJqNGjo3LbpqoFg2plRK/XM3bsWF5/YyUDCwqw2WKpdfvQCiJpMVEtjYM1Th+DFn+A3dd8EyGFoXQbfPU8hINqWX3oJS0mMAatyJwJPcmKMxOl0/LnMfk88kXh0Wz0cFAty29cCUEv9D4XefQVEJdKZVOAv6/Zza6qJrSiQP+MGH43JIcES/te61pRpFdqDLmJ0dS6/Bh1GuJNhpbeiRMlHA6zYcMGZs+ezc6dOxkxYgSjRo36BVf4eeLNBkblJLKupA6XT02nS7WZGJmTEBlXi3BW0ylivm3bNsrLy1sa3CZOnPiz5bKEhARuuOGGVhGqEU4PUc1ifKygH4ndtBq1pNtMdD2BJqUj3t1aUWRIVjzxZgPF9W50osgVg7M4v1caUScgkh1BOBxm+/btPPTQQ6xevZqRI0dy8cUXn3RjpawoeINhNBqRC3unsb/WxbdFtUTpNNS6fQTDstoUpigtJWdJUhCPkTSn08mTTz5JKBTijjvuoCEAgbQ+PLO5ipBUiUEroBEEBmTEYdJrCUgyk5/4kjCou3GXHXZ/oRrBxKSobm7dRrQIeUyUnqsLsslqHgv0BMOcl59CssXAZ/trWLu/GtfBbbDhddXKdeSV0H0URpMei0HL10W1Lef+QUlhc2kDTb4wcyf2+skGMYNWQ4pVPXYpqnOhEUSSrUYyY00/2y/hcrlYuXIl//u//0sgEGDOnDksWLDguDbQp0KqLYpL+2fS4FHjYa1GnXp0EiHCWUyniPmECRMYMmQI/fr1Q6c7sVJWcnIyf/nLXzp4Zf+dXFOQzTcHawlJSitBt+i0dE+yohEFxndLJjv+p5unQD1HP1DrotrpQyuKdE+y0j3JSrekaM7JTTpjdq+SJPHWW2/x6KOPcvDgQa655hr+8pe/nLSQVzd5eXdHBSX1brSiQKrNRJRW5KDdDajjaVpRQFYEgs2+9vFmA5mxJkJy6xqI3++npKQEn89HlcPFS9tq2HW4kd3VTRTb3S0NhkadBh3gCUlqBrksQfUBVcTLdkCXvqo/elp+i63qpB4pZCdE0z89BncgRJnDiy8Ypt4d4Le9UvnTmO788+PdLKw+RLjXeMjqDynd0GhE8pOiyU2MxvmjSNEjHKh1qg19GUcb4sJhmfWldqqafCRaDAzKjGPt/hq+PFBDnduPNyhh0msZlZPIhPxkuicdvwmyrKyMuXPnkpWVxe23387UqVM7RMiPIAgC8cepNESIcDbSKWJ+Kt2nEU4/Vw3pilnUMu/j7ZQ7vBi0In3TYjDptcSa9IzKSWRKv8yWHfxPEaXTcE5eEvtrnRxu8qFpHsnqlmQ9Y0IeDAa59957eemll9BqtSxcuJCrrrqqTWf2L2HO21v4pqgWT1C1CTVoRbrEmok16ZEVBaNOgzckoREFzHoNuQkW4s0GrEYdZr2WUCjE999/z4gRI0hISGDevHkIgsD6Ki8H61w4fMFWQg7gD0m0HHQoitrgtuUDdY58wAXQe7za8IY6v/3yH85BJ0CtO0CFw8Om0noqGr2EZYUYXS3frXqD8f27MXvGlRj1Co9+kUWNV0Kn0TIsO55LB3RBFGBTaT2y1PoGRAEqHT4GNJ+KNXgC/P3T3awrseP0hbAYteQmRCMrMpIM1U0+DthdeAJhPi2sYvWeeK4fnsvkvhkt7wulxcxFICsri3vuuYcRI0bQr1+/yFhqhAi/kE5v39ywYQO7du3C6XSqLlPHcM0115CcHMn77Uj0Wg2XDc3ivH5pSM0BHUfcrwRBbf7SaY4tDh+fWJOegi7xSIp6LY0onNGGN51Oh8vlwmQy8dxzzzF8+PATrgi1x8ubivm0sBpZUQNmJEXBE5TYV9NEUrSReIsRQRAwaEQCkoyCgEmvRdc8Q24RgixY8ADPPvssTz75JNOmTWuxM351724kWaHqmJG/VshhWP8G7PoMDBZ1djx3CGhVwRubE8+/LitgYJcEyhxu3txaxpZyB0V2N2FZQRQE7LWl7H7/dTa94+XSyRcx69y+zBiez3dFtZQ5POg0IgathmqX77i9FL1Sj/Y+PP3tAd7ZXtGy5iZ/iP21LmKMWnqlxrKnugl38xx6UJLZXNqAOxAmO97CwMw4wuEwK1eu5NChQ9x8881YrVZmzpzZrptbhAgRfp5OEXNJktiwYQO33HILRUVF+P1+dDodOp2OQCBAMBhEr9djMpk477zzImLeCQiCQEzU6dv9aEQBzRkcPguFQlRVVWGxWIiLi+Phhx+msbHxtLyX3thSiqyo43WyApKkoKAQRg0P0WqCCCh0jY+myRfEYtBiNmjJS7SQIDUxe9Zc3n77bfLy8sjOzm517aTmeExFbkdCZVn1VP/uFSjbCQlZMPJqSO8BgoBWFLhyUBZPTB9CdHPvQkaMmR7JNt7dXgGKguJ3YYmJw9ilG+KEqxhT0JcqT5iuKSJ2j58mfwijTkMwLGP3BBAQEBUQRIFw85p0GpEBmbF0bTYQqnX52FbpaHPzoShQ7QqQEu1rYygDUNrgpbjWSWaUwrJlS3nooYfIy8vjoosuom/fvscdhTtZ/GEJhyfIuuJagmGZsd2TiTUZTqjiFCHC2UaniHlRURHz589Ho9GwYMECHnvsMUaPHs2ECRMoLi7mq6++wuVyMWPGjEgAS4RfTENDAytXrmTZsmXceOONzJw5E4PBcNpuClN+lEetiroqcqIgEKVT55aPZGb3TrWRGWtiTNc49m/dyJ8WLaKwsJDp06dzzz33kJub2+raE7qn8EN5A/KxYh4OqufiWz6Axmq1wW3IJWrDG5ATb2Z8XhLPXjOy1Y9pRIEEi4F4vUxN6RYc61YRPfmPkNKV2N7DiUqKJ6pZvOvdAfwhibJGD8V1blz+EHqtSILZQEFWPE5/CFEQ6Jpg4Tc9Ulp+hycQJtCO8YpeKxIIhwmE5Va7e7G52iNLIb7dsIHl97/GJ6tXc+655/KXv/zltKbSHcEbDPP6D6U8/lUh5Y1eUCDZauRPo7tz7fCcyBhahP84OkXMv//+ew4cOMC///1vBg0axAsvvECfPn1a8su3b9/O4sWLqaysjJTYIvwidu/ezaOPPsqKFSvo2bMneXl5LWexp4tbxvbg473VeILhVrUHk05DvNmAQash0WKge5IVvUbEqvh48ZknePbpJxEEgXnz5jFjxgxSUlLaXDsr3kyt00tRcyMdoIakbP8YCr8GQVRFvOfYFhMYALNOw5VDuuLyh9oIk+x2UPftu5R9/CayAiFnI6SoO+ysODO5CRZAQVHUYJ31xXXUu4OEFQWdKNDgDTKuewpXDc5GFAXMBm2rY5MUq4mMWDM7fxRiAur185Nt5CVa2G93E5Lk5h4CLYIi4zvwAy+98jZ++2FuvGkmt/x5Jj175CMr4A6E0WvF05YzXtrgYdnafZQ5jo62Vjv9PP3dAYbnJFDQJf6EriMrCuUODyiQFmOKhLFE+NXSKWJut9vR6/X06qW6RomiiCRJyLKMKIr07duXKVOm8OCDDzJz5kxiY9v3ko4Q4QiSJPHll1+yaNEi1q9fz0UXXcSiRYvo3r37KZ2Pt0e/jFgWXdSPJWt2U9XkQxZFjDqR/ukx2KL0KArEmQzoNSKCs4YXn3uML9esJj09naVLlzJixAhMJlO7157/3hY+3H34qKFcU7UaknK4EGLTYeilkN6zZezsCLuqnZQ2eIhp7lc4QklJCf9cOI+tH39MdEoWWVP+BzGxKxpBYEhWHBf3y8BmUtcca9azv9ZJRaOvZRQtIKi7+28O1nJ+r1TiTW07yqP0Gq4f1pUtZfVUOY96EcRE6Vg6bTBWk4GkaCNvb69AVlTLX60ISdEanJYozrn2PoaOn0g5Vtyl9dQ3B/Xoteo0RG6C5ZSaJxVgb1VjKyE/QrXTx+ZD9QzIiGuThncsJXYX//hsL8V2NwiQl2Bh7m96k/ErTwKM8N9Jp4i5VqtFr9cjCAKiKBIdHY3D4cDn82E2m1u+Zrfb8fl8nbGkCGc5+/fvZ9asWRw4cIB7772XuXPnnvYz1x/z+2G5XNwvkze3lFLq8JBiNRGQJAwaEVEU6JFsxeyu5oaZM9m9axdTp05l+fLlWK3tG+bUNHr4/Yvr+exAbfPuVoHKQlj7PDiqoEs/GH8jWNrvwFeAQEhmX7WTbonR2KL0bN26lalTp1JZWcmVV17JH+68m50OhUBYZmhWPMOyE4hqtiwVBIg36wlLSouQA2gEdca9xuWjzh0g3tL+eNigrARW3TSWf321j20VDnokWbllbHeym8/VH7x4IGPyklj+5XYkUeR3I3rTNXYYW8unI+qMBAWBHZWNlNZ7yEkwYzGoNyt2dwBFUcg/RS9/X0hqVTVoed0UtQSvHLHcPQ5N3iC3vLGZvdXOliOD/TVOtlU6+PaO3570uiJE6Cg6RcwTEhIIhUL4fD5sNjbs9L0AACAASURBVBt9+vRh8+bN7Nixg169euF2u1m/fj1AZCQlwnFRFAWv14vZbCY7O5s777wTi8XCJZdc0qFCDqr4xZj0zBiWw8YSO8X1bkI+Se3QjjPTL83GxZOmUVdby/z587njjjuItlpx+kM4PAGCkkyc2UC0UcfHuyu4/c3NFDc072qDXjiwEb5/F6QQDJqs7sg1x68wxEXp2Fhqx2bUUd9QT/mmz3ny74uIjo5m/vz5zJ49m9jYWCb8xHMKhhWSog3ERukISmqVzNBsYyujuvod9/UA4qKjeGDygHYfl0JBoqr3cuD/5tKrVy/Ov/Fl1pXUo2k+KpAVhQZPgEZfkHq3FrNBhwCEJJlD9R66xFlOyJHveGvrmx5LYrSBumPiWmOidIzMTfzZcvnbO8o51OBtna4GVDb6eGlTMdcMzTmptUWI0FF0ipjn5+eTkZHBzp07yc7OZvLkyXz88cfcfvvtDB48mJqaGtauXcuFF14Y6WSP0C7BYJANGzbwySefMHv2bBITE7n++us7vcfCqNMwOCuOWrefqiYPYb+Peh1sLG1g3n3/S0OV6nRoNpupcHjYXFqP3RNAUdQY2WqXj0e+2EOtu9mYpbEKdn4G+74FW7I6P547tMVb3awXCYSVls5ygBijnvHdk1EUhWB9BW+/tpJNn60is2s37v3bXUybNu2EbooNOpFuSdEU13tw+kPqmKKgOrmN7JpAnOnkTFXq6ur497//zdKlSxFFkYEDB+L1B1pG1UCdCvA1N9F5QxI/TuJxBUKEZRk4+a7zLnFmrh2Ww4sbS6hz+0FRRyinD8oiL+HnzZCqmnyEjjMquKPCAUNPemkRInQInSLm3bp1Y9GiRSQlJSGKImPHjmXx4sW8+eabfPvtt5hMJq699lquv/564uNPrDElgsqG4jre3VmBNxgm1WriD8NySI75z4px9Pl8rFixgkcffZTy8nKmTJlCYmLiGWuW3F/rosETwFW2n28/eIOhE6egGTCErmnduWr8OWhEEac/xPs7K6lq8qlz+4LAF/tr+Gx/tbrbkyUo36WmnVXth66DVCFPzG4R8m6J0fz5nDzizEbe2lLCnmoX+Uk29FoRq1FHrF7hyxefY9+mb+g/6lzOv/oPXHXVRSf8usSbDfROjcHuDlJS78bpD6HXiOQmWrhmaM5Jvb4lJSXce++9fPTRR/Tu3Zs777yT888/H19Ywax30+hVPelFgRaP/yidplXF22LQ/ux59s8RE6Xnj6O6MSgznp2VDmRFoU9aDIMy40iM/nlnuTRrFDqN2K6g90k/teS2CBE6gk4Rc5PJxNixY1v+bbVaueKKK5g4cSI+nw+NRoPNZsNisUS62X8B/95YxP0f7aTG5UeSFQxakXe3l/HFzedhNv9njN6EQiHuvvtuli9fjs1m46mnnqJv375nbD2yonCo3o0ABP0+Duz4gdy+g1D6FVDr8uMJSGhEmdW7D/N9WT2SpFDvCbCrqpHKpmZDlnAQCr+FravAXQ+DLz6aPd78/p85Mof81Fim9M2ga4KFST1TcQfDKAp8V1yHwxtEo0jYh44mu3tPRp5/Kbb4hF/0/8eg1TCiayJ6jYbd1Y34ghKp1ihG5SSSHtN+w95PIUkSf/zjH1m/fj1TpkzhnnvuIT8/H41Gg1mj0CXWRK3LT0iSEQWBOJMeTyDcKllNrxXJjre0CvM5WeLNBib2TGVstyQUhV90zQt6pfHK9yVsLXcQkmX0GpEovYac+GguG5B1ymuLEOF0c8YCfHU6HQkJCYTDajOKVquNCPkJoKCeK67ZU8E9729XLVQ16uvmC0lsq3Rw1Uvf8f6fxp3RdZ4OKisrmT59Ohs2bKCgoIClS5dSUFDQ4e+TsKxQ1uChsKaJkCSTmxBNfrIVrSjgdrnwen0giOT0GcSdj7+C2WpD0GgIhiXKHG4qG32U2F0EwzIOb5DP91UTOLLDCwdh8zuwfbXq5nb+7ZCtnjsLAgzJiOW6EXloRIGkaCO2KPWmLMkaRRLqZEjF5i8x5w1Ab7Ey6sLLWtZ9IsE4xxJt1DGuezKj8xKRZAW9RvOL08NkWUYQBDQaDYsXL+a9997j7rvvJirqaIVIFAR6pcag1YhsLXcQCEv0SLZxbn4K5Y1eGtwBTAYtgzLiyE2MPq0OgoafCIc5HnWeAJIi4wtJhCQJH2pa4AtXDcViiOSeR/j10SnvyqamJnbu3EliYmJLCprX62XNmjW89NJLOBwOzj33XK677jrS09M7Y0lnLdWNXm5/+4dWVpqSpKAVaEl+2lHZeCaXeNo4ePAg9fX1XH311dx3333k5HRO09G28ga2VjS0ZL7XOP2U1zuxear4+4NLcEsil9+1BJ1ejy4uHllRqGr04g1LeEMSRXUual1+9tW6+KG8Qb2ILEF9GWx4Eyr3qONmQy6DZNVEJtqgZWC6jUm9M9Bq1CSvbknRxJiOnn2Hw2Fef/11HntgEb+/cyE9R/2GkCSj1YjEmnQUdDl573mtKKI9iWmwkpISPv74Y/r06cPIkSMZOnQoQ4e2f6CsEQV6ptjokWwlLKu58YIgMCgzjpCkoNWcWRvgI3iDYV7YUERZg4+kaCOyrJrgaESBZd8d4J+XFpzpJUaI0IZOEfNNmzbx5z//mfvuu69FzL/++mvmzJmDXq8nMTGRxx9/HEmSmDNnDmZzZI7zePx7QxHv7ahAOsbXXlJAUNTd3a/g8/CkcTgcFBcXM3DgQMaMGcOyZcsYOnQo0dE/37R0OnD6Quw83MiPp5pkSeLdt99i3bsvU3u4gulXXEG3RBNV7jCeQBhvSMIdlMho7lWodftZe7CGw00/6lYv3qKW1V110Ptc6Htei5tbikXP/El9OCcnEb8MAgJpMVGkWqMQBQGvV52XNplMTJw4EaPRyMhx5xKOisETCGHSa8mMNWMzdd4kiCRJrF27lkceeYRvv/2W+fPnM2TIEDSan98FC4KATiO0+rde++t505bUu9lX42oZ2RPFo3c5Xx6oPVPLihDhJ+kUMa+oqMDn8zF8+HBAbWj6/PPPkSSJp556ivj4eJ588klWrVrFjBkzOm0Hdjby4uZDyIoannHELxzU8vuRhuBRXc/OJsJ9+/Zxzz334HA4ePDBBxk8eDDnnntup66h3hNo1fTkcTay+sWn2Pz5hyTERLNo0SKmT59OlCWaek8QXzDMzsONGDQiWlFkX00Tn++rpubISJTPpTa5FX4DWh2MuR66DgSDesM6LCuWxVMGMiE/FaBl/vnIDVllZSX/+Mc/MJvNLFq0iLy8PHJzc1sERlGUTj+e8ng8LF++nKVLl1JeXs7s2bO5/PLL/2PGSmWFdmfU1cdOr7tghAini04Rc6fTiclkajlDq6uro7CwkLFjxzJ8+HD0ej0TJkzgo48+wu12/8zV/rvxh9VxHkFQO4KPSaqkR7KVpVcOOwMrO3kUReHjjz9m9uzZHDp0iMsvv5y0tLQzspZo49H/EtVlxbzx2GL2bVlPetc8Fj38KJdOHNciWmk29f1c7fJjdwd444cS1h1qOBoy4m2Cz56Ew/sgKQfOuQYSuqgWrcDDU/ozY0QuSdHqdbzBMHZ3AI0okGqNYs+e3dx111188cUX9O/fn7vuuouYmJhW4t3ZQm6321m8eDHPP/88oijy8ssvM3HixOM63J2NdEtUeyQKa5xtxHtUTuIZWlWECD9Np4i5KIoEAgHCYfVDrrq6moMHDzJ79uyWxjej0Ug4HG75ngjtc2n/DJZ+fQBFUdCIAoKsqAYfFgN/m9ibm0Z3Py2dwJ2Fy+XixRdfZNGiRfh8PubOncv8+fPP2C4vwWIk2axj7dq1rFz6IDUVh+g5ZDRzHvgXk4b1arMuuTn29Z5VW6l2qWNXSCFVwD97SvVZzxsKw6eDJR4BSIo2MO+3vZk1rmfLNQ7Wudh0yI43KCGFQ1Tu3caKR+6jqrKCq6++mieffBKj8edHqjoal8tFUVERffr04bnnnms5NjsbCYZlwrKMTiO2MpEx6jRcMTiLwpomiuxuJFlBpxHJTbAwb2KfM7jiCBGOT6eIeVpaGuFwmNWrVzN+/PiWlLQ+ffqg0WhQFIWmpiZ0Ot1/TKmuo7jzvN7sPNzExkN2/CEJrUakS6yJeyb1Zcaws+t4QlEUVq9ezcKFC8nKymLevHlceumlZ3RNDQ0NFK19n5ceehC9VsfvZs5m2vV/YlBWUqtmtGBYpt7jZ31xHQtX7zwq5N4m1QBm60eg0cLgydB3IhjUnWuvFCsLL+jDpN6ZLSXyJl+IjSV2fCGJRnst33/+IR+9+AQ2Wwz3LLyPm/900xkVcpfLxf79+xk4cCDZ2dk8/PDDWCyWdoNjOpuwJOPwBfGHZPRakZgo3c92r4clmZ1VjWwssVPm8JKfFM2w7AR6pNhavmdYdgKPXz6EVbsqqXX5yYgxMalXKqm2/5wKRIT/LDpFzIcMGcLIkSNZuHAhK1euZP/+/fzmN79piYP0er1s376d1NRULJZfPl7z30RytJEnpg9hzb5qXP4wogi9km1M7JF6ppd2whwZZRIEgT59+nDrrbdywQUXMHDgwDO9NIqLi7l/4QJ69erFLbfdzjkTfkNKnLVl5yYr6thasd3NS5uKeW9HBQ5fs5ub4zBsWQUlP6jl9P7nQ9YAEEVEAcZ3S+b8Xqn4Qgrriuvolx5DcnQU5Q4P3kCIsgN7+GLli2z+fBU5vQcw9do/Me3KqR3e/Kco4PAGkBQFq7G1GFZUVPB///d/vPXWWyxbtoyxY8eSl5fXoes5Ufwhie2VDsoaPLgDYaJ0GtJsJoZkxbV40LfH2gM1/L+v9lHjUhsUv9xfw6eF1dw9qS89Uo566ecmRnPb+B4d/jwiRDgddIqYZ2ZmsnjxYt555x2Ki4u58MILmTx5MklJSQAEAgE0Gg1XX301CQkJnbGksxZBEMhNsjIzqf0Aj187LpeLF154gS5dujB16lTy8/O56667MBqNZ8xnQFEUFEVBFEV69OjB4sWLGTNmDD169Gjj+V5a7+HrA9U89Oleiuvd6vy4okDZdtj4JjTVQM5QGHg+xKUDAlajlok9UhiTl4JBK+ILSZQ2uFEUhfHdDQSCIXZtWMuq5U9QWbyPURdO47wrrieraw5a/clZqp4o7kCIzaUN1Lv9yIrqvtY/I5b0GBOFhYXcfffdrFmzhkGDBpGenk51o5dHvtjLDz8afxzXLZm7J3V++Xl/rZPdhxtbrG5Dkowr4ESrERh5nLNthzfIuzsqqHUdTXuTFYUDdS6e/e4A/7xscKesPUKE002niLkgCHTr1o077rgDWZbRaDStRlhiY2NZuHAhoih2eGBGhDNHY2MjCxYs4NlnnyUrK4vJkyej0WhamYt0NuFwmJdffpmSkhLmzJmD1WrlpptuanfEKhSW+a6ohr99sP1o9KcUgh1r4If3QAqrISkDzgedWhbPjjNx/wV98ISUVjPUigLlDi/eYJgDm7/m2fvuQBBFLvvzXxh90eXo9AZsUQZsUR137BSWFTaU2Cmpd7eM4jX6gtR7A+SEa/jd1VdRVVXF73//ex544AFi4xNY8PY2/vXtvlZe8WsP1mKL0nHr2M47Pw9JMqUNnlbrAPXGbG91EwVd4tG3Mzh/qN5NldNHez3pLZ4AESKchXSKckqShMfjUW0dm2fIFUWhsbGRuro6nE4naWlpJCZ2fKeoLMvs2rWLJ554gs8//xy73U50dDTDhw9nzpw5DBt2dnWCnw2Ew2GKioqYNWsWX3/9NaNHj+bRRx89oZnkEyEoyfibIy91oohRp0Fzgt7ewWCQDz/8kMrKSi699FL69+/fal0KqogHJYl3tpUx971tzUKugNOuno3v+0a1Yh15NeQUQHOMaGasid1/u5B9dhebS+tb/V5FkZERkGSFUcOGMHz0GPpPmEyvEePRaLWY9VpSbVHUuvxUO4/GAseb1azw00Gty9cSAvNjdmzawI13/oGE+HjuvfdebrnlFqxWK02+EA99XdjmOmFJZsnqnYzOSWRgZmvjGn9IYufhRg7UughJEhkxZgZmxhJnPrWKgywrbdb9Y6TjPBht1B63QTTG9J9hgRzhv5NOEfOSkhKee+45Ro4cyeTJkwF1fnbx4sW8+OKLBINBhgwZwuLFiznnnHNamTScThRF4aOPPmL27NlYrVbOO+88EhMTcTqd1NfXU1sbMYQ43YRCId59913+9re/4XA4uOmmm7jtttvo2rXrabl+ky/IrsNNlNS7CYQlYk16eqbY6J5kPa6gBwIBSktL6dKlC1FRUfz973+noaGBPn1al4oDYYnKRi8Hal18WljFa98f4rDTD4qsdqtv/VANScnsA0MuVc/JUScLeqdYuX1CT8KCmtYVpdO0pISFggGKd23FZjZgGNyF2MxM3npjBY0hgTp3AFEQiInS8dy6gyz9ej+e4NFxxAt6pbF0+lCy40/dWCkQlgk3zzYqioIsSWi0WtJz85lwwcVcf+VlXHLJJeh0qsh5gqHjXqvKHWhZ5xFCksz6kjoO1LpavlZkd1HvDXBh7zTMhpMXT71WQ6LFQI3L10bU02xRGI/TBNc13kJBZhwldnerXb1BK3J1QfZJrydChDNNp4j59u3beeWVV1qEXFEUVq1axcqVK7nmmmtIS0tj5cqVPPvss/Tu3bvDzs337NnDnXfeSVJSEo899hj9+/dv+aByOp3Nhh0RTjcbN24E4O9//zuXXHIJcXEnbzv6Y0KSzM7DjRRWH50HtrsDbC6tJyZK127nsdvt5tVXX2XFihXMmjWLqVOn0rVr1zY3F2FZprDGyfINRWwotrO5vEF1BAv6VAOYnZ9CwAP9JkKv8RCtPqdUq5FL+2fQMyWGaqefXYcbGZgZR/ckK3tqmgiFZbwuJ1+/t4Ioxc/NU8/DEmUkzmYlDshpjucsrHG2EnJQS/Mf7j7MZXtKuXZUj1O+6Y026DDqRLxB2LP5W6pKDjLi/EswW2N48OFH6N/16Ky/PySxvsT+i65vdweobPS1+XqjN8hBu5v+6bEnvXZBgJ6pNhp9QSoavSjN7odJFiNDsxOO64KoEUWmD8pCQT0eaPIFSbIYmdgjlSl9M096PREinGk6Rczr6uoQRZGsLDVtqLGxkc2bN5Obm8vChQux2WzYbDaeeOIJ7HZ7h4n5yy+/THFxMQ8//DAFBa39la3Ws7Oh7NdKXV0dBoMBq9XKHXfcwXXXXUd+fn7LzdPpoNEXpMbpb2Ps4Q9JFNY424h5IBBg3rx5vPrqq8TFxf3ksU6TL8SK7w/x8qYS7J7msTOPA75/Dw6sV8fOxlwHXfqCXj3zT4428IcRuWTEmFrOx6udPvQagQGZsWTGmqh2+tB0iSFv9p8xG3RYre13qtfZ7W12ukd46PNCJvXLPuUxqTiznkyrgdeffYLPV76IwWQiK78PE8aPo1eX1mNnxXY3lQ4vRhH87cR8zx7fgwEZrcXZEwy3WKIey5Eo1FMhJkrPmLxk7G4/9d4gVqOOpGgjVuNPv8fSYkz8cVQ3LumfSUiSMeg0JFmMGM4if4YIEY6lU8Tc5/Oh1+tbziJra2s5ePAg48ePJy4uDp1OR05ODl6vF5+v7Z386aChoYGNGzdisVgYO3Ysjz/+OJ9++ik6nY6JEydy3XXXYTB0bOfwfwOKovDNN99w5ZVXcv3117No0SLS0tI6xNEtJCltGqCO4A22Nh8qKytj8uTJ7Ny5k76DhrDgwX/Rf3D7gSCSLLN6VzlLv96HO9AsqE218NWzULEHErvChXeAWRUvQYCB6THMGJqD3ROgyukjwWzEoBURBQEBgQOFe1iwYAHLly/HarXSv8uFP/ncKpzHN08qafARPNb67yRoqK9n2X1/4Z1XX8Vii+W62fOYNvm39EmPaWWiAur5ulGn4aYx+Ty2dl+r0nayxcCIrLg2aWJWow6tRiDQzlNJtJyec3+zQYvZYCHrFzoYWwxaLIbIGGyE/xw6RczNZjMul4v6+nqSk5MpLS2lpKSEWbNmtXSvy83BIR01nlRRUYHD4SA1NZUpU6ZQXFxMYmIifr+fjz76iFdffZWnnnqKnj17tvlZRVFa1idJUqQcD0iygt3tp9EXQqsRSLNG4XTU88ILL/DAAw8QHx9PTEwMwWCwwwxPrEYt0UYtTb7WuzxBgOzmOFC3282aNWuYO3cu5ZWHGTHpEibfMIt6YxKvbyllZE4i+cnWlp30D2UNfLKngvtW71ZT6UJ+9Xz8m5fA71IDUoZcClHqmXyXWBOPTRtMRaOPdcX2lipBWYOXHslWBqSYeOON11m4cCF2u53XXnuNm2666WefW7/s5OM+ds3QbOJOIVRFURT27NnDnDlzWLduHeeccw6PP/54m56BH3Nkt9srxcbDF/XmvV1VlDmDZFiNrLjxHNLaqRIkWoxkx5nZX+tq8bvXiAIJFgMZMSbcgTA6jYheK/LriVmJEOHspFPEPD8/n5iYGJYsWcKkSZNYtWoVsbGxdOvWDUEQCIfDVFZWYjKZOuyD3+12EwgE2L9/PxqNhuXLlzN06FDsdjuPPPIITzzxBMuWLWPJkiVtTDoKCwv5+OOPCYVCSJLEli1bOP/88ztknWcDvpDEF4VVvL6llN3VTVj0GsbHhyj6bCUfvP8eBQUF3HHHHVx44YWnrWO9Pcx6HflJVtz+MI3Ngq4VRTJio8hNiKa6uppnnnmGZ555Bp3RxJQbbmP05OkYolThkWSFTaX1JFmMxFsMvL+zgt8t/w73ka2kyw5718K21WCOgWHToPtIMJjRawSuKsjmjyPzSIsxUdnkIznaQJM/REhSMGhFqqureHvta7z4wvPEx8fzwAMPcMMNN5zQc4s36/lNjxQ+Laxu9fU+yRZuGtWN6J8pJf8UiqKwb98+qqurmTlzJnfeeefPTpLkJVk5aHfT6A1iNpu5ephqHJOTEE3ScXbZggBDshOwGvXUunxIioLZoMOoEfm+rB5vSMJq1JEdZyYzzhwR9AgRToFOEfPBgwdz44038s9//pPVq1djsVj461//2uIk1dTUxNdff01+fj422/9n7zwDoyjXNnzN9pJk0ztJSAiE0KRD6EWqhQOKIMingiI2QMCCAp5jAdSDBeToUWwgdgVF8YgUEaUFECTUQBII6XWT7Tsz34+FSCSg0hJwrj+a3dmddzZL7nnf93nu2/IH73b+yLKMKIpMnDiR3r17AxAXF8ecOXP46quv2L59O0ePHqVNmza1XqfX6wkODq4R84bgkV2f7MurYNaqPRwvt/nS2rxuti5+CXXhIW4dNYqpUyaTmpp6ycdxagZu1msosDpweSUsRh0xFiNHDh1g9uzZrFu3jo4dO3L9bRPRJbREEGovH3tFiYNFVnbtKGPudxk+IZdlKM6GnV9Bzm6ISIQOwyCqKai1CMCrIzsy7JpGhJoNbD5ajFpQ0TjUH7vbi1eSyD2whzUfvkHOr+l069aNRx55hG7duv1pH4XIACP/GdWZD7dls2jjAYptLoa3iWV6v+a0izu/VLzjx4/j5+dHUFAQffv2JS4ujuTk5D/1by7AoKVnk3AOFFgpqnKiUQvEBppIibSgUZ+9EE+nVtEqJhCnxx9JljlaUs3O42U4T1b25wF5FXb6atUXreVOQeHvyGURcz8/PyZNmsQ//vEPiouLiYiIIDw8vCZpyWQyMXHiREJCQi5Z8ZvBYKjxfe/Tp0+t5wIDA2ndujW7du2ipOTMit3GjRuTkJAA+FqtMjIyLskYrxRe23SIY+W2mp8FtQZt066EpLZj9P0zaN486bKNRa0SiAwwEuZnQEZGLQi43W7uuece0tPTGTVqFLNmzcKqsbDt2JmmIG6vxPPf7WXt4SKqTs3Ij++FTUt9M/PU3tD+RjAFAAI6tcCX9/Smf7OomtY34eR/NSoBs0Zg5w/fs+rtRRQeO8qY/7uDuU/9k+jo6L9cfZ4U6sf0a5sztnNjPJJEsEl3Xv3Zsiyza9cuHnnkEYKCgvjwww8JDAw8owj0j4gMMBJs0uMRJQQBdBpf7OufwaBVY3N5yS611Qj5KaxOD3tOlNP/CrIkVlBoaFw2uzWTyURiYmKdWeVGo7FmpnypCA8Pr1k+r8txzGg0IklSzd746ZzyEQdfAlx92Y42FA4WViJVleI5vg9tQmtUJgv6Fj1ArwG9X718PmqVgNcrIgkCer2euXPnsnHjRmbMmIFWq6XK6WFnbnlNwZwsg9Xh4F/f7qP6VNW41w17v4fNH/nyxruMhBZ9QaNDqxIY1DySF2/qQFJY7c6HuCAThwqtVFRW8uOXH/HNu/9BRmb8o0/x+tOPXVALmV6rJv4Cesq9Xi9r167l/vvvJzc3l9tuuw1RFM97TDqNqk5ntT+DwyPi8tZdoV9c7Tqv91RQUPBxWb1TRVGksLAQq9V61qjTpKSkS2LvGRkZSVJSEps3b2bHjh21+opdLhf79u0jODiYoKDz7339OyCKIubSI1R/+1+8+UfwGzwJXbKvKjzErMfvAvZyL4T8/Hzef/992rdvT8+ePenevTvdu3eved7foKVX0wh255ZTanORkV/BO1uzfEIuSVCR5zOBObwZwhKg7XXQuB2o1ISYdYzr0Jgnh7YmoI7Cs2iLiWtiAnn05WdZt2I5SSktuO2+6dxx8w2XzADpz5CXl8cHH3zAM888Q0hICM8//zzjx4+/qO2BfwWjVo3+LDcCYX5KJ4mCwoVw2cT8+PHjfPLJJ2zevJn8/HycTmedVeHLli2rs6L8QtFoNIwYMYIVK1bw+uuv07FjR+Lj43G73bz33nvs37+fW2655aI5k12NWK1Wli1bxsGPX0YsKcXQbhCa6KYA6DVqBqZG0zTs0iZ8nY1TxW6TJk0iLS2tzsK7pFB/NILAS+v389GOHKxOD0giHNvjs2UtzYEmXeCaITVubgBPD23NXd2SUZ9jQiOztwAAIABJREFUb7hldCBpLZOIMN7K2DvvpmO7awi4hL7qf4TX62XRokXMnz+f3r17M23aNPr27Vuv9R5mvYakMH9KbC7c3t9WwPwNWlpdgIGMgoLCZRLzsrIy5s+fz4cffkjv3r05ePAgTZs2JTk5mczMTLZs2UJycjJ9+vS5pBGo/fv3Z9KkSbz00kvcfvvtNG/enNLSUjZs2EBSUhJ33333RXMnu9rIzs5m3rx5fPjhh0RERPCvZ+exU9WIjFI3Zp2Gke3iGdOpca3M70tNVZXPJtTf35/U1FTefPNNkpKSamojfk+lw80z3+5lxZ7jPiMY0Qu7V0PGel9gSvsbIaUHGH9bRr+pbRx3pjWpU8jzikpYvXY9jZul0io5iamTH6S6upqIiLO3lV0uVCoVnTp1YtKkSTzwwAMkJyfX6yrBKZqE+aPTqDhSXIXN7SXIqKdJuD/hysxcQeGCuCxivmXLFr7++mtmz57N8OHDGTBgAAMHDmT69Ol4vV6+//57Zs6cSWpq6iX9Q2gymXjyySdp1aoVr7zyCsuXL8dkMjF06FBmzpxJYmLi334//Gzk5eWxbt06UlJSWLZsGXHxCbglaoqhTFrNee+lng9FRUXMmjWL6upqFi1aRFBQED169Djr72/j4QJuX7qZ3AoHHkkGeyVsfBeyd4FfMPS+Exq18jm7AQF6DX2bRvDU0Fbo6vD5Lqiw8+xr7/HWc7MZ+n/3MnDUnVwTF0aH+PoTclmWWbp0KUlJSXTr1o3rr7+eQYMGNajuC61aRWKIP3FBZmRAJQioVYLSlqagcIFcFjHPycnB5XIxfPhwTCZTTdTpqWr266+/npycHD744AOGDh16SZe6DQYDY8aMYcyYMZfsHFcLTqeTyspKwsPDSUtLY8mSJXTp0qVmz7U+FpG9Xi9Hjx5l4sSJbNmyhV69euFy+Yqn6hJyh0dk1d5cHl6xk2PldiRRhKKj8POHvv+GNYa+EyDI51Dnr9fQKS6Y61vFcl2r2Bqv9FO43W4kGdYeLiQmtR2Db5tEq279kAQ1GfkVhAcYiAu68BCUv4IoiuTn5zNjxgw+/fRTxowZQ8uWLbFYLJe0z/+vIkoylQ43+wsqyC610zomkMahfph1WhQ1V1C4MC6LmHs8Hvz8/FCr1QiCgMFgwGaz4Xa70el0aLVakpKSOHHiBFar9XIMSeEPyMnJ4b333mP16tW8++67JCcn06NHj3odU3l5OatWreLxxx/H6XRy3333MWXKFCIjI+s8PrfcxrtbjvLGz5kcq7Aju12QudVX6Oa2+9zcOvwD9CZ0ahWdE0IY2zGBHk0iiLYYCTDoagV2lJaW8vnnn2PzgrFFT0IiYxg45u6a5z2iTF6F47KKucPhYOPGjTz99NPs3LmTYcOGMXny5Evq13A+SLLMrtwynl+zjx0nQ2vMOg03tIphar/mF83eVUHh78plEfOAgABEUUSSJDQaDbGxsRw8eJCSkhKio6Pxer3k5+djs9kUq9R6RhRFtm3bxosvvsiKFSu49tpra2a+9YUsy+Tk5LB48WLeeustgoKCePLJJxk+fDiBgYF1vuaLX46xdHsW3+3Px+ERka0lPie3w5t9bm5dRkJSJ9DqCffTM6p9PJP7pNA41L/OSWJOTg4vvvgiy5cvJ6VFK0bNTkOjqr8CN/B1YbzxxhssXryY6upqHn/8ce68886z3tzUJyXVLt7dcpT0Y2U1lrc2t5fPdh8nJdLC2E6Nayx1FRQU/jqXRczj4uLQ6/VkZ2fTqVMn+vbty7/+9S/++c9/cu2113Ls2LGa4qWGNqP4OyGKIitXruSf//wnmZmZPPjggzzwwAPExcX98YsvIXv37uXhhx/mxx9/pFevXjzzzDO0aNGizhYrj1di7v/28t+fM8mrdPhuDgsOw7bPIO8AxDSHzjdDaDyoNSQEm1h0Uwe6NYk4a/Henj17eOCBB9i1axcDBgzg0Ucf44BowO6p7UmgVQtEWS5+W+XZ8Hq9bN26FZPJxHPPPceAAQMa1P746RRanew4TchP4fZKfJNxgtEdElCpFTFXUDhfLouYt27dmiFDhnDkyBG6devGsGHDWLt2LW+99RZvv/02siwTHBzM448/Xu/C8Xfmo48+4o477sDf35+FCxcyduzYs1aGXy7Wrl3LLbfcQnV1NXfffTdz5swhJKRuO9OiKgePfLGT99NzfEVusgRZO2HTMp+bW4u+0H0saHSoBEgO9ePHqQMIDTDWORuXZZkPP/yQCRMmIMsyd911F3PmzCE4OJhGVgff7suvZYLSPMpCXPClX2L3eDxoNBrMZjMvvfQSdruduLi4Bl28KcpyTdjK73G4RZT1OAWFC+OyiHloaCgvvPBCzc9xcXEsXbqU9evXk5GRQUBAAH369CE1NbVB/0G6GjnleKdSqejVqxdDhgzhrrvuYuDAgZe1eMojSlQ7vRRVO1ELvmStHzesZdTNN2EMCGL0/fcy/M4JqIxn9rGLoszq/blM+XQHR0pO2sw6quDAD5D+JehN0Hs8tOiDWiUQ4afnupbRzL2xXZ32qLIsU1BQwKuvvsqiRYsIDg7m4Ycf5p577qlZDYgIMDKibRw5ZTY8XpHYIBOhl3jft6qqipUrV/Kf//yHhQsX0q5duz8MSGkohPnpaRUdyIbMwlrxqRqVwKDUaDQq5d+9gsKFcFkd4E4nICCAG2+8kRtvvLG+hvC3x+FwsGHDBnQ6HT169CA6Opply5ZhNl/eamyPKLG/oJJfT1RQ7fIgeb0EB5hwmWPoNHAYLbr0omWXXhwqc1HuLmRAahRmne+ra3W4+Wh7Ng9+vgOnV/K5uRVnwe5v4eh2iEqBtkMgtgUC0DIqgEevbcENrRth0tX99U9PT2fBggV8++23dOrUiccff5xevXqdcZyfXkOLqMuzLXTixAkWL17MkiVLiIiIoLi4+LKc92IRZTFyW6cEiqudHCi04pVkTDo13RLDuK5VjLJfrqBwgdSbmCvULy6Xi1deeYU333yTpk2b0qpVK8LDwy+7kAMUVTnZl19JtcvDLz+uJT/7MB2HjqTEq+H6u6fXMhIqrnZysMBKu7hgcsqqWbThIP/ZdNgn5F43ZO3wFbqVHIMWfaBlPwiKAUGge2Ioz9xwDV0bh5016Wvz5s088MADZGVlMW7cOO6//36Sk5Mv10dxBrIs89NPPzF37tyamoEZM2aQlpZWb2M6H1SCQI8mEQSZ9ezPr6DU5iY20EzbuCBiA8/MQldQUPhrKGL+NyQvL497772X9evXk5iYyP3331+vnvRlNldNYtmO9d9w/PB+QpPbQHQzPP5nFpQVWh3sOV7MXR+k82t+JQ6PCF6PT8T3fAdI0Gc8JLYHnQkB+EebWF6+ueMfCkdCQgKSJPHMM88watSoei/I3LBhA1OnTuXXX3/l3nvv5eGHHyYmJqZBuLn9VTRqFdfEBtM6JgivKKFVq1Em5AoKFwdFzP9GyLJMeno648ePJyMjg549e/LGG2+QlNSkXsfk9XqRJBFBUDFqyizys48Q2awNh4qq4HelUbIsU1BRxfWv70CU8T3vtMP6N3yz8uBY6Hm7L3tcEDBqVTwxIJWp/VpgPMuyekFBARUVFTRt2pSoqCi2bdv2p3PHLxWSJKFSqWjXrh3R0dFMnTqVcePGnbOmRJJlZNk3Cz7bYb8dU7fJzuVAJQh1uuopKCicP4qY/42w2+18/PHHWK1WHnjgAWbOmoND0LErtwydWiAywESQSVeT032p8Xq9bNy4kf/8902i2/cmtXMv/CxBJLfpgAwkhJgJOq1dzOER2Z5dwvs7ck6+gRvyD8HPH0BFPiR2hI7DISQWgMbBZmb0b849PZqdVdwcDgfTpk2joKCA1157jei4xtg9XgRETDo1Rq3mss4eJUni4MGDpKenM2zYMCwWCytXrjxn0pnDI5JTWs2mzCLKHG5aRgfSvlEwYf6Gmr1ol/fkMUeLKbI6aREVSPv4ECIDDMp+tYLCVYAi5n8DcnJyCA4Oxt/fnwkTJtC/f3/69u3H1pwyDheX1bQM+em1dIgPJjks4JILWGVlJUuXLmXhwoU4nU4mpvUkyKih0i2drGY30Ds5ApvbQ6HVSVGVkx8zi1iVked7A2c1HNzkW1YXPb7I0pb9wORbFh+YEsl9vZoxpEVMndfi9XpRq9VotVqGDx9OVlY2VklL1tEiiqpcqASI8DfSMtpCRMDl6R232+2sXr2aefPmUVBQQFJSEmlpaWcVchkoqLTz1a8nWLknlxKbz9znq19PkJYYyrR+zQn3N+KVJH4+WsxrPx4mp9yGLMOqjDw6xgcztU/zy9JOp6CgcGlRxPwqRpZlNmzYwLPPPku3bt14dObjBEXHExgVR26lg4NFVkTpt2XsapeHffmVRPgbsFzC+M7MzEzmzZvHJ598Qnx8PE8//TTXDhiIV62nyuVBBQQYdViMWgTgpyNFvL+9gB8yC3176/YK2PKxr4fcEgHtboD41qDWolOrmNY3hQndmpzhq37qMzlw4ADvv/8+48ePp3Hjxtxwww0UWe2k51oprLLVHGtzVWH3eBna8tJXW5eUlPD666/z+uuvU11dzb/+9S+aNDn39se7W46wOiOPrdklWJ0eTDoNUQFG3EhsOlJM+0bBjGyfQJnNzco9ueSU2Wo2LTyixLbsUtYfKmBMp8ZorsA9eAUFhd9QxPwqxe1288EHH/DUU09x9OhRmre+htUZuVS5fX/Os0ttmPVq/PW1Z302l5dyu/uSiLksy2zbto3p06ezdetWunbtyhtvvEFiYmLNHnXo76IwfzlWxiNf/kJWSTWVDjdyQSasfQ2sJRDXBrrc5AtJEVTEBRmZd8M1DLsmHqO27j3Z9PR07r//fnbv3k1wcDAPPfQQWq2WCjeU2d21xwsUWB0UWp2X1NmtpKSEBx98kJUrV5KUlMRnn31GmzZtzmnY89muYzy/Zh8VDg9ldheSLGP3eJFkmUZBZrySzObsUka2T8Dq9HC4qOoMYxavJLPjeDkj2ydwGQPvFBQULgGKmF+FlJeXs2jRIp5//nnMZjPvLV2GLiWNYpun5phql4cTlXZaRFkwnFaMpBK4JHvmLpeL1atX8+CDD1JWVsbkyZOZN29encY0sixjdXp486dMFv5wkFKbC6/biefgFuSfPgCvC1L7QOebfIYwQKNAE/8d3YmBqTF1nt/hcPDNN99w1113AfDwww8zceLEmue9olRrleK3sUC124NX0qMShEsyQ9+wYQNff/01gwYN4t///jcJCQl/+Jr3t2dh94i1HpNlqHZ6cXsldBpVzQ2NShDQnqUVz6hVKYFlCgpXAYqYX2WIosijjz7K0qVLSUtLY9asWcS3aMt3BwprHRdlMVLucFNhdxN5ck9YAEL89BfVyexUSMo777zDggULiIuLY86cOdx66611Crkky+zNq+C5NftYvS+PKpcHqbIYee9apIwNCCYLwjWDkFN6gkqFv15D29ggXru5PSkxZ9q8SpJEVlYW//nPf1iwYAGtWrVi6tSpjBw5siaCFyDQqMOoU2M72SIHvshOh0fkeLmdwkonoX56YoPMmPWaCxbA0tJSBEEgKCiIoUOHsmzZMjp16kRExJ/LQz+1iqBWCejUKpwnbWVFWcLlFQkx6xjSwhfrGmLW0TE+hG8yTuA97YbFT6+hf7PIy5pDr6CgcGlQxPwqJDExkfHjxzNp0iRSUlI4UFR1xjFmnYaEYDNGnQadWoVaLRAVYKR1TNBZl6jPB7fbzcKFC3n55ZcZNGgQDz30EN27dz/rEvK3+/JY/MNBNhwuwi2KiHmHkHZ+DXn7UUU3R93+OnSNUtBr1HRJCGFgajTD28QRG3Rm/7jX6+Wnn37ihRdeYP369fzjH/9gypQppKWlnXEjEWExkBTqz778CrySjCTLlNvdiJJM5snPT6MSiA9xkJYYdkGfUWZmJgsXLkSn0zF79mz8/f25/vrr/9J7RAUYyCmzoVYJmHW+sbhE34w8OcyfYW0a0SbG5x0QYNBxU9tGODxetmWXYvd4Cfc3MLB5NO3jQ5VqdgWFqwBFzK8CysvLWbduHe3btychIYEHHngAh8NRE0gSHWBEJQhnJFbFBZlpGxdMiNm3hOyv15zV4vR80Wg09OjRA7PZzJ133kl8fHyd/c0FVgdzv8vg6725HCu343W74eCPyL98C9WlCC36oWpzLYbgCCL8DQxpFcvUPinEBprqdHOTZZlly5Yxf/58ioqKmDZtGhMnTiQ6OrrOcRo0atrEBhFtMZJX6aDa5SFLqPYFtpzEK8lkl1aTEGwmKezM4ro/w/bt25k+fTq//PILI0eOPK/3AJjUoym7T2zD4RHRazVo1GokWWZCWhKjOzQmNshUY3krCNA0PIDJvVPItzpwixJ+eg2xgSb89GdveVNQULhyUMT8Cqe6uprbb7+d7777jjlz5jB16lRMJlPtJWSTjlYxgezOLa/12rhgM80jAlBfQCWzJElkldlxuL1EBhgIMeuRZZmFCxfSsWNH0tLSuOGGGxg6dOhZW6x2Hitj8qfpbM0u8Ymnyw47v4K934PoReh7F6rkzqg0GoL9DNzbqymTejTFoK376+t2u5k6dSpLliwhLCyMV155hREjRvxhPKhRqyYu2ExskIkTFXbyrU5kt7fWMaIkU1jl+MtiLssy7777Lg888AAA9913HzNmzMDf//xuCro3Cee929J4+n+/kllSRahJz93dkrm5fXyd++OCIBDmbyDM31AznnUHC1i44SDZ5XaCzVru7JzE6A4JqM+yv66goNBwUcT8CsXpdLJ9+3Zuv/12CgoKuPHGG7n55pvR689MAQPoEBdCsEnP0RJfVXPjED+Sw/3Pe4lVBoqrnPxwuJDy0/ZvW0QFUnogndmzZzNixAjatGmD2WyusR+VZd9M0StKHC2pZn9BBdO/2EVmSbXvycpC2PopZO+E4FiE3uNRRyRg0qppExPEc8Pa0iXx3ElhWq0WQRBo1aoVCxYsoEePHn/p2lSCbx9apxZw1PG88Sw3EWfD7XazYMECnn32WQIDA5k9ezZ33HHHBbnMqQSBDgkhrJjY+y+/VpZh05FiHlm5i1Kb73dX6XAzd00GXklibKfEs3rXXw2U2lzM+nI3n+0+ht3jJdikZ2qfZkzs0eyibjEpKFxOFDG/AiktLWX58uXMnz8fSZKYMWMG9957L+Hh4Wd9jVolkBzuT3L4+c0Ef4/TI7L7RDkVJ4Xc43JS7XTyqyTTr0Mazz77LH369KkJbskureZISTUOtxetWkVmSRXTPknHVdP47ILjeyH9C7AWQ9Nu0PY6VEER+Ok19E6OYFrf5nRMCK1zPC6Xi507dxIbG0ujRo2YN28epaWlxMfHn9f1WYw6oi0mql21e/EDjFri/6LJilqtJjo6msGDB/Pggw/SrVu38xrTxcLlFfli97EaIT+F3S3ydUYe/VKiaBR0dRrJeEWJf63ew/s7smp+r6U2F3O++RW1SuCB3s3reYQKCueHIuZXIIWFhXz00UfEx8czY8YMBg8efNYZ+aXC6vRQYXcjAy6HnXWfLqWqopT+t9zBsXB/7rvvvppjf8kt5/VNh9lfUEmV00Ol08OxcjueUxppr4CM9ZCx1jfl7zgckrsgmCxEBhiY2D2ZUe0TaHIOZ7q9e/cyYcIExo0bx+TJk/Hz86uVtvZXMWjVtIiyoFYJHCuz4ZVkQsx6UiID/lQPvtvt5v3336d58+Z06dKFm266iaFDh9bUMdQnXkliX0Flnc8dL7djc4t1Pnc1sDevgs1HS85oQxQlmZfWH1DEXOGKRRHzKwi73Y7JZCIpKYn58+cTGhpK06ZNL3tghtMjklVSTW6FHWtxAV8seoaDu7YS17QFbqeT0+vsPF6Rj3Zksyu3DKdHJLOoCufpPd01y+q7fG5uPf8PwhojaLRE+ht4bVRnejeN+MNCrUaNGjF8+HB69OhxTh/zv0KwWU+HuBBSoyzIMjW923+0NVFaWsozzzzD0qVL6dixI5988glms7lWHUN9ohJUNAo0c7DwzC6HELMew1Xcqnas3FazLfR7yk7zYVBQuNJQxPwKQJIklixZwrx580hPTycoKKjelmpLqp18nXGCaqeX7Vu38NVLs7AV55HaqTtjpj9FSGQ0TU4rDttfaPW5j8kgSmA73eik/AR8vQAqi6BJJ+h1Bxh8s+kofwMf3N6NHs0i6+zplmWZjRs34nA46NevH+Hh4Tz11FMX/Xp1GhU6zZ93w8vOzmby5MmsWrWKmJgY5s2bVy8Z8efCqFVxS/tG/HikCJdXqnlcoxIY3CKK6EvodlffpEZaiAgwkG89sxriVHGggsKVyNV7C34VcMpw5eGHH2bGjBkIgsDOnTvrbTxOj8i3+/KpKK9ky9ef8L+XnwCPk9aDR3PbrBeJi4ujdUxgLetTlSDgkSSOlVWzN7/i5BtVQ8Y6+GSOb6+847AaIdeoBIakRvPlPb3peRYhr6ys5L///S9jxoxh5syZFBUVXZ4P4BzIssz69eu5+eab+eGHHxg1ahQ7d+6kdevW9T20MxAEgbTECB4b0IKWURaiLUaSw/wZn5bELe0Srup40ibhAQxuHo2f/rd5jACEmPQ8fX2b+huYgsIFoszMGyiyLLNp0yYWLFjAhg0b6N27NzNnzqRTp071NqZ8q4PjOdms/fQ9tv5vJaFRsfS6aRyJXa6lUYSFHklhRAYYa1VC51XYOVBopbja5SujrsiDPWt8iWeBUdBmECR1hJOz33YxAbw5pkudXuiyLLNv3z4WL17MsmXLSE1NZdq0aYSFnbu6/XIgyzKLFy+mtLSUmTNnMmHCBIKDg+t7WGfFpNPwf12S6J8SRXZpNZEBxvPunb/SmN6/OWaDhi1HSzhRaScp1I/+KVGMbHd+xZIKCg0BRcwbKGvXrmXKlCkUFBRwzz33MHHixPOuzL4YSJLEpg3reG/eXLL276FF554MGD2euGYt0Gg0JIX61aqALrO7mfxJOjtzyzhUVOWbYecdgPQVUHQUGreHawZDaDynqtp0ahWPXptap5B7PB6+++47/v3vf7NhwwZGjRrFtGnTaNOmzQW1eF0Isixjt9vRarXodDpmzZrFiRMn6NmzZ4NbWj8bMYEmYgIbxl7+5UKv1TClT3Py2zkoqnIQE2i6qBbGCgr1gSLmDZSUlBTUajUvv/wyN9544wVVZl8M1qxZw9zZj3Fg3z4GjZ1I/5G34xcYjHAyfCT2d4LQ8bnVHC2p9v0gy8j71sO2z33Z49cM8WWPGwNqjtepVWjVAvGhljPO7XQ6eeedd5g7dy5FRUXMmzePCRMmEBQUdNmL/04hyzI//vgjzz33HFOmTKFfv360bt2ali1b1vTUKzRsoizGS5qGp6BwOVHEvIEgyzK5ubkUFxfTpk0bYmNj2blzZ51hJJcTURRRq9V07NiRVi1bMvXRWWiTO+EVfYVTKkGgZYyFmEATLq/Io5+ls/jnI7hFGZDBaYOtn/haz0wW6HEbNE0D4be0Lo1aQKsWsBi0tIv7rXXr1Mx39uzZvPLKK8TGxvLZZ58xZMiQy/9BnIbL5eLzzz9n0qRJAAwePJhevXqh1WrrRcgl2RcIsz+/nGOldjrFhxBuMSkBKgoKfyMUMW8A2O12fvjhBx577DFCQ0N57bXXaNKkSb0KuSzL7N69my1btjBmzBiCg4NZvnw5arWacru7pvc6+uTspszmYsaKXby1+YjvDSTJt5y+80vI3QdRTaHrLRCZjEGjQq9R4RFlBMG3yh5o1HF/z2a1xpCVlcXEiRPZtGkTAwcO5PHHH6dz587ndT1uUaK4yolHlPDTawk06dD8xahXSZI4evQoCxcuZNGiRbRu3Zrp06czYsSIi9YO91cRJZmfjhQxa9VuduWWI8kyfnoN47sm8fC1LS5JLr2CgkLDQxHzeiY3N5d33nmHl156CYvFwh133EFQUFB9D6umoOvrr7+mQ4cOdOjQoebmIsikI8j0m0i4PCLTv9jJ21uO+h4QvXD4Z9jzHdjKoUVvuGYomIOI8DcwrHUsrWMCKaxwsju/nMgAA2mNw7ipXUKtMZSXl1NSUsL999/PPffcQ1JS0nldS5nNxY+ZRew4XobDIxLmp6dr41A6J4T9pdnrkSNHmDlzJt988w3Dhw9nypQpdO3atV6X1Y+X23hp/X525pbV9PdXu7y8tukwzSMtjO2UWG9jU1BQuHwoYl5PyLLM3r17efbZZ1m1ahXNmjVj/vz5dO3atV7NRURRRJZlNBoN9913H927dycx8eyCUOlw89WvJ34TcrsVdnwJmT+D1gCdb/ZVq+t9BWHdk8J45oZrCDbpEUWJE5V2Agy+mbIgCFRVVbF37166dOlC27Zteeutt0hKSiIgIOCsYzgXHlFiw+FCvt6bh+tk5nd+pYNjZTaCTXpaRAf+6fcym80EBwfzxBNPMG7cOGJiYs5rTBeTnDIb23JK+V0gHi6vxNJtWYqYKyj8TVDEvJ5wOp2sWLGCzz77jFtvvZUFCxbUa0EXQHFxMdOnT2fgwIGMHj26pqDrbMv91S4P6w4V8v3BAt8DZSdg0zI4sR8CI6Hf3b5qdZXv9c0jLbw9tiv+Bt+StEajIj7kt8I+WZYZMWIEpaWlLFy4kLS0NNq2bXtB11RgdbDnREWNkJ+iwuFh7aGCOsX8pyOF6DVqWkcH4XE7+eKLLxg7dixRUVEsWLCgpnq9IeCRpFrGL6dTanNd5tEoKCjUF4qY1xNGo5Fbb72V9u3b07Nnz3qtVnc6nWzbto1x48ZRUlJCWFgYQ4cOxWKx1CnkoiTjESUOFFgprnbicDp9ISk/vQ/WIkhsD33Gg863whBi1tMkWMuWR66r8/yiKPqq4lWqmjjXuLi4i3JtDo+I7XcxpuArGiuscuLyimjVKkRJZnXGCeZe3ISaAAAgAElEQVR+l0GB1YkgS0R4SqhY8zaZe9IJDQ1l0KBBDa7lLNSsp3GImX0F1lqPqwSBG1rH1tOoFBQULjeKmNcjSUlJ570PfDGQZZnCwkKWL1/OvHnzMBgMPProo0yaNAmL5cwWMQCby8vBIisnKuzsOVFBUWEh2T98Bd+/6zN+6TgcWvUHjS/4JTbQxOMDW9AzOQKXV0T/O3exgoICfvzxR1q1akWzZs0YPXo0I0aMuGjBMYFGHaFmPfmVjpprtnlEql1eQsw61h8qJCnUj/xKBy9vOEiB1YnkcWE/tJ0TP32OWFXKzaNupUWLFhdlPBeb5hEWJnRtwvPr9lNQ6URGRqdR0TEuhHHKEruCwt8GRcz/xhw8eJDnn3+eL774gpYtW/LQQw8xZMiQWkvIDo9IfqUDp8eLLMOO42UcLqpCkiUO79tL+qrlHNu6ltCEFNTtr6cwsEmNm1usxcjYjgmoBIGfjxTjcHlpFxdSk3y2b98+5s6dy5o1a3jiiSdISkpCq9Ve1AS4ELOeLo1DySm3YXV4cHglSqpdBBg0xFhMHCuzUVTlJCO/ktxyO6LdinXrKqr3rEelMxLYaxQ3PTCdRo0aXbQxXUz0WjWjOzYmwmIiPaeEMpuLpuEB9G8eRdxfjGpVUFC4clHE/G/KDz/8wPTp0zl48CA33XQT06dPJzU1tdYxlQ43P2QWUXGyFe1oaTU5ZTZEUaTy4A6OrXmf8pyDRHfox4CxEzGFRlNi8yCoBGIsRgIMWiIDfKYckixzpKSaJuH+BBi0rFixgtmzZ5OTk8PIkSMZOHDgJWnFU6sEOieEEmzWsTGziJ3Hywg26UgM8SPwZEW+0yOSb3XgFiU8pblYt32FLjKRwF6jMTRqRoGjYUeC+hu03Ng6lgEpkTi9Ev56TS1LXQUFhasfRcz/ZoiiyKuvvsqMGTPQ6XRMnz6dhx56CH//M325N2YWcaLCzuEiK7tyyymzuX1pU/s2ULbmHWTRS6ub7iO48xBMYUF0iA+hWUQAx8tsVDjOjJN0ixKlldW8vvBN5s6dS1VVFU899RRTp07FYDg/O83s0mo2HSkk0t9E/+ZRdR5j0KppFR1Ei6hAVu4+TlGV64xcdItBi1GrhtjmhAyeiDGpLWpzABqVipuvuTj795cao06D0lauoPD3RBHzvxEOh4MXXniBF154gZDwSGY9+S/G3Doao/7Mr0GpzUVehYOskmoOFFipcrhxlBdS9f1HOPesRR/WiOhrx9CoS2+CzEbSEsMY2S4ejVqFJMtUOCrPeM/ivGM89cYHfPbRcmJiYli+fDmDBw8+r2uxOj0s/uEgS7dnYXeLCAK0ig7k8YEt6RAfUmfmuEoQCPMz+EJfTmKvqiR93TfYy0to2XYwe8uA1r0RBDDrNAxtGUNogGL5qaCg0LBRxPxvgCzLCIKAVqslPqExXfsPYdAtt2Np3JR1hwqJDzaTEhmAUev7OpRUO/l2Xx6rMnI5UlyNy+XElrOPyp8+x5mTgSapAwFpNyLEpVBs92LSi4T66WuWdlMjLeRXOiizuQEQvV4y92xn7QdLyN6/m8GDB/P4449fUDzoj5lFvLstC+fJfHRZhl/zKnjz50waBZmIstTdq58c7k+e1UGF3cWJo4fZ8Pky0r9fRa+ePXh4YhT7HCaOFFtRqQRSIwMZ1qZRnTcGCgoKCg0JRcyvcux2O2+//TYdOnSgc+fOdOgzAHdsayStkWqXl2qXl7KT/cjXxAazO6eUZ9dm8HNWCSXVLkS3C+HARsTd34GzCkPXm9C26Ik6OAK9VoPFoEWjVuP2SjU3DUEmPT2SwskutVFYYWXlh8tZtfwtRIeNadOmcfvttxMbe/5tU5Is8/Xe3BohP4Uswy+55eRVOs4q5qF+BjrGWlj26Qo+fGMxR/fuYtTo0UydMpXWrVvSW6PB5RFB4IzKewUFBYWGiiLmVzlbtmzhqaeeok2bNnz2xQpKnSDrjHCaY5hXksnIryRArWbEWz+SXW5DkgGP02cCc2Q7gtmCecDdmJPb4xU0mHQa4oPNBJv1JIaYcXhEKp0eAk9u2kYEGAk263FFBbBRsqFD4rV336VXr14Xpafe6jxzTx58zmdeSa7zOQCvx82alZ/w33lPUVpSwvz58xk3bhwhISE1hj16rSLiCgoKVxaKmF+FyLKMJEmo1Wr69OnDpEmTGDNmDGqtDpdXPMP6U5RkZn6xk+NVzlNvABX58N2rUHocIpug6XMnoU2aYdFrQBBoHGImJdxCpMWIUatGkmVcHgmMvvNXVFSg0+nwM5v555NzeOLxmQQG/nnr1HMhCAJDWsTwc1bJGdeSFOZHiPnsrW1arRZ/Pz8iwsN579136dOnz0UZ0+XGI/pa7Pq/sga7x8tbY7pS7RIptbnoFB9Cs0gL6r8YJKOgoHDlooh5A8bhEamwu5EBs06Nv0H7h/u3drudDRs2sHHjRqZMmUJkZCRz5swBfMvTAQYdKpWAdHL2Wm53s3R71m9C7nZAzi++7HGHFVJ6QOebUFtCiPI3kBjqj0eUSAjxIz7EXDMeo1aDv8H3dTp06BA33HADt956K0888cRFdU0TJRmr00PrmCC6J4Wz81gZdrcXlcqXqT6mQwKNf9df7XK52L17N6GhoSQmJjJs2DCuu+66s64Q2Nxe7G7fEr5Jq8ZcR4FgfVLt8jBh2WY+2nW85rG+C9dh1Ag0Dg0gwKBhxDWNuKtbcq3UNK9X4kCRFZvbS4S/gYSQ+nMdVFBQuLg0rL9SCjXklFWzMbOIQ0VVyJJMfIgfXRJCaBEViOosM65jx47x9ttv88orrxARE0eH3gMY2jcUo873a1YJAo1DzRRaHRyvsPPriXJ+ziomI/9k5bm9En5dA/t/AL0JOt8EzXuBRkeon4HuSeHEBpoosDoI8dPXCLlJpyYlMgDTyfOYzWbatGlDXFwcXq/3ovWPF1od7Mot50BBJWqVQNfGobSKsiDKoFMLtGsUTLekcNSn9ViXl5fz6aef8sILL9C6dWs++OADjMazV6fnVdjZm1dB0cmK9zA/XxhLbGD9hd/8no93ZPPNvvwzHnd4ZQ4UVmIxaHl3SxapkRaGtPTVJhwstLJq93GWbD1CYZWT9o2CGdUhgZQIC/HBZmIa0PUpKCj8dRQxb4CU2918tCOHX/MqapaRT1Q6KKpyYjHqajl7HSmq4qu9x7HnHWHr5++w5tvVBMYl0+SGCWytMpH3cyaDU6NJDg8gs7iKD9OzOFpSza4TFWSXVv/WD16cDds+g7wDENEE2l0H0SmgUhMfZOLubk1oHxdCuL+BAIOWKqeHcocbk1ZDTKCRwqxDfL4pi0GDBhEbG8vLL79MaGjoRcv5LrO5+PLXXDZnleA+GSyi16hISwxjdIeEmhuJ0ykqKmLu3Ll88MEHBAcHc8stt5wzyKba6WFzVkmtgJKcMi/ldjfXt4ptMDP0tYeKqHad6TcPIMlQ7vBgc1fxza/HGdIyllKbi5fW7eftrUdrQlnWHCxka04p7WIsNIkIpEtC6Fk/RwUFhYaP8i+3AbI7t5zDRVW19oM9osTh4ir25lfWiPnML3fx8Y5jlB3aiXPDu7hL8mjSfQit/jEBnX8g+TaRAnspmcVVJAab+Pf6Q5TaXIiSjFs8mbQlS3A0HTZ/DFXF0LQbdBoBfkEgqAgyann2+jYMuyYOjVqFWhBQqwRESUaUZVSCwNo13zFp0iR0Oh2RkZGkpaURFVW3gcv5kl1mY+fx8hohB1+x289Hi+nbLJLGv1syPnr0KLfddhs7d+6kZ8+ePP/887Rs2fKc2ePHK+xUOtxnPG51esgpqyY16uLs+V8okixz9hI/H25R4tuDhQBkl9pYnp59Rrqa1ella04ZVW6ZI8XVCILAnV3rLytAQUHh/FHEvAEhSjIOj5fj5TYcHhHppFiewitKNW1kS37O5J1N+ynduQ73D8tAq0fd9Rby2gyknX9gzQxUlqHQ6uTVjYeQZWqLgMsO+9bDzq9AEqHrLdBmEAg+wbuhRTSfT+yFug4BVAlgq65m4cKFPPHEE0RHRzN58uQLjiz9PTLg9ooUWh2U2124vCIl1W5UAgSb9UiympzS6lpi7nK5GDFiBJmZmYwdO5annnqKyMjIPzyXR5TOKpLOs8SM1gdtYwP5ck8uds+5bWYr7L5VF48kUXWWmbzT67sxqHZ7+floMWmNQ4kIMKLXqDHplKp+BYUrBUXMGwgeUWL9oULWHy5gd245ueV2LEYdURYjupN7wGqVQGKIH26Ph9179lK8+k28e9f7lsXbDkFKaIdNFMgqrSY20IROo0YANmcVU7tbS4byfNj9LRzeDEEx0Gk4xP1m4tIpPoR3b+9Wp5B7vV727dtXE9LSpUsXHnvsMYYOHXpR/dUlWSa33LeHveloERn5lZTb3ahVAipBIN/qJC7IRJBZjyzL2O121Go1BoOBl156ie3bt3PvvfdiMv25/eAwPwMalQpRqi2SapVAVANygXv42pZkFtt4Z+sRPOLZ5+gpET6LXrUgoNeo6rwh0akFQMbq8PDd/nx2HS8j2Kyjc3wYd6YlkRiqFMkpKFwJKGLeQEjPKeXjHTk4vSJGrQadRk1BlQNBgEZBZtSCQPtGwbSJDeLQoUPs+Hgx3oOboVl3aDMQQhOQBcE3E69yolWrCDXr0WvUlJ/uky56IGc3/PINlORAUmff60MaIQjQOsrCiLbx3NI+oaZn/HRkWWbVqlUsWLCAjIwMRo8ezbRp00hJSbnon0lxlYsfjxRhc3kRJRmXV8TpEdGoBYxaDR5Rwu72EqDXsGfPHpYtW0bv3r0ZNGgQvXr1omfPnufcI/894f4GUqMsZORX1Czn6zQqmkdaiAg4P+/4S8V/b+1M0wh/HG6R9Qfz+eFIca1tGZNOzQO9fL+TGIuRgU0j+XJfXq2VBwEIDzDgOhk0Y9SokfEtv685mI/D42X2kFa1KuIVFBQaJoqYNxC+zjiB0+ubERo0ahoFmTBVq1EJAvHBZronhtEpIRSzXoMlwEJQRIyv2rxpNzD+LiRFlnF5Jcrtbhwe729OaS47ZKyDvd/7Kte73AxN08BkAQRMOg1Lb+9Gy6igM4JIwOftPn/+fN5++20kSWLWrFmMHTuW0NDQS/KZHCmpwu72IgNeUcas0+Dxyti9XtQqgVCznnA/PUs/+IhP33iF3NxcIiMj6d+/P2q1+i8JOfhm4NfEBhEVYKDA6mvViwgwEBlgbJCWrtP7pSLLMiNaxTJj5S6+P1SIKEmE+RmY3CeFIa1iAIiyGJl7Q1tcosiaQ0Unax18lfphfkYKrXZkGQIMWrSndQLsL7TyS24FvZLD6+sSFRQU/iSKmDcQSm2/FV6dCvkwBWmIshi5rVNjwnUS3678lDFjxhAaHk7UwDsQtuUg/24ZXAC0ajUGtZpqlxedRvCJmqsKfloOh34GUxD8YxaExYPqt2Xx54e2pFV0UJ3jO3HiBJMnT+bLL78kNjaWN954g759+6LRXLqvkN3tBdl3TSrB58wW5q9GRibaYsSAl/3fvs//vv8YvB6effZZ7rnnngvKQ9eqVcQGmYk+2arVEEX8dARBoHlMEJ/e3cu35y/7PiujVl3jlS8IAikxgXw+sQ9uUUJGZn9BJe9sOUJWqY0qhxt/vRZ/Q+0ZuFuUqHC46jqtgoJCA0MR8wZCiygLW7JKaj2mEiDcX0+YUc1DDz7IJ598QmxsLL169SI2PAizPg+bV4SThW0+IVfRIsqCv0GLKMl43G5+rcjF87//QukxaNQKut0KgWdWm9/TJ/WMx06RmZnJkSNHSEtL46233iIxMfEifwJnEu5vILvMhizJBJp0BBn1lNldqAUVaredX1YsIfvnb0mIi2Xx4sX069fvop27oYv46Qgnxdt4DhtaQQCjTo0R3zFdG4fRtXEYAG/8dJglm4+c4aZnMWhJVIxlFBSuCBQxbyDc2DqW/EoHuRV2RElGAIIMKjrHBRJq8WPcuHFERUWRmuoT3EGpUaw/lM/2nFI8kowk+Srfw/30eGUZm8uD7Kwmb9cmxFVvoXI6kVoNgNYDwC/4jPO3iQkip8xWyxXMarVy+PBh2rVrR8+ePXnxxRfp0KHDRfFW/zMkhfqTV+ngRIWdYJOeFlEWskqrUQlgcFcRrIe2/7iRJ2fPviR79n8Xbm4bz67jZezJq6gpqDPrNAxsHkmLBtKOp6CgcG4EWf79/bjCufB4PEyfPp1Bgwaddxb32ThSXMWeExUUVjlwV5by/afvccuAntw6elSN3/rpy9o/Hy1m5S/H+DLjBB6vhEatItCgQ69To7eXkrdxBUd+/h96/yCaDLoVa2QrsqpEZFkm2ADFDgj309ExPoT+zaKIshi5sXUjDFo1kiQxc+ZMNm7cyCuvvEKHDh0u6rX+WaqcHo6V2Sizu7FWlLNjRzp9+1+LWgCVvYK40AAiIyL+8v64Qm1qHAcLrZh0Gto2CqZ7Uhh++otj+qOgoPDnsdlshISEUFpa+qftsJWZeQMiKcyfRkFmNv70M3Pmz+SXnTsIEZzceMP1+Pn5nWF4kpYYRvOIAKrcXr7YlU24xYReq0JVdoLdnyykLGs/Sdd0IeWGOzBHJuCV4Vo/PaFmPWpBxukFg1ZFiFmPVq3C5vJS6XBj0BoRBIEmTZqwZ88ewsPrrwDK36AlNcrCoSNHeXPRPNatW0uHRkEMGTIEuPIKs9xeCZUKNOcwr6kP4oP9GNXeRLXLi0Yl4G9QRFxB4UpCEfMGhCiKfLPqK6ZMmUJOTg7jxo1j0aJF57wzC37k05r/L7BZ4egOVN8vRiXI9Lj+Fh57YhaS1syxChtOt5fcSgeHS6pIDgsgIaR277Qsyxw9fAhHiIWEhAQmTJjA+PHja816fVnfvr7ly8Xhw4f5v3Hj2Lp1K3379qVbt26X7dwXizK7i+3ZpeSU2dCpVaRGBdI6JhBDA4pb1apVBJmUNjQFhSsRRcwbCCUlJbz99ts899xzmM1mXn31VSZNmlTn8nGJzcXu7FImfbLd94Asg70C9q6D3asRTAH0vun/GHbr/5EY47M6XfJzJh/tOo5HlHB5RbZkldAhLpieTSLQqQSsZSVs/9/nTP9gCffeey9PPPEERqNvhi7JMoVWJ7/klnO83IZaJdAoyMQ1scGE+RnqbGO7GFRVVfHdd98xefJkXC4X06dP57HHHsNisVyaE14CJFmmwOpkU2YRpTYXapWAW5TYfaIMryTRKSGkwc3SFRQUrjwUMa9nJEli586dvPzyyzVuajNmzKBfv351Cnm53c1Dn+1g6bas3x4szoJfVvvMYCKaoO54Pcn9byQ4wI9Ak47DRVbe25aFRzwVUKLGrJM5XFxN4yAT+rJsVi9fwq6Na7j22mvp3bt3rb15q9ND+rFS8isdgM92NrvUhleU6d004pKFc2RkZDB+/HhiY2OZMmUKI0eOJCAg4JKc61Lg9IgcKKwkPaeMPXnlGDTqmqAaWfbVSLSKDsTfoIi5goLChaGIeT3idDpZsWIFL774Iunp6dx9993cf//9pKSk1GmL+vG2Iyz/JZeVe3J9D8gSZG6DX74GazGkdIdWAxBCownxM3BNbBAWo44vfjn2W7DKSUw6DbLXTfq6b8jd8Bm5WZlMnjyZu+++m6ZNm/7m7Q6UVbsoPpV3fhr5VgcVdvdFF3NRFFGr1TRr1ozp06fTu3dvOnfufNES2C4X23NKOVBYSXapjbKTPgKVTg/NI3xxsQ6PWCs4RkFBQeF8UcS8nnA6nSxZsoSnn34au93OG2+8wYgRI865hPzIV3vILrf7fvA4IX0lZKz3NaR3GuFzg9Ob0GvUjGwXT9PwANQqAYPmzBsDWZLIW/cRJVu+JtjfxOuvv86wYcPObDuTwSPJeKUzmx58FqsXV4xWrFjBhg0bePLJJwkKCmLatGnnzB9vqJTZXBwu9iXfGTS+tDlRlrG7vBRVOUkI8cOk02BQwkwUFBQuAsr6Xj2h0+mIiIggOTmZb775hjvvvPOcQj5w0TqfkMuybxa+7k3YvRrUGhg0GVpdC3qfa5lOrcKoVdf4cA9v0wj/U1ncsozbWsbBJbPIX/chLZom8dFHHzFmzJg6+8cFwVdRXleWt1GrvigFU7IsY7PZmD17NsOHD+ebb75hy5YtvnNcgUIOUOX0Ip28AQow6ggwavFFmoDTK6ISBJpFBNR5o6WgoKDwV1Fm5vWESqXiuuuuY/DgwWdN9RIlmWqXB68k45EkX0zpif2Q/gWUHoeEdtB9bI0JjFmnwd+g4dpmUXz+yzF6JUfQNjYYk0HLowNa8M7mw+zdsZXDK/6LWHqC4TfdzL+enEPz5s3POdYQs44mYf7/3959x0dVpX8c/0zJTCa9NwhJSEgCSWhLCFJCZ6UYdlUUXJrAqgsYWMBd1P2hgghroZhVZAVjKLJLUcFVigUXSUBwaSJKWwhCKoTUSZuZ8/sjMmukiFImMc/79cof3ty589w7Dt/cc849h6P5pVTVWtEAzk462gR7XjHkfwqLxcLhw4eZN28e7733Hl26dGHWrFkMGDDgho7raB4mJ/RaDTVWhclJR0s/N3JLKqmottDSz53EMF9iAutaToQQ4kZJmDuQs/PVV+KqrLVyOKeY3JJKaixWii5cgEPb6n6UDdoPhrjeYKobEObraiQh2JNgz7o72RqrjROFZYT5uBLkYaJPTBChHk4sOryZWk8D9z/yJJP+8Ai+vr4/WqdRr6NtiDfeLgYKSqvQaCDQ3Znm3q71Fub4qZRSZGZm8sQTT3D06FFGjRrFtGnTiImJ+dnHbCi8XQy0DvLkcG4xVlvdIjFR/u54mpzoExOEv1vDWoVNCNG4SZg3IEopzDVWbEpx8OxFvskvrXss7Mwpij/KgL3bwd0PkoZBaBw41QWCh7MTXcJ97UuW6rUaPJydMFdbKK+y8G3Jt/j4+BAT4sfjU/7AuOF30759+580oMxk0BEd4EHEd9O93kiIf191dTUWi4Wnn36aBx54AB+fy6eabaw6hPrgaXLizMUKaiw2Aj1MtPRzw9f15y8EI4QQVyJh3kBUW6zsPFHIP/dls+nLs7g5aUjt1ZrcE1+x9uW5fPvNYbyi4ihOGgdu3qCpC9MANw2PdI/hvxcqUEqh1WqICnDHWV+3BOiB/V/wlyl/YNCgQcx//gWUqw/+LX1+9sjwmxHiOTk5eHh44ObmRs+ePXnvvffw9vZudKPVf4xBryUm0INIf3eUqltiVZrVhRC3QpMO88rKSoqKirBYLPj6+t62BUR+yGpTZOz+Lw//Y499W76lmqkvLsW0axXKUsuAERMY8uBkjEYDW7/8lvMVNSwb3ZVIfw9OXyhn46GznK+oxt/NaB9UFeThTAvfUJycnDhTVMH/bfyCnHILSkGQh4kH72hJ6yDP27ZCWG1tLVlZWTz22GO0atWK1atXYzQaHTpd7K2m0Whw0kmACyFurSY7mr22tpZVq1bRrl07wsPD+cc//uGwWs4Vm3nkUpArBSX5sPcd1LZXqXFypdfYPzJ4zB9wMhhIaObNukf6sH36nUT61/WXh/m4cV/HFvSI9CfQ3RlzUT65R/5DjK+JhJgo1qx/hxZDJnCuzGJf5jKvtJKVn5/iXLH5tpxjSUkJ6enpjB8/nvz8fFq2bInNJs9YCyHEzdBk78yPHTvG6tWrcXV1paioyKG1lFXX1j1GZrNC3nHY/wGcOQRh7bC2uxNjm2QMziYC3J1pHXT542saDQR7uuBhdOKdzVtZ+/JizBcL6R/fglYhXSnWe1BQno9SYPkuQJ10Wi5UVHMkt5RQ7+tblefnOnr0KIsWLeKf//wnkZGRTJ8+nZSUlMsWjhFCCPHzNMkwN5vNrFmzBp1OR3JyMm+99ZZD6/FzM9bdkR/fBf/ZBBUXoeMQiOuDcvPB291EUrgfYb6ueFxlNavKykpWZmSwaNEiTp06xYwZM4iKigLqJjCptdq4aK6hymIFwKDTEeThTJXFckvPrbKykvnz57Nq1SoGDRrEM888Q1xc3C+uf1wIIRypSYb5sWPHWLFiBbNmzSI3N/e2v3+t1cbhnGK+LTbjbtQT6+8GWWvg0FZwdoeeYyEyCXR6fExOJLf0o20z76suaFJaWsq8efNIS0tDo9GwZs0ahg4dap8StlMLXxZuP4q55n/BXW2x4VKlJeAWPyJlMpn43e9+R3h4OI8//jgGg6zKJYQQN1uTa+esqKhgxowZdOrUibvuuuu2N/WWV1vYeOgse7MvcO5CGf/+fB8D70pBe2gr2sAI6D8RWnUFnR69VsPvOrdkbNdWVwxyq9XKyZMnGTduHIsWLaJDhw7s2LGDu+++u97c7hoNRPi4YtRruXQYg15LkLszXi439w5ZKUV+fj6LFi3ik08+wWaz0a9fP5566ikJciGEuEWa1J25zWZjyZIlfPXVV7z55psEBgZe9+us1rrm6dra2hsauPV1XgkXzTXUVFfx5a5P2fbWMgrPZpP069/QZvAISox+HMsrISHUlwB3Zxbc/asrHqesrIwtW7bwzDPPkJOTw4MPPsgf//hHe9P69xWZa+gU5ouPq4HC8mqUqmvajwlwp9py+ZzrP1dtbS179uzhhRdeYPPmzUyZMoWuXbtec3IcIYQQN65Jhfnu3btZvHgxv/vd7+jdu/d1v27v3r2kp6dTVVWFzWbjiy++YNCgQT+rhvPlVSilKPj2NBv/vhClbNw1fgpdB/6WEV1jcdLC0bwS2ob64ex05Xm78/LyWLJkCa+//jpKKebOncu9996Lv7//Fff3d3PGSaclOsCDVt/totHUza3u4Xxz/heoqKhg/fr1LFy4kOPHjzNlyhQmTJiA0SgTpAghxK3WZMK8uLiYRYsW4ebm9pP7biMjI3nwwQexWq1YLBaqqi5fDvR6OTvp0QCefgH0/O0ImkVEE9WuEwajEUnRFsEAACAASURBVGcnHW5GJzq3vPadbGFhIdu2bSMsLIxXXnmF+Pj4a56Pn5uRln5unCwsq9dc7+/uTDOvK88L/1PU1NQwd+5cXn/9dZydnXnjjTcYNGgQ7u7uN3xsIYQQP67JhPmmTZvYvn076enpuLu7U1NTt770peZzi8VCTU0Ner3+sn50Pz8//Pz8gLqm5A0bNvzsOqL83Th1vgx3Lx/6DhsLgAaI9HPDzXj1/mubzUZtbS1Go5H4+HiWLl1KaGgo3t7eP/qeWo2G5KgAPJ2dOPrdFLHRgR50bO6Dk/7GxwzYbDb27dtHREQEr776Kr/61a/s66ELIYS49ZpMmOfn51NaWsqoUaPqhXVlZSU2m40ZM2Ywb948Xn75ZYYOHXrL6gj1dqVbpD9HckupsljRaTUEujvTreWVm8ihLixXrFjB4sWLee+992jevDlt27b9Se/rpNPSKcyXTmE/vrDK9aioqCA3N5fQ0FCcnZ3JyMigoqKCli1b3pTjCyGEuH5NJswTExOZOnXqZYPXdu/eTWZmJt26dSMxMZGIiIhbXktMoCeh3q6UVNZg1OvwMhnQXmPObqvVSkFBAZ6enuTl5dG8efNbXuO1FBYW8sYbb7Bx40aeffZZ+vTpc92DCYUQQtx8TSbMe/XqRa9evS7bPnfuXLKyshg2bBgTJky4bfW4GPS4GK59+Xfv3o2Pjw/R0dE89NBDpKSkOHx50OzsbB5//HG2bt1KfHz8dTXzCyGEuLWa3HPmjYHVarU398+ePZvS0lK8vLyIjY11aF/0xx9/TN++fXn33XcZMGAAr7/+Ou3atXNYPUIIIeo0+TB3dXUlMDAQk8nk6FKw2Wzk5uYyefJk/vznP6PX6xkyZIjDVnP7vm3bttkXSUlNTWX58uVER0fL/OpCCNEANJlm9quZOnUqU6dOdXQZ2Gw2srKyeP7559mxYwfJycnMnz+fDh06OLSuqqoqnJ2d6dChAykpKXTv3p1hw4bJaHUhhGhAmnyYNwSVlZWsXr2av/3tb+Tn5/Pwww+TmppKs2bNHFaTzWbjk08+ISsri9TUVPz9/fnrX//aIFowhBBC1Cdh7mA5OTnMmzePt956C09PT/7617+SkpKCl5eXw2qqqKjgjTfeIC0tjZKSEnr16kVycrIEuRBCNFAS5g505MgRpkyZwqeffkp0dDRr164lNja23iIpt1tRURGzZ89m+fLl6HQ6Vq1axR133OGweoQQQvw4Gb3kIJWVlbz99tscOHCAkSNHsnfvXuLi4hwa5Dk5OYwZM4alS5fSsWNHdu/ezZAhQ2TtcSGEaODkztxBTCYTI0aMoF27dvTs2RMXlxufI/1GHTx4kGPHjvH73/+eadOmER4e7uiShBBCXAcJcweKjIwkMjLSoTXk5eUBEBgYSN++fVm6dCnx8fH2ueiFEEI0fNLM3oQdPXqUiRMn8vjjj1NUVITBYKBXr14S5EII0chImDdR+/bto0+fPmzatAmdTufQvnohhBA3RsK8CVFKUVhYyIIFC7jjjjvQarXMmTOHl156yaGPwgkhhLgx0mfehNTU1PDGG2/w3HPP0alTJ6ZNm0ZKSoqMVhdCiEZOwrwJ0el0xMbGkpqayvDhw4mLi3N0SUIIIW4CCfNfOIvFwoIFC+jZsydJSUkMHDiQ/v37N4hH4YQQQtwcEua/UEop8vPzmTlzJqtXryYxMZGPP/4Yk8mEwWBwdHlCCCFuIhkA9wtksVj47LPPGDt2LO+88w69e/fmtddek7nVhRDiF0ruzH9hrFarfZGUoqIiJk2axNSpUwkICHB0aUIIIW4RCfNfGI1Gw0cffYTFYuH555/nrrvuwsPDw9FlCSGEuIUkzH8hampq0Gq16PV6Xn75ZYqKioiOjkavl49YCCF+6aTPvJGrqanh/fffZ8CAAWzduhWlFEFBQbRp00aCXAghmggJ80bs7NmzLFy4kNGjR5Obm0t5eTkWi8XRZQkhhLjN5NatEbJarezdu5fFixezdu1aevXqxWOPPUavXr1kNjchhGiCJMwboYMHD/LnP/+Zzz//nIkTJzJ16lQiIiLQaqWhRQghmiIJ80YoLCyMhIQExo4dy6hRo6RvXAghmjhJgUbAarVy5swZduzYwf3334+vry9paWlA3aNoQgghmjZpl23gKisr2bp1K6NHj2by5MkcOXIEqAtxCXIhhBAgd+YNmtlsZsmSJSxduhSz2cysWbNo3ry5o8sSQgjRwEiYN1AXLlzg0Ucf5YMPPiA8PJxFixbRt29fjEajo0sTQgjRwEiYN1Dvv/8+69atIzk5mVdffZWYmBhHlySEEKKBkjBvQMrLy6msrMTPz49Ro0ZhsVgYOnQovr6+ji5NCCFEAyZh3kCcO3eOV199lYqKCv70pz8REhLCuHHjHF2WEEKIRkBGszcAmZmZPPjgg6SlpVFYWIjNZnN0SUIIIRoRCXMHqq2tZcWKFYwZM4aPP/6YMWPG8OKLLxISEuLo0oQQQjQi0szuINXV1bz++uv83//9H1qtlmXLljF27Fh5dlwIIcRPJmHuIHq9nhYtWtC/f38mTZpEz549HV2SEEKIRkrC3EF0Oh39+/cnOTkZDw8PR5cjhBCiEZMwdyCTyYTJZHJ0GUIIIRo5GQAnhBBCNHIS5kIIIUQjJ2EuhBBCNHIS5kIIIUQjJ2EuhBBCNHIS5kIIIUQjJ2EuhBBCNHIS5kIIIUQjJ2EuhBBCNHIS5kIIIUQjJ2EuhBBCNHIS5kIIIUQjJ2EuhBBCNHIS5kIIIUQjJ2EuhBBCNHKynvnPoJTizJkzHDlyBI1G4+hyhBBC/IKYzWaUUthstut+jYT5T6SUIiYmhvfff58PPvjgho6TnZ2NzWYjIiJC/ihowCwWC5mZmXTv3h2dTufocsQ1WK1WPvjgAwYNGiSfVQNms9k4cOAAEREReHt7O7qcBqlfv35Yrdbr3l+jlFK3sJ5fpOrqapRSNxTANpuNZcuWUV1dzaRJk9BqpcejoSorK6Nfv378+9//xtnZ2dHliGuoqKggMDCQgoICXFxcHF2OuIra2loeeughJk6cSGJioqPLaZBqampwc3O77pyRO/OfwWg03vAxbDYber0eq9WKwWCQu4gGrLq6Go1Gg9FovCmfvbh1LBYLAAaDQT6rBkyj0aDVauVzuoafel3kdlAIIYRo5HRPP/30044uoqkymUyEhYURFBQkfeYNmEajwd/fn/j4eOkOaeA0Gg1BQUF07txZPqsGTKPR4OnpSXR0NG5ubo4u5xdB+syFEEKIRk7+dBVCCCEaOQlzIYQQopGT0ewOpJTCarWilEKn00kfXwNls9mw2Wxc6pHSaDTodDoZ59CAfP+7BP8bLS3fKce49H259PNj35nvf8fks/t55Go5iM1mY/v27cTHx2MwGJg/f76jSxI/YLPZOHbsGE8++STt27enWbNmtGjRgh49erBixQpHlye+c+HCBV599VV69OhBcHAwAQEBxMfH88QTT3Ds2LGfNIuWuHHffvstixcvZtiwYURFReHs7ExKSgpnz5694v65ubk8++yztGvXjuDgYKKiopg+fTonTpxAhnRdP7kzd5CLFy+SkZFBUVERBoPB0eWIK/jiiy+YPn06eXl53HnnnbRo0YLa2lrOnj1LQUGBo8sT30lLS+Pll18mKSmJ1NRUTCYThw4dIj09nWPHjrFs2TJ8fHwcXWaTcfz4cd5++20sFgsJCQlUV1dfdd/y8nJmzJjB5s2bGTJkCPHx8WRnZ7Ny5UpOnTrFwoULiYiIuI3VN14S5g7y/vvv8+WXX9KvXz+2bNni6HLED+Tl5fHKK69QUFDAsmXLSEpKss/+VlVVRXl5uYMrFFA3S9Yrr7xCmzZteP7554mLi0Or1VJQUMCsWbPIyMjg2LFjdOnSxdGlNhkJCQm88MILuLq6otVqeeSRR66678aNG1m7di3Dhg3jpZdewt/fH7PZjL+/PwsWLOCDDz7g4YcfRq+XqPox0szuABcvXmTOnDn07NmTnj17St9QA/Tll1+ybds2xo8fT3Jycr1pXJ2dnfHz83NgdeKSqqoqzp8/T1hYGDExMfbvkr+/P23btsVms1FcXOzgKpsWf39/unTpQkJCAgEBAVed3VIpxapVq9DpdIwcORJ/f38AXFxcGDt2LK6uruzcuZMLFy7czvIbLUmR28xisZCamopOp2PSpEky13cDVFtby6FDhygvL6dz586sXr2a3/zmNwwYMICZM2dKP2wD4uHhQUREBLm5ufaFi5RSlJSUcOLECdzc3IiMjHR0meIKzp8/z8mTJ9Hr9SQlJdX7naenJwkJCRw/flz+GLtOEua3kVKKjRs38s477zBt2jSioqIcXZK4gqqqKk6ePImbmxt/+ctfePLJJykvL8disbBq1Sp69erFv/71L0eXKb4zd+5cCgsL+ctf/kJGRgbr1q3jqaee4pNPPuHxxx+nefPmji5RXEFeXp59MZEfjmnQ6/UEBQVRWFiI2Wx2UIWNi3RE3EYnTpxg8eLF9OvXj1GjRjm6HHEVVquVkpIS8vPzAXjttdfo06cPVquVd999l8cee4xp06bRoUMHQkNDHVytuPPOO9mzZw8ZGRls3boVg8FAWVkZPXv2pF+/ftL61UCZzWasViseHh6XPbKm0WhwdXWlsrLSvniOuDa5M79NKisrWb16NXl5ecybNw+TyeToksSPUEpx7733ctddd+Hu7o6XlxfDhw+nW7dufPvtt2zfvt3RJQrg5Zdf5sMPP2TcuHGsW7eODRs28Nxzz3HmzBmefPJJCgsLHV2iuIJLAX61x89udJnppkbC/DY5ePAgq1ev5pFHHiE6OtrR5Yhr0Ol09sUfEhMT6/2D4uzsTMeOHbHZbBw/ftxRJYrvfP3118yfP5/Y2FieeOIJ+vfvT48ePZg4cSJjxoxh+/btbNiwwdFliitwdXVFp9NRXFx8WaDbbDbKy8txcXHBycnJQRU2LhLmt8mFCxc4e/Yss2fPJigoCH9/f/z9/Zk8eTJFRUXMnTuX5s2bs2DBAkeX2uQZDAbCwsIArvhIzKVtMqGF43322WcopYiJianX72o0GomJicHLy4svvvjCgRWKqwkJCcHFxQWz2Uxubm6939XW1nLmzBmCg4NxdXV1UIWNi/SZ3yYhISHcd999l/X//Pe//2X//v20atWKdu3ayQQJDYDBYKB169Z4enpy4MABhg8fbr87r62t5eDBg2i1Wlq2bOngSoVGo7HfxVkslnp/aFVUVFBTUyOTMjVQ3t7eJCQkcPLkST799FMeeOAB++9yc3P56quv+M1vfoOvr68Dq2w8JMxvkw4dOpCRkXHZ9oyMDKZPn859993HE0884YDKxA9pNBratm1LYmIi69at4+6776ZLly5YLBa2bdvGzp07CQwMpE+fPo4utcnr0aMHTk5OZGZmsmvXLu644w70ej2nTp3ivffeo6ysjOTkZEeXKa5i/PjxvPPOO6xcuZKkpCQiIyMpLi7mpZdeQqvVkpycjKenp6PLbBQkzIW4grCwMCZOnEhqaiojR46kQ4cO2Gw29u/fj9lsZvny5fameOE4rVq14o9//CMLFixg3LhxxMXF4eLiwpEjRzhx4gQpKSn07dvX0WU2KQUFBcyfP5+vv/6ampoavvzyS7RaLaNGjcJkMtGiRQtefPFF3N3d6dWrFzNmzGDRokWMGDGCqKgosrOzOXToEPfeey9Dhgy56qQz4geUcKg333xT+fr6qrlz5zq6FPEDNptN7du3T6WkpChvb2/l7e2tUlJS1L59+5TNZnN0eeI7VVVVav369So5OVm5u7sro9GoEhIS1EsvvaTOnz/v6PKanNOnT6uuXbsq4Io/cXFx9T6X2tpatWrVKtW+fXvl6uqqIiMj1YsvvqiqqqoceBaNj0YpGcUjhBBCNGYyml0IIYRo5CTMhRBCiEZOwlwIIYRo5CTMhRBCiEZOwlwIIYRo5CTMhRBCiEZOwlwIIYRo5CTMhRCNxsWLF+nbty+PPfYYJSUlji7nit58801atmzJ+vXrf3TfiooK5syZQ3JyMmfOnLkN1YlfKglzIcRlNmzYQGpqKp06dcLFxQWNRkNOTo6jy8Jms1FQUEBxcTE2m83R5VxReXk5586dw2w2/+i+SilKSkooKCi4bBEmIX4KmZtdCHGZ6dOno9Pp8Pf3x9PTk8rKSkeXJIS4BrkzF0Jc5tlnn+W1114jIyODHj16OLocIcSPkDtzIW5Qfn4+H3/8MTt37iQnJwebzUZMTAyjRo2ibdu29fa12Wy8//77bNq0ibNnz2I0GgkODqZv374MGDAADw8P+zFXrFjBnj17KCsrw8vLi+joaIYOHUrHjh3RaDRYLBZ2797NRx99xDfffENxcTGBgYEMHDiQwYMH4+7uDsC5c+eYPXs2YWFhpKam4ubmVq+mrKwsZs+ezX333ce4ceMAGDlypP33JpPpJ18Ts9nMiy++yOHDh3nrrbd48803+fDDD6murqZjx45MnDgRPz+/eq9RSnHmzBnWr1/P559/TllZGc2aNaNbt26kpKRctq51QUEBS5YsYc+ePej1eu68807uv/9++3l/X2ZmJuvWrePkyZM4OTmRmJjI+PHjCQgIsO+Tl5fH2LFjGTFiBO3atWPFihV88803hIaGMmrUKO644w40Gg0rV65ky5YtVFVV0a1bN1JTU6+4ZnptbS3bt29nzZo1fPvtt4SHh9c7jhA3lWPXeRGi8XvmmWeUm5ub8vPzU3FxcSoiIkI5OzsrV1dXtXXr1nr7pqWlKYPBoNzd3VXr1q1VdHS0MplMqkOHDmrv3r1KqbpVpDp37qycnJxUUFCQat++vQoJCVFOTk5qypQpqqamRiml1Pnz51VYWJhyd3dXrVq1UgkJCcrDw0OZTCY1c+ZMVVpaqpRS6uLFi+qhhx5S4eHh6syZM5fVP2rUKOXn56c2b958xfMbPXq0AtS5c+eu+5qUlpaqYcOGqZCQEDVp0iTl7u6uYmJilL+/v9Lr9apjx44qJyen3msOHjyo4uPjlV6vV8HBwapDhw7Kx8dH+fr62ms7f/68io+PV4MGDVKJiYnKy8vLft5OTk5q3rx5l9Uyb9485ezsrEwmk4qMjFRBQUHKyclJdejQQR0/fty+36lTp5TRaFT9+/dX7dq1U2FhYSo2NlY5OTmpmJgYtXv3bjVhwgTl6uqqYmNjlY+Pj9Lr9WrChAn13u/SZ/zAAw+owMBAFRkZqaKjo5VWq1URERFq06ZN9n3LysrU9OnTVUxMjDp58uR1X18hfkjCXIgbtH//fnXkyJF62/7zn/+oqKgo1alTJ3Xx4kWllFLV1dXKw8NDdenSpV6ImM1m9dlnn6nc3FyllFJr165Vrq6u6umnn1Zms9m+34kTJ9Tu3buVxWJRStUt/fnpp5+qkpIS+z7FxcVqxowZKigoSO3atcu+feXKlcrLy0utXLmyXp1nz55Vvr6+qmPHjqqiouKK53cjYa7T6VRoaKj6/PPPlVJKlZeXq9TUVGUwGNSjjz5q/8PEbDarpKQkFRISol577TVVWVlpP8cdO3aoU6dOKaX+F+YGg0FNmDDB/gfL4cOHVffu3ZWPj48qLy+317Fhwwbl4+OjBg0apI4ePaqUUspqtar09HTl6empxo8fb7/Gp06dUhqNRnl7e6uXXnpJVVZWKqvVqtasWaN8fHxUu3btVOfOndWJEyeUUkplZ2erHj16KEAdPnzY/p5paWkKUAEBASotLc1+Llu2bFHBwcFqwIAB6vTp00opCXNx80ifuRA3qH379oSFhXHgwAE+/vhjNm/eTF5eHl27diU3N5f9+/cDUF1djdlsJjQ0lJCQEPvrTSYT3bt3JygoCIALFy5gMpmIjY3FaDTa94uMjCQpKQmdTgeA0Wika9euXLhwgc8++4wtW7aQmZlJYGAger2eXbt22V/bqVMnWrduTXp6er1R4O+++y4VFRWMGDECFxeXm35trFYrs2bNonPnzgC4uroyd+5cQkND2b17N8eOHQNg69atHDhwgOHDhzN69GicnZ3t59ijRw/Cw8PrHTc6OppHH33U3qTepk0b+vXrh9lsZt++fUDdqPJ169ah0+lYunQp0dHRAGi1WsaOHctvf/tb9u/fz9dff20/rlKK7t27c//99+Ps7IxWqyUpKYnExESOHDnC/PnziYyMBKBFixYMHToUjUZDVlZWvfo0Gg2JiYmMHDnSfi49e/Zk3LhxZGVl2c9biJtF+syFuEFHjx4lLS2NPXv2UFhYSHV1NUopKisrcXJyIi8vDwA3Nzf69OnDzp07eeKJJ0hKSiIhIYHY2Fj0+v99FZOTk9FoNPztb38jOzub9u3bk5CQQHBwcL33rampYcWKFaxbt47Tp09TXl6OzWbDYrFQUlJCdna2fd+WLVuSmJhIeno6R44cIT4+ntLSUj788EOMRiPDhw+/JddGq9Vy77331tvm5uZGv3792LJlC9nZ2cTFxbF161Y8PDzo1q3bdfXRx8XF1evv1mg0hISEYDKZOHv2LABnz54lOzsbk8nEP/7xj8uOUVRUxPnz58nPz7dv0+l0REVF1bvWbm5u+Pv7ExAQQPv27esdIyIiAo1GQ0FBQb3tOp2OTp064eXlZd9mNBpJTExEKcU333xD3759f/Q8hbheEuZC3ICysjKee+451qxZw9ixYxk8eDBBQUE4OTmxadMm0tLSqK6uBuoCZ+nSpTz11FOsWbOG1atX4+npSUREBFOmTGHgwIHodDpiY2N55ZVXeOGFF5g3bx6urq54enrSt29fpk6dSsuWLQHYuXMnM2fOJDo6mhkzZtC6dWtcXFw4evQoc+bMqfecs8FgYODAgbz11lssX76chQsXcuDAAb7++muGDBlCs2bNbsn18fT0rBdol4SHh1NVVUV5eTlQN0jPaDTWC+hrcXNzu2zQmcFgQKvVUltbC9R9NhUVFeTk5PDMM89cdoza2lq8vLyoqqqyb9PpdLi5uaHVauttc3JywsvLq952wH7Xfek9L9FqtfVaX6Du83d3d8fDw4Pz589f13kKcb0kzIW4AV9++SV79+5l8ODB/P3vf6/3u3/961+ounEp9m3h4eFkZGRQUlJCVlYW69ev56233uLkyZMsWbKEX//612i1WoYNG8Y999zD6dOn2bp1K2+++SavvPIKRUVFLF68GF9fX9LT0/H29mbu3Ln07t3b/h6VlZVXnFClT58+hIaGsmbNGp566il27dpFdnY26enpt2x0dVlZGTab7bIQLC4utock1DW/W63WesF6LRqN5rKaL/33peut1+vR6/UMGjSIjRs3/uzjXvLDc7gWpRRlZWWXba+pqaGqqupnPSEgxLVIn7kQN6C0tJTKykratWtXb3t1dTU7duy46us8PT0ZOHAgy5cv55///CfZ2dns37+/XghrtVpatmzJH/7wBzIzMxk8eDAHDhzg5MmTAOTk5ODl5VWvSdhms5GdnW1v2v8+g8HA6NGjKS0tZcmSJWRlZdG2bVsSEhJu9DJc1aXH5364be/evfj4+Nhr79q1K2VlZRw4cACr1XpT3js4OJiAgAAOHTp0xWC9laxWK0eOHKGmpsa+zWKxcPr0acxmM1FRUfJ4mripJMyFuAFeXl64uLiQlZXFhQsXgLo743Xr1l02KKqkpIRPPvmEoqIi+zar1YrZbLbf9SmlOHDgAF999VW9ICgrK0MpVe/OsUWLFhQWFnLkyBH7VKBHjx5l9erVlJaWXrHe++67D1dXV5YtW8bevXu5++677U3Ft4JGo2HOnDn2fuza2lreeecddu7cSZs2bYiJiQHg7rvvxsfHh7fffptdu3bZz+dSAF68ePEnv3dAQAC9e/emoKCA559/ntzc3Hq/z8nJ4cCBA1RUVNzgWV7OZrOxc+dOduzYcdlnEx0dTZs2bSTMxU0lzexC3IDWrVvTqVMn1q1bx/jx42nVqhWFhYXs3buXlJQUtm3bZt83Pz+fRx55hNjYWMLDw3Fzc6OgoIAdO3YQGxtLjx490Ol0bNy4kc2bN9OqVStCQkKw2WwcP36crKwshg8fbu8zHzVqFO+99x5z5sxh+/btGAwG9u/fT1lZGWFhYVesNyQkhKFDh7J8+XJCQ0Pp1q2bvan7+zIyMsjMzASwj4r/05/+hIuLC76+vkyYMME+qvtavL29sdls/P73vycuLo7i4mI++ugjAgICePjhh/H29gagWbNmPP3008yaNYvJkyeTlJSEn58fubm55OXlMXPmTJKTk3/SZ6PVahk9ejTffPMNCxcu5IsvviA2NhYXFxfOnz/PqVOnCA4O5tlnn8XV1fUnHfvH6PV6goKCmDVrFr/61a8wGo3s2bOHo0ePMnPmTCIiIm7q+wkhYS7EDfD09OS5557Dzc2NdevWsWPHDuLj43n22We5ePFivTAPDg5mzJgxfPTRR2zYsIHKykp8fX3p1asXkydPpk2bNgAMHjyYc+fOsWvXLjZv3oxer6d58+b2Wdp8fHwA6N69O2lpabzwwgtkZGTg5+fHPffcQ+/evXnqqaeuWvPEiRNJT0+nc+fOREVFXXGfHTt2kJGRAWBv+l+zZg0ajYYWLVqQkpJyXWHu7OxMeno6c+bMYe3atZSXl5OUlMTs2bPp0KFDvX0feOABQkNDWbJkCRs3bqSqqopmzZoxYMAAWrRo8aPvdSVBQUEsWLCA3r17s3z5clatWgWAv78/nTt3ZuTIkQQGBv6sY1+LVqvlnnvuQavV8vrrr/Pf//6X8PBw5s6dy/Dhw6XPXNx0GvX90TlCiF+8f//73wwcOJC5c+cyderUW9LcW1ZWxvjx48nMzOTcuXM3/fhCiPqkz1yIJqSqqopFixYRERFBt27dpN9WiF8IaWYXognIzs4mMzOTr776im3btjFmobYWKAAAAHZJREFUzJhbOopdCHF7SZgL0QQcOnSIZ555hgsXLpCSksK0adOk31aIXxDpMxeiCaiurrY/gmU0GnFxcbmlTexKKcrLy6mtrbUP2BNC3DoS5kIIIUQjJwPghBBCiEZOwlwIIYRo5CTMhRBCiEZOwlwIIYRo5P4fcXEe1grQydEAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a followup to an [earlier blog post](https://greglandrum.github.io/rdkit-blog/posts/2023-06-12-overlapping-ic50-assays1.html) looking at the hazards of combining data from different ChEMBL xC50 (IC50, AC50, XC50, and EC50) data sets. By looking at measurements for the same compound in different assays for the same target we saw the degree of variability which occurs across different xC50 assays. This variability is most likely due to differences in the assays themselves, *not* experimental error (I haven't done the analysis to be able to make that statement with 100% certainty, so I'll stick with \"most likely\").\n",
    "\n",
    "In this post I will repeat the same kind of analysis using Ki data. We'll see that it *is* generally safe to combine data from different Ki assays for the same target.\n",
    "\n",
    "The [earlier post](https://greglandrum.github.io/rdkit-blog/posts/2023-06-12-overlapping-ic50-assays1.html) describes the setup, so I won't repeat that here. The one difference for this post is that I look exclusively at data for which the `standard_type` in ChEMBL is `Ki`.\n",
    "\n",
    "The TL;DR summary: if we're careful about using as much metadata as possible to ascertain which assays can be combined, we get quite good agreement between Ki assays:\n",
    "\n",
    "![results summary](attachment:image.png)\n",
    "\n",
    "For this ~8K data points R$^2$ is 0.88, Spearman's R is 0.94. Less than 10% of the points differ by more than 0.3 log units between the two assays and only 4% differ by more than one log unit. This is hugely better agreement than what we saw with the IC50 results.\n",
    "\n",
    "I've looked into some of the assay pairs where there are significant differences and they are all explainable, but that's a topic for a different post.\n",
    "\n",
    "As always, I'm happy for feedback and comments!\n",
    "\n",
    "The code, along with additional results, is follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-28T13:03:16.839970Z",
     "start_time": "2022-11-28T13:03:16.301427Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import stats\n",
    "from sklearn.metrics import r2_score \n",
    "\n",
    "%load_ext sql\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('tableau-colorblind10')\n",
    "plt.rcParams['font.size'] = '16'\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get assays with an associated document and pchembl_values from the local ChEMBL30 install"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "········\n"
     ]
    }
   ],
   "source": [
    "import getpass\n",
    "pw = getpass.getpass()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set up the tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-28T13:03:19.725682Z",
     "start_time": "2022-11-28T13:03:17.179502Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "38071 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "connection_string = f\"postgresql://ccg_read:{pw}@scotland/chembl_32\"\n",
    "\n",
    "%sql $connection_string \\\n",
    "     drop table if exists Ki_assays\n",
    "\n",
    "\n",
    "%sql \\\n",
    "select assay_id,assays.chembl_id assay_chembl_id,description,tid,targets.chembl_id target_chembl_id,\\\n",
    "    count(distinct(molregno)) cnt,pref_name into temporary table Ki_assays  \\\n",
    "          from activities  \\\n",
    "          join docs using (doc_id)  \\\n",
    "          join assays using(assay_id)  \\\n",
    "          join target_dictionary as targets using (tid) \\\n",
    "          where pchembl_value is not null   \\\n",
    "          and standard_type = 'Ki' \\\n",
    "          and docs.year is not null  \\\n",
    "          and standard_units = 'nM'  \\\n",
    "          and standard_relation = '=' \\\n",
    "          and data_validity_comment is null  \\\n",
    "          and target_type = 'SINGLE PROTEIN' \\\n",
    "          group by (assay_id,assays.chembl_id,description,tid,targets.chembl_id,pref_name) order by cnt desc; \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "Done.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "178420 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql \\\n",
    "     drop table if exists goldilocks\n",
    "\n",
    "\n",
    "%sql \\\n",
    "select assay_id,tid,molregno,pchembl_value into temporary table goldilocks from activities \\\n",
    "  join Ki_assays using (assay_id) \\\n",
    "  where pchembl_value is not null and cnt>=20 and cnt<=100; "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "Done.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15877 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql \\\n",
    "     drop table if exists goldilocks_ovl\n",
    "\n",
    "\n",
    "%sql \\\n",
    "select c1.tid,c1.assay_id aid1,c2.assay_id aid2,count(distinct c1.molregno) ovl into temporary table goldilocks_ovl \\\n",
    " from goldilocks c1 cross join goldilocks c2 \\\n",
    "  join assays a1 on (c1.assay_id=a1.assay_id) \\\n",
    "  join assays a2 on (c2.assay_id=a2.assay_id) \\\n",
    " where c1.assay_id>c2.assay_id and \\\n",
    "  c1.tid=c2.tid and a1.doc_id != a2.doc_id and c1.molregno=c2.molregno \\\n",
    "  and lower(a1.description) not like '%mutant%' and lower(a2.description) not like '%mutant%' \\\n",
    "  and lower(a1.description) not like '%recombinant%' and lower(a2.description) not like '%recombinant%' \\\n",
    " group by (c1.tid,c1.assay_id,c2.assay_id) order by ovl desc;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "Done.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "1032 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql \\\n",
    "     drop table if exists goldilocks_ovl2\n",
    "\n",
    "\n",
    "%sql \\\n",
    "  select ovl.*,count(distinct a1.molregno) a1cnt,count(distinct a2.molregno) a2cnt into temporary table goldilocks_ovl2 \\\n",
    "  from goldilocks_ovl ovl join activities a1 on (aid1=a1.assay_id) join activities a2 on (aid2=a2.assay_id)  \\\n",
    "  where ovl>5 group by (ovl.tid,ovl.aid1,ovl.aid2,ovl.ovl);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "Done.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "1032 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql \\\n",
    "     drop table if exists goldilocks_ovl3\n",
    "\n",
    "\n",
    "%sql \\\n",
    "  select lu3.chembl_id target_chembl_id,lu1.chembl_id assay1_chembl_id,lu2.chembl_id assay2_chembl_id,ovl,a1cnt,a2cnt,aid1,aid2 into temporary table goldilocks_ovl3 from goldilocks_ovl2 \\\n",
    "  join chembl_id_lookup lu1 on (aid1=lu1.entity_id and lu1.entity_type='ASSAY') \\\n",
    "  join chembl_id_lookup lu2 on (aid2=lu2.entity_id and lu2.entity_type='ASSAY') \\\n",
    "  join chembl_id_lookup lu3 on (tid=lu3.entity_id and lu3.entity_type='TARGET');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# All assay parameters are the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "538 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "122 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n"
     ]
    }
   ],
   "source": [
    "d = %sql \\\n",
    "select ovl3.*,a1.assay_organism a1_s,a2.assay_organism a2_s from goldilocks_ovl3 ovl3 \\\n",
    "join assays a1 on (assay1_chembl_id=a1.chembl_id) join assays a2 on (assay2_chembl_id=a2.chembl_id) \\\n",
    "where a1.assay_type = a2.assay_type \\\n",
    "and a1.assay_organism is not distinct from a2.assay_organism \\\n",
    "and a1.assay_category is not distinct from a2.assay_category \\\n",
    "and a1.assay_tax_id is not distinct from a2.assay_tax_id \\\n",
    "and a1.assay_strain is not distinct from a2.assay_strain \\\n",
    "and a1.assay_tissue is not distinct from a2.assay_tissue \\\n",
    "and a1.assay_cell_type is not distinct from a2.assay_cell_type \\\n",
    "and a1.assay_subcellular_fraction is not distinct from a2.assay_subcellular_fraction \\\n",
    "and a1.bao_format is not distinct from a2.bao_format \\\n",
    "order by ovl desc;\n",
    "\n",
    "pts = []\n",
    "aids = []\n",
    "for row in d:\n",
    "    cid1 = row[1]\n",
    "    cid2 = row[2]\n",
    "    aid1 = row[6]\n",
    "    aid2 = row[7]\n",
    "    ad = %sql \\\n",
    "      select a1.molregno,a1.pchembl_value a1_pchembl,a2.pchembl_value a2_pchembl from \\\n",
    "       (select * from activities where pchembl_value is not null and standard_type='Ki' and assay_id=:aid1 \\\n",
    "        and data_validity_comment is null  \\\n",
    "        and standard_relation = '=' \\\n",
    "       ) a1\\\n",
    "       join (select * from activities where pchembl_value is not null and standard_type='Ki' and assay_id=:aid2\\\n",
    "        and data_validity_comment is null  \\\n",
    "        and standard_relation = '=' \\\n",
    "     ) a2 \\\n",
    "       using (molregno) \\\n",
    "       where a1.standard_type = a2.standard_type;    \n",
    "    for row in ad:\n",
    "        pts.append(list(row))\n",
    "        aids.append((cid1,cid2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2=0.88, Spearman R=0.94\n",
      "7824 points. Fraction > 0.3: 0.09, fraction > 1.0: 0.04\n",
      "Fraction with different classifications:\n",
      "\t bin=6: 0.02\n",
      "\t bin=7: 0.05\n",
      "\t bin=8: 0.02\n",
      "\t bin=9: 0.00\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titl = 'all assay parameters match'\n",
    "plt.figure(figsize=(12,12))\n",
    "xp = np.array([x[1] for x in pts])\n",
    "yp = np.array([x[2] for x in pts])\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.scatter(xp,yp,alpha=0.4,edgecolors='none');\n",
    "plt.plot((4,11),(4,11),'k-');\n",
    "plt.plot((4,11),(3,10),'k--');\n",
    "plt.plot((4,11),(5,12),'k--');\n",
    "plt.plot((4,11),(3.7,10.7),'k-.');\n",
    "plt.plot((4,11),(4.3,11.3),'k-.');\n",
    "plt.xlabel('assay1 pchembl')\n",
    "plt.ylabel('assay2 pchembl')\n",
    "plt.title(titl)\n",
    "\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.hexbin(xp,yp,cmap='Blues',bins='log');\n",
    "plt.plot((4,11),(4,11),'k-');\n",
    "plt.plot((4,11),(3,10),'k--');\n",
    "plt.plot((4,11),(5,12),'k--');\n",
    "plt.plot((4,11),(3.7,10.7),'k-.');\n",
    "plt.plot((4,11),(4.3,11.3),'k-.');\n",
    "plt.xlabel('assay1 pchembl')\n",
    "plt.ylabel('assay2 pchembl')\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "delts = np.abs(xp-yp)\n",
    "plt.hist(delts,bins=20);\n",
    "plt.xlabel('delta pchembl');\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "r,p = stats.spearmanr(xp,yp)\n",
    "r2 = r2_score(xp,yp)\n",
    "print(f'R2={r2:.2f}, Spearman R={r:.2f}')\n",
    "\n",
    "\n",
    "\n",
    "npts = len(delts)\n",
    "frac1 = sum(delts>0.3)/npts\n",
    "frac2 = sum(delts>1)/npts\n",
    "print(f'{npts} points. Fraction > 0.3: {frac1:.2f}, fraction > 1.0: {frac2:.2f}')\n",
    "\n",
    "bins = [6,7,8,9]\n",
    "print(f'Fraction with different classifications:')\n",
    "for b in bins:\n",
    "    missed = sum((xp - b)*(yp-b) <0)/ npts\n",
    "    print(f'\\t bin={b}: {missed:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CHEMBL3100637  CHEMBL905129    1.50 28\n",
      "CHEMBL828776   CHEMBL821377    0.94 16\n",
      "CHEMBL951055   CHEMBL925141    0.67 6\n",
      "CHEMBL4427978  CHEMBL951055    0.67 6\n",
      "CHEMBL4035750  CHEMBL951055    0.67 6\n",
      "CHEMBL3782909  CHEMBL951055    0.67 6\n",
      "CHEMBL3761242  CHEMBL951055    0.67 6\n",
      "CHEMBL3404498  CHEMBL951055    0.67 6\n",
      "CHEMBL3270415  CHEMBL951055    0.67 6\n",
      "CHEMBL1678662  CHEMBL951055    0.67 6\n",
      "CHEMBL828776   CHEMBL668411    0.63 26\n",
      "CHEMBL615451   CHEMBL615448    0.54 12\n",
      "CHEMBL615459   CHEMBL615451    0.50 23\n",
      "CHEMBL3097255  CHEMBL951055    0.49 6\n",
      "CHEMBL615459   CHEMBL615448    0.48 13\n",
      "CHEMBL4035750  CHEMBL827065    0.46 11\n",
      "CHEMBL3782909  CHEMBL827065    0.46 11\n",
      "CHEMBL3761242  CHEMBL827065    0.46 11\n",
      "CHEMBL3269990  CHEMBL1059274   0.44 11\n",
      "CHEMBL910617   CHEMBL839581    0.43 16\n",
      "CHEMBL876385   CHEMBL828779    0.29 6\n",
      "CHEMBL839585   CHEMBL827063    0.29 6\n",
      "CHEMBL827891   CHEMBL839585    0.29 6\n",
      "CHEMBL4427978  CHEMBL839585    0.29 6\n",
      "CHEMBL4035750  CHEMBL839585    0.29 6\n",
      "CHEMBL3782909  CHEMBL839585    0.29 6\n",
      "CHEMBL3761242  CHEMBL839585    0.29 6\n",
      "CHEMBL3404498  CHEMBL839585    0.29 6\n",
      "CHEMBL3270415  CHEMBL839585    0.29 6\n",
      "CHEMBL4427978  CHEMBL907133    0.29 6\n",
      "CHEMBL4035750  CHEMBL907133    0.29 6\n",
      "CHEMBL3782909  CHEMBL907133    0.29 6\n",
      "CHEMBL3761242  CHEMBL907133    0.29 6\n",
      "CHEMBL3404498  CHEMBL907133    0.29 6\n",
      "CHEMBL3270415  CHEMBL907133    0.29 6\n",
      "CHEMBL1678662  CHEMBL907133    0.29 6\n",
      "CHEMBL636647   CHEMBL639642    0.28 6\n",
      "CHEMBL1678662  CHEMBL839585    0.25 7\n",
      "CHEMBL1953227  CHEMBL980972    0.25 6\n",
      "CHEMBL876689   CHEMBL827065    0.22 8\n",
      "CHEMBL876374   CHEMBL827065    0.22 8\n",
      "CHEMBL824219   CHEMBL657820    0.22 8\n",
      "CHEMBL4035750  CHEMBL827891    0.21 7\n",
      "CHEMBL3782909  CHEMBL827891    0.21 7\n",
      "CHEMBL3761242  CHEMBL827891    0.21 7\n",
      "CHEMBL830383   CHEMBL828925    0.21 6\n",
      "CHEMBL4427978  CHEMBL830383    0.21 6\n",
      "CHEMBL4035750  CHEMBL830383    0.21 6\n",
      "CHEMBL3782909  CHEMBL830383    0.21 6\n",
      "CHEMBL3761242  CHEMBL830383    0.21 6\n",
      "CHEMBL3404498  CHEMBL830383    0.21 6\n",
      "CHEMBL3270415  CHEMBL830383    0.21 6\n",
      "CHEMBL1678662  CHEMBL830383    0.21 6\n",
      "CHEMBL657825   CHEMBL662721    0.19 9\n",
      "CHEMBL804959   CHEMBL802063    0.19 7\n",
      "CHEMBL4427978  CHEMBL827891    0.19 7\n",
      "CHEMBL3404498  CHEMBL827891    0.19 7\n",
      "CHEMBL3270415  CHEMBL827891    0.19 7\n",
      "CHEMBL3097256  CHEMBL951056    0.17 6\n",
      "CHEMBL827891   CHEMBL828925    0.17 9\n",
      "CHEMBL639642   CHEMBL640787    0.16 13\n",
      "CHEMBL4427978  CHEMBL827065    0.16 11\n",
      "CHEMBL3404498  CHEMBL827065    0.16 11\n",
      "CHEMBL3270415  CHEMBL827065    0.16 11\n",
      "CHEMBL4035750  CHEMBL876374    0.16 16\n",
      "CHEMBL830339   CHEMBL827065    0.16 8\n",
      "CHEMBL769064   CHEMBL765171    0.16 9\n",
      "CHEMBL657820   CHEMBL662721    0.15 11\n",
      "CHEMBL858763   CHEMBL677378    0.15 17\n",
      "CHEMBL4035750  CHEMBL876689    0.15 17\n",
      "CHEMBL3782909  CHEMBL876374    0.15 17\n",
      "CHEMBL3761242  CHEMBL876374    0.15 17\n",
      "CHEMBL1678662  CHEMBL827891    0.15 10\n",
      "CHEMBL828925   CHEMBL827065    0.15 9\n",
      "CHEMBL658750   CHEMBL658214    0.15 9\n",
      "CHEMBL4020039  CHEMBL4007108   0.15 7\n",
      "CHEMBL3737169  CHEMBL830339    0.14 7\n",
      "CHEMBL3782909  CHEMBL876689    0.14 18\n",
      "CHEMBL3761242  CHEMBL876689    0.14 18\n",
      "CHEMBL4035751  CHEMBL828905    0.14 11\n",
      "CHEMBL3782910  CHEMBL828905    0.14 11\n",
      "CHEMBL3761241  CHEMBL828905    0.14 11\n",
      "CHEMBL1678662  CHEMBL827065    0.13 10\n",
      "CHEMBL3097254  CHEMBL951057    0.13 6\n",
      "CHEMBL3424914  CHEMBL1067528   0.12 6\n",
      "CHEMBL658515   CHEMBL658514    0.12 10\n",
      "CHEMBL829822   CHEMBL828778    0.12 9\n",
      "CHEMBL4427978  CHEMBL4035750   0.12 38\n",
      "CHEMBL4035750  CHEMBL3404498   0.12 38\n",
      "CHEMBL4035750  CHEMBL3270415   0.12 38\n",
      "CHEMBL4427978  CHEMBL3782909   0.11 39\n",
      "CHEMBL4427978  CHEMBL3761242   0.11 39\n",
      "CHEMBL659048   CHEMBL658214    0.11 11\n",
      "CHEMBL840097   CHEMBL838622    0.11 6\n",
      "CHEMBL3782909  CHEMBL3404498   0.11 40\n",
      "CHEMBL3782909  CHEMBL3270415   0.11 40\n",
      "CHEMBL3761242  CHEMBL3404498   0.11 40\n",
      "CHEMBL3761242  CHEMBL3270415   0.11 40\n",
      "CHEMBL4035751  CHEMBL828777    0.11 8\n",
      "CHEMBL4001626  CHEMBL828777    0.11 8\n",
      "CHEMBL3782910  CHEMBL828777    0.11 8\n",
      "CHEMBL3761241  CHEMBL828777    0.11 8\n",
      "CHEMBL828776   CHEMBL821378    0.11 10\n",
      "CHEMBL828777   CHEMBL839012    0.10 6\n",
      "CHEMBL824219   CHEMBL657825    0.10 18\n",
      "CHEMBL4035750  CHEMBL830339    0.09 25\n",
      "CHEMBL3782909  CHEMBL830339    0.09 25\n",
      "CHEMBL3761242  CHEMBL830339    0.09 25\n",
      "CHEMBL4427979  CHEMBL828777    0.09 8\n",
      "CHEMBL3767548  CHEMBL828777    0.09 8\n",
      "CHEMBL3404499  CHEMBL828777    0.09 8\n",
      "CHEMBL3270416  CHEMBL828777    0.09 8\n",
      "CHEMBL3737169  CHEMBL2429048   0.09 11\n",
      "CHEMBL1816096  CHEMBL938648    0.09 11\n",
      "CHEMBL3737169  CHEMBL1678662   0.08 12\n",
      "CHEMBL4035750  CHEMBL1678662   0.08 29\n",
      "CHEMBL827891   CHEMBL830339    0.08 8\n",
      "CHEMBL827186   CHEMBL828815    0.08 25\n",
      "CHEMBL3782909  CHEMBL1678662   0.08 30\n",
      "CHEMBL3761242  CHEMBL1678662   0.08 30\n",
      "CHEMBL907134   CHEMBL838677    0.08 6\n",
      "CHEMBL907134   CHEMBL828903    0.08 6\n",
      "CHEMBL907134   CHEMBL828777    0.08 6\n",
      "CHEMBL4427979  CHEMBL907134    0.08 6\n",
      "CHEMBL4035751  CHEMBL907134    0.08 6\n",
      "CHEMBL4001626  CHEMBL907134    0.08 6\n",
      "CHEMBL3782910  CHEMBL907134    0.08 6\n",
      "CHEMBL3767548  CHEMBL907134    0.08 6\n",
      "CHEMBL3761241  CHEMBL907134    0.08 6\n",
      "CHEMBL3404499  CHEMBL907134    0.08 6\n",
      "CHEMBL3270416  CHEMBL907134    0.08 6\n",
      "CHEMBL2049249  CHEMBL907134    0.08 6\n",
      "CHEMBL1678663  CHEMBL907134    0.08 6\n",
      "CHEMBL4035750  CHEMBL3737169   0.08 13\n",
      "CHEMBL4427978  CHEMBL3737169   0.07 14\n",
      "CHEMBL1664119  CHEMBL918669    0.07 8\n",
      "CHEMBL3782909  CHEMBL3737169   0.07 15\n",
      "CHEMBL3761242  CHEMBL3737169   0.07 15\n",
      "CHEMBL3737169  CHEMBL3404498   0.07 15\n",
      "CHEMBL3737169  CHEMBL3270415   0.07 15\n",
      "CHEMBL2330556  CHEMBL2024743   0.06 9\n",
      "CHEMBL843463   CHEMBL881435    0.06 13\n",
      "CHEMBL4427979  CHEMBL4035751   0.06 39\n",
      "CHEMBL4427979  CHEMBL3782910   0.06 39\n",
      "CHEMBL4427979  CHEMBL3761241   0.06 39\n",
      "CHEMBL4035751  CHEMBL3767548   0.06 39\n",
      "CHEMBL4035751  CHEMBL3404499   0.06 39\n",
      "CHEMBL4035751  CHEMBL3270416   0.06 39\n",
      "CHEMBL828973   CHEMBL828276    0.06 7\n",
      "CHEMBL3782910  CHEMBL3767548   0.06 40\n",
      "CHEMBL3782910  CHEMBL3404499   0.06 40\n",
      "CHEMBL3782910  CHEMBL3270416   0.06 40\n",
      "CHEMBL3767548  CHEMBL3761241   0.06 40\n",
      "CHEMBL3761241  CHEMBL3404499   0.06 40\n",
      "CHEMBL3761241  CHEMBL3270416   0.06 40\n",
      "CHEMBL658942   CHEMBL657150    0.06 16\n",
      "CHEMBL925142   CHEMBL839012    0.05 6\n",
      "CHEMBL4035751  CHEMBL839012    0.05 19\n",
      "CHEMBL3782910  CHEMBL839012    0.05 19\n",
      "CHEMBL3761241  CHEMBL839012    0.05 19\n",
      "CHEMBL829640   CHEMBL828905    0.04 8\n",
      "CHEMBL2049249  CHEMBL828932    0.04 29\n",
      "CHEMBL1678663  CHEMBL828932    0.04 29\n",
      "CHEMBL828777   CHEMBL828932    0.04 11\n",
      "CHEMBL1678663  CHEMBL828777    0.04 11\n",
      "CHEMBL925142   CHEMBL829640    0.04 7\n",
      "CHEMBL3116665  CHEMBL839012    0.04 7\n",
      "CHEMBL3116665  CHEMBL829640    0.04 7\n",
      "CHEMBL3116665  CHEMBL828932    0.04 7\n",
      "CHEMBL2429047  CHEMBL839012    0.04 7\n",
      "CHEMBL2429047  CHEMBL829640    0.04 7\n",
      "CHEMBL2429047  CHEMBL828932    0.04 7\n",
      "CHEMBL813627   CHEMBL658079    0.04 9\n",
      "CHEMBL925142   CHEMBL828932    0.04 8\n",
      "CHEMBL657825   CHEMBL653170    0.04 16\n",
      "CHEMBL637899   CHEMBL646021    0.04 6\n",
      "CHEMBL829640   CHEMBL828932    0.03 29\n",
      "CHEMBL4035751  CHEMBL828932    0.03 26\n",
      "CHEMBL3782910  CHEMBL828932    0.03 26\n",
      "CHEMBL3761241  CHEMBL828932    0.03 26\n",
      "CHEMBL1026933  CHEMBL770979    0.03 8\n",
      "CHEMBL2215850  CHEMBL2189030   0.03 6\n",
      "CHEMBL2049249  CHEMBL828777    0.03 9\n",
      "CHEMBL1764161  CHEMBL1273398   0.03 6\n",
      "CHEMBL677601   CHEMBL674886    0.03 7\n",
      "CHEMBL828932   CHEMBL828905    0.03 10\n",
      "CHEMBL2049249  CHEMBL828905    0.03 10\n",
      "CHEMBL1678663  CHEMBL828905    0.03 10\n",
      "CHEMBL817592   CHEMBL818251    0.03 12\n",
      "CHEMBL4035751  CHEMBL829640    0.03 26\n",
      "CHEMBL3782910  CHEMBL829640    0.03 26\n",
      "CHEMBL3761241  CHEMBL829640    0.03 26\n",
      "CHEMBL4427979  CHEMBL1678663   0.02 30\n",
      "CHEMBL3767548  CHEMBL1678663   0.02 30\n",
      "CHEMBL3404499  CHEMBL1678663   0.02 30\n",
      "CHEMBL3270416  CHEMBL1678663   0.02 30\n",
      "CHEMBL875538   CHEMBL658750    0.02 16\n",
      "CHEMBL4427979  CHEMBL2049249   0.02 31\n",
      "CHEMBL3767548  CHEMBL2049249   0.02 31\n",
      "CHEMBL3404499  CHEMBL2049249   0.02 31\n",
      "CHEMBL3270416  CHEMBL2049249   0.02 31\n",
      "CHEMBL4427979  CHEMBL4001626   0.02 36\n",
      "CHEMBL3707679  CHEMBL3130226   0.02 7\n",
      "CHEMBL4001626  CHEMBL3767548   0.02 37\n",
      "CHEMBL4001626  CHEMBL3404499   0.02 37\n",
      "CHEMBL4001626  CHEMBL3270416   0.02 37\n",
      "CHEMBL4427979  CHEMBL828932    0.02 26\n",
      "CHEMBL3767548  CHEMBL828932    0.02 26\n",
      "CHEMBL3404499  CHEMBL828932    0.02 26\n",
      "CHEMBL3270416  CHEMBL828932    0.02 26\n",
      "CHEMBL2049249  CHEMBL839012    0.02 19\n",
      "CHEMBL4001626  CHEMBL1678663   0.02 28\n",
      "CHEMBL1678663  CHEMBL839012    0.02 18\n",
      "CHEMBL876374   CHEMBL828925    0.02 16\n",
      "CHEMBL1678662  CHEMBL876374    0.02 16\n",
      "CHEMBL4427979  CHEMBL829640    0.02 26\n",
      "CHEMBL3767548  CHEMBL829640    0.02 26\n",
      "CHEMBL3404499  CHEMBL829640    0.02 26\n",
      "CHEMBL3270416  CHEMBL829640    0.02 26\n",
      "CHEMBL4001626  CHEMBL2049249   0.02 29\n",
      "CHEMBL951057   CHEMBL829822    0.02 6\n",
      "CHEMBL951057   CHEMBL828906    0.02 6\n",
      "CHEMBL951057   CHEMBL828778    0.02 6\n",
      "CHEMBL876689   CHEMBL828925    0.02 17\n",
      "CHEMBL1678662  CHEMBL876689    0.02 17\n",
      "CHEMBL759583   CHEMBL764115    0.02 11\n",
      "CHEMBL4001626  CHEMBL839012    0.02 18\n",
      "CHEMBL4427979  CHEMBL839012    0.02 19\n",
      "CHEMBL3767548  CHEMBL839012    0.02 19\n",
      "CHEMBL3404499  CHEMBL839012    0.02 19\n",
      "CHEMBL3270416  CHEMBL839012    0.02 19\n",
      "CHEMBL4427978  CHEMBL828925    0.02 24\n",
      "CHEMBL3404498  CHEMBL828925    0.02 24\n",
      "CHEMBL3270415  CHEMBL828925    0.02 24\n",
      "CHEMBL876374   CHEMBL830339    0.02 14\n",
      "CHEMBL821378   CHEMBL821377    0.02 6\n",
      "CHEMBL4427978  CHEMBL830339    0.01 25\n",
      "CHEMBL3404498  CHEMBL830339    0.01 25\n",
      "CHEMBL3270415  CHEMBL830339    0.01 25\n",
      "CHEMBL4427979  CHEMBL841779    0.01 7\n",
      "CHEMBL4035751  CHEMBL841779    0.01 7\n",
      "CHEMBL4001626  CHEMBL841779    0.01 7\n",
      "CHEMBL3782910  CHEMBL841779    0.01 7\n",
      "CHEMBL3767548  CHEMBL841779    0.01 7\n",
      "CHEMBL3761241  CHEMBL841779    0.01 7\n",
      "CHEMBL3404499  CHEMBL841779    0.01 7\n",
      "CHEMBL3270416  CHEMBL841779    0.01 7\n",
      "CHEMBL2049249  CHEMBL841779    0.01 7\n",
      "CHEMBL1678663  CHEMBL841779    0.01 7\n",
      "CHEMBL1010168  CHEMBL946423    0.01 7\n",
      "CHEMBL876689   CHEMBL830339    0.01 15\n",
      "CHEMBL841779   CHEMBL828932    0.01 8\n",
      "CHEMBL4035750  CHEMBL828925    0.01 24\n",
      "CHEMBL4001626  CHEMBL828905    0.01 8\n",
      "CHEMBL3782909  CHEMBL828925    0.01 24\n",
      "CHEMBL3761242  CHEMBL828925    0.01 24\n",
      "CHEMBL4427978  CHEMBL1678662   0.01 30\n",
      "CHEMBL3404498  CHEMBL1678662   0.01 30\n",
      "CHEMBL3270415  CHEMBL1678662   0.01 30\n",
      "CHEMBL4035751  CHEMBL1678663   0.01 30\n",
      "CHEMBL3782910  CHEMBL1678663   0.01 30\n",
      "CHEMBL3761241  CHEMBL1678663   0.01 30\n",
      "CHEMBL663405   CHEMBL661794    0.01 12\n",
      "CHEMBL4035751  CHEMBL2049249   0.01 31\n",
      "CHEMBL3782910  CHEMBL2049249   0.01 31\n",
      "CHEMBL3761241  CHEMBL2049249   0.01 31\n",
      "CHEMBL2049249  CHEMBL829640    0.01 29\n",
      "CHEMBL1678663  CHEMBL829640    0.01 29\n",
      "CHEMBL956658   CHEMBL827890    0.01 7\n",
      "CHEMBL1026932  CHEMBL830381    0.01 7\n",
      "CHEMBL657145   CHEMBL658039    0.01 11\n",
      "CHEMBL4427979  CHEMBL828905    0.01 11\n",
      "CHEMBL3767548  CHEMBL828905    0.01 11\n",
      "CHEMBL3404499  CHEMBL828905    0.01 11\n",
      "CHEMBL3270416  CHEMBL828905    0.01 11\n",
      "CHEMBL828777   CHEMBL829640    0.01 9\n",
      "CHEMBL839012   CHEMBL829640    0.01 16\n",
      "CHEMBL907135   CHEMBL828906    0.01 6\n",
      "CHEMBL907135   CHEMBL828778    0.01 6\n",
      "CHEMBL4001626  CHEMBL829640    0.01 24\n",
      "CHEMBL764924   CHEMBL650169    0.01 9\n",
      "CHEMBL3705472  CHEMBL3130225   0.01 10\n",
      "CHEMBL995612   CHEMBL828277    0.01 7\n",
      "CHEMBL824008   CHEMBL824000    0.01 14\n",
      "CHEMBL821379   CHEMBL668411    0.00 12\n",
      "CHEMBL768259   CHEMBL764924    0.00 12\n",
      "CHEMBL769060   CHEMBL769512    0.00 15\n",
      "CHEMBL657820   CHEMBL653170    0.00 8\n",
      "CHEMBL657025   CHEMBL659048    0.00 8\n",
      "CHEMBL3292987  CHEMBL1944111   0.00 13\n",
      "CHEMBL663411   CHEMBL661794    0.00 15\n",
      "CHEMBL875538   CHEMBL659048    0.00 8\n",
      "CHEMBL658942   CHEMBL657145    0.00 8\n",
      "CHEMBL657825   CHEMBL657820    0.00 12\n",
      "CHEMBL4427978  CHEMBL876374    0.00 17\n",
      "CHEMBL3404498  CHEMBL876374    0.00 17\n",
      "CHEMBL3270415  CHEMBL876374    0.00 17\n",
      "CHEMBL4427978  CHEMBL876689    0.00 18\n",
      "CHEMBL3404498  CHEMBL876689    0.00 18\n",
      "CHEMBL3270415  CHEMBL876689    0.00 18\n",
      "CHEMBL935758   CHEMBL829615    0.00 12\n",
      "CHEMBL911523   CHEMBL870467    0.00 6\n",
      "CHEMBL657150   CHEMBL657145    0.00 12\n",
      "CHEMBL657025   CHEMBL658750    0.00 18\n",
      "CHEMBL1026932  CHEMBL815981    0.00 9\n",
      "CHEMBL839012   CHEMBL828932    0.00 19\n",
      "CHEMBL924958   CHEMBL812385    0.00 12\n",
      "CHEMBL4001626  CHEMBL828932    0.00 24\n",
      "CHEMBL3292988  CHEMBL1944112   0.00 12\n",
      "CHEMBL948917   CHEMBL838818    0.00 13\n",
      "CHEMBL1026911  CHEMBL828769    0.00 26\n",
      "CHEMBL924958   CHEMBL711201    0.00 21\n",
      "CHEMBL768259   CHEMBL650169    0.00 47\n",
      "CHEMBL925155   CHEMBL901108    0.00 60\n",
      "CHEMBL924950   CHEMBL715010    0.00 38\n",
      "CHEMBL984527   CHEMBL864775    0.00 10\n",
      "CHEMBL951056   CHEMBL925142    0.00 6\n",
      "CHEMBL951056   CHEMBL841779    0.00 6\n",
      "CHEMBL951056   CHEMBL838677    0.00 6\n",
      "CHEMBL951056   CHEMBL829640    0.00 6\n",
      "CHEMBL951056   CHEMBL828932    0.00 7\n",
      "CHEMBL951056   CHEMBL828903    0.00 6\n",
      "CHEMBL951056   CHEMBL828777    0.00 6\n",
      "CHEMBL938460   CHEMBL664070    0.00 29\n",
      "CHEMBL925142   CHEMBL841779    0.00 6\n",
      "CHEMBL925142   CHEMBL838677    0.00 6\n",
      "CHEMBL925142   CHEMBL828903    0.00 6\n",
      "CHEMBL925142   CHEMBL828777    0.00 6\n",
      "CHEMBL925141   CHEMBL830339    0.00 6\n",
      "CHEMBL925141   CHEMBL828925    0.00 6\n",
      "CHEMBL924958   CHEMBL710736    0.00 7\n",
      "CHEMBL924951   CHEMBL710807    0.00 39\n",
      "CHEMBL923557   CHEMBL829680    0.00 10\n",
      "CHEMBL877449   CHEMBL828815    0.00 6\n",
      "CHEMBL877449   CHEMBL827186    0.00 7\n",
      "CHEMBL876374   CHEMBL876689    0.00 19\n",
      "CHEMBL875538   CHEMBL657025    0.00 16\n",
      "CHEMBL873716   CHEMBL662722    0.00 20\n",
      "CHEMBL868082   CHEMBL829053    0.00 9\n",
      "CHEMBL867190   CHEMBL827432    0.00 28\n",
      "CHEMBL841779   CHEMBL839012    0.00 6\n",
      "CHEMBL841779   CHEMBL838677    0.00 6\n",
      "CHEMBL841779   CHEMBL829640    0.00 6\n",
      "CHEMBL841779   CHEMBL828903    0.00 6\n",
      "CHEMBL841779   CHEMBL828777    0.00 6\n",
      "CHEMBL840150   CHEMBL828795    0.00 6\n",
      "CHEMBL839630   CHEMBL828796    0.00 6\n",
      "CHEMBL839012   CHEMBL828905    0.00 8\n",
      "CHEMBL838839   CHEMBL763388    0.00 7\n",
      "CHEMBL838677   CHEMBL829640    0.00 6\n",
      "CHEMBL838677   CHEMBL828932    0.00 7\n",
      "CHEMBL838677   CHEMBL828903    0.00 7\n",
      "CHEMBL830339   CHEMBL828925    0.00 27\n",
      "CHEMBL830210   CHEMBL764693    0.00 7\n",
      "CHEMBL829822   CHEMBL828908    0.00 8\n",
      "CHEMBL829822   CHEMBL828906    0.00 6\n",
      "CHEMBL829640   CHEMBL828903    0.00 6\n",
      "CHEMBL829116   CHEMBL828796    0.00 7\n",
      "CHEMBL828932   CHEMBL828903    0.00 7\n",
      "CHEMBL828789   CHEMBL764688    0.00 6\n",
      "CHEMBL828778   CHEMBL828906    0.00 7\n",
      "CHEMBL828777   CHEMBL838677    0.00 7\n",
      "CHEMBL828777   CHEMBL828903    0.00 7\n",
      "CHEMBL828276   CHEMBL829684    0.00 8\n",
      "CHEMBL827891   CHEMBL827063    0.00 6\n",
      "CHEMBL824220   CHEMBL658942    0.00 16\n",
      "CHEMBL824220   CHEMBL658039    0.00 6\n",
      "CHEMBL824220   CHEMBL657150    0.00 19\n",
      "CHEMBL824220   CHEMBL657145    0.00 9\n",
      "CHEMBL824219   CHEMBL657824    0.00 17\n",
      "CHEMBL824219   CHEMBL653170    0.00 15\n",
      "CHEMBL821377   CHEMBL668411    0.00 16\n",
      "CHEMBL817592   CHEMBL817573    0.00 7\n",
      "CHEMBL809836   CHEMBL810658    0.00 6\n",
      "CHEMBL769064   CHEMBL768120    0.00 8\n",
      "CHEMBL718926   CHEMBL710553    0.00 10\n",
      "CHEMBL718849   CHEMBL708140    0.00 10\n",
      "CHEMBL708551   CHEMBL710325    0.00 10\n",
      "CHEMBL689131   CHEMBL687311    0.00 7\n",
      "CHEMBL689131   CHEMBL684200    0.00 7\n",
      "CHEMBL663871   CHEMBL872486    0.00 18\n",
      "CHEMBL663411   CHEMBL663405    0.00 56\n",
      "CHEMBL661794   CHEMBL838886    0.00 7\n",
      "CHEMBL658750   CHEMBL659048    0.00 13\n",
      "CHEMBL657968   CHEMBL657967    0.00 6\n",
      "CHEMBL657968   CHEMBL657042    0.00 6\n",
      "CHEMBL657968   CHEMBL657039    0.00 6\n",
      "CHEMBL657967   CHEMBL657042    0.00 13\n",
      "CHEMBL657967   CHEMBL657039    0.00 32\n",
      "CHEMBL657825   CHEMBL657824    0.00 16\n",
      "CHEMBL657824   CHEMBL657820    0.00 7\n",
      "CHEMBL657824   CHEMBL653170    0.00 15\n",
      "CHEMBL657154   CHEMBL658040    0.00 20\n",
      "CHEMBL657154   CHEMBL657145    0.00 6\n",
      "CHEMBL657150   CHEMBL658039    0.00 9\n",
      "CHEMBL657042   CHEMBL657039    0.00 13\n",
      "CHEMBL650119   CHEMBL649945    0.00 14\n",
      "CHEMBL645212   CHEMBL640356    0.00 9\n",
      "CHEMBL645212   CHEMBL640353    0.00 9\n",
      "CHEMBL620593   CHEMBL619043    0.00 21\n",
      "CHEMBL619046   CHEMBL619043    0.00 21\n",
      "CHEMBL616569   CHEMBL615959    0.00 23\n",
      "CHEMBL616154   CHEMBL616346    0.00 10\n",
      "CHEMBL4619444  CHEMBL3791369   0.00 10\n",
      "CHEMBL4427979  CHEMBL951056    0.00 6\n",
      "CHEMBL4427979  CHEMBL925142    0.00 7\n",
      "CHEMBL4427979  CHEMBL838677    0.00 7\n",
      "CHEMBL4427979  CHEMBL828903    0.00 7\n",
      "CHEMBL4427979  CHEMBL3767548   0.00 39\n",
      "CHEMBL4427979  CHEMBL3404499   0.00 39\n",
      "CHEMBL4427979  CHEMBL3270416   0.00 39\n",
      "CHEMBL4427979  CHEMBL3116665   0.00 11\n",
      "CHEMBL4427979  CHEMBL2429047   0.00 11\n",
      "CHEMBL4427978  CHEMBL925141    0.00 7\n",
      "CHEMBL4427978  CHEMBL827063    0.00 6\n",
      "CHEMBL4427978  CHEMBL3404498   0.00 39\n",
      "CHEMBL4427978  CHEMBL3270415   0.00 39\n",
      "CHEMBL4427978  CHEMBL2429048   0.00 11\n",
      "CHEMBL4035751  CHEMBL951056    0.00 6\n",
      "CHEMBL4035751  CHEMBL925142    0.00 7\n",
      "CHEMBL4035751  CHEMBL838677    0.00 7\n",
      "CHEMBL4035751  CHEMBL828903    0.00 7\n",
      "CHEMBL4035751  CHEMBL4001626   0.00 36\n",
      "CHEMBL4035751  CHEMBL3782910   0.00 39\n",
      "CHEMBL4035751  CHEMBL3761241   0.00 39\n",
      "CHEMBL4035751  CHEMBL3116665   0.00 11\n",
      "CHEMBL4035751  CHEMBL2429047   0.00 11\n",
      "CHEMBL4035750  CHEMBL925141    0.00 7\n",
      "CHEMBL4035750  CHEMBL827063    0.00 6\n",
      "CHEMBL4035750  CHEMBL3782909   0.00 38\n",
      "CHEMBL4035750  CHEMBL3761242   0.00 38\n",
      "CHEMBL4035750  CHEMBL2429048   0.00 10\n",
      "CHEMBL4001626  CHEMBL951056    0.00 6\n",
      "CHEMBL4001626  CHEMBL925142    0.00 7\n",
      "CHEMBL4001626  CHEMBL838677    0.00 7\n",
      "CHEMBL4001626  CHEMBL828903    0.00 7\n",
      "CHEMBL4001626  CHEMBL3782910   0.00 37\n",
      "CHEMBL4001626  CHEMBL3761241   0.00 37\n",
      "CHEMBL4001626  CHEMBL3116665   0.00 11\n",
      "CHEMBL4001626  CHEMBL2429047   0.00 11\n",
      "CHEMBL3791369  CHEMBL3097419   0.00 6\n",
      "CHEMBL3782910  CHEMBL951056    0.00 6\n",
      "CHEMBL3782910  CHEMBL925142    0.00 7\n",
      "CHEMBL3782910  CHEMBL838677    0.00 7\n",
      "CHEMBL3782910  CHEMBL828903    0.00 7\n",
      "CHEMBL3782910  CHEMBL3761241   0.00 40\n",
      "CHEMBL3782910  CHEMBL3116665   0.00 11\n",
      "CHEMBL3782910  CHEMBL2429047   0.00 11\n",
      "CHEMBL3782909  CHEMBL925141    0.00 7\n",
      "CHEMBL3782909  CHEMBL827063    0.00 6\n",
      "CHEMBL3782909  CHEMBL3761242   0.00 40\n",
      "CHEMBL3782909  CHEMBL2429048   0.00 11\n",
      "CHEMBL3767548  CHEMBL951056    0.00 6\n",
      "CHEMBL3767548  CHEMBL925142    0.00 7\n",
      "CHEMBL3767548  CHEMBL838677    0.00 7\n",
      "CHEMBL3767548  CHEMBL828903    0.00 7\n",
      "CHEMBL3767548  CHEMBL3404499   0.00 40\n",
      "CHEMBL3767548  CHEMBL3270416   0.00 40\n",
      "CHEMBL3767548  CHEMBL3116665   0.00 11\n",
      "CHEMBL3767548  CHEMBL2429047   0.00 11\n",
      "CHEMBL3761242  CHEMBL925141    0.00 7\n",
      "CHEMBL3761242  CHEMBL827063    0.00 6\n",
      "CHEMBL3761242  CHEMBL2429048   0.00 11\n",
      "CHEMBL3761241  CHEMBL951056    0.00 6\n",
      "CHEMBL3761241  CHEMBL925142    0.00 7\n",
      "CHEMBL3761241  CHEMBL838677    0.00 7\n",
      "CHEMBL3761241  CHEMBL828903    0.00 7\n",
      "CHEMBL3761241  CHEMBL3116665   0.00 11\n",
      "CHEMBL3761241  CHEMBL2429047   0.00 11\n",
      "CHEMBL3737169  CHEMBL925141    0.00 6\n",
      "CHEMBL3737169  CHEMBL876689    0.00 6\n",
      "CHEMBL3737169  CHEMBL876374    0.00 6\n",
      "CHEMBL3737169  CHEMBL828925    0.00 6\n",
      "CHEMBL3707679  CHEMBL1821390   0.00 8\n",
      "CHEMBL3705472  CHEMBL1821392   0.00 8\n",
      "CHEMBL3404499  CHEMBL951056    0.00 6\n",
      "CHEMBL3404499  CHEMBL925142    0.00 7\n",
      "CHEMBL3404499  CHEMBL838677    0.00 7\n",
      "CHEMBL3404499  CHEMBL828903    0.00 7\n",
      "CHEMBL3404499  CHEMBL3270416   0.00 40\n",
      "CHEMBL3404499  CHEMBL3116665   0.00 11\n",
      "CHEMBL3404499  CHEMBL2429047   0.00 11\n",
      "CHEMBL3404498  CHEMBL925141    0.00 7\n",
      "CHEMBL3404498  CHEMBL827063    0.00 6\n",
      "CHEMBL3404498  CHEMBL3270415   0.00 40\n",
      "CHEMBL3404498  CHEMBL2429048   0.00 11\n",
      "CHEMBL3366665  CHEMBL3377547   0.00 21\n",
      "CHEMBL3366664  CHEMBL3377546   0.00 21\n",
      "CHEMBL3292986  CHEMBL1944110   0.00 12\n",
      "CHEMBL3270416  CHEMBL951056    0.00 6\n",
      "CHEMBL3270416  CHEMBL925142    0.00 7\n",
      "CHEMBL3270416  CHEMBL838677    0.00 7\n",
      "CHEMBL3270416  CHEMBL828903    0.00 7\n",
      "CHEMBL3270416  CHEMBL3116665   0.00 11\n",
      "CHEMBL3270416  CHEMBL2429047   0.00 11\n",
      "CHEMBL3270415  CHEMBL925141    0.00 7\n",
      "CHEMBL3270415  CHEMBL827063    0.00 6\n",
      "CHEMBL3270415  CHEMBL2429048   0.00 11\n",
      "CHEMBL3116665  CHEMBL925142    0.00 6\n",
      "CHEMBL3116665  CHEMBL838677    0.00 6\n",
      "CHEMBL3116665  CHEMBL828903    0.00 6\n",
      "CHEMBL3116665  CHEMBL828777    0.00 6\n",
      "CHEMBL3116665  CHEMBL2429047   0.00 13\n",
      "CHEMBL3116665  CHEMBL2049249   0.00 11\n",
      "CHEMBL3116665  CHEMBL1678663   0.00 11\n",
      "CHEMBL3116663  CHEMBL828815    0.00 7\n",
      "CHEMBL3116663  CHEMBL827186    0.00 6\n",
      "CHEMBL2429048  CHEMBL925141    0.00 6\n",
      "CHEMBL2429048  CHEMBL876689    0.00 6\n",
      "CHEMBL2429048  CHEMBL876374    0.00 6\n",
      "CHEMBL2429048  CHEMBL830339    0.00 6\n",
      "CHEMBL2429048  CHEMBL1678662   0.00 11\n",
      "CHEMBL2429047  CHEMBL925142    0.00 6\n",
      "CHEMBL2429047  CHEMBL838677    0.00 6\n",
      "CHEMBL2429047  CHEMBL828903    0.00 6\n",
      "CHEMBL2429047  CHEMBL828777    0.00 6\n",
      "CHEMBL2429047  CHEMBL2049249   0.00 11\n",
      "CHEMBL2429047  CHEMBL1678663   0.00 11\n",
      "CHEMBL2429045  CHEMBL829116    0.00 6\n",
      "CHEMBL2429045  CHEMBL828796    0.00 6\n",
      "CHEMBL2416135  CHEMBL867233    0.00 8\n",
      "CHEMBL2406600  CHEMBL2404230   0.00 20\n",
      "CHEMBL2049249  CHEMBL951056    0.00 6\n",
      "CHEMBL2049249  CHEMBL925142    0.00 7\n",
      "CHEMBL2049249  CHEMBL838677    0.00 7\n",
      "CHEMBL2049249  CHEMBL828903    0.00 7\n",
      "CHEMBL2049249  CHEMBL1678663   0.00 32\n",
      "CHEMBL1931471  CHEMBL1285944   0.00 22\n",
      "CHEMBL1678663  CHEMBL951056    0.00 6\n",
      "CHEMBL1678663  CHEMBL925142    0.00 7\n",
      "CHEMBL1678663  CHEMBL838677    0.00 8\n",
      "CHEMBL1678663  CHEMBL828903    0.00 7\n",
      "CHEMBL1678662  CHEMBL925141    0.00 7\n",
      "CHEMBL1678662  CHEMBL830339    0.00 28\n",
      "CHEMBL1678662  CHEMBL828925    0.00 27\n",
      "CHEMBL1678662  CHEMBL827063    0.00 6\n",
      "CHEMBL1670560  CHEMBL1030615   0.00 6\n",
      "CHEMBL1272819  CHEMBL886074    0.00 122\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "xp = np.array([x[1] for x in pts])\n",
    "yp = np.array([x[2] for x in pts])\n",
    "\n",
    "delts = yp-xp\n",
    "dd = defaultdict(list)\n",
    "for delt,aid in zip(delts,aids):\n",
    "    dd[aid].append(delt)\n",
    "\n",
    "means = sorted([(abs(np.mean(dd[pr])),pr) for pr in dd],reverse=True)\n",
    "for mean,pr in means:\n",
    "    print(f'{pr[0]:14s} {pr[1]:14s} {mean: 4.2f} {len(dd[pr])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Decimal('0.00'),\n",
       " Decimal('0.00'),\n",
       " Decimal('0.00'),\n",
       " Decimal('0.00'),\n",
       " Decimal('2.00'),\n",
       " Decimal('2.00')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd[('CHEMBL3782909','CHEMBL951055')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>assay_id</th>\n",
       "            <th>chembl_id</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1552240</td>\n",
       "            <td>CHEMBL3761242</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(1552240, 'CHEMBL3761242')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql \\\n",
    "  select assay_id,chembl_id from assays where chembl_id='CHEMBL3761242'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "1 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "1 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>chembl_id</th>\n",
       "            <th>a1_pchembl</th>\n",
       "            <th>a2_pchembl</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>CHEMBL20</td>\n",
       "            <td>6.60</td>\n",
       "            <td>6.60</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CHEMBL19</td>\n",
       "            <td>7.30</td>\n",
       "            <td>7.30</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CHEMBL18</td>\n",
       "            <td>7.60</td>\n",
       "            <td>7.60</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CHEMBL17</td>\n",
       "            <td>5.92</td>\n",
       "            <td>5.92</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CHEMBL218490</td>\n",
       "            <td>4.30</td>\n",
       "            <td>6.30</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>CHEMBL220491</td>\n",
       "            <td>4.35</td>\n",
       "            <td>6.35</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('CHEMBL20', Decimal('6.60'), Decimal('6.60')),\n",
       " ('CHEMBL19', Decimal('7.30'), Decimal('7.30')),\n",
       " ('CHEMBL18', Decimal('7.60'), Decimal('7.60')),\n",
       " ('CHEMBL17', Decimal('5.92'), Decimal('5.92')),\n",
       " ('CHEMBL218490', Decimal('4.30'), Decimal('6.30')),\n",
       " ('CHEMBL220491', Decimal('4.35'), Decimal('6.35'))]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cid1 = 'CHEMBL3761242'\n",
    "cid2 = 'CHEMBL951055'\n",
    "d = %sql \\\n",
    "  select assay_id,chembl_id from assays where chembl_id=:cid1;\n",
    "aid1 = d[0][0]\n",
    "d = %sql \\\n",
    "  select assay_id,chembl_id from assays where chembl_id=:cid2;\n",
    "aid2 = d[0][0]\n",
    "\n",
    "%sql \\\n",
    "select cids.chembl_id,a1.pchembl_value a1_pchembl,a2.pchembl_value a2_pchembl from \\\n",
    "       (select * from activities where pchembl_value is not null and standard_type='Ki' and assay_id=:aid1 \\\n",
    "        and data_validity_comment is null  \\\n",
    "        and standard_relation = '=' \\\n",
    "       ) a1\\\n",
    "       join (select * from activities where pchembl_value is not null and standard_type='Ki' and assay_id=:aid2\\\n",
    "        and data_validity_comment is null  \\\n",
    "        and standard_relation = '=' \\\n",
    "     ) a2 \\\n",
    "       using (molregno) \\\n",
    "       join chembl_id_lookup cids on (molregno=entity_id and entity_type='COMPOUND') \\\n",
    "       where a1.standard_type = a2.standard_type;  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Allow all comparisons (assay_type must be the same)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "1023 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "96 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "91 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "93 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "79 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "58 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "56 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "48 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "47 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "44 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "42 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "40 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "39 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "38 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "37 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "36 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "35 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "33 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "122 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "31 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "60 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "30 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "29 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "27 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "26 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "25 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "45 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "22 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "24 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "21 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "19 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "23 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "18 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "17 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "32 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "16 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "15 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "20 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "13 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "28 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "9 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "8 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "14 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "11 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "12 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "10 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "7 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n",
      " * postgresql://ccg_read:***@scotland/chembl_32\n",
      "6 rows affected.\n"
     ]
    }
   ],
   "source": [
    "d = %sql \\\n",
    "select ovl3.*,a1.assay_organism a1_s,a2.assay_organism a2_s from goldilocks_ovl3 ovl3 \\\n",
    "join assays a1 on (assay1_chembl_id=a1.chembl_id) join assays a2 on (assay2_chembl_id=a2.chembl_id) \\\n",
    "where a1.assay_type = a2.assay_type \\\n",
    "order by ovl desc;\n",
    "\n",
    "pts = []\n",
    "aids = []\n",
    "for row in d:\n",
    "    cid1 = row[1]\n",
    "    cid2 = row[2]\n",
    "    aid1 = row[6]\n",
    "    aid2 = row[7]\n",
    "    ad = %sql \\\n",
    "      select a1.molregno,a1.pchembl_value a1_pchembl,a2.pchembl_value a2_pchembl from \\\n",
    "       (select * from activities where pchembl_value is not null and standard_type='Ki' and assay_id=:aid1 \\\n",
    "        and data_validity_comment is null  \\\n",
    "        and standard_relation = '=' \\\n",
    "       ) a1\\\n",
    "       join (select * from activities where pchembl_value is not null and standard_type='Ki' and assay_id=:aid2\\\n",
    "        and data_validity_comment is null  \\\n",
    "        and standard_relation = '=' \\\n",
    "     ) a2 \\\n",
    "       using (molregno) \\\n",
    "       where a1.standard_type = a2.standard_type;    \n",
    "    for row in ad:\n",
    "        pts.append(list(row))\n",
    "        aids.append((cid1,cid2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2=0.90, Spearman R=0.95\n",
      "13865 points. Fraction > 0.3: 0.10, fraction > 1.0: 0.04\n",
      "Fraction with different classifications:\n",
      "\t bin=6: 0.02\n",
      "\t bin=7: 0.04\n",
      "\t bin=8: 0.02\n",
      "\t bin=9: 0.00\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titl = 'no filters'\n",
    "plt.figure(figsize=(12,12))\n",
    "xp = np.array([x[1] for x in pts])\n",
    "yp = np.array([x[2] for x in pts])\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "plt.scatter(xp,yp,alpha=0.4,edgecolors='none');\n",
    "plt.plot((4,11),(4,11),'k-');\n",
    "plt.plot((4,11),(3,10),'k--');\n",
    "plt.plot((4,11),(5,12),'k--');\n",
    "plt.plot((4,11),(3.7,10.7),'k-.');\n",
    "plt.plot((4,11),(4.3,11.3),'k-.');\n",
    "plt.xlabel('assay1 pchembl')\n",
    "plt.ylabel('assay2 pchembl')\n",
    "plt.title(titl)\n",
    "\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.hexbin(xp,yp,cmap='Blues',bins='log');\n",
    "plt.plot((4,11),(4,11),'k-');\n",
    "plt.plot((4,11),(3,10),'k--');\n",
    "plt.plot((4,11),(5,12),'k--');\n",
    "plt.plot((4,11),(3.7,10.7),'k-.');\n",
    "plt.plot((4,11),(4.3,11.3),'k-.');\n",
    "plt.xlabel('assay1 pchembl')\n",
    "plt.ylabel('assay2 pchembl')\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "delts = np.abs(xp-yp)\n",
    "plt.hist(delts,bins=20);\n",
    "plt.xlabel('delta pchembl');\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "r,p = stats.spearmanr(xp,yp)\n",
    "r2 = r2_score(xp,yp)\n",
    "print(f'R2={r2:.2f}, Spearman R={r:.2f}')\n",
    "\n",
    "\n",
    "\n",
    "npts = len(delts)\n",
    "frac1 = sum(delts>0.3)/npts\n",
    "frac2 = sum(delts>1)/npts\n",
    "print(f'{npts} points. Fraction > 0.3: {frac1:.2f}, fraction > 1.0: {frac2:.2f}')\n",
    "\n",
    "bins = [6,7,8,9]\n",
    "print(f'Fraction with different classifications:')\n",
    "for b in bins:\n",
    "    missed = sum((xp - b)*(yp-b) <0)/ npts\n",
    "    print(f'\\t bin={b}: {missed:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CHEMBL4051151  CHEMBL3736640   1.78 6\n",
      "CHEMBL3887980  CHEMBL3100637   1.60 10\n",
      "CHEMBL3100637  CHEMBL905129    1.50 28\n",
      "CHEMBL828776   CHEMBL821377    0.94 16\n",
      "CHEMBL616594   CHEMBL616592    0.78 12\n",
      "CHEMBL4137136  CHEMBL977319    0.76 6\n",
      "CHEMBL657061   CHEMBL659688    0.74 10\n",
      "CHEMBL616592   CHEMBL616591    0.72 6\n",
      "CHEMBL925141   CHEMBL657825    0.70 6\n",
      "CHEMBL951055   CHEMBL925141    0.67 6\n",
      "CHEMBL4427978  CHEMBL951055    0.67 6\n",
      "CHEMBL4035750  CHEMBL951055    0.67 6\n",
      "CHEMBL3782909  CHEMBL951055    0.67 6\n",
      "CHEMBL3761242  CHEMBL951055    0.67 6\n",
      "CHEMBL3404498  CHEMBL951055    0.67 6\n",
      "CHEMBL3270415  CHEMBL951055    0.67 6\n",
      "CHEMBL1678662  CHEMBL951055    0.67 6\n",
      "CHEMBL828776   CHEMBL668411    0.63 26\n",
      "CHEMBL3614290  CHEMBL913685    0.58 27\n",
      "CHEMBL839013   CHEMBL824221    0.56 14\n",
      "CHEMBL615451   CHEMBL615448    0.54 12\n",
      "CHEMBL615459   CHEMBL615451    0.50 23\n",
      "CHEMBL3097255  CHEMBL951055    0.49 6\n",
      "CHEMBL615459   CHEMBL615448    0.48 13\n",
      "CHEMBL4035750  CHEMBL827065    0.46 11\n",
      "CHEMBL3782909  CHEMBL827065    0.46 11\n",
      "CHEMBL3761242  CHEMBL827065    0.46 11\n",
      "CHEMBL3269990  CHEMBL1059274   0.44 11\n",
      "CHEMBL910617   CHEMBL839581    0.43 16\n",
      "CHEMBL4035750  CHEMBL824219    0.41 14\n",
      "CHEMBL3782909  CHEMBL824219    0.41 14\n",
      "CHEMBL3761242  CHEMBL824219    0.41 14\n",
      "CHEMBL4035750  CHEMBL653170    0.38 13\n",
      "CHEMBL3782909  CHEMBL653170    0.38 13\n",
      "CHEMBL3761242  CHEMBL653170    0.38 13\n",
      "CHEMBL4035750  CHEMBL657825    0.33 25\n",
      "CHEMBL3782909  CHEMBL657825    0.33 25\n",
      "CHEMBL3761242  CHEMBL657825    0.33 25\n",
      "CHEMBL1678662  CHEMBL824219    0.30 15\n",
      "CHEMBL4035750  CHEMBL657824    0.30 13\n",
      "CHEMBL3782909  CHEMBL657824    0.30 13\n",
      "CHEMBL3761242  CHEMBL657824    0.30 13\n",
      "CHEMBL828925   CHEMBL824219    0.30 16\n",
      "CHEMBL925141   CHEMBL662721    0.29 6\n",
      "CHEMBL876385   CHEMBL876689    0.29 6\n",
      "CHEMBL876385   CHEMBL830339    0.29 6\n",
      "CHEMBL876385   CHEMBL828779    0.29 6\n",
      "CHEMBL839585   CHEMBL827063    0.29 6\n",
      "CHEMBL828779   CHEMBL873716    0.29 6\n",
      "CHEMBL828779   CHEMBL839585    0.29 6\n",
      "CHEMBL827891   CHEMBL873716    0.29 6\n",
      "CHEMBL827891   CHEMBL839585    0.29 6\n",
      "CHEMBL827063   CHEMBL873716    0.29 6\n",
      "CHEMBL4427978  CHEMBL873716    0.29 6\n",
      "CHEMBL4427978  CHEMBL839585    0.29 6\n",
      "CHEMBL4035750  CHEMBL873716    0.29 6\n",
      "CHEMBL4035750  CHEMBL839585    0.29 6\n",
      "CHEMBL3782909  CHEMBL873716    0.29 6\n",
      "CHEMBL3782909  CHEMBL839585    0.29 6\n",
      "CHEMBL3761242  CHEMBL873716    0.29 6\n",
      "CHEMBL3761242  CHEMBL839585    0.29 6\n",
      "CHEMBL3404498  CHEMBL873716    0.29 6\n",
      "CHEMBL3404498  CHEMBL839585    0.29 6\n",
      "CHEMBL3270415  CHEMBL873716    0.29 6\n",
      "CHEMBL3270415  CHEMBL839585    0.29 6\n",
      "CHEMBL1678662  CHEMBL873716    0.29 6\n",
      "CHEMBL4427978  CHEMBL907133    0.29 6\n",
      "CHEMBL4035750  CHEMBL907133    0.29 6\n",
      "CHEMBL3782909  CHEMBL907133    0.29 6\n",
      "CHEMBL3761242  CHEMBL907133    0.29 6\n",
      "CHEMBL3404498  CHEMBL907133    0.29 6\n",
      "CHEMBL3270415  CHEMBL907133    0.29 6\n",
      "CHEMBL1678662  CHEMBL907133    0.29 6\n",
      "CHEMBL636647   CHEMBL639642    0.28 6\n",
      "CHEMBL830339   CHEMBL824219    0.27 15\n",
      "CHEMBL876385   CHEMBL828925    0.26 7\n",
      "CHEMBL4427978  CHEMBL876385    0.26 7\n",
      "CHEMBL4035750  CHEMBL876385    0.26 7\n",
      "CHEMBL3782909  CHEMBL876385    0.26 7\n",
      "CHEMBL3761242  CHEMBL876385    0.26 7\n",
      "CHEMBL3404498  CHEMBL876385    0.26 7\n",
      "CHEMBL3270415  CHEMBL876385    0.26 7\n",
      "CHEMBL1678662  CHEMBL876385    0.26 7\n",
      "CHEMBL639642   CHEMBL640367    0.25 6\n",
      "CHEMBL619854   CHEMBL620830    0.25 8\n",
      "CHEMBL876385   CHEMBL657825    0.25 7\n",
      "CHEMBL876385   CHEMBL657820    0.25 7\n",
      "CHEMBL1678662  CHEMBL839585    0.25 7\n",
      "CHEMBL1953227  CHEMBL980972    0.25 6\n",
      "CHEMBL1678662  CHEMBL657824    0.24 13\n",
      "CHEMBL876689   CHEMBL827065    0.22 8\n",
      "CHEMBL876374   CHEMBL827065    0.22 8\n",
      "CHEMBL4775667  CHEMBL3791369   0.22 9\n",
      "CHEMBL876689   CHEMBL662721    0.22 8\n",
      "CHEMBL876374   CHEMBL662721    0.22 8\n",
      "CHEMBL828779   CHEMBL827065    0.22 8\n",
      "CHEMBL828779   CHEMBL662721    0.22 8\n",
      "CHEMBL824219   CHEMBL657820    0.22 8\n",
      "CHEMBL659698   CHEMBL658897    0.22 11\n",
      "CHEMBL4035750  CHEMBL827891    0.21 7\n",
      "CHEMBL3782909  CHEMBL827891    0.21 7\n",
      "CHEMBL3761242  CHEMBL827891    0.21 7\n",
      "CHEMBL830383   CHEMBL828925    0.21 6\n",
      "CHEMBL4427978  CHEMBL830383    0.21 6\n",
      "CHEMBL4035750  CHEMBL830383    0.21 6\n",
      "CHEMBL3782909  CHEMBL830383    0.21 6\n",
      "CHEMBL3761242  CHEMBL830383    0.21 6\n",
      "CHEMBL3404498  CHEMBL830383    0.21 6\n",
      "CHEMBL3270415  CHEMBL830383    0.21 6\n",
      "CHEMBL1678662  CHEMBL830383    0.21 6\n",
      "CHEMBL2429045  CHEMBL829822    0.21 7\n",
      "CHEMBL646021   CHEMBL640787    0.21 9\n",
      "CHEMBL828925   CHEMBL657824    0.20 15\n",
      "CHEMBL1936672  CHEMBL934603    0.20 6\n",
      "CHEMBL828925   CHEMBL662721    0.20 9\n",
      "CHEMBL830383   CHEMBL828779    0.20 6\n",
      "CHEMBL830383   CHEMBL657825    0.20 6\n",
      "CHEMBL830383   CHEMBL657820    0.20 6\n",
      "CHEMBL830339   CHEMBL657824    0.20 13\n",
      "CHEMBL830339   CHEMBL662721    0.19 9\n",
      "CHEMBL657825   CHEMBL662721    0.19 9\n",
      "CHEMBL804959   CHEMBL802063    0.19 7\n",
      "CHEMBL4001626  CHEMBL658039    0.19 11\n",
      "CHEMBL4427978  CHEMBL827891    0.19 7\n",
      "CHEMBL3404498  CHEMBL827891    0.19 7\n",
      "CHEMBL3270415  CHEMBL827891    0.19 7\n",
      "CHEMBL4035750  CHEMBL657820    0.18 11\n",
      "CHEMBL3782909  CHEMBL657820    0.18 11\n",
      "CHEMBL3761242  CHEMBL657820    0.18 11\n",
      "CHEMBL4427978  CHEMBL662721    0.18 10\n",
      "CHEMBL3404498  CHEMBL662721    0.18 10\n",
      "CHEMBL3270415  CHEMBL662721    0.18 10\n",
      "CHEMBL1678662  CHEMBL662721    0.18 10\n",
      "CHEMBL3705964  CHEMBL3632375   0.17 7\n",
      "CHEMBL827065   CHEMBL657825    0.17 10\n",
      "CHEMBL3097256  CHEMBL951056    0.17 6\n",
      "CHEMBL827891   CHEMBL828925    0.17 9\n",
      "CHEMBL919867   CHEMBL730887    0.17 6\n",
      "CHEMBL864183   CHEMBL765836    0.17 6\n",
      "CHEMBL639642   CHEMBL640787    0.16 13\n",
      "CHEMBL4427978  CHEMBL827065    0.16 11\n",
      "CHEMBL3404498  CHEMBL827065    0.16 11\n",
      "CHEMBL3270415  CHEMBL827065    0.16 11\n",
      "CHEMBL4035751  CHEMBL658942    0.16 13\n",
      "CHEMBL3782910  CHEMBL658942    0.16 13\n",
      "CHEMBL3761241  CHEMBL658942    0.16 13\n",
      "CHEMBL4035750  CHEMBL876374    0.16 16\n",
      "CHEMBL830339   CHEMBL827065    0.16 8\n",
      "CHEMBL769064   CHEMBL765171    0.16 9\n",
      "CHEMBL1063127  CHEMBL657156    0.16 6\n",
      "CHEMBL637899   CHEMBL640787    0.16 6\n",
      "CHEMBL657820   CHEMBL662721    0.15 11\n",
      "CHEMBL858763   CHEMBL677378    0.15 17\n",
      "CHEMBL4035750  CHEMBL876689    0.15 17\n",
      "CHEMBL3782909  CHEMBL876374    0.15 17\n",
      "CHEMBL3761242  CHEMBL876374    0.15 17\n",
      "CHEMBL1678662  CHEMBL827891    0.15 10\n",
      "CHEMBL4427978  CHEMBL824219    0.15 14\n",
      "CHEMBL3404498  CHEMBL824219    0.15 14\n",
      "CHEMBL3270415  CHEMBL824219    0.15 14\n",
      "CHEMBL838574   CHEMBL658214    0.15 9\n",
      "CHEMBL828925   CHEMBL827065    0.15 9\n",
      "CHEMBL658750   CHEMBL658214    0.15 9\n",
      "CHEMBL4020039  CHEMBL4007108   0.15 7\n",
      "CHEMBL876374   CHEMBL824219    0.14 12\n",
      "CHEMBL639642   CHEMBL646021    0.14 8\n",
      "CHEMBL3737169  CHEMBL830339    0.14 7\n",
      "CHEMBL3782909  CHEMBL876689    0.14 18\n",
      "CHEMBL3761242  CHEMBL876689    0.14 18\n",
      "CHEMBL4035751  CHEMBL828905    0.14 11\n",
      "CHEMBL3782910  CHEMBL828905    0.14 11\n",
      "CHEMBL3761241  CHEMBL828905    0.14 11\n",
      "CHEMBL638690   CHEMBL643484    0.14 10\n",
      "CHEMBL639427   CHEMBL649101    0.13 9\n",
      "CHEMBL876689   CHEMBL824219    0.13 13\n",
      "CHEMBL1678662  CHEMBL827065    0.13 10\n",
      "CHEMBL3097254  CHEMBL951057    0.13 6\n",
      "CHEMBL1678662  CHEMBL657825    0.13 25\n",
      "CHEMBL980957   CHEMBL648136    0.13 9\n",
      "CHEMBL4427978  CHEMBL657825    0.12 25\n",
      "CHEMBL3404498  CHEMBL657825    0.12 25\n",
      "CHEMBL3270415  CHEMBL657825    0.12 25\n",
      "CHEMBL3424914  CHEMBL1067528   0.12 6\n",
      "CHEMBL658515   CHEMBL658514    0.12 10\n",
      "CHEMBL643484   CHEMBL649108    0.12 8\n",
      "CHEMBL829822   CHEMBL828778    0.12 9\n",
      "CHEMBL828778   CHEMBL657967    0.12 9\n",
      "CHEMBL828778   CHEMBL657039    0.12 9\n",
      "CHEMBL4427978  CHEMBL4035750   0.12 38\n",
      "CHEMBL4035750  CHEMBL3404498   0.12 38\n",
      "CHEMBL4035750  CHEMBL3270415   0.12 38\n",
      "CHEMBL4427978  CHEMBL3782909   0.11 39\n",
      "CHEMBL4427978  CHEMBL3761242   0.11 39\n",
      "CHEMBL2427556  CHEMBL867484    0.11 8\n",
      "CHEMBL659048   CHEMBL658214    0.11 11\n",
      "CHEMBL840097   CHEMBL838622    0.11 6\n",
      "CHEMBL3782909  CHEMBL3404498   0.11 40\n",
      "CHEMBL3782909  CHEMBL3270415   0.11 40\n",
      "CHEMBL3761242  CHEMBL3404498   0.11 40\n",
      "CHEMBL3761242  CHEMBL3270415   0.11 40\n",
      "CHEMBL4035751  CHEMBL828777    0.11 8\n",
      "CHEMBL4001626  CHEMBL828777    0.11 8\n",
      "CHEMBL3782910  CHEMBL828777    0.11 8\n",
      "CHEMBL3761241  CHEMBL828777    0.11 8\n",
      "CHEMBL828776   CHEMBL821378    0.11 10\n",
      "CHEMBL4035751  CHEMBL658039    0.10 10\n",
      "CHEMBL3782910  CHEMBL658039    0.10 10\n",
      "CHEMBL3761241  CHEMBL658039    0.10 10\n",
      "CHEMBL828777   CHEMBL839012    0.10 6\n",
      "CHEMBL824219   CHEMBL657825    0.10 18\n",
      "CHEMBL4035750  CHEMBL830339    0.09 25\n",
      "CHEMBL3782909  CHEMBL830339    0.09 25\n",
      "CHEMBL3761242  CHEMBL830339    0.09 25\n",
      "CHEMBL828932   CHEMBL657156    0.09 7\n",
      "CHEMBL4427979  CHEMBL828777    0.09 8\n",
      "CHEMBL3767548  CHEMBL828777    0.09 8\n",
      "CHEMBL3404499  CHEMBL828777    0.09 8\n",
      "CHEMBL3270416  CHEMBL828777    0.09 8\n",
      "CHEMBL923872   CHEMBL679110    0.09 11\n",
      "CHEMBL876385   CHEMBL830383    0.09 6\n",
      "CHEMBL830383   CHEMBL662721    0.09 6\n",
      "CHEMBL3737169  CHEMBL2429048   0.09 11\n",
      "CHEMBL1816096  CHEMBL938648    0.09 11\n",
      "CHEMBL1670560  CHEMBL925155    0.09 11\n",
      "CHEMBL3705176  CHEMBL961810    0.09 12\n",
      "CHEMBL639642   CHEMBL643484    0.09 6\n",
      "CHEMBL1063127  CHEMBL951056    0.09 6\n",
      "CHEMBL970754   CHEMBL949127    0.09 9\n",
      "CHEMBL2350909  CHEMBL1833395   0.08 6\n",
      "CHEMBL3062224  CHEMBL652822    0.08 14\n",
      "CHEMBL3737169  CHEMBL1678662   0.08 12\n",
      "CHEMBL4035751  CHEMBL657150    0.08 24\n",
      "CHEMBL3782910  CHEMBL657150    0.08 24\n",
      "CHEMBL3761241  CHEMBL657150    0.08 24\n",
      "CHEMBL4035750  CHEMBL1678662   0.08 29\n",
      "CHEMBL827891   CHEMBL830339    0.08 8\n",
      "CHEMBL1063127  CHEMBL828932    0.08 29\n",
      "CHEMBL827186   CHEMBL828815    0.08 25\n",
      "CHEMBL3782909  CHEMBL1678662   0.08 30\n",
      "CHEMBL3761242  CHEMBL1678662   0.08 30\n",
      "CHEMBL907134   CHEMBL838677    0.08 6\n",
      "CHEMBL907134   CHEMBL828903    0.08 6\n",
      "CHEMBL907134   CHEMBL828795    0.08 6\n",
      "CHEMBL907134   CHEMBL828777    0.08 6\n",
      "CHEMBL907134   CHEMBL657154    0.08 6\n",
      "CHEMBL4427979  CHEMBL907134    0.08 6\n",
      "CHEMBL4035751  CHEMBL907134    0.08 6\n",
      "CHEMBL4001626  CHEMBL907134    0.08 6\n",
      "CHEMBL3782910  CHEMBL907134    0.08 6\n",
      "CHEMBL3767548  CHEMBL907134    0.08 6\n",
      "CHEMBL3761241  CHEMBL907134    0.08 6\n",
      "CHEMBL3404499  CHEMBL907134    0.08 6\n",
      "CHEMBL3270416  CHEMBL907134    0.08 6\n",
      "CHEMBL2049249  CHEMBL907134    0.08 6\n",
      "CHEMBL1678663  CHEMBL907134    0.08 6\n",
      "CHEMBL1063127  CHEMBL828905    0.08 10\n",
      "CHEMBL4035750  CHEMBL3737169   0.08 13\n",
      "CHEMBL657061   CHEMBL659689    0.08 10\n",
      "CHEMBL1063127  CHEMBL925142    0.07 7\n",
      "CHEMBL1063127  CHEMBL838677    0.07 7\n",
      "CHEMBL1063127  CHEMBL828903    0.07 7\n",
      "CHEMBL1063127  CHEMBL657154    0.07 7\n",
      "CHEMBL829640   CHEMBL657150    0.07 24\n",
      "CHEMBL677378   CHEMBL677377    0.07 20\n",
      "CHEMBL1063127  CHEMBL658039    0.07 10\n",
      "CHEMBL829640   CHEMBL657156    0.07 6\n",
      "CHEMBL4427979  CHEMBL657156    0.07 6\n",
      "CHEMBL4035751  CHEMBL657156    0.07 6\n",
      "CHEMBL4001626  CHEMBL657156    0.07 6\n",
      "CHEMBL3782910  CHEMBL657156    0.07 6\n",
      "CHEMBL3767548  CHEMBL657156    0.07 6\n",
      "CHEMBL3761241  CHEMBL657156    0.07 6\n",
      "CHEMBL3404499  CHEMBL657156    0.07 6\n",
      "CHEMBL3270416  CHEMBL657156    0.07 6\n",
      "CHEMBL2049249  CHEMBL657156    0.07 6\n",
      "CHEMBL1678663  CHEMBL657156    0.07 6\n",
      "CHEMBL4427978  CHEMBL3737169   0.07 14\n",
      "CHEMBL1664119  CHEMBL918669    0.07 8\n",
      "CHEMBL828932   CHEMBL824220    0.07 18\n",
      "CHEMBL639642   CHEMBL637899    0.07 9\n",
      "CHEMBL1678663  CHEMBL657150    0.07 24\n",
      "CHEMBL4427979  CHEMBL658942    0.07 13\n",
      "CHEMBL3767548  CHEMBL658942    0.07 13\n",
      "CHEMBL3404499  CHEMBL658942    0.07 13\n",
      "CHEMBL3270416  CHEMBL658942    0.07 13\n",
      "CHEMBL3782909  CHEMBL3737169   0.07 15\n",
      "CHEMBL3761242  CHEMBL3737169   0.07 15\n",
      "CHEMBL3737169  CHEMBL3404498   0.07 15\n",
      "CHEMBL3737169  CHEMBL3270415   0.07 15\n",
      "CHEMBL3705176  CHEMBL894567    0.07 15\n",
      "CHEMBL1063127  CHEMBL657150    0.07 24\n",
      "CHEMBL2330556  CHEMBL2024743   0.06 9\n",
      "CHEMBL4001626  CHEMBL658942    0.06 12\n",
      "CHEMBL843463   CHEMBL881435    0.06 13\n",
      "CHEMBL4427979  CHEMBL4035751   0.06 39\n",
      "CHEMBL4427979  CHEMBL3782910   0.06 39\n",
      "CHEMBL4427979  CHEMBL3761241   0.06 39\n",
      "CHEMBL4035751  CHEMBL3767548   0.06 39\n",
      "CHEMBL4035751  CHEMBL3404499   0.06 39\n",
      "CHEMBL4035751  CHEMBL3270416   0.06 39\n",
      "CHEMBL828973   CHEMBL828276    0.06 7\n",
      "CHEMBL3782910  CHEMBL3767548   0.06 40\n",
      "CHEMBL3782910  CHEMBL3404499   0.06 40\n",
      "CHEMBL3782910  CHEMBL3270416   0.06 40\n",
      "CHEMBL3767548  CHEMBL3761241   0.06 40\n",
      "CHEMBL3761241  CHEMBL3404499   0.06 40\n",
      "CHEMBL3761241  CHEMBL3270416   0.06 40\n",
      "CHEMBL617327   CHEMBL617268    0.06 13\n",
      "CHEMBL1063127  CHEMBL841779    0.06 7\n",
      "CHEMBL1063127  CHEMBL840150    0.06 7\n",
      "CHEMBL657061   CHEMBL659697    0.06 6\n",
      "CHEMBL658942   CHEMBL657150    0.06 16\n",
      "CHEMBL4035751  CHEMBL657145    0.06 12\n",
      "CHEMBL4001626  CHEMBL657145    0.06 12\n",
      "CHEMBL3782910  CHEMBL657145    0.06 12\n",
      "CHEMBL3761241  CHEMBL657145    0.06 12\n",
      "CHEMBL645212   CHEMBL640352    0.05 6\n",
      "CHEMBL4035751  CHEMBL824220    0.05 15\n",
      "CHEMBL4001626  CHEMBL824220    0.05 15\n",
      "CHEMBL3782910  CHEMBL824220    0.05 15\n",
      "CHEMBL3761241  CHEMBL824220    0.05 15\n",
      "CHEMBL840097   CHEMBL620800    0.05 11\n",
      "CHEMBL925142   CHEMBL839012    0.05 6\n",
      "CHEMBL925142   CHEMBL658039    0.05 6\n",
      "CHEMBL2049249  CHEMBL657193    0.05 6\n",
      "CHEMBL828925   CHEMBL657825    0.05 24\n",
      "CHEMBL3056710  CHEMBL648011    0.05 15\n",
      "CHEMBL3116665  CHEMBL1063127   0.05 11\n",
      "CHEMBL2429047  CHEMBL1063127   0.05 11\n",
      "CHEMBL4035751  CHEMBL839012    0.05 19\n",
      "CHEMBL3782910  CHEMBL839012    0.05 19\n",
      "CHEMBL3761241  CHEMBL839012    0.05 19\n",
      "CHEMBL4427978  CHEMBL653170    0.05 13\n",
      "CHEMBL3404498  CHEMBL653170    0.05 13\n",
      "CHEMBL3270415  CHEMBL653170    0.05 13\n",
      "CHEMBL1063127  CHEMBL839012    0.05 19\n",
      "CHEMBL858763   CHEMBL677377    0.05 25\n",
      "CHEMBL829640   CHEMBL828905    0.04 8\n",
      "CHEMBL2049249  CHEMBL657150    0.04 24\n",
      "CHEMBL2049249  CHEMBL828932    0.04 29\n",
      "CHEMBL1678663  CHEMBL828932    0.04 29\n",
      "CHEMBL828777   CHEMBL828932    0.04 11\n",
      "CHEMBL1678663  CHEMBL828777    0.04 11\n",
      "CHEMBL925142   CHEMBL829640    0.04 7\n",
      "CHEMBL3116665  CHEMBL839012    0.04 7\n",
      "CHEMBL3116665  CHEMBL829640    0.04 7\n",
      "CHEMBL3116665  CHEMBL828932    0.04 7\n",
      "CHEMBL2429047  CHEMBL839012    0.04 7\n",
      "CHEMBL2429047  CHEMBL829640    0.04 7\n",
      "CHEMBL2429047  CHEMBL828932    0.04 7\n",
      "CHEMBL1063127  CHEMBL828795    0.04 10\n",
      "CHEMBL1678662  CHEMBL653170    0.04 12\n",
      "CHEMBL4427979  CHEMBL1063127   0.04 32\n",
      "CHEMBL881703   CHEMBL662721    0.04 14\n",
      "CHEMBL813627   CHEMBL658079    0.04 9\n",
      "CHEMBL3767548  CHEMBL1063127   0.04 33\n",
      "CHEMBL3404499  CHEMBL1063127   0.04 33\n",
      "CHEMBL3270416  CHEMBL1063127   0.04 33\n",
      "CHEMBL828925   CHEMBL653170    0.04 13\n",
      "CHEMBL925142   CHEMBL828932    0.04 8\n",
      "CHEMBL657825   CHEMBL653170    0.04 16\n",
      "CHEMBL637899   CHEMBL646021    0.04 6\n",
      "CHEMBL3056712  CHEMBL648415    0.04 17\n",
      "CHEMBL3887359  CHEMBL2169117   0.04 23\n",
      "CHEMBL3062587  CHEMBL1026933   0.04 14\n",
      "CHEMBL829640   CHEMBL828932    0.03 29\n",
      "CHEMBL4035751  CHEMBL828932    0.03 26\n",
      "CHEMBL3782910  CHEMBL828932    0.03 26\n",
      "CHEMBL3761241  CHEMBL828932    0.03 26\n",
      "CHEMBL1026933  CHEMBL770979    0.03 8\n",
      "CHEMBL2215850  CHEMBL2189030   0.03 6\n",
      "CHEMBL1638845  CHEMBL692580    0.03 6\n",
      "CHEMBL3056713  CHEMBL648102    0.03 13\n",
      "CHEMBL617323   CHEMBL617268    0.03 24\n",
      "CHEMBL4001626  CHEMBL1063127   0.03 31\n",
      "CHEMBL932239   CHEMBL677437    0.03 8\n",
      "CHEMBL2049249  CHEMBL828777    0.03 9\n",
      "CHEMBL1764161  CHEMBL1273398   0.03 6\n",
      "CHEMBL978937   CHEMBL864338    0.03 20\n",
      "CHEMBL1063127  CHEMBL657145    0.03 13\n",
      "CHEMBL763387   CHEMBL764924    0.03 15\n",
      "CHEMBL3705096  CHEMBL1010203   0.03 18\n",
      "CHEMBL640367   CHEMBL637704    0.03 7\n",
      "CHEMBL828777   CHEMBL657150    0.03 8\n",
      "CHEMBL677601   CHEMBL674886    0.03 7\n",
      "CHEMBL951055   CHEMBL657825    0.03 6\n",
      "CHEMBL1063127  CHEMBL829640    0.03 29\n",
      "CHEMBL923873   CHEMBL805319    0.03 15\n",
      "CHEMBL829640   CHEMBL824220    0.03 16\n",
      "CHEMBL4035750  CHEMBL662721    0.03 10\n",
      "CHEMBL3782909  CHEMBL662721    0.03 10\n",
      "CHEMBL3761242  CHEMBL662721    0.03 10\n",
      "CHEMBL620800   CHEMBL618094    0.03 8\n",
      "CHEMBL828932   CHEMBL828905    0.03 10\n",
      "CHEMBL2049249  CHEMBL828905    0.03 10\n",
      "CHEMBL1678663  CHEMBL828905    0.03 10\n",
      "CHEMBL817592   CHEMBL818251    0.03 12\n",
      "CHEMBL1063127  CHEMBL828777    0.03 9\n",
      "CHEMBL4427978  CHEMBL657824    0.03 13\n",
      "CHEMBL4035751  CHEMBL829640    0.03 26\n",
      "CHEMBL3782910  CHEMBL829640    0.03 26\n",
      "CHEMBL3761241  CHEMBL829640    0.03 26\n",
      "CHEMBL3404498  CHEMBL657824    0.03 13\n",
      "CHEMBL3270415  CHEMBL657824    0.03 13\n",
      "CHEMBL4427979  CHEMBL1678663   0.02 30\n",
      "CHEMBL3767548  CHEMBL1678663   0.02 30\n",
      "CHEMBL3404499  CHEMBL1678663   0.02 30\n",
      "CHEMBL3270416  CHEMBL1678663   0.02 30\n",
      "CHEMBL875538   CHEMBL658750    0.02 16\n",
      "CHEMBL4427979  CHEMBL2049249   0.02 31\n",
      "CHEMBL3767548  CHEMBL2049249   0.02 31\n",
      "CHEMBL3404499  CHEMBL2049249   0.02 31\n",
      "CHEMBL3270416  CHEMBL2049249   0.02 31\n",
      "CHEMBL642306   CHEMBL636766    0.02 25\n",
      "CHEMBL4427979  CHEMBL4001626   0.02 36\n",
      "CHEMBL3707679  CHEMBL3130226   0.02 7\n",
      "CHEMBL4001626  CHEMBL3767548   0.02 37\n",
      "CHEMBL4001626  CHEMBL3404499   0.02 37\n",
      "CHEMBL4001626  CHEMBL3270416   0.02 37\n",
      "CHEMBL615962   CHEMBL616108    0.02 6\n",
      "CHEMBL1072277  CHEMBL958182    0.02 36\n",
      "CHEMBL4775667  CHEMBL3097419   0.02 19\n",
      "CHEMBL4427979  CHEMBL828932    0.02 26\n",
      "CHEMBL3767548  CHEMBL828932    0.02 26\n",
      "CHEMBL3404499  CHEMBL828932    0.02 26\n",
      "CHEMBL3270416  CHEMBL828932    0.02 26\n",
      "CHEMBL4427979  CHEMBL658039    0.02 10\n",
      "CHEMBL3767548  CHEMBL658039    0.02 10\n",
      "CHEMBL3404499  CHEMBL658039    0.02 10\n",
      "CHEMBL3270416  CHEMBL658039    0.02 10\n",
      "CHEMBL2049249  CHEMBL658039    0.02 10\n",
      "CHEMBL1678663  CHEMBL824220    0.02 16\n",
      "CHEMBL1678663  CHEMBL658039    0.02 10\n",
      "CHEMBL1692251  CHEMBL907694    0.02 42\n",
      "CHEMBL830339   CHEMBL657825    0.02 24\n",
      "CHEMBL4427979  CHEMBL657150    0.02 24\n",
      "CHEMBL3767548  CHEMBL657150    0.02 24\n",
      "CHEMBL3404499  CHEMBL657150    0.02 24\n",
      "CHEMBL3270416  CHEMBL657150    0.02 24\n",
      "CHEMBL2049249  CHEMBL839012    0.02 19\n",
      "CHEMBL4001626  CHEMBL1678663   0.02 28\n",
      "CHEMBL642306   CHEMBL649103    0.02 9\n",
      "CHEMBL1678663  CHEMBL839012    0.02 18\n",
      "CHEMBL647729   CHEMBL641299    0.02 25\n",
      "CHEMBL876374   CHEMBL828925    0.02 16\n",
      "CHEMBL1678662  CHEMBL876374    0.02 16\n",
      "CHEMBL1063127  CHEMBL824220    0.02 16\n",
      "CHEMBL4427979  CHEMBL829640    0.02 26\n",
      "CHEMBL3767548  CHEMBL829640    0.02 26\n",
      "CHEMBL3404499  CHEMBL829640    0.02 26\n",
      "CHEMBL3270416  CHEMBL829640    0.02 26\n",
      "CHEMBL4001626  CHEMBL2049249   0.02 29\n",
      "CHEMBL642117   CHEMBL642332    0.02 14\n",
      "CHEMBL3705096  CHEMBL954312    0.02 10\n",
      "CHEMBL951057   CHEMBL829822    0.02 6\n",
      "CHEMBL951057   CHEMBL829116    0.02 6\n",
      "CHEMBL951057   CHEMBL828906    0.02 6\n",
      "CHEMBL951057   CHEMBL828796    0.02 6\n",
      "CHEMBL951057   CHEMBL828778    0.02 6\n",
      "CHEMBL951057   CHEMBL657968    0.02 6\n",
      "CHEMBL951057   CHEMBL657967    0.02 6\n",
      "CHEMBL951057   CHEMBL657042    0.02 6\n",
      "CHEMBL951057   CHEMBL657039    0.02 6\n",
      "CHEMBL946042   CHEMBL655688    0.02 45\n",
      "CHEMBL673590   CHEMBL670975    0.02 6\n",
      "CHEMBL876689   CHEMBL828925    0.02 17\n",
      "CHEMBL1678662  CHEMBL876689    0.02 17\n",
      "CHEMBL923873   CHEMBL805340    0.02 11\n",
      "CHEMBL759583   CHEMBL764115    0.02 11\n",
      "CHEMBL1678663  CHEMBL1063127   0.02 32\n",
      "CHEMBL4001626  CHEMBL839012    0.02 18\n",
      "CHEMBL4427979  CHEMBL839012    0.02 19\n",
      "CHEMBL3767548  CHEMBL839012    0.02 19\n",
      "CHEMBL3404499  CHEMBL839012    0.02 19\n",
      "CHEMBL3270416  CHEMBL839012    0.02 19\n",
      "CHEMBL4427978  CHEMBL828925    0.02 24\n",
      "CHEMBL3404498  CHEMBL828925    0.02 24\n",
      "CHEMBL3270415  CHEMBL828925    0.02 24\n",
      "CHEMBL876374   CHEMBL830339    0.02 14\n",
      "CHEMBL821378   CHEMBL821377    0.02 6\n",
      "CHEMBL2049249  CHEMBL824220    0.02 16\n",
      "CHEMBL2049249  CHEMBL1063127   0.01 35\n",
      "CHEMBL4427978  CHEMBL830339    0.01 25\n",
      "CHEMBL3404498  CHEMBL830339    0.01 25\n",
      "CHEMBL3270415  CHEMBL830339    0.01 25\n",
      "CHEMBL4001626  CHEMBL657150    0.01 21\n",
      "CHEMBL4427979  CHEMBL841779    0.01 7\n",
      "CHEMBL4427979  CHEMBL840150    0.01 7\n",
      "CHEMBL4035751  CHEMBL841779    0.01 7\n",
      "CHEMBL4035751  CHEMBL840150    0.01 7\n",
      "CHEMBL4001626  CHEMBL841779    0.01 7\n",
      "CHEMBL4001626  CHEMBL840150    0.01 7\n",
      "CHEMBL3782910  CHEMBL841779    0.01 7\n",
      "CHEMBL3782910  CHEMBL840150    0.01 7\n",
      "CHEMBL3767548  CHEMBL841779    0.01 7\n",
      "CHEMBL3767548  CHEMBL840150    0.01 7\n",
      "CHEMBL3761241  CHEMBL841779    0.01 7\n",
      "CHEMBL3761241  CHEMBL840150    0.01 7\n",
      "CHEMBL3404499  CHEMBL841779    0.01 7\n",
      "CHEMBL3404499  CHEMBL840150    0.01 7\n",
      "CHEMBL3270416  CHEMBL841779    0.01 7\n",
      "CHEMBL3270416  CHEMBL840150    0.01 7\n",
      "CHEMBL2049249  CHEMBL841779    0.01 7\n",
      "CHEMBL2049249  CHEMBL840150    0.01 7\n",
      "CHEMBL1678663  CHEMBL841779    0.01 7\n",
      "CHEMBL1678663  CHEMBL840150    0.01 7\n",
      "CHEMBL1010168  CHEMBL946423    0.01 7\n",
      "CHEMBL876689   CHEMBL830339    0.01 15\n",
      "CHEMBL841779   CHEMBL828932    0.01 8\n",
      "CHEMBL840150   CHEMBL828932    0.01 8\n",
      "CHEMBL827891   CHEMBL657825    0.01 8\n",
      "CHEMBL4035750  CHEMBL828925    0.01 24\n",
      "CHEMBL4001626  CHEMBL828905    0.01 8\n",
      "CHEMBL3782909  CHEMBL828925    0.01 24\n",
      "CHEMBL3761242  CHEMBL828925    0.01 24\n",
      "CHEMBL4427978  CHEMBL1678662   0.01 30\n",
      "CHEMBL3404498  CHEMBL1678662   0.01 30\n",
      "CHEMBL3270415  CHEMBL1678662   0.01 30\n",
      "CHEMBL4035751  CHEMBL1678663   0.01 30\n",
      "CHEMBL3782910  CHEMBL1678663   0.01 30\n",
      "CHEMBL3761241  CHEMBL1678663   0.01 30\n",
      "CHEMBL663405   CHEMBL661794    0.01 12\n",
      "CHEMBL4051152  CHEMBL3736640   0.01 6\n",
      "CHEMBL4035751  CHEMBL2049249   0.01 31\n",
      "CHEMBL3782910  CHEMBL2049249   0.01 31\n",
      "CHEMBL3761241  CHEMBL2049249   0.01 31\n",
      "CHEMBL2049249  CHEMBL829640    0.01 29\n",
      "CHEMBL1678663  CHEMBL829640    0.01 29\n",
      "CHEMBL956658   CHEMBL827890    0.01 7\n",
      "CHEMBL828932   CHEMBL658039    0.01 10\n",
      "CHEMBL828795   CHEMBL828932    0.01 10\n",
      "CHEMBL4427979  CHEMBL828795    0.01 10\n",
      "CHEMBL4427979  CHEMBL657145    0.01 12\n",
      "CHEMBL4035751  CHEMBL828795    0.01 10\n",
      "CHEMBL4001626  CHEMBL828795    0.01 10\n",
      "CHEMBL3782910  CHEMBL828795    0.01 10\n",
      "CHEMBL3767548  CHEMBL828795    0.01 10\n",
      "CHEMBL3767548  CHEMBL657145    0.01 12\n",
      "CHEMBL3761241  CHEMBL828795    0.01 10\n",
      "CHEMBL3404499  CHEMBL828795    0.01 10\n",
      "CHEMBL3404499  CHEMBL657145    0.01 12\n",
      "CHEMBL3270416  CHEMBL828795    0.01 10\n",
      "CHEMBL3270416  CHEMBL657145    0.01 12\n",
      "CHEMBL2049249  CHEMBL828795    0.01 10\n",
      "CHEMBL2049249  CHEMBL658045    0.01 7\n",
      "CHEMBL1678663  CHEMBL828795    0.01 10\n",
      "CHEMBL1678663  CHEMBL658942    0.01 12\n",
      "CHEMBL1026932  CHEMBL830381    0.01 7\n",
      "CHEMBL828932   CHEMBL658942    0.01 13\n",
      "CHEMBL2049249  CHEMBL657145    0.01 13\n",
      "CHEMBL657145   CHEMBL658039    0.01 11\n",
      "CHEMBL4427979  CHEMBL828905    0.01 11\n",
      "CHEMBL3767548  CHEMBL828905    0.01 11\n",
      "CHEMBL3404499  CHEMBL828905    0.01 11\n",
      "CHEMBL3270416  CHEMBL828905    0.01 11\n",
      "CHEMBL828777   CHEMBL829640    0.01 9\n",
      "CHEMBL839012   CHEMBL829640    0.01 16\n",
      "CHEMBL3887240  CHEMBL2422107   0.01 8\n",
      "CHEMBL1678663  CHEMBL657145    0.01 14\n",
      "CHEMBL907135   CHEMBL829116    0.01 6\n",
      "CHEMBL907135   CHEMBL828906    0.01 6\n",
      "CHEMBL907135   CHEMBL828796    0.01 6\n",
      "CHEMBL907135   CHEMBL828778    0.01 6\n",
      "CHEMBL907135   CHEMBL657968    0.01 6\n",
      "CHEMBL4001626  CHEMBL829640    0.01 24\n",
      "CHEMBL1063127  CHEMBL907134    0.01 6\n",
      "CHEMBL828932   CHEMBL657145    0.01 13\n",
      "CHEMBL2049249  CHEMBL658942    0.01 13\n",
      "CHEMBL1063127  CHEMBL658942    0.01 13\n",
      "CHEMBL4427978  CHEMBL657820    0.01 11\n",
      "CHEMBL3404498  CHEMBL657820    0.01 11\n",
      "CHEMBL3270415  CHEMBL657820    0.01 11\n",
      "CHEMBL764924   CHEMBL650169    0.01 9\n",
      "CHEMBL639739   CHEMBL640367    0.01 9\n",
      "CHEMBL828779   CHEMBL828925    0.01 8\n",
      "CHEMBL1678662  CHEMBL657820    0.01 13\n",
      "CHEMBL3705472  CHEMBL3130225   0.01 10\n",
      "CHEMBL995612   CHEMBL828277    0.01 7\n",
      "CHEMBL824008   CHEMBL824000    0.01 14\n",
      "CHEMBL4427978  CHEMBL828779    0.01 9\n",
      "CHEMBL4035750  CHEMBL828779    0.01 9\n",
      "CHEMBL3782909  CHEMBL828779    0.01 9\n",
      "CHEMBL3761242  CHEMBL828779    0.01 9\n",
      "CHEMBL3404498  CHEMBL828779    0.01 9\n",
      "CHEMBL3270415  CHEMBL828779    0.01 9\n",
      "CHEMBL1678662  CHEMBL828779    0.01 9\n",
      "CHEMBL1931471  CHEMBL811267    0.01 23\n",
      "CHEMBL4035751  CHEMBL1063127   0.00 32\n",
      "CHEMBL3782910  CHEMBL1063127   0.00 33\n",
      "CHEMBL3761241  CHEMBL1063127   0.00 33\n",
      "CHEMBL4427979  CHEMBL824220    0.00 15\n",
      "CHEMBL3767548  CHEMBL824220    0.00 15\n",
      "CHEMBL3404499  CHEMBL824220    0.00 15\n",
      "CHEMBL3270416  CHEMBL824220    0.00 15\n",
      "CHEMBL828925   CHEMBL657820    0.00 11\n",
      "CHEMBL1768188  CHEMBL662838    0.00 23\n",
      "CHEMBL821379   CHEMBL668411    0.00 12\n",
      "CHEMBL768259   CHEMBL764924    0.00 12\n",
      "CHEMBL838622   CHEMBL620800    0.00 10\n",
      "CHEMBL769060   CHEMBL769512    0.00 15\n",
      "CHEMBL657820   CHEMBL653170    0.00 8\n",
      "CHEMBL657025   CHEMBL659048    0.00 8\n",
      "CHEMBL3887805  CHEMBL3705369   0.00 79\n",
      "CHEMBL1285944  CHEMBL811267    0.00 23\n",
      "CHEMBL3888781  CHEMBL3705468   0.00 91\n",
      "CHEMBL3705468  CHEMBL3705059   0.00 96\n",
      "CHEMBL932239   CHEMBL677612    0.00 13\n",
      "CHEMBL3292987  CHEMBL1944111   0.00 13\n",
      "CHEMBL805340   CHEMBL805320    0.00 7\n",
      "CHEMBL640349   CHEMBL640345    0.00 7\n",
      "CHEMBL830339   CHEMBL657820    0.00 11\n",
      "CHEMBL663411   CHEMBL661794    0.00 15\n",
      "CHEMBL932239   CHEMBL677434    0.00 8\n",
      "CHEMBL876374   CHEMBL657825    0.00 16\n",
      "CHEMBL875538   CHEMBL659048    0.00 8\n",
      "CHEMBL838574   CHEMBL875538    0.00 12\n",
      "CHEMBL838574   CHEMBL657025    0.00 12\n",
      "CHEMBL658942   CHEMBL657145    0.00 8\n",
      "CHEMBL657825   CHEMBL657820    0.00 12\n",
      "CHEMBL1176403  CHEMBL617757    0.00 8\n",
      "CHEMBL828932   CHEMBL657150    0.00 25\n",
      "CHEMBL876689   CHEMBL657825    0.00 17\n",
      "CHEMBL4427978  CHEMBL876374    0.00 17\n",
      "CHEMBL3404498  CHEMBL876374    0.00 17\n",
      "CHEMBL3270415  CHEMBL876374    0.00 17\n",
      "CHEMBL4427978  CHEMBL876689    0.00 18\n",
      "CHEMBL3404498  CHEMBL876689    0.00 18\n",
      "CHEMBL3270415  CHEMBL876689    0.00 18\n",
      "CHEMBL935758   CHEMBL829615    0.00 12\n",
      "CHEMBL911523   CHEMBL870467    0.00 6\n",
      "CHEMBL829640   CHEMBL657145    0.00 12\n",
      "CHEMBL657150   CHEMBL657145    0.00 12\n",
      "CHEMBL657025   CHEMBL658750    0.00 18\n",
      "CHEMBL1176403  CHEMBL617752    0.00 13\n",
      "CHEMBL948917   CHEMBL813561    0.00 7\n",
      "CHEMBL679110   CHEMBL878290    0.00 7\n",
      "CHEMBL1026910  CHEMBL828768    0.00 26\n",
      "CHEMBL838574   CHEMBL658750    0.00 18\n",
      "CHEMBL1026932  CHEMBL815981    0.00 9\n",
      "CHEMBL839012   CHEMBL828932    0.00 19\n",
      "CHEMBL984525   CHEMBL864773    0.00 10\n",
      "CHEMBL924958   CHEMBL812385    0.00 12\n",
      "CHEMBL4001626  CHEMBL828932    0.00 24\n",
      "CHEMBL3292988  CHEMBL1944112   0.00 12\n",
      "CHEMBL948917   CHEMBL838818    0.00 13\n",
      "CHEMBL1026911  CHEMBL828769    0.00 26\n",
      "CHEMBL864183   CHEMBL765847    0.00 19\n",
      "CHEMBL835389   CHEMBL671378    0.00 20\n",
      "CHEMBL924958   CHEMBL711201    0.00 21\n",
      "CHEMBL768259   CHEMBL650169    0.00 47\n",
      "CHEMBL763387   CHEMBL650169    0.00 48\n",
      "CHEMBL747013   CHEMBL752503    0.00 26\n",
      "CHEMBL925155   CHEMBL901108    0.00 60\n",
      "CHEMBL924950   CHEMBL715010    0.00 38\n",
      "CHEMBL768259   CHEMBL763387    0.00 58\n",
      "CHEMBL984527   CHEMBL864775    0.00 10\n",
      "CHEMBL951056   CHEMBL925142    0.00 6\n",
      "CHEMBL951056   CHEMBL841779    0.00 6\n",
      "CHEMBL951056   CHEMBL838677    0.00 6\n",
      "CHEMBL951056   CHEMBL829640    0.00 6\n",
      "CHEMBL951056   CHEMBL828932    0.00 7\n",
      "CHEMBL951056   CHEMBL828903    0.00 6\n",
      "CHEMBL951056   CHEMBL828795    0.00 6\n",
      "CHEMBL951056   CHEMBL828777    0.00 6\n",
      "CHEMBL951056   CHEMBL657154    0.00 6\n",
      "CHEMBL951056   CHEMBL657145    0.00 6\n",
      "CHEMBL938460   CHEMBL664070    0.00 29\n",
      "CHEMBL925142   CHEMBL841779    0.00 6\n",
      "CHEMBL925142   CHEMBL838677    0.00 6\n",
      "CHEMBL925142   CHEMBL828903    0.00 6\n",
      "CHEMBL925142   CHEMBL828795    0.00 6\n",
      "CHEMBL925142   CHEMBL828777    0.00 6\n",
      "CHEMBL925142   CHEMBL657154    0.00 6\n",
      "CHEMBL925142   CHEMBL657145    0.00 6\n",
      "CHEMBL925141   CHEMBL830339    0.00 6\n",
      "CHEMBL925141   CHEMBL828925    0.00 6\n",
      "CHEMBL924958   CHEMBL710736    0.00 7\n",
      "CHEMBL924951   CHEMBL710807    0.00 39\n",
      "CHEMBL923872   CHEMBL674427    0.00 15\n",
      "CHEMBL923557   CHEMBL829680    0.00 10\n",
      "CHEMBL918150   CHEMBL817332    0.00 6\n",
      "CHEMBL881704   CHEMBL658039    0.00 14\n",
      "CHEMBL877449   CHEMBL828815    0.00 6\n",
      "CHEMBL877449   CHEMBL827186    0.00 7\n",
      "CHEMBL876689   CHEMBL657824    0.00 11\n",
      "CHEMBL876689   CHEMBL657820    0.00 10\n",
      "CHEMBL876689   CHEMBL653170    0.00 11\n",
      "CHEMBL876385   CHEMBL662721    0.00 6\n",
      "CHEMBL876374   CHEMBL876689    0.00 19\n",
      "CHEMBL876374   CHEMBL828779    0.00 7\n",
      "CHEMBL876374   CHEMBL657824    0.00 10\n",
      "CHEMBL876374   CHEMBL657820    0.00 9\n",
      "CHEMBL876374   CHEMBL653170    0.00 10\n",
      "CHEMBL875538   CHEMBL657025    0.00 16\n",
      "CHEMBL873716   CHEMBL662722    0.00 20\n",
      "CHEMBL868082   CHEMBL829053    0.00 9\n",
      "CHEMBL867190   CHEMBL827432    0.00 28\n",
      "CHEMBL867190   CHEMBL643130    0.00 28\n",
      "CHEMBL864183   CHEMBL766185    0.00 26\n",
      "CHEMBL841779   CHEMBL839012    0.00 6\n",
      "CHEMBL841779   CHEMBL838677    0.00 6\n",
      "CHEMBL841779   CHEMBL829640    0.00 6\n",
      "CHEMBL841779   CHEMBL828903    0.00 6\n",
      "CHEMBL841779   CHEMBL828795    0.00 7\n",
      "CHEMBL841779   CHEMBL828777    0.00 6\n",
      "CHEMBL841779   CHEMBL658039    0.00 6\n",
      "CHEMBL841779   CHEMBL657154    0.00 6\n",
      "CHEMBL841779   CHEMBL657150    0.00 6\n",
      "CHEMBL841779   CHEMBL657145    0.00 7\n",
      "CHEMBL840150   CHEMBL841779    0.00 6\n",
      "CHEMBL840150   CHEMBL839012    0.00 7\n",
      "CHEMBL840150   CHEMBL829640    0.00 6\n",
      "CHEMBL840150   CHEMBL828795    0.00 6\n",
      "CHEMBL840150   CHEMBL658039    0.00 6\n",
      "CHEMBL840150   CHEMBL657150    0.00 7\n",
      "CHEMBL840150   CHEMBL657145    0.00 7\n",
      "CHEMBL839630   CHEMBL829822    0.00 7\n",
      "CHEMBL839630   CHEMBL828796    0.00 6\n",
      "CHEMBL839630   CHEMBL657967    0.00 7\n",
      "CHEMBL839630   CHEMBL657042    0.00 7\n",
      "CHEMBL839630   CHEMBL657039    0.00 7\n",
      "CHEMBL839585   CHEMBL873716    0.00 6\n",
      "CHEMBL839012   CHEMBL828905    0.00 8\n",
      "CHEMBL839012   CHEMBL824220    0.00 14\n",
      "CHEMBL839012   CHEMBL658942    0.00 12\n",
      "CHEMBL839012   CHEMBL658039    0.00 9\n",
      "CHEMBL839012   CHEMBL657150    0.00 18\n",
      "CHEMBL839012   CHEMBL657145    0.00 11\n",
      "CHEMBL838839   CHEMBL763388    0.00 7\n",
      "CHEMBL838677   CHEMBL829640    0.00 6\n",
      "CHEMBL838677   CHEMBL828932    0.00 7\n",
      "CHEMBL838677   CHEMBL828903    0.00 7\n",
      "CHEMBL838677   CHEMBL657154    0.00 7\n",
      "CHEMBL838677   CHEMBL657145    0.00 6\n",
      "CHEMBL838574   CHEMBL659048    0.00 11\n",
      "CHEMBL830339   CHEMBL828925    0.00 27\n",
      "CHEMBL830339   CHEMBL653170    0.00 12\n",
      "CHEMBL830210   CHEMBL764693    0.00 7\n",
      "CHEMBL829822   CHEMBL829116    0.00 6\n",
      "CHEMBL829822   CHEMBL828908    0.00 8\n",
      "CHEMBL829822   CHEMBL828906    0.00 6\n",
      "CHEMBL829822   CHEMBL828796    0.00 9\n",
      "CHEMBL829822   CHEMBL657968    0.00 6\n",
      "CHEMBL829822   CHEMBL657967    0.00 27\n",
      "CHEMBL829822   CHEMBL657042    0.00 13\n",
      "CHEMBL829822   CHEMBL657039    0.00 27\n",
      "CHEMBL829640   CHEMBL828903    0.00 6\n",
      "CHEMBL829640   CHEMBL658942    0.00 12\n",
      "CHEMBL829640   CHEMBL658039    0.00 9\n",
      "CHEMBL829640   CHEMBL657154    0.00 6\n",
      "CHEMBL829627   CHEMBL639433    0.00 27\n",
      "CHEMBL829116   CHEMBL828906    0.00 7\n",
      "CHEMBL829116   CHEMBL828796    0.00 7\n",
      "CHEMBL829116   CHEMBL828778    0.00 7\n",
      "CHEMBL829116   CHEMBL657968    0.00 7\n",
      "CHEMBL829116   CHEMBL657967    0.00 6\n",
      "CHEMBL829116   CHEMBL657042    0.00 6\n",
      "CHEMBL829116   CHEMBL657039    0.00 6\n",
      "CHEMBL828932   CHEMBL828903    0.00 7\n",
      "CHEMBL828932   CHEMBL657154    0.00 7\n",
      "CHEMBL828908   CHEMBL657967    0.00 10\n",
      "CHEMBL828908   CHEMBL657039    0.00 10\n",
      "CHEMBL828906   CHEMBL657968    0.00 7\n",
      "CHEMBL828906   CHEMBL657967    0.00 6\n",
      "CHEMBL828906   CHEMBL657042    0.00 6\n",
      "CHEMBL828906   CHEMBL657039    0.00 6\n",
      "CHEMBL828905   CHEMBL658039    0.00 7\n",
      "CHEMBL828905   CHEMBL657150    0.00 10\n",
      "CHEMBL828903   CHEMBL657154    0.00 7\n",
      "CHEMBL828903   CHEMBL657145    0.00 6\n",
      "CHEMBL828796   CHEMBL828908    0.00 8\n",
      "CHEMBL828796   CHEMBL828906    0.00 7\n",
      "CHEMBL828796   CHEMBL828778    0.00 7\n",
      "CHEMBL828796   CHEMBL657968    0.00 7\n",
      "CHEMBL828796   CHEMBL657967    0.00 9\n",
      "CHEMBL828796   CHEMBL657042    0.00 7\n",
      "CHEMBL828796   CHEMBL657039    0.00 9\n",
      "CHEMBL828795   CHEMBL839012    0.00 8\n",
      "CHEMBL828795   CHEMBL838677    0.00 7\n",
      "CHEMBL828795   CHEMBL829640    0.00 8\n",
      "CHEMBL828795   CHEMBL828905    0.00 8\n",
      "CHEMBL828795   CHEMBL828903    0.00 7\n",
      "CHEMBL828795   CHEMBL828777    0.00 7\n",
      "CHEMBL828795   CHEMBL824220    0.00 6\n",
      "CHEMBL828795   CHEMBL658039    0.00 8\n",
      "CHEMBL828795   CHEMBL657154    0.00 7\n",
      "CHEMBL828795   CHEMBL657150    0.00 8\n",
      "CHEMBL828795   CHEMBL657145    0.00 7\n",
      "CHEMBL828789   CHEMBL764688    0.00 6\n",
      "CHEMBL828779   CHEMBL876689    0.00 7\n",
      "CHEMBL828779   CHEMBL830339    0.00 7\n",
      "CHEMBL828779   CHEMBL827891    0.00 6\n",
      "CHEMBL828779   CHEMBL827063    0.00 6\n",
      "CHEMBL828779   CHEMBL657825    0.00 8\n",
      "CHEMBL828779   CHEMBL657820    0.00 6\n",
      "CHEMBL828778   CHEMBL828906    0.00 7\n",
      "CHEMBL828778   CHEMBL657968    0.00 7\n",
      "CHEMBL828778   CHEMBL657042    0.00 6\n",
      "CHEMBL828777   CHEMBL838677    0.00 7\n",
      "CHEMBL828777   CHEMBL828903    0.00 7\n",
      "CHEMBL828777   CHEMBL657154    0.00 7\n",
      "CHEMBL828777   CHEMBL657145    0.00 6\n",
      "CHEMBL828276   CHEMBL829684    0.00 8\n",
      "CHEMBL827891   CHEMBL827063    0.00 6\n",
      "CHEMBL827890   CHEMBL685307    0.00 11\n",
      "CHEMBL827432   CHEMBL643130    0.00 29\n",
      "CHEMBL827065   CHEMBL662721    0.00 7\n",
      "CHEMBL824220   CHEMBL658942    0.00 16\n",
      "CHEMBL824220   CHEMBL658039    0.00 6\n",
      "CHEMBL824220   CHEMBL657150    0.00 19\n",
      "CHEMBL824220   CHEMBL657145    0.00 9\n",
      "CHEMBL824219   CHEMBL657824    0.00 17\n",
      "CHEMBL824219   CHEMBL653170    0.00 15\n",
      "CHEMBL821377   CHEMBL668411    0.00 16\n",
      "CHEMBL817592   CHEMBL817573    0.00 7\n",
      "CHEMBL809836   CHEMBL810658    0.00 6\n",
      "CHEMBL769064   CHEMBL768120    0.00 8\n",
      "CHEMBL718926   CHEMBL710553    0.00 10\n",
      "CHEMBL718849   CHEMBL708140    0.00 10\n",
      "CHEMBL708551   CHEMBL710325    0.00 10\n",
      "CHEMBL689131   CHEMBL687311    0.00 7\n",
      "CHEMBL689131   CHEMBL684200    0.00 7\n",
      "CHEMBL673590   CHEMBL675803    0.00 6\n",
      "CHEMBL663871   CHEMBL872486    0.00 18\n",
      "CHEMBL663411   CHEMBL663405    0.00 56\n",
      "CHEMBL661794   CHEMBL838886    0.00 7\n",
      "CHEMBL658750   CHEMBL659048    0.00 13\n",
      "CHEMBL657968   CHEMBL657967    0.00 6\n",
      "CHEMBL657968   CHEMBL657042    0.00 6\n",
      "CHEMBL657968   CHEMBL657039    0.00 6\n",
      "CHEMBL657967   CHEMBL657042    0.00 13\n",
      "CHEMBL657967   CHEMBL657039    0.00 32\n",
      "CHEMBL657825   CHEMBL657824    0.00 16\n",
      "CHEMBL657824   CHEMBL657820    0.00 7\n",
      "CHEMBL657824   CHEMBL653170    0.00 15\n",
      "CHEMBL657154   CHEMBL658040    0.00 20\n",
      "CHEMBL657154   CHEMBL657145    0.00 6\n",
      "CHEMBL657150   CHEMBL658039    0.00 9\n",
      "CHEMBL657042   CHEMBL657039    0.00 13\n",
      "CHEMBL650119   CHEMBL649945    0.00 14\n",
      "CHEMBL645212   CHEMBL640356    0.00 9\n",
      "CHEMBL645212   CHEMBL640353    0.00 9\n",
      "CHEMBL642117   CHEMBL639492    0.00 15\n",
      "CHEMBL638724   CHEMBL638717    0.00 24\n",
      "CHEMBL620593   CHEMBL619043    0.00 21\n",
      "CHEMBL619046   CHEMBL619043    0.00 21\n",
      "CHEMBL617327   CHEMBL617323    0.00 9\n",
      "CHEMBL616595   CHEMBL616573    0.00 10\n",
      "CHEMBL616595   CHEMBL616108    0.00 10\n",
      "CHEMBL616594   CHEMBL616591    0.00 6\n",
      "CHEMBL616573   CHEMBL616108    0.00 25\n",
      "CHEMBL616569   CHEMBL615959    0.00 23\n",
      "CHEMBL616154   CHEMBL616346    0.00 10\n",
      "CHEMBL616069   CHEMBL616417    0.00 6\n",
      "CHEMBL4730271  CHEMBL3119243   0.00 11\n",
      "CHEMBL4619444  CHEMBL3791369   0.00 10\n",
      "CHEMBL4427979  CHEMBL951056    0.00 6\n",
      "CHEMBL4427979  CHEMBL925142    0.00 7\n",
      "CHEMBL4427979  CHEMBL838677    0.00 7\n",
      "CHEMBL4427979  CHEMBL828903    0.00 7\n",
      "CHEMBL4427979  CHEMBL657154    0.00 7\n",
      "CHEMBL4427979  CHEMBL3767548   0.00 39\n",
      "CHEMBL4427979  CHEMBL3404499   0.00 39\n",
      "CHEMBL4427979  CHEMBL3270416   0.00 39\n",
      "CHEMBL4427979  CHEMBL3116665   0.00 11\n",
      "CHEMBL4427979  CHEMBL2429047   0.00 11\n",
      "CHEMBL4427978  CHEMBL925141    0.00 7\n",
      "CHEMBL4427978  CHEMBL827063    0.00 6\n",
      "CHEMBL4427978  CHEMBL3404498   0.00 39\n",
      "CHEMBL4427978  CHEMBL3270415   0.00 39\n",
      "CHEMBL4427978  CHEMBL2429048   0.00 11\n",
      "CHEMBL4035751  CHEMBL951056    0.00 6\n",
      "CHEMBL4035751  CHEMBL925142    0.00 7\n",
      "CHEMBL4035751  CHEMBL838677    0.00 7\n",
      "CHEMBL4035751  CHEMBL828903    0.00 7\n",
      "CHEMBL4035751  CHEMBL657154    0.00 7\n",
      "CHEMBL4035751  CHEMBL4001626   0.00 36\n",
      "CHEMBL4035751  CHEMBL3782910   0.00 39\n",
      "CHEMBL4035751  CHEMBL3761241   0.00 39\n",
      "CHEMBL4035751  CHEMBL3116665   0.00 11\n",
      "CHEMBL4035751  CHEMBL2429047   0.00 11\n",
      "CHEMBL4035750  CHEMBL925141    0.00 7\n",
      "CHEMBL4035750  CHEMBL827063    0.00 6\n",
      "CHEMBL4035750  CHEMBL3782909   0.00 38\n",
      "CHEMBL4035750  CHEMBL3761242   0.00 38\n",
      "CHEMBL4035750  CHEMBL2429048   0.00 10\n",
      "CHEMBL4001626  CHEMBL951056    0.00 6\n",
      "CHEMBL4001626  CHEMBL925142    0.00 7\n",
      "CHEMBL4001626  CHEMBL838677    0.00 7\n",
      "CHEMBL4001626  CHEMBL828903    0.00 7\n",
      "CHEMBL4001626  CHEMBL657154    0.00 7\n",
      "CHEMBL4001626  CHEMBL3782910   0.00 37\n",
      "CHEMBL4001626  CHEMBL3761241   0.00 37\n",
      "CHEMBL4001626  CHEMBL3116665   0.00 11\n",
      "CHEMBL4001626  CHEMBL2429047   0.00 11\n",
      "CHEMBL3888781  CHEMBL3705059   0.00 93\n",
      "CHEMBL3887980  CHEMBL905129    0.00 32\n",
      "CHEMBL3887681  CHEMBL3734209   0.00 17\n",
      "CHEMBL3791369  CHEMBL3097419   0.00 6\n",
      "CHEMBL3782910  CHEMBL951056    0.00 6\n",
      "CHEMBL3782910  CHEMBL925142    0.00 7\n",
      "CHEMBL3782910  CHEMBL838677    0.00 7\n",
      "CHEMBL3782910  CHEMBL828903    0.00 7\n",
      "CHEMBL3782910  CHEMBL657154    0.00 7\n",
      "CHEMBL3782910  CHEMBL3761241   0.00 40\n",
      "CHEMBL3782910  CHEMBL3116665   0.00 11\n",
      "CHEMBL3782910  CHEMBL2429047   0.00 11\n",
      "CHEMBL3782909  CHEMBL925141    0.00 7\n",
      "CHEMBL3782909  CHEMBL827063    0.00 6\n",
      "CHEMBL3782909  CHEMBL3761242   0.00 40\n",
      "CHEMBL3782909  CHEMBL2429048   0.00 11\n",
      "CHEMBL3767548  CHEMBL951056    0.00 6\n",
      "CHEMBL3767548  CHEMBL925142    0.00 7\n",
      "CHEMBL3767548  CHEMBL838677    0.00 7\n",
      "CHEMBL3767548  CHEMBL828903    0.00 7\n",
      "CHEMBL3767548  CHEMBL657154    0.00 7\n",
      "CHEMBL3767548  CHEMBL3404499   0.00 40\n",
      "CHEMBL3767548  CHEMBL3270416   0.00 40\n",
      "CHEMBL3767548  CHEMBL3116665   0.00 11\n",
      "CHEMBL3767548  CHEMBL2429047   0.00 11\n",
      "CHEMBL3761242  CHEMBL925141    0.00 7\n",
      "CHEMBL3761242  CHEMBL827063    0.00 6\n",
      "CHEMBL3761242  CHEMBL2429048   0.00 11\n",
      "CHEMBL3761241  CHEMBL951056    0.00 6\n",
      "CHEMBL3761241  CHEMBL925142    0.00 7\n",
      "CHEMBL3761241  CHEMBL838677    0.00 7\n",
      "CHEMBL3761241  CHEMBL828903    0.00 7\n",
      "CHEMBL3761241  CHEMBL657154    0.00 7\n",
      "CHEMBL3761241  CHEMBL3116665   0.00 11\n",
      "CHEMBL3761241  CHEMBL2429047   0.00 11\n",
      "CHEMBL3737169  CHEMBL925141    0.00 6\n",
      "CHEMBL3737169  CHEMBL876689    0.00 6\n",
      "CHEMBL3737169  CHEMBL876374    0.00 6\n",
      "CHEMBL3737169  CHEMBL828925    0.00 6\n",
      "CHEMBL3707679  CHEMBL1821390   0.00 8\n",
      "CHEMBL3705472  CHEMBL1821392   0.00 8\n",
      "CHEMBL3404499  CHEMBL951056    0.00 6\n",
      "CHEMBL3404499  CHEMBL925142    0.00 7\n",
      "CHEMBL3404499  CHEMBL838677    0.00 7\n",
      "CHEMBL3404499  CHEMBL828903    0.00 7\n",
      "CHEMBL3404499  CHEMBL657154    0.00 7\n",
      "CHEMBL3404499  CHEMBL3270416   0.00 40\n",
      "CHEMBL3404499  CHEMBL3116665   0.00 11\n",
      "CHEMBL3404499  CHEMBL2429047   0.00 11\n",
      "CHEMBL3404498  CHEMBL925141    0.00 7\n",
      "CHEMBL3404498  CHEMBL827063    0.00 6\n",
      "CHEMBL3404498  CHEMBL3270415   0.00 40\n",
      "CHEMBL3404498  CHEMBL2429048   0.00 11\n",
      "CHEMBL3366665  CHEMBL3377547   0.00 21\n",
      "CHEMBL3366664  CHEMBL3377546   0.00 21\n",
      "CHEMBL3292986  CHEMBL1944110   0.00 12\n",
      "CHEMBL3270416  CHEMBL951056    0.00 6\n",
      "CHEMBL3270416  CHEMBL925142    0.00 7\n",
      "CHEMBL3270416  CHEMBL838677    0.00 7\n",
      "CHEMBL3270416  CHEMBL828903    0.00 7\n",
      "CHEMBL3270416  CHEMBL657154    0.00 7\n",
      "CHEMBL3270416  CHEMBL3116665   0.00 11\n",
      "CHEMBL3270416  CHEMBL2429047   0.00 11\n",
      "CHEMBL3270415  CHEMBL925141    0.00 7\n",
      "CHEMBL3270415  CHEMBL827063    0.00 6\n",
      "CHEMBL3270415  CHEMBL2429048   0.00 11\n",
      "CHEMBL3116665  CHEMBL925142    0.00 6\n",
      "CHEMBL3116665  CHEMBL838677    0.00 6\n",
      "CHEMBL3116665  CHEMBL828903    0.00 6\n",
      "CHEMBL3116665  CHEMBL828795    0.00 6\n",
      "CHEMBL3116665  CHEMBL828777    0.00 6\n",
      "CHEMBL3116665  CHEMBL657154    0.00 6\n",
      "CHEMBL3116665  CHEMBL2429047   0.00 13\n",
      "CHEMBL3116665  CHEMBL2049249   0.00 11\n",
      "CHEMBL3116665  CHEMBL1678663   0.00 11\n",
      "CHEMBL3116663  CHEMBL828815    0.00 7\n",
      "CHEMBL3116663  CHEMBL827186    0.00 6\n",
      "CHEMBL3116663  CHEMBL2429044   0.00 13\n",
      "CHEMBL3062224  CHEMBL643758    0.00 20\n",
      "CHEMBL2432595  CHEMBL2215099   0.00 8\n",
      "CHEMBL2429048  CHEMBL925141    0.00 6\n",
      "CHEMBL2429048  CHEMBL876689    0.00 6\n",
      "CHEMBL2429048  CHEMBL876374    0.00 6\n",
      "CHEMBL2429048  CHEMBL830339    0.00 6\n",
      "CHEMBL2429048  CHEMBL1678662   0.00 11\n",
      "CHEMBL2429047  CHEMBL925142    0.00 6\n",
      "CHEMBL2429047  CHEMBL838677    0.00 6\n",
      "CHEMBL2429047  CHEMBL828903    0.00 6\n",
      "CHEMBL2429047  CHEMBL828795    0.00 6\n",
      "CHEMBL2429047  CHEMBL828777    0.00 6\n",
      "CHEMBL2429047  CHEMBL657154    0.00 6\n",
      "CHEMBL2429047  CHEMBL2049249   0.00 11\n",
      "CHEMBL2429047  CHEMBL1678663   0.00 11\n",
      "CHEMBL2429045  CHEMBL829116    0.00 6\n",
      "CHEMBL2429045  CHEMBL828906    0.00 6\n",
      "CHEMBL2429045  CHEMBL828796    0.00 6\n",
      "CHEMBL2429045  CHEMBL828778    0.00 6\n",
      "CHEMBL2429045  CHEMBL657968    0.00 6\n",
      "CHEMBL2429044  CHEMBL828815    0.00 7\n",
      "CHEMBL2429044  CHEMBL827186    0.00 6\n",
      "CHEMBL2416135  CHEMBL867233    0.00 8\n",
      "CHEMBL2406600  CHEMBL2404230   0.00 20\n",
      "CHEMBL2050635  CHEMBL753666    0.00 6\n",
      "CHEMBL2049249  CHEMBL951056    0.00 6\n",
      "CHEMBL2049249  CHEMBL925142    0.00 7\n",
      "CHEMBL2049249  CHEMBL838677    0.00 7\n",
      "CHEMBL2049249  CHEMBL828903    0.00 7\n",
      "CHEMBL2049249  CHEMBL657154    0.00 7\n",
      "CHEMBL2049249  CHEMBL1678663   0.00 32\n",
      "CHEMBL1931471  CHEMBL1285944   0.00 22\n",
      "CHEMBL1678663  CHEMBL951056    0.00 6\n",
      "CHEMBL1678663  CHEMBL925142    0.00 7\n",
      "CHEMBL1678663  CHEMBL838677    0.00 8\n",
      "CHEMBL1678663  CHEMBL828903    0.00 7\n",
      "CHEMBL1678663  CHEMBL657154    0.00 7\n",
      "CHEMBL1678662  CHEMBL925141    0.00 7\n",
      "CHEMBL1678662  CHEMBL830339    0.00 28\n",
      "CHEMBL1678662  CHEMBL828925    0.00 27\n",
      "CHEMBL1678662  CHEMBL827063    0.00 6\n",
      "CHEMBL1670560  CHEMBL1030615   0.00 6\n",
      "CHEMBL1645697  CHEMBL1038579   0.00 37\n",
      "CHEMBL1272819  CHEMBL886074    0.00 122\n",
      "CHEMBL1246739  CHEMBL897862    0.00 44\n",
      "CHEMBL1176403  CHEMBL617508    0.00 6\n",
      "CHEMBL1176402  CHEMBL617404    0.00 6\n",
      "CHEMBL1176402  CHEMBL616876    0.00 12\n",
      "CHEMBL1027667  CHEMBL768699    0.00 20\n",
      "CHEMBL1019596  CHEMBL828284    0.00 22\n"
     ]
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "xp = np.array([x[1] for x in pts])\n",
    "yp = np.array([x[2] for x in pts])\n",
    "\n",
    "delts = yp-xp\n",
    "dd = defaultdict(list)\n",
    "for delt,aid in zip(delts,aids):\n",
    "    dd[aid].append(delt)\n",
    "\n",
    "means = sorted([(abs(np.mean(dd[pr])),pr) for pr in dd],reverse=True)\n",
    "for mean,pr in means:\n",
    "    print(f'{pr[0]:14s} {pr[1]:14s} {mean: 4.2f} {len(dd[pr])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}