generate_confs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "# Pulled from trunk on github (e67dc9f64dd787a9bc7294ee1141d3355fe2e831)\n",
    "config_path = None # pass config path\n",
    "cfg = yaml.safe_load(open(config_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2024-11-04 19:40:24.313\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36metflow.utils\u001b[0m:\u001b[36minstantiate_model\u001b[0m:\u001b[36m137\u001b[0m - \u001b[1mLoading BaseFlow with args: {'network_type': 'TorchMDDynamics', 'hidden_channels': 160, 'num_layers': 20, 'num_rbf': 64, 'rbf_type': 'expnorm', 'trainable_rbf': True, 'activation': 'silu', 'neighbor_embedding': True, 'cutoff_lower': 0.0, 'cutoff_upper': 10.0, 'max_z': 100, 'node_attr_dim': 10, 'edge_attr_dim': 1, 'attn_activation': 'silu', 'num_heads': 8, 'distance_influence': 'both', 'reduce_op': 'sum', 'qk_norm': True, 'so3_equivariant': False, 'clip_during_norm': True, 'parity_switch': 'post_hoc', 'sigma': 0.1, 'prior_type': 'harmonic', 'interpolation_type': 'linear', 'optimizer_type': 'AdamW', 'lr': 0.0008, 'weight_decay': 1e-08, 'lr_scheduler_type': 'CosineAnnealingWarmupRestarts', 'first_cycle_steps': 375000, 'cycle_mult': 1.0, 'max_lr': 0.0005, 'min_lr': 1e-08, 'warmup_steps': 0, 'gamma': 0.05, 'last_epoch': -1, 'lr_scheduler_monitor': 'val/loss', 'lr_scheduler_interval': 'step', 'lr_scheduler_frequency': 1}\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from etflow.utils import instantiate_model\n",
    "ckpt_path = None # pass ckpt path\n",
    "model = instantiate_model(cfg['model'], cfg['model_args'])\n",
    "model.load_state_dict(torch.load(ckpt_path)['state_dict'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.to('cuda');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate Conformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[rank: 0] Seed set to 42\n",
      "/mnt/ps/home/CORP/nikhil.shenoy/projects/ETFlow/etflow/commons/utils.py:177: UserWarning: torch.sparse.SparseTensor(indices, values, shape, *, device=) is deprecated.  Please use torch.sparse_coo_tensor(indices, values, shape, dtype=, device=). (Triggered internally at /opt/conda/conda-bld/pytorch_1711403378171/work/torch/csrc/utils/tensor_new.cpp:618.)\n",
      "  bgraph_adj = torch.sparse.LongTensor(edge_index, edge_type, torch.Size([N, N]))\n"
     ]
    }
   ],
   "source": [
    "from etflow.commons.sample import batched_sampling\n",
    "from etflow.commons.featurization import MoleculeFeaturizer\n",
    "\n",
    "feat = MoleculeFeaturizer()\n",
    "smiles = 'CN1C=NC2=C1C(=O)N(C(=O)N2C)C'\n",
    "data = feat.get_data_from_smiles(smiles)\n",
    "num_samples_to_generate = 8\n",
    "gen_position = batched_sampling(model, data, max_batch_size=8, num_samples=num_samples_to_generate, seed=42, device='cuda')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize Conformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import py3Dmol # might have to pip install this for viz\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "IPythonConsole.molSize = (600, 600)   # Change image size\n",
    "IPythonConsole.ipython_useSVG = True  # Change output to SVG\n",
    "IPythonConsole.ipython_3d = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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C2LGaDoyLw/z5+V9qPpBI9rgNk5E7exbNm8PeHjExvK7MQNy8iUaN8q8IXLAAkye/YUh6Ojw989dFW7XC8eNQKLSXkciYcEZo5Jo1g4cHkpKwebPoKKShd95BQED+l1OmYMqU566Uf0F0NLp1y29BW1ts3swWJNIcZ4TG79df0acPXFxw6xZMTUWnIU1IEj76CDt35j9StSpGjYKnJ9zcYGYGAE+f4u+/8euvWL8ez57lPs3UFFu2YOBAAZmJDBaL0PipVHBzw+3b+OUX9OkjOg1pSKnEmDEIDHzxcYUCZcvi6dPnbkyqZm2N7dvxwQe6CUhkNLg0avxMTDB+PADw4npDYmqKtWuxbdtzOysBkCQ8ePCSFuzeHWfOsAWJioAzQlnIyEDVqkhMxMmTz12oRuIlJsLa+nV7TWRlYds27N+Pw4dfsrNS9ero1g39+xdlO3siAsAilI8ZMzB3Lvr2LfRV2qRd3t7Ytw9bt6Jr1zc8MycHcXF48AAPHsDKCmXLolw5lCunk5RExoxFKBcPHqBqVWRn48YNVK8uOg2p3byJunWhUODaNf6/QiQKPyOUi3LlMHAglErecU2fzJyJnBx4e7MFiQTijFBGrl9HnTqwtkZMDMqUEZ2GrlxBo0awsMCtW6hUSXQaIvnijFBGatVCt27IyMC6daKjEICpU6FSYcwYtiCRWJwRysuRI+jcGeXL484dWFmJTiNnZ86gZUvY2iIykie8EInFGaG8dOqExo2RkIAdO0RHkTl/f0gS/u//CtGCd+9qMxCRfLEIZefzzwFg8WJwLUCYw4cRHAw7u9w7HWgiPByurhg2TIupiOSKRSg7AwagcmWEh+PgQdFRZGvmTADw9y/EloEzZiAnB3Z22gtFJFssQtkxN8f//gfwjmui7NmDv/9GhQqF2DLw3Dns3QtbW0ydqs1kRDLFIpSjzz5D6dI4dAgXL4qOIjcqFWbNAoDp02Fjo+moqVMhSfDzQ/ny2otGJFssQjkqVQrDhwPA0qWio8jNjh24dAlVq2LkSE2HHDuGw4dhZ4cJE7SZjEi+WIQyNX48zMywbRtiY0VHkQ+lEl99BQCzZsHSUtNRM2YAwKRJcHDQVjAieWMRylTVqujbF9nZWLVKdBT5+O473LiBd97B4MGaDtm3D6GhcHQsxAeKRFRIvKBevs6dQ7NmsLdHTAxKlBCdxug9e4Z33sHdu/jpJ/Tvr9EQSYK7Oy5cwJIlhbjQgogKiTNC+WraFG3aICkJ330nOoocrF2Lu3dRvz769dN0SFAQLlyAszN8fbWZjEjuOCOUtT170Ls3qlXDrVswMxOdxoilp6N6dSQkYN8+9Oih0RClEvXr49o1rFtXiDNriKjwOCOUtV69ULs27tzB7t2ioxi3pUuRkIBWrTRtQQBbtuDaNdSowbvJEGkbi1DWFAqMGwcACxeKjmLEkpPx7bcAMGeOpkOysnKf/OWXMDfXVjAiAsAipKFD4eiIs2dx4oToKMZq4UI8fowuXdChg6ZD1q1DVBTq1cOAAdpMRkQAi5CsrTF6NMA7rmnJw4dYuRJA7hWEmnj6FAEBADBnDkz4N5RI6/jXjDBmDKytsWcPbt0SHcX4zJmDtDT07o2WLTUdsnw57t1D06bo1UubyYgoF4uQUK4cBg2CSsU7rhW3e/ewfj1MTDB7tqZDUlKwaBEAzJ8PhUJryYgoH4uQAGDiRJiYYNMmJCaKjmJMZs/G06f4+GM0bKjpkMWL8egR2rRB587aTEZE+ViEBABubujeHRkZWLtWdBSjcesWNm2CqWnu7oOaSEzEsmUAcj8jJCKdYBFSLvXeBitW4Nkz0VGMw6xZyMmBtzfc3DQdMn8+UlPh6YlWrbSZjIiewzvLUL4WLXDmDDZuxIgRoqMYuvBwNGwICwvcvInKlTUacv8+atbE06cIC0PjxlrOR0T5OCOkfH5+ALBoEVQq0VEM3fTpUKkwapSmLQjgq6+QkYH+/dmCRDrGGSHly8lB9eqIicHvv6N7d9FpDNfZs2jRAjY2iIzUdE/5O3fg5galEleuoHZtLecjoudwRkj5zMxyt73jxfVvZepUSBI+/1zTFgQwcyaysjB0KFuQSPc4I6TnpKWhcmWkpOD8eS7RFUloKNq1g50doqJgb6/RkKtX0bAhTE1x/TpcXLScj4hexBkhPadkSXh7A+AmhUV0e8MGmJhg8mRNWxDAzJlQKuHryxYkEoIzQnrR3bsIDUX//tz2oNB+++23Xr16ffbuu2sOHYKtrUZjwsLQrBmsrHDrFipW1HJAInoJbsZKL6pcGYMGiQ5hgCRJ+uKLLwDUHjBA0xYEMG0aJAl+fmxBIlE4IyQqHjt27Pj444+rVKly8+ZNS0tLjcYcP442bVC6NKKi4OCg5YBE9HKcEdJzFizIP65fH++//+YhUVHYuTP3uG1bvPuuVoLpOaVS+eWXXwL44osvNG1BADNmAMDEiWxBIoE4I6R8kvTc/nc2NggPf/MJHAcO5Pfl3LmYNk1b8fTZxo0bP/3005o1a0ZERJiZafbvy99/R48ecHREVBRKltRyQCJ6JZ41Sq+UkYExY0SHMARZWVlz584F8NVXX2nagpKUuzfTtGlsQSKxWIT0OgcOYPdu0SH03tq1a6Ojo+vXr9+/f38Nh0Ts26eKjkalShg1SqvZiOiNWIT0BuPG4ckT0SH0WHp6+vz58wHMnTvXxESjv1BKpbLflClOT5+Gz5sHKystBySiN2AR0suVLZt7cPduIfZXl6Hly5fHx8c3a9bM09NTwyFbt269du2abbly73z0kVazEZEmWIT0cm3bomPH3ONly3DhgtA0+iolJeWbb74BEBAQoFAoNBmSnZ399ddfA/j6668tLCy0m4+INMAipFeaPx/q3+05OfD15d5ML7Fo0aLHjx936tSpY96/Gt5k/fr1UVFRdevW/fjjj7WajYg0xCKkV2reHP365R6fPYt164Sm0T+JiYnLly8HoJ7haeLZs2fqDxS//vprDT9QJCJt419Fep3Fi/NvFubvj7g4oWn0zNy5c9PS0nr16vWuxjcRWLFiRWxsrLu7e+/evbWajYg0xyKk16lcGRMn5h6npGDSJKFp9Mn9+/cDAwMVCoX6hjKaePLkifoDxXnz5mn4gSIR6QCLkN5gyhRUq5Z7/OOPOHxYZBj9MXv27KdPnw4YMKBRo0YaDvnmm28ePHjQunXrrl27ajUbERUKi5DewNoaixblfzlmDJ49E5dGP9y+fXvTpk2mpqbq7SY08ejRoyVLlgCYM2eONqMRUaGxCOnN+vXLv5vozZsICBCaRg/MmjUrOzt7+PDhtWrV0nBIQEBAamrq+++/365dO61mI6LCYhGSRpYty78FSkAArl8Xmkaoq1ev7tixw8LCYprG9xePi4tbvXq1QqHQ/PxSItIZFiFppEaN/DNlMjMxduwbnp+Sgtmz8f33OHoUkZHIytJ2QN2ZMWOGSqUaNWqUyxs35vjX119/nZGR0a9fvyZNmmg1GxEVAbdhonwFt2Hq2xc///zcd58+Rb16iIrK/XLXLnz4IfCKbZjCwtC06XPD7e3h6pr7x8kJzs5wdcU77xjY1gvnzp1r3ry5jY1NZGRk+fLlNRly584dNzc3pVJ55cqV2rVrazshERUWN+YlTVlbY9UqdO+e++XEieje/ZW3jHZ0xBdf4M4d3LmDf/7BvXtISkJYGMLCXnxm+fKoWjX/T7VqqFYNVavqaUFOmzZNkiQ/Pz8NWxDArFmzsrKyhg0bxhYk0k+cEVK+188I1Xr3xp49ucfz58PfX9ONeZOSEBWV/+f+fcTF4fp1pKe//PlWVrmzxhfmkS4uEHUN3rFjx9q2bVu6dOmoqCgHzfaUv3HjRr169RQKxfXr111dXbWdkIiKgDNCKpxly3DoEDIyAGDePAwbpulAe3u4u8Pd/cXH8wpSXY3q41u3kJqae/yCFwoyb5W1WjVo+55lM2bMADBp0iQNWxDA9OnTc3JyxowZwxYk0lssQiqcqlUxfTqmTweAtDTMmoW3vFnYqwrywQP880/+H/US6z//ICXllQXZpcs3WVmHqxZQrVo1Z2fnYrmr5/79+0NDQx0dHceNG6fhkLCwsF27dllbW0+dOvXtAxCRlrAIqdAmTsQPP+DaNQDYuBE1a2rlXcqVQ7lyaNbsxcefPs2fOBacSkZH4+rV4KioP194vrm5uaOjo7Ozs2sBTk5Orq6u1tbWGoaRJGnmzJkApk+fXlLjTy9nzJghSdLYsWMrVqyo4RAi0j1+Rkj5NPmMUO3IEXTunHvs6IjExNzj13xGqG3p6bhz5/qdO5H/PC/uFXcKNzU1dXZ2rlq1qouLS8FJZJUqVaz+cwpQUFDQRx995OzsfPv2bQ3r88SJE61bty5RokRUVFTZvG2OiUj/cEZIRdGpEwYMwI4dAPJbUCxbW9StW6tu3Rdv9ZKVlRUbG3v//v24uLioAmJiYu7evXv37t3jx4+/MMTe3j5v4ujs7FytWjX12ubs2bM1n0SqP1CcOHEiW5BIz3FGSPk0nxECiI9HrVpISXnuQYEzwsLKzs6OjY1Vzxrv3LmTN4O8e/du1iuu/3d0dMybOJYpUyYjI8PDw+P9vFNmC/jzzz+7detWpkyZqKioUqVKaflHIaK3whkhFVGFCpg9G59/LjpHUZmbm7u4uLz07jBJSUl5E8e8qeSNGzcSExMTExPDnr8WcsyYMStXriz4iCRJs2bNAuDv788WJNJ/nBFSvkLNCAEolWjaFBcv5j9iQDPCIoiPj8+bOJ46deqvv/5KSkoyMzPLzMwseGLqrl27+vbt6+TkdPv2bRsbG4GBiUgTnBFS0ZmaYtUqtG4NmfxrqkKFChUqVGjRooX6S5VKZWlpmZOT891333366ad5D6q36p05cyZbkMgg8Kbb9Jy8C9U1vINYq1YYOzZ/lL29lvPpExMTk4YNGwJYu3Zt3oM//vjj5cuXq1Wr5u3tLS4aERUCl0aJim7Dhg0jR440NTXNysoyMTHJzs6uXbt2ZGTk5s2bhwwZIjodEWmEM0KiohsxYoSZmZlSqVy/fj2AjRs3RkZGurm5DRw4UHQ0ItIUi5AAQJJw65boEAbIxMREffnEtWvXnj17NnfuXABz5841M+On70QGg0VIALBmDerXx/NXAZBGpk+fDiAoKGjlypWxsbHu7u59+vQRHYq0zsPDo0mTJk2aNOnRo4fmo2bOnNnkX9fUdykkPcB/txKio+Hvj8xMVKggOooBat68efXq1SMjI+fNmwdgzpw5ClHbRJEOXbhw4enTpwAePXqk+ajo6OgLFy6ojzPUe7iQHuCMUO5UKgwfjrQ0fPwx+vUTncYw9e3bF0BSUpKHh0e3bt1ExyGiwmERyt2KFQgJQdmyWLpUdBSD5eXlBUChUKg/IyQiw8IilLXoaMyYAQBr16JcOdFpDFbTpk2rV68uSRIXRYkMEYtQvlQqDBuGJ0/wySfg6R1vqV+/fgB27twpOggRFRqLUL6WLkVoKJycsGyZ6CiGT706+vPPPyuVStFZiKhwWIQydfNm7qLoqlVwcBCdxvC5u7vXqFEjPj7+v7sbEpGeYxHKkUqFTz/F06cYOhQffig6jbFQTwq5OkpkcFiEcrR4MY4dg7MzliwRHcWIcHWUyECxCGXn+nXMmgUA69fLa7MIbWvcuHGtWrUSEhJCQ0NFZyGiQmARykveouiIEXj/fdFpjI76ynqujhIZFm7DJC8BAZg6FRUr4soVTgeL36VLlxo1alS2bNn79+/zvtvGzcbGRn2LNUtLy6ZNm2o46ubNmw8fPlQfnzt3zt3dXVv5qDBYhDJy7RqaNEFmJvbvR/fuotMYqTp16ly7du3w4cOdOnUSnYW0KK8Ii4xFqD+4NCoXOTkYOhTPnmHkSLagFvHKeiKDwxmhXMydixkzULEiwsNhZyc6jfG6cuVKgwYNHB0d4+LiuDpqxPJmhPb29rPUp59p4Icffjh37pz6mB6W9DwAACAASURBVDNC/cG/qLIQEYE5c6BQYONGtqB21a9fv3bt2teuXQsODu7SpYvoOKR1JUuW9PPz0/DJZ8+ezStC0h9cGjV+eYuin32G994TnUYGeGU9kWFhERq/efNw7hyqVcOCBaKjyIO6CHft2pWdnS06CxG9GYvQyF26hLlzoVAgMBAlS4pOIw/16tWrU6fOo0ePgoODRWchojdjERqznBx4eyMrC2PGoGtX0WnkhKujRAaERWjMvv4aYWFwccH8+aKjyMyAAQMA/PLLL1lZWaKzENEbsAiN1sWLmD8fJib4/nuUKCE6jczUqlWrXr16SUlJR48eFZ2FiN6ARWicsrIwdCiyszF2LNq1E51Glrg6SmQoWITG6auvcPkyXF0xZ47oKHL10UcfAdi1axdXR4n0HIvQCF24gIULuSgqmJubW4MGDZKTkw8fPiw6CxG9DovQ2GRm5i6Kjh+Ptm1Fp5E3ro4SGQQWobGZPRtXrsDNjYui4vXv3x/A7t27MzMzRWcholfSy3uNRkfj4EGcP4+HD5GYCEmCoyPKlEHDhujaFW5uovPpr/PnsXgxTEywYQOsrUWnkb133nmnYcOGly5dOnTokKenp+g4VJz8/f3Vn/7aFebuvb17965WrZr62MnJSRvBqAj0afcJScKvv+LLL3H58uueVqMGZs7EJ5/AhNPZ52Rmwt0dV69i0iQsXCg6DQEA5s6dO2PGjCFDhmzevFl0FiJ6Ob0pwqgoDBqEv//W9Pn162PbNtSrp81MBiYg4Oy0aU3d3BQXLsDKSnQaAgBERkbWqFGjVKlSCQkJVvx/hUgv6UcRXr6MTp2QmJj/iJUV2rdHkyZwdISJCR4+xKVLOHoUGRn5zylRAr//jjZtdJ9XD/3999+tW7d2d+++fPmeFi04V9YjTZo0uXDhwt69e3v27Ck6CwmQnp5+9uxZDw8Pc3Nz0Vno5fTgM8LkZPTsmd+CtraYNAkTJrzkxP+MDCxbhnnz8OQJADx5gt69cekSKlXSaWD9k5mZ6e3trVQqO3asxxbUN15eXhcuXNi5cyeLUJ6aNWt27do1bsOrz/Tgl+bo0YiJyT0uVw7Hj2PWrJdf/mZjg6lTcfYsnJ1zH3n8GIMGQaXSUVR9NW3atIiIiNq1a2u+UzbpzEcffaRQKHbv3v3s2TPRWUiA5s2bAzh16pToIPRKooswJATbt+cem5nhwAE0avSGIbVq4Y8/YGmZ+2VoKHbt0mJCvXfq1Klly5aZmZlt3ryZn0LpIVdX1yZNmqSlpf3555+is5AA7777LliE+k10Ea5cmX88cSKaNNFoVP36mD49/8sVK4o5leHIyMgYOnSoUqn09/dv1qyZ6Dj0cryyXs5atWoF4OTJk6KD0CsJPVkmPh6VKyMnBwDMzBAbi/LlNR2bkgJn5/xzZ65eRZ06Wgmp3/z8/JYvX16nTp2wsDBOB/VWdHR09erVS5QokZCQYM0LPGVGpVI5ODikpKTcu3fPOe9jHdInQmeEx4/ntiCA994rRAsCKF0affrkf/nXX8WYy1CcOHFi5cqVXBTVfy4uLk2bNk1LS/vjjz9EZyFdMzExUa/WnD59WnQWejmhRVhwraAIV0EUvJPmf9ffv/wSkydj3z6kpBQpnL7LyMgYNmyYSqWaPn1606ZNRcehN+DqqJypV0f5MaHeElqEFy/mH7/xHJn/KviB4oULL35340YsWoSePVGmDOrWha8vdu587lJFAzd58uTbt283bNhw2rRporPQmw0YMEChUOzduzc9PV10FtI19fky/JhQbwn9jLBBA1y5knt87Rpq1Src8EeP4OiYe1y+POLj878lSTh0CCEhCAnB2bPI2xDOxAR166J9e7Rti7ZtUa7cW/4EogQHB3fq1MnU1PTvv//mxUmGokWLFmfOnPn555/79u0rOgvpVHJycpkyZSwsLFJSUiwsLETHoRcJnREmJ+cflypV6OGlS+cfJyU99y2FAl27Yu5cHD+O5GQcO4aAAHTuDEtLXLmCFSvg5YXy5VG9OoYMwbp1+Oefov4MAqSnp48cOVKSpJkzZ7IFDQhXR2XLzs7Ozc3t2bNnFwsug5HeEDojtLfP78Lk5OeKTUPW1si7SPnp0zffYTMnB5cu4fBhHD+OY8ee+/jQ1RUeHmjdGl26wMWl0El0aNSoUWvXrm3UqNGZM2d40yYDcvfu3apVq1pbWz948MDW1lZ0HNKpTz/9dOPGjUuWLBk/frzoLPQioTNCG5v84yLcdEOlQt42b+bm+ZfYv4aZGdzdMWUKfvsNiYk4dw5Ll8LLC/b2iIrC1q3w9YWrK5yd0b8/1q3D1auFTqVlR48eDQwMtLCw2LJlC1vQsFSuXLlFixYZGRkHDhwQnYV0jZfV6zOhRVhwH6/U1EIPT01F3nTWzg4KReGGq0vRzw9BQXj4EOHhCAyElxccHBAXh5074euLevVyS3HZMoSFQfQNylNTU0eMGCFJ0qxZs+rXry82DBUBV0dli0Woz4QujXp45F9BcewYWrcu3PDr11G7du7xO+/gxo3iSaVS4do1nDiBw4cRHPzciably6NZM7Rujc6d0aRJoav3rfn4+Kxfv75x48anT5/mdNAQxcbGVqlShaujMiRJUpkyZZKSku7evVtJ9vsE6BuhM8KC94K5dKnQwwt+7Fy3bjHkUVOfWerjg6AgPHjw3Mk1CQnYtw/+/mjaFOXLb/zf/5YvX3758mWVTu76ffjw4Q0bNlhaWnJR1HBVqlTp3XffzcjI2L9/v+gspFMKhYJ339ZbQouwRYv847CwQg8vOKTgSxUjhQL16uF//0NQEOLjERmJzZvh44OqVZGUNGvLFj8/v4YNG9rZ2XXp0mXBggXHjx/Pzs7WRpDU1FRvb29Jkr788st63I7YkHF1VLa4Oqq3hC6NXr2av8W8gwPu39fohBc1lQouLvn7N4WG6niH3szbt38IyRWTFwMoVapUmzZt2rZt27Zt26ZNm5qZFc+Oj97e3t99912LFi1OnDhhampaLK9JQsTFxVWqVMnKyiohIaHES7cbIyN16NChrl27tmzZkl2ob0TvUN+kSf5NYbZvx4ABmg48cADvv597XL06bt6EibDZ7f3790+cOHH48OHjx49HRETkPW5jY9O4cePWrVt37ty5devWRb4d6KFDh9577z0LC4uwsLC6xbgITIK0adPm+PHj27dvH6D5f/Bk+FJTUx0cHMzMzJKTk3lzYP0iibVhgwTk/qlSRUpL02hUZqZUu3b+wG+/1XLKQoiLiwsKCho3bpy7u7uiwNk01tbWHh4eU6ZMOXToUEZGhuYvmJycXLlyZQCLFi3SXmzSpWXLlgHo06eP6CCka+rPNU6ePCk6CD1HdBFmZEjVq+dXmpeXlJX1hiFKpTR8eP6QSpWk5GSdZC20hISEvXv3Tpkyxd3d3aTAhNXMzMzd3X3KlCl79+5NflP4IUOGAHj33XdzcnJ0E5u0LS4uzsTExMrKKiUlRXQW0ikfHx8A33zzjegg9BzRRShJ0okTkqlpfrG9/74UG/vKJz94IHl55T9ZoZAOHsz/rh5XRUpKyqFDh/5biqampu7u7uPGjQsKCnr8+PELo3777TcAVlZWV69eFRKbtKRNmzYAfvzxR9FBSKe+//57AH379hUdhJ6jB0UoSdI330gKRX692dpKfn7SkSP5U720NOnYMWnyZMnOLv9pgDRr1nOvs3OnVK6c5OkpBQRI585JSqXOfxKNpKWlqUvRw8Oj4B14TU1N69Sp4+PjExQUlJiYmJSUpL7e6Ft9WvulYrF8+XIAvXv3Fh2EdOr69esAnJ2dRQeh54g+WSbPxo347LP8fXrzWFlBocDTpy8+bmKCb7+Fn99zD06ZgoUL8790dESbNmjXDu3aoUEDgWfTvEZaWtrx48dDQkJCQ0PPnTuXd/WFiYlJ6dKlk5KSWrVqFRoayjNFjUx8fHylSpXMzMwSEhJKF+Euu2SYJEkqV65cYmLiP//8U6VKFdFxKJfeFCGAM2cwaRJCQ9/8zBYtsGjRy6+XiIrC8eM4cQIHD+LOnfzHS5RAy5bo3BkeHmjeHHq5E0pGRsb58+fVJ6CGhoZmZWXZ2NhcvHixZs2aoqNR8Wvfvn1ISMgPP/wwaNAg0VlIdzw9Pffv389zhvWL4Bnpfx06JI0e/dwZNAVPK/Xxkfbvl1QqjV7q3j0pKEjy8ZHq1HnudWxtpc6dpVmzpEOHpGfPtPzzFEViYmL58uUBLF++XHQW0paVK1cC+OCDD0QHIZ2aM2cOgHHjxokOQvn0aUb4guRkJCQgMREqFcqWRdmyKFOm6K8WF4fjx3M3YLp2Lf/22TY2aNw49/ahrVu/eSMnnfj444937Njh4eERGhpqopcruvT2EhISKlasyNVRuTl69GinTp2aNWt25swZ0Vkolx4XofbEx+PYsdwV1PPn80vR3BwNGqBz59wVVGtrIen27NnTu3dvW1vbixcv1qhRQ0gG0o2OHTsGBwdv2bJl8ODBorOQjqSnp9vZ2SkUiuTkZJuCW9GROLIswoIePMDp07l7TVy4gLzbZ5uZoWHD3EZs27YomwYXycOHD+vXr5+QkLBq1arRo0fr5k1JlDVr1owePbpnz5579+4VnYV0p1GjRpcuXQoNDW2j2xtD0qvIvggLSkvD6dO5y6dnziDv9tmmpmjUKHf/+s6dYW+vvQhVq1aNiYlp165dcHCwQufbPJGOPXz40NnZ2cTEJD4+3l6b/12RXhk9evSaNWsWLFgwefJk0VkIELz7hL4pWRKdOyMgAMeP4/FjHDqEKVPg4QFTU4SFYfly9O9/7cMP69at6+vru3PnzkePHhXv+2/fvj0mJkahUMyZM4ctKAdly5Zt165dVlYWZ4Sywm0o9E3x7I1ghEqUyP2wEEBaGo4fR2goQkMPKZURERERERHr1q0zMTGpX79+u3bt2rVr17ZtW0dHx7d8z9u3bwOwtrZuXdg9islgeXl5HTlyZOfOnUOHDhWdhXREXYQn87YlJ9G4NFo4BS/1O378+LNnz/K+5erq2rlzZw8Pj/bt2xftUtmEhAQnJydJkoKDg9u3b19soUmPJSYmOjk5KRSK+Ph4BwcH0XFIRypUqJCQkBAVFeXi4iI6C3FptJBsbGxat26t3kQiLS3t3LlzAQEBnp6epUqVioqKWrdu3dChQ6tWrVq9evUhQ4asW7fuTsGL+t+kfPnyHTp0APDHH39o6wcgPePo6Ni+ffvs7Ow9e/aIzkK606JFC3BSqDc4IyweOTk5ly5dUk8Tjx8/npycnPctJycn9ZaEHh4eb9xN8OTJkx4eHuXLl7979665ubmWU5NeWLduna+vb/fu3X///XfRWUhHAgICpk6dOmbMGPV9FUgsFmHxUyqVFy9ePH78+IkTJ44cOfL48eO8b6lL0cPDo3Xr1k2aNHnpGTH16tW7evXqr7/+2rt3bx2mJmHyVkfj4uLKvM1dI8hwhISEtG/fvkmTJmFhYaKzEItQy5RK5fXr19WfKQYHBycmJuZ9q3z58m3btv1vKX777bcTJkzw9PRU78FEcvDee+8dPHhw48aNI0aMEJ2FdCEjI0N9O6GkpKQSJUqIjiN3LEKdioqKUi+fBgcHx8bG5j1etmzZFi1aqFdQq1SpUrly5ZycnOjoaPXe9GT0NmzYMHLkyG7duh04cEB0FtKRpk2bhoWF8cw4fcAiFCYiIiI0NDQkJCQkJCQuLi7vcQcHBysrq/v373/55ZdffPGFwISkM0lJSRUqVFCpVPfv3y9btqzoOKQLY8eOXbly5bx586ZOnSo6i9zxrFFh6tSp89lnn23fvv3+/fuRkZGbN2/28fFxcXF5/Pjx/fv3AWzatEmVd8s3Mmr29vYdO3bMycnhuaPywcvq9QeLUC+4uroOGTIkMDAwKioqKirq+++/d3BwiI6ODg4OFh2NdMTLywvAzp07RQchHckrQi7LCcci1DsuLi7Dhg0bN24cgI0bN4qOQzrSp08fCwuLo0ePPnjwQHQW0gUXFxcnJ6fExET1LaVIIBahnvL29jY1Nf3ll18KnmhKRszOzq5z5845OTm7d+8WnYV0pGXLluDqqB5gEeqpSpUqdenSJSsra9u2baKzkI5wdVRu+DGhnmAR6i9vb28AGzZsEB2EdKR3794WFhbBwcFcHZUJFqGeYBHqr169epUrV+7KlStnzpwRnYV0wc7OrkuXLkqlcteuXaKzkC64u7tbWFiEh4enpqaKziJrLEL9ZWFhMXjwYPCUGTnh6qisWFtbN2rUSKlUnj17VnQWWWMR6rWRI0cqFIpt27alpaWJzkK68OGHH1pZWf31118F77FARoyro/qARajX3NzcWrVq9eTJk59//ll0FtKFUqVKdenSRaVS8dxRmWAR6gMWob5TnzLD1VH54OqorPCyen3Ae43qu/T0dGdn59TU1KtXr9apU0d0HNK6tLS0cuXKZWVl3b1719nZWXQc0rrKlSvHxsZeu3atVq1aorPIFGeE+s7W1nbAgAEAvvvuO9FZSBdKliz53nvvqVSqX3/9VXQW0gVeVi8ci9AAqFdHN23alJmZKToL6QJXR2WFHxMKxyI0AM2bN2/UqNGjR4+4Va9M9OrVy9ra+tixY+p9SMi4sQiFYxEahuHDh4OnzMhGyZIlu3XrplKpfvnlF9FZSOuaNGliZWUVERGRlJQkOotMsQgNw5AhQ6ytrQ8ePPjPP/+IzkK6wNVR+bC0tGzcuLFKpeJl9aKwCA2DnZ1d7969VSrV999/LzoL6ULPnj2tra1PnDhx79490VlI67g6KhaL0GDkXVCoVCpFZyGtK1GiRPfu3VUqFe+lIAcsQrFYhAajY8eONWrUiI2NPXz4sOgspAtcHZWPVq1aATh9+rRKpRKdRY5YhAZDoVAMGzYMPGVGNnr16mVra3vy5MmYmBjRWUi7nJ2dq1SpkpycfO3aNdFZ5IhFaEi8vb3NzMz27NnD/erkwMbGpnv37pIkcVcmOeDqqEAsQkNSoUKFbt26ZWVlbd26VXQW0gWujsoHi1AgFqGBUZ8ys379et4kVg48PT1tbW1PnTrFy2aMHotQIBahgfH09HR2dr5x4wb/wsiBjY1Njx49JEnilfVGr3HjxjY2NtevX3/8+LHoLLLDIjQwZmZmQ4YMAU+ZkQ2ujsqEubl5kyZNJEk6ffq06CyywyI0POpt63/66afU1FTRWUjr3n///RIlSpw+ffrOnTuis5B2cXVUFBah4XF1dW3btm16evqOHTtEZyGts7Gx8fT0lCSJV9YbPaMswtTU1Oh/PXnyRMNROTk5eaMSEhK0mhAsQgPFbetlhaujMpF3Wb0x3T1q/fr1rv/S/N/ud+/ezRul3nJAq1iEBqlfv3729vZnzpy5dOmS6Cykde+//36pUqXOnDkTGRkpOgtpV8mSJZ89e+bq6tqzZ09/f/9169YdPnw4Pj5edC4jxyI0SNbW1gMHDgTAe3DLgZWVVY8ePQDw3FEjtn379rp166alpeXk5MTExOzbt2/BggW+vr5dunRxcnJycHBo2rRp//79Z8+evXPnzrCwsPT0dNGRjYeZ6ABURD4+PqtWrdq6dWtAQICVlZXoOKRdXl5e27dv37lz5+TJk0VnoWKWkJAwevRo9f2DzMzMcnJyTp48GRsbGxUVFRUVdfXq1StXriQlJYWFhYWFhRUc6OTkVLduXfX6YZ06derWrVu1alVTU1NBP4cBYxEaqgYNGjRp0uT8+fO//vrrxx9/LDoOaVe3bt2srKxiY2PHjBkTEBBQsmRJ0YmoeOzcuXP06NGJiYmlS5eePn365MmTy5Ytqz5rpqCkpCR1KUZEROQVZFxcXFxcXMGnWVhYVKpUSV2KrgXo8AcySCxCA+bt7X3+/PmNGzeyCI1eUFDQs2fPEhISVq9evXfv3oULFw4YMEChUIjORUUXHx8/evToX3/9FUC3bt3WrVsXHR0NoEaNGv99sr29vbu7u7u7e94j6hXUvFJUF2R0dLT6kX379hUcm9eI6o50c3MrUaKE9n9Eg8EiNGCDBg2aNGnSqVMXoqMfu7g4iI5D2vLo0aNJkyYB+PLLLw8cOHDq1KmBAwcuXbp0xYoVzZs3F52OimLnzp2jRo169OhR6dKlFy5cqL44+ODBgwBq1qypySuYmZn9d7aXmZl5+/btgrPG8PDwly6r2tvbF5w41qlTp1atWrJdVmURGrDSpUuPHXs6MLDuxo2KOXNEpyGtGT9+/MOHDzt06DBjxowZM2Zs3bp18uTJZ86ceffddwcNGrR48eKyZcuKzkiaio+PHzVq1O7duwF079593bp1lSpVUn/r9u3b0LgIX8rS0rJu3bp169Yt+OB/l1UjIiKSkpJOnDhx4sSJvKeZm5tXrlw5b9aoLkgXFxdZLDxIZMhCQiRAqlhRyskRHYW04+jRowqFwtra+vbt23kPpqWlzZo1y8LCAoC9vf3SpUuzs7MFhiQNBQUFOTg4ALCzswsMDHzhu3379gWwY8cObcfIzs6OjIw8dOhQYGDglClTPD09XV1dTUxechGBnZ2du7u7l5fXlClTNm/efO7cudTU1EK91zfffJP3aurdAjQRFRWVN0q9GZlWcUZo2Nq2Ra1auH4dBw7A01N0GipuT58+HTlypCRJs2fPrl69et7jJUqUmD179sCBA8ePH3/gwIHx48evXbt22bJlXbt2FZiWXiMuLm7UqFF79uwB8P777wcGBuZNBPPcunULbzcj1NCrllXv3btXcOIYHh4eHx+vybKqm5ubmZkht4m2m5a0LSBAAqTevUXnIC1QXyzRoEGDrKysVz1n7969eb/RPD0979y5o8uEpInXTwTVVCqVra0tgOTkZB3He42EhIRjx45t2LBhypQpH374YZ06dSwtLf/bIxYWFrVq1frggw8mTZq0fv36kJCQtLQ09SsYxIxQIXFbOwOXkIDKlSFJiImBk5PoNFR8Ll++3LRpU6VSefz48f+eT19QVlbWmjVrZsyY8eTJExsbm0mTJvn7+/PqUn0QFxfn6+v722+/AejRo0dgYGDFihVf+sx79+5VqlSpfPny+n8fmfv37xc8HyciIuLOnTsqlargcw4ePNilSxcAixcvnjhxovrBxYsXDxo0SJO3iImJyTsRrHv37r///nux/gT/oe2mJR348EMJkAICROeg4qNUKtXl9/nnn2s4JDY2dvDgwepTGypXrrx582atJqQ3CgoKsre3x2sngnmCg4MBeHh46CZb8crIyLh06dLOnTvnzp07dOjQli1bxsTEqL9VcEZYNDqYEbIIjcG+fRIgVa8uqVSio1AxWbJkCYAqVarkLTFp6K+//mrQoIH6N0jHjh3Dw8O1lJBe486dO+r5EABPT8979+69cci6desADBs2TAfxdMkgipD3GjUG3bqhcmVERiI0VHQUKg4xMTFffPEFgBUrVhT2wud27dpduHBh8+bNjo6OR48ebdy4sZ+fX0pKinaS0oskSVq3bl39+vUPHTpkb28fGBj422+/OTs7v3Hg2187QUXGIjQGpqYYOhQAuC+TcRg7dmxaWtpHH33Uq1evIgw3MTEZMmTIjRs3xo0bp1Kpli9fXr169WXLlr3wKQ4Vuzt37nTt2tXX1zctLc3T0zM8PNzHx0fDsTo7ZVSghQsX3tXMyZMndZpM21NO0o2oKMnERLK2lh4/Fh2F3s5PP/0EoHTp0pqsp73R+fPnW7durf7L7u7ufuLEibd/TfovlUoVGBiovgdsuXLlgoKCCvsK9erVA3DhwgVtxBPIIM4a5YzQSLi4oGNHPH2K7dtFR6G3kJKS8n//938AFi1apMl62hs1btz42LFje/furVKlSlhYWOvWrYcMGaKDLb9lRf2JoHoi6OXlFR4ert5LuVBcXFyqVatW8GpR0hkWofHw9gaAwEDROegtTJ48+d69e23atPn000+L8WV79ux57dq1WbNmWVpabt26tXr16rNnz87MzCzGt5An6d9PBI8cOVKuXLmff/45KCioaHe827t3b3R0NPcVEYJFaDz69IGjIy5fxvnzoqNQkRw7dmz9+vUWFhZr164t9hs82tjYzJ49+8qVK15eXunp6V9++WWDBg20fnmWUYuOju7UqZOvr++TJ0+8vLyuXr2qvkcaGRwWofGwsID6WlWeMmOIsrKyPvvsM0mSpk2bVqdOHS29S40aNYKCgg4dOlSnTp2bN2/26NGjZ8+eBT+PIU2oJ4INGjQIDg4uX778L7/8EhQU5OjoKDoXFRGL0Kiol9N+/BEZGaKjUCHNmzcvIiLCzc1typQp2n6vzp07X7x4cenSpaVKldq3b1/t2rX9/PzS0tK0/b7GISoqquBEMDw8vE+fPq95/owZM2r869y5cxq+y7Fjx/JGzZs3rziC0yuxCI1KvXpo0QIpKfjlF9FRqDBu3ry5YMEChUKxZs0a3dwazdzc3M/P7/r16z4+Pjk5OcuXL69du/aWLVsk3nPx1VQqrF8fVb9+/eDgYCcnp71792oyEXzw4EHkv54+farhe6Wnp+eNevjw4Vtnp9dhERob9SkzXB01IJIkjRo16tmzZz4+Ph06dNDlWzs5OQUGBv79998tW7a8d+/e0KFDO3TocPnyZV1mMBSRkejYEb6+rrVr+3l5eV25cqVnz56iQ1HxYBEam48/RsmSCAnB9euio5Bm1q9ff/To0QoVKsyfP1/zUcV4zmezZs1OnDgRGBhYtmzZkJAQd3f3r746k5RUXC9v8JRKLF6M+vUREgJnZ3z99ZdBQUFlypQRnYuKDYvQ2JQoAfUlTJs2CU5CmoiPj/f39wewfPly9Q2aNXHnzh0XF5fVq1cXVwwTExMfH5+bN29OmTKlYsWWCxc2r1EDy5ZBqSyudzBU6ongxIl4+hReXrh8Gd27m4sORcWMFJlqUgAAIABJREFURWiE1KujmzYhO1t0FHoTPz+/pKSk7t27F+oS7LFjx8bFxZ04caJ4w9jZ2QUEBOzff7h5czx+jPHj0awZjh8v3jcxGCoV1q1DgwYIDYWTE3bvRlAQHBxExyItYBEaoVatUKcOEhKwf7/oKPRaBw4cCAoKsrW1XbVqleajtm3btm/fPgcHh2+//VYbqerWtTx6FHv3wsUFFy6gTRv07ImYGG28lf6KiMC778LXFxkZ8PJCeDg++EB0JtIaM9EBSCtGjMDEidi4Eb17i45Cr5CRkTFmzBgAc+fOdXFx0XDU48eP8+7BVr58ee3F69kTnTtj+XLMmYN9+3D0KCZNgr8/jH6735wcLF6MWbOQmQknJwQGgufEvI3Bgwd37NhRfVylShUNR1WsWPH8v3cGKV26tFaSFaTtm5mSEImJkqWlZGoq/bs7Jumd8ePHA2jWrFlOTo7mo0aMGAGgXbt2Kl1tPnn3rjR4sATk7nlZ+LtJG5LwcKlZMwmQFApp8ODiuYX9yJEj837fhoaGajjqwIEDeaPGjx9fDDno1bg0apzKlEGvXlAqsWWL6Cj0MufOnVuxYoWZmVlgYKCpqamGo0JCQr7//ntLS8s1a9YU+z3YXqVSJWzZgqNHUb8+IiPRvz86d8bVq7p5c93JycGCBXB3x9mzqFoVBw9iyxZofPYSGTYWodHKu6CQm9Dpm5ycHF9fX6VSOWHChMaNG2s4KjMzc9SoUZIkzZgxo3bt2lpN+F8dOuD8eQQGwtERR46gcWP4+SE1VccptCU8HO++C39/ZGXBxwdXrqBzZ9GZSIdYhEarSxdUrYroaAQHi45Cz/v222/Pnz9frVq1mTNnaj5qzpw5165dq1ev3uTJk7WX7TXMzODjgxs3MG4cVCosX45atbBunWH/S0s9EWzaFOfOoVo1HDyIwEBwBwi54ckyRsvEBMOG4csvsXEjOnUSnYb+9c8//3z11VcAAgMDbW1tNRx1/fr1RYsWmZiYrF271sLCQpsB38DBAcuWYehQjBuHEyfg64v167FiBVq2FBiqiK5cwYgROHcOCgV8fPDNN1qvwOHDh9vY2GjyzCdPnmg3ChUk+kNK0qK7dyVTU8nCQnr4UHQU+lePHj0ADB48WPMhSqXSw8MDwJgxY7QXrLBUKikoSKpcOf/Ukvh40Zk0lp0tBQRIFhYSILm4SEeOaPG9Cp4sUzQ8WUbbuDRqzCpVQpcuyMrCtm2ioxAA4Icffti/f3+ZMmW++eYbzUetXbv2xIkTTk5Oc+bM0V62wlIo4OWFa9cwaxYsLLB1K2rVwoIFyMoSnexNLl9Gixbw90d2Nnx8cPky/j29n2SKS6NGztsbf/yBDRswbpzoKLL3+PHjCRMmAFi8eHG5cuU0HBUXFzd9+nQAq1atsrOz02K+IrG1xezZGDQIn3+O/fvh74/vv8fSpejWTXSyl8nOxrff4osvkJUFFxds3Ajd3uQcU6dOrVmzpibPDA8P19INE+glRE9JSbsyM6Vy5SRAOn1adBTZGzZsGID27dsX6hJA9V53PXr00F6w4nLokFS7du4Vh56eUlSU6EDPu3RJatw4dyHXx0dKS9PR+/I6Qv3HpVEjZ2GBwYMBbswkWkhIyObNmy0tLdeuXav5JYD79+/ftWtXqVKl1q5dq9V4xaJzZ1y6hKVLUbIk9u1D3brw94c+nPORnY0FC9CsGS5cgKsrjh5FYCBKlBAdi/QGi9D4jRwJhQLbtunFryR5yszM/OyzzyRJmjVrlpubm4aj0tLSPvvsMwDz5s2rVKmSNgMWG3Nz+Pnh+nUMHoxnz7BgAWrV0uiuDteuISws98/t2xq9V05O/pCoqFc+7dKlFz8RbN9ew5+G5IJFaPzc3NCqFZ48wc6doqPI1VdffXX9+vX69etPnDhR81HTpk2LjY1t3ry5ug4NiLMztmzB33+jRQvcu4ehQ9GhA65ced2QTz5B06a5fxo2fF2x5Xn8OH/IlCkveULBiWD16ggORmAgNL5ihWSERSgL3LZeoPDw8LxLAM3NNd3K7syZM2vWrCnsPdj0SvPmOHkSmzejXDn89ReaNIGvLxIT3zwwIwNjxrztu58+jUaN4O8PpRI+Prh0Ce3ave1rkrFiEcpC//4oVQonTiAiQnQUmVGpVL6+vtnZ2WPGjGnVqpWGo/LuwTZlypRGjRppNaFWmZhgyBBcv5570vK6dXBz02i/3z/+wK5dRXzTZ8/g7w8PD0REcCJIGmERyoKtLQYMAIDvvhMdRWZWr1598uRJJyenr7/+WvNRCxcuvHjxYs2aNdUXThg6e3ssW4YrV9C1ayH2+/3f/5CSUuj3ungRjRtjwQIAmDwZ4eFo27YomUlWWIRyoV4d3bwZmZmio8hGXFyc+m6iq1ev1nxPtdu3b8+ZM0ehUKxevdra2lqbAXWqVi38+Sd27EDlyrhwAW3bYsgQPHz44tPM/r22OS4Os2cX+l1Kl0ZsLOrUwcmTWLDA+HdPpGLBIpSL5s3RqBESE/Hbb6KjyMbo0aOTk5P79u3buzD7I48aNerp06fDhg3rbIw7IHz0Ea5fx6xZsLTE/v0w+c9voEqV8i/GX7EC/27OqikXF/z5J86fR/PmxZCWZIJFKCPDhwM8ZUZXdu3atXv37lKlSi1btkzzUZs2bTp8+LCjo+MC9eqeMbKxwezZuHoV27ahTJmXPGHBgtyCVCrh6/vmDxRf0KoVLC2LISfJB4tQRj75BFZWOHgQ//wjOoqxS01N9fPzAxAQEFCxYkUNRz169Ei9xdLSpUvLli2rxXx6wNUV77338m81aIBBg3KPz51DYKDOQpFMsQhlxMEBH34IlQqbNomOYuymTp0aGxvbokULX19fzUeNHz/+4cOHHTt2HDhwoPayGYT58/Pv/DJ1Ku7fF5qGjB1vui0vn36K7dvx00+YNUt0FON1+vRp9a6BGzduNPnvh2CvEBwc/OOPP9rY2Kxbt07ze7AZq4oVMXUq1OfMpqZi4kQD3kGlX79+NWrUUB+7uLhoOMrNzS1vebxZs2ZaSUZ5RN/slHRKpZI2b5ZSUkTnMF7Z2dkNGzYEMHPmTM1HZWRkVK9eHcDChQu1l02fNWmSe7fuatVyH8nMlGrWzH0QkPbvf3FIQkL+d/v103FeMipcGpUXhQJDhqBUKdE5jFdAQMClS5dq1qw5bdo0zUfNnj07MjKyQYMG48eP1142w2JhgYUL87/088OzZ+LSkFFjERIVm1u3bs2dO1ehUKxZs8ZK40vYLl++vGTJEhMTk8DAQM3vwSYHvXujR4/c49u3MX++0DRkvPgZoVxs2IBHj3KPK1TA0KFvHpKSgrzNf+rWhaentrIZB0mSRo0a9ezZM29v706dOmk4SqVSffbZZ9nZ2Z9//nnLli21mtAQLVuGI0dy54IBARgwALVri85Exkf02izpSK1a+R+oKBTSkSNvHhIdnT9k8GDtRzRwGzduBODo6Pjw4UPNRy1ZsgRAlSpV0nS2Uaxe+u9nhHlmz87/77BDBylvV2N+RkjFhUujciRJGDWK91orTon/397dx9V8938Af1dHqaSUdGOhLE2YmzSjrFBRaox1jVE2N7kQNSwRiqEQV2ImYxf2m5swLoWlJFTuUm5SNMxdSe6lIufm98f3OGwzTpzz/XTO9/V8eOzx7XS+5/vadU2vPt+bz+fOnWnTphFRYmJi06ZNldzr2rVrs2fPJqLly5c3wkKx/yAyktq0kW8fOEDJyUzTgDZCEQpUSQktXsw6hBaZNGnSnTt3+vXrN3ToUOX3mjhxYmVl5RdffPHpp5+qL5umMzCg5ctffDl1KlVXs0sD2ghFKFzz5lFJCesQWuG3337btGmTkZHR999/r/xeycnJu3btMjU1Xbp0qfqyaQcfHxo0SL594wbFxzNNA1oHRSg4TZoQd2fi06fyVeLgXVRXV0+YMIGI5s6d6+DgoOReDx8+nDx5MhEtXrzY1tZWjfm0RULCi7lm4uIwTSCoEopQcGxsaMwY+XZaGm3ZwjSN5ps9e/bly5c7duw4qS6/VkRERJSWlvbs2XP06NHqy6ZN7Oxo1iz5dk3Ni22Ad4ciFKKYmBfP1IeH04MHTNNostOnTycmJurp6a1du1b5RwAPHz78448/6uvrr1q1CrOpKW/yZPrwQ/n2L7/UeYUmgH+CIhQiS0uaOlW+XV5OM2cyTaOxJBLJqFGjnj17Fh4e7uLiouRetbW1//73v2Uy2YwZM5ydndWaUMuIRLRsGXG/OUilNH0660CgLVCEAjV1KrVqJd/+4Qc6epRlGA2VkJBw8uTJFi1axNRlJfUFCxYUFRU5OTlxj1tAnXh6kmJljlOnmEYBLYIiFChDwxePT0ilNHYsicVMA2maa9eucf23YsUK5R8BLCkpWbhwYV3nYIOXLVlCZmasQ4B2QREK1+efk7e3fPvMGarLnf9AoaGhjx8/Hjp0aEBAgJK7yJ7PwRYSEtKrVy+1xtNiVlY0dy7rEKBdUISCtmQJiZ5PNztrFpWWMk2jOTZt2pSSkmJubs5NkKakH3/8MTMz09raOhazR7+bCRPoo49YhwAtgiIUtA4daOJE+XZlJWEJIGU8fPhw6tSpRLRo0SIrKysl9yovL4+MjCSixMTEJk2aqDGfAOjq0ooVpPSaxwBvgP+UhG7OHGreXL69bRulpjJNowmmTJlSVlb2ySefjBw5Uvm9wsLC7t+/7+vrGxgYqL5swuHqSqNGsQ4B2kJHJpOxzgB8aNuWzp8nInJ2pnPn/vStLVtoyBD5tr09nTtHhoZERFeukL29/PWgINqwgbew9dehQ4c8PT319fULCgraKr0g0N69e/38/IyNjc+ePWuv+N8UXlJaKp8FXiSiFi2U2qWmhm7elG8bG5PSg3OAv8KIEOiLL8jXV779xx9vnshxzx66eFHdoeqjp0+fco8ARkVFKd+CijnY5s+fjxb8J82bk4MDOTgo24JEZGgo38XBAS0I7wRFCEREy5aRgYF8OzaWrl37x3c+eUIDB5KjI5makrs7hYXRhg1/HWJqq/nz5xcXFzs5OUVERCi/V1RU1B9//OHq6hoaGqq+bADw1rBCPRAROTrS1Kk0fz4RUU0NRUXRzz+/+p337lG/fpSXRzdvUk4O5eTIX7eyIldX6tqVunYlV1dq1oyn5Ly5cOHCokWLdHV116xZY6D4reFN8vLyli9fLhKJkpKS9PT01JpQWyUl0c2bNGoU2dmxjgJaCtcIheI11wg51dXUrh1duUJEpKNDublkbf26a4RlZXTypPzPsWN0+/afvmtjQy4u8j/du5PSS9XWU1Kp1MPDIzs7e9y4cStXrlRyL7FY3K1bt/z8/GnTpsXFxak1oRbr1o2OH6ejR6lbN9ZRQEthRAhyRka0ciX5+RERyWT0zTe0cePr3m9rS7a2pHia/OVezM2lmzcpNfXFPagv96K7O2nc4wNJSUnZ2dnW1tYLFixQfq+lS5fm5+e3atVqFtZKeAfcMrxGRqxzgPZCEcILvr4UEEApKURER4/Sjh112PfvvZiTQ9nZdPIkFRT8qRf19MjJ6UUvurjIb1Ktt8rLy2fMmEFEK1asMFN6dq+rV6/OnTuXiJKSkoyNjdWYT9uhCEHdUITwJ4mJtH+//EcPd8nw7djaUmAgcY/MicV04cKL8WJeHhUVUVGR/DKkSERt2rwoxY8+In19VfybqE5oaOiDBw/8/PwGDx6s/F4hISFVVVVBQUE+Pj7qyyYEKEJQN1wjFIo3XiNUmD//FQszqfA5wmfPqKREXoo5OXTqFEkkL77boAE5OpK7O7m5kYsLtW3LeAKRPXv29O/f38TE5Ny5c3ZK363xf//3f0FBQRYWFkVFRc2078Yhfpma0qNH9OABmZqyjgJaCkUoFMoXYW0tdewof7OC+h6or6yk/HzKy6MTJygvjy5d+tN3TU0pMDDF3Dzb1dW1a9eurRRrR/GisrKyXbt2169fT0xMnKiYjO5N7t2717Zt24qKinXr1o0YMUKtCYWgQQMSi6m2lpRe+RigbnBqFP5KX5+WL3+xMIW6mZiQhwd5eMi/fPSIzpx5cR61qIjOn/81O3sd911TU9P27du7uLi4uLj07NlT3c+nR0VFXb9+3dXVdfz48crvNWXKlIqKCk9Pz+DgYPVlE4hnz0gsJpEILQhqhBGhUCg/IuQMGUJbtrz4ktUUazdvUkHBwWPHMk+cOJGXl3f7z09pNG/enBspdu3a1dXV1dzcXIWHPnHiRPfu3XV0dI4fP965c2cl9zp48GCvXr309fVPnz7t5OSkwjzC9PAhmZmRqSk9eMA6CmgvjAjh1RIS6Lff6OFDxjFsbMjGxsPPTz5gLCsrO/nckSNHSktLS0tLd+7c+fzNNi7Pubm5vUsvisXisWPHSiSS6dOnK9+CijnYoqOj0YIqgTtlgAcYEQrF55/LH5a3t6etW5XaJSmJfvxRvu3nVx9XQ1X0Yk5OzpEjR6qqql7+ro2Njbu7u5ubm4uLS5cuXYzq8tM0NjZ2xowZrVq1KiwsVP7hh6ioqAULFnTo0OHkyZMNcC5PFS5dovffJweHv148BlAhFCFoCYlEcv78+ZMvefLkieK7IpGoTZs2ivGiq6vra6ZJu3LlSvv27auqqvbt2+et9MXSwsLCLl26SCSSw4cP9+jR413/fYCIiM6epQ8/pPbt6exZ1lFAe6EIQTuJxeILFy4oSvHEiRO1tbWK7zZo0MDR0ZErRXd3906dOr08EaiPj096evqIESPWrVun5OGkUmnPnj1zc3MnTpyYmJio2n8XITt2jD7+mD76iI4dYx0FtBeKUBCkUiotFfScxc+ePTtz5kx2djbXi+fPn5dKpYrvNmrUqGPHjlwv3rhxIyoqysLCori42NLSUsnPX7FixcSJE21sbIqLi03xvJvqHDhAvXuTpycdOMA6CmgvFKEgrF1LEyfSggUUHs46Sv1QWVl5+vRpxXixuLhY8RdBT09PIpFs2LAhKChIyU8rKytzdnZ++PDhjh07Bg4cqLbUQrR7N/n7k58f7d7NOgpoL9w1qv0eP6ZZs6imhqytWUepN0xMTNzd3d3d3bkvKyoq8vLyuCc0jh07dvv2beXnFCUiqVTavXt3IyMjtKDK4a5R4AFGhNpvxgyKjaWPP6bcXNLRYZ2m3ktMTAwLC+vQocOpU6d06zK9W3V1dZ1uTAVlrF9PX31FwcG0fj3rKKC9sEK9lrt+nZYtIx0d+T/hjf797387ODicPXt206ZNddoRLagOGBECD1CEWm7qVKqupmHD6KOPWEfREPr6+tzygTNnznz5RlNgAkUIPEARarMjR2jrVjI0fKcFlQQoKCjI2dn5ypUra9euZZ1F6FCEwAMUodaSySg8nGQyioigFi1Yp9Eoenp63333HRF999131dxPYmCkpoaI6vvSzaDpUIRa6+ef6fhxat6cvv2WdRQNNGjQoI8//vjmzZvLly9nnUXQMCIEHqAItVNNDc2aRUQUG0tKz5QJfxIXF8f98969e6yzCBc3IkQRglqhCLVTXBxdu0ZdutCwYayjaCwPD48+ffo8ePAgPj6edRbhwogQeIAi1EKlpbRkCRFRQgLV5UE4+Ku4uDgdHZ2EhITS0lLWWQQKRQg8wI9JLTRtGlVV0RdfUM+erKNouK5duw4cOLCmpiY2NpZ1FoHiihA3y4BaYWYZbXPsGHXvTgYGVFxMrVqxTqP5Lly40L59ex0dneLi4tatW7OOIzgeHnToEGVlkYcH6yigvTAi1CoyGU2dSjIZTZ6MFlQNJyen4cOHP3v2bM6cOayzCBFOjQIPMCLUKps20ZdfUrNm9Pvv1Lgx6zTa4urVq05OTs+ePcvPz+/YsSPrOMLSrh0VFVFhIbVrxzoKaC+MCLXHkyc0fToR0YIFaEFVatmy5dixY6VS6ezZs1lnERyMCIEHKELtER9PV69Sx4701Veso2idmTNnmpiY7Nq1Kzc3l3UWYUERAg9QhFri1i1avJiIKD6e9PRYp9E6lpaWYWFhRBQZGck6i7CgCIEHuEaoJUaOpP/+lz77jH79lXUULfXw4cPWrVvfvXs3LS3Nx8eHdRyhEIlIIiGxGL/egRphRKgNTp2i9etJX58WLmQdRXuZmppyw8EZM2bg10d+PHlCEgkZGKAFQb1QhNogPJykUpo0iRwdWUfRaqGhoXZ2didPnty2bRvrLIKA86LADxShxtu+nQ4eJEtLiopiHUXbNWzYcObMmUQ0c+ZMsVjMOo72w4zbwA8UoWarrZU/MjF3LpmZsU4jAKNGjfrggw9KSkrWr1/POov2w4gQ+IEi1GwJCfT77+TsTKNHs44iDHp6etHR0UQUHR1dww1YQG1QhMAPFKEGq6igBQuIiJYuJZGIdRrB+OKLL7p06VJaWrpq1SrWWbQcihD4gSLUYLNm0cOH5O9PffuyjiIkOjo68+bNI6IFCxY8evSIdRxthqUngB8oQk1VVEQ//UQiEcXFsY4iPL6+vp6ennfu3Fm6dCnrLNoMI0LgB4pQU33zDYnFNGECJiNmgxsULlmypKKignUWrYUiBH6gCDXSrl20bx81aUKzZrGOIlRubm79+/d//PhxHIbkaoMiBH6ovQhDQ0M7PldWVqbkXjt27FDs9fPPP6s1ocZ59oy+/ZaIKCaGLCxYpxGw2NhYXV3dH3744dq1a6yzaCcUIfBD7fcaXr58+cyZM9z206dPldzr7t27ir1w6ukvVqygkhJycqJx41hHEbYOHToMGTJk48aNc+fOXbNmDes4WkgkOujpeeC99zyJPFlnAW2GU6Ma5v59mj+fiGjJEmrQgHUawZs3b56+vv66deuKi4tZZ9FC5eVZWVlznjw5wDoIaDkUoYaZPZvu3qXeval/f9ZRgMje3n7kyJESiQRr9qoDN2WBIZ6fADVDEWqS8+cpKYn09CghgXUUeG727NlGRkbbt28/duwY6yzaprq6moiMcJEQ1AxFqEmWLMl/9ozGjKEOHVhHgedsbGxCQ0NlMhkGhSqHIgR+oAg1Rlpa2po1Lp6eQXPnso4CfzZ9+vQmTZrs27cvMzOTdRatgiIEfqAINYNEIpk6dSoR+ft3srRknQb+zMzMbMqUKUQ0ffp0rNmrQtw1QhQhqBuKUDOsWrWqsLDQwcEhNDSUdRZ4hfDwcGtr6+PHj+/atYt1Fu2BESHwA0WoAR48eBATE0NES5YsMTAwYB0HXsHY2DgqKoqIoqKiJBIJ6zhaAkUI/EARaoC5c+feuXPH09Nz4MCBrLPAPxo7dqyDg8O5c+c2btzIOouWQBECP1CE9d2lS5dWrlypq6sbHx/POgu8ToMGDaKjo11d/X/6qXNtLes0WoErQjxHCOrG63KugwcPVvLMHqZVU5g8efLTp09Hjx7t4uLCOgu8wfDhwUuXBp84QT/+SBMmsE6j+TAiBH7wWoQFBQV8Hk4LHDhwYNeuXY0aNZqLZyY0ga4uxcTQZ5/R3LkUHEwmJqwDaTgUIfADp0brL6lUyj0yMWPGDBsbG9ZxQCkDB1L37lRRQStWsI6i+VCEwA9eR4QxMTEWyq0bdOTIEdxxsGbNmvz8fDs7u7CwMNZZoA7i4sjDgxYtorFjydycdRqNJZPJampqdHR0cI0Q1I3XIgwODra3t1fmnQ0bNhR4EVZWVkZHRxNRfHw8fiPWLJ98Qt7elJ5OixYRVu19azU1NTKZzNDQUFcXJ65AvfBfWD01f/788vLy7t27BwYGss4CdRYbSzo6lJhIN26wjqKxcF4UeKNJRVhZWenv779mzZo7d+6wzqJef/zxx7Jly3R0dBISEnR0dFjHgTpzcaFBg6imhhYsYB1FY6EIgTeaVIQpKSm7d+8eM2aMtbW1u7v7smXLbt68yTqUWkRERDx58iQoKOijjz5inQXe0oIFJBLRmjV06RLrKJoJRQi80aQi7Nev308//eTn56enp5eTkxMeHm5nZ9e7d++VK1dqUyPm5uZu377d0NDwu+++Y50F3l6bNhQcTM+eUXQ06yiaCTNuA280qQjNzc2//vrr3bt337p1a/369YGBgQ0bNjxw4MCECRPee+89boxYWlrKOuY7kUql4eHhMpksMjKyRYsWrOPAO5k7lwwNadMmOnWKdRQNhBEh8EaTilDBzMwsODg4OTm5oqJi165dQUFBhoaG3Bjxvffea9euXUxMzMWLF1nHfBsbNmw4ceJE8+bNuWV9QKM1b05jx5JUSliy9y2gCIE3GlmECkZGRgEBARs2bLh9+zbXiI0aNSoqKpozZ46joyPXiL///jvrmMqqrq7mVjlfuHChsbEx6zigAlFR1LgxpaTQwYOso2gaTDQKvNHsIlQwNDTkGlExRjQxMeEasU2bNlwjXrhwgXXMN4iNjb1+/Xq3bt2+/PJL1llANZo2pfBwIqKZM1lH0TQYEQJvtKQIFf7eiI0bN+Ya8YMPPuAa8fz586xjvsKNGzeWLl2qo6MTHx+PRya0ybffUrNmlJ1Nv/3GOopGQRECb7StCBUaNmzINeKtW7e4RjQ1NeUasW3btlwjFhUVsY75QkRERHV19ZAhQ9zd3VlnAVVq1IgiIoiIpk0jqZR1Gs2BIgTeaG0RKrzciOnp6ZMmTbK0tOQasV27du3atYuMjMzOzmYb8ujRo5s3b27YsGFsbCzbJKAOEyaQnR2dOUPbtrGOojlQhMAbtc81OmzYMMVT4U2aNFFyry5dukQ/f/yqR48eKkliYGDg5eXl5eW1dOnSI0eObN26dfPmzUVFRUVFRQsXLnRwcPD39w8MDOR/QCaTyaZOncr9s2XLljwfHXjQsCHNnk1jxtDMmTRoEIl4neJXU3HPEeJmGeCBjkwmY52BGYlEwjVicnJyeXk592KrVq0+/fTTwMBANzc3fq7V/fLLL8OHD7eysiopKWncuDFBhY1cAAAVM0lEQVQPRwT+SSTUoQMVF9OPP9Lo0azTaIKIiIjFixcvXLgwgjuzDKA22n9q9DX09PS4x/Bv3Lhx+PDhSZMm2djYXLlyJTExsWfPnvb29mFhYdnZ2Wr9XaGmpiYqKoqIYmNj0YJaTE+PYmKIiKKjqbqacRiNgFOjwBtBF6HC3xvR1tb26tWrXCO2atVKfY24ePHiq1evdurUacSIESr/cKhXAgOpSxcqK6MffmAdRROgCIE3gj41+hpSqTQ3N3fr1q3bt29XTNtmZ2f32WefBQYG9ujRQyVrpJWVlbVp06aqqiorK8vDw+PdPxDqud9+I19fsrCgy5cJ4//XGzJkyJYtWzZt2jRkyBDWWUDLYUT4arq6uooxYmFhYXR09Pvvv3/9+nVujGhtbR0cHJySkiIWi9/lKDNmzKiqqho8eDBaUCD69aNevejuXVqyhHUUHtXU1GRkZERGRnbt2vXp06dK7oURIfAGI8I6OHfu3NatWzdu3KiYtq1p06a+vr6BgYG+vr6iOt4LWFBQ0LVrV5FIVFhY6OjoqIa8UB8dP04ff0zGxnTxIllZsU6jTpcuXdqzZ8+ePXuysrKePHnCvZiZmdmrVy9ldvfy8tq/f396erqXl5c6YwKgCN8K14ibN29WTNtmYWHh5+cXGBjYr1+/Bg0aKPMhHh4ehw4dioiIWLhwoTrDQr3z6aeUkkLh4fSf/7COompisfjo0aOpqakZGRknT57kXtTV1e3cubOXl5e/v7/ylxXc3Nxyc3NzcnJU9QAVwD9BEb4TrhG3bNmimLbN3Ny8f//+gYGBffv21dfX/6cdt27d+q9//atZs2YlJSWmpqZ85YV6obCQOnYkkYjOnyd7e9ZpVOHWrVtpaWmpqalpaWmPHj3iXjQ3N+/Tpw/Xf7a2tnX9zM6dO586daqgoKBTp06qzgvwJyhC1eAacevWrYpp25o0acI9of/3RqytrW3Xrt3FixeTkpJCQkJY5AXGhg+nX36hkSNp7VrWUd6WRCI5depUSkpKampqfn6+4ieJs7NzQECAl5eXh4eHkmdHXsnJyamkpOTChQtt2rRRUWSAV0MRqti5c+dSU1NTUlJycnK4V8zMzAICAgIDA318fAwMDIgoLi5u+vTpzs7Op0+fruuVRdAOV66QkxNJJHTmDDk7s05TF7dv387KyuL67/79+9yLRkZGPXr08Pf3HzRokJ2dnUoOZGdnd+PGjWvXrqnqAwH+CYpQXS5fvpySkrJ161ZFIxoZGfXu3btv375RUVGPHj1KS0vz8fFhGxIYGj+efviBBg/WgAlIpVJpQUFBRkZGSkrKkSNHpM/nDndwcODOfCp+yVMhCwuLe/fu3blzx8LCQrWfDPAXKEK1Kykp2bZt27Zt2woKCrhXdHV1W7ZsuXfvXicnJ7bZgKGbN+n996mmho4coW7dWKd5lXv37u3fv5/rv5s3b3IvGhoaurm5eXl5DRgw4IMPPlDf0Q0NDZ88eVJdXY3pRkHdUIT8uXTp0rZt2+bNm/f48WMi0tHR6dOnz4QJEwICAvT09FinAwYiI2nhQvL2pn37WEd5yblzlJpKGRn0+PHco0flc9/b29t7e3t7eXn5+vo2atRI3RmkUqlIJNLR0RGLxVieE9QNRci3Ll26FBQUDBkyJDU1lWtEW1vbMWPGjB8/vlmzZqzTAa8ePKDWrenePdq/n3r3Zpnk4UNKT6e9e2nvXno+9iNPz/MNGkzy8/Pz8/Pj+Y6Vx48fm5iYGBsbc39HANQKRcg3R0fHixcvlpSUWFlZbd68OTEx8dy5c0Skr68/YMCAkJAQPD4sKLGxNGMGubrSsWPE/8jn8mVKSaHUVDp0iGpr5S9aWZGPDwUEUN++zOaBq6iosLKysrS0rKioYJMAhARFyDcbG5vy8vKysjIbGxvulezs7MTExB07dnATtnXp0mXs2LHDhw/H5FJCUFNDjo5UWko7dtDAgTwdMSeHUlLof/+jq1flL+rpUadO5O9PAQHUpQuDSv6LgwcPenp6tmjR4qoiIoDaoAj5ZmJi8vjx40ePHpmYmLz8ellZ2erVq1euXHn79m0iMjU1HTFiRFhYmIODA6OkwJPvv6fQUHJ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      "text/html": [],
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x15546cf22e30>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# attach it to the mol\n",
    "from etflow.commons.featurization import get_mol_from_smiles\n",
    "from etflow.commons.covmat import set_rdmol_positions\n",
    "\n",
    "# Generate conformer\n",
    "mol = get_mol_from_smiles(smiles)\n",
    "gen_idx = 0 # 0th conformer generated\n",
    "mol = set_rdmol_positions(mol, gen_position[gen_idx])\n",
    "mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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