WLALL_backbone_LSS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSS applied to backbone generation of WLALL pentapeptide"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the different components from their respective repos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mdn_propagator.propagator import Propagator\n",
    "from molgen.models import DDPM\n",
    "from snrv import Snrv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mdtraj as md\n",
    "from pathlib import Path\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and prep data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "trj_fnames = sorted([str(i) for i in Path('data_mdshare/').glob('pentapeptide-*-500ns-impl-solv.xtc')])\n",
    "top_fname = 'data_mdshare/pentapeptide-impl-solv.pdb'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f8520511070>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f85204abd90>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f8520479dc0>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f8520525d00>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f8520479c40>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f8520525a90>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f852052daf0>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f85204d6f40>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f85204c6a30>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f85204b9e80>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f8520432f40>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f85204b27c0>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f852043cf70>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f8520441040>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f8520439880>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f8520451ca0>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f852045b490>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f8520463c10>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f85203ee3d0>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f85203f7b50>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f85203f9490>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f852040ca90>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f85204123d0>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f85204209d0>,\n",
       " <mdtraj.Trajectory with 5001 frames, 94 atoms, 5 residues, and unitcells at 0x7f8520427310>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trjs = [md.load(t, top=top_fname).center_coordinates() for t in trj_fnames]\n",
    "trjs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, torch.Size([5001, 10]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coords_torch = list()\n",
    "for trj in trjs:\n",
    "    t_backbone = trj.atom_slice(trj.top.select('name == CA')).center_coordinates()\n",
    "    pdists = [torch.pdist(p)[None] for p in torch.tensor(t_backbone.xyz)]\n",
    "    #pdists = [torch.pdist(p)[None] for p in torch.tensor(trj.xyz)]\n",
    "    coords_torch.append(torch.cat(pdists))\n",
    "len(coords_torch), coords_torch[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cuda'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device= 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SRV fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from snrv.utils import set_random_seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting random seed to 42\n"
     ]
    }
   ],
   "source": [
    "set_random_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = coords_torch[0].size()[1]\n",
    "output_size = 3\n",
    "hidden_depth = 2\n",
    "hidden_size = 100\n",
    "batch_norm = True\n",
    "dropout_rate = 0.0\n",
    "lr = 1E-2\n",
    "weight_decay = 0.0\n",
    "val_frac = 0.05\n",
    "n_epochs = 300\n",
    "batch_size = 25000\n",
    "VAMPdegree = 2\n",
    "is_reversible = True\n",
    "num_workers = 0\n",
    "\n",
    "model_snrv = Snrv(input_size, output_size, hidden_depth=hidden_depth, hidden_size=hidden_size,\n",
    "            batch_norm=batch_norm, dropout_rate=dropout_rate, lr=lr, weight_decay=weight_decay, \n",
    "            val_frac=val_frac, n_epochs=n_epochs, batch_size=batch_size, \n",
    "            VAMPdegree=VAMPdegree,is_reversible=is_reversible, num_workers=num_workers,\n",
    "            activation=torch.nn.GELU(), device=device)\n",
    "model_snrv = model_snrv.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "lag_n = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 0: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:02<00:00,  2.37batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 0]\t training loss = -1.171\t validation loss = -1.546\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.79batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 1]\t training loss = -1.719\t validation loss = -1.513\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.57batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 2]\t training loss = -1.910\t validation loss = -1.704\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.79batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 3]\t training loss = -2.008\t validation loss = -1.598\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.81batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 4]\t training loss = -2.040\t validation loss = -1.837\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 5: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.81batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 5]\t training loss = -2.064\t validation loss = -1.977\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 6: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.82batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 6]\t training loss = -2.077\t validation loss = -1.973\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 7: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.74batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 7]\t training loss = -2.091\t validation loss = -1.990\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 8: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.80batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 8]\t training loss = -2.101\t validation loss = -2.019\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 9: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.88batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 9]\t training loss = -2.112\t validation loss = -2.022\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 10: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.87batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 10]\t training loss = -2.119\t validation loss = -2.045\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 11: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.77batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 11]\t training loss = -2.128\t validation loss = -2.070\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 12: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.80batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 12]\t training loss = -2.134\t validation loss = -2.075\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 13: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.79batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 13]\t training loss = -2.145\t validation loss = -1.907\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 14: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.78batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 14]\t training loss = -2.152\t validation loss = -1.866\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 15: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.87batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 15]\t training loss = -2.160\t validation loss = -1.975\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 16: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.86batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 16]\t training loss = -2.163\t validation loss = -1.991\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 17: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.86batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 17]\t training loss = -2.169\t validation loss = -1.909\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 18: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.82batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 18]\t training loss = -2.177\t validation loss = -1.929\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 19: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.79batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 19]\t training loss = -2.177\t validation loss = -2.091\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 20: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.86batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 20]\t training loss = -2.176\t validation loss = -1.990\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 21: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.62batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 21]\t training loss = -2.179\t validation loss = -1.852\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 22: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.79batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 22]\t training loss = -2.184\t validation loss = -2.110\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 23: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.82batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 23]\t training loss = -2.192\t validation loss = -2.064\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 24: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.82batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 24]\t training loss = -2.194\t validation loss = -2.083\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 25: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.91batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 25]\t training loss = -2.198\t validation loss = -1.944\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 26: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.83batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 26]\t training loss = -2.199\t validation loss = -2.066\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 27: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.89batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 27]\t training loss = -2.199\t validation loss = -2.148\n"
     ]
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      "[Epoch 28]\t training loss = -2.202\t validation loss = -2.115\n"
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      "Epoch 29: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.82batch/s]\n"
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      "[Epoch 29]\t training loss = -2.205\t validation loss = -2.152\n"
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      "Epoch 30: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.60batch/s]\n"
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      "[Epoch 30]\t training loss = -2.204\t validation loss = -2.122\n"
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      "[Epoch 31]\t training loss = -2.207\t validation loss = -2.105\n"
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      "Epoch 32: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.84batch/s]\n"
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      "[Epoch 32]\t training loss = -2.210\t validation loss = -2.123\n"
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      "[Epoch 33]\t training loss = -2.211\t validation loss = -2.140\n"
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      "[Epoch 34]\t training loss = -2.214\t validation loss = -2.162\n"
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      "[Epoch 35]\t training loss = -2.218\t validation loss = -2.196\n"
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      "[Epoch 36]\t training loss = -2.218\t validation loss = -2.196\n"
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      "Epoch 37: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.82batch/s]\n"
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      "[Epoch 37]\t training loss = -2.218\t validation loss = -2.079\n"
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      "[Epoch 38]\t training loss = -2.223\t validation loss = -2.159\n"
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      "[Epoch 39]\t training loss = -2.225\t validation loss = -2.198\n"
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      "[Epoch 40]\t training loss = -2.223\t validation loss = -2.135\n"
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      "[Epoch 41]\t training loss = -2.226\t validation loss = -2.215\n"
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      "[Epoch 42]\t training loss = -2.230\t validation loss = -2.188\n"
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      "[Epoch 47]\t training loss = -2.242\t validation loss = -2.223\n"
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      "[Epoch 49]\t training loss = -2.240\t validation loss = -2.200\n"
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      "Epoch 50: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.85batch/s]\n"
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      "[Epoch 71]\t training loss = -2.283\t validation loss = -2.290\n"
     ]
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      "[Epoch 72]\t training loss = -2.284\t validation loss = -2.199\n"
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      "Epoch 73: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.79batch/s]\n"
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      "[Epoch 73]\t training loss = -2.283\t validation loss = -2.285\n"
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      "[Epoch 74]\t training loss = -2.281\t validation loss = -2.297\n"
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      "[Epoch 75]\t training loss = -2.286\t validation loss = -2.132\n"
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      "Epoch 76: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.83batch/s]\n"
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      "[Epoch 76]\t training loss = -2.285\t validation loss = -2.155\n"
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      "Epoch 77: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.88batch/s]\n"
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      "[Epoch 77]\t training loss = -2.282\t validation loss = -2.162\n"
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      "Epoch 78: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.74batch/s]\n"
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      "[Epoch 78]\t training loss = -2.286\t validation loss = -2.274\n"
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      "Epoch 79: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.91batch/s]\n"
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      "[Epoch 79]\t training loss = -2.284\t validation loss = -2.288\n"
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      "Epoch 80: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.82batch/s]\n"
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      "[Epoch 80]\t training loss = -2.293\t validation loss = -2.283\n"
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      "Epoch 81: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.90batch/s]\n"
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      "[Epoch 81]\t training loss = -2.294\t validation loss = -2.315\n"
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      "[Epoch 82]\t training loss = -2.297\t validation loss = -2.328\n"
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      "Epoch 84: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.64batch/s]\n"
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      "[Epoch 84]\t training loss = -2.296\t validation loss = -2.325\n"
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      "[Epoch 85]\t training loss = -2.298\t validation loss = -2.323\n"
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      "Epoch 86: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.80batch/s]\n"
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      "[Epoch 86]\t training loss = -2.297\t validation loss = -2.329\n"
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      "[Epoch 87]\t training loss = -2.300\t validation loss = -2.314\n"
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      "[Epoch 90]\t training loss = -2.300\t validation loss = -2.277\n"
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      "Epoch 91: 100%|████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.83batch/s]\n"
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      "[Epoch 93]\t training loss = -2.303\t validation loss = -2.351\n"
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      "[Epoch 94]\t training loss = -2.308\t validation loss = -2.244\n"
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      "[Epoch 115]\t training loss = -2.315\t validation loss = -2.353\n"
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      "[Epoch 159]\t training loss = -2.327\t validation loss = -2.384\n"
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      "[Epoch 247]\t training loss = -2.339\t validation loss = -2.397\n"
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      "[Epoch 248]\t training loss = -2.335\t validation loss = -2.398\n"
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      "[Epoch 250]\t training loss = -2.334\t validation loss = -2.397\n"
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      "[Epoch 251]\t training loss = -2.338\t validation loss = -2.397\n"
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      "[Epoch 254]\t training loss = -2.336\t validation loss = -2.398\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 291: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.65batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 291]\t training loss = -2.340\t validation loss = -2.398\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 292: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.79batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 292]\t training loss = -2.337\t validation loss = -2.399\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 293: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.84batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 293]\t training loss = -2.336\t validation loss = -2.398\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 294: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.88batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 294]\t training loss = -2.334\t validation loss = -2.398\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 295: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.91batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 295]\t training loss = -2.337\t validation loss = -2.399\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 296: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.84batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 296]\t training loss = -2.336\t validation loss = -2.399\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 297: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.90batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 297]\t training loss = -2.338\t validation loss = -2.399\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 298: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.82batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 298]\t training loss = -2.337\t validation loss = -2.399\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 299: 100%|███████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00,  3.85batch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Epoch 299]\t training loss = -2.336\t validation loss = -2.399\n"
     ]
    }
   ],
   "source": [
    "model_snrv.fit(coords_torch, lag=lag_n, scheduler=0.98)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(np.arange(len(model_snrv.training_losses)), model_snrv.training_losses)\n",
    "ax.plot(np.arange(len(model_snrv.validation_losses)), model_snrv.validation_losses)\n",
    "ax.set_xlabel('epoch')\n",
    "ax.set_ylabel('loss')\n",
    "ax.legend(['training','validation'])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_freq = 100 # ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f851cc0e610>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evals = model_snrv.evals.cpu().detach().numpy()\n",
    "plt.bar(range(1,evals.size), -lag_n*save_freq/np.log(evals[1:]))\n",
    "plt.ylabel('Implied timescale (ps)')\n",
    "plt.xticks(range(1,evals.size))\n",
    "plt.xlabel('Timescale index')\n",
    "plt.axhline(lag_n*save_freq, color='k', linestyle=':')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Snrv(\n",
       "  (activation): GELU(approximate=none)\n",
       "  (model): Sequential(\n",
       "    (0): Linear(in_features=10, out_features=100, bias=True)\n",
       "    (1): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (2): GELU(approximate=none)\n",
       "    (3): Linear(in_features=100, out_features=100, bias=True)\n",
       "    (4): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (5): GELU(approximate=none)\n",
       "    (6): Linear(in_features=100, out_features=3, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_snrv.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "evecs = model_snrv.transform(torch.cat(coords_torch)).cpu().detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import deeptime as dt\n",
    "tica = dt.decomposition.TICA(lagtime = 5, dim=2)\n",
    "TICs = tica.fit_transform([a.numpy() for a in coords_torch]).reshape(-1, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize = (15, 7))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for e in range(1, evecs.shape[1]):\n",
    "    evec = evecs[:, e]\n",
    "    ax = axes[e-1]\n",
    "    \n",
    "    im = ax.scatter(TICs[:,0], TICs[:,1], c=evec, s=10, cmap='coolwarm')\n",
    "    ax.set_xlabel('$TIC_0$', fontsize=18)\n",
    "    ax.set_ylabel('$TIC_1$', fontsize=18)\n",
    "    cbar = plt.colorbar(im, ax=ax)\n",
    "    cbar.set_label(f'EV$_{e}$', size=18)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([5001, 2]), 25)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CVs = [model_snrv.transform(x).cpu().detach()[:, 1:] for x in coords_torch]\n",
    "CVs[0].shape, len(CVs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MDN propagator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_mdn = Propagator(dim = CVs[0].size(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/project/andrewferguson/Kirill/env_lss/lib/python3.8/site-packages/lightning_lite/plugins/environments/slurm.py:167: PossibleUserWarning: The `srun` command is available on your system but is not used. HINT: If your intention is to run Lightning on SLURM, prepend your python command with `srun` like so: srun python /project/andrewferguson/Kirill/env_lss/lib/python3.8 ...\n",
      "  rank_zero_warn(\n",
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name    | Type                  | Params\n",
      "--------------------------------------------------\n",
      "0 | mdn     | MixtureDensityNetwork | 33.0 K\n",
      "1 | _scaler | MinMaxScaler          | 0     \n",
      "--------------------------------------------------\n",
      "33.0 K    Trainable params\n",
      "0         Non-trainable params\n",
      "33.0 K    Total params\n",
      "0.132     Total estimated model params size (MB)\n",
      "/project/andrewferguson/Kirill/env_lss/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 48 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d48cf2391d084ac5b992096a5e04b6c1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=25` reached.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Propagator(\n",
       "  (mdn): MixtureDensityNetwork(\n",
       "    (network): MLP(\n",
       "      (mlp): Sequential(\n",
       "        (0): Linear(in_features=2, out_features=128, bias=True)\n",
       "        (1): SiLU()\n",
       "        (2): Linear(in_features=128, out_features=128, bias=True)\n",
       "        (3): SiLU()\n",
       "        (4): Linear(in_features=128, out_features=125, bias=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (_scaler): MinMaxScaler()\n",
       ")"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_mdn.fit(CVs, lag = 10, max_epochs=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51796f68937342f3a84a12bd73a695cb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_steps = int(1E4)\n",
    "x = CVs[0][0][None]\n",
    "synthetic_traj_CVs = model_mdn.gen_synthetic_traj(x, n_steps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DDPM Decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "xyz = list()\n",
    "for trj in trjs:\n",
    "    \n",
    "    t_backbone = trj.atom_slice(trj.top.select('backbone')).center_coordinates()\n",
    "    \n",
    "    n = trj.xyz.shape[0]\n",
    "\n",
    "    xyz.append(torch.tensor(t_backbone.xyz.reshape(n, -1)).float())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_ddpm = DDPM(xyz[0].shape[1], CVs[0].shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name              | Type              | Params\n",
      "--------------------------------------------------------\n",
      "0 | model             | GaussianDiffusion | 4.0 M \n",
      "1 | ema_model         | GaussianDiffusion | 4.0 M \n",
      "2 | _feature_scaler   | MinMaxScaler      | 0     \n",
      "3 | _condition_scaler | MinMaxScaler      | 0     \n",
      "--------------------------------------------------------\n",
      "7.9 M     Trainable params\n",
      "0         Non-trainable params\n",
      "7.9 M     Total params\n",
      "31.749    Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d98abbec102945789fa000b4dc84c959",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=25` reached.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DDPM(\n",
       "  (model): GaussianDiffusion(\n",
       "    (denoise_fn): Unet1D(\n",
       "      (init_conv): Conv1d(1, 32, kernel_size=(7,), stride=(1,), padding=(3,))\n",
       "      (time_mlp): Sequential(\n",
       "        (0): SinusoidalPosEmb()\n",
       "        (1): Linear(in_features=32, out_features=128, bias=True)\n",
       "        (2): GELU(approximate=none)\n",
       "        (3): Linear(in_features=128, out_features=128, bias=True)\n",
       "      )\n",
       "      (downs): ModuleList(\n",
       "        (0): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=64, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=64, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv1d(32, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv1d(128, 32, kernel_size=(1,), stride=(1,))\n",
       "                  (1): LayerNorm()\n",
       "                )\n",
       "              )\n",
       "              (norm): LayerNorm()\n",
       "            )\n",
       "          )\n",
       "          (3): Conv1d(32, 32, kernel_size=(3,), stride=(2,), padding=(1,))\n",
       "        )\n",
       "        (1): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=64, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=64, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv1d(32, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv1d(128, 32, kernel_size=(1,), stride=(1,))\n",
       "                  (1): LayerNorm()\n",
       "                )\n",
       "              )\n",
       "              (norm): LayerNorm()\n",
       "            )\n",
       "          )\n",
       "          (3): Conv1d(32, 64, kernel_size=(3,), stride=(2,), padding=(1,))\n",
       "        )\n",
       "        (2): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 64, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 64, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 64, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 64, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv1d(64, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv1d(128, 64, kernel_size=(1,), stride=(1,))\n",
       "                  (1): LayerNorm()\n",
       "                )\n",
       "              )\n",
       "              (norm): LayerNorm()\n",
       "            )\n",
       "          )\n",
       "          (3): Conv1d(64, 128, kernel_size=(3,), stride=(2,), padding=(1,))\n",
       "        )\n",
       "        (3): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv1d(128, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv1d(128, 128, kernel_size=(1,), stride=(1,))\n",
       "                  (1): LayerNorm()\n",
       "                )\n",
       "              )\n",
       "              (norm): LayerNorm()\n",
       "            )\n",
       "          )\n",
       "          (3): Conv1d(128, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "        )\n",
       "      )\n",
       "      (ups): ModuleList(\n",
       "        (0): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=512, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(384, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv1d(384, 256, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=512, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(384, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv1d(384, 256, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv1d(256, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv1d(128, 256, kernel_size=(1,), stride=(1,))\n",
       "                  (1): LayerNorm()\n",
       "                )\n",
       "              )\n",
       "              (norm): LayerNorm()\n",
       "            )\n",
       "          )\n",
       "          (3): Sequential(\n",
       "            (0): Upsample(scale_factor=2.0, mode=nearest)\n",
       "            (1): Conv1d(256, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          )\n",
       "        )\n",
       "        (1): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(192, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv1d(192, 128, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(192, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv1d(192, 128, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv1d(128, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv1d(128, 128, kernel_size=(1,), stride=(1,))\n",
       "                  (1): LayerNorm()\n",
       "                )\n",
       "              )\n",
       "              (norm): LayerNorm()\n",
       "            )\n",
       "          )\n",
       "          (3): Sequential(\n",
       "            (0): Upsample(scale_factor=2.0, mode=nearest)\n",
       "            (1): Conv1d(128, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          )\n",
       "        )\n",
       "        (2): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(96, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 64, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 64, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv1d(96, 64, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(96, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 64, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 64, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv1d(96, 64, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv1d(64, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv1d(128, 64, kernel_size=(1,), stride=(1,))\n",
       "                  (1): LayerNorm()\n",
       "                )\n",
       "              )\n",
       "              (norm): LayerNorm()\n",
       "            )\n",
       "          )\n",
       "          (3): Sequential(\n",
       "            (0): Upsample(scale_factor=2.0, mode=nearest)\n",
       "            (1): Conv1d(64, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          )\n",
       "        )\n",
       "        (3): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=64, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(64, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv1d(64, 32, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=64, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(64, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv1d(64, 32, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv1d(32, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv1d(128, 32, kernel_size=(1,), stride=(1,))\n",
       "                  (1): LayerNorm()\n",
       "                )\n",
       "              )\n",
       "              (norm): LayerNorm()\n",
       "            )\n",
       "          )\n",
       "          (3): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "        )\n",
       "      )\n",
       "      (mid_block1): ResnetBlock(\n",
       "        (mlp): Sequential(\n",
       "          (0): SiLU()\n",
       "          (1): Linear(in_features=128, out_features=512, bias=True)\n",
       "        )\n",
       "        (block1): Block(\n",
       "          (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (block2): Block(\n",
       "          (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (res_conv): Identity()\n",
       "      )\n",
       "      (mid_attn): Residual(\n",
       "        (fn): PreNorm(\n",
       "          (fn): Attention(\n",
       "            (to_qkv): Conv1d(256, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "            (to_out): Conv1d(128, 256, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (norm): LayerNorm()\n",
       "        )\n",
       "      )\n",
       "      (mid_block2): ResnetBlock(\n",
       "        (mlp): Sequential(\n",
       "          (0): SiLU()\n",
       "          (1): Linear(in_features=128, out_features=512, bias=True)\n",
       "        )\n",
       "        (block1): Block(\n",
       "          (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (block2): Block(\n",
       "          (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (res_conv): Identity()\n",
       "      )\n",
       "      (final_res_block): ResnetBlock(\n",
       "        (mlp): Sequential(\n",
       "          (0): SiLU()\n",
       "          (1): Linear(in_features=128, out_features=64, bias=True)\n",
       "        )\n",
       "        (block1): Block(\n",
       "          (proj): WeightStandardizedConv2d(64, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (block2): Block(\n",
       "          (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (res_conv): Conv1d(64, 32, kernel_size=(1,), stride=(1,))\n",
       "      )\n",
       "      (final_conv): Conv1d(32, 1, kernel_size=(1,), stride=(1,))\n",
       "    )\n",
       "  )\n",
       "  (ema_model): GaussianDiffusion(\n",
       "    (denoise_fn): Unet1D(\n",
       "      (init_conv): Conv1d(1, 32, kernel_size=(7,), stride=(1,), padding=(3,))\n",
       "      (time_mlp): Sequential(\n",
       "        (0): SinusoidalPosEmb()\n",
       "        (1): Linear(in_features=32, out_features=128, bias=True)\n",
       "        (2): GELU(approximate=none)\n",
       "        (3): Linear(in_features=128, out_features=128, bias=True)\n",
       "      )\n",
       "      (downs): ModuleList(\n",
       "        (0): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=64, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=64, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv1d(32, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv1d(128, 32, kernel_size=(1,), stride=(1,))\n",
       "                  (1): LayerNorm()\n",
       "                )\n",
       "              )\n",
       "              (norm): LayerNorm()\n",
       "            )\n",
       "          )\n",
       "          (3): Conv1d(32, 32, kernel_size=(3,), stride=(2,), padding=(1,))\n",
       "        )\n",
       "        (1): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=64, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=64, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv1d(32, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv1d(128, 32, kernel_size=(1,), stride=(1,))\n",
       "                  (1): LayerNorm()\n",
       "                )\n",
       "              )\n",
       "              (norm): LayerNorm()\n",
       "            )\n",
       "          )\n",
       "          (3): Conv1d(32, 64, kernel_size=(3,), stride=(2,), padding=(1,))\n",
       "        )\n",
       "        (2): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 64, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 64, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 64, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 64, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv1d(64, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv1d(128, 64, kernel_size=(1,), stride=(1,))\n",
       "                  (1): LayerNorm()\n",
       "                )\n",
       "              )\n",
       "              (norm): LayerNorm()\n",
       "            )\n",
       "          )\n",
       "          (3): Conv1d(64, 128, kernel_size=(3,), stride=(2,), padding=(1,))\n",
       "        )\n",
       "        (3): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Identity()\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv1d(128, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv1d(128, 128, kernel_size=(1,), stride=(1,))\n",
       "                  (1): LayerNorm()\n",
       "                )\n",
       "              )\n",
       "              (norm): LayerNorm()\n",
       "            )\n",
       "          )\n",
       "          (3): Conv1d(128, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "        )\n",
       "      )\n",
       "      (ups): ModuleList(\n",
       "        (0): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=512, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(384, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv1d(384, 256, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=512, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(384, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv1d(384, 256, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv1d(256, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv1d(128, 256, kernel_size=(1,), stride=(1,))\n",
       "                  (1): LayerNorm()\n",
       "                )\n",
       "              )\n",
       "              (norm): LayerNorm()\n",
       "            )\n",
       "          )\n",
       "          (3): Sequential(\n",
       "            (0): Upsample(scale_factor=2.0, mode=nearest)\n",
       "            (1): Conv1d(256, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          )\n",
       "        )\n",
       "        (1): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(192, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv1d(192, 128, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=256, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(192, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 128, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv1d(192, 128, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv1d(128, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv1d(128, 128, kernel_size=(1,), stride=(1,))\n",
       "                  (1): LayerNorm()\n",
       "                )\n",
       "              )\n",
       "              (norm): LayerNorm()\n",
       "            )\n",
       "          )\n",
       "          (3): Sequential(\n",
       "            (0): Upsample(scale_factor=2.0, mode=nearest)\n",
       "            (1): Conv1d(128, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          )\n",
       "        )\n",
       "        (2): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(96, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 64, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 64, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv1d(96, 64, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=128, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(96, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 64, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 64, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv1d(96, 64, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv1d(64, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv1d(128, 64, kernel_size=(1,), stride=(1,))\n",
       "                  (1): LayerNorm()\n",
       "                )\n",
       "              )\n",
       "              (norm): LayerNorm()\n",
       "            )\n",
       "          )\n",
       "          (3): Sequential(\n",
       "            (0): Upsample(scale_factor=2.0, mode=nearest)\n",
       "            (1): Conv1d(64, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          )\n",
       "        )\n",
       "        (3): ModuleList(\n",
       "          (0): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=64, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(64, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv1d(64, 32, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (1): ResnetBlock(\n",
       "            (mlp): Sequential(\n",
       "              (0): SiLU()\n",
       "              (1): Linear(in_features=128, out_features=64, bias=True)\n",
       "            )\n",
       "            (block1): Block(\n",
       "              (proj): WeightStandardizedConv2d(64, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (block2): Block(\n",
       "              (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "              (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "              (act): SiLU()\n",
       "            )\n",
       "            (res_conv): Conv1d(64, 32, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (2): Residual(\n",
       "            (fn): PreNorm(\n",
       "              (fn): LinearAttention(\n",
       "                (to_qkv): Conv1d(32, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "                (to_out): Sequential(\n",
       "                  (0): Conv1d(128, 32, kernel_size=(1,), stride=(1,))\n",
       "                  (1): LayerNorm()\n",
       "                )\n",
       "              )\n",
       "              (norm): LayerNorm()\n",
       "            )\n",
       "          )\n",
       "          (3): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "        )\n",
       "      )\n",
       "      (mid_block1): ResnetBlock(\n",
       "        (mlp): Sequential(\n",
       "          (0): SiLU()\n",
       "          (1): Linear(in_features=128, out_features=512, bias=True)\n",
       "        )\n",
       "        (block1): Block(\n",
       "          (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (block2): Block(\n",
       "          (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (res_conv): Identity()\n",
       "      )\n",
       "      (mid_attn): Residual(\n",
       "        (fn): PreNorm(\n",
       "          (fn): Attention(\n",
       "            (to_qkv): Conv1d(256, 384, kernel_size=(1,), stride=(1,), bias=False)\n",
       "            (to_out): Conv1d(128, 256, kernel_size=(1,), stride=(1,))\n",
       "          )\n",
       "          (norm): LayerNorm()\n",
       "        )\n",
       "      )\n",
       "      (mid_block2): ResnetBlock(\n",
       "        (mlp): Sequential(\n",
       "          (0): SiLU()\n",
       "          (1): Linear(in_features=128, out_features=512, bias=True)\n",
       "        )\n",
       "        (block1): Block(\n",
       "          (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (block2): Block(\n",
       "          (proj): WeightStandardizedConv2d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          (norm): GroupNorm(8, 256, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (res_conv): Identity()\n",
       "      )\n",
       "      (final_res_block): ResnetBlock(\n",
       "        (mlp): Sequential(\n",
       "          (0): SiLU()\n",
       "          (1): Linear(in_features=128, out_features=64, bias=True)\n",
       "        )\n",
       "        (block1): Block(\n",
       "          (proj): WeightStandardizedConv2d(64, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (block2): Block(\n",
       "          (proj): WeightStandardizedConv2d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "          (norm): GroupNorm(8, 32, eps=1e-05, affine=True)\n",
       "          (act): SiLU()\n",
       "        )\n",
       "        (res_conv): Conv1d(64, 32, kernel_size=(1,), stride=(1,))\n",
       "      )\n",
       "      (final_conv): Conv1d(32, 1, kernel_size=(1,), stride=(1,))\n",
       "    )\n",
       "  )\n",
       "  (_feature_scaler): MinMaxScaler()\n",
       "  (_condition_scaler): MinMaxScaler()\n",
       ")"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_ddpm.fit(xyz, CVs, max_epochs=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decode synthetic traj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ecb00b17577a4b4ba0e21ac55be304f1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "sampling loop time step:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xyz_gen = model_ddpm.generate(synthetic_traj_CVs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "xyz_gen = xyz_gen.reshape(xyz_gen.size(0), -1 , 3).numpy()\n",
    "fake_trj = md.Trajectory(xyz = xyz_gen, topology=t_backbone.top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cda55424aad14feebbd77d7a1f8ed018",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import nglview as nv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93c72cf9ecc04abd99434719f8ffdba1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget(max_frame=9999)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v = nv.show_mdtraj(fake_trj)\n",
    "v.clear_representations()\n",
    "v.add_representation('ball+stick')\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "fake_trj.save_pdb('WLALL_backbone_synthetic_traj.pdb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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