ADP_backbone_LSS.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f31EiiwRKbrt"
      },
      "source": [
        "# LSS applied to backbone generation of Alanine dipeptide (ADP)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "GitHub:\n",
        "https://github.com/Ferg-Lab/LSS\n",
        "\n",
        "Paper:\n",
        "@article{sidky2020molecular,\n",
        "  title={Molecular latent space simulators},\n",
        "  author={Sidky, Hythem and Chen, Wei and Ferguson, Andrew L},\n",
        "  journal={Chemical Science},\n",
        "  volume={11},\n",
        "  number={35},\n",
        "  pages={9459--9467},\n",
        "  year={2020},\n",
        "  publisher={Royal Society of Chemistry}\n",
        "}"
      ],
      "metadata": {
        "id": "9BFdLInmqqHC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Allocating GPU accelerator (~1 min)"
      ],
      "metadata": {
        "id": "Rsz_KDmgRz4n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch"
      ],
      "metadata": {
        "id": "JG6DnjwMmqAC"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if torch.cuda.is_available():\n",
        "  print('GPU available')\n",
        "else:\n",
        "  print('Please set GPU via Edit -> Notebook Settings.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Xuza1E4qRcWM",
        "outputId": "f38a9804-0666-4a79-bca3-b60768a11b4d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU available\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "1svAqZXrKbr0",
        "outputId": "b00c4f19-87e4-4244-aa8d-62e24b15ab44"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "device= 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "device"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Uploading files (~3 mins)\n",
        "### (i) alanine-dipeptide-0-250ns-nowater.xtc\n",
        "### (ii) alanine-dipeptide-nowater.pdb\n",
        "#### N.B. If file upload fails, try using alternate upload means by clicking on file icon in left menu and directly uploading to colab session storage or by uploading to and mounting Google Drive"
      ],
      "metadata": {
        "id": "Ccqa7l2yPTRA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 76
        },
        "id": "WLP1kalcPYH6",
        "outputId": "76c381a9-555f-4e5a-b6ef-1e4aab02c2b5"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d20936df-422e-4c09-846e-9f02d315f654\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d20936df-422e-4c09-846e-9f02d315f654\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving alanine-dipeptide-0-250ns-nowater.xtc to alanine-dipeptide-0-250ns-nowater.xtc\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z8jFqqLaO7y5"
      },
      "source": [
        "## Install necessary packages (~10 mins)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install numpy scipy pandas scikit-learn jupyter ipywidgets==7.7.2 widgetsnbextension jupyter_contrib_nbextensions matplotlib MDTraj tqdm pytest pyemma deeptime einops torch torchvision pytorch-lightning nglview"
      ],
      "metadata": {
        "id": "9Zi6dRJzK4K2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a90c088d-6fed-4961-b999-d0ebd91ab130"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting jupyter\n",
            "  Downloading jupyter-1.0.0-py2.py3-none-any.whl (2.7 kB)\n",
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            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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            "Collecting deeptime\n",
            "  Using cached deeptime-0.4.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n",
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            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert->jupyter) (2023.12.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert->jupyter) (0.34.0)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.1->nbconvert->jupyter) (0.18.0)\n",
            "Requirement already satisfied: anyio<4,>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from jupyter-server>=1.8->nbclassic>=0.4.7->notebook->jupyter) (3.7.1)\n",
            "Requirement already satisfied: websocket-client in /usr/local/lib/python3.10/dist-packages (from jupyter-server>=1.8->nbclassic>=0.4.7->notebook->jupyter) (1.7.0)\n",
            "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook->jupyter) (1.16.0)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<4,>=3.1.0->jupyter-server>=1.8->nbclassic>=0.4.7->notebook->jupyter) (1.3.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook->jupyter) (2.22)\n",
            "Building wheels for collected packages: jupyter_contrib_nbextensions, MDTraj, pyemma, nglview, jupyter_contrib_core\n",
            "  Building wheel for jupyter_contrib_nbextensions (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for jupyter_contrib_nbextensions: filename=jupyter_contrib_nbextensions-0.7.0-py2.py3-none-any.whl size=23428780 sha256=54c5bd7f3580944a65ca0e10aaa94eb282d00aa1b742f1e7d11cf44cf7ee970e\n",
            "  Stored in directory: /root/.cache/pip/wheels/ea/cc/7d/99ef154f984726b1201c0f72cfe9c9d7c5132c1a2ae4d8677f\n",
            "  Building wheel for MDTraj (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for MDTraj: filename=mdtraj-1.9.9-cp310-cp310-linux_x86_64.whl size=7547268 sha256=32e1bb5de92bfa09a7e15562b9bdc207c5e9806ed44e24b16c443424f6a12535\n",
            "  Stored in directory: /root/.cache/pip/wheels/4b/4e/d5/22b44e04aca0780281ffb43717ebb3404bc1a77cf8f345fa73\n",
            "  Building wheel for pyemma (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyemma: filename=pyEMMA-2.5.12-cp310-cp310-linux_x86_64.whl size=2712144 sha256=77d1627e2b6239996c731a801f7e0115fec28a95d27f50ca5677443a47be668c\n",
            "  Stored in directory: /root/.cache/pip/wheels/26/e7/52/8fcd9a27e834967fc71dedff33de9038999783184ec26af7a6\n",
            "  Building wheel for nglview (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for nglview: filename=nglview-3.0.8-py3-none-any.whl size=10216160 sha256=4961aae9cdfc33205c4ada655e58fdcb02fece4057ca67febf30b5cf7e8b4975\n",
            "  Stored in directory: /root/.cache/pip/wheels/2e/6c/59/32bf4aa0134f9c4cdca054f5192839fb4285241e2f17f7d358\n",
            "  Building wheel for jupyter_contrib_core (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for jupyter_contrib_core: filename=jupyter_contrib_core-0.4.2-py2.py3-none-any.whl size=17482 sha256=957d5f23214f03657886c7320d61c13149dadbe9420b613d36cc71066e37d46b\n",
            "  Stored in directory: /root/.cache/pip/wheels/a9/52/88/e0643cdfd68f0562087918c37dd583378648dbc3df68b907f7\n",
            "Successfully built jupyter_contrib_nbextensions MDTraj pyemma nglview jupyter_contrib_core\n",
            "Installing collected packages: jupyter_highlight_selected_word, qtpy, ppft, pox, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, lightning-utilities, jupyterlab-widgets, jedi, einops, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, MDTraj, pathos, nvidia-cusolver-cu12, deeptime, qtconsole, pyemma, torchmetrics, pytorch-lightning, jupyter_contrib_core, jupyter_nbextensions_configurator, ipywidgets, nglview, jupyter_contrib_nbextensions, jupyter\n",
            "  Attempting uninstall: jupyterlab-widgets\n",
            "    Found existing installation: jupyterlab_widgets 3.0.10\n",
            "    Uninstalling jupyterlab_widgets-3.0.10:\n",
            "      Successfully uninstalled jupyterlab_widgets-3.0.10\n",
            "  Attempting uninstall: ipywidgets\n",
            "    Found existing installation: ipywidgets 7.7.1\n",
            "    Uninstalling ipywidgets-7.7.1:\n",
            "      Successfully uninstalled ipywidgets-7.7.1\n",
            "Successfully installed MDTraj-1.9.9 deeptime-0.4.4 dill-0.3.8 einops-0.8.0 ipywidgets-7.7.2 jedi-0.19.1 jupyter-1.0.0 jupyter_contrib_core-0.4.2 jupyter_contrib_nbextensions-0.7.0 jupyter_highlight_selected_word-0.2.0 jupyter_nbextensions_configurator-0.6.3 jupyterlab-widgets-1.1.7 lightning-utilities-0.11.2 multiprocess-0.70.16 nglview-3.0.8 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 pathos-0.3.2 pox-0.3.4 ppft-1.7.6.8 pyemma-2.5.12 pytorch-lightning-2.2.3 qtconsole-5.5.1 qtpy-2.4.1 torchmetrics-1.3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!jupyter nbextension enable --py --sys-prefix widgetsnbextension"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "xeG8h6E2OAbK",
        "outputId": "f40914c2-df9e-4a51-c168-4b233cfe6ec3"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enabling notebook extension jupyter-js-widgets/extension...\n",
            "Paths used for configuration of notebook: \n",
            "    \t/usr/etc/jupyter/nbconfig/notebook.json\n",
            "Paths used for configuration of notebook: \n",
            "    \t\n",
            "      - Validating: \u001b[32mOK\u001b[0m\n",
            "Paths used for configuration of notebook: \n",
            "    \t/usr/etc/jupyter/nbconfig/notebook.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!jupyter nbextension enable nglview --py --sys-prefix"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "px3EEauZOIZa",
        "outputId": "15b8b221-1bfd-4cec-ade9-e5a369c611ab"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enabling notebook extension nglview-js-widgets/extension...\n",
            "Paths used for configuration of notebook: \n",
            "    \t/usr/etc/jupyter/nbconfig/notebook.json\n",
            "Paths used for configuration of notebook: \n",
            "    \t\n",
            "      - Validating: \u001b[32mOK\u001b[0m\n",
            "Paths used for configuration of notebook: \n",
            "    \t/usr/etc/jupyter/nbconfig/notebook.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nglview enable"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "FxYp_tZFOMvb",
        "outputId": "31e8fba6-478a-45d5-d893-de53354e79d2"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enabling notebook extension nglview-js-widgets/extension...\n",
            "Paths used for configuration of notebook: \n",
            "    \t/usr/etc/jupyter/nbconfig/notebook.json\n",
            "Paths used for configuration of notebook: \n",
            "    \t\n",
            "      - Validating: \u001b[32mOK\u001b[0m\n",
            "Paths used for configuration of notebook: \n",
            "    \t/usr/etc/jupyter/nbconfig/notebook.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install git+https://github.com/andrewlferguson/snrv.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "RvR9Kn8KN1q8",
        "outputId": "dc7dfd60-b22b-46d3-ac69-804bdd888a3f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/andrewlferguson/snrv.git\n",
            "  Cloning https://github.com/andrewlferguson/snrv.git to /tmp/pip-req-build-5kuoreb9\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/andrewlferguson/snrv.git /tmp/pip-req-build-5kuoreb9\n",
            "  Resolved https://github.com/andrewlferguson/snrv.git to commit 63aeebc2f0253bec9f5e0ab03615c107256bf34f\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: snrv\n",
            "  Building wheel for snrv (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for snrv: filename=snrv-0.1.0+52.g63aeebc-py3-none-any.whl size=30278 sha256=783c495dc695ac66886bbfe71cd357738c2b9b44080945d7ffb50511f03da77f\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-bvft170g/wheels/d8/83/c6/26e7926d23676778257c4238a0e7ca498b668f07b425672242\n",
            "Successfully built snrv\n",
            "Installing collected packages: snrv\n",
            "Successfully installed snrv-0.1.0+52.g63aeebc\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install git+https://github.com/Ferg-Lab/mdn_propagator.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "77EoLnn6OZNU",
        "outputId": "f683cd66-c7c2-455e-f65a-fb04da3a4923"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/Ferg-Lab/mdn_propagator.git\n",
            "  Cloning https://github.com/Ferg-Lab/mdn_propagator.git to /tmp/pip-req-build-jm9h7_u2\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/Ferg-Lab/mdn_propagator.git /tmp/pip-req-build-jm9h7_u2\n",
            "  Resolved https://github.com/Ferg-Lab/mdn_propagator.git to commit ad8fd32faf84908b2c4f58bf7e16195a7c4f29e8\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: mdn_propagator\n",
            "  Building wheel for mdn_propagator (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for mdn_propagator: filename=mdn_propagator-1.0.0+32.gad8fd32-py3-none-any.whl size=15911 sha256=1a9f05c32d3243520a6373a2fd5d9b077eceda6e26e99925c21c1e640a1e7262\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-n7vhvnqz/wheels/bc/a7/ff/4f2aa2dbe5dc942686e82380dbec7ba232e35df5f5213de831\n",
            "Successfully built mdn_propagator\n",
            "Installing collected packages: mdn_propagator\n",
            "Successfully installed mdn_propagator-1.0.0+32.gad8fd32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install git+https://github.com/Ferg-Lab/molgen.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "vv9Wv6xROlzA",
        "outputId": "268ec33b-2225-46ee-e126-576cb8c5f02b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/Ferg-Lab/molgen.git\n",
            "  Cloning https://github.com/Ferg-Lab/molgen.git to /tmp/pip-req-build-ia6k6i_r\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/Ferg-Lab/molgen.git /tmp/pip-req-build-ia6k6i_r\n",
            "  Resolved https://github.com/Ferg-Lab/molgen.git to commit 533a1ccbcd5d59d5beea36d26a68ff4e6c28816c\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: molgen\n",
            "  Building wheel for molgen (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for molgen: filename=molgen-1.0.0+15.g533a1cc-py3-none-any.whl size=20455 sha256=f387b319ded3f25fcc7bb718006ad88280b2d3ddd567f203d9c7a79c42ababbd\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ty1nv6df/wheels/1e/82/fb/a86e30e540a9156a4dfcf1eb19c92a23f5b728019e949672b1\n",
            "Successfully built molgen\n",
            "Installing collected packages: molgen\n",
            "Successfully installed molgen-1.0.0+15.g533a1cc\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aqzTNQKwKbrv"
      },
      "source": [
        "## Load the different components from their respective repos (~1 min)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "IK7RzQVjKbrw"
      },
      "outputs": [],
      "source": [
        "from mdn_propagator.propagator import Propagator\n",
        "from molgen.models import DDPM\n",
        "from snrv import Snrv\n",
        "from snrv.utils import set_random_seed"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3gzdHO2EKbrw"
      },
      "source": [
        "### Other dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "8pkkc6wwKbrx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "dbefea28e5434ca1858484f3c00a2d97"
          ]
        },
        "outputId": "624ee5ac-7658-49f7-d030-aed5735d4ac2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dbefea28e5434ca1858484f3c00a2d97"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import mdtraj as md\n",
        "from pathlib import Path\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import nglview as nv"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import output\n",
        "output.enable_custom_widget_manager()"
      ],
      "metadata": {
        "id": "E3yUT5TkPMm5"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3yZhY85Kbrx"
      },
      "source": [
        "# Load and prep data (~1 min)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "5HeKUyfvKbry"
      },
      "outputs": [],
      "source": [
        "trj_fnames = sorted([str(i) for i in Path('./').glob('alanine-dipeptide-*-250ns-nowater.xtc')])\n",
        "top_fname = 'alanine-dipeptide-nowater.pdb'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "EJSf9zAyKbry",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "f98321ab-7650-441f-f9ba-6849af438a8e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<mdtraj.Trajectory with 250000 frames, 22 atoms, 3 residues, and unitcells at 0x7b329c7677c0>]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "trjs = [md.load(t, top=top_fname).center_coordinates() for t in trj_fnames]\n",
        "trjs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Sw122IIaKbrz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317,
          "referenced_widgets": [
            "8c3bcad5aa4a406cbc7a6e1702053885",
            "25b7e95abeaf4efcab15db5b11a9609e",
            "262528211cf74ae8a1f9d98f42c53bc5",
            "2d7ac1e1bbb34409bf2ab7ecd8002ddb",
            "cdae90d1405447fba7ab92758b7c587e",
            "a3531be4c5cf4117a1030d4cbc22abf6",
            "5f8fcf183ed548719d6aa8bba10c99ba",
            "9a563d60ec3b477084d6757b81289aa0",
            "6bbaf87e7a3f417e8e7753d15172f988",
            "74b857b18a424649a1ca3119f195e07e",
            "3234aa9f55ca4a3496a560a8d4ac4ea8",
            "8de614e27948429b964f48274ffab662",
            "7eb137763f3144a68f06e818d2efb5bc"
          ]
        },
        "outputId": "5c1db00b-b82a-42ce-d818-d88a40f825ac"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "NGLWidget(max_frame=249999)"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c3bcad5aa4a406cbc7a6e1702053885"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ],
      "source": [
        "v = nv.show_mdtraj(trjs[0])\n",
        "v"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "BKqmbObsKbr0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "be6acdf1-ffae-4f6a-db5b-7dca03714e9d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, torch.Size([250000, 231]))"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "coords_torch = list()\n",
        "for trj in trjs:\n",
        "    #t_backbone = trj.atom_slice(trj.top.select('backbone')).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": "markdown",
      "metadata": {
        "id": "0S8Xvz16Kbr1"
      },
      "source": [
        "# SRV fitting (~5 mins)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "JuMNb0WyKbr1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "9634c082-3bc5-414f-ba69-e6ae5da464ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting random seed to 42\n"
          ]
        }
      ],
      "source": [
        "set_random_seed(42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "rNb19_3gKbr1"
      },
      "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 = 30\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": 21,
      "metadata": {
        "id": "SBuCf9YYKbr1"
      },
      "outputs": [],
      "source": [
        "lag_n = 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "scrolled": true,
        "id": "8ysnrQaxKbr1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "2446bfce-0748-49b5-818a-d97cc3d9a1c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 0: 100%|██████████| 10/10 [00:05<00:00,  1.98batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 0]\t training loss = -2.017\t validation loss = -2.286\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 1: 100%|██████████| 10/10 [00:04<00:00,  2.50batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 1]\t training loss = -2.415\t validation loss = -2.408\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 2: 100%|██████████| 10/10 [00:04<00:00,  2.49batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 2]\t training loss = -2.438\t validation loss = -2.435\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 3: 100%|██████████| 10/10 [00:03<00:00,  2.52batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 3]\t training loss = -2.450\t validation loss = -2.448\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 4: 100%|██████████| 10/10 [00:03<00:00,  2.52batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 4]\t training loss = -2.459\t validation loss = -2.453\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 5: 100%|██████████| 10/10 [00:03<00:00,  2.52batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 5]\t training loss = -2.472\t validation loss = -2.473\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 6: 100%|██████████| 10/10 [00:03<00:00,  2.52batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 6]\t training loss = -2.526\t validation loss = -2.553\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 7: 100%|██████████| 10/10 [00:04<00:00,  2.49batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 7]\t training loss = -2.632\t validation loss = -2.619\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 8: 100%|██████████| 10/10 [00:03<00:00,  2.51batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 8]\t training loss = -2.670\t validation loss = -2.665\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 9: 100%|██████████| 10/10 [00:03<00:00,  2.50batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 9]\t training loss = -2.694\t validation loss = -2.658\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 10: 100%|██████████| 10/10 [00:04<00:00,  2.49batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 10]\t training loss = -2.709\t validation loss = -2.690\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 11: 100%|██████████| 10/10 [00:04<00:00,  2.49batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 11]\t training loss = -2.716\t validation loss = -2.712\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 12: 100%|██████████| 10/10 [00:04<00:00,  2.49batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 12]\t training loss = -2.720\t validation loss = -2.727\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 13: 100%|██████████| 10/10 [00:04<00:00,  2.47batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 13]\t training loss = -2.723\t validation loss = -2.731\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 14: 100%|██████████| 10/10 [00:04<00:00,  2.48batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 14]\t training loss = -2.724\t validation loss = -2.735\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 15: 100%|██████████| 10/10 [00:04<00:00,  2.48batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 15]\t training loss = -2.726\t validation loss = -2.738\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 16: 100%|██████████| 10/10 [00:04<00:00,  2.49batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 16]\t training loss = -2.727\t validation loss = -2.738\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 17: 100%|██████████| 10/10 [00:04<00:00,  2.48batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 17]\t training loss = -2.727\t validation loss = -2.740\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 18: 100%|██████████| 10/10 [00:04<00:00,  2.47batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 18]\t training loss = -2.728\t validation loss = -2.741\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 19: 100%|██████████| 10/10 [00:04<00:00,  2.46batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 19]\t training loss = -2.728\t validation loss = -2.741\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 20: 100%|██████████| 10/10 [00:04<00:00,  2.48batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 20]\t training loss = -2.729\t validation loss = -2.742\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 21: 100%|██████████| 10/10 [00:04<00:00,  2.46batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 21]\t training loss = -2.730\t validation loss = -2.742\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 22: 100%|██████████| 10/10 [00:04<00:00,  2.45batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 22]\t training loss = -2.730\t validation loss = -2.742\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 23: 100%|██████████| 10/10 [00:04<00:00,  2.44batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 23]\t training loss = -2.730\t validation loss = -2.743\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 24: 100%|██████████| 10/10 [00:04<00:00,  2.47batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 24]\t training loss = -2.730\t validation loss = -2.743\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 25: 100%|██████████| 10/10 [00:04<00:00,  2.46batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 25]\t training loss = -2.731\t validation loss = -2.743\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 26: 100%|██████████| 10/10 [00:04<00:00,  2.47batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 26]\t training loss = -2.730\t validation loss = -2.743\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 27: 100%|██████████| 10/10 [00:04<00:00,  2.46batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 27]\t training loss = -2.731\t validation loss = -2.744\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 28: 100%|██████████| 10/10 [00:04<00:00,  2.45batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 28]\t training loss = -2.730\t validation loss = -2.744\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch 29: 100%|██████████| 10/10 [00:04<00:00,  2.44batch/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Epoch 29]\t training loss = -2.730\t validation loss = -2.744\n"
          ]
        }
      ],
      "source": [
        "model_snrv.fit(coords_torch, lag=lag_n, scheduler=0.9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "kKc-O_EdKbr2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "c59d821f-9d9a-4daf-88fc-e51bea47872a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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": 24,
      "metadata": {
        "id": "XymScP_NKbr2"
      },
      "outputs": [],
      "source": [
        "save_freq = 1 # ps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "Y4Mo_MmZKbr2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "45178bc6-0cef-49a0-869a-2225317a73bc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.lines.Line2D at 0x7b3245187370>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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": 26,
      "metadata": {
        "id": "B91B7ugOKbr2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "609dae0b-0fb5-4f64-c2dc-71e3859b4fa6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Snrv(\n",
              "  (activation): GELU(approximate='none')\n",
              "  (model): Sequential(\n",
              "    (0): Linear(in_features=231, 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",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "model_snrv.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "j17fcY7EKbr2"
      },
      "outputs": [],
      "source": [
        "evecs = model_snrv.transform(torch.cat(coords_torch)).cpu().detach().numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "QxJ7nfdnKbr2"
      },
      "outputs": [],
      "source": [
        "trj_cat = md.join(trjs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "NLUKOvbrKbr2"
      },
      "outputs": [],
      "source": [
        "phi = md.compute_phi(trj_cat)[1].flatten()\n",
        "psi = md.compute_psi(trj_cat)[1].flatten()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "3g-BX3PbKbr2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "0c6f20b0-7e7b-469f-df93-c42e724d4b1d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x700 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "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(phi, psi, c=evec, s=10, cmap='coolwarm')\n",
        "    ax.set_xlabel('$\\phi$', fontsize=18)\n",
        "    ax.set_ylabel('$\\psi$', fontsize=18)\n",
        "    ax.set_xlim(-np.pi, np.pi)\n",
        "    ax.set_ylim(-np.pi, np.pi)\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": 31,
      "metadata": {
        "id": "dulko7LMKbr3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "454dbfe0-5aa4-4d58-c5d7-2941d95f4e20"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([250000, 2]), 1)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "CVs = [model_snrv.transform(x).cpu().detach()[:, 1:] for x in coords_torch]\n",
        "CVs[0].shape, len(CVs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HpHzz0XyKbr3"
      },
      "source": [
        "# MDN propagator (~3 mins)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "64ivbfWMKbr3"
      },
      "outputs": [],
      "source": [
        "model_mdn = Propagator(dim = CVs[0].size(1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "IZW6BId2Kbr3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657,
          "referenced_widgets": [
            "b7d566bd864b4157a8d1f3b1468eb544",
            "1afcc2367b2b4ac7b1b287227142b4f1",
            "985b44c960974da9a14796ebc9a53e93",
            "b51b53a0fdce4e8bb1b5853e926806c3",
            "d7d377c61ed2461ea06d1420f15606e2",
            "1b3e494df9984871b699ad6cffe421af",
            "6eede60f15734b10b0586519c8dba2a2",
            "80627b4b1f2d4ccab1baeaa1ae205604",
            "a325a8ff9e0048ed94d774dc6bf9dd82",
            "7fca9868dde94fd79be245eec8764df9",
            "a88e0c900eb24627b3cf30a3d7f2d825"
          ]
        },
        "outputId": "293a986f-0a5f-461c-bbe0-6d67ffd345d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:You are using a CUDA device ('NVIDIA L4') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\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",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=15` in the `DataLoader` to improve performance.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training: |          | 0/? [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b7d566bd864b4157a8d1f3b1468eb544"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "model_mdn.fit(CVs, lag = 10, max_epochs=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "oxNb9b5FKbr3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "0930f27b3533465d91ad5552c166f613",
            "5d4f33ef3814441bbf9165d3e60f21ad",
            "0157c68b46cd4ce8b77f5bd67b1851a7",
            "5766119fe08e4a208f7d081a60c64c3b",
            "4f1f47227a684f94bc66d647fce92740",
            "c275d26e78964158a88eec73b7bf3b08",
            "1a56f17f2ade4ebe82339d5d4cff1682",
            "35e5833510ca40c4a5c6d1c758d58de0",
            "3d06172765d54d81b45714e975696f90",
            "f3f43a45d8224c979aed7a2d874aef68",
            "a722f5fc1d2545af9bea963c70e36304"
          ]
        },
        "outputId": "242dff8a-235f-47cf-9121-f3b6dbf1ec7a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0930f27b3533465d91ad5552c166f613"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ],
      "source": [
        "n_steps = int(1E2)\n",
        "x = CVs[0][0][None]\n",
        "synthetic_traj_CVs = model_mdn.gen_synthetic_traj(x, n_steps)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A9NtuovZKbr4"
      },
      "source": [
        "# DDPM Decoder (~5 mins)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "83wCKw9BKbr4"
      },
      "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": 36,
      "metadata": {
        "id": "URPtzLSHKbr4"
      },
      "outputs": [],
      "source": [
        "model_ddpm = DDPM(xyz[0].shape[1], CVs[0].shape[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "MgXSdkMJKbr4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 13018,
          "referenced_widgets": [
            "2b3726119c7c4176a2b1e0fd5243956e",
            "cf117bce95ea4c3995b9af9a60555b34",
            "378a07778d02425e920e5fe78e97d1ab",
            "bdd1dfae5ca148fb9599f4389205718d",
            "2171f5e70c8d452190d0cffcde7a79c4",
            "a72070278aef45c8993d59c8aed96ab9",
            "5ff0e84cd84242f0afee68d1bceb6081",
            "772ee5a43de44271b174a596956f900e",
            "d8e1a54ef4cd41b7966c5ca01121fee5",
            "77aeaec0e2d844b7b5f8f16eba6d208d",
            "555dce3ac2894cac85d5c94efd96796d"
          ]
        },
        "outputId": "2da8f3a9-4654-4df3-8bb3-70e3b19bd9e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "/usr/local/lib/python3.10/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py:653: Checkpoint directory /content/checkpoints exists and is not empty.\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training: |          | 0/? [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b3726119c7c4176a2b1e0fd5243956e"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=3` reached.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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",
              "          (0-1): 2 x 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",
              "          (0-1): 2 x 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",
              "          (0-1): 2 x 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",
              "          (0-1): 2 x 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",
              "          (0-1): 2 x 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",
              "          (0-1): 2 x 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",
              "          (0-1): 2 x 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",
              "          (0-1): 2 x 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",
              "          (0-1): 2 x 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",
              "          (0-1): 2 x 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",
              "          (0-1): 2 x 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",
              "          (0-1): 2 x 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",
              "          (0-1): 2 x 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",
              "          (0-1): 2 x 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",
              "          (0-1): 2 x 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",
              "          (0-1): 2 x 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",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "model_ddpm.fit(xyz, CVs, max_epochs=3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qovKI7yAKbr4"
      },
      "source": [
        "# Decode synthetic traj (~2 mins)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "wE2Mz-7SKbr4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "8e5ab8edd5f243b78004e6bfbb5711ca",
            "c508ba4a9c7040579392bd1648400981",
            "9f87100db3194278847058eed707bdd8",
            "2b9fbf19eef34be8a41e86e16738135e",
            "3ad52208a3674fbda480fbe2bc48e37c",
            "8c0f5b6c5f2f429b8e2e4ed0d9c6be8e",
            "6f576eb94cb24c6ab44c241f1d254b45",
            "5996f3297e7b42f79affe84f4c76a26c",
            "ff6e3c1b8adb4a1fb7a09272566631fe",
            "5e6b3d5abef8485c9e8b217074c18fad",
            "7dad505c888c40aca716f31c047499e8"
          ]
        },
        "outputId": "63afba3d-0692-4413-a170-e2bdc60bab5b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sampling loop time step:   0%|          | 0/1000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e5ab8edd5f243b78004e6bfbb5711ca"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ],
      "source": [
        "xyz_gen = model_ddpm.generate(synthetic_traj_CVs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "m-j3DeQ3Kbr4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "d2f2ba3e-4b26-4f5c-dc81-42d1effb6bb2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<mdtraj.Trajectory with 100 frames, 8 atoms, 3 residues, without unitcells at 0x7b32459e8190>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "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)\n",
        "fake_trj"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iF_yHOYsKbr4"
      },
      "source": [
        "# Visualize results (~1 min)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "HddeAO7wKbr5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317,
          "referenced_widgets": [
            "39618d1818064f13b06e399f4600cd96",
            "411b435bc2da490fa9adb98cc350c1ff",
            "7aef031a9bea409892ff58118c5f3072",
            "6d993a5a42ae4b9da0f2485525705206",
            "eadc8aa7e7b54137a06e2b971c8a0eac",
            "b17516fa2c3648818a54b8c6d496d6ba",
            "a24bc394db464d52af34822425808ef6",
            "73ccc889e77c45f5a705e73d32cbea5d",
            "49a2db032f0c4e7b825285778419d59b",
            "8f8bee4a2af24094a57de949590fb1d1",
            "00a6d24badb64f7c80212033f259f82d",
            "9c6809e71e6c421e9ceb359de5682f21",
            "161c6d07f77b43bf9d4bf011828e46c1"
          ]
        },
        "outputId": "2aacd26d-4fa1-49be-e3e0-8239cff271fe"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "NGLWidget(max_frame=99)"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "39618d1818064f13b06e399f4600cd96"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          }
        }
      ],
      "source": [
        "v = nv.show_mdtraj(fake_trj)\n",
        "v"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "XHQjfJbTKbr5"
      },
      "outputs": [],
      "source": [
        "fake_trj.save_pdb('ADP_backbone_synthetic_traj.pdb')"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.8"
    },
    "colab": {
      "provenance": [],
      "gpuType": "L4"
    },
    "accelerator": "GPU",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "dbefea28e5434ca1858484f3c00a2d97": {
          "model_module": "nglview-js-widgets",
          "model_name": "ColormakerRegistryModel",
          "model_module_version": "3.0.8",
          "state": {
            "_dom_classes": [],
            "_model_module": "nglview-js-widgets",
            "_model_module_version": "3.0.8",
            "_model_name": "ColormakerRegistryModel",
            "_msg_ar": [],
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