{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f04eee0-5928-4e74-a754-6dc2e528810c",
   "metadata": {},
   "source": [
    "# DunedinPACE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3f514a3-772c-4a14-afdf-5a8376851ff4",
   "metadata": {},
   "source": [
    "## Index\n",
    "1. [Instantiate model class](#Instantiate-model-class)\n",
    "2. [Define clock metadata](#Define-clock-metadata)\n",
    "3. [Download clock dependencies](#Download-clock-dependencies)\n",
    "5. [Load features](#Load-features)\n",
    "6. [Load weights into base model](#Load-weights-into-base-model)\n",
    "7. [Load reference values](#Load-reference-values)\n",
    "8. [Load preprocess and postprocess objects](#Load-preprocess-and-postprocess-objects)\n",
    "10. [Check all clock parameters](#Check-all-clock-parameters)\n",
    "10. [Basic test](#Basic-test)\n",
    "11. [Save torch model](#Save-torch-model)\n",
    "12. [Clear directory](#Clear-directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d95fafdc-643a-40ea-a689-200bd132e90c",
   "metadata": {},
   "source": [
    "Let's first import some packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4adfb4de-cd79-4913-a1af-9e23e9e236c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:50:37.811306Z",
     "iopub.status.busy": "2025-04-07T17:50:37.810909Z",
     "iopub.status.idle": "2025-04-07T17:50:39.184200Z",
     "shell.execute_reply": "2025-04-07T17:50:39.183818Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import inspect\n",
    "import shutil\n",
    "import json\n",
    "import torch\n",
    "import pandas as pd\n",
    "import pyaging as pya"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145082e5-ced4-47ae-88c0-cb69773e3c5a",
   "metadata": {},
   "source": [
    "## Instantiate model class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8aa77372-7ed3-4da7-abc9-d30372106139",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:50:39.185944Z",
     "iopub.status.busy": "2025-04-07T17:50:39.185725Z",
     "iopub.status.idle": "2025-04-07T17:50:39.192877Z",
     "shell.execute_reply": "2025-04-07T17:50:39.192607Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class DunedinPACE(pyagingModel):\n",
      "    def __init__(self):\n",
      "        super().__init__()\n",
      "\n",
      "    def preprocess(self, x):\n",
      "        \"\"\"\n",
      "        Apply quantile normalization on x using gold standard means.\n",
      "        \"\"\"\n",
      "        # Ensure gold_standard_means is a 1D tensor and sorted\n",
      "        sorted_gold_standard = torch.sort(torch.tensor(self.reference_values, device=x.device, dtype=x.dtype))[0]\n",
      "\n",
      "        # Pre-compute the quantile indices\n",
      "        quantile_indices = torch.linspace(0, len(sorted_gold_standard) - 1, steps=x.size(1)).long()\n",
      "\n",
      "        # Prepare a tensor to hold normalized data\n",
      "        normalized_data = torch.empty_like(x, device=x.device, dtype=x.dtype)\n",
      "\n",
      "        for i in range(x.size(0)):\n",
      "            sorted_indices = torch.argsort(x[i, :])\n",
      "            normalized_data[i, sorted_indices] = sorted_gold_standard[quantile_indices]\n",
      "\n",
      "        # Return only the subset from x that is used in the base model\n",
      "        return normalized_data[:, self.preprocess_dependencies[0]]\n",
      "\n",
      "    def postprocess(self, x):\n",
      "        return x\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def print_entire_class(cls):\n",
    "    source = inspect.getsource(cls)\n",
    "    print(source)\n",
    "\n",
    "print_entire_class(pya.models.DunedinPACE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78536494-f1d9-44de-8583-c89a310d2307",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:50:39.194269Z",
     "iopub.status.busy": "2025-04-07T17:50:39.194173Z",
     "iopub.status.idle": "2025-04-07T17:50:39.195804Z",
     "shell.execute_reply": "2025-04-07T17:50:39.195566Z"
    }
   },
   "outputs": [],
   "source": [
    "model = pya.models.DunedinPACE()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51f8615e-01fa-4aa5-b196-3ee2b35d261c",
   "metadata": {},
   "source": [
    "## Define clock metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6601da9e-8adc-44ee-9308-75e3cd31b816",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:50:39.197117Z",
     "iopub.status.busy": "2025-04-07T17:50:39.197033Z",
     "iopub.status.idle": "2025-04-07T17:50:39.199038Z",
     "shell.execute_reply": "2025-04-07T17:50:39.198798Z"
    }
   },
   "outputs": [],
   "source": [
    "model.metadata[\"clock_name\"] = \"dunedinpace\"\n",
    "model.metadata[\"data_type\"] = \"DNA methylation\"  # Paper: DNA-methylation blood-test\n",
    "model.metadata[\"species\"] = \"Homo sapiens\"  # Paper: Dunedin Study 1972–1973 birth cohort\n",
    "model.metadata[\"year\"] = 2022\n",
    "model.metadata[\"approved_by_author\"] = \"✅\"\n",
    "model.metadata[\"citation\"] = \"Belsky, D. W., Caspi, A., Corcoran, D. L., et al. (2022). DunedinPACE, a DNA methylation biomarker of the pace of aging. eLife, 11, e73420.\"\n",
    "model.metadata[\"doi\"] = \"https://doi.org/10.7554/elife.73420\"\n",
    "model.metadata[\"notes\"] = \"Whole-blood elastic-net pace-of-aging biomarker trained at age 45 against a 20-year longitudinal slope composite of 19 organ-system biomarkers. PyAging follows the official 20,000-probe quantile-normalization panel: 173 scoring CpGs plus 19,827 background probes.\"\n",
    "model.metadata[\"research_only\"] = True\n",
    "model.metadata[\"tissue\"] = [\"whole blood\"]  # Paper: blood collected at age 45\n",
    "model.metadata[\"predicts\"] = [\"pace of aging\"]  # Paper: DunedinPACE values quantify how much faster or slower an individual is aging\n",
    "model.metadata[\"training_target\"] = [\"pace of aging\"]  # Paper: The criterion variable was 20-year Pace of Aging.\n",
    "model.metadata[\"unit\"] = [\"biological years per chronological year\"]  # Paper: reference value of 1 year of biological aging per calendar year\n",
    "model.metadata[\"model_type\"] = \"elastic net regression\"  # Paper: using elastic-net regression\n",
    "model.metadata[\"platform\"] = [\"Illumina EPIC\"]  # Paper: DunedinPACE was developed from age-45 Dunedin Study whole-blood methylation measured on Illumina EPIC arrays.\n",
    "model.metadata[\"population\"] = \"adults\"  # Paper: N=817 with methylation and measured Pace of Aging at age 45\n",
    "model.metadata[\"journal\"] = \"eLife\"\n",
    "model.metadata[\"last_author\"] = \"Terrie E. Moffitt\"\n",
    "model.metadata[\"n_features\"] = 20000\n",
    "model.metadata[\"citations\"] = 967\n",
    "model.metadata[\"citations_date\"] = \"2026-07-05\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74492239-5aae-4026-9d90-6bc9c574c110",
   "metadata": {},
   "source": [
    "## Download clock dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeee6c5a-4e0a-4f2a-acef-d657596f453a",
   "metadata": {},
   "source": [
    "#### Download from R package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d81f2c9d-362f-43cb-ad52-012e28217164",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:50:39.200480Z",
     "iopub.status.busy": "2025-04-07T17:50:39.200386Z",
     "iopub.status.idle": "2025-04-07T17:50:39.202718Z",
     "shell.execute_reply": "2025-04-07T17:50:39.202462Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing download.r\n"
     ]
    }
   ],
   "source": [
    "%%writefile download.r\n",
    "\n",
    "options(repos = c(CRAN = \"https://cloud.r-project.org/\"))\n",
    "library(jsonlite)\n",
    "\n",
    "install.packages(\"devtools\")\n",
    "devtools::install_github(\"danbelsky/DunedinPACE\", build_vignettes = FALSE)\n",
    "library(DunedinPACE)\n",
    "\n",
    "PACE_list = list(c(\n",
    "    mPACE_Models$model_names, \n",
    "    mPACE_Models$gold_standard_probes, \n",
    "    mPACE_Models$model_weights, \n",
    "    mPACE_Models$model_intercept,\n",
    "    mPACE_Models$model_means,\n",
    "    mPACE_Models$model_probes,\n",
    "    mPACE_Models$gold_standard_means    \n",
    "))\n",
    "\n",
    "write_json(PACE_list, \"DunedinPACE.json\", digits = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ce2bac8-dd33-46cb-a7b6-14a1d0976f05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:50:39.203879Z",
     "iopub.status.busy": "2025-04-07T17:50:39.203793Z",
     "iopub.status.idle": "2025-04-07T17:51:17.345313Z",
     "shell.execute_reply": "2025-04-07T17:51:17.345040Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.system(\"Rscript download.r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5035b180-3d1b-4432-8ebe-b9c92bd93a7f",
   "metadata": {},
   "source": [
    "## Load features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8025ed7-0013-419b-8cb5-1a2db98f9eba",
   "metadata": {},
   "source": [
    "#### From JSON file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "77face1a-b58f-4f8f-9fe8-1f12037be99a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:17.346883Z",
     "iopub.status.busy": "2025-04-07T17:51:17.346780Z",
     "iopub.status.idle": "2025-04-07T17:51:17.351220Z",
     "shell.execute_reply": "2025-04-07T17:51:17.350910Z"
    }
   },
   "outputs": [],
   "source": [
    "with open('DunedinPACE.json', 'r') as f:\n",
    "    PACE_list = json.load(f)[0]\n",
    "model.features = PACE_list['DunedinPACE']\n",
    "model.base_model_features = PACE_list['DunedinPACE.4']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6d8fa0-4767-4c45-9717-eb1c95e2ddc0",
   "metadata": {},
   "source": [
    "## Load weights into base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e09b3463-4fd4-41b1-ac21-e63ddd223fe0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:17.352641Z",
     "iopub.status.busy": "2025-04-07T17:51:17.352546Z",
     "iopub.status.idle": "2025-04-07T17:51:17.354505Z",
     "shell.execute_reply": "2025-04-07T17:51:17.354270Z"
    }
   },
   "outputs": [],
   "source": [
    "weights = torch.tensor(PACE_list['DunedinPACE.1']).unsqueeze(0).float()\n",
    "intercept = torch.tensor([PACE_list['DunedinPACE.2'][0]]).float()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad261636-5b00-4979-bb1d-67a851f7aa19",
   "metadata": {},
   "source": [
    "#### Linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d7f43b99-26f2-4622-9a76-316712058877",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:17.355857Z",
     "iopub.status.busy": "2025-04-07T17:51:17.355777Z",
     "iopub.status.idle": "2025-04-07T17:51:17.357878Z",
     "shell.execute_reply": "2025-04-07T17:51:17.357647Z"
    }
   },
   "outputs": [],
   "source": [
    "base_model = pya.models.LinearModel(input_dim=len(model.features))\n",
    "\n",
    "base_model.linear.weight.data = weights.float()\n",
    "base_model.linear.bias.data = intercept.float()\n",
    "\n",
    "model.base_model = base_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad8b4c1d-9d57-48b7-9a30-bcfea7b747b1",
   "metadata": {},
   "source": [
    "## Load reference values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ade0f4c9-2298-4fc3-bb72-d200907dd731",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:17.359170Z",
     "iopub.status.busy": "2025-04-07T17:51:17.359082Z",
     "iopub.status.idle": "2025-04-07T17:51:17.360595Z",
     "shell.execute_reply": "2025-04-07T17:51:17.360341Z"
    }
   },
   "outputs": [],
   "source": [
    "model.reference_values = PACE_list['DunedinPACE.5']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af3bcf7b-74a8-4d21-9ccb-4de0c2b0516b",
   "metadata": {},
   "source": [
    "## Load preprocess and postprocess objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7a22fb20-c605-424d-8efb-7620c2c0755c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:17.361910Z",
     "iopub.status.busy": "2025-04-07T17:51:17.361819Z",
     "iopub.status.idle": "2025-04-07T17:51:17.363629Z",
     "shell.execute_reply": "2025-04-07T17:51:17.363379Z"
    }
   },
   "outputs": [],
   "source": [
    "model.preprocess_name = 'quantile_normalization_with_gold_standard'\n",
    "\n",
    "indices = [model.features.index(item) for item in model.base_model_features]\n",
    "model.preprocess_dependencies = [indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ff4a21cb-cf41-44dc-9ed1-95cf8aa15772",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:17.364879Z",
     "iopub.status.busy": "2025-04-07T17:51:17.364799Z",
     "iopub.status.idle": "2025-04-07T17:51:17.366253Z",
     "shell.execute_reply": "2025-04-07T17:51:17.366008Z"
    }
   },
   "outputs": [],
   "source": [
    "model.postprocess_name = None\n",
    "model.postprocess_dependencies = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86e3d6b1-e67e-4f3d-bd39-0ebec5726c3c",
   "metadata": {},
   "source": [
    "## Check all clock parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2168355c-47d9-475d-b816-49f65e74887c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:17.367703Z",
     "iopub.status.busy": "2025-04-07T17:51:17.367609Z",
     "iopub.status.idle": "2025-04-07T17:51:17.371142Z",
     "shell.execute_reply": "2025-04-07T17:51:17.370862Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Attributes:\n",
      "\n",
      "training: True\n",
      "metadata: {'approved_by_author': '✅',\n",
      " 'citation': 'Belsky, Daniel W., et al. \"DunedinPACE, a DNA methylation '\n",
      "             'biomarker of the pace of aging.\" Elife 11 (2022): e73420.',\n",
      " 'clock_name': 'dunedinpace',\n",
      " 'data_type': 'methylation',\n",
      " 'doi': 'https://doi.org/10.7554/eLife.73420',\n",
      " 'notes': 'This model is for research purposes only. Commercial users should '\n",
      "          'contact exclusive DunedinPACE licensee TruDiagnosticTM. The '\n",
      "          'automatic failure if fewer than 80% of the CpG probes are available '\n",
      "          \"is not implemented and left to the user's discretion.\",\n",
      " 'research_only': True,\n",
      " 'species': 'Homo sapiens',\n",
      " 'version': None,\n",
      " 'year': 2022}\n",
      "reference_values: [0.8499678448731, 0.7897610893879, 0.9717597609504, 0.8480138104804, 0.2475593905826, 0.1097207575569, 0.189885225724, 0.3909313843346, 0.9168688056148, 0.2708397202163, 0.8736696325841, 0.5147248840362, 0.9619479966112, 0.2575877641187, 0.9432588276732, 0.8989191043041, 0.580889869872, 0.1242693996089, 0.8932207211244, 0.6638228904263, 0.09830894494804, 0.8640316497494, 0.08535902315207, 0.07942500721274, 0.7473024928443, 0.8737263989611, 0.7534216033511, 0.09034449086512, 0.109486844368, 0.6611683137784]... [Total elements: 20000]\n",
      "preprocess_name: 'quantile_normalization_with_gold_standard'\n",
      "preprocess_dependencies: [[0,\n",
      "  1,\n",
      "  2,\n",
      "  3,\n",
      "  4,\n",
      "  5,\n",
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      "  123,\n",
      "  124,\n",
      "  125,\n",
      "  126,\n",
      "  127,\n",
      "  128,\n",
      "  129,\n",
      "  130,\n",
      "  131,\n",
      "  132,\n",
      "  133,\n",
      "  134,\n",
      "  135,\n",
      "  136,\n",
      "  137,\n",
      "  138,\n",
      "  139,\n",
      "  140,\n",
      "  141,\n",
      "  142,\n",
      "  143,\n",
      "  144,\n",
      "  145,\n",
      "  146,\n",
      "  147,\n",
      "  148,\n",
      "  149,\n",
      "  150,\n",
      "  151,\n",
      "  152,\n",
      "  153,\n",
      "  154,\n",
      "  155,\n",
      "  156,\n",
      "  157,\n",
      "  158,\n",
      "  159,\n",
      "  160,\n",
      "  161,\n",
      "  162,\n",
      "  163,\n",
      "  164,\n",
      "  165,\n",
      "  166,\n",
      "  167,\n",
      "  168,\n",
      "  169,\n",
      "  170,\n",
      "  171,\n",
      "  172]]\n",
      "postprocess_name: None\n",
      "postprocess_dependencies: None\n",
      "features: ['cg00112187', 'cg00151250', 'cg00359421', 'cg00513564', 'cg00532802', 'cg00574958', 'cg00668559', 'cg00782811', 'cg00835193', 'cg01055871', 'cg01101459', 'cg01360413', 'cg01554316', 'cg01936220', 'cg02004723', 'cg02079413', 'cg02229095', 'cg02300147', 'cg02307277', 'cg02571857', 'cg02650017', 'cg02949067', 'cg02997983', 'cg03604011', 'cg03776935', 'cg03810769', 'cg03868770', 'cg04051458', 'cg04105250', 'cg04305539']... [Total elements: 20000]\n",
      "base_model_features: ['cg00112187', 'cg00151250', 'cg00359421', 'cg00513564', 'cg00532802', 'cg00574958', 'cg00668559', 'cg00782811', 'cg00835193', 'cg01055871', 'cg01101459', 'cg01360413', 'cg01554316', 'cg01936220', 'cg02004723', 'cg02079413', 'cg02229095', 'cg02300147', 'cg02307277', 'cg02571857', 'cg02650017', 'cg02949067', 'cg02997983', 'cg03604011', 'cg03776935', 'cg03810769', 'cg03868770', 'cg04051458', 'cg04105250', 'cg04305539']... [Total elements: 173]\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Structure:\n",
      "\n",
      "base_model: LinearModel(\n",
      "  (linear): Linear(in_features=20000, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Parameters and Weights:\n",
      "\n",
      "base_model.linear.weight: [-0.13975931704044342, 0.1523076742887497, 0.1058167889714241, -0.12104399502277374, 0.04729907587170601, -0.2645362913608551, -0.09107177704572678, 0.07626617699861526, -0.05811680853366852, -0.23175522685050964, 0.06270736455917358, 0.015027794055640697, 0.5146545171737671, -0.03559967875480652, 0.009041309356689453, 0.20905275642871857, 0.02438066340982914, 0.14796297252178192, 0.03653242066502571, 0.09093873202800751, -0.5942692160606384, -0.14602923393249512, 0.028370223939418793, 0.12909314036369324, 0.15818408131599426, 0.026989631354808807, -0.038831036537885666, 0.16809432208538055, 0.11093547195196152, -0.012085522525012493]... [Tensor of shape torch.Size([1, 173])]\n",
      "base_model.linear.bias: tensor([-1.9499])\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pya.utils.print_model_details(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "986d0262-e0c7-4036-b687-dee53ba392fb",
   "metadata": {},
   "source": [
    "## Basic test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "936b9877-d076-4ced-99aa-e8d4c58c5caf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:17.372574Z",
     "iopub.status.busy": "2025-04-07T17:51:17.372487Z",
     "iopub.status.idle": "2025-04-07T17:51:17.398592Z",
     "shell.execute_reply": "2025-04-07T17:51:17.398259Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.2938],\n",
       "        [ 0.8014],\n",
       "        [-0.0384],\n",
       "        [-0.2034],\n",
       "        [-0.1848],\n",
       "        [ 0.5177],\n",
       "        [-0.1668],\n",
       "        [ 0.3322],\n",
       "        [-0.8489],\n",
       "        [ 0.7232]], dtype=torch.float64, grad_fn=<AddmmBackward0>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.manual_seed(42)\n",
    "input = torch.randn(10, len(model.features), dtype=float)\n",
    "model.eval()\n",
    "model.to(float)\n",
    "pred = model(input)\n",
    "pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe8299d7-9285-4e22-82fd-b664434b4369",
   "metadata": {},
   "source": [
    "## Save torch model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5ef2fa8d-c80b-4fdd-8555-79c0d541788e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:17.400130Z",
     "iopub.status.busy": "2025-04-07T17:51:17.400026Z",
     "iopub.status.idle": "2025-04-07T17:51:17.412150Z",
     "shell.execute_reply": "2025-04-07T17:51:17.411807Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.save(model, f\"../weights/{model.metadata['clock_name']}.pt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bac6257b-8d08-4a90-8d0b-7f745dc11ac1",
   "metadata": {},
   "source": [
    "## Clear directory\n",
    "<a id=\"10\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "11aeaa70-44c0-42f9-86d7-740e3849a7a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:17.413650Z",
     "iopub.status.busy": "2025-04-07T17:51:17.413558Z",
     "iopub.status.idle": "2025-04-07T17:51:17.417532Z",
     "shell.execute_reply": "2025-04-07T17:51:17.417258Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted file: coefficients.csv\n",
      "Deleted file: coefficients.xlsx\n",
      "Deleted file: download.r\n",
      "Deleted file: DunedinPACE.json\n"
     ]
    }
   ],
   "source": [
    "# Function to remove a folder and all its contents\n",
    "def remove_folder(path):\n",
    "    try:\n",
    "        shutil.rmtree(path)\n",
    "        print(f\"Deleted folder: {path}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error deleting folder {path}: {e}\")\n",
    "\n",
    "# Get a list of all files and folders in the current directory\n",
    "all_items = os.listdir('.')\n",
    "\n",
    "# Loop through the items\n",
    "for item in all_items:\n",
    "    # Check if it's a file and does not end with .ipynb\n",
    "    if os.path.isfile(item) and not item.endswith('.ipynb'):\n",
    "        os.remove(item)\n",
    "        print(f\"Deleted file: {item}\")\n",
    "    # Check if it's a folder\n",
    "    elif os.path.isdir(item):\n",
    "        remove_folder(item)"
   ]
  }
 ],
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