{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2f04eee0-5928-4e74-a754-6dc2e528810c",
   "metadata": {},
   "source": [
    "# Han"
   ]
  },
  {
   "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": "2025-04-07T17:51:46.692625Z",
     "iopub.status.busy": "2025-04-07T17:51:46.692299Z",
     "iopub.status.idle": "2025-04-07T17:51:48.095262Z",
     "shell.execute_reply": "2025-04-07T17:51:48.094929Z"
    }
   },
   "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": "2025-04-07T17:51:48.097001Z",
     "iopub.status.busy": "2025-04-07T17:51:48.096775Z",
     "iopub.status.idle": "2025-04-07T17:51:48.104151Z",
     "shell.execute_reply": "2025-04-07T17:51:48.103862Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class Han(pyagingModel):\n",
      "    def __init__(self):\n",
      "        super().__init__()\n",
      "\n",
      "    def preprocess(self, x):\n",
      "        return x\n",
      "\n",
      "    def postprocess(self, x):\n",
      "        \"\"\"\n",
      "        Applies an anti-logarithmic linear transformation to a PyTorch tensor.\n",
      "        \"\"\"\n",
      "        adult_age = 20\n",
      "\n",
      "        # Create a mask for negative and non-negative values\n",
      "        mask_negative = x < 0\n",
      "        mask_non_negative = ~mask_negative\n",
      "\n",
      "        # Initialize the result tensor\n",
      "        age_tensor = torch.empty_like(x)\n",
      "\n",
      "        # Exponential transformation for negative values\n",
      "        age_tensor[mask_negative] = (1 + adult_age) * torch.exp(x[mask_negative]) - 1\n",
      "\n",
      "        # Linear transformation for non-negative values\n",
      "        age_tensor[mask_non_negative] = (1 + adult_age) * x[mask_non_negative] + adult_age\n",
      "\n",
      "        return age_tensor\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def print_entire_class(cls):\n",
    "    source = inspect.getsource(cls)\n",
    "    print(source)\n",
    "\n",
    "print_entire_class(pya.models.Han)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78536494-f1d9-44de-8583-c89a310d2307",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:48.105530Z",
     "iopub.status.busy": "2025-04-07T17:51:48.105441Z",
     "iopub.status.idle": "2025-04-07T17:51:48.107139Z",
     "shell.execute_reply": "2025-04-07T17:51:48.106894Z"
    }
   },
   "outputs": [],
   "source": [
    "model = pya.models.Han()"
   ]
  },
  {
   "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": "2025-04-07T17:51:48.108426Z",
     "iopub.status.busy": "2025-04-07T17:51:48.108344Z",
     "iopub.status.idle": "2025-04-07T17:51:48.110326Z",
     "shell.execute_reply": "2025-04-07T17:51:48.110083Z"
    }
   },
   "outputs": [],
   "source": [
    "model.metadata[\"clock_name\"] = \"han\"\n",
    "model.metadata[\"data_type\"] = \"DNA methylation\"  # Paper: The predictor uses CpG DNA methylation beta values.\n",
    "model.metadata[\"species\"] = \"Homo sapiens\"  # Paper: The training data consisted of healthy human blood samples.\n",
    "model.metadata[\"year\"] = 2020\n",
    "model.metadata[\"approved_by_author\"] = \"✅\"\n",
    "model.metadata[\"citation\"] = \"Han, Y., et al. “New targeted approaches for epigenetic age predictions.” BMC Biology 18, 71 (2020).\"\n",
    "model.metadata[\"doi\"] = \"https://doi.org/10.1186/s12915-020-00807-2\"\n",
    "model.metadata[\"notes\"] = \"Whole-blood 65-CpG multivariable linear age predictor selected for robust targeted measurement; it fits Horvath-transformed chronological age and inverse-transforms the return to years.\"\n",
    "model.metadata[\"research_only\"] = None\n",
    "model.metadata[\"tissue\"] = [\"whole blood\"]  # Paper: The 973-sample training set consisted of healthy human blood profiles.\n",
    "model.metadata[\"predicts\"] = [\"chronological age\"]  # Paper: The model provides epigenetic predictions of chronological age.\n",
    "model.metadata[\"training_target\"] = [\"chronological age\"]  # Paper: Before fitting the multivariable model, chronological age was transformed with adult.age set to 20 years.\n",
    "model.metadata[\"unit\"] = [\"years\"]  # Paper: pyaging applies the inverse Horvath age transformation and returns age in years.\n",
    "model.metadata[\"model_type\"] = \"linear regression\"  # Paper: A multivariable regression model was established from the 65 CpGs; no penalized selection was used for this 65-CpG microarray model.\n",
    "model.metadata[\"platform\"] = [\"Illumina 450K\"]  # Paper: The 973 training profiles came from seven studies measured on the 450K Illumina BeadChip.\n",
    "model.metadata[\"population\"] = \"all ages\"  # Paper: The training set comprised 973 healthy blood profiles from donors aged 1 to 101 years.\n",
    "model.metadata[\"journal\"] = \"BMC Biology\"\n",
    "model.metadata[\"last_author\"] = \"Wolfgang Wagner\"\n",
    "model.metadata[\"n_features\"] = 65\n",
    "model.metadata[\"citations\"] = 102\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": "code",
   "execution_count": 5,
   "id": "f1965587-a6ac-47ce-bd7a-bb98ca1d91b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:48.111621Z",
     "iopub.status.busy": "2025-04-07T17:51:48.111529Z",
     "iopub.status.idle": "2025-04-07T17:51:48.115249Z",
     "shell.execute_reply": "2025-04-07T17:51:48.114982Z"
    }
   },
   "outputs": [],
   "source": [
    "# from authors\n",
    "cpg_sites = [\n",
    "    \"(Intercept)\",\n",
    "    'cg19283806',\n",
    "    'cg11807280',\n",
    "    'cg00329615',\n",
    "    'cg22454769',\n",
    "    'cg16867657',\n",
    "    'cg22796704',\n",
    "    'cg09809672',\n",
    "    'cg18618815',\n",
    "    'cg25533247',\n",
    "    'cg02286081',\n",
    "    'cg20222376',\n",
    "    'cg19344626',\n",
    "    'cg07082267',\n",
    "    'cg15845821',\n",
    "    'cg11741201',\n",
    "    'cg16054275',\n",
    "    'cg18933331',\n",
    "    'cg20249566',\n",
    "    'cg16604658',\n",
    "    'cg07583137',\n",
    "    'cg16008966',\n",
    "    'cg14556683',\n",
    "    'cg03746976',\n",
    "    'cg14314729',\n",
    "    'cg03431918',\n",
    "    'cg22156456',\n",
    "    'cg23078123',\n",
    "    'cg09748749',\n",
    "    'cg17457912',\n",
    "    'cg06492796',\n",
    "    'cg17593342',\n",
    "    'cg05308819',\n",
    "    'cg22512670',\n",
    "    'cg01820962',\n",
    "    'cg06639320',\n",
    "    'cg03224418',\n",
    "    'cg17436656',\n",
    "    'cg19500607',\n",
    "    'cg03735592',\n",
    "    'cg20669012',\n",
    "    'cg19761273',\n",
    "    'cg07080372',\n",
    "    'cg03638795',\n",
    "    'cg19722847',\n",
    "    'cg24711336',\n",
    "    'cg26935102',\n",
    "    'cg10221746',\n",
    "    'cg02085953',\n",
    "    'cg04604946',\n",
    "    'cg08558886',\n",
    "    'cg22361181',\n",
    "    'cg04208403',\n",
    "    'cg12623930',\n",
    "    'cg21572722',\n",
    "    'cg17885226',\n",
    "    'cg00748589',\n",
    "    'cg13033938',\n",
    "    'cg19784428',\n",
    "    'cg22016779',\n",
    "    'cg01974375',\n",
    "    'cg25256723',\n",
    "    'cg24724428',\n",
    "    'cg07547549',\n",
    "    'cg25410668',\n",
    "    'cg21296230'\n",
    "]\n",
    "\n",
    "coefficients = [\n",
    "    0.711184864,\n",
    "    -0.588354066,\n",
    "    -0.212038592,\n",
    "    0.014351188,\n",
    "    0.051285529,\n",
    "    2.152191741,\n",
    "    -0.689940565,\n",
    "    -0.643729974,\n",
    "    -0.772516118,\n",
    "    0.116662569,\n",
    "    -0.233409678,\n",
    "    0.002802259,\n",
    "    -0.062172432,\n",
    "    -0.224027294,\n",
    "    1.535209377,\n",
    "    0.344367661,\n",
    "    0.188826525,\n",
    "    -0.409150014,\n",
    "    -0.776065004,\n",
    "    0.500336643,\n",
    "    0.06125005,\n",
    "    -0.391624093,\n",
    "    0.100449175,\n",
    "    0.02000403,\n",
    "    0.266044453,\n",
    "    -0.259829677,\n",
    "    0.254063071,\n",
    "    -0.726178338,\n",
    "    -1.141947121,\n",
    "    -0.06322441,\n",
    "    -0.196926134,\n",
    "    0.85613244,\n",
    "    -0.887977059,\n",
    "    -0.334654336,\n",
    "    -0.854110638,\n",
    "    1.916122401,\n",
    "    0.92208575,\n",
    "    -0.070665617,\n",
    "    0.524707402,\n",
    "    0.319375235,\n",
    "    0.376055859,\n",
    "    0.033361038,\n",
    "    -1.458360975,\n",
    "    -0.267930475,\n",
    "    -0.590085273,\n",
    "    0.642506165,\n",
    "    0.470352872,\n",
    "    0.273581649,\n",
    "    -0.637989789,\n",
    "    -1.109388991,\n",
    "    -0.16886654,\n",
    "    0.662451226,\n",
    "    -0.091891613,\n",
    "    0.086290028,\n",
    "    -0.426089316,\n",
    "    0.32615363,\n",
    "    2.535639458,\n",
    "    -3.626802894,\n",
    "    0.097619541,\n",
    "    -0.427604263,\n",
    "    -0.41418774,\n",
    "    -0.27412342,\n",
    "    0.703772384,\n",
    "    -0.110027226,\n",
    "    0.283649813,\n",
    "    0.928585964\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5035b180-3d1b-4432-8ebe-b9c92bd93a7f",
   "metadata": {},
   "source": [
    "## Load features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "77face1a-b58f-4f8f-9fe8-1f12037be99a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:48.116554Z",
     "iopub.status.busy": "2025-04-07T17:51:48.116472Z",
     "iopub.status.idle": "2025-04-07T17:51:48.118568Z",
     "shell.execute_reply": "2025-04-07T17:51:48.118323Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\n",
    "    'feature': cpg_sites,\n",
    "    'coefficient': coefficients\n",
    "})\n",
    "model.features = df['feature'][1:].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6d8fa0-4767-4c45-9717-eb1c95e2ddc0",
   "metadata": {},
   "source": [
    "## Load weights into base model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e09b3463-4fd4-41b1-ac21-e63ddd223fe0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:48.119823Z",
     "iopub.status.busy": "2025-04-07T17:51:48.119742Z",
     "iopub.status.idle": "2025-04-07T17:51:48.121663Z",
     "shell.execute_reply": "2025-04-07T17:51:48.121431Z"
    }
   },
   "outputs": [],
   "source": [
    "weights = torch.tensor(df['coefficient'][1:].tolist()).unsqueeze(0)\n",
    "intercept = torch.tensor([df['coefficient'][0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad261636-5b00-4979-bb1d-67a851f7aa19",
   "metadata": {},
   "source": [
    "#### Linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d7f43b99-26f2-4622-9a76-316712058877",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:48.123025Z",
     "iopub.status.busy": "2025-04-07T17:51:48.122941Z",
     "iopub.status.idle": "2025-04-07T17:51:48.125057Z",
     "shell.execute_reply": "2025-04-07T17:51:48.124808Z"
    }
   },
   "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": 9,
   "id": "ade0f4c9-2298-4fc3-bb72-d200907dd731",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:48.126363Z",
     "iopub.status.busy": "2025-04-07T17:51:48.126279Z",
     "iopub.status.idle": "2025-04-07T17:51:48.127744Z",
     "shell.execute_reply": "2025-04-07T17:51:48.127481Z"
    }
   },
   "outputs": [],
   "source": [
    "model.reference_values = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af3bcf7b-74a8-4d21-9ccb-4de0c2b0516b",
   "metadata": {},
   "source": [
    "## Load preprocess and postprocess objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7a22fb20-c605-424d-8efb-7620c2c0755c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:48.129005Z",
     "iopub.status.busy": "2025-04-07T17:51:48.128920Z",
     "iopub.status.idle": "2025-04-07T17:51:48.130258Z",
     "shell.execute_reply": "2025-04-07T17:51:48.130035Z"
    }
   },
   "outputs": [],
   "source": [
    "model.preprocess_name = None\n",
    "model.preprocess_dependencies = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff4a21cb-cf41-44dc-9ed1-95cf8aa15772",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:48.131493Z",
     "iopub.status.busy": "2025-04-07T17:51:48.131394Z",
     "iopub.status.idle": "2025-04-07T17:51:48.132907Z",
     "shell.execute_reply": "2025-04-07T17:51:48.132639Z"
    }
   },
   "outputs": [],
   "source": [
    "model.postprocess_name = 'anti_log_linear'\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": 12,
   "id": "2168355c-47d9-475d-b816-49f65e74887c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:48.134236Z",
     "iopub.status.busy": "2025-04-07T17:51:48.134147Z",
     "iopub.status.idle": "2025-04-07T17:51:48.137401Z",
     "shell.execute_reply": "2025-04-07T17:51:48.137169Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Attributes:\n",
      "\n",
      "training: True\n",
      "metadata: {'approved_by_author': '✅',\n",
      " 'citation': 'Han, Yang, et al. \"New targeted approaches for epigenetic age '\n",
      "             'predictions.\" BMC biology 18 (2020): 1-15.',\n",
      " 'clock_name': 'han',\n",
      " 'data_type': 'methylation',\n",
      " 'doi': 'https://doi.org/10.1186/s12915-020-00807-2',\n",
      " 'notes': None,\n",
      " 'research_only': None,\n",
      " 'species': 'Homo sapiens',\n",
      " 'version': None,\n",
      " 'year': 2020}\n",
      "reference_values: None\n",
      "preprocess_name: None\n",
      "preprocess_dependencies: None\n",
      "postprocess_name: 'anti_log_linear'\n",
      "postprocess_dependencies: None\n",
      "features: ['cg19283806', 'cg11807280', 'cg00329615', 'cg22454769', 'cg16867657', 'cg22796704', 'cg09809672', 'cg18618815', 'cg25533247', 'cg02286081', 'cg20222376', 'cg19344626', 'cg07082267', 'cg15845821', 'cg11741201', 'cg16054275', 'cg18933331', 'cg20249566', 'cg16604658', 'cg07583137', 'cg16008966', 'cg14556683', 'cg03746976', 'cg14314729', 'cg03431918', 'cg22156456', 'cg23078123', 'cg09748749', 'cg17457912', 'cg06492796']... [Total elements: 65]\n",
      "base_model_features: None\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Structure:\n",
      "\n",
      "base_model: LinearModel(\n",
      "  (linear): Linear(in_features=65, out_features=1, bias=True)\n",
      ")\n",
      "\n",
      "%==================================== Model Details ====================================%\n",
      "Model Parameters and Weights:\n",
      "\n",
      "base_model.linear.weight: [-0.5883540511131287, -0.21203859150409698, 0.014351188205182552, 0.051285527646541595, 2.152191638946533, -0.6899405717849731, -0.6437299847602844, -0.772516131401062, 0.11666256934404373, -0.23340967297554016, 0.0028022590558975935, -0.062172431498765945, -0.22402729094028473, 1.5352094173431396, 0.344367653131485, 0.1888265311717987, -0.40915000438690186, -0.7760649919509888, 0.5003366470336914, 0.061250049620866776, -0.3916240930557251, 0.10044917464256287, 0.0200040303170681, 0.2660444676876068, -0.25982967019081116, 0.25406306982040405, -0.7261783480644226, -1.1419471502304077, -0.0632244125008583, -0.19692613184452057]... [Tensor of shape torch.Size([1, 65])]\n",
      "base_model.linear.bias: tensor([0.7112])\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": 13,
   "id": "936b9877-d076-4ced-99aa-e8d4c58c5caf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:48.138789Z",
     "iopub.status.busy": "2025-04-07T17:51:48.138674Z",
     "iopub.status.idle": "2025-04-07T17:51:48.144871Z",
     "shell.execute_reply": "2025-04-07T17:51:48.144611Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ -1.0000],\n",
       "        [  2.2557],\n",
       "        [310.9586],\n",
       "        [  4.2231],\n",
       "        [ -0.7209],\n",
       "        [161.4707],\n",
       "        [ -0.7927],\n",
       "        [557.5405],\n",
       "        [  0.8421],\n",
       "        [ -0.9992]], dtype=torch.float64, grad_fn=<IndexPutBackward0>)"
      ]
     },
     "execution_count": 13,
     "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": 14,
   "id": "5ef2fa8d-c80b-4fdd-8555-79c0d541788e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:48.146102Z",
     "iopub.status.busy": "2025-04-07T17:51:48.146014Z",
     "iopub.status.idle": "2025-04-07T17:51:48.149610Z",
     "shell.execute_reply": "2025-04-07T17:51:48.149356Z"
    }
   },
   "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": 15,
   "id": "11aeaa70-44c0-42f9-86d7-740e3849a7a6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-07T17:51:48.150930Z",
     "iopub.status.busy": "2025-04-07T17:51:48.150845Z",
     "iopub.status.idle": "2025-04-07T17:51:48.153522Z",
     "shell.execute_reply": "2025-04-07T17:51:48.153219Z"
    }
   },
   "outputs": [],
   "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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