{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e210e5d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:17:45.192780Z",
     "iopub.status.busy": "2025-03-25T08:17:45.192678Z",
     "iopub.status.idle": "2025-03-25T08:17:45.355666Z",
     "shell.execute_reply": "2025-03-25T08:17:45.355278Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n",
    "\n",
    "# Path Configuration\n",
    "from tools.preprocess import *\n",
    "\n",
    "# Processing context\n",
    "trait = \"Chronic_Fatigue_Syndrome\"\n",
    "\n",
    "# Input paths\n",
    "tcga_root_dir = \"../../input/TCGA\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Chronic_Fatigue_Syndrome/TCGA.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Chronic_Fatigue_Syndrome/gene_data/TCGA.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Chronic_Fatigue_Syndrome/clinical_data/TCGA.csv\"\n",
    "json_path = \"../../output/preprocess/Chronic_Fatigue_Syndrome/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e7e812d",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa0b8d64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:17:45.356928Z",
     "iopub.status.busy": "2025-03-25T08:17:45.356785Z",
     "iopub.status.idle": "2025-03-25T08:17:45.362170Z",
     "shell.execute_reply": "2025-03-25T08:17:45.361838Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No suitable directory found for Chronic_Fatigue_Syndrome.\n",
      "Skipping this trait as no suitable data was found.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# 1. Find the most relevant directory for Osteoporosis\n",
    "subdirectories = os.listdir(tcga_root_dir)\n",
    "target_trait = trait.lower().replace(\"_\", \" \")  # Convert to lowercase for case-insensitive matching\n",
    "\n",
    "# Search for related terms to Osteoporosis\n",
    "related_terms = [\"osteoporosis\", \"bone\", \"density\", \"skeletal\", \"bone mineral\", \"fracture\"]\n",
    "matched_dir = None\n",
    "\n",
    "for subdir in subdirectories:\n",
    "    subdir_lower = subdir.lower()\n",
    "    # Check if any related term is in the directory name\n",
    "    if any(term in subdir_lower for term in related_terms):\n",
    "        matched_dir = subdir\n",
    "        print(f\"Found potential match: {subdir}\")\n",
    "        # If exact match found, select it\n",
    "        if \"osteoporosis\" in subdir_lower:\n",
    "            print(f\"Selected as best match: {subdir}\")\n",
    "            matched_dir = subdir\n",
    "            break\n",
    "\n",
    "# If we found a potential match, use it\n",
    "if matched_dir:\n",
    "    print(f\"Selected directory: {matched_dir}\")\n",
    "    \n",
    "    # 2. Get the clinical and genetic data file paths\n",
    "    cohort_dir = os.path.join(tcga_root_dir, matched_dir)\n",
    "    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)\n",
    "    \n",
    "    print(f\"Clinical file: {os.path.basename(clinical_file_path)}\")\n",
    "    print(f\"Genetic file: {os.path.basename(genetic_file_path)}\")\n",
    "    \n",
    "    # 3. Load the data files\n",
    "    clinical_df = pd.read_csv(clinical_file_path, sep='\\t', index_col=0)\n",
    "    genetic_df = pd.read_csv(genetic_file_path, sep='\\t', index_col=0)\n",
    "    \n",
    "    # 4. Print clinical data columns for inspection\n",
    "    print(\"\\nClinical data columns:\")\n",
    "    print(clinical_df.columns.tolist())\n",
    "    \n",
    "    # Print basic information about the datasets\n",
    "    print(f\"\\nClinical data shape: {clinical_df.shape}\")\n",
    "    print(f\"Genetic data shape: {genetic_df.shape}\")\n",
    "    \n",
    "    # Check if we have both gene and trait data\n",
    "    is_gene_available = genetic_df.shape[0] > 0\n",
    "    is_trait_available = clinical_df.shape[0] > 0\n",
    "    \n",
    "else:\n",
    "    print(f\"No suitable directory found for {trait}.\")\n",
    "    is_gene_available = False\n",
    "    is_trait_available = False\n",
    "\n",
    "# Record the data availability\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=\"TCGA\",\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# Exit if no suitable directory was found\n",
    "if not matched_dir:\n",
    "    print(\"Skipping this trait as no suitable data was found.\")"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}