# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Fibromyalgia"

# Input paths
tcga_root_dir = "../DATA/TCGA"

# Output paths
out_data_file = "./output/preprocess/1/Fibromyalgia/TCGA.csv"
out_gene_data_file = "./output/preprocess/1/Fibromyalgia/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/1/Fibromyalgia/clinical_data/TCGA.csv"
json_path = "./output/preprocess/1/Fibromyalgia/cohort_info.json"

import os
import pandas as pd

# 1. Identify subdirectories under tcga_root_dir
subdirectories = os.listdir(tcga_root_dir)

# Search terms related to "Fibromyalgia"
search_terms = ["fibromyalgia", "fibro", "myalgia", "chronic_widespread_pain", "cwp"]

trait_subdir = None
for d in subdirectories:
    d_lower = d.lower()
    if any(term in d_lower for term in search_terms):
        trait_subdir = d
        break

# 2. If none found, skip this trait
if not trait_subdir:
    print(f"No suitable subdirectory found for trait '{trait}'. Skipping...")
    is_gene_available = False
    is_trait_available = False
    validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA",
        info_path=json_path,
        is_gene_available=is_gene_available,
        is_trait_available=is_trait_available
    )
else:
    # 2. Identify file paths
    cohort_path = os.path.join(tcga_root_dir, trait_subdir)
    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_path)

    # 3. Load both files as dataframes
    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t', low_memory=False)
    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t', low_memory=False)

    # 4. Print the column names of the clinical data
    print("Clinical Data Columns:")
    print(clinical_df.columns.tolist())