# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Retinoblastoma"

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

# Output paths
out_data_file = "./output/preprocess/3/Retinoblastoma/TCGA.csv"
out_gene_data_file = "./output/preprocess/3/Retinoblastoma/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/3/Retinoblastoma/clinical_data/TCGA.csv"
json_path = "./output/preprocess/3/Retinoblastoma/cohort_info.json"

# 1. Look for directories related to retinoblastoma (eye/ocular cancer)
available_cohorts = os.listdir(tcga_root_dir)
relevant_dirs = [d for d in available_cohorts if any(term in d.lower() for term in ['eye', 'ocular', 'retina', 'retinoblastoma'])]

# If no exact match found, use ocular melanoma as closest available eye cancer data
if len(relevant_dirs) == 0:
    # Record unavailability and exit
    validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA",
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False
    )
    # Since we need to skip this trait, return empty dataframes to avoid errors in subsequent code
    clinical_df = pd.DataFrame()
    genetic_df = pd.DataFrame()
else:
    # Select the most relevant directory (first match)
    selected_dir = relevant_dirs[0]
    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
    
    # 2. Get file paths for clinical and genetic data
    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
    
    # 3. Load the data files
    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')
    
    # 4. Print clinical data columns
    print("Clinical data columns:")
    print(clinical_df.columns.tolist())
    
    # Record data availability
    is_gene_available = len(genetic_df.columns) > 0 
    is_trait_available = len(clinical_df.columns) > 0
    
    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
    )
# Identify candidate demographic columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Get list of TCGA cohort directories
cohorts = os.listdir(tcga_root_dir)

# Find any clinical files containing Retinoblastoma data
clinical_df = None
for cohort in cohorts:
    cohort_dir = os.path.join(tcga_root_dir, cohort)
    if os.path.isdir(cohort_dir):
        try:
            clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)
            temp_df = pd.read_csv(clinical_file_path, index_col=0)
            if any('retinoblastoma' in str(col).lower() for col in temp_df.columns):
                clinical_df = temp_df
                break
        except:
            continue

if clinical_df is not None:
    # Preview age columns
    age_preview = {}
    for col in candidate_age_cols:
        if col in clinical_df.columns:
            age_preview[col] = clinical_df[col].head().tolist()
    print("Age columns preview:", age_preview)

    # Preview gender columns 
    gender_preview = {}
    for col in candidate_gender_cols:
        if col in clinical_df.columns:
            gender_preview[col] = clinical_df[col].head().tolist()
    print("Gender columns preview:", gender_preview)
else:
    print("No clinical data found containing Retinoblastoma information")