# Path Configuration
from tools.preprocess import *

# Processing context
trait = "COVID-19"

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

# Output paths
out_data_file = "./output/preprocess/3/COVID-19/TCGA.csv"
out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/TCGA.csv"
json_path = "./output/preprocess/3/COVID-19/cohort_info.json"

# Find the lung-related subdirectories as most relevant to COVID-19
lung_dirs = [d for d in os.listdir(tcga_root_dir) if 'LUNG' in d]

if not lung_dirs:
    is_usable = validate_and_save_cohort_info(is_final=False, 
                                             cohort="TCGA",
                                             info_path=json_path,
                                             is_gene_available=False, 
                                             is_trait_available=False)
    raise ValueError("No suitable TCGA cohort found for COVID-19")

# Select the most specific lung cancer cohort
cohort_dir = os.path.join(tcga_root_dir, "TCGA_Lung_Cancer_(LUNG)")

# Get relevant file paths
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

# Load clinical data
clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')

# Load genetic data 
genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')

# Print clinical data columns
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# Step 1: Define candidate columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Step 2: Navigate directory structure and get data
clinical_file_path = None
for subdir in os.listdir(tcga_root_dir):
    subdir_path = os.path.join(tcga_root_dir, subdir)
    if os.path.isdir(subdir_path):
        try:
            clinical_file_path, _ = tcga_get_relevant_filepaths(subdir_path)
            if clinical_file_path:
                break
        except:
            continue

if clinical_file_path:
    clinical_data = pd.read_csv(clinical_file_path, index_col=0, delimiter='\t')

    # Preview age columns
    age_preview = {}
    for col in candidate_age_cols:
        if col in clinical_data.columns:
            age_preview[col] = clinical_data[col].head(5).tolist()
    print("Age columns preview:")
    print(age_preview)

    # Preview gender columns 
    gender_preview = {}
    for col in candidate_gender_cols:
        if col in clinical_data.columns:
            gender_preview[col] = clinical_data[col].head(5).tolist()
    print("\nGender columns preview:")
    print(gender_preview)
else:
    print("No clinical data file found")
# Select appropriate age and gender columns
age_col = 'age_at_initial_pathologic_diagnosis'  # Contains direct age values 
gender_col = 'gender'  # Contains clear gender values

# Print chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# Early validation that this dataset is not suitable for COVID-19
is_usable = validate_and_save_cohort_info(
    is_final=False, 
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=True, 
    is_trait_available=False,  # TCGA data lacks COVID-19 trait information
    note="TCGA cancer data cannot be repurposed for COVID-19 analysis"
)

# Exit early since this dataset is not suitable
raise ValueError("TCGA data is not suitable for COVID-19 analysis. This trait will be skipped.")