# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hemochromatosis"

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

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

# Get subdirectories from TCGA root directory
tcga_subdirs = os.listdir(tcga_root_dir)
tcga_subdirs = [d for d in tcga_subdirs if not d.startswith('.')]

# Review cohort directories for liver data since Hemochromatosis affects liver
selected_cohort = 'TCGA_Liver_Cancer_(LIHC)'
if selected_cohort not in tcga_subdirs:
    # No suitable cohort found - record this and end processing
    validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA", 
        info_path=json_path,
        is_gene_available=True,
        is_trait_available=False
    )
else:
    # Get file paths for clinical and genetic data
    cohort_dir = os.path.join(tcga_root_dir, selected_cohort)
    clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)
    
    # Load the data
    clinical_df = pd.read_csv(clinical_file, index_col=0, sep='\t')
    genetic_df = pd.read_csv(genetic_file, index_col=0, sep='\t')
    
    # Print clinical data columns
    print("Clinical data columns:")
    print(clinical_df.columns.tolist())
# Define candidate columns for age and gender
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Preview the data by extracting candidate columns from clinical data
preview_dict = {}
if len(candidate_age_cols) > 0:
    age_data = clinical_df[candidate_age_cols].head()
    preview_dict.update({col: age_data[col].tolist() for col in candidate_age_cols})

if len(candidate_gender_cols) > 0:
    gender_data = clinical_df[candidate_gender_cols].head() 
    preview_dict.update({col: gender_data[col].tolist() for col in candidate_gender_cols})

# Display preview
print(preview_dict)
# Inspect available columns for age and gender information
age_col = 'age_at_initial_pathologic_diagnosis'  # This column has clear age values
gender_col = 'gender'  # This column has clear gender labels

# Print chosen columns 
print(f"Chosen age column: {age_col}")
print(f"Chosen gender column: {gender_col}")
# Extract and standardize clinical features
selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)
selected_clinical_df.to_csv(out_clinical_data_file)

# Normalize gene symbols and save
normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df)
normalized_genetic_df.to_csv(out_gene_data_file)

# Link clinical and genetic data
linked_data = pd.concat([selected_clinical_df, normalized_genetic_df.T], axis=1)

# Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# Judge whether features are biased and remove biased demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Final validation and save cohort info
note = "Used liver cancer (LIHC) data as a proxy for hemochromatosis since both affect liver function"
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note=note
)

# Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)