# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_cirrhosis"

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

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

# Find cohort directory for liver cancer
cohort_name = "TCGA_Liver_Cancer_(LIHC)"
cohort_dir = os.path.join(tcga_root_dir, cohort_name)

# Get clinical and genetic data file paths 
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

# Load 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')

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

# Import clinical data using root directory directly
clinical_file_path, _ = tcga_get_relevant_filepaths(tcga_root_dir)
clinical_df = pd.read_csv(clinical_file_path, index_col=0)

# Extract and preview age columns
age_preview = clinical_df[candidate_age_cols].head(5).to_dict(orient='list')
print("Age columns preview:")
print(age_preview)

# Extract and preview gender columns 
gender_preview = clinical_df[candidate_gender_cols].head(5).to_dict(orient='list')
print("\nGender columns preview:")
print(gender_preview)
# Find cohort directory for liver cancer
cohort_name = "TCGA_Liver_Cancer_(LIHC)"
cohort_dir = os.path.join(tcga_root_dir, cohort_name)

# Get clinical and genetic data file paths 
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

# Load 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')

# Print clinical columns
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# Inspect age columns
age_candidates = {
    'age_at_initial_pathologic_diagnosis': ['48', '69', '54', '59', '47'],
    'days_to_birth': ['-15552.0', '-25391.0', '-19910.0', '-21669.0', '-17322.0']
}

# Inspect gender columns
gender_candidates = {
    'gender': ['MALE', 'FEMALE', 'MALE', 'MALE', 'MALE']
}

# Select most appropriate columns
age_col = 'age_at_initial_pathologic_diagnosis'  # Contains age values directly
gender_col = 'gender'  # Contains clear gender values

# Print chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# Extract clinical features (trait and demographics)
clinical_data = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)

# Save processed clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_data.to_csv(out_clinical_data_file)

# Normalize gene symbols
normalized_gene_data = normalize_gene_symbols_in_index(genetic_df)

# Save processed gene data 
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# Link clinical and genetic data
linked_data = pd.concat([clinical_data, normalized_gene_data.T], axis=1, join='inner')

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

# Check for biased features and remove biased demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate and save cohort info
note = "Data obtained from TCGA liver cancer cohort (LIHC). Trait is determined by sample type (tumor vs normal)."
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:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)
    print(f"Linked data saved to: {out_data_file}")
else:
    print("Dataset was not usable and was not saved.")