# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hypothyroidism"

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

# Output paths
out_data_file = "./output/preprocess/3/Hypothyroidism/TCGA.csv"
out_gene_data_file = "./output/preprocess/3/Hypothyroidism/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/3/Hypothyroidism/clinical_data/TCGA.csv"
json_path = "./output/preprocess/3/Hypothyroidism/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('.')]

# Select thyroid cancer cohort as most relevant for hypothyroidism
selected_dir = "TCGA_Thyroid_Cancer_(THCA)"
cohort_dir = os.path.join(tcga_root_dir, selected_dir)

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

# 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')

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

# Part 2: Preview existing clinical data
# Print age columns preview
age_preview = {}
for col in candidate_age_cols:
    age_preview[col] = clinical_df[col].head().tolist()
print("Age columns preview:", age_preview)

# Print gender columns preview
gender_preview = {}
for col in candidate_gender_cols:
    gender_preview[col] = clinical_df[col].head().tolist()
print("Gender columns preview:", gender_preview)
# Selecting age column
age_col = "age_at_initial_pathologic_diagnosis"  # Contains direct age values, easier to interpret than days_to_birth

# Selecting gender column
gender_col = "gender"  # Contains standard gender values

# Print chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected 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 thyroid cancer (THCA) data as thyroid disorders are closely related to hypothyroidism"
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)