# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Breast_Cancer"

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

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

# Find the cohort directory for breast cancer
cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Breast_Cancer_(BRCA)')

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

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

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

# Print clinical data column names
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# Identify candidate columns
candidate_age_cols = ["Age_at_Initial_Pathologic_Diagnosis_nature2012", "age_at_initial_pathologic_diagnosis"]
candidate_gender_cols = ["Gender_nature2012", "gender"]

# Get correct file paths using TCGA code
clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, "BRCA"))
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('list')
print("Age columns preview:")
print(age_preview)

# Extract and preview gender columns  
gender_preview = clinical_df[candidate_gender_cols].head(5).to_dict('list')
print("\nGender columns preview:")
print(gender_preview)
# Directly set variables since the output of previous step is missing
age_col = "age_at_initial_pathologic_diagnosis"
gender_col = "gender"

print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# Define demographic columns
age_col = "age_at_initial_pathologic_diagnosis"
gender_col = "gender"

# Get file paths for clinical and genetic data
cohort_dir = os.path.join(tcga_root_dir, 'TCGA_Breast_Cancer_(BRCA)')
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

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

# 1. Extract standardized clinical features
clinical_data = tcga_select_clinical_features(clinical_df, trait="Breast_Cancer", 
                                            age_col=age_col, 
                                            gender_col=gender_col)

# 2. Normalize gene symbols and save
normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_df.to_csv(out_gene_data_file)

# 3. Link clinical and genetic data
linked_data = pd.merge(clinical_data, normalized_gene_df.T, left_index=True, right_index=True)

# 4. Handle missing values 
linked_data = handle_missing_values(linked_data, trait_col="Breast_Cancer")

# 5. Check for bias in features and remove biased demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait="Breast_Cancer")

# 6. Validate and save cohort info
note = "Data contains TCGA breast cancer samples with normalized gene expression."
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
)

# 7. 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)