# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Glioblastoma"

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

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

# 1. Select the relevant subdirectory for glioblastoma
subdirectory = 'TCGA_Glioblastoma_(GBM)'
cohort_dir = os.path.join(tcga_root_dir, subdirectory)

# 2. Get the file paths 
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

# 3. 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')

# 4. Print clinical data columns
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# Define candidate columns for age and gender
candidate_age_cols = ['CDE_DxAge', 'age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Preview age and gender columns from clinical data
age_cols_dict = clinical_df[candidate_age_cols].head(5).to_dict('list')
print(f"Age columns preview: {age_cols_dict}")

gender_cols_dict = clinical_df[candidate_gender_cols].head(5).to_dict('list') 
print(f"Gender columns preview: {gender_cols_dict}")
# Analyze age columns
age_col = 'age_at_initial_pathologic_diagnosis'  # Choose this as it provides direct age values in years

# Analyze gender columns 
gender_col = 'gender'  # Only one gender column available

# Print chosen columns
print(f"Chosen age column: {age_col}")
print(f"Chosen gender column: {gender_col}")
# 1. Extract and standardize clinical features
# Create trait labels from sample IDs (01-09: tumor=1, 10-19: normal=0)
clinical_features = tcga_select_clinical_features(
    clinical_df, 
    trait=trait,
    age_col='age_at_initial_pathologic_diagnosis',
    gender_col='gender'
)
# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_features.to_csv(out_clinical_data_file)

# 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 on sample IDs
linked_data = pd.merge(
    clinical_features, 
    normalized_gene_df.T,
    left_index=True,
    right_index=True,
    how='inner'
)

# 4. Handle missing values systematically 
linked_data = handle_missing_values(linked_data, trait)

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

# 6. Validate data quality and save cohort info
note = "Contains molecular data from tumor and normal samples with patient demographics."
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)