# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sarcoma"

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

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

# 1. Look for directory related to sarcoma
available_cohorts = os.listdir(tcga_root_dir)
selected_dir = 'TCGA_Sarcoma_(SARC)'  # Direct match for our target trait

if selected_dir not in available_cohorts:
    # Record unavailability and exit
    validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA", 
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False
    )
    # Since we need to skip this trait, return empty dataframes to avoid errors in subsequent code
    clinical_df = pd.DataFrame()
    genetic_df = pd.DataFrame()
else:
    # Get the full directory path
    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
    
    # 2. Get file paths for clinical and genetic data
    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())
    
    # Record data availability
    is_gene_available = len(genetic_df.columns) > 0 
    is_trait_available = len(clinical_df.columns) > 0
    
    validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA",
        info_path=json_path,
        is_gene_available=is_gene_available,
        is_trait_available=is_trait_available
    )
# Identify candidate columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Since we don't have direct file access for preview in this step, 
# we'll just define the columns for later use
print(f"Candidate age columns identified: {candidate_age_cols}")
print(f"Candidate gender columns identified: {candidate_gender_cols}")
# Define candidate column names for age and gender information
age_col = 'age_at_initial_pathologic_diagnosis'
gender_col = 'gender'

# Print the chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# Select appropriate demographic columns
age_col = 'age_at_initial_pathologic_diagnosis'  # This is more directly usable than days_to_birth
gender_col = 'gender'

# 1. Extract and standardize clinical features
selected_clinical_df = tcga_select_clinical_features(clinical_df, trait, age_col, gender_col)

# 2. Normalize gene symbols in genetic data
normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_genetic_df.to_csv(out_gene_data_file)

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

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

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

# 6. Validate and save cohort info 
note = f"Sample size after preprocessing: {len(linked_data)}. Number of genes: {len(linked_data.columns) - 3}"
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=is_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)
    print(f"Linked data saved to {out_data_file}")
    print("Shape of final linked data:", linked_data.shape)
else:
    print("Dataset was found to be unusable and was not saved")