# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Epilepsy"
cohort = "GSE63808"

# Input paths
in_trait_dir = "../DATA/GEO/Epilepsy"
in_cohort_dir = "../DATA/GEO/Epilepsy/GSE63808"

# Output paths
out_data_file = "./output/preprocess/3/Epilepsy/GSE63808.csv"
out_gene_data_file = "./output/preprocess/3/Epilepsy/gene_data/GSE63808.csv"
out_clinical_data_file = "./output/preprocess/3/Epilepsy/clinical_data/GSE63808.csv"
json_path = "./output/preprocess/3/Epilepsy/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# From the title and summary, this dataset studies hippocampal tissue gene expression in epilepsy patients
is_gene_available = True 

# 2.1 Variable Availability
# Trait (epilepsy): Available in row 1 as binary phenotype
trait_row = 1
# Age: Not available in characteristics data
age_row = None 
# Gender: Not available in characteristics data
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x: str) -> int:
    """Convert epilepsy status to binary"""
    if not isinstance(x, str):
        return None
    # Extract value after colon
    value = x.split(": ")[-1].lower().strip()
    # Only epilepsy cases are included based on summary
    return 1 if value == "epilepsy" else None

def convert_age(x: str) -> float:
    """Convert age to float - not used since age not available"""
    return None

def convert_gender(x: str) -> int:
    """Convert gender to binary - not used since gender not available"""
    return None

# 3. Save Initial Metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False, 
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=is_trait_available)

# 4. Extract Clinical Features
if trait_row is not None:
    clinical_features = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age, 
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the extracted features
    print("Preview of clinical features:")
    print(preview_df(clinical_features))
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)