# Path Configuration
from tools.preprocess import *

# Processing context
trait = "High-Density_Lipoprotein_Deficiency"
cohort = "GSE34945"

# Input paths
in_trait_dir = "../DATA/GEO/High-Density_Lipoprotein_Deficiency"
in_cohort_dir = "../DATA/GEO/High-Density_Lipoprotein_Deficiency/GSE34945"

# Output paths
out_data_file = "./output/preprocess/3/High-Density_Lipoprotein_Deficiency/GSE34945.csv"
out_gene_data_file = "./output/preprocess/3/High-Density_Lipoprotein_Deficiency/gene_data/GSE34945.csv"
out_clinical_data_file = "./output/preprocess/3/High-Density_Lipoprotein_Deficiency/clinical_data/GSE34945.csv"
json_path = "./output/preprocess/3/High-Density_Lipoprotein_Deficiency/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

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

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability 
# Based on background info, this is a SNP genotyping study using GoldenGate bead array
# Not gene expression data
is_gene_available = False 

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Row Identification
# For trait:
# From field 2, HDL (trait of interest) status can be inferred from APOC3 levels
# Not constant since values vary widely
trait_row = 2

# For age: Not available in characteristics data
age_row = None

# For gender: Not available in characteristics data
gender_row = None

# 2.2 Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    # Extract APOC3 level change after ':'
    if ":" not in value:
        return None
    try:
        change = float(value.split(":")[1].strip())
        # Negative change (reduction) in APOC3 indicates HDL deficiency
        # Using threshold of -20% change
        return 1 if change < -20 else 0
    except:
        return None

def convert_age(value: str) -> Optional[float]:
    return None  # Not used since age data unavailable

def convert_gender(value: str) -> Optional[int]:
    return None  # Not used since gender data unavailable

# 3. Save 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. Clinical Feature Extraction
# Since trait_row is not None, extract clinical features
clinical_df = 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 processed clinical data
preview_df(clinical_df)

# Save to CSV
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_df.to_csv(out_clinical_data_file)