# Path Configuration
from tools.preprocess import *

# Processing context
trait = "LDL_Cholesterol_Levels"
cohort = "GSE34945"

# Input paths
in_trait_dir = "../DATA/GEO/LDL_Cholesterol_Levels"
in_cohort_dir = "../DATA/GEO/LDL_Cholesterol_Levels/GSE34945"

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

# Get paths for relevant files
soft_path, matrix_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_path)

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

# Print dataset background information
print("Background Information:")
print(background_info)
print("\nClinical Features Overview:")
print(json.dumps(sample_chars, indent=2))
# 1. Gene Expression Data Availability
# Based on the background information, this study is about SNPs genotyping, not gene expression
is_gene_available = False

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# LDL levels not directly given, check changed in apoc3 levels as proxy 
trait_row = 2
# Age and gender not available in characteristics
age_row = None  
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Extract numeric value after colon
    if isinstance(x, str) and "percent change in apoc3 levels:" in x:
        try:
            return float(x.split(":")[1].strip())
        except:
            return None
    return None

def convert_age(x):
    return None  # Not available

def convert_gender(x):
    return None  # Not available

# 3. Save Initial Metadata
# Trait data is available since trait_row is not None
is_trait_available = True if trait_row is not None else False
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
if trait_row is not None:
    selected_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 data
    preview = preview_df(selected_clinical_df)
    print("Preview of selected clinical features:")
    print(preview)
    
    # Save to CSV
    selected_clinical_df.to_csv(out_clinical_data_file)