# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Vitamin_D_Levels"
cohort = "GSE118723"

# Input paths
in_trait_dir = "../DATA/GEO/Vitamin_D_Levels"
in_cohort_dir = "../DATA/GEO/Vitamin_D_Levels/GSE118723"

# Output paths
out_data_file = "./output/preprocess/3/Vitamin_D_Levels/GSE118723.csv"
out_gene_data_file = "./output/preprocess/3/Vitamin_D_Levels/gene_data/GSE118723.csv"
out_clinical_data_file = "./output/preprocess/3/Vitamin_D_Levels/clinical_data/GSE118723.csv"
json_path = "./output/preprocess/3/Vitamin_D_Levels/cohort_info.json"



# Request to see vital information before proceeding
print("Please provide the following essential information from previous step:")
print("Sample characteristics dictionary showing clinical variables")
print("Background information about the dataset")
print()
print("This information is needed to:")
print("1. Determine if this dataset has gene expression (not miRNA/methylation)")
print("2. Locate trait (Vitamin D levels), age, and gender data")
print("3. Design appropriate data type conversions")
# Get file paths first
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)
print(f"Matrix file path: {matrix_file_path}")

# Add debugging to check file content 
with gzip.open(matrix_file_path, 'rt') as file:
    first_lines = [next(file) for _ in range(5)]
print("\nFirst 5 lines of matrix file:")
print('\n'.join(first_lines))

# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs and shape
print("\nDataFrame shape:", genetic_data.shape)
print("\nFirst 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))