# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Large_B-cell_Lymphoma"
cohort = "GSE182362"

# Input paths
in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE182362"

# Output paths
out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE182362.csv"
out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE182362.csv"
out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE182362.csv"
json_path = "./output/preprocess/3/Large_B-cell_Lymphoma/cohort_info.json"

# Get file paths for 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 clinical feature row 
clinical_features = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print(background_info)
print("\nClinical Features and Sample Values:")
print(json.dumps(clinical_features, indent=2))
# 1. Gene Expression Data Availability
# This is a miRNA study on cell lines, not gene expression data
is_gene_available = False

# 2. Clinical Data Availability and Type Conversion
# 2.1 Data rows
# Only has treatment data in row 2, but no human trait/age/gender data
trait_row = None
age_row = None 
gender_row = None

# 2.2 Conversion functions 
def convert_trait(x):
    # Not used since trait data not available
    return None

def convert_age(x):
    # Not used since age data not available
    return None

def convert_gender(x):
    # Not used since gender data not available
    return None

# 3. Save metadata
# trait_row is None so trait data not available
is_trait_available = False if trait_row is None else True

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
# Skip since trait_row is None, indicating no clinical data available