# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Esophageal_Cancer"
cohort = "GSE66258"

# Input paths
in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE66258"

# Output paths
out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE66258.csv"
out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE66258.csv"
out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE66258.csv"
json_path = "./output/preprocess/3/Esophageal_Cancer/cohort_info.json"

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

# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data Availability
# Based on the series description, this is a microRNA dataset, not suitable for gene expression analysis
is_gene_available = False

# 2.1 Data Availability
# From sample characteristics:
# - trait: Row 0 shows all samples are ESCC tumor tissue
# - age: Not available 
# - gender: Not available
trait_row = 0
age_row = None 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert ESCC tumor status to binary"""
    if 'esophageal squamous cell carcinoma' in value.lower():
        return 1
    return None

def convert_age(value: str) -> float:
    """Convert age to float"""
    return None # Not used since age data not available

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

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

# 4. Extract clinical features since trait_row is not None
selected_clinical = geo_select_clinical_features(
    clinical_df=clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait
)

# Preview and save
print("Clinical data preview:")
print(preview_df(selected_clinical))

# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)