# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_cirrhosis"
cohort = "GSE150734"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE150734"

# Output paths
out_data_file = "./output/preprocess/3/Liver_cirrhosis/GSE150734.csv"
out_gene_data_file = "./output/preprocess/3/Liver_cirrhosis/gene_data/GSE150734.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_cirrhosis/clinical_data/GSE150734.csv"
json_path = "./output/preprocess/3/Liver_cirrhosis/cohort_info.json"

# Step 1: Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Step 2: Extract background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Step 3: Get dictionary of unique values for each clinical feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Step 4: Print background info and sample characteristics
print("Dataset Background Information:")
print("-" * 80)
print(background_info)
print("\nSample Characteristics:")
print("-" * 80)
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Based on series title and design mentioning "Gene expression profiling", this dataset contains gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# Fibrosis stage (key 0) indicates cirrhosis status
trait_row = 0
def convert_trait(value: str) -> Optional[float]:
    if not value or ':' not in value:
        return None
    stage = value.split(':')[1].strip()
    # Convert fibrosis stage to binary (1 for cirrhosis)
    # Stage ≥ 4 typically indicates cirrhosis, but this dataset only has stages 0-1
    return 0.0  # All samples are non-cirrhotic

# Age and gender not available in characteristics
age_row = None
gender_row = None
convert_age = None  
convert_gender = None

# 3. Save metadata
# is_trait_available=True since 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=True)

# 4. Clinical Feature Extraction
clinical_features = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait)
print("Preview of clinical features:")
print(preview_df(clinical_features))
clinical_features.to_csv(out_clinical_data_file)