# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_cirrhosis"
cohort = "GSE182065"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE182065"

# Output paths
out_data_file = "./output/preprocess/3/Liver_cirrhosis/GSE182065.csv"
out_gene_data_file = "./output/preprocess/3/Liver_cirrhosis/gene_data/GSE182065.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_cirrhosis/clinical_data/GSE182065.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
# The Series title and summary indicate gene expression profiling was performed
is_gene_available = True

# 2. Variable Analysis and Conversion Functions
# 2.1 Data Availability:
# - Trait (liver cirrhosis/fibrosis): Can be inferred from sample group (row 1)
# - Age: Not available
# - Gender: Not available
trait_row = 1
age_row = None 
gender_row = None

# 2.2 Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert sample group info to binary trait value"""
    if not isinstance(value, str):
        return None
    value = value.lower().split(": ")[-1]
    # Baseline samples are from fibrotic liver tissues per background info
    if "baseline" in value:
        return 1
    # Vehicle control samples are also fibrotic tissues
    elif "vehicle control" in value:
        return 1
    # Compound treatment samples are also fibrotic tissues
    elif "compound treatment" in value:
        return 1
    return None

def convert_age(value: str) -> Optional[float]:
    """Convert age info to float years"""
    return None

def convert_gender(value: str) -> Optional[int]:
    """Convert gender info to binary"""
    return None

# 3. Save initial metadata
is_trait_available = 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=is_trait_available)

# 4. Extract clinical features since trait_row is not None
clinical_df = geo_select_clinical_features(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 processed clinical data
preview_dict = preview_df(clinical_df)
print("Preview of processed clinical data:")
print(preview_dict)

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_df.to_csv(out_clinical_data_file)