# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Melanoma"
cohort = "GSE261347"

# Input paths
in_trait_dir = "../DATA/GEO/Melanoma"
in_cohort_dir = "../DATA/GEO/Melanoma/GSE261347"

# Output paths
out_data_file = "./output/preprocess/3/Melanoma/GSE261347.csv"
out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE261347.csv"
out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE261347.csv"
json_path = "./output/preprocess/3/Melanoma/cohort_info.json"

# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

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

# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene expression data availability
# Yes, according to the background info, it contains 1825 gene identifiers from Cancer Transcriptome Atlas
is_gene_available = True

# 2.1 Variable availability 
# Trait (resistance status) is in row 1
trait_row = 1

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

# 2.2 Data type conversion functions
def convert_trait(value: str) -> int:
    """Convert resistance status to binary (0=CTLA4res, 1=PD1res)"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if value == 'CTLA4res':
        return 0
    elif value == 'PD1res':
        return 1
    return None

def convert_age(value: str) -> float:
    """Not used but defined for completeness"""
    return None

def convert_gender(value: str) -> int:
    """Not used but defined for completeness"""
    return None

# 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 available
clinical_features = geo_select_clinical_features(
    clinical_df=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 extracted features
print("Preview of clinical features:")
print(preview_df(clinical_features))

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_features.to_csv(out_clinical_data_file)
print("\nSkipping gene data extraction step since this dataset contains methylation data rather than gene expression data.")