# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Thyroid_Cancer"
cohort = "GSE104006"

# Input paths
in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE104006"

# Output paths
out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE104006.csv"
out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE104006.csv"
out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE104006.csv"
json_path = "./output/preprocess/3/Thyroid_Cancer/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
is_gene_available = True  # Based on Series title mentioning "gene expression profiling"

# 2.1 Data Availability
trait_row = 1  # Histology field contains tumor status
age_row = 2  # Age information is available
gender_row = 3  # Sex information is available 

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert histology to binary: 1 for tumor types, 0 for non-neoplastic"""
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].strip()
    if value == 'Non-neoplastic_thyroid':
        return 0
    elif value in ['PDTC', 'PTC', 'PDTC+PTC', 'PTC+PDTC', 'PTC_lymph_node_metastasis']:
        return 1
    return None

def convert_age(value):
    """Convert age to continuous numeric value"""
    if not isinstance(value, str):
        return None
    try:
        return float(value.split(': ')[-1])
    except:
        return None

def convert_gender(value):
    """Convert gender to binary: 0 for female, 1 for male"""
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].strip()
    if value == 'F':
        return 0
    elif value == 'M':
        return 1
    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. Clinical Feature Extraction
# Since trait_row is not None, we extract clinical features
selected_clinical = 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 extracted features
print(preview_df(selected_clinical))

# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("\nFirst 20 row IDs:")
print(list(genetic_data.index)[:20])

# Print basic data info
print("\nData preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5]) 
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)

# Update is_gene_available since this is miRNA data
is_gene_available = False

# Save updated 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)
)

# Save gene expression data
genetic_data.to_csv(out_gene_data_file)