# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Osteoporosis"
cohort = "GSE152073"

# Input paths
in_trait_dir = "../DATA/GEO/Osteoporosis"
in_cohort_dir = "../DATA/GEO/Osteoporosis/GSE152073"

# Output paths
out_data_file = "./output/preprocess/3/Osteoporosis/GSE152073.csv"
out_gene_data_file = "./output/preprocess/3/Osteoporosis/gene_data/GSE152073.csv"
out_clinical_data_file = "./output/preprocess/3/Osteoporosis/clinical_data/GSE152073.csv"
json_path = "./output/preprocess/3/Osteoporosis/cohort_info.json"

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

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data
# Yes - Based on background info mentioning Affymetrix microarrays and gene expression data
is_gene_available = True

# 2. Variable Availability and Conversion Functions
# 2.1 Row Identifiers 
trait_row = 0  # Inferred from background info stating all subjects have osteoporosis
age_row = 1  # Age data in row 1
gender_row = 0  # Gender data in row 0

# 2.2 Conversion Functions
def convert_trait(x):
    # All subjects have osteoporosis based on study design
    return 1

def convert_age(x):
    try:
        # Extract numeric age value after colon
        age = int(x.split(': ')[1])
        return age
    except:
        return None

def convert_gender(x):
    try:
        gender = x.split(': ')[1].lower()
        if gender == 'female':
            return 0
        elif gender == 'male':
            return 1
        return None
    except:
        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
if trait_row is not None:
    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
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file 
def get_genetic_data_modified(file_path: str, marker: str = "!series_matrix_table_begin") -> pd.DataFrame:
    with gzip.open(file_path, 'rt') as file:
        for i, line in enumerate(file):
            if marker in line:
                skip_rows = i + 1
                break
        else:
            raise ValueError(f"Marker '{marker}' not found in the file.")

    genetic_data = pd.read_csv(file_path, compression='gzip', skiprows=skip_rows, comment='!', 
                             delimiter='\t', on_bad_lines='skip').T
    genetic_data.columns = genetic_data.iloc[0]  # Set first row as column names
    genetic_data = genetic_data.iloc[1:]  # Remove the first row

    return genetic_data

genetic_data = get_genetic_data_modified(matrix_file_path)

# Print outputs to examine structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 column names (probe identifiers):")
print(list(genetic_data.columns)[:20])

print("\nFirst 5 row names (sample IDs):")
print(list(genetic_data.index)[:5])