# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_cirrhosis"
cohort = "GSE285291"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE285291"

# Output paths
out_data_file = "./output/preprocess/3/Liver_cirrhosis/GSE285291.csv"
out_gene_data_file = "./output/preprocess/3/Liver_cirrhosis/gene_data/GSE285291.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_cirrhosis/clinical_data/GSE285291.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 check
# From series title and summary, this is clearly a gene expression study focused on OXPHOS genes
is_gene_available = True

# 2.1 Data availability check
# trait_row: status field indicates cirrhosis status (control vs compensated/decompensated)
trait_row = 1
# age_row: age is not available in sample characteristics 
age_row = None
# gender_row: no gender info in characteristics but summary states all are men
gender_row = None

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

def convert_age(value: str) -> Optional[float]:
    """Convert age to float - not used since age not available"""
    return None

def convert_gender(value: str) -> Optional[int]:
    """Convert gender to binary - not used since gender not available"""
    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
clinical_df = geo_select_clinical_features(clinical_data, 
                                         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_result = preview_df(clinical_df)
print(f"Preview of clinical features: {preview_result}")

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_df.to_csv(out_clinical_data_file)
# 1. Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# 2. Print first 20 row IDs
print("First 20 gene/probe identifiers:")
print(genetic_data.index[:20])
# These identifiers are already in human gene symbol format (e.g. A2M, AADAT, ABL1)
# No mapping needed since they follow standard HGNC gene nomenclature
requires_gene_mapping = False
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# 4. Check for biased features and remove biased demographic ones
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_biased,
    df=linked_data,
    note="All subjects are male according to series summary. Age information not available."
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)