# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hypothyroidism"
cohort = "GSE151158"

# Input paths
in_trait_dir = "../DATA/GEO/Hypothyroidism"
in_cohort_dir = "../DATA/GEO/Hypothyroidism/GSE151158"

# Output paths
out_data_file = "./output/preprocess/3/Hypothyroidism/GSE151158.csv"
out_gene_data_file = "./output/preprocess/3/Hypothyroidism/gene_data/GSE151158.csv"
out_clinical_data_file = "./output/preprocess/3/Hypothyroidism/clinical_data/GSE151158.csv"
json_path = "./output/preprocess/3/Hypothyroidism/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)

# Get unique values for each clinical feature 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True  # Background shows this is gene expression study of 594 genes

# 2.1 Data Availability
trait_row = 12  # hypothyroidism data found in row 12
age_row = 1     # age data found in row 1
gender_row = 2  # gender data found in row 2 as "Sex"

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if pd.isna(x):
        return None
    value = x.split(": ")[1] if ": " in x else x
    if value.upper() == 'Y':
        return 1
    elif value.upper() == 'N': 
        return 0
    return None

def convert_age(x):
    if pd.isna(x):
        return None
    try:
        age = int(x.split(": ")[1])
        return age
    except:
        return None

def convert_gender(x):
    if pd.isna(x):
        return None
    value = x.split(": ")[1] if ": " in x else x
    if value.upper() == 'F':
        return 0
    elif value.upper() == '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
if trait_row is not None:
    clinical_features_df = 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 data
    preview = preview_df(clinical_features_df)
    print(preview)
    
    # Save to CSV
    clinical_features_df.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# These IDs are standard HUGO gene symbols - e.g. ABCB1, ABCF1, ABL1 are well-known gene symbols
# No mapping needed as they are already in the correct format
requires_gene_mapping = False
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(genetic_data)
genetic_data.to_csv(out_gene_data_file)

# Get clinical features
clinical_features = 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
)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)

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

# 4. Judge whether features are biased and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "Dataset contains gene expression data studying hypothyroidism in the context of NAFLD progression, with clinical annotations."
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=note
)

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