# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Huntingtons_Disease"
cohort = "GSE34721"

# Input paths
in_trait_dir = "../DATA/GEO/Huntingtons_Disease"
in_cohort_dir = "../DATA/GEO/Huntingtons_Disease/GSE34721"

# Output paths
out_data_file = "./output/preprocess/3/Huntingtons_Disease/GSE34721.csv"
out_gene_data_file = "./output/preprocess/3/Huntingtons_Disease/gene_data/GSE34721.csv"
out_clinical_data_file = "./output/preprocess/3/Huntingtons_Disease/clinical_data/GSE34721.csv"
json_path = "./output/preprocess/3/Huntingtons_Disease/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  # Yes, based on background info describing genome-wide gene expression data

# 2.1 Variable availability
trait_row = 1  # HTT CAG repeat length is recorded in row 1
gender_row = 0  # Gender is recorded in row 0
age_row = None  # Age data not available

# 2.2 Data type conversion functions
def convert_trait(value: str) -> Optional[int]:
    if not value or ':' not in value:
        return None
    try:
        # Extract CAG repeat length number
        cag_length = int(value.split(': ')[1])
        # HD threshold is typically >35 repeats
        return 1 if cag_length > 35 else 0
    except:
        return None

def convert_gender(value: str) -> Optional[int]:
    if not value or ':' not in value:
        return None
    gender = value.split(': ')[1].lower()
    if gender == 'female':
        return 0
    elif gender == 'male':
        return 1
    return None

def convert_age(value: str) -> Optional[float]:
    # 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=True)  # trait_row is not None

# 4. Extract clinical features
clinical_df = geo_select_clinical_features(clinical_data, 
                                         trait="HD",
                                         trait_row=trait_row,
                                         convert_trait=convert_trait,
                                         gender_row=gender_row,
                                         convert_gender=convert_gender)

print("Preview of clinical data:")
print(preview_df(clinical_df))

# Save clinical data
clinical_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 are Affymetrix probe IDs from an older microarray platform (e.g. HG-U133)
# They need to be mapped to official gene symbols
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)

# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())
print("\nPreview of first few rows:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# Get mapping between probe IDs and gene symbols from annotation data
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')

# Convert probe measurements to gene expression values
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Normalize the gene symbols in the index
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_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, gene_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 from liver tissue samples of patients with various liver conditions including hemochromatosis"
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)