# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cardiovascular_Disease"
cohort = "GSE276839"

# Input paths
in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE276839"

# Output paths
out_data_file = "./output/preprocess/3/Cardiovascular_Disease/GSE276839.csv"
out_gene_data_file = "./output/preprocess/3/Cardiovascular_Disease/gene_data/GSE276839.csv"
out_clinical_data_file = "./output/preprocess/3/Cardiovascular_Disease/clinical_data/GSE276839.csv"
json_path = "./output/preprocess/3/Cardiovascular_Disease/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

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

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# From the series title and summary, this is a transcriptomics study measuring gene expression
# in cardiac tissue samples, so gene expression data should be available
is_gene_available = True

# 2.1 Data Row Identification
# Age groups are in row 0, indicating age data availability 
age_row = 0
# No gender data available in sample characteristics
gender_row = None
# Trait cannot be determined from sample characteristics since all samples are cardiac tissues
# from patients with congenital heart disease
trait_row = None

# 2.2 Data Type Conversion Functions
def convert_age(value: str) -> Optional[float]:
    """Convert age group to numeric values representing approximate age in years"""
    if not isinstance(value, str):
        return None
    value = value.lower().split(': ')[-1]
    if 'neonate' in value:
        return 0.0
    elif 'infant' in value:
        return 0.5  # ~6 months
    elif 'toddler' in value or 'pre school' in value:
        return 2.5  # ~2-3 years
    elif 'school age' in value:
        return 8.0  # ~6-10 years
    elif 'adolescent' in value or 'young adult' in value:
        return 16.0  # ~14-18 years
    return None

# Empty function definitions for trait and gender since data not available
def convert_trait(value: str) -> Optional[int]:
    return None

def convert_gender(value: str) -> Optional[int]:
    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. Skip clinical feature extraction since trait_row is None
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print first 20 row IDs 
print("First 20 gene/probe IDs:")
print(list(genetic_df.index)[:20])
# These IDs are from Affymetrix's ClariomD arrays and need to be mapped to standard gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview column names and first few values
print("Column names and preview of gene annotation data:")
print(preview_df(gene_metadata))
# Extract gene symbols from the full text descriptions
gene_metadata['Gene'] = gene_metadata['SPOT_ID.1'].apply(extract_human_gene_symbols)

# Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'Gene')

# Apply mapping to convert probe measurements to gene expression values 
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Normalize gene symbols to standard format
gene_data = normalize_gene_symbols_in_index(gene_data)
# 1. Save normalized gene data
if not os.path.exists(os.path.dirname(out_gene_data_file)):
    os.makedirs(os.path.dirname(out_gene_data_file))
gene_data.to_csv(out_gene_data_file)

# Record that this dataset can't be used due to missing clinical data
is_usable = validate_and_save_cohort_info(
    is_final=False,  # Initial filtering since clinical data unavailable
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False  # From Step 2, trait_row was None
)