# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hepatitis"
cohort = "GSE124719"

# Input paths
in_trait_dir = "../DATA/GEO/Hepatitis"
in_cohort_dir = "../DATA/GEO/Hepatitis/GSE124719"

# Output paths
out_data_file = "./output/preprocess/3/Hepatitis/GSE124719.csv"
out_gene_data_file = "./output/preprocess/3/Hepatitis/gene_data/GSE124719.csv"
out_clinical_data_file = "./output/preprocess/3/Hepatitis/clinical_data/GSE124719.csv"
json_path = "./output/preprocess/3/Hepatitis/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics 
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# Check if gene expression data is available
is_gene_available = True  # Yes, the series title indicates gene expression data in blood and muscle

# Find rows containing trait, age, and gender info
# For trait: treatment in row 1 indicates hepatitis status (FENDRIXE = HBV vaccine)
trait_row = 1
# For age: found in row 7 and row 11 
age_row = 11  # row 11 has more complete age data
# For gender: found in row 6 and row 10
gender_row = 10  # row 10 has more complete gender data

# Define conversion functions
def convert_trait(value: str) -> int:
    """Convert treatment to binary hepatitis status"""
    if pd.isna(value):
        return None
    value = value.split(': ')[1].strip()
    # FENDRIXE group received HBV vaccine, indicating hepatitis status
    return 1 if value == 'FENDRIXE' else 0

def convert_age(value: str) -> float:
    """Convert age string to numeric value"""
    if pd.isna(value):
        return None
    value = value.split(': ')[1].strip()
    # Extract numeric age value
    return float(value.replace('y', ''))

def convert_gender(value: str) -> int:
    """Convert gender to binary (0=female, 1=male)"""
    if pd.isna(value):
        return None
    value = value.split(': ')[1].strip()
    return 1 if value.lower() == 'male' else 0

# Initial validation and 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
)

# Extract clinical features
clinical_df = geo_select_clinical_features(
    clinical_data,
    trait='Hepatitis',
    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 and save clinical data
print(preview_df(clinical_df))
clinical_df.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug 
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# Review gene identifiers and indicate if mapping is needed
# These are numeric row IDs, not gene symbols, so mapping will be required
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))