# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hepatitis"
cohort = "GSE125860"

# Input paths
in_trait_dir = "../DATA/GEO/Hepatitis"
in_cohort_dir = "../DATA/GEO/Hepatitis/GSE125860"

# Output paths
out_data_file = "./output/preprocess/3/Hepatitis/GSE125860.csv"
out_gene_data_file = "./output/preprocess/3/Hepatitis/gene_data/GSE125860.csv"
out_clinical_data_file = "./output/preprocess/3/Hepatitis/clinical_data/GSE125860.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")
# 1. Gene Expression Data
is_gene_available = True  # Affymetrix arrays indicate gene expression data

# 2.1 Data Row Identification
trait_row = 7  # hepatitis b concentration post-vaccination indicates disease status
age_row = 17  # age information
gender_row = 18  # gender information

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    try:
        # Extract value after colon and strip whitespace
        val = x.split(':', 1)[1].strip()
        if val == 'NA':
            return None
        # Convert to float and binarize based on threshold
        val = float(val.replace('<', '').replace('mIU/mL', '').strip())
        return 1 if val >= 10 else 0  # Common threshold for HBV protection
    except:
        return None

def convert_age(x):
    try:
        return int(x.split(':', 1)[1].strip())
    except:
        return None

def convert_gender(x):
    try:
        gender = x.split(':', 1)[1].strip()
        if gender == 'F':
            return 0
        elif gender == 'M':
            return 1
        return None
    except:
        return None

# 3. Save Initial Metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(False, cohort, json_path, is_gene_available, is_trait_available)

# 4. Clinical Feature Extraction
if trait_row is not None:
    selected_clinical = geo_select_clinical_features(
        clinical_data, 
        trait,
        trait_row,
        convert_trait,
        age_row,
        convert_age,
        gender_row,
        convert_gender
    )
    
    # Preview the data
    preview = preview_df(selected_clinical)
    
    # Save to CSV
    selected_clinical.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)
# Based on the gene identifiers in the data ('AFFX-' prefix suggests Affymetrix probe IDs),
# these are probe IDs that need to be mapped to gene symbols

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))
# Since gene mapping information is missing in the SOFT file
# Print message and save original probe-level data directly
print("Warning: Gene mapping information is not available in the SOFT file.")
print("Saving probe-level expression data without gene mapping.")

# Save probe-level expression data
gene_data.to_csv(out_gene_data_file)