# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hypertension"
cohort = "GSE256539"

# Input paths
in_trait_dir = "../DATA/GEO/Hypertension"
in_cohort_dir = "../DATA/GEO/Hypertension/GSE256539"

# Output paths
out_data_file = "./output/preprocess/3/Hypertension/GSE256539.csv"
out_gene_data_file = "./output/preprocess/3/Hypertension/gene_data/GSE256539.csv"
out_clinical_data_file = "./output/preprocess/3/Hypertension/clinical_data/GSE256539.csv"
json_path = "./output/preprocess/3/Hypertension/cohort_info.json"

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

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# 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 Availability
is_gene_available = True  # According to the background info "whole genome sequencing" is performed

# 2. Variable Availability and Row IDs
trait_row = None  # From background info, the trait (Hypertension) status is constant - all are IPAH patients
age_row = None  # Age info not available 
gender_row = None  # Gender info not available

# Define conversion functions
def convert_trait(val: str) -> int:
    # All subjects have IPAH so should be 1
    return 1 

def convert_age(val: str) -> float:
    return None

def convert_gender(val: str) -> 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
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)
# All identifiers shown in the data appear to be valid human gene symbols.
# For example: A2M, A4GALT, AAAS, AACS, AAGAB etc. are standard HGNC gene symbols.
# These symbols are the official gene nomenclature for human genes, so no mapping is needed.
requires_gene_mapping = False
# 1. Normalize and save gene data
gene_data.index = gene_data.index.str.replace('-mRNA', '')
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 2. Record that dataset lacks trait data
validate_and_save_cohort_info(
    is_final=False, 
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    note="All samples are hypertension patients, so trait data is not suitable for analysis."
)