# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Stroke"
cohort = "GSE38571"

# Input paths
in_trait_dir = "../DATA/GEO/Stroke"
in_cohort_dir = "../DATA/GEO/Stroke/GSE38571"

# Output paths
out_data_file = "./output/preprocess/3/Stroke/GSE38571.csv"
out_gene_data_file = "./output/preprocess/3/Stroke/gene_data/GSE38571.csv"
out_clinical_data_file = "./output/preprocess/3/Stroke/clinical_data/GSE38571.csv"
json_path = "./output/preprocess/3/Stroke/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
# Although gene expression data exists, it's for lung cell differentiation, not stroke-related
is_gene_available = False

# 2.1 Data Availability
# No suitable trait data found in characteristics
trait_row = None  

# Only one gender value (all male) so gender is not useful
gender_row = None  

# No age data available
age_row = None 

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    return None  # Not used since trait_row is None

def convert_age(x):
    return None  # Not used since age_row is None

def convert_gender(x):
    return None  # Not used since gender_row is 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