# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Essential_Thrombocythemia"
cohort = "GSE65161"

# Input paths
in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia"
in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE65161"

# Output paths
out_data_file = "./output/preprocess/3/Essential_Thrombocythemia/GSE65161.csv"
out_gene_data_file = "./output/preprocess/3/Essential_Thrombocythemia/gene_data/GSE65161.csv"
out_clinical_data_file = "./output/preprocess/3/Essential_Thrombocythemia/clinical_data/GSE65161.csv"
json_path = "./output/preprocess/3/Essential_Thrombocythemia/cohort_info.json"

# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data Availability
is_gene_available = False  # Based on the background info, this dataset seems to focus on molecular studies of cell lines rather than gene expression

# 2.1 Data Availability
trait_row = None  # No suitable trait data (Essential Thrombocythemia) can be found - these are cell lines
age_row = None  # No age data available for cell lines
gender_row = None  # No gender data available for cell lines

# 2.2 Data Type Conversion Functions 
def convert_trait(x):
    # Not needed since trait data is unavailable
    return None

def convert_age(x):
    # Not needed since age data is unavailable
    return None

def convert_gender(x):
    # Not needed since gender data is unavailable  
    return None

# 3. Save Initial Metadata
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=False  # trait_row is None
)

# 4. Clinical Feature Extraction
# Skip this step since trait_row is None