# Path Configuration
from tools.preprocess import *

# Processing context
trait = "COVID-19"
cohort = "GSE273225"

# Input paths
in_trait_dir = "../DATA/GEO/COVID-19"
in_cohort_dir = "../DATA/GEO/COVID-19/GSE273225"

# Output paths
out_data_file = "./output/preprocess/3/COVID-19/GSE273225.csv"
out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE273225.csv"
out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE273225.csv"
json_path = "./output/preprocess/3/COVID-19/cohort_info.json"

# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

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

# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene Expression Data Availability
# Based on the Series_overall_design description mentioning nCounter digital gene expression analysis 
# with Immunology V2 panel targeting 579 immune system genes
is_gene_available = True

# 2.1 Data Availability
# For COVID-19 trait - data not available in this transplantation study
trait_row = None

# Age data available in row 3 
age_row = 3

# Gender data available in row 4
gender_row = 4

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    # Not used since trait data not available
    return None

def convert_age(value):
    # Convert age string to numeric value
    try:
        # Extract number after "donor age (y): "
        age = int(value.split(": ")[1])
        return age
    except:
        return None

def convert_gender(value):
    # Convert gender to binary (0=female, 1=male)
    try:
        gender = value.split(": ")[1].lower()
        if gender == "female":
            return 0
        elif gender == "male":
            return 1
        else:
            return None
    except:
        return None

# 3. Save initial metadata
is_usable = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=False  # since trait_row is None
)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)

# Print first few rows with column names to examine data structure
print("Data preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5])
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)

# Verify this is gene expression data and check identifiers
is_gene_available = True

# Save updated 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)
)

# Save gene expression data 
genetic_data.to_csv(out_gene_data_file)
requires_gene_mapping = False
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# Since trait_row is None (no COVID-19 data), skip data linking and update metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=False,
    is_biased=None,
    df=None,
    note="Dataset contains gene expression data from lung transplantation study examining rewarming ischemia effects. No COVID-19 trait data available."
)