# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Celiac_Disease"
cohort = "GSE193442"

# Input paths
in_trait_dir = "../DATA/GEO/Celiac_Disease"
in_cohort_dir = "../DATA/GEO/Celiac_Disease/GSE193442"

# Output paths
out_data_file = "./output/preprocess/3/Celiac_Disease/GSE193442.csv"
out_gene_data_file = "./output/preprocess/3/Celiac_Disease/gene_data/GSE193442.csv"
out_clinical_data_file = "./output/preprocess/3/Celiac_Disease/clinical_data/GSE193442.csv"
json_path = "./output/preprocess/3/Celiac_Disease/cohort_info.json"


# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

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

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# The study looks at transcriptional profiling of cells, suggesting gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# Looking at sample characteristics, there is no trait, age or gender data available
trait_row = None
age_row = None 
gender_row = None

# Define conversion functions (though not needed since data unavailable)
def convert_trait(x):
    return None

def convert_age(x):
    return None

def convert_gender(x):
    return None

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

# 4. Clinical Feature Extraction
# Skip since trait_row is None
# Find subseries directory
parent_dir = os.path.dirname(in_cohort_dir)
available_dirs = os.listdir(parent_dir)
subseries_dirs = [d for d in available_dirs if d.startswith("GSE193442-")]
if not subseries_dirs:
    raise ValueError("No subseries directories found")

# Get paths for first subseries
subseries_dir = os.path.join(parent_dir, subseries_dirs[0])
soft_file, matrix_file = geo_get_relevant_filepaths(subseries_dir)

# Extract gene expression data from matrix file 
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

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

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# Find directories in parent directory containing "GPL" (platform) file
parent_dir = os.path.dirname(in_cohort_dir)
available_dirs = os.listdir(parent_dir)
matching_dirs = [d for d in available_dirs if os.path.exists(os.path.join(parent_dir, d, "GPL*"))]

if not matching_dirs:
    subseries_dir = os.path.join(parent_dir, cohort + "_GPL") 
    matching_dirs = [subseries_dir] if os.path.exists(subseries_dir) else []

if not matching_dirs:
    raise ValueError("No valid gene expression data found")

# Extract gene expression data
subseries_dir = os.path.join(parent_dir, matching_dirs[0])
matrix_file = os.path.join(subseries_dir, "matrix.txt.gz")
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame info
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:") 
print(genetic_df.head().iloc[:, :5])