# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Head_and_Neck_Cancer"
cohort = "GSE156915"

# Input paths
in_trait_dir = "../DATA/GEO/Head_and_Neck_Cancer"
in_cohort_dir = "../DATA/GEO/Head_and_Neck_Cancer/GSE156915"

# Output paths
out_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/GSE156915.csv"
out_gene_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/gene_data/GSE156915.csv"
out_clinical_data_file = "./output/preprocess/3/Head_and_Neck_Cancer/clinical_data/GSE156915.csv"
json_path = "./output/preprocess/3/Head_and_Neck_Cancer/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
# The dataset contains whole transcriptome data which includes gene expression
is_gene_available = True

# 2. Variable Availability and Row Numbers

# No explicit head and neck cancer trait information available in sample characteristics
trait_row = None
convert_trait = None

# Age information not available 
age_row = None
convert_age = None

# Gender information not available
gender_row = None  
convert_gender = 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
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# These appear to be human gene symbols with some special cases
# The format "1060P11.3 /// KIR3DP1" uses /// to separate aliases/alternative names
# Most entries like A1BG, A1BG-AS1, A1CF etc. are standard HUGO gene symbols
# No mapping needed as they are already in gene symbol format
requires_gene_mapping = False
# 1. Normalize gene symbols and save normalized 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)

# No need to do data linking and bias checking since trait data is missing
dummy_data = pd.DataFrame([[0]], columns=['dummy'])
is_biased = True  # Dataset is biased by definition when trait data is missing

note = "Dataset contains gene expression data but lacks trait information needed for association studies."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort, 
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=is_biased,
    df=dummy_data,
    note=note
)

print(f"Dataset {cohort} contains no trait information and will not be used for analysis.")