# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Large_B-cell_Lymphoma"
cohort = "GSE197977"

# Input paths
in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE197977"

# Output paths
out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE197977.csv"
out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE197977.csv"
out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE197977.csv"
json_path = "./output/preprocess/3/Large_B-cell_Lymphoma/cohort_info.json"

# Get file paths for 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 clinical feature row 
clinical_features = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print(background_info)
print("\nClinical Features and Sample Values:")
print(json.dumps(clinical_features, indent=2))
# 1. Gene Expression Data Availability
# Based on the series title and summary, this dataset studies tumor gene expression and immune cell signatures, 
# so it should contain gene expression data
is_gene_available = True

# 2. Data Availability and Type Conversion
# 2.1 Row identifiers 
trait_row = 2  # bestresponse contains response to treatment (CR/PR vs PD/SD)
age_row = None  # Age data not available
gender_row = None  # Gender data not available

# 2.2 Type conversion functions
def convert_trait(value: str) -> int:
    """Convert treatment response to binary outcome
    Complete Response (CR) and Partial Response (PR) -> 1 (response)
    Stable Disease (SD) and Progressive Disease (PD) -> 0 (no response)
    """
    if not value or 'bestresponse:' not in value:
        return None
    response = value.split('bestresponse:')[1].strip()
    if response in ['CR', 'PR']:
        return 1
    elif response in ['SD', 'PD']:
        return 0
    return None

def convert_age(value: str) -> float:
    return None

def convert_gender(value: str) -> int:
    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=trait_row is not None
)

# 4. Extract clinical features
if trait_row is not None:
    clinical_features = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the extracted features
    preview_dict = preview_df(clinical_features)
    
    # Save clinical data
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file)

# Print DataFrame info and dimensions to verify data structure
print("DataFrame info:")
print(genetic_data.info())
print("\nDataFrame dimensions:", genetic_data.shape)

# Print an excerpt of the data to inspect row/column structure
print("\nFirst few rows and columns of data:")
print(genetic_data.head().iloc[:, :5])

# Print first 20 row IDs
print("\nFirst 20 gene/probe IDs:")
print(genetic_data.index[:20].tolist())
# The row indices shown are simple numeric values (1, 2, 3 etc) which are not gene symbols
# We need to map these numeric identifiers to proper gene symbols for biological interpretation
requires_gene_mapping = True
# Report discovery of missing gene annotation
print("Gene Annotation Analysis:")
print("WARNING: Gene probe-to-symbol mapping information is not available in this SOFT file.")
print("The annotation only contains signature names (e.g. TIS.IO360, APM.IO360) rather than human gene symbols.")

# Update validation info to show dataset cannot be used due to missing gene mapping
validate_and_save_cohort_info(
    is_final=False,  
    cohort=cohort,
    info_path=json_path,
    is_gene_available=False,  # Set to False since gene expression data is not mappable
    is_trait_available=trait_row is not None,
    note="Dataset contains numeric probe IDs but lacks gene symbol mapping information"
)