# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Large_B-cell_Lymphoma"
cohort = "GSE248835"

# Input paths
in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE248835"

# Output paths
out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE248835.csv"
out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE248835.csv"
out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE248835.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
is_gene_available = True  # Based on background info mentioning gene expression signatures

# 2.1 Data Availability
trait_row = 10  # histologically.proven.dlbcl.group indicates disease subtype
age_row = None  # Age not available in characteristics
gender_row = None  # Gender not available in characteristics

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if pd.isna(x):
        return None
    val = x.split(': ')[-1]
    # Binary coding: DLBCL+Others as 0, HGBL as 1
    if val == 'DLBCL+Others':
        return 0
    elif val == 'HGBL':
        return 1
    return None
    
def convert_age(x):
    return None  # Not used since age data unavailable
    
def convert_gender(x):
    return None  # Not used since gender data unavailable

# 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:
    selected_df = 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 data
    print("Preview of extracted clinical features:")
    print(preview_df(selected_df))
    
    # Save to CSV
    selected_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file)

# Print first 20 row IDs
print("First 20 gene/probe IDs:")
print(genetic_data.index[:20].tolist())
# These appear to be numerical indices rather than proper gene symbols
# Human gene symbols are typically alphanumeric strings like 'BRCA1', 'TP53', etc.
# Therefore mapping will be required to convert these numeric IDs to gene symbols
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"
)