# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Large_B-cell_Lymphoma"
cohort = "GSE159472"

# Input paths
in_trait_dir = "../DATA/GEO/Large_B-cell_Lymphoma"
in_cohort_dir = "../DATA/GEO/Large_B-cell_Lymphoma/GSE159472"

# Output paths
out_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/GSE159472.csv"
out_gene_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/gene_data/GSE159472.csv"
out_clinical_data_file = "./output/preprocess/3/Large_B-cell_Lymphoma/clinical_data/GSE159472.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 Availability
# Based on background info and series title, this is a microarray expression data for DLBCL
is_gene_available = True 

# 2. Variable Availability and Data Type Conversion
# 2.1 Row Numbers
# Trait (ABC/GCB subtypes) is in row 2
trait_row = 2
# Age and gender not available in characteristics
age_row = None
gender_row = None

# 2.2 Conversion Functions
def convert_trait(x):
    """Convert DLBCL subtype to binary: ABC=1, GCB=0"""
    try:
        if not isinstance(x, str):
            return None
        x = x.split(': ')[1].strip()
        if 'ABC' in x:
            return 1
        elif 'GCB' in x:
            return 0
        return None
    except:
        return None

def convert_age(x):
    return None

def convert_gender(x):
    return None

# 3. Save initial metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False, 
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=is_trait_available)

# 4. Extract clinical features since trait data is available
if trait_row is not None:
    clinical_features = geo_select_clinical_features(
        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
    print("Preview of clinical features:")
    print(preview_df(clinical_features))
    
    # Save to CSV
    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())
# Review gene identifiers - these appear to be Affymetrix probe IDs (e.g. "1007_s_at")
# rather than standard human gene symbols, so mapping will be required
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file)

# Print information about annotation data
print("Gene Annotation Preview:")
print("\nDataFrame Shape:", gene_annotation.shape)
print("\nColumn Names:")
print(gene_annotation.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_annotation))
# Get mapping between gene IDs and gene symbols from annotation data
# 'ID' column matches probe IDs in expression data, 'Gene Symbol' contains human gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Apply mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Print info about the resulting gene expression data
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nFirst few mapped genes and their expression values:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data 
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

# Debug print to check data before handling missing values
print("\nPreview of linked data before handling missing values:")
print(linked_data.head())

# 3. Handle missing values
linked_data = handle_missing_values(df=linked_data, trait_col=trait)

print("\nPreview of linked data after handling missing values:")
print(linked_data.head())

# 4. Check for biases and remove biased demographic features 
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate dataset quality and save metadata
note = ""
if is_biased:
    note = "The trait distribution is severely biased."

is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_biased,
    df=linked_data,
    note=note
)

# 6. Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)