# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Melanoma"
cohort = "GSE215868"

# Input paths
in_trait_dir = "../DATA/GEO/Melanoma"
in_cohort_dir = "../DATA/GEO/Melanoma/GSE215868"

# Output paths
out_data_file = "./output/preprocess/3/Melanoma/GSE215868.csv"
out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE215868.csv"
out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE215868.csv"
json_path = "./output/preprocess/3/Melanoma/cohort_info.json"

# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

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

# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene Expression Data Availability
# Based on background info, this is a gene expression dataset studying melanoma outcomes
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Trait data is available from "long-term benefit" field in key 4
trait_row = 4

# Age data is available in key 0
age_row = 0 

# Gender data not available 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert long-term benefit to binary (0=NO, 1=YES)"""
    if pd.isna(value):
        return None
    value = value.split(": ")[-1]
    if value == "YES":
        return 1
    elif value == "NO":
        return 0
    return None

def convert_age(value: str) -> Optional[float]:
    """Convert age to continuous numeric value"""
    if pd.isna(value):
        return None
    try:
        return float(value.split(": ")[-1])
    except:
        return None

def convert_gender(value: str) -> Optional[int]:
    """Placeholder function since gender data not available"""
    return None

# 3. Save 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
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 extracted features
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# List all files to check and get matrix file content
print("All files in directory:")
files = os.listdir(in_cohort_dir) 
for f in files:
    print(f)

# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs (gene/probe identifiers)
print("\nFirst 20 gene/probe IDs:") 
print(genetic_data.index[:20].tolist())

# Since we found standard gene symbols in the matrix file,
# we need to revise the earlier conclusion about methylation data
is_gene_available = True

# Save updated metadata with corrected gene availability info
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)
)
# These appear to be standard human gene symbols (HGNC symbols)
# Examples like A2M, ABCF1, AKT1 are well-known human gene symbols
# No mapping needed as they are already in the correct format
requires_gene_mapping = False
# 1. Normalize gene symbols and save gene data
gene_data = normalize_gene_symbols_in_index(genetic_data) 
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

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

# 4. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)