# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Melanoma"
cohort = "GSE146264"

# Input paths
in_trait_dir = "../DATA/GEO/Melanoma"
in_cohort_dir = "../DATA/GEO/Melanoma/GSE146264"

# Output paths
out_data_file = "./output/preprocess/3/Melanoma/GSE146264.csv"
out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE146264.csv"
out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE146264.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
is_gene_available = True  # This is an scRNA-seq dataset for CD8+ T cells

# 2. Clinical data availability and conversion
trait_row = 1  # subjectid indicates disease status (P for psoriasis patients, C for controls)
age_row = None  # Age data not available
gender_row = None  # Gender data not available

def convert_trait(x: str) -> int:
    """Convert subject ID to binary trait status
    P = patient = 1, C = control = 0"""
    if not x or ':' not in x:
        return None
    val = x.split(':')[1].strip()
    if val.startswith('P'):  # Patient
        return 1
    elif val.startswith('C'):  # Control
        return 0
    return None

def convert_age(x: str) -> float:
    """Convert age string to float"""
    return None  # Not used since age_row is None

def convert_gender(x: str) -> int:
    """Convert gender string to binary"""
    return None  # Not used since gender_row is 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:
    selected_clinical_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 processed clinical data
    preview = preview_df(selected_clinical_df)
    print("Preview of processed clinical data:")
    print(preview)
    
    # Save clinical data
    selected_clinical_df.to_csv(out_clinical_data_file)
# Try different markers for gene data extraction
markers = ["!series_matrix_table_begin", "!series_matrix_table_begin\t", "!dataset_table_begin"]

for marker in markers:
    genetic_data = get_genetic_data(matrix_file_path, marker=marker)
    if not genetic_data.empty:
        break

if genetic_data.empty:
    print("Warning: No genetic data was extracted from the matrix file.")
    is_gene_available = False
else:
    # Print first 20 row IDs to examine data type
    print("First 20 row IDs:")
    print(list(genetic_data.index)[:20])
    is_gene_available = True
    # Only save if data was successfully extracted
    genetic_data.to_csv(out_gene_data_file)

# Save updated 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)
)
# Peek at file structure
with gzip.open(matrix_file_path, 'rt') as f:
    print("First 10 lines of matrix file:")
    for i, line in enumerate(f):
        if i < 10:
            print(line.strip())
        else:
            break
            
# First try reading as tab-delimited without seeking markers
try:
    genetic_data = pd.read_csv(matrix_file_path, compression='gzip', sep='\t', comment='!', 
                             low_memory=False)
    print("\nLoaded data shape:", genetic_data.shape)
    if not genetic_data.empty:
        if 'ID_REF' in genetic_data.columns:
            genetic_data = genetic_data.rename(columns={'ID_REF': 'ID'})
        genetic_data = genetic_data.set_index(genetic_data.columns[0])
        # Print first 20 row IDs to examine data type
        print("\nFirst 20 row IDs:")
        print(list(genetic_data.index)[:20])
        genetic_data.to_csv(out_gene_data_file)
        is_gene_available = True
    else:
        print("Warning: No genetic data was extracted from the matrix file.")
        is_gene_available = False
        
except Exception as e:
    print(f"Error extracting genetic data: {str(e)}")
    is_gene_available = False

# Save updated 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)
)
requires_gene_mapping = False
# First peek at SOFT file structure
with gzip.open(soft_file_path, 'rt') as f:
    print("First 20 lines of SOFT file:")
    # Store lines that don't start with ^, !, or #
    data_lines = []
    for i, line in enumerate(f):
        if i < 20:
            print(line.strip()) 
        if not any(line.startswith(p) for p in ['^', '!', '#']):
            data_lines.append(line)
        if len(data_lines) >= 5:  # Get first few data lines
            break

# Manual parsing approach since file structure is non-standard
try:
    with gzip.open(soft_file_path, 'rt') as f:
        data_lines = []
        for line in f:
            if not any(line.startswith(p) for p in ['^', '!', '#']):
                data_lines.append(line)
    
    if data_lines:
        gene_metadata = pd.read_csv(io.StringIO(''.join(data_lines)), sep='\t', 
                                  low_memory=False)
        print("\nLoaded data shape:", gene_metadata.shape)
        # Preview column names and first few values
        preview = preview_df(gene_metadata)
        print("\nGene annotation columns and sample values:")
        print(preview)
    else:
        print("Warning: No gene annotation data was found in the SOFT file.")
        
except Exception as e:
    print(f"Error extracting gene annotation data: {str(e)}")
# Check if we have valid gene expression data
if 'genetic_data' not in locals() or genetic_data.empty:
    print("No valid gene expression data available. Skipping data integration.")
    # Create minimal DataFrame to indicate failure
    minimal_df = pd.DataFrame({'Failed': [1]})
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=True,
        is_biased=True,  # Set to True to indicate dataset is unusable
        df=minimal_df,
        note="Failed to extract gene expression data from matrix file."
    )
else:
    # 1. Normalize gene symbols and save 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)

    # 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, normalized_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=True, 
        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)