# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Melanoma"
cohort = "GSE157738"

# Input paths
in_trait_dir = "../DATA/GEO/Melanoma"
in_cohort_dir = "../DATA/GEO/Melanoma/GSE157738"

# Output paths
out_data_file = "./output/preprocess/3/Melanoma/GSE157738.csv"
out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE157738.csv"
out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE157738.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
# Yes, this is gene expression data from Affymetrix Human Gene 2.0 ST Array
is_gene_available = True

# 2.1 Data Availability
# Trait (clinical outcome) is available in row 4 with multiple values
trait_row = 4

# Age and gender data are not available in sample characteristics
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Extract value after colon, strip whitespace
    if not isinstance(x, str):
        return None
    value = x.split(':')[-1].strip()
    # Convert clinical outcomes to binary 
    # NED (No Evidence of Disease) and PR (Partial Response) are positive outcomes
    if value in ['NED1', 'NED2', 'PR']:
        return 1
    # PD (Progressive Disease) and SD (Stable Disease) are negative outcomes
    elif value in ['PD', 'SD']:
        return 0
    return None

def convert_age(x):
    return None  # Age data not available

def convert_gender(x):
    return None  # Gender data not available

# 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. Clinical Feature Extraction
# Since trait_row is not None, we need to extract clinical features
selected_clinical = 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_result = preview_df(selected_clinical)

# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs to examine data type
print("First 20 row IDs:")
print(list(genetic_data.index)[:20])

# After examining the IDs and confirming this is gene expression data:
is_gene_available = True  

# 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)
)

genetic_data.to_csv(out_gene_data_file)
# These numerical IDs appear to be probe IDs, not standard human gene symbols
# They need to be mapped to their corresponding gene symbols for biological interpretation
requires_gene_mapping = True
# First let's examine a few lines from the SOFT file to identify the correct section
import gzip
with gzip.open(soft_file_path, 'rt') as f:
    # Print first 100 lines to see file structure
    for i, line in enumerate(f):
        if i < 100:  # Limit output to first 100 lines
            if 'table_begin' in line.lower():
                print(f"Found table marker at line {i}:")
                print(line.strip())
        else:
            break

# Extract gene annotation with adjusted prefix filtering
gene_metadata = get_gene_annotation(soft_file_path, prefixes=['!Platform_table_begin', '!platform_table_end'])

# Preview to verify we got the annotation data
print("\nGene annotation columns and sample values:")
preview = preview_df(gene_metadata)
print(preview)
# Try loading annotation data with platform-related prefixes
import gzip

def parse_soft_file(file_path):
    probe_to_gene = {}
    within_platform = False
    with gzip.open(file_path, 'rt') as f:
        for line in f:
            line = line.strip()
            if line.startswith('!Platform_table_begin'):
                within_platform = True
                # Get header line
                header = next(f).strip().split('\t')
                id_idx = header.index('ID')
                gene_idx = header.index('Gene Assignment')
                continue
            
            if within_platform:
                if line.startswith('!Platform_table_end'):
                    break
                    
                fields = line.split('\t')
                if len(fields) > max(id_idx, gene_idx):
                    probe_id = fields[id_idx]
                    gene_info = fields[gene_idx]
                    if gene_info != '---':
                        # Extract gene symbol from gene assignment string
                        # Format is typically: gene_id // gene_symbol // gene_name
                        gene_parts = gene_info.split('//')
                        if len(gene_parts) > 1:
                            gene_symbol = gene_parts[1].strip()
                            probe_to_gene[probe_id] = gene_symbol

    # Convert to DataFrame                        
    mapping_df = pd.DataFrame.from_dict(probe_to_gene.items())
    mapping_df.columns = ['ID', 'Gene']
    return mapping_df

# Get mapping between probe IDs and gene symbols
mapping_data = parse_soft_file(soft_file_path)

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

# Preview mapped gene data 
print("\nFirst few gene symbols:")
print(list(gene_data.index)[:10])

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# Parse SOFT file to get probe-to-gene mapping
def parse_soft_file(file_path):
    probe_to_gene = []
    within_platform = False
    
    with gzip.open(file_path, 'rt') as f:
        # Debug printing
        print("First 10 lines of SOFT file:")
        for i, line in enumerate(f):
            if i < 10:
                print(line.strip())
            if i == 10:
                break
        f.seek(0)  # Reset file pointer
            
        for line in f:
            line = line.strip()
            if line.startswith('!Platform_table_begin'):
                within_platform = True
                # Print a few lines after table begin to confirm structure
                print("\nPlatform table header:")
                header = next(f).strip()
                print(header)
                header = header.split('\t')
                try:
                    id_idx = header.index('ID')
                    gene_idx = header.index('Gene Symbol') # Try alternative column name
                except ValueError:
                    # If first attempt fails, print all column names for debugging
                    print("\nAll column names found:")
                    print(header)
                    # Try other common variations
                    gene_idx = next((i for i, col in enumerate(header) 
                                   if 'gene' in col.lower() and 'symbol' in col.lower()), -1)
                    if gene_idx == -1:
                        raise ValueError("Could not find gene symbol column")
                continue
            
            if within_platform:
                if line.startswith('!Platform_table_end'):
                    break
                    
                fields = line.split('\t')
                if len(fields) > max(id_idx, gene_idx):
                    probe_id = fields[id_idx]
                    gene_symbol = fields[gene_idx]
                    if gene_symbol and gene_symbol != '---':
                        probe_to_gene.append([probe_id, gene_symbol])

    mapping_df = pd.DataFrame(probe_to_gene, columns=['ID', 'Gene'])
    print(f"\nFound {len(mapping_df)} probe-to-gene mappings")
    return mapping_df

# Get mapping between probe IDs and gene symbols
mapping_data = parse_soft_file(soft_file_path)

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

# Preview mapped gene data
print("\nFirst few gene symbols:")
print(list(gene_data.index)[:10])

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# Parse SOFT file with more debugging and flexible header parsing
def parse_soft_file(file_path):
    probe_to_gene = []
    within_platform = False
    header_found = False
    
    with gzip.open(file_path, 'rt') as f:
        for line in f:
            line = line.strip()
            
            # Print sections looking for platform metadata
            if line.startswith('^PLATFORM'):
                within_platform = True
                print(f"\nFound platform section: {line}")
                continue
                
            # After platform marker, look for the header line with probe metadata
            if within_platform and not header_found and line.startswith('!Platform_var'):
                header_line = line
                print(f"\nPotential header line found: {header_line}")
                if 'gene symbol' in header_line.lower():
                    print("Found gene symbol column info")
                    
                # Extract column name mapping if this line contains it    
                if ' = ' in line:
                    field_name = line.split(' = ')[1]
                    print(f"Field name: {field_name}")
                    if 'gene symbol' in field_name.lower():
                        gene_col = field_name
                        print(f"Gene column found: {gene_col}")
                        header_found = True
                continue
            
            # After finding header info, process data rows    
            if within_platform and header_found:
                if line.startswith('#') or line.startswith('!'):
                    continue
                    
                fields = line.split('\t')
                if len(fields) < 2:
                    continue
                    
                probe_id = fields[0]
                # Look for gene symbol in likely positions 
                for field in fields[1:]:
                    if '//' in field:  # Common format in GEO: geneID//geneSymbol//geneName
                        parts = field.split('//')
                        if len(parts) > 1:
                            gene_symbol = parts[1].strip()
                            if gene_symbol and gene_symbol not in ['---', '']:
                                probe_to_gene.append([probe_id, gene_symbol])
                                break

    mapping_df = pd.DataFrame(probe_to_gene, columns=['ID', 'Gene'])
    print(f"\nFound {len(mapping_df)} probe-to-gene mappings")
    if len(mapping_df) > 0:
        print("\nFirst few mappings:")
        print(mapping_df.head())
    return mapping_df

# Get mapping between probe IDs and gene symbols
print("Extracting probe-to-gene mappings from SOFT file...")
mapping_data = parse_soft_file(soft_file_path)

if len(mapping_data) > 0:
    # Apply gene mapping to convert probe-level data to gene-level expression
    print("\nConverting probe-level to gene-level expression data...")
    gene_data = apply_gene_mapping(genetic_data, mapping_data)
    
    # Save gene expression data
    print("\nSaving gene expression data...")
    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
    gene_data.to_csv(out_gene_data_file)
    
    # Normalize and link data
    gene_data = normalize_gene_symbols_in_index(gene_data)
    clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
    linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)
    
    # Handle missing values
    linked_data = handle_missing_values(linked_data, trait)
    
    # Judge bias in features and remove biased ones
    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
    
    # 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."
    )
    
    # 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)
else:
    print("Failed to extract gene mappings. Cannot proceed with data processing.")