# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Von_Hippel_Lindau"
cohort = "GSE33093"

# Input paths
in_trait_dir = "../DATA/GEO/Von_Hippel_Lindau"
in_cohort_dir = "../DATA/GEO/Von_Hippel_Lindau/GSE33093"

# Output paths
out_data_file = "./output/preprocess/3/Von_Hippel_Lindau/GSE33093.csv"
out_gene_data_file = "./output/preprocess/3/Von_Hippel_Lindau/gene_data/GSE33093.csv"
out_clinical_data_file = "./output/preprocess/3/Von_Hippel_Lindau/clinical_data/GSE33093.csv"
json_path = "./output/preprocess/3/Von_Hippel_Lindau/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Print shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())

print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
# Based on series description, this is a gene expression study
is_gene_available = True

# 2.1 Data Availability
# Looking at sample characteristics, no explicit trait/age/gender data found in the rows
# The data does not contain VHL status information needed for the trait
trait_row = None 
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # No VHL status data available, function not used
    return None

def convert_age(x):
    # Age conversion function not used since data not available
    return None

def convert_gender(x):
    # Gender conversion function not used since data not available
    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. Clinical Feature Extraction
# Skip since trait_row is None, indicating no clinical data available
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs and shape of data
print("Shape of genetic data:", genetic_data.shape)
print("\nFirst 5 rows with sample columns:")
print(genetic_data.head())
print("\nFirst 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))

# Print first few lines of raw matrix file to inspect format
print("\nFirst few lines of raw matrix file:")
with gzip.open(matrix_file_path, 'rt') as f:
    for i, line in enumerate(f):
        if i < 10:  # Print first 10 lines
            print(line.strip())
        elif "!series_matrix_table_begin" in line:
            print("\nFound table marker at line", i)
            # Print next 3 lines after marker
            for _ in range(3):
                print(next(f).strip())
            break
# The gene identifiers appear to be simple numeric indices (1,2,3...) rather than official gene symbols
# This indicates they are likely probe IDs that need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file with adjusted prefix filtering
gene_annotation = get_gene_annotation(soft_file_path, prefixes=['!Platform_table_begin', '!Platform_table_end'])

# Preview both headers and first few rows
print("Gene annotation column names:")
print(gene_annotation.columns.tolist())

print("\nGene annotation preview:")
preview = preview_df(gene_annotation)
print(preview)

# Also check raw file content around platform table section
print("\nChecking raw SOFT file content around platform table:")
with gzip.open(soft_file_path, 'rt') as f:
    in_platform_table = False
    for i, line in enumerate(f):
        if '!Platform_table_begin' in line:
            print(f"\nFound table begin at line {i}:")
            in_platform_table = True
            # Print header and first few lines
            for _ in range(5):
                print(next(f).strip())
            break
# Since the SOFT file seems to have a non-standard format, let's examine the raw file first
platform_data_lines = []
with gzip.open(soft_file_path, 'rt') as f:
    in_platform_table = False
    for line in f:
        if '!Platform_table_begin' in line:
            in_platform_table = True
            continue
        elif '!Platform_table_end' in line:
            in_platform_table = False
            break
        elif in_platform_table:
            platform_data_lines.append(line.strip())

# Check if we got any platform data
if len(platform_data_lines) > 0:
    # Convert platform data to dataframe
    platform_data = pd.read_csv(io.StringIO('\n'.join(platform_data_lines)), sep='\t', low_memory=False)
    
    # Print columns to verify we have the platform data
    print("Platform data columns:", platform_data.columns.tolist())
    print("\nFirst few rows of platform data:")
    print(platform_data.head())
    
    # Create mapping and apply it if we have the required columns
    id_col = [col for col in platform_data.columns if 'ID' in col.upper()][0] if any('ID' in col.upper() for col in platform_data.columns) else None
    gene_col = [col for col in platform_data.columns if 'GENE' in col.upper() and 'SYMBOL' in col.upper()][0] if any('GENE' in col.upper() and 'SYMBOL' in col.upper() for col in platform_data.columns) else None
    
    if id_col and gene_col:
        mapping_df = get_gene_mapping(platform_data, prob_col=id_col, gene_col=gene_col)
        gene_data = apply_gene_mapping(genetic_data, mapping_df)
        
        # Save gene expression data
        if gene_data is not None:
            os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
            gene_data.to_csv(out_gene_data_file)
            print("\nGene data shape:", gene_data.shape)
            print("First few genes and their expression values:")
            print(gene_data.head())
    else:
        print("Could not identify ID and Gene Symbol columns in platform data")
        gene_data = None
else:
    print("Failed to extract platform table with probe-gene mappings")
    gene_data = None