# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_Cancer"
cohort = "GSE218438"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_Cancer"
in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE218438"

# Output paths
out_data_file = "./output/preprocess/3/Liver_Cancer/GSE218438.csv"
out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/GSE218438.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/GSE218438.csv"
json_path = "./output/preprocess/3/Liver_Cancer/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 Data Availability
# This dataset is about transcriptional profiling using L1000 platform across multiple cell lines
# So it contains gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Looking at the sample characteristics, we can't find trait (disease status), age or gender information
# The data is from cell lines, not human patients
trait_row = None 
age_row = None
gender_row = None

# 2.2 Data Type Conversion
# Although not used since data is unavailable, defining conversion functions as required
def convert_trait(x):
    return None

def convert_age(x):
    return None

def convert_gender(x):
    return None

# 3. Save Metadata 
# Initial validation - trait data is not available
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=False  # trait_row is None
)

# 4. Clinical Feature Extraction
# Skip this step since trait_row is None
# 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())
# These IDs appear to be Affymetrix probe IDs (e.g. "1007_s_at", "AFFX-TrpnX-M_at")
# which need to be mapped to human gene symbols for proper analysis
requires_gene_mapping = True
# Extract gene annotation data with different prefix filtering
# Platform sections in SOFT files often start with "!Platform_table_begin" until "!Platform_table_end"
gene_annotation = get_gene_annotation(soft_file, prefixes=['!Platform_table_begin', '!Platform_table_end'])

# Preview the annotation data structure to identify relevant columns
print("Gene Annotation Data Preview:") 
preview = preview_df(gene_annotation)
print(json.dumps(preview, indent=2))

# Print column names
print("\nAvailable columns:")
print(gene_annotation.columns.tolist())
# Extract gene annotation data using platform table markers
gene_annotation = get_gene_annotation(soft_file, prefixes=['!Platform_table_begin', '!Platform_table_end'])

# Preview the data structure and columns
print("Gene Annotation Data Structure:")
print("DataFrame dimensions:", gene_annotation.shape)
print("\nFirst few rows and columns:")
print(gene_annotation.head())

# Print column names to help identify probe ID and gene symbol columns
print("\nAvailable columns:")
print(gene_annotation.columns.tolist())
# Load the gene annotation data from SOFT file - specifically targeting platform annotation table
with gzip.open(soft_file, 'rt') as f:
    platform_section = False
    table_data = []
    header = None
    for line in f:
        if 'Gene_Symbol' in line or 'Gene Symbol' in line:
            # Found the header line
            header = line.strip().split('\t')
            platform_section = True
            continue
        elif platform_section and line.strip():
            if '!Platform_table_end' in line:
                platform_section = False
            else:
                table_data.append(line.strip().split('\t'))
            
# Create dataframe if we found the header            
if header:
    gene_annotation = pd.DataFrame(table_data, columns=header)
    print("Updated Gene Annotation Data:")
    print(gene_annotation.head())
    print("\nColumns:", gene_annotation.columns.tolist())

    # Extract mapping between probe IDs and gene symbols 
    mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene_Symbol')
    print("\nGene Mapping Preview:")
    print(mapping_data.head())

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

    # Preview the resulting gene expression data
    print("\nGene Expression Data:")
    print(f"Shape: {gene_data.shape}")
    print("\nFirst few rows and columns:")
    print(gene_data.head().iloc[:, :5])
else:
    print("Could not find gene symbol column in platform annotation")