# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_cirrhosis"
cohort = "GSE185529"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE185529"

# Output paths
out_data_file = "./output/preprocess/3/Liver_cirrhosis/GSE185529.csv"
out_gene_data_file = "./output/preprocess/3/Liver_cirrhosis/gene_data/GSE185529.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_cirrhosis/clinical_data/GSE185529.csv"
json_path = "./output/preprocess/3/Liver_cirrhosis/cohort_info.json"

# Step 1: Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# First examine SOFT file contents to identify subseries
with gzip.open(soft_file_path, 'rt') as f:
    soft_content = f.read()

# Look for subseries IDs
subseries_match = re.search(r'!Series_relation = SuperSeries of: (GSE\d+)', soft_content)
if subseries_match:
    subseries_id = subseries_match.group(1)
    subseries_files = [f for f in os.listdir(in_cohort_dir) if subseries_id in f]
    if subseries_files:
        subseries_soft = [f for f in subseries_files if 'soft' in f.lower()][0]
        subseries_matrix = [f for f in subseries_files if 'matrix' in f.lower()][0]
        soft_file_path = os.path.join(in_cohort_dir, subseries_soft)
        matrix_file_path = os.path.join(in_cohort_dir, subseries_matrix)

# Extract background info and clinical data from the appropriate files
background_info, clinical_data = get_background_and_clinical_data(soft_file_path)

if len(clinical_data.columns) <= 2:  # If SOFT file didn't yield enough info, try matrix file
    background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

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

# Print background info and sample characteristics
print("Dataset Background Information:")
print("-" * 80)
print(background_info)
print("\nSample Characteristics:")
print("-" * 80)
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True  # Based on series title which implies gene expression study

# 2.1 Data Availability
trait_row = None  # No disease/control info in characteristics
age_row = None   # No age info in characteristics  
gender_row = None # No gender info in characteristics

# 2.2 Data Type Conversion
# Only define convert_trait since other data not available
def convert_trait(x):
    if x is None:
        return None
    value = x.split(': ')[1].lower() if ': ' in x else x.lower()
    # Return None since we don't have trait data
    return None

# 3. Save metadata
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. Skip clinical feature extraction since trait_row is None
# 1. Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# 2. Print first 20 row IDs
print("First 20 gene/probe identifiers:")
print(genetic_data.index[:20])
# Based on the gene IDs shown ('2824546_st', '2824549_st', etc.), these are
# not standard human gene symbols but rather probe identifiers from an Affymetrix microarray platform.
# They need to be mapped to proper gene symbols for downstream analysis.

requires_gene_mapping = True
# 1. Extract gene annotation data from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# 2. Preview annotation data
print("Column names and first few values in gene annotation data:")
print(preview_df(gene_annotation))
# 2. Extract mapping dataframe with probe IDs and gene symbols
mapping_data = gene_annotation[['probeset_id', 'gene_assignment']].copy()
mapping_data = mapping_data.rename(columns={'probeset_id': 'ID', 'gene_assignment': 'Gene'})
mapping_data = mapping_data.astype({'ID': 'str'})

# Parse the gene_assignment field to extract valid gene symbols
mapping_data['Gene'] = mapping_data['Gene'].apply(lambda x: re.search(r'//\s*(\w+)\s*//', str(x)).group(1) if pd.notnull(x) and '//' in str(x) else None)
mapping_data = mapping_data.dropna()

# 3. Convert probe-level data to gene-level expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview transformed data
print("\nFirst few gene identifiers after mapping:")
print(gene_data.index[:20])
# 2. Extract mapping dataframe with probe IDs and gene symbols 
mapping_data = gene_annotation[['ID', 'gene_assignment']].copy()
mapping_data['ID'] = mapping_data['ID'].astype(str) + '_st'  # Add '_st' suffix to match expression data format

def extract_gene(text):
    if pd.isna(text) or '//' not in str(text):
        return None
    matches = re.findall(r'//\s*(\w+)\s*//', str(text))
    if matches:
        # Convert mouse gene symbols to human by making uppercase
        return matches[0].upper() 
    return None

mapping_data['Gene'] = mapping_data['gene_assignment'].apply(extract_gene)
mapping_data = mapping_data[['ID', 'Gene']].dropna()

# 3. Convert probe-level data to gene-level expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Normalize gene symbols using NCBI database info
gene_data = normalize_gene_symbols_in_index(gene_data)

# Preview transformed data
print("\nFirst few gene identifiers after mapping:")
print(gene_data.index[:20])