# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometriosis"
cohort = "GSE138297"

# Input paths
in_trait_dir = "../DATA/GEO/Endometriosis"
in_cohort_dir = "../DATA/GEO/Endometriosis/GSE138297"

# Output paths
out_data_file = "./output/preprocess/3/Endometriosis/GSE138297.csv"
out_gene_data_file = "./output/preprocess/3/Endometriosis/gene_data/GSE138297.csv"
out_clinical_data_file = "./output/preprocess/3/Endometriosis/clinical_data/GSE138297.csv"
json_path = "./output/preprocess/3/Endometriosis/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability 
is_gene_available = False  # Gene expression data measures IBS response, not suitable for Endometriosis study

# 2.1 Data Availability
trait_row = None  # No Endometriosis data available in this IBS study
age_row = 3  # Age data in years
gender_row = 1  # Gender data encoded as binary

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    return None  # Not needed since trait data is unavailable

def convert_age(value):
    if value is None:
        return None
    try:
        return float(value.split(': ')[-1].strip())
    except:
        return None

def convert_gender(value):
    if value is None:
        return None
    try:
        # Value is already encoded as we want (female=1, male=0)
        return int(value.split(': ')[-1].strip())  
    except:
        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=trait_row is not None)

# 4. Clinical Feature Extraction
# Skip since trait_row is None
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug 
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# The gene identifiers are numeric IDs starting with 16650xxx
# These are not standard human gene symbols and need to be mapped
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# Get file paths and load initial gene expression data
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
gene_data = get_genetic_data(matrix_file)

# Get gene mapping dataframe from annotation data
# 'ID' column in metadata matches IDs in expression data
# 'gene_assignment' contains gene symbols, but needs parsing
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')

# Apply gene mapping to expression data
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Normalize gene symbols to official ones
gene_data = normalize_gene_symbols_in_index(gene_data)
# 1. Save normalized gene data
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Validate and save cohort info
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,  # Changed to False since trait data isn't available
    is_biased=None,  # Not applicable since trait isn't available
    df=None,  # No linked data to provide
    note="Dataset contains gene expression data but lacks endometriosis trait information."
)