# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Arrhythmia"
cohort = "GSE55231"

# Input paths
in_trait_dir = "../DATA/GEO/Arrhythmia"
in_cohort_dir = "../DATA/GEO/Arrhythmia/GSE55231"

# Output paths
out_data_file = "./output/preprocess/3/Arrhythmia/GSE55231.csv"
out_gene_data_file = "./output/preprocess/3/Arrhythmia/gene_data/GSE55231.csv"
out_clinical_data_file = "./output/preprocess/3/Arrhythmia/clinical_data/GSE55231.csv"
json_path = "./output/preprocess/3/Arrhythmia/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
# Based on the background info mentioning "gene expression" and "Illumina Human HT12 Version 4 BeadChips"
# for transcription profiling with >47,000 transcripts, this dataset contains gene expression data
is_gene_available = True

# 2.1 Data Row Identification
# For arrhythmia trait - not explicitly available in sample characteristics
trait_row = None  
# Age is in row 2 (0-based index)
age_row = 2
# Gender is in row 0 (0-based index)
gender_row = 0

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Not needed as trait data is not available
    return None

def convert_age(x):
    # Extract numeric age value after colon
    try:
        age = int(x.split(': ')[1])
        return age  # Return as continuous value
    except:
        return None

def convert_gender(x):
    # Convert to binary: female=0, male=1
    try:
        gender = x.split(': ')[1].lower()
        if gender == 'female':
            return 0
        elif gender == 'male':
            return 1
        return None
    except:
        return None

# 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
# Skip since trait_row is None, meaning clinical data for the trait is not available
# 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 identifiers starting with "ILMN_" are Illumina probe IDs, not gene symbols
# These need to be mapped to standard gene symbols for proper analysis
requires_gene_mapping = True
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file)

# Preview annotation dataframe structure
print("Gene Annotation Preview:")
print("Column names:", gene_annotation.columns.tolist())
print("\nFirst few rows as dictionary:")
print(preview_df(gene_annotation))
# Get file paths and gene expression data again
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)
gene_data = get_genetic_data(matrix_file)

# Map Illumina probe IDs to gene symbols 
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Symbol')

# Convert probe-level measurements to gene expression values
gene_data = apply_gene_mapping(gene_data, mapping_df)

# Preview the mapped gene data
print("Mapped Gene Expression Data Preview:")  
print("Shape:", gene_data.shape)
print("\nFirst few rows:")
print(gene_data.head())
# Since we determined trait data is not available, skip further processing
note = "Dataset lacks trait information necessary for analysis"
validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=False,  # Not applicable but required by function
    df=pd.DataFrame(),  # Empty DataFrame but required by function 
    note=note
)