# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Obesity"
cohort = "GSE271700"

# Input paths
in_trait_dir = "../DATA/GEO/Obesity"
in_cohort_dir = "../DATA/GEO/Obesity/GSE271700"

# Output paths
out_data_file = "./output/preprocess/3/Obesity/GSE271700.csv"
out_gene_data_file = "./output/preprocess/3/Obesity/gene_data/GSE271700.csv"
out_clinical_data_file = "./output/preprocess/3/Obesity/clinical_data/GSE271700.csv"
json_path = "./output/preprocess/3/Obesity/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
# This is whole-genome microarray data, so gene expression data should be available
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = 3  # phenotype indicates diabetes remission response
age_row = 1    # age data available 
gender_row = 0 # gender data available

# 2.2 Data Type Conversion Functions 
def convert_trait(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].strip()
    if value == 'Responder':
        return 1
    elif value == 'Non-Responder':
        return 0
    return None

def convert_age(x):
    if not isinstance(x, str):
        return None
    try:
        return float(x.split(': ')[-1].strip())
    except:
        return None

def convert_gender(x):
    if not isinstance(x, str):
        return None
    value = x.split(': ')[-1].strip()
    if value.lower() == 'female':
        return 0
    elif value.lower() == 'male':
        return 1
    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
# Since trait_row is not None, we extract clinical features
selected_clinical = geo_select_clinical_features(clinical_df=clinical_data,
                                               trait=trait,
                                               trait_row=trait_row,
                                               convert_trait=convert_trait,
                                               age_row=age_row,
                                               convert_age=convert_age,
                                               gender_row=gender_row,
                                               convert_gender=convert_gender)

# Preview the extracted features
print("Preview of selected clinical features:")
print(preview_df(selected_clinical))

# Save to CSV
selected_clinical.to_csv(out_clinical_data_file)
# 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)
# Based on the observance of gene identifiers like "100009676_at", "10000_at", etc,
# these are probe IDs from an Affymetrix microarray platform, not human gene symbols.
# The "_at" suffix is a characteristic identifier format used by Affymetrix arrays.
# These probe IDs need to be mapped to their corresponding gene symbols.

requires_gene_mapping = True
# Get file paths using library function
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene annotation from SOFT file and get meaningful data
# Read first 100 lines to inspect structure
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    print("First 100 lines from SOFT file to inspect structure:")
    for i, line in enumerate(f):
        if i < 100:  # Preview structure
            print(line.strip())
        else:
            break
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)

# Get platform ID from SOFT file
platform_id = None
with gzip.open(soft_file, 'rt', encoding='utf-8') as f:
    for line in f:
        if line.startswith('!Platform_geo_accession'):
            platform_id = line.split('=')[1].strip()
            break

print(f"Dataset uses platform: {platform_id}")
print("Warning: Gene symbol mapping information is not available in the SOFT file or pre-compiled GPL mappings.")
print("Saving probe-level expression data for future mapping when platform annotation becomes available.")

# Save probe-level expression data
gene_data.to_csv(out_gene_data_file)

raise ValueError(
    f"Cannot complete preprocessing: Platform {platform_id} annotation data is required for mapping "
    "probe IDs to gene symbols, but the mapping information is not available. "
    "Please obtain the platform annotation data and rerun preprocessing."
)