# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Glioblastoma"
cohort = "GSE129978"

# Input paths
in_trait_dir = "../DATA/GEO/Glioblastoma"
in_cohort_dir = "../DATA/GEO/Glioblastoma/GSE129978"

# Output paths
out_data_file = "./output/preprocess/3/Glioblastoma/GSE129978.csv"
out_gene_data_file = "./output/preprocess/3/Glioblastoma/gene_data/GSE129978.csv"
out_clinical_data_file = "./output/preprocess/3/Glioblastoma/clinical_data/GSE129978.csv"
json_path = "./output/preprocess/3/Glioblastoma/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 = True  # This is a gene expression analysis study based on series title

# 2. Variable Availability and Data Type Conversion
trait_row = None  # Cell line information doesn't provide suitable trait variation
age_row = None  # No age information available 
gender_row = None  # No gender information available

def convert_trait(x):
    return None

def convert_age(x):
    return None

def convert_gender(x):
    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
# 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 numeric identifiers and the appearance of 'ID_REF' in raw file, 
# these appear to be probe IDs from a microarray platform rather than 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
gene_annotation = get_gene_annotation(soft_file)

# Preview gene annotation data
print("Gene annotation columns and example values:")
print(preview_df(gene_annotation))
# 1. From previous output, gene expression data uses columns 'ID' as identifiers
# Since this is mouse data and we need human data, we should stop here
gene_data = pd.DataFrame() # Empty dataframe to indicate invalid data

# Print warning message
print("WARNING: This dataset contains mouse gene expression data but human data is required.")
print("The preprocessing will stop here and return an empty dataframe.")

# Preview results 
print("\nShape of gene expression data:", gene_data.shape)
print("\nFirst few rows:")
print(gene_data.head())