output/preprocess/Allergies/code/GSE184382.py

# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Allergies"
cohort = "GSE184382"

# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE184382"

# Output paths
out_data_file = "./output/z1/preprocess/Allergies/GSE184382.csv"
out_gene_data_file = "./output/z1/preprocess/Allergies/gene_data/GSE184382.csv"
out_clinical_data_file = "./output/z1/preprocess/Allergies/clinical_data/GSE184382.csv"
json_path = "./output/z1/preprocess/Allergies/cohort_info.json"


# Step 1: Initial Data Loading
import os
from tools.preprocess import *

# Try library helper first, then fall back to a robust recursive search
def find_geo_files_recursive(root_dir: str):
    matrix_candidates = []
    soft_candidates = []
    for dirpath, _, filenames in os.walk(root_dir):
        for fname in filenames:
            lf = fname.lower()
            full_path = os.path.join(dirpath, fname)
            # Prefer GEO series_matrix files
            if ('series_matrix' in lf or 'matrix' in lf) and lf.endswith('.gz'):
                matrix_candidates.append(full_path)
            # SOFT files
            if 'soft' in lf and (lf.endswith('.gz') or lf.endswith('.soft') or lf.endswith('.txt')):
                soft_candidates.append(full_path)
    matrix_candidates.sort()
    soft_candidates.sort()
    return matrix_candidates[0] if matrix_candidates else None, soft_candidates[0] if soft_candidates else None

soft_file = None
matrix_file = None

# Attempt 1: use library helper
try:
    soft_guess, matrix_guess = geo_get_relevant_filepaths(in_cohort_dir)
    # Note: geo_get_relevant_filepaths returns (soft, matrix)
    soft_file = soft_guess
    matrix_file = matrix_guess
except Exception:
    pass

# Attempt 2: recursive search if needed
if matrix_file is None or not os.path.exists(matrix_file):
    rec_matrix, rec_soft = find_geo_files_recursive(in_cohort_dir)
    matrix_file = matrix_file if (matrix_file and os.path.exists(matrix_file)) else rec_matrix
    soft_file = soft_file if (soft_file and os.path.exists(soft_file)) else rec_soft

# Handle missing matrix file gracefully (no hard failure)
if matrix_file is None or not os.path.exists(matrix_file):
    print(f"WARNING: No series matrix file found under {in_cohort_dir}. Skipping data extraction for Step 1.")
    # Record dataset availability status
    validate_and_save_cohort_info(
        is_final=False,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False
    )
    # Fallback outputs for required prints
    background_info = ""
    sample_characteristics_dict = {}
    print("Background Information:")
    print(background_info)
    print("Sample Characteristics Dictionary:")
    print(sample_characteristics_dict)
else:
    # Informative prints on selected files
    print(f"Matrix file selected: {matrix_file}")
    if soft_file is not None:
        print(f"SOFT file selected: {soft_file}")
    else:
        print("WARNING: No SOFT file found. Proceeding with matrix file only for Step 1.")

    # 2. Read the matrix file to obtain background information and sample characteristics data
    background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
    clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
    background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)

    # 3. Obtain the sample characteristics dictionary from the clinical dataframe (limit unique values per feature)
    sample_characteristics_dict = get_unique_values_by_row(clinical_data, max_len=20)

    # 4. Explicitly print out all the background information and the sample characteristics dictionary
    print("Background Information:")
    print(background_info)
    print("Sample Characteristics Dictionary:")
    print(sample_characteristics_dict)