# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Rheumatoid_Arthritis"
cohort = "GSE97475"

# Input paths
in_trait_dir = "../DATA/GEO/Rheumatoid_Arthritis"
in_cohort_dir = "../DATA/GEO/Rheumatoid_Arthritis/GSE97475"

# Output paths
out_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/GSE97475.csv"
out_gene_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/gene_data/GSE97475.csv"
out_clinical_data_file = "./output/preprocess/3/Rheumatoid_Arthritis/clinical_data/GSE97475.csv"
json_path = "./output/preprocess/3/Rheumatoid_Arthritis/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 series title and description, this is a gene expression study that includes PBMCs RNA data
is_gene_available = True

# 2.1 Data Availability
# Trait: Not available since this is healthy control data
trait_row = None

# Age: Available in demographics
age_row = 81

# Gender: Available in demographics 
gender_row = 118

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    return None

def convert_age(x):
    if pd.isna(x):
        return None
    value = x.split(': ')[1]
    try:
        return float(value)
    except:
        return None

def convert_gender(x):
    if pd.isna(x):
        return None
    value = x.split(': ')[1].lower()
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    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)
# 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)
requires_gene_mapping = False
# 1. Normalize gene symbols and save
gene_data = normalize_gene_symbols_in_index(gene_data) 
gene_data.to_csv(out_gene_data_file)

# Since trait_row is None (no trait data available), skip clinical data processing
# and data linking. Instead, just validate and save the 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,  # We know trait is not available
    is_biased=None,  # No trait to check for bias
    df=None,
    note="Dataset contains gene expression profiles from healthy hepatitis B vaccine recipients, but lacks disease trait for comparison."
)