# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Celiac_Disease"
cohort = "GSE20332"

# Input paths
in_trait_dir = "../DATA/GEO/Celiac_Disease"
in_cohort_dir = "../DATA/GEO/Celiac_Disease/GSE20332"

# Output paths
out_data_file = "./output/preprocess/3/Celiac_Disease/GSE20332.csv"
out_gene_data_file = "./output/preprocess/3/Celiac_Disease/gene_data/GSE20332.csv"
out_clinical_data_file = "./output/preprocess/3/Celiac_Disease/clinical_data/GSE20332.csv"
json_path = "./output/preprocess/3/Celiac_Disease/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Based on background info, this is human leukocyte RNA expression data
is_gene_available = True 

# 2. Variable Availability and Row Identification
# No trait, age or gender info in characteristics
trait_row = None
age_row = None  
gender_row = None

# Define conversion functions (though not used in this case)
def convert_trait(x):
    if x is None:
        return None
    value = x.split(": ")[-1].strip().lower()
    # Binary: celiac (1) vs control (0)
    if "celiac" in value or "case" in value:
        return 1
    elif "control" in value or "healthy" in value:
        return 0
    return None

def convert_age(x):
    if x is None:
        return None
    try:
        # Extract numeric age after colon
        value = x.split(": ")[-1].strip()
        return float(value)
    except:
        return None

def convert_gender(x):
    if x is None:
        return None
    value = x.split(": ")[-1].strip().lower()
    # Convert to binary: female (0) vs male (1)
    if "female" in value or "f" == value:
        return 0
    elif "male" in value or "m" == value:
        return 1
    return None

# 3. Save metadata about dataset usability
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. Skip clinical feature extraction since trait_row is None
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# The gene IDs start with "ILMN_" which indicates they are Illumina probe IDs
# These need to be mapped to standard human gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Additional filtering to exclude control probes 
gene_metadata = gene_metadata[gene_metadata['Species'] != 'ILMN Controls']

# Preview filtered annotation data
print("Column names and preview of gene annotation data:")
print(preview_df(gene_metadata))
# Get mapping from annotation data
# ID column matches the probe IDs in expression data
# Symbol column contains the gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Symbol')

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

# Preview the results
print("\nGene expression data shape after mapping:", gene_data.shape)
print("\nPreview of first few rows and columns of gene expression data:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save - this is all we can do since no clinical data available
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)