# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Stomach_Cancer"
cohort = "GSE146361"

# Input paths
in_trait_dir = "../DATA/GEO/Stomach_Cancer"
in_cohort_dir = "../DATA/GEO/Stomach_Cancer/GSE146361"

# Output paths
out_data_file = "./output/preprocess/3/Stomach_Cancer/GSE146361.csv"
out_gene_data_file = "./output/preprocess/3/Stomach_Cancer/gene_data/GSE146361.csv"
out_clinical_data_file = "./output/preprocess/3/Stomach_Cancer/clinical_data/GSE146361.csv"
json_path = "./output/preprocess/3/Stomach_Cancer/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# Check gene expression data availability
# Yes - it contains gene expression data from HumanHT-12 v3.0 Expression BeadChip array
is_gene_available = True

# 2.1 Data Availability
# All samples are gastric cancer cell lines, no healthy controls, so trait data is not usable
trait_row = None  

# Age and gender are not available for cell lines
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # Not needed as trait_row is None
    return None

def convert_age(x):
    # Not needed as age_row is None 
    return None

def convert_gender(x):
    # Not needed as gender_row is None
    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. Skip clinical feature extraction since trait_row is None
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# Check identifiers - Looking at the ID format ('ILMN_' prefix), these are Illumina probe IDs and need mapping to gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and values from annotation dataframe
print("Gene annotation DataFrame preview:")
print(preview_df(gene_annotation))
# 1. Identify columns for probe IDs and gene symbols
# From examining the data, we need 'ID' and 'Symbol' columns
# 'ID' matches the ILMN_ identifiers in the expression data
# 'Symbol' contains the gene symbols we want to map to

# 2. Get mapping between probe IDs and gene symbols
gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# 3. Convert probe measurements to gene expression values
gene_data = apply_gene_mapping(genetic_data, gene_mapping)
# 1. Normalize gene symbols in gene expression data
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)
print("\nGene data shape (normalized gene-level):", gene_data.shape)

# Since we know there's no trait data (all samples are cancer cell lines), mark as biased
trait_biased = True  # No control samples makes it inherently biased
note = "Dataset contains only cancer cell lines without controls. Gene expression data was preprocessed from probe-level to gene-level using gene symbol normalization with NCBI Gene database."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=trait_biased,
    df=gene_data,
    note=note
)

# Don't save the linked data since it's not usable for our analysis