# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Lower_Grade_Glioma"
cohort = "GSE74567"

# Input paths
in_trait_dir = "../DATA/GEO/Lower_Grade_Glioma"
in_cohort_dir = "../DATA/GEO/Lower_Grade_Glioma/GSE74567"

# Output paths
out_data_file = "./output/preprocess/3/Lower_Grade_Glioma/GSE74567.csv"
out_gene_data_file = "./output/preprocess/3/Lower_Grade_Glioma/gene_data/GSE74567.csv"
out_clinical_data_file = "./output/preprocess/3/Lower_Grade_Glioma/clinical_data/GSE74567.csv"
json_path = "./output/preprocess/3/Lower_Grade_Glioma/cohort_info.json"

# Step 1: Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Step 2: Extract background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Step 3: Get dictionary of unique values for each clinical feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Step 4: Print background info and sample characteristics
print("Dataset Background Information:")
print("-" * 80)
print(background_info)
print("\nSample Characteristics:")
print("-" * 80)
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True  # Yes, this dataset contains gene expression data based on background

# 2.1 Data Availability
# The dataset contains cell line data (U251/U373) and experimental construct data
# Not suitable for human trait studies
trait_row = None  # No human trait data available
age_row = None   # No age data
gender_row = None  # No gender data

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

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

def convert_gender(x):
    return None  # Not needed since gender_row is 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 (no clinical data available)
# 1. Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# 2. Print first 20 row IDs
print("First 20 gene/probe identifiers:")
print(genetic_data.index[:20])
requires_gene_mapping = True
# 1. Extract gene annotation data from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# 2. Preview annotation data
print("Column names and first few values in gene annotation data:")
print(preview_df(gene_annotation))

# Preview additional rows to check for gene annotations
print("\nPreview of rows 100-105:")
print(preview_df(gene_annotation.iloc[100:105]))
# 1. ID is in 'ID' column and gene symbol is in 'GENE_SYMBOL' column
# 2. Get mapping between gene identifiers and gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')

# 3. Convert probe data to gene expression data using mapping
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview the first few genes and their expression values
print("First few genes in mapped expression data:")
print(preview_df(gene_data))
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# Skip clinical data linking and missing value handling since no clinical data available

# 2. Final validation and save metadata
# Set is_biased=False since trait data doesn't exist to be biased
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=False,  
    df=normalized_gene_data,
    note="Dataset contains cell line data (U251/U373), not suitable for human trait studies."
)

# Skip saving linked data since not usable