# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Epilepsy"
cohort = "GSE64123"

# Input paths
in_trait_dir = "../DATA/GEO/Epilepsy"
in_cohort_dir = "../DATA/GEO/Epilepsy/GSE64123"

# Output paths
out_data_file = "./output/preprocess/3/Epilepsy/GSE64123.csv"
out_gene_data_file = "./output/preprocess/3/Epilepsy/gene_data/GSE64123.csv"
out_clinical_data_file = "./output/preprocess/3/Epilepsy/clinical_data/GSE64123.csv"
json_path = "./output/preprocess/3/Epilepsy/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
# Yes, this is gene expression data from stem cells, not miRNA or methylation
is_gene_available = True

# 2.1 Data Availability
# This is a drug testing dataset, so patients don't have epilepsy - trait data not available
trait_row = None

# Age and gender not recorded in sample characteristics
age_row = None 
gender_row = None

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

def convert_age(x):
    # Not used since age_row is None
    return None
    
def convert_gender(x):
    # Not used since 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
# 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])
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. Observe the data: 
# In gene expression data, IDs are like '100009676_at', '10000_at'
# In gene annotation, 'ID' contains similar format like '1_at', '10_at'
# 'Description' contains gene names/symbols

# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Description')

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

# Preview results
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nPreview of first few rows and columns:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
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)

# Create a dummy dataframe with just gene data since we lack trait data
df = pd.DataFrame(index=gene_data.index)

# Save metadata indicating dataset is not usable due to lack of trait data
is_usable = validate_and_save_cohort_info(
    is_final=True, 
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,  # No trait data available
    is_biased=True,  # Consider as biased since trait is missing entirely
    df=df,
    note="Drug testing dataset without trait data for epilepsy analysis"
)

# No need to save linked data since dataset is not usable for trait analysis