# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cardiovascular_Disease"
cohort = "GSE285666"

# Input paths
in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE285666"

# Output paths
out_data_file = "./output/preprocess/3/Cardiovascular_Disease/GSE285666.csv"
out_gene_data_file = "./output/preprocess/3/Cardiovascular_Disease/gene_data/GSE285666.csv"
out_clinical_data_file = "./output/preprocess/3/Cardiovascular_Disease/clinical_data/GSE285666.csv"
json_path = "./output/preprocess/3/Cardiovascular_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
# Yes, this dataset contains gene expression data from Affymetrix Human Exon arrays
is_gene_available = True

# 2.1 Data Availability
# trait (Williams syndrome) is available in row 0
trait_row = 0
# Age and gender not available in characteristics
age_row = None 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    # Extract value after colon
    if ':' in value:
        value = value.split(':')[1].strip()
    # Convert to binary (0=control, 1=disease)
    if 'unaffected' in value.lower() or 'control' in value.lower():
        return 0
    elif 'williams syndrome' in value.lower() or 'ws' in value.lower():
        return 1
    return None

def convert_age(value):
    return None

def convert_gender(value):
    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. Clinical Feature Extraction 
if trait_row is not None:
    clinical_df = geo_select_clinical_features(clinical_data,
                                            trait=trait,
                                            trait_row=trait_row,
                                            convert_trait=convert_trait,
                                            age_row=age_row,
                                            convert_age=convert_age,
                                            gender_row=gender_row, 
                                            convert_gender=convert_gender)
    
    # Preview the data
    preview = preview_df(clinical_df)
    print("Preview of clinical data:")
    print(preview)
    
    # Save clinical data
    clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print first 20 row IDs 
print("First 20 gene/probe IDs:")
print(list(genetic_df.index)[:20])
# These identifiers appear to be numeric probe IDs, not human gene symbols
# Numeric probe IDs typically need to be mapped to gene symbols for biological interpretation
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview column names and first few values
print("Column names and preview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. Identify mapping columns:
# ID column contains probe identifiers matching the gene expression data
# gene_assignment contains gene symbols with additional information

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

# 3. Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Preview result 
print("\nFirst 5 genes and their expression values:")
print(preview_df(gene_data, n=5))
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# 4. Check and handle biased features 
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save cohort info
note = "Clinical data structure: binary disease status (Canavan disease) with gender information. Gender distribution is biased with a significant imbalance."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,  
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note=note
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)