# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Canavan_Disease"
cohort = "GSE41445"

# Input paths
in_trait_dir = "../DATA/GEO/Canavan_Disease"
in_cohort_dir = "../DATA/GEO/Canavan_Disease/GSE41445"

# Output paths
out_data_file = "./output/preprocess/3/Canavan_Disease/GSE41445.csv"
out_gene_data_file = "./output/preprocess/3/Canavan_Disease/gene_data/GSE41445.csv"
out_clinical_data_file = "./output/preprocess/3/Canavan_Disease/clinical_data/GSE41445.csv"
json_path = "./output/preprocess/3/Canavan_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, the dataset contains gene expression data from Affymetrix HG-U133_plus2 GeneChips
is_gene_available = True

# 2.1 Row identifiers for variables 
# Disease status related to Canavan disease is in row 2
trait_row = 2
# Age is not available
age_row = None  
# Gender is in row 0
gender_row = 0

# 2.2 Data type conversion functions
def convert_trait(value: str) -> int:
    """Convert disease information to binary: 1 for Canavan disease, 0 for others"""
    if not value or ':' not in value:
        return None
    disease = value.split(':', 1)[1].strip().lower()
    if 'canavan disease' in disease:
        return 1
    return 0

def convert_age(value: str) -> float:
    """Convert age to continuous value"""
    # Not used since age data is not available
    return None

def convert_gender(value: str) -> int:
    """Convert gender to binary: 0 for female, 1 for male"""
    if not value or ':' not in value:
        return None
    gender = value.split(':', 1)[1].strip().lower()
    if gender == 'female':
        return 0
    elif gender == 'male':
        return 1
    return None

# 3. Save metadata for initial filtering
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. Extract clinical features
clinical_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
                                         gender_row=gender_row, convert_gender=convert_gender)
print("Preview of extracted clinical data:")
print(preview_df(clinical_df))

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
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 look like Affymetrix probe IDs (format: XXXXXX_at or XXXXXX_s_at etc)
# rather than standard human gene symbols (e.g. BRCA1, TP53)
# They will need to be mapped to proper gene symbols
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))
# Extract ID and Gene Symbol columns from annotation data
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')

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

# Preview first few rows/columns
print("Preview of gene expression data after mapping:")
print(preview_df(gene_data))

# Save gene expression data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 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)