# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Depression"
cohort = "GSE128387"

# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE128387"

# Output paths
out_data_file = "./output/preprocess/3/Depression/GSE128387.csv"
out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE128387.csv"
out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE128387.csv"
json_path = "./output/preprocess/3/Depression/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. Check gene expression data availability
# From background info, this is a microarray study of gene expression, not miRNA/methylation
is_gene_available = True

# 2.1 Identify data rows
trait_row = 1  # "illness" field indicates depression status
age_row = 2    # "age" field
gender_row = 3 # "Sex" field

# 2.2 Data conversion functions
def convert_trait(value: str) -> int:
    """Convert depression status to binary"""
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].lower()
    if 'major depressive disorder' in value:
        return 1
    return None

def convert_age(value: str) -> float:
    """Convert age to continuous value"""
    if not isinstance(value, str):
        return None
    try:
        age = float(value.split(': ')[-1])
        return age
    except:
        return None

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

# 3. Save metadata
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=trait_row is not None
)

# 4. Extract clinical features
clinical_df = geo_select_clinical_features(
    clinical_df=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 and save clinical data
print("Clinical data preview:")
print(preview_df(clinical_df))

clinical_df.to_csv(out_clinical_data_file)
# 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])
# The gene identifiers appear to be probe IDs 
# (numeric identifiers around 16657xxx) rather than human gene symbols
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. Based on observation:
# - Gene expression data has identifiers like '16657436'
# - In annotation data, 'ID' column has the same format identifiers
# - 'gene_assignment' column contains gene symbol info in the format "//GENE_SYMBOL//"

# 2. Extract ID and gene assignments, then get mapping between them
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')

# 3. Map probe IDs to gene symbols and convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Preview results
print("Gene expression data shape:", gene_data.shape)
print("\nFirst few genes and samples:")
print(gene_data.head().iloc[:, :5])

# Save gene data
gene_data.to_csv(out_gene_data_file)
# 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)

# 2. Link clinical and genetic data
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
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 for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata saving
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="Study of depression in obese patients before and after bariatric surgery"
)

# 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)