# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cardiovascular_Disease"
cohort = "GSE182600"

# Input paths
in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE182600"

# Output paths
out_data_file = "./output/preprocess/3/Cardiovascular_Disease/GSE182600.csv"
out_gene_data_file = "./output/preprocess/3/Cardiovascular_Disease/gene_data/GSE182600.csv"
out_clinical_data_file = "./output/preprocess/3/Cardiovascular_Disease/clinical_data/GSE182600.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
is_gene_available = True  # Contains genome-wide gene expression data per background info

# 2. Variable Availability and Data Type Conversion

# 2.1 Row identification
trait_row = 3  # Outcome Success/Failure indicates cardiovascular disease severity
age_row = 1  # Age data available
gender_row = 2  # Gender F/M data available

# 2.2 Conversion functions
def convert_trait(value: str) -> int:
    """Convert outcome to binary: Failure=0, Success=1"""
    if not value or ':' not in value:
        return None
    val = value.split(':')[1].strip().lower()
    if val == 'success':
        return 1
    elif val in ['failure', 'fail']:
        return 0
    return None

def convert_age(value: str) -> float:
    """Convert age to float"""
    if not value or ':' not in value:
        return None
    try:
        return float(value.split(':')[1].strip())
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender to binary: F=0, M=1"""
    if not value or ':' not in value:
        return None
    val = value.split(':')[1].strip().upper()
    if val == 'F':
        return 0
    elif val == 'M':
        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=True)  # trait_row is not None

# 4. Clinical Feature Extraction
clinical_features = 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 extracted features
print("Preview of extracted clinical features:")
print(preview_df(clinical_features))

# Save clinical features
clinical_features.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])
# These IDs start with "ILMN_", indicating they are Illumina probe IDs
# Illumina probe IDs need to be mapped to standard gene symbols
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Additional filtering to exclude control probes 
gene_metadata = gene_metadata[gene_metadata['Species'] != 'ILMN Controls']

# Preview filtered annotation data
print("Column names and preview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. Identify relevant columns for mapping
# From preview, we can see probe IDs are in 'ID' column and gene symbols are in 'Symbol' column
prob_col = 'ID'
gene_col = 'Symbol'

# 2. Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)

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

# Preview result
print("Shape of gene expression data:", gene_data.shape)
print("\nPreview of mapped gene expression data:")
print(gene_data.head().iloc[:, :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_features, 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 = "Gene expression data from PBMC of cardiogenic shock patients under ECMO treatment. Contains trait (Success/Failure), age, and gender information."
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)