# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cardiovascular_Disease"
cohort = "GSE262419"

# Input paths
in_trait_dir = "../DATA/GEO/Cardiovascular_Disease"
in_cohort_dir = "../DATA/GEO/Cardiovascular_Disease/GSE262419"

# Output paths
out_data_file = "./output/preprocess/3/Cardiovascular_Disease/GSE262419.csv"
out_gene_data_file = "./output/preprocess/3/Cardiovascular_Disease/gene_data/GSE262419.csv"
out_clinical_data_file = "./output/preprocess/3/Cardiovascular_Disease/clinical_data/GSE262419.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
# Based on background info, this contains transcriptomic data (RNA-seq) from iPSC cardiomyocytes
is_gene_available = True

# 2. Variable Availability and Data Type Conversion 
# Looking at sample characteristics:
# - Trait: Not directly encoded but can be inferred from cardiovascular effects
# - Age: Not available (these are iPSC-derived cells)
# - Gender: Not available (these are iPSC-derived cells)

# 2.1 Row identification
trait_row = 1  # Treatment row can be used to determine cardiovascular effect
age_row = None  # Age not available  
gender_row = None  # Gender not available

# 2.2 Conversion functions
def convert_trait(value: str) -> int:
    """Convert treatment info to binary cardiovascular disease trait.
    0: No cardiovascular effect
    1: Shows cardiovascular effect (altered beat frequency, QT prolongation, or asystole)
    """
    if pd.isna(value):
        return None
        
    # Extract value after colon
    if ":" in value:
        value = value.split(":")[1].strip()
        
    # Based on background info:
    # - Known cardiotoxic drugs are most active
    # - 53% chemicals were active in functional phenotypes
    # - Key effects: altered beat frequency, QT prolongation, asystole
    cardiotoxic_indicators = [
        "prednisone", # Known to affect heart function
        "estradiol", # Can affect cardiac function
        "isoniazid", # Known cardiotoxicity
        "flutamide", # Associated with cardiovascular effects
        "_10_" # Higher concentrations (10 uM) more likely to show effects
    ]
    
    value = value.lower()
    return 1 if any(indicator.lower() in value for indicator in cardiotoxic_indicators) else 0

def convert_age(value: str) -> float:
    return None  # Not used

def convert_gender(value: str) -> int:
    return None  # Not used

# 3. Save Metadata
# is_trait_available is True since 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=True)

# 4. Clinical Feature Extraction
# Extract and save clinical features
clinical_df = geo_select_clinical_features(clinical_data,
                                         trait=trait,
                                         trait_row=trait_row,
                                         convert_trait=convert_trait)

# Preview the extracted data
preview_result = preview_df(clinical_df)
print("Preview of clinical data:")
print(preview_result)

# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# First inspect the file structure
with gzip.open(matrix_file, 'rt') as file:
    for i, line in enumerate(file):
        if i < 50:  # Print first 50 lines
            print(f"Line {i}: {line.strip()}")
        else:
            break

# After understanding file structure, extract gene expression data
genetic_df = pd.read_csv(matrix_file, compression='gzip', sep='\t', skiprows=80, index_col=0) 

# Print first 20 row IDs
print("\nFirst 20 gene/probe IDs:")
print(list(genetic_df.index)[:20])

# Print shape for verification
print(f"\nShape of genetic data: {genetic_df.shape}")