# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Breast_Cancer"
cohort = "GSE283522"

# Input paths
in_trait_dir = "../DATA/GEO/Breast_Cancer"
in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE283522"

# Output paths
out_data_file = "./output/preprocess/3/Breast_Cancer/GSE283522.csv"
out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/GSE283522.csv"
out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/GSE283522.csv"
json_path = "./output/preprocess/3/Breast_Cancer/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics 
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# Gene Expression Data Availability
# Based on background info, this is RNA-seq data of breast cancer
is_gene_available = True

# Variable Availability and Data Type Conversion 
# Sample category indicates if sample is tumor or not
trait_row = 6
# Age data available in 5-year ranges
age_row = 2  
# Sex is recorded explicitly
gender_row = 5

def convert_trait(value: str) -> int:
    # Sample category field contains information about tumor status
    if value is None or pd.isna(value):
        return None
    value = value.lower()
    if 'invasive breast cancer' in value:
        return 1
    elif 'true healthy' in value or 'no tumor' in value:
        return 0
    return None

def convert_age(value: str) -> float:
    if value is None or pd.isna(value) or value.endswith('not applicable'):
        return None
        
    # Extract age range and take the midpoint
    parts = value.replace('age: ', '').split(' - ')
    if len(parts) != 2:
        return None
        
    try:
        start = float(parts[0])
        end = float(parts[1])
        return (start + end) / 2
    except:
        return None

def convert_gender(value: str) -> int:
    if value is None or pd.isna(value):
        return None
        
    value = value.lower()
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    return None

# Initial filtering and metadata saving
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)

# Extract clinical features if trait data available
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_data,
        trait="Breast_Cancer", 
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    print("Preview of extracted clinical data:")
    print(preview_df(selected_clinical_df))
    
    selected_clinical_df.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Debug marker line and data format
with gzip.open(matrix_file, 'rt') as f:
    print("First 10 lines after finding marker:")
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Print next 10 lines after marker
            for j in range(10):
                try:
                    next_line = next(f)
                    print(f"Line {j+1}: {next_line[:200]}")  
                except StopIteration:
                    break
            break
            
# Try reading gene expression data with modified settings
gene_data = pd.read_csv(matrix_file, compression='gzip', skiprows=206, sep='\t', index_col=0)

print("\nShape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Save gene expression data  
gene_data.to_csv(out_gene_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract gene expression data using the provided function
gene_data = get_genetic_data(matrix_file)

# Print information about the data
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# After inspecting file format, we see this is RNA-seq data without probe annotations
# Print finding and proceed with an empty annotation dataframe
print("This is RNA-seq data where genes are directly measured without probes.")
print("Gene annotation mapping step will be skipped.")

# Create empty annotation dataframe to maintain pipeline compatibility 
gene_metadata = pd.DataFrame(columns=['ID', 'Gene'])
print("\nEmpty annotation dataframe created with columns:")
print(gene_metadata.columns.tolist())
# Record failure status
validate_and_save_cohort_info(
    is_final=False,  # Use initial filtering to record availability status
    cohort=cohort,
    info_path=json_path,
    is_gene_available=False,
    is_trait_available=False
)