# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE142610"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE142610"

# Output paths
out_data_file = "./output/preprocess/3/Cystic_Fibrosis/GSE142610.csv"
out_gene_data_file = "./output/preprocess/3/Cystic_Fibrosis/gene_data/GSE142610.csv"
out_clinical_data_file = "./output/preprocess/3/Cystic_Fibrosis/clinical_data/GSE142610.csv"
json_path = "./output/preprocess/3/Cystic_Fibrosis/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
# The dataset summary mentions transcription profiling and gene sets, 
# suggesting it contains gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# 2.1 Variable Row Numbers
# The dataset contains cell line samples with different treatments
# All samples are CFBE (cystic fibrosis bronchial epithelial) cells
# No age or gender data as these are cell lines
trait_row = 0  # Cell line info contains CF status
age_row = None  # No age data for cell lines  
gender_row = None  # No gender data for cell lines

# 2.2 Conversion Functions
def convert_trait(value: str) -> int:
    """Convert CF status to binary
    CFBE cells are CF (positive) samples
    """
    if not isinstance(value, str):
        return None
    if 'CFBE' in value:
        return 1  # CF positive
    return None

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

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

# 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
if trait_row is not None:
    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 the clinical data
    print("Preview of extracted clinical features:")
    print(preview_df(clinical_df))
    
    # Save clinical data
    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])
# Most gene identifiers in the data appear to be valid human gene symbols (e.g. A1BG, A1CF, A2M)
# While some identifiers like '7A5' may need mapping, overall these are standard HGNC gene symbols
requires_gene_mapping = False
# 1. Normalize gene symbols and save
genetic_df = normalize_gene_symbols_in_index(genetic_df) 
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
genetic_df.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_df, genetic_df)

# 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="Cell line study comparing deltaF508 CFTR mutant with wildtype CFTR in cystic fibrosis bronchial epithelial cells"
)

# 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)