# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Craniosynostosis"
cohort = "GSE27976"

# Input paths
in_trait_dir = "../DATA/GEO/Craniosynostosis"
in_cohort_dir = "../DATA/GEO/Craniosynostosis/GSE27976"

# Output paths
out_data_file = "./output/preprocess/3/Craniosynostosis/GSE27976.csv"
out_gene_data_file = "./output/preprocess/3/Craniosynostosis/gene_data/GSE27976.csv"
out_clinical_data_file = "./output/preprocess/3/Craniosynostosis/clinical_data/GSE27976.csv"
json_path = "./output/preprocess/3/Craniosynostosis/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
# Yes - dataset contains gene expression data from osteoblasts
is_gene_available = True

# 2.1. Identify data rows
trait_row = 2  # 'type' field contains case/control status
age_row = 0    # 'age months' field contains age data
gender_row = 1  # 'gender' field contains gender data

# 2.2. Data type conversion functions
def convert_trait(value: str) -> int:
    """Convert trait value to binary (0=control, 1=case)"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if 'Control' in value:
        return 0
    elif 'Synostosis' in value:
        return 1
    return None

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

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

# 3. Save initial 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_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 results
preview_result = preview_df(clinical_df)
print("Preview of clinical data:")
print(preview_result)

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
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 IDs appear to be Illumina probe IDs (e.g., 7892501) 
# rather than standard human gene symbols. These will need to be mapped.
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. Identify columns for mapping
# 'ID' in gene annotation matches probe IDs in gene expression data
# 'gene_assignment' contains gene symbols in a complex format

# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')

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

# Print shape of the gene expression data
print("Gene expression data shape:", gene_data.shape)
# 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
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="Dataset contains gene expression from cardiogenic shock patients under ECMO, tracking outcome (Success vs Failure)"
)

# 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)