# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bile_Duct_Cancer"
cohort = "GSE131027"

# Input paths
in_trait_dir = "../DATA/GEO/Bile_Duct_Cancer"
in_cohort_dir = "../DATA/GEO/Bile_Duct_Cancer/GSE131027"

# Output paths
out_data_file = "./output/preprocess/3/Bile_Duct_Cancer/GSE131027.csv"
out_gene_data_file = "./output/preprocess/3/Bile_Duct_Cancer/gene_data/GSE131027.csv"
out_clinical_data_file = "./output/preprocess/3/Bile_Duct_Cancer/clinical_data/GSE131027.csv"
json_path = "./output/preprocess/3/Bile_Duct_Cancer/cohort_info.json"

# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene Expression Data Availability
# Based on the series title and summary, this appears to be a study focusing on genetic mutations and variants
# It's likely there will be gene expression data to study these variations
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability

# Trait (Bile Duct Cancer) data is available in index 1 under 'cancer'
trait_row = 1

# Age and gender are not explicitly available in the characteristics 
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x: str) -> int:
    """Convert cancer type to binary for Bile Duct Cancer"""
    if pd.isna(x):
        return None
    # Extract value after colon and strip whitespace
    value = x.split(':')[1].strip().lower()
    # Return 1 for bile duct cancer, 0 for other cancers
    return 1 if 'bile duct cancer' in value else 0

def convert_age(x: str) -> float:
    """Convert age to continuous value"""
    # Not used since age data is not available
    return None

def convert_gender(x: str) -> int:
    """Convert gender to binary"""
    # Not used since gender data is not available
    return None

# 3. Save Metadata
# Conduct initial filtering - trait data is available (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
# Since trait_row is not None, we proceed with clinical feature extraction
clinical_features = 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 extracted features
preview_result = preview_df(clinical_features)
print("Preview of clinical features:")
print(preview_result)

# Save clinical features to CSV
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs and some data preview to verify structure
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))

print("\nData preview:")
preview_subset = genetic_data.iloc[:5, :5]
print(preview_subset)
# These appear to be Affymetrix probe IDs rather than gene symbols based on the format
# (e.g. '1007_s_at', '1053_at') which is characteristic of older Affymetrix arrays.
# They will need to be mapped to standard human gene symbols.
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path) 

# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# From the previews we can see that:
# - Gene expression data uses probe IDs like '1007_s_at'
# - Gene annotation data has 'ID' column with the same format probe IDs
# - Gene annotation data has 'Gene Symbol' column with human gene symbols

# Get mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')

# Apply the mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview the result
print("\nFirst few genes and their expression values:")
preview_result = preview_df(gene_data)
print(preview_result)

# Save gene expression data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save gene data
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
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

# 3. Handle missing values 
linked_data = handle_missing_values(linked_data, trait)

# 4. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Gene expression data from whole blood. Samples include SJIA patients treated with canakinumab/placebo and healthy controls."
)

# 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)