# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Thyroid_Cancer"
cohort = "GSE138198"

# Input paths
in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE138198"

# Output paths
out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE138198.csv"
out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE138198.csv"
out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE138198.csv"
json_path = "./output/preprocess/3/Thyroid_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
# Dataset uses Affymetrix Human Gene 1.0 ST arrays, which measures gene expression
is_gene_available = True

# 2.1 Data Availability and Row Identification
trait_row = 1  # 'sample type' contains cancer vs normal info
gender_row = 0  # Gender is available
age_row = None  # Age is not available

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if not value or ":" not in value:
        return None
    value = value.split(": ")[1].lower()
    # Convert to binary: 1 for cancer (PTC), 0 for normal
    if "normal" in value:
        return 0
    elif "ptc" in value or "papillary thyroid carcinoma" in value:
        return 1
    return None

def convert_gender(value):
    if not value or ":" not in value:
        return None
    value = value.split(": ")[1].lower()
    if value == "f":
        return 0
    elif value == "m":
        return 1
    return None

# 3. Save Metadata - Initial Filtering
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. Clinical Feature Extraction
if trait_row is not None:
    selected_clinical = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the processed clinical data
    print("Preview of processed clinical data:")
    print(preview_df(selected_clinical))
    
    # Save clinical data
    selected_clinical.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)

# Print first few rows with column names to examine data structure
print("Data preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5])
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)

# Verify this is gene expression data and check identifiers
is_gene_available = True

# Save updated 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)
)

# Save gene expression data 
genetic_data.to_csv(out_gene_data_file)
# The identifiers in the index appear to be numeric IDs (e.g. 7892501, 7892502)
# These are not standard human gene symbols (which are usually alphanumeric like BRCA1, TP53)
# Therefore, a mapping from probe IDs to gene symbols will be required
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)
# Extract probe-to-gene mapping from annotation data
# The 'ID' column matches probe IDs in expression data
# 'gene_assignment' contains gene symbols in a specific format
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'gene_assignment')

# Convert probe-level data to gene-level data using the mapping
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview gene expression data
print("\nGene expression data preview:")
print("Shape:", gene_data.shape)
print("\nFirst few rows:")
print(gene_data.head())
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_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, normalized_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="Dataset contains gene expression data comparing 27 follicular thyroid cancers with 25 follicular thyroid adenomas."
)

# 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)