# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Thyroid_Cancer"
cohort = "GSE151181"

# Input paths
in_trait_dir = "../DATA/GEO/Thyroid_Cancer"
in_cohort_dir = "../DATA/GEO/Thyroid_Cancer/GSE151181"

# Output paths
out_data_file = "./output/preprocess/3/Thyroid_Cancer/GSE151181.csv"
out_gene_data_file = "./output/preprocess/3/Thyroid_Cancer/gene_data/GSE151181.csv"
out_clinical_data_file = "./output/preprocess/3/Thyroid_Cancer/clinical_data/GSE151181.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
# Yes, this dataset contains gene expression data as indicated by the dataset title
is_gene_available = True

# 2.1 Data Availability
# tissue type (row 1) indicates tumor vs normal tissue
trait_row = 1
# Age and gender not available in sample characteristics
age_row = None 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert tissue type to binary (0=normal, 1=tumor)"""
    if not isinstance(value, str):
        return None
    value = value.split(": ")[-1].lower()
    if "non-neoplastic" in value:
        return 0
    elif any(x in value for x in ["tumor", "metastasis"]):
        return 1
    return None

def convert_age(value: str) -> float:
    """Convert age to float"""
    return None

def convert_gender(value: str) -> int:
    """Convert gender to binary"""
    return None

# 3. Save metadata about dataset availability
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 since trait_row is not None
clinical_features_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 extracted features
print(preview_df(clinical_features_df))

# Save clinical data
clinical_features_df.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 IDs in the row index appear to be numeric identifiers (e.g. 23064070)
# rather than standard human gene symbols (e.g. BRCA1, TP53)
# These numeric IDs likely need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data with modified prefix filtering
gene_metadata = get_gene_annotation(soft_file_path, prefixes=['!Platform_table_begin'])

# Clean up column names by removing leading/trailing whitespace
gene_metadata = gene_metadata.rename(columns=lambda x: x.strip())

# Preview column names and first few values
print("\nGene annotation columns preview:")
print(gene_metadata.columns.tolist())

print("\nFirst few rows:")
print(gene_metadata.head())
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path, prefixes=['^', '!', '#'])

# Clean up any whitespace in column names
gene_metadata.columns = gene_metadata.columns.str.strip()

# Preview column names and first few values
preview = preview_df(gene_metadata, n=5)
print("\nGene annotation preview:")
for col, values in preview.items():
    print(f"\n{col}:")
    print(values)
# Update status since we determined this is a miRNA dataset without gene mapping
is_gene_available = False 

# Save updated metadata indicating gene expression data is not available
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)
)