# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_Cancer"
cohort = "GSE212047"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_Cancer"
in_cohort_dir = "../DATA/GEO/Liver_Cancer/GSE212047"

# Output paths
out_data_file = "./output/preprocess/3/Liver_Cancer/GSE212047.csv"
out_gene_data_file = "./output/preprocess/3/Liver_Cancer/gene_data/GSE212047.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_Cancer/clinical_data/GSE212047.csv"
json_path = "./output/preprocess/3/Liver_Cancer/cohort_info.json"

# Get file paths for 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 clinical feature row 
clinical_features = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print(background_info)
print("\nClinical Features and Sample Values:")
print(json.dumps(clinical_features, indent=2))
# 1. Gene Expression Data Availability
# This is a microarray dataset of HSC cells, which should contain gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# This is an animal study (mice) of specific genotypes, not a human study comparing disease cases vs. controls
# So trait data is not available in a way useful for our analysis
trait_row = None  
age_row = None
gender_row = None

def convert_trait(x):
    return None

def convert_age(x):
    return None 

def convert_gender(x):
    return None

# 3. Save Metadata
# The dataset only has genetic data but no trait data for our analysis
is_trait_available = False if trait_row is None else True
validate_and_save_cohort_info(is_final=False, 
                            cohort=cohort,
                            info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=is_trait_available)

# 4. Clinical Feature Extraction
# Skip this step since trait_row is None
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file)

# Print DataFrame info and dimensions to verify data structure
print("DataFrame info:")
print(genetic_data.info())
print("\nDataFrame dimensions:", genetic_data.shape)

# Print an excerpt of the data to inspect row/column structure
print("\nFirst few rows and columns of data:")
print(genetic_data.head().iloc[:, :5])

# Print first 20 row IDs
print("\nFirst 20 gene/probe IDs:")
print(genetic_data.index[:20].tolist())
# Looking at the row indices (gene identifiers), they appear to be numerical identifiers (10338001, etc.)
# rather than standard human gene symbols (which are usually alphanumeric like BRCA1, TP53, etc.)
# These appear to be probe IDs that will need to be mapped to gene symbols.

requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file)

# Extract gene symbols from gene_assignment column
def extract_gene_symbol(assignment):
    if pd.isna(assignment) or assignment == '---':
        return None
    # Get the second part after '//' which typically contains the gene symbol
    parts = assignment.split('//')
    if len(parts) >= 2:
        return parts[1].strip()
    return None

# Create mapping dataframe with ID and extracted gene symbols
mapping_df = pd.DataFrame({
    'ID': gene_annotation['ID'],
    'Gene': gene_annotation['gene_assignment'].apply(extract_gene_symbol)
})

# Preview the mapping data structure
print("Gene Mapping Preview:")
preview = preview_df(mapping_df)
print(json.dumps(preview, indent=2))
# Map gene identifiers between genetic data and annotation data
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Print info about gene expression data after mapping
print("Gene Expression Data after Mapping:")
print(f"Number of genes: {len(gene_data)}")
print("\nFirst few rows and columns:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Create an empty dataframe since this is just a mouse study without usable trait data
empty_df = pd.DataFrame(columns=['trait'])

# Check bias and save metadata 
is_biased = True # Set as biased since this is not even human data
note = "This is a mouse study without usable trait data for human disease analysis."

validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=False,
    is_biased=is_biased,
    df=empty_df,
    note=note
)