# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sarcoma"
cohort = "GSE133228"

# Input paths
in_trait_dir = "../DATA/GEO/Sarcoma"
in_cohort_dir = "../DATA/GEO/Sarcoma/GSE133228"

# Output paths
out_data_file = "./output/preprocess/3/Sarcoma/GSE133228.csv"
out_gene_data_file = "./output/preprocess/3/Sarcoma/gene_data/GSE133228.csv"
out_clinical_data_file = "./output/preprocess/3/Sarcoma/clinical_data/GSE133228.csv"
json_path = "./output/preprocess/3/Sarcoma/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
# Based on the background info, this seems to be a SuperSeries focusing on molecular mechanisms
# and doesn't directly contain gene expression data
is_gene_available = False

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = 2  # "tumor type" row
age_row = 1    # "age" row
gender_row = 0  # "gender" row

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert tumor type to binary"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    # All samples are primary tumor, convert to 1
    if 'primary tumor' in value:
        return 1
    return None

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

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

# 3. Save Metadata
is_trait_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=is_trait_available)

# 4. Clinical Feature Extraction
if trait_row is not None:
    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 the processed data
    preview = preview_df(clinical_df)
    print("Preview of processed clinical data:")
    print(preview)
    
    # Save to CSV
    clinical_df.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# Review gene identifiers - appear to be custom probe IDs (ending in "_at")
# rather than standard human gene symbols
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and values from annotation dataframe
print("Gene annotation DataFrame preview:")
print(preview_df(gene_annotation))
# First, let's see all columns in the annotation data to find gene symbols
print("All columns in gene annotation:")
print(gene_annotation.columns)

# Extract mapping between probe IDs and gene symbols
# Use "GB_ACC" or "Gene Symbol" column if available, otherwise need to extract from Description
mapping_df = gene_annotation[['ID', 'Description']].copy()
mapping_df['Gene'] = mapping_df['Description'].str.extract(r'\((.*?)\)', expand=False)  # Extract text in parentheses
mapping_df = mapping_df[['ID', 'Gene']].dropna()

# Convert probe measurements to gene expression values
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Normalize gene symbols 
gene_data = normalize_gene_symbols_in_index(gene_data)

# Preview results
print("\nShape of gene expression data:", gene_data.shape)
print("\nFirst few rows of gene expression data:")
print(gene_data.head())
print("\nFirst few gene symbols:")
print(list(gene_data.index)[:10])
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
print("Gene data shape after normalization:", gene_data.shape)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
gene_data.to_csv(out_gene_data_file)

# Load clinical data previously processed
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
print("\nClinical data shape:", selected_clinical_df.shape)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
print("\nLinked data shape:", linked_data.shape)

# 3. Handle missing values systematically  
if trait in linked_data.columns:
    linked_data = handle_missing_values(linked_data, trait)

    # 4. Check for bias in trait and demographic features
    trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Final validation and information saving
    note = "This dataset contains gene expression data from myxoid liposarcoma samples, with metastasis status as the trait."
    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=note
    )

    # 6. Save linked data only if usable and not biased
    if is_usable and not trait_biased:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        linked_data.to_csv(out_data_file)
else:
    # Handle case where clinical features were not properly extracted
    note = "Failed to extract clinical trait information from sample characteristics."
    validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path, 
        is_gene_available=True,
        is_trait_available=False,
        is_biased=None,
        df=None,
        note=note
    )