# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Breast_Cancer"
cohort = "GSE207847"

# Input paths
in_trait_dir = "../DATA/GEO/Breast_Cancer"
in_cohort_dir = "../DATA/GEO/Breast_Cancer/GSE207847"

# Output paths
out_data_file = "./output/preprocess/3/Breast_Cancer/GSE207847.csv"
out_gene_data_file = "./output/preprocess/3/Breast_Cancer/gene_data/GSE207847.csv"
out_clinical_data_file = "./output/preprocess/3/Breast_Cancer/clinical_data/GSE207847.csv"
json_path = "./output/preprocess/3/Breast_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 background info mentioning "gene expression profile using Clariom D platform",
# this dataset contains gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability & 2.2 Conversion Functions

# Trait (loco-regional recurrence time)
trait_row = 3
def convert_trait(val):
    if not isinstance(val, str):
        return None
    val = val.split(': ')[-1].strip().upper()
    if val == 'EARLY':
        return 0  # < 2 years
    elif val == 'INTERMEDIATE': 
        return 0.5  # 2-5 years
    elif val == 'LATE':
        return 1  # > 5 years
    return None

# Age - Not available in characteristics 
age_row = None
convert_age = None

# Gender - Constant value "female" for all samples
gender_row = None  # Although present in row 1, it's constant
convert_gender = None

# 3. Save 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
)

# 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,
        age_row=age_row,
        convert_age=convert_age, 
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    print("Preview of selected clinical features:")
    print(preview_df(selected_clinical))
    
    # Save clinical data
    selected_clinical.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
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# The identifiers appear to be using TC (transcript cluster) format from Affymetrix Clariom arrays
# These are not standard gene symbols and will need to be mapped
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)
# Get gene mapping from annotation data
# 'ID' column contains probe IDs matching genetic_data
# 'gene_assignment' contains gene symbols in format "ID // SYMBOL // ..."
prob_col = 'ID'
gene_col = 'gene_assignment'
mapping_data = get_gene_mapping(gene_metadata, prob_col, gene_col)

# Extract valid gene symbols
def extract_gene_symbol(text):
    if not isinstance(text, str):
        return None
    parts = text.split('//')
    if len(parts) >= 2:
        symbol = parts[1].strip()
        # Validate that it looks like a proper gene symbol
        if len(symbol) > 0 and not symbol.startswith('---'):
            return symbol
    return None

# Update mapping before applying
mapping_data['Gene'] = mapping_data['Gene'].apply(extract_gene_symbol)
mapping_data = mapping_data.dropna()

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

# Preview first few gene symbols
print("\nFirst few genes in mapped expression data:")
print(list(gene_data.index[:5]))
# 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 
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=is_trait_available,
    is_biased=trait_biased,
    df=linked_data,
    note="Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence."
)

# 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)