# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Stomach_Cancer"
cohort = "GSE183136"

# Input paths
in_trait_dir = "../DATA/GEO/Stomach_Cancer"
in_cohort_dir = "../DATA/GEO/Stomach_Cancer/GSE183136"

# Output paths
out_data_file = "./output/preprocess/3/Stomach_Cancer/GSE183136.csv"
out_gene_data_file = "./output/preprocess/3/Stomach_Cancer/gene_data/GSE183136.csv"
out_clinical_data_file = "./output/preprocess/3/Stomach_Cancer/clinical_data/GSE183136.csv"
json_path = "./output/preprocess/3/Stomach_Cancer/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)
# Gene Expression Data Availability
is_gene_available = True  # Based on Series info mentioning "mRNA expression profiles"

# Data Availability Analysis
trait_row = 0  # Tumor stage can be used as indicator of stomach cancer severity
age_row = 1  # Age data available
gender_row = 2  # Gender data available

# Data Type Conversion Functions
def convert_trait(x):
    """Convert tumor stage to binary: stages 3-4 -> 1 (severe), stages 1-2 -> 0 (mild)"""
    if not isinstance(x, str):
        return None
    value = x.split(': ')[1] if ': ' in x else x
    try:
        stage = int(value)
        return 1 if stage >= 3 else 0
    except:
        return None

def convert_age(x):
    """Convert age to continuous value"""
    if not isinstance(x, str):
        return None
    value = x.split(': ')[1] if ': ' in x else x
    try:
        return float(value)
    except:
        return None

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

# Initial validation and metadata saving
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)

# Clinical Feature Extraction
if trait_row is not None:
    clinical_features = 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 of extracted clinical features:")
    print(preview_df(clinical_features))
    
    # Save clinical features
    clinical_features.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])
# Gene identifiers have the prefix "ILMN_" which indicates they are Illumina BeadArray identifiers
# These need to be mapped to standardized gene symbols for analysis
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))
# Get mapping between probe IDs and gene symbols
# 'ID' column matches the probe identifiers in expression data
# 'Symbol' column contains the gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

# Convert probe expression to gene expression by applying the mapping
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Normalize gene symbols to standard format and aggregate duplicates
gene_data = normalize_gene_symbols_in_index(gene_data)

# Preview the processed gene expression data
print("\nGenetic data after mapping to gene symbols:")
print(gene_data.head())
print("\nShape:", gene_data.shape)
# 1. Normalize gene symbols in gene expression 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)
print("\nGene data shape (normalized gene-level):", gene_data.shape) 

# 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 using normalized gene-level 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 = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
    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)