# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Stomach_Cancer"
cohort = "GSE161533"

# Input paths
in_trait_dir = "../DATA/GEO/Stomach_Cancer"
in_cohort_dir = "../DATA/GEO/Stomach_Cancer/GSE161533"

# Output paths
out_data_file = "./output/preprocess/3/Stomach_Cancer/GSE161533.csv"
out_gene_data_file = "./output/preprocess/3/Stomach_Cancer/gene_data/GSE161533.csv"
out_clinical_data_file = "./output/preprocess/3/Stomach_Cancer/clinical_data/GSE161533.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)
# 1. Gene Expression Data Availability
# Yes, this is a microarray study using Affymetrix Gene Chip Human Genome U133 plus 2.0 Array
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Trait - can be derived from tissue type in row 0 
trait_row = 0
# Age - available in row 2
age_row = 2  
# Gender - available in row 3
gender_row = 3

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

def convert_age(x: str) -> float:
    """Convert age string to float"""
    if not isinstance(x, str):
        return None
    try:
        return float(x.split(": ")[-1])
    except:
        return None

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

# 3. Save initial 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. Extract clinical features
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 and save clinical data
print(preview_df(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])
# The identifiers like '1007_s_at', '1053_at' etc. are probe IDs from Affymetrix microarray 
# platform. These need to be mapped to 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))
# Extract gene mapping from annotation data
# 'ID' is the identifier matching genetic_data's index
# 'Gene Symbol' is the desired gene symbol column 
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')

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

# Optionally print info about the shape change
print("\nProbes to genes mapping stats:")
print(f"Original probe count: {len(genetic_data)}")
print(f"Gene count after mapping: {len(gene_data)}")
# 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)