# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sjögrens_Syndrome"
cohort = "GSE66795"

# Input paths
in_trait_dir = "../DATA/GEO/Sjögrens_Syndrome"
in_cohort_dir = "../DATA/GEO/Sjögrens_Syndrome/GSE66795"

# Output paths
out_data_file = "./output/preprocess/3/Sjögrens_Syndrome/GSE66795.csv"
out_gene_data_file = "./output/preprocess/3/Sjögrens_Syndrome/gene_data/GSE66795.csv"
out_clinical_data_file = "./output/preprocess/3/Sjögrens_Syndrome/clinical_data/GSE66795.csv"
json_path = "./output/preprocess/3/Sjögrens_Syndrome/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 is a "whole genome microarray" study using whole blood samples,
# so gene expression data is available
is_gene_available = True

# 2.1 Data Availability
# trait_row=2: 'patient group' indicates pSS vs Control status
trait_row = 2
# age_row: Age information is not available in sample characteristics
age_row = None  
# gender_row=3: Gender information is available but shows only females
gender_row = None  # Set to None since it's a constant feature (all female)

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    """Convert pSS status to binary: Control=0, Patient=1"""
    if not isinstance(x, str):
        return None
    x = x.lower().split(': ')[-1]
    if 'control' in x:
        return 0
    elif 'patient' in x:
        return 1
    return None

# No age conversion function needed since age data not available
convert_age = None

# No gender conversion function needed since gender is constant
convert_gender = 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
if trait_row is not None:
    # Extract features using the library function
    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
    preview = preview_df(clinical_features)
    print("Preview of clinical features:", preview)
    
    # Save to CSV
    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 gene identifiers start with "ILMN_" indicating these are Illumina probe IDs
# These need to be mapped to official 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 probe ID and gene symbol mappings from annotation data
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')

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

# Print preview of gene_data to verify conversion
print("\nPreview of mapped gene data:")
print(gene_data.shape)
print(gene_data.head())
# 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)