# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Celiac_Disease"
cohort = "GSE113469"

# Input paths
in_trait_dir = "../DATA/GEO/Celiac_Disease"
in_cohort_dir = "../DATA/GEO/Celiac_Disease/GSE113469"

# Output paths
out_data_file = "./output/preprocess/3/Celiac_Disease/GSE113469.csv"
out_gene_data_file = "./output/preprocess/3/Celiac_Disease/gene_data/GSE113469.csv"
out_clinical_data_file = "./output/preprocess/3/Celiac_Disease/clinical_data/GSE113469.csv"
json_path = "./output/preprocess/3/Celiac_Disease/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# According to the background info, this is PBMC gene expression data
is_gene_available = True

# 2.1 Data Availability
# Trait data in row 0 distinguishes Healthy Control vs Celiac Disease
trait_row = 0
# Age data in row 1
age_row = 1 
# Gender not available
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    # Extract value after colon and convert to binary
    value = value.split(': ')[1].strip()
    if 'Healthy Control' in value:
        return 0
    elif 'Celiac Disease' in value:
        return 1
    return None

def convert_age(value: str) -> float:
    # Extract age value after colon and convert to float
    try:
        age = float(value.split(': ')[1].strip())
        return age
    except:
        return None

def convert_gender(value: str) -> int:
    # Not used since gender data unavailable
    pass

# 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 clinical data
    print("Preview of processed clinical data:")
    print(preview_df(clinical_df))
    
    # Save to CSV
    clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# These are ILMN identifiers from Illumina BeadArray, not gene symbols
# Need to be mapped to human gene symbols
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. Choose mapping columns
# The gene expression data uses ILMN_* identifiers which match the 'ID' column
# Gene symbols are stored in the 'Symbol' column 
prob_col = 'ID'
gene_col = 'Symbol'

# 2. Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_metadata, prob_col, gene_col)

# 3. Apply gene mapping to convert probe-level data to gene expression data
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Print shape and preview gene expression data
print("Gene expression data shape:", gene_data.shape)
print("\nPreview of first few rows and columns:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
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_df, gene_data)

# 3. Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# 4. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata saving
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=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Dataset contains gene expression data from peripheral blood mononuclear cells of celiac patients and controls"
)

# 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)