# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hemochromatosis"
cohort = "GSE50579"

# Input paths
in_trait_dir = "../DATA/GEO/Hemochromatosis"
in_cohort_dir = "../DATA/GEO/Hemochromatosis/GSE50579"

# Output paths
out_data_file = "./output/preprocess/3/Hemochromatosis/GSE50579.csv"
out_gene_data_file = "./output/preprocess/3/Hemochromatosis/gene_data/GSE50579.csv"
out_clinical_data_file = "./output/preprocess/3/Hemochromatosis/clinical_data/GSE50579.csv"
json_path = "./output/preprocess/3/Hemochromatosis/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)

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

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Based on series title "Expression profiling" and no mention of miRNA/methylation
is_gene_available = True

# 2.1 Data Availability
# For trait - index 1 has "etiology: genetic hemochromatosis" 
trait_row = 1

# For gender - index 3 has gender data
gender_row = 3  

# For age - index 5 has age data
age_row = 5

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert trait value to binary (0=control, 1=case)"""
    if pd.isna(value) or value == 'n.d.':
        return None
    if 'hemochromatosis' in value.lower():
        return 1
    return 0

def convert_age(value: str) -> float:
    """Convert age value to continuous numeric"""
    if pd.isna(value) or value == 'n.d.':
        return None
    age = value.split(':')[1].strip()
    if age == 'n.d.':
        return None
    return float(age)

def convert_gender(value: str) -> int:
    """Convert gender value to binary (0=female, 1=male)"""
    if pd.isna(value) or value == 'n.d.':
        return None
    gender = value.split(':')[1].strip()
    if gender == 'n.d.':
        return None
    return 1 if gender.lower() == 'male' else 0

# 3. Save metadata
# Initial filtering using validate_and_save_cohort_info
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_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 extracted features
    print("Preview of extracted clinical features:")
    print(preview_df(clinical_features))
    
    # Save clinical features
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# The identifiers look like probe IDs (Agilent microarray probes starting with A_19_P), not standard human gene symbols
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)

# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())
print("\nPreview of first few rows:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# Extract gene mapping columns from annotation data
# 'ID' column contains probe IDs matching gene expression data
# 'GENE_SYMBOL' contains the target gene symbols 
gene_mapping = get_gene_mapping(gene_metadata, 'ID', 'GENE_SYMBOL')

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

# Preview the first few rows of gene expression data
print("Preview of gene expression data after mapping:")
print(preview_df(gene_data))
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# Get clinical features 
clinical_features = 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
)

# 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 whether features are biased and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "Dataset contains gene expression data from skeletal muscle biopsies and height measurements from subjects"
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=is_biased,
    df=linked_data,
    note=note
)

# 6. Save the linked data only if it's usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)