# Path Configuration
from tools.preprocess import *

# Processing context
trait = "HIV_Resistance"
cohort = "GSE33580"

# Input paths
in_trait_dir = "../DATA/GEO/HIV_Resistance"
in_cohort_dir = "../DATA/GEO/HIV_Resistance/GSE33580"

# Output paths
out_data_file = "./output/preprocess/3/HIV_Resistance/GSE33580.csv"
out_gene_data_file = "./output/preprocess/3/HIV_Resistance/gene_data/GSE33580.csv"
out_clinical_data_file = "./output/preprocess/3/HIV_Resistance/clinical_data/GSE33580.csv"
json_path = "./output/preprocess/3/HIV_Resistance/cohort_info.json"

# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data Availability
# Yes - the background info mentions "gene expression analysis" and "Affymetrix microarrays"
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# Trait (HIV Resistance) is in row 1
trait_row = 1
# Age data not available 
age_row = None
# Gender data not available - appears to be all female from background info
gender_row = None

def convert_trait(x):
    """Convert HIV status to binary (1=resistant, 0=negative)"""
    if not isinstance(x, str):
        return None
    x = x.lower().split(': ')[-1]
    if 'resistant' in x:
        return 1
    elif 'negative' in x:
        return 0
    return None

def convert_age(x):
    """Placeholder since age not available"""
    return None
    
def convert_gender(x):
    """Placeholder since gender not available"""
    return None

# 3. Save 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. 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
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# The identifiers shown are Affymetrix probe IDs (e.g. '1007_s_at', '1053_at')
# These need to be mapped to HGNC gene symbols for standardization
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file_path)

# Preview column names and first few values
preview_dict = preview_df(gene_annotation)
print("Column names and preview values:")
for col, values in preview_dict.items():
    print(f"\n{col}:")
    print(values)
# 1. Identify columns for mapping
# 'ID' in gene annotation matches probe IDs in gene expression data
# 'Gene Symbol' contains the gene symbols we want to map to
prob_col = 'ID'
gene_col = 'Gene Symbol'

# 2. Get mapping dataframe
mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col)

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

# Preview result
print("\nFirst few rows and columns of gene expression data:")
print(gene_data.iloc[:5, :5])
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# Read the processed clinical data file 
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# Link clinical and genetic data using the normalized gene data
linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)

# Handle missing values systematically
linked_data = handle_missing_values(linked_data, trait)

# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate data quality and save cohort info
note = "Gene expression data from glucocorticoid sensitivity study."
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
)

# 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)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")