# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Lactose_Intolerance"
cohort = "GSE138297"

# Input paths
in_trait_dir = "../DATA/GEO/Lactose_Intolerance"
in_cohort_dir = "../DATA/GEO/Lactose_Intolerance/GSE138297"

# Output paths
out_data_file = "./output/preprocess/3/Lactose_Intolerance/GSE138297.csv"
out_gene_data_file = "./output/preprocess/3/Lactose_Intolerance/gene_data/GSE138297.csv"
out_clinical_data_file = "./output/preprocess/3/Lactose_Intolerance/clinical_data/GSE138297.csv"
json_path = "./output/preprocess/3/Lactose_Intolerance/cohort_info.json"

# Get file paths for 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 clinical feature row 
clinical_features = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print(background_info)
print("\nClinical Features and Sample Values:")
print(json.dumps(clinical_features, indent=2))
# 1. Gene Expression Data Availability
# Based on background info mentioning "Microarray analysis", gene expression data is available
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# Trait can be inferred from experimental condition (autologous vs allogenic)
trait_row = 6

def convert_trait(value):
    # Extract value after colon
    if ':' in value:
        value = value.split(':', 1)[1].strip()
    # Convert to binary: autologous = 0 (control), allogenic = 1 (treated)
    if 'Autologous' in value:
        return 0
    elif 'Allogenic' in value:  
        return 1
    return None

# Age is available in row 3
age_row = 3

def convert_age(value):
    if ':' in value:
        value = value.split(':', 1)[1].strip()
        try:
            return float(value) 
        except:
            return None
    return None

# Gender is available in row 1
gender_row = 1

def convert_gender(value):
    if ':' in value:
        value = value.split(':', 1)[1].strip()
        try:
            # Data already coded as female=1, male=0
            # But we need to reverse it to match our convention (female=0, male=1)
            return 1 - int(value)
        except:
            return None
    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 since trait_row is not None
selected_clinical = 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 results
print(preview_df(selected_clinical))

# Save clinical data
selected_clinical.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file)

# Print first 20 row IDs
print("First 20 gene/probe IDs:")
print(genetic_data.index[:20].tolist())
# Review the IDs - they appear to be probe IDs, not human gene symbols
# The format looks like Illumina probe IDs that need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation from SOFT file
gene_annotation = get_gene_annotation(soft_file)

# Preview column names and first few values
print("Gene Annotation Preview:")
print(preview_df(gene_annotation))
# Extract gene mapping from annotation data 
# 'ID' column matches probe IDs in expression data
# 'gene_assignment' column contains gene symbols
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')

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

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical, gene_data)

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

# 4. Check for biases and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate dataset quality and save metadata
note = ""
if is_biased:
    note = "The trait distribution is severely biased."

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 linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)