# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Metabolic_Rate"
cohort = "GSE106800"

# Input paths
in_trait_dir = "../DATA/GEO/Metabolic_Rate"
in_cohort_dir = "../DATA/GEO/Metabolic_Rate/GSE106800"

# Output paths
out_data_file = "./output/preprocess/3/Metabolic_Rate/GSE106800.csv"
out_gene_data_file = "./output/preprocess/3/Metabolic_Rate/gene_data/GSE106800.csv"
out_clinical_data_file = "./output/preprocess/3/Metabolic_Rate/clinical_data/GSE106800.csv"
json_path = "./output/preprocess/3/Metabolic_Rate/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
# Background info mentions microarray analysis on muscle biopsies, so gene data likely exists
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# Metabolic rate (trait) can be inferred from fasting glucose, insulin & FFA measurements in rows 8,9,10
# Age data is in row 2, Gender in row 0
trait_row = 9  # Using insulin level as proxy for metabolic rate since title mentions insulin resistance
age_row = 2
gender_row = 0

def convert_trait(value):
    # Extract numeric insulin value after colon as continuous trait
    try:
        return float(value.split(': ')[1])
    except:
        return None

def convert_age(value):
    # Extract numeric age value after colon
    try:
        return float(value.split(': ')[1])
    except:
        return None

def convert_gender(value):
    # Convert gender to binary (female=0, male=1)
    try:
        gender = value.split(': ')[1].lower()
        if gender == 'male':
            return 1
        elif gender == 'female':
            return 0
        return None
    except:
        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:
    selected_clinical = 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
    )
    
    print("Preview of selected clinical features:")
    print(preview_df(selected_clinical))
    
    # Save clinical data
    selected_clinical.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])
# Based on the gene identifiers shown (e.g., '16650001', '16650003', etc.), 
# these appear to be probe IDs from a microarray platform rather than human gene symbols.
# They need to be mapped to standard gene symbols for analysis.
requires_gene_mapping = True
# Extract gene annotation data
gene_annotation = get_gene_annotation(soft_file_path)

# Display column names and preview data
print("Column names:")
print(gene_annotation.columns)

print("\nPreview of gene annotation data:")
print(preview_df(gene_annotation))
# From inspection, 'ID' stores gene identifiers matching those in expression data,
# and 'gene_assignment' contains gene symbols
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

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

# Preview results
print("Gene expression data shape:", gene_data.shape)
print("\nFirst 5 gene symbols:", gene_data.index[:5])
print("\nPreview of expression values:")
print(gene_data.head())

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(gene_data)
genetic_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)

# 3. Handle missing values systematically  
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 = "Contains gene expression data with metabolic rate (inferred from multicentric occurrence-free survival days) measurements"
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
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)