# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Heart_rate"
cohort = "GSE34788"

# Input paths
in_trait_dir = "../DATA/GEO/Heart_rate"
in_cohort_dir = "../DATA/GEO/Heart_rate/GSE34788"

# Output paths
out_data_file = "./output/preprocess/3/Heart_rate/GSE34788.csv"
out_gene_data_file = "./output/preprocess/3/Heart_rate/gene_data/GSE34788.csv"
out_clinical_data_file = "./output/preprocess/3/Heart_rate/clinical_data/GSE34788.csv"
json_path = "./output/preprocess/3/Heart_rate/cohort_info.json"

# Get file paths
soft_path, matrix_path = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_path)

# Get unique values by row in clinical data and limit the number shown
sample_chars = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in sample_chars.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene Expression Data Availability
# Based on the summary mentioning "microarray analyses on mRNA", this dataset contains gene expression data
is_gene_available = True

# 2.1 Data Availability
trait_row = 6  # Heart rate data available in row 6
gender_row = 1  # Gender data available in row 1
age_row = None  # Age data not available in sample characteristics

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert heart rate response to binary: 0 for low responders, 1 for high responders"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'low' in value:
        return 0
    elif 'high' in value:
        return 1
    return None

def convert_gender(value: str) -> int:
    """Convert gender to binary: 0 for female, 1 for male"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'female' in value:
        return 0
    elif 'male' in value:
        return 1
    return None

convert_age = None  # Not needed since age data is not available

# 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_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)
    
    # Save clinical features
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data
gene_data = get_genetic_data(matrix_path)

# Print first 20 probe/gene IDs
print("First 20 probe/gene IDs:")
print(gene_data.index[:20].tolist())
# These identifiers appear to be numerical probe IDs, not human gene symbols
# They look like Illumina BeadArray probe IDs which will need to be mapped to gene symbols
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_annotation = get_gene_annotation(soft_path)

# Preview column names and first few values
column_preview = preview_df(gene_annotation)
print("\nGene annotation columns and sample values:")
print(column_preview)
# Get gene mapping between gene names and probes
# 'ID' in gene annotation matches probe IDs in gene expression data
# 'gene_assignment' contains information about gene symbols
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='gene_assignment')

# Apply gene mapping to convert probe level data to gene level data 
gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_data)

# Normalize gene symbols to standard format and aggregate duplicate genes
gene_data = normalize_gene_symbols_in_index(gene_data)

# Preview updated gene data
print("\nFirst 20 gene symbols after mapping:")
print(gene_data.index[:20].tolist())
# 1. Normalize gene symbols and save gene data
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(clinical_features, gene_data)

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

# 4. Check for biases and remove biased demographic features
trait_type = 'binary' if len(linked_data[trait].unique()) == 2 else 'continuous'
if trait_type == "binary":
    is_biased = judge_binary_variable_biased(linked_data, trait)
else:
    is_biased = judge_continuous_variable_biased(linked_data, trait)

# Remove biased demographic features
if "Age" in linked_data.columns:
    if judge_continuous_variable_biased(linked_data, "Age"):
        linked_data = linked_data.drop(columns="Age")
if "Gender" in linked_data.columns:
    if judge_binary_variable_biased(linked_data, "Gender"):
        linked_data = linked_data.drop(columns="Gender")

# 5. Validate and save cohort info
note = "The dataset contains before/after exercise measurements for each subject. We merged them to increase statistical power."
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=is_trait_available,
    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)