# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Mesothelioma"
cohort = "GSE64738"

# Input paths
in_trait_dir = "../DATA/GEO/Mesothelioma"
in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE64738"

# Output paths
out_data_file = "./output/preprocess/3/Mesothelioma/GSE64738.csv"
out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE64738.csv"
out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE64738.csv"
json_path = "./output/preprocess/3/Mesothelioma/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
# Dataset mentions Affymetrix microarrays for gene expression profiling
is_gene_available = True

# 2.1 Data Availability 
# From sample characteristics, trait status can be extracted from index 0
trait_row = 0

# Age and gender data not available in sample characteristics
age_row = None  
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    """Convert cell type to binary trait - 1 for mesothelioma cell/xenograft, 0 for normal"""
    if not value:
        return None
    value = value.lower()
    if 'mesothelioma' in value:
        return 1
    return None
    
# No age/gender conversion functions needed since data not available
convert_age = None
convert_gender = None

# 3. Save Initial 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. Extract Clinical Features
# Since trait_row is not None, proceed with feature extraction
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)

# Preview the selected features
preview_df(selected_clinical)

# Save clinical data
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
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])
# Review the gene identifiers
# The IDs have the format like "1007_s_at" which are Affymetrix probe IDs
# These need to be mapped to human gene symbols for consistency
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))
# Get gene mapping dataframe
mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

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

# Save gene data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True) 
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 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 = "Dataset contains gene expression data from mesothelioma samples, but case/control ratio is heavily imbalanced."
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)