# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Mesothelioma"
cohort = "GSE131027"

# Input paths
in_trait_dir = "../DATA/GEO/Mesothelioma"
in_cohort_dir = "../DATA/GEO/Mesothelioma/GSE131027"

# Output paths
out_data_file = "./output/preprocess/3/Mesothelioma/GSE131027.csv"
out_gene_data_file = "./output/preprocess/3/Mesothelioma/gene_data/GSE131027.csv"
out_clinical_data_file = "./output/preprocess/3/Mesothelioma/clinical_data/GSE131027.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
is_gene_available = False  # This dataset appears to focus on genetic mutations/variants rather than gene expression

# 2. Clinical Data Availability and Conversion Functions
# 2.1 Row numbers for data extraction
trait_row = 1  # Cancer type information is in row 1
age_row = None  # Age information not available
gender_row = None  # Gender information not available

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    """Convert cancer type to binary for Mesothelioma"""
    if pd.isna(x):
        return None
    if isinstance(x, str):
        x = x.split(": ")[-1]  # Get value after colon
        if x == "Mesothelioma":
            return 1
        else:
            return 0
    return None

# 3. Save Metadata - Initial Filtering
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:
    selected_clinical_df = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait
    )
    
    # Preview the processed data
    print("Preview of processed clinical data:")
    print(preview_df(selected_clinical_df))
    
    # Save to CSV
    selected_clinical_df.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])
# The IDs like "1007_s_at", "1053_at" etc. appear to be Affymetrix probe IDs
# These need to be mapped to standard human gene symbols
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 using probe ID and gene symbol columns
mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Gene Symbol')

# Convert probe-level measurements to gene expression values
gene_data = apply_gene_mapping(genetic_data, mapping_df)

# Preview the converted data
print("Preview of mapped gene expression data:")
print(gene_data.head())
print("\nShape:", gene_data.shape)
# 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
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=""
)

# 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)