# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Kidney_Chromophobe"
cohort = "GSE19982"

# Input paths
in_trait_dir = "../DATA/GEO/Kidney_Chromophobe"
in_cohort_dir = "../DATA/GEO/Kidney_Chromophobe/GSE19982"

# Output paths
out_data_file = "./output/preprocess/3/Kidney_Chromophobe/GSE19982.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_Chromophobe/gene_data/GSE19982.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_Chromophobe/clinical_data/GSE19982.csv"
json_path = "./output/preprocess/3/Kidney_Chromophobe/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)

# Get unique values for each clinical feature 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability 
# The background info shows this is mRNA profiling data (not miRNA/methylation)
is_gene_available = True

# 2.1 Data Availability and 2.2 Data Type Conversion
# Trait: Key 0 has disease state info, binary classification of chromophobe RCC vs oncocytoma
trait_row = 0 

def convert_trait(value):
    if not isinstance(value, str):
        return None
    value = value.lower()
    if 'chromophobe' in value:
        return 1  # Chromophobe RCC is positive class
    elif 'oncocytoma' in value:
        return 0  # Oncocytoma is negative class
    return None

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

def convert_age(value):
    return None

def convert_gender(value): 
    return None

# 3. Save Metadata
# is_trait_available is True since trait_row is not None
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
# Since trait_row is not None, we extract clinical features
clinical_df = 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 and save clinical data
print("Preview of clinical features:")
print(preview_df(clinical_df))

# Create directory if doesn't exist
os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)

# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
# These look like Affymetrix probe IDs rather than human gene symbols
# Format is typical of Affymetrix arrays with numeric_at pattern
requires_gene_mapping = True
# Extract gene annotation data from SOFT file
gene_metadata = get_gene_annotation(soft_file_path)

# Display information about the annotation data
print("Column names:")
print(gene_metadata.columns.tolist())

# Look at general data statistics 
print("\nData shape:", gene_metadata.shape)

# Preview the first few rows
print("\nPreview of the annotation data:")
print(json.dumps(preview_df(gene_metadata), indent=2))
# Extract gene mapping from annotation data
# 'ID' column in annotation matches probe IDs in expression data
# 'Gene Symbol' column contains the target gene symbols
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'Gene Symbol')

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

# Preview the results
print("\nShape of probe data:", genetic_data.shape)
print("Shape of gene data:", gene_data.shape)
print("\nFirst 10 gene symbols:")
print(gene_data.index[:10].tolist())
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

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

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

# Early exit if trait values are all NaN
if linked_data[trait].isna().all():
    is_biased = True
    linked_data = None
else:
    # 4. Judge whether features are biased and remove biased demographic features
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "Dataset from a cancer gene expression study using oligonucleotide microarrays, containing samples of kidney chromophobe tumors and normal tissues."
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 the linked data only if it's usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)