# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Melanoma"
cohort = "GSE148319"

# Input paths
in_trait_dir = "../DATA/GEO/Melanoma"
in_cohort_dir = "../DATA/GEO/Melanoma/GSE148319"

# Output paths
out_data_file = "./output/preprocess/3/Melanoma/GSE148319.csv"
out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE148319.csv"
out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE148319.csv"
json_path = "./output/preprocess/3/Melanoma/cohort_info.json"

# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene expression data availability
# Based on background info, this is a study with gene expression data from tumor xenografts
is_gene_available = True

# 2.1 Data availability
# For trait - can be inferred from cell line info in row 8 
trait_row = 8

# Age & gender not recorded in the sample characteristics
age_row = None  
gender_row = None

# 2.2 Data type conversion functions
def convert_trait(value: str) -> int:
    """Convert cell line info to binary melanoma status"""
    if "melanoma" in value.lower():
        return 1
    elif "oral carcinoma" in value.lower():
        return 0
    return None

def convert_age(value: str) -> float:
    return None

def convert_gender(value: str) -> int:
    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. Extract clinical features since 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,
                                                  age_row=age_row,
                                                  convert_age=convert_age, 
                                                  gender_row=gender_row,
                                                  convert_gender=convert_gender)

# Preview the processed clinical data
print(preview_df(selected_clinical_df))

# Save clinical data
selected_clinical_df.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs to examine data type
print("First 20 row IDs:")
print(list(genetic_data.index)[:20])

# After examining the IDs and confirming this is gene expression data:
is_gene_available = True  

# Save updated 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)
)

genetic_data.to_csv(out_gene_data_file)
# The pattern of identifiers shows these are probe IDs from an Affymetrix microarray platform 
# (format: number_at, number_s_at, number_x_at)
# They need to be mapped to human gene symbols for proper analysis
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file_path) 

# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# Extract gene mapping from annotation data
# ID column contains probe IDs that match gene expression data
# Gene Symbol column contains standardized human gene symbols
mapping_df = get_gene_mapping(gene_metadata, "ID", "Gene Symbol")

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

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data  
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)

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

# 4. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
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=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Gene expression data from melanoma patients receiving PD-1 immunotherapy, with long-term benefit as outcome."
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)