# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Melanoma"
cohort = "GSE200904"

# Input paths
in_trait_dir = "../DATA/GEO/Melanoma"
in_cohort_dir = "../DATA/GEO/Melanoma/GSE200904"

# Output paths
out_data_file = "./output/preprocess/3/Melanoma/GSE200904.csv"
out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE200904.csv"
out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE200904.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 series title "NanoString nCounter" and overall design mentioning "Multiplex gene expression analysis"
is_gene_available = True

# 2.1 Data Availability
# All samples are melanoma cases, can use scan_id row
trait_row = 0
# No age or gender data available
age_row = None  
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    # All samples are melanoma cases from tissue microarrays
    return 1

def convert_age(x):
    return None

def convert_gender(x):
    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. Clinical Feature Extraction
if trait_row is not None:
    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_df(clinical_df)
    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)
# These appear to be standard human gene symbols (e.g. AKT1, CD274/PD-L1, CD8A) commonly used in immunology/oncology
# No mapping required as they are already in the correct format
requires_gene_mapping = False
# 1. Normalize gene symbols and save gene data
gene_data = normalize_gene_symbols_in_index(genetic_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
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_features, 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)