# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Esophageal_Cancer"
cohort = "GSE156915"

# Input paths
in_trait_dir = "../DATA/GEO/Esophageal_Cancer"
in_cohort_dir = "../DATA/GEO/Esophageal_Cancer/GSE156915"

# Output paths
out_data_file = "./output/preprocess/3/Esophageal_Cancer/GSE156915.csv"
out_gene_data_file = "./output/preprocess/3/Esophageal_Cancer/gene_data/GSE156915.csv"
out_clinical_data_file = "./output/preprocess/3/Esophageal_Cancer/clinical_data/GSE156915.csv"
json_path = "./output/preprocess/3/Esophageal_Cancer/cohort_info.json"

# Get relevant file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

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

# Get dictionary of unique values per row in clinical data
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("Background Information:")
print("-" * 50)
print(background_info)
print("\n")

# Print clinical data unique values
print("Sample Characteristics:")
print("-" * 50)
for row, values in unique_values_dict.items():
    print(f"{row}:")
    print(f"  {values}")
    print()
# 1. Gene Expression Data Availability
# From the background info, we can see this is a gene expression study investigating
# DNA damage immune response in colorectal cancer
is_gene_available = True

# 2.1 Data Availability 
# Looking at the sample characteristics:
# - Row 0 shows DDIR status which indicates DNA damage response status
trait_row = 0  
# Age and gender info not available in sample characteristics
age_row = None
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if pd.isna(x):
        return None
    # Extract value after colon and strip whitespace
    val = x.split(':')[1].strip()
    # DDIR NEG = control = 0, DDIR POS = case = 1
    if 'NEG' in val:
        return 0
    elif 'POS' in val:
        return 1
    return None

def convert_age(x):
    # Not available
    return None

def convert_gender(x):
    # Not available 
    return 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
if trait_row is not None:
    clinical_df = 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 extracted features
    preview = preview_df(clinical_df)
    print("Preview of clinical features:")
    print(preview)
    
    # Save to CSV
    clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 probe IDs
print("First 20 probe IDs:")
print(genetic_data.index[:20])
# These appear to be human gene symbols, with some RNA genes and pseudogenes
# The identifiers match official HGNC gene symbols and nomenclature patterns
requires_gene_mapping = False
# 1. Normalize gene symbols and save normalized gene data
normalized_gene_data = normalize_gene_symbols_in_index(genetic_data) 
normalized_gene_data.to_csv(out_gene_data_file)

# Read the processed clinical and gene data files
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
gene_data = pd.read_csv(out_gene_data_file, index_col=0)

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

# Handle missing values systematically 
linked_data = handle_missing_values(linked_data, trait)

# Detect bias in trait and demographic features, remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Validate data quality and save cohort info
note = ("This dataset studies gene expression profiles in esophageal squamous cell carcinoma, "
        "comparing tumor samples with matched nonmalignant mucosa. The sample size is moderate with paired samples.")
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
)

# Save linked data if usable
if is_usable:
    linked_data.to_csv(out_data_file)
else:
    print(f"Dataset {cohort} did not pass quality validation and will not be saved.")