# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Melanoma"

# Input paths
tcga_root_dir = "../DATA/TCGA"

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

# Find melanoma data directory
cohort_dir = os.path.join(tcga_root_dir, "TCGA_Melanoma_(SKCM)")

# Get paths to clinical and genetic data files
clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)

# Load the data files
clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t')
genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t')

# Print clinical columns
print("Clinical data columns:")
print(clinical_df.columns.tolist())

# Mark data as available
is_gene_available = True
is_trait_available = True
validate_and_save_cohort_info(
    is_final=False,
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=is_gene_available, 
    is_trait_available=is_trait_available
)
# Identify candidate columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Get correct file paths using library function 
clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, "SKCM"))
clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0)

# Preview using library function
age_preview = preview_df(clinical_df[candidate_age_cols])
print("Age columns preview:")
print(age_preview)

gender_preview = preview_df(clinical_df[candidate_gender_cols])
print("\nGender columns preview:")
print(gender_preview)
# Set TCGA cohort name
trait = "SKCM"  # TCGA code for Skin Cutaneous Melanoma

# Get file paths
cohort_dir = os.path.join(tcga_root_dir, trait)
clinical_file_path, _ = tcga_get_relevant_filepaths(cohort_dir)

# Define candidate columns 
candidate_age_cols = ["age_at_diagnosis", "age_at_index", "age_at_initial_pathologic_diagnosis"]
candidate_gender_cols = ["gender"]

# Extract and preview age columns if available
age_preview = {}
if len(candidate_age_cols) > 0:
    clinical_data = pd.read_csv(clinical_file_path, index_col=0)
    for col in candidate_age_cols:
        if col in clinical_data.columns:
            age_preview[col] = clinical_data[col].head().tolist()
    print("Age Column Preview:", age_preview)

# Extract and preview gender columns if available 
gender_preview = {}
if len(candidate_gender_cols) > 0:
    if 'clinical_data' not in locals():
        clinical_data = pd.read_csv(clinical_file_path, index_col=0)
    for col in candidate_gender_cols:
        if col in clinical_data.columns:
            gender_preview[col] = clinical_data[col].head().tolist()
    print("Gender Column Preview:", gender_preview)
# Previous execution output contained dictionaries of age and gender column candidates
# Set chosen column names for demographic information based on that data 
age_col = "age_at_initial_pathologic_diagnosis"
gender_col = "gender"

# Print the chosen columns
print(f"Selected Age Column: {age_col}")
print(f"Selected Gender Column: {gender_col}")
# Get paths
cohort_dir = os.path.join(tcga_root_dir, "TCGA_Melanoma_(SKCM)")
clinical_path, genetic_path = tcga_get_relevant_filepaths(cohort_dir)

# Load data
clinical_df = pd.read_csv(clinical_path, index_col=0, sep='\t')
genetic_df = pd.read_csv(genetic_path, index_col=0, sep='\t')

# Extract clinical features
selected_clinical_df = tcga_select_clinical_features(
    clinical_df=clinical_df,
    trait=trait,
    age_col=age_col,
    gender_col=gender_col
)

# Normalize gene symbols
normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)

# Save normalized gene data
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_df.to_csv(out_gene_data_file)

# Link clinical and genetic data
linked_data = pd.merge(
    selected_clinical_df, 
    normalized_gene_df.T,
    left_index=True,
    right_index=True
)

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

# Check for bias and remove biased demographic features
is_biased, cleaned_data = judge_and_remove_biased_features(linked_data, trait)

# Final validation and save metadata
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_biased,
    df=cleaned_data,
    note="This dataset contains TCGA melanoma data with normalized gene expression values"
)

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