# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Anorexia_Nervosa"

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

# Output paths
out_data_file = "./output/z1/preprocess/Anorexia_Nervosa/TCGA.csv"
out_gene_data_file = "./output/z1/preprocess/Anorexia_Nervosa/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z1/preprocess/Anorexia_Nervosa/clinical_data/TCGA.csv"
json_path = "./output/z1/preprocess/Anorexia_Nervosa/cohort_info.json"


# Step 1: Initial Data Loading
import os
import pandas as pd

# Discover available TCGA cohort directories
all_entries = os.listdir(tcga_root_dir)
cohort_dirs = [d for d in all_entries if os.path.isdir(os.path.join(tcga_root_dir, d))]

# Define keywords related to the trait to find a relevant cohort (none expected for Anorexia Nervosa in TCGA)
trait_keywords = {
    "anorexia", "nervosa", "eating", "appetite", "weight", "body_mass", "bmi", "cachexia"
}

# Score directories by presence of any keyword
def score_dir(name: str) -> int:
    lname = name.lower()
    return sum(1 for kw in trait_keywords if kw in lname)

scored = [(d, score_dir(d)) for d in cohort_dirs]
# Select the best match if any positive score
scored.sort(key=lambda x: x[1], reverse=True)
selected_dir = scored[0][0] if scored and scored[0][1] > 0 else None

if selected_dir is None:
    # No suitable TCGA cohort for Anorexia Nervosa; mark as unavailable and complete this task
    _ = validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA",
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False
    )
    clinical_df = None
    genetic_df = None
    print("No suitable TCGA cohort found for the trait; skipping TCGA for this trait.")
else:
    cohort_path = os.path.join(tcga_root_dir, selected_dir)
    clinical_fp, genetic_fp = tcga_get_relevant_filepaths(cohort_path)

    # Load dataframes
    clinical_df = pd.read_csv(clinical_fp, sep='\t', index_col=0, low_memory=False)
    genetic_df = pd.read_csv(genetic_fp, sep='\t', index_col=0, low_memory=False)

    # Print clinical column names
    print(clinical_df.columns.tolist())