# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Duchenne_Muscular_Dystrophy"

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

# Output paths
out_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/TCGA.csv"
out_gene_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/clinical_data/TCGA.csv"
json_path = "./output/z2/preprocess/Duchenne_Muscular_Dystrophy/cohort_info.json"


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

# Discover available subdirectories (cohorts)
subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]

# Try to find a TCGA cohort relevant to Duchenne Muscular Dystrophy (DMD) — TCGA is cancer-focused, so expect none.
terms = ["duchenne muscular dystrophy", "dystrophin", "dmd"]
lower_map = {d.lower(): d for d in subdirs}

def score_dir(name: str) -> int:
    name_l = name.lower()
    score = 0
    if "duchenne muscular dystrophy" in name_l:
        score += 3
    if "dystrophin" in name_l:
        score += 2
    if "dmd" in name_l:
        score += 1
    return score

scored = [(score_dir(d), d) for d in subdirs]
scored = [item for item in scored if item[0] > 0]

if len(scored) == 0:
    print("No suitable TCGA cohort matches Duchenne Muscular Dystrophy. Skipping this trait for TCGA.")
    # Record unusable dataset for this trait within TCGA
    validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA_Duchenne_Muscular_Dystrophy",
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False
    )
    # Prepare empty placeholders to avoid downstream NameErrors if any
    clinical_df = pd.DataFrame()
    genetic_df = pd.DataFrame()
else:
    # Select the best-matching cohort
    selected_dir = sorted(scored, key=lambda x: (-x[0], len(x[1])))[0][1]
    cohort_dir = os.path.join(tcga_root_dir, selected_dir)
    print(f"Selected TCGA cohort: {selected_dir}")

    # Identify relevant file paths
    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

    # Load clinical and genetic data
    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False)
    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False)

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