"""Train/test split (by numeric case id range) + k-fold cross-validation."""
import os, glob
import numpy as np
from .utils import numeric_id


def list_processed(proc_dir):
    cases = [os.path.splitext(os.path.basename(p))[0]
             for p in glob.glob(os.path.join(proc_dir, "*.npz"))]
    return sorted(cases, key=numeric_id)


def _in(num, rng):
    return bool(rng) and rng[0] <= num <= rng[1]


def make_split(proc_dir, cfg):
    cases = list_processed(proc_dir)
    sp = cfg["split"]
    exclude_cases = set(sp.get("exclude_cases", []) or [])   # specific case ids to drop
    train, test = [], []
    for c in cases:
        n = numeric_id(c)
        if n in exclude_cases:
            continue
        if _in(n, sp.get("exclude_range")):
            continue
        if _in(n, sp.get("test_range")):
            test.append(c)
        elif _in(n, sp.get("train_range")):
            train.append(c)
    return train, test


def kfold(train_cases, n_folds=5, seed=42):
    rng = np.random.default_rng(seed)
    idx = np.arange(len(train_cases))
    rng.shuffle(idx)
    folds = np.array_split(idx, n_folds)
    out = []
    for i in range(n_folds):
        val_idx = folds[i]
        tr_idx = np.concatenate([folds[j] for j in range(n_folds) if j != i]) \
            if n_folds > 1 else val_idx
        out.append(([train_cases[j] for j in tr_idx],
                    [train_cases[j] for j in val_idx]))
    return out