| """Property tests for rtc.masking (see its docstring). |
| |
| Run: ``python -m pytest tests/`` or ``python tests/test_masking.py``. |
| """ |
| import numpy as np |
|
|
| from rtc.masking import _SCHEDULES, get_prefix_weights_np as W |
|
|
| |
| got = W(start=2, end=6, total=10, schedule="linear") |
| want = np.array([1, 1, 4/5, 3/5, 2/5, 1/5, 0, 0, 0, 0]) |
| assert np.allclose(got, want), f"linear mismatch:\n got={got}\nwant={want}" |
| print(f"linear(2,6,10) = {got} == reference example OK") |
|
|
| for sched in _SCHEDULES: |
| for start, end, total in [(0, 8, 16), (2, 6, 10), (4, 10, 16), (0, 0, 8), |
| (8, 8, 16), (6, 4, 16), (3, 16, 16)]: |
| w = W(start, end, total, sched) |
| s, e = min(start, end), min(max(end, 0), total) |
| assert w.shape == (total,) |
| assert np.all((w >= 0) & (w <= 1)), f"{sched} {start},{end}: out of [0,1]" |
| |
| if sched != "ones": |
| assert np.all(w[:s] == 1.0), f"{sched} {start},{end},{total}: prefix not 1" |
| |
| assert np.all(np.diff(w) <= 1e-12), f"{sched} {start},{end}: not monotone" |
| |
| if sched != "ones" or e < total: |
| assert np.all(w[e:] == 0.0), f"{sched} {start},{end},{total}: tail not 0" |
| print(f"properties 1-3 hold for all of {_SCHEDULES} across 7 (start,end,total) cases OK") |
|
|
| |
| z = W(2, 6, 10, "zeros") |
| assert np.array_equal(z, np.array([1., 1., 0, 0, 0, 0, 0, 0, 0, 0])), z |
| print(f"zeros(2,6,10) = {z} (naive hard-mask baseline) OK") |
|
|
| e = W(2, 6, 10, "exp") |
| print(f"exp(2,6,10) = {np.round(e, 4)}") |
| assert e[0] == e[1] == 1.0 and e[6] == 0.0 |
| assert np.all(e[2:6] <= W(2, 6, 10, "linear")[2:6] + 1e-12), "exp should not exceed linear" |
| print("exp endpoints + convexity vs linear OK") |
|
|
