107 lines
3.3 KiB
Python
107 lines
3.3 KiB
Python
"""Tests for trajectory post-processing primitives."""
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from __future__ import annotations
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import numpy as np
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from ai_mouse._postprocess import gaussian_smooth
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def test_gaussian_smooth_preserves_endpoints() -> None:
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x = np.array([1.0, 5.0, 3.0, 8.0, 2.0, 6.0, 4.0])
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result = gaussian_smooth(x, sigma=1.0)
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assert result[0] == 1.0
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assert result[-1] == 4.0
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def test_gaussian_smooth_short_input_unchanged() -> None:
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x = np.array([1.0, 2.0, 3.0])
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result = gaussian_smooth(x, sigma=1.0)
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assert np.array_equal(result, x)
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def test_gaussian_smooth_constant_unchanged() -> None:
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x = np.full(20, 7.5)
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result = gaussian_smooth(x, sigma=1.0)
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assert np.allclose(result, x, atol=1e-6)
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from ai_mouse._postprocess import snap_endpoints
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def test_snap_endpoints_pins_first_and_last() -> None:
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forward = np.linspace(0.1, 0.9, 16)
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lateral = np.full(16, 0.5)
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f, l = snap_endpoints(forward.copy(), lateral.copy(), seq_len=16)
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assert f[0] == 0.0
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assert l[0] == 0.0
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assert f[-1] == 1.0
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assert l[-1] == 0.0
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def test_snap_endpoints_preserves_middle() -> None:
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forward = np.linspace(0.0, 1.0, 16)
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lateral = np.zeros(16)
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f, _ = snap_endpoints(forward.copy(), lateral.copy(), seq_len=16, n_snap=4)
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# Points before the last n_snap should be unchanged
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assert np.allclose(f[1 : 16 - 4], forward[1 : 16 - 4], atol=1e-6)
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from ai_mouse._postprocess import enforce_forward_monotonic, smooth_start
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def test_smooth_start_dampens_lateral() -> None:
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forward = np.linspace(0, 1, 16)
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lateral = np.full(16, 1.0)
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forward[0] = lateral[0] = 0.0 # invariant: snap already done
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_, l = smooth_start(forward.copy(), lateral.copy(), n=4)
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# Lateral at points 1-4 should be < original (dampened)
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assert l[1] < 1.0
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assert l[4] < 1.0
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# Lateral at point 5+ unchanged
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assert l[5] == 1.0
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def test_enforce_forward_monotonic_repairs_inversions() -> None:
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f = np.array([0.0, 0.4, 0.3, 0.6, 0.5, 1.0])
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out = enforce_forward_monotonic(f.copy())
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assert np.all(np.diff(out) > 0), out
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def test_enforce_forward_monotonic_clips_to_unit_interval() -> None:
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f = np.array([-0.1, 0.5, 1.2])
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out = enforce_forward_monotonic(f.copy())
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assert out[0] == 0.0
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assert out[-1] == 1.0
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from ai_mouse._postprocess import build_timestamps, resample_arc
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def test_resample_arc_identity_when_same_length() -> None:
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pts = np.array([[0.0, 0.0], [1.0, 1.0], [2.0, 0.0], [3.0, 1.0]])
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out = resample_arc(pts, 4)
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assert np.allclose(out, pts, atol=1e-6)
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def test_resample_arc_changes_length() -> None:
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pts = np.array([[float(i), 0.0] for i in range(10)])
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out = resample_arc(pts, 5)
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assert out.shape == (5, 2)
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# Endpoints preserved
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assert np.allclose(out[0], pts[0])
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assert np.allclose(out[-1], pts[-1])
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def test_build_timestamps_strictly_increasing() -> None:
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log_dt = np.array([0.0, 2.0, 2.5, 3.0, 2.0])
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ts = build_timestamps(log_dt, total_duration_ms=200.0)
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assert ts[0] == 0
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assert np.all(np.diff(ts) >= 1) # at least 1 ms apart
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def test_build_timestamps_total_close_to_target() -> None:
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log_dt = np.array([1.0] * 10)
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ts = build_timestamps(log_dt, total_duration_ms=300.0)
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# Last timestamp should be roughly total - one slot
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assert abs(ts[-1] - 270) < 60 # tolerant of clipping
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