"""Tests for trajectory post-processing primitives."""
from __future__ import annotations

import numpy as np

from ai_mouse._postprocess import gaussian_smooth


def test_gaussian_smooth_preserves_endpoints() -> None:
    x = np.array([1.0, 5.0, 3.0, 8.0, 2.0, 6.0, 4.0])
    result = gaussian_smooth(x, sigma=1.0)
    assert result[0] == 1.0
    assert result[-1] == 4.0


def test_gaussian_smooth_short_input_unchanged() -> None:
    x = np.array([1.0, 2.0, 3.0])
    result = gaussian_smooth(x, sigma=1.0)
    assert np.array_equal(result, x)


def test_gaussian_smooth_constant_unchanged() -> None:
    x = np.full(20, 7.5)
    result = gaussian_smooth(x, sigma=1.0)
    assert np.allclose(result, x, atol=1e-6)


from ai_mouse._postprocess import snap_endpoints


def test_snap_endpoints_pins_first_and_last() -> None:
    forward = np.linspace(0.1, 0.9, 16)
    lateral = np.full(16, 0.5)
    f, l = snap_endpoints(forward.copy(), lateral.copy(), seq_len=16)
    assert f[0] == 0.0
    assert l[0] == 0.0
    assert f[-1] == 1.0
    assert l[-1] == 0.0


def test_snap_endpoints_preserves_middle() -> None:
    forward = np.linspace(0.0, 1.0, 16)
    lateral = np.zeros(16)
    f, _ = snap_endpoints(forward.copy(), lateral.copy(), seq_len=16, n_snap=4)
    # Points before the last n_snap should be unchanged
    assert np.allclose(f[1 : 16 - 4], forward[1 : 16 - 4], atol=1e-6)


from ai_mouse._postprocess import enforce_forward_monotonic, smooth_start


def test_smooth_start_dampens_lateral() -> None:
    forward = np.linspace(0, 1, 16)
    lateral = np.full(16, 1.0)
    forward[0] = lateral[0] = 0.0  # invariant: snap already done
    _, l = smooth_start(forward.copy(), lateral.copy(), n=4)
    # Lateral at points 1-4 should be < original (dampened)
    assert l[1] < 1.0
    assert l[4] < 1.0
    # Lateral at point 5+ unchanged
    assert l[5] == 1.0


def test_enforce_forward_monotonic_repairs_inversions() -> None:
    f = np.array([0.0, 0.4, 0.3, 0.6, 0.5, 1.0])
    out = enforce_forward_monotonic(f.copy())
    assert np.all(np.diff(out) > 0), out


def test_enforce_forward_monotonic_clips_to_unit_interval() -> None:
    f = np.array([-0.1, 0.5, 1.2])
    out = enforce_forward_monotonic(f.copy())
    assert out[0] == 0.0
    assert out[-1] == 1.0