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@@ -36,19 +36,22 @@ def test_nice_linspace(start, stop, num, expected):
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(0, 5, 2, True, np.array([0., 2., 4.])), # basic
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(0, 5, 2, False, np.array([0., 2.])), # no endpoint
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(-1, 2, 1.5, True, np.array([-1.5, 0., 1.5])) # step doesn't fit exactly
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(-1, 2, -1.5, True, np.array([-1.5, 0., 1.5])),
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( 5, 0, -2, True, np.array([ 4., 2., 0.])),
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])
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def test_nice_steps_centered(start, stop, step, endpoint, expected):
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np.testing.assert_allclose(nice_steps_centered(start, stop, step, endpoint), expected)
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@pytest.mark.parametrize("start, stop, step, endpoint, expected", [
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(0, 5, 2, True, np.array([0., 2., 4., 6.])), # Normal case with endpoint overshooting
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(0, 5, 2, False, np.array([0., 2., 4.])), # No endpoint
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(-1, 2, 1.5, True, np.array([-1., 0.5, 2.])), # Needs fractional alignment to include stop
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(-1, 2, 1.5, False, np.array([-1., 0.5])), # Same range but no endpoint
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(-2, 1, 1.2, True, np.array([-2., -0.8, 0.4, 1.6])), # Step overshoots
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(5, 0, -2, True, np.array([5., 3., 1., -1.])), # Backward steps with overshoot
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(5, 0, -2, False, np.array([5., 3., 1.])), # No endpoint, backward
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(0, 5, 2, True, np.array([0., 2., 4.])),
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(0, 5, 2, False, np.array([0., 2.])),
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(-1, 2, 1.5, True, np.array([-1., 0.5, 2.])),
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(-1, 2, 1.5, False, np.array([-1., 0.5])),
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(-2, 1, 1.2, True, np.array([-2., -0.8, 0.4])),
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(5, 0, -2, True, np.array([5., 3., 1.])),
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(5, 0, -2, False, np.array([5., 3.])),
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])
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def test_nice_steps_left_aligned(start, stop, step, endpoint, expected):
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np.testing.assert_allclose(nice_steps_left_aligned(start, stop, step, endpoint), expected)
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