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Make vector projections independent of direction scale #242

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Make vector projections independent of direction scale #242

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9 changes: 8 additions & 1 deletion spatialmath/base/vectors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -832,7 +832,14 @@ def project(v1: ArrayLike3, v2: ArrayLike3) -> ArrayLike3:
:return: vector projection of v1 onto v2 (parrallel to v2)
:rtype: ndarray(n)
"""
return np.dot(v1, v2) * v2
v1 = getvector(v1)
v2 = getvector(v2)
scale = np.max(np.abs(v2))
if scale == 0:
return np.zeros_like(v2)
# Normalize the scale before dot products to avoid over/underflow.
v2 = v2 / scale
return (np.dot(v1, v2) / np.dot(v2, v2)) * v2


def orthogonalize(v1: ArrayLike3, v2: ArrayLike3, normalize: bool = True) -> ArrayLike3:
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17 changes: 17 additions & 0 deletions tests/base/test_vectors.py
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Expand Up @@ -33,6 +33,23 @@ class TestVector(unittest.TestCase):
def tearDownClass(cls):
plt.close("all")

def test_project_nonunit_direction(self):
for scale in (1, 2, -3, 1e-200, 1e200):
direction = np.array([scale, scale, 0])
nt.assert_allclose(project([3, 1, 4], direction), [2, 2, 0])
nt.assert_allclose(project((3, 1, 4), (2, 2, 0)), [2, 2, 0])

def test_orthogonalize_nonunit_direction(self):
result = orthogonalize([3, 1, 4], [2, 2, 0], normalize=False)
nt.assert_allclose(result, [1, -1, 4])
self.assertAlmostEqual(np.dot(result, [2, 2, 0]), 0)
normalized = orthogonalize([3, 1, 4], [2, 2, 0])
self.assertAlmostEqual(np.linalg.norm(normalized), 1)
self.assertAlmostEqual(np.dot(normalized, [2, 2, 0]), 0)

def test_project_zero_direction(self):
nt.assert_array_equal(project([1, 2, 3], [0, 0, 0]), [0, 0, 0])

def test_unit(self):
nt.assert_array_almost_equal(unitvec([1, 0, 0]), np.r_[1, 0, 0])
nt.assert_array_almost_equal(unitvec([0, 1, 0]), np.r_[0, 1, 0])
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