def test_array_api(self):
a = AD(1, [1., 0])
b = AD(2, [0, 1.])
x = np.array([a, b])
self.assertEqual(x.dtype, object)
# Idempotent check.
numpy_compare.assert_equal(x, x)
# Conversion.
with self.assertRaises(TypeError):
# Avoid implicit coercion, as this will imply information loss.
xf = np.zeros(2, dtype=np.float)
xf[:] = x
with self.assertRaises(TypeError):
# We could define `__float__` to allow this, but then that will
# enable implicit coercion, which we should avoid.
xf = x.astype(dtype=np.float)
# Presently, does not convert.
x = np.zeros((3, 3), dtype=AD)
self.assertFalse(isinstance(x[0, 0], AD))
x = np.eye(3).astype(AD)
self.assertFalse(isinstance(x[0, 0], AD))
# Test implicit conversion.
numpy_compare.assert_equal(
autodiff_vector_pass_through([1, 2]), # int
[AD(1., []), AD(2., [])])
numpy_compare.assert_equal(
autodiff_vector_pass_through([1., 2.]), # float
[AD(1., []), AD(2., [])])
def test_array_api(self):
a = AD(1, [1., 0])
b = AD(2, [0, 1.])
x = np.array([a, b])
self.assertEqual(x.dtype, object)
# Idempotent check.
npc.assert_equal(x, x)
# Conversion.
with self.assertRaises(TypeError):
# Avoid implicit coercion, as this will imply information loss.
xf = np.zeros(2, dtype=np.float)
xf[:] = x
with self.assertRaises(TypeError):
# We could define `__float__` to allow this, but then that will
# enable implicit coercion, which we should avoid.
xf = x.astype(dtype=np.float)
# Presently, does not convert.
x = np.zeros((3, 3), dtype=AD)
self.assertFalse(isinstance(x[0, 0], AD))
x = np.eye(3).astype(AD)
self.assertFalse(isinstance(x[0, 0], AD))
# Test implicit conversion.
npc.assert_equal(
autodiff_vector_pass_through([1, 2]), # int
[AD(1., []), AD(2., [])])
npc.assert_equal(
autodiff_vector_pass_through([1., 2.]), # float
[AD(1., []), AD(2., [])])
def test_scalar_api(self):
a = AD(1, [1., 0])
self.assertEqual(a.value(), 1.)
npc.assert_equal(a.derivatives(), [1., 0])
self.assertEqual(str(a), "AD{1.0, nderiv=2}")
self.assertEqual(repr(a), "<AutoDiffXd 1.0 nderiv=2>")
npc.assert_equal(a, a)
# Test construction from `float` and `int`.
npc.assert_equal(AD(1), AD(1., []))
npc.assert_equal(AD(1.), AD(1., []))
# Test implicit conversion.
npc.assert_equal(
autodiff_scalar_pass_through(1), # int
AD(1., []))
npc.assert_equal(
autodiff_scalar_pass_through(1.), # float
AD(1., []))
# Test multi-element pass-through.
x = np.array([AD(1.), AD(2.), AD(3.)])
npc.assert_equal(autodiff_vector_pass_through(x), x)
# Ensure fixed-size vectors are correctly converted (#9886).
npc.assert_equal(autodiff_vector3_pass_through(x), x)
# Ensure we can copy.
npc.assert_equal(copy.copy(a), a)
npc.assert_equal(copy.deepcopy(a), a)
def test_scalar_api(self):
a = AD(1, [1., 0])
self.assertEqual(a.value(), 1.)
npc.assert_equal(a.derivatives(), [1., 0])
self.assertEqual(str(a), "AD{1.0, nderiv=2}")
self.assertEqual(repr(a), "<AutoDiffXd 1.0 nderiv=2>")
npc.assert_equal(a, a)
# Test construction from `float` and `int`.
npc.assert_equal(AD(1), AD(1., []))
npc.assert_equal(AD(1.), AD(1., []))
# Test implicit conversion.
npc.assert_equal(
autodiff_scalar_pass_through(1), # int
AD(1., []))
npc.assert_equal(
autodiff_scalar_pass_through(1.), # float
AD(1., []))
# Test multi-element pass-through.
x = np.array([AD(1.), AD(2.), AD(3.)])
npc.assert_equal(autodiff_vector_pass_through(x), x)
# Ensure fixed-size vectors are correctly converted (#9886).
npc.assert_equal(autodiff_vector3_pass_through(x), x)
# Ensure we can copy.
npc.assert_equal(copy.copy(a), a)
npc.assert_equal(copy.deepcopy(a), a)
def test_scalar_api(self):
a = AD(1, [1., 0])
self.assertEqual(a.value(), 1.)
numpy_compare.assert_equal(a.derivatives(), [1., 0])
self.assertEqual(str(a), "AD{1.0, nderiv=2}")
self.assertEqual(repr(a), "<AutoDiffXd 1.0 nderiv=2>")
numpy_compare.assert_equal(a, a)
# Test construction from `float` and `int`.
numpy_compare.assert_equal(AD(1), AD(1., []))
numpy_compare.assert_equal(AD(1.), AD(1., []))
# Test implicit conversion from a simple dtype to AutoDiff.
numpy_compare.assert_equal(
autodiff_scalar_pass_through(1), # int
AD(1., []))
numpy_compare.assert_equal(
autodiff_scalar_pass_through(1.), # float
AD(1., []))
# Test explicit conversion to float.
with self.assertRaises(TypeError) as cm:
float(a)
self.assertIn(
"not 'pydrake.autodiffutils.AutoDiffXd'", str(cm.exception))
a_scalar = np.array(a)
with self.assertRaises(TypeError) as cm:
float(a_scalar)
if np.lib.NumpyVersion(np.__version__) < "1.14.0":
self.assertEqual(
"don't know how to convert scalar number to float",
str(cm.exception))
else:
self.assertEqual(
"float() argument must be a string or a number, not "
"'pydrake.autodiffutils.AutoDiffXd'",
str(cm.exception))
# Test multi-element pass-through.
x = np.array([AD(1.), AD(2.), AD(3.)])
numpy_compare.assert_equal(autodiff_vector_pass_through(x), x)
# Ensure fixed-size vectors are correctly converted (#9886).
numpy_compare.assert_equal(autodiff_vector3_pass_through(x), x)
# Ensure we can copy.
numpy_compare.assert_equal(copy.copy(a), a)
numpy_compare.assert_equal(copy.deepcopy(a), a)
# Ensure that we can pickle.
assert_pickle(self, a, lambda x: x)
