def test_invalid(self):
""" Test it errors when indices has too few dimensions """
a = np.ones((10, 10))
ai = np.ones((10, 2), dtype=np.intp)
# sanity check
take_along_axis(a, ai, axis=1)
# not enough indices
assert_raises(ValueError, take_along_axis, a, np.array(1), axis=1)
# bool arrays not allowed
assert_raises(IndexError, take_along_axis, a, ai.astype(bool), axis=1)
# float arrays not allowed
assert_raises(IndexError, take_along_axis, a, ai.astype(float), axis=1)
# invalid axis
assert_raises(np.AxisError, take_along_axis, a, ai, axis=10)
def test_empty(self):
""" Test everything is ok with empty results, even with inserted dims """
a = np.ones((3, 4, 5))
ai = np.ones((3, 0, 5), dtype=np.intp)
actual = take_along_axis(a, ai, axis=1)
assert_equal(actual.shape, ai.shape)
def test_empty(self):
""" Test everything is ok with empty results, even with inserted dims """
a = np.ones((3, 4, 5))
ai = np.ones((3, 0, 5), dtype=np.intp)
actual = take_along_axis(a, ai, axis=1)
assert_equal(actual.shape, ai.shape)
def test_invalid(self):
""" Test it errors when indices has too few dimensions """
a = np.ones((10, 10))
ai = np.ones((10, 2), dtype=np.intp)
# sanity check
take_along_axis(a, ai, axis=1)
# not enough indices
assert_raises(ValueError, take_along_axis, a, np.array(1), axis=1)
# bool arrays not allowed
assert_raises(IndexError, take_along_axis, a, ai.astype(bool), axis=1)
# float arrays not allowed
assert_raises(IndexError, take_along_axis, a, ai.astype(float), axis=1)
# invalid axis
assert_raises(np.AxisError, take_along_axis, a, ai, axis=10)
def test_argequivalent(self):
""" Test it translates from arg<func> to <func> """
from numpy.random import rand
a = rand(3, 4, 5)
funcs = [
(np.sort, np.argsort, dict()),
(_add_keepdims(np.min), _add_keepdims(np.argmin), dict()),
(_add_keepdims(np.max), _add_keepdims(np.argmax), dict()),
(np.partition, np.argpartition, dict(kth=2)),
]
for func, argfunc, kwargs in funcs:
for axis in list(range(a.ndim)) + [None]:
a_func = func(a, axis=axis, **kwargs)
ai_func = argfunc(a, axis=axis, **kwargs)
assert_equal(a_func, take_along_axis(a, ai_func, axis=axis))
def test_argequivalent(self):
""" Test it translates from arg<func> to <func> """
from numpy.random import rand
a = rand(3, 4, 5)
funcs = [
(np.sort, np.argsort, dict()),
(_add_keepdims(np.min), _add_keepdims(np.argmin), dict()),
(_add_keepdims(np.max), _add_keepdims(np.argmax), dict()),
(np.partition, np.argpartition, dict(kth=2)),
]
for func, argfunc, kwargs in funcs:
for axis in list(range(a.ndim)) + [None]:
a_func = func(a, axis=axis, **kwargs)
ai_func = argfunc(a, axis=axis, **kwargs)
assert_equal(a_func, take_along_axis(a, ai_func, axis=axis))
def test_broadcast(self):
""" Test that non-indexing dimensions are broadcast in both directions """
a = np.ones((3, 4, 1))
ai = np.ones((1, 2, 5), dtype=np.intp)
actual = take_along_axis(a, ai, axis=1)
assert_equal(actual.shape, (3, 2, 5))
def test_broadcast(self):
""" Test that non-indexing dimensions are broadcast in both directions """
a = np.ones((3, 4, 1))
ai = np.arange(10, dtype=np.intp).reshape((1, 2, 5)) % 4
put_along_axis(a, ai, 20, axis=1)
assert_equal(take_along_axis(a, ai, axis=1), 20)
def test_broadcast(self):
""" Test that non-indexing dimensions are broadcast in both directions """
a = np.ones((3, 4, 1))
ai = np.arange(10, dtype=np.intp).reshape((1, 2, 5)) % 4
put_along_axis(a, ai, 20, axis=1)
assert_equal(take_along_axis(a, ai, axis=1), 20)
def test_broadcast(self):
""" Test that non-indexing dimensions are broadcast in both directions """
a = np.ones((3, 4, 1))
ai = np.ones((1, 2, 5), dtype=np.intp)
actual = take_along_axis(a, ai, axis=1)
assert_equal(actual.shape, (3, 2, 5))