def test_density(self):
# Check that the integral of the density equals 1.
n = 100
v = dpnp.random.rand(n)
a, b = dpnp.histogram(v, density=True)
area = dpnp.sum(a * dpnp.diff(b)[0])[0]
numpy.testing.assert_almost_equal(area, 1)
# Check with non-constant bin widths
v = dpnp.arange(10)
bins = [0, 1, 3, 6, 10]
a, b = dpnp.histogram(v, bins, density=True)
numpy.testing.assert_array_equal(a, .1)
numpy.testing.assert_equal(dpnp.sum(a * dpnp.diff(b))[0], 1)
# Test that passing False works too
a, b = dpnp.histogram(v, bins, density=False)
numpy.testing.assert_array_equal(a, [1, 2, 3, 4])
# Variable bin widths are especially useful to deal with
# infinities.
v = dpnp.arange(10)
bins = [0, 1, 3, 6, numpy.inf]
a, b = dpnp.histogram(v, bins, density=True)
numpy.testing.assert_array_equal(a, [.1, .1, .1, 0.])
# Taken from a bug report from N. Becker on the numpy-discussion
# mailing list Aug. 6, 2010.
counts, dmy = dpnp.histogram([1, 2, 3, 4], [0.5, 1.5, numpy.inf],
density=True)
numpy.testing.assert_equal(counts, [.25, 0])
def test_diff(array):
a = numpy.array(array)
ia = inp.array(a)
expected = numpy.diff(a)
result = inp.diff(ia)
numpy.testing.assert_allclose(expected, result)
def test_diff(array):
np_a = numpy.array(array)
dpnp_a = dpnp.array(array)
expected = numpy.diff(np_a)
result = dpnp.diff(dpnp_a)
numpy.testing.assert_allclose(expected, result)