JayKimDevolved/deepseek

c011401 verified 3 months ago

80.6 kB

	from __future__ import division, absolute_import, print_function

	import warnings

	import numpy as np
	from numpy.testing import (
	run_module_suite, TestCase, assert_, assert_equal, assert_array_equal,
	assert_almost_equal, assert_array_almost_equal, assert_raises,
	assert_allclose, assert_array_max_ulp, assert_warns, assert_raises_regex
	)
	from numpy.random import rand
	from numpy.lib import *
	from numpy.compat import long


	class TestAny(TestCase):
	def test_basic(self):
	y1 = [0, 0, 1, 0]
	y2 = [0, 0, 0, 0]
	y3 = [1, 0, 1, 0]
	assert_(np.any(y1))
	assert_(np.any(y3))
	assert_(not np.any(y2))

	def test_nd(self):
	y1 = [[0, 0, 0], [0, 1, 0], [1, 1, 0]]
	assert_(np.any(y1))
	assert_array_equal(np.sometrue(y1, axis=0), [1, 1, 0])
	assert_array_equal(np.sometrue(y1, axis=1), [0, 1, 1])


	class TestAll(TestCase):
	def test_basic(self):
	y1 = [0, 1, 1, 0]
	y2 = [0, 0, 0, 0]
	y3 = [1, 1, 1, 1]
	assert_(not np.all(y1))
	assert_(np.all(y3))
	assert_(not np.all(y2))
	assert_(np.all(~np.array(y2)))

	def test_nd(self):
	y1 = [[0, 0, 1], [0, 1, 1], [1, 1, 1]]
	assert_(not np.all(y1))
	assert_array_equal(np.alltrue(y1, axis=0), [0, 0, 1])
	assert_array_equal(np.alltrue(y1, axis=1), [0, 0, 1])


	class TestCopy(TestCase):
	def test_basic(self):
	a = np.array([[1, 2], [3, 4]])
	a_copy = np.copy(a)
	assert_array_equal(a, a_copy)
	a_copy[0, 0] = 10
	assert_equal(a[0, 0], 1)
	assert_equal(a_copy[0, 0], 10)

	def test_order(self):
	# It turns out that people rely on np.copy() preserving order by
	# default; changing this broke scikit-learn:
	# https://github.com/scikit-learn/scikit-learn/commit/7842748cf777412c506a8c0ed28090711d3a3783
	a = np.array([[1, 2], [3, 4]])
	assert_(a.flags.c_contiguous)
	assert_(not a.flags.f_contiguous)
	a_fort = np.array([[1, 2], [3, 4]], order="F")
	assert_(not a_fort.flags.c_contiguous)
	assert_(a_fort.flags.f_contiguous)
	a_copy = np.copy(a)
	assert_(a_copy.flags.c_contiguous)
	assert_(not a_copy.flags.f_contiguous)
	a_fort_copy = np.copy(a_fort)
	assert_(not a_fort_copy.flags.c_contiguous)
	assert_(a_fort_copy.flags.f_contiguous)


	class TestAverage(TestCase):
	def test_basic(self):
	y1 = np.array([1, 2, 3])
	assert_(average(y1, axis=0) == 2.)
	y2 = np.array([1., 2., 3.])
	assert_(average(y2, axis=0) == 2.)
	y3 = [0., 0., 0.]
	assert_(average(y3, axis=0) == 0.)

	y4 = np.ones((4, 4))
	y4[0, 1] = 0
	y4[1, 0] = 2
	assert_almost_equal(y4.mean(0), average(y4, 0))
	assert_almost_equal(y4.mean(1), average(y4, 1))

	y5 = rand(5, 5)
	assert_almost_equal(y5.mean(0), average(y5, 0))
	assert_almost_equal(y5.mean(1), average(y5, 1))

	y6 = np.matrix(rand(5, 5))
	assert_array_equal(y6.mean(0), average(y6, 0))

	def test_weights(self):
	y = np.arange(10)
	w = np.arange(10)
	actual = average(y, weights=w)
	desired = (np.arange(10) ** 2).sum()*1. / np.arange(10).sum()
	assert_almost_equal(actual, desired)

	y1 = np.array([[1, 2, 3], [4, 5, 6]])
	w0 = [1, 2]
	actual = average(y1, weights=w0, axis=0)
	desired = np.array([3., 4., 5.])
	assert_almost_equal(actual, desired)

	w1 = [0, 0, 1]
	actual = average(y1, weights=w1, axis=1)
	desired = np.array([3., 6.])
	assert_almost_equal(actual, desired)

	# This should raise an error. Can we test for that ?
	# assert_equal(average(y1, weights=w1), 9./2.)

	# 2D Case
	w2 = [[0, 0, 1], [0, 0, 2]]
	desired = np.array([3., 6.])
	assert_array_equal(average(y1, weights=w2, axis=1), desired)
	assert_equal(average(y1, weights=w2), 5.)

	def test_returned(self):
	y = np.array([[1, 2, 3], [4, 5, 6]])

	# No weights
	avg, scl = average(y, returned=True)
	assert_equal(scl, 6.)

	avg, scl = average(y, 0, returned=True)
	assert_array_equal(scl, np.array([2., 2., 2.]))

	avg, scl = average(y, 1, returned=True)
	assert_array_equal(scl, np.array([3., 3.]))

	# With weights
	w0 = [1, 2]
	avg, scl = average(y, weights=w0, axis=0, returned=True)
	assert_array_equal(scl, np.array([3., 3., 3.]))

	w1 = [1, 2, 3]
	avg, scl = average(y, weights=w1, axis=1, returned=True)
	assert_array_equal(scl, np.array([6., 6.]))

	w2 = [[0, 0, 1], [1, 2, 3]]
	avg, scl = average(y, weights=w2, axis=1, returned=True)
	assert_array_equal(scl, np.array([1., 6.]))


	class TestSelect(TestCase):
	choices = [np.array([1, 2, 3]),
	np.array([4, 5, 6]),
	np.array([7, 8, 9])]
	conditions = [np.array([False, False, False]),
	np.array([False, True, False]),
	np.array([False, False, True])]

	def _select(self, cond, values, default=0):
	output = []
	for m in range(len(cond)):
	output += [V[m] for V, C in zip(values, cond) if C[m]] or [default]
	return output

	def test_basic(self):
	choices = self.choices
	conditions = self.conditions
	assert_array_equal(select(conditions, choices, default=15),
	self._select(conditions, choices, default=15))

	assert_equal(len(choices), 3)
	assert_equal(len(conditions), 3)

	def test_broadcasting(self):
	conditions = [np.array(True), np.array([False, True, False])]
	choices = [1, np.arange(12).reshape(4, 3)]
	assert_array_equal(select(conditions, choices), np.ones((4, 3)))
	# default can broadcast too:
	assert_equal(select([True], [0], default=[0]).shape, (1,))

	def test_return_dtype(self):
	assert_equal(select(self.conditions, self.choices, 1j).dtype,
	np.complex_)
	# But the conditions need to be stronger then the scalar default
	# if it is scalar.
	choices = [choice.astype(np.int8) for choice in self.choices]
	assert_equal(select(self.conditions, choices).dtype, np.int8)

	d = np.array([1, 2, 3, np.nan, 5, 7])
	m = np.isnan(d)
	assert_equal(select([m], [d]), [0, 0, 0, np.nan, 0, 0])

	def test_deprecated_empty(self):
	with warnings.catch_warnings(record=True):
	warnings.simplefilter("always")
	assert_equal(select([], [], 3j), 3j)

	with warnings.catch_warnings():
	warnings.simplefilter("always")
	assert_warns(DeprecationWarning, select, [], [])
	warnings.simplefilter("error")
	assert_raises(DeprecationWarning, select, [], [])

	def test_non_bool_deprecation(self):
	choices = self.choices
	conditions = self.conditions[:]
	with warnings.catch_warnings():
	warnings.filterwarnings("always")
	conditions[0] = conditions[0].astype(np.int_)
	assert_warns(DeprecationWarning, select, conditions, choices)
	conditions[0] = conditions[0].astype(np.uint8)
	assert_warns(DeprecationWarning, select, conditions, choices)
	warnings.filterwarnings("error")
	assert_raises(DeprecationWarning, select, conditions, choices)

	def test_many_arguments(self):
	# This used to be limited by NPY_MAXARGS == 32
	conditions = [np.array([False])] * 100
	choices = [np.array([1])] * 100
	select(conditions, choices)


	class TestInsert(TestCase):
	def test_basic(self):
	a = [1, 2, 3]
	assert_equal(insert(a, 0, 1), [1, 1, 2, 3])
	assert_equal(insert(a, 3, 1), [1, 2, 3, 1])
	assert_equal(insert(a, [1, 1, 1], [1, 2, 3]), [1, 1, 2, 3, 2, 3])
	assert_equal(insert(a, 1, [1, 2, 3]), [1, 1, 2, 3, 2, 3])
	assert_equal(insert(a, [1, -1, 3], 9), [1, 9, 2, 9, 3, 9])
	assert_equal(insert(a, slice(-1, None, -1), 9), [9, 1, 9, 2, 9, 3])
	assert_equal(insert(a, [-1, 1, 3], [7, 8, 9]), [1, 8, 2, 7, 3, 9])
	b = np.array([0, 1], dtype=np.float64)
	assert_equal(insert(b, 0, b[0]), [0., 0., 1.])
	assert_equal(insert(b, [], []), b)
	# Bools will be treated differently in the future:
	#assert_equal(insert(a, np.array([True]*4), 9), [9,1,9,2,9,3,9])
	with warnings.catch_warnings(record=True) as w:
	warnings.filterwarnings('always', '', FutureWarning)
	assert_equal(
	insert(a, np.array([True]*4), 9), [1, 9, 9, 9, 9, 2, 3])
	assert_(w[0].category is FutureWarning)

	def test_multidim(self):
	a = [[1, 1, 1]]
	r = [[2, 2, 2],
	[1, 1, 1]]
	assert_equal(insert(a, 0, [1]), [1, 1, 1, 1])
	assert_equal(insert(a, 0, [2, 2, 2], axis=0), r)
	assert_equal(insert(a, 0, 2, axis=0), r)
	assert_equal(insert(a, 2, 2, axis=1), [[1, 1, 2, 1]])

	a = np.array([[1, 1], [2, 2], [3, 3]])
	b = np.arange(1, 4).repeat(3).reshape(3, 3)
	c = np.concatenate(
	(a[:, 0:1], np.arange(1, 4).repeat(3).reshape(3, 3).T,
	a[:, 1:2]), axis=1)
	assert_equal(insert(a, [1], [[1], [2], [3]], axis=1), b)
	assert_equal(insert(a, [1], [1, 2, 3], axis=1), c)
	# scalars behave differently, in this case exactly opposite:
	assert_equal(insert(a, 1, [1, 2, 3], axis=1), b)
	assert_equal(insert(a, 1, [[1], [2], [3]], axis=1), c)

	a = np.arange(4).reshape(2, 2)
	assert_equal(insert(a[:, :1], 1, a[:, 1], axis=1), a)
	assert_equal(insert(a[:1, :], 1, a[1, :], axis=0), a)

	# negative axis value
	a = np.arange(24).reshape((2, 3, 4))
	assert_equal(insert(a, 1, a[:, :, 3], axis=-1),
	insert(a, 1, a[:, :, 3], axis=2))
	assert_equal(insert(a, 1, a[:, 2, :], axis=-2),
	insert(a, 1, a[:, 2, :], axis=1))

	# invalid axis value
	assert_raises(IndexError, insert, a, 1, a[:, 2, :], axis=3)
	assert_raises(IndexError, insert, a, 1, a[:, 2, :], axis=-4)

	# negative axis value
	a = np.arange(24).reshape((2,3,4))
	assert_equal(insert(a, 1, a[:,:,3], axis=-1),
	insert(a, 1, a[:,:,3], axis=2))
	assert_equal(insert(a, 1, a[:,2,:], axis=-2),
	insert(a, 1, a[:,2,:], axis=1))

	def test_0d(self):
	# This is an error in the future
	a = np.array(1)
	with warnings.catch_warnings(record=True) as w:
	warnings.filterwarnings('always', '', DeprecationWarning)
	assert_equal(insert(a, [], 2, axis=0), np.array(2))
	assert_(w[0].category is DeprecationWarning)

	def test_subclass(self):
	class SubClass(np.ndarray):
	pass
	a = np.arange(10).view(SubClass)
	assert_(isinstance(np.insert(a, 0, [0]), SubClass))
	assert_(isinstance(np.insert(a, [], []), SubClass))
	assert_(isinstance(np.insert(a, [0, 1], [1, 2]), SubClass))
	assert_(isinstance(np.insert(a, slice(1, 2), [1, 2]), SubClass))
	assert_(isinstance(np.insert(a, slice(1, -2, -1), []), SubClass))
	# This is an error in the future:
	a = np.array(1).view(SubClass)
	assert_(isinstance(np.insert(a, 0, [0]), SubClass))

	def test_index_array_copied(self):
	x = np.array([1, 1, 1])
	np.insert([0, 1, 2], x, [3, 4, 5])
	assert_equal(x, np.array([1, 1, 1]))

	def test_structured_array(self):
	a = np.array([(1, 'a'), (2, 'b'), (3, 'c')],
	dtype=[('foo', 'i'), ('bar', 'a1')])
	val = (4, 'd')
	b = np.insert(a, 0, val)
	assert_array_equal(b[0], np.array(val, dtype=b.dtype))
	val = [(4, 'd')] * 2
	b = np.insert(a, [0, 2], val)
	assert_array_equal(b[[0, 3]], np.array(val, dtype=b.dtype))


	class TestAmax(TestCase):
	def test_basic(self):
	a = [3, 4, 5, 10, -3, -5, 6.0]
	assert_equal(np.amax(a), 10.0)
	b = [[3, 6.0, 9.0],
	[4, 10.0, 5.0],
	[8, 3.0, 2.0]]
	assert_equal(np.amax(b, axis=0), [8.0, 10.0, 9.0])
	assert_equal(np.amax(b, axis=1), [9.0, 10.0, 8.0])


	class TestAmin(TestCase):
	def test_basic(self):
	a = [3, 4, 5, 10, -3, -5, 6.0]
	assert_equal(np.amin(a), -5.0)
	b = [[3, 6.0, 9.0],
	[4, 10.0, 5.0],
	[8, 3.0, 2.0]]
	assert_equal(np.amin(b, axis=0), [3.0, 3.0, 2.0])
	assert_equal(np.amin(b, axis=1), [3.0, 4.0, 2.0])


	class TestPtp(TestCase):
	def test_basic(self):
	a = [3, 4, 5, 10, -3, -5, 6.0]
	assert_equal(np.ptp(a, axis=0), 15.0)
	b = [[3, 6.0, 9.0],
	[4, 10.0, 5.0],
	[8, 3.0, 2.0]]
	assert_equal(np.ptp(b, axis=0), [5.0, 7.0, 7.0])
	assert_equal(np.ptp(b, axis=-1), [6.0, 6.0, 6.0])


	class TestCumsum(TestCase):
	def test_basic(self):
	ba = [1, 2, 10, 11, 6, 5, 4]
	ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
	for ctype in [np.int8, np.uint8, np.int16, np.uint16, np.int32,
	np.uint32, np.float32, np.float64, np.complex64, np.complex128]:
	a = np.array(ba, ctype)
	a2 = np.array(ba2, ctype)

	tgt = np.array([1, 3, 13, 24, 30, 35, 39], ctype)
	assert_array_equal(np.cumsum(a, axis=0), tgt)

	tgt = np.array(
	[[1, 2, 3, 4], [6, 8, 10, 13], [16, 11, 14, 18]], ctype)
	assert_array_equal(np.cumsum(a2, axis=0), tgt)

	tgt = np.array(
	[[1, 3, 6, 10], [5, 11, 18, 27], [10, 13, 17, 22]], ctype)
	assert_array_equal(np.cumsum(a2, axis=1), tgt)


	class TestProd(TestCase):
	def test_basic(self):
	ba = [1, 2, 10, 11, 6, 5, 4]
	ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
	for ctype in [np.int16, np.uint16, np.int32, np.uint32,
	np.float32, np.float64, np.complex64, np.complex128]:
	a = np.array(ba, ctype)
	a2 = np.array(ba2, ctype)
	if ctype in ['1', 'b']:
	self.assertRaises(ArithmeticError, prod, a)
	self.assertRaises(ArithmeticError, prod, a2, 1)
	self.assertRaises(ArithmeticError, prod, a)
	else:
	assert_equal(np.prod(a, axis=0), 26400)
	assert_array_equal(np.prod(a2, axis=0),
	np.array([50, 36, 84, 180], ctype))
	assert_array_equal(np.prod(a2, axis=-1),
	np.array([24, 1890, 600], ctype))


	class TestCumprod(TestCase):
	def test_basic(self):
	ba = [1, 2, 10, 11, 6, 5, 4]
	ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
	for ctype in [np.int16, np.uint16, np.int32, np.uint32,
	np.float32, np.float64, np.complex64, np.complex128]:
	a = np.array(ba, ctype)
	a2 = np.array(ba2, ctype)
	if ctype in ['1', 'b']:
	self.assertRaises(ArithmeticError, cumprod, a)
	self.assertRaises(ArithmeticError, cumprod, a2, 1)
	self.assertRaises(ArithmeticError, cumprod, a)
	else:
	assert_array_equal(np.cumprod(a, axis=-1),
	np.array([1, 2, 20, 220,
	1320, 6600, 26400], ctype))
	assert_array_equal(np.cumprod(a2, axis=0),
	np.array([[1, 2, 3, 4],
	[5, 12, 21, 36],
	[50, 36, 84, 180]], ctype))
	assert_array_equal(np.cumprod(a2, axis=-1),
	np.array([[1, 2, 6, 24],
	[5, 30, 210, 1890],
	[10, 30, 120, 600]], ctype))


	class TestDiff(TestCase):
	def test_basic(self):
	x = [1, 4, 6, 7, 12]
	out = np.array([3, 2, 1, 5])
	out2 = np.array([-1, -1, 4])
	out3 = np.array([0, 5])
	assert_array_equal(diff(x), out)
	assert_array_equal(diff(x, n=2), out2)
	assert_array_equal(diff(x, n=3), out3)

	def test_nd(self):
	x = 20 * rand(10, 20, 30)
	out1 = x[:, :, 1:] - x[:, :, :-1]
	out2 = out1[:, :, 1:] - out1[:, :, :-1]
	out3 = x[1:, :, :] - x[:-1, :, :]
	out4 = out3[1:, :, :] - out3[:-1, :, :]
	assert_array_equal(diff(x), out1)
	assert_array_equal(diff(x, n=2), out2)
	assert_array_equal(diff(x, axis=0), out3)
	assert_array_equal(diff(x, n=2, axis=0), out4)


	class TestDelete(TestCase):
	def setUp(self):
	self.a = np.arange(5)
	self.nd_a = np.arange(5).repeat(2).reshape(1, 5, 2)

	def _check_inverse_of_slicing(self, indices):
	a_del = delete(self.a, indices)
	nd_a_del = delete(self.nd_a, indices, axis=1)
	msg = 'Delete failed for obj: %r' % indices
	# NOTE: The cast should be removed after warning phase for bools
	if not isinstance(indices, (slice, int, long, np.integer)):
	indices = np.asarray(indices, dtype=np.intp)
	indices = indices[(indices >= 0) & (indices < 5)]
	assert_array_equal(setxor1d(a_del, self.a[indices, ]), self.a,
	err_msg=msg)
	xor = setxor1d(nd_a_del[0, :, 0], self.nd_a[0, indices, 0])
	assert_array_equal(xor, self.nd_a[0, :, 0], err_msg=msg)

	def test_slices(self):
	lims = [-6, -2, 0, 1, 2, 4, 5]
	steps = [-3, -1, 1, 3]
	for start in lims:
	for stop in lims:
	for step in steps:
	s = slice(start, stop, step)
	self._check_inverse_of_slicing(s)

	def test_fancy(self):
	# Deprecation/FutureWarning tests should be kept after change.
	self._check_inverse_of_slicing(np.array([[0, 1], [2, 1]]))
	with warnings.catch_warnings():
	warnings.filterwarnings('error', category=DeprecationWarning)
	assert_raises(DeprecationWarning, delete, self.a, [100])
	assert_raises(DeprecationWarning, delete, self.a, [-100])
	with warnings.catch_warnings(record=True) as w:
	warnings.filterwarnings('always', category=FutureWarning)
	self._check_inverse_of_slicing([0, -1, 2, 2])
	obj = np.array([True, False, False], dtype=bool)
	self._check_inverse_of_slicing(obj)
	assert_(w[0].category is FutureWarning)
	assert_(w[1].category is FutureWarning)

	def test_single(self):
	self._check_inverse_of_slicing(0)
	self._check_inverse_of_slicing(-4)

	def test_0d(self):
	a = np.array(1)
	with warnings.catch_warnings(record=True) as w:
	warnings.filterwarnings('always', '', DeprecationWarning)
	assert_equal(delete(a, [], axis=0), a)
	assert_(w[0].category is DeprecationWarning)

	def test_subclass(self):
	class SubClass(np.ndarray):
	pass
	a = self.a.view(SubClass)
	assert_(isinstance(delete(a, 0), SubClass))
	assert_(isinstance(delete(a, []), SubClass))
	assert_(isinstance(delete(a, [0, 1]), SubClass))
	assert_(isinstance(delete(a, slice(1, 2)), SubClass))
	assert_(isinstance(delete(a, slice(1, -2)), SubClass))


	class TestGradient(TestCase):
	def test_basic(self):
	v = [[1, 1], [3, 4]]
	x = np.array(v)
	dx = [np.array([[2., 3.], [2., 3.]]),
	np.array([[0., 0.], [1., 1.]])]
	assert_array_equal(gradient(x), dx)
	assert_array_equal(gradient(v), dx)

	def test_badargs(self):
	# for 2D array, gradient can take 0, 1, or 2 extra args
	x = np.array([[1, 1], [3, 4]])
	assert_raises(SyntaxError, gradient, x, np.array([1., 1.]),
	np.array([1., 1.]), np.array([1., 1.]))

	def test_masked(self):
	# Make sure that gradient supports subclasses like masked arrays
	x = np.ma.array([[1, 1], [3, 4]])
	assert_equal(type(gradient(x)[0]), type(x))

	def test_datetime64(self):
	# Make sure gradient() can handle special types like datetime64
	x = np.array(
	['1910-08-16', '1910-08-11', '1910-08-10', '1910-08-12',
	'1910-10-12', '1910-12-12', '1912-12-12'],
	dtype='datetime64[D]')
	dx = np.array(
	[-7, -3, 0, 31, 61, 396, 1066],
	dtype='timedelta64[D]')
	assert_array_equal(gradient(x), dx)
	assert_(dx.dtype == np.dtype('timedelta64[D]'))

	def test_timedelta64(self):
	# Make sure gradient() can handle special types like timedelta64
	x = np.array(
	[-5, -3, 10, 12, 61, 321, 300],
	dtype='timedelta64[D]')
	dx = np.array(
	[-3, 7, 7, 25, 154, 119, -161],
	dtype='timedelta64[D]')
	assert_array_equal(gradient(x), dx)
	assert_(dx.dtype == np.dtype('timedelta64[D]'))

	def test_second_order_accurate(self):
	# Testing that the relative numerical error is less that 3% for
	# this example problem. This corresponds to second order
	# accurate finite differences for all interior and boundary
	# points.
	x = np.linspace(0, 1, 10)
	dx = x[1] - x[0]
	y = 2 * x ** 3 + 4 * x ** 2 + 2 * x
	analytical = 6 * x ** 2 + 8 * x + 2
	num_error = np.abs((np.gradient(y, dx) / analytical) - 1)
	assert_(np.all(num_error < 0.03) == True)


	class TestAngle(TestCase):
	def test_basic(self):
	x = [1 + 3j, np.sqrt(2) / 2.0 + 1j * np.sqrt(2) / 2,
	1, 1j, -1, -1j, 1 - 3j, -1 + 3j]
	y = angle(x)
	yo = [
	np.arctan(3.0 / 1.0),
	np.arctan(1.0), 0, np.pi / 2, np.pi, -np.pi / 2.0,
	-np.arctan(3.0 / 1.0), np.pi - np.arctan(3.0 / 1.0)]
	z = angle(x, deg=1)
	zo = np.array(yo) * 180 / np.pi
	assert_array_almost_equal(y, yo, 11)
	assert_array_almost_equal(z, zo, 11)


	class TestTrimZeros(TestCase):
	""" only testing for integer splits.
	"""
	def test_basic(self):
	a = np.array([0, 0, 1, 2, 3, 4, 0])
	res = trim_zeros(a)
	assert_array_equal(res, np.array([1, 2, 3, 4]))

	def test_leading_skip(self):
	a = np.array([0, 0, 1, 0, 2, 3, 4, 0])
	res = trim_zeros(a)
	assert_array_equal(res, np.array([1, 0, 2, 3, 4]))

	def test_trailing_skip(self):
	a = np.array([0, 0, 1, 0, 2, 3, 0, 4, 0])
	res = trim_zeros(a)
	assert_array_equal(res, np.array([1, 0, 2, 3, 0, 4]))


	class TestExtins(TestCase):
	def test_basic(self):
	a = np.array([1, 3, 2, 1, 2, 3, 3])
	b = extract(a > 1, a)
	assert_array_equal(b, [3, 2, 2, 3, 3])

	def test_place(self):
	a = np.array([1, 4, 3, 2, 5, 8, 7])
	place(a, [0, 1, 0, 1, 0, 1, 0], [2, 4, 6])
	assert_array_equal(a, [1, 2, 3, 4, 5, 6, 7])

	def test_both(self):
	a = rand(10)
	mask = a > 0.5
	ac = a.copy()
	c = extract(mask, a)
	place(a, mask, 0)
	place(a, mask, c)
	assert_array_equal(a, ac)


	class TestVectorize(TestCase):
	def test_simple(self):
	def addsubtract(a, b):
	if a > b:
	return a - b
	else:
	return a + b
	f = vectorize(addsubtract)
	r = f([0, 3, 6, 9], [1, 3, 5, 7])
	assert_array_equal(r, [1, 6, 1, 2])

	def test_scalar(self):
	def addsubtract(a, b):
	if a > b:
	return a - b
	else:
	return a + b
	f = vectorize(addsubtract)
	r = f([0, 3, 6, 9], 5)
	assert_array_equal(r, [5, 8, 1, 4])

	def test_large(self):
	x = np.linspace(-3, 2, 10000)
	f = vectorize(lambda x: x)
	y = f(x)
	assert_array_equal(y, x)

	def test_ufunc(self):
	import math
	f = vectorize(math.cos)
	args = np.array([0, 0.5np.pi, np.pi, 1.5np.pi, 2*np.pi])
	r1 = f(args)
	r2 = np.cos(args)
	assert_array_equal(r1, r2)

	def test_keywords(self):
	import math

	def foo(a, b=1):
	return a + b
	f = vectorize(foo)
	args = np.array([1, 2, 3])
	r1 = f(args)
	r2 = np.array([2, 3, 4])
	assert_array_equal(r1, r2)
	r1 = f(args, 2)
	r2 = np.array([3, 4, 5])
	assert_array_equal(r1, r2)

	def test_keywords_no_func_code(self):
	# This needs to test a function that has keywords but
	# no func_code attribute, since otherwise vectorize will
	# inspect the func_code.
	import random
	try:
	f = vectorize(random.randrange)
	except:
	raise AssertionError()

	def test_keywords2_ticket_2100(self):
	r"""Test kwarg support: enhancement ticket 2100"""
	import math

	def foo(a, b=1):
	return a + b
	f = vectorize(foo)
	args = np.array([1, 2, 3])
	r1 = f(a=args)
	r2 = np.array([2, 3, 4])
	assert_array_equal(r1, r2)
	r1 = f(b=1, a=args)
	assert_array_equal(r1, r2)
	r1 = f(args, b=2)
	r2 = np.array([3, 4, 5])
	assert_array_equal(r1, r2)

	def test_keywords3_ticket_2100(self):
	"""Test excluded with mixed positional and kwargs: ticket 2100"""
	def mypolyval(x, p):
	_p = list(p)
	res = _p.pop(0)
	while _p:
	res = res*x + _p.pop(0)
	return res
	vpolyval = np.vectorize(mypolyval, excluded=['p', 1])
	ans = [3, 6]
	assert_array_equal(ans, vpolyval(x=[0, 1], p=[1, 2, 3]))
	assert_array_equal(ans, vpolyval([0, 1], p=[1, 2, 3]))
	assert_array_equal(ans, vpolyval([0, 1], [1, 2, 3]))

	def test_keywords4_ticket_2100(self):
	"""Test vectorizing function with no positional args."""
	@vectorize
	def f(**kw):
	res = 1.0
	for _k in kw:
	res *= kw[_k]
	return res
	assert_array_equal(f(a=[1, 2], b=[3, 4]), [3, 8])

	def test_keywords5_ticket_2100(self):
	"""Test vectorizing function with no kwargs args."""
	@vectorize
	def f(*v):
	return np.prod(v)
	assert_array_equal(f([1, 2], [3, 4]), [3, 8])

	def test_coverage1_ticket_2100(self):
	def foo():
	return 1
	f = vectorize(foo)
	assert_array_equal(f(), 1)

	def test_assigning_docstring(self):
	def foo(x):
	return x
	doc = "Provided documentation"
	f = vectorize(foo, doc=doc)
	assert_equal(f.__doc__, doc)

	def test_UnboundMethod_ticket_1156(self):
	"""Regression test for issue 1156"""
	class Foo:
	b = 2

	def bar(self, a):
	return a**self.b
	assert_array_equal(vectorize(Foo().bar)(np.arange(9)),
	np.arange(9)**2)
	assert_array_equal(vectorize(Foo.bar)(Foo(), np.arange(9)),
	np.arange(9)**2)

	def test_execution_order_ticket_1487(self):
	"""Regression test for dependence on execution order: issue 1487"""
	f1 = vectorize(lambda x: x)
	res1a = f1(np.arange(3))
	res1b = f1(np.arange(0.1, 3))
	f2 = vectorize(lambda x: x)
	res2b = f2(np.arange(0.1, 3))
	res2a = f2(np.arange(3))
	assert_equal(res1a, res2a)
	assert_equal(res1b, res2b)

	def test_string_ticket_1892(self):
	"""Test vectorization over strings: issue 1892."""
	f = np.vectorize(lambda x: x)
	s = '0123456789'*10
	assert_equal(s, f(s))
	#z = f(np.array([s,s]))
	#assert_array_equal([s,s], f(s))

	def test_cache(self):
	"""Ensure that vectorized func called exactly once per argument."""
	_calls = [0]

	@vectorize
	def f(x):
	_calls[0] += 1
	return x**2
	f.cache = True
	x = np.arange(5)
	assert_array_equal(f(x), x*x)
	assert_equal(_calls[0], len(x))

	def test_otypes(self):
	f = np.vectorize(lambda x: x)
	f.otypes = 'i'
	x = np.arange(5)
	assert_array_equal(f(x), x)


	class TestDigitize(TestCase):
	def test_forward(self):
	x = np.arange(-6, 5)
	bins = np.arange(-5, 5)
	assert_array_equal(digitize(x, bins), np.arange(11))

	def test_reverse(self):
	x = np.arange(5, -6, -1)
	bins = np.arange(5, -5, -1)
	assert_array_equal(digitize(x, bins), np.arange(11))

	def test_random(self):
	x = rand(10)
	bin = np.linspace(x.min(), x.max(), 10)
	assert_(np.all(digitize(x, bin) != 0))

	def test_right_basic(self):
	x = [1, 5, 4, 10, 8, 11, 0]
	bins = [1, 5, 10]
	default_answer = [1, 2, 1, 3, 2, 3, 0]
	assert_array_equal(digitize(x, bins), default_answer)
	right_answer = [0, 1, 1, 2, 2, 3, 0]
	assert_array_equal(digitize(x, bins, True), right_answer)

	def test_right_open(self):
	x = np.arange(-6, 5)
	bins = np.arange(-6, 4)
	assert_array_equal(digitize(x, bins, True), np.arange(11))

	def test_right_open_reverse(self):
	x = np.arange(5, -6, -1)
	bins = np.arange(4, -6, -1)
	assert_array_equal(digitize(x, bins, True), np.arange(11))

	def test_right_open_random(self):
	x = rand(10)
	bins = np.linspace(x.min(), x.max(), 10)
	assert_(np.all(digitize(x, bins, True) != 10))

	def test_monotonic(self):
	x = [-1, 0, 1, 2]
	bins = [0, 0, 1]
	assert_array_equal(digitize(x, bins, False), [0, 2, 3, 3])
	assert_array_equal(digitize(x, bins, True), [0, 0, 2, 3])
	bins = [1, 1, 0]
	assert_array_equal(digitize(x, bins, False), [3, 2, 0, 0])
	assert_array_equal(digitize(x, bins, True), [3, 3, 2, 0])
	bins = [1, 1, 1, 1]
	assert_array_equal(digitize(x, bins, False), [0, 0, 4, 4])
	assert_array_equal(digitize(x, bins, True), [0, 0, 0, 4])
	bins = [0, 0, 1, 0]
	assert_raises(ValueError, digitize, x, bins)
	bins = [1, 1, 0, 1]
	assert_raises(ValueError, digitize, x, bins)


	class TestUnwrap(TestCase):
	def test_simple(self):
	#check that unwrap removes jumps greather that 2*pi
	assert_array_equal(unwrap([1, 1 + 2 * np.pi]), [1, 1])
	#check that unwrap maintans continuity
	assert_(np.all(diff(unwrap(rand(10) * 100)) < np.pi))


	class TestFilterwindows(TestCase):
	def test_hanning(self):
	#check symmetry
	w = hanning(10)
	assert_array_almost_equal(w, flipud(w), 7)
	#check known value
	assert_almost_equal(np.sum(w, axis=0), 4.500, 4)

	def test_hamming(self):
	#check symmetry
	w = hamming(10)
	assert_array_almost_equal(w, flipud(w), 7)
	#check known value
	assert_almost_equal(np.sum(w, axis=0), 4.9400, 4)

	def test_bartlett(self):
	#check symmetry
	w = bartlett(10)
	assert_array_almost_equal(w, flipud(w), 7)
	#check known value
	assert_almost_equal(np.sum(w, axis=0), 4.4444, 4)

	def test_blackman(self):
	#check symmetry
	w = blackman(10)
	assert_array_almost_equal(w, flipud(w), 7)
	#check known value
	assert_almost_equal(np.sum(w, axis=0), 3.7800, 4)


	class TestTrapz(TestCase):
	def test_simple(self):
	x = np.arange(-10, 10, .1)
	r = trapz(np.exp(-.5x2) / np.sqrt(2np.pi), dx=0.1)
	#check integral of normal equals 1
	assert_almost_equal(r, 1, 7)

	def test_ndim(self):
	x = np.linspace(0, 1, 3)
	y = np.linspace(0, 2, 8)
	z = np.linspace(0, 3, 13)

	wx = np.ones_like(x) * (x[1] - x[0])
	wx[0] /= 2
	wx[-1] /= 2
	wy = np.ones_like(y) * (y[1] - y[0])
	wy[0] /= 2
	wy[-1] /= 2
	wz = np.ones_like(z) * (z[1] - z[0])
	wz[0] /= 2
	wz[-1] /= 2

	q = x[:, None, None] + y[None, :, None] + z[None, None, :]

	qx = (q * wx[:, None, None]).sum(axis=0)
	qy = (q * wy[None, :, None]).sum(axis=1)
	qz = (q * wz[None, None, :]).sum(axis=2)

	# n-d `x`
	r = trapz(q, x=x[:, None, None], axis=0)
	assert_almost_equal(r, qx)
	r = trapz(q, x=y[None, :, None], axis=1)
	assert_almost_equal(r, qy)
	r = trapz(q, x=z[None, None, :], axis=2)
	assert_almost_equal(r, qz)

	# 1-d `x`
	r = trapz(q, x=x, axis=0)
	assert_almost_equal(r, qx)
	r = trapz(q, x=y, axis=1)
	assert_almost_equal(r, qy)
	r = trapz(q, x=z, axis=2)
	assert_almost_equal(r, qz)

	def test_masked(self):
	#Testing that masked arrays behave as if the function is 0 where
	#masked
	x = np.arange(5)
	y = x * x
	mask = x == 2
	ym = np.ma.array(y, mask=mask)
	r = 13.0 # sum(0.5 * (0 + 1) * 1.0 + 0.5 * (9 + 16))
	assert_almost_equal(trapz(ym, x), r)

	xm = np.ma.array(x, mask=mask)
	assert_almost_equal(trapz(ym, xm), r)

	xm = np.ma.array(x, mask=mask)
	assert_almost_equal(trapz(y, xm), r)

	def test_matrix(self):
	#Test to make sure matrices give the same answer as ndarrays
	x = np.linspace(0, 5)
	y = x * x
	r = trapz(y, x)
	mx = np.matrix(x)
	my = np.matrix(y)
	mr = trapz(my, mx)
	assert_almost_equal(mr, r)


	class TestSinc(TestCase):
	def test_simple(self):
	assert_(sinc(0) == 1)
	w = sinc(np.linspace(-1, 1, 100))
	#check symmetry
	assert_array_almost_equal(w, flipud(w), 7)

	def test_array_like(self):
	x = [0, 0.5]
	y1 = sinc(np.array(x))
	y2 = sinc(list(x))
	y3 = sinc(tuple(x))
	assert_array_equal(y1, y2)
	assert_array_equal(y1, y3)


	class TestHistogram(TestCase):
	def setUp(self):
	pass

	def tearDown(self):
	pass

	def test_simple(self):
	n = 100
	v = rand(n)
	(a, b) = histogram(v)
	#check if the sum of the bins equals the number of samples
	assert_equal(np.sum(a, axis=0), n)
	#check that the bin counts are evenly spaced when the data is from a
	# linear function
	(a, b) = histogram(np.linspace(0, 10, 100))
	assert_array_equal(a, 10)

	def test_one_bin(self):
	# Ticket 632
	hist, edges = histogram([1, 2, 3, 4], [1, 2])
	assert_array_equal(hist, [2, ])
	assert_array_equal(edges, [1, 2])
	assert_raises(ValueError, histogram, [1, 2], bins=0)
	h, e = histogram([1, 2], bins=1)
	assert_equal(h, np.array([2]))
	assert_allclose(e, np.array([1., 2.]))

	def test_normed(self):
	# Check that the integral of the density equals 1.
	n = 100
	v = rand(n)
	a, b = histogram(v, normed=True)
	area = np.sum(a * diff(b))
	assert_almost_equal(area, 1)

	# Check with non-constant bin widths (buggy but backwards compatible)
	v = np.arange(10)
	bins = [0, 1, 5, 9, 10]
	a, b = histogram(v, bins, normed=True)
	area = np.sum(a * diff(b))
	assert_almost_equal(area, 1)

	def test_density(self):
	# Check that the integral of the density equals 1.
	n = 100
	v = rand(n)
	a, b = histogram(v, density=True)
	area = np.sum(a * diff(b))
	assert_almost_equal(area, 1)

	# Check with non-constant bin widths
	v = np.arange(10)
	bins = [0, 1, 3, 6, 10]
	a, b = histogram(v, bins, density=True)
	assert_array_equal(a, .1)
	assert_equal(np.sum(a*diff(b)), 1)

	# Variale bin widths are especially useful to deal with
	# infinities.
	v = np.arange(10)
	bins = [0, 1, 3, 6, np.inf]
	a, b = histogram(v, bins, density=True)
	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 = np.histogram(
	[1, 2, 3, 4], [0.5, 1.5, np.inf], density=True)
	assert_equal(counts, [.25, 0])

	def test_outliers(self):
	# Check that outliers are not tallied
	a = np.arange(10) + .5

	# Lower outliers
	h, b = histogram(a, range=[0, 9])
	assert_equal(h.sum(), 9)

	# Upper outliers
	h, b = histogram(a, range=[1, 10])
	assert_equal(h.sum(), 9)

	# Normalization
	h, b = histogram(a, range=[1, 9], normed=True)
	assert_almost_equal((h * diff(b)).sum(), 1, decimal=15)

	# Weights
	w = np.arange(10) + .5
	h, b = histogram(a, range=[1, 9], weights=w, normed=True)
	assert_equal((h * diff(b)).sum(), 1)

	h, b = histogram(a, bins=8, range=[1, 9], weights=w)
	assert_equal(h, w[1:-1])

	def test_type(self):
	# Check the type of the returned histogram
	a = np.arange(10) + .5
	h, b = histogram(a)
	assert_(issubdtype(h.dtype, int))

	h, b = histogram(a, normed=True)
	assert_(issubdtype(h.dtype, float))

	h, b = histogram(a, weights=np.ones(10, int))
	assert_(issubdtype(h.dtype, int))

	h, b = histogram(a, weights=np.ones(10, float))
	assert_(issubdtype(h.dtype, float))

	def test_f32_rounding(self):
	# gh-4799, check that the rounding of the edges works with float32
	x = np.array([276.318359 , -69.593948 , 21.329449], dtype=np.float32)
	y = np.array([5005.689453, 4481.327637, 6010.369629], dtype=np.float32)
	counts_hist, xedges, yedges = np.histogram2d(x, y, bins=100)
	assert_equal(counts_hist.sum(), 3.)

	def test_weights(self):
	v = rand(100)
	w = np.ones(100) * 5
	a, b = histogram(v)
	na, nb = histogram(v, normed=True)
	wa, wb = histogram(v, weights=w)
	nwa, nwb = histogram(v, weights=w, normed=True)
	assert_array_almost_equal(a * 5, wa)
	assert_array_almost_equal(na, nwa)

	# Check weights are properly applied.
	v = np.linspace(0, 10, 10)
	w = np.concatenate((np.zeros(5), np.ones(5)))
	wa, wb = histogram(v, bins=np.arange(11), weights=w)
	assert_array_almost_equal(wa, w)

	# Check with integer weights
	wa, wb = histogram([1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1])
	assert_array_equal(wa, [4, 5, 0, 1])
	wa, wb = histogram(
	[1, 2, 2, 4], bins=4, weights=[4, 3, 2, 1], normed=True)
	assert_array_almost_equal(wa, np.array([4, 5, 0, 1]) / 10. / 3. * 4)

	# Check weights with non-uniform bin widths
	a, b = histogram(
	np.arange(9), [0, 1, 3, 6, 10],
	weights=[2, 1, 1, 1, 1, 1, 1, 1, 1], density=True)
	assert_almost_equal(a, [.2, .1, .1, .075])

	def test_empty(self):
	a, b = histogram([], bins=([0, 1]))
	assert_array_equal(a, np.array([0]))
	assert_array_equal(b, np.array([0, 1]))


	class TestHistogramdd(TestCase):
	def test_simple(self):
	x = np.array([[-.5, .5, 1.5], [-.5, 1.5, 2.5], [-.5, 2.5, .5],
	[.5, .5, 1.5], [.5, 1.5, 2.5], [.5, 2.5, 2.5]])
	H, edges = histogramdd(x, (2, 3, 3),
	range=[[-1, 1], [0, 3], [0, 3]])
	answer = np.array([[[0, 1, 0], [0, 0, 1], [1, 0, 0]],
	[[0, 1, 0], [0, 0, 1], [0, 0, 1]]])
	assert_array_equal(H, answer)

	# Check normalization
	ed = [[-2, 0, 2], [0, 1, 2, 3], [0, 1, 2, 3]]
	H, edges = histogramdd(x, bins=ed, normed=True)
	assert_(np.all(H == answer / 12.))

	# Check that H has the correct shape.
	H, edges = histogramdd(x, (2, 3, 4),
	range=[[-1, 1], [0, 3], [0, 4]],
	normed=True)
	answer = np.array([[[0, 1, 0, 0], [0, 0, 1, 0], [1, 0, 0, 0]],
	[[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 1, 0]]])
	assert_array_almost_equal(H, answer / 6., 4)
	# Check that a sequence of arrays is accepted and H has the correct
	# shape.
	z = [np.squeeze(y) for y in split(x, 3, axis=1)]
	H, edges = histogramdd(
	z, bins=(4, 3, 2), range=[[-2, 2], [0, 3], [0, 2]])
	answer = np.array([[[0, 0], [0, 0], [0, 0]],
	[[0, 1], [0, 0], [1, 0]],
	[[0, 1], [0, 0], [0, 0]],
	[[0, 0], [0, 0], [0, 0]]])
	assert_array_equal(H, answer)

	Z = np.zeros((5, 5, 5))
	Z[list(range(5)), list(range(5)), list(range(5))] = 1.
	H, edges = histogramdd([np.arange(5), np.arange(5), np.arange(5)], 5)
	assert_array_equal(H, Z)

	def test_shape_3d(self):
	# All possible permutations for bins of different lengths in 3D.
	bins = ((5, 4, 6), (6, 4, 5), (5, 6, 4), (4, 6, 5), (6, 5, 4),
	(4, 5, 6))
	r = rand(10, 3)
	for b in bins:
	H, edges = histogramdd(r, b)
	assert_(H.shape == b)

	def test_shape_4d(self):
	# All possible permutations for bins of different lengths in 4D.
	bins = ((7, 4, 5, 6), (4, 5, 7, 6), (5, 6, 4, 7), (7, 6, 5, 4),
	(5, 7, 6, 4), (4, 6, 7, 5), (6, 5, 7, 4), (7, 5, 4, 6),
	(7, 4, 6, 5), (6, 4, 7, 5), (6, 7, 5, 4), (4, 6, 5, 7),
	(4, 7, 5, 6), (5, 4, 6, 7), (5, 7, 4, 6), (6, 7, 4, 5),
	(6, 5, 4, 7), (4, 7, 6, 5), (4, 5, 6, 7), (7, 6, 4, 5),
	(5, 4, 7, 6), (5, 6, 7, 4), (6, 4, 5, 7), (7, 5, 6, 4))

	r = rand(10, 4)
	for b in bins:
	H, edges = histogramdd(r, b)
	assert_(H.shape == b)

	def test_weights(self):
	v = rand(100, 2)
	hist, edges = histogramdd(v)
	n_hist, edges = histogramdd(v, normed=True)
	w_hist, edges = histogramdd(v, weights=np.ones(100))
	assert_array_equal(w_hist, hist)
	w_hist, edges = histogramdd(v, weights=np.ones(100) * 2, normed=True)
	assert_array_equal(w_hist, n_hist)
	w_hist, edges = histogramdd(v, weights=np.ones(100, int) * 2)
	assert_array_equal(w_hist, 2 * hist)

	def test_identical_samples(self):
	x = np.zeros((10, 2), int)
	hist, edges = histogramdd(x, bins=2)
	assert_array_equal(edges[0], np.array([-0.5, 0., 0.5]))

	def test_empty(self):
	a, b = histogramdd([[], []], bins=([0, 1], [0, 1]))
	assert_array_max_ulp(a, np.array([[0.]]))
	a, b = np.histogramdd([[], [], []], bins=2)
	assert_array_max_ulp(a, np.zeros((2, 2, 2)))

	def test_bins_errors(self):
	"""There are two ways to specify bins. Check for the right errors when
	mixing those."""
	x = np.arange(8).reshape(2, 4)
	assert_raises(ValueError, np.histogramdd, x, bins=[-1, 2, 4, 5])
	assert_raises(ValueError, np.histogramdd, x, bins=[1, 0.99, 1, 1])
	assert_raises(
	ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 2, 3]])
	assert_raises(
	ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 3, -3]])
	assert_(np.histogramdd(x, bins=[1, 1, 1, [1, 2, 3, 4]]))

	def test_inf_edges(self):
	"""Test using +/-inf bin edges works. See #1788."""
	with np.errstate(invalid='ignore'):
	x = np.arange(6).reshape(3, 2)
	expected = np.array([[1, 0], [0, 1], [0, 1]])
	h, e = np.histogramdd(x, bins=[3, [-np.inf, 2, 10]])
	assert_allclose(h, expected)
	h, e = np.histogramdd(x, bins=[3, np.array([-1, 2, np.inf])])
	assert_allclose(h, expected)
	h, e = np.histogramdd(x, bins=[3, [-np.inf, 3, np.inf]])
	assert_allclose(h, expected)

	def test_rightmost_binedge(self):
	"""Test event very close to rightmost binedge.
	See Github issue #4266"""
	x = [0.9999999995]
	bins = [[0.,0.5,1.0]]
	hist, _ = histogramdd(x, bins=bins)
	assert_(hist[0] == 0.0)
	assert_(hist[1] == 1.)
	x = [1.0]
	bins = [[0.,0.5,1.0]]
	hist, _ = histogramdd(x, bins=bins)
	assert_(hist[0] == 0.0)
	assert_(hist[1] == 1.)
	x = [1.0000000001]
	bins = [[0.,0.5,1.0]]
	hist, _ = histogramdd(x, bins=bins)
	assert_(hist[0] == 0.0)
	assert_(hist[1] == 1.)
	x = [1.0001]
	bins = [[0.,0.5,1.0]]
	hist, _ = histogramdd(x, bins=bins)
	assert_(hist[0] == 0.0)
	assert_(hist[1] == 0.0)


	class TestUnique(TestCase):
	def test_simple(self):
	x = np.array([4, 3, 2, 1, 1, 2, 3, 4, 0])
	assert_(np.all(unique(x) == [0, 1, 2, 3, 4]))
	assert_(unique(np.array([1, 1, 1, 1, 1])) == np.array([1]))
	x = ['widget', 'ham', 'foo', 'bar', 'foo', 'ham']
	assert_(np.all(unique(x) == ['bar', 'foo', 'ham', 'widget']))
	x = np.array([5 + 6j, 1 + 1j, 1 + 10j, 10, 5 + 6j])
	assert_(np.all(unique(x) == [1 + 1j, 1 + 10j, 5 + 6j, 10]))


	class TestCheckFinite(TestCase):
	def test_simple(self):
	a = [1, 2, 3]
	b = [1, 2, np.inf]
	c = [1, 2, np.nan]
	np.lib.asarray_chkfinite(a)
	assert_raises(ValueError, np.lib.asarray_chkfinite, b)
	assert_raises(ValueError, np.lib.asarray_chkfinite, c)

	def test_dtype_order(self):
	"""Regression test for missing dtype and order arguments"""
	a = [1, 2, 3]
	a = np.lib.asarray_chkfinite(a, order='F', dtype=np.float64)
	assert_(a.dtype == np.float64)


	class TestCorrCoef(TestCase):
	A = np.array(
	[[0.15391142, 0.18045767, 0.14197213],
	[0.70461506, 0.96474128, 0.27906989],
	[0.9297531, 0.32296769, 0.19267156]])
	B = np.array(
	[[0.10377691, 0.5417086, 0.49807457],
	[0.82872117, 0.77801674, 0.39226705],
	[0.9314666, 0.66800209, 0.03538394]])
	res1 = np.array(
	[[1., 0.9379533, -0.04931983],
	[0.9379533, 1., 0.30007991],
	[-0.04931983, 0.30007991, 1.]])
	res2 = np.array(
	[[1., 0.9379533, -0.04931983, 0.30151751, 0.66318558, 0.51532523],
	[0.9379533, 1., 0.30007991, -0.04781421, 0.88157256, 0.78052386],
	[-0.04931983, 0.30007991, 1., -0.96717111, 0.71483595, 0.83053601],
	[0.30151751, -0.04781421, -0.96717111, 1., -0.51366032, -0.66173113],
	[0.66318558, 0.88157256, 0.71483595, -0.51366032, 1., 0.98317823],
	[0.51532523, 0.78052386, 0.83053601, -0.66173113, 0.98317823, 1.]])

	def test_non_array(self):
	assert_almost_equal(np.corrcoef([0, 1, 0], [1, 0, 1]),
	[[1., -1.], [-1., 1.]])

	def test_simple(self):
	assert_almost_equal(corrcoef(self.A), self.res1)
	assert_almost_equal(corrcoef(self.A, self.B), self.res2)

	def test_ddof(self):
	assert_almost_equal(corrcoef(self.A, ddof=-1), self.res1)
	assert_almost_equal(corrcoef(self.A, self.B, ddof=-1), self.res2)

	def test_complex(self):
	x = np.array([[1, 2, 3], [1j, 2j, 3j]])
	assert_allclose(corrcoef(x), np.array([[1., -1.j], [1.j, 1.]]))

	def test_xy(self):
	x = np.array([[1, 2, 3]])
	y = np.array([[1j, 2j, 3j]])
	assert_allclose(np.corrcoef(x, y), np.array([[1., -1.j], [1.j, 1.]]))

	def test_empty(self):
	with warnings.catch_warnings(record=True):
	warnings.simplefilter('always', RuntimeWarning)
	assert_array_equal(corrcoef(np.array([])), np.nan)
	assert_array_equal(corrcoef(np.array([]).reshape(0, 2)),
	np.array([]).reshape(0, 0))
	assert_array_equal(corrcoef(np.array([]).reshape(2, 0)),
	np.array([[np.nan, np.nan], [np.nan, np.nan]]))

	def test_wrong_ddof(self):
	x = np.array([[0, 2], [1, 1], [2, 0]]).T
	with warnings.catch_warnings(record=True):
	warnings.simplefilter('always', RuntimeWarning)
	assert_array_equal(corrcoef(x, ddof=5),
	np.array([[np.nan, np.nan], [np.nan, np.nan]]))


	class TestCov(TestCase):
	def test_basic(self):
	x = np.array([[0, 2], [1, 1], [2, 0]]).T
	assert_allclose(cov(x), np.array([[1., -1.], [-1., 1.]]))

	def test_complex(self):
	x = np.array([[1, 2, 3], [1j, 2j, 3j]])
	assert_allclose(cov(x), np.array([[1., -1.j], [1.j, 1.]]))

	def test_xy(self):
	x = np.array([[1, 2, 3]])
	y = np.array([[1j, 2j, 3j]])
	assert_allclose(cov(x, y), np.array([[1., -1.j], [1.j, 1.]]))

	def test_empty(self):
	with warnings.catch_warnings(record=True):
	warnings.simplefilter('always', RuntimeWarning)
	assert_array_equal(cov(np.array([])), np.nan)
	assert_array_equal(cov(np.array([]).reshape(0, 2)),
	np.array([]).reshape(0, 0))
	assert_array_equal(cov(np.array([]).reshape(2, 0)),
	np.array([[np.nan, np.nan], [np.nan, np.nan]]))

	def test_wrong_ddof(self):
	x = np.array([[0, 2], [1, 1], [2, 0]]).T
	with warnings.catch_warnings(record=True):
	warnings.simplefilter('always', RuntimeWarning)
	assert_array_equal(cov(x, ddof=5),
	np.array([[np.inf, -np.inf], [-np.inf, np.inf]]))


	class Test_I0(TestCase):
	def test_simple(self):
	assert_almost_equal(
	i0(0.5),
	np.array(1.0634833707413234))

	A = np.array([0.49842636, 0.6969809, 0.22011976, 0.0155549])
	assert_almost_equal(
	i0(A),
	np.array([1.06307822, 1.12518299, 1.01214991, 1.00006049]))

	B = np.array([[0.827002, 0.99959078],
	[0.89694769, 0.39298162],
	[0.37954418, 0.05206293],
	[0.36465447, 0.72446427],
	[0.48164949, 0.50324519]])
	assert_almost_equal(
	i0(B),
	np.array([[1.17843223, 1.26583466],
	[1.21147086, 1.03898290],
	[1.03633899, 1.00067775],
	[1.03352052, 1.13557954],
	[1.05884290, 1.06432317]]))


	class TestKaiser(TestCase):
	def test_simple(self):
	assert_(np.isfinite(kaiser(1, 1.0)))
	assert_almost_equal(kaiser(0, 1.0),
	np.array([]))
	assert_almost_equal(kaiser(2, 1.0),
	np.array([0.78984831, 0.78984831]))
	assert_almost_equal(kaiser(5, 1.0),
	np.array([0.78984831, 0.94503323, 1.,
	0.94503323, 0.78984831]))
	assert_almost_equal(kaiser(5, 1.56789),
	np.array([0.58285404, 0.88409679, 1.,
	0.88409679, 0.58285404]))

	def test_int_beta(self):
	kaiser(3, 4)


	class TestMsort(TestCase):
	def test_simple(self):
	A = np.array([[0.44567325, 0.79115165, 0.54900530],
	[0.36844147, 0.37325583, 0.96098397],
	[0.64864341, 0.52929049, 0.39172155]])
	assert_almost_equal(
	msort(A),
	np.array([[0.36844147, 0.37325583, 0.39172155],
	[0.44567325, 0.52929049, 0.54900530],
	[0.64864341, 0.79115165, 0.96098397]]))


	class TestMeshgrid(TestCase):
	def test_simple(self):
	[X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7])
	assert_array_equal(X, np.array([[1, 2, 3],
	[1, 2, 3],
	[1, 2, 3],
	[1, 2, 3]]))
	assert_array_equal(Y, np.array([[4, 4, 4],
	[5, 5, 5],
	[6, 6, 6],
	[7, 7, 7]]))

	def test_single_input(self):
	[X] = meshgrid([1, 2, 3, 4])
	assert_array_equal(X, np.array([1, 2, 3, 4]))

	def test_no_input(self):
	args = []
	assert_array_equal([], meshgrid(*args))

	def test_indexing(self):
	x = [1, 2, 3]
	y = [4, 5, 6, 7]
	[X, Y] = meshgrid(x, y, indexing='ij')
	assert_array_equal(X, np.array([[1, 1, 1, 1],
	[2, 2, 2, 2],
	[3, 3, 3, 3]]))
	assert_array_equal(Y, np.array([[4, 5, 6, 7],
	[4, 5, 6, 7],
	[4, 5, 6, 7]]))

	# Test expected shapes:
	z = [8, 9]
	assert_(meshgrid(x, y)[0].shape == (4, 3))
	assert_(meshgrid(x, y, indexing='ij')[0].shape == (3, 4))
	assert_(meshgrid(x, y, z)[0].shape == (4, 3, 2))
	assert_(meshgrid(x, y, z, indexing='ij')[0].shape == (3, 4, 2))

	assert_raises(ValueError, meshgrid, x, y, indexing='notvalid')

	def test_sparse(self):
	[X, Y] = meshgrid([1, 2, 3], [4, 5, 6, 7], sparse=True)
	assert_array_equal(X, np.array([[1, 2, 3]]))
	assert_array_equal(Y, np.array([[4], [5], [6], [7]]))

	def test_invalid_arguments(self):
	# Test that meshgrid complains about invalid arguments
	# Regression test for issue #4755:
	# https://github.com/numpy/numpy/issues/4755
	assert_raises(TypeError, meshgrid,
	[1, 2, 3], [4, 5, 6, 7], indices='ij')


	class TestPiecewise(TestCase):
	def test_simple(self):
	# Condition is single bool list
	x = piecewise([0, 0], [True, False], [1])
	assert_array_equal(x, [1, 0])

	# List of conditions: single bool list
	x = piecewise([0, 0], [[True, False]], [1])
	assert_array_equal(x, [1, 0])

	# Conditions is single bool array
	x = piecewise([0, 0], np.array([True, False]), [1])
	assert_array_equal(x, [1, 0])

	# Condition is single int array
	x = piecewise([0, 0], np.array([1, 0]), [1])
	assert_array_equal(x, [1, 0])

	# List of conditions: int array
	x = piecewise([0, 0], [np.array([1, 0])], [1])
	assert_array_equal(x, [1, 0])

	x = piecewise([0, 0], [[False, True]], [lambda x:-1])
	assert_array_equal(x, [0, -1])

	def test_two_conditions(self):
	x = piecewise([1, 2], [[True, False], [False, True]], [3, 4])
	assert_array_equal(x, [3, 4])

	def test_default(self):
	# No value specified for x[1], should be 0
	x = piecewise([1, 2], [True, False], [2])
	assert_array_equal(x, [2, 0])

	# Should set x[1] to 3
	x = piecewise([1, 2], [True, False], [2, 3])
	assert_array_equal(x, [2, 3])

	def test_0d(self):
	x = np.array(3)
	y = piecewise(x, x > 3, [4, 0])
	assert_(y.ndim == 0)
	assert_(y == 0)

	x = 5
	y = piecewise(x, [[True], [False]], [1, 0])
	assert_(y.ndim == 0)
	assert_(y == 1)

	def test_0d_comparison(self):
	x = 3
	y = piecewise(x, [x <= 3, x > 3], [4, 0])


	class TestBincount(TestCase):
	def test_simple(self):
	y = np.bincount(np.arange(4))
	assert_array_equal(y, np.ones(4))

	def test_simple2(self):
	y = np.bincount(np.array([1, 5, 2, 4, 1]))
	assert_array_equal(y, np.array([0, 2, 1, 0, 1, 1]))

	def test_simple_weight(self):
	x = np.arange(4)
	w = np.array([0.2, 0.3, 0.5, 0.1])
	y = np.bincount(x, w)
	assert_array_equal(y, w)

	def test_simple_weight2(self):
	x = np.array([1, 2, 4, 5, 2])
	w = np.array([0.2, 0.3, 0.5, 0.1, 0.2])
	y = np.bincount(x, w)
	assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1]))

	def test_with_minlength(self):
	x = np.array([0, 1, 0, 1, 1])
	y = np.bincount(x, minlength=3)
	assert_array_equal(y, np.array([2, 3, 0]))

	def test_with_minlength_smaller_than_maxvalue(self):
	x = np.array([0, 1, 1, 2, 2, 3, 3])
	y = np.bincount(x, minlength=2)
	assert_array_equal(y, np.array([1, 2, 2, 2]))

	def test_with_minlength_and_weights(self):
	x = np.array([1, 2, 4, 5, 2])
	w = np.array([0.2, 0.3, 0.5, 0.1, 0.2])
	y = np.bincount(x, w, 8)
	assert_array_equal(y, np.array([0, 0.2, 0.5, 0, 0.5, 0.1, 0, 0]))

	def test_empty(self):
	x = np.array([], dtype=int)
	y = np.bincount(x)
	assert_array_equal(x, y)

	def test_empty_with_minlength(self):
	x = np.array([], dtype=int)
	y = np.bincount(x, minlength=5)
	assert_array_equal(y, np.zeros(5, dtype=int))

	def test_with_incorrect_minlength(self):
	x = np.array([], dtype=int)
	assert_raises_regex(TypeError, "an integer is required",
	lambda: np.bincount(x, minlength="foobar"))
	assert_raises_regex(ValueError, "must be positive",
	lambda: np.bincount(x, minlength=-1))
	assert_raises_regex(ValueError, "must be positive",
	lambda: np.bincount(x, minlength=0))

	x = np.arange(5)
	assert_raises_regex(TypeError, "an integer is required",
	lambda: np.bincount(x, minlength="foobar"))
	assert_raises_regex(ValueError, "minlength must be positive",
	lambda: np.bincount(x, minlength=-1))
	assert_raises_regex(ValueError, "minlength must be positive",
	lambda: np.bincount(x, minlength=0))


	class TestInterp(TestCase):
	def test_exceptions(self):
	assert_raises(ValueError, interp, 0, [], [])
	assert_raises(ValueError, interp, 0, [0], [1, 2])

	def test_basic(self):
	x = np.linspace(0, 1, 5)
	y = np.linspace(0, 1, 5)
	x0 = np.linspace(0, 1, 50)
	assert_almost_equal(np.interp(x0, x, y), x0)

	def test_right_left_behavior(self):
	assert_equal(interp([-1, 0, 1], [0], [1]), [1, 1, 1])
	assert_equal(interp([-1, 0, 1], [0], [1], left=0), [0, 1, 1])
	assert_equal(interp([-1, 0, 1], [0], [1], right=0), [1, 1, 0])
	assert_equal(interp([-1, 0, 1], [0], [1], left=0, right=0), [0, 1, 0])

	def test_scalar_interpolation_point(self):
	x = np.linspace(0, 1, 5)
	y = np.linspace(0, 1, 5)
	x0 = 0
	assert_almost_equal(np.interp(x0, x, y), x0)
	x0 = .3
	assert_almost_equal(np.interp(x0, x, y), x0)
	x0 = np.float32(.3)
	assert_almost_equal(np.interp(x0, x, y), x0)
	x0 = np.float64(.3)
	assert_almost_equal(np.interp(x0, x, y), x0)
	x0 = np.nan
	assert_almost_equal(np.interp(x0, x, y), x0)

	def test_zero_dimensional_interpolation_point(self):
	x = np.linspace(0, 1, 5)
	y = np.linspace(0, 1, 5)
	x0 = np.array(.3)
	assert_almost_equal(np.interp(x0, x, y), x0)
	x0 = np.array(.3, dtype=object)
	assert_almost_equal(np.interp(x0, x, y), .3)

	def test_if_len_x_is_small(self):
	xp = np.arange(0, 10, 0.0001)
	fp = np.sin(xp)
	assert_almost_equal(np.interp(np.pi, xp, fp), 0.0)


	def compare_results(res, desired):
	for i in range(len(desired)):
	assert_array_equal(res[i], desired[i])


	class TestScoreatpercentile(TestCase):

	def test_basic(self):
	x = np.arange(8) * 0.5
	assert_equal(np.percentile(x, 0), 0.)
	assert_equal(np.percentile(x, 100), 3.5)
	assert_equal(np.percentile(x, 50), 1.75)

	def test_api(self):
	d = np.ones(5)
	np.percentile(d, 5, None, None, False)
	np.percentile(d, 5, None, None, False, 'linear')
	o = np.ones((1,))
	np.percentile(d, 5, None, o, False, 'linear')

	def test_2D(self):
	x = np.array([[1, 1, 1],
	[1, 1, 1],
	[4, 4, 3],
	[1, 1, 1],
	[1, 1, 1]])
	assert_array_equal(np.percentile(x, 50, axis=0), [1, 1, 1])

	def test_linear(self):

	# Test defaults
	assert_equal(np.percentile(range(10), 50), 4.5)

	# explicitly specify interpolation_method 'fraction' (the default)
	assert_equal(np.percentile(range(10), 50,
	interpolation='linear'), 4.5)

	def test_lower_higher(self):

	# interpolation_method 'lower'/'higher'
	assert_equal(np.percentile(range(10), 50,
	interpolation='lower'), 4)
	assert_equal(np.percentile(range(10), 50,
	interpolation='higher'), 5)

	def test_midpoint(self):
	assert_equal(np.percentile(range(10), 51,
	interpolation='midpoint'), 4.5)

	def test_nearest(self):
	assert_equal(np.percentile(range(10), 51,
	interpolation='nearest'), 5)
	assert_equal(np.percentile(range(10), 49,
	interpolation='nearest'), 4)

	def test_sequence(self):
	x = np.arange(8) * 0.5
	assert_equal(np.percentile(x, [0, 100, 50]), [0, 3.5, 1.75])

	def test_axis(self):
	x = np.arange(12).reshape(3, 4)

	assert_equal(np.percentile(x, (25, 50, 100)), [2.75, 5.5, 11.0])

	r0 = [[2, 3, 4, 5], [4, 5, 6, 7], [8, 9, 10, 11]]
	assert_equal(np.percentile(x, (25, 50, 100), axis=0), r0)

	r1 = [[0.75, 1.5, 3], [4.75, 5.5, 7], [8.75, 9.5, 11]]
	assert_equal(np.percentile(x, (25, 50, 100), axis=1), np.array(r1).T)

	# ensure qth axis is always first as with np.array(old_percentile(..))
	x = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
	assert_equal(np.percentile(x, (25, 50)).shape, (2,))
	assert_equal(np.percentile(x, (25, 50, 75)).shape, (3,))
	assert_equal(np.percentile(x, (25, 50), axis=0).shape, (2, 4, 5, 6))
	assert_equal(np.percentile(x, (25, 50), axis=1).shape, (2, 3, 5, 6))
	assert_equal(np.percentile(x, (25, 50), axis=2).shape, (2, 3, 4, 6))
	assert_equal(np.percentile(x, (25, 50), axis=3).shape, (2, 3, 4, 5))
	assert_equal(np.percentile(x, (25, 50, 75), axis=1).shape, (3, 3, 5, 6))
	assert_equal(np.percentile(x, (25, 50),
	interpolation="higher").shape, (2,))
	assert_equal(np.percentile(x, (25, 50, 75),
	interpolation="higher").shape, (3,))
	assert_equal(np.percentile(x, (25, 50), axis=0,
	interpolation="higher").shape, (2, 4, 5, 6))
	assert_equal(np.percentile(x, (25, 50), axis=1,
	interpolation="higher").shape, (2, 3, 5, 6))
	assert_equal(np.percentile(x, (25, 50), axis=2,
	interpolation="higher").shape, (2, 3, 4, 6))
	assert_equal(np.percentile(x, (25, 50), axis=3,
	interpolation="higher").shape, (2, 3, 4, 5))
	assert_equal(np.percentile(x, (25, 50, 75), axis=1,
	interpolation="higher").shape, (3, 3, 5, 6))

	def test_scalar_q(self):
	# test for no empty dimensions for compatiblity with old percentile
	x = np.arange(12).reshape(3, 4)
	assert_equal(np.percentile(x, 50), 5.5)
	self.assertTrue(np.isscalar(np.percentile(x, 50)))
	r0 = np.array([ 4., 5., 6., 7.])
	assert_equal(np.percentile(x, 50, axis=0), r0)
	assert_equal(np.percentile(x, 50, axis=0).shape, r0.shape)
	r1 = np.array([ 1.5, 5.5, 9.5])
	assert_almost_equal(np.percentile(x, 50, axis=1), r1)
	assert_equal(np.percentile(x, 50, axis=1).shape, r1.shape)

	out = np.empty(1)
	assert_equal(np.percentile(x, 50, out=out), 5.5)
	assert_equal(out, 5.5)
	out = np.empty(4)
	assert_equal(np.percentile(x, 50, axis=0, out=out), r0)
	assert_equal(out, r0)
	out = np.empty(3)
	assert_equal(np.percentile(x, 50, axis=1, out=out), r1)
	assert_equal(out, r1)

	# test for no empty dimensions for compatiblity with old percentile
	x = np.arange(12).reshape(3, 4)
	assert_equal(np.percentile(x, 50, interpolation='lower'), 5.)
	self.assertTrue(np.isscalar(np.percentile(x, 50)))
	r0 = np.array([ 4., 5., 6., 7.])
	c0 = np.percentile(x, 50, interpolation='lower', axis=0)
	assert_equal(c0, r0)
	assert_equal(c0.shape, r0.shape)
	r1 = np.array([ 1., 5., 9.])
	c1 = np.percentile(x, 50, interpolation='lower', axis=1)
	assert_almost_equal(c1, r1)
	assert_equal(c1.shape, r1.shape)

	out = np.empty((), dtype=x.dtype)
	c = np.percentile(x, 50, interpolation='lower', out=out)
	assert_equal(c, 5)
	assert_equal(out, 5)
	out = np.empty(4, dtype=x.dtype)
	c = np.percentile(x, 50, interpolation='lower', axis=0, out=out)
	assert_equal(c, r0)
	assert_equal(out, r0)
	out = np.empty(3, dtype=x.dtype)
	c = np.percentile(x, 50, interpolation='lower', axis=1, out=out)
	assert_equal(c, r1)
	assert_equal(out, r1)

	def test_exception(self):
	assert_raises(ValueError, np.percentile, [1, 2], 56,
	interpolation='foobar')
	assert_raises(ValueError, np.percentile, [1], 101)
	assert_raises(ValueError, np.percentile, [1], -1)
	assert_raises(ValueError, np.percentile, [1], list(range(50)) + [101])
	assert_raises(ValueError, np.percentile, [1], list(range(50)) + [-0.1])

	def test_percentile_list(self):
	assert_equal(np.percentile([1, 2, 3], 0), 1)

	def test_percentile_out(self):
	x = np.array([1, 2, 3])
	y = np.zeros((3,))
	p = (1, 2, 3)
	np.percentile(x, p, out=y)
	assert_equal(y, np.percentile(x, p))

	x = np.array([[1, 2, 3],
	[4, 5, 6]])

	y = np.zeros((3, 3))
	np.percentile(x, p, axis=0, out=y)
	assert_equal(y, np.percentile(x, p, axis=0))

	y = np.zeros((3, 2))
	np.percentile(x, p, axis=1, out=y)
	assert_equal(y, np.percentile(x, p, axis=1))

	x = np.arange(12).reshape(3, 4)
	# q.dim > 1, float
	r0 = np.array([[2., 3., 4., 5.], [4., 5., 6., 7.]])
	out = np.empty((2, 4))
	assert_equal(np.percentile(x, (25, 50), axis=0, out=out), r0)
	assert_equal(out, r0)
	r1 = np.array([[0.75, 4.75, 8.75], [1.5, 5.5, 9.5]])
	out = np.empty((2, 3))
	assert_equal(np.percentile(x, (25, 50), axis=1, out=out), r1)
	assert_equal(out, r1)

	# q.dim > 1, int
	r0 = np.array([[0, 1, 2, 3], [4, 5, 6, 7]])
	out = np.empty((2, 4), dtype=x.dtype)
	c = np.percentile(x, (25, 50), interpolation='lower', axis=0, out=out)
	assert_equal(c, r0)
	assert_equal(out, r0)
	r1 = np.array([[0, 4, 8], [1, 5, 9]])
	out = np.empty((2, 3), dtype=x.dtype)
	c = np.percentile(x, (25, 50), interpolation='lower', axis=1, out=out)
	assert_equal(c, r1)
	assert_equal(out, r1)

	def test_percentile_empty_dim(self):
	# empty dims are preserved
	d = np.arange(11*2).reshape(11, 1, 2, 1)
	assert_array_equal(np.percentile(d, 50, axis=0).shape, (1, 2, 1))
	assert_array_equal(np.percentile(d, 50, axis=1).shape, (11, 2, 1))
	assert_array_equal(np.percentile(d, 50, axis=2).shape, (11, 1, 1))
	assert_array_equal(np.percentile(d, 50, axis=3).shape, (11, 1, 2))
	assert_array_equal(np.percentile(d, 50, axis=-1).shape, (11, 1, 2))
	assert_array_equal(np.percentile(d, 50, axis=-2).shape, (11, 1, 1))
	assert_array_equal(np.percentile(d, 50, axis=-3).shape, (11, 2, 1))
	assert_array_equal(np.percentile(d, 50, axis=-4).shape, (1, 2, 1))

	assert_array_equal(np.percentile(d, 50, axis=2,
	interpolation='midpoint').shape,
	(11, 1, 1))
	assert_array_equal(np.percentile(d, 50, axis=-2,
	interpolation='midpoint').shape,
	(11, 1, 1))

	assert_array_equal(np.array(np.percentile(d, [10, 50], axis=0)).shape,
	(2, 1, 2, 1))
	assert_array_equal(np.array(np.percentile(d, [10, 50], axis=1)).shape,
	(2, 11, 2, 1))
	assert_array_equal(np.array(np.percentile(d, [10, 50], axis=2)).shape,
	(2, 11, 1, 1))
	assert_array_equal(np.array(np.percentile(d, [10, 50], axis=3)).shape,
	(2, 11, 1, 2))


	def test_percentile_no_overwrite(self):
	a = np.array([2, 3, 4, 1])
	np.percentile(a, [50], overwrite_input=False)
	assert_equal(a, np.array([2, 3, 4, 1]))

	a = np.array([2, 3, 4, 1])
	np.percentile(a, [50])
	assert_equal(a, np.array([2, 3, 4, 1]))

	def test_no_p_overwrite(self):
	p = np.linspace(0., 100., num=5)
	np.percentile(np.arange(100.), p, interpolation="midpoint")
	assert_array_equal(p, np.linspace(0., 100., num=5))
	p = np.linspace(0., 100., num=5).tolist()
	np.percentile(np.arange(100.), p, interpolation="midpoint")
	assert_array_equal(p, np.linspace(0., 100., num=5).tolist())

	def test_percentile_overwrite(self):
	a = np.array([2, 3, 4, 1])
	b = np.percentile(a, [50], overwrite_input=True)
	assert_equal(b, np.array([2.5]))

	b = np.percentile([2, 3, 4, 1], [50], overwrite_input=True)
	assert_equal(b, np.array([2.5]))

	def test_extended_axis(self):
	o = np.random.normal(size=(71, 23))
	x = np.dstack([o] * 10)
	assert_equal(np.percentile(x, 30, axis=(0, 1)), np.percentile(o, 30))
	x = np.rollaxis(x, -1, 0)
	assert_equal(np.percentile(x, 30, axis=(-2, -1)), np.percentile(o, 30))
	x = x.swapaxes(0, 1).copy()
	assert_equal(np.percentile(x, 30, axis=(0, -1)), np.percentile(o, 30))
	x = x.swapaxes(0, 1).copy()

	assert_equal(np.percentile(x, [25, 60], axis=(0, 1, 2)),
	np.percentile(x, [25, 60], axis=None))
	assert_equal(np.percentile(x, [25, 60], axis=(0,)),
	np.percentile(x, [25, 60], axis=0))

	d = np.arange(3 * 5 * 7 * 11).reshape(3, 5, 7, 11)
	np.random.shuffle(d)
	assert_equal(np.percentile(d, 25, axis=(0, 1, 2))[0],
	np.percentile(d[:, :, :, 0].flatten(), 25))
	assert_equal(np.percentile(d, [10, 90], axis=(0, 1, 3))[:, 1],
	np.percentile(d[:, :, 1, :].flatten(), [10, 90]))
	assert_equal(np.percentile(d, 25, axis=(3, 1, -4))[2],
	np.percentile(d[:, :, 2, :].flatten(), 25))
	assert_equal(np.percentile(d, 25, axis=(3, 1, 2))[2],
	np.percentile(d[2, :, :, :].flatten(), 25))
	assert_equal(np.percentile(d, 25, axis=(3, 2))[2, 1],
	np.percentile(d[2, 1, :, :].flatten(), 25))
	assert_equal(np.percentile(d, 25, axis=(1, -2))[2, 1],
	np.percentile(d[2, :, :, 1].flatten(), 25))
	assert_equal(np.percentile(d, 25, axis=(1, 3))[2, 2],
	np.percentile(d[2, :, 2, :].flatten(), 25))

	def test_extended_axis_invalid(self):
	d = np.ones((3, 5, 7, 11))
	assert_raises(IndexError, np.percentile, d, axis=-5, q=25)
	assert_raises(IndexError, np.percentile, d, axis=(0, -5), q=25)
	assert_raises(IndexError, np.percentile, d, axis=4, q=25)
	assert_raises(IndexError, np.percentile, d, axis=(0, 4), q=25)
	assert_raises(ValueError, np.percentile, d, axis=(1, 1), q=25)

	def test_keepdims(self):
	d = np.ones((3, 5, 7, 11))
	assert_equal(np.percentile(d, 7, axis=None, keepdims=True).shape,
	(1, 1, 1, 1))
	assert_equal(np.percentile(d, 7, axis=(0, 1), keepdims=True).shape,
	(1, 1, 7, 11))
	assert_equal(np.percentile(d, 7, axis=(0, 3), keepdims=True).shape,
	(1, 5, 7, 1))
	assert_equal(np.percentile(d, 7, axis=(1,), keepdims=True).shape,
	(3, 1, 7, 11))
	assert_equal(np.percentile(d, 7, (0, 1, 2, 3), keepdims=True).shape,
	(1, 1, 1, 1))
	assert_equal(np.percentile(d, 7, axis=(0, 1, 3), keepdims=True).shape,
	(1, 1, 7, 1))

	assert_equal(np.percentile(d, [1, 7], axis=(0, 1, 3),
	keepdims=True).shape, (2, 1, 1, 7, 1))
	assert_equal(np.percentile(d, [1, 7], axis=(0, 3),
	keepdims=True).shape, (2, 1, 5, 7, 1))


	class TestMedian(TestCase):
	def test_basic(self):
	a0 = np.array(1)
	a1 = np.arange(2)
	a2 = np.arange(6).reshape(2, 3)
	assert_equal(np.median(a0), 1)
	assert_allclose(np.median(a1), 0.5)
	assert_allclose(np.median(a2), 2.5)
	assert_allclose(np.median(a2, axis=0), [1.5, 2.5, 3.5])
	assert_equal(np.median(a2, axis=1), [1, 4])
	assert_allclose(np.median(a2, axis=None), 2.5)

	a = np.array([0.0444502, 0.0463301, 0.141249, 0.0606775])
	assert_almost_equal((a[1] + a[3]) / 2., np.median(a))
	a = np.array([0.0463301, 0.0444502, 0.141249])
	assert_equal(a[0], np.median(a))
	a = np.array([0.0444502, 0.141249, 0.0463301])
	assert_equal(a[-1], np.median(a))
	# check array scalar result
	assert_equal(np.median(a).ndim, 0)
	a[1] = np.nan
	assert_equal(np.median(a).ndim, 0)

	def test_axis_keyword(self):
	a3 = np.array([[2, 3],
	[0, 1],
	[6, 7],
	[4, 5]])
	for a in [a3, np.random.randint(0, 100, size=(2, 3, 4))]:
	orig = a.copy()
	np.median(a, axis=None)
	for ax in range(a.ndim):
	np.median(a, axis=ax)
	assert_array_equal(a, orig)

	assert_allclose(np.median(a3, axis=0), [3, 4])
	assert_allclose(np.median(a3.T, axis=1), [3, 4])
	assert_allclose(np.median(a3), 3.5)
	assert_allclose(np.median(a3, axis=None), 3.5)
	assert_allclose(np.median(a3.T), 3.5)

	def test_overwrite_keyword(self):
	a3 = np.array([[2, 3],
	[0, 1],
	[6, 7],
	[4, 5]])
	a0 = np.array(1)
	a1 = np.arange(2)
	a2 = np.arange(6).reshape(2, 3)
	assert_allclose(np.median(a0.copy(), overwrite_input=True), 1)
	assert_allclose(np.median(a1.copy(), overwrite_input=True), 0.5)
	assert_allclose(np.median(a2.copy(), overwrite_input=True), 2.5)
	assert_allclose(np.median(a2.copy(), overwrite_input=True, axis=0),
	[1.5, 2.5, 3.5])
	assert_allclose(
	np.median(a2.copy(), overwrite_input=True, axis=1), [1, 4])
	assert_allclose(
	np.median(a2.copy(), overwrite_input=True, axis=None), 2.5)
	assert_allclose(
	np.median(a3.copy(), overwrite_input=True, axis=0), [3, 4])
	assert_allclose(np.median(a3.T.copy(), overwrite_input=True, axis=1),
	[3, 4])

	a4 = np.arange(3 * 4 * 5, dtype=np.float32).reshape((3, 4, 5))
	map(np.random.shuffle, a4)
	assert_allclose(np.median(a4, axis=None),
	np.median(a4.copy(), axis=None, overwrite_input=True))
	assert_allclose(np.median(a4, axis=0),
	np.median(a4.copy(), axis=0, overwrite_input=True))
	assert_allclose(np.median(a4, axis=1),
	np.median(a4.copy(), axis=1, overwrite_input=True))
	assert_allclose(np.median(a4, axis=2),
	np.median(a4.copy(), axis=2, overwrite_input=True))

	def test_array_like(self):
	x = [1, 2, 3]
	assert_almost_equal(np.median(x), 2)
	x2 = [x]
	assert_almost_equal(np.median(x2), 2)
	assert_allclose(np.median(x2, axis=0), x)

	def test_subclass(self):
	# gh-3846
	class MySubClass(np.ndarray):
	def __new__(cls, input_array, info=None):
	obj = np.asarray(input_array).view(cls)
	obj.info = info
	return obj

	def mean(self, axis=None, dtype=None, out=None):
	return -7

	a = MySubClass([1,2,3])
	assert_equal(np.median(a), -7)

	def test_object(self):
	o = np.arange(7.);
	assert_(type(np.median(o.astype(object))), float)
	o[2] = np.nan
	assert_(type(np.median(o.astype(object))), float)

	def test_extended_axis(self):
	o = np.random.normal(size=(71, 23))
	x = np.dstack([o] * 10)
	assert_equal(np.median(x, axis=(0, 1)), np.median(o))
	x = np.rollaxis(x, -1, 0)
	assert_equal(np.median(x, axis=(-2, -1)), np.median(o))
	x = x.swapaxes(0, 1).copy()
	assert_equal(np.median(x, axis=(0, -1)), np.median(o))

	assert_equal(np.median(x, axis=(0, 1, 2)), np.median(x, axis=None))
	assert_equal(np.median(x, axis=(0, )), np.median(x, axis=0))
	assert_equal(np.median(x, axis=(-1, )), np.median(x, axis=-1))

	d = np.arange(3 * 5 * 7 * 11).reshape(3, 5, 7, 11)
	np.random.shuffle(d)
	assert_equal(np.median(d, axis=(0, 1, 2))[0],
	np.median(d[:, :, :, 0].flatten()))
	assert_equal(np.median(d, axis=(0, 1, 3))[1],
	np.median(d[:, :, 1, :].flatten()))
	assert_equal(np.median(d, axis=(3, 1, -4))[2],
	np.median(d[:, :, 2, :].flatten()))
	assert_equal(np.median(d, axis=(3, 1, 2))[2],
	np.median(d[2, :, :, :].flatten()))
	assert_equal(np.median(d, axis=(3, 2))[2, 1],
	np.median(d[2, 1, :, :].flatten()))
	assert_equal(np.median(d, axis=(1, -2))[2, 1],
	np.median(d[2, :, :, 1].flatten()))
	assert_equal(np.median(d, axis=(1, 3))[2, 2],
	np.median(d[2, :, 2, :].flatten()))

	def test_extended_axis_invalid(self):
	d = np.ones((3, 5, 7, 11))
	assert_raises(IndexError, np.median, d, axis=-5)
	assert_raises(IndexError, np.median, d, axis=(0, -5))
	assert_raises(IndexError, np.median, d, axis=4)
	assert_raises(IndexError, np.median, d, axis=(0, 4))
	assert_raises(ValueError, np.median, d, axis=(1, 1))

	def test_keepdims(self):
	d = np.ones((3, 5, 7, 11))
	assert_equal(np.median(d, axis=None, keepdims=True).shape,
	(1, 1, 1, 1))
	assert_equal(np.median(d, axis=(0, 1), keepdims=True).shape,
	(1, 1, 7, 11))
	assert_equal(np.median(d, axis=(0, 3), keepdims=True).shape,
	(1, 5, 7, 1))
	assert_equal(np.median(d, axis=(1,), keepdims=True).shape,
	(3, 1, 7, 11))
	assert_equal(np.median(d, axis=(0, 1, 2, 3), keepdims=True).shape,
	(1, 1, 1, 1))
	assert_equal(np.median(d, axis=(0, 1, 3), keepdims=True).shape,
	(1, 1, 7, 1))



	class TestAdd_newdoc_ufunc(TestCase):

	def test_ufunc_arg(self):
	assert_raises(TypeError, add_newdoc_ufunc, 2, "blah")
	assert_raises(ValueError, add_newdoc_ufunc, np.add, "blah")

	def test_string_arg(self):
	assert_raises(TypeError, add_newdoc_ufunc, np.add, 3)


	class TestAdd_newdoc(TestCase):
	def test_add_doc(self):
	# test np.add_newdoc
	tgt = "Current flat index into the array."
	self.assertEqual(np.core.flatiter.index.__doc__[:len(tgt)], tgt)
	self.assertTrue(len(np.core.ufunc.identity.__doc__) > 300)
	self.assertTrue(len(np.lib.index_tricks.mgrid.__doc__) > 300)


	if __name__ == "__main__":
	run_module_suite()