from __future__ import division, absolute_import, print_function

import operator
import warnings
import sys
import decimal
import types
from fractions import Fraction
import pytest

import numpy as np
from numpy import ma
from numpy.testing import (
    assert_, assert_equal, assert_array_equal, assert_almost_equal,
    assert_array_almost_equal, assert_raises, assert_allclose, IS_PYPY,
    assert_warns, assert_raises_regex, suppress_warnings, HAS_REFCOUNT,
    )
import numpy.lib.function_base as nfb
from numpy.random import rand
from numpy.lib import (
    add_newdoc_ufunc, angle, average, bartlett, blackman, corrcoef, cov,
    delete, diff, digitize, extract, flipud, gradient, hamming, hanning,
    i0, insert, interp, kaiser, meshgrid, msort, piecewise, place, rot90,
    select, setxor1d, sinc, trapz, trim_zeros, unwrap, unique, vectorize
    )

from numpy.compat import long

PY2 = sys.version_info[0] == 2

def get_mat(n):
    data = np.arange(n)
    data = np.add.outer(data, data)
    return data


def _make_complex(real, imag):
    """
    Like real + 1j * imag, but behaves as expected when imag contains non-finite
    values
    """
    ret = np.zeros(np.broadcast(real, imag).shape, np.complex_)
    ret.real = real
    ret.imag = imag
    return ret


class TestRot90(object):
    def test_basic(self):
        assert_raises(ValueError, rot90, np.ones(4))
        assert_raises(ValueError, rot90, np.ones((2,2,2)), axes=(0,1,2))
        assert_raises(ValueError, rot90, np.ones((2,2)), axes=(0,2))
        assert_raises(ValueError, rot90, np.ones((2,2)), axes=(1,1))
        assert_raises(ValueError, rot90, np.ones((2,2,2)), axes=(-2,1))

        a = [[0, 1, 2],
             [3, 4, 5]]
        b1 = [[2, 5],
              [1, 4],
              [0, 3]]
        b2 = [[5, 4, 3],
              [2, 1, 0]]
        b3 = [[3, 0],
              [4, 1],
              [5, 2]]
        b4 = [[0, 1, 2],
              [3, 4, 5]]

        for k in range(-3, 13, 4):
            assert_equal(rot90(a, k=k), b1)
        for k in range(-2, 13, 4):
            assert_equal(rot90(a, k=k), b2)
        for k in range(-1, 13, 4):
            assert_equal(rot90(a, k=k), b3)
        for k in range(0, 13, 4):
            assert_equal(rot90(a, k=k), b4)

        assert_equal(rot90(rot90(a, axes=(0,1)), axes=(1,0)), a)
        assert_equal(rot90(a, k=1, axes=(1,0)), rot90(a, k=-1, axes=(0,1)))

    def test_axes(self):
        a = np.ones((50, 40, 3))
        assert_equal(rot90(a).shape, (40, 50, 3))
        assert_equal(rot90(a, axes=(0,2)), rot90(a, axes=(0,-1)))
        assert_equal(rot90(a, axes=(1,2)), rot90(a, axes=(-2,-1)))

    def test_rotation_axes(self):
        a = np.arange(8).reshape((2,2,2))

        a_rot90_01 = [[[2, 3],
                       [6, 7]],
                      [[0, 1],
                       [4, 5]]]
        a_rot90_12 = [[[1, 3],
                       [0, 2]],
                      [[5, 7],
                       [4, 6]]]
        a_rot90_20 = [[[4, 0],
                       [6, 2]],
                      [[5, 1],
                       [7, 3]]]
        a_rot90_10 = [[[4, 5],
                       [0, 1]],
                      [[6, 7],
                       [2, 3]]]

        assert_equal(rot90(a, axes=(0, 1)), a_rot90_01)
        assert_equal(rot90(a, axes=(1, 0)), a_rot90_10)
        assert_equal(rot90(a, axes=(1, 2)), a_rot90_12)

        for k in range(1,5):
            assert_equal(rot90(a, k=k, axes=(2, 0)),
                         rot90(a_rot90_20, k=k-1, axes=(2, 0)))


class TestFlip(object):

    def test_axes(self):
        assert_raises(np.AxisError, np.flip, np.ones(4), axis=1)
        assert_raises(np.AxisError, np.flip, np.ones((4, 4)), axis=2)
        assert_raises(np.AxisError, np.flip, np.ones((4, 4)), axis=-3)
        assert_raises(np.AxisError, np.flip, np.ones((4, 4)), axis=(0, 3))

    def test_basic_lr(self):
        a = get_mat(4)
        b = a[:, ::-1]
        assert_equal(np.flip(a, 1), b)
        a = [[0, 1, 2],
             [3, 4, 5]]
        b = [[2, 1, 0],
             [5, 4, 3]]
        assert_equal(np.flip(a, 1), b)

    def test_basic_ud(self):
        a = get_mat(4)
        b = a[::-1, :]
        assert_equal(np.flip(a, 0), b)
        a = [[0, 1, 2],
             [3, 4, 5]]
        b = [[3, 4, 5],
             [0, 1, 2]]
        assert_equal(np.flip(a, 0), b)

    def test_3d_swap_axis0(self):
        a = np.array([[[0, 1],
                       [2, 3]],
                      [[4, 5],
                       [6, 7]]])

        b = np.array([[[4, 5],
                       [6, 7]],
                      [[0, 1],
                       [2, 3]]])

        assert_equal(np.flip(a, 0), b)

    def test_3d_swap_axis1(self):
        a = np.array([[[0, 1],
                       [2, 3]],
                      [[4, 5],
                       [6, 7]]])

        b = np.array([[[2, 3],
                       [0, 1]],
                      [[6, 7],
                       [4, 5]]])

        assert_equal(np.flip(a, 1), b)

    def test_3d_swap_axis2(self):
        a = np.array([[[0, 1],
                       [2, 3]],
                      [[4, 5],
                       [6, 7]]])

        b = np.array([[[1, 0],
                       [3, 2]],
                      [[5, 4],
                       [7, 6]]])

        assert_equal(np.flip(a, 2), b)

    def test_4d(self):
        a = np.arange(2 * 3 * 4 * 5).reshape(2, 3, 4, 5)
        for i in range(a.ndim):
            assert_equal(np.flip(a, i),
                         np.flipud(a.swapaxes(0, i)).swapaxes(i, 0))

    def test_default_axis(self):
        a = np.array([[1, 2, 3],
                      [4, 5, 6]])
        b = np.array([[6, 5, 4],
                      [3, 2, 1]])
        assert_equal(np.flip(a), b)

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

        assert_equal(np.flip(a, axis=()), a)

        b = np.array([[[5, 4],
                       [7, 6]],
                      [[1, 0],
                       [3, 2]]])

        assert_equal(np.flip(a, axis=(0, 2)), b)

        c = np.array([[[3, 2],
                       [1, 0]],
                      [[7, 6],
                       [5, 4]]])

        assert_equal(np.flip(a, axis=(1, 2)), c)


class TestAny(object):

    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(object):

    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(object):

    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:
        # github.com/scikit-learn/scikit-learn/commit/7842748cf777412c506a8c0ed28090711d3a3783  # noqa
        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(object):

    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))

    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.)

        y3 = rand(5).astype(np.float32)
        w3 = rand(5).astype(np.float64)

        assert_(np.average(y3, weights=w3).dtype == np.result_type(y3, w3))

    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.]))

    def test_subclasses(self):
        class subclass(np.ndarray):
            pass
        a = np.array([[1,2],[3,4]]).view(subclass)
        w = np.array([[1,2],[3,4]]).view(subclass)

        assert_equal(type(np.average(a)), subclass)
        assert_equal(type(np.average(a, weights=w)), subclass)

    def test_upcasting(self):
        typs = [('i4', 'i4', 'f8'), ('i4', 'f4', 'f8'), ('f4', 'i4', 'f8'),
                 ('f4', 'f4', 'f4'), ('f4', 'f8', 'f8')]
        for at, wt, rt in typs:
            a = np.array([[1,2],[3,4]], dtype=at)
            w = np.array([[1,2],[3,4]], dtype=wt)
            assert_equal(np.average(a, weights=w).dtype, np.dtype(rt))

    def test_object_dtype(self):
        a = np.array([decimal.Decimal(x) for x in range(10)])
        w = np.array([decimal.Decimal(1) for _ in range(10)])
        w /= w.sum()
        assert_almost_equal(a.mean(0), average(a, weights=w))

class TestSelect(object):
    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):
        assert_raises(ValueError, select, [], [], 3j)
        assert_raises(ValueError, select, [], [])

    def test_non_bool_deprecation(self):
        choices = self.choices
        conditions = self.conditions[:]
        conditions[0] = conditions[0].astype(np.int_)
        assert_raises(TypeError, select, conditions, choices)
        conditions[0] = conditions[0].astype(np.uint8)
        assert_raises(TypeError, select, conditions, choices)
        assert_raises(TypeError, 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(object):

    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(np.AxisError, insert, a, 1, a[:, 2, :], axis=3)
        assert_raises(np.AxisError, 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(object):

    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(object):

    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(object):

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

        assert_equal(b.ptp(axis=0, keepdims=True), [[5.0, 7.0, 7.0]])
        assert_equal(b.ptp(axis=(0,1), keepdims=True), [[8.0]])


class TestCumsum(object):

    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(object):

    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']:
                assert_raises(ArithmeticError, np.prod, a)
                assert_raises(ArithmeticError, np.prod, a2, 1)
            else:
                assert_equal(a.prod(axis=0), 26400)
                assert_array_equal(a2.prod(axis=0),
                                   np.array([50, 36, 84, 180], ctype))
                assert_array_equal(a2.prod(axis=-1),
                                   np.array([24, 1890, 600], ctype))


class TestCumprod(object):

    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']:
                assert_raises(ArithmeticError, np.cumprod, a)
                assert_raises(ArithmeticError, np.cumprod, a2, 1)
                assert_raises(ArithmeticError, np.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(object):

    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)

        x = [1.1, 2.2, 3.0, -0.2, -0.1]
        out = np.array([1.1, 0.8, -3.2, 0.1])
        assert_almost_equal(diff(x), out)

        x = [True, True, False, False]
        out = np.array([False, True, False])
        out2 = np.array([True, True])
        assert_array_equal(diff(x), out)
        assert_array_equal(diff(x, n=2), out2)

    def test_axis(self):
        x = np.zeros((10, 20, 30))
        x[:, 1::2, :] = 1
        exp = np.ones((10, 19, 30))
        exp[:, 1::2, :] = -1
        assert_array_equal(diff(x), np.zeros((10, 20, 29)))
        assert_array_equal(diff(x, axis=-1), np.zeros((10, 20, 29)))
        assert_array_equal(diff(x, axis=0), np.zeros((9, 20, 30)))
        assert_array_equal(diff(x, axis=1), exp)
        assert_array_equal(diff(x, axis=-2), exp)
        assert_raises(np.AxisError, diff, x, axis=3)
        assert_raises(np.AxisError, diff, x, axis=-4)

        x = np.array(1.11111111111, np.float64)
        assert_raises(ValueError, diff, x)

    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)

    def test_n(self):
        x = list(range(3))
        assert_raises(ValueError, diff, x, n=-1)
        output = [diff(x, n=n) for n in range(1, 5)]
        expected = [[1, 1], [0], [], []]
        assert_(diff(x, n=0) is x)
        for n, (expected, out) in enumerate(zip(expected, output), start=1):
            assert_(type(out) is np.ndarray)
            assert_array_equal(out, expected)
            assert_equal(out.dtype, np.int_)
            assert_equal(len(out), max(0, len(x) - n))

    def test_times(self):
        x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64)
        expected = [
            np.array([1, 1], dtype='timedelta64[D]'),
            np.array([0], dtype='timedelta64[D]'),
        ]
        expected.extend([np.array([], dtype='timedelta64[D]')] * 3)
        for n, exp in enumerate(expected, start=1):
            out = diff(x, n=n)
            assert_array_equal(out, exp)
            assert_equal(out.dtype, exp.dtype)

    def test_subclass(self):
        x = ma.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]],
                     mask=[[False, False], [True, False],
                           [False, True], [True, True], [False, False]])
        out = diff(x)
        assert_array_equal(out.data, [[1], [1], [1], [1], [1]])
        assert_array_equal(out.mask, [[False], [True],
                                      [True], [True], [False]])
        assert_(type(out) is type(x))

        out3 = diff(x, n=3)
        assert_array_equal(out3.data, [[], [], [], [], []])
        assert_array_equal(out3.mask, [[], [], [], [], []])
        assert_(type(out3) is type(x))

    def test_prepend(self):
        x = np.arange(5) + 1
        assert_array_equal(diff(x, prepend=0), np.ones(5))
        assert_array_equal(diff(x, prepend=[0]), np.ones(5))
        assert_array_equal(np.cumsum(np.diff(x, prepend=0)), x)
        assert_array_equal(diff(x, prepend=[-1, 0]), np.ones(6))

        x = np.arange(4).reshape(2, 2)
        result = np.diff(x, axis=1, prepend=0)
        expected = [[0, 1], [2, 1]]
        assert_array_equal(result, expected)
        result = np.diff(x, axis=1, prepend=[[0], [0]])
        assert_array_equal(result, expected)

        result = np.diff(x, axis=0, prepend=0)
        expected = [[0, 1], [2, 2]]
        assert_array_equal(result, expected)
        result = np.diff(x, axis=0, prepend=[[0, 0]])
        assert_array_equal(result, expected)

        assert_raises(ValueError, np.diff, x, prepend=np.zeros((3,3)))

        assert_raises(np.AxisError, diff, x, prepend=0, axis=3)

    def test_append(self):
        x = np.arange(5)
        result = diff(x, append=0)
        expected = [1, 1, 1, 1, -4]
        assert_array_equal(result, expected)
        result = diff(x, append=[0])
        assert_array_equal(result, expected)
        result = diff(x, append=[0, 2])
        expected = expected + [2]
        assert_array_equal(result, expected)

        x = np.arange(4).reshape(2, 2)
        result = np.diff(x, axis=1, append=0)
        expected = [[1, -1], [1, -3]]
        assert_array_equal(result, expected)
        result = np.diff(x, axis=1, append=[[0], [0]])
        assert_array_equal(result, expected)

        result = np.diff(x, axis=0, append=0)
        expected = [[2, 2], [-2, -3]]
        assert_array_equal(result, expected)
        result = np.diff(x, axis=0, append=[[0, 0]])
        assert_array_equal(result, expected)

        assert_raises(ValueError, np.diff, x, append=np.zeros((3,3)))

        assert_raises(np.AxisError, diff, x, append=0, axis=3)


class TestDelete(object):

    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))

    def test_array_order_preserve(self):
        # See gh-7113
        k = np.arange(10).reshape(2, 5, order='F')
        m = delete(k, slice(60, None), axis=1)

        # 'k' is Fortran ordered, and 'm' should have the
        # same ordering as 'k' and NOT become C ordered
        assert_equal(m.flags.c_contiguous, k.flags.c_contiguous)
        assert_equal(m.flags.f_contiguous, k.flags.f_contiguous)


class TestGradient(object):

    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_args(self):
        dx = np.cumsum(np.ones(5))
        dx_uneven = [1., 2., 5., 9., 11.]
        f_2d = np.arange(25).reshape(5, 5)

        # distances must be scalars or have size equal to gradient[axis]
        gradient(np.arange(5), 3.)
        gradient(np.arange(5), np.array(3.))
        gradient(np.arange(5), dx)
        # dy is set equal to dx because scalar
        gradient(f_2d, 1.5)
        gradient(f_2d, np.array(1.5))

        gradient(f_2d, dx_uneven, dx_uneven)
        # mix between even and uneven spaces and
        # mix between scalar and vector
        gradient(f_2d, dx, 2)

        # 2D but axis specified
        gradient(f_2d, dx, axis=1)

        # 2d coordinate arguments are not yet allowed
        assert_raises_regex(ValueError, '.*scalars or 1d',
            gradient, f_2d, np.stack([dx]*2, axis=-1), 1)

    def test_badargs(self):
        f_2d = np.arange(25).reshape(5, 5)
        x = np.cumsum(np.ones(5))

        # wrong sizes
        assert_raises(ValueError, gradient, f_2d, x, np.ones(2))
        assert_raises(ValueError, gradient, f_2d, 1, np.ones(2))
        assert_raises(ValueError, gradient, f_2d, np.ones(2), np.ones(2))
        # wrong number of arguments
        assert_raises(TypeError, gradient, f_2d, x)
        assert_raises(TypeError, gradient, f_2d, x, axis=(0,1))
        assert_raises(TypeError, gradient, f_2d, x, x, x)
        assert_raises(TypeError, gradient, f_2d, 1, 1, 1)
        assert_raises(TypeError, gradient, f_2d, x, x, axis=1)
        assert_raises(TypeError, gradient, f_2d, 1, 1, axis=1)

    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(
            [-5, -3, 0, 31, 61, 396, 731],
            dtype='timedelta64[D]')
        assert_array_equal(gradient(x), dx)
        assert_(dx.dtype == np.dtype('timedelta64[D]'))

    def test_masked(self):
        # Make sure that gradient supports subclasses like masked arrays
        x = np.ma.array([[1, 1], [3, 4]],
                        mask=[[False, False], [False, False]])
        out = gradient(x)[0]
        assert_equal(type(out), type(x))
        # And make sure that the output and input don't have aliased mask
        # arrays
        assert_(x._mask is not out._mask)
        # Also check that edge_order=2 doesn't alter the original mask
        x2 = np.ma.arange(5)
        x2[2] = np.ma.masked
        np.gradient(x2, edge_order=2)
        assert_array_equal(x2.mask, [False, False, True, False, False])

    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, edge_order=2) / analytical) - 1)
        assert_(np.all(num_error < 0.03) == True)

        # test with unevenly spaced
        np.random.seed(0)
        x = np.sort(np.random.random(10))
        y = 2 * x ** 3 + 4 * x ** 2 + 2 * x
        analytical = 6 * x ** 2 + 8 * x + 2
        num_error = np.abs((np.gradient(y, x, edge_order=2) / analytical) - 1)
        assert_(np.all(num_error < 0.03) == True)

    def test_spacing(self):
        f = np.array([0, 2., 3., 4., 5., 5.])
        f = np.tile(f, (6,1)) + f.reshape(-1, 1)
        x_uneven = np.array([0., 0.5, 1., 3., 5., 7.])
        x_even = np.arange(6.)

        fdx_even_ord1 = np.tile([2., 1.5, 1., 1., 0.5, 0.], (6,1))
        fdx_even_ord2 = np.tile([2.5, 1.5, 1., 1., 0.5, -0.5], (6,1))
        fdx_uneven_ord1 = np.tile([4., 3., 1.7, 0.5, 0.25, 0.], (6,1))
        fdx_uneven_ord2 = np.tile([5., 3., 1.7, 0.5, 0.25, -0.25], (6,1))

        # evenly spaced
        for edge_order, exp_res in [(1, fdx_even_ord1), (2, fdx_even_ord2)]:
            res1 = gradient(f, 1., axis=(0,1), edge_order=edge_order)
            res2 = gradient(f, x_even, x_even,
                            axis=(0,1), edge_order=edge_order)
            res3 = gradient(f, x_even, x_even,
                            axis=None, edge_order=edge_order)
            assert_array_equal(res1, res2)
            assert_array_equal(res2, res3)
            assert_almost_equal(res1[0], exp_res.T)
            assert_almost_equal(res1[1], exp_res)

            res1 = gradient(f, 1., axis=0, edge_order=edge_order)
            res2 = gradient(f, x_even, axis=0, edge_order=edge_order)
            assert_(res1.shape == res2.shape)
            assert_almost_equal(res2, exp_res.T)

            res1 = gradient(f, 1., axis=1, edge_order=edge_order)
            res2 = gradient(f, x_even, axis=1, edge_order=edge_order)
            assert_(res1.shape == res2.shape)
            assert_array_equal(res2, exp_res)

        # unevenly spaced
        for edge_order, exp_res in [(1, fdx_uneven_ord1), (2, fdx_uneven_ord2)]:
            res1 = gradient(f, x_uneven, x_uneven,
                            axis=(0,1), edge_order=edge_order)
            res2 = gradient(f, x_uneven, x_uneven,
                            axis=None, edge_order=edge_order)
            assert_array_equal(res1, res2)
            assert_almost_equal(res1[0], exp_res.T)
            assert_almost_equal(res1[1], exp_res)

            res1 = gradient(f, x_uneven, axis=0, edge_order=edge_order)
            assert_almost_equal(res1, exp_res.T)

            res1 = gradient(f, x_uneven, axis=1, edge_order=edge_order)
            assert_almost_equal(res1, exp_res)

        # mixed
        res1 = gradient(f, x_even, x_uneven, axis=(0,1), edge_order=1)
        res2 = gradient(f, x_uneven, x_even, axis=(1,0), edge_order=1)
        assert_array_equal(res1[0], res2[1])
        assert_array_equal(res1[1], res2[0])
        assert_almost_equal(res1[0], fdx_even_ord1.T)
        assert_almost_equal(res1[1], fdx_uneven_ord1)

        res1 = gradient(f, x_even, x_uneven, axis=(0,1), edge_order=2)
        res2 = gradient(f, x_uneven, x_even, axis=(1,0), edge_order=2)
        assert_array_equal(res1[0], res2[1])
        assert_array_equal(res1[1], res2[0])
        assert_almost_equal(res1[0], fdx_even_ord2.T)
        assert_almost_equal(res1[1], fdx_uneven_ord2)

    def test_specific_axes(self):
        # Testing that gradient can work on a given axis only
        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, axis=0), dx[0])
        assert_array_equal(gradient(x, axis=1), dx[1])
        assert_array_equal(gradient(x, axis=-1), dx[1])
        assert_array_equal(gradient(x, axis=(1, 0)), [dx[1], dx[0]])

        # test axis=None which means all axes
        assert_almost_equal(gradient(x, axis=None), [dx[0], dx[1]])
        # and is the same as no axis keyword given
        assert_almost_equal(gradient(x, axis=None), gradient(x))

        # test vararg order
        assert_array_equal(gradient(x, 2, 3, axis=(1, 0)),
                           [dx[1]/2.0, dx[0]/3.0])
        # test maximal number of varargs
        assert_raises(TypeError, gradient, x, 1, 2, axis=1)

        assert_raises(np.AxisError, gradient, x, axis=3)
        assert_raises(np.AxisError, gradient, x, axis=-3)
        # assert_raises(TypeError, gradient, x, axis=[1,])

    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(
            [2, 7, 7, 25, 154, 119, -21],
            dtype='timedelta64[D]')
        assert_array_equal(gradient(x), dx)
        assert_(dx.dtype == np.dtype('timedelta64[D]'))

    def test_inexact_dtypes(self):
        for dt in [np.float16, np.float32, np.float64]:
            # dtypes should not be promoted in a different way to what diff does
            x = np.array([1, 2, 3], dtype=dt)
            assert_equal(gradient(x).dtype, np.diff(x).dtype)

    def test_values(self):
        # needs at least 2 points for edge_order ==1
        gradient(np.arange(2), edge_order=1)
        # needs at least 3 points for edge_order ==1
        gradient(np.arange(3), edge_order=2)

        assert_raises(ValueError, gradient, np.arange(0), edge_order=1)
        assert_raises(ValueError, gradient, np.arange(0), edge_order=2)
        assert_raises(ValueError, gradient, np.arange(1), edge_order=1)
        assert_raises(ValueError, gradient, np.arange(1), edge_order=2)
        assert_raises(ValueError, gradient, np.arange(2), edge_order=2)

    @pytest.mark.parametrize('f_dtype', [np.uint8, np.uint16,
                                         np.uint32, np.uint64])
    def test_f_decreasing_unsigned_int(self, f_dtype):
        f = np.array([5, 4, 3, 2, 1], dtype=f_dtype)
        g = gradient(f)
        assert_array_equal(g, [-1]*len(f))

    @pytest.mark.parametrize('f_dtype', [np.int8, np.int16,
                                         np.int32, np.int64])
    def test_f_signed_int_big_jump(self, f_dtype):
        maxint = np.iinfo(f_dtype).max
        x = np.array([1, 3])
        f = np.array([-1, maxint], dtype=f_dtype)
        dfdx = gradient(f, x)
        assert_array_equal(dfdx, [(maxint + 1) // 2]*2)

    @pytest.mark.parametrize('x_dtype', [np.uint8, np.uint16,
                                         np.uint32, np.uint64])
    def test_x_decreasing_unsigned(self, x_dtype):
        x = np.array([3, 2, 1], dtype=x_dtype)
        f = np.array([0, 2, 4])
        dfdx = gradient(f, x)
        assert_array_equal(dfdx, [-2]*len(x))

    @pytest.mark.parametrize('x_dtype', [np.int8, np.int16,
                                         np.int32, np.int64])
    def test_x_signed_int_big_jump(self, x_dtype):
        minint = np.iinfo(x_dtype).min
        maxint = np.iinfo(x_dtype).max
        x = np.array([-1, maxint], dtype=x_dtype)
        f = np.array([minint // 2, 0])
        dfdx = gradient(f, x)
        assert_array_equal(dfdx, [0.5, 0.5])


class TestAngle(object):

    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=True)
        zo = np.array(yo) * 180 / np.pi
        assert_array_almost_equal(y, yo, 11)
        assert_array_almost_equal(z, zo, 11)

    def test_subclass(self):
        x = np.ma.array([1 + 3j, 1, np.sqrt(2)/2 * (1 + 1j)])
        x[1] = np.ma.masked
        expected = np.ma.array([np.arctan(3.0 / 1.0), 0, np.arctan(1.0)])
        expected[1] = np.ma.masked
        actual = angle(x)
        assert_equal(type(actual), type(expected))
        assert_equal(actual.mask, expected.mask)
        assert_equal(actual, expected)


class TestTrimZeros(object):

    """
    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(object):

    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):
        # Make sure that non-np.ndarray objects
        # raise an error instead of doing nothing
        assert_raises(TypeError, place, [1, 2, 3], [True, False], [0, 1])

        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])

        place(a, np.zeros(7), [])
        assert_array_equal(a, np.arange(1, 8))

        place(a, [1, 0, 1, 0, 1, 0, 1], [8, 9])
        assert_array_equal(a, [8, 2, 9, 4, 8, 6, 9])
        assert_raises_regex(ValueError, "Cannot insert from an empty array",
                            lambda: place(a, [0, 0, 0, 0, 0, 1, 0], []))

        # See Issue #6974
        a = np.array(['12', '34'])
        place(a, [0, 1], '9')
        assert_array_equal(a, ['12', '9'])

    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(object):

    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, 0.5 * np.pi, np.pi, 1.5 * np.pi, 2 * np.pi])
        r1 = f(args)
        r2 = np.cos(args)
        assert_array_almost_equal(r1, r2)

    def test_keywords(self):

        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:
            vectorize(random.randrange)  # Should succeed
        except Exception:
            raise AssertionError()

    def test_keywords2_ticket_2100(self):
        # Test kwarg support: enhancement ticket 2100

        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):
            """Original documentation"""
            return x

        f = vectorize(foo)
        assert_equal(f.__doc__, foo.__doc__)

        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))

    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)

    def test_parse_gufunc_signature(self):
        assert_equal(nfb._parse_gufunc_signature('(x)->()'), ([('x',)], [()]))
        assert_equal(nfb._parse_gufunc_signature('(x,y)->()'),
                     ([('x', 'y')], [()]))
        assert_equal(nfb._parse_gufunc_signature('(x),(y)->()'),
                     ([('x',), ('y',)], [()]))
        assert_equal(nfb._parse_gufunc_signature('(x)->(y)'),
                     ([('x',)], [('y',)]))
        assert_equal(nfb._parse_gufunc_signature('(x)->(y),()'),
                     ([('x',)], [('y',), ()]))
        assert_equal(nfb._parse_gufunc_signature('(),(a,b,c),(d)->(d,e)'),
                     ([(), ('a', 'b', 'c'), ('d',)], [('d', 'e')]))
        with assert_raises(ValueError):
            nfb._parse_gufunc_signature('(x)(y)->()')
        with assert_raises(ValueError):
            nfb._parse_gufunc_signature('(x),(y)->')
        with assert_raises(ValueError):
            nfb._parse_gufunc_signature('((x))->(x)')

    def test_signature_simple(self):
        def addsubtract(a, b):
            if a > b:
                return a - b
            else:
                return a + b

        f = vectorize(addsubtract, signature='(),()->()')
        r = f([0, 3, 6, 9], [1, 3, 5, 7])
        assert_array_equal(r, [1, 6, 1, 2])

    def test_signature_mean_last(self):
        def mean(a):
            return a.mean()

        f = vectorize(mean, signature='(n)->()')
        r = f([[1, 3], [2, 4]])
        assert_array_equal(r, [2, 3])

    def test_signature_center(self):
        def center(a):
            return a - a.mean()

        f = vectorize(center, signature='(n)->(n)')
        r = f([[1, 3], [2, 4]])
        assert_array_equal(r, [[-1, 1], [-1, 1]])

    def test_signature_two_outputs(self):
        f = vectorize(lambda x: (x, x), signature='()->(),()')
        r = f([1, 2, 3])
        assert_(isinstance(r, tuple) and len(r) == 2)
        assert_array_equal(r[0], [1, 2, 3])
        assert_array_equal(r[1], [1, 2, 3])

    def test_signature_outer(self):
        f = vectorize(np.outer, signature='(a),(b)->(a,b)')
        r = f([1, 2], [1, 2, 3])
        assert_array_equal(r, [[1, 2, 3], [2, 4, 6]])

        r = f([[[1, 2]]], [1, 2, 3])
        assert_array_equal(r, [[[[1, 2, 3], [2, 4, 6]]]])

        r = f([[1, 0], [2, 0]], [1, 2, 3])
        assert_array_equal(r, [[[1, 2, 3], [0, 0, 0]],
                               [[2, 4, 6], [0, 0, 0]]])

        r = f([1, 2], [[1, 2, 3], [0, 0, 0]])
        assert_array_equal(r, [[[1, 2, 3], [2, 4, 6]],
                               [[0, 0, 0], [0, 0, 0]]])

    def test_signature_computed_size(self):
        f = vectorize(lambda x: x[:-1], signature='(n)->(m)')
        r = f([1, 2, 3])
        assert_array_equal(r, [1, 2])

        r = f([[1, 2, 3], [2, 3, 4]])
        assert_array_equal(r, [[1, 2], [2, 3]])

    def test_signature_excluded(self):

        def foo(a, b=1):
            return a + b

        f = vectorize(foo, signature='()->()', excluded={'b'})
        assert_array_equal(f([1, 2, 3]), [2, 3, 4])
        assert_array_equal(f([1, 2, 3], b=0), [1, 2, 3])

    def test_signature_otypes(self):
        f = vectorize(lambda x: x, signature='(n)->(n)', otypes=['float64'])
        r = f([1, 2, 3])
        assert_equal(r.dtype, np.dtype('float64'))
        assert_array_equal(r, [1, 2, 3])

    def test_signature_invalid_inputs(self):
        f = vectorize(operator.add, signature='(n),(n)->(n)')
        with assert_raises_regex(TypeError, 'wrong number of positional'):
            f([1, 2])
        with assert_raises_regex(
                ValueError, 'does not have enough dimensions'):
            f(1, 2)
        with assert_raises_regex(
                ValueError, 'inconsistent size for core dimension'):
            f([1, 2], [1, 2, 3])

        f = vectorize(operator.add, signature='()->()')
        with assert_raises_regex(TypeError, 'wrong number of positional'):
            f(1, 2)

    def test_signature_invalid_outputs(self):

        f = vectorize(lambda x: x[:-1], signature='(n)->(n)')
        with assert_raises_regex(
                ValueError, 'inconsistent size for core dimension'):
            f([1, 2, 3])

        f = vectorize(lambda x: x, signature='()->(),()')
        with assert_raises_regex(ValueError, 'wrong number of outputs'):
            f(1)

        f = vectorize(lambda x: (x, x), signature='()->()')
        with assert_raises_regex(ValueError, 'wrong number of outputs'):
            f([1, 2])

    def test_size_zero_output(self):
        # see issue 5868
        f = np.vectorize(lambda x: x)
        x = np.zeros([0, 5], dtype=int)
        with assert_raises_regex(ValueError, 'otypes'):
            f(x)

        f.otypes = 'i'
        assert_array_equal(f(x), x)

        f = np.vectorize(lambda x: x, signature='()->()')
        with assert_raises_regex(ValueError, 'otypes'):
            f(x)

        f = np.vectorize(lambda x: x, signature='()->()', otypes='i')
        assert_array_equal(f(x), x)

        f = np.vectorize(lambda x: x, signature='(n)->(n)', otypes='i')
        assert_array_equal(f(x), x)

        f = np.vectorize(lambda x: x, signature='(n)->(n)')
        assert_array_equal(f(x.T), x.T)

        f = np.vectorize(lambda x: [x], signature='()->(n)', otypes='i')
        with assert_raises_regex(ValueError, 'new output dimensions'):
            f(x)


class TestLeaks(object):
    class A(object):
        iters = 20

        def bound(self, *args):
            return 0

        @staticmethod
        def unbound(*args):
            return 0

    @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
    @pytest.mark.parametrize('name, incr', [
            ('bound', A.iters),
            ('unbound', 0),
            ])
    def test_frompyfunc_leaks(self, name, incr):
        # exposed in gh-11867 as np.vectorized, but the problem stems from
        # frompyfunc.
        # class.attribute = np.frompyfunc(<method>) creates a
        # reference cycle if <method> is a bound class method. It requires a
        # gc collection cycle to break the cycle (on CPython 3)
        import gc
        A_func = getattr(self.A, name)
        gc.disable()
        try:
            refcount = sys.getrefcount(A_func)
            for i in range(self.A.iters):
                a = self.A()
                a.f = np.frompyfunc(getattr(a, name), 1, 1)
                out = a.f(np.arange(10))
            a = None
            if PY2:
                assert_equal(sys.getrefcount(A_func), refcount)
            else:
                # A.func is part of a reference cycle if incr is non-zero
                assert_equal(sys.getrefcount(A_func), refcount + incr)
            for i in range(5):
                gc.collect()
            assert_equal(sys.getrefcount(A_func), refcount)
        finally:
            gc.enable()

class TestDigitize(object):

    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)

    def test_casting_error(self):
        x = [1, 2, 3 + 1.j]
        bins = [1, 2, 3]
        assert_raises(TypeError, digitize, x, bins)
        x, bins = bins, x
        assert_raises(TypeError, digitize, x, bins)

    def test_return_type(self):
        # Functions returning indices should always return base ndarrays
        class A(np.ndarray):
            pass
        a = np.arange(5).view(A)
        b = np.arange(1, 3).view(A)
        assert_(not isinstance(digitize(b, a, False), A))
        assert_(not isinstance(digitize(b, a, True), A))

    def test_large_integers_increasing(self):
        # gh-11022
        x = 2**54  # loses precision in a float
        assert_equal(np.digitize(x, [x - 1, x + 1]), 1)

    @pytest.mark.xfail(
        reason="gh-11022: np.core.multiarray._monoticity loses precision")
    def test_large_integers_decreasing(self):
        # gh-11022
        x = 2**54  # loses precision in a float
        assert_equal(np.digitize(x, [x + 1, x - 1]), 1)


class TestUnwrap(object):

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


class TestFilterwindows(object):

    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(object):

    def test_simple(self):
        x = np.arange(-10, 10, .1)
        r = trapz(np.exp(-.5 * x ** 2) / np.sqrt(2 * np.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)


class TestSinc(object):

    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 TestUnique(object):

    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(object):

    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(object):
    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):
        tgt1 = corrcoef(self.A)
        assert_almost_equal(tgt1, self.res1)
        assert_(np.all(np.abs(tgt1) <= 1.0))

        tgt2 = corrcoef(self.A, self.B)
        assert_almost_equal(tgt2, self.res2)
        assert_(np.all(np.abs(tgt2) <= 1.0))

    def test_ddof(self):
        # ddof raises DeprecationWarning
        with suppress_warnings() as sup:
            warnings.simplefilter("always")
            assert_warns(DeprecationWarning, corrcoef, self.A, ddof=-1)
            sup.filter(DeprecationWarning)
            # ddof has no or negligible effect on the function
            assert_almost_equal(corrcoef(self.A, ddof=-1), self.res1)
            assert_almost_equal(corrcoef(self.A, self.B, ddof=-1), self.res2)
            assert_almost_equal(corrcoef(self.A, ddof=3), self.res1)
            assert_almost_equal(corrcoef(self.A, self.B, ddof=3), self.res2)

    def test_bias(self):
        # bias raises DeprecationWarning
        with suppress_warnings() as sup:
            warnings.simplefilter("always")
            assert_warns(DeprecationWarning, corrcoef, self.A, self.B, 1, 0)
            assert_warns(DeprecationWarning, corrcoef, self.A, bias=0)
            sup.filter(DeprecationWarning)
            # bias has no or negligible effect on the function
            assert_almost_equal(corrcoef(self.A, bias=1), self.res1)

    def test_complex(self):
        x = np.array([[1, 2, 3], [1j, 2j, 3j]])
        res = corrcoef(x)
        tgt = np.array([[1., -1.j], [1.j, 1.]])
        assert_allclose(res, tgt)
        assert_(np.all(np.abs(res) <= 1.0))

    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_extreme(self):
        x = [[1e-100, 1e100], [1e100, 1e-100]]
        with np.errstate(all='raise'):
            c = corrcoef(x)
        assert_array_almost_equal(c, np.array([[1., -1.], [-1., 1.]]))
        assert_(np.all(np.abs(c) <= 1.0))


class TestCov(object):
    x1 = np.array([[0, 2], [1, 1], [2, 0]]).T
    res1 = np.array([[1., -1.], [-1., 1.]])
    x2 = np.array([0.0, 1.0, 2.0], ndmin=2)
    frequencies = np.array([1, 4, 1])
    x2_repeats = np.array([[0.0], [1.0], [1.0], [1.0], [1.0], [2.0]]).T
    res2 = np.array([[0.4, -0.4], [-0.4, 0.4]])
    unit_frequencies = np.ones(3, dtype=np.integer)
    weights = np.array([1.0, 4.0, 1.0])
    res3 = np.array([[2. / 3., -2. / 3.], [-2. / 3., 2. / 3.]])
    unit_weights = np.ones(3)
    x3 = np.array([0.3942, 0.5969, 0.7730, 0.9918, 0.7964])

    def test_basic(self):
        assert_allclose(cov(self.x1), self.res1)

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

    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):
        with warnings.catch_warnings(record=True):
            warnings.simplefilter('always', RuntimeWarning)
            assert_array_equal(cov(self.x1, ddof=5),
                               np.array([[np.inf, -np.inf],
                                         [-np.inf, np.inf]]))

    def test_1D_rowvar(self):
        assert_allclose(cov(self.x3), cov(self.x3, rowvar=False))
        y = np.array([0.0780, 0.3107, 0.2111, 0.0334, 0.8501])
        assert_allclose(cov(self.x3, y), cov(self.x3, y, rowvar=False))

    def test_1D_variance(self):
        assert_allclose(cov(self.x3, ddof=1), np.var(self.x3, ddof=1))

    def test_fweights(self):
        assert_allclose(cov(self.x2, fweights=self.frequencies),
                        cov(self.x2_repeats))
        assert_allclose(cov(self.x1, fweights=self.frequencies),
                        self.res2)
        assert_allclose(cov(self.x1, fweights=self.unit_frequencies),
                        self.res1)
        nonint = self.frequencies + 0.5
        assert_raises(TypeError, cov, self.x1, fweights=nonint)
        f = np.ones((2, 3), dtype=np.integer)
        assert_raises(RuntimeError, cov, self.x1, fweights=f)
        f = np.ones(2, dtype=np.integer)
        assert_raises(RuntimeError, cov, self.x1, fweights=f)
        f = -1 * np.ones(3, dtype=np.integer)
        assert_raises(ValueError, cov, self.x1, fweights=f)

    def test_aweights(self):
        assert_allclose(cov(self.x1, aweights=self.weights), self.res3)
        assert_allclose(cov(self.x1, aweights=3.0 * self.weights),
                        cov(self.x1, aweights=self.weights))
        assert_allclose(cov(self.x1, aweights=self.unit_weights), self.res1)
        w = np.ones((2, 3))
        assert_raises(RuntimeError, cov, self.x1, aweights=w)
        w = np.ones(2)
        assert_raises(RuntimeError, cov, self.x1, aweights=w)
        w = -1.0 * np.ones(3)
        assert_raises(ValueError, cov, self.x1, aweights=w)

    def test_unit_fweights_and_aweights(self):
        assert_allclose(cov(self.x2, fweights=self.frequencies,
                            aweights=self.unit_weights),
                        cov(self.x2_repeats))
        assert_allclose(cov(self.x1, fweights=self.frequencies,
                            aweights=self.unit_weights),
                        self.res2)
        assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
                            aweights=self.unit_weights),
                        self.res1)
        assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
                            aweights=self.weights),
                        self.res3)
        assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
                            aweights=3.0 * self.weights),
                        cov(self.x1, aweights=self.weights))
        assert_allclose(cov(self.x1, fweights=self.unit_frequencies,
                            aweights=self.unit_weights),
                        self.res1)


class Test_I0(object):

    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])
        expected = np.array([1.06307822, 1.12518299, 1.01214991, 1.00006049])
        assert_almost_equal(i0(A), expected)
        assert_almost_equal(i0(-A), expected)

        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]]))
        # Regression test for gh-11205
        i0_0 = np.i0([0.])
        assert_equal(i0_0.shape, (1,))
        assert_array_equal(np.i0([0.]), np.array([1.]))

    def test_non_array(self):
        a = np.arange(4)

        class array_like:
            __array_interface__ = a.__array_interface__

            def __array_wrap__(self, arr):
                return self

        # E.g. pandas series survive ufunc calls through array-wrap:
        assert isinstance(np.abs(array_like()), array_like)
        exp = np.i0(a)
        res = np.i0(array_like())

        assert_array_equal(exp, res)


class TestKaiser(object):

    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(object):

    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(object):

    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))
        assert_array_equal([], meshgrid(*args, copy=False))

    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')

    def test_return_type(self):
        # Test for appropriate dtype in returned arrays.
        # Regression test for issue #5297
        # https://github.com/numpy/numpy/issues/5297
        x = np.arange(0, 10, dtype=np.float32)
        y = np.arange(10, 20, dtype=np.float64)

        X, Y = np.meshgrid(x,y)

        assert_(X.dtype == x.dtype)
        assert_(Y.dtype == y.dtype)

        # copy
        X, Y = np.meshgrid(x,y, copy=True)

        assert_(X.dtype == x.dtype)
        assert_(Y.dtype == y.dtype)

        # sparse
        X, Y = np.meshgrid(x,y, sparse=True)

        assert_(X.dtype == x.dtype)
        assert_(Y.dtype == y.dtype)

    def test_writeback(self):
        # Issue 8561
        X = np.array([1.1, 2.2])
        Y = np.array([3.3, 4.4])
        x, y = np.meshgrid(X, Y, sparse=False, copy=True)

        x[0, :] = 0
        assert_equal(x[0, :], 0)
        assert_equal(x[1, :], X)


class TestPiecewise(object):

    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])

        assert_raises_regex(ValueError, '1 or 2 functions are expected',
            piecewise, [0, 0], [[False, True]], [])
        assert_raises_regex(ValueError, '1 or 2 functions are expected',
            piecewise, [0, 0], [[False, True]], [1, 2, 3])

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

    def test_scalar_domains_three_conditions(self):
        x = piecewise(3, [True, False, False], [4, 2, 0])
        assert_equal(x, 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)

        # With 3 ranges (It was failing, before)
        y = piecewise(x, [False, False, True], [1, 2, 3])
        assert_array_equal(y, 3)

    def test_0d_comparison(self):
        x = 3
        y = piecewise(x, [x <= 3, x > 3], [4, 0])  # Should succeed.
        assert_equal(y, 4)

        # With 3 ranges (It was failing, before)
        x = 4
        y = piecewise(x, [x <= 3, (x > 3) * (x <= 5), x > 5], [1, 2, 3])
        assert_array_equal(y, 2)

        assert_raises_regex(ValueError, '2 or 3 functions are expected',
            piecewise, x, [x <= 3, x > 3], [1])
        assert_raises_regex(ValueError, '2 or 3 functions are expected',
            piecewise, x, [x <= 3, x > 3], [1, 1, 1, 1])

    def test_0d_0d_condition(self):
        x = np.array(3)
        c = np.array(x > 3)
        y = piecewise(x, [c], [1, 2])
        assert_equal(y, 2)

    def test_multidimensional_extrafunc(self):
        x = np.array([[-2.5, -1.5, -0.5],
                      [0.5, 1.5, 2.5]])
        y = piecewise(x, [x < 0, x >= 2], [-1, 1, 3])
        assert_array_equal(y, np.array([[-1., -1., -1.],
                                        [3., 3., 1.]]))


class TestBincount(object):

    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]))
        x = []
        y = np.bincount(x, minlength=0)
        assert_array_equal(y, np.array([]))

    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]))
        y = np.bincount(x, minlength=0)
        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,
                            "'str' object cannot be interpreted",
                            lambda: np.bincount(x, minlength="foobar"))
        assert_raises_regex(ValueError,
                            "must not be negative",
                            lambda: np.bincount(x, minlength=-1))

        x = np.arange(5)
        assert_raises_regex(TypeError,
                            "'str' object cannot be interpreted",
                            lambda: np.bincount(x, minlength="foobar"))
        assert_raises_regex(ValueError,
                            "must not be negative",
                            lambda: np.bincount(x, minlength=-1))

    @pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
    def test_dtype_reference_leaks(self):
        # gh-6805
        intp_refcount = sys.getrefcount(np.dtype(np.intp))
        double_refcount = sys.getrefcount(np.dtype(np.double))

        for j in range(10):
            np.bincount([1, 2, 3])
        assert_equal(sys.getrefcount(np.dtype(np.intp)), intp_refcount)
        assert_equal(sys.getrefcount(np.dtype(np.double)), double_refcount)

        for j in range(10):
            np.bincount([1, 2, 3], [4, 5, 6])
        assert_equal(sys.getrefcount(np.dtype(np.intp)), intp_refcount)
        assert_equal(sys.getrefcount(np.dtype(np.double)), double_refcount)


class TestInterp(object):

    def test_exceptions(self):
        assert_raises(ValueError, interp, 0, [], [])
        assert_raises(ValueError, interp, 0, [0], [1, 2])
        assert_raises(ValueError, interp, 0, [0, 1], [1, 2], period=0)
        assert_raises(ValueError, interp, 0, [], [], period=360)
        assert_raises(ValueError, interp, 0, [0], [1, 2], period=360)

    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):
        # Needs range of sizes to test different code paths.
        # size ==1 is special cased, 1 < size < 5 is linear search, and
        # size >= 5 goes through local search and possibly binary search.
        for size in range(1, 10):
            xp = np.arange(size, dtype=np.double)
            yp = np.ones(size, dtype=np.double)
            incpts = np.array([-1, 0, size - 1, size], dtype=np.double)
            decpts = incpts[::-1]

            incres = interp(incpts, xp, yp)
            decres = interp(decpts, xp, yp)
            inctgt = np.array([1, 1, 1, 1], dtype=float)
            dectgt = inctgt[::-1]
            assert_equal(incres, inctgt)
            assert_equal(decres, dectgt)

            incres = interp(incpts, xp, yp, left=0)
            decres = interp(decpts, xp, yp, left=0)
            inctgt = np.array([0, 1, 1, 1], dtype=float)
            dectgt = inctgt[::-1]
            assert_equal(incres, inctgt)
            assert_equal(decres, dectgt)

            incres = interp(incpts, xp, yp, right=2)
            decres = interp(decpts, xp, yp, right=2)
            inctgt = np.array([1, 1, 1, 2], dtype=float)
            dectgt = inctgt[::-1]
            assert_equal(incres, inctgt)
            assert_equal(decres, dectgt)

            incres = interp(incpts, xp, yp, left=0, right=2)
            decres = interp(decpts, xp, yp, left=0, right=2)
            inctgt = np.array([0, 1, 1, 2], dtype=float)
            dectgt = inctgt[::-1]
            assert_equal(incres, inctgt)
            assert_equal(decres, dectgt)

    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_non_finite_behavior_exact_x(self):
        x = [1, 2, 2.5, 3, 4]
        xp = [1, 2, 3, 4]
        fp = [1, 2, np.inf, 4]
        assert_almost_equal(np.interp(x, xp, fp), [1, 2, np.inf, np.inf, 4])
        fp = [1, 2, np.nan, 4]
        assert_almost_equal(np.interp(x, xp, fp), [1, 2, np.nan, np.nan, 4])

    @pytest.fixture(params=[
        lambda x: np.float_(x),
        lambda x: _make_complex(x, 0),
        lambda x: _make_complex(0, x),
        lambda x: _make_complex(x, np.multiply(x, -2))
    ], ids=[
        'real',
        'complex-real',
        'complex-imag',
        'complex-both'
    ])
    def sc(self, request):
        """ scale function used by the below tests """
        return request.param

    def test_non_finite_any_nan(self, sc):
        """ test that nans are propagated """
        assert_equal(np.interp(0.5, [np.nan,      1], sc([     0,     10])), sc(np.nan))
        assert_equal(np.interp(0.5, [     0, np.nan], sc([     0,     10])), sc(np.nan))
        assert_equal(np.interp(0.5, [     0,      1], sc([np.nan,     10])), sc(np.nan))
        assert_equal(np.interp(0.5, [     0,      1], sc([     0, np.nan])), sc(np.nan))

    def test_non_finite_inf(self, sc):
        """ Test that interp between opposite infs gives nan """
        assert_equal(np.interp(0.5, [-np.inf, +np.inf], sc([      0,      10])), sc(np.nan))
        assert_equal(np.interp(0.5, [      0,       1], sc([-np.inf, +np.inf])), sc(np.nan))
        assert_equal(np.interp(0.5, [      0,       1], sc([+np.inf, -np.inf])), sc(np.nan))

        # unless the y values are equal
        assert_equal(np.interp(0.5, [-np.inf, +np.inf], sc([     10,      10])), sc(10))

    def test_non_finite_half_inf_xf(self, sc):
        """ Test that interp where both axes have a bound at inf gives nan """
        assert_equal(np.interp(0.5, [-np.inf,       1], sc([-np.inf,      10])), sc(np.nan))
        assert_equal(np.interp(0.5, [-np.inf,       1], sc([+np.inf,      10])), sc(np.nan))
        assert_equal(np.interp(0.5, [-np.inf,       1], sc([      0, -np.inf])), sc(np.nan))
        assert_equal(np.interp(0.5, [-np.inf,       1], sc([      0, +np.inf])), sc(np.nan))
        assert_equal(np.interp(0.5, [      0, +np.inf], sc([-np.inf,      10])), sc(np.nan))
        assert_equal(np.interp(0.5, [      0, +np.inf], sc([+np.inf,      10])), sc(np.nan))
        assert_equal(np.interp(0.5, [      0, +np.inf], sc([      0, -np.inf])), sc(np.nan))
        assert_equal(np.interp(0.5, [      0, +np.inf], sc([      0, +np.inf])), sc(np.nan))

    def test_non_finite_half_inf_x(self, sc):
        """ Test interp where the x axis has a bound at inf """
        assert_equal(np.interp(0.5, [-np.inf, -np.inf], sc([0, 10])), sc(10))
        assert_equal(np.interp(0.5, [-np.inf, 1      ], sc([0, 10])), sc(10))
        assert_equal(np.interp(0.5, [      0, +np.inf], sc([0, 10])), sc(0))
        assert_equal(np.interp(0.5, [+np.inf, +np.inf], sc([0, 10])), sc(0))

    def test_non_finite_half_inf_f(self, sc):
        """ Test interp where the f axis has a bound at inf """
        assert_equal(np.interp(0.5, [0, 1], sc([      0, -np.inf])), sc(-np.inf))
        assert_equal(np.interp(0.5, [0, 1], sc([      0, +np.inf])), sc(+np.inf))
        assert_equal(np.interp(0.5, [0, 1], sc([-np.inf,      10])), sc(-np.inf))
        assert_equal(np.interp(0.5, [0, 1], sc([+np.inf,      10])), sc(+np.inf))
        assert_equal(np.interp(0.5, [0, 1], sc([-np.inf, -np.inf])), sc(-np.inf))
        assert_equal(np.interp(0.5, [0, 1], sc([+np.inf, +np.inf])), sc(+np.inf))

    def test_complex_interp(self):
        # test complex interpolation
        x = np.linspace(0, 1, 5)
        y = np.linspace(0, 1, 5) + (1 + np.linspace(0, 1, 5))*1.0j
        x0 = 0.3
        y0 = x0 + (1+x0)*1.0j
        assert_almost_equal(np.interp(x0, x, y), y0)
        # test complex left and right
        x0 = -1
        left = 2 + 3.0j
        assert_almost_equal(np.interp(x0, x, y, left=left), left)
        x0 = 2.0
        right = 2 + 3.0j
        assert_almost_equal(np.interp(x0, x, y, right=right), right)
        # test complex non finite
        x = [1, 2, 2.5, 3, 4]
        xp = [1, 2, 3, 4]
        fp = [1, 2+1j, np.inf, 4]
        y = [1, 2+1j, np.inf+0.5j, np.inf, 4]
        assert_almost_equal(np.interp(x, xp, fp), y)
        # test complex periodic
        x = [-180, -170, -185, 185, -10, -5, 0, 365]
        xp = [190, -190, 350, -350]
        fp = [5+1.0j, 10+2j, 3+3j, 4+4j]
        y = [7.5+1.5j, 5.+1.0j, 8.75+1.75j, 6.25+1.25j, 3.+3j, 3.25+3.25j,
             3.5+3.5j, 3.75+3.75j]
        assert_almost_equal(np.interp(x, xp, fp, period=360), y)

    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)

        xp = np.array([0, 2, 4])
        fp = np.array([1, -1, 1])

        actual = np.interp(np.array(1), xp, fp)
        assert_equal(actual, 0)
        assert_(isinstance(actual, np.float64))

        actual = np.interp(np.array(4.5), xp, fp, period=4)
        assert_equal(actual, 0.5)
        assert_(isinstance(actual, np.float64))

    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 test_period(self):
        x = [-180, -170, -185, 185, -10, -5, 0, 365]
        xp = [190, -190, 350, -350]
        fp = [5, 10, 3, 4]
        y = [7.5, 5., 8.75, 6.25, 3., 3.25, 3.5, 3.75]
        assert_almost_equal(np.interp(x, xp, fp, period=360), y)
        x = np.array(x, order='F').reshape(2, -1)
        y = np.array(y, order='C').reshape(2, -1)
        assert_almost_equal(np.interp(x, xp, fp, period=360), y)


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


class TestPercentile(object):

    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)
        x[1] = np.nan
        assert_equal(np.percentile(x, 0), np.nan)
        assert_equal(np.percentile(x, 0, interpolation='nearest'), np.nan)

    def test_fraction(self):
        x = [Fraction(i, 2) for i in range(8)]

        p = np.percentile(x, Fraction(0))
        assert_equal(p, Fraction(0))
        assert_equal(type(p), Fraction)

        p = np.percentile(x, Fraction(100))
        assert_equal(p, Fraction(7, 2))
        assert_equal(type(p), Fraction)

        p = np.percentile(x, Fraction(50))
        assert_equal(p, Fraction(7, 4))
        assert_equal(type(p), Fraction)

    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 'linear' (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)
        assert_equal(np.percentile(range(11), 51,
                                   interpolation='midpoint'), 5.5)
        assert_equal(np.percentile(range(11), 50,
                                   interpolation='midpoint'), 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 compatibility with old percentile
        x = np.arange(12).reshape(3, 4)
        assert_equal(np.percentile(x, 50), 5.5)
        assert_(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 compatibility with old percentile
        x = np.arange(12).reshape(3, 4)
        assert_equal(np.percentile(x, 50, interpolation='lower'), 5.)
        assert_(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.moveaxis(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.ravel())
        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(np.AxisError, np.percentile, d, axis=-5, q=25)
        assert_raises(np.AxisError, np.percentile, d, axis=(0, -5), q=25)
        assert_raises(np.AxisError, np.percentile, d, axis=4, q=25)
        assert_raises(np.AxisError, np.percentile, d, axis=(0, 4), q=25)
        # each of these refers to the same axis twice
        assert_raises(ValueError, np.percentile, d, axis=(1, 1), q=25)
        assert_raises(ValueError, np.percentile, d, axis=(-1, -1), q=25)
        assert_raises(ValueError, np.percentile, d, axis=(3, -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))

    def test_out(self):
        o = np.zeros((4,))
        d = np.ones((3, 4))
        assert_equal(np.percentile(d, 0, 0, out=o), o)
        assert_equal(np.percentile(d, 0, 0, interpolation='nearest', out=o), o)
        o = np.zeros((3,))
        assert_equal(np.percentile(d, 1, 1, out=o), o)
        assert_equal(np.percentile(d, 1, 1, interpolation='nearest', out=o), o)

        o = np.zeros(())
        assert_equal(np.percentile(d, 2, out=o), o)
        assert_equal(np.percentile(d, 2, interpolation='nearest', out=o), o)

    def test_out_nan(self):
        with warnings.catch_warnings(record=True):
            warnings.filterwarnings('always', '', RuntimeWarning)
            o = np.zeros((4,))
            d = np.ones((3, 4))
            d[2, 1] = np.nan
            assert_equal(np.percentile(d, 0, 0, out=o), o)
            assert_equal(
                np.percentile(d, 0, 0, interpolation='nearest', out=o), o)
            o = np.zeros((3,))
            assert_equal(np.percentile(d, 1, 1, out=o), o)
            assert_equal(
                np.percentile(d, 1, 1, interpolation='nearest', out=o), o)
            o = np.zeros(())
            assert_equal(np.percentile(d, 1, out=o), o)
            assert_equal(
                np.percentile(d, 1, interpolation='nearest', out=o), o)

    def test_nan_behavior(self):
        a = np.arange(24, dtype=float)
        a[2] = np.nan
        assert_equal(np.percentile(a, 0.3), np.nan)
        assert_equal(np.percentile(a, 0.3, axis=0), np.nan)
        assert_equal(np.percentile(a, [0.3, 0.6], axis=0),
                     np.array([np.nan] * 2))

        a = np.arange(24, dtype=float).reshape(2, 3, 4)
        a[1, 2, 3] = np.nan
        a[1, 1, 2] = np.nan

        # no axis
        assert_equal(np.percentile(a, 0.3), np.nan)
        assert_equal(np.percentile(a, 0.3).ndim, 0)

        # axis0 zerod
        b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, 0)
        b[2, 3] = np.nan
        b[1, 2] = np.nan
        assert_equal(np.percentile(a, 0.3, 0), b)

        # axis0 not zerod
        b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4),
                          [0.3, 0.6], 0)
        b[:, 2, 3] = np.nan
        b[:, 1, 2] = np.nan
        assert_equal(np.percentile(a, [0.3, 0.6], 0), b)

        # axis1 zerod
        b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, 1)
        b[1, 3] = np.nan
        b[1, 2] = np.nan
        assert_equal(np.percentile(a, 0.3, 1), b)
        # axis1 not zerod
        b = np.percentile(
            np.arange(24, dtype=float).reshape(2, 3, 4), [0.3, 0.6], 1)
        b[:, 1, 3] = np.nan
        b[:, 1, 2] = np.nan
        assert_equal(np.percentile(a, [0.3, 0.6], 1), b)

        # axis02 zerod
        b = np.percentile(
            np.arange(24, dtype=float).reshape(2, 3, 4), 0.3, (0, 2))
        b[1] = np.nan
        b[2] = np.nan
        assert_equal(np.percentile(a, 0.3, (0, 2)), b)
        # axis02 not zerod
        b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4),
                          [0.3, 0.6], (0, 2))
        b[:, 1] = np.nan
        b[:, 2] = np.nan
        assert_equal(np.percentile(a, [0.3, 0.6], (0, 2)), b)
        # axis02 not zerod with nearest interpolation
        b = np.percentile(np.arange(24, dtype=float).reshape(2, 3, 4),
                          [0.3, 0.6], (0, 2), interpolation='nearest')
        b[:, 1] = np.nan
        b[:, 2] = np.nan
        assert_equal(np.percentile(
            a, [0.3, 0.6], (0, 2), interpolation='nearest'), b)


class TestQuantile(object):
    # most of this is already tested by TestPercentile

    def test_basic(self):
        x = np.arange(8) * 0.5
        assert_equal(np.quantile(x, 0), 0.)
        assert_equal(np.quantile(x, 1), 3.5)
        assert_equal(np.quantile(x, 0.5), 1.75)

    def test_fraction(self):
        # fractional input, integral quantile
        x = [Fraction(i, 2) for i in range(8)]

        q = np.quantile(x, 0)
        assert_equal(q, 0)
        assert_equal(type(q), Fraction)

        q = np.quantile(x, 1)
        assert_equal(q, Fraction(7, 2))
        assert_equal(type(q), Fraction)

        q = np.quantile(x, Fraction(1, 2))
        assert_equal(q, Fraction(7, 4))
        assert_equal(type(q), Fraction)

        # repeat with integral input but fractional quantile
        x = np.arange(8)
        assert_equal(np.quantile(x, Fraction(1, 2)), Fraction(7, 2))

    def test_no_p_overwrite(self):
        # this is worth retesting, because quantile does not make a copy
        p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
        p = p0.copy()
        np.quantile(np.arange(100.), p, interpolation="midpoint")
        assert_array_equal(p, p0)

        p0 = p0.tolist()
        p = p.tolist()
        np.quantile(np.arange(100.), p, interpolation="midpoint")
        assert_array_equal(p, p0)


class TestMedian(object):

    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))
        np.random.shuffle(a4.ravel())
        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_out(self):
        o = np.zeros((4,))
        d = np.ones((3, 4))
        assert_equal(np.median(d, 0, out=o), o)
        o = np.zeros((3,))
        assert_equal(np.median(d, 1, out=o), o)
        o = np.zeros(())
        assert_equal(np.median(d, out=o), o)

    def test_out_nan(self):
        with warnings.catch_warnings(record=True):
            warnings.filterwarnings('always', '', RuntimeWarning)
            o = np.zeros((4,))
            d = np.ones((3, 4))
            d[2, 1] = np.nan
            assert_equal(np.median(d, 0, out=o), o)
            o = np.zeros((3,))
            assert_equal(np.median(d, 1, out=o), o)
            o = np.zeros(())
            assert_equal(np.median(d, out=o), o)

    def test_nan_behavior(self):
        a = np.arange(24, dtype=float)
        a[2] = np.nan
        assert_equal(np.median(a), np.nan)
        assert_equal(np.median(a, axis=0), np.nan)

        a = np.arange(24, dtype=float).reshape(2, 3, 4)
        a[1, 2, 3] = np.nan
        a[1, 1, 2] = np.nan

        # no axis
        assert_equal(np.median(a), np.nan)
        assert_equal(np.median(a).ndim, 0)

        # axis0
        b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), 0)
        b[2, 3] = np.nan
        b[1, 2] = np.nan
        assert_equal(np.median(a, 0), b)

        # axis1
        b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), 1)
        b[1, 3] = np.nan
        b[1, 2] = np.nan
        assert_equal(np.median(a, 1), b)

        # axis02
        b = np.median(np.arange(24, dtype=float).reshape(2, 3, 4), (0, 2))
        b[1] = np.nan
        b[2] = np.nan
        assert_equal(np.median(a, (0, 2)), b)

    def test_empty(self):
        # mean(empty array) emits two warnings: empty slice and divide by 0
        a = np.array([], dtype=float)
        with warnings.catch_warnings(record=True) as w:
            warnings.filterwarnings('always', '', RuntimeWarning)
            assert_equal(np.median(a), np.nan)
            assert_(w[0].category is RuntimeWarning)
            assert_equal(len(w), 2)

        # multiple dimensions
        a = np.array([], dtype=float, ndmin=3)
        # no axis
        with warnings.catch_warnings(record=True) as w:
            warnings.filterwarnings('always', '', RuntimeWarning)
            assert_equal(np.median(a), np.nan)
            assert_(w[0].category is RuntimeWarning)

        # axis 0 and 1
        b = np.array([], dtype=float, ndmin=2)
        assert_equal(np.median(a, axis=0), b)
        assert_equal(np.median(a, axis=1), b)

        # axis 2
        b = np.array(np.nan, dtype=float, ndmin=2)
        with warnings.catch_warnings(record=True) as w:
            warnings.filterwarnings('always', '', RuntimeWarning)
            assert_equal(np.median(a, axis=2), b)
            assert_(w[0].category is RuntimeWarning)

    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.moveaxis(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.ravel())
        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(np.AxisError, np.median, d, axis=-5)
        assert_raises(np.AxisError, np.median, d, axis=(0, -5))
        assert_raises(np.AxisError, np.median, d, axis=4)
        assert_raises(np.AxisError, 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(object):

    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(object):

    @pytest.mark.skipif(sys.flags.optimize == 2, reason="Python running -OO")
    @pytest.mark.xfail(IS_PYPY, reason="PyPy does not modify tp_doc")
    def test_add_doc(self):
        # test np.add_newdoc
        tgt = "Current flat index into the array."
        assert_equal(np.core.flatiter.index.__doc__[:len(tgt)], tgt)
        assert_(len(np.core.ufunc.identity.__doc__) > 300)
        assert_(len(np.lib.index_tricks.mgrid.__doc__) > 300)

class TestSortComplex(object):

    @pytest.mark.parametrize("type_in, type_out", [
        ('l', 'D'),
        ('h', 'F'),
        ('H', 'F'),
        ('b', 'F'),
        ('B', 'F'),
        ('g', 'G'),
        ])
    def test_sort_real(self, type_in, type_out):
        # sort_complex() type casting for real input types
        a = np.array([5, 3, 6, 2, 1], dtype=type_in)
        actual = np.sort_complex(a)
        expected = np.sort(a).astype(type_out)
        assert_equal(actual, expected)
        assert_equal(actual.dtype, expected.dtype)

    def test_sort_complex(self):
        # sort_complex() handling of complex input
        a = np.array([2 + 3j, 1 - 2j, 1 - 3j, 2 + 1j], dtype='D')
        expected = np.array([1 - 3j, 1 - 2j, 2 + 1j, 2 + 3j], dtype='D')
        actual = np.sort_complex(a)
        assert_equal(actual, expected)
        assert_equal(actual.dtype, expected.dtype)
