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✏️ Editing: test_multiarray.py

from __future__ import annotations

import collections.abc
import tempfile
import sys
import warnings
import operator
import io
import itertools
import functools
import ctypes
import os
import gc
import re
import weakref
import pytest
from contextlib import contextmanager

from numpy.compat import pickle

import pathlib
import builtins
from decimal import Decimal
import mmap

import numpy as np
import numpy.core._multiarray_tests as _multiarray_tests
from numpy.core._rational_tests import rational
from numpy.testing import (
    assert_, assert_raises, assert_warns, assert_equal, assert_almost_equal,
    assert_array_equal, assert_raises_regex, assert_array_almost_equal,
    assert_allclose, IS_PYPY, IS_PYSTON, HAS_REFCOUNT, assert_array_less,
    runstring, temppath, suppress_warnings, break_cycles, _SUPPORTS_SVE,
    )
from numpy.testing._private.utils import requires_memory, _no_tracing
from numpy.core.tests._locales import CommaDecimalPointLocale
from numpy.lib.recfunctions import repack_fields
from numpy.core.multiarray import _get_ndarray_c_version

# Need to test an object that does not fully implement math interface
from datetime import timedelta, datetime

def assert_arg_sorted(arr, arg):
    # resulting array should be sorted and arg values should be unique
    assert_equal(arr[arg], np.sort(arr))
    assert_equal(np.sort(arg), np.arange(len(arg)))

def _aligned_zeros(shape, dtype=float, order="C", align=None):
    """
    Allocate a new ndarray with aligned memory.

The ndarray is guaranteed *not* aligned to twice the requested alignment.
    Eg, if align=4, guarantees it is not aligned to 8. If align=None uses
    dtype.alignment."""
    dtype = np.dtype(dtype)
    if dtype == np.dtype(object):
        # Can't do this, fall back to standard allocation (which
        # should always be sufficiently aligned)
        if align is not None:
            raise ValueError("object array alignment not supported")
        return np.zeros(shape, dtype=dtype, order=order)
    if align is None:
        align = dtype.alignment
    if not hasattr(shape, '__len__'):
        shape = (shape,)
    size = functools.reduce(operator.mul, shape) * dtype.itemsize
    buf = np.empty(size + 2*align + 1, np.uint8)

ptr = buf.__array_interface__['data'][0]
    offset = ptr % align
    if offset != 0:
        offset = align - offset
    if (ptr % (2*align)) == 0:
        offset += align

# Note: slices producing 0-size arrays do not necessarily change
    # data pointer --- so we use and allocate size+1
    buf = buf[offset:offset+size+1][:-1]
    buf.fill(0)
    data = np.ndarray(shape, dtype, buf, order=order)
    return data

class TestFlags:
    def setup_method(self):
        self.a = np.arange(10)

def test_writeable(self):
        mydict = locals()
        self.a.flags.writeable = False
        assert_raises(ValueError, runstring, 'self.a[0] = 3', mydict)
        assert_raises(ValueError, runstring, 'self.a[0:1].itemset(3)', mydict)
        self.a.flags.writeable = True
        self.a[0] = 5
        self.a[0] = 0

def test_writeable_any_base(self):
        # Ensure that any base being writeable is sufficient to change flag;
        # this is especially interesting for arrays from an array interface.
        arr = np.arange(10)

class subclass(np.ndarray):
            pass

# Create subclass so base will not be collapsed, this is OK to change
        view1 = arr.view(subclass)
        view2 = view1[...]
        arr.flags.writeable = False
        view2.flags.writeable = False
        view2.flags.writeable = True  # Can be set to True again.

arr = np.arange(10)

class frominterface:
            def __init__(self, arr):
                self.arr = arr
                self.__array_interface__ = arr.__array_interface__

view1 = np.asarray(frominterface)
        view2 = view1[...]
        view2.flags.writeable = False
        view2.flags.writeable = True

view1.flags.writeable = False
        view2.flags.writeable = False
        with assert_raises(ValueError):
            # Must assume not writeable, since only base is not:
            view2.flags.writeable = True

def test_writeable_from_readonly(self):
        # gh-9440 - make sure fromstring, from buffer on readonly buffers
        # set writeable False
        data = b'\x00' * 100
        vals = np.frombuffer(data, 'B')
        assert_raises(ValueError, vals.setflags, write=True)
        types = np.dtype( [('vals', 'u1'), ('res3', 'S4')] )
        values = np.core.records.fromstring(data, types)
        vals = values['vals']
        assert_raises(ValueError, vals.setflags, write=True)

def test_writeable_from_buffer(self):
        data = bytearray(b'\x00' * 100)
        vals = np.frombuffer(data, 'B')
        assert_(vals.flags.writeable)
        vals.setflags(write=False)
        assert_(vals.flags.writeable is False)
        vals.setflags(write=True)
        assert_(vals.flags.writeable)
        types = np.dtype( [('vals', 'u1'), ('res3', 'S4')] )
        values = np.core.records.fromstring(data, types)
        vals = values['vals']
        assert_(vals.flags.writeable)
        vals.setflags(write=False)
        assert_(vals.flags.writeable is False)
        vals.setflags(write=True)
        assert_(vals.flags.writeable)

@pytest.mark.skipif(IS_PYPY, reason="PyPy always copies")
    def test_writeable_pickle(self):
        import pickle
        # Small arrays will be copied without setting base.
        # See condition for using PyArray_SetBaseObject in
        # array_setstate.
        a = np.arange(1000)
        for v in range(pickle.HIGHEST_PROTOCOL):
            vals = pickle.loads(pickle.dumps(a, v))
            assert_(vals.flags.writeable)
            assert_(isinstance(vals.base, bytes))

def test_writeable_from_c_data(self):
        # Test that the writeable flag can be changed for an array wrapping
        # low level C-data, but not owning its data.
        # Also see that this is deprecated to change from python.
        from numpy.core._multiarray_tests import get_c_wrapping_array

arr_writeable = get_c_wrapping_array(True)
        assert not arr_writeable.flags.owndata
        assert arr_writeable.flags.writeable
        view = arr_writeable[...]

# Toggling the writeable flag works on the view:
        view.flags.writeable = False
        assert not view.flags.writeable
        view.flags.writeable = True
        assert view.flags.writeable
        # Flag can be unset on the arr_writeable:
        arr_writeable.flags.writeable = False

arr_readonly = get_c_wrapping_array(False)
        assert not arr_readonly.flags.owndata
        assert not arr_readonly.flags.writeable

for arr in [arr_writeable, arr_readonly]:
            view = arr[...]
            view.flags.writeable = False  # make sure it is readonly
            arr.flags.writeable = False
            assert not arr.flags.writeable

with assert_raises(ValueError):
                view.flags.writeable = True

with warnings.catch_warnings():
                warnings.simplefilter("error", DeprecationWarning)
                with assert_raises(DeprecationWarning):
                    arr.flags.writeable = True

with assert_warns(DeprecationWarning):
                arr.flags.writeable = True

def test_warnonwrite(self):
        a = np.arange(10)
        a.flags._warn_on_write = True
        with warnings.catch_warnings(record=True) as w:
            warnings.filterwarnings('always')
            a[1] = 10
            a[2] = 10
            # only warn once
            assert_(len(w) == 1)

@pytest.mark.parametrize(["flag", "flag_value", "writeable"],
            [("writeable", True, True),
             # Delete _warn_on_write after deprecation and simplify
             # the parameterization:
             ("_warn_on_write", True, False),
             ("writeable", False, False)])
    def test_readonly_flag_protocols(self, flag, flag_value, writeable):
        a = np.arange(10)
        setattr(a.flags, flag, flag_value)

class MyArr():
            __array_struct__ = a.__array_struct__

assert memoryview(a).readonly is not writeable
        assert a.__array_interface__['data'][1] is not writeable
        assert np.asarray(MyArr()).flags.writeable is writeable

def test_otherflags(self):
        assert_equal(self.a.flags.carray, True)
        assert_equal(self.a.flags['C'], True)
        assert_equal(self.a.flags.farray, False)
        assert_equal(self.a.flags.behaved, True)
        assert_equal(self.a.flags.fnc, False)
        assert_equal(self.a.flags.forc, True)
        assert_equal(self.a.flags.owndata, True)
        assert_equal(self.a.flags.writeable, True)
        assert_equal(self.a.flags.aligned, True)
        assert_equal(self.a.flags.writebackifcopy, False)
        assert_equal(self.a.flags['X'], False)
        assert_equal(self.a.flags['WRITEBACKIFCOPY'], False)

def test_string_align(self):
        a = np.zeros(4, dtype=np.dtype('|S4'))
        assert_(a.flags.aligned)
        # not power of two are accessed byte-wise and thus considered aligned
        a = np.zeros(5, dtype=np.dtype('|S4'))
        assert_(a.flags.aligned)

def test_void_align(self):
        a = np.zeros(4, dtype=np.dtype([("a", "i4"), ("b", "i4")]))
        assert_(a.flags.aligned)

class TestHash:
    # see #3793
    def test_int(self):
        for st, ut, s in [(np.int8, np.uint8, 8),
                          (np.int16, np.uint16, 16),
                          (np.int32, np.uint32, 32),
                          (np.int64, np.uint64, 64)]:
            for i in range(1, s):
                assert_equal(hash(st(-2**i)), hash(-2**i),
                             err_msg="%r: -2**%d" % (st, i))
                assert_equal(hash(st(2**(i - 1))), hash(2**(i - 1)),
                             err_msg="%r: 2**%d" % (st, i - 1))
                assert_equal(hash(st(2**i - 1)), hash(2**i - 1),
                             err_msg="%r: 2**%d - 1" % (st, i))

i = max(i - 1, 1)
                assert_equal(hash(ut(2**(i - 1))), hash(2**(i - 1)),
                             err_msg="%r: 2**%d" % (ut, i - 1))
                assert_equal(hash(ut(2**i - 1)), hash(2**i - 1),
                             err_msg="%r: 2**%d - 1" % (ut, i))

class TestAttributes:
    def setup_method(self):
        self.one = np.arange(10)
        self.two = np.arange(20).reshape(4, 5)
        self.three = np.arange(60, dtype=np.float64).reshape(2, 5, 6)

def test_attributes(self):
        assert_equal(self.one.shape, (10,))
        assert_equal(self.two.shape, (4, 5))
        assert_equal(self.three.shape, (2, 5, 6))
        self.three.shape = (10, 3, 2)
        assert_equal(self.three.shape, (10, 3, 2))
        self.three.shape = (2, 5, 6)
        assert_equal(self.one.strides, (self.one.itemsize,))
        num = self.two.itemsize
        assert_equal(self.two.strides, (5*num, num))
        num = self.three.itemsize
        assert_equal(self.three.strides, (30*num, 6*num, num))
        assert_equal(self.one.ndim, 1)
        assert_equal(self.two.ndim, 2)
        assert_equal(self.three.ndim, 3)
        num = self.two.itemsize
        assert_equal(self.two.size, 20)
        assert_equal(self.two.nbytes, 20*num)
        assert_equal(self.two.itemsize, self.two.dtype.itemsize)
        assert_equal(self.two.base, np.arange(20))

def test_dtypeattr(self):
        assert_equal(self.one.dtype, np.dtype(np.int_))
        assert_equal(self.three.dtype, np.dtype(np.float_))
        assert_equal(self.one.dtype.char, 'l')
        assert_equal(self.three.dtype.char, 'd')
        assert_(self.three.dtype.str[0] in '<>')
        assert_equal(self.one.dtype.str[1], 'i')
        assert_equal(self.three.dtype.str[1], 'f')

def test_int_subclassing(self):
        # Regression test for https://github.com/numpy/numpy/pull/3526

numpy_int = np.int_(0)

# int_ doesn't inherit from Python int, because it's not fixed-width
        assert_(not isinstance(numpy_int, int))

def test_stridesattr(self):
        x = self.one

def make_array(size, offset, strides):
            return np.ndarray(size, buffer=x, dtype=int,
                              offset=offset*x.itemsize,
                              strides=strides*x.itemsize)

assert_equal(make_array(4, 4, -1), np.array([4, 3, 2, 1]))
        assert_raises(ValueError, make_array, 4, 4, -2)
        assert_raises(ValueError, make_array, 4, 2, -1)
        assert_raises(ValueError, make_array, 8, 3, 1)
        assert_equal(make_array(8, 3, 0), np.array([3]*8))
        # Check behavior reported in gh-2503:
        assert_raises(ValueError, make_array, (2, 3), 5, np.array([-2, -3]))
        make_array(0, 0, 10)

def test_set_stridesattr(self):
        x = self.one

def make_array(size, offset, strides):
            try:
                r = np.ndarray([size], dtype=int, buffer=x,
                               offset=offset*x.itemsize)
            except Exception as e:
                raise RuntimeError(e)
            r.strides = strides = strides*x.itemsize
            return r

assert_equal(make_array(4, 4, -1), np.array([4, 3, 2, 1]))
        assert_equal(make_array(7, 3, 1), np.array([3, 4, 5, 6, 7, 8, 9]))
        assert_raises(ValueError, make_array, 4, 4, -2)
        assert_raises(ValueError, make_array, 4, 2, -1)
        assert_raises(RuntimeError, make_array, 8, 3, 1)
        # Check that the true extent of the array is used.
        # Test relies on as_strided base not exposing a buffer.
        x = np.lib.stride_tricks.as_strided(np.arange(1), (10, 10), (0, 0))

def set_strides(arr, strides):
            arr.strides = strides

assert_raises(ValueError, set_strides, x, (10*x.itemsize, x.itemsize))

# Test for offset calculations:
        x = np.lib.stride_tricks.as_strided(np.arange(10, dtype=np.int8)[-1],
                                                    shape=(10,), strides=(-1,))
        assert_raises(ValueError, set_strides, x[::-1], -1)
        a = x[::-1]
        a.strides = 1
        a[::2].strides = 2

# test 0d
        arr_0d = np.array(0)
        arr_0d.strides = ()
        assert_raises(TypeError, set_strides, arr_0d, None)

def test_fill(self):
        for t in "?bhilqpBHILQPfdgFDGO":
            x = np.empty((3, 2, 1), t)
            y = np.empty((3, 2, 1), t)
            x.fill(1)
            y[...] = 1
            assert_equal(x, y)

def test_fill_max_uint64(self):
        x = np.empty((3, 2, 1), dtype=np.uint64)
        y = np.empty((3, 2, 1), dtype=np.uint64)
        value = 2**64 - 1
        y[...] = value
        x.fill(value)
        assert_array_equal(x, y)

def test_fill_struct_array(self):
        # Filling from a scalar
        x = np.array([(0, 0.0), (1, 1.0)], dtype='i4,f8')
        x.fill(x[0])
        assert_equal(x['f1'][1], x['f1'][0])
        # Filling from a tuple that can be converted
        # to a scalar
        x = np.zeros(2, dtype=[('a', 'f8'), ('b', 'i4')])
        x.fill((3.5, -2))
        assert_array_equal(x['a'], [3.5, 3.5])
        assert_array_equal(x['b'], [-2, -2])

def test_fill_readonly(self):
        # gh-22922
        a = np.zeros(11)
        a.setflags(write=False)
        with pytest.raises(ValueError, match=".*read-only"):
            a.fill(0)

class TestArrayConstruction:
    def test_array(self):
        d = np.ones(6)
        r = np.array([d, d])
        assert_equal(r, np.ones((2, 6)))

d = np.ones(6)
        tgt = np.ones((2, 6))
        r = np.array([d, d])
        assert_equal(r, tgt)
        tgt[1] = 2
        r = np.array([d, d + 1])
        assert_equal(r, tgt)

d = np.ones(6)
        r = np.array([[d, d]])
        assert_equal(r, np.ones((1, 2, 6)))

d = np.ones(6)
        r = np.array([[d, d], [d, d]])
        assert_equal(r, np.ones((2, 2, 6)))

d = np.ones((6, 6))
        r = np.array([d, d])
        assert_equal(r, np.ones((2, 6, 6)))

d = np.ones((6, ))
        r = np.array([[d, d + 1], d + 2], dtype=object)
        assert_equal(len(r), 2)
        assert_equal(r[0], [d, d + 1])
        assert_equal(r[1], d + 2)

tgt = np.ones((2, 3), dtype=bool)
        tgt[0, 2] = False
        tgt[1, 0:2] = False
        r = np.array([[True, True, False], [False, False, True]])
        assert_equal(r, tgt)
        r = np.array([[True, False], [True, False], [False, True]])
        assert_equal(r, tgt.T)

def test_array_empty(self):
        assert_raises(TypeError, np.array)

def test_0d_array_shape(self):
        assert np.ones(np.array(3)).shape == (3,)

def test_array_copy_false(self):
        d = np.array([1, 2, 3])
        e = np.array(d, copy=False)
        d[1] = 3
        assert_array_equal(e, [1, 3, 3])
        e = np.array(d, copy=False, order='F')
        d[1] = 4
        assert_array_equal(e, [1, 4, 3])
        e[2] = 7
        assert_array_equal(d, [1, 4, 7])

def test_array_copy_true(self):
        d = np.array([[1,2,3], [1, 2, 3]])
        e = np.array(d, copy=True)
        d[0, 1] = 3
        e[0, 2] = -7
        assert_array_equal(e, [[1, 2, -7], [1, 2, 3]])
        assert_array_equal(d, [[1, 3, 3], [1, 2, 3]])
        e = np.array(d, copy=True, order='F')
        d[0, 1] = 5
        e[0, 2] = 7
        assert_array_equal(e, [[1, 3, 7], [1, 2, 3]])
        assert_array_equal(d, [[1, 5, 3], [1,2,3]])

def test_array_cont(self):
        d = np.ones(10)[::2]
        assert_(np.ascontiguousarray(d).flags.c_contiguous)
        assert_(np.ascontiguousarray(d).flags.f_contiguous)
        assert_(np.asfortranarray(d).flags.c_contiguous)
        assert_(np.asfortranarray(d).flags.f_contiguous)
        d = np.ones((10, 10))[::2,::2]
        assert_(np.ascontiguousarray(d).flags.c_contiguous)
        assert_(np.asfortranarray(d).flags.f_contiguous)

@pytest.mark.parametrize("func",
            [np.array,
             np.asarray,
             np.asanyarray,
             np.ascontiguousarray,
             np.asfortranarray])
    def test_bad_arguments_error(self, func):
        with pytest.raises(TypeError):
            func(3, dtype="bad dtype")
        with pytest.raises(TypeError):
            func()  # missing arguments
        with pytest.raises(TypeError):
            func(1, 2, 3, 4, 5, 6, 7, 8)  # too many arguments

@pytest.mark.parametrize("func",
            [np.array,
             np.asarray,
             np.asanyarray,
             np.ascontiguousarray,
             np.asfortranarray])
    def test_array_as_keyword(self, func):
        # This should likely be made positional only, but do not change
        # the name accidentally.
        if func is np.array:
            func(object=3)
        else:
            func(a=3)

class TestAssignment:
    def test_assignment_broadcasting(self):
        a = np.arange(6).reshape(2, 3)

# Broadcasting the input to the output
        a[...] = np.arange(3)
        assert_equal(a, [[0, 1, 2], [0, 1, 2]])
        a[...] = np.arange(2).reshape(2, 1)
        assert_equal(a, [[0, 0, 0], [1, 1, 1]])

# For compatibility with <= 1.5, a limited version of broadcasting
        # the output to the input.
        #
        # This behavior is inconsistent with NumPy broadcasting
        # in general, because it only uses one of the two broadcasting
        # rules (adding a new "1" dimension to the left of the shape),
        # applied to the output instead of an input. In NumPy 2.0, this kind
        # of broadcasting assignment will likely be disallowed.
        a[...] = np.arange(6)[::-1].reshape(1, 2, 3)
        assert_equal(a, [[5, 4, 3], [2, 1, 0]])
        # The other type of broadcasting would require a reduction operation.

def assign(a, b):
            a[...] = b

assert_raises(ValueError, assign, a, np.arange(12).reshape(2, 2, 3))

def test_assignment_errors(self):
        # Address issue #2276
        class C:
            pass
        a = np.zeros(1)

def assign(v):
            a[0] = v

assert_raises((AttributeError, TypeError), assign, C())
        assert_raises(ValueError, assign, [1])

def test_unicode_assignment(self):
        # gh-5049
        from numpy.core.numeric import set_string_function

@contextmanager
        def inject_str(s):
            """ replace ndarray.__str__ temporarily """
            set_string_function(lambda x: s, repr=False)
            try:
                yield
            finally:
                set_string_function(None, repr=False)

a1d = np.array(['test'])
        a0d = np.array('done')
        with inject_str('bad'):
            a1d[0] = a0d  # previously this would invoke __str__
        assert_equal(a1d[0], 'done')

# this would crash for the same reason
        np.array([np.array('\xe5\xe4\xf6')])

def test_stringlike_empty_list(self):
        # gh-8902
        u = np.array(['done'])
        b = np.array([b'done'])

class bad_sequence:
            def __getitem__(self): pass
            def __len__(self): raise RuntimeError

assert_raises(ValueError, operator.setitem, u, 0, [])
        assert_raises(ValueError, operator.setitem, b, 0, [])

assert_raises(ValueError, operator.setitem, u, 0, bad_sequence())
        assert_raises(ValueError, operator.setitem, b, 0, bad_sequence())

def test_longdouble_assignment(self):
        # only relevant if longdouble is larger than float
        # we're looking for loss of precision

for dtype in (np.longdouble, np.longcomplex):
            # gh-8902
            tinyb = np.nextafter(np.longdouble(0), 1).astype(dtype)
            tinya = np.nextafter(np.longdouble(0), -1).astype(dtype)

# construction
            tiny1d = np.array([tinya])
            assert_equal(tiny1d[0], tinya)

# scalar = scalar
            tiny1d[0] = tinyb
            assert_equal(tiny1d[0], tinyb)

# 0d = scalar
            tiny1d[0, ...] = tinya
            assert_equal(tiny1d[0], tinya)

# 0d = 0d
            tiny1d[0, ...] = tinyb[...]
            assert_equal(tiny1d[0], tinyb)

# scalar = 0d
            tiny1d[0] = tinyb[...]
            assert_equal(tiny1d[0], tinyb)

arr = np.array([np.array(tinya)])
            assert_equal(arr[0], tinya)

def test_cast_to_string(self):
        # cast to str should do "str(scalar)", not "str(scalar.item())"
        # Example: In python2, str(float) is truncated, so we want to avoid
        # str(np.float64(...).item()) as this would incorrectly truncate.
        a = np.zeros(1, dtype='S20')
        a[:] = np.array(['1.12345678901234567890'], dtype='f8')
        assert_equal(a[0], b"1.1234567890123457")

class TestDtypedescr:
    def test_construction(self):
        d1 = np.dtype('i4')
        assert_equal(d1, np.dtype(np.int32))
        d2 = np.dtype('f8')
        assert_equal(d2, np.dtype(np.float64))

def test_byteorders(self):
        assert_(np.dtype('<i4') != np.dtype('>i4'))
        assert_(np.dtype([('a', '<i4')]) != np.dtype([('a', '>i4')]))

def test_structured_non_void(self):
        fields = [('a', '<i2'), ('b', '<i2')]
        dt_int = np.dtype(('i4', fields))
        assert_equal(str(dt_int), "(numpy.int32, [('a', '<i2'), ('b', '<i2')])")

# gh-9821
        arr_int = np.zeros(4, dt_int)
        assert_equal(repr(arr_int),
            "array([0, 0, 0, 0], dtype=(numpy.int32, [('a', '<i2'), ('b', '<i2')]))")

class TestZeroRank:
    def setup_method(self):
        self.d = np.array(0), np.array('x', object)

def test_ellipsis_subscript(self):
        a, b = self.d
        assert_equal(a[...], 0)
        assert_equal(b[...], 'x')
        assert_(a[...].base is a)  # `a[...] is a` in numpy <1.9.
        assert_(b[...].base is b)  # `b[...] is b` in numpy <1.9.

def test_empty_subscript(self):
        a, b = self.d
        assert_equal(a[()], 0)
        assert_equal(b[()], 'x')
        assert_(type(a[()]) is a.dtype.type)
        assert_(type(b[()]) is str)

def test_invalid_subscript(self):
        a, b = self.d
        assert_raises(IndexError, lambda x: x[0], a)
        assert_raises(IndexError, lambda x: x[0], b)
        assert_raises(IndexError, lambda x: x[np.array([], int)], a)
        assert_raises(IndexError, lambda x: x[np.array([], int)], b)

def test_ellipsis_subscript_assignment(self):
        a, b = self.d
        a[...] = 42
        assert_equal(a, 42)
        b[...] = ''
        assert_equal(b.item(), '')

def test_empty_subscript_assignment(self):
        a, b = self.d
        a[()] = 42
        assert_equal(a, 42)
        b[()] = ''
        assert_equal(b.item(), '')

def test_invalid_subscript_assignment(self):
        a, b = self.d

def assign(x, i, v):
            x[i] = v

assert_raises(IndexError, assign, a, 0, 42)
        assert_raises(IndexError, assign, b, 0, '')
        assert_raises(ValueError, assign, a, (), '')

def test_newaxis(self):
        a, b = self.d
        assert_equal(a[np.newaxis].shape, (1,))
        assert_equal(a[..., np.newaxis].shape, (1,))
        assert_equal(a[np.newaxis, ...].shape, (1,))
        assert_equal(a[..., np.newaxis].shape, (1,))
        assert_equal(a[np.newaxis, ..., np.newaxis].shape, (1, 1))
        assert_equal(a[..., np.newaxis, np.newaxis].shape, (1, 1))
        assert_equal(a[np.newaxis, np.newaxis, ...].shape, (1, 1))
        assert_equal(a[(np.newaxis,)*10].shape, (1,)*10)

def test_invalid_newaxis(self):
        a, b = self.d

def subscript(x, i):
            x[i]

assert_raises(IndexError, subscript, a, (np.newaxis, 0))
        assert_raises(IndexError, subscript, a, (np.newaxis,)*50)

def test_constructor(self):
        x = np.ndarray(())
        x[()] = 5
        assert_equal(x[()], 5)
        y = np.ndarray((), buffer=x)
        y[()] = 6
        assert_equal(x[()], 6)

# strides and shape must be the same length
        with pytest.raises(ValueError):
            np.ndarray((2,), strides=())
        with pytest.raises(ValueError):
            np.ndarray((), strides=(2,))

def test_output(self):
        x = np.array(2)
        assert_raises(ValueError, np.add, x, [1], x)

def test_real_imag(self):
        # contiguity checks are for gh-11245
        x = np.array(1j)
        xr = x.real
        xi = x.imag

assert_equal(xr, np.array(0))
        assert_(type(xr) is np.ndarray)
        assert_equal(xr.flags.contiguous, True)
        assert_equal(xr.flags.f_contiguous, True)

assert_equal(xi, np.array(1))
        assert_(type(xi) is np.ndarray)
        assert_equal(xi.flags.contiguous, True)
        assert_equal(xi.flags.f_contiguous, True)

class TestScalarIndexing:
    def setup_method(self):
        self.d = np.array([0, 1])[0]

def test_ellipsis_subscript(self):
        a = self.d
        assert_equal(a[...], 0)
        assert_equal(a[...].shape, ())

def test_empty_subscript(self):
        a = self.d
        assert_equal(a[()], 0)
        assert_equal(a[()].shape, ())

def test_invalid_subscript(self):
        a = self.d
        assert_raises(IndexError, lambda x: x[0], a)
        assert_raises(IndexError, lambda x: x[np.array([], int)], a)

def test_invalid_subscript_assignment(self):
        a = self.d

def assign(x, i, v):
            x[i] = v

assert_raises(TypeError, assign, a, 0, 42)

def test_newaxis(self):
        a = self.d
        assert_equal(a[np.newaxis].shape, (1,))
        assert_equal(a[..., np.newaxis].shape, (1,))
        assert_equal(a[np.newaxis, ...].shape, (1,))
        assert_equal(a[..., np.newaxis].shape, (1,))
        assert_equal(a[np.newaxis, ..., np.newaxis].shape, (1, 1))
        assert_equal(a[..., np.newaxis, np.newaxis].shape, (1, 1))
        assert_equal(a[np.newaxis, np.newaxis, ...].shape, (1, 1))
        assert_equal(a[(np.newaxis,)*10].shape, (1,)*10)

def test_invalid_newaxis(self):
        a = self.d

def subscript(x, i):
            x[i]

assert_raises(IndexError, subscript, a, (np.newaxis, 0))
        assert_raises(IndexError, subscript, a, (np.newaxis,)*50)

def test_overlapping_assignment(self):
        # With positive strides
        a = np.arange(4)
        a[:-1] = a[1:]
        assert_equal(a, [1, 2, 3, 3])

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

# With positive and negative strides
        a = np.arange(4)
        a[:] = a[::-1]
        assert_equal(a, [3, 2, 1, 0])

a = np.arange(6).reshape(2, 3)
        a[::-1,:] = a[:, ::-1]
        assert_equal(a, [[5, 4, 3], [2, 1, 0]])

a = np.arange(6).reshape(2, 3)
        a[::-1, ::-1] = a[:, ::-1]
        assert_equal(a, [[3, 4, 5], [0, 1, 2]])

# With just one element overlapping
        a = np.arange(5)
        a[:3] = a[2:]
        assert_equal(a, [2, 3, 4, 3, 4])

a = np.arange(5)
        a[2:] = a[:3]
        assert_equal(a, [0, 1, 0, 1, 2])

a = np.arange(5)
        a[2::-1] = a[2:]
        assert_equal(a, [4, 3, 2, 3, 4])

a = np.arange(5)
        a[2:] = a[2::-1]
        assert_equal(a, [0, 1, 2, 1, 0])

a = np.arange(5)
        a[2::-1] = a[:1:-1]
        assert_equal(a, [2, 3, 4, 3, 4])

a = np.arange(5)
        a[:1:-1] = a[2::-1]
        assert_equal(a, [0, 1, 0, 1, 2])

class TestCreation:
    """
    Test the np.array constructor
    """
    def test_from_attribute(self):
        class x:
            def __array__(self, dtype=None):
                pass

assert_raises(ValueError, np.array, x())

def test_from_string(self):
        types = np.typecodes['AllInteger'] + np.typecodes['Float']
        nstr = ['123', '123']
        result = np.array([123, 123], dtype=int)
        for type in types:
            msg = 'String conversion for %s' % type
            assert_equal(np.array(nstr, dtype=type), result, err_msg=msg)

def test_void(self):
        arr = np.array([], dtype='V')
        assert arr.dtype == 'V8'  # current default
        # Same length scalars (those that go to the same void) work:
        arr = np.array([b"1234", b"1234"], dtype="V")
        assert arr.dtype == "V4"

# Promoting different lengths will fail (pre 1.20 this worked)
        # by going via S5 and casting to V5.
        with pytest.raises(TypeError):
            np.array([b"1234", b"12345"], dtype="V")
        with pytest.raises(TypeError):
            np.array([b"12345", b"1234"], dtype="V")

# Check the same for the casting path:
        arr = np.array([b"1234", b"1234"], dtype="O").astype("V")
        assert arr.dtype == "V4"
        with pytest.raises(TypeError):
            np.array([b"1234", b"12345"], dtype="O").astype("V")

@pytest.mark.parametrize("idx",
            [pytest.param(Ellipsis, id="arr"), pytest.param((), id="scalar")])
    def test_structured_void_promotion(self, idx):
        arr = np.array(
            [np.array(1, dtype="i,i")[idx], np.array(2, dtype='i,i')[idx]],
            dtype="V")
        assert_array_equal(arr, np.array([(1, 1), (2, 2)], dtype="i,i"))
        # The following fails to promote the two dtypes, resulting in an error
        with pytest.raises(TypeError):
            np.array(
                [np.array(1, dtype="i,i")[idx], np.array(2, dtype='i,i,i')[idx]],
                dtype="V")

def test_too_big_error(self):
        # 45341 is the smallest integer greater than sqrt(2**31 - 1).
        # 3037000500 is the smallest integer greater than sqrt(2**63 - 1).
        # We want to make sure that the square byte array with those dimensions
        # is too big on 32 or 64 bit systems respectively.
        if np.iinfo('intp').max == 2**31 - 1:
            shape = (46341, 46341)
        elif np.iinfo('intp').max == 2**63 - 1:
            shape = (3037000500, 3037000500)
        else:
            return
        assert_raises(ValueError, np.empty, shape, dtype=np.int8)
        assert_raises(ValueError, np.zeros, shape, dtype=np.int8)
        assert_raises(ValueError, np.ones, shape, dtype=np.int8)

@pytest.mark.skipif(np.dtype(np.intp).itemsize != 8,
                        reason="malloc may not fail on 32 bit systems")
    def test_malloc_fails(self):
        # This test is guaranteed to fail due to a too large allocation
        with assert_raises(np.core._exceptions._ArrayMemoryError):
            np.empty(np.iinfo(np.intp).max, dtype=np.uint8)

def test_zeros(self):
        types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
        for dt in types:
            d = np.zeros((13,), dtype=dt)
            assert_equal(np.count_nonzero(d), 0)
            # true for ieee floats
            assert_equal(d.sum(), 0)
            assert_(not d.any())

d = np.zeros(2, dtype='(2,4)i4')
            assert_equal(np.count_nonzero(d), 0)
            assert_equal(d.sum(), 0)
            assert_(not d.any())

d = np.zeros(2, dtype='4i4')
            assert_equal(np.count_nonzero(d), 0)
            assert_equal(d.sum(), 0)
            assert_(not d.any())

d = np.zeros(2, dtype='(2,4)i4, (2,4)i4')
            assert_equal(np.count_nonzero(d), 0)

@pytest.mark.slow
    def test_zeros_big(self):
        # test big array as they might be allocated different by the system
        types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
        for dt in types:
            d = np.zeros((30 * 1024**2,), dtype=dt)
            assert_(not d.any())
            # This test can fail on 32-bit systems due to insufficient
            # contiguous memory. Deallocating the previous array increases the
            # chance of success.
            del(d)

def test_zeros_obj(self):
        # test initialization from PyLong(0)
        d = np.zeros((13,), dtype=object)
        assert_array_equal(d, [0] * 13)
        assert_equal(np.count_nonzero(d), 0)

def test_zeros_obj_obj(self):
        d = np.zeros(10, dtype=[('k', object, 2)])
        assert_array_equal(d['k'], 0)

def test_zeros_like_like_zeros(self):
        # test zeros_like returns the same as zeros
        for c in np.typecodes['All']:
            if c == 'V':
                continue
            d = np.zeros((3,3), dtype=c)
            assert_array_equal(np.zeros_like(d), d)
            assert_equal(np.zeros_like(d).dtype, d.dtype)
        # explicitly check some special cases
        d = np.zeros((3,3), dtype='S5')
        assert_array_equal(np.zeros_like(d), d)
        assert_equal(np.zeros_like(d).dtype, d.dtype)
        d = np.zeros((3,3), dtype='U5')
        assert_array_equal(np.zeros_like(d), d)
        assert_equal(np.zeros_like(d).dtype, d.dtype)

d = np.zeros((3,3), dtype='<i4')
        assert_array_equal(np.zeros_like(d), d)
        assert_equal(np.zeros_like(d).dtype, d.dtype)
        d = np.zeros((3,3), dtype='>i4')
        assert_array_equal(np.zeros_like(d), d)
        assert_equal(np.zeros_like(d).dtype, d.dtype)

d = np.zeros((3,3), dtype='<M8[s]')
        assert_array_equal(np.zeros_like(d), d)
        assert_equal(np.zeros_like(d).dtype, d.dtype)
        d = np.zeros((3,3), dtype='>M8[s]')
        assert_array_equal(np.zeros_like(d), d)
        assert_equal(np.zeros_like(d).dtype, d.dtype)

d = np.zeros((3,3), dtype='f4,f4')
        assert_array_equal(np.zeros_like(d), d)
        assert_equal(np.zeros_like(d).dtype, d.dtype)

def test_empty_unicode(self):
        # don't throw decode errors on garbage memory
        for i in range(5, 100, 5):
            d = np.empty(i, dtype='U')
            str(d)

def test_sequence_non_homogeneous(self):
        assert_equal(np.array([4, 2**80]).dtype, object)
        assert_equal(np.array([4, 2**80, 4]).dtype, object)
        assert_equal(np.array([2**80, 4]).dtype, object)
        assert_equal(np.array([2**80] * 3).dtype, object)
        assert_equal(np.array([[1, 1],[1j, 1j]]).dtype, complex)
        assert_equal(np.array([[1j, 1j],[1, 1]]).dtype, complex)
        assert_equal(np.array([[1, 1, 1],[1, 1j, 1.], [1, 1, 1]]).dtype, complex)

def test_non_sequence_sequence(self):
        """Should not segfault.

Class Fail breaks the sequence protocol for new style classes, i.e.,
        those derived from object. Class Map is a mapping type indicated by
        raising a ValueError. At some point we may raise a warning instead
        of an error in the Fail case.

"""
        class Fail:
            def __len__(self):
                return 1

def __getitem__(self, index):
                raise ValueError()

class Map:
            def __len__(self):
                return 1

def __getitem__(self, index):
                raise KeyError()

a = np.array([Map()])
        assert_(a.shape == (1,))
        assert_(a.dtype == np.dtype(object))
        assert_raises(ValueError, np.array, [Fail()])

def test_no_len_object_type(self):
        # gh-5100, want object array from iterable object without len()
        class Point2:
            def __init__(self):
                pass

def __getitem__(self, ind):
                if ind in [0, 1]:
                    return ind
                else:
                    raise IndexError()
        d = np.array([Point2(), Point2(), Point2()])
        assert_equal(d.dtype, np.dtype(object))

def test_false_len_sequence(self):
        # gh-7264, segfault for this example
        class C:
            def __getitem__(self, i):
                raise IndexError
            def __len__(self):
                return 42

a = np.array(C()) # segfault?
        assert_equal(len(a), 0)

def test_false_len_iterable(self):
        # Special case where a bad __getitem__ makes us fall back on __iter__:
        class C:
            def __getitem__(self, x):
                raise Exception
            def __iter__(self):
                return iter(())
            def __len__(self):
                return 2

a = np.empty(2)
        with assert_raises(ValueError):
            a[:] = C()  # Segfault!

np.array(C()) == list(C())

def test_failed_len_sequence(self):
        # gh-7393
        class A:
            def __init__(self, data):
                self._data = data
            def __getitem__(self, item):
                return type(self)(self._data[item])
            def __len__(self):
                return len(self._data)

# len(d) should give 3, but len(d[0]) will fail
        d = A([1,2,3])
        assert_equal(len(np.array(d)), 3)

def test_array_too_big(self):
        # Test that array creation succeeds for arrays addressable by intp
        # on the byte level and fails for too large arrays.
        buf = np.zeros(100)

max_bytes = np.iinfo(np.intp).max
        for dtype in ["intp", "S20", "b"]:
            dtype = np.dtype(dtype)
            itemsize = dtype.itemsize

np.ndarray(buffer=buf, strides=(0,),
                       shape=(max_bytes//itemsize,), dtype=dtype)
            assert_raises(ValueError, np.ndarray, buffer=buf, strides=(0,),
                          shape=(max_bytes//itemsize + 1,), dtype=dtype)

def _ragged_creation(self, seq):
        # without dtype=object, the ragged object raises
        with pytest.raises(ValueError, match=".*detected shape was"):
            a = np.array(seq)

return np.array(seq, dtype=object)

def test_ragged_ndim_object(self):
        # Lists of mismatching depths are treated as object arrays
        a = self._ragged_creation([[1], 2, 3])
        assert_equal(a.shape, (3,))
        assert_equal(a.dtype, object)

a = self._ragged_creation([1, [2], 3])
        assert_equal(a.shape, (3,))
        assert_equal(a.dtype, object)

a = self._ragged_creation([1, 2, [3]])
        assert_equal(a.shape, (3,))
        assert_equal(a.dtype, object)

def test_ragged_shape_object(self):
        # The ragged dimension of a list is turned into an object array
        a = self._ragged_creation([[1, 1], [2], [3]])
        assert_equal(a.shape, (3,))
        assert_equal(a.dtype, object)

a = self._ragged_creation([[1], [2, 2], [3]])
        assert_equal(a.shape, (3,))
        assert_equal(a.dtype, object)

a = self._ragged_creation([[1], [2], [3, 3]])
        assert a.shape == (3,)
        assert a.dtype == object

def test_array_of_ragged_array(self):
        outer = np.array([None, None])
        outer[0] = outer[1] = np.array([1, 2, 3])
        assert np.array(outer).shape == (2,)
        assert np.array([outer]).shape == (1, 2)

outer_ragged = np.array([None, None])
        outer_ragged[0] = np.array([1, 2, 3])
        outer_ragged[1] = np.array([1, 2, 3, 4])
        # should both of these emit deprecation warnings?
        assert np.array(outer_ragged).shape == (2,)
        assert np.array([outer_ragged]).shape == (1, 2,)

def test_deep_nonragged_object(self):
        # None of these should raise, even though they are missing dtype=object
        a = np.array([[[Decimal(1)]]])
        a = np.array([1, Decimal(1)])
        a = np.array([[1], [Decimal(1)]])

@pytest.mark.parametrize("dtype", [object, "O,O", "O,(3)O", "(2,3)O"])
    @pytest.mark.parametrize("function", [
            np.ndarray, np.empty,
            lambda shape, dtype: np.empty_like(np.empty(shape, dtype=dtype))])
    def test_object_initialized_to_None(self, function, dtype):
        # NumPy has support for object fields to be NULL (meaning None)
        # but generally, we should always fill with the proper None, and
        # downstream may rely on that.  (For fully initialized arrays!)
        arr = function(3, dtype=dtype)
        # We expect a fill value of None, which is not NULL:
        expected = np.array(None).tobytes()
        expected = expected * (arr.nbytes // len(expected))
        assert arr.tobytes() == expected

@pytest.mark.parametrize("func", [
        np.array, np.asarray, np.asanyarray, np.ascontiguousarray,
        np.asfortranarray])
    def test_creation_from_dtypemeta(self, func):
        dtype = np.dtype('i')
        arr1 = func([1, 2, 3], dtype=dtype)
        arr2 = func([1, 2, 3], dtype=type(dtype))
        assert_array_equal(arr1, arr2)
        assert arr2.dtype == dtype

class TestStructured:
    def test_subarray_field_access(self):
        a = np.zeros((3, 5), dtype=[('a', ('i4', (2, 2)))])
        a['a'] = np.arange(60).reshape(3, 5, 2, 2)

# Since the subarray is always in C-order, a transpose
        # does not swap the subarray:
        assert_array_equal(a.T['a'], a['a'].transpose(1, 0, 2, 3))

# In Fortran order, the subarray gets appended
        # like in all other cases, not prepended as a special case
        b = a.copy(order='F')
        assert_equal(a['a'].shape, b['a'].shape)
        assert_equal(a.T['a'].shape, a.T.copy()['a'].shape)

def test_subarray_comparison(self):
        # Check that comparisons between record arrays with
        # multi-dimensional field types work properly
        a = np.rec.fromrecords(
            [([1, 2, 3], 'a', [[1, 2], [3, 4]]), ([3, 3, 3], 'b', [[0, 0], [0, 0]])],
            dtype=[('a', ('f4', 3)), ('b', object), ('c', ('i4', (2, 2)))])
        b = a.copy()
        assert_equal(a == b, [True, True])
        assert_equal(a != b, [False, False])
        b[1].b = 'c'
        assert_equal(a == b, [True, False])
        assert_equal(a != b, [False, True])
        for i in range(3):
            b[0].a = a[0].a
            b[0].a[i] = 5
            assert_equal(a == b, [False, False])
            assert_equal(a != b, [True, True])
        for i in range(2):
            for j in range(2):
                b = a.copy()
                b[0].c[i, j] = 10
                assert_equal(a == b, [False, True])
                assert_equal(a != b, [True, False])

# Check that broadcasting with a subarray works, including cases that
        # require promotion to work:
        a = np.array([[(0,)], [(1,)]], dtype=[('a', 'f8')])
        b = np.array([(0,), (0,), (1,)], dtype=[('a', 'f8')])
        assert_equal(a == b, [[True, True, False], [False, False, True]])
        assert_equal(b == a, [[True, True, False], [False, False, True]])
        a = np.array([[(0,)], [(1,)]], dtype=[('a', 'f8', (1,))])
        b = np.array([(0,), (0,), (1,)], dtype=[('a', 'f8', (1,))])
        assert_equal(a == b, [[True, True, False], [False, False, True]])
        assert_equal(b == a, [[True, True, False], [False, False, True]])
        a = np.array([[([0, 0],)], [([1, 1],)]], dtype=[('a', 'f8', (2,))])
        b = np.array([([0, 0],), ([0, 1],), ([1, 1],)], dtype=[('a', 'f8', (2,))])
        assert_equal(a == b, [[True, False, False], [False, False, True]])
        assert_equal(b == a, [[True, False, False], [False, False, True]])

# Check that broadcasting Fortran-style arrays with a subarray work
        a = np.array([[([0, 0],)], [([1, 1],)]], dtype=[('a', 'f8', (2,))], order='F')
        b = np.array([([0, 0],), ([0, 1],), ([1, 1],)], dtype=[('a', 'f8', (2,))])
        assert_equal(a == b, [[True, False, False], [False, False, True]])
        assert_equal(b == a, [[True, False, False], [False, False, True]])

# Check that incompatible sub-array shapes don't result to broadcasting
        x = np.zeros((1,), dtype=[('a', ('f4', (1, 2))), ('b', 'i1')])
        y = np.zeros((1,), dtype=[('a', ('f4', (2,))), ('b', 'i1')])
        # The main importance is that it does not return True:
        with pytest.raises(TypeError):
            x == y

x = np.zeros((1,), dtype=[('a', ('f4', (2, 1))), ('b', 'i1')])
        y = np.zeros((1,), dtype=[('a', ('f4', (2,))), ('b', 'i1')])
        # The main importance is that it does not return True:
        with pytest.raises(TypeError):
            x == y

def test_empty_structured_array_comparison(self):
        # Check that comparison works on empty arrays with nontrivially
        # shaped fields
        a = np.zeros(0, [('a', '<f8', (1, 1))])
        assert_equal(a, a)
        a = np.zeros(0, [('a', '<f8', (1,))])
        assert_equal(a, a)
        a = np.zeros((0, 0), [('a', '<f8', (1, 1))])
        assert_equal(a, a)
        a = np.zeros((1, 0, 1), [('a', '<f8', (1, 1))])
        assert_equal(a, a)

@pytest.mark.parametrize("op", [operator.eq, operator.ne])
    def test_structured_array_comparison_bad_broadcasts(self, op):
        a = np.zeros(3, dtype='i,i')
        b = np.array([], dtype="i,i")
        with pytest.raises(ValueError):
            op(a, b)

def test_structured_comparisons_with_promotion(self):
        # Check that structured arrays can be compared so long as their
        # dtypes promote fine:
        a = np.array([(5, 42), (10, 1)], dtype=[('a', '>i8'), ('b', '<f8')])
        b = np.array([(5, 43), (10, 1)], dtype=[('a', '<i8'), ('b', '>f8')])
        assert_equal(a == b, [False, True])
        assert_equal(a != b, [True, False])

a = np.array([(5, 42), (10, 1)], dtype=[('a', '>f8'), ('b', '<f8')])
        b = np.array([(5, 43), (10, 1)], dtype=[('a', '<i8'), ('b', '>i8')])
        assert_equal(a == b, [False, True])
        assert_equal(a != b, [True, False])

# Including with embedded subarray dtype (although subarray comparison
        # itself may still be a bit weird and compare the raw data)
        a = np.array([(5, 42), (10, 1)], dtype=[('a', '10>f8'), ('b', '5<f8')])
        b = np.array([(5, 43), (10, 1)], dtype=[('a', '10<i8'), ('b', '5>i8')])
        assert_equal(a == b, [False, True])
        assert_equal(a != b, [True, False])

@pytest.mark.parametrize("op", [
            operator.eq, lambda x, y: operator.eq(y, x),
            operator.ne, lambda x, y: operator.ne(y, x)])
    def test_void_comparison_failures(self, op):
        # In principle, one could decide to return an array of False for some
        # if comparisons are impossible.  But right now we return TypeError
        # when "void" dtype are involved.
        x = np.zeros(3, dtype=[('a', 'i1')])
        y = np.zeros(3)
        # Cannot compare non-structured to structured:
        with pytest.raises(TypeError):
            op(x, y)

# Added title prevents promotion, but casts are OK:
        y = np.zeros(3, dtype=[(('title', 'a'), 'i1')])
        assert np.can_cast(y.dtype, x.dtype)
        with pytest.raises(TypeError):
            op(x, y)

x = np.zeros(3, dtype="V7")
        y = np.zeros(3, dtype="V8")
        with pytest.raises(TypeError):
            op(x, y)

def test_casting(self):
        # Check that casting a structured array to change its byte order
        # works
        a = np.array([(1,)], dtype=[('a', '<i4')])
        assert_(np.can_cast(a.dtype, [('a', '>i4')], casting='unsafe'))
        b = a.astype([('a', '>i4')])
        assert_equal(b, a.byteswap().newbyteorder())
        assert_equal(a['a'][0], b['a'][0])

# Check that equality comparison works on structured arrays if
        # they are 'equiv'-castable
        a = np.array([(5, 42), (10, 1)], dtype=[('a', '>i4'), ('b', '<f8')])
        b = np.array([(5, 42), (10, 1)], dtype=[('a', '<i4'), ('b', '>f8')])
        assert_(np.can_cast(a.dtype, b.dtype, casting='equiv'))
        assert_equal(a == b, [True, True])

# Check that 'equiv' casting can change byte order
        assert_(np.can_cast(a.dtype, b.dtype, casting='equiv'))
        c = a.astype(b.dtype, casting='equiv')
        assert_equal(a == c, [True, True])

# Check that 'safe' casting can change byte order and up-cast
        # fields
        t = [('a', '<i8'), ('b', '>f8')]
        assert_(np.can_cast(a.dtype, t, casting='safe'))
        c = a.astype(t, casting='safe')
        assert_equal((c == np.array([(5, 42), (10, 1)], dtype=t)),
                     [True, True])

# Check that 'same_kind' casting can change byte order and
        # change field widths within a "kind"
        t = [('a', '<i4'), ('b', '>f4')]
        assert_(np.can_cast(a.dtype, t, casting='same_kind'))
        c = a.astype(t, casting='same_kind')
        assert_equal((c == np.array([(5, 42), (10, 1)], dtype=t)),
                     [True, True])

# Check that casting fails if the casting rule should fail on
        # any of the fields
        t = [('a', '>i8'), ('b', '<f4')]
        assert_(not np.can_cast(a.dtype, t, casting='safe'))
        assert_raises(TypeError, a.astype, t, casting='safe')
        t = [('a', '>i2'), ('b', '<f8')]
        assert_(not np.can_cast(a.dtype, t, casting='equiv'))
        assert_raises(TypeError, a.astype, t, casting='equiv')
        t = [('a', '>i8'), ('b', '<i2')]
        assert_(not np.can_cast(a.dtype, t, casting='same_kind'))
        assert_raises(TypeError, a.astype, t, casting='same_kind')
        assert_(not np.can_cast(a.dtype, b.dtype, casting='no'))
        assert_raises(TypeError, a.astype, b.dtype, casting='no')

# Check that non-'unsafe' casting can't change the set of field names
        for casting in ['no', 'safe', 'equiv', 'same_kind']:
            t = [('a', '>i4')]
            assert_(not np.can_cast(a.dtype, t, casting=casting))
            t = [('a', '>i4'), ('b', '<f8'), ('c', 'i4')]
            assert_(not np.can_cast(a.dtype, t, casting=casting))

def test_objview(self):
        # https://github.com/numpy/numpy/issues/3286
        a = np.array([], dtype=[('a', 'f'), ('b', 'f'), ('c', 'O')])
        a[['a', 'b']]  # TypeError?

# https://github.com/numpy/numpy/issues/3253
        dat2 = np.zeros(3, [('A', 'i'), ('B', '|O')])
        dat2[['B', 'A']]  # TypeError?

def test_setfield(self):
        # https://github.com/numpy/numpy/issues/3126
        struct_dt = np.dtype([('elem', 'i4', 5),])
        dt = np.dtype([('field', 'i4', 10),('struct', struct_dt)])
        x = np.zeros(1, dt)
        x[0]['field'] = np.ones(10, dtype='i4')
        x[0]['struct'] = np.ones(1, dtype=struct_dt)
        assert_equal(x[0]['field'], np.ones(10, dtype='i4'))

def test_setfield_object(self):
        # make sure object field assignment with ndarray value
        # on void scalar mimics setitem behavior
        b = np.zeros(1, dtype=[('x', 'O')])
        # next line should work identically to b['x'][0] = np.arange(3)
        b[0]['x'] = np.arange(3)
        assert_equal(b[0]['x'], np.arange(3))

# check that broadcasting check still works
        c = np.zeros(1, dtype=[('x', 'O', 5)])

def testassign():
            c[0]['x'] = np.arange(3)

assert_raises(ValueError, testassign)

def test_zero_width_string(self):
        # Test for PR #6430 / issues #473, #4955, #2585

dt = np.dtype([('I', int), ('S', 'S0')])

x = np.zeros(4, dtype=dt)

assert_equal(x['S'], [b'', b'', b'', b''])
        assert_equal(x['S'].itemsize, 0)

x['S'] = ['a', 'b', 'c', 'd']
        assert_equal(x['S'], [b'', b'', b'', b''])
        assert_equal(x['I'], [0, 0, 0, 0])

# Variation on test case from #4955
        x['S'][x['I'] == 0] = 'hello'
        assert_equal(x['S'], [b'', b'', b'', b''])
        assert_equal(x['I'], [0, 0, 0, 0])

# Variation on test case from #2585
        x['S'] = 'A'
        assert_equal(x['S'], [b'', b'', b'', b''])
        assert_equal(x['I'], [0, 0, 0, 0])

# Allow zero-width dtypes in ndarray constructor
        y = np.ndarray(4, dtype=x['S'].dtype)
        assert_equal(y.itemsize, 0)
        assert_equal(x['S'], y)

# More tests for indexing an array with zero-width fields
        assert_equal(np.zeros(4, dtype=[('a', 'S0,S0'),
                                        ('b', 'u1')])['a'].itemsize, 0)
        assert_equal(np.empty(3, dtype='S0,S0').itemsize, 0)
        assert_equal(np.zeros(4, dtype='S0,u1')['f0'].itemsize, 0)

xx = x['S'].reshape((2, 2))
        assert_equal(xx.itemsize, 0)
        assert_equal(xx, [[b'', b''], [b'', b'']])
        # check for no uninitialized memory due to viewing S0 array
        assert_equal(xx[:].dtype, xx.dtype)
        assert_array_equal(eval(repr(xx), dict(array=np.array)), xx)

b = io.BytesIO()
        np.save(b, xx)

b.seek(0)
        yy = np.load(b)
        assert_equal(yy.itemsize, 0)
        assert_equal(xx, yy)

with temppath(suffix='.npy') as tmp:
            np.save(tmp, xx)
            yy = np.load(tmp)
            assert_equal(yy.itemsize, 0)
            assert_equal(xx, yy)

def test_base_attr(self):
        a = np.zeros(3, dtype='i4,f4')
        b = a[0]
        assert_(b.base is a)

def test_assignment(self):
        def testassign(arr, v):
            c = arr.copy()
            c[0] = v  # assign using setitem
            c[1:] = v # assign using "dtype_transfer" code paths
            return c

dt = np.dtype([('foo', 'i8'), ('bar', 'i8')])
        arr = np.ones(2, dt)
        v1 = np.array([(2,3)], dtype=[('foo', 'i8'), ('bar', 'i8')])
        v2 = np.array([(2,3)], dtype=[('bar', 'i8'), ('foo', 'i8')])
        v3 = np.array([(2,3)], dtype=[('bar', 'i8'), ('baz', 'i8')])
        v4 = np.array([(2,)],  dtype=[('bar', 'i8')])
        v5 = np.array([(2,3)], dtype=[('foo', 'f8'), ('bar', 'f8')])
        w = arr.view({'names': ['bar'], 'formats': ['i8'], 'offsets': [8]})

ans = np.array([(2,3),(2,3)], dtype=dt)
        assert_equal(testassign(arr, v1), ans)
        assert_equal(testassign(arr, v2), ans)
        assert_equal(testassign(arr, v3), ans)
        assert_raises(TypeError, lambda: testassign(arr, v4))
        assert_equal(testassign(arr, v5), ans)
        w[:] = 4
        assert_equal(arr, np.array([(1,4),(1,4)], dtype=dt))

# test field-reordering, assignment by position, and self-assignment
        a = np.array([(1,2,3)],
                     dtype=[('foo', 'i8'), ('bar', 'i8'), ('baz', 'f4')])
        a[['foo', 'bar']] = a[['bar', 'foo']]
        assert_equal(a[0].item(), (2,1,3))

# test that this works even for 'simple_unaligned' structs
        # (ie, that PyArray_EquivTypes cares about field order too)
        a = np.array([(1,2)], dtype=[('a', 'i4'), ('b', 'i4')])
        a[['a', 'b']] = a[['b', 'a']]
        assert_equal(a[0].item(), (2,1))

def test_scalar_assignment(self):
        with assert_raises(ValueError):
            arr = np.arange(25).reshape(5, 5)
            arr.itemset(3)

def test_structuredscalar_indexing(self):
        # test gh-7262
        x = np.empty(shape=1, dtype="(2)3S,(2)3U")
        assert_equal(x[["f0","f1"]][0], x[0][["f0","f1"]])
        assert_equal(x[0], x[0][()])

def test_multiindex_titles(self):
        a = np.zeros(4, dtype=[(('a', 'b'), 'i'), ('c', 'i'), ('d', 'i')])
        assert_raises(KeyError, lambda : a[['a','c']])
        assert_raises(KeyError, lambda : a[['a','a']])
        assert_raises(ValueError, lambda : a[['b','b']])  # field exists, but repeated
        a[['b','c']]  # no exception

def test_structured_cast_promotion_fieldorder(self):
        # gh-15494
        # dtypes with different field names are not promotable
        A = ("a", "<i8")
        B = ("b", ">i8")
        ab = np.array([(1, 2)], dtype=[A, B])
        ba = np.array([(1, 2)], dtype=[B, A])
        assert_raises(TypeError, np.concatenate, ab, ba)
        assert_raises(TypeError, np.result_type, ab.dtype, ba.dtype)
        assert_raises(TypeError, np.promote_types, ab.dtype, ba.dtype)

# dtypes with same field names/order but different memory offsets
        # and byte-order are promotable to packed nbo.
        assert_equal(np.promote_types(ab.dtype, ba[['a', 'b']].dtype),
                     repack_fields(ab.dtype.newbyteorder('N')))

# gh-13667
        # dtypes with different fieldnames but castable field types are castable
        assert_equal(np.can_cast(ab.dtype, ba.dtype), True)
        assert_equal(ab.astype(ba.dtype).dtype, ba.dtype)
        assert_equal(np.can_cast('f8,i8', [('f0', 'f8'), ('f1', 'i8')]), True)
        assert_equal(np.can_cast('f8,i8', [('f1', 'f8'), ('f0', 'i8')]), True)
        assert_equal(np.can_cast('f8,i8', [('f1', 'i8'), ('f0', 'f8')]), False)
        assert_equal(np.can_cast('f8,i8', [('f1', 'i8'), ('f0', 'f8')],
                                 casting='unsafe'), True)

ab[:] = ba  # make sure assignment still works

# tests of type-promotion of corresponding fields
        dt1 = np.dtype([("", "i4")])
        dt2 = np.dtype([("", "i8")])
        assert_equal(np.promote_types(dt1, dt2), np.dtype([('f0', 'i8')]))
        assert_equal(np.promote_types(dt2, dt1), np.dtype([('f0', 'i8')]))
        assert_raises(TypeError, np.promote_types, dt1, np.dtype([("", "V3")]))
        assert_equal(np.promote_types('i4,f8', 'i8,f4'),
                     np.dtype([('f0', 'i8'), ('f1', 'f8')]))
        # test nested case
        dt1nest = np.dtype([("", dt1)])
        dt2nest = np.dtype([("", dt2)])
        assert_equal(np.promote_types(dt1nest, dt2nest),
                     np.dtype([('f0', np.dtype([('f0', 'i8')]))]))

# note that offsets are lost when promoting:
        dt = np.dtype({'names': ['x'], 'formats': ['i4'], 'offsets': [8]})
        a = np.ones(3, dtype=dt)
        assert_equal(np.concatenate([a, a]).dtype, np.dtype([('x', 'i4')]))

@pytest.mark.parametrize("dtype_dict", [
            dict(names=["a", "b"], formats=["i4", "f"], itemsize=100),
            dict(names=["a", "b"], formats=["i4", "f"],
                 offsets=[0, 12])])
    @pytest.mark.parametrize("align", [True, False])
    def test_structured_promotion_packs(self, dtype_dict, align):
        # Structured dtypes are packed when promoted (we consider the packed
        # form to be "canonical"), so tere is no extra padding.
        dtype = np.dtype(dtype_dict, align=align)
        # Remove non "canonical" dtype options:
        dtype_dict.pop("itemsize", None)
        dtype_dict.pop("offsets", None)
        expected = np.dtype(dtype_dict, align=align)

res = np.promote_types(dtype, dtype)
        assert res.itemsize == expected.itemsize
        assert res.fields == expected.fields

# But the "expected" one, should just be returned unchanged:
        res = np.promote_types(expected, expected)
        assert res is expected

def test_structured_asarray_is_view(self):
        # A scalar viewing an array preserves its view even when creating a
        # new array. This test documents behaviour, it may not be the best
        # desired behaviour.
        arr = np.array([1], dtype="i,i")
        scalar = arr[0]
        assert not scalar.flags.owndata  # view into the array
        assert np.asarray(scalar).base is scalar
        # But never when a dtype is passed in:
        assert np.asarray(scalar, dtype=scalar.dtype).base is None
        # A scalar which owns its data does not have this property.
        # It is not easy to create one, one method is to use pickle:
        scalar = pickle.loads(pickle.dumps(scalar))
        assert scalar.flags.owndata
        assert np.asarray(scalar).base is None

class TestBool:
    def test_test_interning(self):
        a0 = np.bool_(0)
        b0 = np.bool_(False)
        assert_(a0 is b0)
        a1 = np.bool_(1)
        b1 = np.bool_(True)
        assert_(a1 is b1)
        assert_(np.array([True])[0] is a1)
        assert_(np.array(True)[()] is a1)

def test_sum(self):
        d = np.ones(101, dtype=bool)
        assert_equal(d.sum(), d.size)
        assert_equal(d[::2].sum(), d[::2].size)
        assert_equal(d[::-2].sum(), d[::-2].size)

d = np.frombuffer(b'\xff\xff' * 100, dtype=bool)
        assert_equal(d.sum(), d.size)
        assert_equal(d[::2].sum(), d[::2].size)
        assert_equal(d[::-2].sum(), d[::-2].size)

def check_count_nonzero(self, power, length):
        powers = [2 ** i for i in range(length)]
        for i in range(2**power):
            l = [(i & x) != 0 for x in powers]
            a = np.array(l, dtype=bool)
            c = builtins.sum(l)
            assert_equal(np.count_nonzero(a), c)
            av = a.view(np.uint8)
            av *= 3
            assert_equal(np.count_nonzero(a), c)
            av *= 4
            assert_equal(np.count_nonzero(a), c)
            av[av != 0] = 0xFF
            assert_equal(np.count_nonzero(a), c)

def test_count_nonzero(self):
        # check all 12 bit combinations in a length 17 array
        # covers most cases of the 16 byte unrolled code
        self.check_count_nonzero(12, 17)

@pytest.mark.slow
    def test_count_nonzero_all(self):
        # check all combinations in a length 17 array
        # covers all cases of the 16 byte unrolled code
        self.check_count_nonzero(17, 17)

def test_count_nonzero_unaligned(self):
        # prevent mistakes as e.g. gh-4060
        for o in range(7):
            a = np.zeros((18,), dtype=bool)[o+1:]
            a[:o] = True
            assert_equal(np.count_nonzero(a), builtins.sum(a.tolist()))
            a = np.ones((18,), dtype=bool)[o+1:]
            a[:o] = False
            assert_equal(np.count_nonzero(a), builtins.sum(a.tolist()))

def _test_cast_from_flexible(self, dtype):
        # empty string -> false
        for n in range(3):
            v = np.array(b'', (dtype, n))
            assert_equal(bool(v), False)
            assert_equal(bool(v[()]), False)
            assert_equal(v.astype(bool), False)
            assert_(isinstance(v.astype(bool), np.ndarray))
            assert_(v[()].astype(bool) is np.False_)

# anything else -> true
        for n in range(1, 4):
            for val in [b'a', b'0', b' ']:
                v = np.array(val, (dtype, n))
                assert_equal(bool(v), True)
                assert_equal(bool(v[()]), True)
                assert_equal(v.astype(bool), True)
                assert_(isinstance(v.astype(bool), np.ndarray))
                assert_(v[()].astype(bool) is np.True_)

def test_cast_from_void(self):
        self._test_cast_from_flexible(np.void)

@pytest.mark.xfail(reason="See gh-9847")
    def test_cast_from_unicode(self):
        self._test_cast_from_flexible(np.str_)

@pytest.mark.xfail(reason="See gh-9847")
    def test_cast_from_bytes(self):
        self._test_cast_from_flexible(np.bytes_)

class TestZeroSizeFlexible:
    @staticmethod
    def _zeros(shape, dtype=str):
        dtype = np.dtype(dtype)
        if dtype == np.void:
            return np.zeros(shape, dtype=(dtype, 0))

# not constructable directly
        dtype = np.dtype([('x', dtype, 0)])
        return np.zeros(shape, dtype=dtype)['x']

def test_create(self):
        zs = self._zeros(10, bytes)
        assert_equal(zs.itemsize, 0)
        zs = self._zeros(10, np.void)
        assert_equal(zs.itemsize, 0)
        zs = self._zeros(10, str)
        assert_equal(zs.itemsize, 0)

def _test_sort_partition(self, name, kinds, **kwargs):
        # Previously, these would all hang
        for dt in [bytes, np.void, str]:
            zs = self._zeros(10, dt)
            sort_method = getattr(zs, name)
            sort_func = getattr(np, name)
            for kind in kinds:
                sort_method(kind=kind, **kwargs)
                sort_func(zs, kind=kind, **kwargs)

def test_sort(self):
        self._test_sort_partition('sort', kinds='qhs')

def test_argsort(self):
        self._test_sort_partition('argsort', kinds='qhs')

def test_partition(self):
        self._test_sort_partition('partition', kinds=['introselect'], kth=2)

def test_argpartition(self):
        self._test_sort_partition('argpartition', kinds=['introselect'], kth=2)

def test_resize(self):
        # previously an error
        for dt in [bytes, np.void, str]:
            zs = self._zeros(10, dt)
            zs.resize(25)
            zs.resize((10, 10))

def test_view(self):
        for dt in [bytes, np.void, str]:
            zs = self._zeros(10, dt)

# viewing as itself should be allowed
            assert_equal(zs.view(dt).dtype, np.dtype(dt))

# viewing as any non-empty type gives an empty result
            assert_equal(zs.view((dt, 1)).shape, (0,))

def test_dumps(self):
        zs = self._zeros(10, int)
        assert_equal(zs, pickle.loads(zs.dumps()))

def test_pickle(self):
        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
            for dt in [bytes, np.void, str]:
                zs = self._zeros(10, dt)
                p = pickle.dumps(zs, protocol=proto)
                zs2 = pickle.loads(p)

assert_equal(zs.dtype, zs2.dtype)

def test_pickle_empty(self):
        """Checking if an empty array pickled and un-pickled will not cause a
        segmentation fault"""
        arr = np.array([]).reshape(999999, 0)
        pk_dmp = pickle.dumps(arr)
        pk_load = pickle.loads(pk_dmp)

assert pk_load.size == 0

@pytest.mark.skipif(pickle.HIGHEST_PROTOCOL < 5,
                        reason="requires pickle protocol 5")
    def test_pickle_with_buffercallback(self):
        array = np.arange(10)
        buffers = []
        bytes_string = pickle.dumps(array, buffer_callback=buffers.append,
                                    protocol=5)
        array_from_buffer = pickle.loads(bytes_string, buffers=buffers)
        # when using pickle protocol 5 with buffer callbacks,
        # array_from_buffer is reconstructed from a buffer holding a view
        # to the initial array's data, so modifying an element in array
        # should modify it in array_from_buffer too.
        array[0] = -1
        assert array_from_buffer[0] == -1, array_from_buffer[0]

class TestMethods:

sort_kinds = ['quicksort', 'heapsort', 'stable']

def test_all_where(self):
        a = np.array([[True, False, True],
                      [False, False, False],
                      [True, True, True]])
        wh_full = np.array([[True, False, True],
                            [False, False, False],
                            [True, False, True]])
        wh_lower = np.array([[False],
                             [False],
                             [True]])
        for _ax in [0, None]:
            assert_equal(a.all(axis=_ax, where=wh_lower),
                        np.all(a[wh_lower[:,0],:], axis=_ax))
            assert_equal(np.all(a, axis=_ax, where=wh_lower),
                         a[wh_lower[:,0],:].all(axis=_ax))

assert_equal(a.all(where=wh_full), True)
        assert_equal(np.all(a, where=wh_full), True)
        assert_equal(a.all(where=False), True)
        assert_equal(np.all(a, where=False), True)

def test_any_where(self):
        a = np.array([[True, False, True],
                      [False, False, False],
                      [True, True, True]])
        wh_full = np.array([[False, True, False],
                            [True, True, True],
                            [False, False, False]])
        wh_middle = np.array([[False],
                              [True],
                              [False]])
        for _ax in [0, None]:
            assert_equal(a.any(axis=_ax, where=wh_middle),
                         np.any(a[wh_middle[:,0],:], axis=_ax))
            assert_equal(np.any(a, axis=_ax, where=wh_middle),
                         a[wh_middle[:,0],:].any(axis=_ax))
        assert_equal(a.any(where=wh_full), False)
        assert_equal(np.any(a, where=wh_full), False)
        assert_equal(a.any(where=False), False)
        assert_equal(np.any(a, where=False), False)

def test_compress(self):
        tgt = [[5, 6, 7, 8, 9]]
        arr = np.arange(10).reshape(2, 5)
        out = arr.compress([0, 1], axis=0)
        assert_equal(out, tgt)

tgt = [[1, 3], [6, 8]]
        out = arr.compress([0, 1, 0, 1, 0], axis=1)
        assert_equal(out, tgt)

tgt = [[1], [6]]
        arr = np.arange(10).reshape(2, 5)
        out = arr.compress([0, 1], axis=1)
        assert_equal(out, tgt)

arr = np.arange(10).reshape(2, 5)
        out = arr.compress([0, 1])
        assert_equal(out, 1)

def test_choose(self):
        x = 2*np.ones((3,), dtype=int)
        y = 3*np.ones((3,), dtype=int)
        x2 = 2*np.ones((2, 3), dtype=int)
        y2 = 3*np.ones((2, 3), dtype=int)
        ind = np.array([0, 0, 1])

A = ind.choose((x, y))
        assert_equal(A, [2, 2, 3])

A = ind.choose((x2, y2))
        assert_equal(A, [[2, 2, 3], [2, 2, 3]])

A = ind.choose((x, y2))
        assert_equal(A, [[2, 2, 3], [2, 2, 3]])

oned = np.ones(1)
        # gh-12031, caused SEGFAULT
        assert_raises(TypeError, oned.choose,np.void(0), [oned])

out = np.array(0)
        ret = np.choose(np.array(1), [10, 20, 30], out=out)
        assert out is ret
        assert_equal(out[()], 20)

# gh-6272 check overlap on out
        x = np.arange(5)
        y = np.choose([0,0,0], [x[:3], x[:3], x[:3]], out=x[1:4], mode='wrap')
        assert_equal(y, np.array([0, 1, 2]))

def test_prod(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, a.prod)
                assert_raises(ArithmeticError, a2.prod, axis=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))

@pytest.mark.parametrize('dtype', [None, object])
    def test_repeat(self, dtype):
        m = np.array([1, 2, 3, 4, 5, 6], dtype=dtype)
        m_rect = m.reshape((2, 3))

A = m.repeat([1, 3, 2, 1, 1, 2])
        assert_equal(A, [1, 2, 2, 2, 3,
                         3, 4, 5, 6, 6])

A = m.repeat(2)
        assert_equal(A, [1, 1, 2, 2, 3, 3,
                         4, 4, 5, 5, 6, 6])

A = m_rect.repeat([2, 1], axis=0)
        assert_equal(A, [[1, 2, 3],
                         [1, 2, 3],
                         [4, 5, 6]])

A = m_rect.repeat([1, 3, 2], axis=1)
        assert_equal(A, [[1, 2, 2, 2, 3, 3],
                         [4, 5, 5, 5, 6, 6]])

A = m_rect.repeat(2, axis=0)
        assert_equal(A, [[1, 2, 3],
                         [1, 2, 3],
                         [4, 5, 6],
                         [4, 5, 6]])

A = m_rect.repeat(2, axis=1)
        assert_equal(A, [[1, 1, 2, 2, 3, 3],
                         [4, 4, 5, 5, 6, 6]])

def test_reshape(self):
        arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])

tgt = [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]
        assert_equal(arr.reshape(2, 6), tgt)

tgt = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
        assert_equal(arr.reshape(3, 4), tgt)

tgt = [[1, 10, 8, 6], [4, 2, 11, 9], [7, 5, 3, 12]]
        assert_equal(arr.reshape((3, 4), order='F'), tgt)

tgt = [[1, 4, 7, 10], [2, 5, 8, 11], [3, 6, 9, 12]]
        assert_equal(arr.T.reshape((3, 4), order='C'), tgt)

def test_round(self):
        def check_round(arr, expected, *round_args):
            assert_equal(arr.round(*round_args), expected)
            # With output array
            out = np.zeros_like(arr)
            res = arr.round(*round_args, out=out)
            assert_equal(out, expected)
            assert out is res

check_round(np.array([1.2, 1.5]), [1, 2])
        check_round(np.array(1.5), 2)
        check_round(np.array([12.2, 15.5]), [10, 20], -1)
        check_round(np.array([12.15, 15.51]), [12.2, 15.5], 1)
        # Complex rounding
        check_round(np.array([4.5 + 1.5j]), [4 + 2j])
        check_round(np.array([12.5 + 15.5j]), [10 + 20j], -1)

def test_squeeze(self):
        a = np.array([[[1], [2], [3]]])
        assert_equal(a.squeeze(), [1, 2, 3])
        assert_equal(a.squeeze(axis=(0,)), [[1], [2], [3]])
        assert_raises(ValueError, a.squeeze, axis=(1,))
        assert_equal(a.squeeze(axis=(2,)), [[1, 2, 3]])

def test_transpose(self):
        a = np.array([[1, 2], [3, 4]])
        assert_equal(a.transpose(), [[1, 3], [2, 4]])
        assert_raises(ValueError, lambda: a.transpose(0))
        assert_raises(ValueError, lambda: a.transpose(0, 0))
        assert_raises(ValueError, lambda: a.transpose(0, 1, 2))

def test_sort(self):
        # test ordering for floats and complex containing nans. It is only
        # necessary to check the less-than comparison, so sorts that
        # only follow the insertion sort path are sufficient. We only
        # test doubles and complex doubles as the logic is the same.

# check doubles
        msg = "Test real sort order with nans"
        a = np.array([np.nan, 1, 0])
        b = np.sort(a)
        assert_equal(b, a[::-1], msg)
        # check complex
        msg = "Test complex sort order with nans"
        a = np.zeros(9, dtype=np.complex128)
        a.real += [np.nan, np.nan, np.nan, 1, 0, 1, 1, 0, 0]
        a.imag += [np.nan, 1, 0, np.nan, np.nan, 1, 0, 1, 0]
        b = np.sort(a)
        assert_equal(b, a[::-1], msg)

# all c scalar sorts use the same code with different types
    # so it suffices to run a quick check with one type. The number
    # of sorted items must be greater than ~50 to check the actual
    # algorithm because quick and merge sort fall over to insertion
    # sort for small arrays.

@pytest.mark.parametrize('dtype', [np.uint8, np.uint16, np.uint32, np.uint64,
                                       np.float16, np.float32, np.float64,
                                       np.longdouble])
    def test_sort_unsigned(self, dtype):
        a = np.arange(101, dtype=dtype)
        b = a[::-1].copy()
        for kind in self.sort_kinds:
            msg = "scalar sort, kind=%s" % kind
            c = a.copy()
            c.sort(kind=kind)
            assert_equal(c, a, msg)
            c = b.copy()
            c.sort(kind=kind)
            assert_equal(c, a, msg)

@pytest.mark.parametrize('dtype',
                             [np.int8, np.int16, np.int32, np.int64, np.float16,
                              np.float32, np.float64, np.longdouble])
    def test_sort_signed(self, dtype):
        a = np.arange(-50, 51, dtype=dtype)
        b = a[::-1].copy()
        for kind in self.sort_kinds:
            msg = "scalar sort, kind=%s" % (kind)
            c = a.copy()
            c.sort(kind=kind)
            assert_equal(c, a, msg)
            c = b.copy()
            c.sort(kind=kind)
            assert_equal(c, a, msg)

@pytest.mark.parametrize('dtype', [np.float32, np.float64, np.longdouble])
    @pytest.mark.parametrize('part', ['real', 'imag'])
    def test_sort_complex(self, part, dtype):
        # test complex sorts. These use the same code as the scalars
        # but the compare function differs.
        cdtype = {
            np.single: np.csingle,
            np.double: np.cdouble,
            np.longdouble: np.clongdouble,
        }[dtype]
        a = np.arange(-50, 51, dtype=dtype)
        b = a[::-1].copy()
        ai = (a * (1+1j)).astype(cdtype)
        bi = (b * (1+1j)).astype(cdtype)
        setattr(ai, part, 1)
        setattr(bi, part, 1)
        for kind in self.sort_kinds:
            msg = "complex sort, %s part == 1, kind=%s" % (part, kind)
            c = ai.copy()
            c.sort(kind=kind)
            assert_equal(c, ai, msg)
            c = bi.copy()
            c.sort(kind=kind)
            assert_equal(c, ai, msg)

def test_sort_complex_byte_swapping(self):
        # test sorting of complex arrays requiring byte-swapping, gh-5441
        for endianness in '<>':
            for dt in np.typecodes['Complex']:
                arr = np.array([1+3.j, 2+2.j, 3+1.j], dtype=endianness + dt)
                c = arr.copy()
                c.sort()
                msg = 'byte-swapped complex sort, dtype={0}'.format(dt)
                assert_equal(c, arr, msg)

@pytest.mark.parametrize('dtype', [np.bytes_, np.str_])
    def test_sort_string(self, dtype):
        # np.array will perform the encoding to bytes for us in the bytes test
        a = np.array(['aaaaaaaa' + chr(i) for i in range(101)], dtype=dtype)
        b = a[::-1].copy()
        for kind in self.sort_kinds:
            msg = "kind=%s" % kind
            c = a.copy()
            c.sort(kind=kind)
            assert_equal(c, a, msg)
            c = b.copy()
            c.sort(kind=kind)
            assert_equal(c, a, msg)

def test_sort_object(self):
        # test object array sorts.
        a = np.empty((101,), dtype=object)
        a[:] = list(range(101))
        b = a[::-1]
        for kind in ['q', 'h', 'm']:
            msg = "kind=%s" % kind
            c = a.copy()
            c.sort(kind=kind)
            assert_equal(c, a, msg)
            c = b.copy()
            c.sort(kind=kind)
            assert_equal(c, a, msg)

@pytest.mark.parametrize("dt", [
            np.dtype([('f', float), ('i', int)]),
            np.dtype([('f', float), ('i', object)])])
    @pytest.mark.parametrize("step", [1, 2])
    def test_sort_structured(self, dt, step):
        # test record array sorts.
        a = np.array([(i, i) for i in range(101*step)], dtype=dt)
        b = a[::-1]
        for kind in ['q', 'h', 'm']:
            msg = "kind=%s" % kind
            c = a.copy()[::step]
            indx = c.argsort(kind=kind)
            c.sort(kind=kind)
            assert_equal(c, a[::step], msg)
            assert_equal(a[::step][indx], a[::step], msg)
            c = b.copy()[::step]
            indx = c.argsort(kind=kind)
            c.sort(kind=kind)
            assert_equal(c, a[step-1::step], msg)
            assert_equal(b[::step][indx], a[step-1::step], msg)

@pytest.mark.parametrize('dtype', ['datetime64[D]', 'timedelta64[D]'])
    def test_sort_time(self, dtype):
        # test datetime64 and timedelta64 sorts.
        a = np.arange(0, 101, dtype=dtype)
        b = a[::-1]
        for kind in ['q', 'h', 'm']:
            msg = "kind=%s" % kind
            c = a.copy()
            c.sort(kind=kind)
            assert_equal(c, a, msg)
            c = b.copy()
            c.sort(kind=kind)
            assert_equal(c, a, msg)

def test_sort_axis(self):
        # check axis handling. This should be the same for all type
        # specific sorts, so we only check it for one type and one kind
        a = np.array([[3, 2], [1, 0]])
        b = np.array([[1, 0], [3, 2]])
        c = np.array([[2, 3], [0, 1]])
        d = a.copy()
        d.sort(axis=0)
        assert_equal(d, b, "test sort with axis=0")
        d = a.copy()
        d.sort(axis=1)
        assert_equal(d, c, "test sort with axis=1")
        d = a.copy()
        d.sort()
        assert_equal(d, c, "test sort with default axis")

def test_sort_size_0(self):
        # check axis handling for multidimensional empty arrays
        a = np.array([])
        a.shape = (3, 2, 1, 0)
        for axis in range(-a.ndim, a.ndim):
            msg = 'test empty array sort with axis={0}'.format(axis)
            assert_equal(np.sort(a, axis=axis), a, msg)
        msg = 'test empty array sort with axis=None'
        assert_equal(np.sort(a, axis=None), a.ravel(), msg)

def test_sort_bad_ordering(self):
        # test generic class with bogus ordering,
        # should not segfault.
        class Boom:
            def __lt__(self, other):
                return True

a = np.array([Boom()] * 100, dtype=object)
        for kind in self.sort_kinds:
            msg = "kind=%s" % kind
            c = a.copy()
            c.sort(kind=kind)
            assert_equal(c, a, msg)

def test_void_sort(self):
        # gh-8210 - previously segfaulted
        for i in range(4):
            rand = np.random.randint(256, size=4000, dtype=np.uint8)
            arr = rand.view('V4')
            arr[::-1].sort()

dt = np.dtype([('val', 'i4', (1,))])
        for i in range(4):
            rand = np.random.randint(256, size=4000, dtype=np.uint8)
            arr = rand.view(dt)
            arr[::-1].sort()

def test_sort_raises(self):
        #gh-9404
        arr = np.array([0, datetime.now(), 1], dtype=object)
        for kind in self.sort_kinds:
            assert_raises(TypeError, arr.sort, kind=kind)
        #gh-3879
        class Raiser:
            def raises_anything(*args, **kwargs):
                raise TypeError("SOMETHING ERRORED")
            __eq__ = __ne__ = __lt__ = __gt__ = __ge__ = __le__ = raises_anything
        arr = np.array([[Raiser(), n] for n in range(10)]).reshape(-1)
        np.random.shuffle(arr)
        for kind in self.sort_kinds:
            assert_raises(TypeError, arr.sort, kind=kind)

def test_sort_degraded(self):
        # test degraded dataset would take minutes to run with normal qsort
        d = np.arange(1000000)
        do = d.copy()
        x = d
        # create a median of 3 killer where each median is the sorted second
        # last element of the quicksort partition
        while x.size > 3:
            mid = x.size // 2
            x[mid], x[-2] = x[-2], x[mid]
            x = x[:-2]

assert_equal(np.sort(d), do)
        assert_equal(d[np.argsort(d)], do)

def test_copy(self):
        def assert_fortran(arr):
            assert_(arr.flags.fortran)
            assert_(arr.flags.f_contiguous)
            assert_(not arr.flags.c_contiguous)

def assert_c(arr):
            assert_(not arr.flags.fortran)
            assert_(not arr.flags.f_contiguous)
            assert_(arr.flags.c_contiguous)

a = np.empty((2, 2), order='F')
        # Test copying a Fortran array
        assert_c(a.copy())
        assert_c(a.copy('C'))
        assert_fortran(a.copy('F'))
        assert_fortran(a.copy('A'))

# Now test starting with a C array.
        a = np.empty((2, 2), order='C')
        assert_c(a.copy())
        assert_c(a.copy('C'))
        assert_fortran(a.copy('F'))
        assert_c(a.copy('A'))

@pytest.mark.parametrize("dtype", ['O', np.int32, 'i,O'])
    def test__deepcopy__(self, dtype):
        # Force the entry of NULLs into array
        a = np.empty(4, dtype=dtype)
        ctypes.memset(a.ctypes.data, 0, a.nbytes)

# Ensure no error is raised, see gh-21833
        b = a.__deepcopy__({})

a[0] = 42
        with pytest.raises(AssertionError):
            assert_array_equal(a, b)

def test__deepcopy__catches_failure(self):
        class MyObj:
            def __deepcopy__(self, *args, **kwargs):
                raise RuntimeError

arr = np.array([1, MyObj(), 3], dtype='O')
        with pytest.raises(RuntimeError):
            arr.__deepcopy__({})

def test_sort_order(self):
        # Test sorting an array with fields
        x1 = np.array([21, 32, 14])
        x2 = np.array(['my', 'first', 'name'])
        x3 = np.array([3.1, 4.5, 6.2])
        r = np.rec.fromarrays([x1, x2, x3], names='id,word,number')

r.sort(order=['id'])
        assert_equal(r.id, np.array([14, 21, 32]))
        assert_equal(r.word, np.array(['name', 'my', 'first']))
        assert_equal(r.number, np.array([6.2, 3.1, 4.5]))

r.sort(order=['word'])
        assert_equal(r.id, np.array([32, 21, 14]))
        assert_equal(r.word, np.array(['first', 'my', 'name']))
        assert_equal(r.number, np.array([4.5, 3.1, 6.2]))

r.sort(order=['number'])
        assert_equal(r.id, np.array([21, 32, 14]))
        assert_equal(r.word, np.array(['my', 'first', 'name']))
        assert_equal(r.number, np.array([3.1, 4.5, 6.2]))

assert_raises_regex(ValueError, 'duplicate',
            lambda: r.sort(order=['id', 'id']))

if sys.byteorder == 'little':
            strtype = '>i2'
        else:
            strtype = '<i2'
        mydtype = [('name', 'U5'), ('col2', strtype)]
        r = np.array([('a', 1), ('b', 255), ('c', 3), ('d', 258)],
                     dtype=mydtype)
        r.sort(order='col2')
        assert_equal(r['col2'], [1, 3, 255, 258])
        assert_equal(r, np.array([('a', 1), ('c', 3), ('b', 255), ('d', 258)],
                                 dtype=mydtype))

def test_argsort(self):
        # all c scalar argsorts use the same code with different types
        # so it suffices to run a quick check with one type. The number
        # of sorted items must be greater than ~50 to check the actual
        # algorithm because quick and merge sort fall over to insertion
        # sort for small arrays.

for dtype in [np.int32, np.uint32, np.float32]:
            a = np.arange(101, dtype=dtype)
            b = a[::-1].copy()
            for kind in self.sort_kinds:
                msg = "scalar argsort, kind=%s, dtype=%s" % (kind, dtype)
                assert_equal(a.copy().argsort(kind=kind), a, msg)
                assert_equal(b.copy().argsort(kind=kind), b, msg)

# test complex argsorts. These use the same code as the scalars
        # but the compare function differs.
        ai = a*1j + 1
        bi = b*1j + 1
        for kind in self.sort_kinds:
            msg = "complex argsort, kind=%s" % kind
            assert_equal(ai.copy().argsort(kind=kind), a, msg)
            assert_equal(bi.copy().argsort(kind=kind), b, msg)
        ai = a + 1j
        bi = b + 1j
        for kind in self.sort_kinds:
            msg = "complex argsort, kind=%s" % kind
            assert_equal(ai.copy().argsort(kind=kind), a, msg)
            assert_equal(bi.copy().argsort(kind=kind), b, msg)

# test argsort of complex arrays requiring byte-swapping, gh-5441
        for endianness in '<>':
            for dt in np.typecodes['Complex']:
                arr = np.array([1+3.j, 2+2.j, 3+1.j], dtype=endianness + dt)
                msg = 'byte-swapped complex argsort, dtype={0}'.format(dt)
                assert_equal(arr.argsort(),
                             np.arange(len(arr), dtype=np.intp), msg)

# test string argsorts.
        s = 'aaaaaaaa'
        a = np.array([s + chr(i) for i in range(101)])
        b = a[::-1].copy()
        r = np.arange(101)
        rr = r[::-1]
        for kind in self.sort_kinds:
            msg = "string argsort, kind=%s" % kind
            assert_equal(a.copy().argsort(kind=kind), r, msg)
            assert_equal(b.copy().argsort(kind=kind), rr, msg)

# test unicode argsorts.
        s = 'aaaaaaaa'
        a = np.array([s + chr(i) for i in range(101)], dtype=np.str_)
        b = a[::-1]
        r = np.arange(101)
        rr = r[::-1]
        for kind in self.sort_kinds:
            msg = "unicode argsort, kind=%s" % kind
            assert_equal(a.copy().argsort(kind=kind), r, msg)
            assert_equal(b.copy().argsort(kind=kind), rr, msg)

# test object array argsorts.
        a = np.empty((101,), dtype=object)
        a[:] = list(range(101))
        b = a[::-1]
        r = np.arange(101)
        rr = r[::-1]
        for kind in self.sort_kinds:
            msg = "object argsort, kind=%s" % kind
            assert_equal(a.copy().argsort(kind=kind), r, msg)
            assert_equal(b.copy().argsort(kind=kind), rr, msg)

# test structured array argsorts.
        dt = np.dtype([('f', float), ('i', int)])
        a = np.array([(i, i) for i in range(101)], dtype=dt)
        b = a[::-1]
        r = np.arange(101)
        rr = r[::-1]
        for kind in self.sort_kinds:
            msg = "structured array argsort, kind=%s" % kind
            assert_equal(a.copy().argsort(kind=kind), r, msg)
            assert_equal(b.copy().argsort(kind=kind), rr, msg)

# test datetime64 argsorts.
        a = np.arange(0, 101, dtype='datetime64[D]')
        b = a[::-1]
        r = np.arange(101)
        rr = r[::-1]
        for kind in ['q', 'h', 'm']:
            msg = "datetime64 argsort, kind=%s" % kind
            assert_equal(a.copy().argsort(kind=kind), r, msg)
            assert_equal(b.copy().argsort(kind=kind), rr, msg)

# test timedelta64 argsorts.
        a = np.arange(0, 101, dtype='timedelta64[D]')
        b = a[::-1]
        r = np.arange(101)
        rr = r[::-1]
        for kind in ['q', 'h', 'm']:
            msg = "timedelta64 argsort, kind=%s" % kind
            assert_equal(a.copy().argsort(kind=kind), r, msg)
            assert_equal(b.copy().argsort(kind=kind), rr, msg)

# check axis handling. This should be the same for all type
        # specific argsorts, so we only check it for one type and one kind
        a = np.array([[3, 2], [1, 0]])
        b = np.array([[1, 1], [0, 0]])
        c = np.array([[1, 0], [1, 0]])
        assert_equal(a.copy().argsort(axis=0), b)
        assert_equal(a.copy().argsort(axis=1), c)
        assert_equal(a.copy().argsort(), c)

# check axis handling for multidimensional empty arrays
        a = np.array([])
        a.shape = (3, 2, 1, 0)
        for axis in range(-a.ndim, a.ndim):
            msg = 'test empty array argsort with axis={0}'.format(axis)
            assert_equal(np.argsort(a, axis=axis),
                         np.zeros_like(a, dtype=np.intp), msg)
        msg = 'test empty array argsort with axis=None'
        assert_equal(np.argsort(a, axis=None),
                     np.zeros_like(a.ravel(), dtype=np.intp), msg)

# check that stable argsorts are stable
        r = np.arange(100)
        # scalars
        a = np.zeros(100)
        assert_equal(a.argsort(kind='m'), r)
        # complex
        a = np.zeros(100, dtype=complex)
        assert_equal(a.argsort(kind='m'), r)
        # string
        a = np.array(['aaaaaaaaa' for i in range(100)])
        assert_equal(a.argsort(kind='m'), r)
        # unicode
        a = np.array(['aaaaaaaaa' for i in range(100)], dtype=np.str_)
        assert_equal(a.argsort(kind='m'), r)

def test_sort_unicode_kind(self):
        d = np.arange(10)
        k = b'\xc3\xa4'.decode("UTF8")
        assert_raises(ValueError, d.sort, kind=k)
        assert_raises(ValueError, d.argsort, kind=k)

@pytest.mark.parametrize('a', [
        np.array([0, 1, np.nan], dtype=np.float16),
        np.array([0, 1, np.nan], dtype=np.float32),
        np.array([0, 1, np.nan]),
    ])
    def test_searchsorted_floats(self, a):
        # test for floats arrays containing nans. Explicitly test
        # half, single, and double precision floats to verify that
        # the NaN-handling is correct.
        msg = "Test real (%s) searchsorted with nans, side='l'" % a.dtype
        b = a.searchsorted(a, side='left')
        assert_equal(b, np.arange(3), msg)
        msg = "Test real (%s) searchsorted with nans, side='r'" % a.dtype
        b = a.searchsorted(a, side='right')
        assert_equal(b, np.arange(1, 4), msg)
        # check keyword arguments
        a.searchsorted(v=1)
        x = np.array([0, 1, np.nan], dtype='float32')
        y = np.searchsorted(x, x[-1])
        assert_equal(y, 2)

def test_searchsorted_complex(self):
        # test for complex arrays containing nans.
        # The search sorted routines use the compare functions for the
        # array type, so this checks if that is consistent with the sort
        # order.
        # check double complex
        a = np.zeros(9, dtype=np.complex128)
        a.real += [0, 0, 1, 1, 0, 1, np.nan, np.nan, np.nan]
        a.imag += [0, 1, 0, 1, np.nan, np.nan, 0, 1, np.nan]
        msg = "Test complex searchsorted with nans, side='l'"
        b = a.searchsorted(a, side='left')
        assert_equal(b, np.arange(9), msg)
        msg = "Test complex searchsorted with nans, side='r'"
        b = a.searchsorted(a, side='right')
        assert_equal(b, np.arange(1, 10), msg)
        msg = "Test searchsorted with little endian, side='l'"
        a = np.array([0, 128], dtype='<i4')
        b = a.searchsorted(np.array(128, dtype='<i4'))
        assert_equal(b, 1, msg)
        msg = "Test searchsorted with big endian, side='l'"
        a = np.array([0, 128], dtype='>i4')
        b = a.searchsorted(np.array(128, dtype='>i4'))
        assert_equal(b, 1, msg)

def test_searchsorted_n_elements(self):
        # Check 0 elements
        a = np.ones(0)
        b = a.searchsorted([0, 1, 2], 'left')
        assert_equal(b, [0, 0, 0])
        b = a.searchsorted([0, 1, 2], 'right')
        assert_equal(b, [0, 0, 0])
        a = np.ones(1)
        # Check 1 element
        b = a.searchsorted([0, 1, 2], 'left')
        assert_equal(b, [0, 0, 1])
        b = a.searchsorted([0, 1, 2], 'right')
        assert_equal(b, [0, 1, 1])
        # Check all elements equal
        a = np.ones(2)
        b = a.searchsorted([0, 1, 2], 'left')
        assert_equal(b, [0, 0, 2])
        b = a.searchsorted([0, 1, 2], 'right')
        assert_equal(b, [0, 2, 2])

def test_searchsorted_unaligned_array(self):
        # Test searching unaligned array
        a = np.arange(10)
        aligned = np.empty(a.itemsize * a.size + 1, 'uint8')
        unaligned = aligned[1:].view(a.dtype)
        unaligned[:] = a
        # Test searching unaligned array
        b = unaligned.searchsorted(a, 'left')
        assert_equal(b, a)
        b = unaligned.searchsorted(a, 'right')
        assert_equal(b, a + 1)
        # Test searching for unaligned keys
        b = a.searchsorted(unaligned, 'left')
        assert_equal(b, a)
        b = a.searchsorted(unaligned, 'right')
        assert_equal(b, a + 1)

def test_searchsorted_resetting(self):
        # Test smart resetting of binsearch indices
        a = np.arange(5)
        b = a.searchsorted([6, 5, 4], 'left')
        assert_equal(b, [5, 5, 4])
        b = a.searchsorted([6, 5, 4], 'right')
        assert_equal(b, [5, 5, 5])

def test_searchsorted_type_specific(self):
        # Test all type specific binary search functions
        types = ''.join((np.typecodes['AllInteger'], np.typecodes['AllFloat'],
                         np.typecodes['Datetime'], '?O'))
        for dt in types:
            if dt == 'M':
                dt = 'M8[D]'
            if dt == '?':
                a = np.arange(2, dtype=dt)
                out = np.arange(2)
            else:
                a = np.arange(0, 5, dtype=dt)
                out = np.arange(5)
            b = a.searchsorted(a, 'left')
            assert_equal(b, out)
            b = a.searchsorted(a, 'right')
            assert_equal(b, out + 1)
            # Test empty array, use a fresh array to get warnings in
            # valgrind if access happens.
            e = np.ndarray(shape=0, buffer=b'', dtype=dt)
            b = e.searchsorted(a, 'left')
            assert_array_equal(b, np.zeros(len(a), dtype=np.intp))
            b = a.searchsorted(e, 'left')
            assert_array_equal(b, np.zeros(0, dtype=np.intp))

def test_searchsorted_unicode(self):
        # Test searchsorted on unicode strings.

# 1.6.1 contained a string length miscalculation in
        # arraytypes.c.src:UNICODE_compare() which manifested as
        # incorrect/inconsistent results from searchsorted.
        a = np.array(['P:\\20x_dapi_cy3\\20x_dapi_cy3_20100185_1',
                      'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100186_1',
                      'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100187_1',
                      'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100189_1',
                      'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100190_1',
                      'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100191_1',
                      'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100192_1',
                      'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100193_1',
                      'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100194_1',
                      'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100195_1',
                      'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100196_1',
                      'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100197_1',
                      'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100198_1',
                      'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100199_1'],
                     dtype=np.str_)
        ind = np.arange(len(a))
        assert_equal([a.searchsorted(v, 'left') for v in a], ind)
        assert_equal([a.searchsorted(v, 'right') for v in a], ind + 1)
        assert_equal([a.searchsorted(a[i], 'left') for i in ind], ind)
        assert_equal([a.searchsorted(a[i], 'right') for i in ind], ind + 1)

def test_searchsorted_with_invalid_sorter(self):
        a = np.array([5, 2, 1, 3, 4])
        s = np.argsort(a)
        assert_raises(TypeError, np.searchsorted, a, 0,
                      sorter=np.array((1, (2, 3)), dtype=object))
        assert_raises(TypeError, np.searchsorted, a, 0, sorter=[1.1])
        assert_raises(ValueError, np.searchsorted, a, 0, sorter=[1, 2, 3, 4])
        assert_raises(ValueError, np.searchsorted, a, 0, sorter=[1, 2, 3, 4, 5, 6])

# bounds check
        assert_raises(ValueError, np.searchsorted, a, 4, sorter=[0, 1, 2, 3, 5])
        assert_raises(ValueError, np.searchsorted, a, 0, sorter=[-1, 0, 1, 2, 3])
        assert_raises(ValueError, np.searchsorted, a, 0, sorter=[4, 0, -1, 2, 3])

def test_searchsorted_with_sorter(self):
        a = np.random.rand(300)
        s = a.argsort()
        b = np.sort(a)
        k = np.linspace(0, 1, 20)
        assert_equal(b.searchsorted(k), a.searchsorted(k, sorter=s))

a = np.array([0, 1, 2, 3, 5]*20)
        s = a.argsort()
        k = [0, 1, 2, 3, 5]
        expected = [0, 20, 40, 60, 80]
        assert_equal(a.searchsorted(k, side='left', sorter=s), expected)
        expected = [20, 40, 60, 80, 100]
        assert_equal(a.searchsorted(k, side='right', sorter=s), expected)

# Test searching unaligned array
        keys = np.arange(10)
        a = keys.copy()
        np.random.shuffle(s)
        s = a.argsort()
        aligned = np.empty(a.itemsize * a.size + 1, 'uint8')
        unaligned = aligned[1:].view(a.dtype)
        # Test searching unaligned array
        unaligned[:] = a
        b = unaligned.searchsorted(keys, 'left', s)
        assert_equal(b, keys)
        b = unaligned.searchsorted(keys, 'right', s)
        assert_equal(b, keys + 1)
        # Test searching for unaligned keys
        unaligned[:] = keys
        b = a.searchsorted(unaligned, 'left', s)
        assert_equal(b, keys)
        b = a.searchsorted(unaligned, 'right', s)
        assert_equal(b, keys + 1)

# Test all type specific indirect binary search functions
        types = ''.join((np.typecodes['AllInteger'], np.typecodes['AllFloat'],
                         np.typecodes['Datetime'], '?O'))
        for dt in types:
            if dt == 'M':
                dt = 'M8[D]'
            if dt == '?':
                a = np.array([1, 0], dtype=dt)
                # We want the sorter array to be of a type that is different
                # from np.intp in all platforms, to check for #4698
                s = np.array([1, 0], dtype=np.int16)
                out = np.array([1, 0])
            else:
                a = np.array([3, 4, 1, 2, 0], dtype=dt)
                # We want the sorter array to be of a type that is different
                # from np.intp in all platforms, to check for #4698
                s = np.array([4, 2, 3, 0, 1], dtype=np.int16)
                out = np.array([3, 4, 1, 2, 0], dtype=np.intp)
            b = a.searchsorted(a, 'left', s)
            assert_equal(b, out)
            b = a.searchsorted(a, 'right', s)
            assert_equal(b, out + 1)
            # Test empty array, use a fresh array to get warnings in
            # valgrind if access happens.
            e = np.ndarray(shape=0, buffer=b'', dtype=dt)
            b = e.searchsorted(a, 'left', s[:0])
            assert_array_equal(b, np.zeros(len(a), dtype=np.intp))
            b = a.searchsorted(e, 'left', s)
            assert_array_equal(b, np.zeros(0, dtype=np.intp))

# Test non-contiguous sorter array
        a = np.array([3, 4, 1, 2, 0])
        srt = np.empty((10,), dtype=np.intp)
        srt[1::2] = -1
        srt[::2] = [4, 2, 3, 0, 1]
        s = srt[::2]
        out = np.array([3, 4, 1, 2, 0], dtype=np.intp)
        b = a.searchsorted(a, 'left', s)
        assert_equal(b, out)
        b = a.searchsorted(a, 'right', s)
        assert_equal(b, out + 1)

def test_searchsorted_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)
        s = np.arange(5).view(A)
        assert_(not isinstance(a.searchsorted(b, 'left'), A))
        assert_(not isinstance(a.searchsorted(b, 'right'), A))
        assert_(not isinstance(a.searchsorted(b, 'left', s), A))
        assert_(not isinstance(a.searchsorted(b, 'right', s), A))

@pytest.mark.parametrize("dtype", np.typecodes["All"])
    def test_argpartition_out_of_range(self, dtype):
        # Test out of range values in kth raise an error, gh-5469
        d = np.arange(10).astype(dtype=dtype)
        assert_raises(ValueError, d.argpartition, 10)
        assert_raises(ValueError, d.argpartition, -11)

@pytest.mark.parametrize("dtype", np.typecodes["All"])
    def test_partition_out_of_range(self, dtype):
        # Test out of range values in kth raise an error, gh-5469
        d = np.arange(10).astype(dtype=dtype)
        assert_raises(ValueError, d.partition, 10)
        assert_raises(ValueError, d.partition, -11)

def test_argpartition_integer(self):
        # Test non-integer values in kth raise an error/
        d = np.arange(10)
        assert_raises(TypeError, d.argpartition, 9.)
        # Test also for generic type argpartition, which uses sorting
        # and used to not bound check kth
        d_obj = np.arange(10, dtype=object)
        assert_raises(TypeError, d_obj.argpartition, 9.)

def test_partition_integer(self):
        # Test out of range values in kth raise an error, gh-5469
        d = np.arange(10)
        assert_raises(TypeError, d.partition, 9.)
        # Test also for generic type partition, which uses sorting
        # and used to not bound check kth
        d_obj = np.arange(10, dtype=object)
        assert_raises(TypeError, d_obj.partition, 9.)

@pytest.mark.parametrize("kth_dtype", np.typecodes["AllInteger"])
    def test_partition_empty_array(self, kth_dtype):
        # check axis handling for multidimensional empty arrays
        kth = np.array(0, dtype=kth_dtype)[()]
        a = np.array([])
        a.shape = (3, 2, 1, 0)
        for axis in range(-a.ndim, a.ndim):
            msg = 'test empty array partition with axis={0}'.format(axis)
            assert_equal(np.partition(a, kth, axis=axis), a, msg)
        msg = 'test empty array partition with axis=None'
        assert_equal(np.partition(a, kth, axis=None), a.ravel(), msg)

@pytest.mark.parametrize("kth_dtype", np.typecodes["AllInteger"])
    def test_argpartition_empty_array(self, kth_dtype):
        # check axis handling for multidimensional empty arrays
        kth = np.array(0, dtype=kth_dtype)[()]
        a = np.array([])
        a.shape = (3, 2, 1, 0)
        for axis in range(-a.ndim, a.ndim):
            msg = 'test empty array argpartition with axis={0}'.format(axis)
            assert_equal(np.partition(a, kth, axis=axis),
                         np.zeros_like(a, dtype=np.intp), msg)
        msg = 'test empty array argpartition with axis=None'
        assert_equal(np.partition(a, kth, axis=None),
                     np.zeros_like(a.ravel(), dtype=np.intp), msg)

def test_partition(self):
        d = np.arange(10)
        assert_raises(TypeError, np.partition, d, 2, kind=1)
        assert_raises(ValueError, np.partition, d, 2, kind="nonsense")
        assert_raises(ValueError, np.argpartition, d, 2, kind="nonsense")
        assert_raises(ValueError, d.partition, 2, axis=0, kind="nonsense")
        assert_raises(ValueError, d.argpartition, 2, axis=0, kind="nonsense")
        for k in ("introselect",):
            d = np.array([])
            assert_array_equal(np.partition(d, 0, kind=k), d)
            assert_array_equal(np.argpartition(d, 0, kind=k), d)
            d = np.ones(1)
            assert_array_equal(np.partition(d, 0, kind=k)[0], d)
            assert_array_equal(d[np.argpartition(d, 0, kind=k)],
                               np.partition(d, 0, kind=k))

# kth not modified
            kth = np.array([30, 15, 5])
            okth = kth.copy()
            np.partition(np.arange(40), kth)
            assert_array_equal(kth, okth)

for r in ([2, 1], [1, 2], [1, 1]):
                d = np.array(r)
                tgt = np.sort(d)
                assert_array_equal(np.partition(d, 0, kind=k)[0], tgt[0])
                assert_array_equal(np.partition(d, 1, kind=k)[1], tgt[1])
                assert_array_equal(d[np.argpartition(d, 0, kind=k)],
                                   np.partition(d, 0, kind=k))
                assert_array_equal(d[np.argpartition(d, 1, kind=k)],
                                   np.partition(d, 1, kind=k))
                for i in range(d.size):
                    d[i:].partition(0, kind=k)
                assert_array_equal(d, tgt)

for r in ([3, 2, 1], [1, 2, 3], [2, 1, 3], [2, 3, 1],
                      [1, 1, 1], [1, 2, 2], [2, 2, 1], [1, 2, 1]):
                d = np.array(r)
                tgt = np.sort(d)
                assert_array_equal(np.partition(d, 0, kind=k)[0], tgt[0])
                assert_array_equal(np.partition(d, 1, kind=k)[1], tgt[1])
                assert_array_equal(np.partition(d, 2, kind=k)[2], tgt[2])
                assert_array_equal(d[np.argpartition(d, 0, kind=k)],
                                   np.partition(d, 0, kind=k))
                assert_array_equal(d[np.argpartition(d, 1, kind=k)],
                                   np.partition(d, 1, kind=k))
                assert_array_equal(d[np.argpartition(d, 2, kind=k)],
                                   np.partition(d, 2, kind=k))
                for i in range(d.size):
                    d[i:].partition(0, kind=k)
                assert_array_equal(d, tgt)

d = np.ones(50)
            assert_array_equal(np.partition(d, 0, kind=k), d)
            assert_array_equal(d[np.argpartition(d, 0, kind=k)],
                               np.partition(d, 0, kind=k))

# sorted
            d = np.arange(49)
            assert_equal(np.partition(d, 5, kind=k)[5], 5)
            assert_equal(np.partition(d, 15, kind=k)[15], 15)
            assert_array_equal(d[np.argpartition(d, 5, kind=k)],
                               np.partition(d, 5, kind=k))
            assert_array_equal(d[np.argpartition(d, 15, kind=k)],
                               np.partition(d, 15, kind=k))

# rsorted
            d = np.arange(47)[::-1]
            assert_equal(np.partition(d, 6, kind=k)[6], 6)
            assert_equal(np.partition(d, 16, kind=k)[16], 16)
            assert_array_equal(d[np.argpartition(d, 6, kind=k)],
                               np.partition(d, 6, kind=k))
            assert_array_equal(d[np.argpartition(d, 16, kind=k)],
                               np.partition(d, 16, kind=k))

assert_array_equal(np.partition(d, -6, kind=k),
                               np.partition(d, 41, kind=k))
            assert_array_equal(np.partition(d, -16, kind=k),
                               np.partition(d, 31, kind=k))
            assert_array_equal(d[np.argpartition(d, -6, kind=k)],
                               np.partition(d, 41, kind=k))

# median of 3 killer, O(n^2) on pure median 3 pivot quickselect
            # exercises the median of median of 5 code used to keep O(n)
            d = np.arange(1000000)
            x = np.roll(d, d.size // 2)
            mid = x.size // 2 + 1
            assert_equal(np.partition(x, mid)[mid], mid)
            d = np.arange(1000001)
            x = np.roll(d, d.size // 2 + 1)
            mid = x.size // 2 + 1
            assert_equal(np.partition(x, mid)[mid], mid)

# max
            d = np.ones(10)
            d[1] = 4
            assert_equal(np.partition(d, (2, -1))[-1], 4)
            assert_equal(np.partition(d, (2, -1))[2], 1)
            assert_equal(d[np.argpartition(d, (2, -1))][-1], 4)
            assert_equal(d[np.argpartition(d, (2, -1))][2], 1)
            d[1] = np.nan
            assert_(np.isnan(d[np.argpartition(d, (2, -1))][-1]))
            assert_(np.isnan(np.partition(d, (2, -1))[-1]))

# equal elements
            d = np.arange(47) % 7
            tgt = np.sort(np.arange(47) % 7)
            np.random.shuffle(d)
            for i in range(d.size):
                assert_equal(np.partition(d, i, kind=k)[i], tgt[i])
            assert_array_equal(d[np.argpartition(d, 6, kind=k)],
                               np.partition(d, 6, kind=k))
            assert_array_equal(d[np.argpartition(d, 16, kind=k)],
                               np.partition(d, 16, kind=k))
            for i in range(d.size):
                d[i:].partition(0, kind=k)
            assert_array_equal(d, tgt)

d = np.array([0, 1, 2, 3, 4, 5, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
                          7, 7, 7, 7, 7, 9])
            kth = [0, 3, 19, 20]
            assert_equal(np.partition(d, kth, kind=k)[kth], (0, 3, 7, 7))
            assert_equal(d[np.argpartition(d, kth, kind=k)][kth], (0, 3, 7, 7))

d = np.array([2, 1])
            d.partition(0, kind=k)
            assert_raises(ValueError, d.partition, 2)
            assert_raises(np.AxisError, d.partition, 3, axis=1)
            assert_raises(ValueError, np.partition, d, 2)
            assert_raises(np.AxisError, np.partition, d, 2, axis=1)
            assert_raises(ValueError, d.argpartition, 2)
            assert_raises(np.AxisError, d.argpartition, 3, axis=1)
            assert_raises(ValueError, np.argpartition, d, 2)
            assert_raises(np.AxisError, np.argpartition, d, 2, axis=1)
            d = np.arange(10).reshape((2, 5))
            d.partition(1, axis=0, kind=k)
            d.partition(4, axis=1, kind=k)
            np.partition(d, 1, axis=0, kind=k)
            np.partition(d, 4, axis=1, kind=k)
            np.partition(d, 1, axis=None, kind=k)
            np.partition(d, 9, axis=None, kind=k)
            d.argpartition(1, axis=0, kind=k)
            d.argpartition(4, axis=1, kind=k)
            np.argpartition(d, 1, axis=0, kind=k)
            np.argpartition(d, 4, axis=1, kind=k)
            np.argpartition(d, 1, axis=None, kind=k)
            np.argpartition(d, 9, axis=None, kind=k)
            assert_raises(ValueError, d.partition, 2, axis=0)
            assert_raises(ValueError, d.partition, 11, axis=1)
            assert_raises(TypeError, d.partition, 2, axis=None)
            assert_raises(ValueError, np.partition, d, 9, axis=1)
            assert_raises(ValueError, np.partition, d, 11, axis=None)
            assert_raises(ValueError, d.argpartition, 2, axis=0)
            assert_raises(ValueError, d.argpartition, 11, axis=1)
            assert_raises(ValueError, np.argpartition, d, 9, axis=1)
            assert_raises(ValueError, np.argpartition, d, 11, axis=None)

td = [(dt, s) for dt in [np.int32, np.float32, np.complex64]
                  for s in (9, 16)]
            for dt, s in td:
                aae = assert_array_equal
                at = assert_

d = np.arange(s, dtype=dt)
                np.random.shuffle(d)
                d1 = np.tile(np.arange(s, dtype=dt), (4, 1))
                map(np.random.shuffle, d1)
                d0 = np.transpose(d1)
                for i in range(d.size):
                    p = np.partition(d, i, kind=k)
                    assert_equal(p[i], i)
                    # all before are smaller
                    assert_array_less(p[:i], p[i])
                    # all after are larger
                    assert_array_less(p[i], p[i + 1:])
                    aae(p, d[np.argpartition(d, i, kind=k)])

p = np.partition(d1, i, axis=1, kind=k)
                    aae(p[:, i], np.array([i] * d1.shape[0], dtype=dt))
                    # array_less does not seem to work right
                    at((p[:, :i].T <= p[:, i]).all(),
                       msg="%d: %r <= %r" % (i, p[:, i], p[:, :i].T))
                    at((p[:, i + 1:].T > p[:, i]).all(),
                       msg="%d: %r < %r" % (i, p[:, i], p[:, i + 1:].T))
                    aae(p, d1[np.arange(d1.shape[0])[:, None],
                        np.argpartition(d1, i, axis=1, kind=k)])

p = np.partition(d0, i, axis=0, kind=k)
                    aae(p[i, :], np.array([i] * d1.shape[0], dtype=dt))
                    # array_less does not seem to work right
                    at((p[:i, :] <= p[i, :]).all(),
                       msg="%d: %r <= %r" % (i, p[i, :], p[:i, :]))
                    at((p[i + 1:, :] > p[i, :]).all(),
                       msg="%d: %r < %r" % (i, p[i, :], p[:, i + 1:]))
                    aae(p, d0[np.argpartition(d0, i, axis=0, kind=k),
                        np.arange(d0.shape[1])[None, :]])

# check inplace
                    dc = d.copy()
                    dc.partition(i, kind=k)
                    assert_equal(dc, np.partition(d, i, kind=k))
                    dc = d0.copy()
                    dc.partition(i, axis=0, kind=k)
                    assert_equal(dc, np.partition(d0, i, axis=0, kind=k))
                    dc = d1.copy()
                    dc.partition(i, axis=1, kind=k)
                    assert_equal(dc, np.partition(d1, i, axis=1, kind=k))

def assert_partitioned(self, d, kth):
        prev = 0
        for k in np.sort(kth):
            assert_array_less(d[prev:k], d[k], err_msg='kth %d' % k)
            assert_((d[k:] >= d[k]).all(),
                    msg="kth %d, %r not greater equal %d" % (k, d[k:], d[k]))
            prev = k + 1

def test_partition_iterative(self):
            d = np.arange(17)
            kth = (0, 1, 2, 429, 231)
            assert_raises(ValueError, d.partition, kth)
            assert_raises(ValueError, d.argpartition, kth)
            d = np.arange(10).reshape((2, 5))
            assert_raises(ValueError, d.partition, kth, axis=0)
            assert_raises(ValueError, d.partition, kth, axis=1)
            assert_raises(ValueError, np.partition, d, kth, axis=1)
            assert_raises(ValueError, np.partition, d, kth, axis=None)

d = np.array([3, 4, 2, 1])
            p = np.partition(d, (0, 3))
            self.assert_partitioned(p, (0, 3))
            self.assert_partitioned(d[np.argpartition(d, (0, 3))], (0, 3))

assert_array_equal(p, np.partition(d, (-3, -1)))
            assert_array_equal(p, d[np.argpartition(d, (-3, -1))])

d = np.arange(17)
            np.random.shuffle(d)
            d.partition(range(d.size))
            assert_array_equal(np.arange(17), d)
            np.random.shuffle(d)
            assert_array_equal(np.arange(17), d[d.argpartition(range(d.size))])

# test unsorted kth
            d = np.arange(17)
            np.random.shuffle(d)
            keys = np.array([1, 3, 8, -2])
            np.random.shuffle(d)
            p = np.partition(d, keys)
            self.assert_partitioned(p, keys)
            p = d[np.argpartition(d, keys)]
            self.assert_partitioned(p, keys)
            np.random.shuffle(keys)
            assert_array_equal(np.partition(d, keys), p)
            assert_array_equal(d[np.argpartition(d, keys)], p)

# equal kth
            d = np.arange(20)[::-1]
            self.assert_partitioned(np.partition(d, [5]*4), [5])
            self.assert_partitioned(np.partition(d, [5]*4 + [6, 13]),
                                    [5]*4 + [6, 13])
            self.assert_partitioned(d[np.argpartition(d, [5]*4)], [5])
            self.assert_partitioned(d[np.argpartition(d, [5]*4 + [6, 13])],
                                    [5]*4 + [6, 13])

d = np.arange(12)
            np.random.shuffle(d)
            d1 = np.tile(np.arange(12), (4, 1))
            map(np.random.shuffle, d1)
            d0 = np.transpose(d1)

kth = (1, 6, 7, -1)
            p = np.partition(d1, kth, axis=1)
            pa = d1[np.arange(d1.shape[0])[:, None],
                    d1.argpartition(kth, axis=1)]
            assert_array_equal(p, pa)
            for i in range(d1.shape[0]):
                self.assert_partitioned(p[i,:], kth)
            p = np.partition(d0, kth, axis=0)
            pa = d0[np.argpartition(d0, kth, axis=0),
                    np.arange(d0.shape[1])[None,:]]
            assert_array_equal(p, pa)
            for i in range(d0.shape[1]):
                self.assert_partitioned(p[:, i], kth)

def test_partition_cdtype(self):
        d = np.array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41),
                   ('Lancelot', 1.9, 38)],
                  dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])

tgt = np.sort(d, order=['age', 'height'])
        assert_array_equal(np.partition(d, range(d.size),
                                        order=['age', 'height']),
                           tgt)
        assert_array_equal(d[np.argpartition(d, range(d.size),
                                             order=['age', 'height'])],
                           tgt)
        for k in range(d.size):
            assert_equal(np.partition(d, k, order=['age', 'height'])[k],
                        tgt[k])
            assert_equal(d[np.argpartition(d, k, order=['age', 'height'])][k],
                         tgt[k])

d = np.array(['Galahad', 'Arthur', 'zebra', 'Lancelot'])
        tgt = np.sort(d)
        assert_array_equal(np.partition(d, range(d.size)), tgt)
        for k in range(d.size):
            assert_equal(np.partition(d, k)[k], tgt[k])
            assert_equal(d[np.argpartition(d, k)][k], tgt[k])

def test_partition_unicode_kind(self):
        d = np.arange(10)
        k = b'\xc3\xa4'.decode("UTF8")
        assert_raises(ValueError, d.partition, 2, kind=k)
        assert_raises(ValueError, d.argpartition, 2, kind=k)

def test_partition_fuzz(self):
        # a few rounds of random data testing
        for j in range(10, 30):
            for i in range(1, j - 2):
                d = np.arange(j)
                np.random.shuffle(d)
                d = d % np.random.randint(2, 30)
                idx = np.random.randint(d.size)
                kth = [0, idx, i, i + 1]
                tgt = np.sort(d)[kth]
                assert_array_equal(np.partition(d, kth)[kth], tgt,
                                   err_msg="data: %r\n kth: %r" % (d, kth))

@pytest.mark.parametrize("kth_dtype", np.typecodes["AllInteger"])
    def test_argpartition_gh5524(self, kth_dtype):
        #  A test for functionality of argpartition on lists.
        kth = np.array(1, dtype=kth_dtype)[()]
        d = [6, 7, 3, 2, 9, 0]
        p = np.argpartition(d, kth)
        self.assert_partitioned(np.array(d)[p],[1])

def test_flatten(self):
        x0 = np.array([[1, 2, 3], [4, 5, 6]], np.int32)
        x1 = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], np.int32)
        y0 = np.array([1, 2, 3, 4, 5, 6], np.int32)
        y0f = np.array([1, 4, 2, 5, 3, 6], np.int32)
        y1 = np.array([1, 2, 3, 4, 5, 6, 7, 8], np.int32)
        y1f = np.array([1, 5, 3, 7, 2, 6, 4, 8], np.int32)
        assert_equal(x0.flatten(), y0)
        assert_equal(x0.flatten('F'), y0f)
        assert_equal(x0.flatten('F'), x0.T.flatten())
        assert_equal(x1.flatten(), y1)
        assert_equal(x1.flatten('F'), y1f)
        assert_equal(x1.flatten('F'), x1.T.flatten())

@pytest.mark.parametrize('func', (np.dot, np.matmul))
    def test_arr_mult(self, func):
        a = np.array([[1, 0], [0, 1]])
        b = np.array([[0, 1], [1, 0]])
        c = np.array([[9, 1], [1, -9]])
        d = np.arange(24).reshape(4, 6)
        ddt = np.array(
            [[  55,  145,  235,  325],
             [ 145,  451,  757, 1063],
             [ 235,  757, 1279, 1801],
             [ 325, 1063, 1801, 2539]]
        )
        dtd = np.array(
            [[504, 540, 576, 612, 648, 684],
             [540, 580, 620, 660, 700, 740],
             [576, 620, 664, 708, 752, 796],
             [612, 660, 708, 756, 804, 852],
             [648, 700, 752, 804, 856, 908],
             [684, 740, 796, 852, 908, 964]]
        )

# gemm vs syrk optimizations
        for et in [np.float32, np.float64, np.complex64, np.complex128]:
            eaf = a.astype(et)
            assert_equal(func(eaf, eaf), eaf)
            assert_equal(func(eaf.T, eaf), eaf)
            assert_equal(func(eaf, eaf.T), eaf)
            assert_equal(func(eaf.T, eaf.T), eaf)
            assert_equal(func(eaf.T.copy(), eaf), eaf)
            assert_equal(func(eaf, eaf.T.copy()), eaf)
            assert_equal(func(eaf.T.copy(), eaf.T.copy()), eaf)

# syrk validations
        for et in [np.float32, np.float64, np.complex64, np.complex128]:
            eaf = a.astype(et)
            ebf = b.astype(et)
            assert_equal(func(ebf, ebf), eaf)
            assert_equal(func(ebf.T, ebf), eaf)
            assert_equal(func(ebf, ebf.T), eaf)
            assert_equal(func(ebf.T, ebf.T), eaf)

# syrk - different shape, stride, and view validations
        for et in [np.float32, np.float64, np.complex64, np.complex128]:
            edf = d.astype(et)
            assert_equal(
                func(edf[::-1, :], edf.T),
                func(edf[::-1, :].copy(), edf.T.copy())
            )
            assert_equal(
                func(edf[:, ::-1], edf.T),
                func(edf[:, ::-1].copy(), edf.T.copy())
            )
            assert_equal(
                func(edf, edf[::-1, :].T),
                func(edf, edf[::-1, :].T.copy())
            )
            assert_equal(
                func(edf, edf[:, ::-1].T),
                func(edf, edf[:, ::-1].T.copy())
            )
            assert_equal(
                func(edf[:edf.shape[0] // 2, :], edf[::2, :].T),
                func(edf[:edf.shape[0] // 2, :].copy(), edf[::2, :].T.copy())
            )
            assert_equal(
                func(edf[::2, :], edf[:edf.shape[0] // 2, :].T),
                func(edf[::2, :].copy(), edf[:edf.shape[0] // 2, :].T.copy())
            )

# syrk - different shape
        for et in [np.float32, np.float64, np.complex64, np.complex128]:
            edf = d.astype(et)
            eddtf = ddt.astype(et)
            edtdf = dtd.astype(et)
            assert_equal(func(edf, edf.T), eddtf)
            assert_equal(func(edf.T, edf), edtdf)

@pytest.mark.parametrize('func', (np.dot, np.matmul))
    @pytest.mark.parametrize('dtype', 'ifdFD')
    def test_no_dgemv(self, func, dtype):
        # check vector arg for contiguous before gemv
        # gh-12156
        a = np.arange(8.0, dtype=dtype).reshape(2, 4)
        b = np.broadcast_to(1., (4, 1))
        ret1 = func(a, b)
        ret2 = func(a, b.copy())
        assert_equal(ret1, ret2)

ret1 = func(b.T, a.T)
        ret2 = func(b.T.copy(), a.T)
        assert_equal(ret1, ret2)

# check for unaligned data
        dt = np.dtype(dtype)
        a = np.zeros(8 * dt.itemsize // 2 + 1, dtype='int16')[1:].view(dtype)
        a = a.reshape(2, 4)
        b = a[0]
        # make sure it is not aligned
        assert_(a.__array_interface__['data'][0] % dt.itemsize != 0)
        ret1 = func(a, b)
        ret2 = func(a.copy(), b.copy())
        assert_equal(ret1, ret2)

ret1 = func(b.T, a.T)
        ret2 = func(b.T.copy(), a.T.copy())
        assert_equal(ret1, ret2)

def test_dot(self):
        a = np.array([[1, 0], [0, 1]])
        b = np.array([[0, 1], [1, 0]])
        c = np.array([[9, 1], [1, -9]])
        # function versus methods
        assert_equal(np.dot(a, b), a.dot(b))
        assert_equal(np.dot(np.dot(a, b), c), a.dot(b).dot(c))

# test passing in an output array
        c = np.zeros_like(a)
        a.dot(b, c)
        assert_equal(c, np.dot(a, b))

# test keyword args
        c = np.zeros_like(a)
        a.dot(b=b, out=c)
        assert_equal(c, np.dot(a, b))

def test_dot_type_mismatch(self):
        c = 1.
        A = np.array((1,1), dtype='i,i')

assert_raises(TypeError, np.dot, c, A)
        assert_raises(TypeError, np.dot, A, c)

def test_dot_out_mem_overlap(self):
        np.random.seed(1)

# Test BLAS and non-BLAS code paths, including all dtypes
        # that dot() supports
        dtypes = [np.dtype(code) for code in np.typecodes['All']
                  if code not in 'USVM']
        for dtype in dtypes:
            a = np.random.rand(3, 3).astype(dtype)

# Valid dot() output arrays must be aligned
            b = _aligned_zeros((3, 3), dtype=dtype)
            b[...] = np.random.rand(3, 3)

y = np.dot(a, b)
            x = np.dot(a, b, out=b)
            assert_equal(x, y, err_msg=repr(dtype))

# Check invalid output array
            assert_raises(ValueError, np.dot, a, b, out=b[::2])
            assert_raises(ValueError, np.dot, a, b, out=b.T)

def test_dot_matmul_out(self):
        # gh-9641
        class Sub(np.ndarray):
            pass
        a = np.ones((2, 2)).view(Sub)
        b = np.ones((2, 2)).view(Sub)
        out = np.ones((2, 2))

# make sure out can be any ndarray (not only subclass of inputs)
        np.dot(a, b, out=out)
        np.matmul(a, b, out=out)

def test_dot_matmul_inner_array_casting_fails(self):

class A:
            def __array__(self, *args, **kwargs):
                raise NotImplementedError

# Don't override the error from calling __array__()
        assert_raises(NotImplementedError, np.dot, A(), A())
        assert_raises(NotImplementedError, np.matmul, A(), A())
        assert_raises(NotImplementedError, np.inner, A(), A())

def test_matmul_out(self):
        # overlapping memory
        a = np.arange(18).reshape(2, 3, 3)
        b = np.matmul(a, a)
        c = np.matmul(a, a, out=a)
        assert_(c is a)
        assert_equal(c, b)
        a = np.arange(18).reshape(2, 3, 3)
        c = np.matmul(a, a, out=a[::-1, ...])
        assert_(c.base is a.base)
        assert_equal(c, b)

def test_diagonal(self):
        a = np.arange(12).reshape((3, 4))
        assert_equal(a.diagonal(), [0, 5, 10])
        assert_equal(a.diagonal(0), [0, 5, 10])
        assert_equal(a.diagonal(1), [1, 6, 11])
        assert_equal(a.diagonal(-1), [4, 9])
        assert_raises(np.AxisError, a.diagonal, axis1=0, axis2=5)
        assert_raises(np.AxisError, a.diagonal, axis1=5, axis2=0)
        assert_raises(np.AxisError, a.diagonal, axis1=5, axis2=5)
        assert_raises(ValueError, a.diagonal, axis1=1, axis2=1)

b = np.arange(8).reshape((2, 2, 2))
        assert_equal(b.diagonal(), [[0, 6], [1, 7]])
        assert_equal(b.diagonal(0), [[0, 6], [1, 7]])
        assert_equal(b.diagonal(1), [[2], [3]])
        assert_equal(b.diagonal(-1), [[4], [5]])
        assert_raises(ValueError, b.diagonal, axis1=0, axis2=0)
        assert_equal(b.diagonal(0, 1, 2), [[0, 3], [4, 7]])
        assert_equal(b.diagonal(0, 0, 1), [[0, 6], [1, 7]])
        assert_equal(b.diagonal(offset=1, axis1=0, axis2=2), [[1], [3]])
        # Order of axis argument doesn't matter:
        assert_equal(b.diagonal(0, 2, 1), [[0, 3], [4, 7]])

def test_diagonal_view_notwriteable(self):
        a = np.eye(3).diagonal()
        assert_(not a.flags.writeable)
        assert_(not a.flags.owndata)

a = np.diagonal(np.eye(3))
        assert_(not a.flags.writeable)
        assert_(not a.flags.owndata)

a = np.diag(np.eye(3))
        assert_(not a.flags.writeable)
        assert_(not a.flags.owndata)

def test_diagonal_memleak(self):
        # Regression test for a bug that crept in at one point
        a = np.zeros((100, 100))
        if HAS_REFCOUNT:
            assert_(sys.getrefcount(a) < 50)
        for i in range(100):
            a.diagonal()
        if HAS_REFCOUNT:
            assert_(sys.getrefcount(a) < 50)

def test_size_zero_memleak(self):
        # Regression test for issue 9615
        # Exercises a special-case code path for dot products of length
        # zero in cblasfuncs (making it is specific to floating dtypes).
        a = np.array([], dtype=np.float64)
        x = np.array(2.0)
        for _ in range(100):
            np.dot(a, a, out=x)
        if HAS_REFCOUNT:
            assert_(sys.getrefcount(x) < 50)

def test_trace(self):
        a = np.arange(12).reshape((3, 4))
        assert_equal(a.trace(), 15)
        assert_equal(a.trace(0), 15)
        assert_equal(a.trace(1), 18)
        assert_equal(a.trace(-1), 13)

b = np.arange(8).reshape((2, 2, 2))
        assert_equal(b.trace(), [6, 8])
        assert_equal(b.trace(0), [6, 8])
        assert_equal(b.trace(1), [2, 3])
        assert_equal(b.trace(-1), [4, 5])
        assert_equal(b.trace(0, 0, 1), [6, 8])
        assert_equal(b.trace(0, 0, 2), [5, 9])
        assert_equal(b.trace(0, 1, 2), [3, 11])
        assert_equal(b.trace(offset=1, axis1=0, axis2=2), [1, 3])

out = np.array(1)
        ret = a.trace(out=out)
        assert ret is out

def test_trace_subclass(self):
        # The class would need to overwrite trace to ensure single-element
        # output also has the right subclass.
        class MyArray(np.ndarray):
            pass

b = np.arange(8).reshape((2, 2, 2)).view(MyArray)
        t = b.trace()
        assert_(isinstance(t, MyArray))

def test_put(self):
        icodes = np.typecodes['AllInteger']
        fcodes = np.typecodes['AllFloat']
        for dt in icodes + fcodes + 'O':
            tgt = np.array([0, 1, 0, 3, 0, 5], dtype=dt)

# test 1-d
            a = np.zeros(6, dtype=dt)
            a.put([1, 3, 5], [1, 3, 5])
            assert_equal(a, tgt)

# test 2-d
            a = np.zeros((2, 3), dtype=dt)
            a.put([1, 3, 5], [1, 3, 5])
            assert_equal(a, tgt.reshape(2, 3))

for dt in '?':
            tgt = np.array([False, True, False, True, False, True], dtype=dt)

# test 1-d
            a = np.zeros(6, dtype=dt)
            a.put([1, 3, 5], [True]*3)
            assert_equal(a, tgt)

# test 2-d
            a = np.zeros((2, 3), dtype=dt)
            a.put([1, 3, 5], [True]*3)
            assert_equal(a, tgt.reshape(2, 3))

# check must be writeable
        a = np.zeros(6)
        a.flags.writeable = False
        assert_raises(ValueError, a.put, [1, 3, 5], [1, 3, 5])

# when calling np.put, make sure a
        # TypeError is raised if the object
        # isn't an ndarray
        bad_array = [1, 2, 3]
        assert_raises(TypeError, np.put, bad_array, [0, 2], 5)

def test_ravel(self):
        a = np.array([[0, 1], [2, 3]])
        assert_equal(a.ravel(), [0, 1, 2, 3])
        assert_(not a.ravel().flags.owndata)
        assert_equal(a.ravel('F'), [0, 2, 1, 3])
        assert_equal(a.ravel(order='C'), [0, 1, 2, 3])
        assert_equal(a.ravel(order='F'), [0, 2, 1, 3])
        assert_equal(a.ravel(order='A'), [0, 1, 2, 3])
        assert_(not a.ravel(order='A').flags.owndata)
        assert_equal(a.ravel(order='K'), [0, 1, 2, 3])
        assert_(not a.ravel(order='K').flags.owndata)
        assert_equal(a.ravel(), a.reshape(-1))

a = np.array([[0, 1], [2, 3]], order='F')
        assert_equal(a.ravel(), [0, 1, 2, 3])
        assert_equal(a.ravel(order='A'), [0, 2, 1, 3])
        assert_equal(a.ravel(order='K'), [0, 2, 1, 3])
        assert_(not a.ravel(order='A').flags.owndata)
        assert_(not a.ravel(order='K').flags.owndata)
        assert_equal(a.ravel(), a.reshape(-1))
        assert_equal(a.ravel(order='A'), a.reshape(-1, order='A'))

a = np.array([[0, 1], [2, 3]])[::-1, :]
        assert_equal(a.ravel(), [2, 3, 0, 1])
        assert_equal(a.ravel(order='C'), [2, 3, 0, 1])
        assert_equal(a.ravel(order='F'), [2, 0, 3, 1])
        assert_equal(a.ravel(order='A'), [2, 3, 0, 1])
        # 'K' doesn't reverse the axes of negative strides
        assert_equal(a.ravel(order='K'), [2, 3, 0, 1])
        assert_(a.ravel(order='K').flags.owndata)

# Test simple 1-d copy behaviour:
        a = np.arange(10)[::2]
        assert_(a.ravel('K').flags.owndata)
        assert_(a.ravel('C').flags.owndata)
        assert_(a.ravel('F').flags.owndata)

# Not contiguous and 1-sized axis with non matching stride
        a = np.arange(2**3 * 2)[::2]
        a = a.reshape(2, 1, 2, 2).swapaxes(-1, -2)
        strides = list(a.strides)
        strides[1] = 123
        a.strides = strides
        assert_(a.ravel(order='K').flags.owndata)
        assert_equal(a.ravel('K'), np.arange(0, 15, 2))

# contiguous and 1-sized axis with non matching stride works:
        a = np.arange(2**3)
        a = a.reshape(2, 1, 2, 2).swapaxes(-1, -2)
        strides = list(a.strides)
        strides[1] = 123
        a.strides = strides
        assert_(np.may_share_memory(a.ravel(order='K'), a))
        assert_equal(a.ravel(order='K'), np.arange(2**3))

# Test negative strides (not very interesting since non-contiguous):
        a = np.arange(4)[::-1].reshape(2, 2)
        assert_(a.ravel(order='C').flags.owndata)
        assert_(a.ravel(order='K').flags.owndata)
        assert_equal(a.ravel('C'), [3, 2, 1, 0])
        assert_equal(a.ravel('K'), [3, 2, 1, 0])

# 1-element tidy strides test:
        a = np.array([[1]])
        a.strides = (123, 432)
        # If the following stride is not 8, NPY_RELAXED_STRIDES_DEBUG is
        # messing them up on purpose:
        if np.ones(1).strides == (8,):
            assert_(np.may_share_memory(a.ravel('K'), a))
            assert_equal(a.ravel('K').strides, (a.dtype.itemsize,))

for order in ('C', 'F', 'A', 'K'):
            # 0-d corner case:
            a = np.array(0)
            assert_equal(a.ravel(order), [0])
            assert_(np.may_share_memory(a.ravel(order), a))

# Test that certain non-inplace ravels work right (mostly) for 'K':
        b = np.arange(2**4 * 2)[::2].reshape(2, 2, 2, 2)
        a = b[..., ::2]
        assert_equal(a.ravel('K'), [0, 4, 8, 12, 16, 20, 24, 28])
        assert_equal(a.ravel('C'), [0, 4, 8, 12, 16, 20, 24, 28])
        assert_equal(a.ravel('A'), [0, 4, 8, 12, 16, 20, 24, 28])
        assert_equal(a.ravel('F'), [0, 16, 8, 24, 4, 20, 12, 28])

a = b[::2, ...]
        assert_equal(a.ravel('K'), [0, 2, 4, 6, 8, 10, 12, 14])
        assert_equal(a.ravel('C'), [0, 2, 4, 6, 8, 10, 12, 14])
        assert_equal(a.ravel('A'), [0, 2, 4, 6, 8, 10, 12, 14])
        assert_equal(a.ravel('F'), [0, 8, 4, 12, 2, 10, 6, 14])

def test_ravel_subclass(self):
        class ArraySubclass(np.ndarray):
            pass

a = np.arange(10).view(ArraySubclass)
        assert_(isinstance(a.ravel('C'), ArraySubclass))
        assert_(isinstance(a.ravel('F'), ArraySubclass))
        assert_(isinstance(a.ravel('A'), ArraySubclass))
        assert_(isinstance(a.ravel('K'), ArraySubclass))

a = np.arange(10)[::2].view(ArraySubclass)
        assert_(isinstance(a.ravel('C'), ArraySubclass))
        assert_(isinstance(a.ravel('F'), ArraySubclass))
        assert_(isinstance(a.ravel('A'), ArraySubclass))
        assert_(isinstance(a.ravel('K'), ArraySubclass))

def test_swapaxes(self):
        a = np.arange(1*2*3*4).reshape(1, 2, 3, 4).copy()
        idx = np.indices(a.shape)
        assert_(a.flags['OWNDATA'])
        b = a.copy()
        # check exceptions
        assert_raises(np.AxisError, a.swapaxes, -5, 0)
        assert_raises(np.AxisError, a.swapaxes, 4, 0)
        assert_raises(np.AxisError, a.swapaxes, 0, -5)
        assert_raises(np.AxisError, a.swapaxes, 0, 4)

for i in range(-4, 4):
            for j in range(-4, 4):
                for k, src in enumerate((a, b)):
                    c = src.swapaxes(i, j)
                    # check shape
                    shape = list(src.shape)
                    shape[i] = src.shape[j]
                    shape[j] = src.shape[i]
                    assert_equal(c.shape, shape, str((i, j, k)))
                    # check array contents
                    i0, i1, i2, i3 = [dim-1 for dim in c.shape]
                    j0, j1, j2, j3 = [dim-1 for dim in src.shape]
                    assert_equal(src[idx[j0], idx[j1], idx[j2], idx[j3]],
                                 c[idx[i0], idx[i1], idx[i2], idx[i3]],
                                 str((i, j, k)))
                    # check a view is always returned, gh-5260
                    assert_(not c.flags['OWNDATA'], str((i, j, k)))
                    # check on non-contiguous input array
                    if k == 1:
                        b = c

def test_conjugate(self):
        a = np.array([1-1j, 1+1j, 23+23.0j])
        ac = a.conj()
        assert_equal(a.real, ac.real)
        assert_equal(a.imag, -ac.imag)
        assert_equal(ac, a.conjugate())
        assert_equal(ac, np.conjugate(a))

a = np.array([1-1j, 1+1j, 23+23.0j], 'F')
        ac = a.conj()
        assert_equal(a.real, ac.real)
        assert_equal(a.imag, -ac.imag)
        assert_equal(ac, a.conjugate())
        assert_equal(ac, np.conjugate(a))

a = np.array([1, 2, 3])
        ac = a.conj()
        assert_equal(a, ac)
        assert_equal(ac, a.conjugate())
        assert_equal(ac, np.conjugate(a))

a = np.array([1.0, 2.0, 3.0])
        ac = a.conj()
        assert_equal(a, ac)
        assert_equal(ac, a.conjugate())
        assert_equal(ac, np.conjugate(a))

a = np.array([1-1j, 1+1j, 1, 2.0], object)
        ac = a.conj()
        assert_equal(ac, [k.conjugate() for k in a])
        assert_equal(ac, a.conjugate())
        assert_equal(ac, np.conjugate(a))

a = np.array([1-1j, 1, 2.0, 'f'], object)
        assert_raises(TypeError, lambda: a.conj())
        assert_raises(TypeError, lambda: a.conjugate())

def test_conjugate_out(self):
        # Minimal test for the out argument being passed on correctly
        # NOTE: The ability to pass `out` is currently undocumented!
        a = np.array([1-1j, 1+1j, 23+23.0j])
        out = np.empty_like(a)
        res = a.conjugate(out)
        assert res is out
        assert_array_equal(out, a.conjugate())

def test__complex__(self):
        dtypes = ['i1', 'i2', 'i4', 'i8',
                  'u1', 'u2', 'u4', 'u8',
                  'f', 'd', 'g', 'F', 'D', 'G',
                  '?', 'O']
        for dt in dtypes:
            a = np.array(7, dtype=dt)
            b = np.array([7], dtype=dt)
            c = np.array([[[[[7]]]]], dtype=dt)

msg = 'dtype: {0}'.format(dt)
            ap = complex(a)
            assert_equal(ap, a, msg)

with assert_warns(DeprecationWarning):
                bp = complex(b)
            assert_equal(bp, b, msg)

with assert_warns(DeprecationWarning):
                cp = complex(c)
            assert_equal(cp, c, msg)

def test__complex__should_not_work(self):
        dtypes = ['i1', 'i2', 'i4', 'i8',
                  'u1', 'u2', 'u4', 'u8',
                  'f', 'd', 'g', 'F', 'D', 'G',
                  '?', 'O']
        for dt in dtypes:
            a = np.array([1, 2, 3], dtype=dt)
            assert_raises(TypeError, complex, a)

dt = np.dtype([('a', 'f8'), ('b', 'i1')])
        b = np.array((1.0, 3), dtype=dt)
        assert_raises(TypeError, complex, b)

c = np.array([(1.0, 3), (2e-3, 7)], dtype=dt)
        assert_raises(TypeError, complex, c)

d = np.array('1+1j')
        assert_raises(TypeError, complex, d)

e = np.array(['1+1j'], 'U')
        with assert_warns(DeprecationWarning):
            assert_raises(TypeError, complex, e)

class TestCequenceMethods:
    def test_array_contains(self):
        assert_(4.0 in np.arange(16.).reshape(4,4))
        assert_(20.0 not in np.arange(16.).reshape(4,4))

class TestBinop:
    def test_inplace(self):
        # test refcount 1 inplace conversion
        assert_array_almost_equal(np.array([0.5]) * np.array([1.0, 2.0]),
                                  [0.5, 1.0])

d = np.array([0.5, 0.5])[::2]
        assert_array_almost_equal(d * (d * np.array([1.0, 2.0])),
                                  [0.25, 0.5])

a = np.array([0.5])
        b = np.array([0.5])
        c = a + b
        c = a - b
        c = a * b
        c = a / b
        assert_equal(a, b)
        assert_almost_equal(c, 1.)

c = a + b * 2. / b * a - a / b
        assert_equal(a, b)
        assert_equal(c, 0.5)

# true divide
        a = np.array([5])
        b = np.array([3])
        c = (a * a) / b

assert_almost_equal(c, 25 / 3)
        assert_equal(a, 5)
        assert_equal(b, 3)

# ndarray.__rop__ always calls ufunc
    # ndarray.__iop__ always calls ufunc
    # ndarray.__op__, __rop__:
    #   - defer if other has __array_ufunc__ and it is None
    #           or other is not a subclass and has higher array priority
    #   - else, call ufunc
    def test_ufunc_binop_interaction(self):
        # Python method name (without underscores)
        #   -> (numpy ufunc, has_in_place_version, preferred_dtype)
        ops = {
            'add':      (np.add, True, float),
            'sub':      (np.subtract, True, float),
            'mul':      (np.multiply, True, float),
            'truediv':  (np.true_divide, True, float),
            'floordiv': (np.floor_divide, True, float),
            'mod':      (np.remainder, True, float),
            'divmod':   (np.divmod, False, float),
            'pow':      (np.power, True, int),
            'lshift':   (np.left_shift, True, int),
            'rshift':   (np.right_shift, True, int),
            'and':      (np.bitwise_and, True, int),
            'xor':      (np.bitwise_xor, True, int),
            'or':       (np.bitwise_or, True, int),
            'matmul':   (np.matmul, True, float),
            # 'ge':       (np.less_equal, False),
            # 'gt':       (np.less, False),
            # 'le':       (np.greater_equal, False),
            # 'lt':       (np.greater, False),
            # 'eq':       (np.equal, False),
            # 'ne':       (np.not_equal, False),
        }

class Coerced(Exception):
            pass

def array_impl(self):
            raise Coerced

def op_impl(self, other):
            return "forward"

def rop_impl(self, other):
            return "reverse"

def iop_impl(self, other):
            return "in-place"

def array_ufunc_impl(self, ufunc, method, *args, **kwargs):
            return ("__array_ufunc__", ufunc, method, args, kwargs)

# Create an object with the given base, in the given module, with a
        # bunch of placeholder __op__ methods, and optionally a
        # __array_ufunc__ and __array_priority__.
        def make_obj(base, array_priority=False, array_ufunc=False,
                     alleged_module="__main__"):
            class_namespace = {"__array__": array_impl}
            if array_priority is not False:
                class_namespace["__array_priority__"] = array_priority
            for op in ops:
                class_namespace["__{0}__".format(op)] = op_impl
                class_namespace["__r{0}__".format(op)] = rop_impl
                class_namespace["__i{0}__".format(op)] = iop_impl
            if array_ufunc is not False:
                class_namespace["__array_ufunc__"] = array_ufunc
            eval_namespace = {"base": base,
                              "class_namespace": class_namespace,
                              "__name__": alleged_module,
                              }
            MyType = eval("type('MyType', (base,), class_namespace)",
                          eval_namespace)
            if issubclass(MyType, np.ndarray):
                # Use this range to avoid special case weirdnesses around
                # divide-by-0, pow(x, 2), overflow due to pow(big, big), etc.
                return np.arange(3, 7).reshape(2, 2).view(MyType)
            else:
                return MyType()

def check(obj, binop_override_expected, ufunc_override_expected,
                  inplace_override_expected, check_scalar=True):
            for op, (ufunc, has_inplace, dtype) in ops.items():
                err_msg = ('op: %s, ufunc: %s, has_inplace: %s, dtype: %s'
                           % (op, ufunc, has_inplace, dtype))
                check_objs = [np.arange(3, 7, dtype=dtype).reshape(2, 2)]
                if check_scalar:
                    check_objs.append(check_objs[0][0])
                for arr in check_objs:
                    arr_method = getattr(arr, "__{0}__".format(op))

def first_out_arg(result):
                        if op == "divmod":
                            assert_(isinstance(result, tuple))
                            return result[0]
                        else:
                            return result

# arr __op__ obj
                    if binop_override_expected:
                        assert_equal(arr_method(obj), NotImplemented, err_msg)
                    elif ufunc_override_expected:
                        assert_equal(arr_method(obj)[0], "__array_ufunc__",
                                     err_msg)
                    else:
                        if (isinstance(obj, np.ndarray) and
                            (type(obj).__array_ufunc__ is
                             np.ndarray.__array_ufunc__)):
                            # __array__ gets ignored
                            res = first_out_arg(arr_method(obj))
                            assert_(res.__class__ is obj.__class__, err_msg)
                        else:
                            assert_raises((TypeError, Coerced),
                                          arr_method, obj, err_msg=err_msg)
                    # obj __op__ arr
                    arr_rmethod = getattr(arr, "__r{0}__".format(op))
                    if ufunc_override_expected:
                        res = arr_rmethod(obj)
                        assert_equal(res[0], "__array_ufunc__",
                                     err_msg=err_msg)
                        assert_equal(res[1], ufunc, err_msg=err_msg)
                    else:
                        if (isinstance(obj, np.ndarray) and
                                (type(obj).__array_ufunc__ is
                                 np.ndarray.__array_ufunc__)):
                            # __array__ gets ignored
                            res = first_out_arg(arr_rmethod(obj))
                            assert_(res.__class__ is obj.__class__, err_msg)
                        else:
                            # __array_ufunc__ = "asdf" creates a TypeError
                            assert_raises((TypeError, Coerced),
                                          arr_rmethod, obj, err_msg=err_msg)

# arr __iop__ obj
                    # array scalars don't have in-place operators
                    if has_inplace and isinstance(arr, np.ndarray):
                        arr_imethod = getattr(arr, "__i{0}__".format(op))
                        if inplace_override_expected:
                            assert_equal(arr_method(obj), NotImplemented,
                                         err_msg=err_msg)
                        elif ufunc_override_expected:
                            res = arr_imethod(obj)
                            assert_equal(res[0], "__array_ufunc__", err_msg)
                            assert_equal(res[1], ufunc, err_msg)
                            assert_(type(res[-1]["out"]) is tuple, err_msg)
                            assert_(res[-1]["out"][0] is arr, err_msg)
                        else:
                            if (isinstance(obj, np.ndarray) and
                                    (type(obj).__array_ufunc__ is
                                    np.ndarray.__array_ufunc__)):
                                # __array__ gets ignored
                                assert_(arr_imethod(obj) is arr, err_msg)
                            else:
                                assert_raises((TypeError, Coerced),
                                              arr_imethod, obj,
                                              err_msg=err_msg)

op_fn = getattr(operator, op, None)
                    if op_fn is None:
                        op_fn = getattr(operator, op + "_", None)
                    if op_fn is None:
                        op_fn = getattr(builtins, op)
                    assert_equal(op_fn(obj, arr), "forward", err_msg)
                    if not isinstance(obj, np.ndarray):
                        if binop_override_expected:
                            assert_equal(op_fn(arr, obj), "reverse", err_msg)
                        elif ufunc_override_expected:
                            assert_equal(op_fn(arr, obj)[0], "__array_ufunc__",
                                         err_msg)
                    if ufunc_override_expected:
                        assert_equal(ufunc(obj, arr)[0], "__array_ufunc__",
                                     err_msg)

# No array priority, no array_ufunc -> nothing called
        check(make_obj(object), False, False, False)
        # Negative array priority, no array_ufunc -> nothing called
        # (has to be very negative, because scalar priority is -1000000.0)
        check(make_obj(object, array_priority=-2**30), False, False, False)
        # Positive array priority, no array_ufunc -> binops and iops only
        check(make_obj(object, array_priority=1), True, False, True)
        # ndarray ignores array_priority for ndarray subclasses
        check(make_obj(np.ndarray, array_priority=1), False, False, False,
              check_scalar=False)
        # Positive array_priority and array_ufunc -> array_ufunc only
        check(make_obj(object, array_priority=1,
                       array_ufunc=array_ufunc_impl), False, True, False)
        check(make_obj(np.ndarray, array_priority=1,
                       array_ufunc=array_ufunc_impl), False, True, False)
        # array_ufunc set to None -> defer binops only
        check(make_obj(object, array_ufunc=None), True, False, False)
        check(make_obj(np.ndarray, array_ufunc=None), True, False, False,
              check_scalar=False)

@pytest.mark.parametrize("priority", [None, "runtime error"])
    def test_ufunc_binop_bad_array_priority(self, priority):
        # Mainly checks that this does not crash.  The second array has a lower
        # priority than -1 ("error value").  If the __radd__ actually exists,
        # bad things can happen (I think via the scalar paths).
        # In principle both of these can probably just be errors in the future.
        class BadPriority:
            @property
            def __array_priority__(self):
                if priority == "runtime error":
                    raise RuntimeError("RuntimeError in __array_priority__!")
                return priority

def __radd__(self, other):
                return "result"

class LowPriority(np.ndarray):
            __array_priority__ = -1000

# Priority failure uses the same as scalars (smaller -1000).  So the
        # LowPriority wins with 'result' for each element (inner operation).
        res = np.arange(3).view(LowPriority) + BadPriority()
        assert res.shape == (3,)
        assert res[0] == 'result'

def test_ufunc_override_normalize_signature(self):
        # gh-5674
        class SomeClass:
            def __array_ufunc__(self, ufunc, method, *inputs, **kw):
                return kw

a = SomeClass()
        kw = np.add(a, [1])
        assert_('sig' not in kw and 'signature' not in kw)
        kw = np.add(a, [1], sig='ii->i')
        assert_('sig' not in kw and 'signature' in kw)
        assert_equal(kw['signature'], 'ii->i')
        kw = np.add(a, [1], signature='ii->i')
        assert_('sig' not in kw and 'signature' in kw)
        assert_equal(kw['signature'], 'ii->i')

def test_array_ufunc_index(self):
        # Check that index is set appropriately, also if only an output
        # is passed on (latter is another regression tests for github bug 4753)
        # This also checks implicitly that 'out' is always a tuple.
        class CheckIndex:
            def __array_ufunc__(self, ufunc, method, *inputs, **kw):
                for i, a in enumerate(inputs):
                    if a is self:
                        return i
                # calls below mean we must be in an output.
                for j, a in enumerate(kw['out']):
                    if a is self:
                        return (j,)

a = CheckIndex()
        dummy = np.arange(2.)
        # 1 input, 1 output
        assert_equal(np.sin(a), 0)
        assert_equal(np.sin(dummy, a), (0,))
        assert_equal(np.sin(dummy, out=a), (0,))
        assert_equal(np.sin(dummy, out=(a,)), (0,))
        assert_equal(np.sin(a, a), 0)
        assert_equal(np.sin(a, out=a), 0)
        assert_equal(np.sin(a, out=(a,)), 0)
        # 1 input, 2 outputs
        assert_equal(np.modf(dummy, a), (0,))
        assert_equal(np.modf(dummy, None, a), (1,))
        assert_equal(np.modf(dummy, dummy, a), (1,))
        assert_equal(np.modf(dummy, out=(a, None)), (0,))
        assert_equal(np.modf(dummy, out=(a, dummy)), (0,))
        assert_equal(np.modf(dummy, out=(None, a)), (1,))
        assert_equal(np.modf(dummy, out=(dummy, a)), (1,))
        assert_equal(np.modf(a, out=(dummy, a)), 0)
        with assert_raises(TypeError):
            # Out argument must be tuple, since there are multiple outputs
            np.modf(dummy, out=a)

assert_raises(ValueError, np.modf, dummy, out=(a,))

# 2 inputs, 1 output
        assert_equal(np.add(a, dummy), 0)
        assert_equal(np.add(dummy, a), 1)
        assert_equal(np.add(dummy, dummy, a), (0,))
        assert_equal(np.add(dummy, a, a), 1)
        assert_equal(np.add(dummy, dummy, out=a), (0,))
        assert_equal(np.add(dummy, dummy, out=(a,)), (0,))
        assert_equal(np.add(a, dummy, out=a), 0)

def test_out_override(self):
        # regression test for github bug 4753
        class OutClass(np.ndarray):
            def __array_ufunc__(self, ufunc, method, *inputs, **kw):
                if 'out' in kw:
                    tmp_kw = kw.copy()
                    tmp_kw.pop('out')
                    func = getattr(ufunc, method)
                    kw['out'][0][...] = func(*inputs, **tmp_kw)

A = np.array([0]).view(OutClass)
        B = np.array([5])
        C = np.array([6])
        np.multiply(C, B, A)
        assert_equal(A[0], 30)
        assert_(isinstance(A, OutClass))
        A[0] = 0
        np.multiply(C, B, out=A)
        assert_equal(A[0], 30)
        assert_(isinstance(A, OutClass))

def test_pow_override_with_errors(self):
        # regression test for gh-9112
        class PowerOnly(np.ndarray):
            def __array_ufunc__(self, ufunc, method, *inputs, **kw):
                if ufunc is not np.power:
                    raise NotImplementedError
                return "POWER!"
        # explicit cast to float, to ensure the fast power path is taken.
        a = np.array(5., dtype=np.float64).view(PowerOnly)
        assert_equal(a ** 2.5, "POWER!")
        with assert_raises(NotImplementedError):
            a ** 0.5
        with assert_raises(NotImplementedError):
            a ** 0
        with assert_raises(NotImplementedError):
            a ** 1
        with assert_raises(NotImplementedError):
            a ** -1
        with assert_raises(NotImplementedError):
            a ** 2

def test_pow_array_object_dtype(self):
        # test pow on arrays of object dtype
        class SomeClass:
            def __init__(self, num=None):
                self.num = num

# want to ensure a fast pow path is not taken
            def __mul__(self, other):
                raise AssertionError('__mul__ should not be called')

def __div__(self, other):
                raise AssertionError('__div__ should not be called')

def __pow__(self, exp):
                return SomeClass(num=self.num ** exp)

def __eq__(self, other):
                if isinstance(other, SomeClass):
                    return self.num == other.num

__rpow__ = __pow__

def pow_for(exp, arr):
            return np.array([x ** exp for x in arr])

obj_arr = np.array([SomeClass(1), SomeClass(2), SomeClass(3)])

assert_equal(obj_arr ** 0.5, pow_for(0.5, obj_arr))
        assert_equal(obj_arr ** 0, pow_for(0, obj_arr))
        assert_equal(obj_arr ** 1, pow_for(1, obj_arr))
        assert_equal(obj_arr ** -1, pow_for(-1, obj_arr))
        assert_equal(obj_arr ** 2, pow_for(2, obj_arr))

def test_pos_array_ufunc_override(self):
        class A(np.ndarray):
            def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
                return getattr(ufunc, method)(*[i.view(np.ndarray) for
                                                i in inputs], **kwargs)
        tst = np.array('foo').view(A)
        with assert_raises(TypeError):
            +tst

class TestTemporaryElide:
    # elision is only triggered on relatively large arrays

def test_extension_incref_elide(self):
        # test extension (e.g. cython) calling PyNumber_* slots without
        # increasing the reference counts
        #
        # def incref_elide(a):
        #    d = input.copy() # refcount 1
        #    return d, d + d # PyNumber_Add without increasing refcount
        from numpy.core._multiarray_tests import incref_elide
        d = np.ones(100000)
        orig, res = incref_elide(d)
        d + d
        # the return original should not be changed to an inplace operation
        assert_array_equal(orig, d)
        assert_array_equal(res, d + d)

def test_extension_incref_elide_stack(self):
        # scanning if the refcount == 1 object is on the python stack to check
        # that we are called directly from python is flawed as object may still
        # be above the stack pointer and we have no access to the top of it
        #
        # def incref_elide_l(d):
        #    return l[4] + l[4] # PyNumber_Add without increasing refcount
        from numpy.core._multiarray_tests import incref_elide_l
        # padding with 1 makes sure the object on the stack is not overwritten
        l = [1, 1, 1, 1, np.ones(100000)]
        res = incref_elide_l(l)
        # the return original should not be changed to an inplace operation
        assert_array_equal(l[4], np.ones(100000))
        assert_array_equal(res, l[4] + l[4])

def test_temporary_with_cast(self):
        # check that we don't elide into a temporary which would need casting
        d = np.ones(200000, dtype=np.int64)
        assert_equal(((d + d) + 2**222).dtype, np.dtype('O'))

r = ((d + d) / 2)
        assert_equal(r.dtype, np.dtype('f8'))

r = np.true_divide((d + d), 2)
        assert_equal(r.dtype, np.dtype('f8'))

r = ((d + d) / 2.)
        assert_equal(r.dtype, np.dtype('f8'))

r = ((d + d) // 2)
        assert_equal(r.dtype, np.dtype(np.int64))

# commutative elision into the astype result
        f = np.ones(100000, dtype=np.float32)
        assert_equal(((f + f) + f.astype(np.float64)).dtype, np.dtype('f8'))

# no elision into lower type
        d = f.astype(np.float64)
        assert_equal(((f + f) + d).dtype, d.dtype)
        l = np.ones(100000, dtype=np.longdouble)
        assert_equal(((d + d) + l).dtype, l.dtype)

# test unary abs with different output dtype
        for dt in (np.complex64, np.complex128, np.clongdouble):
            c = np.ones(100000, dtype=dt)
            r = abs(c * 2.0)
            assert_equal(r.dtype, np.dtype('f%d' % (c.itemsize // 2)))

def test_elide_broadcast(self):
        # test no elision on broadcast to higher dimension
        # only triggers elision code path in debug mode as triggering it in
        # normal mode needs 256kb large matching dimension, so a lot of memory
        d = np.ones((2000, 1), dtype=int)
        b = np.ones((2000), dtype=bool)
        r = (1 - d) + b
        assert_equal(r, 1)
        assert_equal(r.shape, (2000, 2000))

def test_elide_scalar(self):
        # check inplace op does not create ndarray from scalars
        a = np.bool_()
        assert_(type(~(a & a)) is np.bool_)

def test_elide_scalar_readonly(self):
        # The imaginary part of a real array is readonly. This needs to go
        # through fast_scalar_power which is only called for powers of
        # +1, -1, 0, 0.5, and 2, so use 2. Also need valid refcount for
        # elision which can be gotten for the imaginary part of a real
        # array. Should not error.
        a = np.empty(100000, dtype=np.float64)
        a.imag ** 2

def test_elide_readonly(self):
        # don't try to elide readonly temporaries
        r = np.asarray(np.broadcast_to(np.zeros(1), 100000).flat) * 0.0
        assert_equal(r, 0)

def test_elide_updateifcopy(self):
        a = np.ones(2**20)[::2]
        b = a.flat.__array__() + 1
        del b
        assert_equal(a, 1)

class TestCAPI:
    def test_IsPythonScalar(self):
        from numpy.core._multiarray_tests import IsPythonScalar
        assert_(IsPythonScalar(b'foobar'))
        assert_(IsPythonScalar(1))
        assert_(IsPythonScalar(2**80))
        assert_(IsPythonScalar(2.))
        assert_(IsPythonScalar("a"))

@pytest.mark.parametrize("converter",
             [_multiarray_tests.run_scalar_intp_converter,
              _multiarray_tests.run_scalar_intp_from_sequence])
    def test_intp_sequence_converters(self, converter):
        # Test simple values (-1 is special for error return paths)
        assert converter(10) == (10,)
        assert converter(-1) == (-1,)
        # A 0-D array looks a bit like a sequence but must take the integer
        # path:
        assert converter(np.array(123)) == (123,)
        # Test simple sequences (intp_from_sequence only supports length 1):
        assert converter((10,)) == (10,)
        assert converter(np.array([11])) == (11,)

@pytest.mark.parametrize("converter",
             [_multiarray_tests.run_scalar_intp_converter,
              _multiarray_tests.run_scalar_intp_from_sequence])
    @pytest.mark.skipif(IS_PYPY and sys.implementation.version <= (7, 3, 8),
            reason="PyPy bug in error formatting")
    def test_intp_sequence_converters_errors(self, converter):
        with pytest.raises(TypeError,
                match="expected a sequence of integers or a single integer, "):
            converter(object())
        with pytest.raises(TypeError,
                match="expected a sequence of integers or a single integer, "
                      "got '32.0'"):
            converter(32.)
        with pytest.raises(TypeError,
                match="'float' object cannot be interpreted as an integer"):
            converter([32.])
        with pytest.raises(ValueError,
                match="Maximum allowed dimension"):
            # These converters currently convert overflows to a ValueError
            converter(2**64)

class TestSubscripting:
    def test_test_zero_rank(self):
        x = np.array([1, 2, 3])
        assert_(isinstance(x[0], np.int_))
        assert_(type(x[0, ...]) is np.ndarray)

class TestPickling:
    @pytest.mark.skipif(pickle.HIGHEST_PROTOCOL >= 5,
                        reason=('this tests the error messages when trying to'
                                'protocol 5 although it is not available'))
    def test_correct_protocol5_error_message(self):
        array = np.arange(10)

def test_record_array_with_object_dtype(self):
        my_object = object()

arr_with_object = np.array(
                [(my_object, 1, 2.0)],
                dtype=[('a', object), ('b', int), ('c', float)])
        arr_without_object = np.array(
                [('xxx', 1, 2.0)],
                dtype=[('a', str), ('b', int), ('c', float)])

for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
            depickled_arr_with_object = pickle.loads(
                    pickle.dumps(arr_with_object, protocol=proto))
            depickled_arr_without_object = pickle.loads(
                    pickle.dumps(arr_without_object, protocol=proto))

assert_equal(arr_with_object.dtype,
                         depickled_arr_with_object.dtype)
            assert_equal(arr_without_object.dtype,
                         depickled_arr_without_object.dtype)

@pytest.mark.skipif(pickle.HIGHEST_PROTOCOL < 5,
                        reason="requires pickle protocol 5")
    def test_f_contiguous_array(self):
        f_contiguous_array = np.array([[1, 2, 3], [4, 5, 6]], order='F')
        buffers = []

# When using pickle protocol 5, Fortran-contiguous arrays can be
        # serialized using out-of-band buffers
        bytes_string = pickle.dumps(f_contiguous_array, protocol=5,
                                    buffer_callback=buffers.append)

assert len(buffers) > 0

depickled_f_contiguous_array = pickle.loads(bytes_string,
                                                    buffers=buffers)

assert_equal(f_contiguous_array, depickled_f_contiguous_array)

def test_non_contiguous_array(self):
        non_contiguous_array = np.arange(12).reshape(3, 4)[:, :2]
        assert not non_contiguous_array.flags.c_contiguous
        assert not non_contiguous_array.flags.f_contiguous

# make sure non-contiguous arrays can be pickled-depickled
        # using any protocol
        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
            depickled_non_contiguous_array = pickle.loads(
                    pickle.dumps(non_contiguous_array, protocol=proto))

assert_equal(non_contiguous_array, depickled_non_contiguous_array)

def test_roundtrip(self):
        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
            carray = np.array([[2, 9], [7, 0], [3, 8]])
            DATA = [
                carray,
                np.transpose(carray),
                np.array([('xxx', 1, 2.0)], dtype=[('a', (str, 3)), ('b', int),
                                                   ('c', float)])
            ]

refs = [weakref.ref(a) for a in DATA]
            for a in DATA:
                assert_equal(
                        a, pickle.loads(pickle.dumps(a, protocol=proto)),
                        err_msg="%r" % a)
            del a, DATA, carray
            break_cycles()
            # check for reference leaks (gh-12793)
            for ref in refs:
                assert ref() is None

def _loads(self, obj):
        return pickle.loads(obj, encoding='latin1')

# version 0 pickles, using protocol=2 to pickle
    # version 0 doesn't have a version field
    def test_version0_int8(self):
        s = b'\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x04\x85cnumpy\ndtype\nq\x04U\x02i1K\x00K\x01\x87Rq\x05(U\x01|NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89U\x04\x01\x02\x03\x04tb.'
        a = np.array([1, 2, 3, 4], dtype=np.int8)
        p = self._loads(s)
        assert_equal(a, p)

def test_version0_float32(self):
        s = b'\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x04\x85cnumpy\ndtype\nq\x04U\x02f4K\x00K\x01\x87Rq\x05(U\x01<NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89U\x10\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@tb.'
        a = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32)
        p = self._loads(s)
        assert_equal(a, p)

def test_version0_object(self):
        s = b'\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x02\x85cnumpy\ndtype\nq\x04U\x02O8K\x00K\x01\x87Rq\x05(U\x01|NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89]q\x06(}q\x07U\x01aK\x01s}q\x08U\x01bK\x02setb.'
        a = np.array([{'a': 1}, {'b': 2}])
        p = self._loads(s)
        assert_equal(a, p)

# version 1 pickles, using protocol=2 to pickle
    def test_version1_int8(self):
        s = b'\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x01K\x04\x85cnumpy\ndtype\nq\x04U\x02i1K\x00K\x01\x87Rq\x05(K\x01U\x01|NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89U\x04\x01\x02\x03\x04tb.'
        a = np.array([1, 2, 3, 4], dtype=np.int8)
        p = self._loads(s)
        assert_equal(a, p)

def test_version1_float32(self):
        s = b'\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x01K\x04\x85cnumpy\ndtype\nq\x04U\x02f4K\x00K\x01\x87Rq\x05(K\x01U\x01<NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89U\x10\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@tb.'
        a = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32)
        p = self._loads(s)
        assert_equal(a, p)

def test_version1_object(self):
        s = b'\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x01K\x02\x85cnumpy\ndtype\nq\x04U\x02O8K\x00K\x01\x87Rq\x05(K\x01U\x01|NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89]q\x06(}q\x07U\x01aK\x01s}q\x08U\x01bK\x02setb.'
        a = np.array([{'a': 1}, {'b': 2}])
        p = self._loads(s)
        assert_equal(a, p)

def test_subarray_int_shape(self):
        s = b"cnumpy.core.multiarray\n_reconstruct\np0\n(cnumpy\nndarray\np1\n(I0\ntp2\nS'b'\np3\ntp4\nRp5\n(I1\n(I1\ntp6\ncnumpy\ndtype\np7\n(S'V6'\np8\nI0\nI1\ntp9\nRp10\n(I3\nS'|'\np11\nN(S'a'\np12\ng3\ntp13\n(dp14\ng12\n(g7\n(S'V4'\np15\nI0\nI1\ntp16\nRp17\n(I3\nS'|'\np18\n(g7\n(S'i1'\np19\nI0\nI1\ntp20\nRp21\n(I3\nS'|'\np22\nNNNI-1\nI-1\nI0\ntp23\nb(I2\nI2\ntp24\ntp25\nNNI4\nI1\nI0\ntp26\nbI0\ntp27\nsg3\n(g7\n(S'V2'\np28\nI0\nI1\ntp29\nRp30\n(I3\nS'|'\np31\n(g21\nI2\ntp32\nNNI2\nI1\nI0\ntp33\nbI4\ntp34\nsI6\nI1\nI0\ntp35\nbI00\nS'\\x01\\x01\\x01\\x01\\x01\\x02'\np36\ntp37\nb."
        a = np.array([(1, (1, 2))], dtype=[('a', 'i1', (2, 2)), ('b', 'i1', 2)])
        p = self._loads(s)
        assert_equal(a, p)

def test_datetime64_byteorder(self):
        original = np.array([['2015-02-24T00:00:00.000000000']], dtype='datetime64[ns]')

original_byte_reversed = original.copy(order='K')
        original_byte_reversed.dtype = original_byte_reversed.dtype.newbyteorder('S')
        original_byte_reversed.byteswap(inplace=True)

new = pickle.loads(pickle.dumps(original_byte_reversed))

assert_equal(original.dtype, new.dtype)

class TestFancyIndexing:
    def test_list(self):
        x = np.ones((1, 1))
        x[:, [0]] = 2.0
        assert_array_equal(x, np.array([[2.0]]))

x = np.ones((1, 1, 1))
        x[:, :, [0]] = 2.0
        assert_array_equal(x, np.array([[[2.0]]]))

def test_tuple(self):
        x = np.ones((1, 1))
        x[:, (0,)] = 2.0
        assert_array_equal(x, np.array([[2.0]]))
        x = np.ones((1, 1, 1))
        x[:, :, (0,)] = 2.0
        assert_array_equal(x, np.array([[[2.0]]]))

def test_mask(self):
        x = np.array([1, 2, 3, 4])
        m = np.array([0, 1, 0, 0], bool)
        assert_array_equal(x[m], np.array([2]))

def test_mask2(self):
        x = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
        m = np.array([0, 1], bool)
        m2 = np.array([[0, 1, 0, 0], [1, 0, 0, 0]], bool)
        m3 = np.array([[0, 1, 0, 0], [0, 0, 0, 0]], bool)
        assert_array_equal(x[m], np.array([[5, 6, 7, 8]]))
        assert_array_equal(x[m2], np.array([2, 5]))
        assert_array_equal(x[m3], np.array([2]))

def test_assign_mask(self):
        x = np.array([1, 2, 3, 4])
        m = np.array([0, 1, 0, 0], bool)
        x[m] = 5
        assert_array_equal(x, np.array([1, 5, 3, 4]))

def test_assign_mask2(self):
        xorig = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
        m = np.array([0, 1], bool)
        m2 = np.array([[0, 1, 0, 0], [1, 0, 0, 0]], bool)
        m3 = np.array([[0, 1, 0, 0], [0, 0, 0, 0]], bool)
        x = xorig.copy()
        x[m] = 10
        assert_array_equal(x, np.array([[1, 2, 3, 4], [10, 10, 10, 10]]))
        x = xorig.copy()
        x[m2] = 10
        assert_array_equal(x, np.array([[1, 10, 3, 4], [10, 6, 7, 8]]))
        x = xorig.copy()
        x[m3] = 10
        assert_array_equal(x, np.array([[1, 10, 3, 4], [5, 6, 7, 8]]))

class TestStringCompare:
    def test_string(self):
        g1 = np.array(["This", "is", "example"])
        g2 = np.array(["This", "was", "example"])
        assert_array_equal(g1 == g2, [g1[i] == g2[i] for i in [0, 1, 2]])
        assert_array_equal(g1 != g2, [g1[i] != g2[i] for i in [0, 1, 2]])
        assert_array_equal(g1 <= g2, [g1[i] <= g2[i] for i in [0, 1, 2]])
        assert_array_equal(g1 >= g2, [g1[i] >= g2[i] for i in [0, 1, 2]])
        assert_array_equal(g1 < g2, [g1[i] < g2[i] for i in [0, 1, 2]])
        assert_array_equal(g1 > g2, [g1[i] > g2[i] for i in [0, 1, 2]])

def test_mixed(self):
        g1 = np.array(["spam", "spa", "spammer", "and eggs"])
        g2 = "spam"
        assert_array_equal(g1 == g2, [x == g2 for x in g1])
        assert_array_equal(g1 != g2, [x != g2 for x in g1])
        assert_array_equal(g1 < g2, [x < g2 for x in g1])
        assert_array_equal(g1 > g2, [x > g2 for x in g1])
        assert_array_equal(g1 <= g2, [x <= g2 for x in g1])
        assert_array_equal(g1 >= g2, [x >= g2 for x in g1])

def test_unicode(self):
        g1 = np.array(["This", "is", "example"])
        g2 = np.array(["This", "was", "example"])
        assert_array_equal(g1 == g2, [g1[i] == g2[i] for i in [0, 1, 2]])
        assert_array_equal(g1 != g2, [g1[i] != g2[i] for i in [0, 1, 2]])
        assert_array_equal(g1 <= g2, [g1[i] <= g2[i] for i in [0, 1, 2]])
        assert_array_equal(g1 >= g2, [g1[i] >= g2[i] for i in [0, 1, 2]])
        assert_array_equal(g1 < g2,  [g1[i] < g2[i] for i in [0, 1, 2]])
        assert_array_equal(g1 > g2,  [g1[i] > g2[i] for i in [0, 1, 2]])

class TestArgmaxArgminCommon:

sizes = [(), (3,), (3, 2), (2, 3),
             (3, 3), (2, 3, 4), (4, 3, 2),
             (1, 2, 3, 4), (2, 3, 4, 1),
             (3, 4, 1, 2), (4, 1, 2, 3),
             (64,), (128,), (256,)]

@pytest.mark.parametrize("size, axis", itertools.chain(*[[(size, axis)
        for axis in list(range(-len(size), len(size))) + [None]]
        for size in sizes]))
    @pytest.mark.parametrize('method', [np.argmax, np.argmin])
    def test_np_argmin_argmax_keepdims(self, size, axis, method):

arr = np.random.normal(size=size)

# contiguous arrays
        if axis is None:
            new_shape = [1 for _ in range(len(size))]
        else:
            new_shape = list(size)
            new_shape[axis] = 1
        new_shape = tuple(new_shape)

_res_orig = method(arr, axis=axis)
        res_orig = _res_orig.reshape(new_shape)
        res = method(arr, axis=axis, keepdims=True)
        assert_equal(res, res_orig)
        assert_(res.shape == new_shape)
        outarray = np.empty(res.shape, dtype=res.dtype)
        res1 = method(arr, axis=axis, out=outarray,
                            keepdims=True)
        assert_(res1 is outarray)
        assert_equal(res, outarray)

if len(size) > 0:
            wrong_shape = list(new_shape)
            if axis is not None:
                wrong_shape[axis] = 2
            else:
                wrong_shape[0] = 2
            wrong_outarray = np.empty(wrong_shape, dtype=res.dtype)
            with pytest.raises(ValueError):
                method(arr.T, axis=axis,
                        out=wrong_outarray, keepdims=True)

# non-contiguous arrays
        if axis is None:
            new_shape = [1 for _ in range(len(size))]
        else:
            new_shape = list(size)[::-1]
            new_shape[axis] = 1
        new_shape = tuple(new_shape)

_res_orig = method(arr.T, axis=axis)
        res_orig = _res_orig.reshape(new_shape)
        res = method(arr.T, axis=axis, keepdims=True)
        assert_equal(res, res_orig)
        assert_(res.shape == new_shape)
        outarray = np.empty(new_shape[::-1], dtype=res.dtype)
        outarray = outarray.T
        res1 = method(arr.T, axis=axis, out=outarray,
                            keepdims=True)
        assert_(res1 is outarray)
        assert_equal(res, outarray)

if len(size) > 0:
            # one dimension lesser for non-zero sized
            # array should raise an error
            with pytest.raises(ValueError):
                method(arr[0], axis=axis,
                        out=outarray, keepdims=True)

@pytest.mark.parametrize('method', ['max', 'min'])
    def test_all(self, method):
        a = np.random.normal(0, 1, (4, 5, 6, 7, 8))
        arg_method = getattr(a, 'arg' + method)
        val_method = getattr(a, method)
        for i in range(a.ndim):
            a_maxmin = val_method(i)
            aarg_maxmin = arg_method(i)
            axes = list(range(a.ndim))
            axes.remove(i)
            assert_(np.all(a_maxmin == aarg_maxmin.choose(
                                        *a.transpose(i, *axes))))

@pytest.mark.parametrize('method', ['argmax', 'argmin'])
    def test_output_shape(self, method):
        # see also gh-616
        a = np.ones((10, 5))
        arg_method = getattr(a, method)
        # Check some simple shape mismatches
        out = np.ones(11, dtype=np.int_)
        assert_raises(ValueError, arg_method, -1, out)

out = np.ones((2, 5), dtype=np.int_)
        assert_raises(ValueError, arg_method, -1, out)

# these could be relaxed possibly (used to allow even the previous)
        out = np.ones((1, 10), dtype=np.int_)
        assert_raises(ValueError, arg_method, -1, out)

out = np.ones(10, dtype=np.int_)
        arg_method(-1, out=out)
        assert_equal(out, arg_method(-1))

@pytest.mark.parametrize('ndim', [0, 1])
    @pytest.mark.parametrize('method', ['argmax', 'argmin'])
    def test_ret_is_out(self, ndim, method):
        a = np.ones((4,) + (256,)*ndim)
        arg_method = getattr(a, method)
        out = np.empty((256,)*ndim, dtype=np.intp)
        ret = arg_method(axis=0, out=out)
        assert ret is out

@pytest.mark.parametrize('np_array, method, idx, val',
        [(np.zeros, 'argmax', 5942, "as"),
         (np.ones, 'argmin', 6001, "0")])
    def test_unicode(self, np_array, method, idx, val):
        d = np_array(6031, dtype='<U9')
        arg_method = getattr(d, method)
        d[idx] = val
        assert_equal(arg_method(), idx)

@pytest.mark.parametrize('arr_method, np_method',
        [('argmax', np.argmax),
         ('argmin', np.argmin)])
    def test_np_vs_ndarray(self, arr_method, np_method):
        # make sure both ndarray.argmax/argmin and
        # numpy.argmax/argmin support out/axis args
        a = np.random.normal(size=(2, 3))
        arg_method = getattr(a, arr_method)

# check positional args
        out1 = np.zeros(2, dtype=int)
        out2 = np.zeros(2, dtype=int)
        assert_equal(arg_method(1, out1), np_method(a, 1, out2))
        assert_equal(out1, out2)

# check keyword args
        out1 = np.zeros(3, dtype=int)
        out2 = np.zeros(3, dtype=int)
        assert_equal(arg_method(out=out1, axis=0),
                     np_method(a, out=out2, axis=0))
        assert_equal(out1, out2)

@pytest.mark.leaks_references(reason="replaces None with NULL.")
    @pytest.mark.parametrize('method, vals',
        [('argmax', (10, 30)),
         ('argmin', (30, 10))])
    def test_object_with_NULLs(self, method, vals):
        # See gh-6032
        a = np.empty(4, dtype='O')
        arg_method = getattr(a, method)
        ctypes.memset(a.ctypes.data, 0, a.nbytes)
        assert_equal(arg_method(), 0)
        a[3] = vals[0]
        assert_equal(arg_method(), 3)
        a[1] = vals[1]
        assert_equal(arg_method(), 1)

class TestArgmax:
    usg_data = [
        ([1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], 0),
        ([3, 3, 3, 3,  2,  2,  2,  2], 0),
        ([0, 1, 2, 3,  4,  5,  6,  7], 7),
        ([7, 6, 5, 4,  3,  2,  1,  0], 0)
    ]
    sg_data = usg_data + [
        ([1, 2, 3, 4, -4, -3, -2, -1], 3),
        ([1, 2, 3, 4, -1, -2, -3, -4], 3)
    ]
    darr = [(np.array(d[0], dtype=t), d[1]) for d, t in (
        itertools.product(usg_data, (
            np.uint8, np.uint16, np.uint32, np.uint64
        ))
    )]
    darr = darr + [(np.array(d[0], dtype=t), d[1]) for d, t in (
        itertools.product(sg_data, (
            np.int8, np.int16, np.int32, np.int64, np.float32, np.float64
        ))
    )]
    darr = darr + [(np.array(d[0], dtype=t), d[1]) for d, t in (
        itertools.product((
            ([0, 1, 2, 3, np.nan], 4),
            ([0, 1, 2, np.nan, 3], 3),
            ([np.nan, 0, 1, 2, 3], 0),
            ([np.nan, 0, np.nan, 2, 3], 0),
            # To hit the tail of SIMD multi-level(x4, x1) inner loops
            # on variant SIMD widthes
            ([1] * (2*5-1) + [np.nan], 2*5-1),
            ([1] * (4*5-1) + [np.nan], 4*5-1),
            ([1] * (8*5-1) + [np.nan], 8*5-1),
            ([1] * (16*5-1) + [np.nan], 16*5-1),
            ([1] * (32*5-1) + [np.nan], 32*5-1)
        ), (
            np.float32, np.float64
        ))
    )]
    nan_arr = darr + [
        ([0, 1, 2, 3, complex(0, np.nan)], 4),
        ([0, 1, 2, 3, complex(np.nan, 0)], 4),
        ([0, 1, 2, complex(np.nan, 0), 3], 3),
        ([0, 1, 2, complex(0, np.nan), 3], 3),
        ([complex(0, np.nan), 0, 1, 2, 3], 0),
        ([complex(np.nan, np.nan), 0, 1, 2, 3], 0),
        ([complex(np.nan, 0), complex(np.nan, 2), complex(np.nan, 1)], 0),
        ([complex(np.nan, np.nan), complex(np.nan, 2), complex(np.nan, 1)], 0),
        ([complex(np.nan, 0), complex(np.nan, 2), complex(np.nan, np.nan)], 0),

([complex(0, 0), complex(0, 2), complex(0, 1)], 1),
        ([complex(1, 0), complex(0, 2), complex(0, 1)], 0),
        ([complex(1, 0), complex(0, 2), complex(1, 1)], 2),

([np.datetime64('1923-04-14T12:43:12'),
          np.datetime64('1994-06-21T14:43:15'),
          np.datetime64('2001-10-15T04:10:32'),
          np.datetime64('1995-11-25T16:02:16'),
          np.datetime64('2005-01-04T03:14:12'),
          np.datetime64('2041-12-03T14:05:03')], 5),
        ([np.datetime64('1935-09-14T04:40:11'),
          np.datetime64('1949-10-12T12:32:11'),
          np.datetime64('2010-01-03T05:14:12'),
          np.datetime64('2015-11-20T12:20:59'),
          np.datetime64('1932-09-23T10:10:13'),
          np.datetime64('2014-10-10T03:50:30')], 3),
        # Assorted tests with NaTs
        ([np.datetime64('NaT'),
          np.datetime64('NaT'),
          np.datetime64('2010-01-03T05:14:12'),
          np.datetime64('NaT'),
          np.datetime64('2015-09-23T10:10:13'),
          np.datetime64('1932-10-10T03:50:30')], 0),
        ([np.datetime64('2059-03-14T12:43:12'),
          np.datetime64('1996-09-21T14:43:15'),
          np.datetime64('NaT'),
          np.datetime64('2022-12-25T16:02:16'),
          np.datetime64('1963-10-04T03:14:12'),
          np.datetime64('2013-05-08T18:15:23')], 2),
        ([np.timedelta64(2, 's'),
          np.timedelta64(1, 's'),
          np.timedelta64('NaT', 's'),
          np.timedelta64(3, 's')], 2),
        ([np.timedelta64('NaT', 's')] * 3, 0),

([timedelta(days=5, seconds=14), timedelta(days=2, seconds=35),
          timedelta(days=-1, seconds=23)], 0),
        ([timedelta(days=1, seconds=43), timedelta(days=10, seconds=5),
          timedelta(days=5, seconds=14)], 1),
        ([timedelta(days=10, seconds=24), timedelta(days=10, seconds=5),
          timedelta(days=10, seconds=43)], 2),

([False, False, False, False, True], 4),
        ([False, False, False, True, False], 3),
        ([True, False, False, False, False], 0),
        ([True, False, True, False, False], 0),
    ]

@pytest.mark.parametrize('data', nan_arr)
    def test_combinations(self, data):
        arr, pos = data
        with suppress_warnings() as sup:
            sup.filter(RuntimeWarning,
                        "invalid value encountered in reduce")
            val = np.max(arr)

assert_equal(np.argmax(arr), pos, err_msg="%r" % arr)
        assert_equal(arr[np.argmax(arr)], val, err_msg="%r" % arr)

# add padding to test SIMD loops
        rarr = np.repeat(arr, 129)
        rpos = pos * 129
        assert_equal(np.argmax(rarr), rpos, err_msg="%r" % rarr)
        assert_equal(rarr[np.argmax(rarr)], val, err_msg="%r" % rarr)

padd = np.repeat(np.min(arr), 513)
        rarr = np.concatenate((arr, padd))
        rpos = pos
        assert_equal(np.argmax(rarr), rpos, err_msg="%r" % rarr)
        assert_equal(rarr[np.argmax(rarr)], val, err_msg="%r" % rarr)

def test_maximum_signed_integers(self):

a = np.array([1, 2**7 - 1, -2**7], dtype=np.int8)
        assert_equal(np.argmax(a), 1)
        a = a.repeat(129)
        assert_equal(np.argmax(a), 129)

a = np.array([1, 2**15 - 1, -2**15], dtype=np.int16)
        assert_equal(np.argmax(a), 1)
        a = a.repeat(129)
        assert_equal(np.argmax(a), 129)

a = np.array([1, 2**31 - 1, -2**31], dtype=np.int32)
        assert_equal(np.argmax(a), 1)
        a = a.repeat(129)
        assert_equal(np.argmax(a), 129)

a = np.array([1, 2**63 - 1, -2**63], dtype=np.int64)
        assert_equal(np.argmax(a), 1)
        a = a.repeat(129)
        assert_equal(np.argmax(a), 129)

class TestArgmin:
    usg_data = [
        ([1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], 8),
        ([3, 3, 3, 3,  2,  2,  2,  2], 4),
        ([0, 1, 2, 3,  4,  5,  6,  7], 0),
        ([7, 6, 5, 4,  3,  2,  1,  0], 7)
    ]
    sg_data = usg_data + [
        ([1, 2, 3, 4, -4, -3, -2, -1], 4),
        ([1, 2, 3, 4, -1, -2, -3, -4], 7)
    ]
    darr = [(np.array(d[0], dtype=t), d[1]) for d, t in (
        itertools.product(usg_data, (
            np.uint8, np.uint16, np.uint32, np.uint64
        ))
    )]
    darr = darr + [(np.array(d[0], dtype=t), d[1]) for d, t in (
        itertools.product(sg_data, (
            np.int8, np.int16, np.int32, np.int64, np.float32, np.float64
        ))
    )]
    darr = darr + [(np.array(d[0], dtype=t), d[1]) for d, t in (
        itertools.product((
            ([0, 1, 2, 3, np.nan], 4),
            ([0, 1, 2, np.nan, 3], 3),
            ([np.nan, 0, 1, 2, 3], 0),
            ([np.nan, 0, np.nan, 2, 3], 0),
            # To hit the tail of SIMD multi-level(x4, x1) inner loops
            # on variant SIMD widthes
            ([1] * (2*5-1) + [np.nan], 2*5-1),
            ([1] * (4*5-1) + [np.nan], 4*5-1),
            ([1] * (8*5-1) + [np.nan], 8*5-1),
            ([1] * (16*5-1) + [np.nan], 16*5-1),
            ([1] * (32*5-1) + [np.nan], 32*5-1)
        ), (
            np.float32, np.float64
        ))
    )]
    nan_arr = darr + [
        ([0, 1, 2, 3, complex(0, np.nan)], 4),
        ([0, 1, 2, 3, complex(np.nan, 0)], 4),
        ([0, 1, 2, complex(np.nan, 0), 3], 3),
        ([0, 1, 2, complex(0, np.nan), 3], 3),
        ([complex(0, np.nan), 0, 1, 2, 3], 0),
        ([complex(np.nan, np.nan), 0, 1, 2, 3], 0),
        ([complex(np.nan, 0), complex(np.nan, 2), complex(np.nan, 1)], 0),
        ([complex(np.nan, np.nan), complex(np.nan, 2), complex(np.nan, 1)], 0),
        ([complex(np.nan, 0), complex(np.nan, 2), complex(np.nan, np.nan)], 0),

([complex(0, 0), complex(0, 2), complex(0, 1)], 0),
        ([complex(1, 0), complex(0, 2), complex(0, 1)], 2),
        ([complex(1, 0), complex(0, 2), complex(1, 1)], 1),

([np.datetime64('1923-04-14T12:43:12'),
          np.datetime64('1994-06-21T14:43:15'),
          np.datetime64('2001-10-15T04:10:32'),
          np.datetime64('1995-11-25T16:02:16'),
          np.datetime64('2005-01-04T03:14:12'),
          np.datetime64('2041-12-03T14:05:03')], 0),
        ([np.datetime64('1935-09-14T04:40:11'),
          np.datetime64('1949-10-12T12:32:11'),
          np.datetime64('2010-01-03T05:14:12'),
          np.datetime64('2014-11-20T12:20:59'),
          np.datetime64('2015-09-23T10:10:13'),
          np.datetime64('1932-10-10T03:50:30')], 5),
        # Assorted tests with NaTs
        ([np.datetime64('NaT'),
          np.datetime64('NaT'),
          np.datetime64('2010-01-03T05:14:12'),
          np.datetime64('NaT'),
          np.datetime64('2015-09-23T10:10:13'),
          np.datetime64('1932-10-10T03:50:30')], 0),
        ([np.datetime64('2059-03-14T12:43:12'),
          np.datetime64('1996-09-21T14:43:15'),
          np.datetime64('NaT'),
          np.datetime64('2022-12-25T16:02:16'),
          np.datetime64('1963-10-04T03:14:12'),
          np.datetime64('2013-05-08T18:15:23')], 2),
        ([np.timedelta64(2, 's'),
          np.timedelta64(1, 's'),
          np.timedelta64('NaT', 's'),
          np.timedelta64(3, 's')], 2),
        ([np.timedelta64('NaT', 's')] * 3, 0),

([timedelta(days=5, seconds=14), timedelta(days=2, seconds=35),
          timedelta(days=-1, seconds=23)], 2),
        ([timedelta(days=1, seconds=43), timedelta(days=10, seconds=5),
          timedelta(days=5, seconds=14)], 0),
        ([timedelta(days=10, seconds=24), timedelta(days=10, seconds=5),
          timedelta(days=10, seconds=43)], 1),

([True, True, True, True, False], 4),
        ([True, True, True, False, True], 3),
        ([False, True, True, True, True], 0),
        ([False, True, False, True, True], 0),
    ]

@pytest.mark.parametrize('data', nan_arr)
    def test_combinations(self, data):
        arr, pos = data
        with suppress_warnings() as sup:
            sup.filter(RuntimeWarning,
                       "invalid value encountered in reduce")
            min_val = np.min(arr)

assert_equal(np.argmin(arr), pos, err_msg="%r" % arr)
        assert_equal(arr[np.argmin(arr)], min_val, err_msg="%r" % arr)

# add padding to test SIMD loops
        rarr = np.repeat(arr, 129)
        rpos = pos * 129
        assert_equal(np.argmin(rarr), rpos, err_msg="%r" % rarr)
        assert_equal(rarr[np.argmin(rarr)], min_val, err_msg="%r" % rarr)

padd = np.repeat(np.max(arr), 513)
        rarr = np.concatenate((arr, padd))
        rpos = pos
        assert_equal(np.argmin(rarr), rpos, err_msg="%r" % rarr)
        assert_equal(rarr[np.argmin(rarr)], min_val, err_msg="%r" % rarr)

def test_minimum_signed_integers(self):

a = np.array([1, -2**7, -2**7 + 1, 2**7 - 1], dtype=np.int8)
        assert_equal(np.argmin(a), 1)
        a = a.repeat(129)
        assert_equal(np.argmin(a), 129)

a = np.array([1, -2**15, -2**15 + 1, 2**15 - 1], dtype=np.int16)
        assert_equal(np.argmin(a), 1)
        a = a.repeat(129)
        assert_equal(np.argmin(a), 129)

a = np.array([1, -2**31, -2**31 + 1, 2**31 - 1], dtype=np.int32)
        assert_equal(np.argmin(a), 1)
        a = a.repeat(129)
        assert_equal(np.argmin(a), 129)

a = np.array([1, -2**63, -2**63 + 1, 2**63 - 1], dtype=np.int64)
        assert_equal(np.argmin(a), 1)
        a = a.repeat(129)
        assert_equal(np.argmin(a), 129)

class TestMinMax:

def test_scalar(self):
        assert_raises(np.AxisError, np.amax, 1, 1)
        assert_raises(np.AxisError, np.amin, 1, 1)

assert_equal(np.amax(1, axis=0), 1)
        assert_equal(np.amin(1, axis=0), 1)
        assert_equal(np.amax(1, axis=None), 1)
        assert_equal(np.amin(1, axis=None), 1)

def test_axis(self):
        assert_raises(np.AxisError, np.amax, [1, 2, 3], 1000)
        assert_equal(np.amax([[1, 2, 3]], axis=1), 3)

def test_datetime(self):
        # Do not ignore NaT
        for dtype in ('m8[s]', 'm8[Y]'):
            a = np.arange(10).astype(dtype)
            assert_equal(np.amin(a), a[0])
            assert_equal(np.amax(a), a[9])
            a[3] = 'NaT'
            assert_equal(np.amin(a), a[3])
            assert_equal(np.amax(a), a[3])

class TestNewaxis:
    def test_basic(self):
        sk = np.array([0, -0.1, 0.1])
        res = 250*sk[:, np.newaxis]
        assert_almost_equal(res.ravel(), 250*sk)

class TestClip:
    def _check_range(self, x, cmin, cmax):
        assert_(np.all(x >= cmin))
        assert_(np.all(x <= cmax))

def _clip_type(self, type_group, array_max,
                   clip_min, clip_max, inplace=False,
                   expected_min=None, expected_max=None):
        if expected_min is None:
            expected_min = clip_min
        if expected_max is None:
            expected_max = clip_max

for T in np.sctypes[type_group]:
            if sys.byteorder == 'little':
                byte_orders = ['=', '>']
            else:
                byte_orders = ['<', '=']

for byteorder in byte_orders:
                dtype = np.dtype(T).newbyteorder(byteorder)

x = (np.random.random(1000) * array_max).astype(dtype)
                if inplace:
                    # The tests that call us pass clip_min and clip_max that
                    # might not fit in the destination dtype. They were written
                    # assuming the previous unsafe casting, which now must be
                    # passed explicitly to avoid a warning.
                    x.clip(clip_min, clip_max, x, casting='unsafe')
                else:
                    x = x.clip(clip_min, clip_max)
                    byteorder = '='

if x.dtype.byteorder == '|':
                    byteorder = '|'
                assert_equal(x.dtype.byteorder, byteorder)
                self._check_range(x, expected_min, expected_max)
        return x

def test_basic(self):
        for inplace in [False, True]:
            self._clip_type(
                'float', 1024, -12.8, 100.2, inplace=inplace)
            self._clip_type(
                'float', 1024, 0, 0, inplace=inplace)

self._clip_type(
                'int', 1024, -120, 100, inplace=inplace)
            self._clip_type(
                'int', 1024, 0, 0, inplace=inplace)

self._clip_type(
                'uint', 1024, 0, 0, inplace=inplace)
            self._clip_type(
                'uint', 1024, -120, 100, inplace=inplace, expected_min=0)

def test_record_array(self):
        rec = np.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
                       dtype=[('x', '<f8'), ('y', '<f8'), ('z', '<f8')])
        y = rec['x'].clip(-0.3, 0.5)
        self._check_range(y, -0.3, 0.5)

def test_max_or_min(self):
        val = np.array([0, 1, 2, 3, 4, 5, 6, 7])
        x = val.clip(3)
        assert_(np.all(x >= 3))
        x = val.clip(min=3)
        assert_(np.all(x >= 3))
        x = val.clip(max=4)
        assert_(np.all(x <= 4))

def test_nan(self):
        input_arr = np.array([-2., np.nan, 0.5, 3., 0.25, np.nan])
        result = input_arr.clip(-1, 1)
        expected = np.array([-1., np.nan, 0.5, 1., 0.25, np.nan])
        assert_array_equal(result, expected)

class TestCompress:
    def test_axis(self):
        tgt = [[5, 6, 7, 8, 9]]
        arr = np.arange(10).reshape(2, 5)
        out = np.compress([0, 1], arr, axis=0)
        assert_equal(out, tgt)

tgt = [[1, 3], [6, 8]]
        out = np.compress([0, 1, 0, 1, 0], arr, axis=1)
        assert_equal(out, tgt)

def test_truncate(self):
        tgt = [[1], [6]]
        arr = np.arange(10).reshape(2, 5)
        out = np.compress([0, 1], arr, axis=1)
        assert_equal(out, tgt)

def test_flatten(self):
        arr = np.arange(10).reshape(2, 5)
        out = np.compress([0, 1], arr)
        assert_equal(out, 1)

class TestPutmask:
    def tst_basic(self, x, T, mask, val):
        np.putmask(x, mask, val)
        assert_equal(x[mask], np.array(val, T))

def test_ip_types(self):
        unchecked_types = [bytes, str, np.void]

x = np.random.random(1000)*100
        mask = x < 40

for val in [-100, 0, 15]:
            for types in np.sctypes.values():
                for T in types:
                    if T not in unchecked_types:
                        if val < 0 and np.dtype(T).kind == "u":
                            val = np.iinfo(T).max - 99
                        self.tst_basic(x.copy().astype(T), T, mask, val)

# Also test string of a length which uses an untypical length
            dt = np.dtype("S3")
            self.tst_basic(x.astype(dt), dt.type, mask, dt.type(val)[:3])

def test_mask_size(self):
        assert_raises(ValueError, np.putmask, np.array([1, 2, 3]), [True], 5)

@pytest.mark.parametrize('dtype', ('>i4', '<i4'))
    def test_byteorder(self, dtype):
        x = np.array([1, 2, 3], dtype)
        np.putmask(x, [True, False, True], -1)
        assert_array_equal(x, [-1, 2, -1])

def test_record_array(self):
        # Note mixed byteorder.
        rec = np.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
                      dtype=[('x', '<f8'), ('y', '>f8'), ('z', '<f8')])
        np.putmask(rec['x'], [True, False], 10)
        assert_array_equal(rec['x'], [10, 5])
        assert_array_equal(rec['y'], [2, 4])
        assert_array_equal(rec['z'], [3, 3])
        np.putmask(rec['y'], [True, False], 11)
        assert_array_equal(rec['x'], [10, 5])
        assert_array_equal(rec['y'], [11, 4])
        assert_array_equal(rec['z'], [3, 3])

def test_overlaps(self):
        # gh-6272 check overlap
        x = np.array([True, False, True, False])
        np.putmask(x[1:4], [True, True, True], x[:3])
        assert_equal(x, np.array([True, True, False, True]))

x = np.array([True, False, True, False])
        np.putmask(x[1:4], x[:3], [True, False, True])
        assert_equal(x, np.array([True, True, True, True]))

def test_writeable(self):
        a = np.arange(5)
        a.flags.writeable = False

with pytest.raises(ValueError):
            np.putmask(a, a >= 2, 3)

def test_kwargs(self):
        x = np.array([0, 0])
        np.putmask(x, [0, 1], [-1, -2])
        assert_array_equal(x, [0, -2])

x = np.array([0, 0])
        np.putmask(x, mask=[0, 1], values=[-1, -2])
        assert_array_equal(x, [0, -2])

x = np.array([0, 0])
        np.putmask(x, values=[-1, -2],  mask=[0, 1])
        assert_array_equal(x, [0, -2])

with pytest.raises(TypeError):
            np.putmask(a=x, values=[-1, -2],  mask=[0, 1])

class TestTake:
    def tst_basic(self, x):
        ind = list(range(x.shape[0]))
        assert_array_equal(x.take(ind, axis=0), x)

def test_ip_types(self):
        unchecked_types = [bytes, str, np.void]

x = np.random.random(24)*100
        x.shape = 2, 3, 4
        for types in np.sctypes.values():
            for T in types:
                if T not in unchecked_types:
                    self.tst_basic(x.copy().astype(T))

# Also test string of a length which uses an untypical length
            self.tst_basic(x.astype("S3"))

def test_raise(self):
        x = np.random.random(24)*100
        x.shape = 2, 3, 4
        assert_raises(IndexError, x.take, [0, 1, 2], axis=0)
        assert_raises(IndexError, x.take, [-3], axis=0)
        assert_array_equal(x.take([-1], axis=0)[0], x[1])

def test_clip(self):
        x = np.random.random(24)*100
        x.shape = 2, 3, 4
        assert_array_equal(x.take([-1], axis=0, mode='clip')[0], x[0])
        assert_array_equal(x.take([2], axis=0, mode='clip')[0], x[1])

def test_wrap(self):
        x = np.random.random(24)*100
        x.shape = 2, 3, 4
        assert_array_equal(x.take([-1], axis=0, mode='wrap')[0], x[1])
        assert_array_equal(x.take([2], axis=0, mode='wrap')[0], x[0])
        assert_array_equal(x.take([3], axis=0, mode='wrap')[0], x[1])

@pytest.mark.parametrize('dtype', ('>i4', '<i4'))
    def test_byteorder(self, dtype):
        x = np.array([1, 2, 3], dtype)
        assert_array_equal(x.take([0, 2, 1]), [1, 3, 2])

def test_record_array(self):
        # Note mixed byteorder.
        rec = np.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
                      dtype=[('x', '<f8'), ('y', '>f8'), ('z', '<f8')])
        rec1 = rec.take([1])
        assert_(rec1['x'] == 5.0 and rec1['y'] == 4.0)

def test_out_overlap(self):
        # gh-6272 check overlap on out
        x = np.arange(5)
        y = np.take(x, [1, 2, 3], out=x[2:5], mode='wrap')
        assert_equal(y, np.array([1, 2, 3]))

@pytest.mark.parametrize('shape', [(1, 2), (1,), ()])
    def test_ret_is_out(self, shape):
        # 0d arrays should not be an exception to this rule
        x = np.arange(5)
        inds = np.zeros(shape, dtype=np.intp)
        out = np.zeros(shape, dtype=x.dtype)
        ret = np.take(x, inds, out=out)
        assert ret is out

class TestLexsort:
    @pytest.mark.parametrize('dtype',[
        np.uint8, np.uint16, np.uint32, np.uint64,
        np.int8, np.int16, np.int32, np.int64,
        np.float16, np.float32, np.float64
    ])
    def test_basic(self, dtype):
        a = np.array([1, 2, 1, 3, 1, 5], dtype=dtype)
        b = np.array([0, 4, 5, 6, 2, 3], dtype=dtype)
        idx = np.lexsort((b, a))
        expected_idx = np.array([0, 4, 2, 1, 3, 5])
        assert_array_equal(idx, expected_idx)
        assert_array_equal(a[idx], np.sort(a))

def test_mixed(self):
        a = np.array([1, 2, 1, 3, 1, 5])
        b = np.array([0, 4, 5, 6, 2, 3], dtype='datetime64[D]')

idx = np.lexsort((b, a))
        expected_idx = np.array([0, 4, 2, 1, 3, 5])
        assert_array_equal(idx, expected_idx)

def test_datetime(self):
        a = np.array([0,0,0], dtype='datetime64[D]')
        b = np.array([2,1,0], dtype='datetime64[D]')
        idx = np.lexsort((b, a))
        expected_idx = np.array([2, 1, 0])
        assert_array_equal(idx, expected_idx)

a = np.array([0,0,0], dtype='timedelta64[D]')
        b = np.array([2,1,0], dtype='timedelta64[D]')
        idx = np.lexsort((b, a))
        expected_idx = np.array([2, 1, 0])
        assert_array_equal(idx, expected_idx)

def test_object(self):  # gh-6312
        a = np.random.choice(10, 1000)
        b = np.random.choice(['abc', 'xy', 'wz', 'efghi', 'qwst', 'x'], 1000)

for u in a, b:
            left = np.lexsort((u.astype('O'),))
            right = np.argsort(u, kind='mergesort')
            assert_array_equal(left, right)

for u, v in (a, b), (b, a):
            idx = np.lexsort((u, v))
            assert_array_equal(idx, np.lexsort((u.astype('O'), v)))
            assert_array_equal(idx, np.lexsort((u, v.astype('O'))))
            u, v = np.array(u, dtype='object'), np.array(v, dtype='object')
            assert_array_equal(idx, np.lexsort((u, v)))

def test_invalid_axis(self): # gh-7528
        x = np.linspace(0., 1., 42*3).reshape(42, 3)
        assert_raises(np.AxisError, np.lexsort, x, axis=2)

class TestIO:
    """Test tofile, fromfile, tobytes, and fromstring"""

@pytest.fixture()
    def x(self):
        shape = (2, 4, 3)
        rand = np.random.random
        x = rand(shape) + rand(shape).astype(complex) * 1j
        x[0, :, 1] = [np.nan, np.inf, -np.inf, np.nan]
        return x

@pytest.fixture(params=["string", "path_obj"])
    def tmp_filename(self, tmp_path, request):
        # This fixture covers two cases:
        # one where the filename is a string and
        # another where it is a pathlib object
        filename = tmp_path / "file"
        if request.param == "string":
            filename = str(filename)
        yield filename

def test_nofile(self):
        # this should probably be supported as a file
        # but for now test for proper errors
        b = io.BytesIO()
        assert_raises(OSError, np.fromfile, b, np.uint8, 80)
        d = np.ones(7)
        assert_raises(OSError, lambda x: x.tofile(b), d)

def test_bool_fromstring(self):
        v = np.array([True, False, True, False], dtype=np.bool_)
        y = np.fromstring('1 0 -2.3 0.0', sep=' ', dtype=np.bool_)
        assert_array_equal(v, y)

def test_uint64_fromstring(self):
        d = np.fromstring("9923372036854775807 104783749223640",
                          dtype=np.uint64, sep=' ')
        e = np.array([9923372036854775807, 104783749223640], dtype=np.uint64)
        assert_array_equal(d, e)

def test_int64_fromstring(self):
        d = np.fromstring("-25041670086757 104783749223640",
                          dtype=np.int64, sep=' ')
        e = np.array([-25041670086757, 104783749223640], dtype=np.int64)
        assert_array_equal(d, e)

def test_fromstring_count0(self):
        d = np.fromstring("1,2", sep=",", dtype=np.int64, count=0)
        assert d.shape == (0,)

def test_empty_files_text(self, tmp_filename):
        with open(tmp_filename, 'w') as f:
            pass
        y = np.fromfile(tmp_filename)
        assert_(y.size == 0, "Array not empty")

def test_empty_files_binary(self, tmp_filename):
        with open(tmp_filename, 'wb') as f:
            pass
        y = np.fromfile(tmp_filename, sep=" ")
        assert_(y.size == 0, "Array not empty")

def test_roundtrip_file(self, x, tmp_filename):
        with open(tmp_filename, 'wb') as f:
            x.tofile(f)
        # NB. doesn't work with flush+seek, due to use of C stdio
        with open(tmp_filename, 'rb') as f:
            y = np.fromfile(f, dtype=x.dtype)
        assert_array_equal(y, x.flat)

def test_roundtrip(self, x, tmp_filename):
        x.tofile(tmp_filename)
        y = np.fromfile(tmp_filename, dtype=x.dtype)
        assert_array_equal(y, x.flat)

def test_roundtrip_dump_pathlib(self, x, tmp_filename):
        p = pathlib.Path(tmp_filename)
        x.dump(p)
        y = np.load(p, allow_pickle=True)
        assert_array_equal(y, x)

def test_roundtrip_binary_str(self, x):
        s = x.tobytes()
        y = np.frombuffer(s, dtype=x.dtype)
        assert_array_equal(y, x.flat)

s = x.tobytes('F')
        y = np.frombuffer(s, dtype=x.dtype)
        assert_array_equal(y, x.flatten('F'))

def test_roundtrip_str(self, x):
        x = x.real.ravel()
        s = "@".join(map(str, x))
        y = np.fromstring(s, sep="@")
        # NB. str imbues less precision
        nan_mask = ~np.isfinite(x)
        assert_array_equal(x[nan_mask], y[nan_mask])
        assert_array_almost_equal(x[~nan_mask], y[~nan_mask], decimal=5)

def test_roundtrip_repr(self, x):
        x = x.real.ravel()
        s = "@".join(map(repr, x))
        y = np.fromstring(s, sep="@")
        assert_array_equal(x, y)

def test_unseekable_fromfile(self, x, tmp_filename):
        # gh-6246
        x.tofile(tmp_filename)

def fail(*args, **kwargs):
            raise OSError('Can not tell or seek')

with io.open(tmp_filename, 'rb', buffering=0) as f:
            f.seek = fail
            f.tell = fail
            assert_raises(OSError, np.fromfile, f, dtype=x.dtype)

def test_io_open_unbuffered_fromfile(self, x, tmp_filename):
        # gh-6632
        x.tofile(tmp_filename)
        with io.open(tmp_filename, 'rb', buffering=0) as f:
            y = np.fromfile(f, dtype=x.dtype)
            assert_array_equal(y, x.flat)

def test_largish_file(self, tmp_filename):
        # check the fallocate path on files > 16MB
        d = np.zeros(4 * 1024 ** 2)
        d.tofile(tmp_filename)
        assert_equal(os.path.getsize(tmp_filename), d.nbytes)
        assert_array_equal(d, np.fromfile(tmp_filename))
        # check offset
        with open(tmp_filename, "r+b") as f:
            f.seek(d.nbytes)
            d.tofile(f)
            assert_equal(os.path.getsize(tmp_filename), d.nbytes * 2)
        # check append mode (gh-8329)
        open(tmp_filename, "w").close()  # delete file contents
        with open(tmp_filename, "ab") as f:
            d.tofile(f)
        assert_array_equal(d, np.fromfile(tmp_filename))
        with open(tmp_filename, "ab") as f:
            d.tofile(f)
        assert_equal(os.path.getsize(tmp_filename), d.nbytes * 2)

def test_io_open_buffered_fromfile(self, x, tmp_filename):
        # gh-6632
        x.tofile(tmp_filename)
        with io.open(tmp_filename, 'rb', buffering=-1) as f:
            y = np.fromfile(f, dtype=x.dtype)
        assert_array_equal(y, x.flat)

def test_file_position_after_fromfile(self, tmp_filename):
        # gh-4118
        sizes = [io.DEFAULT_BUFFER_SIZE//8,
                 io.DEFAULT_BUFFER_SIZE,
                 io.DEFAULT_BUFFER_SIZE*8]

for size in sizes:
            with open(tmp_filename, 'wb') as f:
                f.seek(size-1)
                f.write(b'\0')

for mode in ['rb', 'r+b']:
                err_msg = "%d %s" % (size, mode)

with open(tmp_filename, mode) as f:
                    f.read(2)
                    np.fromfile(f, dtype=np.float64, count=1)
                    pos = f.tell()
                assert_equal(pos, 10, err_msg=err_msg)

def test_file_position_after_tofile(self, tmp_filename):
        # gh-4118
        sizes = [io.DEFAULT_BUFFER_SIZE//8,
                 io.DEFAULT_BUFFER_SIZE,
                 io.DEFAULT_BUFFER_SIZE*8]

for size in sizes:
            err_msg = "%d" % (size,)

with open(tmp_filename, 'wb') as f:
                f.seek(size-1)
                f.write(b'\0')
                f.seek(10)
                f.write(b'12')
                np.array([0], dtype=np.float64).tofile(f)
                pos = f.tell()
            assert_equal(pos, 10 + 2 + 8, err_msg=err_msg)

with open(tmp_filename, 'r+b') as f:
                f.read(2)
                f.seek(0, 1)  # seek between read&write required by ANSI C
                np.array([0], dtype=np.float64).tofile(f)
                pos = f.tell()
            assert_equal(pos, 10, err_msg=err_msg)

def test_load_object_array_fromfile(self, tmp_filename):
        # gh-12300
        with open(tmp_filename, 'w') as f:
            # Ensure we have a file with consistent contents
            pass

with open(tmp_filename, 'rb') as f:
            assert_raises_regex(ValueError, "Cannot read into object array",
                                np.fromfile, f, dtype=object)

assert_raises_regex(ValueError, "Cannot read into object array",
                            np.fromfile, tmp_filename, dtype=object)

def test_fromfile_offset(self, x, tmp_filename):
        with open(tmp_filename, 'wb') as f:
            x.tofile(f)

with open(tmp_filename, 'rb') as f:
            y = np.fromfile(f, dtype=x.dtype, offset=0)
            assert_array_equal(y, x.flat)

with open(tmp_filename, 'rb') as f:
            count_items = len(x.flat) // 8
            offset_items = len(x.flat) // 4
            offset_bytes = x.dtype.itemsize * offset_items
            y = np.fromfile(
                f, dtype=x.dtype, count=count_items, offset=offset_bytes
            )
            assert_array_equal(
                y, x.flat[offset_items:offset_items+count_items]
            )

# subsequent seeks should stack
            offset_bytes = x.dtype.itemsize
            z = np.fromfile(f, dtype=x.dtype, offset=offset_bytes)
            assert_array_equal(z, x.flat[offset_items+count_items+1:])

with open(tmp_filename, 'wb') as f:
            x.tofile(f, sep=",")

with open(tmp_filename, 'rb') as f:
            assert_raises_regex(
                    TypeError,
                    "'offset' argument only permitted for binary files",
                    np.fromfile, tmp_filename, dtype=x.dtype,
                    sep=",", offset=1)

@pytest.mark.skipif(IS_PYPY, reason="bug in PyPy's PyNumber_AsSsize_t")
    def test_fromfile_bad_dup(self, x, tmp_filename):
        def dup_str(fd):
            return 'abc'

def dup_bigint(fd):
            return 2**68

old_dup = os.dup
        try:
            with open(tmp_filename, 'wb') as f:
                x.tofile(f)
                for dup, exc in ((dup_str, TypeError), (dup_bigint, OSError)):
                    os.dup = dup
                    assert_raises(exc, np.fromfile, f)
        finally:
            os.dup = old_dup

def _check_from(self, s, value, filename, **kw):
        if 'sep' not in kw:
            y = np.frombuffer(s, **kw)
        else:
            y = np.fromstring(s, **kw)
        assert_array_equal(y, value)

with open(filename, 'wb') as f:
            f.write(s)
        y = np.fromfile(filename, **kw)
        assert_array_equal(y, value)

@pytest.fixture(params=["period", "comma"])
    def decimal_sep_localization(self, request):
        """
        Including this fixture in a test will automatically
        execute it with both types of decimal separator.

So::

def test_decimal(decimal_sep_localization):
                pass

is equivalent to the following two tests::

def test_decimal_period_separator():
                pass

def test_decimal_comma_separator():
                with CommaDecimalPointLocale():
                    pass
        """
        if request.param == "period":
            yield
        elif request.param == "comma":
            with CommaDecimalPointLocale():
                yield
        else:
            assert False, request.param

def test_nan(self, tmp_filename, decimal_sep_localization):
        self._check_from(
            b"nan +nan -nan NaN nan(foo) +NaN(BAR) -NAN(q_u_u_x_)",
            [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
            tmp_filename,
            sep=' ')

def test_inf(self, tmp_filename, decimal_sep_localization):
        self._check_from(
            b"inf +inf -inf infinity -Infinity iNfInItY -inF",
            [np.inf, np.inf, -np.inf, np.inf, -np.inf, np.inf, -np.inf],
            tmp_filename,
            sep=' ')

def test_numbers(self, tmp_filename, decimal_sep_localization):
        self._check_from(
            b"1.234 -1.234 .3 .3e55 -123133.1231e+133",
            [1.234, -1.234, .3, .3e55, -123133.1231e+133],
            tmp_filename,
            sep=' ')

def test_binary(self, tmp_filename):
        self._check_from(
            b'\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@',
            np.array([1, 2, 3, 4]),
            tmp_filename,
            dtype='<f4')

def test_string(self, tmp_filename):
        self._check_from(b'1,2,3,4', [1., 2., 3., 4.], tmp_filename, sep=',')

def test_counted_string(self, tmp_filename, decimal_sep_localization):
        self._check_from(
            b'1,2,3,4', [1., 2., 3., 4.], tmp_filename, count=4, sep=',')
        self._check_from(
            b'1,2,3,4', [1., 2., 3.], tmp_filename, count=3, sep=',')
        self._check_from(
            b'1,2,3,4', [1., 2., 3., 4.], tmp_filename, count=-1, sep=',')

def test_string_with_ws(self, tmp_filename):
        self._check_from(
            b'1 2  3     4   ', [1, 2, 3, 4], tmp_filename, dtype=int, sep=' ')

def test_counted_string_with_ws(self, tmp_filename):
        self._check_from(
            b'1 2  3     4   ', [1, 2, 3], tmp_filename, count=3, dtype=int,
            sep=' ')

def test_ascii(self, tmp_filename, decimal_sep_localization):
        self._check_from(
            b'1 , 2 , 3 , 4', [1., 2., 3., 4.], tmp_filename, sep=',')
        self._check_from(
            b'1,2,3,4', [1., 2., 3., 4.], tmp_filename, dtype=float, sep=',')

def test_malformed(self, tmp_filename, decimal_sep_localization):
        with assert_warns(DeprecationWarning):
            self._check_from(
                b'1.234 1,234', [1.234, 1.], tmp_filename, sep=' ')

def test_long_sep(self, tmp_filename):
        self._check_from(
            b'1_x_3_x_4_x_5', [1, 3, 4, 5], tmp_filename, sep='_x_')

def test_dtype(self, tmp_filename):
        v = np.array([1, 2, 3, 4], dtype=np.int_)
        self._check_from(b'1,2,3,4', v, tmp_filename, sep=',', dtype=np.int_)

def test_dtype_bool(self, tmp_filename):
        # can't use _check_from because fromstring can't handle True/False
        v = np.array([True, False, True, False], dtype=np.bool_)
        s = b'1,0,-2.3,0'
        with open(tmp_filename, 'wb') as f:
            f.write(s)
        y = np.fromfile(tmp_filename, sep=',', dtype=np.bool_)
        assert_(y.dtype == '?')
        assert_array_equal(y, v)

def test_tofile_sep(self, tmp_filename, decimal_sep_localization):
        x = np.array([1.51, 2, 3.51, 4], dtype=float)
        with open(tmp_filename, 'w') as f:
            x.tofile(f, sep=',')
        with open(tmp_filename, 'r') as f:
            s = f.read()
        #assert_equal(s, '1.51,2.0,3.51,4.0')
        y = np.array([float(p) for p in s.split(',')])
        assert_array_equal(x,y)

def test_tofile_format(self, tmp_filename, decimal_sep_localization):
        x = np.array([1.51, 2, 3.51, 4], dtype=float)
        with open(tmp_filename, 'w') as f:
            x.tofile(f, sep=',', format='%.2f')
        with open(tmp_filename, 'r') as f:
            s = f.read()
        assert_equal(s, '1.51,2.00,3.51,4.00')

def test_tofile_cleanup(self, tmp_filename):
        x = np.zeros((10), dtype=object)
        with open(tmp_filename, 'wb') as f:
            assert_raises(OSError, lambda: x.tofile(f, sep=''))
        # Dup-ed file handle should be closed or remove will fail on Windows OS
        os.remove(tmp_filename)

# Also make sure that we close the Python handle
        assert_raises(OSError, lambda: x.tofile(tmp_filename))
        os.remove(tmp_filename)

def test_fromfile_subarray_binary(self, tmp_filename):
        # Test subarray dtypes which are absorbed into the shape
        x = np.arange(24, dtype="i4").reshape(2, 3, 4)
        x.tofile(tmp_filename)
        res = np.fromfile(tmp_filename, dtype="(3,4)i4")
        assert_array_equal(x, res)

x_str = x.tobytes()
        with assert_warns(DeprecationWarning):
            # binary fromstring is deprecated
            res = np.fromstring(x_str, dtype="(3,4)i4")
            assert_array_equal(x, res)

def test_parsing_subarray_unsupported(self, tmp_filename):
        # We currently do not support parsing subarray dtypes
        data = "12,42,13," * 50
        with pytest.raises(ValueError):
            expected = np.fromstring(data, dtype="(3,)i", sep=",")

with open(tmp_filename, "w") as f:
            f.write(data)

with pytest.raises(ValueError):
            np.fromfile(tmp_filename, dtype="(3,)i", sep=",")

def test_read_shorter_than_count_subarray(self, tmp_filename):
        # Test that requesting more values does not cause any problems
        # in conjunction with subarray dimensions being absorbed into the
        # array dimension.
        expected = np.arange(511 * 10, dtype="i").reshape(-1, 10)

binary = expected.tobytes()
        with pytest.raises(ValueError):
            with pytest.warns(DeprecationWarning):
                np.fromstring(binary, dtype="(10,)i", count=10000)

expected.tofile(tmp_filename)
        res = np.fromfile(tmp_filename, dtype="(10,)i", count=10000)
        assert_array_equal(res, expected)

class TestFromBuffer:
    @pytest.mark.parametrize('byteorder', ['<', '>'])
    @pytest.mark.parametrize('dtype', [float, int, complex])
    def test_basic(self, byteorder, dtype):
        dt = np.dtype(dtype).newbyteorder(byteorder)
        x = (np.random.random((4, 7)) * 5).astype(dt)
        buf = x.tobytes()
        assert_array_equal(np.frombuffer(buf, dtype=dt), x.flat)

@pytest.mark.parametrize("obj", [np.arange(10), b"12345678"])
    def test_array_base(self, obj):
        # Objects (including NumPy arrays), which do not use the
        # `release_buffer` slot should be directly used as a base object.
        # See also gh-21612
        new = np.frombuffer(obj)
        assert new.base is obj

def test_empty(self):
        assert_array_equal(np.frombuffer(b''), np.array([]))

@pytest.mark.skipif(IS_PYPY,
            reason="PyPy's memoryview currently does not track exports. See: "
                   "https://foss.heptapod.net/pypy/pypy/-/issues/3724")
    def test_mmap_close(self):
        # The old buffer protocol was not safe for some things that the new
        # one is.  But `frombuffer` always used the old one for a long time.
        # Checks that it is safe with the new one (using memoryviews)
        with tempfile.TemporaryFile(mode='wb') as tmp:
            tmp.write(b"asdf")
            tmp.flush()
            mm = mmap.mmap(tmp.fileno(), 0)
            arr = np.frombuffer(mm, dtype=np.uint8)
            with pytest.raises(BufferError):
                mm.close()  # cannot close while array uses the buffer
            del arr
            mm.close()

class TestFlat:
    def setup_method(self):
        a0 = np.arange(20.0)
        a = a0.reshape(4, 5)
        a0.shape = (4, 5)
        a.flags.writeable = False
        self.a = a
        self.b = a[::2, ::2]
        self.a0 = a0
        self.b0 = a0[::2, ::2]

def test_contiguous(self):
        testpassed = False
        try:
            self.a.flat[12] = 100.0
        except ValueError:
            testpassed = True
        assert_(testpassed)
        assert_(self.a.flat[12] == 12.0)

def test_discontiguous(self):
        testpassed = False
        try:
            self.b.flat[4] = 100.0
        except ValueError:
            testpassed = True
        assert_(testpassed)
        assert_(self.b.flat[4] == 12.0)

def test___array__(self):
        c = self.a.flat.__array__()
        d = self.b.flat.__array__()
        e = self.a0.flat.__array__()
        f = self.b0.flat.__array__()

assert_(c.flags.writeable is False)
        assert_(d.flags.writeable is False)
        assert_(e.flags.writeable is True)
        assert_(f.flags.writeable is False)
        assert_(c.flags.writebackifcopy is False)
        assert_(d.flags.writebackifcopy is False)
        assert_(e.flags.writebackifcopy is False)
        assert_(f.flags.writebackifcopy is False)

@pytest.mark.skipif(not HAS_REFCOUNT, reason="Python lacks refcounts")
    def test_refcount(self):
        # includes regression test for reference count error gh-13165
        inds = [np.intp(0), np.array([True]*self.a.size), np.array([0]), None]
        indtype = np.dtype(np.intp)
        rc_indtype = sys.getrefcount(indtype)
        for ind in inds:
            rc_ind = sys.getrefcount(ind)
            for _ in range(100):
                try:
                    self.a.flat[ind]
                except IndexError:
                    pass
            assert_(abs(sys.getrefcount(ind) - rc_ind) < 50)
            assert_(abs(sys.getrefcount(indtype) - rc_indtype) < 50)

def test_index_getset(self):
        it = np.arange(10).reshape(2, 1, 5).flat
        with pytest.raises(AttributeError):
            it.index = 10

for _ in it:
            pass
        # Check the value of `.index` is updated correctly (see also gh-19153)
        # If the type was incorrect, this would show up on big-endian machines
        assert it.index == it.base.size

class TestResize:

@_no_tracing
    def test_basic(self):
        x = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
        if IS_PYPY:
            x.resize((5, 5), refcheck=False)
        else:
            x.resize((5, 5))
        assert_array_equal(x.flat[:9],
                np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]).flat)
        assert_array_equal(x[9:].flat, 0)

def test_check_reference(self):
        x = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
        y = x
        assert_raises(ValueError, x.resize, (5, 1))
        del y  # avoid pyflakes unused variable warning.

@_no_tracing
    def test_int_shape(self):
        x = np.eye(3)
        if IS_PYPY:
            x.resize(3, refcheck=False)
        else:
            x.resize(3)
        assert_array_equal(x, np.eye(3)[0,:])

def test_none_shape(self):
        x = np.eye(3)
        x.resize(None)
        assert_array_equal(x, np.eye(3))
        x.resize()
        assert_array_equal(x, np.eye(3))

def test_0d_shape(self):
        # to it multiple times to test it does not break alloc cache gh-9216
        for i in range(10):
            x = np.empty((1,))
            x.resize(())
            assert_equal(x.shape, ())
            assert_equal(x.size, 1)
            x = np.empty(())
            x.resize((1,))
            assert_equal(x.shape, (1,))
            assert_equal(x.size, 1)

def test_invalid_arguments(self):
        assert_raises(TypeError, np.eye(3).resize, 'hi')
        assert_raises(ValueError, np.eye(3).resize, -1)
        assert_raises(TypeError, np.eye(3).resize, order=1)
        assert_raises(TypeError, np.eye(3).resize, refcheck='hi')

@_no_tracing
    def test_freeform_shape(self):
        x = np.eye(3)
        if IS_PYPY:
            x.resize(3, 2, 1, refcheck=False)
        else:
            x.resize(3, 2, 1)
        assert_(x.shape == (3, 2, 1))

@_no_tracing
    def test_zeros_appended(self):
        x = np.eye(3)
        if IS_PYPY:
            x.resize(2, 3, 3, refcheck=False)
        else:
            x.resize(2, 3, 3)
        assert_array_equal(x[0], np.eye(3))
        assert_array_equal(x[1], np.zeros((3, 3)))

@_no_tracing
    def test_obj_obj(self):
        # check memory is initialized on resize, gh-4857
        a = np.ones(10, dtype=[('k', object, 2)])
        if IS_PYPY:
            a.resize(15, refcheck=False)
        else:
            a.resize(15,)
        assert_equal(a.shape, (15,))
        assert_array_equal(a['k'][-5:], 0)
        assert_array_equal(a['k'][:-5], 1)

def test_empty_view(self):
        # check that sizes containing a zero don't trigger a reallocate for
        # already empty arrays
        x = np.zeros((10, 0), int)
        x_view = x[...]
        x_view.resize((0, 10))
        x_view.resize((0, 100))

def test_check_weakref(self):
        x = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
        xref = weakref.ref(x)
        assert_raises(ValueError, x.resize, (5, 1))
        del xref  # avoid pyflakes unused variable warning.

class TestRecord:
    def test_field_rename(self):
        dt = np.dtype([('f', float), ('i', int)])
        dt.names = ['p', 'q']
        assert_equal(dt.names, ['p', 'q'])

def test_multiple_field_name_occurrence(self):
        def test_dtype_init():
            np.dtype([("A", "f8"), ("B", "f8"), ("A", "f8")])

# Error raised when multiple fields have the same name
        assert_raises(ValueError, test_dtype_init)

def test_bytes_fields(self):
        # Bytes are not allowed in field names and not recognized in titles
        # on Py3
        assert_raises(TypeError, np.dtype, [(b'a', int)])
        assert_raises(TypeError, np.dtype, [(('b', b'a'), int)])

dt = np.dtype([((b'a', 'b'), int)])
        assert_raises(TypeError, dt.__getitem__, b'a')

x = np.array([(1,), (2,), (3,)], dtype=dt)
        assert_raises(IndexError, x.__getitem__, b'a')

y = x[0]
        assert_raises(IndexError, y.__getitem__, b'a')

def test_multiple_field_name_unicode(self):
        def test_dtype_unicode():
            np.dtype([("\u20B9", "f8"), ("B", "f8"), ("\u20B9", "f8")])

# Error raised when multiple fields have the same name(unicode included)
        assert_raises(ValueError, test_dtype_unicode)

def test_fromarrays_unicode(self):
        # A single name string provided to fromarrays() is allowed to be unicode
        # on both Python 2 and 3:
        x = np.core.records.fromarrays(
            [[0], [1]], names='a,b', formats='i4,i4')
        assert_equal(x['a'][0], 0)
        assert_equal(x['b'][0], 1)

def test_unicode_order(self):
        # Test that we can sort with order as a unicode field name in both Python 2 and
        # 3:
        name = 'b'
        x = np.array([1, 3, 2], dtype=[(name, int)])
        x.sort(order=name)
        assert_equal(x['b'], np.array([1, 2, 3]))

def test_field_names(self):
        # Test unicode and 8-bit / byte strings can be used
        a = np.zeros((1,), dtype=[('f1', 'i4'),
                                  ('f2', 'i4'),
                                  ('f3', [('sf1', 'i4')])])
        # byte string indexing fails gracefully
        assert_raises(IndexError, a.__setitem__, b'f1', 1)
        assert_raises(IndexError, a.__getitem__, b'f1')
        assert_raises(IndexError, a['f1'].__setitem__, b'sf1', 1)
        assert_raises(IndexError, a['f1'].__getitem__, b'sf1')
        b = a.copy()
        fn1 = str('f1')
        b[fn1] = 1
        assert_equal(b[fn1], 1)
        fnn = str('not at all')
        assert_raises(ValueError, b.__setitem__, fnn, 1)
        assert_raises(ValueError, b.__getitem__, fnn)
        b[0][fn1] = 2
        assert_equal(b[fn1], 2)
        # Subfield
        assert_raises(ValueError, b[0].__setitem__, fnn, 1)
        assert_raises(ValueError, b[0].__getitem__, fnn)
        # Subfield
        fn3 = str('f3')
        sfn1 = str('sf1')
        b[fn3][sfn1] = 1
        assert_equal(b[fn3][sfn1], 1)
        assert_raises(ValueError, b[fn3].__setitem__, fnn, 1)
        assert_raises(ValueError, b[fn3].__getitem__, fnn)
        # multiple subfields
        fn2 = str('f2')
        b[fn2] = 3

assert_equal(b[['f1', 'f2']][0].tolist(), (2, 3))
        assert_equal(b[['f2', 'f1']][0].tolist(), (3, 2))
        assert_equal(b[['f1', 'f3']][0].tolist(), (2, (1,)))

# non-ascii unicode field indexing is well behaved
        assert_raises(ValueError, a.__setitem__, '\u03e0', 1)
        assert_raises(ValueError, a.__getitem__, '\u03e0')

def test_record_hash(self):
        a = np.array([(1, 2), (1, 2)], dtype='i1,i2')
        a.flags.writeable = False
        b = np.array([(1, 2), (3, 4)], dtype=[('num1', 'i1'), ('num2', 'i2')])
        b.flags.writeable = False
        c = np.array([(1, 2), (3, 4)], dtype='i1,i2')
        c.flags.writeable = False
        assert_(hash(a[0]) == hash(a[1]))
        assert_(hash(a[0]) == hash(b[0]))
        assert_(hash(a[0]) != hash(b[1]))
        assert_(hash(c[0]) == hash(a[0]) and c[0] == a[0])

def test_record_no_hash(self):
        a = np.array([(1, 2), (1, 2)], dtype='i1,i2')
        assert_raises(TypeError, hash, a[0])

def test_empty_structure_creation(self):
        # make sure these do not raise errors (gh-5631)
        np.array([()], dtype={'names': [], 'formats': [],
                           'offsets': [], 'itemsize': 12})
        np.array([(), (), (), (), ()], dtype={'names': [], 'formats': [],
                                           'offsets': [], 'itemsize': 12})

def test_multifield_indexing_view(self):
        a = np.ones(3, dtype=[('a', 'i4'), ('b', 'f4'), ('c', 'u4')])
        v = a[['a', 'c']]
        assert_(v.base is a)
        assert_(v.dtype == np.dtype({'names': ['a', 'c'],
                                     'formats': ['i4', 'u4'],
                                     'offsets': [0, 8]}))
        v[:] = (4,5)
        assert_equal(a[0].item(), (4, 1, 5))

class TestView:
    def test_basic(self):
        x = np.array([(1, 2, 3, 4), (5, 6, 7, 8)],
                     dtype=[('r', np.int8), ('g', np.int8),
                            ('b', np.int8), ('a', np.int8)])
        # We must be specific about the endianness here:
        y = x.view(dtype='<i4')
        # ... and again without the keyword.
        z = x.view('<i4')
        assert_array_equal(y, z)
        assert_array_equal(y, [67305985, 134678021])

def _mean(a, **args):
    return a.mean(**args)

def _var(a, **args):
    return a.var(**args)

def _std(a, **args):
    return a.std(**args)

class TestStats:

funcs = [_mean, _var, _std]

def setup_method(self):
        np.random.seed(range(3))
        self.rmat = np.random.random((4, 5))
        self.cmat = self.rmat + 1j * self.rmat
        self.omat = np.array([Decimal(repr(r)) for r in self.rmat.flat])
        self.omat = self.omat.reshape(4, 5)

def test_python_type(self):
        for x in (np.float16(1.), 1, 1., 1+0j):
            assert_equal(np.mean([x]), 1.)
            assert_equal(np.std([x]), 0.)
            assert_equal(np.var([x]), 0.)

def test_keepdims(self):
        mat = np.eye(3)
        for f in self.funcs:
            for axis in [0, 1]:
                res = f(mat, axis=axis, keepdims=True)
                assert_(res.ndim == mat.ndim)
                assert_(res.shape[axis] == 1)
            for axis in [None]:
                res = f(mat, axis=axis, keepdims=True)
                assert_(res.shape == (1, 1))

def test_out(self):
        mat = np.eye(3)
        for f in self.funcs:
            out = np.zeros(3)
            tgt = f(mat, axis=1)
            res = f(mat, axis=1, out=out)
            assert_almost_equal(res, out)
            assert_almost_equal(res, tgt)
        out = np.empty(2)
        assert_raises(ValueError, f, mat, axis=1, out=out)
        out = np.empty((2, 2))
        assert_raises(ValueError, f, mat, axis=1, out=out)

def test_dtype_from_input(self):

icodes = np.typecodes['AllInteger']
        fcodes = np.typecodes['AllFloat']

# object type
        for f in self.funcs:
            mat = np.array([[Decimal(1)]*3]*3)
            tgt = mat.dtype.type
            res = f(mat, axis=1).dtype.type
            assert_(res is tgt)
            # scalar case
            res = type(f(mat, axis=None))
            assert_(res is Decimal)

# integer types
        for f in self.funcs:
            for c in icodes:
                mat = np.eye(3, dtype=c)
                tgt = np.float64
                res = f(mat, axis=1).dtype.type
                assert_(res is tgt)
                # scalar case
                res = f(mat, axis=None).dtype.type
                assert_(res is tgt)

# mean for float types
        for f in [_mean]:
            for c in fcodes:
                mat = np.eye(3, dtype=c)
                tgt = mat.dtype.type
                res = f(mat, axis=1).dtype.type
                assert_(res is tgt)
                # scalar case
                res = f(mat, axis=None).dtype.type
                assert_(res is tgt)

# var, std for float types
        for f in [_var, _std]:
            for c in fcodes:
                mat = np.eye(3, dtype=c)
                # deal with complex types
                tgt = mat.real.dtype.type
                res = f(mat, axis=1).dtype.type
                assert_(res is tgt)
                # scalar case
                res = f(mat, axis=None).dtype.type
                assert_(res is tgt)

def test_dtype_from_dtype(self):
        mat = np.eye(3)

# stats for integer types
        # FIXME:
        # this needs definition as there are lots places along the line
        # where type casting may take place.

# for f in self.funcs:
        #    for c in np.typecodes['AllInteger']:
        #        tgt = np.dtype(c).type
        #        res = f(mat, axis=1, dtype=c).dtype.type
        #        assert_(res is tgt)
        #        # scalar case
        #        res = f(mat, axis=None, dtype=c).dtype.type
        #        assert_(res is tgt)

# stats for float types
        for f in self.funcs:
            for c in np.typecodes['AllFloat']:
                tgt = np.dtype(c).type
                res = f(mat, axis=1, dtype=c).dtype.type
                assert_(res is tgt)
                # scalar case
                res = f(mat, axis=None, dtype=c).dtype.type
                assert_(res is tgt)

def test_ddof(self):
        for f in [_var]:
            for ddof in range(3):
                dim = self.rmat.shape[1]
                tgt = f(self.rmat, axis=1) * dim
                res = f(self.rmat, axis=1, ddof=ddof) * (dim - ddof)
        for f in [_std]:
            for ddof in range(3):
                dim = self.rmat.shape[1]
                tgt = f(self.rmat, axis=1) * np.sqrt(dim)
                res = f(self.rmat, axis=1, ddof=ddof) * np.sqrt(dim - ddof)
                assert_almost_equal(res, tgt)
                assert_almost_equal(res, tgt)

def test_ddof_too_big(self):
        dim = self.rmat.shape[1]
        for f in [_var, _std]:
            for ddof in range(dim, dim + 2):
                with warnings.catch_warnings(record=True) as w:
                    warnings.simplefilter('always')
                    res = f(self.rmat, axis=1, ddof=ddof)
                    assert_(not (res < 0).any())
                    assert_(len(w) > 0)
                    assert_(issubclass(w[0].category, RuntimeWarning))

def test_empty(self):
        A = np.zeros((0, 3))
        for f in self.funcs:
            for axis in [0, None]:
                with warnings.catch_warnings(record=True) as w:
                    warnings.simplefilter('always')
                    assert_(np.isnan(f(A, axis=axis)).all())
                    assert_(len(w) > 0)
                    assert_(issubclass(w[0].category, RuntimeWarning))
            for axis in [1]:
                with warnings.catch_warnings(record=True) as w:
                    warnings.simplefilter('always')
                    assert_equal(f(A, axis=axis), np.zeros([]))

def test_mean_values(self):
        for mat in [self.rmat, self.cmat, self.omat]:
            for axis in [0, 1]:
                tgt = mat.sum(axis=axis)
                res = _mean(mat, axis=axis) * mat.shape[axis]
                assert_almost_equal(res, tgt)
            for axis in [None]:
                tgt = mat.sum(axis=axis)
                res = _mean(mat, axis=axis) * np.prod(mat.shape)
                assert_almost_equal(res, tgt)

def test_mean_float16(self):
        # This fail if the sum inside mean is done in float16 instead
        # of float32.
        assert_(_mean(np.ones(100000, dtype='float16')) == 1)

def test_mean_axis_error(self):
        # Ensure that AxisError is raised instead of IndexError when axis is
        # out of bounds, see gh-15817.
        with assert_raises(np.exceptions.AxisError):
            np.arange(10).mean(axis=2)

def test_mean_where(self):
        a = np.arange(16).reshape((4, 4))
        wh_full = np.array([[False, True, False, True],
                            [True, False, True, False],
                            [True, True, False, False],
                            [False, False, True, True]])
        wh_partial = np.array([[False],
                               [True],
                               [True],
                               [False]])
        _cases = [(1, True, [1.5, 5.5, 9.5, 13.5]),
                  (0, wh_full, [6., 5., 10., 9.]),
                  (1, wh_full, [2., 5., 8.5, 14.5]),
                  (0, wh_partial, [6., 7., 8., 9.])]
        for _ax, _wh, _res in _cases:
            assert_allclose(a.mean(axis=_ax, where=_wh),
                            np.array(_res))
            assert_allclose(np.mean(a, axis=_ax, where=_wh),
                            np.array(_res))

a3d = np.arange(16).reshape((2, 2, 4))
        _wh_partial = np.array([False, True, True, False])
        _res = [[1.5, 5.5], [9.5, 13.5]]
        assert_allclose(a3d.mean(axis=2, where=_wh_partial),
                        np.array(_res))
        assert_allclose(np.mean(a3d, axis=2, where=_wh_partial),
                        np.array(_res))

with pytest.warns(RuntimeWarning) as w:
            assert_allclose(a.mean(axis=1, where=wh_partial),
                            np.array([np.nan, 5.5, 9.5, np.nan]))
        with pytest.warns(RuntimeWarning) as w:
            assert_equal(a.mean(where=False), np.nan)
        with pytest.warns(RuntimeWarning) as w:
            assert_equal(np.mean(a, where=False), np.nan)

def test_var_values(self):
        for mat in [self.rmat, self.cmat, self.omat]:
            for axis in [0, 1, None]:
                msqr = _mean(mat * mat.conj(), axis=axis)
                mean = _mean(mat, axis=axis)
                tgt = msqr - mean * mean.conjugate()
                res = _var(mat, axis=axis)
                assert_almost_equal(res, tgt)

@pytest.mark.parametrize(('complex_dtype', 'ndec'), (
        ('complex64', 6),
        ('complex128', 7),
        ('clongdouble', 7),
    ))
    def test_var_complex_values(self, complex_dtype, ndec):
        # Test fast-paths for every builtin complex type
        for axis in [0, 1, None]:
            mat = self.cmat.copy().astype(complex_dtype)
            msqr = _mean(mat * mat.conj(), axis=axis)
            mean = _mean(mat, axis=axis)
            tgt = msqr - mean * mean.conjugate()
            res = _var(mat, axis=axis)
            assert_almost_equal(res, tgt, decimal=ndec)

def test_var_dimensions(self):
        # _var paths for complex number introduce additions on views that
        # increase dimensions. Ensure this generalizes to higher dims
        mat = np.stack([self.cmat]*3)
        for axis in [0, 1, 2, -1, None]:
            msqr = _mean(mat * mat.conj(), axis=axis)
            mean = _mean(mat, axis=axis)
            tgt = msqr - mean * mean.conjugate()
            res = _var(mat, axis=axis)
            assert_almost_equal(res, tgt)

def test_var_complex_byteorder(self):
        # Test that var fast-path does not cause failures for complex arrays
        # with non-native byteorder
        cmat = self.cmat.copy().astype('complex128')
        cmat_swapped = cmat.astype(cmat.dtype.newbyteorder())
        assert_almost_equal(cmat.var(), cmat_swapped.var())

def test_var_axis_error(self):
        # Ensure that AxisError is raised instead of IndexError when axis is
        # out of bounds, see gh-15817.
        with assert_raises(np.exceptions.AxisError):
            np.arange(10).var(axis=2)

def test_var_where(self):
        a = np.arange(25).reshape((5, 5))
        wh_full = np.array([[False, True, False, True, True],
                            [True, False, True, True, False],
                            [True, True, False, False, True],
                            [False, True, True, False, True],
                            [True, False, True, True, False]])
        wh_partial = np.array([[False],
                               [True],
                               [True],
                               [False],
                               [True]])
        _cases = [(0, True, [50., 50., 50., 50., 50.]),
                  (1, True, [2., 2., 2., 2., 2.])]
        for _ax, _wh, _res in _cases:
            assert_allclose(a.var(axis=_ax, where=_wh),
                            np.array(_res))
            assert_allclose(np.var(a, axis=_ax, where=_wh),
                            np.array(_res))

a3d = np.arange(16).reshape((2, 2, 4))
        _wh_partial = np.array([False, True, True, False])
        _res = [[0.25, 0.25], [0.25, 0.25]]
        assert_allclose(a3d.var(axis=2, where=_wh_partial),
                        np.array(_res))
        assert_allclose(np.var(a3d, axis=2, where=_wh_partial),
                        np.array(_res))

assert_allclose(np.var(a, axis=1, where=wh_full),
                        np.var(a[wh_full].reshape((5, 3)), axis=1))
        assert_allclose(np.var(a, axis=0, where=wh_partial),
                        np.var(a[wh_partial[:,0]], axis=0))
        with pytest.warns(RuntimeWarning) as w:
            assert_equal(a.var(where=False), np.nan)
        with pytest.warns(RuntimeWarning) as w:
            assert_equal(np.var(a, where=False), np.nan)

def test_std_values(self):
        for mat in [self.rmat, self.cmat, self.omat]:
            for axis in [0, 1, None]:
                tgt = np.sqrt(_var(mat, axis=axis))
                res = _std(mat, axis=axis)
                assert_almost_equal(res, tgt)

def test_std_where(self):
        a = np.arange(25).reshape((5,5))[::-1]
        whf = np.array([[False, True, False, True, True],
                        [True, False, True, False, True],
                        [True, True, False, True, False],
                        [True, False, True, True, False],
                        [False, True, False, True, True]])
        whp = np.array([[False],
                        [False],
                        [True],
                        [True],
                        [False]])
        _cases = [
            (0, True, 7.07106781*np.ones((5))),
            (1, True, 1.41421356*np.ones((5))),
            (0, whf,
             np.array([4.0824829 , 8.16496581, 5., 7.39509973, 8.49836586])),
            (0, whp, 2.5*np.ones((5)))
        ]
        for _ax, _wh, _res in _cases:
            assert_allclose(a.std(axis=_ax, where=_wh), _res)
            assert_allclose(np.std(a, axis=_ax, where=_wh), _res)

a3d = np.arange(16).reshape((2, 2, 4))
        _wh_partial = np.array([False, True, True, False])
        _res = [[0.5, 0.5], [0.5, 0.5]]
        assert_allclose(a3d.std(axis=2, where=_wh_partial),
                        np.array(_res))
        assert_allclose(np.std(a3d, axis=2, where=_wh_partial),
                        np.array(_res))

assert_allclose(a.std(axis=1, where=whf),
                        np.std(a[whf].reshape((5,3)), axis=1))
        assert_allclose(np.std(a, axis=1, where=whf),
                        (a[whf].reshape((5,3))).std(axis=1))
        assert_allclose(a.std(axis=0, where=whp),
                        np.std(a[whp[:,0]], axis=0))
        assert_allclose(np.std(a, axis=0, where=whp),
                        (a[whp[:,0]]).std(axis=0))
        with pytest.warns(RuntimeWarning) as w:
            assert_equal(a.std(where=False), np.nan)
        with pytest.warns(RuntimeWarning) as w:
            assert_equal(np.std(a, where=False), np.nan)

def test_subclass(self):
        class TestArray(np.ndarray):
            def __new__(cls, data, info):
                result = np.array(data)
                result = result.view(cls)
                result.info = info
                return result

def __array_finalize__(self, obj):
                self.info = getattr(obj, "info", '')

dat = TestArray([[1, 2, 3, 4], [5, 6, 7, 8]], 'jubba')
        res = dat.mean(1)
        assert_(res.info == dat.info)
        res = dat.std(1)
        assert_(res.info == dat.info)
        res = dat.var(1)
        assert_(res.info == dat.info)

class TestVdot:
    def test_basic(self):
        dt_numeric = np.typecodes['AllFloat'] + np.typecodes['AllInteger']
        dt_complex = np.typecodes['Complex']

# test real
        a = np.eye(3)
        for dt in dt_numeric + 'O':
            b = a.astype(dt)
            res = np.vdot(b, b)
            assert_(np.isscalar(res))
            assert_equal(np.vdot(b, b), 3)

# test complex
        a = np.eye(3) * 1j
        for dt in dt_complex + 'O':
            b = a.astype(dt)
            res = np.vdot(b, b)
            assert_(np.isscalar(res))
            assert_equal(np.vdot(b, b), 3)

# test boolean
        b = np.eye(3, dtype=bool)
        res = np.vdot(b, b)
        assert_(np.isscalar(res))
        assert_equal(np.vdot(b, b), True)

def test_vdot_array_order(self):
        a = np.array([[1, 2], [3, 4]], order='C')
        b = np.array([[1, 2], [3, 4]], order='F')
        res = np.vdot(a, a)

# integer arrays are exact
        assert_equal(np.vdot(a, b), res)
        assert_equal(np.vdot(b, a), res)
        assert_equal(np.vdot(b, b), res)

def test_vdot_uncontiguous(self):
        for size in [2, 1000]:
            # Different sizes match different branches in vdot.
            a = np.zeros((size, 2, 2))
            b = np.zeros((size, 2, 2))
            a[:, 0, 0] = np.arange(size)
            b[:, 0, 0] = np.arange(size) + 1
            # Make a and b uncontiguous:
            a = a[..., 0]
            b = b[..., 0]

assert_equal(np.vdot(a, b),
                         np.vdot(a.flatten(), b.flatten()))
            assert_equal(np.vdot(a, b.copy()),
                         np.vdot(a.flatten(), b.flatten()))
            assert_equal(np.vdot(a.copy(), b),
                         np.vdot(a.flatten(), b.flatten()))
            assert_equal(np.vdot(a.copy('F'), b),
                         np.vdot(a.flatten(), b.flatten()))
            assert_equal(np.vdot(a, b.copy('F')),
                         np.vdot(a.flatten(), b.flatten()))

class TestDot:
    def setup_method(self):
        np.random.seed(128)
        self.A = np.random.rand(4, 2)
        self.b1 = np.random.rand(2, 1)
        self.b2 = np.random.rand(2)
        self.b3 = np.random.rand(1, 2)
        self.b4 = np.random.rand(4)
        self.N = 7

def test_dotmatmat(self):
        A = self.A
        res = np.dot(A.transpose(), A)
        tgt = np.array([[1.45046013, 0.86323640],
                        [0.86323640, 0.84934569]])
        assert_almost_equal(res, tgt, decimal=self.N)

def test_dotmatvec(self):
        A, b1 = self.A, self.b1
        res = np.dot(A, b1)
        tgt = np.array([[0.32114320], [0.04889721],
                        [0.15696029], [0.33612621]])
        assert_almost_equal(res, tgt, decimal=self.N)

def test_dotmatvec2(self):
        A, b2 = self.A, self.b2
        res = np.dot(A, b2)
        tgt = np.array([0.29677940, 0.04518649, 0.14468333, 0.31039293])
        assert_almost_equal(res, tgt, decimal=self.N)

def test_dotvecmat(self):
        A, b4 = self.A, self.b4
        res = np.dot(b4, A)
        tgt = np.array([1.23495091, 1.12222648])
        assert_almost_equal(res, tgt, decimal=self.N)

def test_dotvecmat2(self):
        b3, A = self.b3, self.A
        res = np.dot(b3, A.transpose())
        tgt = np.array([[0.58793804, 0.08957460, 0.30605758, 0.62716383]])
        assert_almost_equal(res, tgt, decimal=self.N)

def test_dotvecmat3(self):
        A, b4 = self.A, self.b4
        res = np.dot(A.transpose(), b4)
        tgt = np.array([1.23495091, 1.12222648])
        assert_almost_equal(res, tgt, decimal=self.N)

def test_dotvecvecouter(self):
        b1, b3 = self.b1, self.b3
        res = np.dot(b1, b3)
        tgt = np.array([[0.20128610, 0.08400440], [0.07190947, 0.03001058]])
        assert_almost_equal(res, tgt, decimal=self.N)

def test_dotvecvecinner(self):
        b1, b3 = self.b1, self.b3
        res = np.dot(b3, b1)
        tgt = np.array([[ 0.23129668]])
        assert_almost_equal(res, tgt, decimal=self.N)

def test_dotcolumnvect1(self):
        b1 = np.ones((3, 1))
        b2 = [5.3]
        res = np.dot(b1, b2)
        tgt = np.array([5.3, 5.3, 5.3])
        assert_almost_equal(res, tgt, decimal=self.N)

def test_dotcolumnvect2(self):
        b1 = np.ones((3, 1)).transpose()
        b2 = [6.2]
        res = np.dot(b2, b1)
        tgt = np.array([6.2, 6.2, 6.2])
        assert_almost_equal(res, tgt, decimal=self.N)

def test_dotvecscalar(self):
        np.random.seed(100)
        b1 = np.random.rand(1, 1)
        b2 = np.random.rand(1, 4)
        res = np.dot(b1, b2)
        tgt = np.array([[0.15126730, 0.23068496, 0.45905553, 0.00256425]])
        assert_almost_equal(res, tgt, decimal=self.N)

def test_dotvecscalar2(self):
        np.random.seed(100)
        b1 = np.random.rand(4, 1)
        b2 = np.random.rand(1, 1)
        res = np.dot(b1, b2)
        tgt = np.array([[0.00256425],[0.00131359],[0.00200324],[ 0.00398638]])
        assert_almost_equal(res, tgt, decimal=self.N)

def test_all(self):
        dims = [(), (1,), (1, 1)]
        dout = [(), (1,), (1, 1), (1,), (), (1,), (1, 1), (1,), (1, 1)]
        for dim, (dim1, dim2) in zip(dout, itertools.product(dims, dims)):
            b1 = np.zeros(dim1)
            b2 = np.zeros(dim2)
            res = np.dot(b1, b2)
            tgt = np.zeros(dim)
            assert_(res.shape == tgt.shape)
            assert_almost_equal(res, tgt, decimal=self.N)

def test_vecobject(self):
        class Vec:
            def __init__(self, sequence=None):
                if sequence is None:
                    sequence = []
                self.array = np.array(sequence)

def __add__(self, other):
                out = Vec()
                out.array = self.array + other.array
                return out

def __sub__(self, other):
                out = Vec()
                out.array = self.array - other.array
                return out

def __mul__(self, other):  # with scalar
                out = Vec(self.array.copy())
                out.array *= other
                return out

def __rmul__(self, other):
                return self*other

U_non_cont = np.transpose([[1., 1.], [1., 2.]])
        U_cont = np.ascontiguousarray(U_non_cont)
        x = np.array([Vec([1., 0.]), Vec([0., 1.])])
        zeros = np.array([Vec([0., 0.]), Vec([0., 0.])])
        zeros_test = np.dot(U_cont, x) - np.dot(U_non_cont, x)
        assert_equal(zeros[0].array, zeros_test[0].array)
        assert_equal(zeros[1].array, zeros_test[1].array)

def test_dot_2args(self):
        from numpy.core.multiarray import dot

a = np.array([[1, 2], [3, 4]], dtype=float)
        b = np.array([[1, 0], [1, 1]], dtype=float)
        c = np.array([[3, 2], [7, 4]], dtype=float)

d = dot(a, b)
        assert_allclose(c, d)

def test_dot_3args(self):
        from numpy.core.multiarray import dot

np.random.seed(22)
        f = np.random.random_sample((1024, 16))
        v = np.random.random_sample((16, 32))

r = np.empty((1024, 32))
        for i in range(12):
            dot(f, v, r)
        if HAS_REFCOUNT:
            assert_equal(sys.getrefcount(r), 2)
        r2 = dot(f, v, out=None)
        assert_array_equal(r2, r)
        assert_(r is dot(f, v, out=r))

v = v[:, 0].copy()  # v.shape == (16,)
        r = r[:, 0].copy()  # r.shape == (1024,)
        r2 = dot(f, v)
        assert_(r is dot(f, v, r))
        assert_array_equal(r2, r)

def test_dot_3args_errors(self):
        from numpy.core.multiarray import dot

np.random.seed(22)
        f = np.random.random_sample((1024, 16))
        v = np.random.random_sample((16, 32))

r = np.empty((1024, 31))
        assert_raises(ValueError, dot, f, v, r)

r = np.empty((1024,))
        assert_raises(ValueError, dot, f, v, r)

r = np.empty((32,))
        assert_raises(ValueError, dot, f, v, r)

r = np.empty((32, 1024))
        assert_raises(ValueError, dot, f, v, r)
        assert_raises(ValueError, dot, f, v, r.T)

r = np.empty((1024, 64))
        assert_raises(ValueError, dot, f, v, r[:, ::2])
        assert_raises(ValueError, dot, f, v, r[:, :32])

r = np.empty((1024, 32), dtype=np.float32)
        assert_raises(ValueError, dot, f, v, r)

r = np.empty((1024, 32), dtype=int)
        assert_raises(ValueError, dot, f, v, r)

def test_dot_out_result(self):
        x = np.ones((), dtype=np.float16)
        y = np.ones((5,), dtype=np.float16)
        z = np.zeros((5,), dtype=np.float16)
        res = x.dot(y, out=z)
        assert np.array_equal(res, y)
        assert np.array_equal(z, y)

def test_dot_out_aliasing(self):
        x = np.ones((), dtype=np.float16)
        y = np.ones((5,), dtype=np.float16)
        z = np.zeros((5,), dtype=np.float16)
        res = x.dot(y, out=z)
        z[0] = 2
        assert np.array_equal(res, z)

def test_dot_array_order(self):
        a = np.array([[1, 2], [3, 4]], order='C')
        b = np.array([[1, 2], [3, 4]], order='F')
        res = np.dot(a, a)

# integer arrays are exact
        assert_equal(np.dot(a, b), res)
        assert_equal(np.dot(b, a), res)
        assert_equal(np.dot(b, b), res)

def test_accelerate_framework_sgemv_fix(self):

def aligned_array(shape, align, dtype, order='C'):
            d = dtype(0)
            N = np.prod(shape)
            tmp = np.zeros(N * d.nbytes + align, dtype=np.uint8)
            address = tmp.__array_interface__["data"][0]
            for offset in range(align):
                if (address + offset) % align == 0:
                    break
            tmp = tmp[offset:offset+N*d.nbytes].view(dtype=dtype)
            return tmp.reshape(shape, order=order)

def as_aligned(arr, align, dtype, order='C'):
            aligned = aligned_array(arr.shape, align, dtype, order)
            aligned[:] = arr[:]
            return aligned

def assert_dot_close(A, X, desired):
            assert_allclose(np.dot(A, X), desired, rtol=1e-5, atol=1e-7)

m = aligned_array(100, 15, np.float32)
        s = aligned_array((100, 100), 15, np.float32)
        np.dot(s, m)  # this will always segfault if the bug is present

testdata = itertools.product((15, 32), (10000,), (200, 89), ('C', 'F'))
        for align, m, n, a_order in testdata:
            # Calculation in double precision
            A_d = np.random.rand(m, n)
            X_d = np.random.rand(n)
            desired = np.dot(A_d, X_d)
            # Calculation with aligned single precision
            A_f = as_aligned(A_d, align, np.float32, order=a_order)
            X_f = as_aligned(X_d, align, np.float32)
            assert_dot_close(A_f, X_f, desired)
            # Strided A rows
            A_d_2 = A_d[::2]
            desired = np.dot(A_d_2, X_d)
            A_f_2 = A_f[::2]
            assert_dot_close(A_f_2, X_f, desired)
            # Strided A columns, strided X vector
            A_d_22 = A_d_2[:, ::2]
            X_d_2 = X_d[::2]
            desired = np.dot(A_d_22, X_d_2)
            A_f_22 = A_f_2[:, ::2]
            X_f_2 = X_f[::2]
            assert_dot_close(A_f_22, X_f_2, desired)
            # Check the strides are as expected
            if a_order == 'F':
                assert_equal(A_f_22.strides, (8, 8 * m))
            else:
                assert_equal(A_f_22.strides, (8 * n, 8))
            assert_equal(X_f_2.strides, (8,))
            # Strides in A rows + cols only
            X_f_2c = as_aligned(X_f_2, align, np.float32)
            assert_dot_close(A_f_22, X_f_2c, desired)
            # Strides just in A cols
            A_d_12 = A_d[:, ::2]
            desired = np.dot(A_d_12, X_d_2)
            A_f_12 = A_f[:, ::2]
            assert_dot_close(A_f_12, X_f_2c, desired)
            # Strides in A cols and X
            assert_dot_close(A_f_12, X_f_2, desired)

@pytest.mark.slow
    @pytest.mark.parametrize("dtype", [np.float64, np.complex128])
    @requires_memory(free_bytes=18e9)  # complex case needs 18GiB+
    def test_huge_vectordot(self, dtype):
        # Large vector multiplications are chunked with 32bit BLAS
        # Test that the chunking does the right thing, see also gh-22262
        data = np.ones(2**30+100, dtype=dtype)
        res = np.dot(data, data)
        assert res == 2**30+100

def test_dtype_discovery_fails(self):
        # See gh-14247, error checking was missing for failed dtype discovery
        class BadObject(object):
            def __array__(self):
                raise TypeError("just this tiny mint leaf")

with pytest.raises(TypeError):
            np.dot(BadObject(), BadObject())

with pytest.raises(TypeError):
            np.dot(3.0, BadObject())

class MatmulCommon:
    """Common tests for '@' operator and numpy.matmul.

"""
    # Should work with these types. Will want to add
    # "O" at some point
    types = "?bhilqBHILQefdgFDGO"

def test_exceptions(self):
        dims = [
            ((1,), (2,)),            # mismatched vector vector
            ((2, 1,), (2,)),         # mismatched matrix vector
            ((2,), (1, 2)),          # mismatched vector matrix
            ((1, 2), (3, 1)),        # mismatched matrix matrix
            ((1,), ()),              # vector scalar
            ((), (1)),               # scalar vector
            ((1, 1), ()),            # matrix scalar
            ((), (1, 1)),            # scalar matrix
            ((2, 2, 1), (3, 1, 2)),  # cannot broadcast
            ]

for dt, (dm1, dm2) in itertools.product(self.types, dims):
            a = np.ones(dm1, dtype=dt)
            b = np.ones(dm2, dtype=dt)
            assert_raises(ValueError, self.matmul, a, b)

def test_shapes(self):
        dims = [
            ((1, 1), (2, 1, 1)),     # broadcast first argument
            ((2, 1, 1), (1, 1)),     # broadcast second argument
            ((2, 1, 1), (2, 1, 1)),  # matrix stack sizes match
            ]

for dt, (dm1, dm2) in itertools.product(self.types, dims):
            a = np.ones(dm1, dtype=dt)
            b = np.ones(dm2, dtype=dt)
            res = self.matmul(a, b)
            assert_(res.shape == (2, 1, 1))

# vector vector returns scalars.
        for dt in self.types:
            a = np.ones((2,), dtype=dt)
            b = np.ones((2,), dtype=dt)
            c = self.matmul(a, b)
            assert_(np.array(c).shape == ())

def test_result_types(self):
        mat = np.ones((1,1))
        vec = np.ones((1,))
        for dt in self.types:
            m = mat.astype(dt)
            v = vec.astype(dt)
            for arg in [(m, v), (v, m), (m, m)]:
                res = self.matmul(*arg)
                assert_(res.dtype == dt)

# vector vector returns scalars
            if dt != "O":
                res = self.matmul(v, v)
                assert_(type(res) is np.dtype(dt).type)

def test_scalar_output(self):
        vec1 = np.array([2])
        vec2 = np.array([3, 4]).reshape(1, -1)
        tgt = np.array([6, 8])
        for dt in self.types[1:]:
            v1 = vec1.astype(dt)
            v2 = vec2.astype(dt)
            res = self.matmul(v1, v2)
            assert_equal(res, tgt)
            res = self.matmul(v2.T, v1)
            assert_equal(res, tgt)

# boolean type
        vec = np.array([True, True], dtype='?').reshape(1, -1)
        res = self.matmul(vec[:, 0], vec)
        assert_equal(res, True)

def test_vector_vector_values(self):
        vec1 = np.array([1, 2])
        vec2 = np.array([3, 4]).reshape(-1, 1)
        tgt1 = np.array([11])
        tgt2 = np.array([[3, 6], [4, 8]])
        for dt in self.types[1:]:
            v1 = vec1.astype(dt)
            v2 = vec2.astype(dt)
            res = self.matmul(v1, v2)
            assert_equal(res, tgt1)
            # no broadcast, we must make v1 into a 2d ndarray
            res = self.matmul(v2, v1.reshape(1, -1))
            assert_equal(res, tgt2)

# boolean type
        vec = np.array([True, True], dtype='?')
        res = self.matmul(vec, vec)
        assert_equal(res, True)

def test_vector_matrix_values(self):
        vec = np.array([1, 2])
        mat1 = np.array([[1, 2], [3, 4]])
        mat2 = np.stack([mat1]*2, axis=0)
        tgt1 = np.array([7, 10])
        tgt2 = np.stack([tgt1]*2, axis=0)
        for dt in self.types[1:]:
            v = vec.astype(dt)
            m1 = mat1.astype(dt)
            m2 = mat2.astype(dt)
            res = self.matmul(v, m1)
            assert_equal(res, tgt1)
            res = self.matmul(v, m2)
            assert_equal(res, tgt2)

# boolean type
        vec = np.array([True, False])
        mat1 = np.array([[True, False], [False, True]])
        mat2 = np.stack([mat1]*2, axis=0)
        tgt1 = np.array([True, False])
        tgt2 = np.stack([tgt1]*2, axis=0)

res = self.matmul(vec, mat1)
        assert_equal(res, tgt1)
        res = self.matmul(vec, mat2)
        assert_equal(res, tgt2)

def test_matrix_vector_values(self):
        vec = np.array([1, 2])
        mat1 = np.array([[1, 2], [3, 4]])
        mat2 = np.stack([mat1]*2, axis=0)
        tgt1 = np.array([5, 11])
        tgt2 = np.stack([tgt1]*2, axis=0)
        for dt in self.types[1:]:
            v = vec.astype(dt)
            m1 = mat1.astype(dt)
            m2 = mat2.astype(dt)
            res = self.matmul(m1, v)
            assert_equal(res, tgt1)
            res = self.matmul(m2, v)
            assert_equal(res, tgt2)

res = self.matmul(vec, mat1)
        assert_equal(res, tgt1)
        res = self.matmul(vec, mat2)
        assert_equal(res, tgt2)

def test_matrix_matrix_values(self):
        mat1 = np.array([[1, 2], [3, 4]])
        mat2 = np.array([[1, 0], [1, 1]])
        mat12 = np.stack([mat1, mat2], axis=0)
        mat21 = np.stack([mat2, mat1], axis=0)
        tgt11 = np.array([[7, 10], [15, 22]])
        tgt12 = np.array([[3, 2], [7, 4]])
        tgt21 = np.array([[1, 2], [4, 6]])
        tgt12_21 = np.stack([tgt12, tgt21], axis=0)
        tgt11_12 = np.stack((tgt11, tgt12), axis=0)
        tgt11_21 = np.stack((tgt11, tgt21), axis=0)
        for dt in self.types[1:]:
            m1 = mat1.astype(dt)
            m2 = mat2.astype(dt)
            m12 = mat12.astype(dt)
            m21 = mat21.astype(dt)

# matrix @ matrix
            res = self.matmul(m1, m2)
            assert_equal(res, tgt12)
            res = self.matmul(m2, m1)
            assert_equal(res, tgt21)

# stacked @ matrix
            res = self.matmul(m12, m1)
            assert_equal(res, tgt11_21)

# matrix @ stacked
            res = self.matmul(m1, m12)
            assert_equal(res, tgt11_12)

# stacked @ stacked
            res = self.matmul(m12, m21)
            assert_equal(res, tgt12_21)

# boolean type
        m1 = np.array([[1, 1], [0, 0]], dtype=np.bool_)
        m2 = np.array([[1, 0], [1, 1]], dtype=np.bool_)
        m12 = np.stack([m1, m2], axis=0)
        m21 = np.stack([m2, m1], axis=0)
        tgt11 = m1
        tgt12 = m1
        tgt21 = np.array([[1, 1], [1, 1]], dtype=np.bool_)
        tgt12_21 = np.stack([tgt12, tgt21], axis=0)
        tgt11_12 = np.stack((tgt11, tgt12), axis=0)
        tgt11_21 = np.stack((tgt11, tgt21), axis=0)

# matrix @ matrix
        res = self.matmul(m1, m2)
        assert_equal(res, tgt12)
        res = self.matmul(m2, m1)
        assert_equal(res, tgt21)

# stacked @ matrix
        res = self.matmul(m12, m1)
        assert_equal(res, tgt11_21)

# matrix @ stacked
        res = self.matmul(m1, m12)
        assert_equal(res, tgt11_12)

# stacked @ stacked
        res = self.matmul(m12, m21)
        assert_equal(res, tgt12_21)

class TestMatmul(MatmulCommon):
    matmul = np.matmul

def test_out_arg(self):
        a = np.ones((5, 2), dtype=float)
        b = np.array([[1, 3], [5, 7]], dtype=float)
        tgt = np.dot(a, b)

# test as positional argument
        msg = "out positional argument"
        out = np.zeros((5, 2), dtype=float)
        self.matmul(a, b, out)
        assert_array_equal(out, tgt, err_msg=msg)

# test as keyword argument
        msg = "out keyword argument"
        out = np.zeros((5, 2), dtype=float)
        self.matmul(a, b, out=out)
        assert_array_equal(out, tgt, err_msg=msg)

# test out with not allowed type cast (safe casting)
        msg = "Cannot cast ufunc .* output"
        out = np.zeros((5, 2), dtype=np.int32)
        assert_raises_regex(TypeError, msg, self.matmul, a, b, out=out)

# test out with type upcast to complex
        out = np.zeros((5, 2), dtype=np.complex128)
        c = self.matmul(a, b, out=out)
        assert_(c is out)
        with suppress_warnings() as sup:
            sup.filter(np.ComplexWarning, '')
            c = c.astype(tgt.dtype)
        assert_array_equal(c, tgt)

def test_empty_out(self):
        # Check that the output cannot be broadcast, so that it cannot be
        # size zero when the outer dimensions (iterator size) has size zero.
        arr = np.ones((0, 1, 1))
        out = np.ones((1, 1, 1))
        assert self.matmul(arr, arr).shape == (0, 1, 1)

with pytest.raises(ValueError, match=r"non-broadcastable"):
            self.matmul(arr, arr, out=out)

def test_out_contiguous(self):
        a = np.ones((5, 2), dtype=float)
        b = np.array([[1, 3], [5, 7]], dtype=float)
        v = np.array([1, 3], dtype=float)
        tgt = np.dot(a, b)
        tgt_mv = np.dot(a, v)

# test out non-contiguous
        out = np.ones((5, 2, 2), dtype=float)
        c = self.matmul(a, b, out=out[..., 0])
        assert c.base is out
        assert_array_equal(c, tgt)
        c = self.matmul(a, v, out=out[:, 0, 0])
        assert_array_equal(c, tgt_mv)
        c = self.matmul(v, a.T, out=out[:, 0, 0])
        assert_array_equal(c, tgt_mv)

# test out contiguous in only last dim
        out = np.ones((10, 2), dtype=float)
        c = self.matmul(a, b, out=out[::2, :])
        assert_array_equal(c, tgt)

# test transposes of out, args
        out = np.ones((5, 2), dtype=float)
        c = self.matmul(b.T, a.T, out=out.T)
        assert_array_equal(out, tgt)

m1 = np.arange(15.).reshape(5, 3)
    m2 = np.arange(21.).reshape(3, 7)
    m3 = np.arange(30.).reshape(5, 6)[:, ::2]  # non-contiguous
    vc = np.arange(10.)
    vr = np.arange(6.)
    m0 = np.zeros((3, 0))
    @pytest.mark.parametrize('args', (
            # matrix-matrix
            (m1, m2), (m2.T, m1.T), (m2.T.copy(), m1.T), (m2.T, m1.T.copy()),
            # matrix-matrix-transpose, contiguous and non
            (m1, m1.T), (m1.T, m1), (m1, m3.T), (m3, m1.T),
            (m3, m3.T), (m3.T, m3),
            # matrix-matrix non-contiguous
            (m3, m2), (m2.T, m3.T), (m2.T.copy(), m3.T),
            # vector-matrix, matrix-vector, contiguous
            (m1, vr[:3]), (vc[:5], m1), (m1.T, vc[:5]), (vr[:3], m1.T),
            # vector-matrix, matrix-vector, vector non-contiguous
            (m1, vr[::2]), (vc[::2], m1), (m1.T, vc[::2]), (vr[::2], m1.T),
            # vector-matrix, matrix-vector, matrix non-contiguous
            (m3, vr[:3]), (vc[:5], m3), (m3.T, vc[:5]), (vr[:3], m3.T),
            # vector-matrix, matrix-vector, both non-contiguous
            (m3, vr[::2]), (vc[::2], m3), (m3.T, vc[::2]), (vr[::2], m3.T),
            # size == 0
            (m0, m0.T), (m0.T, m0), (m1, m0), (m0.T, m1.T),
        ))
    def test_dot_equivalent(self, args):
        r1 = np.matmul(*args)
        r2 = np.dot(*args)
        assert_equal(r1, r2)

r3 = np.matmul(args[0].copy(), args[1].copy())
        assert_equal(r1, r3)

def test_matmul_object(self):
        import fractions

f = np.vectorize(fractions.Fraction)
        def random_ints():
            return np.random.randint(1, 1000, size=(10, 3, 3))
        M1 = f(random_ints(), random_ints())
        M2 = f(random_ints(), random_ints())

M3 = self.matmul(M1, M2)

[N1, N2, N3] = [a.astype(float) for a in [M1, M2, M3]]

assert_allclose(N3, self.matmul(N1, N2))

def test_matmul_object_type_scalar(self):
        from fractions import Fraction as F
        v = np.array([F(2,3), F(5,7)])
        res = self.matmul(v, v)
        assert_(type(res) is F)

def test_matmul_empty(self):
        a = np.empty((3, 0), dtype=object)
        b = np.empty((0, 3), dtype=object)
        c = np.zeros((3, 3))
        assert_array_equal(np.matmul(a, b), c)

def test_matmul_exception_multiply(self):
        # test that matmul fails if `__mul__` is missing
        class add_not_multiply():
            def __add__(self, other):
                return self
        a = np.full((3,3), add_not_multiply())
        with assert_raises(TypeError):
            b = np.matmul(a, a)

def test_matmul_exception_add(self):
        # test that matmul fails if `__add__` is missing
        class multiply_not_add():
            def __mul__(self, other):
                return self
        a = np.full((3,3), multiply_not_add())
        with assert_raises(TypeError):
            b = np.matmul(a, a)

def test_matmul_bool(self):
        # gh-14439
        a = np.array([[1, 0],[1, 1]], dtype=bool)
        assert np.max(a.view(np.uint8)) == 1
        b = np.matmul(a, a)
        # matmul with boolean output should always be 0, 1
        assert np.max(b.view(np.uint8)) == 1

rg = np.random.default_rng(np.random.PCG64(43))
        d = rg.integers(2, size=4*5, dtype=np.int8)
        d = d.reshape(4, 5) > 0
        out1 = np.matmul(d, d.reshape(5, 4))
        out2 = np.dot(d, d.reshape(5, 4))
        assert_equal(out1, out2)

c = np.matmul(np.zeros((2, 0), dtype=bool), np.zeros(0, dtype=bool))
        assert not np.any(c)

class TestMatmulOperator(MatmulCommon):
    import operator
    matmul = operator.matmul

def test_array_priority_override(self):

class A:
            __array_priority__ = 1000

def __matmul__(self, other):
                return "A"

def __rmatmul__(self, other):
                return "A"

a = A()
        b = np.ones(2)
        assert_equal(self.matmul(a, b), "A")
        assert_equal(self.matmul(b, a), "A")

def test_matmul_raises(self):
        assert_raises(TypeError, self.matmul, np.int8(5), np.int8(5))
        assert_raises(TypeError, self.matmul, np.void(b'abc'), np.void(b'abc'))
        assert_raises(TypeError, self.matmul, np.arange(10), np.void(b'abc'))

class TestMatmulInplace:
    DTYPES = {}
    for i in MatmulCommon.types:
        for j in MatmulCommon.types:
            if np.can_cast(j, i):
                DTYPES[f"{i}-{j}"] = (np.dtype(i), np.dtype(j))

@pytest.mark.parametrize("dtype1,dtype2", DTYPES.values(), ids=DTYPES)
    def test_basic(self, dtype1: np.dtype, dtype2: np.dtype) -> None:
        a = np.arange(10).reshape(5, 2).astype(dtype1)
        a_id = id(a)
        b = np.ones((2, 2), dtype=dtype2)

ref = a @ b
        a @= b

assert id(a) == a_id
        assert a.dtype == dtype1
        assert a.shape == (5, 2)
        if dtype1.kind in "fc":
            np.testing.assert_allclose(a, ref)
        else:
            np.testing.assert_array_equal(a, ref)

SHAPES = {
        "2d_large": ((10**5, 10), (10, 10)),
        "3d_large": ((10**4, 10, 10), (1, 10, 10)),
        "1d": ((3,), (3,)),
        "2d_1d": ((3, 3), (3,)),
        "1d_2d": ((3,), (3, 3)),
        "2d_broadcast": ((3, 3), (3, 1)),
        "2d_broadcast_reverse": ((1, 3), (3, 3)),
        "3d_broadcast1": ((3, 3, 3), (1, 3, 1)),
        "3d_broadcast2": ((3, 3, 3), (1, 3, 3)),
        "3d_broadcast3": ((3, 3, 3), (3, 3, 1)),
        "3d_broadcast_reverse1": ((1, 3, 3), (3, 3, 3)),
        "3d_broadcast_reverse2": ((3, 1, 3), (3, 3, 3)),
        "3d_broadcast_reverse3": ((1, 1, 3), (3, 3, 3)),
    }

@pytest.mark.parametrize("a_shape,b_shape", SHAPES.values(), ids=SHAPES)
    def test_shapes(self, a_shape: tuple[int, ...], b_shape: tuple[int, ...]):
        a_size = np.prod(a_shape)
        a = np.arange(a_size).reshape(a_shape).astype(np.float64)
        a_id = id(a)

b_size = np.prod(b_shape)
        b = np.arange(b_size).reshape(b_shape)

ref = a @ b
        if ref.shape != a_shape:
            with pytest.raises(ValueError):
                a @= b
            return
        else:
            a @= b

assert id(a) == a_id
        assert a.dtype.type == np.float64
        assert a.shape == a_shape
        np.testing.assert_allclose(a, ref)

def test_matmul_axes():
    a = np.arange(3*4*5).reshape(3, 4, 5)
    c = np.matmul(a, a, axes=[(-2, -1), (-1, -2), (1, 2)])
    assert c.shape == (3, 4, 4)
    d = np.matmul(a, a, axes=[(-2, -1), (-1, -2), (0, 1)])
    assert d.shape == (4, 4, 3)
    e = np.swapaxes(d, 0, 2)
    assert_array_equal(e, c)
    f = np.matmul(a, np.arange(3), axes=[(1, 0), (0), (0)])
    assert f.shape == (4, 5)

class TestInner:

def test_inner_type_mismatch(self):
        c = 1.
        A = np.array((1,1), dtype='i,i')

assert_raises(TypeError, np.inner, c, A)
        assert_raises(TypeError, np.inner, A, c)

def test_inner_scalar_and_vector(self):
        for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
            sca = np.array(3, dtype=dt)[()]
            vec = np.array([1, 2], dtype=dt)
            desired = np.array([3, 6], dtype=dt)
            assert_equal(np.inner(vec, sca), desired)
            assert_equal(np.inner(sca, vec), desired)

def test_vecself(self):
        # Ticket 844.
        # Inner product of a vector with itself segfaults or give
        # meaningless result
        a = np.zeros(shape=(1, 80), dtype=np.float64)
        p = np.inner(a, a)
        assert_almost_equal(p, 0, decimal=14)

def test_inner_product_with_various_contiguities(self):
        # github issue 6532
        for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
            # check an inner product involving a matrix transpose
            A = np.array([[1, 2], [3, 4]], dtype=dt)
            B = np.array([[1, 3], [2, 4]], dtype=dt)
            C = np.array([1, 1], dtype=dt)
            desired = np.array([4, 6], dtype=dt)
            assert_equal(np.inner(A.T, C), desired)
            assert_equal(np.inner(C, A.T), desired)
            assert_equal(np.inner(B, C), desired)
            assert_equal(np.inner(C, B), desired)
            # check a matrix product
            desired = np.array([[7, 10], [15, 22]], dtype=dt)
            assert_equal(np.inner(A, B), desired)
            # check the syrk vs. gemm paths
            desired = np.array([[5, 11], [11, 25]], dtype=dt)
            assert_equal(np.inner(A, A), desired)
            assert_equal(np.inner(A, A.copy()), desired)
            # check an inner product involving an aliased and reversed view
            a = np.arange(5).astype(dt)
            b = a[::-1]
            desired = np.array(10, dtype=dt).item()
            assert_equal(np.inner(b, a), desired)

def test_3d_tensor(self):
        for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
            a = np.arange(24).reshape(2,3,4).astype(dt)
            b = np.arange(24, 48).reshape(2,3,4).astype(dt)
            desired = np.array(
                [[[[ 158,  182,  206],
                   [ 230,  254,  278]],

[[ 566,  654,  742],
                   [ 830,  918, 1006]],

[[ 974, 1126, 1278],
                   [1430, 1582, 1734]]],

[[[1382, 1598, 1814],
                   [2030, 2246, 2462]],

[[1790, 2070, 2350],
                   [2630, 2910, 3190]],

[[2198, 2542, 2886],
                   [3230, 3574, 3918]]]]
            ).astype(dt)
            assert_equal(np.inner(a, b), desired)
            assert_equal(np.inner(b, a).transpose(2,3,0,1), desired)

class TestChoose:
    def setup_method(self):
        self.x = 2*np.ones((3,), dtype=int)
        self.y = 3*np.ones((3,), dtype=int)
        self.x2 = 2*np.ones((2, 3), dtype=int)
        self.y2 = 3*np.ones((2, 3), dtype=int)
        self.ind = [0, 0, 1]

def test_basic(self):
        A = np.choose(self.ind, (self.x, self.y))
        assert_equal(A, [2, 2, 3])

def test_broadcast1(self):
        A = np.choose(self.ind, (self.x2, self.y2))
        assert_equal(A, [[2, 2, 3], [2, 2, 3]])

def test_broadcast2(self):
        A = np.choose(self.ind, (self.x, self.y2))
        assert_equal(A, [[2, 2, 3], [2, 2, 3]])

@pytest.mark.parametrize("ops",
        [(1000, np.array([1], dtype=np.uint8)),
         (-1, np.array([1], dtype=np.uint8)),
         (1., np.float32(3)),
         (1., np.array([3], dtype=np.float32))],)
    def test_output_dtype(self, ops):
        expected_dt = np.result_type(*ops)
        assert(np.choose([0], ops).dtype == expected_dt)

class TestRepeat:
    def setup_method(self):
        self.m = np.array([1, 2, 3, 4, 5, 6])
        self.m_rect = self.m.reshape((2, 3))

def test_basic(self):
        A = np.repeat(self.m, [1, 3, 2, 1, 1, 2])
        assert_equal(A, [1, 2, 2, 2, 3,
                         3, 4, 5, 6, 6])

def test_broadcast1(self):
        A = np.repeat(self.m, 2)
        assert_equal(A, [1, 1, 2, 2, 3, 3,
                         4, 4, 5, 5, 6, 6])

def test_axis_spec(self):
        A = np.repeat(self.m_rect, [2, 1], axis=0)
        assert_equal(A, [[1, 2, 3],
                         [1, 2, 3],
                         [4, 5, 6]])

A = np.repeat(self.m_rect, [1, 3, 2], axis=1)
        assert_equal(A, [[1, 2, 2, 2, 3, 3],
                         [4, 5, 5, 5, 6, 6]])

def test_broadcast2(self):
        A = np.repeat(self.m_rect, 2, axis=0)
        assert_equal(A, [[1, 2, 3],
                         [1, 2, 3],
                         [4, 5, 6],
                         [4, 5, 6]])

A = np.repeat(self.m_rect, 2, axis=1)
        assert_equal(A, [[1, 1, 2, 2, 3, 3],
                         [4, 4, 5, 5, 6, 6]])

# TODO: test for multidimensional
NEIGH_MODE = {'zero': 0, 'one': 1, 'constant': 2, 'circular': 3, 'mirror': 4}

@pytest.mark.parametrize('dt', [float, Decimal], ids=['float', 'object'])
class TestNeighborhoodIter:
    # Simple, 2d tests
    def test_simple2d(self, dt):
        # Test zero and one padding for simple data type
        x = np.array([[0, 1], [2, 3]], dtype=dt)
        r = [np.array([[0, 0, 0], [0, 0, 1]], dtype=dt),
             np.array([[0, 0, 0], [0, 1, 0]], dtype=dt),
             np.array([[0, 0, 1], [0, 2, 3]], dtype=dt),
             np.array([[0, 1, 0], [2, 3, 0]], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator(
                x, [-1, 0, -1, 1], x[0], NEIGH_MODE['zero'])
        assert_array_equal(l, r)

r = [np.array([[1, 1, 1], [1, 0, 1]], dtype=dt),
             np.array([[1, 1, 1], [0, 1, 1]], dtype=dt),
             np.array([[1, 0, 1], [1, 2, 3]], dtype=dt),
             np.array([[0, 1, 1], [2, 3, 1]], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator(
                x, [-1, 0, -1, 1], x[0], NEIGH_MODE['one'])
        assert_array_equal(l, r)

r = [np.array([[4, 4, 4], [4, 0, 1]], dtype=dt),
             np.array([[4, 4, 4], [0, 1, 4]], dtype=dt),
             np.array([[4, 0, 1], [4, 2, 3]], dtype=dt),
             np.array([[0, 1, 4], [2, 3, 4]], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator(
                x, [-1, 0, -1, 1], 4, NEIGH_MODE['constant'])
        assert_array_equal(l, r)

# Test with start in the middle
        r = [np.array([[4, 0, 1], [4, 2, 3]], dtype=dt),
             np.array([[0, 1, 4], [2, 3, 4]], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator(
                x, [-1, 0, -1, 1], 4, NEIGH_MODE['constant'], 2)
        assert_array_equal(l, r)

def test_mirror2d(self, dt):
        x = np.array([[0, 1], [2, 3]], dtype=dt)
        r = [np.array([[0, 0, 1], [0, 0, 1]], dtype=dt),
             np.array([[0, 1, 1], [0, 1, 1]], dtype=dt),
             np.array([[0, 0, 1], [2, 2, 3]], dtype=dt),
             np.array([[0, 1, 1], [2, 3, 3]], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator(
                x, [-1, 0, -1, 1], x[0], NEIGH_MODE['mirror'])
        assert_array_equal(l, r)

# Simple, 1d tests
    def test_simple(self, dt):
        # Test padding with constant values
        x = np.linspace(1, 5, 5).astype(dt)
        r = [[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 0]]
        l = _multiarray_tests.test_neighborhood_iterator(
                x, [-1, 1], x[0], NEIGH_MODE['zero'])
        assert_array_equal(l, r)

r = [[1, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 1]]
        l = _multiarray_tests.test_neighborhood_iterator(
                x, [-1, 1], x[0], NEIGH_MODE['one'])
        assert_array_equal(l, r)

r = [[x[4], 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, x[4]]]
        l = _multiarray_tests.test_neighborhood_iterator(
                x, [-1, 1], x[4], NEIGH_MODE['constant'])
        assert_array_equal(l, r)

# Test mirror modes
    def test_mirror(self, dt):
        x = np.linspace(1, 5, 5).astype(dt)
        r = np.array([[2, 1, 1, 2, 3], [1, 1, 2, 3, 4], [1, 2, 3, 4, 5],
                [2, 3, 4, 5, 5], [3, 4, 5, 5, 4]], dtype=dt)
        l = _multiarray_tests.test_neighborhood_iterator(
                x, [-2, 2], x[1], NEIGH_MODE['mirror'])
        assert_([i.dtype == dt for i in l])
        assert_array_equal(l, r)

# Circular mode
    def test_circular(self, dt):
        x = np.linspace(1, 5, 5).astype(dt)
        r = np.array([[4, 5, 1, 2, 3], [5, 1, 2, 3, 4], [1, 2, 3, 4, 5],
                [2, 3, 4, 5, 1], [3, 4, 5, 1, 2]], dtype=dt)
        l = _multiarray_tests.test_neighborhood_iterator(
                x, [-2, 2], x[0], NEIGH_MODE['circular'])
        assert_array_equal(l, r)

# Test stacking neighborhood iterators
class TestStackedNeighborhoodIter:
    # Simple, 1d test: stacking 2 constant-padded neigh iterators
    def test_simple_const(self):
        dt = np.float64
        # Test zero and one padding for simple data type
        x = np.array([1, 2, 3], dtype=dt)
        r = [np.array([0], dtype=dt),
             np.array([0], dtype=dt),
             np.array([1], dtype=dt),
             np.array([2], dtype=dt),
             np.array([3], dtype=dt),
             np.array([0], dtype=dt),
             np.array([0], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator_oob(
                x, [-2, 4], NEIGH_MODE['zero'], [0, 0], NEIGH_MODE['zero'])
        assert_array_equal(l, r)

r = [np.array([1, 0, 1], dtype=dt),
             np.array([0, 1, 2], dtype=dt),
             np.array([1, 2, 3], dtype=dt),
             np.array([2, 3, 0], dtype=dt),
             np.array([3, 0, 1], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator_oob(
                x, [-1, 3], NEIGH_MODE['zero'], [-1, 1], NEIGH_MODE['one'])
        assert_array_equal(l, r)

# 2nd simple, 1d test: stacking 2 neigh iterators, mixing const padding and
    # mirror padding
    def test_simple_mirror(self):
        dt = np.float64
        # Stacking zero on top of mirror
        x = np.array([1, 2, 3], dtype=dt)
        r = [np.array([0, 1, 1], dtype=dt),
             np.array([1, 1, 2], dtype=dt),
             np.array([1, 2, 3], dtype=dt),
             np.array([2, 3, 3], dtype=dt),
             np.array([3, 3, 0], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator_oob(
                x, [-1, 3], NEIGH_MODE['mirror'], [-1, 1], NEIGH_MODE['zero'])
        assert_array_equal(l, r)

# Stacking mirror on top of zero
        x = np.array([1, 2, 3], dtype=dt)
        r = [np.array([1, 0, 0], dtype=dt),
             np.array([0, 0, 1], dtype=dt),
             np.array([0, 1, 2], dtype=dt),
             np.array([1, 2, 3], dtype=dt),
             np.array([2, 3, 0], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator_oob(
                x, [-1, 3], NEIGH_MODE['zero'], [-2, 0], NEIGH_MODE['mirror'])
        assert_array_equal(l, r)

# Stacking mirror on top of zero: 2nd
        x = np.array([1, 2, 3], dtype=dt)
        r = [np.array([0, 1, 2], dtype=dt),
             np.array([1, 2, 3], dtype=dt),
             np.array([2, 3, 0], dtype=dt),
             np.array([3, 0, 0], dtype=dt),
             np.array([0, 0, 3], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator_oob(
                x, [-1, 3], NEIGH_MODE['zero'], [0, 2], NEIGH_MODE['mirror'])
        assert_array_equal(l, r)

# Stacking mirror on top of zero: 3rd
        x = np.array([1, 2, 3], dtype=dt)
        r = [np.array([1, 0, 0, 1, 2], dtype=dt),
             np.array([0, 0, 1, 2, 3], dtype=dt),
             np.array([0, 1, 2, 3, 0], dtype=dt),
             np.array([1, 2, 3, 0, 0], dtype=dt),
             np.array([2, 3, 0, 0, 3], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator_oob(
                x, [-1, 3], NEIGH_MODE['zero'], [-2, 2], NEIGH_MODE['mirror'])
        assert_array_equal(l, r)

# 3rd simple, 1d test: stacking 2 neigh iterators, mixing const padding and
    # circular padding
    def test_simple_circular(self):
        dt = np.float64
        # Stacking zero on top of mirror
        x = np.array([1, 2, 3], dtype=dt)
        r = [np.array([0, 3, 1], dtype=dt),
             np.array([3, 1, 2], dtype=dt),
             np.array([1, 2, 3], dtype=dt),
             np.array([2, 3, 1], dtype=dt),
             np.array([3, 1, 0], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator_oob(
                x, [-1, 3], NEIGH_MODE['circular'], [-1, 1], NEIGH_MODE['zero'])
        assert_array_equal(l, r)

# Stacking mirror on top of zero
        x = np.array([1, 2, 3], dtype=dt)
        r = [np.array([3, 0, 0], dtype=dt),
             np.array([0, 0, 1], dtype=dt),
             np.array([0, 1, 2], dtype=dt),
             np.array([1, 2, 3], dtype=dt),
             np.array([2, 3, 0], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator_oob(
                x, [-1, 3], NEIGH_MODE['zero'], [-2, 0], NEIGH_MODE['circular'])
        assert_array_equal(l, r)

# Stacking mirror on top of zero: 2nd
        x = np.array([1, 2, 3], dtype=dt)
        r = [np.array([0, 1, 2], dtype=dt),
             np.array([1, 2, 3], dtype=dt),
             np.array([2, 3, 0], dtype=dt),
             np.array([3, 0, 0], dtype=dt),
             np.array([0, 0, 1], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator_oob(
                x, [-1, 3], NEIGH_MODE['zero'], [0, 2], NEIGH_MODE['circular'])
        assert_array_equal(l, r)

# Stacking mirror on top of zero: 3rd
        x = np.array([1, 2, 3], dtype=dt)
        r = [np.array([3, 0, 0, 1, 2], dtype=dt),
             np.array([0, 0, 1, 2, 3], dtype=dt),
             np.array([0, 1, 2, 3, 0], dtype=dt),
             np.array([1, 2, 3, 0, 0], dtype=dt),
             np.array([2, 3, 0, 0, 1], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator_oob(
                x, [-1, 3], NEIGH_MODE['zero'], [-2, 2], NEIGH_MODE['circular'])
        assert_array_equal(l, r)

# 4th simple, 1d test: stacking 2 neigh iterators, but with lower iterator
    # being strictly within the array
    def test_simple_strict_within(self):
        dt = np.float64
        # Stacking zero on top of zero, first neighborhood strictly inside the
        # array
        x = np.array([1, 2, 3], dtype=dt)
        r = [np.array([1, 2, 3, 0], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator_oob(
                x, [1, 1], NEIGH_MODE['zero'], [-1, 2], NEIGH_MODE['zero'])
        assert_array_equal(l, r)

# Stacking mirror on top of zero, first neighborhood strictly inside the
        # array
        x = np.array([1, 2, 3], dtype=dt)
        r = [np.array([1, 2, 3, 3], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator_oob(
                x, [1, 1], NEIGH_MODE['zero'], [-1, 2], NEIGH_MODE['mirror'])
        assert_array_equal(l, r)

# Stacking mirror on top of zero, first neighborhood strictly inside the
        # array
        x = np.array([1, 2, 3], dtype=dt)
        r = [np.array([1, 2, 3, 1], dtype=dt)]
        l = _multiarray_tests.test_neighborhood_iterator_oob(
                x, [1, 1], NEIGH_MODE['zero'], [-1, 2], NEIGH_MODE['circular'])
        assert_array_equal(l, r)

class TestWarnings:

def test_complex_warning(self):
        x = np.array([1, 2])
        y = np.array([1-2j, 1+2j])

with warnings.catch_warnings():
            warnings.simplefilter("error", np.ComplexWarning)
            assert_raises(np.ComplexWarning, x.__setitem__, slice(None), y)
            assert_equal(x, [1, 2])

class TestMinScalarType:

def test_usigned_shortshort(self):
        dt = np.min_scalar_type(2**8-1)
        wanted = np.dtype('uint8')
        assert_equal(wanted, dt)

def test_usigned_short(self):
        dt = np.min_scalar_type(2**16-1)
        wanted = np.dtype('uint16')
        assert_equal(wanted, dt)

def test_usigned_int(self):
        dt = np.min_scalar_type(2**32-1)
        wanted = np.dtype('uint32')
        assert_equal(wanted, dt)

def test_usigned_longlong(self):
        dt = np.min_scalar_type(2**63-1)
        wanted = np.dtype('uint64')
        assert_equal(wanted, dt)

def test_object(self):
        dt = np.min_scalar_type(2**64)
        wanted = np.dtype('O')
        assert_equal(wanted, dt)

from numpy.core._internal import _dtype_from_pep3118

class TestPEP3118Dtype:
    def _check(self, spec, wanted):
        dt = np.dtype(wanted)
        actual = _dtype_from_pep3118(spec)
        assert_equal(actual, dt,
                     err_msg="spec %r != dtype %r" % (spec, wanted))

def test_native_padding(self):
        align = np.dtype('i').alignment
        for j in range(8):
            if j == 0:
                s = 'bi'
            else:
                s = 'b%dxi' % j
            self._check('@'+s, {'f0': ('i1', 0),
                                'f1': ('i', align*(1 + j//align))})
            self._check('='+s, {'f0': ('i1', 0),
                                'f1': ('i', 1+j)})

def test_native_padding_2(self):
        # Native padding should work also for structs and sub-arrays
        self._check('x3T{xi}', {'f0': (({'f0': ('i', 4)}, (3,)), 4)})
        self._check('^x3T{xi}', {'f0': (({'f0': ('i', 1)}, (3,)), 1)})

def test_trailing_padding(self):
        # Trailing padding should be included, *and*, the item size
        # should match the alignment if in aligned mode
        align = np.dtype('i').alignment
        size = np.dtype('i').itemsize

def aligned(n):
            return align*(1 + (n-1)//align)

base = dict(formats=['i'], names=['f0'])

self._check('ix',    dict(itemsize=aligned(size + 1), **base))
        self._check('ixx',   dict(itemsize=aligned(size + 2), **base))
        self._check('ixxx',  dict(itemsize=aligned(size + 3), **base))
        self._check('ixxxx', dict(itemsize=aligned(size + 4), **base))
        self._check('i7x',   dict(itemsize=aligned(size + 7), **base))

self._check('^ix',    dict(itemsize=size + 1, **base))
        self._check('^ixx',   dict(itemsize=size + 2, **base))
        self._check('^ixxx',  dict(itemsize=size + 3, **base))
        self._check('^ixxxx', dict(itemsize=size + 4, **base))
        self._check('^i7x',   dict(itemsize=size + 7, **base))

def test_native_padding_3(self):
        dt = np.dtype(
                [('a', 'b'), ('b', 'i'),
                    ('sub', np.dtype('b,i')), ('c', 'i')],
                align=True)
        self._check("T{b:a:xxxi:b:T{b:f0:=i:f1:}:sub:xxxi:c:}", dt)

dt = np.dtype(
                [('a', 'b'), ('b', 'i'), ('c', 'b'), ('d', 'b'),
                    ('e', 'b'), ('sub', np.dtype('b,i', align=True))])
        self._check("T{b:a:=i:b:b:c:b:d:b:e:T{b:f0:xxxi:f1:}:sub:}", dt)

def test_padding_with_array_inside_struct(self):
        dt = np.dtype(
                [('a', 'b'), ('b', 'i'), ('c', 'b', (3,)),
                    ('d', 'i')],
                align=True)
        self._check("T{b:a:xxxi:b:3b:c:xi:d:}", dt)

def test_byteorder_inside_struct(self):
        # The byte order after @T{=i} should be '=', not '@'.
        # Check this by noting the absence of native alignment.
        self._check('@T{^i}xi', {'f0': ({'f0': ('i', 0)}, 0),
                                 'f1': ('i', 5)})

def test_intra_padding(self):
        # Natively aligned sub-arrays may require some internal padding
        align = np.dtype('i').alignment
        size = np.dtype('i').itemsize

def aligned(n):
            return (align*(1 + (n-1)//align))

self._check('(3)T{ix}', (dict(
            names=['f0'],
            formats=['i'],
            offsets=[0],
            itemsize=aligned(size + 1)
        ), (3,)))

def test_char_vs_string(self):
        dt = np.dtype('c')
        self._check('c', dt)

dt = np.dtype([('f0', 'S1', (4,)), ('f1', 'S4')])
        self._check('4c4s', dt)

def test_field_order(self):
        # gh-9053 - previously, we relied on dictionary key order
        self._check("(0)I:a:f:b:", [('a', 'I', (0,)), ('b', 'f')])
        self._check("(0)I:b:f:a:", [('b', 'I', (0,)), ('a', 'f')])

def test_unnamed_fields(self):
        self._check('ii',     [('f0', 'i'), ('f1', 'i')])
        self._check('ii:f0:', [('f1', 'i'), ('f0', 'i')])

self._check('i', 'i')
        self._check('i:f0:', [('f0', 'i')])

class TestNewBufferProtocol:
    """ Test PEP3118 buffers """

def _check_roundtrip(self, obj):
        obj = np.asarray(obj)
        x = memoryview(obj)
        y = np.asarray(x)
        y2 = np.array(x)
        assert_(not y.flags.owndata)
        assert_(y2.flags.owndata)

assert_equal(y.dtype, obj.dtype)
        assert_equal(y.shape, obj.shape)
        assert_array_equal(obj, y)

assert_equal(y2.dtype, obj.dtype)
        assert_equal(y2.shape, obj.shape)
        assert_array_equal(obj, y2)

def test_roundtrip(self):
        x = np.array([1, 2, 3, 4, 5], dtype='i4')
        self._check_roundtrip(x)

x = np.array([[1, 2], [3, 4]], dtype=np.float64)
        self._check_roundtrip(x)

x = np.zeros((3, 3, 3), dtype=np.float32)[:, 0,:]
        self._check_roundtrip(x)

dt = [('a', 'b'),
              ('b', 'h'),
              ('c', 'i'),
              ('d', 'l'),
              ('dx', 'q'),
              ('e', 'B'),
              ('f', 'H'),
              ('g', 'I'),
              ('h', 'L'),
              ('hx', 'Q'),
              ('i', np.single),
              ('j', np.double),
              ('k', np.longdouble),
              ('ix', np.csingle),
              ('jx', np.cdouble),
              ('kx', np.clongdouble),
              ('l', 'S4'),
              ('m', 'U4'),
              ('n', 'V3'),
              ('o', '?'),
              ('p', np.half),
              ]
        x = np.array(
                [(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                    b'aaaa', 'bbbb', b'xxx', True, 1.0)],
                dtype=dt)
        self._check_roundtrip(x)

x = np.array(([[1, 2], [3, 4]],), dtype=[('a', (int, (2, 2)))])
        self._check_roundtrip(x)

x = np.array([1, 2, 3], dtype='>i2')
        self._check_roundtrip(x)

x = np.array([1, 2, 3], dtype='<i2')
        self._check_roundtrip(x)

x = np.array([1, 2, 3], dtype='>i4')
        self._check_roundtrip(x)

x = np.array([1, 2, 3], dtype='<i4')
        self._check_roundtrip(x)

# check long long can be represented as non-native
        x = np.array([1, 2, 3], dtype='>q')
        self._check_roundtrip(x)

# Native-only data types can be passed through the buffer interface
        # only in native byte order
        if sys.byteorder == 'little':
            x = np.array([1, 2, 3], dtype='>g')
            assert_raises(ValueError, self._check_roundtrip, x)
            x = np.array([1, 2, 3], dtype='<g')
            self._check_roundtrip(x)
        else:
            x = np.array([1, 2, 3], dtype='>g')
            self._check_roundtrip(x)
            x = np.array([1, 2, 3], dtype='<g')
            assert_raises(ValueError, self._check_roundtrip, x)

def test_roundtrip_half(self):
        half_list = [
            1.0,
            -2.0,
            6.5504 * 10**4,  # (max half precision)
            2**-14,  # ~= 6.10352 * 10**-5 (minimum positive normal)
            2**-24,  # ~= 5.96046 * 10**-8 (minimum strictly positive subnormal)
            0.0,
            -0.0,
            float('+inf'),
            float('-inf'),
            0.333251953125,  # ~= 1/3
        ]

x = np.array(half_list, dtype='>e')
        self._check_roundtrip(x)
        x = np.array(half_list, dtype='<e')
        self._check_roundtrip(x)

def test_roundtrip_single_types(self):
        for typ in np.sctypeDict.values():
            dtype = np.dtype(typ)

if dtype.char in 'Mm':
                # datetimes cannot be used in buffers
                continue
            if dtype.char == 'V':
                # skip void
                continue

x = np.zeros(4, dtype=dtype)
            self._check_roundtrip(x)

if dtype.char not in 'qQgG':
                dt = dtype.newbyteorder('<')
                x = np.zeros(4, dtype=dt)
                self._check_roundtrip(x)

dt = dtype.newbyteorder('>')
                x = np.zeros(4, dtype=dt)
                self._check_roundtrip(x)

def test_roundtrip_scalar(self):
        # Issue #4015.
        self._check_roundtrip(0)

def test_invalid_buffer_format(self):
        # datetime64 cannot be used fully in a buffer yet
        # Should be fixed in the next Numpy major release
        dt = np.dtype([('a', 'uint16'), ('b', 'M8[s]')])
        a = np.empty(3, dt)
        assert_raises((ValueError, BufferError), memoryview, a)
        assert_raises((ValueError, BufferError), memoryview, np.array((3), 'M8[D]'))

def test_export_simple_1d(self):
        x = np.array([1, 2, 3, 4, 5], dtype='i')
        y = memoryview(x)
        assert_equal(y.format, 'i')
        assert_equal(y.shape, (5,))
        assert_equal(y.ndim, 1)
        assert_equal(y.strides, (4,))
        assert_equal(y.suboffsets, ())
        assert_equal(y.itemsize, 4)

def test_export_simple_nd(self):
        x = np.array([[1, 2], [3, 4]], dtype=np.float64)
        y = memoryview(x)
        assert_equal(y.format, 'd')
        assert_equal(y.shape, (2, 2))
        assert_equal(y.ndim, 2)
        assert_equal(y.strides, (16, 8))
        assert_equal(y.suboffsets, ())
        assert_equal(y.itemsize, 8)

def test_export_discontiguous(self):
        x = np.zeros((3, 3, 3), dtype=np.float32)[:, 0,:]
        y = memoryview(x)
        assert_equal(y.format, 'f')
        assert_equal(y.shape, (3, 3))
        assert_equal(y.ndim, 2)
        assert_equal(y.strides, (36, 4))
        assert_equal(y.suboffsets, ())
        assert_equal(y.itemsize, 4)

def test_export_record(self):
        dt = [('a', 'b'),
              ('b', 'h'),
              ('c', 'i'),
              ('d', 'l'),
              ('dx', 'q'),
              ('e', 'B'),
              ('f', 'H'),
              ('g', 'I'),
              ('h', 'L'),
              ('hx', 'Q'),
              ('i', np.single),
              ('j', np.double),
              ('k', np.longdouble),
              ('ix', np.csingle),
              ('jx', np.cdouble),
              ('kx', np.clongdouble),
              ('l', 'S4'),
              ('m', 'U4'),
              ('n', 'V3'),
              ('o', '?'),
              ('p', np.half),
              ]
        x = np.array(
                [(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                    b'aaaa', 'bbbb', b'   ', True, 1.0)],
                dtype=dt)
        y = memoryview(x)
        assert_equal(y.shape, (1,))
        assert_equal(y.ndim, 1)
        assert_equal(y.suboffsets, ())

sz = sum([np.dtype(b).itemsize for a, b in dt])
        if np.dtype('l').itemsize == 4:
            assert_equal(y.format, 'T{b:a:=h:b:i:c:l:d:q:dx:B:e:@H:f:=I:g:L:h:Q:hx:f:i:d:j:^g:k:=Zf:ix:Zd:jx:^Zg:kx:4s:l:=4w:m:3x:n:?:o:@e:p:}')
        else:
            assert_equal(y.format, 'T{b:a:=h:b:i:c:q:d:q:dx:B:e:@H:f:=I:g:Q:h:Q:hx:f:i:d:j:^g:k:=Zf:ix:Zd:jx:^Zg:kx:4s:l:=4w:m:3x:n:?:o:@e:p:}')
        # Cannot test if NPY_RELAXED_STRIDES_DEBUG changes the strides
        if not (np.ones(1).strides[0] == np.iinfo(np.intp).max):
            assert_equal(y.strides, (sz,))
        assert_equal(y.itemsize, sz)

def test_export_subarray(self):
        x = np.array(([[1, 2], [3, 4]],), dtype=[('a', ('i', (2, 2)))])
        y = memoryview(x)
        assert_equal(y.format, 'T{(2,2)i:a:}')
        assert_equal(y.shape, ())
        assert_equal(y.ndim, 0)
        assert_equal(y.strides, ())
        assert_equal(y.suboffsets, ())
        assert_equal(y.itemsize, 16)

def test_export_endian(self):
        x = np.array([1, 2, 3], dtype='>i')
        y = memoryview(x)
        if sys.byteorder == 'little':
            assert_equal(y.format, '>i')
        else:
            assert_equal(y.format, 'i')

x = np.array([1, 2, 3], dtype='<i')
        y = memoryview(x)
        if sys.byteorder == 'little':
            assert_equal(y.format, 'i')
        else:
            assert_equal(y.format, '<i')

def test_export_flags(self):
        # Check SIMPLE flag, see also gh-3613 (exception should be BufferError)
        assert_raises(ValueError,
                      _multiarray_tests.get_buffer_info,
                       np.arange(5)[::2], ('SIMPLE',))

@pytest.mark.parametrize(["obj", "error"], [
            pytest.param(np.array([1, 2], dtype=rational), ValueError, id="array"),
            pytest.param(rational(1, 2), TypeError, id="scalar")])
    def test_export_and_pickle_user_dtype(self, obj, error):
        # User dtypes should export successfully when FORMAT was not requested.
        with pytest.raises(error):
            _multiarray_tests.get_buffer_info(obj, ("STRIDED_RO", "FORMAT"))

_multiarray_tests.get_buffer_info(obj, ("STRIDED_RO",))

# This is currently also necessary to implement pickling:
        pickle_obj = pickle.dumps(obj)
        res = pickle.loads(pickle_obj)
        assert_array_equal(res, obj)

def test_padding(self):
        for j in range(8):
            x = np.array([(1,), (2,)], dtype={'f0': (int, j)})
            self._check_roundtrip(x)

def test_reference_leak(self):
        if HAS_REFCOUNT:
            count_1 = sys.getrefcount(np.core._internal)
        a = np.zeros(4)
        b = memoryview(a)
        c = np.asarray(b)
        if HAS_REFCOUNT:
            count_2 = sys.getrefcount(np.core._internal)
            assert_equal(count_1, count_2)
        del c  # avoid pyflakes unused variable warning.

def test_padded_struct_array(self):
        dt1 = np.dtype(
                [('a', 'b'), ('b', 'i'), ('sub', np.dtype('b,i')), ('c', 'i')],
                align=True)
        x1 = np.arange(dt1.itemsize, dtype=np.int8).view(dt1)
        self._check_roundtrip(x1)

dt2 = np.dtype(
                [('a', 'b'), ('b', 'i'), ('c', 'b', (3,)), ('d', 'i')],
                align=True)
        x2 = np.arange(dt2.itemsize, dtype=np.int8).view(dt2)
        self._check_roundtrip(x2)

dt3 = np.dtype(
                [('a', 'b'), ('b', 'i'), ('c', 'b'), ('d', 'b'),
                    ('e', 'b'), ('sub', np.dtype('b,i', align=True))])
        x3 = np.arange(dt3.itemsize, dtype=np.int8).view(dt3)
        self._check_roundtrip(x3)

@pytest.mark.valgrind_error(reason="leaks buffer info cache temporarily.")
    def test_relaxed_strides(self, c=np.ones((1, 10, 10), dtype='i8')):
        # Note: c defined as parameter so that it is persistent and leak
        # checks will notice gh-16934 (buffer info cache leak).
        c.strides = (-1, 80, 8)  # strides need to be fixed at export

assert_(memoryview(c).strides == (800, 80, 8))

# Writing C-contiguous data to a BytesIO buffer should work
        fd = io.BytesIO()
        fd.write(c.data)

fortran = c.T
        assert_(memoryview(fortran).strides == (8, 80, 800))

arr = np.ones((1, 10))
        if arr.flags.f_contiguous:
            shape, strides = _multiarray_tests.get_buffer_info(
                    arr, ['F_CONTIGUOUS'])
            assert_(strides[0] == 8)
            arr = np.ones((10, 1), order='F')
            shape, strides = _multiarray_tests.get_buffer_info(
                    arr, ['C_CONTIGUOUS'])
            assert_(strides[-1] == 8)

@pytest.mark.valgrind_error(reason="leaks buffer info cache temporarily.")
    @pytest.mark.skipif(not np.ones((10, 1), order="C").flags.f_contiguous,
            reason="Test is unnecessary (but fails) without relaxed strides.")
    def test_relaxed_strides_buffer_info_leak(self, arr=np.ones((1, 10))):
        """Test that alternating export of C- and F-order buffers from
        an array which is both C- and F-order when relaxed strides is
        active works.
        This test defines array in the signature to ensure leaking more
        references every time the test is run (catching the leak with
        pytest-leaks).
        """
        for i in range(10):
            _, s = _multiarray_tests.get_buffer_info(arr, ['F_CONTIGUOUS'])
            assert s == (8, 8)
            _, s = _multiarray_tests.get_buffer_info(arr, ['C_CONTIGUOUS'])
            assert s == (80, 8)

def test_out_of_order_fields(self):
        dt = np.dtype(dict(
            formats=['<i4', '<i4'],
            names=['one', 'two'],
            offsets=[4, 0],
            itemsize=8
        ))

# overlapping fields cannot be represented by PEP3118
        arr = np.empty(1, dt)
        with assert_raises(ValueError):
            memoryview(arr)

def test_max_dims(self):
        a = np.ones((1,) * 32)
        self._check_roundtrip(a)

@pytest.mark.slow
    def test_error_too_many_dims(self):
        def make_ctype(shape, scalar_type):
            t = scalar_type
            for dim in shape[::-1]:
                t = dim * t
            return t

# construct a memoryview with 33 dimensions
        c_u8_33d = make_ctype((1,)*33, ctypes.c_uint8)
        m = memoryview(c_u8_33d())
        assert_equal(m.ndim, 33)

assert_raises_regex(
            RuntimeError, "ndim",
            np.array, m)

# The above seems to create some deep cycles, clean them up for
        # easier reference count debugging:
        del c_u8_33d, m
        for i in range(33):
            if gc.collect() == 0:
                break

def test_error_pointer_type(self):
        # gh-6741
        m = memoryview(ctypes.pointer(ctypes.c_uint8()))
        assert_('&' in m.format)

assert_raises_regex(
            ValueError, "format string",
            np.array, m)

def test_error_message_unsupported(self):
        # wchar has no corresponding numpy type - if this changes in future, we
        # need a better way to construct an invalid memoryview format.
        t = ctypes.c_wchar * 4
        with assert_raises(ValueError) as cm:
            np.array(t())

exc = cm.exception
        with assert_raises_regex(
            NotImplementedError,
            r"Unrepresentable .* 'u' \(UCS-2 strings\)"
        ):
            raise exc.__cause__

def test_ctypes_integer_via_memoryview(self):
        # gh-11150, due to bpo-10746
        for c_integer in {ctypes.c_int, ctypes.c_long, ctypes.c_longlong}:
            value = c_integer(42)
            with warnings.catch_warnings(record=True):
                warnings.filterwarnings('always', r'.*\bctypes\b', RuntimeWarning)
                np.asarray(value)

def test_ctypes_struct_via_memoryview(self):
        # gh-10528
        class foo(ctypes.Structure):
            _fields_ = [('a', ctypes.c_uint8), ('b', ctypes.c_uint32)]
        f = foo(a=1, b=2)

with warnings.catch_warnings(record=True):
            warnings.filterwarnings('always', r'.*\bctypes\b', RuntimeWarning)
            arr = np.asarray(f)

assert_equal(arr['a'], 1)
        assert_equal(arr['b'], 2)
        f.a = 3
        assert_equal(arr['a'], 3)

@pytest.mark.parametrize("obj", [np.ones(3), np.ones(1, dtype="i,i")[()]])
    def test_error_if_stored_buffer_info_is_corrupted(self, obj):
        """
        If a user extends a NumPy array before 1.20 and then runs it
        on NumPy 1.20+. A C-subclassed array might in theory modify
        the new buffer-info field. This checks that an error is raised
        if this happens (for buffer export), an error is written on delete.
        This is a sanity check to help users transition to safe code, it
        may be deleted at any point.
        """
        # corrupt buffer info:
        _multiarray_tests.corrupt_or_fix_bufferinfo(obj)
        name = type(obj)
        with pytest.raises(RuntimeError,
                    match=f".*{name} appears to be C subclassed"):
            memoryview(obj)
        # Fix buffer info again before we delete (or we lose the memory)
        _multiarray_tests.corrupt_or_fix_bufferinfo(obj)

def test_no_suboffsets(self):
        try:
            import _testbuffer
        except ImportError:
            raise pytest.skip("_testbuffer is not available")

for shape in [(2, 3), (2, 3, 4)]:
            data = list(range(np.prod(shape)))
            buffer = _testbuffer.ndarray(data, shape, format='i',
                                         flags=_testbuffer.ND_PIL)
            msg = "NumPy currently does not support.*suboffsets"
            with pytest.raises(BufferError, match=msg):
                np.asarray(buffer)
            with pytest.raises(BufferError, match=msg):
                np.asarray([buffer])

# Also check (unrelated and more limited but similar) frombuffer:
            with pytest.raises(BufferError):
                np.frombuffer(buffer)

class TestArrayCreationCopyArgument(object):

class RaiseOnBool:

def __bool__(self):
            raise ValueError

true_vals = [True, np._CopyMode.ALWAYS, np.True_]
    false_vals = [False, np._CopyMode.IF_NEEDED, np.False_]

def test_scalars(self):
        # Test both numpy and python scalars
        for dtype in np.typecodes["All"]:
            arr = np.zeros((), dtype=dtype)
            scalar = arr[()]
            pyscalar = arr.item(0)

# Test never-copy raises error:
            assert_raises(ValueError, np.array, scalar,
                            copy=np._CopyMode.NEVER)
            assert_raises(ValueError, np.array, pyscalar,
                            copy=np._CopyMode.NEVER)
            assert_raises(ValueError, np.array, pyscalar,
                            copy=self.RaiseOnBool())
            assert_raises(ValueError, _multiarray_tests.npy_ensurenocopy,
                            [1])
            # Casting with a dtype (to unsigned integers) can be special:
            with pytest.raises(ValueError):
                np.array(pyscalar, dtype=np.int64, copy=np._CopyMode.NEVER)

def test_compatible_cast(self):

# Some types are compatible even though they are different, no
        # copy is necessary for them. This is mostly true for some integers
        def int_types(byteswap=False):
            int_types = (np.typecodes["Integer"] +
                         np.typecodes["UnsignedInteger"])
            for int_type in int_types:
                yield np.dtype(int_type)
                if byteswap:
                    yield np.dtype(int_type).newbyteorder()

for int1 in int_types():
            for int2 in int_types(True):
                arr = np.arange(10, dtype=int1)

for copy in self.true_vals:
                    res = np.array(arr, copy=copy, dtype=int2)
                    assert res is not arr and res.flags.owndata
                    assert_array_equal(res, arr)

if int1 == int2:
                    # Casting is not necessary, base check is sufficient here
                    for copy in self.false_vals:
                        res = np.array(arr, copy=copy, dtype=int2)
                        assert res is arr or res.base is arr

res = np.array(arr,
                                   copy=np._CopyMode.NEVER,
                                   dtype=int2)
                    assert res is arr or res.base is arr

else:
                    # Casting is necessary, assert copy works:
                    for copy in self.false_vals:
                        res = np.array(arr, copy=copy, dtype=int2)
                        assert res is not arr and res.flags.owndata
                        assert_array_equal(res, arr)

assert_raises(ValueError, np.array,
                                  arr, copy=np._CopyMode.NEVER,
                                  dtype=int2)
                    assert_raises(ValueError, np.array,
                                  arr, copy=None,
                                  dtype=int2)

def test_buffer_interface(self):

# Buffer interface gives direct memory access (no copy)
        arr = np.arange(10)
        view = memoryview(arr)

# Checking bases is a bit tricky since numpy creates another
        # memoryview, so use may_share_memory.
        for copy in self.true_vals:
            res = np.array(view, copy=copy)
            assert not np.may_share_memory(arr, res)
        for copy in self.false_vals:
            res = np.array(view, copy=copy)
            assert np.may_share_memory(arr, res)
        res = np.array(view, copy=np._CopyMode.NEVER)
        assert np.may_share_memory(arr, res)

def test_array_interfaces(self):
        # Array interface gives direct memory access (much like a memoryview)
        base_arr = np.arange(10)

class ArrayLike:
            __array_interface__ = base_arr.__array_interface__

arr = ArrayLike()

for copy, val in [(True, None), (np._CopyMode.ALWAYS, None),
                          (False, arr), (np._CopyMode.IF_NEEDED, arr),
                          (np._CopyMode.NEVER, arr)]:
            res = np.array(arr, copy=copy)
            assert res.base is val

def test___array__(self):
        base_arr = np.arange(10)

class ArrayLike:
            def __array__(self):
                # __array__ should return a copy, numpy cannot know this
                # however.
                return base_arr

arr = ArrayLike()

for copy in self.true_vals:
            res = np.array(arr, copy=copy)
            assert_array_equal(res, base_arr)
            # An additional copy is currently forced by numpy in this case,
            # you could argue, numpy does not trust the ArrayLike. This
            # may be open for change:
            assert res is not base_arr

for copy in self.false_vals:
            res = np.array(arr, copy=False)
            assert_array_equal(res, base_arr)
            assert res is base_arr  # numpy trusts the ArrayLike

with pytest.raises(ValueError):
            np.array(arr, copy=np._CopyMode.NEVER)

@pytest.mark.parametrize(
            "arr", [np.ones(()), np.arange(81).reshape((9, 9))])
    @pytest.mark.parametrize("order1", ["C", "F", None])
    @pytest.mark.parametrize("order2", ["C", "F", "A", "K"])
    def test_order_mismatch(self, arr, order1, order2):
        # The order is the main (python side) reason that can cause
        # a never-copy to fail.
        # Prepare C-order, F-order and non-contiguous arrays:
        arr = arr.copy(order1)
        if order1 == "C":
            assert arr.flags.c_contiguous
        elif order1 == "F":
            assert arr.flags.f_contiguous
        elif arr.ndim != 0:
            # Make array non-contiguous
            arr = arr[::2, ::2]
            assert not arr.flags.forc

# Whether a copy is necessary depends on the order of arr:
        if order2 == "C":
            no_copy_necessary = arr.flags.c_contiguous
        elif order2 == "F":
            no_copy_necessary = arr.flags.f_contiguous
        else:
            # Keeporder and Anyorder are OK with non-contiguous output.
            # This is not consistent with the `astype` behaviour which
            # enforces contiguity for "A". It is probably historic from when
            # "K" did not exist.
            no_copy_necessary = True

# Test it for both the array and a memoryview
        for view in [arr, memoryview(arr)]:
            for copy in self.true_vals:
                res = np.array(view, copy=copy, order=order2)
                assert res is not arr and res.flags.owndata
                assert_array_equal(arr, res)

if no_copy_necessary:
                for copy in self.false_vals:
                    res = np.array(view, copy=copy, order=order2)
                    # res.base.obj refers to the memoryview
                    if not IS_PYPY:
                        assert res is arr or res.base.obj is arr

res = np.array(view, copy=np._CopyMode.NEVER,
                               order=order2)
                if not IS_PYPY:
                    assert res is arr or res.base.obj is arr
            else:
                for copy in self.false_vals:
                    res = np.array(arr, copy=copy, order=order2)
                    assert_array_equal(arr, res)
                assert_raises(ValueError, np.array,
                              view, copy=np._CopyMode.NEVER,
                              order=order2)
                assert_raises(ValueError, np.array,
                              view, copy=None,
                              order=order2)

def test_striding_not_ok(self):
        arr = np.array([[1, 2, 4], [3, 4, 5]])
        assert_raises(ValueError, np.array,
                      arr.T, copy=np._CopyMode.NEVER,
                      order='C')
        assert_raises(ValueError, np.array,
                      arr.T, copy=np._CopyMode.NEVER,
                      order='C', dtype=np.int64)
        assert_raises(ValueError, np.array,
                      arr, copy=np._CopyMode.NEVER,
                      order='F')
        assert_raises(ValueError, np.array,
                      arr, copy=np._CopyMode.NEVER,
                      order='F', dtype=np.int64)

class TestArrayAttributeDeletion:

def test_multiarray_writable_attributes_deletion(self):
        # ticket #2046, should not seqfault, raise AttributeError
        a = np.ones(2)
        attr = ['shape', 'strides', 'data', 'dtype', 'real', 'imag', 'flat']
        with suppress_warnings() as sup:
            sup.filter(DeprecationWarning, "Assigning the 'data' attribute")
            for s in attr:
                assert_raises(AttributeError, delattr, a, s)

def test_multiarray_not_writable_attributes_deletion(self):
        a = np.ones(2)
        attr = ["ndim", "flags", "itemsize", "size", "nbytes", "base",
                "ctypes", "T", "__array_interface__", "__array_struct__",
                "__array_priority__", "__array_finalize__"]
        for s in attr:
            assert_raises(AttributeError, delattr, a, s)

def test_multiarray_flags_writable_attribute_deletion(self):
        a = np.ones(2).flags
        attr = ['writebackifcopy', 'updateifcopy', 'aligned', 'writeable']
        for s in attr:
            assert_raises(AttributeError, delattr, a, s)

def test_multiarray_flags_not_writable_attribute_deletion(self):
        a = np.ones(2).flags
        attr = ["contiguous", "c_contiguous", "f_contiguous", "fortran",
                "owndata", "fnc", "forc", "behaved", "carray", "farray",
                "num"]
        for s in attr:
            assert_raises(AttributeError, delattr, a, s)

class TestArrayInterface():
    class Foo:
        def __init__(self, value):
            self.value = value
            self.iface = {'typestr': 'f8'}

def __float__(self):
            return float(self.value)

@property
        def __array_interface__(self):
            return self.iface

f = Foo(0.5)

@pytest.mark.parametrize('val, iface, expected', [
        (f, {}, 0.5),
        ([f], {}, [0.5]),
        ([f, f], {}, [0.5, 0.5]),
        (f, {'shape': ()}, 0.5),
        (f, {'shape': None}, TypeError),
        (f, {'shape': (1, 1)}, [[0.5]]),
        (f, {'shape': (2,)}, ValueError),
        (f, {'strides': ()}, 0.5),
        (f, {'strides': (2,)}, ValueError),
        (f, {'strides': 16}, TypeError),
        ])
    def test_scalar_interface(self, val, iface, expected):
        # Test scalar coercion within the array interface
        self.f.iface = {'typestr': 'f8'}
        self.f.iface.update(iface)
        if HAS_REFCOUNT:
            pre_cnt = sys.getrefcount(np.dtype('f8'))
        if isinstance(expected, type):
            assert_raises(expected, np.array, val)
        else:
            result = np.array(val)
            assert_equal(np.array(val), expected)
            assert result.dtype == 'f8'
            del result
        if HAS_REFCOUNT:
            post_cnt = sys.getrefcount(np.dtype('f8'))
            assert_equal(pre_cnt, post_cnt)

def test_interface_no_shape():
    class ArrayLike:
        array = np.array(1)
        __array_interface__ = array.__array_interface__
    assert_equal(np.array(ArrayLike()), 1)

def test_array_interface_itemsize():
    # See gh-6361
    my_dtype = np.dtype({'names': ['A', 'B'], 'formats': ['f4', 'f4'],
                         'offsets': [0, 8], 'itemsize': 16})
    a = np.ones(10, dtype=my_dtype)
    descr_t = np.dtype(a.__array_interface__['descr'])
    typestr_t = np.dtype(a.__array_interface__['typestr'])
    assert_equal(descr_t.itemsize, typestr_t.itemsize)

def test_array_interface_empty_shape():
    # See gh-7994
    arr = np.array([1, 2, 3])
    interface1 = dict(arr.__array_interface__)
    interface1['shape'] = ()

class DummyArray1:
        __array_interface__ = interface1

# NOTE: Because Py2 str/Py3 bytes supports the buffer interface, setting
    # the interface data to bytes would invoke the bug this tests for, that
    # __array_interface__ with shape=() is not allowed if the data is an object
    # exposing the buffer interface
    interface2 = dict(interface1)
    interface2['data'] = arr[0].tobytes()

class DummyArray2:
        __array_interface__ = interface2

arr1 = np.asarray(DummyArray1())
    arr2 = np.asarray(DummyArray2())
    arr3 = arr[:1].reshape(())
    assert_equal(arr1, arr2)
    assert_equal(arr1, arr3)

def test_array_interface_offset():
    arr = np.array([1, 2, 3], dtype='int32')
    interface = dict(arr.__array_interface__)
    interface['data'] = memoryview(arr)
    interface['shape'] = (2,)
    interface['offset'] = 4

class DummyArray:
        __array_interface__ = interface

arr1 = np.asarray(DummyArray())
    assert_equal(arr1, arr[1:])

def test_array_interface_unicode_typestr():
    arr = np.array([1, 2, 3], dtype='int32')
    interface = dict(arr.__array_interface__)
    interface['typestr'] = '\N{check mark}'

class DummyArray:
        __array_interface__ = interface

# should not be UnicodeEncodeError
    with pytest.raises(TypeError):
        np.asarray(DummyArray())

def test_flat_element_deletion():
    it = np.ones(3).flat
    try:
        del it[1]
        del it[1:2]
    except TypeError:
        pass
    except Exception:
        raise AssertionError

def test_scalar_element_deletion():
    a = np.zeros(2, dtype=[('x', 'int'), ('y', 'int')])
    assert_raises(ValueError, a[0].__delitem__, 'x')

class TestMapIter:
    def test_mapiter(self):
        # The actual tests are within the C code in
        # multiarray/_multiarray_tests.c.src

a = np.arange(12).reshape((3, 4)).astype(float)
        index = ([1, 1, 2, 0],
                 [0, 0, 2, 3])
        vals = [50, 50, 30, 16]

_multiarray_tests.test_inplace_increment(a, index, vals)
        assert_equal(a, [[0.00, 1., 2.0, 19.],
                         [104., 5., 6.0, 7.0],
                         [8.00, 9., 40., 11.]])

b = np.arange(6).astype(float)
        index = (np.array([1, 2, 0]),)
        vals = [50, 4, 100.1]
        _multiarray_tests.test_inplace_increment(b, index, vals)
        assert_equal(b, [100.1,  51.,   6.,   3.,   4.,   5.])

class TestAsCArray:
    def test_1darray(self):
        array = np.arange(24, dtype=np.double)
        from_c = _multiarray_tests.test_as_c_array(array, 3)
        assert_equal(array[3], from_c)

def test_2darray(self):
        array = np.arange(24, dtype=np.double).reshape(3, 8)
        from_c = _multiarray_tests.test_as_c_array(array, 2, 4)
        assert_equal(array[2, 4], from_c)

def test_3darray(self):
        array = np.arange(24, dtype=np.double).reshape(2, 3, 4)
        from_c = _multiarray_tests.test_as_c_array(array, 1, 2, 3)
        assert_equal(array[1, 2, 3], from_c)

class TestConversion:
    def test_array_scalar_relational_operation(self):
        # All integer
        for dt1 in np.typecodes['AllInteger']:
            assert_(1 > np.array(0, dtype=dt1), "type %s failed" % (dt1,))
            assert_(not 1 < np.array(0, dtype=dt1), "type %s failed" % (dt1,))

for dt2 in np.typecodes['AllInteger']:
                assert_(np.array(1, dtype=dt1) > np.array(0, dtype=dt2),
                        "type %s and %s failed" % (dt1, dt2))
                assert_(not np.array(1, dtype=dt1) < np.array(0, dtype=dt2),
                        "type %s and %s failed" % (dt1, dt2))

# Unsigned integers
        for dt1 in 'BHILQP':
            assert_(-1 < np.array(1, dtype=dt1), "type %s failed" % (dt1,))
            assert_(not -1 > np.array(1, dtype=dt1), "type %s failed" % (dt1,))
            assert_(-1 != np.array(1, dtype=dt1), "type %s failed" % (dt1,))

# Unsigned vs signed
            for dt2 in 'bhilqp':
                assert_(np.array(1, dtype=dt1) > np.array(-1, dtype=dt2),
                        "type %s and %s failed" % (dt1, dt2))
                assert_(not np.array(1, dtype=dt1) < np.array(-1, dtype=dt2),
                        "type %s and %s failed" % (dt1, dt2))
                assert_(np.array(1, dtype=dt1) != np.array(-1, dtype=dt2),
                        "type %s and %s failed" % (dt1, dt2))

# Signed integers and floats
        for dt1 in 'bhlqp' + np.typecodes['Float']:
            assert_(1 > np.array(-1, dtype=dt1), "type %s failed" % (dt1,))
            assert_(not 1 < np.array(-1, dtype=dt1), "type %s failed" % (dt1,))
            assert_(-1 == np.array(-1, dtype=dt1), "type %s failed" % (dt1,))

for dt2 in 'bhlqp' + np.typecodes['Float']:
                assert_(np.array(1, dtype=dt1) > np.array(-1, dtype=dt2),
                        "type %s and %s failed" % (dt1, dt2))
                assert_(not np.array(1, dtype=dt1) < np.array(-1, dtype=dt2),
                        "type %s and %s failed" % (dt1, dt2))
                assert_(np.array(-1, dtype=dt1) == np.array(-1, dtype=dt2),
                        "type %s and %s failed" % (dt1, dt2))

def test_to_bool_scalar(self):
        assert_equal(bool(np.array([False])), False)
        assert_equal(bool(np.array([True])), True)
        assert_equal(bool(np.array([[42]])), True)
        assert_raises(ValueError, bool, np.array([1, 2]))

class NotConvertible:
            def __bool__(self):
                raise NotImplementedError

assert_raises(NotImplementedError, bool, np.array(NotConvertible()))
        assert_raises(NotImplementedError, bool, np.array([NotConvertible()]))
        if IS_PYSTON:
            pytest.skip("Pyston disables recursion checking")

self_containing = np.array([None])
        self_containing[0] = self_containing

Error = RecursionError

assert_raises(Error, bool, self_containing)  # previously stack overflow
        self_containing[0] = None  # resolve circular reference

def test_to_int_scalar(self):
        # gh-9972 means that these aren't always the same
        int_funcs = (int, lambda x: x.__int__())
        for int_func in int_funcs:
            assert_equal(int_func(np.array(0)), 0)
            with assert_warns(DeprecationWarning):
                assert_equal(int_func(np.array([1])), 1)
            with assert_warns(DeprecationWarning):
                assert_equal(int_func(np.array([[42]])), 42)
            assert_raises(TypeError, int_func, np.array([1, 2]))

# gh-9972
            assert_equal(4, int_func(np.array('4')))
            assert_equal(5, int_func(np.bytes_(b'5')))
            assert_equal(6, int_func(np.str_('6')))

# The delegation of int() to __trunc__ was deprecated in
            # Python 3.11.
            if sys.version_info < (3, 11):
                class HasTrunc:
                    def __trunc__(self):
                        return 3
                assert_equal(3, int_func(np.array(HasTrunc())))
                with assert_warns(DeprecationWarning):
                    assert_equal(3, int_func(np.array([HasTrunc()])))
            else:
                pass

class NotConvertible:
                def __int__(self):
                    raise NotImplementedError
            assert_raises(NotImplementedError,
                int_func, np.array(NotConvertible()))
            with assert_warns(DeprecationWarning):
                assert_raises(NotImplementedError,
                    int_func, np.array([NotConvertible()]))

class TestWhere:
    def test_basic(self):
        dts = [bool, np.int16, np.int32, np.int64, np.double, np.complex128,
               np.longdouble, np.clongdouble]
        for dt in dts:
            c = np.ones(53, dtype=bool)
            assert_equal(np.where( c, dt(0), dt(1)), dt(0))
            assert_equal(np.where(~c, dt(0), dt(1)), dt(1))
            assert_equal(np.where(True, dt(0), dt(1)), dt(0))
            assert_equal(np.where(False, dt(0), dt(1)), dt(1))
            d = np.ones_like(c).astype(dt)
            e = np.zeros_like(d)
            r = d.astype(dt)
            c[7] = False
            r[7] = e[7]
            assert_equal(np.where(c, e, e), e)
            assert_equal(np.where(c, d, e), r)
            assert_equal(np.where(c, d, e[0]), r)
            assert_equal(np.where(c, d[0], e), r)
            assert_equal(np.where(c[::2], d[::2], e[::2]), r[::2])
            assert_equal(np.where(c[1::2], d[1::2], e[1::2]), r[1::2])
            assert_equal(np.where(c[::3], d[::3], e[::3]), r[::3])
            assert_equal(np.where(c[1::3], d[1::3], e[1::3]), r[1::3])
            assert_equal(np.where(c[::-2], d[::-2], e[::-2]), r[::-2])
            assert_equal(np.where(c[::-3], d[::-3], e[::-3]), r[::-3])
            assert_equal(np.where(c[1::-3], d[1::-3], e[1::-3]), r[1::-3])

def test_exotic(self):
        # object
        assert_array_equal(np.where(True, None, None), np.array(None))
        # zero sized
        m = np.array([], dtype=bool).reshape(0, 3)
        b = np.array([], dtype=np.float64).reshape(0, 3)
        assert_array_equal(np.where(m, 0, b), np.array([]).reshape(0, 3))

# object cast
        d = np.array([-1.34, -0.16, -0.54, -0.31, -0.08, -0.95, 0.000, 0.313,
                      0.547, -0.18, 0.876, 0.236, 1.969, 0.310, 0.699, 1.013,
                      1.267, 0.229, -1.39, 0.487])
        nan = float('NaN')
        e = np.array(['5z', '0l', nan, 'Wz', nan, nan, 'Xq', 'cs', nan, nan,
                     'QN', nan, nan, 'Fd', nan, nan, 'kp', nan, '36', 'i1'],
                     dtype=object)
        m = np.array([0, 0, 1, 0, 1, 1, 0, 0, 1, 1,
                      0, 1, 1, 0, 1, 1, 0, 1, 0, 0], dtype=bool)

r = e[:]
        r[np.where(m)] = d[np.where(m)]
        assert_array_equal(np.where(m, d, e), r)

r = e[:]
        r[np.where(~m)] = d[np.where(~m)]
        assert_array_equal(np.where(m, e, d), r)

assert_array_equal(np.where(m, e, e), e)

# minimal dtype result with NaN scalar (e.g required by pandas)
        d = np.array([1., 2.], dtype=np.float32)
        e = float('NaN')
        assert_equal(np.where(True, d, e).dtype, np.float32)
        e = float('Infinity')
        assert_equal(np.where(True, d, e).dtype, np.float32)
        e = float('-Infinity')
        assert_equal(np.where(True, d, e).dtype, np.float32)
        # also check upcast
        e = float(1e150)
        assert_equal(np.where(True, d, e).dtype, np.float64)

def test_ndim(self):
        c = [True, False]
        a = np.zeros((2, 25))
        b = np.ones((2, 25))
        r = np.where(np.array(c)[:,np.newaxis], a, b)
        assert_array_equal(r[0], a[0])
        assert_array_equal(r[1], b[0])

a = a.T
        b = b.T
        r = np.where(c, a, b)
        assert_array_equal(r[:,0], a[:,0])
        assert_array_equal(r[:,1], b[:,0])

def test_dtype_mix(self):
        c = np.array([False, True, False, False, False, False, True, False,
                     False, False, True, False])
        a = np.uint32(1)
        b = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.],
                      dtype=np.float64)
        r = np.array([5., 1., 3., 2., -1., -4., 1., -10., 10., 1., 1., 3.],
                     dtype=np.float64)
        assert_equal(np.where(c, a, b), r)

a = a.astype(np.float32)
        b = b.astype(np.int64)
        assert_equal(np.where(c, a, b), r)

# non bool mask
        c = c.astype(int)
        c[c != 0] = 34242324
        assert_equal(np.where(c, a, b), r)
        # invert
        tmpmask = c != 0
        c[c == 0] = 41247212
        c[tmpmask] = 0
        assert_equal(np.where(c, b, a), r)

def test_foreign(self):
        c = np.array([False, True, False, False, False, False, True, False,
                     False, False, True, False])
        r = np.array([5., 1., 3., 2., -1., -4., 1., -10., 10., 1., 1., 3.],
                     dtype=np.float64)
        a = np.ones(1, dtype='>i4')
        b = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.],
                     dtype=np.float64)
        assert_equal(np.where(c, a, b), r)

b = b.astype('>f8')
        assert_equal(np.where(c, a, b), r)

a = a.astype('<i4')
        assert_equal(np.where(c, a, b), r)

c = c.astype('>i4')
        assert_equal(np.where(c, a, b), r)

def test_error(self):
        c = [True, True]
        a = np.ones((4, 5))
        b = np.ones((5, 5))
        assert_raises(ValueError, np.where, c, a, a)
        assert_raises(ValueError, np.where, c[0], a, b)

def test_string(self):
        # gh-4778 check strings are properly filled with nulls
        a = np.array("abc")
        b = np.array("x" * 753)
        assert_equal(np.where(True, a, b), "abc")
        assert_equal(np.where(False, b, a), "abc")

# check native datatype sized strings
        a = np.array("abcd")
        b = np.array("x" * 8)
        assert_equal(np.where(True, a, b), "abcd")
        assert_equal(np.where(False, b, a), "abcd")

def test_empty_result(self):
        # pass empty where result through an assignment which reads the data of
        # empty arrays, error detectable with valgrind, see gh-8922
        x = np.zeros((1, 1))
        ibad = np.vstack(np.where(x == 99.))
        assert_array_equal(ibad,
                           np.atleast_2d(np.array([[],[]], dtype=np.intp)))

def test_largedim(self):
        # invalid read regression gh-9304
        shape = [10, 2, 3, 4, 5, 6]
        np.random.seed(2)
        array = np.random.rand(*shape)

for i in range(10):
            benchmark = array.nonzero()
            result = array.nonzero()
            assert_array_equal(benchmark, result)

def test_kwargs(self):
        a = np.zeros(1)
        with assert_raises(TypeError):
            np.where(a, x=a, y=a)

if not IS_PYPY:
    # sys.getsizeof() is not valid on PyPy
    class TestSizeOf:

def test_empty_array(self):
            x = np.array([])
            assert_(sys.getsizeof(x) > 0)

def check_array(self, dtype):
            elem_size = dtype(0).itemsize

for length in [10, 50, 100, 500]:
                x = np.arange(length, dtype=dtype)
                assert_(sys.getsizeof(x) > length * elem_size)

def test_array_int32(self):
            self.check_array(np.int32)

def test_array_int64(self):
            self.check_array(np.int64)

def test_array_float32(self):
            self.check_array(np.float32)

def test_array_float64(self):
            self.check_array(np.float64)

def test_view(self):
            d = np.ones(100)
            assert_(sys.getsizeof(d[...]) < sys.getsizeof(d))

def test_reshape(self):
            d = np.ones(100)
            assert_(sys.getsizeof(d) < sys.getsizeof(d.reshape(100, 1, 1).copy()))

@_no_tracing
        def test_resize(self):
            d = np.ones(100)
            old = sys.getsizeof(d)
            d.resize(50)
            assert_(old > sys.getsizeof(d))
            d.resize(150)
            assert_(old < sys.getsizeof(d))

def test_error(self):
            d = np.ones(100)
            assert_raises(TypeError, d.__sizeof__, "a")

class TestHashing:

def test_arrays_not_hashable(self):
        x = np.ones(3)
        assert_raises(TypeError, hash, x)

def test_collections_hashable(self):
        x = np.array([])
        assert_(not isinstance(x, collections.abc.Hashable))

class TestArrayPriority:
    # This will go away when __array_priority__ is settled, meanwhile
    # it serves to check unintended changes.
    op = operator
    binary_ops = [
        op.pow, op.add, op.sub, op.mul, op.floordiv, op.truediv, op.mod,
        op.and_, op.or_, op.xor, op.lshift, op.rshift, op.mod, op.gt,
        op.ge, op.lt, op.le, op.ne, op.eq
        ]

class Foo(np.ndarray):
        __array_priority__ = 100.

def __new__(cls, *args, **kwargs):
            return np.array(*args, **kwargs).view(cls)

class Bar(np.ndarray):
        __array_priority__ = 101.

def __new__(cls, *args, **kwargs):
            return np.array(*args, **kwargs).view(cls)

class Other:
        __array_priority__ = 1000.

def _all(self, other):
            return self.__class__()

__add__ = __radd__ = _all
        __sub__ = __rsub__ = _all
        __mul__ = __rmul__ = _all
        __pow__ = __rpow__ = _all
        __div__ = __rdiv__ = _all
        __mod__ = __rmod__ = _all
        __truediv__ = __rtruediv__ = _all
        __floordiv__ = __rfloordiv__ = _all
        __and__ = __rand__ = _all
        __xor__ = __rxor__ = _all
        __or__ = __ror__ = _all
        __lshift__ = __rlshift__ = _all
        __rshift__ = __rrshift__ = _all
        __eq__ = _all
        __ne__ = _all
        __gt__ = _all
        __ge__ = _all
        __lt__ = _all
        __le__ = _all

def test_ndarray_subclass(self):
        a = np.array([1, 2])
        b = self.Bar([1, 2])
        for f in self.binary_ops:
            msg = repr(f)
            assert_(isinstance(f(a, b), self.Bar), msg)
            assert_(isinstance(f(b, a), self.Bar), msg)

def test_ndarray_other(self):
        a = np.array([1, 2])
        b = self.Other()
        for f in self.binary_ops:
            msg = repr(f)
            assert_(isinstance(f(a, b), self.Other), msg)
            assert_(isinstance(f(b, a), self.Other), msg)

def test_subclass_subclass(self):
        a = self.Foo([1, 2])
        b = self.Bar([1, 2])
        for f in self.binary_ops:
            msg = repr(f)
            assert_(isinstance(f(a, b), self.Bar), msg)
            assert_(isinstance(f(b, a), self.Bar), msg)

def test_subclass_other(self):
        a = self.Foo([1, 2])
        b = self.Other()
        for f in self.binary_ops:
            msg = repr(f)
            assert_(isinstance(f(a, b), self.Other), msg)
            assert_(isinstance(f(b, a), self.Other), msg)

class TestBytestringArrayNonzero:

def test_empty_bstring_array_is_falsey(self):
        assert_(not np.array([''], dtype=str))

def test_whitespace_bstring_array_is_falsey(self):
        a = np.array(['spam'], dtype=str)
        a[0] = '  \0\0'
        assert_(not a)

def test_all_null_bstring_array_is_falsey(self):
        a = np.array(['spam'], dtype=str)
        a[0] = '\0\0\0\0'
        assert_(not a)

def test_null_inside_bstring_array_is_truthy(self):
        a = np.array(['spam'], dtype=str)
        a[0] = ' \0 \0'
        assert_(a)

class TestUnicodeEncoding:
    """
    Tests for encoding related bugs, such as UCS2 vs UCS4, round-tripping
    issues, etc
    """
    def test_round_trip(self):
        """ Tests that GETITEM, SETITEM, and PyArray_Scalar roundtrip """
        # gh-15363
        arr = np.zeros(shape=(), dtype="U1")
        for i in range(1, sys.maxunicode + 1):
            expected = chr(i)
            arr[()] = expected
            assert arr[()] == expected
            assert arr.item() == expected

def test_assign_scalar(self):
        # gh-3258
        l = np.array(['aa', 'bb'])
        l[:] = np.str_('cc')
        assert_equal(l, ['cc', 'cc'])

def test_fill_scalar(self):
        # gh-7227
        l = np.array(['aa', 'bb'])
        l.fill(np.str_('cc'))
        assert_equal(l, ['cc', 'cc'])

class TestUnicodeArrayNonzero:

def test_empty_ustring_array_is_falsey(self):
        assert_(not np.array([''], dtype=np.str_))

def test_whitespace_ustring_array_is_falsey(self):
        a = np.array(['eggs'], dtype=np.str_)
        a[0] = '  \0\0'
        assert_(not a)

def test_all_null_ustring_array_is_falsey(self):
        a = np.array(['eggs'], dtype=np.str_)
        a[0] = '\0\0\0\0'
        assert_(not a)

def test_null_inside_ustring_array_is_truthy(self):
        a = np.array(['eggs'], dtype=np.str_)
        a[0] = ' \0 \0'
        assert_(a)

class TestFormat:

def test_0d(self):
        a = np.array(np.pi)
        assert_equal('{:0.3g}'.format(a), '3.14')
        assert_equal('{:0.3g}'.format(a[()]), '3.14')

def test_1d_no_format(self):
        a = np.array([np.pi])
        assert_equal('{}'.format(a), str(a))

def test_1d_format(self):
        # until gh-5543, ensure that the behaviour matches what it used to be
        a = np.array([np.pi])
        assert_raises(TypeError, '{:30}'.format, a)

from numpy.testing import IS_PYPY

class TestCTypes:

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

assert_equal(ctypes, test_arr.ctypes._ctypes)
        assert_equal(tuple(test_arr.ctypes.shape), (2, 3))

def test_ctypes_is_not_available(self):
        from numpy.core import _internal
        _internal.ctypes = None
        try:
            test_arr = np.array([[1, 2, 3], [4, 5, 6]])

assert_(isinstance(test_arr.ctypes._ctypes,
                               _internal._missing_ctypes))
            assert_equal(tuple(test_arr.ctypes.shape), (2, 3))
        finally:
            _internal.ctypes = ctypes

def _make_readonly(x):
        x.flags.writeable = False
        return x

@pytest.mark.parametrize('arr', [
        np.array([1, 2, 3]),
        np.array([['one', 'two'], ['three', 'four']]),
        np.array((1, 2), dtype='i4,i4'),
        np.zeros((2,), dtype=
            np.dtype(dict(
                formats=['<i4', '<i4'],
                names=['a', 'b'],
                offsets=[0, 2],
                itemsize=6
            ))
        ),
        np.array([None], dtype=object),
        np.array([]),
        np.empty((0, 0)),
        _make_readonly(np.array([1, 2, 3])),
    ], ids=[
        '1d',
        '2d',
        'structured',
        'overlapping',
        'object',
        'empty',
        'empty-2d',
        'readonly'
    ])
    def test_ctypes_data_as_holds_reference(self, arr):
        # gh-9647
        # create a copy to ensure that pytest does not mess with the refcounts
        arr = arr.copy()

arr_ref = weakref.ref(arr)

ctypes_ptr = arr.ctypes.data_as(ctypes.c_void_p)

# `ctypes_ptr` should hold onto `arr`
        del arr
        break_cycles()
        assert_(arr_ref() is not None, "ctypes pointer did not hold onto a reference")

# but when the `ctypes_ptr` object dies, so should `arr`
        del ctypes_ptr
        if IS_PYPY:
            # Pypy does not recycle arr objects immediately. Trigger gc to
            # release arr. Cpython uses refcounts. An explicit call to gc
            # should not be needed here.
            break_cycles()
        assert_(arr_ref() is None, "unknowable whether ctypes pointer holds a reference")

def test_ctypes_as_parameter_holds_reference(self):
        arr = np.array([None]).copy()

arr_ref = weakref.ref(arr)

ctypes_ptr = arr.ctypes._as_parameter_

# `ctypes_ptr` should hold onto `arr`
        del arr
        break_cycles()
        assert_(arr_ref() is not None, "ctypes pointer did not hold onto a reference")

# but when the `ctypes_ptr` object dies, so should `arr`
        del ctypes_ptr
        if IS_PYPY:
            break_cycles()
        assert_(arr_ref() is None, "unknowable whether ctypes pointer holds a reference")

class TestWritebackIfCopy:
    # all these tests use the WRITEBACKIFCOPY mechanism
    def test_argmax_with_out(self):
        mat = np.eye(5)
        out = np.empty(5, dtype='i2')
        res = np.argmax(mat, 0, out=out)
        assert_equal(res, range(5))

def test_argmin_with_out(self):
        mat = -np.eye(5)
        out = np.empty(5, dtype='i2')
        res = np.argmin(mat, 0, out=out)
        assert_equal(res, range(5))

def test_insert_noncontiguous(self):
        a = np.arange(6).reshape(2,3).T # force non-c-contiguous
        # uses arr_insert
        np.place(a, a>2, [44, 55])
        assert_equal(a, np.array([[0, 44], [1, 55], [2, 44]]))
        # hit one of the failing paths
        assert_raises(ValueError, np.place, a, a>20, [])

def test_put_noncontiguous(self):
        a = np.arange(6).reshape(2,3).T # force non-c-contiguous
        np.put(a, [0, 2], [44, 55])
        assert_equal(a, np.array([[44, 3], [55, 4], [2, 5]]))

def test_putmask_noncontiguous(self):
        a = np.arange(6).reshape(2,3).T # force non-c-contiguous
        # uses arr_putmask
        np.putmask(a, a>2, a**2)
        assert_equal(a, np.array([[0, 9], [1, 16], [2, 25]]))

def test_take_mode_raise(self):
        a = np.arange(6, dtype='int')
        out = np.empty(2, dtype='int')
        np.take(a, [0, 2], out=out, mode='raise')
        assert_equal(out, np.array([0, 2]))

def test_choose_mod_raise(self):
        a = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
        out = np.empty((3,3), dtype='int')
        choices = [-10, 10]
        np.choose(a, choices, out=out, mode='raise')
        assert_equal(out, np.array([[ 10, -10,  10],
                                    [-10,  10, -10],
                                    [ 10, -10,  10]]))

def test_flatiter__array__(self):
        a = np.arange(9).reshape(3,3)
        b = a.T.flat
        c = b.__array__()
        # triggers the WRITEBACKIFCOPY resolution, assuming refcount semantics
        del c

def test_dot_out(self):
        # if HAVE_CBLAS, will use WRITEBACKIFCOPY
        a = np.arange(9, dtype=float).reshape(3,3)
        b = np.dot(a, a, out=a)
        assert_equal(b, np.array([[15, 18, 21], [42, 54, 66], [69, 90, 111]]))

def test_view_assign(self):
        from numpy.core._multiarray_tests import npy_create_writebackifcopy, npy_resolve

arr = np.arange(9).reshape(3, 3).T
        arr_wb = npy_create_writebackifcopy(arr)
        assert_(arr_wb.flags.writebackifcopy)
        assert_(arr_wb.base is arr)
        arr_wb[...] = -100
        npy_resolve(arr_wb)
        # arr changes after resolve, even though we assigned to arr_wb
        assert_equal(arr, -100)
        # after resolve, the two arrays no longer reference each other
        assert_(arr_wb.ctypes.data != 0)
        assert_equal(arr_wb.base, None)
        # assigning to arr_wb does not get transferred to arr
        arr_wb[...] = 100
        assert_equal(arr, -100)

@pytest.mark.leaks_references(
            reason="increments self in dealloc; ignore since deprecated path.")
    def test_dealloc_warning(self):
        with suppress_warnings() as sup:
            sup.record(RuntimeWarning)
            arr = np.arange(9).reshape(3, 3)
            v = arr.T
            _multiarray_tests.npy_abuse_writebackifcopy(v)
            assert len(sup.log) == 1

def test_view_discard_refcount(self):
        from numpy.core._multiarray_tests import npy_create_writebackifcopy, npy_discard

arr = np.arange(9).reshape(3, 3).T
        orig = arr.copy()
        if HAS_REFCOUNT:
            arr_cnt = sys.getrefcount(arr)
        arr_wb = npy_create_writebackifcopy(arr)
        assert_(arr_wb.flags.writebackifcopy)
        assert_(arr_wb.base is arr)
        arr_wb[...] = -100
        npy_discard(arr_wb)
        # arr remains unchanged after discard
        assert_equal(arr, orig)
        # after discard, the two arrays no longer reference each other
        assert_(arr_wb.ctypes.data != 0)
        assert_equal(arr_wb.base, None)
        if HAS_REFCOUNT:
            assert_equal(arr_cnt, sys.getrefcount(arr))
        # assigning to arr_wb does not get transferred to arr
        arr_wb[...] = 100
        assert_equal(arr, orig)

class TestArange:
    def test_infinite(self):
        assert_raises_regex(
            ValueError, "size exceeded",
            np.arange, 0, np.inf
        )

def test_nan_step(self):
        assert_raises_regex(
            ValueError, "cannot compute length",
            np.arange, 0, 1, np.nan
        )

def test_zero_step(self):
        assert_raises(ZeroDivisionError, np.arange, 0, 10, 0)
        assert_raises(ZeroDivisionError, np.arange, 0.0, 10.0, 0.0)

# empty range
        assert_raises(ZeroDivisionError, np.arange, 0, 0, 0)
        assert_raises(ZeroDivisionError, np.arange, 0.0, 0.0, 0.0)

def test_require_range(self):
        assert_raises(TypeError, np.arange)
        assert_raises(TypeError, np.arange, step=3)
        assert_raises(TypeError, np.arange, dtype='int64')
        assert_raises(TypeError, np.arange, start=4)

def test_start_stop_kwarg(self):
        keyword_stop = np.arange(stop=3)
        keyword_zerotostop = np.arange(start=0, stop=3)
        keyword_start_stop = np.arange(start=3, stop=9)

assert len(keyword_stop) == 3
        assert len(keyword_zerotostop) == 3
        assert len(keyword_start_stop) == 6
        assert_array_equal(keyword_stop, keyword_zerotostop)

def test_arange_booleans(self):
        # Arange makes some sense for booleans and works up to length 2.
        # But it is weird since `arange(2, 4, dtype=bool)` works.
        # Arguably, much or all of this could be deprecated/removed.
        res = np.arange(False, dtype=bool)
        assert_array_equal(res, np.array([], dtype="bool"))

res = np.arange(True, dtype="bool")
        assert_array_equal(res, [False])

res = np.arange(2, dtype="bool")
        assert_array_equal(res, [False, True])

# This case is especially weird, but drops out without special case:
        res = np.arange(6, 8, dtype="bool")
        assert_array_equal(res, [True, True])

with pytest.raises(TypeError):
            np.arange(3, dtype="bool")

@pytest.mark.parametrize("dtype", ["S3", "U", "5i"])
    def test_rejects_bad_dtypes(self, dtype):
        dtype = np.dtype(dtype)
        DType_name = re.escape(str(type(dtype)))
        with pytest.raises(TypeError,
                match=rf"arange\(\) not supported for inputs .* {DType_name}"):
            np.arange(2, dtype=dtype)

def test_rejects_strings(self):
        # Explicitly test error for strings which may call "b" - "a":
        DType_name = re.escape(str(type(np.array("a").dtype)))
        with pytest.raises(TypeError,
                match=rf"arange\(\) not supported for inputs .* {DType_name}"):
            np.arange("a", "b")

def test_byteswapped(self):
        res_be = np.arange(1, 1000, dtype=">i4")
        res_le = np.arange(1, 1000, dtype="<i4")
        assert res_be.dtype == ">i4"
        assert res_le.dtype == "<i4"
        assert_array_equal(res_le, res_be)

@pytest.mark.parametrize("which", [0, 1, 2])
    def test_error_paths_and_promotion(self, which):
        args = [0, 1, 2]  # start, stop, and step
        args[which] = np.float64(2.)  # should ensure float64 output

assert np.arange(*args).dtype == np.float64

# Cover stranger error path, test only to achieve code coverage!
        args[which] = [None, []]
        with pytest.raises(ValueError):
            # Fails discovering start dtype
            np.arange(*args)

class TestArrayFinalize:
    """ Tests __array_finalize__ """

def test_receives_base(self):
        # gh-11237
        class SavesBase(np.ndarray):
            def __array_finalize__(self, obj):
                self.saved_base = self.base

a = np.array(1).view(SavesBase)
        assert_(a.saved_base is a.base)

def test_bad_finalize1(self):
        class BadAttributeArray(np.ndarray):
            @property
            def __array_finalize__(self):
                raise RuntimeError("boohoo!")

with pytest.raises(TypeError, match="not callable"):
            np.arange(10).view(BadAttributeArray)

def test_bad_finalize2(self):
        class BadAttributeArray(np.ndarray):
            def __array_finalize__(self):
                raise RuntimeError("boohoo!")

with pytest.raises(TypeError, match="takes 1 positional"):
            np.arange(10).view(BadAttributeArray)

def test_bad_finalize3(self):
        class BadAttributeArray(np.ndarray):
            def __array_finalize__(self, obj):
                raise RuntimeError("boohoo!")

with pytest.raises(RuntimeError, match="boohoo!"):
            np.arange(10).view(BadAttributeArray)

def test_lifetime_on_error(self):
        # gh-11237
        class RaisesInFinalize(np.ndarray):
            def __array_finalize__(self, obj):
                # crash, but keep this object alive
                raise Exception(self)

# a plain object can't be weakref'd
        class Dummy: pass

# get a weak reference to an object within an array
        obj_arr = np.array(Dummy())
        obj_ref = weakref.ref(obj_arr[()])

# get an array that crashed in __array_finalize__
        with assert_raises(Exception) as e:
            obj_arr.view(RaisesInFinalize)

obj_subarray = e.exception.args[0]
        del e
        assert_(isinstance(obj_subarray, RaisesInFinalize))

# reference should still be held by obj_arr
        break_cycles()
        assert_(obj_ref() is not None, "object should not already be dead")

del obj_arr
        break_cycles()
        assert_(obj_ref() is not None, "obj_arr should not hold the last reference")

del obj_subarray
        break_cycles()
        assert_(obj_ref() is None, "no references should remain")

def test_can_use_super(self):
        class SuperFinalize(np.ndarray):
            def __array_finalize__(self, obj):
                self.saved_result = super().__array_finalize__(obj)

a = np.array(1).view(SuperFinalize)
        assert_(a.saved_result is None)

def test_orderconverter_with_nonASCII_unicode_ordering():
    # gh-7475
    a = np.arange(5)
    assert_raises(ValueError, a.flatten, order='\xe2')

def test_equal_override():
    # gh-9153: ndarray.__eq__ uses special logic for structured arrays, which
    # did not respect overrides with __array_priority__ or __array_ufunc__.
    # The PR fixed this for __array_priority__ and __array_ufunc__ = None.
    class MyAlwaysEqual:
        def __eq__(self, other):
            return "eq"

def __ne__(self, other):
            return "ne"

class MyAlwaysEqualOld(MyAlwaysEqual):
        __array_priority__ = 10000

class MyAlwaysEqualNew(MyAlwaysEqual):
        __array_ufunc__ = None

array = np.array([(0, 1), (2, 3)], dtype='i4,i4')
    for my_always_equal_cls in MyAlwaysEqualOld, MyAlwaysEqualNew:
        my_always_equal = my_always_equal_cls()
        assert_equal(my_always_equal == array, 'eq')
        assert_equal(array == my_always_equal, 'eq')
        assert_equal(my_always_equal != array, 'ne')
        assert_equal(array != my_always_equal, 'ne')

@pytest.mark.parametrize("op", [operator.eq, operator.ne])
@pytest.mark.parametrize(["dt1", "dt2"], [
        ([("f", "i")], [("f", "i")]),  # structured comparison (successful)
        ("M8", "d"),  # impossible comparison: result is all True or False
        ("d", "d"),  # valid comparison
        ])
def test_equal_subclass_no_override(op, dt1, dt2):
    # Test how the three different possible code-paths deal with subclasses

class MyArr(np.ndarray):
        called_wrap = 0

def __array_wrap__(self, new):
            type(self).called_wrap += 1
            return super().__array_wrap__(new)

numpy_arr = np.zeros(5, dtype=dt1)
    my_arr = np.zeros(5, dtype=dt2).view(MyArr)

assert type(op(numpy_arr, my_arr)) is MyArr
    assert type(op(my_arr, numpy_arr)) is MyArr
    # We expect 2 calls (more if there were more fields):
    assert MyArr.called_wrap == 2

@pytest.mark.parametrize(["dt1", "dt2"], [
        ("M8[ns]", "d"),
        ("M8[s]", "l"),
        ("m8[ns]", "d"),
        # Missing: ("m8[ns]", "l") as timedelta currently promotes ints
        ("M8[s]", "m8[s]"),
        ("S5", "U5"),
        # Structured/void dtypes have explicit paths not tested here.
])
def test_no_loop_gives_all_true_or_false(dt1, dt2):
    # Make sure they broadcast to test result shape, use random values, since
    # the actual value should be ignored
    arr1 = np.random.randint(5, size=100).astype(dt1)
    arr2 = np.random.randint(5, size=99)[:, np.newaxis].astype(dt2)

res = arr1 == arr2
    assert res.shape == (99, 100)
    assert res.dtype == bool
    assert not res.any()

res = arr1 != arr2
    assert res.shape == (99, 100)
    assert res.dtype == bool
    assert res.all()

# incompatible shapes raise though
    arr2 = np.random.randint(5, size=99).astype(dt2)
    with pytest.raises(ValueError):
        arr1 == arr2

with pytest.raises(ValueError):
        arr1 != arr2

# Basic test with another operation:
    with pytest.raises(np.core._exceptions._UFuncNoLoopError):
        arr1 > arr2

@pytest.mark.parametrize("op", [
        operator.eq, operator.ne, operator.le, operator.lt, operator.ge,
        operator.gt])
def test_comparisons_forwards_error(op):
    class NotArray:
        def __array__(self):
            raise TypeError("run you fools")

with pytest.raises(TypeError, match="run you fools"):
        op(np.arange(2), NotArray())

with pytest.raises(TypeError, match="run you fools"):
        op(NotArray(), np.arange(2))

def test_richcompare_scalar_boolean_singleton_return():
    # These are currently guaranteed to be the boolean singletons, but maybe
    # returning NumPy booleans would also be OK:
    assert (np.array(0) == "a") is False
    assert (np.array(0) != "a") is True
    assert (np.int16(0) == "a") is False
    assert (np.int16(0) != "a") is True

@pytest.mark.parametrize("op", [
        operator.eq, operator.ne, operator.le, operator.lt, operator.ge,
        operator.gt])
def test_ragged_comparison_fails(op):
    # This needs to convert the internal array to True/False, which fails:
    a = np.array([1, np.array([1, 2, 3])], dtype=object)
    b = np.array([1, np.array([1, 2, 3])], dtype=object)

with pytest.raises(ValueError, match="The truth value.*ambiguous"):
        op(a, b)

@pytest.mark.parametrize(
    ["fun", "npfun"],
    [
        (_multiarray_tests.npy_cabs, np.absolute),
        (_multiarray_tests.npy_carg, np.angle)
    ]
)
@pytest.mark.parametrize("x", [1, np.inf, -np.inf, np.nan])
@pytest.mark.parametrize("y", [1, np.inf, -np.inf, np.nan])
@pytest.mark.parametrize("test_dtype", np.complexfloating.__subclasses__())
def test_npymath_complex(fun, npfun, x, y, test_dtype):
    # Smoketest npymath functions
    z = test_dtype(complex(x, y))
    got = fun(z)
    expected = npfun(z)
    assert_allclose(got, expected)

def test_npymath_real():
    # Smoketest npymath functions
    from numpy.core._multiarray_tests import (
        npy_log10, npy_cosh, npy_sinh, npy_tan, npy_tanh)

funcs = {npy_log10: np.log10,
             npy_cosh: np.cosh,
             npy_sinh: np.sinh,
             npy_tan: np.tan,
             npy_tanh: np.tanh}
    vals = (1, np.inf, -np.inf, np.nan)
    types = (np.float32, np.float64, np.longdouble)

with np.errstate(all='ignore'):
        for fun, npfun in funcs.items():
            for x, t in itertools.product(vals, types):
                z = t(x)
                got = fun(z)
                expected = npfun(z)
                assert_allclose(got, expected)

def test_uintalignment_and_alignment():
    # alignment code needs to satisfy these requirements:
    #  1. numpy structs match C struct layout
    #  2. ufuncs/casting is safe wrt to aligned access
    #  3. copy code is safe wrt to "uint alidned" access
    #
    # Complex types are the main problem, whose alignment may not be the same
    # as their "uint alignment".
    #
    # This test might only fail on certain platforms, where uint64 alignment is
    # not equal to complex64 alignment. The second 2 tests will only fail
    # for DEBUG=1.

d1 = np.dtype('u1,c8', align=True)
    d2 = np.dtype('u4,c8', align=True)
    d3 = np.dtype({'names': ['a', 'b'], 'formats': ['u1', d1]}, align=True)

assert_equal(np.zeros(1, dtype=d1)['f1'].flags['ALIGNED'], True)
    assert_equal(np.zeros(1, dtype=d2)['f1'].flags['ALIGNED'], True)
    assert_equal(np.zeros(1, dtype='u1,c8')['f1'].flags['ALIGNED'], False)

# check that C struct matches numpy struct size
    s = _multiarray_tests.get_struct_alignments()
    for d, (alignment, size) in zip([d1,d2,d3], s):
        assert_equal(d.alignment, alignment)
        assert_equal(d.itemsize, size)

# check that ufuncs don't complain in debug mode
    # (this is probably OK if the aligned flag is true above)
    src = np.zeros((2,2), dtype=d1)['f1']  # 4-byte aligned, often
    np.exp(src)  # assert fails?

# check that copy code doesn't complain in debug mode
    dst = np.zeros((2,2), dtype='c8')
    dst[:,1] = src[:,1]  # assert in lowlevel_strided_loops fails?

class TestAlignment:
    # adapted from scipy._lib.tests.test__util.test__aligned_zeros
    # Checks that unusual memory alignments don't trip up numpy.
    # In particular, check RELAXED_STRIDES don't trip alignment assertions in
    # NDEBUG mode for size-0 arrays (gh-12503)

def check(self, shape, dtype, order, align):
        err_msg = repr((shape, dtype, order, align))
        x = _aligned_zeros(shape, dtype, order, align=align)
        if align is None:
            align = np.dtype(dtype).alignment
        assert_equal(x.__array_interface__['data'][0] % align, 0)
        if hasattr(shape, '__len__'):
            assert_equal(x.shape, shape, err_msg)
        else:
            assert_equal(x.shape, (shape,), err_msg)
        assert_equal(x.dtype, dtype)
        if order == "C":
            assert_(x.flags.c_contiguous, err_msg)
        elif order == "F":
            if x.size > 0:
                assert_(x.flags.f_contiguous, err_msg)
        elif order is None:
            assert_(x.flags.c_contiguous, err_msg)
        else:
            raise ValueError()

def test_various_alignments(self):
        for align in [1, 2, 3, 4, 8, 12, 16, 32, 64, None]:
            for n in [0, 1, 3, 11]:
                for order in ["C", "F", None]:
                    for dtype in list(np.typecodes["All"]) + ['i4,i4,i4']:
                        if dtype == 'O':
                            # object dtype can't be misaligned
                            continue
                        for shape in [n, (1, 2, 3, n)]:
                            self.check(shape, np.dtype(dtype), order, align)

def test_strided_loop_alignments(self):
        # particularly test that complex64 and float128 use right alignment
        # code-paths, since these are particularly problematic. It is useful to
        # turn on USE_DEBUG for this test, so lowlevel-loop asserts are run.
        for align in [1, 2, 4, 8, 12, 16, None]:
            xf64 = _aligned_zeros(3, np.float64)

xc64 = _aligned_zeros(3, np.complex64, align=align)
            xf128 = _aligned_zeros(3, np.longdouble, align=align)

# test casting, both to and from misaligned
            with suppress_warnings() as sup:
                sup.filter(np.ComplexWarning, "Casting complex values")
                xc64.astype('f8')
            xf64.astype(np.complex64)
            test = xc64 + xf64

xf128.astype('f8')
            xf64.astype(np.longdouble)
            test = xf128 + xf64

test = xf128 + xc64

# test copy, both to and from misaligned
            # contig copy
            xf64[:] = xf64.copy()
            xc64[:] = xc64.copy()
            xf128[:] = xf128.copy()
            # strided copy
            xf64[::2] = xf64[::2].copy()
            xc64[::2] = xc64[::2].copy()
            xf128[::2] = xf128[::2].copy()

def test_getfield():
    a = np.arange(32, dtype='uint16')
    if sys.byteorder == 'little':
        i = 0
        j = 1
    else:
        i = 1
        j = 0
    b = a.getfield('int8', i)
    assert_equal(b, a)
    b = a.getfield('int8', j)
    assert_equal(b, 0)
    pytest.raises(ValueError, a.getfield, 'uint8', -1)
    pytest.raises(ValueError, a.getfield, 'uint8', 16)
    pytest.raises(ValueError, a.getfield, 'uint64', 0)

class TestViewDtype:
    """
    Verify that making a view of a non-contiguous array works as expected.
    """
    def test_smaller_dtype_multiple(self):
        # x is non-contiguous
        x = np.arange(10, dtype='<i4')[::2]
        with pytest.raises(ValueError,
                           match='the last axis must be contiguous'):
            x.view('<i2')
        expected = [[0, 0], [2, 0], [4, 0], [6, 0], [8, 0]]
        assert_array_equal(x[:, np.newaxis].view('<i2'), expected)

def test_smaller_dtype_not_multiple(self):
        # x is non-contiguous
        x = np.arange(5, dtype='<i4')[::2]

with pytest.raises(ValueError,
                           match='the last axis must be contiguous'):
            x.view('S3')
        with pytest.raises(ValueError,
                           match='When changing to a smaller dtype'):
            x[:, np.newaxis].view('S3')

# Make sure the problem is because of the dtype size
        expected = [[b''], [b'\x02'], [b'\x04']]
        assert_array_equal(x[:, np.newaxis].view('S4'), expected)

def test_larger_dtype_multiple(self):
        # x is non-contiguous in the first dimension, contiguous in the last
        x = np.arange(20, dtype='<i2').reshape(10, 2)[::2, :]
        expected = np.array([[65536], [327684], [589832],
                             [851980], [1114128]], dtype='<i4')
        assert_array_equal(x.view('<i4'), expected)

def test_larger_dtype_not_multiple(self):
        # x is non-contiguous in the first dimension, contiguous in the last
        x = np.arange(20, dtype='<i2').reshape(10, 2)[::2, :]
        with pytest.raises(ValueError,
                           match='When changing to a larger dtype'):
            x.view('S3')
        # Make sure the problem is because of the dtype size
        expected = [[b'\x00\x00\x01'], [b'\x04\x00\x05'], [b'\x08\x00\t'],
                    [b'\x0c\x00\r'], [b'\x10\x00\x11']]
        assert_array_equal(x.view('S4'), expected)

def test_f_contiguous(self):
        # x is F-contiguous
        x = np.arange(4 * 3, dtype='<i4').reshape(4, 3).T
        with pytest.raises(ValueError,
                           match='the last axis must be contiguous'):
            x.view('<i2')

def test_non_c_contiguous(self):
        # x is contiguous in axis=-1, but not C-contiguous in other axes
        x = np.arange(2 * 3 * 4, dtype='i1').\
                    reshape(2, 3, 4).transpose(1, 0, 2)
        expected = [[[256, 770], [3340, 3854]],
                    [[1284, 1798], [4368, 4882]],
                    [[2312, 2826], [5396, 5910]]]
        assert_array_equal(x.view('<i2'), expected)

@pytest.mark.xfail(_SUPPORTS_SVE, reason="gh-22982")
# Test various array sizes that hit different code paths in quicksort-avx512
@pytest.mark.parametrize("N", np.arange(1, 512))
@pytest.mark.parametrize("dtype", ['e', 'f', 'd'])
def test_sort_float(N, dtype):
    # Regular data with nan sprinkled
    np.random.seed(42)
    arr = -0.5 + np.random.sample(N).astype(dtype)
    arr[np.random.choice(arr.shape[0], 3)] = np.nan
    assert_equal(np.sort(arr, kind='quick'), np.sort(arr, kind='heap'))

# (2) with +INF
    infarr = np.inf*np.ones(N, dtype=dtype)
    infarr[np.random.choice(infarr.shape[0], 5)] = -1.0
    assert_equal(np.sort(infarr, kind='quick'), np.sort(infarr, kind='heap'))

# (3) with -INF
    neginfarr = -np.inf*np.ones(N, dtype=dtype)
    neginfarr[np.random.choice(neginfarr.shape[0], 5)] = 1.0
    assert_equal(np.sort(neginfarr, kind='quick'),
                 np.sort(neginfarr, kind='heap'))

# (4) with +/-INF
    infarr = np.inf*np.ones(N, dtype=dtype)
    infarr[np.random.choice(infarr.shape[0], (int)(N/2))] = -np.inf
    assert_equal(np.sort(infarr, kind='quick'), np.sort(infarr, kind='heap'))

def test_sort_float16():
    arr = np.arange(65536, dtype=np.int16)
    temp = np.frombuffer(arr.tobytes(), dtype=np.float16)
    data = np.copy(temp)
    np.random.shuffle(data)
    data_backup = data
    assert_equal(np.sort(data, kind='quick'),
            np.sort(data_backup, kind='heap'))

@pytest.mark.parametrize("N", np.arange(1, 512))
@pytest.mark.parametrize("dtype", ['h', 'H', 'i', 'I', 'l', 'L'])
def test_sort_int(N, dtype):
    # Random data with MAX and MIN sprinkled
    minv = np.iinfo(dtype).min
    maxv = np.iinfo(dtype).max
    arr = np.random.randint(low=minv, high=maxv-1, size=N, dtype=dtype)
    arr[np.random.choice(arr.shape[0], 10)] = minv
    arr[np.random.choice(arr.shape[0], 10)] = maxv
    assert_equal(np.sort(arr, kind='quick'), np.sort(arr, kind='heap'))

def test_sort_uint():
    # Random data with NPY_MAX_UINT32 sprinkled
    rng = np.random.default_rng(42)
    N = 2047
    maxv = np.iinfo(np.uint32).max
    arr = rng.integers(low=0, high=maxv, size=N).astype('uint32')
    arr[np.random.choice(arr.shape[0], 10)] = maxv
    assert_equal(np.sort(arr, kind='quick'), np.sort(arr, kind='heap'))

def test_private_get_ndarray_c_version():
    assert isinstance(_get_ndarray_c_version(), int)

@pytest.mark.parametrize("N", np.arange(1, 512))
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_argsort_float(N, dtype):
    rnd = np.random.RandomState(116112)
    # (1) Regular data with a few nan: doesn't use vectorized sort
    arr = -0.5 + rnd.random(N).astype(dtype)
    arr[rnd.choice(arr.shape[0], 3)] = np.nan
    assert_arg_sorted(arr, np.argsort(arr, kind='quick'))

# (2) Random data with inf at the end of array
    # See: https://github.com/intel/x86-simd-sort/pull/39
    arr = -0.5 + rnd.rand(N).astype(dtype)
    arr[N-1] = np.inf
    assert_arg_sorted(arr, np.argsort(arr, kind='quick'))

@pytest.mark.parametrize("N", np.arange(2, 512))
@pytest.mark.parametrize("dtype", [np.int32, np.uint32, np.int64, np.uint64])
def test_argsort_int(N, dtype):
    rnd = np.random.RandomState(1100710816)
    # (1) random data with min and max values
    minv = np.iinfo(dtype).min
    maxv = np.iinfo(dtype).max
    arr = rnd.randint(low=minv, high=maxv, size=N, dtype=dtype)
    i, j = rnd.choice(N, 2, replace=False)
    arr[i] = minv
    arr[j] = maxv
    assert_arg_sorted(arr, np.argsort(arr, kind='quick'))

# (2) random data with max value at the end of array
    # See: https://github.com/intel/x86-simd-sort/pull/39
    arr = rnd.randint(low=minv, high=maxv, size=N, dtype=dtype)
    arr[N-1] = maxv
    assert_arg_sorted(arr, np.argsort(arr, kind='quick'))

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