import sys
import os
import itertools
import pytest
import weakref

import numpy as np
from numpy.testing import (
    assert_equal, assert_array_equal, assert_almost_equal,

            

Reported by Pylint.

              class _GenericTest:

    def _test_equal(self, a, b):
        self._assert_func(a, b)

    def _test_not_equal(self, a, b):
        with assert_raises(AssertionError):
            self._assert_func(a, b)


            

Reported by Pylint.

              
    def _test_not_equal(self, a, b):
        with assert_raises(AssertionError):
            self._assert_func(a, b)

    def test_array_rank1_eq(self):
        """Test two equal array of rank 1 are found equal."""
        a = np.array([1, 2])
        b = np.array([1, 2])

            

Reported by Pylint.

              class TestULP:

    def test_equal(self):
        x = np.random.randn(10)
        assert_array_max_ulp(x, x, maxulp=0)

    def test_single(self):
        # Generate 1 + small deviation, check that adding eps gives a few UNL
        x = np.ones(10).astype(np.float32)

            

Reported by Pylint.

                  def test_single(self):
        # Generate 1 + small deviation, check that adding eps gives a few UNL
        x = np.ones(10).astype(np.float32)
        x += 0.01 * np.random.randn(10).astype(np.float32)
        eps = np.finfo(np.float32).eps
        assert_array_max_ulp(x, x+eps, maxulp=20)

    def test_double(self):
        # Generate 1 + small deviation, check that adding eps gives a few UNL

            

Reported by Pylint.

                  def test_double(self):
        # Generate 1 + small deviation, check that adding eps gives a few UNL
        x = np.ones(10).astype(np.float64)
        x += 0.01 * np.random.randn(10).astype(np.float64)
        eps = np.finfo(np.float64).eps
        assert_array_max_ulp(x, x+eps, maxulp=200)

    def test_inf(self):
        for dt in [np.float32, np.float64]:

            

Reported by Pylint.

              
    # serial test scenario -- the __warningregistry__
    # attribute should be present
    class mod:
        def __init__(self):
            self.__warningregistry__ = {'warning1':1,
                                        'warning2':2}

    mod_inst = mod()

            

Reported by Pylint.

              class TestArrayEqual(_GenericTest):

    def setup(self):
        self._assert_func = assert_array_equal

    def test_generic_rank1(self):
        """Test rank 1 array for all dtypes."""
        def foo(t):
            a = np.empty(2, t)

            

Reported by Pylint.

                      a = np.ma.MaskedArray([3., 4., 6.5], mask=[False, True, False])
        b = np.array([3., np.nan, 6.5])
        self._test_equal(a, b)
        self._test_equal(b, a)
        a = np.ma.MaskedArray([3., 4., 6.5], mask=[True, False, False])
        b = np.array([np.inf, 4., 6.5])
        self._test_equal(a, b)
        self._test_equal(b, a)


            

Reported by Pylint.

                      a = np.ma.MaskedArray([3., 4., 6.5], mask=[True, False, False])
        b = np.array([np.inf, 4., 6.5])
        self._test_equal(a, b)
        self._test_equal(b, a)

    def test_subclass_that_overrides_eq(self):
        # While we cannot guarantee testing functions will always work for
        # subclasses, the tests should ideally rely only on subclasses having
        # comparison operators, not on them being able to store booleans

            

Reported by Pylint.

              import numpy.typing as npt

# Can't directly import `np.float128` as it is not available on all platforms
f16: np.floating[npt._128Bit]

c16 = np.complex128()
f8 = np.float64()
i8 = np.int64()
u8 = np.uint64()

            

Reported by Pylint.

              # Can't directly import `np.float128` as it is not available on all platforms
f16: np.floating[npt._128Bit]

c16 = np.complex128()
f8 = np.float64()
i8 = np.int64()
u8 = np.uint64()

c8 = np.complex64()

            

Reported by Pylint.

              f16: np.floating[npt._128Bit]

c16 = np.complex128()
f8 = np.float64()
i8 = np.int64()
u8 = np.uint64()

c8 = np.complex64()
f4 = np.float32()

            

Reported by Pylint.

              c16 = np.complex128()
f8 = np.float64()
i8 = np.int64()
u8 = np.uint64()

c8 = np.complex64()
f4 = np.float32()
i4 = np.int32()
u4 = np.uint32()

            

Reported by Pylint.

              i8 = np.int64()
u8 = np.uint64()

c8 = np.complex64()
f4 = np.float32()
i4 = np.int32()
u4 = np.uint32()

dt = np.datetime64(0, "D")

            

Reported by Pylint.

              u8 = np.uint64()

c8 = np.complex64()
f4 = np.float32()
i4 = np.int32()
u4 = np.uint32()

dt = np.datetime64(0, "D")
td = np.timedelta64(0, "D")

            

Reported by Pylint.

              c8 = np.complex64()
f4 = np.float32()
i4 = np.int32()
u4 = np.uint32()

dt = np.datetime64(0, "D")
td = np.timedelta64(0, "D")

b_ = np.bool_()

            

Reported by Pylint.

              dt = np.datetime64(0, "D")
td = np.timedelta64(0, "D")

b_ = np.bool_()

b = bool()
c = complex()
f = float()
i = int()

            

Reported by Pylint.

              f = float()
i = int()

AR_b: np.ndarray[Any, np.dtype[np.bool_]]
AR_u: np.ndarray[Any, np.dtype[np.uint32]]
AR_i: np.ndarray[Any, np.dtype[np.int64]]
AR_f: np.ndarray[Any, np.dtype[np.float64]]
AR_c: np.ndarray[Any, np.dtype[np.complex128]]
AR_m: np.ndarray[Any, np.dtype[np.timedelta64]]

            

Reported by Pylint.

              i = int()

AR_b: np.ndarray[Any, np.dtype[np.bool_]]
AR_u: np.ndarray[Any, np.dtype[np.uint32]]
AR_i: np.ndarray[Any, np.dtype[np.int64]]
AR_f: np.ndarray[Any, np.dtype[np.float64]]
AR_c: np.ndarray[Any, np.dtype[np.complex128]]
AR_m: np.ndarray[Any, np.dtype[np.timedelta64]]
AR_M: np.ndarray[Any, np.dtype[np.datetime64]]

            

Reported by Pylint.

              import numpy
import numpy as np
import datetime
import pytest
from numpy.testing import (
    assert_, assert_equal, assert_raises, assert_warns, suppress_warnings,
    assert_raises_regex, assert_array_equal,
    )
from numpy.compat import pickle

            

Reported by Pylint.

                                   np.datetime64('1950-03-12T13', 'm'))

        # Default construction means NaT
        assert_equal(np.datetime64(), np.datetime64('NaT'))

        # Some basic strings and repr
        assert_equal(str(np.datetime64('NaT')), 'NaT')
        assert_equal(repr(np.datetime64('NaT')),
                     "numpy.datetime64('NaT')")

            

Reported by Pylint.

                      assert_equal(np.datetime64(None), np.datetime64('NaT'))

        # Default construction of NaT is in generic units
        assert_equal(np.datetime64().dtype, np.dtype('M8'))
        assert_equal(np.datetime64('NaT').dtype, np.dtype('M8'))

        # Construction from integers requires a specified unit
        assert_raises(ValueError, np.datetime64, 17)


            

Reported by Pylint.

                                   np.timedelta64(2, 'm'))

        # Default construction means 0
        assert_equal(np.timedelta64(), np.timedelta64(0))

        # None gets constructed as NaT
        assert_equal(np.timedelta64(None), np.timedelta64('NaT'))

        # Some basic strings and repr

            

Reported by Pylint.

                  def test_datetime_y2038(self):
        # Test parsing on either side of the Y2038 boundary
        a = np.datetime64('2038-01-19T03:14:07')
        assert_equal(a.view(np.int64), 2**31 - 1)
        a = np.datetime64('2038-01-19T03:14:08')
        assert_equal(a.view(np.int64), 2**31)

        # Test parsing on either side of the Y2038 boundary with
        # a manually specified timezone offset

            

Reported by Pylint.

                      a = np.datetime64('2038-01-19T03:14:07')
        assert_equal(a.view(np.int64), 2**31 - 1)
        a = np.datetime64('2038-01-19T03:14:08')
        assert_equal(a.view(np.int64), 2**31)

        # Test parsing on either side of the Y2038 boundary with
        # a manually specified timezone offset
        with assert_warns(DeprecationWarning):
            a = np.datetime64('2038-01-19T04:14:07+0100')

            

Reported by Pylint.

                      # a manually specified timezone offset
        with assert_warns(DeprecationWarning):
            a = np.datetime64('2038-01-19T04:14:07+0100')
            assert_equal(a.view(np.int64), 2**31 - 1)
        with assert_warns(DeprecationWarning):
            a = np.datetime64('2038-01-19T04:14:08+0100')
            assert_equal(a.view(np.int64), 2**31)

        # Test parsing a date after Y2038

            

Reported by Pylint.

                          assert_equal(a.view(np.int64), 2**31 - 1)
        with assert_warns(DeprecationWarning):
            a = np.datetime64('2038-01-19T04:14:08+0100')
            assert_equal(a.view(np.int64), 2**31)

        # Test parsing a date after Y2038
        a = np.datetime64('2038-01-20T13:21:14')
        assert_equal(str(a), '2038-01-20T13:21:14')


            

Reported by Pylint.

              
import numpy
import numpy as np
import datetime
import pytest
from numpy.testing import (
    assert_, assert_equal, assert_raises, assert_warns, suppress_warnings,
    assert_raises_regex, assert_array_equal,
    )

            

Reported by Pylint.

              try:
    RecursionError
except NameError:
    RecursionError = RuntimeError  # python < 3.5


class TestDateTime:
    def test_datetime_dtype_creation(self):
        for unit in ['Y', 'M', 'W', 'D',

            

Reported by Pylint.

                          flag = 1
            log.info(self.__class__.__name__ + ':')
            if hasattr(self, 'calc_info'):
                self.calc_info()
            if notfound_action:
                if not self.has_info():
                    if notfound_action == 1:
                        warnings.warn(self.notfounderror.__doc__, stacklevel=2)
                    elif notfound_action == 2:

            

Reported by Pylint.

                      env_var = self.dir_env_var
        if env_var:
            if is_sequence(env_var):
                e0 = env_var[-1]
                for e in env_var:
                    if e in os.environ:
                        e0 = e
                        break
                if not env_var[0] == e0:

            

Reported by Pylint.

                      if env_var:
            if is_sequence(env_var):
                e0 = env_var[-1]
                for e in env_var:
                    if e in os.environ:
                        e0 = e
                        break
                if not env_var[0] == e0:
                    log.info('Setting %s=%s' % (env_var[0], e0))

            

Reported by Pylint.

                                  if e in os.environ:
                        e0 = e
                        break
                if not env_var[0] == e0:
                    log.info('Setting %s=%s' % (env_var[0], e0))
                env_var = e0
        if env_var and env_var in os.environ:
            d = os.environ[env_var]
            if d == 'None':

            

Reported by Pylint.

                                      e0 = e
                        break
                if not env_var[0] == e0:
                    log.info('Setting %s=%s' % (env_var[0], e0))
                env_var = e0
        if env_var and env_var in os.environ:
            d = os.environ[env_var]
            if d == 'None':
                log.info('Disabled %s: %s',

            

Reported by Pylint.

                                  b = os.path.splitext(b)[0]
                    if b[:3] == 'lib':
                        log.info('Replacing _lib_names[0]==%r with %r' \
                              % (self._lib_names[0], b[3:]))
                        self._lib_names[0] = b[3:]
            else:
                ds = d.split(os.pathsep)
                ds2 = []
                for d in ds:

            

Reported by Pylint.

                                  if b[:3] == 'lib':
                        log.info('Replacing _lib_names[0]==%r with %r' \
                              % (self._lib_names[0], b[3:]))
                        self._lib_names[0] = b[3:]
            else:
                ds = d.split(os.pathsep)
                ds2 = []
                for d in ds:
                    if os.path.isdir(d):

            

Reported by Pylint.

              
    def get_libraries(self, key='libraries'):
        if hasattr(self, '_lib_names'):
            return self.get_libs(key, default=self._lib_names)
        else:
            return self.get_libs(key, '')

    def library_extensions(self):
        c = customized_ccompiler()

            

Reported by Pylint.

                      if mklroot is None:
            system_info.__init__(self)
        else:
            from .cpuinfo import cpu
            if cpu.is_Itanium():
                plt = '64'
            elif cpu.is_Intel() and cpu.is_64bit():
                plt = 'intel64'
            else:

            

Reported by Pylint.

              global_compiler = None

def customized_ccompiler():
    global global_compiler
    if not global_compiler:
        global_compiler = _customized_ccompiler()
    return global_compiler



            

Reported by Pylint.

              

class Object:
    def __array__(self) -> np.ndarray[Any, np.dtype[np.object_]]:
        ret = np.empty((), dtype=object)
        ret[()] = self
        return ret

    def __sub__(self, value: Any) -> Object:

            

Reported by Pylint.

                      return self


AR_b: np.ndarray[Any, np.dtype[np.bool_]] = np.array([True])
AR_u: np.ndarray[Any, np.dtype[np.uint32]] = np.array([1], dtype=np.uint32)
AR_i: np.ndarray[Any, np.dtype[np.int64]] = np.array([1])
AR_f: np.ndarray[Any, np.dtype[np.float64]] = np.array([1.0])
AR_c: np.ndarray[Any, np.dtype[np.complex128]] = np.array([1j])
AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] = np.array([np.timedelta64(1, "D")])

            

Reported by Pylint.

              

AR_b: np.ndarray[Any, np.dtype[np.bool_]] = np.array([True])
AR_u: np.ndarray[Any, np.dtype[np.uint32]] = np.array([1], dtype=np.uint32)
AR_i: np.ndarray[Any, np.dtype[np.int64]] = np.array([1])
AR_f: np.ndarray[Any, np.dtype[np.float64]] = np.array([1.0])
AR_c: np.ndarray[Any, np.dtype[np.complex128]] = np.array([1j])
AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] = np.array([np.timedelta64(1, "D")])
AR_M: np.ndarray[Any, np.dtype[np.datetime64]] = np.array([np.datetime64(1, "D")])

            

Reported by Pylint.

              
AR_b: np.ndarray[Any, np.dtype[np.bool_]] = np.array([True])
AR_u: np.ndarray[Any, np.dtype[np.uint32]] = np.array([1], dtype=np.uint32)
AR_i: np.ndarray[Any, np.dtype[np.int64]] = np.array([1])
AR_f: np.ndarray[Any, np.dtype[np.float64]] = np.array([1.0])
AR_c: np.ndarray[Any, np.dtype[np.complex128]] = np.array([1j])
AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] = np.array([np.timedelta64(1, "D")])
AR_M: np.ndarray[Any, np.dtype[np.datetime64]] = np.array([np.datetime64(1, "D")])
AR_O: np.ndarray[Any, np.dtype[np.object_]] = np.array([Object()])

            

Reported by Pylint.

              AR_b: np.ndarray[Any, np.dtype[np.bool_]] = np.array([True])
AR_u: np.ndarray[Any, np.dtype[np.uint32]] = np.array([1], dtype=np.uint32)
AR_i: np.ndarray[Any, np.dtype[np.int64]] = np.array([1])
AR_f: np.ndarray[Any, np.dtype[np.float64]] = np.array([1.0])
AR_c: np.ndarray[Any, np.dtype[np.complex128]] = np.array([1j])
AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] = np.array([np.timedelta64(1, "D")])
AR_M: np.ndarray[Any, np.dtype[np.datetime64]] = np.array([np.datetime64(1, "D")])
AR_O: np.ndarray[Any, np.dtype[np.object_]] = np.array([Object()])


            

Reported by Pylint.

              AR_u: np.ndarray[Any, np.dtype[np.uint32]] = np.array([1], dtype=np.uint32)
AR_i: np.ndarray[Any, np.dtype[np.int64]] = np.array([1])
AR_f: np.ndarray[Any, np.dtype[np.float64]] = np.array([1.0])
AR_c: np.ndarray[Any, np.dtype[np.complex128]] = np.array([1j])
AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] = np.array([np.timedelta64(1, "D")])
AR_M: np.ndarray[Any, np.dtype[np.datetime64]] = np.array([np.datetime64(1, "D")])
AR_O: np.ndarray[Any, np.dtype[np.object_]] = np.array([Object()])

AR_LIKE_b = [True]

            

Reported by Pylint.

              AR_i: np.ndarray[Any, np.dtype[np.int64]] = np.array([1])
AR_f: np.ndarray[Any, np.dtype[np.float64]] = np.array([1.0])
AR_c: np.ndarray[Any, np.dtype[np.complex128]] = np.array([1j])
AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] = np.array([np.timedelta64(1, "D")])
AR_M: np.ndarray[Any, np.dtype[np.datetime64]] = np.array([np.datetime64(1, "D")])
AR_O: np.ndarray[Any, np.dtype[np.object_]] = np.array([Object()])

AR_LIKE_b = [True]
AR_LIKE_u = [np.uint32(1)]

            

Reported by Pylint.

              AR_f: np.ndarray[Any, np.dtype[np.float64]] = np.array([1.0])
AR_c: np.ndarray[Any, np.dtype[np.complex128]] = np.array([1j])
AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] = np.array([np.timedelta64(1, "D")])
AR_M: np.ndarray[Any, np.dtype[np.datetime64]] = np.array([np.datetime64(1, "D")])
AR_O: np.ndarray[Any, np.dtype[np.object_]] = np.array([Object()])

AR_LIKE_b = [True]
AR_LIKE_u = [np.uint32(1)]
AR_LIKE_i = [1]

            

Reported by Pylint.

              AR_c: np.ndarray[Any, np.dtype[np.complex128]] = np.array([1j])
AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] = np.array([np.timedelta64(1, "D")])
AR_M: np.ndarray[Any, np.dtype[np.datetime64]] = np.array([np.datetime64(1, "D")])
AR_O: np.ndarray[Any, np.dtype[np.object_]] = np.array([Object()])

AR_LIKE_b = [True]
AR_LIKE_u = [np.uint32(1)]
AR_LIKE_i = [1]
AR_LIKE_f = [1.0]

            

Reported by Pylint.

              -AR_f

+c16
+c8
+f8
+f4
+i8
+i4
+u8

            

Reported by Pylint.

              import functools
import operator

import pytest

import numpy as np
from numpy.core._multiarray_tests import array_indexing
from itertools import product
from numpy.testing import (

            

Reported by Pylint.

              import pytest

import numpy as np
from numpy.core._multiarray_tests import array_indexing
from itertools import product
from numpy.testing import (
    assert_, assert_equal, assert_raises, assert_raises_regex,
    assert_array_equal, assert_warns, HAS_REFCOUNT,
    )

            

Reported by Pylint.

                      dt = np.dtype([("", dt)] * 2)
        dt = np.dtype([("", dt)] * 2)
        # The array should be large enough to likely run into threading issues
        arr = np.random.uniform(size=(6000, 8)).view(dt)[:, 0]

        rng = np.random.default_rng()
        def func(arr):
            indx = rng.integers(0, len(arr), size=6000, dtype=np.intp)
            arr[indx]

            

Reported by Pylint.

                      # Empty tuple index creates a view
        a = np.array([1, 2, 3])
        assert_equal(a[()], a)
        assert_(a[()].base is a)
        a = np.array(0)
        assert_(isinstance(a[()], np.int_))

    def test_void_scalar_empty_tuple(self):
        s = np.zeros((), dtype='V4')

            

Reported by Pylint.

                      assert_equal(a[()], a)
        assert_(a[()].base is a)
        a = np.array(0)
        assert_(isinstance(a[()], np.int_))

    def test_void_scalar_empty_tuple(self):
        s = np.zeros((), dtype='V4')
        assert_equal(s[()].dtype, s.dtype)
        assert_equal(s[()], s)

            

Reported by Pylint.

                      a = np.array([[1, 2, 3],
                      [4, 5, 6],
                      [7, 8, 9]])
        assert_(a[...] is not a)
        assert_equal(a[...], a)
        # `a[...]` was `a` in numpy <1.9.
        assert_(a[...].base is a)

        # Slicing with ellipsis can skip an

            

Reported by Pylint.

                      assert_(a[...] is not a)
        assert_equal(a[...], a)
        # `a[...]` was `a` in numpy <1.9.
        assert_(a[...].base is a)

        # Slicing with ellipsis can skip an
        # arbitrary number of dimensions
        assert_equal(a[0, ...], a[0])
        assert_equal(a[0, ...], a[0,:])

            

Reported by Pylint.

                      # Before `...` would return a itself.
        a = np.arange(5)

        assert_(a is not a[()])
        assert_(a is not a[...])
        assert_(a is not a[:])

    def test_broaderrors_indexing(self):
        a = np.zeros((5, 5))

            

Reported by Pylint.

                      a = np.arange(5)

        assert_(a is not a[()])
        assert_(a is not a[...])
        assert_(a is not a[:])

    def test_broaderrors_indexing(self):
        a = np.zeros((5, 5))
        assert_raises(IndexError, a.__getitem__, ([0, 1], [0, 1, 2]))

            

Reported by Pylint.

              
        assert_(a is not a[()])
        assert_(a is not a[...])
        assert_(a is not a[:])

    def test_broaderrors_indexing(self):
        a = np.zeros((5, 5))
        assert_raises(IndexError, a.__getitem__, ([0, 1], [0, 1, 2]))
        assert_raises(IndexError, a.__setitem__, ([0, 1], [0, 1, 2]), 0)

            

Reported by Pylint.

              
    def test_it(self):
        assert_equal(self.A.shape, (4,))
        assert_equal(self.A.upper()[:2].tobytes(), b'AB')

class TestComparisons:
    def setup(self):
        self.A = np.array([['abc', '123'],
                           ['789', 'xyz']]).view(np.chararray)

            

Reported by Pylint.

                      assert_array_equal(np.char.str_len(self.B), [[3, 0], [5, 9], [12, 5]])

    def test_count(self):
        assert_(issubclass(self.A.count('').dtype.type, np.integer))
        assert_array_equal(self.A.count('a'), [[1, 0], [0, 1], [0, 0]])
        assert_array_equal(self.A.count('123'), [[0, 0], [1, 0], [1, 0]])
        # Python doesn't seem to like counting NULL characters
        # assert_array_equal(self.A.count('\0'), [[0, 0], [0, 0], [1, 0]])
        assert_array_equal(self.A.count('a', 0, 2), [[1, 0], [0, 0], [0, 0]])

            

Reported by Pylint.

              
    def test_count(self):
        assert_(issubclass(self.A.count('').dtype.type, np.integer))
        assert_array_equal(self.A.count('a'), [[1, 0], [0, 1], [0, 0]])
        assert_array_equal(self.A.count('123'), [[0, 0], [1, 0], [1, 0]])
        # Python doesn't seem to like counting NULL characters
        # assert_array_equal(self.A.count('\0'), [[0, 0], [0, 0], [1, 0]])
        assert_array_equal(self.A.count('a', 0, 2), [[1, 0], [0, 0], [0, 0]])
        assert_array_equal(self.B.count('a'), [[0, 0], [0, 1], [0, 0]])

            

Reported by Pylint.

                  def test_count(self):
        assert_(issubclass(self.A.count('').dtype.type, np.integer))
        assert_array_equal(self.A.count('a'), [[1, 0], [0, 1], [0, 0]])
        assert_array_equal(self.A.count('123'), [[0, 0], [1, 0], [1, 0]])
        # Python doesn't seem to like counting NULL characters
        # assert_array_equal(self.A.count('\0'), [[0, 0], [0, 0], [1, 0]])
        assert_array_equal(self.A.count('a', 0, 2), [[1, 0], [0, 0], [0, 0]])
        assert_array_equal(self.B.count('a'), [[0, 0], [0, 1], [0, 0]])
        assert_array_equal(self.B.count('123'), [[0, 0], [1, 0], [1, 0]])

            

Reported by Pylint.

                      assert_array_equal(self.A.count('123'), [[0, 0], [1, 0], [1, 0]])
        # Python doesn't seem to like counting NULL characters
        # assert_array_equal(self.A.count('\0'), [[0, 0], [0, 0], [1, 0]])
        assert_array_equal(self.A.count('a', 0, 2), [[1, 0], [0, 0], [0, 0]])
        assert_array_equal(self.B.count('a'), [[0, 0], [0, 1], [0, 0]])
        assert_array_equal(self.B.count('123'), [[0, 0], [1, 0], [1, 0]])
        # assert_array_equal(self.B.count('\0'), [[0, 0], [0, 0], [1, 0]])

    def test_endswith(self):

            

Reported by Pylint.

                      # Python doesn't seem to like counting NULL characters
        # assert_array_equal(self.A.count('\0'), [[0, 0], [0, 0], [1, 0]])
        assert_array_equal(self.A.count('a', 0, 2), [[1, 0], [0, 0], [0, 0]])
        assert_array_equal(self.B.count('a'), [[0, 0], [0, 1], [0, 0]])
        assert_array_equal(self.B.count('123'), [[0, 0], [1, 0], [1, 0]])
        # assert_array_equal(self.B.count('\0'), [[0, 0], [0, 0], [1, 0]])

    def test_endswith(self):
        assert_(issubclass(self.A.endswith('').dtype.type, np.bool_))

            

Reported by Pylint.

                      # assert_array_equal(self.A.count('\0'), [[0, 0], [0, 0], [1, 0]])
        assert_array_equal(self.A.count('a', 0, 2), [[1, 0], [0, 0], [0, 0]])
        assert_array_equal(self.B.count('a'), [[0, 0], [0, 1], [0, 0]])
        assert_array_equal(self.B.count('123'), [[0, 0], [1, 0], [1, 0]])
        # assert_array_equal(self.B.count('\0'), [[0, 0], [0, 0], [1, 0]])

    def test_endswith(self):
        assert_(issubclass(self.A.endswith('').dtype.type, np.bool_))
        assert_array_equal(self.A.endswith(' '), [[1, 0], [0, 0], [1, 0]])

            

Reported by Pylint.

                      # assert_array_equal(self.B.count('\0'), [[0, 0], [0, 0], [1, 0]])

    def test_endswith(self):
        assert_(issubclass(self.A.endswith('').dtype.type, np.bool_))
        assert_array_equal(self.A.endswith(' '), [[1, 0], [0, 0], [1, 0]])
        assert_array_equal(self.A.endswith('3', 0, 3), [[0, 0], [1, 0], [1, 0]])

        def fail():
            self.A.endswith('3', 'fdjk')

            

Reported by Pylint.

              
    def test_endswith(self):
        assert_(issubclass(self.A.endswith('').dtype.type, np.bool_))
        assert_array_equal(self.A.endswith(' '), [[1, 0], [0, 0], [1, 0]])
        assert_array_equal(self.A.endswith('3', 0, 3), [[0, 0], [1, 0], [1, 0]])

        def fail():
            self.A.endswith('3', 'fdjk')


            

Reported by Pylint.

                  def test_endswith(self):
        assert_(issubclass(self.A.endswith('').dtype.type, np.bool_))
        assert_array_equal(self.A.endswith(' '), [[1, 0], [0, 0], [1, 0]])
        assert_array_equal(self.A.endswith('3', 0, 3), [[0, 0], [1, 0], [1, 0]])

        def fail():
            self.A.endswith('3', 'fdjk')

        assert_raises(TypeError, fail)

            

Reported by Pylint.

                  assert_array_almost_equal, assert_raises, assert_allclose,
    assert_array_max_ulp, assert_raises_regex, suppress_warnings,
    )
import pytest


class TestHistogram:

    def setup(self):

            

Reported by Pylint.

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

            

Reported by Pylint.

                          rec = sup.record(np.VisibleDeprecationWarning, '.*normed.*')
            # Check that the integral of the density equals 1.
            n = 100
            v = np.random.rand(n)
            a, b = histogram(v, normed=True)
            area = np.sum(a * np.diff(b))
            assert_almost_equal(area, 1)
            assert_equal(len(rec), 1)


            

Reported by Pylint.

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

        # Check with non-constant bin widths

            

Reported by Pylint.

                          assert_array_equal(edges, int_edges)

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

            

Reported by Pylint.

                      """Verify that Scott's rule and Stone's rule converges for normally distributed data"""

        def nbins_ratio(seed, size):
            rng = np.random.RandomState(seed)
            x = rng.normal(loc=0, scale=2, size=size)
            a, b = len(np.histogram(x, 'stone')[0]), len(np.histogram(x, 'scott')[0])
            return a / (a + b)

        ll = [[nbins_ratio(seed, size) for size in np.geomspace(start=10, stop=100, num=4).round().astype(int)]

            

Reported by Pylint.

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

    def test_shape_4d(self):

            

Reported by Pylint.

                              (6, 5, 4, 7), (4, 7, 6, 5), (4, 5, 6, 7), (7, 6, 4, 5),
                (5, 4, 7, 6), (5, 6, 7, 4), (6, 4, 5, 7), (7, 5, 6, 4))

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

    def test_weights(self):

            

Reported by Pylint.

                          assert_(H.shape == b)

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

            

Reported by Pylint.

                      assert_(hist[1] == 0.0)

    def test_finite_range(self):
        vals = np.random.random((100, 3))
        histogramdd(vals, range=[[0.0, 1.0], [0.25, 0.75], [0.25, 0.5]])
        assert_raises(ValueError, histogramdd, vals,
                      range=[[0.0, 1.0], [0.25, 0.75], [0.25, np.inf]])
        assert_raises(ValueError, histogramdd, vals,
                      range=[[0.0, 1.0], [np.nan, 0.75], [0.25, 0.5]])

            

Reported by Pylint.

              import warnings
import pytest

import numpy as np
from numpy.lib.nanfunctions import _nan_mask, _replace_nan
from numpy.testing import (
    assert_, assert_equal, assert_almost_equal, assert_no_warnings,
    assert_raises, assert_array_equal, suppress_warnings
    )

            

Reported by Pylint.

                      for f in self.nanfuncs:
            d = np.ones((3, 5, 7, 11))
            # Randomly set some elements to NaN:
            rs = np.random.RandomState(0)
            d[rs.rand(*d.shape) < 0.5] = np.nan
            res = f(d, axis=None)
            assert_equal(res.shape, (1155,))
            for axis in np.arange(4):
                res = f(d, axis=axis)

            

Reported by Pylint.

              
        d = np.ones((3, 5, 7, 11))
        # Randomly set some elements to NaN:
        w = np.random.random((4, 200)) * np.array(d.shape)[:, None]
        w = w.astype(np.intp)
        d[tuple(w)] = np.nan
        with suppress_warnings() as sup:
            sup.filter(RuntimeWarning)
            res = np.nanmedian(d, axis=None, keepdims=True)

            

Reported by Pylint.

                          assert_equal(res.shape, (1, 1, 7, 1))

    def test_out(self):
        mat = np.random.rand(3, 3)
        nan_mat = np.insert(mat, [0, 2], np.nan, axis=1)
        resout = np.zeros(3)
        tgt = np.median(mat, axis=1)
        res = np.nanmedian(nan_mat, axis=1, out=resout)
        assert_almost_equal(res, resout)

            

Reported by Pylint.

                  def test_small_large(self):
        # test the small and large code paths, current cutoff 400 elements
        for s in [5, 20, 51, 200, 1000]:
            d = np.random.randn(4, s)
            # Randomly set some elements to NaN:
            w = np.random.randint(0, d.size, size=d.size // 5)
            d.ravel()[w] = np.nan
            d[:,0] = 1.  # ensure at least one good value
            # use normal median without nans to compare

            

Reported by Pylint.

                      for s in [5, 20, 51, 200, 1000]:
            d = np.random.randn(4, s)
            # Randomly set some elements to NaN:
            w = np.random.randint(0, d.size, size=d.size // 5)
            d.ravel()[w] = np.nan
            d[:,0] = 1.  # ensure at least one good value
            # use normal median without nans to compare
            tgt = []
            for x in d:

            

Reported by Pylint.

              
        d = np.ones((3, 5, 7, 11))
        # Randomly set some elements to NaN:
        w = np.random.random((4, 200)) * np.array(d.shape)[:, None]
        w = w.astype(np.intp)
        d[tuple(w)] = np.nan
        with suppress_warnings() as sup:
            sup.filter(RuntimeWarning)
            res = np.nanpercentile(d, 90, axis=None, keepdims=True)

            

Reported by Pylint.

                          assert_equal(res.shape, (1, 1, 7, 1))

    def test_out(self):
        mat = np.random.rand(3, 3)
        nan_mat = np.insert(mat, [0, 2], np.nan, axis=1)
        resout = np.zeros(3)
        tgt = np.percentile(mat, 42, axis=1)
        res = np.nanpercentile(nan_mat, 42, axis=1, out=resout)
        assert_almost_equal(res, resout)

            

Reported by Pylint.

                          for axis in [None, 0, 1]:
                tgt = rf(mat, axis=axis, keepdims=True)
                res = nf(mat, axis=axis, keepdims=True)
                assert_(res.ndim == tgt.ndim)

    def test_out(self):
        mat = np.eye(3)
        for nf, rf in zip(self.nanfuncs, self.stdfuncs):
            resout = np.zeros(3)

            

Reported by Pylint.

                              mat = np.eye(3, dtype=c)
                tgt = rf(mat, axis=1).dtype.type
                res = nf(mat, axis=1).dtype.type
                assert_(res is tgt)
                # scalar case
                tgt = rf(mat, axis=None).dtype.type
                res = nf(mat, axis=None).dtype.type
                assert_(res is tgt)


            

Reported by Pylint.

              """Tests for the array padding functions.

"""
import pytest

import numpy as np
from numpy.testing import assert_array_equal, assert_allclose, assert_equal
from numpy.lib.arraypad import _as_pairs


            

Reported by Pylint.

              

def test_legacy_vector_functionality():
    def _padwithtens(vector, pad_width, iaxis, kwargs):
        vector[:pad_width[0]] = 10
        vector[-pad_width[1]:] = 10

    a = np.arange(6).reshape(2, 3)
    a = np.pad(a, 2, _padwithtens)

            

Reported by Pylint.

              

def test_legacy_vector_functionality():
    def _padwithtens(vector, pad_width, iaxis, kwargs):
        vector[:pad_width[0]] = 10
        vector[-pad_width[1]:] = 10

    a = np.arange(6).reshape(2, 3)
    a = np.pad(a, 2, _padwithtens)

            

Reported by Pylint.

              """Tests for the array padding functions.

"""
import pytest

import numpy as np
from numpy.testing import assert_array_equal, assert_allclose, assert_equal
from numpy.lib.arraypad import _as_pairs


            

Reported by Pylint.

              }


class TestAsPairs:
    def test_single_value(self):
        """Test casting for a single value."""
        expected = np.array([[3, 3]] * 10)
        for x in (3, [3], [[3]]):
            result = _as_pairs(x, 10)

            

Reported by Pylint.

              

class TestAsPairs:
    def test_single_value(self):
        """Test casting for a single value."""
        expected = np.array([[3, 3]] * 10)
        for x in (3, [3], [[3]]):
            result = _as_pairs(x, 10)
            assert_equal(result, expected)

            

Reported by Pylint.

                  def test_single_value(self):
        """Test casting for a single value."""
        expected = np.array([[3, 3]] * 10)
        for x in (3, [3], [[3]]):
            result = _as_pairs(x, 10)
            assert_equal(result, expected)
        # Test with dtype=object
        obj = object()
        assert_equal(

            

Reported by Pylint.

                          np.array([[obj, obj]] * 10)
        )

    def test_two_values(self):
        """Test proper casting for two different values."""
        # Broadcasting in the first dimension with numbers
        expected = np.array([[3, 4]] * 10)
        for x in ([3, 4], [[3, 4]]):
            result = _as_pairs(x, 10)

            

Reported by Pylint.

                      """Test proper casting for two different values."""
        # Broadcasting in the first dimension with numbers
        expected = np.array([[3, 4]] * 10)
        for x in ([3, 4], [[3, 4]]):
            result = _as_pairs(x, 10)
            assert_equal(result, expected)
        # and with dtype=object
        obj = object()
        assert_equal(

            

Reported by Pylint.

                          np.array([["a", "a"], [obj, obj]])
        )

    def test_with_none(self):
        expected = ((None, None), (None, None), (None, None))
        assert_equal(
            _as_pairs(None, 3, as_index=False),
            expected
        )

            

Reported by Pylint.