code: # pylint: disable-msg=W0400,W0511,W0611,W0612,W0614,R0201,E1102
  """Tests suite for MaskedArray & subclassing.
  
  :author: Pierre Gerard-Marchant
  :contact: pierregm_at_uga_dot_edu
  """
  from __future__ import division, absolute_import, print_function
  
  __author__ = "Pierre GF Gerard-Marchant"
  
  import warnings
  import pickle
  import operator
  import itertools
  from functools import reduce
  
  
  import numpy as np
  import numpy.ma.core
  import numpy.core.fromnumeric as fromnumeric
  import numpy.core.umath as umath
  from numpy.testing import (
      run_module_suite, assert_raises, assert_warns, suppress_warnings
      )
  from numpy import ndarray
  from numpy.compat import asbytes, asbytes_nested
  from numpy.ma.testutils import (
      assert_, assert_array_equal, assert_equal, assert_almost_equal,
      assert_equal_records, fail_if_equal, assert_not_equal,
      assert_mask_equal
      )
  from numpy.ma.core import (
      MAError, MaskError, MaskType, MaskedArray, abs, absolute, add, all,
      allclose, allequal, alltrue, angle, anom, arange, arccos, arccosh, arctan2,
      arcsin, arctan, argsort, array, asarray, choose, concatenate,
      conjugate, cos, cosh, count, default_fill_value, diag, divide, empty,
      empty_like, equal, exp, flatten_mask, filled, fix_invalid,
      flatten_structured_array, fromflex, getmask, getmaskarray, greater,
      greater_equal, identity, inner, isMaskedArray, less, less_equal, log,
      log10, make_mask, make_mask_descr, mask_or, masked, masked_array,
      masked_equal, masked_greater, masked_greater_equal, masked_inside,
      masked_less, masked_less_equal, masked_not_equal, masked_outside,
      masked_print_option, masked_values, masked_where, max, maximum,
      maximum_fill_value, min, minimum, minimum_fill_value, mod, multiply,
      mvoid, nomask, not_equal, ones, outer, power, product, put, putmask,
      ravel, repeat, reshape, resize, shape, sin, sinh, sometrue, sort, sqrt,
      subtract, sum, take, tan, tanh, transpose, where, zeros,
      )
  
  pi = np.pi
  
  
  suppress_copy_mask_on_assignment = suppress_warnings()
  suppress_copy_mask_on_assignment.filter(
      numpy.ma.core.MaskedArrayFutureWarning,
      "setting an item on a masked array which has a shared mask will not copy")
  
  
  class TestMaskedArray(object):
      # Base test class for MaskedArrays.
  
      def setup(self):
          # Base data definition.
          x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
          y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
          a10 = 10.
          m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
          m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
          xm = masked_array(x, mask=m1)
          ym = masked_array(y, mask=m2)
          z = np.array([-.5, 0., .5, .8])
          zm = masked_array(z, mask=[0, 1, 0, 0])
          xf = np.where(m1, 1e+20, x)
          xm.set_fill_value(1e+20)
          self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf)
  
      def test_basicattributes(self):
          # Tests some basic array attributes.
          a = array([1, 3, 2])
          b = array([1, 3, 2], mask=[1, 0, 1])
          assert_equal(a.ndim, 1)
          assert_equal(b.ndim, 1)
          assert_equal(a.size, 3)
          assert_equal(b.size, 3)
          assert_equal(a.shape, (3,))
          assert_equal(b.shape, (3,))
  
      def test_basic0d(self):
          # Checks masking a scalar
          x = masked_array(0)
          assert_equal(str(x), '0')
          x = masked_array(0, mask=True)
          assert_equal(str(x), str(masked_print_option))
          x = masked_array(0, mask=False)
          assert_equal(str(x), '0')
          x = array(0, mask=1)
          assert_(x.filled().dtype is x._data.dtype)
  
      def test_basic1d(self):
          # Test of basic array creation and properties in 1 dimension.
          (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
          assert_(not isMaskedArray(x))
          assert_(isMaskedArray(xm))
          assert_((xm - ym).filled(0).any())
          fail_if_equal(xm.mask.astype(int), ym.mask.astype(int))
          s = x.shape
          assert_equal(np.shape(xm), s)
          assert_equal(xm.shape, s)
          assert_equal(xm.dtype, x.dtype)
          assert_equal(zm.dtype, z.dtype)
          assert_equal(xm.size, reduce(lambda x, y:x * y, s))
          assert_equal(count(xm), len(m1) - reduce(lambda x, y:x + y, m1))
          assert_array_equal(xm, xf)
          assert_array_equal(filled(xm, 1.e20), xf)
          assert_array_equal(x, xm)
  
      def test_basic2d(self):
          # Test of basic array creation and properties in 2 dimensions.
          (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
          for s in [(4, 3), (6, 2)]:
              x.shape = s
              y.shape = s
              xm.shape = s
              ym.shape = s
              xf.shape = s
  
              assert_(not isMaskedArray(x))
              assert_(isMaskedArray(xm))
              assert_equal(shape(xm), s)
              assert_equal(xm.shape, s)
              assert_equal(xm.size, reduce(lambda x, y:x * y, s))
              assert_equal(count(xm), len(m1) - reduce(lambda x, y:x + y, m1))
              assert_equal(xm, xf)
              assert_equal(filled(xm, 1.e20), xf)
              assert_equal(x, xm)
  
      def test_concatenate_basic(self):
          # Tests concatenations.
          (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
          # basic concatenation
          assert_equal(np.concatenate((x, y)), concatenate((xm, ym)))
          assert_equal(np.concatenate((x, y)), concatenate((x, y)))
          assert_equal(np.concatenate((x, y)), concatenate((xm, y)))
          assert_equal(np.concatenate((x, y, x)), concatenate((x, ym, x)))
  
      def test_concatenate_alongaxis(self):
          # Tests concatenations.
          (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
          # Concatenation along an axis
          s = (3, 4)
          x.shape = y.shape = xm.shape = ym.shape = s
          assert_equal(xm.mask, np.reshape(m1, s))
          assert_equal(ym.mask, np.reshape(m2, s))
          xmym = concatenate((xm, ym), 1)
          assert_equal(np.concatenate((x, y), 1), xmym)
          assert_equal(np.concatenate((xm.mask, ym.mask), 1), xmym._mask)
  
          x = zeros(2)
          y = array(ones(2), mask=[False, True])
          z = concatenate((x, y))
          assert_array_equal(z, [0, 0, 1, 1])
          assert_array_equal(z.mask, [False, False, False, True])
          z = concatenate((y, x))
          assert_array_equal(z, [1, 1, 0, 0])
          assert_array_equal(z.mask, [False, True, False, False])
  
      def test_concatenate_flexible(self):
          # Tests the concatenation on flexible arrays.
          data = masked_array(list(zip(np.random.rand(10),
                                       np.arange(10))),
                              dtype=[('a', float), ('b', int)])
  
          test = concatenate([data[:5], data[5:]])
          assert_equal_records(test, data)
  
      def test_creation_ndmin(self):
          # Check the use of ndmin
          x = array([1, 2, 3], mask=[1, 0, 0], ndmin=2)
          assert_equal(x.shape, (1, 3))
          assert_equal(x._data, [[1, 2, 3]])
          assert_equal(x._mask, [[1, 0, 0]])
  
      def test_creation_ndmin_from_maskedarray(self):
          # Make sure we're not losing the original mask w/ ndmin
          x = array([1, 2, 3])
          x[-1] = masked
          xx = array(x, ndmin=2, dtype=float)
          assert_equal(x.shape, x._mask.shape)
          assert_equal(xx.shape, xx._mask.shape)
  
      def test_creation_maskcreation(self):
          # Tests how masks are initialized at the creation of Maskedarrays.
          data = arange(24, dtype=float)
          data[[3, 6, 15]] = masked
          dma_1 = MaskedArray(data)
          assert_equal(dma_1.mask, data.mask)
          dma_2 = MaskedArray(dma_1)
          assert_equal(dma_2.mask, dma_1.mask)
          dma_3 = MaskedArray(dma_1, mask=[1, 0, 0, 0] * 6)
          fail_if_equal(dma_3.mask, dma_1.mask)
  
          x = array([1, 2, 3], mask=True)
          assert_equal(x._mask, [True, True, True])
          x = array([1, 2, 3], mask=False)
          assert_equal(x._mask, [False, False, False])
          y = array([1, 2, 3], mask=x._mask, copy=False)
          assert_(np.may_share_memory(x.mask, y.mask))
          y = array([1, 2, 3], mask=x._mask, copy=True)
          assert_(not np.may_share_memory(x.mask, y.mask))
  
      def test_creation_with_list_of_maskedarrays(self):
          # Tests creating a masked array from a list of masked arrays.
          x = array(np.arange(5), mask=[1, 0, 0, 0, 0])
          data = array((x, x[::-1]))
          assert_equal(data, [[0, 1, 2, 3, 4], [4, 3, 2, 1, 0]])
          assert_equal(data._mask, [[1, 0, 0, 0, 0], [0, 0, 0, 0, 1]])
  
          x.mask = nomask
          data = array((x, x[::-1]))
          assert_equal(data, [[0, 1, 2, 3, 4], [4, 3, 2, 1, 0]])
          assert_(data.mask is nomask)
  
      def test_creation_from_ndarray_with_padding(self):
          x = np.array([('A', 0)], dtype={'names':['f0','f1'],
                                          'formats':['S4','i8'],
                                          'offsets':[0,8]})
          data = array(x) # used to fail due to 'V' padding field in x.dtype.descr
  
      def test_asarray(self):
          (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
          xm.fill_value = -9999
          xm._hardmask = True
          xmm = asarray(xm)
          assert_equal(xmm._data, xm._data)
          assert_equal(xmm._mask, xm._mask)
          assert_equal(xmm.fill_value, xm.fill_value)
          assert_equal(xmm._hardmask, xm._hardmask)
  
      def test_asarray_default_order(self):
          # See Issue #6646
          m = np.eye(3).T
          assert_(not m.flags.c_contiguous)
  
          new_m = asarray(m)
          assert_(new_m.flags.c_contiguous)
  
      def test_asarray_enforce_order(self):
          # See Issue #6646
          m = np.eye(3).T
          assert_(not m.flags.c_contiguous)
  
          new_m = asarray(m, order='C')
          assert_(new_m.flags.c_contiguous)
  
      def test_fix_invalid(self):
          # Checks fix_invalid.
          with np.errstate(invalid='ignore'):
              data = masked_array([np.nan, 0., 1.], mask=[0, 0, 1])
              data_fixed = fix_invalid(data)
              assert_equal(data_fixed._data, [data.fill_value, 0., 1.])
              assert_equal(data_fixed._mask, [1., 0., 1.])
  
      def test_maskedelement(self):
          # Test of masked element
          x = arange(6)
          x[1] = masked
          assert_(str(masked) == '--')
          assert_(x[1] is masked)
          assert_equal(filled(x[1], 0), 0)
  
      def test_set_element_as_object(self):
          # Tests setting elements with object
          a = empty(1, dtype=object)
          x = (1, 2, 3, 4, 5)
          a[0] = x
          assert_equal(a[0], x)
          assert_(a[0] is x)
  
          import datetime
          dt = datetime.datetime.now()
          a[0] = dt
          assert_(a[0] is dt)
  
      def test_indexing(self):
          # Tests conversions and indexing
          x1 = np.array([1, 2, 4, 3])
          x2 = array(x1, mask=[1, 0, 0, 0])
          x3 = array(x1, mask=[0, 1, 0, 1])
          x4 = array(x1)
          # test conversion to strings
          str(x2)  # raises?
          repr(x2)  # raises?
          assert_equal(np.sort(x1), sort(x2, endwith=False))
          # tests of indexing
          assert_(type(x2[1]) is type(x1[1]))
          assert_(x1[1] == x2[1])
          assert_(x2[0] is masked)
          assert_equal(x1[2], x2[2])
          assert_equal(x1[2:5], x2[2:5])
          assert_equal(x1[:], x2[:])
          assert_equal(x1[1:], x3[1:])
          x1[2] = 9
          x2[2] = 9
          assert_equal(x1, x2)
          x1[1:3] = 99
          x2[1:3] = 99
          assert_equal(x1, x2)
          x2[1] = masked
          assert_equal(x1, x2)
          x2[1:3] = masked
          assert_equal(x1, x2)
          x2[:] = x1
          x2[1] = masked
          assert_(allequal(getmask(x2), array([0, 1, 0, 0])))
          x3[:] = masked_array([1, 2, 3, 4], [0, 1, 1, 0])
          assert_(allequal(getmask(x3), array([0, 1, 1, 0])))
          x4[:] = masked_array([1, 2, 3, 4], [0, 1, 1, 0])
          assert_(allequal(getmask(x4), array([0, 1, 1, 0])))
          assert_(allequal(x4, array([1, 2, 3, 4])))
          x1 = np.arange(5) * 1.0
          x2 = masked_values(x1, 3.0)
          assert_equal(x1, x2)
          assert_(allequal(array([0, 0, 0, 1, 0], MaskType), x2.mask))
          assert_equal(3.0, x2.fill_value)
          x1 = array([1, 'hello', 2, 3], object)
          x2 = np.array([1, 'hello', 2, 3], object)
          s1 = x1[1]
          s2 = x2[1]
          assert_equal(type(s2), str)
          assert_equal(type(s1), str)
          assert_equal(s1, s2)
          assert_(x1[1:1].shape == (0,))
  
      def test_matrix_indexing(self):
          # Tests conversions and indexing
          x1 = np.matrix([[1, 2, 3], [4, 3, 2]])
          x2 = array(x1, mask=[[1, 0, 0], [0, 1, 0]])
          x3 = array(x1, mask=[[0, 1, 0], [1, 0, 0]])
          x4 = array(x1)
          # test conversion to strings
          str(x2)  # raises?
          repr(x2)  # raises?
          # tests of indexing
          assert_(type(x2[1, 0]) is type(x1[1, 0]))
          assert_(x1[1, 0] == x2[1, 0])
          assert_(x2[1, 1] is masked)
          assert_equal(x1[0, 2], x2[0, 2])
          assert_equal(x1[0, 1:], x2[0, 1:])
          assert_equal(x1[:, 2], x2[:, 2])
          assert_equal(x1[:], x2[:])
          assert_equal(x1[1:], x3[1:])
          x1[0, 2] = 9
          x2[0, 2] = 9
          assert_equal(x1, x2)
          x1[0, 1:] = 99
          x2[0, 1:] = 99
          assert_equal(x1, x2)
          x2[0, 1] = masked
          assert_equal(x1, x2)
          x2[0, 1:] = masked
          assert_equal(x1, x2)
          x2[0, :] = x1[0, :]
          x2[0, 1] = masked
          assert_(allequal(getmask(x2), np.array([[0, 1, 0], [0, 1, 0]])))
          x3[1, :] = masked_array([1, 2, 3], [1, 1, 0])
          assert_(allequal(getmask(x3)[1], array([1, 1, 0])))
          assert_(allequal(getmask(x3[1]), array([1, 1, 0])))
          x4[1, :] = masked_array([1, 2, 3], [1, 1, 0])
          assert_(allequal(getmask(x4[1]), array([1, 1, 0])))
          assert_(allequal(x4[1], array([1, 2, 3])))
          x1 = np.matrix(np.arange(5) * 1.0)
          x2 = masked_values(x1, 3.0)
          assert_equal(x1, x2)
          assert_(allequal(array([0, 0, 0, 1, 0], MaskType), x2.mask))
          assert_equal(3.0, x2.fill_value)
  
      @suppress_copy_mask_on_assignment
      def test_copy(self):
          # Tests of some subtle points of copying and sizing.
          n = [0, 0, 1, 0, 0]
          m = make_mask(n)
          m2 = make_mask(m)
          assert_(m is m2)
          m3 = make_mask(m, copy=1)
          assert_(m is not m3)
  
          x1 = np.arange(5)
          y1 = array(x1, mask=m)
          assert_equal(y1._data.__array_interface__, x1.__array_interface__)
          assert_(allequal(x1, y1.data))
          assert_equal(y1._mask.__array_interface__, m.__array_interface__)
  
          y1a = array(y1)
          assert_(y1a._data.__array_interface__ ==
                          y1._data.__array_interface__)
          assert_(y1a.mask is y1.mask)
  
          y2 = array(x1, mask=m3)
          assert_(y2._data.__array_interface__ == x1.__array_interface__)
          assert_(y2._mask.__array_interface__ == m3.__array_interface__)
          assert_(y2[2] is masked)
          y2[2] = 9
          assert_(y2[2] is not masked)
          assert_(y2._mask.__array_interface__ == m3.__array_interface__)
          assert_(allequal(y2.mask, 0))
  
          y2a = array(x1, mask=m, copy=1)
          assert_(y2a._data.__array_interface__ != x1.__array_interface__)
          #assert_( y2a.mask is not m)
          assert_(y2a._mask.__array_interface__ != m.__array_interface__)
          assert_(y2a[2] is masked)
          y2a[2] = 9
          assert_(y2a[2] is not masked)
          #assert_( y2a.mask is not m)
          assert_(y2a._mask.__array_interface__ != m.__array_interface__)
          assert_(allequal(y2a.mask, 0))
  
          y3 = array(x1 * 1.0, mask=m)
          assert_(filled(y3).dtype is (x1 * 1.0).dtype)
  
          x4 = arange(4)
          x4[2] = masked
          y4 = resize(x4, (8,))
          assert_equal(concatenate([x4, x4]), y4)
          assert_equal(getmask(y4), [0, 0, 1, 0, 0, 0, 1, 0])
          y5 = repeat(x4, (2, 2, 2, 2), axis=0)
          assert_equal(y5, [0, 0, 1, 1, 2, 2, 3, 3])
          y6 = repeat(x4, 2, axis=0)
          assert_equal(y5, y6)
          y7 = x4.repeat((2, 2, 2, 2), axis=0)
          assert_equal(y5, y7)
          y8 = x4.repeat(2, 0)
          assert_equal(y5, y8)
  
          y9 = x4.copy()
          assert_equal(y9._data, x4._data)
          assert_equal(y9._mask, x4._mask)
  
          x = masked_array([1, 2, 3], mask=[0, 1, 0])
          # Copy is False by default
          y = masked_array(x)
          assert_equal(y._data.ctypes.data, x._data.ctypes.data)
          assert_equal(y._mask.ctypes.data, x._mask.ctypes.data)
          y = masked_array(x, copy=True)
          assert_not_equal(y._data.ctypes.data, x._data.ctypes.data)
          assert_not_equal(y._mask.ctypes.data, x._mask.ctypes.data)
  
      def test_copy_on_python_builtins(self):
          # Tests copy works on python builtins (issue#8019)
          assert_(isMaskedArray(np.ma.copy([1,2,3])))
          assert_(isMaskedArray(np.ma.copy((1,2,3))))
  
      def test_copy_immutable(self):
          # Tests that the copy method is immutable, GitHub issue #5247
          a = np.ma.array([1, 2, 3])
          b = np.ma.array([4, 5, 6])
          a_copy_method = a.copy
          b.copy
          assert_equal(a_copy_method(), [1, 2, 3])
  
      def test_deepcopy(self):
          from copy import deepcopy
          a = array([0, 1, 2], mask=[False, True, False])
          copied = deepcopy(a)
          assert_equal(copied.mask, a.mask)
          assert_not_equal(id(a._mask), id(copied._mask))
  
          copied[1] = 1
          assert_equal(copied.mask, [0, 0, 0])
          assert_equal(a.mask, [0, 1, 0])
  
          copied = deepcopy(a)
          assert_equal(copied.mask, a.mask)
          copied.mask[1] = False
          assert_equal(copied.mask, [0, 0, 0])
          assert_equal(a.mask, [0, 1, 0])
  
      def test_str_repr(self):
          a = array([0, 1, 2], mask=[False, True, False])
          assert_equal(str(a), '[0 -- 2]')
          assert_equal(repr(a), 'masked_array(data = [0 -- 2],\n'
                                '             mask = [False  True False],\n'
                                '       fill_value = 999999)\n')
  
          a = np.ma.arange(2000)
          a[1:50] = np.ma.masked
          assert_equal(
              repr(a),
              'masked_array(data = [0 -- -- ..., 1997 1998 1999],\n'
              '             mask = [False  True  True ..., False False False],\n'
              '       fill_value = 999999)\n'
          )
  
      def test_pickling(self):
          # Tests pickling
          for dtype in (int, float, str, object):
              a = arange(10).astype(dtype)
              a.fill_value = 999
  
              masks = ([0, 0, 0, 1, 0, 1, 0, 1, 0, 1],  # partially masked
                       True,                            # Fully masked
                       False)                           # Fully unmasked
  
              for mask in masks:
                  a.mask = mask
                  a_pickled = pickle.loads(a.dumps())
                  assert_equal(a_pickled._mask, a._mask)
                  assert_equal(a_pickled._data, a._data)
                  if dtype in (object, int):
                      assert_equal(a_pickled.fill_value, 999)
                  else:
                      assert_equal(a_pickled.fill_value, dtype(999))
                  assert_array_equal(a_pickled.mask, mask)
  
      def test_pickling_subbaseclass(self):
          # Test pickling w/ a subclass of ndarray
          a = array(np.matrix(list(range(10))), mask=[1, 0, 1, 0, 0] * 2)
          a_pickled = pickle.loads(a.dumps())
          assert_equal(a_pickled._mask, a._mask)
          assert_equal(a_pickled, a)
          assert_(isinstance(a_pickled._data, np.matrix))
  
      def test_pickling_maskedconstant(self):
          # Test pickling MaskedConstant
          mc = np.ma.masked
          mc_pickled = pickle.loads(mc.dumps())
          assert_equal(mc_pickled._baseclass, mc._baseclass)
          assert_equal(mc_pickled._mask, mc._mask)
          assert_equal(mc_pickled._data, mc._data)
  
      def test_pickling_wstructured(self):
          # Tests pickling w/ structured array
          a = array([(1, 1.), (2, 2.)], mask=[(0, 0), (0, 1)],
                    dtype=[('a', int), ('b', float)])
          a_pickled = pickle.loads(a.dumps())
          assert_equal(a_pickled._mask, a._mask)
          assert_equal(a_pickled, a)
  
      def test_pickling_keepalignment(self):
          # Tests pickling w/ F_CONTIGUOUS arrays
          a = arange(10)
          a.shape = (-1, 2)
          b = a.T
          test = pickle.loads(pickle.dumps(b))
          assert_equal(test, b)
  
      def test_single_element_subscript(self):
          # Tests single element subscripts of Maskedarrays.
          a = array([1, 3, 2])
          b = array([1, 3, 2], mask=[1, 0, 1])
          assert_equal(a[0].shape, ())
          assert_equal(b[0].shape, ())
          assert_equal(b[1].shape, ())
  
      def test_topython(self):
          # Tests some communication issues with Python.
          assert_equal(1, int(array(1)))
          assert_equal(1.0, float(array(1)))
          assert_equal(1, int(array([[[1]]])))
          assert_equal(1.0, float(array([[1]])))
          assert_raises(TypeError, float, array([1, 1]))
  
          with suppress_warnings() as sup:
              sup.filter(UserWarning, 'Warning: converting a masked element')
              assert_(np.isnan(float(array([1], mask=[1]))))
  
              a = array([1, 2, 3], mask=[1, 0, 0])
              assert_raises(TypeError, lambda: float(a))
              assert_equal(float(a[-1]), 3.)
              assert_(np.isnan(float(a[0])))
          assert_raises(TypeError, int, a)
          assert_equal(int(a[-1]), 3)
          assert_raises(MAError, lambda:int(a[0]))
  
      def test_oddfeatures_1(self):
          # Test of other odd features
          x = arange(20)
          x = x.reshape(4, 5)
          x.flat[5] = 12
          assert_(x[1, 0] == 12)
          z = x + 10j * x
          assert_equal(z.real, x)
          assert_equal(z.imag, 10 * x)
          assert_equal((z * conjugate(z)).real, 101 * x * x)
          z.imag[...] = 0.0
  
          x = arange(10)
          x[3] = masked
          assert_(str(x[3]) == str(masked))
          c = x >= 8
          assert_(count(where(c, masked, masked)) == 0)
          assert_(shape(where(c, masked, masked)) == c.shape)
  
          z = masked_where(c, x)
          assert_(z.dtype is x.dtype)
          assert_(z[3] is masked)
          assert_(z[4] is not masked)
          assert_(z[7] is not masked)
          assert_(z[8] is masked)
          assert_(z[9] is masked)
          assert_equal(x, z)
  
      def test_oddfeatures_2(self):
          # Tests some more features.
          x = array([1., 2., 3., 4., 5.])
          c = array([1, 1, 1, 0, 0])
          x[2] = masked
          z = where(c, x, -x)
          assert_equal(z, [1., 2., 0., -4., -5])
          c[0] = masked
          z = where(c, x, -x)
          assert_equal(z, [1., 2., 0., -4., -5])
          assert_(z[0] is masked)
          assert_(z[1] is not masked)
          assert_(z[2] is masked)
  
      @suppress_copy_mask_on_assignment
      def test_oddfeatures_3(self):
          # Tests some generic features
          atest = array([10], mask=True)
          btest = array([20])
          idx = atest.mask
          atest[idx] = btest[idx]
          assert_equal(atest, [20])
  
      def test_filled_with_object_dtype(self):
          a = np.ma.masked_all(1, dtype='O')
          assert_equal(a.filled('x')[0], 'x')
  
      def test_filled_with_flexible_dtype(self):
          # Test filled w/ flexible dtype
          flexi = array([(1, 1, 1)],
                        dtype=[('i', int), ('s', '|S8'), ('f', float)])
          flexi[0] = masked
          assert_equal(flexi.filled(),
                       np.array([(default_fill_value(0),
                                  default_fill_value('0'),
                                  default_fill_value(0.),)], dtype=flexi.dtype))
          flexi[0] = masked
          assert_equal(flexi.filled(1),
                       np.array([(1, '1', 1.)], dtype=flexi.dtype))
  
      def test_filled_with_mvoid(self):
          # Test filled w/ mvoid
          ndtype = [('a', int), ('b', float)]
          a = mvoid((1, 2.), mask=[(0, 1)], dtype=ndtype)
          # Filled using default
          test = a.filled()
          assert_equal(tuple(test), (1, default_fill_value(1.)))
          # Explicit fill_value
          test = a.filled((-1, -1))
          assert_equal(tuple(test), (1, -1))
          # Using predefined filling values
          a.fill_value = (-999, -999)
          assert_equal(tuple(a.filled()), (1, -999))
  
      def test_filled_with_nested_dtype(self):
          # Test filled w/ nested dtype
          ndtype = [('A', int), ('B', [('BA', int), ('BB', int)])]
          a = array([(1, (1, 1)), (2, (2, 2))],
                    mask=[(0, (1, 0)), (0, (0, 1))], dtype=ndtype)
          test = a.filled(0)
          control = np.array([(1, (0, 1)), (2, (2, 0))], dtype=ndtype)
          assert_equal(test, control)
  
          test = a['B'].filled(0)
          control = np.array([(0, 1), (2, 0)], dtype=a['B'].dtype)
          assert_equal(test, control)
  
          # test if mask gets set correctly (see #6760)
          Z = numpy.ma.zeros(2, numpy.dtype([("A", "(2,2)i1,(2,2)i1", (2,2))]))
          assert_equal(Z.data.dtype, numpy.dtype([('A', [('f0', 'i1', (2, 2)),
                                            ('f1', 'i1', (2, 2))], (2, 2))]))
          assert_equal(Z.mask.dtype, numpy.dtype([('A', [('f0', '?', (2, 2)),
                                            ('f1', '?', (2, 2))], (2, 2))]))
  
      def test_filled_with_f_order(self):
          # Test filled w/ F-contiguous array
          a = array(np.array([(0, 1, 2), (4, 5, 6)], order='F'),
                    mask=np.array([(0, 0, 1), (1, 0, 0)], order='F'),
                    order='F')  # this is currently ignored
          assert_(a.flags['F_CONTIGUOUS'])
          assert_(a.filled(0).flags['F_CONTIGUOUS'])
  
      def test_optinfo_propagation(self):
          # Checks that _optinfo dictionary isn't back-propagated
          x = array([1, 2, 3, ], dtype=float)
          x._optinfo['info'] = '???'
          y = x.copy()
          assert_equal(y._optinfo['info'], '???')
          y._optinfo['info'] = '!!!'
          assert_equal(x._optinfo['info'], '???')
  
      def test_fancy_printoptions(self):
          # Test printing a masked array w/ fancy dtype.
          fancydtype = np.dtype([('x', int), ('y', [('t', int), ('s', float)])])
          test = array([(1, (2, 3.0)), (4, (5, 6.0))],
                       mask=[(1, (0, 1)), (0, (1, 0))],
                       dtype=fancydtype)
          control = "[(--, (2, --)) (4, (--, 6.0))]"
          assert_equal(str(test), control)
  
          # Test 0-d array with multi-dimensional dtype
          t_2d0 = masked_array(data = (0, [[0.0, 0.0, 0.0],
                                          [0.0, 0.0, 0.0]],
                                      0.0),
                               mask = (False, [[True, False, True],
                                               [False, False, True]],
                                       False),
                               dtype = "int, (2,3)float, float")
          control = "(0, [[--, 0.0, --], [0.0, 0.0, --]], 0.0)"
          assert_equal(str(t_2d0), control)
  
  
      def test_flatten_structured_array(self):
          # Test flatten_structured_array on arrays
          # On ndarray
          ndtype = [('a', int), ('b', float)]
          a = np.array([(1, 1), (2, 2)], dtype=ndtype)
          test = flatten_structured_array(a)
          control = np.array([[1., 1.], [2., 2.]], dtype=float)
          assert_equal(test, control)
          assert_equal(test.dtype, control.dtype)
          # On masked_array
          a = array([(1, 1), (2, 2)], mask=[(0, 1), (1, 0)], dtype=ndtype)
          test = flatten_structured_array(a)
          control = array([[1., 1.], [2., 2.]],
                          mask=[[0, 1], [1, 0]], dtype=float)
          assert_equal(test, control)
          assert_equal(test.dtype, control.dtype)
          assert_equal(test.mask, control.mask)
          # On masked array with nested structure
          ndtype = [('a', int), ('b', [('ba', int), ('bb', float)])]
          a = array([(1, (1, 1.1)), (2, (2, 2.2))],
                    mask=[(0, (1, 0)), (1, (0, 1))], dtype=ndtype)
          test = flatten_structured_array(a)
          control = array([[1., 1., 1.1], [2., 2., 2.2]],
                          mask=[[0, 1, 0], [1, 0, 1]], dtype=float)
          assert_equal(test, control)
          assert_equal(test.dtype, control.dtype)
          assert_equal(test.mask, control.mask)
          # Keeping the initial shape
          ndtype = [('a', int), ('b', float)]
          a = np.array([[(1, 1), ], [(2, 2), ]], dtype=ndtype)
          test = flatten_structured_array(a)
          control = np.array([[[1., 1.], ], [[2., 2.], ]], dtype=float)
          assert_equal(test, control)
          assert_equal(test.dtype, control.dtype)
  
      def test_void0d(self):
          # Test creating a mvoid object
          ndtype = [('a', int), ('b', int)]
          a = np.array([(1, 2,)], dtype=ndtype)[0]
          f = mvoid(a)
          assert_(isinstance(f, mvoid))
  
          a = masked_array([(1, 2)], mask=[(1, 0)], dtype=ndtype)[0]
          assert_(isinstance(a, mvoid))
  
          a = masked_array([(1, 2), (1, 2)], mask=[(1, 0), (0, 0)], dtype=ndtype)
          f = mvoid(a._data[0], a._mask[0])
          assert_(isinstance(f, mvoid))
  
      def test_mvoid_getitem(self):
          # Test mvoid.__getitem__
          ndtype = [('a', int), ('b', int)]
          a = masked_array([(1, 2,), (3, 4)], mask=[(0, 0), (1, 0)],
                           dtype=ndtype)
          # w/o mask
          f = a[0]
          assert_(isinstance(f, mvoid))
          assert_equal((f[0], f['a']), (1, 1))
          assert_equal(f['b'], 2)
          # w/ mask
          f = a[1]
          assert_(isinstance(f, mvoid))
          assert_(f[0] is masked)
          assert_(f['a'] is masked)
          assert_equal(f[1], 4)
  
          # exotic dtype
          A = masked_array(data=[([0,1],)],
                           mask=[([True, False],)],
                           dtype=[("A", ">i2", (2,))])
          assert_equal(A[0]["A"], A["A"][0])
          assert_equal(A[0]["A"], masked_array(data=[0, 1],
                           mask=[True, False], dtype=">i2"))
  
      def test_mvoid_iter(self):
          # Test iteration on __getitem__
          ndtype = [('a', int), ('b', int)]
          a = masked_array([(1, 2,), (3, 4)], mask=[(0, 0), (1, 0)],
                           dtype=ndtype)
          # w/o mask
          assert_equal(list(a[0]), [1, 2])
          # w/ mask
          assert_equal(list(a[1]), [masked, 4])
  
      def test_mvoid_print(self):
          # Test printing a mvoid
          mx = array([(1, 1), (2, 2)], dtype=[('a', int), ('b', int)])
          assert_equal(str(mx[0]), "(1, 1)")
          mx['b'][0] = masked
          ini_display = masked_print_option._display
          masked_print_option.set_display("-X-")
          try:
              assert_equal(str(mx[0]), "(1, -X-)")
              assert_equal(repr(mx[0]), "(1, -X-)")
          finally:
              masked_print_option.set_display(ini_display)
  
          # also check if there are object datatypes (see gh-7493)
          mx = array([(1,), (2,)], dtype=[('a', 'O')])
          assert_equal(str(mx[0]), "(1,)")
  
      def test_mvoid_multidim_print(self):
  
          # regression test for gh-6019
          t_ma = masked_array(data = [([1, 2, 3],)],
                              mask = [([False, True, False],)],
                              fill_value = ([999999, 999999, 999999],),
                              dtype = [('a', '<i4', (3,))])
          assert_(str(t_ma[0]) == "([1, --, 3],)")
          assert_(repr(t_ma[0]) == "([1, --, 3],)")
  
          # additional tests with structured arrays
  
          t_2d = masked_array(data = [([[1, 2], [3,4]],)],
                              mask = [([[False, True], [True, False]],)],
                              dtype = [('a', '<i4', (2,2))])
          assert_(str(t_2d[0]) == "([[1, --], [--, 4]],)")
          assert_(repr(t_2d[0]) == "([[1, --], [--, 4]],)")
  
          t_0d = masked_array(data = [(1,2)],
                              mask = [(True,False)],
                              dtype = [('a', '<i4'), ('b', '<i4')])
          assert_(str(t_0d[0]) == "(--, 2)")
          assert_(repr(t_0d[0]) == "(--, 2)")
  
          t_2d = masked_array(data = [([[1, 2], [3,4]], 1)],
                              mask = [([[False, True], [True, False]], False)],
                              dtype = [('a', '<i4', (2,2)), ('b', float)])
          assert_(str(t_2d[0]) == "([[1, --], [--, 4]], 1.0)")
          assert_(repr(t_2d[0]) == "([[1, --], [--, 4]], 1.0)")
  
          t_ne = masked_array(data=[(1, (1, 1))],
                              mask=[(True, (True, False))],
                              dtype = [('a', '<i4'), ('b', 'i4,i4')])
          assert_(str(t_ne[0]) == "(--, (--, 1))")
          assert_(repr(t_ne[0]) == "(--, (--, 1))")
  
      def test_object_with_array(self):
          mx1 = masked_array([1.], mask=[True])
          mx2 = masked_array([1., 2.])
          mx = masked_array([mx1, mx2], mask=[False, True])
          assert_(mx[0] is mx1)
          assert_(mx[1] is not mx2)
          assert_(np.all(mx[1].data == mx2.data))
          assert_(np.all(mx[1].mask))
          # check that we return a view.
          mx[1].data[0] = 0.
          assert_(mx2[0] == 0.)
  
  
  class TestMaskedArrayArithmetic(object):
      # Base test class for MaskedArrays.
  
      def setup(self):
          # Base data definition.
          x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
          y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
          a10 = 10.
          m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
          m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
          xm = masked_array(x, mask=m1)
          ym = masked_array(y, mask=m2)
          z = np.array([-.5, 0., .5, .8])
          zm = masked_array(z, mask=[0, 1, 0, 0])
          xf = np.where(m1, 1e+20, x)
          xm.set_fill_value(1e+20)
          self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf)
          self.err_status = np.geterr()
          np.seterr(divide='ignore', invalid='ignore')
  
      def teardown(self):
          np.seterr(**self.err_status)
  
      def test_basic_arithmetic(self):
          # Test of basic arithmetic.
          (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
          a2d = array([[1, 2], [0, 4]])
          a2dm = masked_array(a2d, [[0, 0], [1, 0]])
          assert_equal(a2d * a2d, a2d * a2dm)
          assert_equal(a2d + a2d, a2d + a2dm)
          assert_equal(a2d - a2d, a2d - a2dm)
          for s in [(12,), (4, 3), (2, 6)]:
              x = x.reshape(s)
              y = y.reshape(s)
              xm = xm.reshape(s)
              ym = ym.reshape(s)
              xf = xf.reshape(s)
              assert_equal(-x, -xm)
              assert_equal(x + y, xm + ym)
              assert_equal(x - y, xm - ym)
              assert_equal(x * y, xm * ym)
              assert_equal(x / y, xm / ym)
              assert_equal(a10 + y, a10 + ym)
              assert_equal(a10 - y, a10 - ym)
              assert_equal(a10 * y, a10 * ym)
              assert_equal(a10 / y, a10 / ym)
              assert_equal(x + a10, xm + a10)
              assert_equal(x - a10, xm - a10)
              assert_equal(x * a10, xm * a10)
              assert_equal(x / a10, xm / a10)
              assert_equal(x ** 2, xm ** 2)
              assert_equal(abs(x) ** 2.5, abs(xm) ** 2.5)
              assert_equal(x ** y, xm ** ym)
              assert_equal(np.add(x, y), add(xm, ym))
              assert_equal(np.subtract(x, y), subtract(xm, ym))
              assert_equal(np.multiply(x, y), multiply(xm, ym))
              assert_equal(np.divide(x, y), divide(xm, ym))
  
      def test_divide_on_different_shapes(self):
          x = arange(6, dtype=float)
          x.shape = (2, 3)
          y = arange(3, dtype=float)
  
          z = x / y
          assert_equal(z, [[-1., 1., 1.], [-1., 4., 2.5]])
          assert_equal(z.mask, [[1, 0, 0], [1, 0, 0]])
  
          z = x / y[None,:]
          assert_equal(z, [[-1., 1., 1.], [-1., 4., 2.5]])
          assert_equal(z.mask, [[1, 0, 0], [1, 0, 0]])
  
          y = arange(2, dtype=float)
          z = x / y[:, None]
          assert_equal(z, [[-1., -1., -1.], [3., 4., 5.]])
          assert_equal(z.mask, [[1, 1, 1], [0, 0, 0]])
  
      def test_mixed_arithmetic(self):
          # Tests mixed arithmetics.
          na = np.array([1])
          ma = array([1])
          assert_(isinstance(na + ma, MaskedArray))
          assert_(isinstance(ma + na, MaskedArray))
  
      def test_limits_arithmetic(self):
          tiny = np.finfo(float).tiny
          a = array([tiny, 1. / tiny, 0.])
          assert_equal(getmaskarray(a / 2), [0, 0, 0])
          assert_equal(getmaskarray(2 / a), [1, 0, 1])
  
      def test_masked_singleton_arithmetic(self):
          # Tests some scalar arithmetics on MaskedArrays.
          # Masked singleton should remain masked no matter what
          xm = array(0, mask=1)
          assert_((1 / array(0)).mask)
          assert_((1 + xm).mask)
          assert_((-xm).mask)
          assert_(maximum(xm, xm).mask)
          assert_(minimum(xm, xm).mask)
  
      def test_masked_singleton_equality(self):
          # Tests (in)equality on masked singleton
          a = array([1, 2, 3], mask=[1, 1, 0])
          assert_((a[0] == 0) is masked)
          assert_((a[0] != 0) is masked)
          assert_equal((a[-1] == 0), False)
          assert_equal((a[-1] != 0), True)
  
      def test_arithmetic_with_masked_singleton(self):
          # Checks that there's no collapsing to masked
          x = masked_array([1, 2])
          y = x * masked
          assert_equal(y.shape, x.shape)
          assert_equal(y._mask, [True, True])
          y = x[0] * masked
          assert_(y is masked)
          y = x + masked
          assert_equal(y.shape, x.shape)
          assert_equal(y._mask, [True, True])
  
      def test_arithmetic_with_masked_singleton_on_1d_singleton(self):
          # Check that we're not losing the shape of a singleton
          x = masked_array([1, ])
          y = x + masked
          assert_equal(y.shape, x.shape)
          assert_equal(y.mask, [True, ])
  
      def test_scalar_arithmetic(self):
          x = array(0, mask=0)
          assert_equal(x.filled().ctypes.data, x.ctypes.data)
          # Make sure we don't lose the shape in some circumstances
          xm = array((0, 0)) / 0.
          assert_equal(xm.shape, (2,))
          assert_equal(xm.mask, [1, 1])
  
      def test_basic_ufuncs(self):
          # Test various functions such as sin, cos.
          (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
          assert_equal(np.cos(x), cos(xm))
          assert_equal(np.cosh(x), cosh(xm))
          assert_equal(np.sin(x), sin(xm))
          assert_equal(np.sinh(x), sinh(xm))
          assert_equal(np.tan(x), tan(xm))
          assert_equal(np.tanh(x), tanh(xm))
          assert_equal(np.sqrt(abs(x)), sqrt(xm))
          assert_equal(np.log(abs(x)), log(xm))
          assert_equal(np.log10(abs(x)), log10(xm))
          assert_equal(np.exp(x), exp(xm))
          assert_equal(np.arcsin(z), arcsin(zm))
          assert_equal(np.arccos(z), arccos(zm))
          assert_equal(np.arctan(z), arctan(zm))
          assert_equal(np.arctan2(x, y), arctan2(xm, ym))
          assert_equal(np.absolute(x), absolute(xm))
          assert_equal(np.angle(x + 1j*y), angle(xm + 1j*ym))
          assert_equal(np.angle(x + 1j*y, deg=True), angle(xm + 1j*ym, deg=True))
          assert_equal(np.equal(x, y), equal(xm, ym))
          assert_equal(np.not_equal(x, y), not_equal(xm, ym))
          assert_equal(np.less(x, y), less(xm, ym))
          assert_equal(np.greater(x, y), greater(xm, ym))
          assert_equal(np.less_equal(x, y), less_equal(xm, ym))
          assert_equal(np.greater_equal(x, y), greater_equal(xm, ym))
          assert_equal(np.conjugate(x), conjugate(xm))
  
      def test_count_func(self):
          # Tests count
          assert_equal(1, count(1))
          assert_equal(0, array(1, mask=[1]))
  
          ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
          res = count(ott)
          assert_(res.dtype.type is np.intp)
          assert_equal(3, res)
  
          ott = ott.reshape((2, 2))
          res = count(ott)
          assert_(res.dtype.type is np.intp)
          assert_equal(3, res)
          res = count(ott, 0)
          assert_(isinstance(res, ndarray))
          assert_equal([1, 2], res)
          assert_(getmask(res) is nomask)
  
          ott = array([0., 1., 2., 3.])
          res = count(ott, 0)
          assert_(isinstance(res, ndarray))
          assert_(res.dtype.type is np.intp)
          assert_raises(np.AxisError, ott.count, axis=1)
  
      def test_count_on_python_builtins(self):
          # Tests count works on python builtins (issue#8019)
          assert_equal(3, count([1,2,3]))
          assert_equal(2, count((1,2)))
  
      def test_minmax_func(self):
          # Tests minimum and maximum.
          (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
          # max doesn't work if shaped
          xr = np.ravel(x)
          xmr = ravel(xm)
          # following are true because of careful selection of data
          assert_equal(max(xr), maximum.reduce(xmr))
          assert_equal(min(xr), minimum.reduce(xmr))
  
          assert_equal(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3])
          assert_equal(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9])
          x = arange(5)
          y = arange(5) - 2
          x[3] = masked
          y[0] = masked
          assert_equal(minimum(x, y), where(less(x, y), x, y))
          assert_equal(maximum(x, y), where(greater(x, y), x, y))
          assert_(minimum.reduce(x) == 0)
          assert_(maximum.reduce(x) == 4)
  
          x = arange(4).reshape(2, 2)
          x[-1, -1] = masked
          assert_equal(maximum.reduce(x, axis=None), 2)
  
      def test_minimummaximum_func(self):
          a = np.ones((2, 2))
          aminimum = minimum(a, a)
          assert_(isinstance(aminimum, MaskedArray))
          assert_equal(aminimum, np.minimum(a, a))
  
          aminimum = minimum.outer(a, a)
          assert_(isinstance(aminimum, MaskedArray))
          assert_equal(aminimum, np.minimum.outer(a, a))
  
          amaximum = maximum(a, a)
          assert_(isinstance(amaximum, MaskedArray))
          assert_equal(amaximum, np.maximum(a, a))
  
          amaximum = maximum.outer(a, a)
          assert_(isinstance(amaximum, MaskedArray))
          assert_equal(amaximum, np.maximum.outer(a, a))
  
      def test_minmax_reduce(self):
          # Test np.min/maximum.reduce on array w/ full False mask
          a = array([1, 2, 3], mask=[False, False, False])
          b = np.maximum.reduce(a)
          assert_equal(b, 3)
  
      def test_minmax_funcs_with_output(self):
          # Tests the min/max functions with explicit outputs
          mask = np.random.rand(12).round()
          xm = array(np.random.uniform(0, 10, 12), mask=mask)
          xm.shape = (3, 4)
          for funcname in ('min', 'max'):
              # Initialize
              npfunc = getattr(np, funcname)
              mafunc = getattr(numpy.ma.core, funcname)
              # Use the np version
              nout = np.empty((4,), dtype=int)
              try:
                  result = npfunc(xm, axis=0, out=nout)
              except MaskError:
                  pass
              nout = np.empty((4,), dtype=float)
              result = npfunc(xm, axis=0, out=nout)
              assert_(result is nout)
              # Use the ma version
              nout.fill(-999)
              result = mafunc(xm, axis=0, out=nout)
              assert_(result is nout)
  
      def test_minmax_methods(self):
          # Additional tests on max/min
          (_, _, _, _, _, xm, _, _, _, _) = self.d
          xm.shape = (xm.size,)
          assert_equal(xm.max(), 10)
          assert_(xm[0].max() is masked)
          assert_(xm[0].max(0) is masked)
          assert_(xm[0].max(-1) is masked)
          assert_equal(xm.min(), -10.)
          assert_(xm[0].min() is masked)
          assert_(xm[0].min(0) is masked)
          assert_(xm[0].min(-1) is masked)
          assert_equal(xm.ptp(), 20.)
          assert_(xm[0].ptp() is masked)
          assert_(xm[0].ptp(0) is masked)
          assert_(xm[0].ptp(-1) is masked)
  
          x = array([1, 2, 3], mask=True)
          assert_(x.min() is masked)
          assert_(x.max() is masked)
          assert_(x.ptp() is masked)
  
      def test_addsumprod(self):
          # Tests add, sum, product.
          (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
          assert_equal(np.add.reduce(x), add.reduce(x))
          assert_equal(np.add.accumulate(x), add.accumulate(x))
          assert_equal(4, sum(array(4), axis=0))
          assert_equal(4, sum(array(4), axis=0))
          assert_equal(np.sum(x, axis=0), sum(x, axis=0))
          assert_equal(np.sum(filled(xm, 0), axis=0), sum(xm, axis=0))
          assert_equal(np.sum(x, 0), sum(x, 0))
          assert_equal(np.product(x, axis=0), product(x, axis=0))
          assert_equal(np.product(x, 0), product(x, 0))
          assert_equal(np.product(filled(xm, 1), axis=0), product(xm, axis=0))
          s = (3, 4)
          x.shape = y.shape = xm.shape = ym.shape = s
          if len(s) > 1:
              assert_equal(np.concatenate((x, y), 1), concatenate((xm, ym), 1))
              assert_equal(np.add.reduce(x, 1), add.reduce(x, 1))
              assert_equal(np.sum(x, 1), sum(x, 1))
              assert_equal(np.product(x, 1), product(x, 1))
  
      def test_binops_d2D(self):
          # Test binary operations on 2D data
          a = array([[1.], [2.], [3.]], mask=[[False], [True], [True]])
          b = array([[2., 3.], [4., 5.], [6., 7.]])
  
          test = a * b
          control = array([[2., 3.], [2., 2.], [3., 3.]],
                          mask=[[0, 0], [1, 1], [1, 1]])
          assert_equal(test, control)
          assert_equal(test.data, control.data)
          assert_equal(test.mask, control.mask)
  
          test = b * a
          control = array([[2., 3.], [4., 5.], [6., 7.]],
                          mask=[[0, 0], [1, 1], [1, 1]])
          assert_equal(test, control)
          assert_equal(test.data, control.data)
          assert_equal(test.mask, control.mask)
  
          a = array([[1.], [2.], [3.]])
          b = array([[2., 3.], [4., 5.], [6., 7.]],
                    mask=[[0, 0], [0, 0], [0, 1]])
          test = a * b
          control = array([[2, 3], [8, 10], [18, 3]],
                          mask=[[0, 0], [0, 0], [0, 1]])
          assert_equal(test, control)
          assert_equal(test.data, control.data)
          assert_equal(test.mask, control.mask)
  
          test = b * a
          control = array([[2, 3], [8, 10], [18, 7]],
                          mask=[[0, 0], [0, 0], [0, 1]])
          assert_equal(test, control)
          assert_equal(test.data, control.data)
          assert_equal(test.mask, control.mask)
  
      def test_domained_binops_d2D(self):
          # Test domained binary operations on 2D data
          a = array([[1.], [2.], [3.]], mask=[[False], [True], [True]])
          b = array([[2., 3.], [4., 5.], [6., 7.]])
  
          test = a / b
          control = array([[1. / 2., 1. / 3.], [2., 2.], [3., 3.]],
                          mask=[[0, 0], [1, 1], [1, 1]])
          assert_equal(test, control)
          assert_equal(test.data, control.data)
          assert_equal(test.mask, control.mask)
  
          test = b / a
          control = array([[2. / 1., 3. / 1.], [4., 5.], [6., 7.]],
                          mask=[[0, 0], [1, 1], [1, 1]])
          assert_equal(test, control)
          assert_equal(test.data, control.data)
          assert_equal(test.mask, control.mask)
  
          a = array([[1.], [2.], [3.]])
          b = array([[2., 3.], [4., 5.], [6., 7.]],
                    mask=[[0, 0], [0, 0], [0, 1]])
          test = a / b
          control = array([[1. / 2, 1. / 3], [2. / 4, 2. / 5], [3. / 6, 3]],
                          mask=[[0, 0], [0, 0], [0, 1]])
          assert_equal(test, control)
          assert_equal(test.data, control.data)
          assert_equal(test.mask, control.mask)
  
          test = b / a
          control = array([[2 / 1., 3 / 1.], [4 / 2., 5 / 2.], [6 / 3., 7]],
                          mask=[[0, 0], [0, 0], [0, 1]])
          assert_equal(test, control)
          assert_equal(test.data, control.data)
          assert_equal(test.mask, control.mask)
  
      def test_noshrinking(self):
          # Check that we don't shrink a mask when not wanted
          # Binary operations
          a = masked_array([1., 2., 3.], mask=[False, False, False],
                           shrink=False)
          b = a + 1
          assert_equal(b.mask, [0, 0, 0])
          # In place binary operation
          a += 1
          assert_equal(a.mask, [0, 0, 0])
          # Domained binary operation
          b = a / 1.
          assert_equal(b.mask, [0, 0, 0])
          # In place binary operation
          a /= 1.
          assert_equal(a.mask, [0, 0, 0])
  
      def test_ufunc_nomask(self):
          # check the case ufuncs should set the mask to false
          m = np.ma.array([1])
          # check we don't get array([False], dtype=bool)
          assert_equal(np.true_divide(m, 5).mask.shape, ())
  
      def test_noshink_on_creation(self):
          # Check that the mask is not shrunk on array creation when not wanted
          a = np.ma.masked_values([1., 2.5, 3.1], 1.5, shrink=False)
          assert_equal(a.mask, [0, 0, 0])
  
      def test_mod(self):
          # Tests mod
          (x, y, a10, m1, m2, xm, ym, z, zm, xf) = self.d
          assert_equal(mod(x, y), mod(xm, ym))
          test = mod(ym, xm)
          assert_equal(test, np.mod(ym, xm))
          assert_equal(test.mask, mask_or(xm.mask, ym.mask))
          test = mod(xm, ym)
          assert_equal(test, np.mod(xm, ym))
          assert_equal(test.mask, mask_or(mask_or(xm.mask, ym.mask), (ym == 0)))
  
      def test_TakeTransposeInnerOuter(self):
          # Test of take, transpose, inner, outer products
          x = arange(24)
          y = np.arange(24)
          x[5:6] = masked
          x = x.reshape(2, 3, 4)
          y = y.reshape(2, 3, 4)
          assert_equal(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1)))
          assert_equal(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1))
          assert_equal(np.inner(filled(x, 0), filled(y, 0)),
                       inner(x, y))
          assert_equal(np.outer(filled(x, 0), filled(y, 0)),
                       outer(x, y))
          y = array(['abc', 1, 'def', 2, 3], object)
          y[2] = masked
          t = take(y, [0, 3, 4])
          assert_(t[0] == 'abc')
          assert_(t[1] == 2)
          assert_(t[2] == 3)
  
      def test_imag_real(self):
          # Check complex
          xx = array([1 + 10j, 20 + 2j], mask=[1, 0])
          assert_equal(xx.imag, [10, 2])
          assert_equal(xx.imag.filled(), [1e+20, 2])
          assert_equal(xx.imag.dtype, xx._data.imag.dtype)
          assert_equal(xx.real, [1, 20])
          assert_equal(xx.real.filled(), [1e+20, 20])
          assert_equal(xx.real.dtype, xx._data.real.dtype)
  
      def test_methods_with_output(self):
          xm = array(np.random.uniform(0, 10, 12)).reshape(3, 4)
          xm[:, 0] = xm[0] = xm[-1, -1] = masked
  
          funclist = ('sum', 'prod', 'var', 'std', 'max', 'min', 'ptp', 'mean',)
  
          for funcname in funclist:
              npfunc = getattr(np, funcname)
              xmmeth = getattr(xm, funcname)
              # A ndarray as explicit input
              output = np.empty(4, dtype=float)
              output.fill(-9999)
              result = npfunc(xm, axis=0, out=output)
              # ... the result should be the given output
              assert_(result is output)
              assert_equal(result, xmmeth(axis=0, out=output))
  
              output = empty(4, dtype=int)
              result = xmmeth(axis=0, out=output)
              assert_(result is output)
              assert_(output[0] is masked)
  
      def test_count_mean_with_matrix(self):
          m = np.ma.array(np.matrix([[1,2],[3,4]]), mask=np.zeros((2,2)))
  
          assert_equal(m.count(axis=0).shape, (1,2))
          assert_equal(m.count(axis=1).shape, (2,1))
  
          #make sure broadcasting inside mean and var work
          assert_equal(m.mean(axis=0), [[2., 3.]])
          assert_equal(m.mean(axis=1), [[1.5], [3.5]])
  
      def test_eq_on_structured(self):
          # Test the equality of structured arrays
          ndtype = [('A', int), ('B', int)]
          a = array([(1, 1), (2, 2)], mask=[(0, 1), (0, 0)], dtype=ndtype)
          test = (a == a)
          assert_equal(test.data, [True, True])
          assert_equal(test.mask, [False, False])
          test = (a == a[0])
          assert_equal(test.data, [True, False])
          assert_equal(test.mask, [False, False])
          b = array([(1, 1), (2, 2)], mask=[(1, 0), (0, 0)], dtype=ndtype)
          test = (a == b)
          assert_equal(test.data, [False, True])
          assert_equal(test.mask, [True, False])
          test = (a[0] == b)
          assert_equal(test.data, [False, False])
          assert_equal(test.mask, [True, False])
          b = array([(1, 1), (2, 2)], mask=[(0, 1), (1, 0)], dtype=ndtype)
          test = (a == b)
          assert_equal(test.data, [True, True])
          assert_equal(test.mask, [False, False])
          # complicated dtype, 2-dimensional array.
          ndtype = [('A', int), ('B', [('BA', int), ('BB', int)])]
          a = array([[(1, (1, 1)), (2, (2, 2))],
                     [(3, (3, 3)), (4, (4, 4))]],
                    mask=[[(0, (1, 0)), (0, (0, 1))],
                          [(1, (0, 0)), (1, (1, 1))]], dtype=ndtype)
          test = (a[0, 0] == a)
          assert_equal(test.data, [[True, False], [False, False]])
          assert_equal(test.mask, [[False, False], [False, True]])
  
      def test_ne_on_structured(self):
          # Test the equality of structured arrays
          ndtype = [('A', int), ('B', int)]
          a = array([(1, 1), (2, 2)], mask=[(0, 1), (0, 0)], dtype=ndtype)
          test = (a != a)
          assert_equal(test.data, [False, False])
          assert_equal(test.mask, [False, False])
          test = (a != a[0])
          assert_equal(test.data, [False, True])
          assert_equal(test.mask, [False, False])
          b = array([(1, 1), (2, 2)], mask=[(1, 0), (0, 0)], dtype=ndtype)
          test = (a != b)
          assert_equal(test.data, [True, False])
          assert_equal(test.mask, [True, False])
          test = (a[0] != b)
          assert_equal(test.data, [True, True])
          assert_equal(test.mask, [True, False])
          b = array([(1, 1), (2, 2)], mask=[(0, 1), (1, 0)], dtype=ndtype)
          test = (a != b)
          assert_equal(test.data, [False, False])
          assert_equal(test.mask, [False, False])
          # complicated dtype, 2-dimensional array.
          ndtype = [('A', int), ('B', [('BA', int), ('BB', int)])]
          a = array([[(1, (1, 1)), (2, (2, 2))],
                     [(3, (3, 3)), (4, (4, 4))]],
                    mask=[[(0, (1, 0)), (0, (0, 1))],
                          [(1, (0, 0)), (1, (1, 1))]], dtype=ndtype)
          test = (a[0, 0] != a)
          assert_equal(test.data, [[False, True], [True, True]])
          assert_equal(test.mask, [[False, False], [False, True]])
  
      def test_eq_ne_structured_extra(self):
          # ensure simple examples are symmetric and make sense.
          # from https://github.com/numpy/numpy/pull/8590#discussion_r101126465
          dt = np.dtype('i4,i4')
          for m1 in (mvoid((1, 2), mask=(0, 0), dtype=dt),
                     mvoid((1, 2), mask=(0, 1), dtype=dt),
                     mvoid((1, 2), mask=(1, 0), dtype=dt),
                     mvoid((1, 2), mask=(1, 1), dtype=dt)):
              ma1 = m1.view(MaskedArray)
              r1 = ma1.view('2i4')
              for m2 in (np.array((1, 1), dtype=dt),
                         mvoid((1, 1), dtype=dt),
                         mvoid((1, 0), mask=(0, 1), dtype=dt),
                         mvoid((3, 2), mask=(0, 1), dtype=dt)):
                  ma2 = m2.view(MaskedArray)
                  r2 = ma2.view('2i4')
                  eq_expected = (r1 == r2).all()
                  assert_equal(m1 == m2, eq_expected)
                  assert_equal(m2 == m1, eq_expected)
                  assert_equal(ma1 == m2, eq_expected)
                  assert_equal(m1 == ma2, eq_expected)
                  assert_equal(ma1 == ma2, eq_expected)
                  # Also check it is the same if we do it element by element.
                  el_by_el = [m1[name] == m2[name] for name in dt.names]
                  assert_equal(array(el_by_el, dtype=bool).all(), eq_expected)
                  ne_expected = (r1 != r2).any()
                  assert_equal(m1 != m2, ne_expected)
                  assert_equal(m2 != m1, ne_expected)
                  assert_equal(ma1 != m2, ne_expected)
                  assert_equal(m1 != ma2, ne_expected)
                  assert_equal(ma1 != ma2, ne_expected)
                  el_by_el = [m1[name] != m2[name] for name in dt.names]
                  assert_equal(array(el_by_el, dtype=bool).any(), ne_expected)
  
      def test_eq_with_None(self):
          # Really, comparisons with None should not be done, but check them
          # anyway. Note that pep8 will flag these tests.
          # Deprecation is in place for arrays, and when it happens this
          # test will fail (and have to be changed accordingly).
  
          # With partial mask
          with suppress_warnings() as sup:
              sup.filter(FutureWarning, "Comparison to `None`")
              a = array([None, 1], mask=[0, 1])
              assert_equal(a == None, array([True, False], mask=[0, 1]))
              assert_equal(a.data == None, [True, False])
              assert_equal(a != None, array([False, True], mask=[0, 1]))
              # With nomask
              a = array([None, 1], mask=False)
              assert_equal(a == None, [True, False])
              assert_equal(a != None, [False, True])
              # With complete mask
              a = array([None, 2], mask=True)
              assert_equal(a == None, array([False, True], mask=True))
              assert_equal(a != None, array([True, False], mask=True))
              # Fully masked, even comparison to None should return "masked"
              a = masked
              assert_equal(a == None, masked)
  
      def test_eq_with_scalar(self):
          a = array(1)
          assert_equal(a == 1, True)
          assert_equal(a == 0, False)
          assert_equal(a != 1, False)
          assert_equal(a != 0, True)
          b = array(1, mask=True)
          assert_equal(b == 0, masked)
          assert_equal(b == 1, masked)
          assert_equal(b != 0, masked)
          assert_equal(b != 1, masked)
  
      def test_eq_different_dimensions(self):
          m1 = array([1, 1], mask=[0, 1])
          # test comparison with both masked and regular arrays.
          for m2 in (array([[0, 1], [1, 2]]),
                     np.array([[0, 1], [1, 2]])):
              test = (m1 == m2)
              assert_equal(test.data, [[False, False],
                                       [True, False]])
              assert_equal(test.mask, [[False, True],
                                       [False, True]])
  
      def test_numpyarithmetics(self):
          # Check that the mask is not back-propagated when using numpy functions
          a = masked_array([-1, 0, 1, 2, 3], mask=[0, 0, 0, 0, 1])
          control = masked_array([np.nan, np.nan, 0, np.log(2), -1],
                                 mask=[1, 1, 0, 0, 1])
  
          test = log(a)
          assert_equal(test, control)
          assert_equal(test.mask, control.mask)
          assert_equal(a.mask, [0, 0, 0, 0, 1])
  
          test = np.log(a)
          assert_equal(test, control)
          assert_equal(test.mask, control.mask)
          assert_equal(a.mask, [0, 0, 0, 0, 1])
  
  
  class TestMaskedArrayAttributes(object):
  
      def test_keepmask(self):
          # Tests the keep mask flag
          x = masked_array([1, 2, 3], mask=[1, 0, 0])
          mx = masked_array(x)
          assert_equal(mx.mask, x.mask)
          mx = masked_array(x, mask=[0, 1, 0], keep_mask=False)
          assert_equal(mx.mask, [0, 1, 0])
          mx = masked_array(x, mask=[0, 1, 0], keep_mask=True)
          assert_equal(mx.mask, [1, 1, 0])
          # We default to true
          mx = masked_array(x, mask=[0, 1, 0])
          assert_equal(mx.mask, [1, 1, 0])
  
      def test_hardmask(self):
          # Test hard_mask
          d = arange(5)
          n = [0, 0, 0, 1, 1]
          m = make_mask(n)
          xh = array(d, mask=m, hard_mask=True)
          # We need to copy, to avoid updating d in xh !
          xs = array(d, mask=m, hard_mask=False, copy=True)
          xh[[1, 4]] = [10, 40]
          xs[[1, 4]] = [10, 40]
          assert_equal(xh._data, [0, 10, 2, 3, 4])
          assert_equal(xs._data, [0, 10, 2, 3, 40])
          assert_equal(xs.mask, [0, 0, 0, 1, 0])
          assert_(xh._hardmask)
          assert_(not xs._hardmask)
          xh[1:4] = [10, 20, 30]
          xs[1:4] = [10, 20, 30]
          assert_equal(xh._data, [0, 10, 20, 3, 4])
          assert_equal(xs._data, [0, 10, 20, 30, 40])
          assert_equal(xs.mask, nomask)
          xh[0] = masked
          xs[0] = masked
          assert_equal(xh.mask, [1, 0, 0, 1, 1])
          assert_equal(xs.mask, [1, 0, 0, 0, 0])
          xh[:] = 1
          xs[:] = 1
          assert_equal(xh._data, [0, 1, 1, 3, 4])
          assert_equal(xs._data, [1, 1, 1, 1, 1])
          assert_equal(xh.mask, [1, 0, 0, 1, 1])
          assert_equal(xs.mask, nomask)
          # Switch to soft mask
          xh.soften_mask()
          xh[:] = arange(5)
          assert_equal(xh._data, [0, 1, 2, 3, 4])
          assert_equal(xh.mask, nomask)
          # Switch back to hard mask
          xh.harden_mask()
          xh[xh < 3] = masked
          assert_equal(xh._data, [0, 1, 2, 3, 4])
          assert_equal(xh._mask, [1, 1, 1, 0, 0])
          xh[filled(xh > 1, False)] = 5
          assert_equal(xh._data, [0, 1, 2, 5, 5])
          assert_equal(xh._mask, [1, 1, 1, 0, 0])
  
          xh = array([[1, 2], [3, 4]], mask=[[1, 0], [0, 0]], hard_mask=True)
          xh[0] = 0
          assert_equal(xh._data, [[1, 0], [3, 4]])
          assert_equal(xh._mask, [[1, 0], [0, 0]])
          xh[-1, -1] = 5
          assert_equal(xh._data, [[1, 0], [3, 5]])
          assert_equal(xh._mask, [[1, 0], [0, 0]])
          xh[filled(xh < 5, False)] = 2
          assert_equal(xh._data, [[1, 2], [2, 5]])
          assert_equal(xh._mask, [[1, 0], [0, 0]])
  
      def test_hardmask_again(self):
          # Another test of hardmask
          d = arange(5)
          n = [0, 0, 0, 1, 1]
          m = make_mask(n)
          xh = array(d, mask=m, hard_mask=True)
          xh[4:5] = 999
          xh[0:1] = 999
          assert_equal(xh._data, [999, 1, 2, 3, 4])
  
      def test_hardmask_oncemore_yay(self):
          # OK, yet another test of hardmask
          # Make sure that harden_mask/soften_mask//unshare_mask returns self
          a = array([1, 2, 3], mask=[1, 0, 0])
          b = a.harden_mask()
          assert_equal(a, b)
          b[0] = 0
          assert_equal(a, b)
          assert_equal(b, array([1, 2, 3], mask=[1, 0, 0]))
          a = b.soften_mask()
          a[0] = 0
          assert_equal(a, b)
          assert_equal(b, array([0, 2, 3], mask=[0, 0, 0]))
  
      def test_smallmask(self):
          # Checks the behaviour of _smallmask
          a = arange(10)
          a[1] = masked
          a[1] = 1
          assert_equal(a._mask, nomask)
          a = arange(10)
          a._smallmask = False
          a[1] = masked
          a[1] = 1
          assert_equal(a._mask, zeros(10))
  
      def test_shrink_mask(self):
          # Tests .shrink_mask()
          a = array([1, 2, 3], mask=[0, 0, 0])
          b = a.shrink_mask()
          assert_equal(a, b)
          assert_equal(a.mask, nomask)
  
      def test_flat(self):
          # Test that flat can return all types of items [#4585, #4615]
          # test simple access
          test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
          assert_equal(test.flat[1], 2)
          assert_equal(test.flat[2], masked)
          assert_(np.all(test.flat[0:2] == test[0, 0:2]))
          # Test flat on masked_matrices
          test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
          test.flat = masked_array([3, 2, 1], mask=[1, 0, 0])
          control = masked_array(np.matrix([[3, 2, 1]]), mask=[1, 0, 0])
          assert_equal(test, control)
          # Test setting
          test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1])
          testflat = test.flat
          testflat[:] = testflat[[2, 1, 0]]
          assert_equal(test, control)
          testflat[0] = 9
          assert_equal(test[0, 0], 9)
          # test 2-D record array
          # ... on structured array w/ masked records
          x = array([[(1, 1.1, 'one'), (2, 2.2, 'two'), (3, 3.3, 'thr')],
                     [(4, 4.4, 'fou'), (5, 5.5, 'fiv'), (6, 6.6, 'six')]],
                    dtype=[('a', int), ('b', float), ('c', '|S8')])
          x['a'][0, 1] = masked
          x['b'][1, 0] = masked
          x['c'][0, 2] = masked
          x[-1, -1] = masked
          xflat = x.flat
          assert_equal(xflat[0], x[0, 0])
          assert_equal(xflat[1], x[0, 1])
          assert_equal(xflat[2], x[0, 2])
          assert_equal(xflat[:3], x[0])
          assert_equal(xflat[3], x[1, 0])
          assert_equal(xflat[4], x[1, 1])
          assert_equal(xflat[5], x[1, 2])
          assert_equal(xflat[3:], x[1])
          assert_equal(xflat[-1], x[-1, -1])
          i = 0
          j = 0
          for xf in xflat:
              assert_equal(xf, x[j, i])
              i += 1
              if i >= x.shape[-1]:
                  i = 0
                  j += 1
          # test that matrices keep the correct shape (#4615)
          a = masked_array(np.matrix(np.eye(2)), mask=0)
          b = a.flat
          b01 = b[:2]
          assert_equal(b01.data, array([[1., 0.]]))
          assert_equal(b01.mask, array([[False, False]]))
  
      def test_assign_dtype(self):
          # check that the mask's dtype is updated when dtype is changed
          a = np.zeros(4, dtype='f4,i4')
  
          m = np.ma.array(a)
          m.dtype = np.dtype('f4')
          repr(m)  # raises?
          assert_equal(m.dtype, np.dtype('f4'))
  
          # check that dtype changes that change shape of mask too much
          # are not allowed
          def assign():
              m = np.ma.array(a)
              m.dtype = np.dtype('f8')
          assert_raises(ValueError, assign)
  
          b = a.view(dtype='f4', type=np.ma.MaskedArray)  # raises?
          assert_equal(b.dtype, np.dtype('f4'))
  
          # check that nomask is preserved
          a = np.zeros(4, dtype='f4')
          m = np.ma.array(a)
          m.dtype = np.dtype('f4,i4')
          assert_equal(m.dtype, np.dtype('f4,i4'))
          assert_equal(m._mask, np.ma.nomask)
  
  
  class TestFillingValues(object):
  
      def test_check_on_scalar(self):
          # Test _check_fill_value set to valid and invalid values
          _check_fill_value = np.ma.core._check_fill_value
  
          fval = _check_fill_value(0, int)
          assert_equal(fval, 0)
          fval = _check_fill_value(None, int)
          assert_equal(fval, default_fill_value(0))
  
          fval = _check_fill_value(0, "|S3")
          assert_equal(fval, b"0")
          fval = _check_fill_value(None, "|S3")
          assert_equal(fval, default_fill_value(b"camelot!"))
          assert_raises(TypeError, _check_fill_value, 1e+20, int)
          assert_raises(TypeError, _check_fill_value, 'stuff', int)
  
      def test_check_on_fields(self):
          # Tests _check_fill_value with records
          _check_fill_value = np.ma.core._check_fill_value
          ndtype = [('a', int), ('b', float), ('c', "|S3")]
          # A check on a list should return a single record
          fval = _check_fill_value([-999, -12345678.9, "???"], ndtype)
          assert_(isinstance(fval, ndarray))
          assert_equal(fval.item(), [-999, -12345678.9, b"???"])
          # A check on None should output the defaults
          fval = _check_fill_value(None, ndtype)
          assert_(isinstance(fval, ndarray))
          assert_equal(fval.item(), [default_fill_value(0),
                                     default_fill_value(0.),
                                     asbytes(default_fill_value("0"))])
          #.....Using a structured type as fill_value should work
          fill_val = np.array((-999, -12345678.9, "???"), dtype=ndtype)
          fval = _check_fill_value(fill_val, ndtype)
          assert_(isinstance(fval, ndarray))
          assert_equal(fval.item(), [-999, -12345678.9, b"???"])
  
          #.....Using a flexible type w/ a different type shouldn't matter
          # BEHAVIOR in 1.5 and earlier: match structured types by position
          #fill_val = np.array((-999, -12345678.9, "???"),
          #                    dtype=[("A", int), ("B", float), ("C", "|S3")])
          # BEHAVIOR in 1.6 and later: match structured types by name
          fill_val = np.array(("???", -999, -12345678.9),
                              dtype=[("c", "|S3"), ("a", int), ("b", float), ])
          # suppress deprecation warning in 1.12 (remove in 1.13)
          with assert_warns(FutureWarning):
              fval = _check_fill_value(fill_val, ndtype)
          assert_(isinstance(fval, ndarray))
          assert_equal(fval.item(), [-999, -12345678.9, b"???"])
  
          #.....Using an object-array shouldn't matter either
          fill_val = np.ndarray(shape=(1,), dtype=object)
          fill_val[0] = (-999, -12345678.9, b"???")
          fval = _check_fill_value(fill_val, object)
          assert_(isinstance(fval, ndarray))
          assert_equal(fval.item(), [-999, -12345678.9, b"???"])
          # NOTE: This test was never run properly as "fill_value" rather than
          # "fill_val" was assigned.  Written properly, it fails.
          #fill_val = np.array((-999, -12345678.9, "???"))
          #fval = _check_fill_value(fill_val, ndtype)
          #assert_(isinstance(fval, ndarray))
          #assert_equal(fval.item(), [-999, -12345678.9, b"???"])
          #.....One-field-only flexible type should work as well
          ndtype = [("a", int)]
          fval = _check_fill_value(-999999999, ndtype)
          assert_(isinstance(fval, ndarray))
          assert_equal(fval.item(), (-999999999,))
  
      def test_fillvalue_conversion(self):
          # Tests the behavior of fill_value during conversion
          # We had a tailored comment to make sure special attributes are
          # properly dealt with
          a = array([b'3', b'4', b'5'])
          a._optinfo.update({'comment':"updated!"})
  
          b = array(a, dtype=int)
          assert_equal(b._data, [3, 4, 5])
          assert_equal(b.fill_value, default_fill_value(0))
  
          b = array(a, dtype=float)
          assert_equal(b._data, [3, 4, 5])
          assert_equal(b.fill_value, default_fill_value(0.))
  
          b = a.astype(int)
          assert_equal(b._data, [3, 4, 5])
          assert_equal(b.fill_value, default_fill_value(0))
          assert_equal(b._optinfo['comment'], "updated!")
  
          b = a.astype([('a', '|S3')])
          assert_equal(b['a']._data, a._data)
          assert_equal(b['a'].fill_value, a.fill_value)
  
      def test_default_fill_value(self):
          # check all calling conventions
          f1 = default_fill_value(1.)
          f2 = default_fill_value(np.array(1.))
          f3 = default_fill_value(np.array(1.).dtype)
          assert_equal(f1, f2)
          assert_equal(f1, f3)
  
      def test_default_fill_value_structured(self):
          fields = array([(1, 1, 1)],
                        dtype=[('i', int), ('s', '|S8'), ('f', float)])
  
          f1 = default_fill_value(fields)
          f2 = default_fill_value(fields.dtype)
          expected = np.array((default_fill_value(0),
                               default_fill_value('0'),
                               default_fill_value(0.)), dtype=fields.dtype)
          assert_equal(f1, expected)
          assert_equal(f2, expected)
  
      def test_default_fill_value_void(self):
          dt = np.dtype([('v', 'V7')])
          f = default_fill_value(dt)
          assert_equal(f['v'], np.array(default_fill_value(dt['v']), dt['v']))
  
      def test_fillvalue(self):
          # Yet more fun with the fill_value
          data = masked_array([1, 2, 3], fill_value=-999)
          series = data[[0, 2, 1]]
          assert_equal(series._fill_value, data._fill_value)
  
          mtype = [('f', float), ('s', '|S3')]
          x = array([(1, 'a'), (2, 'b'), (pi, 'pi')], dtype=mtype)
          x.fill_value = 999
          assert_equal(x.fill_value.item(), [999., b'999'])
          assert_equal(x['f'].fill_value, 999)
          assert_equal(x['s'].fill_value, b'999')
  
          x.fill_value = (9, '???')
          assert_equal(x.fill_value.item(), (9, b'???'))
          assert_equal(x['f'].fill_value, 9)
          assert_equal(x['s'].fill_value, b'???')
  
          x = array([1, 2, 3.1])
          x.fill_value = 999
          assert_equal(np.asarray(x.fill_value).dtype, float)
          assert_equal(x.fill_value, 999.)
          assert_equal(x._fill_value, np.array(999.))
  
      def test_fillvalue_exotic_dtype(self):
          # Tests yet more exotic flexible dtypes
          _check_fill_value = np.ma.core._check_fill_value
          ndtype = [('i', int), ('s', '|S8'), ('f', float)]
          control = np.array((default_fill_value(0),
                              default_fill_value('0'),
                              default_fill_value(0.),),
                             dtype=ndtype)
          assert_equal(_check_fill_value(None, ndtype), control)
          # The shape shouldn't matter
          ndtype = [('f0', float, (2, 2))]
          control = np.array((default_fill_value(0.),),
                             dtype=[('f0', float)]).astype(ndtype)
          assert_equal(_check_fill_value(None, ndtype), control)
          control = np.array((0,), dtype=[('f0', float)]).astype(ndtype)
          assert_equal(_check_fill_value(0, ndtype), control)
  
          ndtype = np.dtype("int, (2,3)float, float")
          control = np.array((default_fill_value(0),
                              default_fill_value(0.),
                              default_fill_value(0.),),
                             dtype="int, float, float").astype(ndtype)
          test = _check_fill_value(None, ndtype)
          assert_equal(test, control)
          control = np.array((0, 0, 0), dtype="int, float, float").astype(ndtype)
          assert_equal(_check_fill_value(0, ndtype), control)
          # but when indexing, fill value should become scalar not tuple
          # See issue #6723
          M = masked_array(control)
          assert_equal(M["f1"].fill_value.ndim, 0)
  
      def test_fillvalue_datetime_timedelta(self):
          # Test default fillvalue for datetime64 and timedelta64 types.
          # See issue #4476, this would return '?' which would cause errors
          # elsewhere
  
          for timecode in ("as", "fs", "ps", "ns", "us", "ms", "s", "m",
                           "h", "D", "W", "M", "Y"):
              control = numpy.datetime64("NaT", timecode)
              test = default_fill_value(numpy.dtype("<M8[" + timecode + "]"))
              np.testing.assert_equal(test, control)
  
              control = numpy.timedelta64("NaT", timecode)
              test = default_fill_value(numpy.dtype("<m8[" + timecode + "]"))
              np.testing.assert_equal(test, control)
  
      def test_extremum_fill_value(self):
          # Tests extremum fill values for flexible type.
          a = array([(1, (2, 3)), (4, (5, 6))],
                    dtype=[('A', int), ('B', [('BA', int), ('BB', int)])])
          test = a.fill_value
          assert_equal(test.dtype, a.dtype)
          assert_equal(test['A'], default_fill_value(a['A']))
          assert_equal(test['B']['BA'], default_fill_value(a['B']['BA']))
          assert_equal(test['B']['BB'], default_fill_value(a['B']['BB']))
  
          test = minimum_fill_value(a)
          assert_equal(test.dtype, a.dtype)
          assert_equal(test[0], minimum_fill_value(a['A']))
          assert_equal(test[1][0], minimum_fill_value(a['B']['BA']))
          assert_equal(test[1][1], minimum_fill_value(a['B']['BB']))
          assert_equal(test[1], minimum_fill_value(a['B']))
  
          test = maximum_fill_value(a)
          assert_equal(test.dtype, a.dtype)
          assert_equal(test[0], maximum_fill_value(a['A']))
          assert_equal(test[1][0], maximum_fill_value(a['B']['BA']))
          assert_equal(test[1][1], maximum_fill_value(a['B']['BB']))
          assert_equal(test[1], maximum_fill_value(a['B']))
  
      def test_extremum_fill_value_subdtype(self):
          a = array(([2, 3, 4],), dtype=[('value', np.int8, 3)])
  
          test = minimum_fill_value(a)
          assert_equal(test.dtype, a.dtype)
          assert_equal(test[0], np.full(3, minimum_fill_value(a['value'])))
  
          test = maximum_fill_value(a)
          assert_equal(test.dtype, a.dtype)
          assert_equal(test[0], np.full(3, maximum_fill_value(a['value'])))
  
      def test_fillvalue_individual_fields(self):
          # Test setting fill_value on individual fields
          ndtype = [('a', int), ('b', int)]
          # Explicit fill_value
          a = array(list(zip([1, 2, 3], [4, 5, 6])),
                    fill_value=(-999, -999), dtype=ndtype)
          aa = a['a']
          aa.set_fill_value(10)
          assert_equal(aa._fill_value, np.array(10))
          assert_equal(tuple(a.fill_value), (10, -999))
          a.fill_value['b'] = -10
          assert_equal(tuple(a.fill_value), (10, -10))
          # Implicit fill_value
          t = array(list(zip([1, 2, 3], [4, 5, 6])), dtype=ndtype)
          tt = t['a']
          tt.set_fill_value(10)
          assert_equal(tt._fill_value, np.array(10))
          assert_equal(tuple(t.fill_value), (10, default_fill_value(0)))
  
      def test_fillvalue_implicit_structured_array(self):
          # Check that fill_value is always defined for structured arrays
          ndtype = ('b', float)
          adtype = ('a', float)
          a = array([(1.,), (2.,)], mask=[(False,), (False,)],
                    fill_value=(np.nan,), dtype=np.dtype([adtype]))
          b = empty(a.shape, dtype=[adtype, ndtype])
          b['a'] = a['a']
          b['a'].set_fill_value(a['a'].fill_value)
          f = b._fill_value[()]
          assert_(np.isnan(f[0]))
          assert_equal(f[-1], default_fill_value(1.))
  
      def test_fillvalue_as_arguments(self):
          # Test adding a fill_value parameter to empty/ones/zeros
          a = empty(3, fill_value=999.)
          assert_equal(a.fill_value, 999.)
  
          a = ones(3, fill_value=999., dtype=float)
          assert_equal(a.fill_value, 999.)
  
          a = zeros(3, fill_value=0., dtype=complex)
          assert_equal(a.fill_value, 0.)
  
          a = identity(3, fill_value=0., dtype=complex)
          assert_equal(a.fill_value, 0.)
  
      def test_shape_argument(self):
          # Test that shape can be provides as an argument
          # GH issue 6106
          a = empty(shape=(3, ))
          assert_equal(a.shape, (3, ))
  
          a = ones(shape=(3, ), dtype=float)
          assert_equal(a.shape, (3, ))
  
          a = zeros(shape=(3, ), dtype=complex)
          assert_equal(a.shape, (3, ))
  
      def test_fillvalue_in_view(self):
          # Test the behavior of fill_value in view
  
          # Create initial masked array
          x = array([1, 2, 3], fill_value=1, dtype=np.int64)
  
          # Check that fill_value is preserved by default
          y = x.view()
          assert_(y.fill_value == 1)
  
          # Check that fill_value is preserved if dtype is specified and the
          # dtype is an ndarray sub-class and has a _fill_value attribute
          y = x.view(MaskedArray)
          assert_(y.fill_value == 1)
  
          # Check that fill_value is preserved if type is specified and the
          # dtype is an ndarray sub-class and has a _fill_value attribute (by
          # default, the first argument is dtype, not type)
          y = x.view(type=MaskedArray)
          assert_(y.fill_value == 1)
  
          # Check that code does not crash if passed an ndarray sub-class that
          # does not have a _fill_value attribute
          y = x.view(np.ndarray)
          y = x.view(type=np.ndarray)
  
          # Check that fill_value can be overridden with view
          y = x.view(MaskedArray, fill_value=2)
          assert_(y.fill_value == 2)
  
          # Check that fill_value can be overridden with view (using type=)
          y = x.view(type=MaskedArray, fill_value=2)
          assert_(y.fill_value == 2)
  
          # Check that fill_value gets reset if passed a dtype but not a
          # fill_value. This is because even though in some cases one can safely
          # cast the fill_value, e.g. if taking an int64 view of an int32 array,
          # in other cases, this cannot be done (e.g. int32 view of an int64
          # array with a large fill_value).
          y = x.view(dtype=np.int32)
          assert_(y.fill_value == 999999)
  
      def test_fillvalue_bytes_or_str(self):
          # Test whether fill values work as expected for structured dtypes
          # containing bytes or str.  See issue #7259.
          a = empty(shape=(3, ), dtype="(2)3S,(2)3U")
          assert_equal(a["f0"].fill_value, default_fill_value(b"spam"))
          assert_equal(a["f1"].fill_value, default_fill_value("eggs"))
  
  
  class TestUfuncs(object):
      # Test class for the application of ufuncs on MaskedArrays.
  
      def setup(self):
          # Base data definition.
          self.d = (array([1.0, 0, -1, pi / 2] * 2, mask=[0, 1] + [0] * 6),
                    array([1.0, 0, -1, pi / 2] * 2, mask=[1, 0] + [0] * 6),)
          self.err_status = np.geterr()
          np.seterr(divide='ignore', invalid='ignore')
  
      def teardown(self):
          np.seterr(**self.err_status)
  
      def test_testUfuncRegression(self):
          # Tests new ufuncs on MaskedArrays.
          for f in ['sqrt', 'log', 'log10', 'exp', 'conjugate',
                    'sin', 'cos', 'tan',
                    'arcsin', 'arccos', 'arctan',
                    'sinh', 'cosh', 'tanh',
                    'arcsinh',
                    'arccosh',
                    'arctanh',
                    'absolute', 'fabs', 'negative',
                    'floor', 'ceil',
                    'logical_not',
                    'add', 'subtract', 'multiply',
                    'divide', 'true_divide', 'floor_divide',
                    'remainder', 'fmod', 'hypot', 'arctan2',
                    'equal', 'not_equal', 'less_equal', 'greater_equal',
                    'less', 'greater',
                    'logical_and', 'logical_or', 'logical_xor',
                    ]:
              try:
                  uf = getattr(umath, f)
              except AttributeError:
                  uf = getattr(fromnumeric, f)
              mf = getattr(numpy.ma.core, f)
              args = self.d[:uf.nin]
              ur = uf(*args)
              mr = mf(*args)
              assert_equal(ur.filled(0), mr.filled(0), f)
              assert_mask_equal(ur.mask, mr.mask, err_msg=f)
  
      def test_reduce(self):
          # Tests reduce on MaskedArrays.
          a = self.d[0]
          assert_(not alltrue(a, axis=0))
          assert_(sometrue(a, axis=0))
          assert_equal(sum(a[:3], axis=0), 0)
          assert_equal(product(a, axis=0), 0)
          assert_equal(add.reduce(a), pi)
  
      def test_minmax(self):
          # Tests extrema on MaskedArrays.
          a = arange(1, 13).reshape(3, 4)
          amask = masked_where(a < 5, a)
          assert_equal(amask.max(), a.max())
          assert_equal(amask.min(), 5)
          assert_equal(amask.max(0), a.max(0))
          assert_equal(amask.min(0), [5, 6, 7, 8])
          assert_(amask.max(1)[0].mask)
          assert_(amask.min(1)[0].mask)
  
      def test_ndarray_mask(self):
          # Check that the mask of the result is a ndarray (not a MaskedArray...)
          a = masked_array([-1, 0, 1, 2, 3], mask=[0, 0, 0, 0, 1])
          test = np.sqrt(a)
          control = masked_array([-1, 0, 1, np.sqrt(2), -1],
                                 mask=[1, 0, 0, 0, 1])
          assert_equal(test, control)
          assert_equal(test.mask, control.mask)
          assert_(not isinstance(test.mask, MaskedArray))
  
      def test_treatment_of_NotImplemented(self):
          # Check that NotImplemented is returned at appropriate places
  
          a = masked_array([1., 2.], mask=[1, 0])
          assert_raises(TypeError, operator.mul, a, "abc")
          assert_raises(TypeError, operator.truediv, a, "abc")
  
          class MyClass(object):
              __array_priority__ = a.__array_priority__ + 1
  
              def __mul__(self, other):
                  return "My mul"
  
              def __rmul__(self, other):
                  return "My rmul"
  
          me = MyClass()
          assert_(me * a == "My mul")
          assert_(a * me == "My rmul")
  
          # and that __array_priority__ is respected
          class MyClass2(object):
              __array_priority__ = 100
  
              def __mul__(self, other):
                  return "Me2mul"
  
              def __rmul__(self, other):
                  return "Me2rmul"
  
              def __rdiv__(self, other):
                  return "Me2rdiv"
  
              __rtruediv__ = __rdiv__
  
          me_too = MyClass2()
          assert_(a.__mul__(me_too) is NotImplemented)
          assert_(all(multiply.outer(a, me_too) == "Me2rmul"))
          assert_(a.__truediv__(me_too) is NotImplemented)
          assert_(me_too * a == "Me2mul")
          assert_(a * me_too == "Me2rmul")
          assert_(a / me_too == "Me2rdiv")
  
      def test_no_masked_nan_warnings(self):
          # check that a nan in masked position does not
          # cause ufunc warnings
  
          m = np.ma.array([0.5, np.nan], mask=[0,1])
  
          with warnings.catch_warnings():
              warnings.filterwarnings("error")
  
              # test unary and binary ufuncs
              exp(m)
              add(m, 1)
              m > 0
  
              # test different unary domains
              sqrt(m)
              log(m)
              tan(m)
              arcsin(m)
              arccos(m)
              arccosh(m)
  
              # test binary domains
              divide(m, 2)
  
              # also check that allclose uses ma ufuncs, to avoid warning
              allclose(m, 0.5)
  
  class TestMaskedArrayInPlaceArithmetics(object):
      # Test MaskedArray Arithmetics
  
      def setup(self):
          x = arange(10)
          y = arange(10)
          xm = arange(10)
          xm[2] = masked
          self.intdata = (x, y, xm)
          self.floatdata = (x.astype(float), y.astype(float), xm.astype(float))
          self.othertypes = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
          self.othertypes = [np.dtype(_).type for _ in self.othertypes]
          self.uint8data = (
              x.astype(np.uint8),
              y.astype(np.uint8),
              xm.astype(np.uint8)
          )
  
      def test_inplace_addition_scalar(self):
          # Test of inplace additions
          (x, y, xm) = self.intdata
          xm[2] = masked
          x += 1
          assert_equal(x, y + 1)
          xm += 1
          assert_equal(xm, y + 1)
  
          (x, _, xm) = self.floatdata
          id1 = x.data.ctypes._data
          x += 1.
          assert_(id1 == x.data.ctypes._data)
          assert_equal(x, y + 1.)
  
      def test_inplace_addition_array(self):
          # Test of inplace additions
          (x, y, xm) = self.intdata
          m = xm.mask
          a = arange(10, dtype=np.int16)
          a[-1] = masked
          x += a
          xm += a
          assert_equal(x, y + a)
          assert_equal(xm, y + a)
          assert_equal(xm.mask, mask_or(m, a.mask))
  
      def test_inplace_subtraction_scalar(self):
          # Test of inplace subtractions
          (x, y, xm) = self.intdata
          x -= 1
          assert_equal(x, y - 1)
          xm -= 1
          assert_equal(xm, y - 1)
  
      def test_inplace_subtraction_array(self):
          # Test of inplace subtractions
          (x, y, xm) = self.floatdata
          m = xm.mask
          a = arange(10, dtype=float)
          a[-1] = masked
          x -= a
          xm -= a
          assert_equal(x, y - a)
          assert_equal(xm, y - a)
          assert_equal(xm.mask, mask_or(m, a.mask))
  
      def test_inplace_multiplication_scalar(self):
          # Test of inplace multiplication
          (x, y, xm) = self.floatdata
          x *= 2.0
          assert_equal(x, y * 2)
          xm *= 2.0
          assert_equal(xm, y * 2)
  
      def test_inplace_multiplication_array(self):
          # Test of inplace multiplication
          (x, y, xm) = self.floatdata
          m = xm.mask
          a = arange(10, dtype=float)
          a[-1] = masked
          x *= a
          xm *= a
          assert_equal(x, y * a)
          assert_equal(xm, y * a)
          assert_equal(xm.mask, mask_or(m, a.mask))
  
      def test_inplace_division_scalar_int(self):
          # Test of inplace division
          (x, y, xm) = self.intdata
          x = arange(10) * 2
          xm = arange(10) * 2
          xm[2] = masked
          x //= 2
          assert_equal(x, y)
          xm //= 2
          assert_equal(xm, y)
  
      def test_inplace_division_scalar_float(self):
          # Test of inplace division
          (x, y, xm) = self.floatdata
          x /= 2.0
          assert_equal(x, y / 2.0)
          xm /= arange(10)
          assert_equal(xm, ones((10,)))
  
      def test_inplace_division_array_float(self):
          # Test of inplace division
          (x, y, xm) = self.floatdata
          m = xm.mask
          a = arange(10, dtype=float)
          a[-1] = masked
          x /= a
          xm /= a
          assert_equal(x, y / a)
          assert_equal(xm, y / a)
          assert_equal(xm.mask, mask_or(mask_or(m, a.mask), (a == 0)))
  
      def test_inplace_division_misc(self):
  
          x = [1., 1., 1., -2., pi / 2., 4., 5., -10., 10., 1., 2., 3.]
          y = [5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.]
          m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
          m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
          xm = masked_array(x, mask=m1)
          ym = masked_array(y, mask=m2)
  
          z = xm / ym
          assert_equal(z._mask, [1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1])
          assert_equal(z._data,
                       [1., 1., 1., -1., -pi / 2., 4., 5., 1., 1., 1., 2., 3.])
  
          xm = xm.copy()
          xm /= ym
          assert_equal(xm._mask, [1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1])
          assert_equal(z._data,
                       [1., 1., 1., -1., -pi / 2., 4., 5., 1., 1., 1., 2., 3.])
  
      def test_datafriendly_add(self):
          # Test keeping data w/ (inplace) addition
          x = array([1, 2, 3], mask=[0, 0, 1])
          # Test add w/ scalar
          xx = x + 1
          assert_equal(xx.data, [2, 3, 3])
          assert_equal(xx.mask, [0, 0, 1])
          # Test iadd w/ scalar
          x += 1
          assert_equal(x.data, [2, 3, 3])
          assert_equal(x.mask, [0, 0, 1])
          # Test add w/ array
          x = array([1, 2, 3], mask=[0, 0, 1])
          xx = x + array([1, 2, 3], mask=[1, 0, 0])
          assert_equal(xx.data, [1, 4, 3])
          assert_equal(xx.mask, [1, 0, 1])
          # Test iadd w/ array
          x = array([1, 2, 3], mask=[0, 0, 1])
          x += array([1, 2, 3], mask=[1, 0, 0])
          assert_equal(x.data, [1, 4, 3])
          assert_equal(x.mask, [1, 0, 1])
  
      def test_datafriendly_sub(self):
          # Test keeping data w/ (inplace) subtraction
          # Test sub w/ scalar
          x = array([1, 2, 3], mask=[0, 0, 1])
          xx = x - 1
          assert_equal(xx.data, [0, 1, 3])
          assert_equal(xx.mask, [0, 0, 1])
          # Test isub w/ scalar
          x = array([1, 2, 3], mask=[0, 0, 1])
          x -= 1
          assert_equal(x.data, [0, 1, 3])
          assert_equal(x.mask, [0, 0, 1])
          # Test sub w/ array
          x = array([1, 2, 3], mask=[0, 0, 1])
          xx = x - array([1, 2, 3], mask=[1, 0, 0])
          assert_equal(xx.data, [1, 0, 3])
          assert_equal(xx.mask, [1, 0, 1])
          # Test isub w/ array
          x = array([1, 2, 3], mask=[0, 0, 1])
          x -= array([1, 2, 3], mask=[1, 0, 0])
          assert_equal(x.data, [1, 0, 3])
          assert_equal(x.mask, [1, 0, 1])
  
      def test_datafriendly_mul(self):
          # Test keeping data w/ (inplace) multiplication
          # Test mul w/ scalar
          x = array([1, 2, 3], mask=[0, 0, 1])
          xx = x * 2
          assert_equal(xx.data, [2, 4, 3])
          assert_equal(xx.mask, [0, 0, 1])
          # Test imul w/ scalar
          x = array([1, 2, 3], mask=[0, 0, 1])
          x *= 2
          assert_equal(x.data, [2, 4, 3])
          assert_equal(x.mask, [0, 0, 1])
          # Test mul w/ array
          x = array([1, 2, 3], mask=[0, 0, 1])
          xx = x * array([10, 20, 30], mask=[1, 0, 0])
          assert_equal(xx.data, [1, 40, 3])
          assert_equal(xx.mask, [1, 0, 1])
          # Test imul w/ array
          x = array([1, 2, 3], mask=[0, 0, 1])
          x *= array([10, 20, 30], mask=[1, 0, 0])
          assert_equal(x.data, [1, 40, 3])
          assert_equal(x.mask, [1, 0, 1])
  
      def test_datafriendly_div(self):
          # Test keeping data w/ (inplace) division
          # Test div on scalar
          x = array([1, 2, 3], mask=[0, 0, 1])
          xx = x / 2.
          assert_equal(xx.data, [1 / 2., 2 / 2., 3])
          assert_equal(xx.mask, [0, 0, 1])
          # Test idiv on scalar
          x = array([1., 2., 3.], mask=[0, 0, 1])
          x /= 2.
          assert_equal(x.data, [1 / 2., 2 / 2., 3])
          assert_equal(x.mask, [0, 0, 1])
          # Test div on array
          x = array([1., 2., 3.], mask=[0, 0, 1])
          xx = x / array([10., 20., 30.], mask=[1, 0, 0])
          assert_equal(xx.data, [1., 2. / 20., 3.])
          assert_equal(xx.mask, [1, 0, 1])
          # Test idiv on array
          x = array([1., 2., 3.], mask=[0, 0, 1])
          x /= array([10., 20., 30.], mask=[1, 0, 0])
          assert_equal(x.data, [1., 2 / 20., 3.])
          assert_equal(x.mask, [1, 0, 1])
  
      def test_datafriendly_pow(self):
          # Test keeping data w/ (inplace) power
          # Test pow on scalar
          x = array([1., 2., 3.], mask=[0, 0, 1])
          xx = x ** 2.5
          assert_equal(xx.data, [1., 2. ** 2.5, 3.])
          assert_equal(xx.mask, [0, 0, 1])
          # Test ipow on scalar
          x **= 2.5
          assert_equal(x.data, [1., 2. ** 2.5, 3])
          assert_equal(x.mask, [0, 0, 1])
  
      def test_datafriendly_add_arrays(self):
          a = array([[1, 1], [3, 3]])
          b = array([1, 1], mask=[0, 0])
          a += b
          assert_equal(a, [[2, 2], [4, 4]])
          if a.mask is not nomask:
              assert_equal(a.mask, [[0, 0], [0, 0]])
  
          a = array([[1, 1], [3, 3]])
          b = array([1, 1], mask=[0, 1])
          a += b
          assert_equal(a, [[2, 2], [4, 4]])
          assert_equal(a.mask, [[0, 1], [0, 1]])
  
      def test_datafriendly_sub_arrays(self):
          a = array([[1, 1], [3, 3]])
          b = array([1, 1], mask=[0, 0])
          a -= b
          assert_equal(a, [[0, 0], [2, 2]])
          if a.mask is not nomask:
              assert_equal(a.mask, [[0, 0], [0, 0]])
  
          a = array([[1, 1], [3, 3]])
          b = array([1, 1], mask=[0, 1])
          a -= b
          assert_equal(a, [[0, 0], [2, 2]])
          assert_equal(a.mask, [[0, 1], [0, 1]])
  
      def test_datafriendly_mul_arrays(self):
          a = array([[1, 1], [3, 3]])
          b = array([1, 1], mask=[0, 0])
          a *= b
          assert_equal(a, [[1, 1], [3, 3]])
          if a.mask is not nomask:
              assert_equal(a.mask, [[0, 0], [0, 0]])
  
          a = array([[1, 1], [3, 3]])
          b = array([1, 1], mask=[0, 1])
          a *= b
          assert_equal(a, [[1, 1], [3, 3]])
          assert_equal(a.mask, [[0, 1], [0, 1]])
  
      def test_inplace_addition_scalar_type(self):
          # Test of inplace additions
          for t in self.othertypes:
              with warnings.catch_warnings(record=True) as w:
                  warnings.filterwarnings("always")
                  (x, y, xm) = (_.astype(t) for _ in self.uint8data)
                  xm[2] = masked
                  x += t(1)
                  assert_equal(x, y + t(1))
                  xm += t(1)
                  assert_equal(xm, y + t(1))
  
                  assert_equal(len(w), 0, "Failed on type=%s." % t)
  
      def test_inplace_addition_array_type(self):
          # Test of inplace additions
          for t in self.othertypes:
              with warnings.catch_warnings(record=True) as w:
                  warnings.filterwarnings("always")
                  (x, y, xm) = (_.astype(t) for _ in self.uint8data)
                  m = xm.mask
                  a = arange(10, dtype=t)
                  a[-1] = masked
                  x += a
                  xm += a
                  assert_equal(x, y + a)
                  assert_equal(xm, y + a)
                  assert_equal(xm.mask, mask_or(m, a.mask))
  
                  assert_equal(len(w), 0, "Failed on type=%s." % t)
  
      def test_inplace_subtraction_scalar_type(self):
          # Test of inplace subtractions
          for t in self.othertypes:
              with warnings.catch_warnings(record=True) as w:
                  warnings.filterwarnings("always")
                  (x, y, xm) = (_.astype(t) for _ in self.uint8data)
                  x -= t(1)
                  assert_equal(x, y - t(1))
                  xm -= t(1)
                  assert_equal(xm, y - t(1))
  
                  assert_equal(len(w), 0, "Failed on type=%s." % t)
  
      def test_inplace_subtraction_array_type(self):
          # Test of inplace subtractions
          for t in self.othertypes:
              with warnings.catch_warnings(record=True) as w:
                  warnings.filterwarnings("always")
                  (x, y, xm) = (_.astype(t) for _ in self.uint8data)
                  m = xm.mask
                  a = arange(10, dtype=t)
                  a[-1] = masked
                  x -= a
                  xm -= a
                  assert_equal(x, y - a)
                  assert_equal(xm, y - a)
                  assert_equal(xm.mask, mask_or(m, a.mask))
  
                  assert_equal(len(w), 0, "Failed on type=%s." % t)
  
      def test_inplace_multiplication_scalar_type(self):
          # Test of inplace multiplication
          for t in self.othertypes:
              with warnings.catch_warnings(record=True) as w:
                  warnings.filterwarnings("always")
                  (x, y, xm) = (_.astype(t) for _ in self.uint8data)
                  x *= t(2)
                  assert_equal(x, y * t(2))
                  xm *= t(2)
                  assert_equal(xm, y * t(2))
  
                  assert_equal(len(w), 0, "Failed on type=%s." % t)
  
      def test_inplace_multiplication_array_type(self):
          # Test of inplace multiplication
          for t in self.othertypes:
              with warnings.catch_warnings(record=True) as w:
                  warnings.filterwarnings("always")
                  (x, y, xm) = (_.astype(t) for _ in self.uint8data)
                  m = xm.mask
                  a = arange(10, dtype=t)
                  a[-1] = masked
                  x *= a
                  xm *= a
                  assert_equal(x, y * a)
                  assert_equal(xm, y * a)
                  assert_equal(xm.mask, mask_or(m, a.mask))
  
                  assert_equal(len(w), 0, "Failed on type=%s." % t)
  
      def test_inplace_floor_division_scalar_type(self):
          # Test of inplace division
          for t in self.othertypes:
              with warnings.catch_warnings(record=True) as w:
                  warnings.filterwarnings("always")
                  (x, y, xm) = (_.astype(t) for _ in self.uint8data)
                  x = arange(10, dtype=t) * t(2)
                  xm = arange(10, dtype=t) * t(2)
                  xm[2] = masked
                  x //= t(2)
                  xm //= t(2)
                  assert_equal(x, y)
                  assert_equal(xm, y)
  
                  assert_equal(len(w), 0, "Failed on type=%s." % t)
  
      def test_inplace_floor_division_array_type(self):
          # Test of inplace division
          for t in self.othertypes:
              with warnings.catch_warnings(record=True) as w:
                  warnings.filterwarnings("always")
                  (x, y, xm) = (_.astype(t) for _ in self.uint8data)
                  m = xm.mask
                  a = arange(10, dtype=t)
                  a[-1] = masked
                  x //= a
                  xm //= a
                  assert_equal(x, y // a)
                  assert_equal(xm, y // a)
                  assert_equal(
                      xm.mask,
                      mask_or(mask_or(m, a.mask), (a == t(0)))
                  )
  
                  assert_equal(len(w), 0, "Failed on type=%s." % t)
  
      def test_inplace_division_scalar_type(self):
          # Test of inplace division
          for t in self.othertypes:
              with suppress_warnings() as sup:
                  sup.record(UserWarning)
  
                  (x, y, xm) = (_.astype(t) for _ in self.uint8data)
                  x = arange(10, dtype=t) * t(2)
                  xm = arange(10, dtype=t) * t(2)
                  xm[2] = masked
  
                  # May get a DeprecationWarning or a TypeError.
                  #
                  # This is a consequence of the fact that this is true divide
                  # and will require casting to float for calculation and
                  # casting back to the original type. This will only be raised
                  # with integers. Whether it is an error or warning is only
                  # dependent on how stringent the casting rules are.
                  #
                  # Will handle the same way.
                  try:
                      x /= t(2)
                      assert_equal(x, y)
                  except (DeprecationWarning, TypeError) as e:
                      warnings.warn(str(e), stacklevel=1)
                  try:
                      xm /= t(2)
                      assert_equal(xm, y)
                  except (DeprecationWarning, TypeError) as e:
                      warnings.warn(str(e), stacklevel=1)
  
                  if issubclass(t, np.integer):
                      assert_equal(len(sup.log), 2, "Failed on type=%s." % t)
                  else:
                      assert_equal(len(sup.log), 0, "Failed on type=%s." % t)
  
      def test_inplace_division_array_type(self):
          # Test of inplace division
          for t in self.othertypes:
              with suppress_warnings() as sup:
                  sup.record(UserWarning)
                  (x, y, xm) = (_.astype(t) for _ in self.uint8data)
                  m = xm.mask
                  a = arange(10, dtype=t)
                  a[-1] = masked
  
                  # May get a DeprecationWarning or a TypeError.
                  #
                  # This is a consequence of the fact that this is true divide
                  # and will require casting to float for calculation and
                  # casting back to the original type. This will only be raised
                  # with integers. Whether it is an error or warning is only
                  # dependent on how stringent the casting rules are.
                  #
                  # Will handle the same way.
                  try:
                      x /= a
                      assert_equal(x, y / a)
                  except (DeprecationWarning, TypeError) as e:
                      warnings.warn(str(e), stacklevel=1)
                  try:
                      xm /= a
                      assert_equal(xm, y / a)
                      assert_equal(
                          xm.mask,
                          mask_or(mask_or(m, a.mask), (a == t(0)))
                      )
                  except (DeprecationWarning, TypeError) as e:
                      warnings.warn(str(e), stacklevel=1)
  
                  if issubclass(t, np.integer):
                      assert_equal(len(sup.log), 2, "Failed on type=%s." % t)
                  else:
                      assert_equal(len(sup.log), 0, "Failed on type=%s." % t)
  
      def test_inplace_pow_type(self):
          # Test keeping data w/ (inplace) power
          for t in self.othertypes:
              with warnings.catch_warnings(record=True) as w:
                  warnings.filterwarnings("always")
                  # Test pow on scalar
                  x = array([1, 2, 3], mask=[0, 0, 1], dtype=t)
                  xx = x ** t(2)
                  xx_r = array([1, 2 ** 2, 3], mask=[0, 0, 1], dtype=t)
                  assert_equal(xx.data, xx_r.data)
                  assert_equal(xx.mask, xx_r.mask)
                  # Test ipow on scalar
                  x **= t(2)
                  assert_equal(x.data, xx_r.data)
                  assert_equal(x.mask, xx_r.mask)
  
                  assert_equal(len(w), 0, "Failed on type=%s." % t)
  
  
  class TestMaskedArrayMethods(object):
      # Test class for miscellaneous MaskedArrays methods.
      def setup(self):
          # Base data definition.
          x = np.array([8.375, 7.545, 8.828, 8.5, 1.757, 5.928,
                        8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
                        3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
                        6.04, 9.63, 7.712, 3.382, 4.489, 6.479,
                        7.189, 9.645, 5.395, 4.961, 9.894, 2.893,
                        7.357, 9.828, 6.272, 3.758, 6.693, 0.993])
          X = x.reshape(6, 6)
          XX = x.reshape(3, 2, 2, 3)
  
          m = np.array([0, 1, 0, 1, 0, 0,
                       1, 0, 1, 1, 0, 1,
                       0, 0, 0, 1, 0, 1,
                       0, 0, 0, 1, 1, 1,
                       1, 0, 0, 1, 0, 0,
                       0, 0, 1, 0, 1, 0])
          mx = array(data=x, mask=m)
          mX = array(data=X, mask=m.reshape(X.shape))
          mXX = array(data=XX, mask=m.reshape(XX.shape))
  
          m2 = np.array([1, 1, 0, 1, 0, 0,
                        1, 1, 1, 1, 0, 1,
                        0, 0, 1, 1, 0, 1,
                        0, 0, 0, 1, 1, 1,
                        1, 0, 0, 1, 1, 0,
                        0, 0, 1, 0, 1, 1])
          m2x = array(data=x, mask=m2)
          m2X = array(data=X, mask=m2.reshape(X.shape))
          m2XX = array(data=XX, mask=m2.reshape(XX.shape))
          self.d = (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX)
  
      def test_generic_methods(self):
          # Tests some MaskedArray methods.
          a = array([1, 3, 2])
          assert_equal(a.any(), a._data.any())
          assert_equal(a.all(), a._data.all())
          assert_equal(a.argmax(), a._data.argmax())
          assert_equal(a.argmin(), a._data.argmin())
          assert_equal(a.choose(0, 1, 2, 3, 4), a._data.choose(0, 1, 2, 3, 4))
          assert_equal(a.compress([1, 0, 1]), a._data.compress([1, 0, 1]))
          assert_equal(a.conj(), a._data.conj())
          assert_equal(a.conjugate(), a._data.conjugate())
  
          m = array([[1, 2], [3, 4]])
          assert_equal(m.diagonal(), m._data.diagonal())
          assert_equal(a.sum(), a._data.sum())
          assert_equal(a.take([1, 2]), a._data.take([1, 2]))
          assert_equal(m.transpose(), m._data.transpose())
  
      def test_allclose(self):
          # Tests allclose on arrays
          a = np.random.rand(10)
          b = a + np.random.rand(10) * 1e-8
          assert_(allclose(a, b))
          # Test allclose w/ infs
          a[0] = np.inf
          assert_(not allclose(a, b))
          b[0] = np.inf
          assert_(allclose(a, b))
          # Test allclose w/ masked
          a = masked_array(a)
          a[-1] = masked
          assert_(allclose(a, b, masked_equal=True))
          assert_(not allclose(a, b, masked_equal=False))
          # Test comparison w/ scalar
          a *= 1e-8
          a[0] = 0
          assert_(allclose(a, 0, masked_equal=True))
  
          # Test that the function works for MIN_INT integer typed arrays
          a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
          assert_(allclose(a, a))
  
      def test_allany(self):
          # Checks the any/all methods/functions.
          x = np.array([[0.13, 0.26, 0.90],
                        [0.28, 0.33, 0.63],
                        [0.31, 0.87, 0.70]])
          m = np.array([[True, False, False],
                        [False, False, False],
                        [True, True, False]], dtype=np.bool_)
          mx = masked_array(x, mask=m)
          mxbig = (mx > 0.5)
          mxsmall = (mx < 0.5)
  
          assert_(not mxbig.all())
          assert_(mxbig.any())
          assert_equal(mxbig.all(0), [False, False, True])
          assert_equal(mxbig.all(1), [False, False, True])
          assert_equal(mxbig.any(0), [False, False, True])
          assert_equal(mxbig.any(1), [True, True, True])
  
          assert_(not mxsmall.all())
          assert_(mxsmall.any())
          assert_equal(mxsmall.all(0), [True, True, False])
          assert_equal(mxsmall.all(1), [False, False, False])
          assert_equal(mxsmall.any(0), [True, True, False])
          assert_equal(mxsmall.any(1), [True, True, False])
  
      def test_allany_onmatrices(self):
          x = np.array([[0.13, 0.26, 0.90],
                        [0.28, 0.33, 0.63],
                        [0.31, 0.87, 0.70]])
          X = np.matrix(x)
          m = np.array([[True, False, False],
                        [False, False, False],
                        [True, True, False]], dtype=np.bool_)
          mX = masked_array(X, mask=m)
          mXbig = (mX > 0.5)
          mXsmall = (mX < 0.5)
  
          assert_(not mXbig.all())
          assert_(mXbig.any())
          assert_equal(mXbig.all(0), np.matrix([False, False, True]))
          assert_equal(mXbig.all(1), np.matrix([False, False, True]).T)
          assert_equal(mXbig.any(0), np.matrix([False, False, True]))
          assert_equal(mXbig.any(1), np.matrix([True, True, True]).T)
  
          assert_(not mXsmall.all())
          assert_(mXsmall.any())
          assert_equal(mXsmall.all(0), np.matrix([True, True, False]))
          assert_equal(mXsmall.all(1), np.matrix([False, False, False]).T)
          assert_equal(mXsmall.any(0), np.matrix([True, True, False]))
          assert_equal(mXsmall.any(1), np.matrix([True, True, False]).T)
  
      def test_allany_oddities(self):
          # Some fun with all and any
          store = empty((), dtype=bool)
          full = array([1, 2, 3], mask=True)
  
          assert_(full.all() is masked)
          full.all(out=store)
          assert_(store)
          assert_(store._mask, True)
          assert_(store is not masked)
  
          store = empty((), dtype=bool)
          assert_(full.any() is masked)
          full.any(out=store)
          assert_(not store)
          assert_(store._mask, True)
          assert_(store is not masked)
  
      def test_argmax_argmin(self):
          # Tests argmin & argmax on MaskedArrays.
          (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
  
          assert_equal(mx.argmin(), 35)
          assert_equal(mX.argmin(), 35)
          assert_equal(m2x.argmin(), 4)
          assert_equal(m2X.argmin(), 4)
          assert_equal(mx.argmax(), 28)
          assert_equal(mX.argmax(), 28)
          assert_equal(m2x.argmax(), 31)
          assert_equal(m2X.argmax(), 31)
  
          assert_equal(mX.argmin(0), [2, 2, 2, 5, 0, 5])
          assert_equal(m2X.argmin(0), [2, 2, 4, 5, 0, 4])
          assert_equal(mX.argmax(0), [0, 5, 0, 5, 4, 0])
          assert_equal(m2X.argmax(0), [5, 5, 0, 5, 1, 0])
  
          assert_equal(mX.argmin(1), [4, 1, 0, 0, 5, 5, ])
          assert_equal(m2X.argmin(1), [4, 4, 0, 0, 5, 3])
          assert_equal(mX.argmax(1), [2, 4, 1, 1, 4, 1])
          assert_equal(m2X.argmax(1), [2, 4, 1, 1, 1, 1])
  
      def test_clip(self):
          # Tests clip on MaskedArrays.
          x = np.array([8.375, 7.545, 8.828, 8.5, 1.757, 5.928,
                        8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
                        3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
                        6.04, 9.63, 7.712, 3.382, 4.489, 6.479,
                        7.189, 9.645, 5.395, 4.961, 9.894, 2.893,
                        7.357, 9.828, 6.272, 3.758, 6.693, 0.993])
          m = np.array([0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1,
                        0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1,
                        1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0])
          mx = array(x, mask=m)
          clipped = mx.clip(2, 8)
          assert_equal(clipped.mask, mx.mask)
          assert_equal(clipped._data, x.clip(2, 8))
          assert_equal(clipped._data, mx._data.clip(2, 8))
  
      def test_compress(self):
          # test compress
          a = masked_array([1., 2., 3., 4., 5.], fill_value=9999)
          condition = (a > 1.5) & (a < 3.5)
          assert_equal(a.compress(condition), [2., 3.])
  
          a[[2, 3]] = masked
          b = a.compress(condition)
          assert_equal(b._data, [2., 3.])
          assert_equal(b._mask, [0, 1])
          assert_equal(b.fill_value, 9999)
          assert_equal(b, a[condition])
  
          condition = (a < 4.)
          b = a.compress(condition)
          assert_equal(b._data, [1., 2., 3.])
          assert_equal(b._mask, [0, 0, 1])
          assert_equal(b.fill_value, 9999)
          assert_equal(b, a[condition])
  
          a = masked_array([[10, 20, 30], [40, 50, 60]],
                           mask=[[0, 0, 1], [1, 0, 0]])
          b = a.compress(a.ravel() >= 22)
          assert_equal(b._data, [30, 40, 50, 60])
          assert_equal(b._mask, [1, 1, 0, 0])
  
          x = np.array([3, 1, 2])
          b = a.compress(x >= 2, axis=1)
          assert_equal(b._data, [[10, 30], [40, 60]])
          assert_equal(b._mask, [[0, 1], [1, 0]])
  
      def test_compressed(self):
          # Tests compressed
          a = array([1, 2, 3, 4], mask=[0, 0, 0, 0])
          b = a.compressed()
          assert_equal(b, a)
          a[0] = masked
          b = a.compressed()
          assert_equal(b, [2, 3, 4])
  
          a = array(np.matrix([1, 2, 3, 4]), mask=[0, 0, 0, 0])
          b = a.compressed()
          assert_equal(b, a)
          assert_(isinstance(b, np.matrix))
          a[0, 0] = masked
          b = a.compressed()
          assert_equal(b, [[2, 3, 4]])
  
      def test_empty(self):
          # Tests empty/like
          datatype = [('a', int), ('b', float), ('c', '|S8')]
          a = masked_array([(1, 1.1, '1.1'), (2, 2.2, '2.2'), (3, 3.3, '3.3')],
                           dtype=datatype)
          assert_equal(len(a.fill_value.item()), len(datatype))
  
          b = empty_like(a)
          assert_equal(b.shape, a.shape)
          assert_equal(b.fill_value, a.fill_value)
  
          b = empty(len(a), dtype=datatype)
          assert_equal(b.shape, a.shape)
          assert_equal(b.fill_value, a.fill_value)
  
          # check empty_like mask handling
          a = masked_array([1, 2, 3], mask=[False, True, False])
          b = empty_like(a)
          assert_(not np.may_share_memory(a.mask, b.mask))
          b = a.view(masked_array)
          assert_(np.may_share_memory(a.mask, b.mask))
  
      @suppress_copy_mask_on_assignment
      def test_put(self):
          # Tests put.
          d = arange(5)
          n = [0, 0, 0, 1, 1]
          m = make_mask(n)
          x = array(d, mask=m)
          assert_(x[3] is masked)
          assert_(x[4] is masked)
          x[[1, 4]] = [10, 40]
          assert_(x[3] is masked)
          assert_(x[4] is not masked)
          assert_equal(x, [0, 10, 2, -1, 40])
  
          x = masked_array(arange(10), mask=[1, 0, 0, 0, 0] * 2)
          i = [0, 2, 4, 6]
          x.put(i, [6, 4, 2, 0])
          assert_equal(x, asarray([6, 1, 4, 3, 2, 5, 0, 7, 8, 9, ]))
          assert_equal(x.mask, [0, 0, 0, 0, 0, 1, 0, 0, 0, 0])
          x.put(i, masked_array([0, 2, 4, 6], [1, 0, 1, 0]))
          assert_array_equal(x, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ])
          assert_equal(x.mask, [1, 0, 0, 0, 1, 1, 0, 0, 0, 0])
  
          x = masked_array(arange(10), mask=[1, 0, 0, 0, 0] * 2)
          put(x, i, [6, 4, 2, 0])
          assert_equal(x, asarray([6, 1, 4, 3, 2, 5, 0, 7, 8, 9, ]))
          assert_equal(x.mask, [0, 0, 0, 0, 0, 1, 0, 0, 0, 0])
          put(x, i, masked_array([0, 2, 4, 6], [1, 0, 1, 0]))
          assert_array_equal(x, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ])
          assert_equal(x.mask, [1, 0, 0, 0, 1, 1, 0, 0, 0, 0])
  
      def test_put_nomask(self):
          # GitHub issue 6425
          x = zeros(10)
          z = array([3., -1.], mask=[False, True])
  
          x.put([1, 2], z)
          assert_(x[0] is not masked)
          assert_equal(x[0], 0)
          assert_(x[1] is not masked)
          assert_equal(x[1], 3)
          assert_(x[2] is masked)
          assert_(x[3] is not masked)
          assert_equal(x[3], 0)
  
      def test_put_hardmask(self):
          # Tests put on hardmask
          d = arange(5)
          n = [0, 0, 0, 1, 1]
          m = make_mask(n)
          xh = array(d + 1, mask=m, hard_mask=True, copy=True)
          xh.put([4, 2, 0, 1, 3], [1, 2, 3, 4, 5])
          assert_equal(xh._data, [3, 4, 2, 4, 5])
  
      def test_putmask(self):
          x = arange(6) + 1
          mx = array(x, mask=[0, 0, 0, 1, 1, 1])
          mask = [0, 0, 1, 0, 0, 1]
          # w/o mask, w/o masked values
          xx = x.copy()
          putmask(xx, mask, 99)
          assert_equal(xx, [1, 2, 99, 4, 5, 99])
          # w/ mask, w/o masked values
          mxx = mx.copy()
          putmask(mxx, mask, 99)
          assert_equal(mxx._data, [1, 2, 99, 4, 5, 99])
          assert_equal(mxx._mask, [0, 0, 0, 1, 1, 0])
          # w/o mask, w/ masked values
          values = array([10, 20, 30, 40, 50, 60], mask=[1, 1, 1, 0, 0, 0])
          xx = x.copy()
          putmask(xx, mask, values)
          assert_equal(xx._data, [1, 2, 30, 4, 5, 60])
          assert_equal(xx._mask, [0, 0, 1, 0, 0, 0])
          # w/ mask, w/ masked values
          mxx = mx.copy()
          putmask(mxx, mask, values)
          assert_equal(mxx._data, [1, 2, 30, 4, 5, 60])
          assert_equal(mxx._mask, [0, 0, 1, 1, 1, 0])
          # w/ mask, w/ masked values + hardmask
          mxx = mx.copy()
          mxx.harden_mask()
          putmask(mxx, mask, values)
          assert_equal(mxx, [1, 2, 30, 4, 5, 60])
  
      def test_ravel(self):
          # Tests ravel
          a = array([[1, 2, 3, 4, 5]], mask=[[0, 1, 0, 0, 0]])
          aravel = a.ravel()
          assert_equal(aravel._mask.shape, aravel.shape)
          a = array([0, 0], mask=[1, 1])
          aravel = a.ravel()
          assert_equal(aravel._mask.shape, a.shape)
          a = array(np.matrix([1, 2, 3, 4, 5]), mask=[[0, 1, 0, 0, 0]])
          aravel = a.ravel()
          assert_equal(aravel.shape, (1, 5))
          assert_equal(aravel._mask.shape, a.shape)
          # Checks that small_mask is preserved
          a = array([1, 2, 3, 4], mask=[0, 0, 0, 0], shrink=False)
          assert_equal(a.ravel()._mask, [0, 0, 0, 0])
          # Test that the fill_value is preserved
          a.fill_value = -99
          a.shape = (2, 2)
          ar = a.ravel()
          assert_equal(ar._mask, [0, 0, 0, 0])
          assert_equal(ar._data, [1, 2, 3, 4])
          assert_equal(ar.fill_value, -99)
          # Test index ordering
          assert_equal(a.ravel(order='C'), [1, 2, 3, 4])
          assert_equal(a.ravel(order='F'), [1, 3, 2, 4])
  
      def test_reshape(self):
          # Tests reshape
          x = arange(4)
          x[0] = masked
          y = x.reshape(2, 2)
          assert_equal(y.shape, (2, 2,))
          assert_equal(y._mask.shape, (2, 2,))
          assert_equal(x.shape, (4,))
          assert_equal(x._mask.shape, (4,))
  
      def test_sort(self):
          # Test sort
          x = array([1, 4, 2, 3], mask=[0, 1, 0, 0], dtype=np.uint8)
  
          sortedx = sort(x)
          assert_equal(sortedx._data, [1, 2, 3, 4])
          assert_equal(sortedx._mask, [0, 0, 0, 1])
  
          sortedx = sort(x, endwith=False)
          assert_equal(sortedx._data, [4, 1, 2, 3])
          assert_equal(sortedx._mask, [1, 0, 0, 0])
  
          x.sort()
          assert_equal(x._data, [1, 2, 3, 4])
          assert_equal(x._mask, [0, 0, 0, 1])
  
          x = array([1, 4, 2, 3], mask=[0, 1, 0, 0], dtype=np.uint8)
          x.sort(endwith=False)
          assert_equal(x._data, [4, 1, 2, 3])
          assert_equal(x._mask, [1, 0, 0, 0])
  
          x = [1, 4, 2, 3]
          sortedx = sort(x)
          assert_(not isinstance(sorted, MaskedArray))
  
          x = array([0, 1, -1, -2, 2], mask=nomask, dtype=np.int8)
          sortedx = sort(x, endwith=False)
          assert_equal(sortedx._data, [-2, -1, 0, 1, 2])
          x = array([0, 1, -1, -2, 2], mask=[0, 1, 0, 0, 1], dtype=np.int8)
          sortedx = sort(x, endwith=False)
          assert_equal(sortedx._data, [1, 2, -2, -1, 0])
          assert_equal(sortedx._mask, [1, 1, 0, 0, 0])
  
      def test_argsort_matches_sort(self):
          x = array([1, 4, 2, 3], mask=[0, 1, 0, 0], dtype=np.uint8)
  
          for kwargs in [dict(),
                         dict(endwith=True),
                         dict(endwith=False),
                         dict(fill_value=2),
                         dict(fill_value=2, endwith=True),
                         dict(fill_value=2, endwith=False)]:
              sortedx = sort(x, **kwargs)
              argsortedx = x[argsort(x, **kwargs)]
              assert_equal(sortedx._data, argsortedx._data)
              assert_equal(sortedx._mask, argsortedx._mask)
  
      def test_sort_2d(self):
          # Check sort of 2D array.
          # 2D array w/o mask
          a = masked_array([[8, 4, 1], [2, 0, 9]])
          a.sort(0)
          assert_equal(a, [[2, 0, 1], [8, 4, 9]])
          a = masked_array([[8, 4, 1], [2, 0, 9]])
          a.sort(1)
          assert_equal(a, [[1, 4, 8], [0, 2, 9]])
          # 2D array w/mask
          a = masked_array([[8, 4, 1], [2, 0, 9]], mask=[[1, 0, 0], [0, 0, 1]])
          a.sort(0)
          assert_equal(a, [[2, 0, 1], [8, 4, 9]])
          assert_equal(a._mask, [[0, 0, 0], [1, 0, 1]])
          a = masked_array([[8, 4, 1], [2, 0, 9]], mask=[[1, 0, 0], [0, 0, 1]])
          a.sort(1)
          assert_equal(a, [[1, 4, 8], [0, 2, 9]])
          assert_equal(a._mask, [[0, 0, 1], [0, 0, 1]])
          # 3D
          a = masked_array([[[7, 8, 9], [4, 5, 6], [1, 2, 3]],
                            [[1, 2, 3], [7, 8, 9], [4, 5, 6]],
                            [[7, 8, 9], [1, 2, 3], [4, 5, 6]],
                            [[4, 5, 6], [1, 2, 3], [7, 8, 9]]])
          a[a % 4 == 0] = masked
          am = a.copy()
          an = a.filled(99)
          am.sort(0)
          an.sort(0)
          assert_equal(am, an)
          am = a.copy()
          an = a.filled(99)
          am.sort(1)
          an.sort(1)
          assert_equal(am, an)
          am = a.copy()
          an = a.filled(99)
          am.sort(2)
          an.sort(2)
          assert_equal(am, an)
  
      def test_sort_flexible(self):
          # Test sort on structured dtype.
          a = array(
              data=[(3, 3), (3, 2), (2, 2), (2, 1), (1, 0), (1, 1), (1, 2)],
              mask=[(0, 0), (0, 1), (0, 0), (0, 0), (1, 0), (0, 0), (0, 0)],
              dtype=[('A', int), ('B', int)])
          mask_last = array(
              data=[(1, 1), (1, 2), (2, 1), (2, 2), (3, 3), (3, 2), (1, 0)],
              mask=[(0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 1), (1, 0)],
              dtype=[('A', int), ('B', int)])
          mask_first = array(
              data=[(1, 0), (1, 1), (1, 2), (2, 1), (2, 2), (3, 2), (3, 3)],
              mask=[(1, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 1), (0, 0)],
              dtype=[('A', int), ('B', int)])
  
          test = sort(a)
          assert_equal(test, mask_last)
          assert_equal(test.mask, mask_last.mask)
  
          test = sort(a, endwith=False)
          assert_equal(test, mask_first)
          assert_equal(test.mask, mask_first.mask)
  
          # Test sort on dtype with subarray (gh-8069)
          dt = np.dtype([('v', int, 2)])
          a = a.view(dt)
          mask_last = mask_last.view(dt)
          mask_first = mask_first.view(dt)
  
          test = sort(a)
          assert_equal(test, mask_last)
          assert_equal(test.mask, mask_last.mask)
  
          test = sort(a, endwith=False)
          assert_equal(test, mask_first)
          assert_equal(test.mask, mask_first.mask)
  
      def test_argsort(self):
          # Test argsort
          a = array([1, 5, 2, 4, 3], mask=[1, 0, 0, 1, 0])
          assert_equal(np.argsort(a), argsort(a))
  
      def test_squeeze(self):
          # Check squeeze
          data = masked_array([[1, 2, 3]])
          assert_equal(data.squeeze(), [1, 2, 3])
          data = masked_array([[1, 2, 3]], mask=[[1, 1, 1]])
          assert_equal(data.squeeze(), [1, 2, 3])
          assert_equal(data.squeeze()._mask, [1, 1, 1])
          data = masked_array([[1]], mask=True)
          assert_(data.squeeze() is masked)
  
      def test_swapaxes(self):
          # Tests swapaxes on MaskedArrays.
          x = np.array([8.375, 7.545, 8.828, 8.5, 1.757, 5.928,
                        8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
                        3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
                        6.04, 9.63, 7.712, 3.382, 4.489, 6.479,
                        7.189, 9.645, 5.395, 4.961, 9.894, 2.893,
                        7.357, 9.828, 6.272, 3.758, 6.693, 0.993])
          m = np.array([0, 1, 0, 1, 0, 0,
                        1, 0, 1, 1, 0, 1,
                        0, 0, 0, 1, 0, 1,
                        0, 0, 0, 1, 1, 1,
                        1, 0, 0, 1, 0, 0,
                        0, 0, 1, 0, 1, 0])
          mX = array(x, mask=m).reshape(6, 6)
          mXX = mX.reshape(3, 2, 2, 3)
  
          mXswapped = mX.swapaxes(0, 1)
          assert_equal(mXswapped[-1], mX[:, -1])
  
          mXXswapped = mXX.swapaxes(0, 2)
          assert_equal(mXXswapped.shape, (2, 2, 3, 3))
  
      def test_take(self):
          # Tests take
          x = masked_array([10, 20, 30, 40], [0, 1, 0, 1])
          assert_equal(x.take([0, 0, 3]), masked_array([10, 10, 40], [0, 0, 1]))
          assert_equal(x.take([0, 0, 3]), x[[0, 0, 3]])
          assert_equal(x.take([[0, 1], [0, 1]]),
                       masked_array([[10, 20], [10, 20]], [[0, 1], [0, 1]]))
  
          # assert_equal crashes when passed np.ma.mask
          assert_(x[1] is np.ma.masked)
          assert_(x.take(1) is np.ma.masked)
  
          x = array([[10, 20, 30], [40, 50, 60]], mask=[[0, 0, 1], [1, 0, 0, ]])
          assert_equal(x.take([0, 2], axis=1),
                       array([[10, 30], [40, 60]], mask=[[0, 1], [1, 0]]))
          assert_equal(take(x, [0, 2], axis=1),
                       array([[10, 30], [40, 60]], mask=[[0, 1], [1, 0]]))
  
      def test_take_masked_indices(self):
          # Test take w/ masked indices
          a = np.array((40, 18, 37, 9, 22))
          indices = np.arange(3)[None,:] + np.arange(5)[:, None]
          mindices = array(indices, mask=(indices >= len(a)))
          # No mask
          test = take(a, mindices, mode='clip')
          ctrl = array([[40, 18, 37],
                        [18, 37, 9],
                        [37, 9, 22],
                        [9, 22, 22],
                        [22, 22, 22]])
          assert_equal(test, ctrl)
          # Masked indices
          test = take(a, mindices)
          ctrl = array([[40, 18, 37],
                        [18, 37, 9],
                        [37, 9, 22],
                        [9, 22, 40],
                        [22, 40, 40]])
          ctrl[3, 2] = ctrl[4, 1] = ctrl[4, 2] = masked
          assert_equal(test, ctrl)
          assert_equal(test.mask, ctrl.mask)
          # Masked input + masked indices
          a = array((40, 18, 37, 9, 22), mask=(0, 1, 0, 0, 0))
          test = take(a, mindices)
          ctrl[0, 1] = ctrl[1, 0] = masked
          assert_equal(test, ctrl)
          assert_equal(test.mask, ctrl.mask)
  
      def test_tolist(self):
          # Tests to list
          # ... on 1D
          x = array(np.arange(12))
          x[[1, -2]] = masked
          xlist = x.tolist()
          assert_(xlist[1] is None)
          assert_(xlist[-2] is None)
          # ... on 2D
          x.shape = (3, 4)
          xlist = x.tolist()
          ctrl = [[0, None, 2, 3], [4, 5, 6, 7], [8, 9, None, 11]]
          assert_equal(xlist[0], [0, None, 2, 3])
          assert_equal(xlist[1], [4, 5, 6, 7])
          assert_equal(xlist[2], [8, 9, None, 11])
          assert_equal(xlist, ctrl)
          # ... on structured array w/ masked records
          x = array(list(zip([1, 2, 3],
                             [1.1, 2.2, 3.3],
                             ['one', 'two', 'thr'])),
                    dtype=[('a', int), ('b', float), ('c', '|S8')])
          x[-1] = masked
          assert_equal(x.tolist(),
                       [(1, 1.1, b'one'),
                        (2, 2.2, b'two'),
                        (None, None, None)])
          # ... on structured array w/ masked fields
          a = array([(1, 2,), (3, 4)], mask=[(0, 1), (0, 0)],
                    dtype=[('a', int), ('b', int)])
          test = a.tolist()
          assert_equal(test, [[1, None], [3, 4]])
          # ... on mvoid
          a = a[0]
          test = a.tolist()
          assert_equal(test, [1, None])
  
      def test_tolist_specialcase(self):
          # Test mvoid.tolist: make sure we return a standard Python object
          a = array([(0, 1), (2, 3)], dtype=[('a', int), ('b', int)])
          # w/o mask: each entry is a np.void whose elements are standard Python
          for entry in a:
              for item in entry.tolist():
                  assert_(not isinstance(item, np.generic))
          # w/ mask: each entry is a ma.void whose elements should be
          # standard Python
          a.mask[0] = (0, 1)
          for entry in a:
              for item in entry.tolist():
                  assert_(not isinstance(item, np.generic))
  
      def test_toflex(self):
          # Test the conversion to records
          data = arange(10)
          record = data.toflex()
          assert_equal(record['_data'], data._data)
          assert_equal(record['_mask'], data._mask)
  
          data[[0, 1, 2, -1]] = masked
          record = data.toflex()
          assert_equal(record['_data'], data._data)
          assert_equal(record['_mask'], data._mask)
  
          ndtype = [('i', int), ('s', '|S3'), ('f', float)]
          data = array([(i, s, f) for (i, s, f) in zip(np.arange(10),
                                                       'ABCDEFGHIJKLM',
                                                       np.random.rand(10))],
                       dtype=ndtype)
          data[[0, 1, 2, -1]] = masked
          record = data.toflex()
          assert_equal(record['_data'], data._data)
          assert_equal(record['_mask'], data._mask)
  
          ndtype = np.dtype("int, (2,3)float, float")
          data = array([(i, f, ff) for (i, f, ff) in zip(np.arange(10),
                                                         np.random.rand(10),
                                                         np.random.rand(10))],
                       dtype=ndtype)
          data[[0, 1, 2, -1]] = masked
          record = data.toflex()
          assert_equal_records(record['_data'], data._data)
          assert_equal_records(record['_mask'], data._mask)
  
      def test_fromflex(self):
          # Test the reconstruction of a masked_array from a record
          a = array([1, 2, 3])
          test = fromflex(a.toflex())
          assert_equal(test, a)
          assert_equal(test.mask, a.mask)
  
          a = array([1, 2, 3], mask=[0, 0, 1])
          test = fromflex(a.toflex())
          assert_equal(test, a)
          assert_equal(test.mask, a.mask)
  
          a = array([(1, 1.), (2, 2.), (3, 3.)], mask=[(1, 0), (0, 0), (0, 1)],
                    dtype=[('A', int), ('B', float)])
          test = fromflex(a.toflex())
          assert_equal(test, a)
          assert_equal(test.data, a.data)
  
      def test_arraymethod(self):
          # Test a _arraymethod w/ n argument
          marray = masked_array([[1, 2, 3, 4, 5]], mask=[0, 0, 1, 0, 0])
          control = masked_array([[1], [2], [3], [4], [5]],
                                 mask=[0, 0, 1, 0, 0])
          assert_equal(marray.T, control)
          assert_equal(marray.transpose(), control)
  
          assert_equal(MaskedArray.cumsum(marray.T, 0), control.cumsum(0))
  
  
  class TestMaskedArrayMathMethods(object):
  
      def setup(self):
          # Base data definition.
          x = np.array([8.375, 7.545, 8.828, 8.5, 1.757, 5.928,
                        8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
                        3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
                        6.04, 9.63, 7.712, 3.382, 4.489, 6.479,
                        7.189, 9.645, 5.395, 4.961, 9.894, 2.893,
                        7.357, 9.828, 6.272, 3.758, 6.693, 0.993])
          X = x.reshape(6, 6)
          XX = x.reshape(3, 2, 2, 3)
  
          m = np.array([0, 1, 0, 1, 0, 0,
                       1, 0, 1, 1, 0, 1,
                       0, 0, 0, 1, 0, 1,
                       0, 0, 0, 1, 1, 1,
                       1, 0, 0, 1, 0, 0,
                       0, 0, 1, 0, 1, 0])
          mx = array(data=x, mask=m)
          mX = array(data=X, mask=m.reshape(X.shape))
          mXX = array(data=XX, mask=m.reshape(XX.shape))
  
          m2 = np.array([1, 1, 0, 1, 0, 0,
                        1, 1, 1, 1, 0, 1,
                        0, 0, 1, 1, 0, 1,
                        0, 0, 0, 1, 1, 1,
                        1, 0, 0, 1, 1, 0,
                        0, 0, 1, 0, 1, 1])
          m2x = array(data=x, mask=m2)
          m2X = array(data=X, mask=m2.reshape(X.shape))
          m2XX = array(data=XX, mask=m2.reshape(XX.shape))
          self.d = (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX)
  
      def test_cumsumprod(self):
          # Tests cumsum & cumprod on MaskedArrays.
          (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
          mXcp = mX.cumsum(0)
          assert_equal(mXcp._data, mX.filled(0).cumsum(0))
          mXcp = mX.cumsum(1)
          assert_equal(mXcp._data, mX.filled(0).cumsum(1))
  
          mXcp = mX.cumprod(0)
          assert_equal(mXcp._data, mX.filled(1).cumprod(0))
          mXcp = mX.cumprod(1)
          assert_equal(mXcp._data, mX.filled(1).cumprod(1))
  
      def test_cumsumprod_with_output(self):
          # Tests cumsum/cumprod w/ output
          xm = array(np.random.uniform(0, 10, 12)).reshape(3, 4)
          xm[:, 0] = xm[0] = xm[-1, -1] = masked
  
          for funcname in ('cumsum', 'cumprod'):
              npfunc = getattr(np, funcname)
              xmmeth = getattr(xm, funcname)
  
              # A ndarray as explicit input
              output = np.empty((3, 4), dtype=float)
              output.fill(-9999)
              result = npfunc(xm, axis=0, out=output)
              # ... the result should be the given output
              assert_(result is output)
              assert_equal(result, xmmeth(axis=0, out=output))
  
              output = empty((3, 4), dtype=int)
              result = xmmeth(axis=0, out=output)
              assert_(result is output)
  
      def test_ptp(self):
          # Tests ptp on MaskedArrays.
          (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
          (n, m) = X.shape
          assert_equal(mx.ptp(), mx.compressed().ptp())
          rows = np.zeros(n, float)
          cols = np.zeros(m, float)
          for k in range(m):
              cols[k] = mX[:, k].compressed().ptp()
          for k in range(n):
              rows[k] = mX[k].compressed().ptp()
          assert_equal(mX.ptp(0), cols)
          assert_equal(mX.ptp(1), rows)
  
      def test_add_object(self):
          x = masked_array(['a', 'b'], mask=[1, 0], dtype=object)
          y = x + 'x'
          assert_equal(y[1], 'bx')
          assert_(y.mask[0])
  
      def test_sum_object(self):
          # Test sum on object dtype
          a = masked_array([1, 2, 3], mask=[1, 0, 0], dtype=object)
          assert_equal(a.sum(), 5)
          a = masked_array([[1, 2, 3], [4, 5, 6]], dtype=object)
          assert_equal(a.sum(axis=0), [5, 7, 9])
  
      def test_prod_object(self):
          # Test prod on object dtype
          a = masked_array([1, 2, 3], mask=[1, 0, 0], dtype=object)
          assert_equal(a.prod(), 2 * 3)
          a = masked_array([[1, 2, 3], [4, 5, 6]], dtype=object)
          assert_equal(a.prod(axis=0), [4, 10, 18])
  
      def test_meananom_object(self):
          # Test mean/anom on object dtype
          a = masked_array([1, 2, 3], dtype=object)
          assert_equal(a.mean(), 2)
          assert_equal(a.anom(), [-1, 0, 1])
  
      def test_trace(self):
          # Tests trace on MaskedArrays.
          (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
          mXdiag = mX.diagonal()
          assert_equal(mX.trace(), mX.diagonal().compressed().sum())
          assert_almost_equal(mX.trace(),
                              X.trace() - sum(mXdiag.mask * X.diagonal(),
                                              axis=0))
          assert_equal(np.trace(mX), mX.trace())
  
      def test_dot(self):
          # Tests dot on MaskedArrays.
          (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
          fx = mx.filled(0)
          r = mx.dot(mx)
          assert_almost_equal(r.filled(0), fx.dot(fx))
          assert_(r.mask is nomask)
  
          fX = mX.filled(0)
          r = mX.dot(mX)
          assert_almost_equal(r.filled(0), fX.dot(fX))
          assert_(r.mask[1,3])
          r1 = empty_like(r)
          mX.dot(mX, out=r1)
          assert_almost_equal(r, r1)
  
          mYY = mXX.swapaxes(-1, -2)
          fXX, fYY = mXX.filled(0), mYY.filled(0)
          r = mXX.dot(mYY)
          assert_almost_equal(r.filled(0), fXX.dot(fYY))
          r1 = empty_like(r)
          mXX.dot(mYY, out=r1)
          assert_almost_equal(r, r1)
  
      def test_dot_shape_mismatch(self):
          # regression test
          x = masked_array([[1,2],[3,4]], mask=[[0,1],[0,0]])
          y = masked_array([[1,2],[3,4]], mask=[[0,1],[0,0]])
          z = masked_array([[0,1],[3,3]])
          x.dot(y, out=z)
          assert_almost_equal(z.filled(0), [[1, 0], [15, 16]])
          assert_almost_equal(z.mask, [[0, 1], [0, 0]])
  
      def test_varmean_nomask(self):
          # gh-5769
          foo = array([1,2,3,4], dtype='f8')
          bar = array([1,2,3,4], dtype='f8')
          assert_equal(type(foo.mean()), np.float64)
          assert_equal(type(foo.var()), np.float64)
          assert((foo.mean() == bar.mean()) is np.bool_(True))
  
          # check array type is preserved and out works
          foo = array(np.arange(16).reshape((4,4)), dtype='f8')
          bar = empty(4, dtype='f4')
          assert_equal(type(foo.mean(axis=1)), MaskedArray)
          assert_equal(type(foo.var(axis=1)), MaskedArray)
          assert_(foo.mean(axis=1, out=bar) is bar)
          assert_(foo.var(axis=1, out=bar) is bar)
  
      def test_varstd(self):
          # Tests var & std on MaskedArrays.
          (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
          assert_almost_equal(mX.var(axis=None), mX.compressed().var())
          assert_almost_equal(mX.std(axis=None), mX.compressed().std())
          assert_almost_equal(mX.std(axis=None, ddof=1),
                              mX.compressed().std(ddof=1))
          assert_almost_equal(mX.var(axis=None, ddof=1),
                              mX.compressed().var(ddof=1))
          assert_equal(mXX.var(axis=3).shape, XX.var(axis=3).shape)
          assert_equal(mX.var().shape, X.var().shape)
          (mXvar0, mXvar1) = (mX.var(axis=0), mX.var(axis=1))
          assert_almost_equal(mX.var(axis=None, ddof=2),
                              mX.compressed().var(ddof=2))
          assert_almost_equal(mX.std(axis=None, ddof=2),
                              mX.compressed().std(ddof=2))
          for k in range(6):
              assert_almost_equal(mXvar1[k], mX[k].compressed().var())
              assert_almost_equal(mXvar0[k], mX[:, k].compressed().var())
              assert_almost_equal(np.sqrt(mXvar0[k]),
                                  mX[:, k].compressed().std())
  
      @suppress_copy_mask_on_assignment
      def test_varstd_specialcases(self):
          # Test a special case for var
          nout = np.array(-1, dtype=float)
          mout = array(-1, dtype=float)
  
          x = array(arange(10), mask=True)
          for methodname in ('var', 'std'):
              method = getattr(x, methodname)
              assert_(method() is masked)
              assert_(method(0) is masked)
              assert_(method(-1) is masked)
              # Using a masked array as explicit output
              method(out=mout)
              assert_(mout is not masked)
              assert_equal(mout.mask, True)
              # Using a ndarray as explicit output
              method(out=nout)
              assert_(np.isnan(nout))
  
          x = array(arange(10), mask=True)
          x[-1] = 9
          for methodname in ('var', 'std'):
              method = getattr(x, methodname)
              assert_(method(ddof=1) is masked)
              assert_(method(0, ddof=1) is masked)
              assert_(method(-1, ddof=1) is masked)
              # Using a masked array as explicit output
              method(out=mout, ddof=1)
              assert_(mout is not masked)
              assert_equal(mout.mask, True)
              # Using a ndarray as explicit output
              method(out=nout, ddof=1)
              assert_(np.isnan(nout))
  
      def test_varstd_ddof(self):
          a = array([[1, 1, 0], [1, 1, 0]], mask=[[0, 0, 1], [0, 0, 1]])
          test = a.std(axis=0, ddof=0)
          assert_equal(test.filled(0), [0, 0, 0])
          assert_equal(test.mask, [0, 0, 1])
          test = a.std(axis=0, ddof=1)
          assert_equal(test.filled(0), [0, 0, 0])
          assert_equal(test.mask, [0, 0, 1])
          test = a.std(axis=0, ddof=2)
          assert_equal(test.filled(0), [0, 0, 0])
          assert_equal(test.mask, [1, 1, 1])
  
      def test_diag(self):
          # Test diag
          x = arange(9).reshape((3, 3))
          x[1, 1] = masked
          out = np.diag(x)
          assert_equal(out, [0, 4, 8])
          out = diag(x)
          assert_equal(out, [0, 4, 8])
          assert_equal(out.mask, [0, 1, 0])
          out = diag(out)
          control = array([[0, 0, 0], [0, 4, 0], [0, 0, 8]],
                          mask=[[0, 0, 0], [0, 1, 0], [0, 0, 0]])
          assert_equal(out, control)
  
      def test_axis_methods_nomask(self):
          # Test the combination nomask & methods w/ axis
          a = array([[1, 2, 3], [4, 5, 6]])
  
          assert_equal(a.sum(0), [5, 7, 9])
          assert_equal(a.sum(-1), [6, 15])
          assert_equal(a.sum(1), [6, 15])
  
          assert_equal(a.prod(0), [4, 10, 18])
          assert_equal(a.prod(-1), [6, 120])
          assert_equal(a.prod(1), [6, 120])
  
          assert_equal(a.min(0), [1, 2, 3])
          assert_equal(a.min(-1), [1, 4])
          assert_equal(a.min(1), [1, 4])
  
          assert_equal(a.max(0), [4, 5, 6])
          assert_equal(a.max(-1), [3, 6])
          assert_equal(a.max(1), [3, 6])
  
  
  class TestMaskedArrayMathMethodsComplex(object):
      # Test class for miscellaneous MaskedArrays methods.
      def setup(self):
          # Base data definition.
          x = np.array([8.375j, 7.545j, 8.828j, 8.5j, 1.757j, 5.928,
                        8.43, 7.78, 9.865, 5.878, 8.979, 4.732,
                        3.012, 6.022, 5.095, 3.116, 5.238, 3.957,
                        6.04, 9.63, 7.712, 3.382, 4.489, 6.479j,
                        7.189j, 9.645, 5.395, 4.961, 9.894, 2.893,
                        7.357, 9.828, 6.272, 3.758, 6.693, 0.993j])
          X = x.reshape(6, 6)
          XX = x.reshape(3, 2, 2, 3)
  
          m = np.array([0, 1, 0, 1, 0, 0,
                       1, 0, 1, 1, 0, 1,
                       0, 0, 0, 1, 0, 1,
                       0, 0, 0, 1, 1, 1,
                       1, 0, 0, 1, 0, 0,
                       0, 0, 1, 0, 1, 0])
          mx = array(data=x, mask=m)
          mX = array(data=X, mask=m.reshape(X.shape))
          mXX = array(data=XX, mask=m.reshape(XX.shape))
  
          m2 = np.array([1, 1, 0, 1, 0, 0,
                        1, 1, 1, 1, 0, 1,
                        0, 0, 1, 1, 0, 1,
                        0, 0, 0, 1, 1, 1,
                        1, 0, 0, 1, 1, 0,
                        0, 0, 1, 0, 1, 1])
          m2x = array(data=x, mask=m2)
          m2X = array(data=X, mask=m2.reshape(X.shape))
          m2XX = array(data=XX, mask=m2.reshape(XX.shape))
          self.d = (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX)
  
      def test_varstd(self):
          # Tests var & std on MaskedArrays.
          (x, X, XX, m, mx, mX, mXX, m2x, m2X, m2XX) = self.d
          assert_almost_equal(mX.var(axis=None), mX.compressed().var())
          assert_almost_equal(mX.std(axis=None), mX.compressed().std())
          assert_equal(mXX.var(axis=3).shape, XX.var(axis=3).shape)
          assert_equal(mX.var().shape, X.var().shape)
          (mXvar0, mXvar1) = (mX.var(axis=0), mX.var(axis=1))
          assert_almost_equal(mX.var(axis=None, ddof=2),
                              mX.compressed().var(ddof=2))
          assert_almost_equal(mX.std(axis=None, ddof=2),
                              mX.compressed().std(ddof=2))
          for k in range(6):
              assert_almost_equal(mXvar1[k], mX[k].compressed().var())
              assert_almost_equal(mXvar0[k], mX[:, k].compressed().var())
              assert_almost_equal(np.sqrt(mXvar0[k]),
                                  mX[:, k].compressed().std())
  
  
  class TestMaskedArrayFunctions(object):
      # Test class for miscellaneous functions.
  
      def setup(self):
          x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
          y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
          m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
          m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
          xm = masked_array(x, mask=m1)
          ym = masked_array(y, mask=m2)
          xm.set_fill_value(1e+20)
          self.info = (xm, ym)
  
      def test_masked_where_bool(self):
          x = [1, 2]
          y = masked_where(False, x)
          assert_equal(y, [1, 2])
          assert_equal(y[1], 2)
  
      def test_masked_equal_wlist(self):
          x = [1, 2, 3]
          mx = masked_equal(x, 3)
          assert_equal(mx, x)
          assert_equal(mx._mask, [0, 0, 1])
          mx = masked_not_equal(x, 3)
          assert_equal(mx, x)
          assert_equal(mx._mask, [1, 1, 0])
  
      def test_masked_equal_fill_value(self):
          x = [1, 2, 3]
          mx = masked_equal(x, 3)
          assert_equal(mx._mask, [0, 0, 1])
          assert_equal(mx.fill_value, 3)
  
      def test_masked_where_condition(self):
          # Tests masking functions.
          x = array([1., 2., 3., 4., 5.])
          x[2] = masked
          assert_equal(masked_where(greater(x, 2), x), masked_greater(x, 2))
          assert_equal(masked_where(greater_equal(x, 2), x),
                       masked_greater_equal(x, 2))
          assert_equal(masked_where(less(x, 2), x), masked_less(x, 2))
          assert_equal(masked_where(less_equal(x, 2), x),
                       masked_less_equal(x, 2))
          assert_equal(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2))
          assert_equal(masked_where(equal(x, 2), x), masked_equal(x, 2))
          assert_equal(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2))
          assert_equal(masked_where([1, 1, 0, 0, 0], [1, 2, 3, 4, 5]),
                       [99, 99, 3, 4, 5])
  
      def test_masked_where_oddities(self):
          # Tests some generic features.
          atest = ones((10, 10, 10), dtype=float)
          btest = zeros(atest.shape, MaskType)
          ctest = masked_where(btest, atest)
          assert_equal(atest, ctest)
  
      def test_masked_where_shape_constraint(self):
          a = arange(10)
          try:
              test = masked_equal(1, a)
          except IndexError:
              pass
          else:
              raise AssertionError("Should have failed...")
          test = masked_equal(a, 1)
          assert_equal(test.mask, [0, 1, 0, 0, 0, 0, 0, 0, 0, 0])
  
      def test_masked_where_structured(self):
          # test that masked_where on a structured array sets a structured
          # mask (see issue #2972)
          a = np.zeros(10, dtype=[("A", "<f2"), ("B", "<f4")])
          am = np.ma.masked_where(a["A"] < 5, a)
          assert_equal(am.mask.dtype.names, am.dtype.names)
          assert_equal(am["A"],
                      np.ma.masked_array(np.zeros(10), np.ones(10)))
  
      def test_masked_otherfunctions(self):
          assert_equal(masked_inside(list(range(5)), 1, 3),
                       [0, 199, 199, 199, 4])
          assert_equal(masked_outside(list(range(5)), 1, 3), [199, 1, 2, 3, 199])
          assert_equal(masked_inside(array(list(range(5)),
                                           mask=[1, 0, 0, 0, 0]), 1, 3).mask,
                       [1, 1, 1, 1, 0])
          assert_equal(masked_outside(array(list(range(5)),
                                            mask=[0, 1, 0, 0, 0]), 1, 3).mask,
                       [1, 1, 0, 0, 1])
          assert_equal(masked_equal(array(list(range(5)),
                                          mask=[1, 0, 0, 0, 0]), 2).mask,
                       [1, 0, 1, 0, 0])
          assert_equal(masked_not_equal(array([2, 2, 1, 2, 1],
                                              mask=[1, 0, 0, 0, 0]), 2).mask,
                       [1, 0, 1, 0, 1])
  
      def test_round(self):
          a = array([1.23456, 2.34567, 3.45678, 4.56789, 5.67890],
                    mask=[0, 1, 0, 0, 0])
          assert_equal(a.round(), [1., 2., 3., 5., 6.])
          assert_equal(a.round(1), [1.2, 2.3, 3.5, 4.6, 5.7])
          assert_equal(a.round(3), [1.235, 2.346, 3.457, 4.568, 5.679])
          b = empty_like(a)
          a.round(out=b)
          assert_equal(b, [1., 2., 3., 5., 6.])
  
          x = array([1., 2., 3., 4., 5.])
          c = array([1, 1, 1, 0, 0])
          x[2] = masked
          z = where(c, x, -x)
          assert_equal(z, [1., 2., 0., -4., -5])
          c[0] = masked
          z = where(c, x, -x)
          assert_equal(z, [1., 2., 0., -4., -5])
          assert_(z[0] is masked)
          assert_(z[1] is not masked)
          assert_(z[2] is masked)
  
      def test_round_with_output(self):
          # Testing round with an explicit output
  
          xm = array(np.random.uniform(0, 10, 12)).reshape(3, 4)
          xm[:, 0] = xm[0] = xm[-1, -1] = masked
  
          # A ndarray as explicit input
          output = np.empty((3, 4), dtype=float)
          output.fill(-9999)
          result = np.round(xm, decimals=2, out=output)
          # ... the result should be the given output
          assert_(result is output)
          assert_equal(result, xm.round(decimals=2, out=output))
  
          output = empty((3, 4), dtype=float)
          result = xm.round(decimals=2, out=output)
          assert_(result is output)
  
      def test_round_with_scalar(self):
          # Testing round with scalar/zero dimension input
          # GH issue 2244
          a = array(1.1, mask=[False])
          assert_equal(a.round(), 1)
  
          a = array(1.1, mask=[True])
          assert_(a.round() is masked)
  
          a = array(1.1, mask=[False])
          output = np.empty(1, dtype=float)
          output.fill(-9999)
          a.round(out=output)
          assert_equal(output, 1)
  
          a = array(1.1, mask=[False])
          output = array(-9999., mask=[True])
          a.round(out=output)
          assert_equal(output[()], 1)
  
          a = array(1.1, mask=[True])
          output = array(-9999., mask=[False])
          a.round(out=output)
          assert_(output[()] is masked)
  
      def test_identity(self):
          a = identity(5)
          assert_(isinstance(a, MaskedArray))
          assert_equal(a, np.identity(5))
  
      def test_power(self):
          x = -1.1
          assert_almost_equal(power(x, 2.), 1.21)
          assert_(power(x, masked) is masked)
          x = array([-1.1, -1.1, 1.1, 1.1, 0.])
          b = array([0.5, 2., 0.5, 2., -1.], mask=[0, 0, 0, 0, 1])
          y = power(x, b)
          assert_almost_equal(y, [0, 1.21, 1.04880884817, 1.21, 0.])
          assert_equal(y._mask, [1, 0, 0, 0, 1])
          b.mask = nomask
          y = power(x, b)
          assert_equal(y._mask, [1, 0, 0, 0, 1])
          z = x ** b
          assert_equal(z._mask, y._mask)
          assert_almost_equal(z, y)
          assert_almost_equal(z._data, y._data)
          x **= b
          assert_equal(x._mask, y._mask)
          assert_almost_equal(x, y)
          assert_almost_equal(x._data, y._data)
  
      def test_power_with_broadcasting(self):
          # Test power w/ broadcasting
          a2 = np.array([[1., 2., 3.], [4., 5., 6.]])
          a2m = array(a2, mask=[[1, 0, 0], [0, 0, 1]])
          b1 = np.array([2, 4, 3])
          b2 = np.array([b1, b1])
          b2m = array(b2, mask=[[0, 1, 0], [0, 1, 0]])
  
          ctrl = array([[1 ** 2, 2 ** 4, 3 ** 3], [4 ** 2, 5 ** 4, 6 ** 3]],
                       mask=[[1, 1, 0], [0, 1, 1]])
          # No broadcasting, base & exp w/ mask
          test = a2m ** b2m
          assert_equal(test, ctrl)
          assert_equal(test.mask, ctrl.mask)
          # No broadcasting, base w/ mask, exp w/o mask
          test = a2m ** b2
          assert_equal(test, ctrl)
          assert_equal(test.mask, a2m.mask)
          # No broadcasting, base w/o mask, exp w/ mask
          test = a2 ** b2m
          assert_equal(test, ctrl)
          assert_equal(test.mask, b2m.mask)
  
          ctrl = array([[2 ** 2, 4 ** 4, 3 ** 3], [2 ** 2, 4 ** 4, 3 ** 3]],
                       mask=[[0, 1, 0], [0, 1, 0]])
          test = b1 ** b2m
          assert_equal(test, ctrl)
          assert_equal(test.mask, ctrl.mask)
          test = b2m ** b1
          assert_equal(test, ctrl)
          assert_equal(test.mask, ctrl.mask)
  
      def test_where(self):
          # Test the where function
          x = np.array([1., 1., 1., -2., pi/2.0, 4., 5., -10., 10., 1., 2., 3.])
          y = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.])
          m1 = [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
          m2 = [0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1]
          xm = masked_array(x, mask=m1)
          ym = masked_array(y, mask=m2)
          xm.set_fill_value(1e+20)
  
          d = where(xm > 2, xm, -9)
          assert_equal(d, [-9., -9., -9., -9., -9., 4.,
                           -9., -9., 10., -9., -9., 3.])
          assert_equal(d._mask, xm._mask)
          d = where(xm > 2, -9, ym)
          assert_equal(d, [5., 0., 3., 2., -1., -9.,
                           -9., -10., -9., 1., 0., -9.])
          assert_equal(d._mask, [1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0])
          d = where(xm > 2, xm, masked)
          assert_equal(d, [-9., -9., -9., -9., -9., 4.,
                           -9., -9., 10., -9., -9., 3.])
          tmp = xm._mask.copy()
          tmp[(xm <= 2).filled(True)] = True
          assert_equal(d._mask, tmp)
  
          ixm = xm.astype(int)
          d = where(ixm > 2, ixm, masked)
          assert_equal(d, [-9, -9, -9, -9, -9, 4, -9, -9, 10, -9, -9, 3])
          assert_equal(d.dtype, ixm.dtype)
  
      def test_where_object(self):
          a = np.array(None)
          b = masked_array(None)
          r = b.copy()
          assert_equal(np.ma.where(True, a, a), r)
          assert_equal(np.ma.where(True, b, b), r)
  
      def test_where_with_masked_choice(self):
          x = arange(10)
          x[3] = masked
          c = x >= 8
          # Set False to masked
          z = where(c, x, masked)
          assert_(z.dtype is x.dtype)
          assert_(z[3] is masked)
          assert_(z[4] is masked)
          assert_(z[7] is masked)
          assert_(z[8] is not masked)
          assert_(z[9] is not masked)
          assert_equal(x, z)
          # Set True to masked
          z = where(c, masked, x)
          assert_(z.dtype is x.dtype)
          assert_(z[3] is masked)
          assert_(z[4] is not masked)
          assert_(z[7] is not masked)
          assert_(z[8] is masked)
          assert_(z[9] is masked)
  
      def test_where_with_masked_condition(self):
          x = array([1., 2., 3., 4., 5.])
          c = array([1, 1, 1, 0, 0])
          x[2] = masked
          z = where(c, x, -x)
          assert_equal(z, [1., 2., 0., -4., -5])
          c[0] = masked
          z = where(c, x, -x)
          assert_equal(z, [1., 2., 0., -4., -5])
          assert_(z[0] is masked)
          assert_(z[1] is not masked)
          assert_(z[2] is masked)
  
          x = arange(1, 6)
          x[-1] = masked
          y = arange(1, 6) * 10
          y[2] = masked
          c = array([1, 1, 1, 0, 0], mask=[1, 0, 0, 0, 0])
          cm = c.filled(1)
          z = where(c, x, y)
          zm = where(cm, x, y)
          assert_equal(z, zm)
          assert_(getmask(zm) is nomask)
          assert_equal(zm, [1, 2, 3, 40, 50])
          z = where(c, masked, 1)
          assert_equal(z, [99, 99, 99, 1, 1])
          z = where(c, 1, masked)
          assert_equal(z, [99, 1, 1, 99, 99])
  
      def test_where_type(self):
          # Test the type conservation with where
          x = np.arange(4, dtype=np.int32)
          y = np.arange(4, dtype=np.float32) * 2.2
          test = where(x > 1.5, y, x).dtype
          control = np.find_common_type([np.int32, np.float32], [])
          assert_equal(test, control)
  
      def test_where_broadcast(self):
          # Issue 8599
          x = np.arange(9).reshape(3, 3)
          y = np.zeros(3)
          core = np.where([1, 0, 1], x, y)
          ma = where([1, 0, 1], x, y)
  
          assert_equal(core, ma)
          assert_equal(core.dtype, ma.dtype)
  
      def test_where_structured(self):
          # Issue 8600
          dt = np.dtype([('a', int), ('b', int)])
          x = np.array([(1, 2), (3, 4), (5, 6)], dtype=dt)
          y = np.array((10, 20), dtype=dt)
          core = np.where([0, 1, 1], x, y)
          ma = np.where([0, 1, 1], x, y)
  
          assert_equal(core, ma)
          assert_equal(core.dtype, ma.dtype)
  
      def test_where_structured_masked(self):
          dt = np.dtype([('a', int), ('b', int)])
          x = np.array([(1, 2), (3, 4), (5, 6)], dtype=dt)
  
          ma = where([0, 1, 1], x, masked)
          expected = masked_where([1, 0, 0], x)
  
          assert_equal(ma.dtype, expected.dtype)
          assert_equal(ma, expected)
          assert_equal(ma.mask, expected.mask)
  
      def test_choose(self):
          # Test choose
          choices = [[0, 1, 2, 3], [10, 11, 12, 13],
                     [20, 21, 22, 23], [30, 31, 32, 33]]
          chosen = choose([2, 3, 1, 0], choices)
          assert_equal(chosen, array([20, 31, 12, 3]))
          chosen = choose([2, 4, 1, 0], choices, mode='clip')
          assert_equal(chosen, array([20, 31, 12, 3]))
          chosen = choose([2, 4, 1, 0], choices, mode='wrap')
          assert_equal(chosen, array([20, 1, 12, 3]))
          # Check with some masked indices
          indices_ = array([2, 4, 1, 0], mask=[1, 0, 0, 1])
          chosen = choose(indices_, choices, mode='wrap')
          assert_equal(chosen, array([99, 1, 12, 99]))
          assert_equal(chosen.mask, [1, 0, 0, 1])
          # Check with some masked choices
          choices = array(choices, mask=[[0, 0, 0, 1], [1, 1, 0, 1],
                                         [1, 0, 0, 0], [0, 0, 0, 0]])
          indices_ = [2, 3, 1, 0]
          chosen = choose(indices_, choices, mode='wrap')
          assert_equal(chosen, array([20, 31, 12, 3]))
          assert_equal(chosen.mask, [1, 0, 0, 1])
  
      def test_choose_with_out(self):
          # Test choose with an explicit out keyword
          choices = [[0, 1, 2, 3], [10, 11, 12, 13],
                     [20, 21, 22, 23], [30, 31, 32, 33]]
          store = empty(4, dtype=int)
          chosen = choose([2, 3, 1, 0], choices, out=store)
          assert_equal(store, array([20, 31, 12, 3]))
          assert_(store is chosen)
          # Check with some masked indices + out
          store = empty(4, dtype=int)
          indices_ = array([2, 3, 1, 0], mask=[1, 0, 0, 1])
          chosen = choose(indices_, choices, mode='wrap', out=store)
          assert_equal(store, array([99, 31, 12, 99]))
          assert_equal(store.mask, [1, 0, 0, 1])
          # Check with some masked choices + out ina ndarray !
          choices = array(choices, mask=[[0, 0, 0, 1], [1, 1, 0, 1],
                                         [1, 0, 0, 0], [0, 0, 0, 0]])
          indices_ = [2, 3, 1, 0]
          store = empty(4, dtype=int).view(ndarray)
          chosen = choose(indices_, choices, mode='wrap', out=store)
          assert_equal(store, array([999999, 31, 12, 999999]))
  
      def test_reshape(self):
          a = arange(10)
          a[0] = masked
          # Try the default
          b = a.reshape((5, 2))
          assert_equal(b.shape, (5, 2))
          assert_(b.flags['C'])
          # Try w/ arguments as list instead of tuple
          b = a.reshape(5, 2)
          assert_equal(b.shape, (5, 2))
          assert_(b.flags['C'])
          # Try w/ order
          b = a.reshape((5, 2), order='F')
          assert_equal(b.shape, (5, 2))
          assert_(b.flags['F'])
          # Try w/ order
          b = a.reshape(5, 2, order='F')
          assert_equal(b.shape, (5, 2))
          assert_(b.flags['F'])
  
          c = np.reshape(a, (2, 5))
          assert_(isinstance(c, MaskedArray))
          assert_equal(c.shape, (2, 5))
          assert_(c[0, 0] is masked)
          assert_(c.flags['C'])
  
      def test_make_mask_descr(self):
          # Flexible
          ntype = [('a', float), ('b', float)]
          test = make_mask_descr(ntype)
          assert_equal(test, [('a', bool), ('b', bool)])
          assert_(test is make_mask_descr(test))
  
          # Standard w/ shape
          ntype = (float, 2)
          test = make_mask_descr(ntype)
          assert_equal(test, (bool, 2))
          assert_(test is make_mask_descr(test))
  
          # Standard standard
          ntype = float
          test = make_mask_descr(ntype)
          assert_equal(test, np.dtype(bool))
          assert_(test is make_mask_descr(test))
  
          # Nested
          ntype = [('a', float), ('b', [('ba', float), ('bb', float)])]
          test = make_mask_descr(ntype)
          control = np.dtype([('a', 'b1'), ('b', [('ba', 'b1'), ('bb', 'b1')])])
          assert_equal(test, control)
          assert_(test is make_mask_descr(test))
  
          # Named+ shape
          ntype = [('a', (float, 2))]
          test = make_mask_descr(ntype)
          assert_equal(test, np.dtype([('a', (bool, 2))]))
          assert_(test is make_mask_descr(test))
  
          # 2 names
          ntype = [(('A', 'a'), float)]
          test = make_mask_descr(ntype)
          assert_equal(test, np.dtype([(('A', 'a'), bool)]))
          assert_(test is make_mask_descr(test))
  
          # nested boolean types should preserve identity
          base_type = np.dtype([('a', int, 3)])
          base_mtype = make_mask_descr(base_type)
          sub_type = np.dtype([('a', int), ('b', base_mtype)])
          test = make_mask_descr(sub_type)
          assert_equal(test, np.dtype([('a', bool), ('b', [('a', bool, 3)])]))
          assert_(test.fields['b'][0] is base_mtype)
  
      def test_make_mask(self):
          # Test make_mask
          # w/ a list as an input
          mask = [0, 1]
          test = make_mask(mask)
          assert_equal(test.dtype, MaskType)
          assert_equal(test, [0, 1])
          # w/ a ndarray as an input
          mask = np.array([0, 1], dtype=bool)
          test = make_mask(mask)
          assert_equal(test.dtype, MaskType)
          assert_equal(test, [0, 1])
          # w/ a flexible-type ndarray as an input - use default
          mdtype = [('a', bool), ('b', bool)]
          mask = np.array([(0, 0), (0, 1)], dtype=mdtype)
          test = make_mask(mask)
          assert_equal(test.dtype, MaskType)
          assert_equal(test, [1, 1])
          # w/ a flexible-type ndarray as an input - use input dtype
          mdtype = [('a', bool), ('b', bool)]
          mask = np.array([(0, 0), (0, 1)], dtype=mdtype)
          test = make_mask(mask, dtype=mask.dtype)
          assert_equal(test.dtype, mdtype)
          assert_equal(test, mask)
          # w/ a flexible-type ndarray as an input - use input dtype
          mdtype = [('a', float), ('b', float)]
          bdtype = [('a', bool), ('b', bool)]
          mask = np.array([(0, 0), (0, 1)], dtype=mdtype)
          test = make_mask(mask, dtype=mask.dtype)
          assert_equal(test.dtype, bdtype)
          assert_equal(test, np.array([(0, 0), (0, 1)], dtype=bdtype))
          # Ensure this also works for void
          mask = np.array((False, True), dtype='?,?')[()]
          assert_(isinstance(mask, np.void))
          test = make_mask(mask, dtype=mask.dtype)
          assert_equal(test, mask)
          assert_(test is not mask)
          mask = np.array((0, 1), dtype='i4,i4')[()]
          test2 = make_mask(mask, dtype=mask.dtype)
          assert_equal(test2, test)
          # test that nomask is returned when m is nomask.
          bools = [True, False]
          dtypes = [MaskType, float]
          msgformat = 'copy=%s, shrink=%s, dtype=%s'
          for cpy, shr, dt in itertools.product(bools, bools, dtypes):
              res = make_mask(nomask, copy=cpy, shrink=shr, dtype=dt)
              assert_(res is nomask, msgformat % (cpy, shr, dt))
  
      def test_mask_or(self):
          # Initialize
          mtype = [('a', bool), ('b', bool)]
          mask = np.array([(0, 0), (0, 1), (1, 0), (0, 0)], dtype=mtype)
          # Test using nomask as input
          test = mask_or(mask, nomask)
          assert_equal(test, mask)
          test = mask_or(nomask, mask)
          assert_equal(test, mask)
          # Using False as input
          test = mask_or(mask, False)
          assert_equal(test, mask)
          # Using another array w / the same dtype
          other = np.array([(0, 1), (0, 1), (0, 1), (0, 1)], dtype=mtype)
          test = mask_or(mask, other)
          control = np.array([(0, 1), (0, 1), (1, 1), (0, 1)], dtype=mtype)
          assert_equal(test, control)
          # Using another array w / a different dtype
          othertype = [('A', bool), ('B', bool)]
          other = np.array([(0, 1), (0, 1), (0, 1), (0, 1)], dtype=othertype)
          try:
              test = mask_or(mask, other)
          except ValueError:
              pass
          # Using nested arrays
          dtype = [('a', bool), ('b', [('ba', bool), ('bb', bool)])]
          amask = np.array([(0, (1, 0)), (0, (1, 0))], dtype=dtype)
          bmask = np.array([(1, (0, 1)), (0, (0, 0))], dtype=dtype)
          cntrl = np.array([(1, (1, 1)), (0, (1, 0))], dtype=dtype)
          assert_equal(mask_or(amask, bmask), cntrl)
  
      def test_flatten_mask(self):
          # Tests flatten mask
          # Standard dtype
          mask = np.array([0, 0, 1], dtype=bool)
          assert_equal(flatten_mask(mask), mask)
          # Flexible dtype
          mask = np.array([(0, 0), (0, 1)], dtype=[('a', bool), ('b', bool)])
          test = flatten_mask(mask)
          control = np.array([0, 0, 0, 1], dtype=bool)
          assert_equal(test, control)
  
          mdtype = [('a', bool), ('b', [('ba', bool), ('bb', bool)])]
          data = [(0, (0, 0)), (0, (0, 1))]
          mask = np.array(data, dtype=mdtype)
          test = flatten_mask(mask)
          control = np.array([0, 0, 0, 0, 0, 1], dtype=bool)
          assert_equal(test, control)
  
      def test_on_ndarray(self):
          # Test functions on ndarrays
          a = np.array([1, 2, 3, 4])
          m = array(a, mask=False)
          test = anom(a)
          assert_equal(test, m.anom())
          test = reshape(a, (2, 2))
          assert_equal(test, m.reshape(2, 2))
  
      def test_compress(self):
          # Test compress function on ndarray and masked array
          # Address Github #2495.
          arr = np.arange(8)
          arr.shape = 4, 2
          cond = np.array([True, False, True, True])
          control = arr[[0, 2, 3]]
          test = np.ma.compress(cond, arr, axis=0)
          assert_equal(test, control)
          marr = np.ma.array(arr)
          test = np.ma.compress(cond, marr, axis=0)
          assert_equal(test, control)
  
      def test_compressed(self):
          # Test ma.compressed function.
          # Address gh-4026
          a = np.ma.array([1, 2])
          test = np.ma.compressed(a)
          assert_(type(test) is np.ndarray)
  
          # Test case when input data is ndarray subclass
          class A(np.ndarray):
              pass
  
          a = np.ma.array(A(shape=0))
          test = np.ma.compressed(a)
          assert_(type(test) is A)
  
          # Test that compress flattens
          test = np.ma.compressed([[1],[2]])
          assert_equal(test.ndim, 1)
          test = np.ma.compressed([[[[[1]]]]])
          assert_equal(test.ndim, 1)
  
          # Test case when input is MaskedArray subclass
          class M(MaskedArray):
              pass
  
          test = np.ma.compressed(M(shape=(0,1,2)))
          assert_equal(test.ndim, 1)
  
          # with .compressed() overridden
          class M(MaskedArray):
              def compressed(self):
                  return 42
  
          test = np.ma.compressed(M(shape=(0,1,2)))
          assert_equal(test, 42)
  
      def test_convolve(self):
          a = masked_equal(np.arange(5), 2)
          b = np.array([1, 1])
          test = np.ma.convolve(a, b)
          assert_equal(test, masked_equal([0, 1, -1, -1, 7, 4], -1))
  
          test = np.ma.convolve(a, b, propagate_mask=False)
          assert_equal(test, masked_equal([0, 1, 1, 3, 7, 4], -1))
  
          test = np.ma.convolve([1, 1], [1, 1, 1])
          assert_equal(test, masked_equal([1, 2, 2, 1], -1))
  
          a = [1, 1]
          b = masked_equal([1, -1, -1, 1], -1)
          test = np.ma.convolve(a, b, propagate_mask=False)
          assert_equal(test, masked_equal([1, 1, -1, 1, 1], -1))
          test = np.ma.convolve(a, b, propagate_mask=True)
          assert_equal(test, masked_equal([-1, -1, -1, -1, -1], -1))
  
  
  class TestMaskedFields(object):
  
      def setup(self):
          ilist = [1, 2, 3, 4, 5]
          flist = [1.1, 2.2, 3.3, 4.4, 5.5]
          slist = ['one', 'two', 'three', 'four', 'five']
          ddtype = [('a', int), ('b', float), ('c', '|S8')]
          mdtype = [('a', bool), ('b', bool), ('c', bool)]
          mask = [0, 1, 0, 0, 1]
          base = array(list(zip(ilist, flist, slist)), mask=mask, dtype=ddtype)
          self.data = dict(base=base, mask=mask, ddtype=ddtype, mdtype=mdtype)
  
      def test_set_records_masks(self):
          base = self.data['base']
          mdtype = self.data['mdtype']
          # Set w/ nomask or masked
          base.mask = nomask
          assert_equal_records(base._mask, np.zeros(base.shape, dtype=mdtype))
          base.mask = masked
          assert_equal_records(base._mask, np.ones(base.shape, dtype=mdtype))
          # Set w/ simple boolean
          base.mask = False
          assert_equal_records(base._mask, np.zeros(base.shape, dtype=mdtype))
          base.mask = True
          assert_equal_records(base._mask, np.ones(base.shape, dtype=mdtype))
          # Set w/ list
          base.mask = [0, 0, 0, 1, 1]
          assert_equal_records(base._mask,
                               np.array([(x, x, x) for x in [0, 0, 0, 1, 1]],
                                        dtype=mdtype))
  
      def test_set_record_element(self):
          # Check setting an element of a record)
          base = self.data['base']
          (base_a, base_b, base_c) = (base['a'], base['b'], base['c'])
          base[0] = (pi, pi, 'pi')
  
          assert_equal(base_a.dtype, int)
          assert_equal(base_a._data, [3, 2, 3, 4, 5])
  
          assert_equal(base_b.dtype, float)
          assert_equal(base_b._data, [pi, 2.2, 3.3, 4.4, 5.5])
  
          assert_equal(base_c.dtype, '|S8')
          assert_equal(base_c._data,
                       [b'pi', b'two', b'three', b'four', b'five'])
  
      def test_set_record_slice(self):
          base = self.data['base']
          (base_a, base_b, base_c) = (base['a'], base['b'], base['c'])
          base[:3] = (pi, pi, 'pi')
  
          assert_equal(base_a.dtype, int)
          assert_equal(base_a._data, [3, 3, 3, 4, 5])
  
          assert_equal(base_b.dtype, float)
          assert_equal(base_b._data, [pi, pi, pi, 4.4, 5.5])
  
          assert_equal(base_c.dtype, '|S8')
          assert_equal(base_c._data,
                       [b'pi', b'pi', b'pi', b'four', b'five'])
  
      def test_mask_element(self):
          "Check record access"
          base = self.data['base']
          base[0] = masked
  
          for n in ('a', 'b', 'c'):
              assert_equal(base[n].mask, [1, 1, 0, 0, 1])
              assert_equal(base[n]._data, base._data[n])
  
      def test_getmaskarray(self):
          # Test getmaskarray on flexible dtype
          ndtype = [('a', int), ('b', float)]
          test = empty(3, dtype=ndtype)
          assert_equal(getmaskarray(test),
                       np.array([(0, 0), (0, 0), (0, 0)],
                                dtype=[('a', '|b1'), ('b', '|b1')]))
          test[:] = masked
          assert_equal(getmaskarray(test),
                       np.array([(1, 1), (1, 1), (1, 1)],
                                dtype=[('a', '|b1'), ('b', '|b1')]))
  
      def test_view(self):
          # Test view w/ flexible dtype
          iterator = list(zip(np.arange(10), np.random.rand(10)))
          data = np.array(iterator)
          a = array(iterator, dtype=[('a', float), ('b', float)])
          a.mask[0] = (1, 0)
          controlmask = np.array([1] + 19 * [0], dtype=bool)
          # Transform globally to simple dtype
          test = a.view(float)
          assert_equal(test, data.ravel())
          assert_equal(test.mask, controlmask)
          # Transform globally to dty
          test = a.view((float, 2))
          assert_equal(test, data)
          assert_equal(test.mask, controlmask.reshape(-1, 2))
  
          test = a.view((float, 2), np.matrix)
          assert_equal(test, data)
          assert_(isinstance(test, np.matrix))
  
      def test_getitem(self):
          ndtype = [('a', float), ('b', float)]
          a = array(list(zip(np.random.rand(10), np.arange(10))), dtype=ndtype)
          a.mask = np.array(list(zip([0, 0, 0, 0, 0, 0, 0, 0, 1, 1],
                                     [1, 0, 0, 0, 0, 0, 0, 0, 1, 0])),
                            dtype=[('a', bool), ('b', bool)])
  
          def _test_index(i):
              assert_equal(type(a[i]), mvoid)
              assert_equal_records(a[i]._data, a._data[i])
              assert_equal_records(a[i]._mask, a._mask[i])
  
              assert_equal(type(a[i, ...]), MaskedArray)
              assert_equal_records(a[i,...]._data, a._data[i,...])
              assert_equal_records(a[i,...]._mask, a._mask[i,...])
  
          _test_index(1)   # No mask
          _test_index(0)   # One element masked
          _test_index(-2)  # All element masked
  
      def test_setitem(self):
          # Issue 4866: check that one can set individual items in [record][col]
          # and [col][record] order
          ndtype = np.dtype([('a', float), ('b', int)])
          ma = np.ma.MaskedArray([(1.0, 1), (2.0, 2)], dtype=ndtype)
          ma['a'][1] = 3.0
          assert_equal(ma['a'], np.array([1.0, 3.0]))
          ma[1]['a'] = 4.0
          assert_equal(ma['a'], np.array([1.0, 4.0]))
          # Issue 2403
          mdtype = np.dtype([('a', bool), ('b', bool)])
          # soft mask
          control = np.array([(False, True), (True, True)], dtype=mdtype)
          a = np.ma.masked_all((2,), dtype=ndtype)
          a['a'][0] = 2
          assert_equal(a.mask, control)
          a = np.ma.masked_all((2,), dtype=ndtype)
          a[0]['a'] = 2
          assert_equal(a.mask, control)
          # hard mask
          control = np.array([(True, True), (True, True)], dtype=mdtype)
          a = np.ma.masked_all((2,), dtype=ndtype)
          a.harden_mask()
          a['a'][0] = 2
          assert_equal(a.mask, control)
          a = np.ma.masked_all((2,), dtype=ndtype)
          a.harden_mask()
          a[0]['a'] = 2
          assert_equal(a.mask, control)
  
      def test_setitem_scalar(self):
          # 8510
          mask_0d = np.ma.masked_array(1, mask=True)
          arr = np.ma.arange(3)
          arr[0] = mask_0d
          assert_array_equal(arr.mask, [True, False, False])
  
      def test_element_len(self):
          # check that len() works for mvoid (Github issue #576)
          for rec in self.data['base']:
              assert_equal(len(rec), len(self.data['ddtype']))
  
  
  class TestMaskedObjectArray(object):
  
      def test_getitem(self):
          arr = np.ma.array([None, None])
          for dt in [float, object]:
              a0 = np.eye(2).astype(dt)
              a1 = np.eye(3).astype(dt)
              arr[0] = a0
              arr[1] = a1
  
              assert_(arr[0] is a0)
              assert_(arr[1] is a1)
              assert_(isinstance(arr[0,...], MaskedArray))
              assert_(isinstance(arr[1,...], MaskedArray))
              assert_(arr[0,...][()] is a0)
              assert_(arr[1,...][()] is a1)
  
              arr[0] = np.ma.masked
  
              assert_(arr[1] is a1)
              assert_(isinstance(arr[0,...], MaskedArray))
              assert_(isinstance(arr[1,...], MaskedArray))
              assert_equal(arr[0,...].mask, True)
              assert_(arr[1,...][()] is a1)
  
              # gh-5962 - object arrays of arrays do something special
              assert_equal(arr[0].data, a0)
              assert_equal(arr[0].mask, True)
              assert_equal(arr[0,...][()].data, a0)
              assert_equal(arr[0,...][()].mask, True)
  
      def test_nested_ma(self):
  
          arr = np.ma.array([None, None])
          # set the first object to be an unmasked masked constant. A little fiddly
          arr[0,...] = np.array([np.ma.masked], object)[0,...]
  
          # check the above line did what we were aiming for
          assert_(arr.data[0] is np.ma.masked)
  
          # test that getitem returned the value by identity
          assert_(arr[0] is np.ma.masked)
  
          # now mask the masked value!
          arr[0] = np.ma.masked
          assert_(arr[0] is np.ma.masked)
  
  
  class TestMaskedView(object):
  
      def setup(self):
          iterator = list(zip(np.arange(10), np.random.rand(10)))
          data = np.array(iterator)
          a = array(iterator, dtype=[('a', float), ('b', float)])
          a.mask[0] = (1, 0)
          controlmask = np.array([1] + 19 * [0], dtype=bool)
          self.data = (data, a, controlmask)
  
      def test_view_to_nothing(self):
          (data, a, controlmask) = self.data
          test = a.view()
          assert_(isinstance(test, MaskedArray))
          assert_equal(test._data, a._data)
          assert_equal(test._mask, a._mask)
  
      def test_view_to_type(self):
          (data, a, controlmask) = self.data
          test = a.view(np.ndarray)
          assert_(not isinstance(test, MaskedArray))
          assert_equal(test, a._data)
          assert_equal_records(test, data.view(a.dtype).squeeze())
  
      def test_view_to_simple_dtype(self):
          (data, a, controlmask) = self.data
          # View globally
          test = a.view(float)
          assert_(isinstance(test, MaskedArray))
          assert_equal(test, data.ravel())
          assert_equal(test.mask, controlmask)
  
      def test_view_to_flexible_dtype(self):
          (data, a, controlmask) = self.data
  
          test = a.view([('A', float), ('B', float)])
          assert_equal(test.mask.dtype.names, ('A', 'B'))
          assert_equal(test['A'], a['a'])
          assert_equal(test['B'], a['b'])
  
          test = a[0].view([('A', float), ('B', float)])
          assert_(isinstance(test, MaskedArray))
          assert_equal(test.mask.dtype.names, ('A', 'B'))
          assert_equal(test['A'], a['a'][0])
          assert_equal(test['B'], a['b'][0])
  
          test = a[-1].view([('A', float), ('B', float)])
          assert_(isinstance(test, MaskedArray))
          assert_equal(test.dtype.names, ('A', 'B'))
          assert_equal(test['A'], a['a'][-1])
          assert_equal(test['B'], a['b'][-1])
  
      def test_view_to_subdtype(self):
          (data, a, controlmask) = self.data
          # View globally
          test = a.view((float, 2))
          assert_(isinstance(test, MaskedArray))
          assert_equal(test, data)
          assert_equal(test.mask, controlmask.reshape(-1, 2))
          # View on 1 masked element
          test = a[0].view((float, 2))
          assert_(isinstance(test, MaskedArray))
          assert_equal(test, data[0])
          assert_equal(test.mask, (1, 0))
          # View on 1 unmasked element
          test = a[-1].view((float, 2))
          assert_(isinstance(test, MaskedArray))
          assert_equal(test, data[-1])
  
      def test_view_to_dtype_and_type(self):
          (data, a, controlmask) = self.data
  
          test = a.view((float, 2), np.matrix)
          assert_equal(test, data)
          assert_(isinstance(test, np.matrix))
          assert_(not isinstance(test, MaskedArray))
  
  class TestOptionalArgs(object):
      def test_ndarrayfuncs(self):
          # test axis arg behaves the same as ndarray (including multiple axes)
  
          d = np.arange(24.0).reshape((2,3,4))
          m = np.zeros(24, dtype=bool).reshape((2,3,4))
          # mask out last element of last dimension
          m[:,:,-1] = True
          a = np.ma.array(d, mask=m)
  
          def testaxis(f, a, d):
              numpy_f = numpy.__getattribute__(f)
              ma_f = np.ma.__getattribute__(f)
  
              # test axis arg
              assert_equal(ma_f(a, axis=1)[...,:-1], numpy_f(d[...,:-1], axis=1))
              assert_equal(ma_f(a, axis=(0,1))[...,:-1],
                           numpy_f(d[...,:-1], axis=(0,1)))
  
          def testkeepdims(f, a, d):
              numpy_f = numpy.__getattribute__(f)
              ma_f = np.ma.__getattribute__(f)
  
              # test keepdims arg
              assert_equal(ma_f(a, keepdims=True).shape,
                           numpy_f(d, keepdims=True).shape)
              assert_equal(ma_f(a, keepdims=False).shape,
                           numpy_f(d, keepdims=False).shape)
  
              # test both at once
              assert_equal(ma_f(a, axis=1, keepdims=True)[...,:-1],
                           numpy_f(d[...,:-1], axis=1, keepdims=True))
              assert_equal(ma_f(a, axis=(0,1), keepdims=True)[...,:-1],
                           numpy_f(d[...,:-1], axis=(0,1), keepdims=True))
  
          for f in ['sum', 'prod', 'mean', 'var', 'std']:
              testaxis(f, a, d)
              testkeepdims(f, a, d)
  
          for f in ['min', 'max']:
              testaxis(f, a, d)
  
          d = (np.arange(24).reshape((2,3,4))%2 == 0)
          a = np.ma.array(d, mask=m)
          for f in ['all', 'any']:
              testaxis(f, a, d)
              testkeepdims(f, a, d)
  
      def test_count(self):
          # test np.ma.count specially
  
          d = np.arange(24.0).reshape((2,3,4))
          m = np.zeros(24, dtype=bool).reshape((2,3,4))
          m[:,0,:] = True
          a = np.ma.array(d, mask=m)
  
          assert_equal(count(a), 16)
          assert_equal(count(a, axis=1), 2*ones((2,4)))
          assert_equal(count(a, axis=(0,1)), 4*ones((4,)))
          assert_equal(count(a, keepdims=True), 16*ones((1,1,1)))
          assert_equal(count(a, axis=1, keepdims=True), 2*ones((2,1,4)))
          assert_equal(count(a, axis=(0,1), keepdims=True), 4*ones((1,1,4)))
          assert_equal(count(a, axis=-2), 2*ones((2,4)))
          assert_raises(ValueError, count, a, axis=(1,1))
          assert_raises(np.AxisError, count, a, axis=3)
  
          # check the 'nomask' path
          a = np.ma.array(d, mask=nomask)
  
          assert_equal(count(a), 24)
          assert_equal(count(a, axis=1), 3*ones((2,4)))
          assert_equal(count(a, axis=(0,1)), 6*ones((4,)))
          assert_equal(count(a, keepdims=True), 24*ones((1,1,1)))
          assert_equal(np.ndim(count(a, keepdims=True)), 3)
          assert_equal(count(a, axis=1, keepdims=True), 3*ones((2,1,4)))
          assert_equal(count(a, axis=(0,1), keepdims=True), 6*ones((1,1,4)))
          assert_equal(count(a, axis=-2), 3*ones((2,4)))
          assert_raises(ValueError, count, a, axis=(1,1))
          assert_raises(np.AxisError, count, a, axis=3)
  
          # check the 'masked' singleton
          assert_equal(count(np.ma.masked), 0)
  
          # check 0-d arrays do not allow axis > 0
          assert_raises(np.AxisError, count, np.ma.array(1), axis=1)
  
  
  class TestMaskedConstant(object):
      def _do_add_test(self, add):
          # sanity check
          assert_(add(np.ma.masked, 1) is np.ma.masked)
  
          # now try with a vector
          vector = np.array([1, 2, 3])
          result = add(np.ma.masked, vector)
  
          # lots of things could go wrong here
          assert_(result is not np.ma.masked)
          assert_(not isinstance(result, np.ma.core.MaskedConstant))
          assert_equal(result.shape, vector.shape)
          assert_equal(np.ma.getmask(result), np.ones(vector.shape, dtype=bool))
  
      def test_ufunc(self):
          self._do_add_test(np.add)
  
      def test_operator(self):
          self._do_add_test(lambda a, b: a + b)
  
      def test_ctor(self):
          m = np.ma.array(np.ma.masked)
  
          # most importantly, we do not want to create a new MaskedConstant
          # instance
          assert_(not isinstance(m, np.ma.core.MaskedConstant))
          assert_(m is not np.ma.masked)
  
  
  def test_masked_array():
      a = np.ma.array([0, 1, 2, 3], mask=[0, 0, 1, 0])
      assert_equal(np.argwhere(a), [[1], [3]])
  
  def test_append_masked_array():
      a = np.ma.masked_equal([1,2,3], value=2)
      b = np.ma.masked_equal([4,3,2], value=2)
  
      result = np.ma.append(a, b)
      expected_data = [1, 2, 3, 4, 3, 2]
      expected_mask = [False, True, False, False, False, True]
      assert_array_equal(result.data, expected_data)
      assert_array_equal(result.mask, expected_mask)
  
      a = np.ma.masked_all((2,2))
      b = np.ma.ones((3,1))
  
      result = np.ma.append(a, b)
      expected_data = [1] * 3
      expected_mask = [True] * 4 + [False] * 3
      assert_array_equal(result.data[-3], expected_data)
      assert_array_equal(result.mask, expected_mask)
  
      result = np.ma.append(a, b, axis=None)
      assert_array_equal(result.data[-3], expected_data)
      assert_array_equal(result.mask, expected_mask)
  
  
  def test_append_masked_array_along_axis():
      a = np.ma.masked_equal([1,2,3], value=2)
      b = np.ma.masked_values([[4, 5, 6], [7, 8, 9]], 7)
  
      # When `axis` is specified, `values` must have the correct shape.
      assert_raises(ValueError, np.ma.append, a, b, axis=0)
  
      result = np.ma.append(a[np.newaxis,:], b, axis=0)
      expected = np.ma.arange(1, 10)
      expected[[1, 6]] = np.ma.masked
      expected = expected.reshape((3,3))
      assert_array_equal(result.data, expected.data)
      assert_array_equal(result.mask, expected.mask)
  
  
  def test_default_fill_value_complex():
      # regression test for Python 3, where 'unicode' was not defined
      assert_(default_fill_value(1 + 1j) == 1.e20 + 0.0j)
  
  
  def test_ufunc_with_output():
      # check that giving an output argument always returns that output.
      # Regression test for gh-8416.
      x = array([1., 2., 3.], mask=[0, 0, 1])
      y = np.add(x, 1., out=x)
      assert_(y is x)
  
  def test_astype():
      descr = [('v', int, 3), ('x', [('y', float)])]
      x = array(([1, 2, 3], (1.0,)), dtype=descr)
      assert_equal(x, x.astype(descr))
  
  
  ###############################################################################
  if __name__ == "__main__":
      run_module_suite()
  
apis: numpy.ones numpy.ma.core.maximum_fill_value numpy.multiply numpy.subtract numpy.testing.assert_equal numpy.ma.core.mvoid numpy.ma.core.ones numpy.asarray numpy.ma.core.allclose numpy.testing.assert_warns numpy.ma.core.empty numpy.ma.core.filled numpy.ma.core.min numpy.ma.core.angle numpy.ma.core.minimum.outer numpy.transpose numpy.arccos numpy.bool_ numpy.ma.core.flatten_structured_array numpy.ma.MaskedArray numpy.absolute numpy.ma.core.cosh numpy.ma.core.take numpy.ma.core.allequal numpy.ma.__getattribute__ numpy.ma.core.greater numpy.ma.convolve numpy.ma.core.getmask numpy.ma.core.divide numpy.ma.core.shape numpy.ma.core.ravel numpy.ma.core.getmaskarray numpy.ma.core.max numpy.maximum.reduce numpy.ma.core.arccosh numpy.ma.testutils.assert_equal_records numpy.arcsin numpy.divide numpy.ma.testutils.fail_if_equal numpy.ma.core.masked_print_option.set_display numpy.matrix numpy.arctan numpy.exp numpy.errstate numpy.ma.copy numpy.round numpy.array numpy.find_common_type numpy.ma.testutils.assert_ numpy.ma.core.maximum numpy.ma.core.arccos numpy.ma.core.masked_equal numpy.geterr numpy.argwhere numpy.ma.core.sqrt numpy.ma.core.diag numpy.add numpy.ma.core.MaskedArray.cumsum numpy.ma.core.MaskedArray numpy.ma.core.equal numpy.ma.core.mod numpy.ma.core.arctan numpy.ma.core.reshape numpy.add.reduce numpy.ma.core.repeat numpy.reshape numpy.ma.core.flatten_mask numpy.seterr numpy.ma.core.zeros numpy.ma.core.empty_like numpy.ma.core.maximum.reduce numpy.ma.core.masked_less numpy.ma.core.add numpy.equal numpy.ma.core.less_equal numpy.ma.masked_all numpy.greater numpy.ma.core.add.accumulate numpy.ma.core.masked_array numpy.ma.core.minimum_fill_value numpy.ma.core.sort numpy.may_share_memory numpy.finfo numpy.testing.assert_raises numpy.arctan2 numpy.ma.core.masked_less_equal numpy.ma.core.less numpy.ma.ones numpy.not_equal numpy.ma.core.argsort numpy.ma.core.putmask numpy.ma.array numpy.ma.core.arctan2 numpy.angle numpy.product numpy.ma.core.tanh numpy.sqrt numpy.ma.compress numpy.concatenate numpy.ma.testutils.assert_not_equal numpy.sin numpy.sum numpy.ma.compressed numpy.ma.core.greater_equal numpy.minimum.outer numpy.ma.core.isMaskedArray numpy.ma.core.product numpy.take numpy.less numpy.testing.run_module_suite numpy.argsort numpy.trace numpy.log numpy.ma.core.masked_not_equal numpy.ma.core.masked_greater_equal numpy.ma.testutils.assert_almost_equal numpy.ma.core.exp numpy.ma.core.alltrue numpy.ma.core.asarray numpy.ma.core.resize numpy.ma.core.minimum.reduce numpy.ma.core.array numpy.ma.core.sinh numpy.random.rand numpy.ma.core.put numpy.isnan numpy.ma.core.minimum numpy.random.uniform numpy.ma.testutils.assert_array_equal numpy.ma.core.transpose numpy.zeros numpy.ma.masked_equal numpy.ma.getmask numpy.maximum.outer numpy.ma.core.sum numpy.arange numpy.sort numpy.ma.core.multiply.outer numpy.ma.core.not_equal numpy.cosh numpy.ma.core.anom numpy.ma.core.conjugate numpy.ma.core.subtract numpy.ma.core.multiply numpy.true_divide numpy.ma.core.sin numpy.ma.core.absolute numpy.ma.masked_values numpy.less_equal numpy.ma.core.default_fill_value numpy.ma.core.add.reduce numpy.ma.testutils.assert_equal numpy.diag numpy.dtype numpy.ma.testutils.assert_mask_equal numpy.ma.core.cos numpy.greater_equal numpy.ma.core.log numpy.ma.core.masked_greater numpy.ma.core.count numpy.ma.core.where numpy.tanh numpy.ma.core.log10 numpy.ma.core.inner numpy.ma.core.fix_invalid numpy.ma.core.tan numpy.ma.core.arange numpy.ma.masked_where numpy.ma.core.masked_where numpy.ma.masked_array numpy.ma.core.arcsin numpy.cos numpy.ndarray numpy.ma.core.sometrue numpy.ma.core.make_mask_descr numpy.where numpy.ma.arange numpy.identity numpy.minimum numpy.ma.where numpy.eye numpy.ma.core.abs numpy.sinh numpy.mod numpy.all numpy.tan numpy.ma.core.masked_values numpy.maximum numpy.ma.core.power numpy.ma.core.concatenate numpy.ma.core.mask_or numpy.ma.append numpy.ma.core.maximum.outer numpy.empty numpy.add.accumulate numpy.conjugate numpy.ma.core.identity numpy.ravel numpy.ma.core.outer numpy.ma.core.make_mask numpy.shape numpy.iinfo numpy.testing.suppress_warnings numpy.ma.core.choose