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.github/workflows/main.yml

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+# This is a basic workflow to help you get started with Actions
+
+name: CI
+
+# Controls when the workflow will run
+on:
+  # Triggers the workflow on push or pull request events but only for the main branch
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+# A workflow run is made up of one or more jobs that can run sequentially or in parallel
+jobs:
+  # This workflow contains a single job called "build"
+  build:
+    # The type of runner that the job will run on
+    runs-on: ubuntu-latest
+
+    # Steps represent a sequence of tasks that will be executed as part of the job
+    steps:
+
+      - name: Checkout code
+        uses: actions/checkout@v2
+
+      # Runs a single command using the runners shell
+      - name: docker compose
+        run: docker-compose up -d

venv/lib/python3.13/site-packages/fsspec-2025.10.0.dist-info/licenses/LICENSE

@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) 2018, Martin Durant
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

venv/lib/python3.13/site-packages/numpy/ma/tests/test_arrayobject.py

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+import pytest
+
+import numpy as np
+from numpy.ma import masked_array
+from numpy.testing import assert_array_equal
+
+
+def test_matrix_transpose_raises_error_for_1d():
+    msg = "matrix transpose with ndim < 2 is undefined"
+    ma_arr = masked_array(data=[1, 2, 3, 4, 5, 6],
+                          mask=[1, 0, 1, 1, 1, 0])
+    with pytest.raises(ValueError, match=msg):
+        ma_arr.mT
+
+
+def test_matrix_transpose_equals_transpose_2d():
+    ma_arr = masked_array(data=[[1, 2, 3], [4, 5, 6]],
+                          mask=[[1, 0, 1], [1, 1, 0]])
+    assert_array_equal(ma_arr.T, ma_arr.mT)
+
+
+ARRAY_SHAPES_TO_TEST = (
+    (5, 2),
+    (5, 2, 3),
+    (5, 2, 3, 4),
+)
+
+
+@pytest.mark.parametrize("shape", ARRAY_SHAPES_TO_TEST)
+def test_matrix_transpose_equals_swapaxes(shape):
+    num_of_axes = len(shape)
+    vec = np.arange(shape[-1])
+    arr = np.broadcast_to(vec, shape)
+
+    rng = np.random.default_rng(42)
+    mask = rng.choice([0, 1], size=shape)
+    ma_arr = masked_array(data=arr, mask=mask)
+
+    tgt = np.swapaxes(arr, num_of_axes - 2, num_of_axes - 1)
+    assert_array_equal(tgt, ma_arr.mT)

venv/lib/python3.13/site-packages/numpy/ma/tests/test_deprecations.py

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+"""Test deprecation and future warnings.
+
+"""
+import io
+import textwrap
+
+import pytest
+
+import numpy as np
+from numpy.ma.core import MaskedArrayFutureWarning
+from numpy.ma.testutils import assert_equal
+from numpy.testing import assert_warns
+
+
+class TestArgsort:
+    """ gh-8701 """
+    def _test_base(self, argsort, cls):
+        arr_0d = np.array(1).view(cls)
+        argsort(arr_0d)
+
+        arr_1d = np.array([1, 2, 3]).view(cls)
+        argsort(arr_1d)
+
+        # argsort has a bad default for >1d arrays
+        arr_2d = np.array([[1, 2], [3, 4]]).view(cls)
+        result = assert_warns(
+            np.ma.core.MaskedArrayFutureWarning, argsort, arr_2d)
+        assert_equal(result, argsort(arr_2d, axis=None))
+
+        # should be no warnings for explicitly specifying it
+        argsort(arr_2d, axis=None)
+        argsort(arr_2d, axis=-1)
+
+    def test_function_ndarray(self):
+        return self._test_base(np.ma.argsort, np.ndarray)
+
+    def test_function_maskedarray(self):
+        return self._test_base(np.ma.argsort, np.ma.MaskedArray)
+
+    def test_method(self):
+        return self._test_base(np.ma.MaskedArray.argsort, np.ma.MaskedArray)
+
+
+class TestMinimumMaximum:
+
+    def test_axis_default(self):
+        # NumPy 1.13, 2017-05-06
+
+        data1d = np.ma.arange(6)
+        data2d = data1d.reshape(2, 3)
+
+        ma_min = np.ma.minimum.reduce
+        ma_max = np.ma.maximum.reduce
+
+        # check that the default axis is still None, but warns on 2d arrays
+        result = assert_warns(MaskedArrayFutureWarning, ma_max, data2d)
+        assert_equal(result, ma_max(data2d, axis=None))
+
+        result = assert_warns(MaskedArrayFutureWarning, ma_min, data2d)
+        assert_equal(result, ma_min(data2d, axis=None))
+
+        # no warnings on 1d, as both new and old defaults are equivalent
+        result = ma_min(data1d)
+        assert_equal(result, ma_min(data1d, axis=None))
+        assert_equal(result, ma_min(data1d, axis=0))
+
+        result = ma_max(data1d)
+        assert_equal(result, ma_max(data1d, axis=None))
+        assert_equal(result, ma_max(data1d, axis=0))
+
+
+class TestFromtextfile:
+    def test_fromtextfile_delimitor(self):
+        # NumPy 1.22.0, 2021-09-23
+
+        textfile = io.StringIO(textwrap.dedent(
+            """
+            A,B,C,D
+            'string 1';1;1.0;'mixed column'
+            'string 2';2;2.0;
+            'string 3';3;3.0;123
+            'string 4';4;4.0;3.14
+            """
+        ))
+
+        with pytest.warns(DeprecationWarning):
+            result = np.ma.mrecords.fromtextfile(textfile, delimitor=';')

venv/lib/python3.13/site-packages/numpy/ma/tests/test_extras.py

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+"""Tests suite for MaskedArray.
+Adapted from the original test_ma by Pierre Gerard-Marchant
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+
+"""
+import itertools
+import warnings
+
+import pytest
+
+import numpy as np
+from numpy._core.numeric import normalize_axis_tuple
+from numpy.ma.core import (
+    MaskedArray,
+    arange,
+    array,
+    count,
+    getmaskarray,
+    masked,
+    masked_array,
+    nomask,
+    ones,
+    shape,
+    zeros,
+)
+from numpy.ma.extras import (
+    _covhelper,
+    apply_along_axis,
+    apply_over_axes,
+    atleast_1d,
+    atleast_2d,
+    atleast_3d,
+    average,
+    clump_masked,
+    clump_unmasked,
+    compress_nd,
+    compress_rowcols,
+    corrcoef,
+    cov,
+    diagflat,
+    dot,
+    ediff1d,
+    flatnotmasked_contiguous,
+    in1d,
+    intersect1d,
+    isin,
+    mask_rowcols,
+    masked_all,
+    masked_all_like,
+    median,
+    mr_,
+    ndenumerate,
+    notmasked_contiguous,
+    notmasked_edges,
+    polyfit,
+    setdiff1d,
+    setxor1d,
+    stack,
+    union1d,
+    unique,
+    vstack,
+)
+from numpy.ma.testutils import (
+    assert_,
+    assert_almost_equal,
+    assert_array_equal,
+    assert_equal,
+)
+from numpy.testing import assert_warns, suppress_warnings
+
+
+class TestGeneric:
+    #
+    def test_masked_all(self):
+        # Tests masked_all
+        # Standard dtype
+        test = masked_all((2,), dtype=float)
+        control = array([1, 1], mask=[1, 1], dtype=float)
+        assert_equal(test, control)
+        # Flexible dtype
+        dt = np.dtype({'names': ['a', 'b'], 'formats': ['f', 'f']})
+        test = masked_all((2,), dtype=dt)
+        control = array([(0, 0), (0, 0)], mask=[(1, 1), (1, 1)], dtype=dt)
+        assert_equal(test, control)
+        test = masked_all((2, 2), dtype=dt)
+        control = array([[(0, 0), (0, 0)], [(0, 0), (0, 0)]],
+                        mask=[[(1, 1), (1, 1)], [(1, 1), (1, 1)]],
+                        dtype=dt)
+        assert_equal(test, control)
+        # Nested dtype
+        dt = np.dtype([('a', 'f'), ('b', [('ba', 'f'), ('bb', 'f')])])
+        test = masked_all((2,), dtype=dt)
+        control = array([(1, (1, 1)), (1, (1, 1))],
+                        mask=[(1, (1, 1)), (1, (1, 1))], dtype=dt)
+        assert_equal(test, control)
+        test = masked_all((2,), dtype=dt)
+        control = array([(1, (1, 1)), (1, (1, 1))],
+                        mask=[(1, (1, 1)), (1, (1, 1))], dtype=dt)
+        assert_equal(test, control)
+        test = masked_all((1, 1), dtype=dt)
+        control = array([[(1, (1, 1))]], mask=[[(1, (1, 1))]], dtype=dt)
+        assert_equal(test, control)
+
+    def test_masked_all_with_object_nested(self):
+        # Test masked_all works with nested array with dtype of an 'object'
+        # refers to issue #15895
+        my_dtype = np.dtype([('b', ([('c', object)], (1,)))])
+        masked_arr = np.ma.masked_all((1,), my_dtype)
+
+        assert_equal(type(masked_arr['b']), np.ma.core.MaskedArray)
+        assert_equal(type(masked_arr['b']['c']), np.ma.core.MaskedArray)
+        assert_equal(len(masked_arr['b']['c']), 1)
+        assert_equal(masked_arr['b']['c'].shape, (1, 1))
+        assert_equal(masked_arr['b']['c']._fill_value.shape, ())
+
+    def test_masked_all_with_object(self):
+        # same as above except that the array is not nested
+        my_dtype = np.dtype([('b', (object, (1,)))])
+        masked_arr = np.ma.masked_all((1,), my_dtype)
+
+        assert_equal(type(masked_arr['b']), np.ma.core.MaskedArray)
+        assert_equal(len(masked_arr['b']), 1)
+        assert_equal(masked_arr['b'].shape, (1, 1))
+        assert_equal(masked_arr['b']._fill_value.shape, ())
+
+    def test_masked_all_like(self):
+        # Tests masked_all
+        # Standard dtype
+        base = array([1, 2], dtype=float)
+        test = masked_all_like(base)
+        control = array([1, 1], mask=[1, 1], dtype=float)
+        assert_equal(test, control)
+        # Flexible dtype
+        dt = np.dtype({'names': ['a', 'b'], 'formats': ['f', 'f']})
+        base = array([(0, 0), (0, 0)], mask=[(1, 1), (1, 1)], dtype=dt)
+        test = masked_all_like(base)
+        control = array([(10, 10), (10, 10)], mask=[(1, 1), (1, 1)], dtype=dt)
+        assert_equal(test, control)
+        # Nested dtype
+        dt = np.dtype([('a', 'f'), ('b', [('ba', 'f'), ('bb', 'f')])])
+        control = array([(1, (1, 1)), (1, (1, 1))],
+                        mask=[(1, (1, 1)), (1, (1, 1))], dtype=dt)
+        test = masked_all_like(control)
+        assert_equal(test, control)
+
+    def check_clump(self, f):
+        for i in range(1, 7):
+            for j in range(2**i):
+                k = np.arange(i, dtype=int)
+                ja = np.full(i, j, dtype=int)
+                a = masked_array(2**k)
+                a.mask = (ja & (2**k)) != 0
+                s = 0
+                for sl in f(a):
+                    s += a.data[sl].sum()
+                if f == clump_unmasked:
+                    assert_equal(a.compressed().sum(), s)
+                else:
+                    a.mask = ~a.mask
+                    assert_equal(a.compressed().sum(), s)
+
+    def test_clump_masked(self):
+        # Test clump_masked
+        a = masked_array(np.arange(10))
+        a[[0, 1, 2, 6, 8, 9]] = masked
+        #
+        test = clump_masked(a)
+        control = [slice(0, 3), slice(6, 7), slice(8, 10)]
+        assert_equal(test, control)
+
+        self.check_clump(clump_masked)
+
+    def test_clump_unmasked(self):
+        # Test clump_unmasked
+        a = masked_array(np.arange(10))
+        a[[0, 1, 2, 6, 8, 9]] = masked
+        test = clump_unmasked(a)
+        control = [slice(3, 6), slice(7, 8), ]
+        assert_equal(test, control)
+
+        self.check_clump(clump_unmasked)
+
+    def test_flatnotmasked_contiguous(self):
+        # Test flatnotmasked_contiguous
+        a = arange(10)
+        # No mask
+        test = flatnotmasked_contiguous(a)
+        assert_equal(test, [slice(0, a.size)])
+        # mask of all false
+        a.mask = np.zeros(10, dtype=bool)
+        assert_equal(test, [slice(0, a.size)])
+        # Some mask
+        a[(a < 3) | (a > 8) | (a == 5)] = masked
+        test = flatnotmasked_contiguous(a)
+        assert_equal(test, [slice(3, 5), slice(6, 9)])
+        #
+        a[:] = masked
+        test = flatnotmasked_contiguous(a)
+        assert_equal(test, [])
+
+
+class TestAverage:
+    # Several tests of average. Why so many ? Good point...
+    def test_testAverage1(self):
+        # Test of average.
+        ott = array([0., 1., 2., 3.], mask=[True, False, False, False])
+        assert_equal(2.0, average(ott, axis=0))
+        assert_equal(2.0, average(ott, weights=[1., 1., 2., 1.]))
+        result, wts = average(ott, weights=[1., 1., 2., 1.], returned=True)
+        assert_equal(2.0, result)
+        assert_(wts == 4.0)
+        ott[:] = masked
+        assert_equal(average(ott, axis=0).mask, [True])
+        ott = array([0., 1., 2., 3.], mask=[True, False, False, False])
+        ott = ott.reshape(2, 2)
+        ott[:, 1] = masked
+        assert_equal(average(ott, axis=0), [2.0, 0.0])
+        assert_equal(average(ott, axis=1).mask[0], [True])
+        assert_equal([2., 0.], average(ott, axis=0))
+        result, wts = average(ott, axis=0, returned=True)
+        assert_equal(wts, [1., 0.])
+
+    def test_testAverage2(self):
+        # More tests of average.
+        w1 = [0, 1, 1, 1, 1, 0]
+        w2 = [[0, 1, 1, 1, 1, 0], [1, 0, 0, 0, 0, 1]]
+        x = arange(6, dtype=np.float64)
+        assert_equal(average(x, axis=0), 2.5)
+        assert_equal(average(x, axis=0, weights=w1), 2.5)
+        y = array([arange(6, dtype=np.float64), 2.0 * arange(6)])
+        assert_equal(average(y, None), np.add.reduce(np.arange(6)) * 3. / 12.)
+        assert_equal(average(y, axis=0), np.arange(6) * 3. / 2.)
+        assert_equal(average(y, axis=1),
+                     [average(x, axis=0), average(x, axis=0) * 2.0])
+        assert_equal(average(y, None, weights=w2), 20. / 6.)
+        assert_equal(average(y, axis=0, weights=w2),
+                     [0., 1., 2., 3., 4., 10.])
+        assert_equal(average(y, axis=1),
+                     [average(x, axis=0), average(x, axis=0) * 2.0])
+        m1 = zeros(6)
+        m2 = [0, 0, 1, 1, 0, 0]
+        m3 = [[0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 1, 0]]
+        m4 = ones(6)
+        m5 = [0, 1, 1, 1, 1, 1]
+        assert_equal(average(masked_array(x, m1), axis=0), 2.5)
+        assert_equal(average(masked_array(x, m2), axis=0), 2.5)
+        assert_equal(average(masked_array(x, m4), axis=0).mask, [True])
+        assert_equal(average(masked_array(x, m5), axis=0), 0.0)
+        assert_equal(count(average(masked_array(x, m4), axis=0)), 0)
+        z = masked_array(y, m3)
+        assert_equal(average(z, None), 20. / 6.)
+        assert_equal(average(z, axis=0), [0., 1., 99., 99., 4.0, 7.5])
+        assert_equal(average(z, axis=1), [2.5, 5.0])
+        assert_equal(average(z, axis=0, weights=w2),
+                     [0., 1., 99., 99., 4.0, 10.0])
+
+    def test_testAverage3(self):
+        # Yet more tests of average!
+        a = arange(6)
+        b = arange(6) * 3
+        r1, w1 = average([[a, b], [b, a]], axis=1, returned=True)
+        assert_equal(shape(r1), shape(w1))
+        assert_equal(r1.shape, w1.shape)
+        r2, w2 = average(ones((2, 2, 3)), axis=0, weights=[3, 1], returned=True)
+        assert_equal(shape(w2), shape(r2))
+        r2, w2 = average(ones((2, 2, 3)), returned=True)
+        assert_equal(shape(w2), shape(r2))
+        r2, w2 = average(ones((2, 2, 3)), weights=ones((2, 2, 3)), returned=True)
+        assert_equal(shape(w2), shape(r2))
+        a2d = array([[1, 2], [0, 4]], float)
+        a2dm = masked_array(a2d, [[False, False], [True, False]])
+        a2da = average(a2d, axis=0)
+        assert_equal(a2da, [0.5, 3.0])
+        a2dma = average(a2dm, axis=0)
+        assert_equal(a2dma, [1.0, 3.0])
+        a2dma = average(a2dm, axis=None)
+        assert_equal(a2dma, 7. / 3.)
+        a2dma = average(a2dm, axis=1)
+        assert_equal(a2dma, [1.5, 4.0])
+
+    def test_testAverage4(self):
+        # Test that `keepdims` works with average
+        x = np.array([2, 3, 4]).reshape(3, 1)
+        b = np.ma.array(x, mask=[[False], [False], [True]])
+        w = np.array([4, 5, 6]).reshape(3, 1)
+        actual = average(b, weights=w, axis=1, keepdims=True)
+        desired = masked_array([[2.], [3.], [4.]], [[False], [False], [True]])
+        assert_equal(actual, desired)
+
+    def test_weight_and_input_dims_different(self):
+        # this test mirrors a test for np.average()
+        # in lib/test/test_function_base.py
+        y = np.arange(12).reshape(2, 2, 3)
+        w = np.array([0., 0., 1., .5, .5, 0., 0., .5, .5, 1., 0., 0.])\
+            .reshape(2, 2, 3)
+
+        m = np.full((2, 2, 3), False)
+        yma = np.ma.array(y, mask=m)
+        subw0 = w[:, :, 0]
+
+        actual = average(yma, axis=(0, 1), weights=subw0)
+        desired = masked_array([7., 8., 9.], mask=[False, False, False])
+        assert_almost_equal(actual, desired)
+
+        m = np.full((2, 2, 3), False)
+        m[:, :, 0] = True
+        m[0, 0, 1] = True
+        yma = np.ma.array(y, mask=m)
+        actual = average(yma, axis=(0, 1), weights=subw0)
+        desired = masked_array(
+            [np.nan, 8., 9.],
+            mask=[True, False, False])
+        assert_almost_equal(actual, desired)
+
+        m = np.full((2, 2, 3), False)
+        yma = np.ma.array(y, mask=m)
+
+        subw1 = w[1, :, :]
+        actual = average(yma, axis=(1, 2), weights=subw1)
+        desired = masked_array([2.25, 8.25], mask=[False, False])
+        assert_almost_equal(actual, desired)
+
+        # here the weights have the wrong shape for the specified axes
+        with pytest.raises(
+                ValueError,
+                match="Shape of weights must be consistent with "
+                      "shape of a along specified axis"):
+            average(yma, axis=(0, 1, 2), weights=subw0)
+
+        with pytest.raises(
+                ValueError,
+                match="Shape of weights must be consistent with "
+                      "shape of a along specified axis"):
+            average(yma, axis=(0, 1), weights=subw1)
+
+        # swapping the axes should be same as transposing weights
+        actual = average(yma, axis=(1, 0), weights=subw0)
+        desired = average(yma, axis=(0, 1), weights=subw0.T)
+        assert_almost_equal(actual, desired)
+
+    def test_onintegers_with_mask(self):
+        # Test average on integers with mask
+        a = average(array([1, 2]))
+        assert_equal(a, 1.5)
+        a = average(array([1, 2, 3, 4], mask=[False, False, True, True]))
+        assert_equal(a, 1.5)
+
+    def test_complex(self):
+        # Test with complex data.
+        # (Regression test for https://github.com/numpy/numpy/issues/2684)
+        mask = np.array([[0, 0, 0, 1, 0],
+                         [0, 1, 0, 0, 0]], dtype=bool)
+        a = masked_array([[0, 1 + 2j, 3 + 4j, 5 + 6j, 7 + 8j],
+                          [9j, 0 + 1j, 2 + 3j, 4 + 5j, 7 + 7j]],
+                         mask=mask)
+
+        av = average(a)
+        expected = np.average(a.compressed())
+        assert_almost_equal(av.real, expected.real)
+        assert_almost_equal(av.imag, expected.imag)
+
+        av0 = average(a, axis=0)
+        expected0 = average(a.real, axis=0) + average(a.imag, axis=0) * 1j
+        assert_almost_equal(av0.real, expected0.real)
+        assert_almost_equal(av0.imag, expected0.imag)
+
+        av1 = average(a, axis=1)
+        expected1 = average(a.real, axis=1) + average(a.imag, axis=1) * 1j
+        assert_almost_equal(av1.real, expected1.real)
+        assert_almost_equal(av1.imag, expected1.imag)
+
+        # Test with the 'weights' argument.
+        wts = np.array([[0.5, 1.0, 2.0, 1.0, 0.5],
+                        [1.0, 1.0, 1.0, 1.0, 1.0]])
+        wav = average(a, weights=wts)
+        expected = np.average(a.compressed(), weights=wts[~mask])
+        assert_almost_equal(wav.real, expected.real)
+        assert_almost_equal(wav.imag, expected.imag)
+
+        wav0 = average(a, weights=wts, axis=0)
+        expected0 = (average(a.real, weights=wts, axis=0) +
+                     average(a.imag, weights=wts, axis=0) * 1j)
+        assert_almost_equal(wav0.real, expected0.real)
+        assert_almost_equal(wav0.imag, expected0.imag)
+
+        wav1 = average(a, weights=wts, axis=1)
+        expected1 = (average(a.real, weights=wts, axis=1) +
+                     average(a.imag, weights=wts, axis=1) * 1j)
+        assert_almost_equal(wav1.real, expected1.real)
+        assert_almost_equal(wav1.imag, expected1.imag)
+
+    @pytest.mark.parametrize(
+        'x, axis, expected_avg, weights, expected_wavg, expected_wsum',
+        [([1, 2, 3], None, [2.0], [3, 4, 1], [1.75], [8.0]),
+         ([[1, 2, 5], [1, 6, 11]], 0, [[1.0, 4.0, 8.0]],
+          [1, 3], [[1.0, 5.0, 9.5]], [[4, 4, 4]])],
+    )
+    def test_basic_keepdims(self, x, axis, expected_avg,
+                            weights, expected_wavg, expected_wsum):
+        avg = np.ma.average(x, axis=axis, keepdims=True)
+        assert avg.shape == np.shape(expected_avg)
+        assert_array_equal(avg, expected_avg)
+
+        wavg = np.ma.average(x, axis=axis, weights=weights, keepdims=True)
+        assert wavg.shape == np.shape(expected_wavg)
+        assert_array_equal(wavg, expected_wavg)
+
+        wavg, wsum = np.ma.average(x, axis=axis, weights=weights,
+                                   returned=True, keepdims=True)
+        assert wavg.shape == np.shape(expected_wavg)
+        assert_array_equal(wavg, expected_wavg)
+        assert wsum.shape == np.shape(expected_wsum)
+        assert_array_equal(wsum, expected_wsum)
+
+    def test_masked_weights(self):
+        # Test with masked weights.
+        # (Regression test for https://github.com/numpy/numpy/issues/10438)
+        a = np.ma.array(np.arange(9).reshape(3, 3),
+                        mask=[[1, 0, 0], [1, 0, 0], [0, 0, 0]])
+        weights_unmasked = masked_array([5, 28, 31], mask=False)
+        weights_masked = masked_array([5, 28, 31], mask=[1, 0, 0])
+
+        avg_unmasked = average(a, axis=0,
+                               weights=weights_unmasked, returned=False)
+        expected_unmasked = np.array([6.0, 5.21875, 6.21875])
+        assert_almost_equal(avg_unmasked, expected_unmasked)
+
+        avg_masked = average(a, axis=0, weights=weights_masked, returned=False)
+        expected_masked = np.array([6.0, 5.576271186440678, 6.576271186440678])
+        assert_almost_equal(avg_masked, expected_masked)
+
+        # weights should be masked if needed
+        # depending on the array mask. This is to avoid summing
+        # masked nan or other values that are not cancelled by a zero
+        a = np.ma.array([1.0,   2.0,   3.0,  4.0],
+                   mask=[False, False, True, True])
+        avg_unmasked = average(a, weights=[1, 1, 1, np.nan])
+
+        assert_almost_equal(avg_unmasked, 1.5)
+
+        a = np.ma.array([
+            [1.0, 2.0, 3.0, 4.0],
+            [5.0, 6.0, 7.0, 8.0],
+            [9.0, 1.0, 2.0, 3.0],
+        ], mask=[
+            [False, True, True, False],
+            [True, False, True, True],
+            [True, False, True, False],
+        ])
+
+        avg_masked = np.ma.average(a, weights=[1, np.nan, 1], axis=0)
+        avg_expected = np.ma.array([1.0, np.nan, np.nan, 3.5],
+                              mask=[False, True, True, False])
+
+        assert_almost_equal(avg_masked, avg_expected)
+        assert_equal(avg_masked.mask, avg_expected.mask)
+
+
+class TestConcatenator:
+    # Tests for mr_, the equivalent of r_ for masked arrays.
+
+    def test_1d(self):
+        # Tests mr_ on 1D arrays.
+        assert_array_equal(mr_[1, 2, 3, 4, 5, 6], array([1, 2, 3, 4, 5, 6]))
+        b = ones(5)
+        m = [1, 0, 0, 0, 0]
+        d = masked_array(b, mask=m)
+        c = mr_[d, 0, 0, d]
+        assert_(isinstance(c, MaskedArray))
+        assert_array_equal(c, [1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1])
+        assert_array_equal(c.mask, mr_[m, 0, 0, m])
+
+    def test_2d(self):
+        # Tests mr_ on 2D arrays.
+        a_1 = np.random.rand(5, 5)
+        a_2 = np.random.rand(5, 5)
+        m_1 = np.round(np.random.rand(5, 5), 0)
+        m_2 = np.round(np.random.rand(5, 5), 0)
+        b_1 = masked_array(a_1, mask=m_1)
+        b_2 = masked_array(a_2, mask=m_2)
+        # append columns
+        d = mr_['1', b_1, b_2]
+        assert_(d.shape == (5, 10))
+        assert_array_equal(d[:, :5], b_1)
+        assert_array_equal(d[:, 5:], b_2)
+        assert_array_equal(d.mask, np.r_['1', m_1, m_2])
+        d = mr_[b_1, b_2]
+        assert_(d.shape == (10, 5))
+        assert_array_equal(d[:5, :], b_1)
+        assert_array_equal(d[5:, :], b_2)
+        assert_array_equal(d.mask, np.r_[m_1, m_2])
+
+    def test_masked_constant(self):
+        actual = mr_[np.ma.masked, 1]
+        assert_equal(actual.mask, [True, False])
+        assert_equal(actual.data[1], 1)
+
+        actual = mr_[[1, 2], np.ma.masked]
+        assert_equal(actual.mask, [False, False, True])
+        assert_equal(actual.data[:2], [1, 2])
+
+
+class TestNotMasked:
+    # Tests notmasked_edges and notmasked_contiguous.
+
+    def test_edges(self):
+        # Tests unmasked_edges
+        data = masked_array(np.arange(25).reshape(5, 5),
+                            mask=[[0, 0, 1, 0, 0],
+                                  [0, 0, 0, 1, 1],
+                                  [1, 1, 0, 0, 0],
+                                  [0, 0, 0, 0, 0],
+                                  [1, 1, 1, 0, 0]],)
+        test = notmasked_edges(data, None)
+        assert_equal(test, [0, 24])
+        test = notmasked_edges(data, 0)
+        assert_equal(test[0], [(0, 0, 1, 0, 0), (0, 1, 2, 3, 4)])
+        assert_equal(test[1], [(3, 3, 3, 4, 4), (0, 1, 2, 3, 4)])
+        test = notmasked_edges(data, 1)
+        assert_equal(test[0], [(0, 1, 2, 3, 4), (0, 0, 2, 0, 3)])
+        assert_equal(test[1], [(0, 1, 2, 3, 4), (4, 2, 4, 4, 4)])
+        #
+        test = notmasked_edges(data.data, None)
+        assert_equal(test, [0, 24])
+        test = notmasked_edges(data.data, 0)
+        assert_equal(test[0], [(0, 0, 0, 0, 0), (0, 1, 2, 3, 4)])
+        assert_equal(test[1], [(4, 4, 4, 4, 4), (0, 1, 2, 3, 4)])
+        test = notmasked_edges(data.data, -1)
+        assert_equal(test[0], [(0, 1, 2, 3, 4), (0, 0, 0, 0, 0)])
+        assert_equal(test[1], [(0, 1, 2, 3, 4), (4, 4, 4, 4, 4)])
+        #
+        data[-2] = masked
+        test = notmasked_edges(data, 0)
+        assert_equal(test[0], [(0, 0, 1, 0, 0), (0, 1, 2, 3, 4)])
+        assert_equal(test[1], [(1, 1, 2, 4, 4), (0, 1, 2, 3, 4)])
+        test = notmasked_edges(data, -1)
+        assert_equal(test[0], [(0, 1, 2, 4), (0, 0, 2, 3)])
+        assert_equal(test[1], [(0, 1, 2, 4), (4, 2, 4, 4)])
+
+    def test_contiguous(self):
+        # Tests notmasked_contiguous
+        a = masked_array(np.arange(24).reshape(3, 8),
+                         mask=[[0, 0, 0, 0, 1, 1, 1, 1],
+                               [1, 1, 1, 1, 1, 1, 1, 1],
+                               [0, 0, 0, 0, 0, 0, 1, 0]])
+        tmp = notmasked_contiguous(a, None)
+        assert_equal(tmp, [
+            slice(0, 4, None),
+            slice(16, 22, None),
+            slice(23, 24, None)
+        ])
+
+        tmp = notmasked_contiguous(a, 0)
+        assert_equal(tmp, [
+            [slice(0, 1, None), slice(2, 3, None)],
+            [slice(0, 1, None), slice(2, 3, None)],
+            [slice(0, 1, None), slice(2, 3, None)],
+            [slice(0, 1, None), slice(2, 3, None)],
+            [slice(2, 3, None)],
+            [slice(2, 3, None)],
+            [],
+            [slice(2, 3, None)]
+        ])
+        #
+        tmp = notmasked_contiguous(a, 1)
+        assert_equal(tmp, [
+            [slice(0, 4, None)],
+            [],
+            [slice(0, 6, None), slice(7, 8, None)]
+        ])
+
+
+class TestCompressFunctions:
+
+    def test_compress_nd(self):
+        # Tests compress_nd
+        x = np.array(list(range(3 * 4 * 5))).reshape(3, 4, 5)
+        m = np.zeros((3, 4, 5)).astype(bool)
+        m[1, 1, 1] = True
+        x = array(x, mask=m)
+
+        # axis=None
+        a = compress_nd(x)
+        assert_equal(a, [[[ 0,  2,  3,  4],
+                          [10, 12, 13, 14],
+                          [15, 17, 18, 19]],
+                         [[40, 42, 43, 44],
+                          [50, 52, 53, 54],
+                          [55, 57, 58, 59]]])
+
+        # axis=0
+        a = compress_nd(x, 0)
+        assert_equal(a, [[[ 0,  1,  2,  3,  4],
+                          [ 5,  6,  7,  8,  9],
+                          [10, 11, 12, 13, 14],
+                          [15, 16, 17, 18, 19]],
+                         [[40, 41, 42, 43, 44],
+                          [45, 46, 47, 48, 49],
+                          [50, 51, 52, 53, 54],
+                          [55, 56, 57, 58, 59]]])
+
+        # axis=1
+        a = compress_nd(x, 1)
+        assert_equal(a, [[[ 0,  1,  2,  3,  4],
+                          [10, 11, 12, 13, 14],
+                          [15, 16, 17, 18, 19]],
+                         [[20, 21, 22, 23, 24],
+                          [30, 31, 32, 33, 34],
+                          [35, 36, 37, 38, 39]],
+                         [[40, 41, 42, 43, 44],
+                          [50, 51, 52, 53, 54],
+                          [55, 56, 57, 58, 59]]])
+
+        a2 = compress_nd(x, (1,))
+        a3 = compress_nd(x, -2)
+        a4 = compress_nd(x, (-2,))
+        assert_equal(a, a2)
+        assert_equal(a, a3)
+        assert_equal(a, a4)
+
+        # axis=2
+        a = compress_nd(x, 2)
+        assert_equal(a, [[[ 0, 2,  3,  4],
+                          [ 5, 7,  8,  9],
+                          [10, 12, 13, 14],
+                          [15, 17, 18, 19]],
+                         [[20, 22, 23, 24],
+                          [25, 27, 28, 29],
+                          [30, 32, 33, 34],
+                          [35, 37, 38, 39]],
+                         [[40, 42, 43, 44],
+                          [45, 47, 48, 49],
+                          [50, 52, 53, 54],
+                          [55, 57, 58, 59]]])
+
+        a2 = compress_nd(x, (2,))
+        a3 = compress_nd(x, -1)
+        a4 = compress_nd(x, (-1,))
+        assert_equal(a, a2)
+        assert_equal(a, a3)
+        assert_equal(a, a4)
+
+        # axis=(0, 1)
+        a = compress_nd(x, (0, 1))
+        assert_equal(a, [[[ 0,  1,  2,  3,  4],
+                          [10, 11, 12, 13, 14],
+                          [15, 16, 17, 18, 19]],
+                         [[40, 41, 42, 43, 44],
+                          [50, 51, 52, 53, 54],
+                          [55, 56, 57, 58, 59]]])
+        a2 = compress_nd(x, (0, -2))
+        assert_equal(a, a2)
+
+        # axis=(1, 2)
+        a = compress_nd(x, (1, 2))
+        assert_equal(a, [[[ 0,  2,  3,  4],
+                          [10, 12, 13, 14],
+                          [15, 17, 18, 19]],
+                         [[20, 22, 23, 24],
+                          [30, 32, 33, 34],
+                          [35, 37, 38, 39]],
+                         [[40, 42, 43, 44],
+                          [50, 52, 53, 54],
+                          [55, 57, 58, 59]]])
+
+        a2 = compress_nd(x, (-2, 2))
+        a3 = compress_nd(x, (1, -1))
+        a4 = compress_nd(x, (-2, -1))
+        assert_equal(a, a2)
+        assert_equal(a, a3)
+        assert_equal(a, a4)
+
+        # axis=(0, 2)
+        a = compress_nd(x, (0, 2))
+        assert_equal(a, [[[ 0,  2,  3,  4],
+                          [ 5,  7,  8,  9],
+                          [10, 12, 13, 14],
+                          [15, 17, 18, 19]],
+                         [[40, 42, 43, 44],
+                          [45, 47, 48, 49],
+                          [50, 52, 53, 54],
+                          [55, 57, 58, 59]]])
+
+        a2 = compress_nd(x, (0, -1))
+        assert_equal(a, a2)
+
+    def test_compress_rowcols(self):
+        # Tests compress_rowcols
+        x = array(np.arange(9).reshape(3, 3),
+                  mask=[[1, 0, 0], [0, 0, 0], [0, 0, 0]])
+        assert_equal(compress_rowcols(x), [[4, 5], [7, 8]])
+        assert_equal(compress_rowcols(x, 0), [[3, 4, 5], [6, 7, 8]])
+        assert_equal(compress_rowcols(x, 1), [[1, 2], [4, 5], [7, 8]])
+        x = array(x._data, mask=[[0, 0, 0], [0, 1, 0], [0, 0, 0]])
+        assert_equal(compress_rowcols(x), [[0, 2], [6, 8]])
+        assert_equal(compress_rowcols(x, 0), [[0, 1, 2], [6, 7, 8]])
+        assert_equal(compress_rowcols(x, 1), [[0, 2], [3, 5], [6, 8]])
+        x = array(x._data, mask=[[1, 0, 0], [0, 1, 0], [0, 0, 0]])
+        assert_equal(compress_rowcols(x), [[8]])
+        assert_equal(compress_rowcols(x, 0), [[6, 7, 8]])
+        assert_equal(compress_rowcols(x, 1,), [[2], [5], [8]])
+        x = array(x._data, mask=[[1, 0, 0], [0, 1, 0], [0, 0, 1]])
+        assert_equal(compress_rowcols(x).size, 0)
+        assert_equal(compress_rowcols(x, 0).size, 0)
+        assert_equal(compress_rowcols(x, 1).size, 0)
+
+    def test_mask_rowcols(self):
+        # Tests mask_rowcols.
+        x = array(np.arange(9).reshape(3, 3),
+                  mask=[[1, 0, 0], [0, 0, 0], [0, 0, 0]])
+        assert_equal(mask_rowcols(x).mask,
+                     [[1, 1, 1], [1, 0, 0], [1, 0, 0]])
+        assert_equal(mask_rowcols(x, 0).mask,
+                     [[1, 1, 1], [0, 0, 0], [0, 0, 0]])
+        assert_equal(mask_rowcols(x, 1).mask,
+                     [[1, 0, 0], [1, 0, 0], [1, 0, 0]])
+        x = array(x._data, mask=[[0, 0, 0], [0, 1, 0], [0, 0, 0]])
+        assert_equal(mask_rowcols(x).mask,
+                     [[0, 1, 0], [1, 1, 1], [0, 1, 0]])
+        assert_equal(mask_rowcols(x, 0).mask,
+                     [[0, 0, 0], [1, 1, 1], [0, 0, 0]])
+        assert_equal(mask_rowcols(x, 1).mask,
+                     [[0, 1, 0], [0, 1, 0], [0, 1, 0]])
+        x = array(x._data, mask=[[1, 0, 0], [0, 1, 0], [0, 0, 0]])
+        assert_equal(mask_rowcols(x).mask,
+                     [[1, 1, 1], [1, 1, 1], [1, 1, 0]])
+        assert_equal(mask_rowcols(x, 0).mask,
+                     [[1, 1, 1], [1, 1, 1], [0, 0, 0]])
+        assert_equal(mask_rowcols(x, 1,).mask,
+                     [[1, 1, 0], [1, 1, 0], [1, 1, 0]])
+        x = array(x._data, mask=[[1, 0, 0], [0, 1, 0], [0, 0, 1]])
+        assert_(mask_rowcols(x).all() is masked)
+        assert_(mask_rowcols(x, 0).all() is masked)
+        assert_(mask_rowcols(x, 1).all() is masked)
+        assert_(mask_rowcols(x).mask.all())
+        assert_(mask_rowcols(x, 0).mask.all())
+        assert_(mask_rowcols(x, 1).mask.all())
+
+    @pytest.mark.parametrize("axis", [None, 0, 1])
+    @pytest.mark.parametrize(["func", "rowcols_axis"],
+                             [(np.ma.mask_rows, 0), (np.ma.mask_cols, 1)])
+    def test_mask_row_cols_axis_deprecation(self, axis, func, rowcols_axis):
+        # Test deprecation of the axis argument to `mask_rows` and `mask_cols`
+        x = array(np.arange(9).reshape(3, 3),
+                  mask=[[1, 0, 0], [0, 0, 0], [0, 0, 0]])
+
+        with assert_warns(DeprecationWarning):
+            res = func(x, axis=axis)
+            assert_equal(res, mask_rowcols(x, rowcols_axis))
+
+    def test_dot(self):
+        # Tests dot product
+        n = np.arange(1, 7)
+        #
+        m = [1, 0, 0, 0, 0, 0]
+        a = masked_array(n, mask=m).reshape(2, 3)
+        b = masked_array(n, mask=m).reshape(3, 2)
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask, [[1, 1], [1, 0]])
+        c = dot(b, a, strict=True)
+        assert_equal(c.mask, [[1, 1, 1], [1, 0, 0], [1, 0, 0]])
+        c = dot(a, b, strict=False)
+        assert_equal(c, np.dot(a.filled(0), b.filled(0)))
+        c = dot(b, a, strict=False)
+        assert_equal(c, np.dot(b.filled(0), a.filled(0)))
+        #
+        m = [0, 0, 0, 0, 0, 1]
+        a = masked_array(n, mask=m).reshape(2, 3)
+        b = masked_array(n, mask=m).reshape(3, 2)
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask, [[0, 1], [1, 1]])
+        c = dot(b, a, strict=True)
+        assert_equal(c.mask, [[0, 0, 1], [0, 0, 1], [1, 1, 1]])
+        c = dot(a, b, strict=False)
+        assert_equal(c, np.dot(a.filled(0), b.filled(0)))
+        assert_equal(c, dot(a, b))
+        c = dot(b, a, strict=False)
+        assert_equal(c, np.dot(b.filled(0), a.filled(0)))
+        #
+        m = [0, 0, 0, 0, 0, 0]
+        a = masked_array(n, mask=m).reshape(2, 3)
+        b = masked_array(n, mask=m).reshape(3, 2)
+        c = dot(a, b)
+        assert_equal(c.mask, nomask)
+        c = dot(b, a)
+        assert_equal(c.mask, nomask)
+        #
+        a = masked_array(n, mask=[1, 0, 0, 0, 0, 0]).reshape(2, 3)
+        b = masked_array(n, mask=[0, 0, 0, 0, 0, 0]).reshape(3, 2)
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask, [[1, 1], [0, 0]])
+        c = dot(a, b, strict=False)
+        assert_equal(c, np.dot(a.filled(0), b.filled(0)))
+        c = dot(b, a, strict=True)
+        assert_equal(c.mask, [[1, 0, 0], [1, 0, 0], [1, 0, 0]])
+        c = dot(b, a, strict=False)
+        assert_equal(c, np.dot(b.filled(0), a.filled(0)))
+        #
+        a = masked_array(n, mask=[0, 0, 0, 0, 0, 1]).reshape(2, 3)
+        b = masked_array(n, mask=[0, 0, 0, 0, 0, 0]).reshape(3, 2)
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask, [[0, 0], [1, 1]])
+        c = dot(a, b)
+        assert_equal(c, np.dot(a.filled(0), b.filled(0)))
+        c = dot(b, a, strict=True)
+        assert_equal(c.mask, [[0, 0, 1], [0, 0, 1], [0, 0, 1]])
+        c = dot(b, a, strict=False)
+        assert_equal(c, np.dot(b.filled(0), a.filled(0)))
+        #
+        a = masked_array(n, mask=[0, 0, 0, 0, 0, 1]).reshape(2, 3)
+        b = masked_array(n, mask=[0, 0, 1, 0, 0, 0]).reshape(3, 2)
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask, [[1, 0], [1, 1]])
+        c = dot(a, b, strict=False)
+        assert_equal(c, np.dot(a.filled(0), b.filled(0)))
+        c = dot(b, a, strict=True)
+        assert_equal(c.mask, [[0, 0, 1], [1, 1, 1], [0, 0, 1]])
+        c = dot(b, a, strict=False)
+        assert_equal(c, np.dot(b.filled(0), a.filled(0)))
+        #
+        a = masked_array(np.arange(8).reshape(2, 2, 2),
+                         mask=[[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        b = masked_array(np.arange(8).reshape(2, 2, 2),
+                         mask=[[[0, 0], [0, 0]], [[0, 0], [0, 1]]])
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask,
+                     [[[[1, 1], [1, 1]], [[0, 0], [0, 1]]],
+                      [[[0, 0], [0, 1]], [[0, 0], [0, 1]]]])
+        c = dot(a, b, strict=False)
+        assert_equal(c.mask,
+                     [[[[0, 0], [0, 1]], [[0, 0], [0, 0]]],
+                      [[[0, 0], [0, 0]], [[0, 0], [0, 0]]]])
+        c = dot(b, a, strict=True)
+        assert_equal(c.mask,
+                     [[[[1, 0], [0, 0]], [[1, 0], [0, 0]]],
+                      [[[1, 0], [0, 0]], [[1, 1], [1, 1]]]])
+        c = dot(b, a, strict=False)
+        assert_equal(c.mask,
+                     [[[[0, 0], [0, 0]], [[0, 0], [0, 0]]],
+                      [[[0, 0], [0, 0]], [[1, 0], [0, 0]]]])
+        #
+        a = masked_array(np.arange(8).reshape(2, 2, 2),
+                         mask=[[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        b = 5.
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        c = dot(a, b, strict=False)
+        assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        c = dot(b, a, strict=True)
+        assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        c = dot(b, a, strict=False)
+        assert_equal(c.mask, [[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        #
+        a = masked_array(np.arange(8).reshape(2, 2, 2),
+                         mask=[[[1, 0], [0, 0]], [[0, 0], [0, 0]]])
+        b = masked_array(np.arange(2), mask=[0, 1])
+        c = dot(a, b, strict=True)
+        assert_equal(c.mask, [[1, 1], [1, 1]])
+        c = dot(a, b, strict=False)
+        assert_equal(c.mask, [[1, 0], [0, 0]])
+
+    def test_dot_returns_maskedarray(self):
+        # See gh-6611
+        a = np.eye(3)
+        b = array(a)
+        assert_(type(dot(a, a)) is MaskedArray)
+        assert_(type(dot(a, b)) is MaskedArray)
+        assert_(type(dot(b, a)) is MaskedArray)
+        assert_(type(dot(b, b)) is MaskedArray)
+
+    def test_dot_out(self):
+        a = array(np.eye(3))
+        out = array(np.zeros((3, 3)))
+        res = dot(a, a, out=out)
+        assert_(res is out)
+        assert_equal(a, res)
+
+
+class TestApplyAlongAxis:
+    # Tests 2D functions
+    def test_3d(self):
+        a = arange(12.).reshape(2, 2, 3)
+
+        def myfunc(b):
+            return b[1]
+
+        xa = apply_along_axis(myfunc, 2, a)
+        assert_equal(xa, [[1, 4], [7, 10]])
+
+    # Tests kwargs functions
+    def test_3d_kwargs(self):
+        a = arange(12).reshape(2, 2, 3)
+
+        def myfunc(b, offset=0):
+            return b[1 + offset]
+
+        xa = apply_along_axis(myfunc, 2, a, offset=1)
+        assert_equal(xa, [[2, 5], [8, 11]])
+
+
+class TestApplyOverAxes:
+    # Tests apply_over_axes
+    def test_basic(self):
+        a = arange(24).reshape(2, 3, 4)
+        test = apply_over_axes(np.sum, a, [0, 2])
+        ctrl = np.array([[[60], [92], [124]]])
+        assert_equal(test, ctrl)
+        a[(a % 2).astype(bool)] = masked
+        test = apply_over_axes(np.sum, a, [0, 2])
+        ctrl = np.array([[[28], [44], [60]]])
+        assert_equal(test, ctrl)
+
+
+class TestMedian:
+    def test_pytype(self):
+        r = np.ma.median([[np.inf, np.inf], [np.inf, np.inf]], axis=-1)
+        assert_equal(r, np.inf)
+
+    def test_inf(self):
+        # test that even which computes handles inf / x = masked
+        r = np.ma.median(np.ma.masked_array([[np.inf, np.inf],
+                                             [np.inf, np.inf]]), axis=-1)
+        assert_equal(r, np.inf)
+        r = np.ma.median(np.ma.masked_array([[np.inf, np.inf],
+                                             [np.inf, np.inf]]), axis=None)
+        assert_equal(r, np.inf)
+        # all masked
+        r = np.ma.median(np.ma.masked_array([[np.inf, np.inf],
+                                             [np.inf, np.inf]], mask=True),
+                         axis=-1)
+        assert_equal(r.mask, True)
+        r = np.ma.median(np.ma.masked_array([[np.inf, np.inf],
+                                             [np.inf, np.inf]], mask=True),
+                         axis=None)
+        assert_equal(r.mask, True)
+
+    def test_non_masked(self):
+        x = np.arange(9)
+        assert_equal(np.ma.median(x), 4.)
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        x = range(8)
+        assert_equal(np.ma.median(x), 3.5)
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        x = 5
+        assert_equal(np.ma.median(x), 5.)
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        # integer
+        x = np.arange(9 * 8).reshape(9, 8)
+        assert_equal(np.ma.median(x, axis=0), np.median(x, axis=0))
+        assert_equal(np.ma.median(x, axis=1), np.median(x, axis=1))
+        assert_(np.ma.median(x, axis=1) is not MaskedArray)
+        # float
+        x = np.arange(9 * 8.).reshape(9, 8)
+        assert_equal(np.ma.median(x, axis=0), np.median(x, axis=0))
+        assert_equal(np.ma.median(x, axis=1), np.median(x, axis=1))
+        assert_(np.ma.median(x, axis=1) is not MaskedArray)
+
+    def test_docstring_examples(self):
+        "test the examples given in the docstring of ma.median"
+        x = array(np.arange(8), mask=[0] * 4 + [1] * 4)
+        assert_equal(np.ma.median(x), 1.5)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        x = array(np.arange(10).reshape(2, 5), mask=[0] * 6 + [1] * 4)
+        assert_equal(np.ma.median(x), 2.5)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        ma_x = np.ma.median(x, axis=-1, overwrite_input=True)
+        assert_equal(ma_x, [2., 5.])
+        assert_equal(ma_x.shape, (2,), "shape mismatch")
+        assert_(type(ma_x) is MaskedArray)
+
+    def test_axis_argument_errors(self):
+        msg = "mask = %s, ndim = %s, axis = %s, overwrite_input = %s"
+        for ndmin in range(5):
+            for mask in [False, True]:
+                x = array(1, ndmin=ndmin, mask=mask)
+
+                # Valid axis values should not raise exception
+                args = itertools.product(range(-ndmin, ndmin), [False, True])
+                for axis, over in args:
+                    try:
+                        np.ma.median(x, axis=axis, overwrite_input=over)
+                    except Exception:
+                        raise AssertionError(msg % (mask, ndmin, axis, over))
+
+                # Invalid axis values should raise exception
+                args = itertools.product([-(ndmin + 1), ndmin], [False, True])
+                for axis, over in args:
+                    try:
+                        np.ma.median(x, axis=axis, overwrite_input=over)
+                    except np.exceptions.AxisError:
+                        pass
+                    else:
+                        raise AssertionError(msg % (mask, ndmin, axis, over))
+
+    def test_masked_0d(self):
+        # Check values
+        x = array(1, mask=False)
+        assert_equal(np.ma.median(x), 1)
+        x = array(1, mask=True)
+        assert_equal(np.ma.median(x), np.ma.masked)
+
+    def test_masked_1d(self):
+        x = array(np.arange(5), mask=True)
+        assert_equal(np.ma.median(x), np.ma.masked)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is np.ma.core.MaskedConstant)
+        x = array(np.arange(5), mask=False)
+        assert_equal(np.ma.median(x), 2.)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        x = array(np.arange(5), mask=[0, 1, 0, 0, 0])
+        assert_equal(np.ma.median(x), 2.5)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        x = array(np.arange(5), mask=[0, 1, 1, 1, 1])
+        assert_equal(np.ma.median(x), 0.)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        # integer
+        x = array(np.arange(5), mask=[0, 1, 1, 0, 0])
+        assert_equal(np.ma.median(x), 3.)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        # float
+        x = array(np.arange(5.), mask=[0, 1, 1, 0, 0])
+        assert_equal(np.ma.median(x), 3.)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        # integer
+        x = array(np.arange(6), mask=[0, 1, 1, 1, 1, 0])
+        assert_equal(np.ma.median(x), 2.5)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        # float
+        x = array(np.arange(6.), mask=[0, 1, 1, 1, 1, 0])
+        assert_equal(np.ma.median(x), 2.5)
+        assert_equal(np.ma.median(x).shape, (), "shape mismatch")
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+
+    def test_1d_shape_consistency(self):
+        assert_equal(np.ma.median(array([1, 2, 3], mask=[0, 0, 0])).shape,
+                     np.ma.median(array([1, 2, 3], mask=[0, 1, 0])).shape)
+
+    def test_2d(self):
+        # Tests median w/ 2D
+        (n, p) = (101, 30)
+        x = masked_array(np.linspace(-1., 1., n),)
+        x[:10] = x[-10:] = masked
+        z = masked_array(np.empty((n, p), dtype=float))
+        z[:, 0] = x[:]
+        idx = np.arange(len(x))
+        for i in range(1, p):
+            np.random.shuffle(idx)
+            z[:, i] = x[idx]
+        assert_equal(median(z[:, 0]), 0)
+        assert_equal(median(z), 0)
+        assert_equal(median(z, axis=0), np.zeros(p))
+        assert_equal(median(z.T, axis=1), np.zeros(p))
+
+    def test_2d_waxis(self):
+        # Tests median w/ 2D arrays and different axis.
+        x = masked_array(np.arange(30).reshape(10, 3))
+        x[:3] = x[-3:] = masked
+        assert_equal(median(x), 14.5)
+        assert_(type(np.ma.median(x)) is not MaskedArray)
+        assert_equal(median(x, axis=0), [13.5, 14.5, 15.5])
+        assert_(type(np.ma.median(x, axis=0)) is MaskedArray)
+        assert_equal(median(x, axis=1), [0, 0, 0, 10, 13, 16, 19, 0, 0, 0])
+        assert_(type(np.ma.median(x, axis=1)) is MaskedArray)
+        assert_equal(median(x, axis=1).mask, [1, 1, 1, 0, 0, 0, 0, 1, 1, 1])
+
+    def test_3d(self):
+        # Tests median w/ 3D
+        x = np.ma.arange(24).reshape(3, 4, 2)
+        x[x % 3 == 0] = masked
+        assert_equal(median(x, 0), [[12, 9], [6, 15], [12, 9], [18, 15]])
+        x.shape = (4, 3, 2)
+        assert_equal(median(x, 0), [[99, 10], [11, 99], [13, 14]])
+        x = np.ma.arange(24).reshape(4, 3, 2)
+        x[x % 5 == 0] = masked
+        assert_equal(median(x, 0), [[12, 10], [8, 9], [16, 17]])
+
+    def test_neg_axis(self):
+        x = masked_array(np.arange(30).reshape(10, 3))
+        x[:3] = x[-3:] = masked
+        assert_equal(median(x, axis=-1), median(x, axis=1))
+
+    def test_out_1d(self):
+        # integer float even odd
+        for v in (30, 30., 31, 31.):
+            x = masked_array(np.arange(v))
+            x[:3] = x[-3:] = masked
+            out = masked_array(np.ones(()))
+            r = median(x, out=out)
+            if v == 30:
+                assert_equal(out, 14.5)
+            else:
+                assert_equal(out, 15.)
+            assert_(r is out)
+            assert_(type(r) is MaskedArray)
+
+    def test_out(self):
+        # integer float even odd
+        for v in (40, 40., 30, 30.):
+            x = masked_array(np.arange(v).reshape(10, -1))
+            x[:3] = x[-3:] = masked
+            out = masked_array(np.ones(10))
+            r = median(x, axis=1, out=out)
+            if v == 30:
+                e = masked_array([0.] * 3 + [10, 13, 16, 19] + [0.] * 3,
+                                 mask=[True] * 3 + [False] * 4 + [True] * 3)
+            else:
+                e = masked_array([0.] * 3 + [13.5, 17.5, 21.5, 25.5] + [0.] * 3,
+                                 mask=[True] * 3 + [False] * 4 + [True] * 3)
+            assert_equal(r, e)
+            assert_(r is out)
+            assert_(type(r) is MaskedArray)
+
+    @pytest.mark.parametrize(
+        argnames='axis',
+        argvalues=[
+            None,
+            1,
+            (1, ),
+            (0, 1),
+            (-3, -1),
+        ]
+    )
+    def test_keepdims_out(self, axis):
+        mask = np.zeros((3, 5, 7, 11), dtype=bool)
+        # Randomly set some elements to True:
+        w = np.random.random((4, 200)) * np.array(mask.shape)[:, None]
+        w = w.astype(np.intp)
+        mask[tuple(w)] = np.nan
+        d = masked_array(np.ones(mask.shape), mask=mask)
+        if axis is None:
+            shape_out = (1,) * d.ndim
+        else:
+            axis_norm = normalize_axis_tuple(axis, d.ndim)
+            shape_out = tuple(
+                1 if i in axis_norm else d.shape[i] for i in range(d.ndim))
+        out = masked_array(np.empty(shape_out))
+        result = median(d, axis=axis, keepdims=True, out=out)
+        assert result is out
+        assert_equal(result.shape, shape_out)
+
+    def test_single_non_masked_value_on_axis(self):
+        data = [[1., 0.],
+                [0., 3.],
+                [0., 0.]]
+        masked_arr = np.ma.masked_equal(data, 0)
+        expected = [1., 3.]
+        assert_array_equal(np.ma.median(masked_arr, axis=0),
+                           expected)
+
+    def test_nan(self):
+        for mask in (False, np.zeros(6, dtype=bool)):
+            dm = np.ma.array([[1, np.nan, 3], [1, 2, 3]])
+            dm.mask = mask
+
+            # scalar result
+            r = np.ma.median(dm, axis=None)
+            assert_(np.isscalar(r))
+            assert_array_equal(r, np.nan)
+            r = np.ma.median(dm.ravel(), axis=0)
+            assert_(np.isscalar(r))
+            assert_array_equal(r, np.nan)
+
+            r = np.ma.median(dm, axis=0)
+            assert_equal(type(r), MaskedArray)
+            assert_array_equal(r, [1, np.nan, 3])
+            r = np.ma.median(dm, axis=1)
+            assert_equal(type(r), MaskedArray)
+            assert_array_equal(r, [np.nan, 2])
+            r = np.ma.median(dm, axis=-1)
+            assert_equal(type(r), MaskedArray)
+            assert_array_equal(r, [np.nan, 2])
+
+        dm = np.ma.array([[1, np.nan, 3], [1, 2, 3]])
+        dm[:, 2] = np.ma.masked
+        assert_array_equal(np.ma.median(dm, axis=None), np.nan)
+        assert_array_equal(np.ma.median(dm, axis=0), [1, np.nan, 3])
+        assert_array_equal(np.ma.median(dm, axis=1), [np.nan, 1.5])
+
+    def test_out_nan(self):
+        o = np.ma.masked_array(np.zeros((4,)))
+        d = np.ma.masked_array(np.ones((3, 4)))
+        d[2, 1] = np.nan
+        d[2, 2] = np.ma.masked
+        assert_equal(np.ma.median(d, 0, out=o), o)
+        o = np.ma.masked_array(np.zeros((3,)))
+        assert_equal(np.ma.median(d, 1, out=o), o)
+        o = np.ma.masked_array(np.zeros(()))
+        assert_equal(np.ma.median(d, out=o), o)
+
+    def test_nan_behavior(self):
+        a = np.ma.masked_array(np.arange(24, dtype=float))
+        a[::3] = np.ma.masked
+        a[2] = np.nan
+        assert_array_equal(np.ma.median(a), np.nan)
+        assert_array_equal(np.ma.median(a, axis=0), np.nan)
+
+        a = np.ma.masked_array(np.arange(24, dtype=float).reshape(2, 3, 4))
+        a.mask = np.arange(a.size) % 2 == 1
+        aorig = a.copy()
+        a[1, 2, 3] = np.nan
+        a[1, 1, 2] = np.nan
+
+        # no axis
+        assert_array_equal(np.ma.median(a), np.nan)
+        assert_(np.isscalar(np.ma.median(a)))
+
+        # axis0
+        b = np.ma.median(aorig, axis=0)
+        b[2, 3] = np.nan
+        b[1, 2] = np.nan
+        assert_equal(np.ma.median(a, 0), b)
+
+        # axis1
+        b = np.ma.median(aorig, axis=1)
+        b[1, 3] = np.nan
+        b[1, 2] = np.nan
+        assert_equal(np.ma.median(a, 1), b)
+
+        # axis02
+        b = np.ma.median(aorig, axis=(0, 2))
+        b[1] = np.nan
+        b[2] = np.nan
+        assert_equal(np.ma.median(a, (0, 2)), b)
+
+    def test_ambigous_fill(self):
+        # 255 is max value, used as filler for sort
+        a = np.array([[3, 3, 255], [3, 3, 255]], dtype=np.uint8)
+        a = np.ma.masked_array(a, mask=a == 3)
+        assert_array_equal(np.ma.median(a, axis=1), 255)
+        assert_array_equal(np.ma.median(a, axis=1).mask, False)
+        assert_array_equal(np.ma.median(a, axis=0), a[0])
+        assert_array_equal(np.ma.median(a), 255)
+
+    def test_special(self):
+        for inf in [np.inf, -np.inf]:
+            a = np.array([[inf, np.nan], [np.nan, np.nan]])
+            a = np.ma.masked_array(a, mask=np.isnan(a))
+            assert_equal(np.ma.median(a, axis=0), [inf, np.nan])
+            assert_equal(np.ma.median(a, axis=1), [inf, np.nan])
+            assert_equal(np.ma.median(a), inf)
+
+            a = np.array([[np.nan, np.nan, inf], [np.nan, np.nan, inf]])
+            a = np.ma.masked_array(a, mask=np.isnan(a))
+            assert_array_equal(np.ma.median(a, axis=1), inf)
+            assert_array_equal(np.ma.median(a, axis=1).mask, False)
+            assert_array_equal(np.ma.median(a, axis=0), a[0])
+            assert_array_equal(np.ma.median(a), inf)
+
+            # no mask
+            a = np.array([[inf, inf], [inf, inf]])
+            assert_equal(np.ma.median(a), inf)
+            assert_equal(np.ma.median(a, axis=0), inf)
+            assert_equal(np.ma.median(a, axis=1), inf)
+
+            a = np.array([[inf, 7, -inf, -9],
+                          [-10, np.nan, np.nan, 5],
+                          [4, np.nan, np.nan, inf]],
+                          dtype=np.float32)
+            a = np.ma.masked_array(a, mask=np.isnan(a))
+            if inf > 0:
+                assert_equal(np.ma.median(a, axis=0), [4., 7., -inf, 5.])
+                assert_equal(np.ma.median(a), 4.5)
+            else:
+                assert_equal(np.ma.median(a, axis=0), [-10., 7., -inf, -9.])
+                assert_equal(np.ma.median(a), -2.5)
+            assert_equal(np.ma.median(a, axis=1), [-1., -2.5, inf])
+
+            for i in range(10):
+                for j in range(1, 10):
+                    a = np.array([([np.nan] * i) + ([inf] * j)] * 2)
+                    a = np.ma.masked_array(a, mask=np.isnan(a))
+                    assert_equal(np.ma.median(a), inf)
+                    assert_equal(np.ma.median(a, axis=1), inf)
+                    assert_equal(np.ma.median(a, axis=0),
+                                 ([np.nan] * i) + [inf] * j)
+
+    def test_empty(self):
+        # empty arrays
+        a = np.ma.masked_array(np.array([], dtype=float))
+        with suppress_warnings() as w:
+            w.record(RuntimeWarning)
+            assert_array_equal(np.ma.median(a), np.nan)
+            assert_(w.log[0].category is RuntimeWarning)
+
+        # multiple dimensions
+        a = np.ma.masked_array(np.array([], dtype=float, ndmin=3))
+        # no axis
+        with suppress_warnings() as w:
+            w.record(RuntimeWarning)
+            warnings.filterwarnings('always', '', RuntimeWarning)
+            assert_array_equal(np.ma.median(a), np.nan)
+            assert_(w.log[0].category is RuntimeWarning)
+
+        # axis 0 and 1
+        b = np.ma.masked_array(np.array([], dtype=float, ndmin=2))
+        assert_equal(np.ma.median(a, axis=0), b)
+        assert_equal(np.ma.median(a, axis=1), b)
+
+        # axis 2
+        b = np.ma.masked_array(np.array(np.nan, dtype=float, ndmin=2))
+        with warnings.catch_warnings(record=True) as w:
+            warnings.filterwarnings('always', '', RuntimeWarning)
+            assert_equal(np.ma.median(a, axis=2), b)
+            assert_(w[0].category is RuntimeWarning)
+
+    def test_object(self):
+        o = np.ma.masked_array(np.arange(7.))
+        assert_(type(np.ma.median(o.astype(object))), float)
+        o[2] = np.nan
+        assert_(type(np.ma.median(o.astype(object))), float)
+
+
+class TestCov:
+
+    def setup_method(self):
+        self.data = array(np.random.rand(12))
+
+    def test_covhelper(self):
+        x = self.data
+        # Test not mask output type is a float.
+        assert_(_covhelper(x, rowvar=True)[1].dtype, np.float32)
+        assert_(_covhelper(x, y=x, rowvar=False)[1].dtype, np.float32)
+        # Test not mask output is equal after casting to float.
+        mask = x > 0.5
+        assert_array_equal(
+            _covhelper(
+                np.ma.masked_array(x, mask), rowvar=True
+            )[1].astype(bool),
+            ~mask.reshape(1, -1),
+        )
+        assert_array_equal(
+            _covhelper(
+                np.ma.masked_array(x, mask), y=x, rowvar=False
+            )[1].astype(bool),
+            np.vstack((~mask, ~mask)),
+        )
+
+    def test_1d_without_missing(self):
+        # Test cov on 1D variable w/o missing values
+        x = self.data
+        assert_almost_equal(np.cov(x), cov(x))
+        assert_almost_equal(np.cov(x, rowvar=False), cov(x, rowvar=False))
+        assert_almost_equal(np.cov(x, rowvar=False, bias=True),
+                            cov(x, rowvar=False, bias=True))
+
+    def test_2d_without_missing(self):
+        # Test cov on 1 2D variable w/o missing values
+        x = self.data.reshape(3, 4)
+        assert_almost_equal(np.cov(x), cov(x))
+        assert_almost_equal(np.cov(x, rowvar=False), cov(x, rowvar=False))
+        assert_almost_equal(np.cov(x, rowvar=False, bias=True),
+                            cov(x, rowvar=False, bias=True))
+
+    def test_1d_with_missing(self):
+        # Test cov 1 1D variable w/missing values
+        x = self.data
+        x[-1] = masked
+        x -= x.mean()
+        nx = x.compressed()
+        assert_almost_equal(np.cov(nx), cov(x))
+        assert_almost_equal(np.cov(nx, rowvar=False), cov(x, rowvar=False))
+        assert_almost_equal(np.cov(nx, rowvar=False, bias=True),
+                            cov(x, rowvar=False, bias=True))
+        #
+        try:
+            cov(x, allow_masked=False)
+        except ValueError:
+            pass
+        #
+        # 2 1D variables w/ missing values
+        nx = x[1:-1]
+        assert_almost_equal(np.cov(nx, nx[::-1]), cov(x, x[::-1]))
+        assert_almost_equal(np.cov(nx, nx[::-1], rowvar=False),
+                            cov(x, x[::-1], rowvar=False))
+        assert_almost_equal(np.cov(nx, nx[::-1], rowvar=False, bias=True),
+                            cov(x, x[::-1], rowvar=False, bias=True))
+
+    def test_2d_with_missing(self):
+        # Test cov on 2D variable w/ missing value
+        x = self.data
+        x[-1] = masked
+        x = x.reshape(3, 4)
+        valid = np.logical_not(getmaskarray(x)).astype(int)
+        frac = np.dot(valid, valid.T)
+        xf = (x - x.mean(1)[:, None]).filled(0)
+        assert_almost_equal(cov(x),
+                            np.cov(xf) * (x.shape[1] - 1) / (frac - 1.))
+        assert_almost_equal(cov(x, bias=True),
+                            np.cov(xf, bias=True) * x.shape[1] / frac)
+        frac = np.dot(valid.T, valid)
+        xf = (x - x.mean(0)).filled(0)
+        assert_almost_equal(cov(x, rowvar=False),
+                            (np.cov(xf, rowvar=False) *
+                             (x.shape[0] - 1) / (frac - 1.)))
+        assert_almost_equal(cov(x, rowvar=False, bias=True),
+                            (np.cov(xf, rowvar=False, bias=True) *
+                             x.shape[0] / frac))
+
+
+class TestCorrcoef:
+
+    def setup_method(self):
+        self.data = array(np.random.rand(12))
+        self.data2 = array(np.random.rand(12))
+
+    def test_ddof(self):
+        # ddof raises DeprecationWarning
+        x, y = self.data, self.data2
+        expected = np.corrcoef(x)
+        expected2 = np.corrcoef(x, y)
+        with suppress_warnings() as sup:
+            warnings.simplefilter("always")
+            assert_warns(DeprecationWarning, corrcoef, x, ddof=-1)
+            sup.filter(DeprecationWarning, "bias and ddof have no effect")
+            # ddof has no or negligible effect on the function
+            assert_almost_equal(np.corrcoef(x, ddof=0), corrcoef(x, ddof=0))
+            assert_almost_equal(corrcoef(x, ddof=-1), expected)
+            assert_almost_equal(corrcoef(x, y, ddof=-1), expected2)
+            assert_almost_equal(corrcoef(x, ddof=3), expected)
+            assert_almost_equal(corrcoef(x, y, ddof=3), expected2)
+
+    def test_bias(self):
+        x, y = self.data, self.data2
+        expected = np.corrcoef(x)
+        # bias raises DeprecationWarning
+        with suppress_warnings() as sup:
+            warnings.simplefilter("always")
+            assert_warns(DeprecationWarning, corrcoef, x, y, True, False)
+            assert_warns(DeprecationWarning, corrcoef, x, y, True, True)
+            assert_warns(DeprecationWarning, corrcoef, x, bias=False)
+            sup.filter(DeprecationWarning, "bias and ddof have no effect")
+            # bias has no or negligible effect on the function
+            assert_almost_equal(corrcoef(x, bias=1), expected)
+
+    def test_1d_without_missing(self):
+        # Test cov on 1D variable w/o missing values
+        x = self.data
+        assert_almost_equal(np.corrcoef(x), corrcoef(x))
+        assert_almost_equal(np.corrcoef(x, rowvar=False),
+                            corrcoef(x, rowvar=False))
+        with suppress_warnings() as sup:
+            sup.filter(DeprecationWarning, "bias and ddof have no effect")
+            assert_almost_equal(np.corrcoef(x, rowvar=False, bias=True),
+                                corrcoef(x, rowvar=False, bias=True))
+
+    def test_2d_without_missing(self):
+        # Test corrcoef on 1 2D variable w/o missing values
+        x = self.data.reshape(3, 4)
+        assert_almost_equal(np.corrcoef(x), corrcoef(x))
+        assert_almost_equal(np.corrcoef(x, rowvar=False),
+                            corrcoef(x, rowvar=False))
+        with suppress_warnings() as sup:
+            sup.filter(DeprecationWarning, "bias and ddof have no effect")
+            assert_almost_equal(np.corrcoef(x, rowvar=False, bias=True),
+                                corrcoef(x, rowvar=False, bias=True))
+
+    def test_1d_with_missing(self):
+        # Test corrcoef 1 1D variable w/missing values
+        x = self.data
+        x[-1] = masked
+        x -= x.mean()
+        nx = x.compressed()
+        assert_almost_equal(np.corrcoef(nx), corrcoef(x))
+        assert_almost_equal(np.corrcoef(nx, rowvar=False),
+                            corrcoef(x, rowvar=False))
+        with suppress_warnings() as sup:
+            sup.filter(DeprecationWarning, "bias and ddof have no effect")
+            assert_almost_equal(np.corrcoef(nx, rowvar=False, bias=True),
+                                corrcoef(x, rowvar=False, bias=True))
+        try:
+            corrcoef(x, allow_masked=False)
+        except ValueError:
+            pass
+        # 2 1D variables w/ missing values
+        nx = x[1:-1]
+        assert_almost_equal(np.corrcoef(nx, nx[::-1]), corrcoef(x, x[::-1]))
+        assert_almost_equal(np.corrcoef(nx, nx[::-1], rowvar=False),
+                            corrcoef(x, x[::-1], rowvar=False))
+        with suppress_warnings() as sup:
+            sup.filter(DeprecationWarning, "bias and ddof have no effect")
+            # ddof and bias have no or negligible effect on the function
+            assert_almost_equal(np.corrcoef(nx, nx[::-1]),
+                                corrcoef(x, x[::-1], bias=1))
+            assert_almost_equal(np.corrcoef(nx, nx[::-1]),
+                                corrcoef(x, x[::-1], ddof=2))
+
+    def test_2d_with_missing(self):
+        # Test corrcoef on 2D variable w/ missing value
+        x = self.data
+        x[-1] = masked
+        x = x.reshape(3, 4)
+
+        test = corrcoef(x)
+        control = np.corrcoef(x)
+        assert_almost_equal(test[:-1, :-1], control[:-1, :-1])
+        with suppress_warnings() as sup:
+            sup.filter(DeprecationWarning, "bias and ddof have no effect")
+            # ddof and bias have no or negligible effect on the function
+            assert_almost_equal(corrcoef(x, ddof=-2)[:-1, :-1],
+                                control[:-1, :-1])
+            assert_almost_equal(corrcoef(x, ddof=3)[:-1, :-1],
+                                control[:-1, :-1])
+            assert_almost_equal(corrcoef(x, bias=1)[:-1, :-1],
+                                control[:-1, :-1])
+
+
+class TestPolynomial:
+    #
+    def test_polyfit(self):
+        # Tests polyfit
+        # On ndarrays
+        x = np.random.rand(10)
+        y = np.random.rand(20).reshape(-1, 2)
+        assert_almost_equal(polyfit(x, y, 3), np.polyfit(x, y, 3))
+        # ON 1D maskedarrays
+        x = x.view(MaskedArray)
+        x[0] = masked
+        y = y.view(MaskedArray)
+        y[0, 0] = y[-1, -1] = masked
+        #
+        (C, R, K, S, D) = polyfit(x, y[:, 0], 3, full=True)
+        (c, r, k, s, d) = np.polyfit(x[1:], y[1:, 0].compressed(), 3,
+                                     full=True)
+        for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)):
+            assert_almost_equal(a, a_)
+        #
+        (C, R, K, S, D) = polyfit(x, y[:, -1], 3, full=True)
+        (c, r, k, s, d) = np.polyfit(x[1:-1], y[1:-1, -1], 3, full=True)
+        for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)):
+            assert_almost_equal(a, a_)
+        #
+        (C, R, K, S, D) = polyfit(x, y, 3, full=True)
+        (c, r, k, s, d) = np.polyfit(x[1:-1], y[1:-1, :], 3, full=True)
+        for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)):
+            assert_almost_equal(a, a_)
+        #
+        w = np.random.rand(10) + 1
+        wo = w.copy()
+        xs = x[1:-1]
+        ys = y[1:-1]
+        ws = w[1:-1]
+        (C, R, K, S, D) = polyfit(x, y, 3, full=True, w=w)
+        (c, r, k, s, d) = np.polyfit(xs, ys, 3, full=True, w=ws)
+        assert_equal(w, wo)
+        for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)):
+            assert_almost_equal(a, a_)
+
+    def test_polyfit_with_masked_NaNs(self):
+        x = np.random.rand(10)
+        y = np.random.rand(20).reshape(-1, 2)
+
+        x[0] = np.nan
+        y[-1, -1] = np.nan
+        x = x.view(MaskedArray)
+        y = y.view(MaskedArray)
+        x[0] = masked
+        y[-1, -1] = masked
+
+        (C, R, K, S, D) = polyfit(x, y, 3, full=True)
+        (c, r, k, s, d) = np.polyfit(x[1:-1], y[1:-1, :], 3, full=True)
+        for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)):
+            assert_almost_equal(a, a_)
+
+
+class TestArraySetOps:
+
+    def test_unique_onlist(self):
+        # Test unique on list
+        data = [1, 1, 1, 2, 2, 3]
+        test = unique(data, return_index=True, return_inverse=True)
+        assert_(isinstance(test[0], MaskedArray))
+        assert_equal(test[0], masked_array([1, 2, 3], mask=[0, 0, 0]))
+        assert_equal(test[1], [0, 3, 5])
+        assert_equal(test[2], [0, 0, 0, 1, 1, 2])
+
+    def test_unique_onmaskedarray(self):
+        # Test unique on masked data w/use_mask=True
+        data = masked_array([1, 1, 1, 2, 2, 3], mask=[0, 0, 1, 0, 1, 0])
+        test = unique(data, return_index=True, return_inverse=True)
+        assert_equal(test[0], masked_array([1, 2, 3, -1], mask=[0, 0, 0, 1]))
+        assert_equal(test[1], [0, 3, 5, 2])
+        assert_equal(test[2], [0, 0, 3, 1, 3, 2])
+        #
+        data.fill_value = 3
+        data = masked_array(data=[1, 1, 1, 2, 2, 3],
+                            mask=[0, 0, 1, 0, 1, 0], fill_value=3)
+        test = unique(data, return_index=True, return_inverse=True)
+        assert_equal(test[0], masked_array([1, 2, 3, -1], mask=[0, 0, 0, 1]))
+        assert_equal(test[1], [0, 3, 5, 2])
+        assert_equal(test[2], [0, 0, 3, 1, 3, 2])
+
+    def test_unique_allmasked(self):
+        # Test all masked
+        data = masked_array([1, 1, 1], mask=True)
+        test = unique(data, return_index=True, return_inverse=True)
+        assert_equal(test[0], masked_array([1, ], mask=[True]))
+        assert_equal(test[1], [0])
+        assert_equal(test[2], [0, 0, 0])
+        #
+        # Test masked
+        data = masked
+        test = unique(data, return_index=True, return_inverse=True)
+        assert_equal(test[0], masked_array(masked))
+        assert_equal(test[1], [0])
+        assert_equal(test[2], [0])
+
+    def test_ediff1d(self):
+        # Tests mediff1d
+        x = masked_array(np.arange(5), mask=[1, 0, 0, 0, 1])
+        control = array([1, 1, 1, 4], mask=[1, 0, 0, 1])
+        test = ediff1d(x)
+        assert_equal(test, control)
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+
+    def test_ediff1d_tobegin(self):
+        # Test ediff1d w/ to_begin
+        x = masked_array(np.arange(5), mask=[1, 0, 0, 0, 1])
+        test = ediff1d(x, to_begin=masked)
+        control = array([0, 1, 1, 1, 4], mask=[1, 1, 0, 0, 1])
+        assert_equal(test, control)
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+        #
+        test = ediff1d(x, to_begin=[1, 2, 3])
+        control = array([1, 2, 3, 1, 1, 1, 4], mask=[0, 0, 0, 1, 0, 0, 1])
+        assert_equal(test, control)
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+
+    def test_ediff1d_toend(self):
+        # Test ediff1d w/ to_end
+        x = masked_array(np.arange(5), mask=[1, 0, 0, 0, 1])
+        test = ediff1d(x, to_end=masked)
+        control = array([1, 1, 1, 4, 0], mask=[1, 0, 0, 1, 1])
+        assert_equal(test, control)
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+        #
+        test = ediff1d(x, to_end=[1, 2, 3])
+        control = array([1, 1, 1, 4, 1, 2, 3], mask=[1, 0, 0, 1, 0, 0, 0])
+        assert_equal(test, control)
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+
+    def test_ediff1d_tobegin_toend(self):
+        # Test ediff1d w/ to_begin and to_end
+        x = masked_array(np.arange(5), mask=[1, 0, 0, 0, 1])
+        test = ediff1d(x, to_end=masked, to_begin=masked)
+        control = array([0, 1, 1, 1, 4, 0], mask=[1, 1, 0, 0, 1, 1])
+        assert_equal(test, control)
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+        #
+        test = ediff1d(x, to_end=[1, 2, 3], to_begin=masked)
+        control = array([0, 1, 1, 1, 4, 1, 2, 3],
+                        mask=[1, 1, 0, 0, 1, 0, 0, 0])
+        assert_equal(test, control)
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+
+    def test_ediff1d_ndarray(self):
+        # Test ediff1d w/ a ndarray
+        x = np.arange(5)
+        test = ediff1d(x)
+        control = array([1, 1, 1, 1], mask=[0, 0, 0, 0])
+        assert_equal(test, control)
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+        #
+        test = ediff1d(x, to_end=masked, to_begin=masked)
+        control = array([0, 1, 1, 1, 1, 0], mask=[1, 0, 0, 0, 0, 1])
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test.filled(0), control.filled(0))
+        assert_equal(test.mask, control.mask)
+
+    def test_intersect1d(self):
+        # Test intersect1d
+        x = array([1, 3, 3, 3], mask=[0, 0, 0, 1])
+        y = array([3, 1, 1, 1], mask=[0, 0, 0, 1])
+        test = intersect1d(x, y)
+        control = array([1, 3, -1], mask=[0, 0, 1])
+        assert_equal(test, control)
+
+    def test_setxor1d(self):
+        # Test setxor1d
+        a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
+        b = array([1, 2, 3, 4, 5, -1], mask=[0, 0, 0, 0, 0, 1])
+        test = setxor1d(a, b)
+        assert_equal(test, array([3, 4, 7]))
+        #
+        a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
+        b = [1, 2, 3, 4, 5]
+        test = setxor1d(a, b)
+        assert_equal(test, array([3, 4, 7, -1], mask=[0, 0, 0, 1]))
+        #
+        a = array([1, 2, 3])
+        b = array([6, 5, 4])
+        test = setxor1d(a, b)
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test, [1, 2, 3, 4, 5, 6])
+        #
+        a = array([1, 8, 2, 3], mask=[0, 1, 0, 0])
+        b = array([6, 5, 4, 8], mask=[0, 0, 0, 1])
+        test = setxor1d(a, b)
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test, [1, 2, 3, 4, 5, 6])
+        #
+        assert_array_equal([], setxor1d([], []))
+
+    def test_setxor1d_unique(self):
+        # Test setxor1d with assume_unique=True
+        a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
+        b = [1, 2, 3, 4, 5]
+        test = setxor1d(a, b, assume_unique=True)
+        assert_equal(test, array([3, 4, 7, -1], mask=[0, 0, 0, 1]))
+        #
+        a = array([1, 8, 2, 3], mask=[0, 1, 0, 0])
+        b = array([6, 5, 4, 8], mask=[0, 0, 0, 1])
+        test = setxor1d(a, b, assume_unique=True)
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test, [1, 2, 3, 4, 5, 6])
+        #
+        a = array([[1], [8], [2], [3]])
+        b = array([[6, 5], [4, 8]])
+        test = setxor1d(a, b, assume_unique=True)
+        assert_(isinstance(test, MaskedArray))
+        assert_equal(test, [1, 2, 3, 4, 5, 6])
+
+    def test_isin(self):
+        # the tests for in1d cover most of isin's behavior
+        # if in1d is removed, would need to change those tests to test
+        # isin instead.
+        a = np.arange(24).reshape([2, 3, 4])
+        mask = np.zeros([2, 3, 4])
+        mask[1, 2, 0] = 1
+        a = array(a, mask=mask)
+        b = array(data=[0, 10, 20, 30,  1,  3, 11, 22, 33],
+                  mask=[0,  1,  0,  1,  0,  1,  0,  1,  0])
+        ec = zeros((2, 3, 4), dtype=bool)
+        ec[0, 0, 0] = True
+        ec[0, 0, 1] = True
+        ec[0, 2, 3] = True
+        c = isin(a, b)
+        assert_(isinstance(c, MaskedArray))
+        assert_array_equal(c, ec)
+        # compare results of np.isin to ma.isin
+        d = np.isin(a, b[~b.mask]) & ~a.mask
+        assert_array_equal(c, d)
+
+    def test_in1d(self):
+        # Test in1d
+        a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
+        b = array([1, 2, 3, 4, 5, -1], mask=[0, 0, 0, 0, 0, 1])
+        test = in1d(a, b)
+        assert_equal(test, [True, True, True, False, True])
+        #
+        a = array([5, 5, 2, 1, -1], mask=[0, 0, 0, 0, 1])
+        b = array([1, 5, -1], mask=[0, 0, 1])
+        test = in1d(a, b)
+        assert_equal(test, [True, True, False, True, True])
+        #
+        assert_array_equal([], in1d([], []))
+
+    def test_in1d_invert(self):
+        # Test in1d's invert parameter
+        a = array([1, 2, 5, 7, -1], mask=[0, 0, 0, 0, 1])
+        b = array([1, 2, 3, 4, 5, -1], mask=[0, 0, 0, 0, 0, 1])
+        assert_equal(np.invert(in1d(a, b)), in1d(a, b, invert=True))
+
+        a = array([5, 5, 2, 1, -1], mask=[0, 0, 0, 0, 1])
+        b = array([1, 5, -1], mask=[0, 0, 1])
+        assert_equal(np.invert(in1d(a, b)), in1d(a, b, invert=True))
+
+        assert_array_equal([], in1d([], [], invert=True))
+
+    def test_union1d(self):
+        # Test union1d
+        a = array([1, 2, 5, 7, 5, -1], mask=[0, 0, 0, 0, 0, 1])
+        b = array([1, 2, 3, 4, 5, -1], mask=[0, 0, 0, 0, 0, 1])
+        test = union1d(a, b)
+        control = array([1, 2, 3, 4, 5, 7, -1], mask=[0, 0, 0, 0, 0, 0, 1])
+        assert_equal(test, control)
+
+        # Tests gh-10340, arguments to union1d should be
+        # flattened if they are not already 1D
+        x = array([[0, 1, 2], [3, 4, 5]], mask=[[0, 0, 0], [0, 0, 1]])
+        y = array([0, 1, 2, 3, 4], mask=[0, 0, 0, 0, 1])
+        ez = array([0, 1, 2, 3, 4, 5], mask=[0, 0, 0, 0, 0, 1])
+        z = union1d(x, y)
+        assert_equal(z, ez)
+        #
+        assert_array_equal([], union1d([], []))
+
+    def test_setdiff1d(self):
+        # Test setdiff1d
+        a = array([6, 5, 4, 7, 7, 1, 2, 1], mask=[0, 0, 0, 0, 0, 0, 0, 1])
+        b = array([2, 4, 3, 3, 2, 1, 5])
+        test = setdiff1d(a, b)
+        assert_equal(test, array([6, 7, -1], mask=[0, 0, 1]))
+        #
+        a = arange(10)
+        b = arange(8)
+        assert_equal(setdiff1d(a, b), array([8, 9]))
+        a = array([], np.uint32, mask=[])
+        assert_equal(setdiff1d(a, []).dtype, np.uint32)
+
+    def test_setdiff1d_char_array(self):
+        # Test setdiff1d_charray
+        a = np.array(['a', 'b', 'c'])
+        b = np.array(['a', 'b', 's'])
+        assert_array_equal(setdiff1d(a, b), np.array(['c']))
+
+
+class TestShapeBase:
+
+    def test_atleast_2d(self):
+        # Test atleast_2d
+        a = masked_array([0, 1, 2], mask=[0, 1, 0])
+        b = atleast_2d(a)
+        assert_equal(b.shape, (1, 3))
+        assert_equal(b.mask.shape, b.data.shape)
+        assert_equal(a.shape, (3,))
+        assert_equal(a.mask.shape, a.data.shape)
+        assert_equal(b.mask.shape, b.data.shape)
+
+    def test_shape_scalar(self):
+        # the atleast and diagflat function should work with scalars
+        # GitHub issue #3367
+        # Additionally, the atleast functions should accept multiple scalars
+        # correctly
+        b = atleast_1d(1.0)
+        assert_equal(b.shape, (1,))
+        assert_equal(b.mask.shape, b.shape)
+        assert_equal(b.data.shape, b.shape)
+
+        b = atleast_1d(1.0, 2.0)
+        for a in b:
+            assert_equal(a.shape, (1,))
+            assert_equal(a.mask.shape, a.shape)
+            assert_equal(a.data.shape, a.shape)
+
+        b = atleast_2d(1.0)
+        assert_equal(b.shape, (1, 1))
+        assert_equal(b.mask.shape, b.shape)
+        assert_equal(b.data.shape, b.shape)
+
+        b = atleast_2d(1.0, 2.0)
+        for a in b:
+            assert_equal(a.shape, (1, 1))
+            assert_equal(a.mask.shape, a.shape)
+            assert_equal(a.data.shape, a.shape)
+
+        b = atleast_3d(1.0)
+        assert_equal(b.shape, (1, 1, 1))
+        assert_equal(b.mask.shape, b.shape)
+        assert_equal(b.data.shape, b.shape)
+
+        b = atleast_3d(1.0, 2.0)
+        for a in b:
+            assert_equal(a.shape, (1, 1, 1))
+            assert_equal(a.mask.shape, a.shape)
+            assert_equal(a.data.shape, a.shape)
+
+        b = diagflat(1.0)
+        assert_equal(b.shape, (1, 1))
+        assert_equal(b.mask.shape, b.data.shape)
+
+
+class TestNDEnumerate:
+
+    def test_ndenumerate_nomasked(self):
+        ordinary = np.arange(6.).reshape((1, 3, 2))
+        empty_mask = np.zeros_like(ordinary, dtype=bool)
+        with_mask = masked_array(ordinary, mask=empty_mask)
+        assert_equal(list(np.ndenumerate(ordinary)),
+                     list(ndenumerate(ordinary)))
+        assert_equal(list(ndenumerate(ordinary)),
+                     list(ndenumerate(with_mask)))
+        assert_equal(list(ndenumerate(with_mask)),
+                     list(ndenumerate(with_mask, compressed=False)))
+
+    def test_ndenumerate_allmasked(self):
+        a = masked_all(())
+        b = masked_all((100,))
+        c = masked_all((2, 3, 4))
+        assert_equal(list(ndenumerate(a)), [])
+        assert_equal(list(ndenumerate(b)), [])
+        assert_equal(list(ndenumerate(b, compressed=False)),
+                     list(zip(np.ndindex((100,)), 100 * [masked])))
+        assert_equal(list(ndenumerate(c)), [])
+        assert_equal(list(ndenumerate(c, compressed=False)),
+                     list(zip(np.ndindex((2, 3, 4)), 2 * 3 * 4 * [masked])))
+
+    def test_ndenumerate_mixedmasked(self):
+        a = masked_array(np.arange(12).reshape((3, 4)),
+                         mask=[[1, 1, 1, 1],
+                               [1, 1, 0, 1],
+                               [0, 0, 0, 0]])
+        items = [((1, 2), 6),
+                 ((2, 0), 8), ((2, 1), 9), ((2, 2), 10), ((2, 3), 11)]
+        assert_equal(list(ndenumerate(a)), items)
+        assert_equal(len(list(ndenumerate(a, compressed=False))), a.size)
+        for coordinate, value in ndenumerate(a, compressed=False):
+            assert_equal(a[coordinate], value)
+
+
+class TestStack:
+
+    def test_stack_1d(self):
+        a = masked_array([0, 1, 2], mask=[0, 1, 0])
+        b = masked_array([9, 8, 7], mask=[1, 0, 0])
+
+        c = stack([a, b], axis=0)
+        assert_equal(c.shape, (2, 3))
+        assert_array_equal(a.mask, c[0].mask)
+        assert_array_equal(b.mask, c[1].mask)
+
+        d = vstack([a, b])
+        assert_array_equal(c.data, d.data)
+        assert_array_equal(c.mask, d.mask)
+
+        c = stack([a, b], axis=1)
+        assert_equal(c.shape, (3, 2))
+        assert_array_equal(a.mask, c[:, 0].mask)
+        assert_array_equal(b.mask, c[:, 1].mask)
+
+    def test_stack_masks(self):
+        a = masked_array([0, 1, 2], mask=True)
+        b = masked_array([9, 8, 7], mask=False)
+
+        c = stack([a, b], axis=0)
+        assert_equal(c.shape, (2, 3))
+        assert_array_equal(a.mask, c[0].mask)
+        assert_array_equal(b.mask, c[1].mask)
+
+        d = vstack([a, b])
+        assert_array_equal(c.data, d.data)
+        assert_array_equal(c.mask, d.mask)
+
+        c = stack([a, b], axis=1)
+        assert_equal(c.shape, (3, 2))
+        assert_array_equal(a.mask, c[:, 0].mask)
+        assert_array_equal(b.mask, c[:, 1].mask)
+
+    def test_stack_nd(self):
+        # 2D
+        shp = (3, 2)
+        d1 = np.random.randint(0, 10, shp)
+        d2 = np.random.randint(0, 10, shp)
+        m1 = np.random.randint(0, 2, shp).astype(bool)
+        m2 = np.random.randint(0, 2, shp).astype(bool)
+        a1 = masked_array(d1, mask=m1)
+        a2 = masked_array(d2, mask=m2)
+
+        c = stack([a1, a2], axis=0)
+        c_shp = (2,) + shp
+        assert_equal(c.shape, c_shp)
+        assert_array_equal(a1.mask, c[0].mask)
+        assert_array_equal(a2.mask, c[1].mask)
+
+        c = stack([a1, a2], axis=-1)
+        c_shp = shp + (2,)
+        assert_equal(c.shape, c_shp)
+        assert_array_equal(a1.mask, c[..., 0].mask)
+        assert_array_equal(a2.mask, c[..., 1].mask)
+
+        # 4D
+        shp = (3, 2, 4, 5,)
+        d1 = np.random.randint(0, 10, shp)
+        d2 = np.random.randint(0, 10, shp)
+        m1 = np.random.randint(0, 2, shp).astype(bool)
+        m2 = np.random.randint(0, 2, shp).astype(bool)
+        a1 = masked_array(d1, mask=m1)
+        a2 = masked_array(d2, mask=m2)
+
+        c = stack([a1, a2], axis=0)
+        c_shp = (2,) + shp
+        assert_equal(c.shape, c_shp)
+        assert_array_equal(a1.mask, c[0].mask)
+        assert_array_equal(a2.mask, c[1].mask)
+
+        c = stack([a1, a2], axis=-1)
+        c_shp = shp + (2,)
+        assert_equal(c.shape, c_shp)
+        assert_array_equal(a1.mask, c[..., 0].mask)
+        assert_array_equal(a2.mask, c[..., 1].mask)

venv/lib/python3.13/site-packages/numpy/ma/tests/test_mrecords.py

@@ -0,0 +1,497 @@
+"""Tests suite for mrecords.
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+
+"""
+import pickle
+
+import numpy as np
+import numpy.ma as ma
+from numpy._core.records import fromarrays as recfromarrays
+from numpy._core.records import fromrecords as recfromrecords
+from numpy._core.records import recarray
+from numpy.ma import masked, nomask
+from numpy.ma.mrecords import (
+    MaskedRecords,
+    addfield,
+    fromarrays,
+    fromrecords,
+    fromtextfile,
+    mrecarray,
+)
+from numpy.ma.testutils import (
+    assert_,
+    assert_equal,
+    assert_equal_records,
+)
+from numpy.testing import temppath
+
+
+class TestMRecords:
+
+    ilist = [1, 2, 3, 4, 5]
+    flist = [1.1, 2.2, 3.3, 4.4, 5.5]
+    slist = [b'one', b'two', b'three', b'four', b'five']
+    ddtype = [('a', int), ('b', float), ('c', '|S8')]
+    mask = [0, 1, 0, 0, 1]
+    base = ma.array(list(zip(ilist, flist, slist)), mask=mask, dtype=ddtype)
+
+    def test_byview(self):
+        # Test creation by view
+        base = self.base
+        mbase = base.view(mrecarray)
+        assert_equal(mbase.recordmask, base.recordmask)
+        assert_equal_records(mbase._mask, base._mask)
+        assert_(isinstance(mbase._data, recarray))
+        assert_equal_records(mbase._data, base._data.view(recarray))
+        for field in ('a', 'b', 'c'):
+            assert_equal(base[field], mbase[field])
+        assert_equal_records(mbase.view(mrecarray), mbase)
+
+    def test_get(self):
+        # Tests fields retrieval
+        base = self.base.copy()
+        mbase = base.view(mrecarray)
+        # As fields..........
+        for field in ('a', 'b', 'c'):
+            assert_equal(getattr(mbase, field), mbase[field])
+            assert_equal(base[field], mbase[field])
+        # as elements .......
+        mbase_first = mbase[0]
+        assert_(isinstance(mbase_first, mrecarray))
+        assert_equal(mbase_first.dtype, mbase.dtype)
+        assert_equal(mbase_first.tolist(), (1, 1.1, b'one'))
+        # Used to be mask, now it's recordmask
+        assert_equal(mbase_first.recordmask, nomask)
+        assert_equal(mbase_first._mask.item(), (False, False, False))
+        assert_equal(mbase_first['a'], mbase['a'][0])
+        mbase_last = mbase[-1]
+        assert_(isinstance(mbase_last, mrecarray))
+        assert_equal(mbase_last.dtype, mbase.dtype)
+        assert_equal(mbase_last.tolist(), (None, None, None))
+        # Used to be mask, now it's recordmask
+        assert_equal(mbase_last.recordmask, True)
+        assert_equal(mbase_last._mask.item(), (True, True, True))
+        assert_equal(mbase_last['a'], mbase['a'][-1])
+        assert_(mbase_last['a'] is masked)
+        # as slice ..........
+        mbase_sl = mbase[:2]
+        assert_(isinstance(mbase_sl, mrecarray))
+        assert_equal(mbase_sl.dtype, mbase.dtype)
+        # Used to be mask, now it's recordmask
+        assert_equal(mbase_sl.recordmask, [0, 1])
+        assert_equal_records(mbase_sl.mask,
+                             np.array([(False, False, False),
+                                       (True, True, True)],
+                                      dtype=mbase._mask.dtype))
+        assert_equal_records(mbase_sl, base[:2].view(mrecarray))
+        for field in ('a', 'b', 'c'):
+            assert_equal(getattr(mbase_sl, field), base[:2][field])
+
+    def test_set_fields(self):
+        # Tests setting fields.
+        base = self.base.copy()
+        mbase = base.view(mrecarray)
+        mbase = mbase.copy()
+        mbase.fill_value = (999999, 1e20, 'N/A')
+        # Change the data, the mask should be conserved
+        mbase.a._data[:] = 5
+        assert_equal(mbase['a']._data, [5, 5, 5, 5, 5])
+        assert_equal(mbase['a']._mask, [0, 1, 0, 0, 1])
+        # Change the elements, and the mask will follow
+        mbase.a = 1
+        assert_equal(mbase['a']._data, [1] * 5)
+        assert_equal(ma.getmaskarray(mbase['a']), [0] * 5)
+        # Use to be _mask, now it's recordmask
+        assert_equal(mbase.recordmask, [False] * 5)
+        assert_equal(mbase._mask.tolist(),
+                     np.array([(0, 0, 0),
+                               (0, 1, 1),
+                               (0, 0, 0),
+                               (0, 0, 0),
+                               (0, 1, 1)],
+                              dtype=bool))
+        # Set a field to mask ........................
+        mbase.c = masked
+        # Use to be mask, and now it's still mask !
+        assert_equal(mbase.c.mask, [1] * 5)
+        assert_equal(mbase.c.recordmask, [1] * 5)
+        assert_equal(ma.getmaskarray(mbase['c']), [1] * 5)
+        assert_equal(ma.getdata(mbase['c']), [b'N/A'] * 5)
+        assert_equal(mbase._mask.tolist(),
+                     np.array([(0, 0, 1),
+                               (0, 1, 1),
+                               (0, 0, 1),
+                               (0, 0, 1),
+                               (0, 1, 1)],
+                              dtype=bool))
+        # Set fields by slices .......................
+        mbase = base.view(mrecarray).copy()
+        mbase.a[3:] = 5
+        assert_equal(mbase.a, [1, 2, 3, 5, 5])
+        assert_equal(mbase.a._mask, [0, 1, 0, 0, 0])
+        mbase.b[3:] = masked
+        assert_equal(mbase.b, base['b'])
+        assert_equal(mbase.b._mask, [0, 1, 0, 1, 1])
+        # Set fields globally..........................
+        ndtype = [('alpha', '|S1'), ('num', int)]
+        data = ma.array([('a', 1), ('b', 2), ('c', 3)], dtype=ndtype)
+        rdata = data.view(MaskedRecords)
+        val = ma.array([10, 20, 30], mask=[1, 0, 0])
+
+        rdata['num'] = val
+        assert_equal(rdata.num, val)
+        assert_equal(rdata.num.mask, [1, 0, 0])
+
+    def test_set_fields_mask(self):
+        # Tests setting the mask of a field.
+        base = self.base.copy()
+        # This one has already a mask....
+        mbase = base.view(mrecarray)
+        mbase['a'][-2] = masked
+        assert_equal(mbase.a, [1, 2, 3, 4, 5])
+        assert_equal(mbase.a._mask, [0, 1, 0, 1, 1])
+        # This one has not yet
+        mbase = fromarrays([np.arange(5), np.random.rand(5)],
+                           dtype=[('a', int), ('b', float)])
+        mbase['a'][-2] = masked
+        assert_equal(mbase.a, [0, 1, 2, 3, 4])
+        assert_equal(mbase.a._mask, [0, 0, 0, 1, 0])
+
+    def test_set_mask(self):
+        base = self.base.copy()
+        mbase = base.view(mrecarray)
+        # Set the mask to True .......................
+        mbase.mask = masked
+        assert_equal(ma.getmaskarray(mbase['b']), [1] * 5)
+        assert_equal(mbase['a']._mask, mbase['b']._mask)
+        assert_equal(mbase['a']._mask, mbase['c']._mask)
+        assert_equal(mbase._mask.tolist(),
+                     np.array([(1, 1, 1)] * 5, dtype=bool))
+        # Delete the mask ............................
+        mbase.mask = nomask
+        assert_equal(ma.getmaskarray(mbase['c']), [0] * 5)
+        assert_equal(mbase._mask.tolist(),
+                     np.array([(0, 0, 0)] * 5, dtype=bool))
+
+    def test_set_mask_fromarray(self):
+        base = self.base.copy()
+        mbase = base.view(mrecarray)
+        # Sets the mask w/ an array
+        mbase.mask = [1, 0, 0, 0, 1]
+        assert_equal(mbase.a.mask, [1, 0, 0, 0, 1])
+        assert_equal(mbase.b.mask, [1, 0, 0, 0, 1])
+        assert_equal(mbase.c.mask, [1, 0, 0, 0, 1])
+        # Yay, once more !
+        mbase.mask = [0, 0, 0, 0, 1]
+        assert_equal(mbase.a.mask, [0, 0, 0, 0, 1])
+        assert_equal(mbase.b.mask, [0, 0, 0, 0, 1])
+        assert_equal(mbase.c.mask, [0, 0, 0, 0, 1])
+
+    def test_set_mask_fromfields(self):
+        mbase = self.base.copy().view(mrecarray)
+
+        nmask = np.array(
+            [(0, 1, 0), (0, 1, 0), (1, 0, 1), (1, 0, 1), (0, 0, 0)],
+            dtype=[('a', bool), ('b', bool), ('c', bool)])
+        mbase.mask = nmask
+        assert_equal(mbase.a.mask, [0, 0, 1, 1, 0])
+        assert_equal(mbase.b.mask, [1, 1, 0, 0, 0])
+        assert_equal(mbase.c.mask, [0, 0, 1, 1, 0])
+        # Reinitialize and redo
+        mbase.mask = False
+        mbase.fieldmask = nmask
+        assert_equal(mbase.a.mask, [0, 0, 1, 1, 0])
+        assert_equal(mbase.b.mask, [1, 1, 0, 0, 0])
+        assert_equal(mbase.c.mask, [0, 0, 1, 1, 0])
+
+    def test_set_elements(self):
+        base = self.base.copy()
+        # Set an element to mask .....................
+        mbase = base.view(mrecarray).copy()
+        mbase[-2] = masked
+        assert_equal(
+            mbase._mask.tolist(),
+            np.array([(0, 0, 0), (1, 1, 1), (0, 0, 0), (1, 1, 1), (1, 1, 1)],
+                     dtype=bool))
+        # Used to be mask, now it's recordmask!
+        assert_equal(mbase.recordmask, [0, 1, 0, 1, 1])
+        # Set slices .................................
+        mbase = base.view(mrecarray).copy()
+        mbase[:2] = (5, 5, 5)
+        assert_equal(mbase.a._data, [5, 5, 3, 4, 5])
+        assert_equal(mbase.a._mask, [0, 0, 0, 0, 1])
+        assert_equal(mbase.b._data, [5., 5., 3.3, 4.4, 5.5])
+        assert_equal(mbase.b._mask, [0, 0, 0, 0, 1])
+        assert_equal(mbase.c._data,
+                     [b'5', b'5', b'three', b'four', b'five'])
+        assert_equal(mbase.b._mask, [0, 0, 0, 0, 1])
+
+        mbase = base.view(mrecarray).copy()
+        mbase[:2] = masked
+        assert_equal(mbase.a._data, [1, 2, 3, 4, 5])
+        assert_equal(mbase.a._mask, [1, 1, 0, 0, 1])
+        assert_equal(mbase.b._data, [1.1, 2.2, 3.3, 4.4, 5.5])
+        assert_equal(mbase.b._mask, [1, 1, 0, 0, 1])
+        assert_equal(mbase.c._data,
+                     [b'one', b'two', b'three', b'four', b'five'])
+        assert_equal(mbase.b._mask, [1, 1, 0, 0, 1])
+
+    def test_setslices_hardmask(self):
+        # Tests setting slices w/ hardmask.
+        base = self.base.copy()
+        mbase = base.view(mrecarray)
+        mbase.harden_mask()
+        try:
+            mbase[-2:] = (5, 5, 5)
+            assert_equal(mbase.a._data, [1, 2, 3, 5, 5])
+            assert_equal(mbase.b._data, [1.1, 2.2, 3.3, 5, 5.5])
+            assert_equal(mbase.c._data,
+                         [b'one', b'two', b'three', b'5', b'five'])
+            assert_equal(mbase.a._mask, [0, 1, 0, 0, 1])
+            assert_equal(mbase.b._mask, mbase.a._mask)
+            assert_equal(mbase.b._mask, mbase.c._mask)
+        except NotImplementedError:
+            # OK, not implemented yet...
+            pass
+        except AssertionError:
+            raise
+        else:
+            raise Exception("Flexible hard masks should be supported !")
+        # Not using a tuple should crash
+        try:
+            mbase[-2:] = 3
+        except (NotImplementedError, TypeError):
+            pass
+        else:
+            raise TypeError("Should have expected a readable buffer object!")
+
+    def test_hardmask(self):
+        # Test hardmask
+        base = self.base.copy()
+        mbase = base.view(mrecarray)
+        mbase.harden_mask()
+        assert_(mbase._hardmask)
+        mbase.mask = nomask
+        assert_equal_records(mbase._mask, base._mask)
+        mbase.soften_mask()
+        assert_(not mbase._hardmask)
+        mbase.mask = nomask
+        # So, the mask of a field is no longer set to nomask...
+        assert_equal_records(mbase._mask,
+                             ma.make_mask_none(base.shape, base.dtype))
+        assert_(ma.make_mask(mbase['b']._mask) is nomask)
+        assert_equal(mbase['a']._mask, mbase['b']._mask)
+
+    def test_pickling(self):
+        # Test pickling
+        base = self.base.copy()
+        mrec = base.view(mrecarray)
+        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
+            _ = pickle.dumps(mrec, protocol=proto)
+            mrec_ = pickle.loads(_)
+            assert_equal(mrec_.dtype, mrec.dtype)
+            assert_equal_records(mrec_._data, mrec._data)
+            assert_equal(mrec_._mask, mrec._mask)
+            assert_equal_records(mrec_._mask, mrec._mask)
+
+    def test_filled(self):
+        # Test filling the array
+        _a = ma.array([1, 2, 3], mask=[0, 0, 1], dtype=int)
+        _b = ma.array([1.1, 2.2, 3.3], mask=[0, 0, 1], dtype=float)
+        _c = ma.array(['one', 'two', 'three'], mask=[0, 0, 1], dtype='|S8')
+        ddtype = [('a', int), ('b', float), ('c', '|S8')]
+        mrec = fromarrays([_a, _b, _c], dtype=ddtype,
+                          fill_value=(99999, 99999., 'N/A'))
+        mrecfilled = mrec.filled()
+        assert_equal(mrecfilled['a'], np.array((1, 2, 99999), dtype=int))
+        assert_equal(mrecfilled['b'], np.array((1.1, 2.2, 99999.),
+                                               dtype=float))
+        assert_equal(mrecfilled['c'], np.array(('one', 'two', 'N/A'),
+                                               dtype='|S8'))
+
+    def test_tolist(self):
+        # Test tolist.
+        _a = ma.array([1, 2, 3], mask=[0, 0, 1], dtype=int)
+        _b = ma.array([1.1, 2.2, 3.3], mask=[0, 0, 1], dtype=float)
+        _c = ma.array(['one', 'two', 'three'], mask=[1, 0, 0], dtype='|S8')
+        ddtype = [('a', int), ('b', float), ('c', '|S8')]
+        mrec = fromarrays([_a, _b, _c], dtype=ddtype,
+                          fill_value=(99999, 99999., 'N/A'))
+
+        assert_equal(mrec.tolist(),
+                     [(1, 1.1, None), (2, 2.2, b'two'),
+                      (None, None, b'three')])
+
+    def test_withnames(self):
+        # Test the creation w/ format and names
+        x = mrecarray(1, formats=float, names='base')
+        x[0]['base'] = 10
+        assert_equal(x['base'][0], 10)
+
+    def test_exotic_formats(self):
+        # Test that 'exotic' formats are processed properly
+        easy = mrecarray(1, dtype=[('i', int), ('s', '|S8'), ('f', float)])
+        easy[0] = masked
+        assert_equal(easy.filled(1).item(), (1, b'1', 1.))
+
+        solo = mrecarray(1, dtype=[('f0', '<f8', (2, 2))])
+        solo[0] = masked
+        assert_equal(solo.filled(1).item(),
+                     np.array((1,), dtype=solo.dtype).item())
+
+        mult = mrecarray(2, dtype="i4, (2,3)float, float")
+        mult[0] = masked
+        mult[1] = (1, 1, 1)
+        mult.filled(0)
+        assert_equal_records(mult.filled(0),
+                             np.array([(0, 0, 0), (1, 1, 1)],
+                                      dtype=mult.dtype))
+
+
+class TestView:
+
+    def setup_method(self):
+        (a, b) = (np.arange(10), np.random.rand(10))
+        ndtype = [('a', float), ('b', float)]
+        arr = np.array(list(zip(a, b)), dtype=ndtype)
+
+        mrec = fromarrays([a, b], dtype=ndtype, fill_value=(-9., -99.))
+        mrec.mask[3] = (False, True)
+        self.data = (mrec, a, b, arr)
+
+    def test_view_by_itself(self):
+        (mrec, a, b, arr) = self.data
+        test = mrec.view()
+        assert_(isinstance(test, MaskedRecords))
+        assert_equal_records(test, mrec)
+        assert_equal_records(test._mask, mrec._mask)
+
+    def test_view_simple_dtype(self):
+        (mrec, a, b, arr) = self.data
+        ntype = (float, 2)
+        test = mrec.view(ntype)
+        assert_(isinstance(test, ma.MaskedArray))
+        assert_equal(test, np.array(list(zip(a, b)), dtype=float))
+        assert_(test[3, 1] is ma.masked)
+
+    def test_view_flexible_type(self):
+        (mrec, a, b, arr) = self.data
+        alttype = [('A', float), ('B', float)]
+        test = mrec.view(alttype)
+        assert_(isinstance(test, MaskedRecords))
+        assert_equal_records(test, arr.view(alttype))
+        assert_(test['B'][3] is masked)
+        assert_equal(test.dtype, np.dtype(alttype))
+        assert_(test._fill_value is None)
+
+
+##############################################################################
+class TestMRecordsImport:
+
+    _a = ma.array([1, 2, 3], mask=[0, 0, 1], dtype=int)
+    _b = ma.array([1.1, 2.2, 3.3], mask=[0, 0, 1], dtype=float)
+    _c = ma.array([b'one', b'two', b'three'],
+                  mask=[0, 0, 1], dtype='|S8')
+    ddtype = [('a', int), ('b', float), ('c', '|S8')]
+    mrec = fromarrays([_a, _b, _c], dtype=ddtype,
+                      fill_value=(b'99999', b'99999.',
+                                  b'N/A'))
+    nrec = recfromarrays((_a._data, _b._data, _c._data), dtype=ddtype)
+    data = (mrec, nrec, ddtype)
+
+    def test_fromarrays(self):
+        _a = ma.array([1, 2, 3], mask=[0, 0, 1], dtype=int)
+        _b = ma.array([1.1, 2.2, 3.3], mask=[0, 0, 1], dtype=float)
+        _c = ma.array(['one', 'two', 'three'], mask=[0, 0, 1], dtype='|S8')
+        (mrec, nrec, _) = self.data
+        for (f, l) in zip(('a', 'b', 'c'), (_a, _b, _c)):
+            assert_equal(getattr(mrec, f)._mask, l._mask)
+        # One record only
+        _x = ma.array([1, 1.1, 'one'], mask=[1, 0, 0], dtype=object)
+        assert_equal_records(fromarrays(_x, dtype=mrec.dtype), mrec[0])
+
+    def test_fromrecords(self):
+        # Test construction from records.
+        (mrec, nrec, ddtype) = self.data
+        # ......
+        palist = [(1, 'abc', 3.7000002861022949, 0),
+                  (2, 'xy', 6.6999998092651367, 1),
+                  (0, ' ', 0.40000000596046448, 0)]
+        pa = recfromrecords(palist, names='c1, c2, c3, c4')
+        mpa = fromrecords(palist, names='c1, c2, c3, c4')
+        assert_equal_records(pa, mpa)
+        # .....
+        _mrec = fromrecords(nrec)
+        assert_equal(_mrec.dtype, mrec.dtype)
+        for field in _mrec.dtype.names:
+            assert_equal(getattr(_mrec, field), getattr(mrec._data, field))
+
+        _mrec = fromrecords(nrec.tolist(), names='c1,c2,c3')
+        assert_equal(_mrec.dtype, [('c1', int), ('c2', float), ('c3', '|S5')])
+        for (f, n) in zip(('c1', 'c2', 'c3'), ('a', 'b', 'c')):
+            assert_equal(getattr(_mrec, f), getattr(mrec._data, n))
+
+        _mrec = fromrecords(mrec)
+        assert_equal(_mrec.dtype, mrec.dtype)
+        assert_equal_records(_mrec._data, mrec.filled())
+        assert_equal_records(_mrec._mask, mrec._mask)
+
+    def test_fromrecords_wmask(self):
+        # Tests construction from records w/ mask.
+        (mrec, nrec, ddtype) = self.data
+
+        _mrec = fromrecords(nrec.tolist(), dtype=ddtype, mask=[0, 1, 0,])
+        assert_equal_records(_mrec._data, mrec._data)
+        assert_equal(_mrec._mask.tolist(), [(0, 0, 0), (1, 1, 1), (0, 0, 0)])
+
+        _mrec = fromrecords(nrec.tolist(), dtype=ddtype, mask=True)
+        assert_equal_records(_mrec._data, mrec._data)
+        assert_equal(_mrec._mask.tolist(), [(1, 1, 1), (1, 1, 1), (1, 1, 1)])
+
+        _mrec = fromrecords(nrec.tolist(), dtype=ddtype, mask=mrec._mask)
+        assert_equal_records(_mrec._data, mrec._data)
+        assert_equal(_mrec._mask.tolist(), mrec._mask.tolist())
+
+        _mrec = fromrecords(nrec.tolist(), dtype=ddtype,
+                            mask=mrec._mask.tolist())
+        assert_equal_records(_mrec._data, mrec._data)
+        assert_equal(_mrec._mask.tolist(), mrec._mask.tolist())
+
+    def test_fromtextfile(self):
+        # Tests reading from a text file.
+        fcontent = (
+"""#
+'One (S)','Two (I)','Three (F)','Four (M)','Five (-)','Six (C)'
+'strings',1,1.0,'mixed column',,1
+'with embedded "double quotes"',2,2.0,1.0,,1
+'strings',3,3.0E5,3,,1
+'strings',4,-1e-10,,,1
+""")
+        with temppath() as path:
+            with open(path, 'w') as f:
+                f.write(fcontent)
+            mrectxt = fromtextfile(path, delimiter=',', varnames='ABCDEFG')
+        assert_(isinstance(mrectxt, MaskedRecords))
+        assert_equal(mrectxt.F, [1, 1, 1, 1])
+        assert_equal(mrectxt.E._mask, [1, 1, 1, 1])
+        assert_equal(mrectxt.C, [1, 2, 3.e+5, -1e-10])
+
+    def test_addfield(self):
+        # Tests addfield
+        (mrec, nrec, ddtype) = self.data
+        (d, m) = ([100, 200, 300], [1, 0, 0])
+        mrec = addfield(mrec, ma.array(d, mask=m))
+        assert_equal(mrec.f3, d)
+        assert_equal(mrec.f3._mask, m)
+
+
+def test_record_array_with_object_field():
+    # Trac #1839
+    y = ma.masked_array(
+        [(1, '2'), (3, '4')],
+        mask=[(0, 0), (0, 1)],
+        dtype=[('a', int), ('b', object)])
+    # getting an item used to fail
+    y[1]

venv/lib/python3.13/site-packages/numpy/ma/tests/test_old_ma.py

@@ -0,0 +1,942 @@
+import pickle
+from functools import reduce
+
+import pytest
+
+import numpy as np
+import numpy._core.fromnumeric as fromnumeric
+import numpy._core.umath as umath
+from numpy.ma import (
+    MaskedArray,
+    MaskType,
+    absolute,
+    add,
+    all,
+    allclose,
+    allequal,
+    alltrue,
+    arange,
+    arccos,
+    arcsin,
+    arctan,
+    arctan2,
+    array,
+    average,
+    choose,
+    concatenate,
+    conjugate,
+    cos,
+    cosh,
+    count,
+    divide,
+    equal,
+    exp,
+    filled,
+    getmask,
+    greater,
+    greater_equal,
+    inner,
+    isMaskedArray,
+    less,
+    less_equal,
+    log,
+    log10,
+    make_mask,
+    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,
+    maximum,
+    minimum,
+    multiply,
+    nomask,
+    nonzero,
+    not_equal,
+    ones,
+    outer,
+    product,
+    put,
+    ravel,
+    repeat,
+    resize,
+    shape,
+    sin,
+    sinh,
+    sometrue,
+    sort,
+    sqrt,
+    subtract,
+    sum,
+    take,
+    tan,
+    tanh,
+    transpose,
+    where,
+    zeros,
+)
+from numpy.testing import (
+    assert_,
+    assert_equal,
+    assert_raises,
+)
+
+pi = np.pi
+
+
+def eq(v, w, msg=''):
+    result = allclose(v, w)
+    if not result:
+        print(f'Not eq:{msg}\n{v}\n----{w}')
+    return result
+
+
+class TestMa:
+
+    def setup_method(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.])
+        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 = array(x, mask=m1)
+        ym = array(y, mask=m2)
+        z = np.array([-.5, 0., .5, .8])
+        zm = array(z, mask=[0, 1, 0, 0])
+        xf = np.where(m1, 1e+20, x)
+        s = x.shape
+        xm.set_fill_value(1e+20)
+        self.d = (x, y, a10, m1, m2, xm, ym, z, zm, xf, s)
+
+    def test_testBasic1d(self):
+        # Test of basic array creation and properties in 1 dimension.
+        (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
+        assert_(not isMaskedArray(x))
+        assert_(isMaskedArray(xm))
+        assert_equal(shape(xm), s)
+        assert_equal(xm.shape, s)
+        assert_equal(xm.dtype, x.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_(eq(xm, xf))
+        assert_(eq(filled(xm, 1.e20), xf))
+        assert_(eq(x, xm))
+
+    @pytest.mark.parametrize("s", [(4, 3), (6, 2)])
+    def test_testBasic2d(self, s):
+        # Test of basic array creation and properties in 2 dimensions.
+        (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
+        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_(eq(xm, xf))
+        assert_(eq(filled(xm, 1.e20), xf))
+        assert_(eq(x, xm))
+
+    def test_testArithmetic(self):
+        # Test of basic arithmetic.
+        (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
+        a2d = array([[1, 2], [0, 4]])
+        a2dm = masked_array(a2d, [[0, 0], [1, 0]])
+        assert_(eq(a2d * a2d, a2d * a2dm))
+        assert_(eq(a2d + a2d, a2d + a2dm))
+        assert_(eq(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_(eq(-x, -xm))
+            assert_(eq(x + y, xm + ym))
+            assert_(eq(x - y, xm - ym))
+            assert_(eq(x * y, xm * ym))
+            with np.errstate(divide='ignore', invalid='ignore'):
+                assert_(eq(x / y, xm / ym))
+            assert_(eq(a10 + y, a10 + ym))
+            assert_(eq(a10 - y, a10 - ym))
+            assert_(eq(a10 * y, a10 * ym))
+            with np.errstate(divide='ignore', invalid='ignore'):
+                assert_(eq(a10 / y, a10 / ym))
+            assert_(eq(x + a10, xm + a10))
+            assert_(eq(x - a10, xm - a10))
+            assert_(eq(x * a10, xm * a10))
+            assert_(eq(x / a10, xm / a10))
+            assert_(eq(x ** 2, xm ** 2))
+            assert_(eq(abs(x) ** 2.5, abs(xm) ** 2.5))
+            assert_(eq(x ** y, xm ** ym))
+            assert_(eq(np.add(x, y), add(xm, ym)))
+            assert_(eq(np.subtract(x, y), subtract(xm, ym)))
+            assert_(eq(np.multiply(x, y), multiply(xm, ym)))
+            with np.errstate(divide='ignore', invalid='ignore'):
+                assert_(eq(np.divide(x, y), divide(xm, ym)))
+
+    def test_testMixedArithmetic(self):
+        na = np.array([1])
+        ma = array([1])
+        assert_(isinstance(na + ma, MaskedArray))
+        assert_(isinstance(ma + na, MaskedArray))
+
+    def test_testUfuncs1(self):
+        # Test various functions such as sin, cos.
+        (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
+        assert_(eq(np.cos(x), cos(xm)))
+        assert_(eq(np.cosh(x), cosh(xm)))
+        assert_(eq(np.sin(x), sin(xm)))
+        assert_(eq(np.sinh(x), sinh(xm)))
+        assert_(eq(np.tan(x), tan(xm)))
+        assert_(eq(np.tanh(x), tanh(xm)))
+        with np.errstate(divide='ignore', invalid='ignore'):
+            assert_(eq(np.sqrt(abs(x)), sqrt(xm)))
+            assert_(eq(np.log(abs(x)), log(xm)))
+            assert_(eq(np.log10(abs(x)), log10(xm)))
+        assert_(eq(np.exp(x), exp(xm)))
+        assert_(eq(np.arcsin(z), arcsin(zm)))
+        assert_(eq(np.arccos(z), arccos(zm)))
+        assert_(eq(np.arctan(z), arctan(zm)))
+        assert_(eq(np.arctan2(x, y), arctan2(xm, ym)))
+        assert_(eq(np.absolute(x), absolute(xm)))
+        assert_(eq(np.equal(x, y), equal(xm, ym)))
+        assert_(eq(np.not_equal(x, y), not_equal(xm, ym)))
+        assert_(eq(np.less(x, y), less(xm, ym)))
+        assert_(eq(np.greater(x, y), greater(xm, ym)))
+        assert_(eq(np.less_equal(x, y), less_equal(xm, ym)))
+        assert_(eq(np.greater_equal(x, y), greater_equal(xm, ym)))
+        assert_(eq(np.conjugate(x), conjugate(xm)))
+        assert_(eq(np.concatenate((x, y)), concatenate((xm, ym))))
+        assert_(eq(np.concatenate((x, y)), concatenate((x, y))))
+        assert_(eq(np.concatenate((x, y)), concatenate((xm, y))))
+        assert_(eq(np.concatenate((x, y, x)), concatenate((x, ym, x))))
+
+    def test_xtestCount(self):
+        # Test count
+        ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
+        assert_(count(ott).dtype.type is np.intp)
+        assert_equal(3, count(ott))
+        assert_equal(1, count(1))
+        assert_(eq(0, array(1, mask=[1])))
+        ott = ott.reshape((2, 2))
+        assert_(count(ott).dtype.type is np.intp)
+        assert_(isinstance(count(ott, 0), np.ndarray))
+        assert_(count(ott).dtype.type is np.intp)
+        assert_(eq(3, count(ott)))
+        assert_(getmask(count(ott, 0)) is nomask)
+        assert_(eq([1, 2], count(ott, 0)))
+
+    def test_testMinMax(self):
+        # Test minimum and maximum.
+        (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
+        xr = np.ravel(x)  # max doesn't work if shaped
+        xmr = ravel(xm)
+
+        # true because of careful selection of data
+        assert_(eq(max(xr), maximum.reduce(xmr)))
+        assert_(eq(min(xr), minimum.reduce(xmr)))
+
+    def test_testAddSumProd(self):
+        # Test add, sum, product.
+        (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
+        assert_(eq(np.add.reduce(x), add.reduce(x)))
+        assert_(eq(np.add.accumulate(x), add.accumulate(x)))
+        assert_(eq(4, sum(array(4), axis=0)))
+        assert_(eq(4, sum(array(4), axis=0)))
+        assert_(eq(np.sum(x, axis=0), sum(x, axis=0)))
+        assert_(eq(np.sum(filled(xm, 0), axis=0), sum(xm, axis=0)))
+        assert_(eq(np.sum(x, 0), sum(x, 0)))
+        assert_(eq(np.prod(x, axis=0), product(x, axis=0)))
+        assert_(eq(np.prod(x, 0), product(x, 0)))
+        assert_(eq(np.prod(filled(xm, 1), axis=0),
+                           product(xm, axis=0)))
+        if len(s) > 1:
+            assert_(eq(np.concatenate((x, y), 1),
+                               concatenate((xm, ym), 1)))
+            assert_(eq(np.add.reduce(x, 1), add.reduce(x, 1)))
+            assert_(eq(np.sum(x, 1), sum(x, 1)))
+            assert_(eq(np.prod(x, 1), product(x, 1)))
+
+    def test_testCI(self):
+        # Test of 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_(eq(np.sort(x1), sort(x2, fill_value=0)))
+        # tests of indexing
+        assert_(type(x2[1]) is type(x1[1]))
+        assert_(x1[1] == x2[1])
+        assert_(x2[0] is masked)
+        assert_(eq(x1[2], x2[2]))
+        assert_(eq(x1[2:5], x2[2:5]))
+        assert_(eq(x1[:], x2[:]))
+        assert_(eq(x1[1:], x3[1:]))
+        x1[2] = 9
+        x2[2] = 9
+        assert_(eq(x1, x2))
+        x1[1:3] = 99
+        x2[1:3] = 99
+        assert_(eq(x1, x2))
+        x2[1] = masked
+        assert_(eq(x1, x2))
+        x2[1:3] = masked
+        assert_(eq(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_(eq(x1, x2))
+        assert_(allequal(array([0, 0, 0, 1, 0], MaskType), x2.mask))
+        assert_(eq(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_testCopySize(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=True)
+        assert_(m is not m3)
+
+        x1 = np.arange(5)
+        y1 = array(x1, mask=m)
+        assert_(y1._data is not x1)
+        assert_(allequal(x1, y1._data))
+        assert_(y1._mask is m)
+
+        y1a = array(y1, copy=0)
+        # For copy=False, one might expect that the array would just
+        # passed on, i.e., that it would be "is" instead of "==".
+        # See gh-4043 for discussion.
+        assert_(y1a._mask.__array_interface__ ==
+                y1._mask.__array_interface__)
+
+        y2 = array(x1, mask=m3, copy=0)
+        assert_(y2._mask is m3)
+        assert_(y2[2] is masked)
+        y2[2] = 9
+        assert_(y2[2] is not masked)
+        assert_(y2._mask is m3)
+        assert_(allequal(y2.mask, 0))
+
+        y2a = array(x1, mask=m, copy=1)
+        assert_(y2a._mask is not m)
+        assert_(y2a[2] is masked)
+        y2a[2] = 9
+        assert_(y2a[2] is not masked)
+        assert_(y2a._mask is not m)
+        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_(eq(concatenate([x4, x4]), y4))
+        assert_(eq(getmask(y4), [0, 0, 1, 0, 0, 0, 1, 0]))
+        y5 = repeat(x4, (2, 2, 2, 2), axis=0)
+        assert_(eq(y5, [0, 0, 1, 1, 2, 2, 3, 3]))
+        y6 = repeat(x4, 2, axis=0)
+        assert_(eq(y5, y6))
+
+    def test_testPut(self):
+        # Test of put
+        d = arange(5)
+        n = [0, 0, 0, 1, 1]
+        m = make_mask(n)
+        m2 = m.copy()
+        x = array(d, mask=m)
+        assert_(x[3] is masked)
+        assert_(x[4] is masked)
+        x[[1, 4]] = [10, 40]
+        assert_(x._mask is m)
+        assert_(x[3] is masked)
+        assert_(x[4] is not masked)
+        assert_(eq(x, [0, 10, 2, -1, 40]))
+
+        x = array(d, mask=m2, copy=True)
+        x.put([0, 1, 2], [-1, 100, 200])
+        assert_(x._mask is not m2)
+        assert_(x[3] is masked)
+        assert_(x[4] is masked)
+        assert_(eq(x, [-1, 100, 200, 0, 0]))
+
+    def test_testPut2(self):
+        # Test of put
+        d = arange(5)
+        x = array(d, mask=[0, 0, 0, 0, 0])
+        z = array([10, 40], mask=[1, 0])
+        assert_(x[2] is not masked)
+        assert_(x[3] is not masked)
+        x[2:4] = z
+        assert_(x[2] is masked)
+        assert_(x[3] is not masked)
+        assert_(eq(x, [0, 1, 10, 40, 4]))
+
+        d = arange(5)
+        x = array(d, mask=[0, 0, 0, 0, 0])
+        y = x[2:4]
+        z = array([10, 40], mask=[1, 0])
+        assert_(x[2] is not masked)
+        assert_(x[3] is not masked)
+        y[:] = z
+        assert_(y[0] is masked)
+        assert_(y[1] is not masked)
+        assert_(eq(y, [10, 40]))
+        assert_(x[2] is masked)
+        assert_(x[3] is not masked)
+        assert_(eq(x, [0, 1, 10, 40, 4]))
+
+    def test_testMaPut(self):
+        (x, y, a10, m1, m2, xm, ym, z, zm, xf, s) = self.d
+        m = [1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1]
+        i = np.nonzero(m)[0]
+        put(ym, i, zm)
+        assert_(all(take(ym, i, axis=0) == zm))
+
+    def test_testOddFeatures(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_(eq(z.real, x))
+        assert_(eq(z.imag, 10 * x))
+        assert_(eq((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 = 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_(eq(x, z))
+        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)
+        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_(eq(x, z))
+        x = array([1., 2., 3., 4., 5.])
+        c = array([1, 1, 1, 0, 0])
+        x[2] = masked
+        z = where(c, x, -x)
+        assert_(eq(z, [1., 2., 0., -4., -5]))
+        c[0] = masked
+        z = where(c, x, -x)
+        assert_(eq(z, [1., 2., 0., -4., -5]))
+        assert_(z[0] is masked)
+        assert_(z[1] is not masked)
+        assert_(z[2] is masked)
+        assert_(eq(masked_where(greater(x, 2), x), masked_greater(x, 2)))
+        assert_(eq(masked_where(greater_equal(x, 2), x),
+                   masked_greater_equal(x, 2)))
+        assert_(eq(masked_where(less(x, 2), x), masked_less(x, 2)))
+        assert_(eq(masked_where(less_equal(x, 2), x), masked_less_equal(x, 2)))
+        assert_(eq(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2)))
+        assert_(eq(masked_where(equal(x, 2), x), masked_equal(x, 2)))
+        assert_(eq(masked_where(not_equal(x, 2), x), masked_not_equal(x, 2)))
+        assert_(eq(masked_inside(list(range(5)), 1, 3), [0, 199, 199, 199, 4]))
+        assert_(eq(masked_outside(list(range(5)), 1, 3), [199, 1, 2, 3, 199]))
+        assert_(eq(masked_inside(array(list(range(5)),
+                                       mask=[1, 0, 0, 0, 0]), 1, 3).mask,
+                   [1, 1, 1, 1, 0]))
+        assert_(eq(masked_outside(array(list(range(5)),
+                                        mask=[0, 1, 0, 0, 0]), 1, 3).mask,
+                   [1, 1, 0, 0, 1]))
+        assert_(eq(masked_equal(array(list(range(5)),
+                                      mask=[1, 0, 0, 0, 0]), 2).mask,
+                   [1, 0, 1, 0, 0]))
+        assert_(eq(masked_not_equal(array([2, 2, 1, 2, 1],
+                                          mask=[1, 0, 0, 0, 0]), 2).mask,
+                   [1, 0, 1, 0, 1]))
+        assert_(eq(masked_where([1, 1, 0, 0, 0], [1, 2, 3, 4, 5]),
+                   [99, 99, 3, 4, 5]))
+        atest = ones((10, 10, 10), dtype=np.float32)
+        btest = zeros(atest.shape, MaskType)
+        ctest = masked_where(btest, atest)
+        assert_(eq(atest, ctest))
+        z = choose(c, (-x, x))
+        assert_(eq(z, [1., 2., 0., -4., -5]))
+        assert_(z[0] is masked)
+        assert_(z[1] is not masked)
+        assert_(z[2] is masked)
+        x = arange(6)
+        x[5] = masked
+        y = arange(6) * 10
+        y[2] = masked
+        c = array([1, 1, 1, 0, 0, 0], mask=[1, 0, 0, 0, 0, 0])
+        cm = c.filled(1)
+        z = where(c, x, y)
+        zm = where(cm, x, y)
+        assert_(eq(z, zm))
+        assert_(getmask(zm) is nomask)
+        assert_(eq(zm, [0, 1, 2, 30, 40, 50]))
+        z = where(c, masked, 1)
+        assert_(eq(z, [99, 99, 99, 1, 1, 1]))
+        z = where(c, 1, masked)
+        assert_(eq(z, [99, 1, 1, 99, 99, 99]))
+
+    def test_testMinMax2(self):
+        # Test of minimum, maximum.
+        assert_(eq(minimum([1, 2, 3], [4, 0, 9]), [1, 0, 3]))
+        assert_(eq(maximum([1, 2, 3], [4, 0, 9]), [4, 2, 9]))
+        x = arange(5)
+        y = arange(5) - 2
+        x[3] = masked
+        y[0] = masked
+        assert_(eq(minimum(x, y), where(less(x, y), x, y)))
+        assert_(eq(maximum(x, y), where(greater(x, y), x, y)))
+        assert_(minimum.reduce(x) == 0)
+        assert_(maximum.reduce(x) == 4)
+
+    def test_testTakeTransposeInnerOuter(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_(eq(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1))))
+        assert_(eq(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1)))
+        assert_(eq(np.inner(filled(x, 0), filled(y, 0)),
+                   inner(x, y)))
+        assert_(eq(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_testInplace(self):
+        # Test of inplace operations and rich comparisons
+        y = arange(10)
+
+        x = arange(10)
+        xm = arange(10)
+        xm[2] = masked
+        x += 1
+        assert_(eq(x, y + 1))
+        xm += 1
+        assert_(eq(x, y + 1))
+
+        x = arange(10)
+        xm = arange(10)
+        xm[2] = masked
+        x -= 1
+        assert_(eq(x, y - 1))
+        xm -= 1
+        assert_(eq(xm, y - 1))
+
+        x = arange(10) * 1.0
+        xm = arange(10) * 1.0
+        xm[2] = masked
+        x *= 2.0
+        assert_(eq(x, y * 2))
+        xm *= 2.0
+        assert_(eq(xm, y * 2))
+
+        x = arange(10) * 2
+        xm = arange(10)
+        xm[2] = masked
+        x //= 2
+        assert_(eq(x, y))
+        xm //= 2
+        assert_(eq(x, y))
+
+        x = arange(10) * 1.0
+        xm = arange(10) * 1.0
+        xm[2] = masked
+        x /= 2.0
+        assert_(eq(x, y / 2.0))
+        xm /= arange(10)
+        assert_(eq(xm, ones((10,))))
+
+        x = arange(10).astype(np.float32)
+        xm = arange(10)
+        xm[2] = masked
+        x += 1.
+        assert_(eq(x, y + 1.))
+
+    def test_testPickle(self):
+        # Test of pickling
+        x = arange(12)
+        x[4:10:2] = masked
+        x = x.reshape(4, 3)
+        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
+            s = pickle.dumps(x, protocol=proto)
+            y = pickle.loads(s)
+            assert_(eq(x, y))
+
+    def test_testMasked(self):
+        # Test of masked element
+        xx = arange(6)
+        xx[1] = masked
+        assert_(str(masked) == '--')
+        assert_(xx[1] is masked)
+        assert_equal(filled(xx[1], 0), 0)
+
+    def test_testAverage1(self):
+        # Test of average.
+        ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
+        assert_(eq(2.0, average(ott, axis=0)))
+        assert_(eq(2.0, average(ott, weights=[1., 1., 2., 1.])))
+        result, wts = average(ott, weights=[1., 1., 2., 1.], returned=True)
+        assert_(eq(2.0, result))
+        assert_(wts == 4.0)
+        ott[:] = masked
+        assert_(average(ott, axis=0) is masked)
+        ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
+        ott = ott.reshape(2, 2)
+        ott[:, 1] = masked
+        assert_(eq(average(ott, axis=0), [2.0, 0.0]))
+        assert_(average(ott, axis=1)[0] is masked)
+        assert_(eq([2., 0.], average(ott, axis=0)))
+        result, wts = average(ott, axis=0, returned=True)
+        assert_(eq(wts, [1., 0.]))
+
+    def test_testAverage2(self):
+        # More tests of average.
+        w1 = [0, 1, 1, 1, 1, 0]
+        w2 = [[0, 1, 1, 1, 1, 0], [1, 0, 0, 0, 0, 1]]
+        x = arange(6)
+        assert_(allclose(average(x, axis=0), 2.5))
+        assert_(allclose(average(x, axis=0, weights=w1), 2.5))
+        y = array([arange(6), 2.0 * arange(6)])
+        assert_(allclose(average(y, None),
+                                 np.add.reduce(np.arange(6)) * 3. / 12.))
+        assert_(allclose(average(y, axis=0), np.arange(6) * 3. / 2.))
+        assert_(allclose(average(y, axis=1),
+                                 [average(x, axis=0), average(x, axis=0) * 2.0]))
+        assert_(allclose(average(y, None, weights=w2), 20. / 6.))
+        assert_(allclose(average(y, axis=0, weights=w2),
+                                 [0., 1., 2., 3., 4., 10.]))
+        assert_(allclose(average(y, axis=1),
+                                 [average(x, axis=0), average(x, axis=0) * 2.0]))
+        m1 = zeros(6)
+        m2 = [0, 0, 1, 1, 0, 0]
+        m3 = [[0, 0, 1, 1, 0, 0], [0, 1, 1, 1, 1, 0]]
+        m4 = ones(6)
+        m5 = [0, 1, 1, 1, 1, 1]
+        assert_(allclose(average(masked_array(x, m1), axis=0), 2.5))
+        assert_(allclose(average(masked_array(x, m2), axis=0), 2.5))
+        assert_(average(masked_array(x, m4), axis=0) is masked)
+        assert_equal(average(masked_array(x, m5), axis=0), 0.0)
+        assert_equal(count(average(masked_array(x, m4), axis=0)), 0)
+        z = masked_array(y, m3)
+        assert_(allclose(average(z, None), 20. / 6.))
+        assert_(allclose(average(z, axis=0),
+                                 [0., 1., 99., 99., 4.0, 7.5]))
+        assert_(allclose(average(z, axis=1), [2.5, 5.0]))
+        assert_(allclose(average(z, axis=0, weights=w2),
+                                 [0., 1., 99., 99., 4.0, 10.0]))
+
+        a = arange(6)
+        b = arange(6) * 3
+        r1, w1 = average([[a, b], [b, a]], axis=1, returned=True)
+        assert_equal(shape(r1), shape(w1))
+        assert_equal(r1.shape, w1.shape)
+        r2, w2 = average(ones((2, 2, 3)), axis=0, weights=[3, 1], returned=True)
+        assert_equal(shape(w2), shape(r2))
+        r2, w2 = average(ones((2, 2, 3)), returned=True)
+        assert_equal(shape(w2), shape(r2))
+        r2, w2 = average(ones((2, 2, 3)), weights=ones((2, 2, 3)), returned=True)
+        assert_(shape(w2) == shape(r2))
+        a2d = array([[1, 2], [0, 4]], float)
+        a2dm = masked_array(a2d, [[0, 0], [1, 0]])
+        a2da = average(a2d, axis=0)
+        assert_(eq(a2da, [0.5, 3.0]))
+        a2dma = average(a2dm, axis=0)
+        assert_(eq(a2dma, [1.0, 3.0]))
+        a2dma = average(a2dm, axis=None)
+        assert_(eq(a2dma, 7. / 3.))
+        a2dma = average(a2dm, axis=1)
+        assert_(eq(a2dma, [1.5, 4.0]))
+
+    def test_testToPython(self):
+        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]))
+        assert_raises(ValueError, bool, array([0, 1]))
+        assert_raises(ValueError, bool, array([0, 0], mask=[0, 1]))
+
+    def test_testScalarArithmetic(self):
+        xm = array(0, mask=1)
+        # TODO FIXME: Find out what the following raises a warning in r8247
+        with np.errstate(divide='ignore'):
+            assert_((1 / array(0)).mask)
+        assert_((1 + xm).mask)
+        assert_((-xm).mask)
+        assert_((-xm).mask)
+        assert_(maximum(xm, xm).mask)
+        assert_(minimum(xm, xm).mask)
+        assert_(xm.filled().dtype is xm._data.dtype)
+        x = array(0, mask=0)
+        assert_(x.filled() == x._data)
+        assert_equal(str(xm), str(masked_print_option))
+
+    def test_testArrayMethods(self):
+        a = array([1, 3, 2])
+        assert_(eq(a.any(), a._data.any()))
+        assert_(eq(a.all(), a._data.all()))
+        assert_(eq(a.argmax(), a._data.argmax()))
+        assert_(eq(a.argmin(), a._data.argmin()))
+        assert_(eq(a.choose(0, 1, 2, 3, 4),
+                           a._data.choose(0, 1, 2, 3, 4)))
+        assert_(eq(a.compress([1, 0, 1]), a._data.compress([1, 0, 1])))
+        assert_(eq(a.conj(), a._data.conj()))
+        assert_(eq(a.conjugate(), a._data.conjugate()))
+        m = array([[1, 2], [3, 4]])
+        assert_(eq(m.diagonal(), m._data.diagonal()))
+        assert_(eq(a.sum(), a._data.sum()))
+        assert_(eq(a.take([1, 2]), a._data.take([1, 2])))
+        assert_(eq(m.transpose(), m._data.transpose()))
+
+    def test_testArrayAttributes(self):
+        a = array([1, 3, 2])
+        assert_equal(a.ndim, 1)
+
+    def test_testAPI(self):
+        assert_(not [m for m in dir(np.ndarray)
+                     if m not in dir(MaskedArray) and
+                     not m.startswith('_')])
+
+    def test_testSingleElementSubscript(self):
+        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_assignment_by_condition(self):
+        # Test for gh-18951
+        a = array([1, 2, 3, 4], mask=[1, 0, 1, 0])
+        c = a >= 3
+        a[c] = 5
+        assert_(a[2] is masked)
+
+    def test_assignment_by_condition_2(self):
+        # gh-19721
+        a = masked_array([0, 1], mask=[False, False])
+        b = masked_array([0, 1], mask=[True, True])
+        mask = a < 1
+        b[mask] = a[mask]
+        expected_mask = [False, True]
+        assert_equal(b.mask, expected_mask)
+
+
+class TestUfuncs:
+    def setup_method(self):
+        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),)
+
+    def test_testUfuncRegression(self):
+        f_invalid_ignore = [
+            'sqrt', 'arctanh', 'arcsin', 'arccos',
+            'arccosh', 'arctanh', 'log', 'log10', 'divide',
+            'true_divide', 'floor_divide', 'remainder', 'fmod']
+        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(np.ma, f)
+            args = self.d[:uf.nin]
+            with np.errstate():
+                if f in f_invalid_ignore:
+                    np.seterr(invalid='ignore')
+                if f in ['arctanh', 'log', 'log10']:
+                    np.seterr(divide='ignore')
+                ur = uf(*args)
+                mr = mf(*args)
+            assert_(eq(ur.filled(0), mr.filled(0), f))
+            assert_(eqmask(ur.mask, mr.mask))
+
+    def test_reduce(self):
+        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)
+
+    def test_minmax(self):
+        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_((amask.max(0) == a.max(0)).all())
+        assert_((amask.min(0) == [5, 6, 7, 8]).all())
+        assert_(amask.max(1)[0].mask)
+        assert_(amask.min(1)[0].mask)
+
+    def test_nonzero(self):
+        for t in "?bhilqpBHILQPfdgFDGO":
+            x = array([1, 0, 2, 0], mask=[0, 0, 1, 1])
+            assert_(eq(nonzero(x), [0]))
+
+
+class TestArrayMethods:
+
+    def setup_method(self):
+        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))
+
+        self.d = (x, X, XX, m, mx, mX, mXX)
+
+    def test_trace(self):
+        (x, X, XX, m, mx, mX, mXX,) = self.d
+        mXdiag = mX.diagonal()
+        assert_equal(mX.trace(), mX.diagonal().compressed().sum())
+        assert_(eq(mX.trace(),
+                           X.trace() - sum(mXdiag.mask * X.diagonal(),
+                                           axis=0)))
+
+    def test_clip(self):
+        (x, X, XX, m, mx, mX, mXX,) = self.d
+        clipped = mx.clip(2, 8)
+        assert_(eq(clipped.mask, mx.mask))
+        assert_(eq(clipped._data, x.clip(2, 8)))
+        assert_(eq(clipped._data, mx._data.clip(2, 8)))
+
+    def test_ptp(self):
+        (x, X, XX, m, mx, mX, mXX,) = self.d
+        (n, m) = X.shape
+        # print(type(mx), mx.compressed())
+        # raise Exception()
+        assert_equal(mx.ptp(), np.ptp(mx.compressed()))
+        rows = np.zeros(n, np.float64)
+        cols = np.zeros(m, np.float64)
+        for k in range(m):
+            cols[k] = np.ptp(mX[:, k].compressed())
+        for k in range(n):
+            rows[k] = np.ptp(mX[k].compressed())
+        assert_(eq(mX.ptp(0), cols))
+        assert_(eq(mX.ptp(1), rows))
+
+    def test_swapaxes(self):
+        (x, X, XX, m, mx, mX, mXX,) = self.d
+        mXswapped = mX.swapaxes(0, 1)
+        assert_(eq(mXswapped[-1], mX[:, -1]))
+        mXXswapped = mXX.swapaxes(0, 2)
+        assert_equal(mXXswapped.shape, (2, 2, 3, 3))
+
+    def test_cumprod(self):
+        (x, X, XX, m, mx, mX, mXX,) = self.d
+        mXcp = mX.cumprod(0)
+        assert_(eq(mXcp._data, mX.filled(1).cumprod(0)))
+        mXcp = mX.cumprod(1)
+        assert_(eq(mXcp._data, mX.filled(1).cumprod(1)))
+
+    def test_cumsum(self):
+        (x, X, XX, m, mx, mX, mXX,) = self.d
+        mXcp = mX.cumsum(0)
+        assert_(eq(mXcp._data, mX.filled(0).cumsum(0)))
+        mXcp = mX.cumsum(1)
+        assert_(eq(mXcp._data, mX.filled(0).cumsum(1)))
+
+    def test_varstd(self):
+        (x, X, XX, m, mx, mX, mXX,) = self.d
+        assert_(eq(mX.var(axis=None), mX.compressed().var()))
+        assert_(eq(mX.std(axis=None), mX.compressed().std()))
+        assert_(eq(mXX.var(axis=3).shape, XX.var(axis=3).shape))
+        assert_(eq(mX.var().shape, X.var().shape))
+        (mXvar0, mXvar1) = (mX.var(axis=0), mX.var(axis=1))
+        for k in range(6):
+            assert_(eq(mXvar1[k], mX[k].compressed().var()))
+            assert_(eq(mXvar0[k], mX[:, k].compressed().var()))
+            assert_(eq(np.sqrt(mXvar0[k]),
+                               mX[:, k].compressed().std()))
+
+
+def eqmask(m1, m2):
+    if m1 is nomask:
+        return m2 is nomask
+    if m2 is nomask:
+        return m1 is nomask
+    return (m1 == m2).all()

venv/lib/python3.13/site-packages/numpy/ma/tests/test_regression.py

@@ -0,0 +1,100 @@
+import numpy as np
+from numpy.testing import (
+    assert_,
+    assert_allclose,
+    assert_array_equal,
+    suppress_warnings,
+)
+
+
+class TestRegression:
+    def test_masked_array_create(self):
+        # Ticket #17
+        x = np.ma.masked_array([0, 1, 2, 3, 0, 4, 5, 6],
+                               mask=[0, 0, 0, 1, 1, 1, 0, 0])
+        assert_array_equal(np.ma.nonzero(x), [[1, 2, 6, 7]])
+
+    def test_masked_array(self):
+        # Ticket #61
+        np.ma.array(1, mask=[1])
+
+    def test_mem_masked_where(self):
+        # Ticket #62
+        from numpy.ma import MaskType, masked_where
+        a = np.zeros((1, 1))
+        b = np.zeros(a.shape, MaskType)
+        c = masked_where(b, a)
+        a - c
+
+    def test_masked_array_multiply(self):
+        # Ticket #254
+        a = np.ma.zeros((4, 1))
+        a[2, 0] = np.ma.masked
+        b = np.zeros((4, 2))
+        a * b
+        b * a
+
+    def test_masked_array_repeat(self):
+        # Ticket #271
+        np.ma.array([1], mask=False).repeat(10)
+
+    def test_masked_array_repr_unicode(self):
+        # Ticket #1256
+        repr(np.ma.array("Unicode"))
+
+    def test_atleast_2d(self):
+        # Ticket #1559
+        a = np.ma.masked_array([0.0, 1.2, 3.5], mask=[False, True, False])
+        b = np.atleast_2d(a)
+        assert_(a.mask.ndim == 1)
+        assert_(b.mask.ndim == 2)
+
+    def test_set_fill_value_unicode_py3(self):
+        # Ticket #2733
+        a = np.ma.masked_array(['a', 'b', 'c'], mask=[1, 0, 0])
+        a.fill_value = 'X'
+        assert_(a.fill_value == 'X')
+
+    def test_var_sets_maskedarray_scalar(self):
+        # Issue gh-2757
+        a = np.ma.array(np.arange(5), mask=True)
+        mout = np.ma.array(-1, dtype=float)
+        a.var(out=mout)
+        assert_(mout._data == 0)
+
+    def test_ddof_corrcoef(self):
+        # See gh-3336
+        x = np.ma.masked_equal([1, 2, 3, 4, 5], 4)
+        y = np.array([2, 2.5, 3.1, 3, 5])
+        # this test can be removed after deprecation.
+        with suppress_warnings() as sup:
+            sup.filter(DeprecationWarning, "bias and ddof have no effect")
+            r0 = np.ma.corrcoef(x, y, ddof=0)
+            r1 = np.ma.corrcoef(x, y, ddof=1)
+            # ddof should not have an effect (it gets cancelled out)
+            assert_allclose(r0.data, r1.data)
+
+    def test_mask_not_backmangled(self):
+        # See gh-10314.  Test case taken from gh-3140.
+        a = np.ma.MaskedArray([1., 2.], mask=[False, False])
+        assert_(a.mask.shape == (2,))
+        b = np.tile(a, (2, 1))
+        # Check that the above no longer changes a.shape to (1, 2)
+        assert_(a.mask.shape == (2,))
+        assert_(b.shape == (2, 2))
+        assert_(b.mask.shape == (2, 2))
+
+    def test_empty_list_on_structured(self):
+        # See gh-12464. Indexing with empty list should give empty result.
+        ma = np.ma.MaskedArray([(1, 1.), (2, 2.), (3, 3.)], dtype='i4,f4')
+        assert_array_equal(ma[[]], ma[:0])
+
+    def test_masked_array_tobytes_fortran(self):
+        ma = np.ma.arange(4).reshape((2, 2))
+        assert_array_equal(ma.tobytes(order='F'), ma.T.tobytes())
+
+    def test_structured_array(self):
+        # see gh-22041
+        np.ma.array((1, (b"", b"")),
+                    dtype=[("x", np.int_),
+                          ("y", [("i", np.void), ("j", np.void)])])

venv/lib/python3.13/site-packages/numpy/ma/tests/test_subclassing.py

@@ -0,0 +1,469 @@
+"""Tests suite for MaskedArray & subclassing.
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+
+"""
+import numpy as np
+from numpy.lib.mixins import NDArrayOperatorsMixin
+from numpy.ma.core import (
+    MaskedArray,
+    add,
+    arange,
+    array,
+    asanyarray,
+    asarray,
+    divide,
+    hypot,
+    log,
+    masked,
+    masked_array,
+    nomask,
+)
+from numpy.ma.testutils import assert_equal
+from numpy.testing import assert_, assert_raises
+
+# from numpy.ma.core import (
+
+def assert_startswith(a, b):
+    # produces a better error message than assert_(a.startswith(b))
+    assert_equal(a[:len(b)], b)
+
+class SubArray(np.ndarray):
+    # Defines a generic np.ndarray subclass, that stores some metadata
+    # in the  dictionary `info`.
+    def __new__(cls, arr, info={}):
+        x = np.asanyarray(arr).view(cls)
+        x.info = info.copy()
+        return x
+
+    def __array_finalize__(self, obj):
+        super().__array_finalize__(obj)
+        self.info = getattr(obj, 'info', {}).copy()
+
+    def __add__(self, other):
+        result = super().__add__(other)
+        result.info['added'] = result.info.get('added', 0) + 1
+        return result
+
+    def __iadd__(self, other):
+        result = super().__iadd__(other)
+        result.info['iadded'] = result.info.get('iadded', 0) + 1
+        return result
+
+
+subarray = SubArray
+
+
+class SubMaskedArray(MaskedArray):
+    """Pure subclass of MaskedArray, keeping some info on subclass."""
+    def __new__(cls, info=None, **kwargs):
+        obj = super().__new__(cls, **kwargs)
+        obj._optinfo['info'] = info
+        return obj
+
+
+class MSubArray(SubArray, MaskedArray):
+
+    def __new__(cls, data, info={}, mask=nomask):
+        subarr = SubArray(data, info)
+        _data = MaskedArray.__new__(cls, data=subarr, mask=mask)
+        _data.info = subarr.info
+        return _data
+
+    @property
+    def _series(self):
+        _view = self.view(MaskedArray)
+        _view._sharedmask = False
+        return _view
+
+
+msubarray = MSubArray
+
+
+# Also a subclass that overrides __str__, __repr__ and __setitem__, disallowing
+# setting to non-class values (and thus np.ma.core.masked_print_option)
+# and overrides __array_wrap__, updating the info dict, to check that this
+# doesn't get destroyed by MaskedArray._update_from.  But this one also needs
+# its own iterator...
+class CSAIterator:
+    """
+    Flat iterator object that uses its own setter/getter
+    (works around ndarray.flat not propagating subclass setters/getters
+    see https://github.com/numpy/numpy/issues/4564)
+    roughly following MaskedIterator
+    """
+    def __init__(self, a):
+        self._original = a
+        self._dataiter = a.view(np.ndarray).flat
+
+    def __iter__(self):
+        return self
+
+    def __getitem__(self, indx):
+        out = self._dataiter.__getitem__(indx)
+        if not isinstance(out, np.ndarray):
+            out = out.__array__()
+        out = out.view(type(self._original))
+        return out
+
+    def __setitem__(self, index, value):
+        self._dataiter[index] = self._original._validate_input(value)
+
+    def __next__(self):
+        return next(self._dataiter).__array__().view(type(self._original))
+
+
+class ComplicatedSubArray(SubArray):
+
+    def __str__(self):
+        return f'myprefix {self.view(SubArray)} mypostfix'
+
+    def __repr__(self):
+        # Return a repr that does not start with 'name('
+        return f'<{self.__class__.__name__} {self}>'
+
+    def _validate_input(self, value):
+        if not isinstance(value, ComplicatedSubArray):
+            raise ValueError("Can only set to MySubArray values")
+        return value
+
+    def __setitem__(self, item, value):
+        # validation ensures direct assignment with ndarray or
+        # masked_print_option will fail
+        super().__setitem__(item, self._validate_input(value))
+
+    def __getitem__(self, item):
+        # ensure getter returns our own class also for scalars
+        value = super().__getitem__(item)
+        if not isinstance(value, np.ndarray):  # scalar
+            value = value.__array__().view(ComplicatedSubArray)
+        return value
+
+    @property
+    def flat(self):
+        return CSAIterator(self)
+
+    @flat.setter
+    def flat(self, value):
+        y = self.ravel()
+        y[:] = value
+
+    def __array_wrap__(self, obj, context=None, return_scalar=False):
+        obj = super().__array_wrap__(obj, context, return_scalar)
+        if context is not None and context[0] is np.multiply:
+            obj.info['multiplied'] = obj.info.get('multiplied', 0) + 1
+
+        return obj
+
+
+class WrappedArray(NDArrayOperatorsMixin):
+    """
+    Wrapping a MaskedArray rather than subclassing to test that
+    ufunc deferrals are commutative.
+    See: https://github.com/numpy/numpy/issues/15200)
+    """
+    __slots__ = ('_array', 'attrs')
+    __array_priority__ = 20
+
+    def __init__(self, array, **attrs):
+        self._array = array
+        self.attrs = attrs
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}(\n{self._array}\n{self.attrs}\n)"
+
+    def __array__(self, dtype=None, copy=None):
+        return np.asarray(self._array)
+
+    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+        if method == '__call__':
+            inputs = [arg._array if isinstance(arg, self.__class__) else arg
+                      for arg in inputs]
+            return self.__class__(ufunc(*inputs, **kwargs), **self.attrs)
+        else:
+            return NotImplemented
+
+
+class TestSubclassing:
+    # Test suite for masked subclasses of ndarray.
+
+    def setup_method(self):
+        x = np.arange(5, dtype='float')
+        mx = msubarray(x, mask=[0, 1, 0, 0, 0])
+        self.data = (x, mx)
+
+    def test_data_subclassing(self):
+        # Tests whether the subclass is kept.
+        x = np.arange(5)
+        m = [0, 0, 1, 0, 0]
+        xsub = SubArray(x)
+        xmsub = masked_array(xsub, mask=m)
+        assert_(isinstance(xmsub, MaskedArray))
+        assert_equal(xmsub._data, xsub)
+        assert_(isinstance(xmsub._data, SubArray))
+
+    def test_maskedarray_subclassing(self):
+        # Tests subclassing MaskedArray
+        (x, mx) = self.data
+        assert_(isinstance(mx._data, subarray))
+
+    def test_masked_unary_operations(self):
+        # Tests masked_unary_operation
+        (x, mx) = self.data
+        with np.errstate(divide='ignore'):
+            assert_(isinstance(log(mx), msubarray))
+            assert_equal(log(x), np.log(x))
+
+    def test_masked_binary_operations(self):
+        # Tests masked_binary_operation
+        (x, mx) = self.data
+        # Result should be a msubarray
+        assert_(isinstance(add(mx, mx), msubarray))
+        assert_(isinstance(add(mx, x), msubarray))
+        # Result should work
+        assert_equal(add(mx, x), mx + x)
+        assert_(isinstance(add(mx, mx)._data, subarray))
+        assert_(isinstance(add.outer(mx, mx), msubarray))
+        assert_(isinstance(hypot(mx, mx), msubarray))
+        assert_(isinstance(hypot(mx, x), msubarray))
+
+    def test_masked_binary_operations2(self):
+        # Tests domained_masked_binary_operation
+        (x, mx) = self.data
+        xmx = masked_array(mx.data.__array__(), mask=mx.mask)
+        assert_(isinstance(divide(mx, mx), msubarray))
+        assert_(isinstance(divide(mx, x), msubarray))
+        assert_equal(divide(mx, mx), divide(xmx, xmx))
+
+    def test_attributepropagation(self):
+        x = array(arange(5), mask=[0] + [1] * 4)
+        my = masked_array(subarray(x))
+        ym = msubarray(x)
+        #
+        z = (my + 1)
+        assert_(isinstance(z, MaskedArray))
+        assert_(not isinstance(z, MSubArray))
+        assert_(isinstance(z._data, SubArray))
+        assert_equal(z._data.info, {})
+        #
+        z = (ym + 1)
+        assert_(isinstance(z, MaskedArray))
+        assert_(isinstance(z, MSubArray))
+        assert_(isinstance(z._data, SubArray))
+        assert_(z._data.info['added'] > 0)
+        # Test that inplace methods from data get used (gh-4617)
+        ym += 1
+        assert_(isinstance(ym, MaskedArray))
+        assert_(isinstance(ym, MSubArray))
+        assert_(isinstance(ym._data, SubArray))
+        assert_(ym._data.info['iadded'] > 0)
+        #
+        ym._set_mask([1, 0, 0, 0, 1])
+        assert_equal(ym._mask, [1, 0, 0, 0, 1])
+        ym._series._set_mask([0, 0, 0, 0, 1])
+        assert_equal(ym._mask, [0, 0, 0, 0, 1])
+        #
+        xsub = subarray(x, info={'name': 'x'})
+        mxsub = masked_array(xsub)
+        assert_(hasattr(mxsub, 'info'))
+        assert_equal(mxsub.info, xsub.info)
+
+    def test_subclasspreservation(self):
+        # Checks that masked_array(...,subok=True) preserves the class.
+        x = np.arange(5)
+        m = [0, 0, 1, 0, 0]
+        xinfo = list(zip(x, m))
+        xsub = MSubArray(x, mask=m, info={'xsub': xinfo})
+        #
+        mxsub = masked_array(xsub, subok=False)
+        assert_(not isinstance(mxsub, MSubArray))
+        assert_(isinstance(mxsub, MaskedArray))
+        assert_equal(mxsub._mask, m)
+        #
+        mxsub = asarray(xsub)
+        assert_(not isinstance(mxsub, MSubArray))
+        assert_(isinstance(mxsub, MaskedArray))
+        assert_equal(mxsub._mask, m)
+        #
+        mxsub = masked_array(xsub, subok=True)
+        assert_(isinstance(mxsub, MSubArray))
+        assert_equal(mxsub.info, xsub.info)
+        assert_equal(mxsub._mask, xsub._mask)
+        #
+        mxsub = asanyarray(xsub)
+        assert_(isinstance(mxsub, MSubArray))
+        assert_equal(mxsub.info, xsub.info)
+        assert_equal(mxsub._mask, m)
+
+    def test_subclass_items(self):
+        """test that getter and setter go via baseclass"""
+        x = np.arange(5)
+        xcsub = ComplicatedSubArray(x)
+        mxcsub = masked_array(xcsub, mask=[True, False, True, False, False])
+        # getter should  return a ComplicatedSubArray, even for single item
+        # first check we wrote ComplicatedSubArray correctly
+        assert_(isinstance(xcsub[1], ComplicatedSubArray))
+        assert_(isinstance(xcsub[1, ...], ComplicatedSubArray))
+        assert_(isinstance(xcsub[1:4], ComplicatedSubArray))
+
+        # now that it propagates inside the MaskedArray
+        assert_(isinstance(mxcsub[1], ComplicatedSubArray))
+        assert_(isinstance(mxcsub[1, ...].data, ComplicatedSubArray))
+        assert_(mxcsub[0] is masked)
+        assert_(isinstance(mxcsub[0, ...].data, ComplicatedSubArray))
+        assert_(isinstance(mxcsub[1:4].data, ComplicatedSubArray))
+
+        # also for flattened version (which goes via MaskedIterator)
+        assert_(isinstance(mxcsub.flat[1].data, ComplicatedSubArray))
+        assert_(mxcsub.flat[0] is masked)
+        assert_(isinstance(mxcsub.flat[1:4].base, ComplicatedSubArray))
+
+        # setter should only work with ComplicatedSubArray input
+        # first check we wrote ComplicatedSubArray correctly
+        assert_raises(ValueError, xcsub.__setitem__, 1, x[4])
+        # now that it propagates inside the MaskedArray
+        assert_raises(ValueError, mxcsub.__setitem__, 1, x[4])
+        assert_raises(ValueError, mxcsub.__setitem__, slice(1, 4), x[1:4])
+        mxcsub[1] = xcsub[4]
+        mxcsub[1:4] = xcsub[1:4]
+        # also for flattened version (which goes via MaskedIterator)
+        assert_raises(ValueError, mxcsub.flat.__setitem__, 1, x[4])
+        assert_raises(ValueError, mxcsub.flat.__setitem__, slice(1, 4), x[1:4])
+        mxcsub.flat[1] = xcsub[4]
+        mxcsub.flat[1:4] = xcsub[1:4]
+
+    def test_subclass_nomask_items(self):
+        x = np.arange(5)
+        xcsub = ComplicatedSubArray(x)
+        mxcsub_nomask = masked_array(xcsub)
+
+        assert_(isinstance(mxcsub_nomask[1, ...].data, ComplicatedSubArray))
+        assert_(isinstance(mxcsub_nomask[0, ...].data, ComplicatedSubArray))
+
+        assert_(isinstance(mxcsub_nomask[1], ComplicatedSubArray))
+        assert_(isinstance(mxcsub_nomask[0], ComplicatedSubArray))
+
+    def test_subclass_repr(self):
+        """test that repr uses the name of the subclass
+        and 'array' for np.ndarray"""
+        x = np.arange(5)
+        mx = masked_array(x, mask=[True, False, True, False, False])
+        assert_startswith(repr(mx), 'masked_array')
+        xsub = SubArray(x)
+        mxsub = masked_array(xsub, mask=[True, False, True, False, False])
+        assert_startswith(repr(mxsub),
+            f'masked_{SubArray.__name__}(data=[--, 1, --, 3, 4]')
+
+    def test_subclass_str(self):
+        """test str with subclass that has overridden str, setitem"""
+        # first without override
+        x = np.arange(5)
+        xsub = SubArray(x)
+        mxsub = masked_array(xsub, mask=[True, False, True, False, False])
+        assert_equal(str(mxsub), '[-- 1 -- 3 4]')
+
+        xcsub = ComplicatedSubArray(x)
+        assert_raises(ValueError, xcsub.__setitem__, 0,
+                      np.ma.core.masked_print_option)
+        mxcsub = masked_array(xcsub, mask=[True, False, True, False, False])
+        assert_equal(str(mxcsub), 'myprefix [-- 1 -- 3 4] mypostfix')
+
+    def test_pure_subclass_info_preservation(self):
+        # Test that ufuncs and methods conserve extra information consistently;
+        # see gh-7122.
+        arr1 = SubMaskedArray('test', data=[1, 2, 3, 4, 5, 6])
+        arr2 = SubMaskedArray(data=[0, 1, 2, 3, 4, 5])
+        diff1 = np.subtract(arr1, arr2)
+        assert_('info' in diff1._optinfo)
+        assert_(diff1._optinfo['info'] == 'test')
+        diff2 = arr1 - arr2
+        assert_('info' in diff2._optinfo)
+        assert_(diff2._optinfo['info'] == 'test')
+
+
+class ArrayNoInheritance:
+    """Quantity-like class that does not inherit from ndarray"""
+    def __init__(self, data, units):
+        self.magnitude = data
+        self.units = units
+
+    def __getattr__(self, attr):
+        return getattr(self.magnitude, attr)
+
+
+def test_array_no_inheritance():
+    data_masked = np.ma.array([1, 2, 3], mask=[True, False, True])
+    data_masked_units = ArrayNoInheritance(data_masked, 'meters')
+
+    # Get the masked representation of the Quantity-like class
+    new_array = np.ma.array(data_masked_units)
+    assert_equal(data_masked.data, new_array.data)
+    assert_equal(data_masked.mask, new_array.mask)
+    # Test sharing the mask
+    data_masked.mask = [True, False, False]
+    assert_equal(data_masked.mask, new_array.mask)
+    assert_(new_array.sharedmask)
+
+    # Get the masked representation of the Quantity-like class
+    new_array = np.ma.array(data_masked_units, copy=True)
+    assert_equal(data_masked.data, new_array.data)
+    assert_equal(data_masked.mask, new_array.mask)
+    # Test that the mask is not shared when copy=True
+    data_masked.mask = [True, False, True]
+    assert_equal([True, False, False], new_array.mask)
+    assert_(not new_array.sharedmask)
+
+    # Get the masked representation of the Quantity-like class
+    new_array = np.ma.array(data_masked_units, keep_mask=False)
+    assert_equal(data_masked.data, new_array.data)
+    # The change did not affect the original mask
+    assert_equal(data_masked.mask, [True, False, True])
+    # Test that the mask is False and not shared when keep_mask=False
+    assert_(not new_array.mask)
+    assert_(not new_array.sharedmask)
+
+
+class TestClassWrapping:
+    # Test suite for classes that wrap MaskedArrays
+
+    def setup_method(self):
+        m = np.ma.masked_array([1, 3, 5], mask=[False, True, False])
+        wm = WrappedArray(m)
+        self.data = (m, wm)
+
+    def test_masked_unary_operations(self):
+        # Tests masked_unary_operation
+        (m, wm) = self.data
+        with np.errstate(divide='ignore'):
+            assert_(isinstance(np.log(wm), WrappedArray))
+
+    def test_masked_binary_operations(self):
+        # Tests masked_binary_operation
+        (m, wm) = self.data
+        # Result should be a WrappedArray
+        assert_(isinstance(np.add(wm, wm), WrappedArray))
+        assert_(isinstance(np.add(m, wm), WrappedArray))
+        assert_(isinstance(np.add(wm, m), WrappedArray))
+        # add and '+' should call the same ufunc
+        assert_equal(np.add(m, wm), m + wm)
+        assert_(isinstance(np.hypot(m, wm), WrappedArray))
+        assert_(isinstance(np.hypot(wm, m), WrappedArray))
+        # Test domained binary operations
+        assert_(isinstance(np.divide(wm, m), WrappedArray))
+        assert_(isinstance(np.divide(m, wm), WrappedArray))
+        assert_equal(np.divide(wm, m) * m, np.divide(m, m) * wm)
+        # Test broadcasting
+        m2 = np.stack([m, m])
+        assert_(isinstance(np.divide(wm, m2), WrappedArray))
+        assert_(isinstance(np.divide(m2, wm), WrappedArray))
+        assert_equal(np.divide(m2, wm), np.divide(wm, m2))
+
+    def test_mixins_have_slots(self):
+        mixin = NDArrayOperatorsMixin()
+        # Should raise an error
+        assert_raises(AttributeError, mixin.__setattr__, "not_a_real_attr", 1)
+
+        m = np.ma.masked_array([1, 3, 5], mask=[False, True, False])
+        wm = WrappedArray(m)
+        assert_raises(AttributeError, wm.__setattr__, "not_an_attr", 2)

venv/lib/python3.13/site-packages/numpy/matrixlib/tests/test_defmatrix.py

@@ -0,0 +1,455 @@
+import collections.abc
+
+import numpy as np
+from numpy import asmatrix, bmat, matrix
+from numpy.linalg import matrix_power
+from numpy.testing import (
+    assert_,
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_equal,
+    assert_equal,
+    assert_raises,
+)
+
+
+class TestCtor:
+    def test_basic(self):
+        A = np.array([[1, 2], [3, 4]])
+        mA = matrix(A)
+        assert_(np.all(mA.A == A))
+
+        B = bmat("A,A;A,A")
+        C = bmat([[A, A], [A, A]])
+        D = np.array([[1, 2, 1, 2],
+                      [3, 4, 3, 4],
+                      [1, 2, 1, 2],
+                      [3, 4, 3, 4]])
+        assert_(np.all(B.A == D))
+        assert_(np.all(C.A == D))
+
+        E = np.array([[5, 6], [7, 8]])
+        AEresult = matrix([[1, 2, 5, 6], [3, 4, 7, 8]])
+        assert_(np.all(bmat([A, E]) == AEresult))
+
+        vec = np.arange(5)
+        mvec = matrix(vec)
+        assert_(mvec.shape == (1, 5))
+
+    def test_exceptions(self):
+        # Check for ValueError when called with invalid string data.
+        assert_raises(ValueError, matrix, "invalid")
+
+    def test_bmat_nondefault_str(self):
+        A = np.array([[1, 2], [3, 4]])
+        B = np.array([[5, 6], [7, 8]])
+        Aresult = np.array([[1, 2, 1, 2],
+                            [3, 4, 3, 4],
+                            [1, 2, 1, 2],
+                            [3, 4, 3, 4]])
+        mixresult = np.array([[1, 2, 5, 6],
+                              [3, 4, 7, 8],
+                              [5, 6, 1, 2],
+                              [7, 8, 3, 4]])
+        assert_(np.all(bmat("A,A;A,A") == Aresult))
+        assert_(np.all(bmat("A,A;A,A", ldict={'A': B}) == Aresult))
+        assert_raises(TypeError, bmat, "A,A;A,A", gdict={'A': B})
+        assert_(
+            np.all(bmat("A,A;A,A", ldict={'A': A}, gdict={'A': B}) == Aresult))
+        b2 = bmat("A,B;C,D", ldict={'A': A, 'B': B}, gdict={'C': B, 'D': A})
+        assert_(np.all(b2 == mixresult))
+
+
+class TestProperties:
+    def test_sum(self):
+        """Test whether matrix.sum(axis=1) preserves orientation.
+        Fails in NumPy <= 0.9.6.2127.
+        """
+        M = matrix([[1, 2, 0, 0],
+                   [3, 4, 0, 0],
+                   [1, 2, 1, 2],
+                   [3, 4, 3, 4]])
+        sum0 = matrix([8, 12, 4, 6])
+        sum1 = matrix([3, 7, 6, 14]).T
+        sumall = 30
+        assert_array_equal(sum0, M.sum(axis=0))
+        assert_array_equal(sum1, M.sum(axis=1))
+        assert_equal(sumall, M.sum())
+
+        assert_array_equal(sum0, np.sum(M, axis=0))
+        assert_array_equal(sum1, np.sum(M, axis=1))
+        assert_equal(sumall, np.sum(M))
+
+    def test_prod(self):
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(x.prod(), 720)
+        assert_equal(x.prod(0), matrix([[4, 10, 18]]))
+        assert_equal(x.prod(1), matrix([[6], [120]]))
+
+        assert_equal(np.prod(x), 720)
+        assert_equal(np.prod(x, axis=0), matrix([[4, 10, 18]]))
+        assert_equal(np.prod(x, axis=1), matrix([[6], [120]]))
+
+        y = matrix([0, 1, 3])
+        assert_(y.prod() == 0)
+
+    def test_max(self):
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(x.max(), 6)
+        assert_equal(x.max(0), matrix([[4, 5, 6]]))
+        assert_equal(x.max(1), matrix([[3], [6]]))
+
+        assert_equal(np.max(x), 6)
+        assert_equal(np.max(x, axis=0), matrix([[4, 5, 6]]))
+        assert_equal(np.max(x, axis=1), matrix([[3], [6]]))
+
+    def test_min(self):
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(x.min(), 1)
+        assert_equal(x.min(0), matrix([[1, 2, 3]]))
+        assert_equal(x.min(1), matrix([[1], [4]]))
+
+        assert_equal(np.min(x), 1)
+        assert_equal(np.min(x, axis=0), matrix([[1, 2, 3]]))
+        assert_equal(np.min(x, axis=1), matrix([[1], [4]]))
+
+    def test_ptp(self):
+        x = np.arange(4).reshape((2, 2))
+        mx = x.view(np.matrix)
+        assert_(mx.ptp() == 3)
+        assert_(np.all(mx.ptp(0) == np.array([2, 2])))
+        assert_(np.all(mx.ptp(1) == np.array([1, 1])))
+
+    def test_var(self):
+        x = np.arange(9).reshape((3, 3))
+        mx = x.view(np.matrix)
+        assert_equal(x.var(ddof=0), mx.var(ddof=0))
+        assert_equal(x.var(ddof=1), mx.var(ddof=1))
+
+    def test_basic(self):
+        import numpy.linalg as linalg
+
+        A = np.array([[1., 2.],
+                      [3., 4.]])
+        mA = matrix(A)
+        assert_(np.allclose(linalg.inv(A), mA.I))
+        assert_(np.all(np.array(np.transpose(A) == mA.T)))
+        assert_(np.all(np.array(np.transpose(A) == mA.H)))
+        assert_(np.all(A == mA.A))
+
+        B = A + 2j * A
+        mB = matrix(B)
+        assert_(np.allclose(linalg.inv(B), mB.I))
+        assert_(np.all(np.array(np.transpose(B) == mB.T)))
+        assert_(np.all(np.array(np.transpose(B).conj() == mB.H)))
+
+    def test_pinv(self):
+        x = matrix(np.arange(6).reshape(2, 3))
+        xpinv = matrix([[-0.77777778,  0.27777778],
+                        [-0.11111111,  0.11111111],
+                        [ 0.55555556, -0.05555556]])
+        assert_almost_equal(x.I, xpinv)
+
+    def test_comparisons(self):
+        A = np.arange(100).reshape(10, 10)
+        mA = matrix(A)
+        mB = matrix(A) + 0.1
+        assert_(np.all(mB == A + 0.1))
+        assert_(np.all(mB == matrix(A + 0.1)))
+        assert_(not np.any(mB == matrix(A - 0.1)))
+        assert_(np.all(mA < mB))
+        assert_(np.all(mA <= mB))
+        assert_(np.all(mA <= mA))
+        assert_(not np.any(mA < mA))
+
+        assert_(not np.any(mB < mA))
+        assert_(np.all(mB >= mA))
+        assert_(np.all(mB >= mB))
+        assert_(not np.any(mB > mB))
+
+        assert_(np.all(mA == mA))
+        assert_(not np.any(mA == mB))
+        assert_(np.all(mB != mA))
+
+        assert_(not np.all(abs(mA) > 0))
+        assert_(np.all(abs(mB > 0)))
+
+    def test_asmatrix(self):
+        A = np.arange(100).reshape(10, 10)
+        mA = asmatrix(A)
+        A[0, 0] = -10
+        assert_(A[0, 0] == mA[0, 0])
+
+    def test_noaxis(self):
+        A = matrix([[1, 0], [0, 1]])
+        assert_(A.sum() == matrix(2))
+        assert_(A.mean() == matrix(0.5))
+
+    def test_repr(self):
+        A = matrix([[1, 0], [0, 1]])
+        assert_(repr(A) == "matrix([[1, 0],\n        [0, 1]])")
+
+    def test_make_bool_matrix_from_str(self):
+        A = matrix('True; True; False')
+        B = matrix([[True], [True], [False]])
+        assert_array_equal(A, B)
+
+class TestCasting:
+    def test_basic(self):
+        A = np.arange(100).reshape(10, 10)
+        mA = matrix(A)
+
+        mB = mA.copy()
+        O = np.ones((10, 10), np.float64) * 0.1
+        mB = mB + O
+        assert_(mB.dtype.type == np.float64)
+        assert_(np.all(mA != mB))
+        assert_(np.all(mB == mA + 0.1))
+
+        mC = mA.copy()
+        O = np.ones((10, 10), np.complex128)
+        mC = mC * O
+        assert_(mC.dtype.type == np.complex128)
+        assert_(np.all(mA != mB))
+
+
+class TestAlgebra:
+    def test_basic(self):
+        import numpy.linalg as linalg
+
+        A = np.array([[1., 2.], [3., 4.]])
+        mA = matrix(A)
+
+        B = np.identity(2)
+        for i in range(6):
+            assert_(np.allclose((mA ** i).A, B))
+            B = np.dot(B, A)
+
+        Ainv = linalg.inv(A)
+        B = np.identity(2)
+        for i in range(6):
+            assert_(np.allclose((mA ** -i).A, B))
+            B = np.dot(B, Ainv)
+
+        assert_(np.allclose((mA * mA).A, np.dot(A, A)))
+        assert_(np.allclose((mA + mA).A, (A + A)))
+        assert_(np.allclose((3 * mA).A, (3 * A)))
+
+        mA2 = matrix(A)
+        mA2 *= 3
+        assert_(np.allclose(mA2.A, 3 * A))
+
+    def test_pow(self):
+        """Test raising a matrix to an integer power works as expected."""
+        m = matrix("1. 2.; 3. 4.")
+        m2 = m.copy()
+        m2 **= 2
+        mi = m.copy()
+        mi **= -1
+        m4 = m2.copy()
+        m4 **= 2
+        assert_array_almost_equal(m2, m**2)
+        assert_array_almost_equal(m4, np.dot(m2, m2))
+        assert_array_almost_equal(np.dot(mi, m), np.eye(2))
+
+    def test_scalar_type_pow(self):
+        m = matrix([[1, 2], [3, 4]])
+        for scalar_t in [np.int8, np.uint8]:
+            two = scalar_t(2)
+            assert_array_almost_equal(m ** 2, m ** two)
+
+    def test_notimplemented(self):
+        '''Check that 'not implemented' operations produce a failure.'''
+        A = matrix([[1., 2.],
+                    [3., 4.]])
+
+        # __rpow__
+        with assert_raises(TypeError):
+            1.0**A
+
+        # __mul__ with something not a list, ndarray, tuple, or scalar
+        with assert_raises(TypeError):
+            A * object()
+
+
+class TestMatrixReturn:
+    def test_instance_methods(self):
+        a = matrix([1.0], dtype='f8')
+        methodargs = {
+            'astype': ('intc',),
+            'clip': (0.0, 1.0),
+            'compress': ([1],),
+            'repeat': (1,),
+            'reshape': (1,),
+            'swapaxes': (0, 0),
+            'dot': np.array([1.0]),
+            }
+        excluded_methods = [
+            'argmin', 'choose', 'dump', 'dumps', 'fill', 'getfield',
+            'getA', 'getA1', 'item', 'nonzero', 'put', 'putmask', 'resize',
+            'searchsorted', 'setflags', 'setfield', 'sort',
+            'partition', 'argpartition', 'newbyteorder', 'to_device',
+            'take', 'tofile', 'tolist', 'tobytes', 'all', 'any',
+            'sum', 'argmax', 'argmin', 'min', 'max', 'mean', 'var', 'ptp',
+            'prod', 'std', 'ctypes', 'itemset', 'bitwise_count',
+            ]
+        for attrib in dir(a):
+            if attrib.startswith('_') or attrib in excluded_methods:
+                continue
+            f = getattr(a, attrib)
+            if isinstance(f, collections.abc.Callable):
+                # reset contents of a
+                a.astype('f8')
+                a.fill(1.0)
+                args = methodargs.get(attrib, ())
+                b = f(*args)
+                assert_(type(b) is matrix, f"{attrib}")
+        assert_(type(a.real) is matrix)
+        assert_(type(a.imag) is matrix)
+        c, d = matrix([0.0]).nonzero()
+        assert_(type(c) is np.ndarray)
+        assert_(type(d) is np.ndarray)
+
+
+class TestIndexing:
+    def test_basic(self):
+        x = asmatrix(np.zeros((3, 2), float))
+        y = np.zeros((3, 1), float)
+        y[:, 0] = [0.8, 0.2, 0.3]
+        x[:, 1] = y > 0.5
+        assert_equal(x, [[0, 1], [0, 0], [0, 0]])
+
+
+class TestNewScalarIndexing:
+    a = matrix([[1, 2], [3, 4]])
+
+    def test_dimesions(self):
+        a = self.a
+        x = a[0]
+        assert_equal(x.ndim, 2)
+
+    def test_array_from_matrix_list(self):
+        a = self.a
+        x = np.array([a, a])
+        assert_equal(x.shape, [2, 2, 2])
+
+    def test_array_to_list(self):
+        a = self.a
+        assert_equal(a.tolist(), [[1, 2], [3, 4]])
+
+    def test_fancy_indexing(self):
+        a = self.a
+        x = a[1, [0, 1, 0]]
+        assert_(isinstance(x, matrix))
+        assert_equal(x, matrix([[3,  4,  3]]))
+        x = a[[1, 0]]
+        assert_(isinstance(x, matrix))
+        assert_equal(x, matrix([[3, 4], [1, 2]]))
+        x = a[[[1], [0]], [[1, 0], [0, 1]]]
+        assert_(isinstance(x, matrix))
+        assert_equal(x, matrix([[4, 3], [1, 2]]))
+
+    def test_matrix_element(self):
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(x[0][0], matrix([[1, 2, 3]]))
+        assert_equal(x[0][0].shape, (1, 3))
+        assert_equal(x[0].shape, (1, 3))
+        assert_equal(x[:, 0].shape, (2, 1))
+
+        x = matrix(0)
+        assert_equal(x[0, 0], 0)
+        assert_equal(x[0], 0)
+        assert_equal(x[:, 0].shape, x.shape)
+
+    def test_scalar_indexing(self):
+        x = asmatrix(np.zeros((3, 2), float))
+        assert_equal(x[0, 0], x[0][0])
+
+    def test_row_column_indexing(self):
+        x = asmatrix(np.eye(2))
+        assert_array_equal(x[0, :], [[1, 0]])
+        assert_array_equal(x[1, :], [[0, 1]])
+        assert_array_equal(x[:, 0], [[1], [0]])
+        assert_array_equal(x[:, 1], [[0], [1]])
+
+    def test_boolean_indexing(self):
+        A = np.arange(6)
+        A.shape = (3, 2)
+        x = asmatrix(A)
+        assert_array_equal(x[:, np.array([True, False])], x[:, 0])
+        assert_array_equal(x[np.array([True, False, False]), :], x[0, :])
+
+    def test_list_indexing(self):
+        A = np.arange(6)
+        A.shape = (3, 2)
+        x = asmatrix(A)
+        assert_array_equal(x[:, [1, 0]], x[:, ::-1])
+        assert_array_equal(x[[2, 1, 0], :], x[::-1, :])
+
+
+class TestPower:
+    def test_returntype(self):
+        a = np.array([[0, 1], [0, 0]])
+        assert_(type(matrix_power(a, 2)) is np.ndarray)
+        a = asmatrix(a)
+        assert_(type(matrix_power(a, 2)) is matrix)
+
+    def test_list(self):
+        assert_array_equal(matrix_power([[0, 1], [0, 0]], 2), [[0, 0], [0, 0]])
+
+
+class TestShape:
+
+    a = np.array([[1], [2]])
+    m = matrix([[1], [2]])
+
+    def test_shape(self):
+        assert_equal(self.a.shape, (2, 1))
+        assert_equal(self.m.shape, (2, 1))
+
+    def test_numpy_ravel(self):
+        assert_equal(np.ravel(self.a).shape, (2,))
+        assert_equal(np.ravel(self.m).shape, (2,))
+
+    def test_member_ravel(self):
+        assert_equal(self.a.ravel().shape, (2,))
+        assert_equal(self.m.ravel().shape, (1, 2))
+
+    def test_member_flatten(self):
+        assert_equal(self.a.flatten().shape, (2,))
+        assert_equal(self.m.flatten().shape, (1, 2))
+
+    def test_numpy_ravel_order(self):
+        x = np.array([[1, 2, 3], [4, 5, 6]])
+        assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6])
+        assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6])
+        assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6])
+        assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6])
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6])
+        assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6])
+        assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6])
+        assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6])
+
+    def test_matrix_ravel_order(self):
+        x = matrix([[1, 2, 3], [4, 5, 6]])
+        assert_equal(x.ravel(), [[1, 2, 3, 4, 5, 6]])
+        assert_equal(x.ravel(order='F'), [[1, 4, 2, 5, 3, 6]])
+        assert_equal(x.T.ravel(), [[1, 4, 2, 5, 3, 6]])
+        assert_equal(x.T.ravel(order='A'), [[1, 2, 3, 4, 5, 6]])
+
+    def test_array_memory_sharing(self):
+        assert_(np.may_share_memory(self.a, self.a.ravel()))
+        assert_(not np.may_share_memory(self.a, self.a.flatten()))
+
+    def test_matrix_memory_sharing(self):
+        assert_(np.may_share_memory(self.m, self.m.ravel()))
+        assert_(not np.may_share_memory(self.m, self.m.flatten()))
+
+    def test_expand_dims_matrix(self):
+        # matrices are always 2d - so expand_dims only makes sense when the
+        # type is changed away from matrix.
+        a = np.arange(10).reshape((2, 5)).view(np.matrix)
+        expanded = np.expand_dims(a, axis=1)
+        assert_equal(expanded.ndim, 3)
+        assert_(not isinstance(expanded, np.matrix))

venv/lib/python3.13/site-packages/numpy/matrixlib/tests/test_interaction.py

@@ -0,0 +1,360 @@
+"""Tests of interaction of matrix with other parts of numpy.
+
+Note that tests with MaskedArray and linalg are done in separate files.
+"""
+import textwrap
+import warnings
+
+import pytest
+
+import numpy as np
+from numpy.testing import (
+    assert_,
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_equal,
+    assert_equal,
+    assert_raises,
+    assert_raises_regex,
+)
+
+
+def test_fancy_indexing():
+    # The matrix class messes with the shape. While this is always
+    # weird (getitem is not used, it does not have setitem nor knows
+    # about fancy indexing), this tests gh-3110
+    # 2018-04-29: moved here from core.tests.test_index.
+    m = np.matrix([[1, 2], [3, 4]])
+
+    assert_(isinstance(m[[0, 1, 0], :], np.matrix))
+
+    # gh-3110. Note the transpose currently because matrices do *not*
+    # support dimension fixing for fancy indexing correctly.
+    x = np.asmatrix(np.arange(50).reshape(5, 10))
+    assert_equal(x[:2, np.array(-1)], x[:2, -1].T)
+
+
+def test_polynomial_mapdomain():
+    # test that polynomial preserved matrix subtype.
+    # 2018-04-29: moved here from polynomial.tests.polyutils.
+    dom1 = [0, 4]
+    dom2 = [1, 3]
+    x = np.matrix([dom1, dom1])
+    res = np.polynomial.polyutils.mapdomain(x, dom1, dom2)
+    assert_(isinstance(res, np.matrix))
+
+
+def test_sort_matrix_none():
+    # 2018-04-29: moved here from core.tests.test_multiarray
+    a = np.matrix([[2, 1, 0]])
+    actual = np.sort(a, axis=None)
+    expected = np.matrix([[0, 1, 2]])
+    assert_equal(actual, expected)
+    assert_(type(expected) is np.matrix)
+
+
+def test_partition_matrix_none():
+    # gh-4301
+    # 2018-04-29: moved here from core.tests.test_multiarray
+    a = np.matrix([[2, 1, 0]])
+    actual = np.partition(a, 1, axis=None)
+    expected = np.matrix([[0, 1, 2]])
+    assert_equal(actual, expected)
+    assert_(type(expected) is np.matrix)
+
+
+def test_dot_scalar_and_matrix_of_objects():
+    # Ticket #2469
+    # 2018-04-29: moved here from core.tests.test_multiarray
+    arr = np.matrix([1, 2], dtype=object)
+    desired = np.matrix([[3, 6]], dtype=object)
+    assert_equal(np.dot(arr, 3), desired)
+    assert_equal(np.dot(3, arr), desired)
+
+
+def test_inner_scalar_and_matrix():
+    # 2018-04-29: moved here from core.tests.test_multiarray
+    for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
+        sca = np.array(3, dtype=dt)[()]
+        arr = np.matrix([[1, 2], [3, 4]], dtype=dt)
+        desired = np.matrix([[3, 6], [9, 12]], dtype=dt)
+        assert_equal(np.inner(arr, sca), desired)
+        assert_equal(np.inner(sca, arr), desired)
+
+
+def test_inner_scalar_and_matrix_of_objects():
+    # Ticket #4482
+    # 2018-04-29: moved here from core.tests.test_multiarray
+    arr = np.matrix([1, 2], dtype=object)
+    desired = np.matrix([[3, 6]], dtype=object)
+    assert_equal(np.inner(arr, 3), desired)
+    assert_equal(np.inner(3, arr), desired)
+
+
+def test_iter_allocate_output_subtype():
+    # Make sure that the subtype with priority wins
+    # 2018-04-29: moved here from core.tests.test_nditer, given the
+    # matrix specific shape test.
+
+    # matrix vs ndarray
+    a = np.matrix([[1, 2], [3, 4]])
+    b = np.arange(4).reshape(2, 2).T
+    i = np.nditer([a, b, None], [],
+                  [['readonly'], ['readonly'], ['writeonly', 'allocate']])
+    assert_(type(i.operands[2]) is np.matrix)
+    assert_(type(i.operands[2]) is not np.ndarray)
+    assert_equal(i.operands[2].shape, (2, 2))
+
+    # matrix always wants things to be 2D
+    b = np.arange(4).reshape(1, 2, 2)
+    assert_raises(RuntimeError, np.nditer, [a, b, None], [],
+                  [['readonly'], ['readonly'], ['writeonly', 'allocate']])
+    # but if subtypes are disabled, the result can still work
+    i = np.nditer([a, b, None], [],
+                  [['readonly'], ['readonly'],
+                   ['writeonly', 'allocate', 'no_subtype']])
+    assert_(type(i.operands[2]) is np.ndarray)
+    assert_(type(i.operands[2]) is not np.matrix)
+    assert_equal(i.operands[2].shape, (1, 2, 2))
+
+
+def like_function():
+    # 2018-04-29: moved here from core.tests.test_numeric
+    a = np.matrix([[1, 2], [3, 4]])
+    for like_function in np.zeros_like, np.ones_like, np.empty_like:
+        b = like_function(a)
+        assert_(type(b) is np.matrix)
+
+        c = like_function(a, subok=False)
+        assert_(type(c) is not np.matrix)
+
+
+def test_array_astype():
+    # 2018-04-29: copied here from core.tests.test_api
+    # subok=True passes through a matrix
+    a = np.matrix([[0, 1, 2], [3, 4, 5]], dtype='f4')
+    b = a.astype('f4', subok=True, copy=False)
+    assert_(a is b)
+
+    # subok=True is default, and creates a subtype on a cast
+    b = a.astype('i4', copy=False)
+    assert_equal(a, b)
+    assert_equal(type(b), np.matrix)
+
+    # subok=False never returns a matrix
+    b = a.astype('f4', subok=False, copy=False)
+    assert_equal(a, b)
+    assert_(not (a is b))
+    assert_(type(b) is not np.matrix)
+
+
+def test_stack():
+    # 2018-04-29: copied here from core.tests.test_shape_base
+    # check np.matrix cannot be stacked
+    m = np.matrix([[1, 2], [3, 4]])
+    assert_raises_regex(ValueError, 'shape too large to be a matrix',
+                        np.stack, [m, m])
+
+
+def test_object_scalar_multiply():
+    # Tickets #2469 and #4482
+    # 2018-04-29: moved here from core.tests.test_ufunc
+    arr = np.matrix([1, 2], dtype=object)
+    desired = np.matrix([[3, 6]], dtype=object)
+    assert_equal(np.multiply(arr, 3), desired)
+    assert_equal(np.multiply(3, arr), desired)
+
+
+def test_nanfunctions_matrices():
+    # Check that it works and that type and
+    # shape are preserved
+    # 2018-04-29: moved here from core.tests.test_nanfunctions
+    mat = np.matrix(np.eye(3))
+    for f in [np.nanmin, np.nanmax]:
+        res = f(mat, axis=0)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (1, 3))
+        res = f(mat, axis=1)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (3, 1))
+        res = f(mat)
+        assert_(np.isscalar(res))
+    # check that rows of nan are dealt with for subclasses (#4628)
+    mat[1] = np.nan
+    for f in [np.nanmin, np.nanmax]:
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always')
+            res = f(mat, axis=0)
+            assert_(isinstance(res, np.matrix))
+            assert_(not np.any(np.isnan(res)))
+            assert_(len(w) == 0)
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always')
+            res = f(mat, axis=1)
+            assert_(isinstance(res, np.matrix))
+            assert_(np.isnan(res[1, 0]) and not np.isnan(res[0, 0])
+                    and not np.isnan(res[2, 0]))
+            assert_(len(w) == 1, 'no warning raised')
+            assert_(issubclass(w[0].category, RuntimeWarning))
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter('always')
+            res = f(mat)
+            assert_(np.isscalar(res))
+            assert_(res != np.nan)
+            assert_(len(w) == 0)
+
+
+def test_nanfunctions_matrices_general():
+    # Check that it works and that type and
+    # shape are preserved
+    # 2018-04-29: moved here from core.tests.test_nanfunctions
+    mat = np.matrix(np.eye(3))
+    for f in (np.nanargmin, np.nanargmax, np.nansum, np.nanprod,
+              np.nanmean, np.nanvar, np.nanstd):
+        res = f(mat, axis=0)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (1, 3))
+        res = f(mat, axis=1)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (3, 1))
+        res = f(mat)
+        assert_(np.isscalar(res))
+
+    for f in np.nancumsum, np.nancumprod:
+        res = f(mat, axis=0)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (3, 3))
+        res = f(mat, axis=1)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (3, 3))
+        res = f(mat)
+        assert_(isinstance(res, np.matrix))
+        assert_(res.shape == (1, 3 * 3))
+
+
+def test_average_matrix():
+    # 2018-04-29: moved here from core.tests.test_function_base.
+    y = np.matrix(np.random.rand(5, 5))
+    assert_array_equal(y.mean(0), np.average(y, 0))
+
+    a = np.matrix([[1, 2], [3, 4]])
+    w = np.matrix([[1, 2], [3, 4]])
+
+    r = np.average(a, axis=0, weights=w)
+    assert_equal(type(r), np.matrix)
+    assert_equal(r, [[2.5, 10.0 / 3]])
+
+
+def test_dot_matrix():
+    # Test to make sure matrices give the same answer as ndarrays
+    # 2018-04-29: moved here from core.tests.test_function_base.
+    x = np.linspace(0, 5)
+    y = np.linspace(-5, 0)
+    mx = np.matrix(x)
+    my = np.matrix(y)
+    r = np.dot(x, y)
+    mr = np.dot(mx, my.T)
+    assert_almost_equal(mr, r)
+
+
+def test_ediff1d_matrix():
+    # 2018-04-29: moved here from core.tests.test_arraysetops.
+    assert isinstance(np.ediff1d(np.matrix(1)), np.matrix)
+    assert isinstance(np.ediff1d(np.matrix(1), to_begin=1), np.matrix)
+
+
+def test_apply_along_axis_matrix():
+    # this test is particularly malicious because matrix
+    # refuses to become 1d
+    # 2018-04-29: moved here from core.tests.test_shape_base.
+    def double(row):
+        return row * 2
+
+    m = np.matrix([[0, 1], [2, 3]])
+    expected = np.matrix([[0, 2], [4, 6]])
+
+    result = np.apply_along_axis(double, 0, m)
+    assert_(isinstance(result, np.matrix))
+    assert_array_equal(result, expected)
+
+    result = np.apply_along_axis(double, 1, m)
+    assert_(isinstance(result, np.matrix))
+    assert_array_equal(result, expected)
+
+
+def test_kron_matrix():
+    # 2018-04-29: moved here from core.tests.test_shape_base.
+    a = np.ones([2, 2])
+    m = np.asmatrix(a)
+    assert_equal(type(np.kron(a, a)), np.ndarray)
+    assert_equal(type(np.kron(m, m)), np.matrix)
+    assert_equal(type(np.kron(a, m)), np.matrix)
+    assert_equal(type(np.kron(m, a)), np.matrix)
+
+
+class TestConcatenatorMatrix:
+    # 2018-04-29: moved here from core.tests.test_index_tricks.
+    def test_matrix(self):
+        a = [1, 2]
+        b = [3, 4]
+
+        ab_r = np.r_['r', a, b]
+        ab_c = np.r_['c', a, b]
+
+        assert_equal(type(ab_r), np.matrix)
+        assert_equal(type(ab_c), np.matrix)
+
+        assert_equal(np.array(ab_r), [[1, 2, 3, 4]])
+        assert_equal(np.array(ab_c), [[1], [2], [3], [4]])
+
+        assert_raises(ValueError, lambda: np.r_['rc', a, b])
+
+    def test_matrix_scalar(self):
+        r = np.r_['r', [1, 2], 3]
+        assert_equal(type(r), np.matrix)
+        assert_equal(np.array(r), [[1, 2, 3]])
+
+    def test_matrix_builder(self):
+        a = np.array([1])
+        b = np.array([2])
+        c = np.array([3])
+        d = np.array([4])
+        actual = np.r_['a, b; c, d']
+        expected = np.bmat([[a, b], [c, d]])
+
+        assert_equal(actual, expected)
+        assert_equal(type(actual), type(expected))
+
+
+def test_array_equal_error_message_matrix():
+    # 2018-04-29: moved here from testing.tests.test_utils.
+    with pytest.raises(AssertionError) as exc_info:
+        assert_equal(np.array([1, 2]), np.matrix([1, 2]))
+    msg = str(exc_info.value)
+    msg_reference = textwrap.dedent("""\
+
+    Arrays are not equal
+
+    (shapes (2,), (1, 2) mismatch)
+     ACTUAL: array([1, 2])
+     DESIRED: matrix([[1, 2]])""")
+    assert_equal(msg, msg_reference)
+
+
+def test_array_almost_equal_matrix():
+    # Matrix slicing keeps things 2-D, while array does not necessarily.
+    # See gh-8452.
+    # 2018-04-29: moved here from testing.tests.test_utils.
+    m1 = np.matrix([[1., 2.]])
+    m2 = np.matrix([[1., np.nan]])
+    m3 = np.matrix([[1., -np.inf]])
+    m4 = np.matrix([[np.nan, np.inf]])
+    m5 = np.matrix([[1., 2.], [np.nan, np.inf]])
+    for assert_func in assert_array_almost_equal, assert_almost_equal:
+        for m in m1, m2, m3, m4, m5:
+            assert_func(m, m)
+            a = np.array(m)
+            assert_func(a, m)
+            assert_func(m, a)

venv/lib/python3.13/site-packages/numpy/matrixlib/tests/test_masked_matrix.py

@@ -0,0 +1,240 @@
+import pickle
+
+import numpy as np
+from numpy.ma.core import (
+    MaskedArray,
+    MaskType,
+    add,
+    allequal,
+    divide,
+    getmask,
+    hypot,
+    log,
+    masked,
+    masked_array,
+    masked_values,
+    nomask,
+)
+from numpy.ma.extras import mr_
+from numpy.ma.testutils import assert_, assert_array_equal, assert_equal, assert_raises
+
+
+class MMatrix(MaskedArray, np.matrix,):
+
+    def __new__(cls, data, mask=nomask):
+        mat = np.matrix(data)
+        _data = MaskedArray.__new__(cls, data=mat, mask=mask)
+        return _data
+
+    def __array_finalize__(self, obj):
+        np.matrix.__array_finalize__(self, obj)
+        MaskedArray.__array_finalize__(self, obj)
+
+    @property
+    def _series(self):
+        _view = self.view(MaskedArray)
+        _view._sharedmask = False
+        return _view
+
+
+class TestMaskedMatrix:
+    def test_matrix_indexing(self):
+        # Tests conversions and indexing
+        x1 = np.matrix([[1, 2, 3], [4, 3, 2]])
+        x2 = masked_array(x1, mask=[[1, 0, 0], [0, 1, 0]])
+        x3 = masked_array(x1, mask=[[0, 1, 0], [1, 0, 0]])
+        x4 = masked_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], masked_array([1, 1, 0])))
+        assert_(allequal(getmask(x3[1]), masked_array([1, 1, 0])))
+        x4[1, :] = masked_array([1, 2, 3], [1, 1, 0])
+        assert_(allequal(getmask(x4[1]), masked_array([1, 1, 0])))
+        assert_(allequal(x4[1], masked_array([1, 2, 3])))
+        x1 = np.matrix(np.arange(5) * 1.0)
+        x2 = masked_values(x1, 3.0)
+        assert_equal(x1, x2)
+        assert_(allequal(masked_array([0, 0, 0, 1, 0], dtype=MaskType),
+                         x2.mask))
+        assert_equal(3.0, x2.fill_value)
+
+    def test_pickling_subbaseclass(self):
+        # Test pickling w/ a subclass of ndarray
+        a = masked_array(np.matrix(list(range(10))), mask=[1, 0, 1, 0, 0] * 2)
+        for proto in range(2, pickle.HIGHEST_PROTOCOL + 1):
+            a_pickled = pickle.loads(pickle.dumps(a, protocol=proto))
+            assert_equal(a_pickled._mask, a._mask)
+            assert_equal(a_pickled, a)
+            assert_(isinstance(a_pickled._data, np.matrix))
+
+    def test_count_mean_with_matrix(self):
+        m = masked_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_flat(self):
+        # Test that flat can return items even for matrices [#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
+        # 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, np.array([[1., 0.]]))
+        assert_equal(b01.mask, np.array([[False, 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_compressed(self):
+        a = masked_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_ravel(self):
+        a = masked_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)
+
+    def test_view(self):
+        # Test view w/ flexible dtype
+        iterator = list(zip(np.arange(10), np.random.rand(10)))
+        data = np.array(iterator)
+        a = masked_array(iterator, dtype=[('a', float), ('b', float)])
+        a.mask[0] = (1, 0)
+        test = a.view((float, 2), np.matrix)
+        assert_equal(test, data)
+        assert_(isinstance(test, np.matrix))
+        assert_(not isinstance(test, MaskedArray))
+
+
+class TestSubclassing:
+    # Test suite for masked subclasses of ndarray.
+
+    def setup_method(self):
+        x = np.arange(5, dtype='float')
+        mx = MMatrix(x, mask=[0, 1, 0, 0, 0])
+        self.data = (x, mx)
+
+    def test_maskedarray_subclassing(self):
+        # Tests subclassing MaskedArray
+        (x, mx) = self.data
+        assert_(isinstance(mx._data, np.matrix))
+
+    def test_masked_unary_operations(self):
+        # Tests masked_unary_operation
+        (x, mx) = self.data
+        with np.errstate(divide='ignore'):
+            assert_(isinstance(log(mx), MMatrix))
+            assert_equal(log(x), np.log(x))
+
+    def test_masked_binary_operations(self):
+        # Tests masked_binary_operation
+        (x, mx) = self.data
+        # Result should be a MMatrix
+        assert_(isinstance(add(mx, mx), MMatrix))
+        assert_(isinstance(add(mx, x), MMatrix))
+        # Result should work
+        assert_equal(add(mx, x), mx + x)
+        assert_(isinstance(add(mx, mx)._data, np.matrix))
+        with assert_raises(TypeError):
+            add.outer(mx, mx)
+        assert_(isinstance(hypot(mx, mx), MMatrix))
+        assert_(isinstance(hypot(mx, x), MMatrix))
+
+    def test_masked_binary_operations2(self):
+        # Tests domained_masked_binary_operation
+        (x, mx) = self.data
+        xmx = masked_array(mx.data.__array__(), mask=mx.mask)
+        assert_(isinstance(divide(mx, mx), MMatrix))
+        assert_(isinstance(divide(mx, x), MMatrix))
+        assert_equal(divide(mx, mx), divide(xmx, xmx))
+
+class TestConcatenator:
+    # Tests for mr_, the equivalent of r_ for masked arrays.
+
+    def test_matrix_builder(self):
+        assert_raises(np.ma.MAError, lambda: mr_['1, 2; 3, 4'])
+
+    def test_matrix(self):
+        # Test consistency with unmasked version.  If we ever deprecate
+        # matrix, this test should either still pass, or both actual and
+        # expected should fail to be build.
+        actual = mr_['r', 1, 2, 3]
+        expected = np.ma.array(np.r_['r', 1, 2, 3])
+        assert_array_equal(actual, expected)
+
+        # outer type is masked array, inner type is matrix
+        assert_equal(type(actual), type(expected))
+        assert_equal(type(actual.data), type(expected.data))

venv/lib/python3.13/site-packages/numpy/matrixlib/tests/test_matrix_linalg.py

@@ -0,0 +1,105 @@
+""" Test functions for linalg module using the matrix class."""
+import numpy as np
+from numpy.linalg.tests.test_linalg import (
+    CondCases,
+    DetCases,
+    EigCases,
+    EigvalsCases,
+    InvCases,
+    LinalgCase,
+    LinalgTestCase,
+    LstsqCases,
+    PinvCases,
+    SolveCases,
+    SVDCases,
+    _TestNorm2D,
+    _TestNormDoubleBase,
+    _TestNormInt64Base,
+    _TestNormSingleBase,
+    apply_tag,
+)
+from numpy.linalg.tests.test_linalg import TestQR as _TestQR
+
+CASES = []
+
+# square test cases
+CASES += apply_tag('square', [
+    LinalgCase("0x0_matrix",
+               np.empty((0, 0), dtype=np.double).view(np.matrix),
+               np.empty((0, 1), dtype=np.double).view(np.matrix),
+               tags={'size-0'}),
+    LinalgCase("matrix_b_only",
+               np.array([[1., 2.], [3., 4.]]),
+               np.matrix([2., 1.]).T),
+    LinalgCase("matrix_a_and_b",
+               np.matrix([[1., 2.], [3., 4.]]),
+               np.matrix([2., 1.]).T),
+])
+
+# hermitian test-cases
+CASES += apply_tag('hermitian', [
+    LinalgCase("hmatrix_a_and_b",
+               np.matrix([[1., 2.], [2., 1.]]),
+               None),
+])
+# No need to make generalized or strided cases for matrices.
+
+
+class MatrixTestCase(LinalgTestCase):
+    TEST_CASES = CASES
+
+
+class TestSolveMatrix(SolveCases, MatrixTestCase):
+    pass
+
+
+class TestInvMatrix(InvCases, MatrixTestCase):
+    pass
+
+
+class TestEigvalsMatrix(EigvalsCases, MatrixTestCase):
+    pass
+
+
+class TestEigMatrix(EigCases, MatrixTestCase):
+    pass
+
+
+class TestSVDMatrix(SVDCases, MatrixTestCase):
+    pass
+
+
+class TestCondMatrix(CondCases, MatrixTestCase):
+    pass
+
+
+class TestPinvMatrix(PinvCases, MatrixTestCase):
+    pass
+
+
+class TestDetMatrix(DetCases, MatrixTestCase):
+    pass
+
+
+class TestLstsqMatrix(LstsqCases, MatrixTestCase):
+    pass
+
+
+class _TestNorm2DMatrix(_TestNorm2D):
+    array = np.matrix
+
+
+class TestNormDoubleMatrix(_TestNorm2DMatrix, _TestNormDoubleBase):
+    pass
+
+
+class TestNormSingleMatrix(_TestNorm2DMatrix, _TestNormSingleBase):
+    pass
+
+
+class TestNormInt64Matrix(_TestNorm2DMatrix, _TestNormInt64Base):
+    pass
+
+
+class TestQRMatrix(_TestQR):
+    array = np.matrix

venv/lib/python3.13/site-packages/numpy/matrixlib/tests/test_multiarray.py

@@ -0,0 +1,17 @@
+import numpy as np
+from numpy.testing import assert_, assert_array_equal, assert_equal
+
+
+class TestView:
+    def test_type(self):
+        x = np.array([1, 2, 3])
+        assert_(isinstance(x.view(np.matrix), np.matrix))
+
+    def test_keywords(self):
+        x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
+        # We must be specific about the endianness here:
+        y = x.view(dtype='<i2', type=np.matrix)
+        assert_array_equal(y, [[513]])
+
+        assert_(isinstance(y, np.matrix))
+        assert_equal(y.dtype, np.dtype('<i2'))

venv/lib/python3.13/site-packages/numpy/matrixlib/tests/test_numeric.py

@@ -0,0 +1,18 @@
+import numpy as np
+from numpy.testing import assert_equal
+
+
+class TestDot:
+    def test_matscalar(self):
+        b1 = np.matrix(np.ones((3, 3), dtype=complex))
+        assert_equal(b1 * 1.0, b1)
+
+
+def test_diagonal():
+    b1 = np.matrix([[1, 2], [3, 4]])
+    diag_b1 = np.matrix([[1, 4]])
+    array_b1 = np.array([1, 4])
+
+    assert_equal(b1.diagonal(), diag_b1)
+    assert_equal(np.diagonal(b1), array_b1)
+    assert_equal(np.diag(b1), array_b1)

venv/lib/python3.13/site-packages/numpy/matrixlib/tests/test_regression.py

@@ -0,0 +1,31 @@
+import numpy as np
+from numpy.testing import assert_, assert_equal, assert_raises
+
+
+class TestRegression:
+    def test_kron_matrix(self):
+        # Ticket #71
+        x = np.matrix('[1 0; 1 0]')
+        assert_equal(type(np.kron(x, x)), type(x))
+
+    def test_matrix_properties(self):
+        # Ticket #125
+        a = np.matrix([1.0], dtype=float)
+        assert_(type(a.real) is np.matrix)
+        assert_(type(a.imag) is np.matrix)
+        c, d = np.matrix([0.0]).nonzero()
+        assert_(type(c) is np.ndarray)
+        assert_(type(d) is np.ndarray)
+
+    def test_matrix_multiply_by_1d_vector(self):
+        # Ticket #473
+        def mul():
+            np.asmatrix(np.eye(2)) * np.ones(2)
+
+        assert_raises(ValueError, mul)
+
+    def test_matrix_std_argmax(self):
+        # Ticket #83
+        x = np.asmatrix(np.random.uniform(0, 1, (3, 3)))
+        assert_equal(x.std().shape, ())
+        assert_equal(x.argmax().shape, ())