comparison DEPENDENCIES/mingw32/Python27/Lib/site-packages/numpy/ma/tests/test_extras.py @ 87:2a2c65a20a8b

Add Python libs and headers

+# pylint: disable-msg=W0611, W0612, W0511
+"""Tests suite for MaskedArray.
+Adapted from the original test_ma by Pierre Gerard-Marchant
+
+:author: Pierre Gerard-Marchant
+:contact: pierregm_at_uga_dot_edu
+:version: $Id: test_extras.py 3473 2007-10-29 15:18:13Z jarrod.millman $
+
+"""
+from __future__ import division, absolute_import, print_function
+
+__author__ = "Pierre GF Gerard-Marchant ($Author: jarrod.millman $)"
+__version__ = '1.0'
+__revision__ = "$Revision: 3473 $"
+__date__ = '$Date: 2007-10-29 17:18:13 +0200 (Mon, 29 Oct 2007) $'
+
+import numpy as np
+from numpy.testing import TestCase, run_module_suite
+from numpy.ma.testutils import (rand, assert_, assert_array_equal,
+                                assert_equal, assert_almost_equal)
+from numpy.ma.core import (array, arange, masked, MaskedArray, masked_array,
+                           getmaskarray, shape, nomask, ones, zeros, count)
+from numpy.ma.extras import (
+    atleast_2d, mr_, dot, polyfit,
+    cov, corrcoef, median, average,
+    unique, setxor1d, setdiff1d, union1d, intersect1d, in1d, ediff1d,
+    apply_over_axes, apply_along_axis,
+    compress_rowcols, mask_rowcols,
+    clump_masked, clump_unmasked,
+    flatnotmasked_contiguous, notmasked_contiguous, notmasked_edges,
+    masked_all, masked_all_like)
+
+
+class TestGeneric(TestCase):
+    #
+    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_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 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)
+
+    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)
+
+    def test_flatnotmasked_contiguous(self):
+        # Test flatnotmasked_contiguous
+        a = arange(10)
+        # No mask
+        test = flatnotmasked_contiguous(a)
+        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, None)
+
+
+class TestAverage(TestCase):
+    # 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=1)
+        assert_equal(2.0, result)
+        self.assertTrue(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=1)
+        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.float_)
+        assert_equal(average(x, axis=0), 2.5)
+        assert_equal(average(x, axis=0, weights=w1), 2.5)
+        y = array([arange(6, dtype=np.float_), 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=1)
+        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=1)
+        assert_equal(shape(w2), shape(r2))
+        r2, w2 = average(ones((2, 2, 3)), returned=1)
+        assert_equal(shape(w2), shape(r2))
+        r2, w2 = average(ones((2, 2, 3)), weights=ones((2, 2, 3)), returned=1)
+        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_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)
+
+
+class TestConcatenator(TestCase):
+    # 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]
+        self.assertTrue(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 = rand(5, 5)
+        a_2 = rand(5, 5)
+        m_1 = np.round_(rand(5, 5), 0)
+        m_2 = np.round_(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]
+        self.assertTrue(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]
+        self.assertTrue(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])
+
+
+class TestNotMasked(TestCase):
+    # 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[-1], slice(23, 24, None))
+        assert_equal(tmp[-2], slice(16, 22, None))
+        assert_equal(tmp[-3], slice(0, 4, None))
+        #
+        tmp = notmasked_contiguous(a, 0)
+        self.assertTrue(len(tmp[-1]) == 1)
+        self.assertTrue(tmp[-2] is None)
+        assert_equal(tmp[-3], tmp[-1])
+        self.assertTrue(len(tmp[0]) == 2)
+        #
+        tmp = notmasked_contiguous(a, 1)
+        assert_equal(tmp[0][-1], slice(0, 4, None))
+        self.assertTrue(tmp[1] is None)
+        assert_equal(tmp[2][-1], slice(7, 8, None))
+        assert_equal(tmp[2][-2], slice(0, 6, None))
+
+
+class Test2DFunctions(TestCase):
+    # Tests 2D functions
+    def test_compress2d(self):
+        # Tests compress2d
+        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]])
+        self.assertTrue(mask_rowcols(x).all() is masked)
+        self.assertTrue(mask_rowcols(x, 0).all() is masked)
+        self.assertTrue(mask_rowcols(x, 1).all() is masked)
+        self.assertTrue(mask_rowcols(x).mask.all())
+        self.assertTrue(mask_rowcols(x, 0).mask.all())
+        self.assertTrue(mask_rowcols(x, 1).mask.all())
+
+    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, True)
+        assert_equal(c.mask, [[1, 1], [1, 0]])
+        c = dot(b, a, True)
+        assert_equal(c.mask, [[1, 1, 1], [1, 0, 0], [1, 0, 0]])
+        c = dot(a, b, False)
+        assert_equal(c, np.dot(a.filled(0), b.filled(0)))
+        c = dot(b, a, 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, True)
+        assert_equal(c.mask, [[0, 1], [1, 1]])
+        c = dot(b, a, True)
+        assert_equal(c.mask, [[0, 0, 1], [0, 0, 1], [1, 1, 1]])
+        c = dot(a, b, False)
+        assert_equal(c, np.dot(a.filled(0), b.filled(0)))
+        assert_equal(c, dot(a, b))
+        c = dot(b, a, 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, True)
+        assert_equal(c.mask, [[1, 1], [0, 0]])
+        c = dot(a, b, False)
+        assert_equal(c, np.dot(a.filled(0), b.filled(0)))
+        c = dot(b, a, True)
+        assert_equal(c.mask, [[1, 0, 0], [1, 0, 0], [1, 0, 0]])
+        c = dot(b, a, 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, 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, True)
+        assert_equal(c.mask, [[0, 0, 1], [0, 0, 1], [0, 0, 1]])
+        c = dot(b, a, 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, True)
+        assert_equal(c.mask, [[1, 0], [1, 1]])
+        c = dot(a, b, False)
+        assert_equal(c, np.dot(a.filled(0), b.filled(0)))
+        c = dot(b, a, True)
+        assert_equal(c.mask, [[0, 0, 1], [1, 1, 1], [0, 0, 1]])
+        c = dot(b, a, False)
+        assert_equal(c, np.dot(b.filled(0), a.filled(0)))
+
+
+class TestApplyAlongAxis(TestCase):
+    # 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(TestCase):
+    # 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(np.bool)] = masked
+        test = apply_over_axes(np.sum, a, [0, 2])
+        ctrl = np.array([[[28], [44], [60]]])
+        assert_equal(test, ctrl)
+
+
+class TestMedian(TestCase):
+    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_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_equal(median(x, axis=0), [13.5, 14.5, 15.5])
+        assert_equal(median(x, axis=1), [0, 0, 0, 10, 13, 16, 19, 0, 0, 0])
+        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(self):
+        x = masked_array(np.arange(30).reshape(10, 3))
+        x[:3] = x[-3:] = masked
+        out = masked_array(np.ones(10))
+        r = median(x, axis=1, out=out)
+        assert_equal(r, out)
+        assert_(type(r) == MaskedArray)
+
+
+class TestCov(TestCase):
+
+    def setUp(self):
+        self.data = array(np.random.rand(12))
+
+    def test_1d_wo_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_wo_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_w_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_w_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(TestCase):
+
+    def setUp(self):
+        self.data = array(np.random.rand(12))
+
+    def test_ddof(self):
+        # Test ddof keyword
+        x = self.data
+        assert_almost_equal(np.corrcoef(x, ddof=0), corrcoef(x, ddof=0))
+
+    def test_1d_wo_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))
+        assert_almost_equal(np.corrcoef(x, rowvar=False, bias=True),
+                            corrcoef(x, rowvar=False, bias=True))
+
+    def test_2d_wo_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))
+        assert_almost_equal(np.corrcoef(x, rowvar=False, bias=True),
+                            corrcoef(x, rowvar=False, bias=True))
+
+    def test_1d_w_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))
+        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))
+        assert_almost_equal(np.corrcoef(nx, nx[::-1], rowvar=False, bias=True),
+                            corrcoef(x, x[::-1], rowvar=False, bias=True))
+
+    def test_2d_w_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])
+
+
+class TestPolynomial(TestCase):
+    #
+    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_)
+
+
+class TestArraySetOps(TestCase):
+
+    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)
+        self.assertTrue(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.data, control.data)
+        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.data, control.data)
+        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.data, control.data)
+        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.data, control.data)
+        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.data, control.data)
+        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.data, control.data)
+        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.data, control.data)
+        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)
+        self.assertTrue(isinstance(test, MaskedArray))
+        assert_equal(test.data, control.data)
+        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])
+        self.assertTrue(isinstance(test, MaskedArray))
+        assert_equal(test.data, control.data)
+        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_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)
+        #
+        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]))
+
+    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(TestCase):
+    #
+    def test_atleast2d(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)
+
+
+###############################################################################
+#------------------------------------------------------------------------------
+if __name__ == "__main__":
+    run_module_suite()