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Edit File: test_extras.py
# 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 $ """ import warnings import itertools import pytest import numpy as np from numpy.core.numeric import normalize_axis_tuple from numpy.testing import ( assert_warns, suppress_warnings ) from numpy.ma.testutils import ( 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_1d, atleast_2d, atleast_3d, mr_, dot, polyfit, cov, corrcoef, median, average, unique, setxor1d, setdiff1d, union1d, intersect1d, in1d, ediff1d, apply_over_axes, apply_along_axis, compress_nd, compress_rowcols, mask_rowcols, clump_masked, clump_unmasked, flatnotmasked_contiguous, notmasked_contiguous, notmasked_edges, masked_all, masked_all_like, isin, diagflat, ndenumerate, stack, vstack ) 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.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=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_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))) 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.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(0, 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_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_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)