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Code Editor : test_twodim_base.py
"""Test functions for matrix module """ from numpy.testing import ( assert_equal, assert_array_equal, assert_array_max_ulp, assert_array_almost_equal, assert_raises, assert_ ) from numpy import ( arange, add, fliplr, flipud, zeros, ones, eye, array, diag, histogram2d, tri, mask_indices, triu_indices, triu_indices_from, tril_indices, tril_indices_from, vander, ) import numpy as np from numpy.core.tests.test_overrides import requires_array_function def get_mat(n): data = arange(n) data = add.outer(data, data) return data class TestEye: def test_basic(self): assert_equal(eye(4), array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])) assert_equal(eye(4, dtype='f'), array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], 'f')) assert_equal(eye(3) == 1, eye(3, dtype=bool)) def test_diag(self): assert_equal(eye(4, k=1), array([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0]])) assert_equal(eye(4, k=-1), array([[0, 0, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])) def test_2d(self): assert_equal(eye(4, 3), array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 0]])) assert_equal(eye(3, 4), array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])) def test_diag2d(self): assert_equal(eye(3, 4, k=2), array([[0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0]])) assert_equal(eye(4, 3, k=-2), array([[0, 0, 0], [0, 0, 0], [1, 0, 0], [0, 1, 0]])) def test_eye_bounds(self): assert_equal(eye(2, 2, 1), [[0, 1], [0, 0]]) assert_equal(eye(2, 2, -1), [[0, 0], [1, 0]]) assert_equal(eye(2, 2, 2), [[0, 0], [0, 0]]) assert_equal(eye(2, 2, -2), [[0, 0], [0, 0]]) assert_equal(eye(3, 2, 2), [[0, 0], [0, 0], [0, 0]]) assert_equal(eye(3, 2, 1), [[0, 1], [0, 0], [0, 0]]) assert_equal(eye(3, 2, -1), [[0, 0], [1, 0], [0, 1]]) assert_equal(eye(3, 2, -2), [[0, 0], [0, 0], [1, 0]]) assert_equal(eye(3, 2, -3), [[0, 0], [0, 0], [0, 0]]) def test_strings(self): assert_equal(eye(2, 2, dtype='S3'), [[b'1', b''], [b'', b'1']]) def test_bool(self): assert_equal(eye(2, 2, dtype=bool), [[True, False], [False, True]]) def test_order(self): mat_c = eye(4, 3, k=-1) mat_f = eye(4, 3, k=-1, order='F') assert_equal(mat_c, mat_f) assert mat_c.flags.c_contiguous assert not mat_c.flags.f_contiguous assert not mat_f.flags.c_contiguous assert mat_f.flags.f_contiguous class TestDiag: def test_vector(self): vals = (100 * arange(5)).astype('l') b = zeros((5, 5)) for k in range(5): b[k, k] = vals[k] assert_equal(diag(vals), b) b = zeros((7, 7)) c = b.copy() for k in range(5): b[k, k + 2] = vals[k] c[k + 2, k] = vals[k] assert_equal(diag(vals, k=2), b) assert_equal(diag(vals, k=-2), c) def test_matrix(self, vals=None): if vals is None: vals = (100 * get_mat(5) + 1).astype('l') b = zeros((5,)) for k in range(5): b[k] = vals[k, k] assert_equal(diag(vals), b) b = b * 0 for k in range(3): b[k] = vals[k, k + 2] assert_equal(diag(vals, 2), b[:3]) for k in range(3): b[k] = vals[k + 2, k] assert_equal(diag(vals, -2), b[:3]) def test_fortran_order(self): vals = array((100 * get_mat(5) + 1), order='F', dtype='l') self.test_matrix(vals) def test_diag_bounds(self): A = [[1, 2], [3, 4], [5, 6]] assert_equal(diag(A, k=2), []) assert_equal(diag(A, k=1), [2]) assert_equal(diag(A, k=0), [1, 4]) assert_equal(diag(A, k=-1), [3, 6]) assert_equal(diag(A, k=-2), [5]) assert_equal(diag(A, k=-3), []) def test_failure(self): assert_raises(ValueError, diag, [[[1]]]) class TestFliplr: def test_basic(self): assert_raises(ValueError, fliplr, ones(4)) a = get_mat(4) b = a[:, ::-1] assert_equal(fliplr(a), b) a = [[0, 1, 2], [3, 4, 5]] b = [[2, 1, 0], [5, 4, 3]] assert_equal(fliplr(a), b) class TestFlipud: def test_basic(self): a = get_mat(4) b = a[::-1, :] assert_equal(flipud(a), b) a = [[0, 1, 2], [3, 4, 5]] b = [[3, 4, 5], [0, 1, 2]] assert_equal(flipud(a), b) class TestHistogram2d: def test_simple(self): x = array( [0.41702200, 0.72032449, 1.1437481e-4, 0.302332573, 0.146755891]) y = array( [0.09233859, 0.18626021, 0.34556073, 0.39676747, 0.53881673]) xedges = np.linspace(0, 1, 10) yedges = np.linspace(0, 1, 10) H = histogram2d(x, y, (xedges, yedges))[0] answer = array( [[0, 0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]]) assert_array_equal(H.T, answer) H = histogram2d(x, y, xedges)[0] assert_array_equal(H.T, answer) H, xedges, yedges = histogram2d(list(range(10)), list(range(10))) assert_array_equal(H, eye(10, 10)) assert_array_equal(xedges, np.linspace(0, 9, 11)) assert_array_equal(yedges, np.linspace(0, 9, 11)) def test_asym(self): x = array([1, 1, 2, 3, 4, 4, 4, 5]) y = array([1, 3, 2, 0, 1, 2, 3, 4]) H, xed, yed = histogram2d( x, y, (6, 5), range=[[0, 6], [0, 5]], density=True) answer = array( [[0., 0, 0, 0, 0], [0, 1, 0, 1, 0], [0, 0, 1, 0, 0], [1, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 1]]) assert_array_almost_equal(H, answer/8., 3) assert_array_equal(xed, np.linspace(0, 6, 7)) assert_array_equal(yed, np.linspace(0, 5, 6)) def test_density(self): x = array([1, 2, 3, 1, 2, 3, 1, 2, 3]) y = array([1, 1, 1, 2, 2, 2, 3, 3, 3]) H, xed, yed = histogram2d( x, y, [[1, 2, 3, 5], [1, 2, 3, 5]], density=True) answer = array([[1, 1, .5], [1, 1, .5], [.5, .5, .25]])/9. assert_array_almost_equal(H, answer, 3) def test_all_outliers(self): r = np.random.rand(100) + 1. + 1e6 # histogramdd rounds by decimal=6 H, xed, yed = histogram2d(r, r, (4, 5), range=([0, 1], [0, 1])) assert_array_equal(H, 0) def test_empty(self): a, edge1, edge2 = histogram2d([], [], bins=([0, 1], [0, 1])) assert_array_max_ulp(a, array([[0.]])) a, edge1, edge2 = histogram2d([], [], bins=4) assert_array_max_ulp(a, np.zeros((4, 4))) def test_binparameter_combination(self): x = array( [0, 0.09207008, 0.64575234, 0.12875982, 0.47390599, 0.59944483, 1]) y = array( [0, 0.14344267, 0.48988575, 0.30558665, 0.44700682, 0.15886423, 1]) edges = (0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1) H, xe, ye = histogram2d(x, y, (edges, 4)) answer = array( [[2., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 1., 0., 0.], [1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 1.]]) assert_array_equal(H, answer) assert_array_equal(ye, array([0., 0.25, 0.5, 0.75, 1])) H, xe, ye = histogram2d(x, y, (4, edges)) answer = array( [[1., 1., 0., 1., 0., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.], [0., 1., 0., 0., 1., 0., 0., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]]) assert_array_equal(H, answer) assert_array_equal(xe, array([0., 0.25, 0.5, 0.75, 1])) @requires_array_function def test_dispatch(self): class ShouldDispatch: def __array_function__(self, function, types, args, kwargs): return types, args, kwargs xy = [1, 2] s_d = ShouldDispatch() r = histogram2d(s_d, xy) # Cannot use assert_equal since that dispatches... assert_(r == ((ShouldDispatch,), (s_d, xy), {})) r = histogram2d(xy, s_d) assert_(r == ((ShouldDispatch,), (xy, s_d), {})) r = histogram2d(xy, xy, bins=s_d) assert_(r, ((ShouldDispatch,), (xy, xy), dict(bins=s_d))) r = histogram2d(xy, xy, bins=[s_d, 5]) assert_(r, ((ShouldDispatch,), (xy, xy), dict(bins=[s_d, 5]))) assert_raises(Exception, histogram2d, xy, xy, bins=[s_d]) r = histogram2d(xy, xy, weights=s_d) assert_(r, ((ShouldDispatch,), (xy, xy), dict(weights=s_d))) class TestTri: def test_dtype(self): out = array([[1, 0, 0], [1, 1, 0], [1, 1, 1]]) assert_array_equal(tri(3), out) assert_array_equal(tri(3, dtype=bool), out.astype(bool)) def test_tril_triu_ndim2(): for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']: a = np.ones((2, 2), dtype=dtype) b = np.tril(a) c = np.triu(a) assert_array_equal(b, [[1, 0], [1, 1]]) assert_array_equal(c, b.T) # should return the same dtype as the original array assert_equal(b.dtype, a.dtype) assert_equal(c.dtype, a.dtype) def test_tril_triu_ndim3(): for dtype in np.typecodes['AllFloat'] + np.typecodes['AllInteger']: a = np.array([ [[1, 1], [1, 1]], [[1, 1], [1, 0]], [[1, 1], [0, 0]], ], dtype=dtype) a_tril_desired = np.array([ [[1, 0], [1, 1]], [[1, 0], [1, 0]], [[1, 0], [0, 0]], ], dtype=dtype) a_triu_desired = np.array([ [[1, 1], [0, 1]], [[1, 1], [0, 0]], [[1, 1], [0, 0]], ], dtype=dtype) a_triu_observed = np.triu(a) a_tril_observed = np.tril(a) assert_array_equal(a_triu_observed, a_triu_desired) assert_array_equal(a_tril_observed, a_tril_desired) assert_equal(a_triu_observed.dtype, a.dtype) assert_equal(a_tril_observed.dtype, a.dtype) def test_tril_triu_with_inf(): # Issue 4859 arr = np.array([[1, 1, np.inf], [1, 1, 1], [np.inf, 1, 1]]) out_tril = np.array([[1, 0, 0], [1, 1, 0], [np.inf, 1, 1]]) out_triu = out_tril.T assert_array_equal(np.triu(arr), out_triu) assert_array_equal(np.tril(arr), out_tril) def test_tril_triu_dtype(): # Issue 4916 # tril and triu should return the same dtype as input for c in np.typecodes['All']: if c == 'V': continue arr = np.zeros((3, 3), dtype=c) assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype) # check special cases arr = np.array([['2001-01-01T12:00', '2002-02-03T13:56'], ['2004-01-01T12:00', '2003-01-03T13:45']], dtype='datetime64') assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype) arr = np.zeros((3,3), dtype='f4,f4') assert_equal(np.triu(arr).dtype, arr.dtype) assert_equal(np.tril(arr).dtype, arr.dtype) def test_mask_indices(): # simple test without offset iu = mask_indices(3, np.triu) a = np.arange(9).reshape(3, 3) assert_array_equal(a[iu], array([0, 1, 2, 4, 5, 8])) # Now with an offset iu1 = mask_indices(3, np.triu, 1) assert_array_equal(a[iu1], array([1, 2, 5])) def test_tril_indices(): # indices without and with offset il1 = tril_indices(4) il2 = tril_indices(4, k=2) il3 = tril_indices(4, m=5) il4 = tril_indices(4, k=2, m=5) a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]) b = np.arange(1, 21).reshape(4, 5) # indexing: assert_array_equal(a[il1], array([1, 5, 6, 9, 10, 11, 13, 14, 15, 16])) assert_array_equal(b[il3], array([1, 6, 7, 11, 12, 13, 16, 17, 18, 19])) # And for assigning values: a[il1] = -1 assert_array_equal(a, array([[-1, 2, 3, 4], [-1, -1, 7, 8], [-1, -1, -1, 12], [-1, -1, -1, -1]])) b[il3] = -1 assert_array_equal(b, array([[-1, 2, 3, 4, 5], [-1, -1, 8, 9, 10], [-1, -1, -1, 14, 15], [-1, -1, -1, -1, 20]])) # These cover almost the whole array (two diagonals right of the main one): a[il2] = -10 assert_array_equal(a, array([[-10, -10, -10, 4], [-10, -10, -10, -10], [-10, -10, -10, -10], [-10, -10, -10, -10]])) b[il4] = -10 assert_array_equal(b, array([[-10, -10, -10, 4, 5], [-10, -10, -10, -10, 10], [-10, -10, -10, -10, -10], [-10, -10, -10, -10, -10]])) class TestTriuIndices: def test_triu_indices(self): iu1 = triu_indices(4) iu2 = triu_indices(4, k=2) iu3 = triu_indices(4, m=5) iu4 = triu_indices(4, k=2, m=5) a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]]) b = np.arange(1, 21).reshape(4, 5) # Both for indexing: assert_array_equal(a[iu1], array([1, 2, 3, 4, 6, 7, 8, 11, 12, 16])) assert_array_equal(b[iu3], array([1, 2, 3, 4, 5, 7, 8, 9, 10, 13, 14, 15, 19, 20])) # And for assigning values: a[iu1] = -1 assert_array_equal(a, array([[-1, -1, -1, -1], [5, -1, -1, -1], [9, 10, -1, -1], [13, 14, 15, -1]])) b[iu3] = -1 assert_array_equal(b, array([[-1, -1, -1, -1, -1], [6, -1, -1, -1, -1], [11, 12, -1, -1, -1], [16, 17, 18, -1, -1]])) # These cover almost the whole array (two diagonals right of the # main one): a[iu2] = -10 assert_array_equal(a, array([[-1, -1, -10, -10], [5, -1, -1, -10], [9, 10, -1, -1], [13, 14, 15, -1]])) b[iu4] = -10 assert_array_equal(b, array([[-1, -1, -10, -10, -10], [6, -1, -1, -10, -10], [11, 12, -1, -1, -10], [16, 17, 18, -1, -1]])) class TestTrilIndicesFrom: def test_exceptions(self): assert_raises(ValueError, tril_indices_from, np.ones((2,))) assert_raises(ValueError, tril_indices_from, np.ones((2, 2, 2))) # assert_raises(ValueError, tril_indices_from, np.ones((2, 3))) class TestTriuIndicesFrom: def test_exceptions(self): assert_raises(ValueError, triu_indices_from, np.ones((2,))) assert_raises(ValueError, triu_indices_from, np.ones((2, 2, 2))) # assert_raises(ValueError, triu_indices_from, np.ones((2, 3))) class TestVander: def test_basic(self): c = np.array([0, 1, -2, 3]) v = vander(c) powers = np.array([[0, 0, 0, 0, 1], [1, 1, 1, 1, 1], [16, -8, 4, -2, 1], [81, 27, 9, 3, 1]]) # Check default value of N: assert_array_equal(v, powers[:, 1:]) # Check a range of N values, including 0 and 5 (greater than default) m = powers.shape[1] for n in range(6): v = vander(c, N=n) assert_array_equal(v, powers[:, m-n:m]) def test_dtypes(self): c = array([11, -12, 13], dtype=np.int8) v = vander(c) expected = np.array([[121, 11, 1], [144, -12, 1], [169, 13, 1]]) assert_array_equal(v, expected) c = array([1.0+1j, 1.0-1j]) v = vander(c, N=3) expected = np.array([[2j, 1+1j, 1], [-2j, 1-1j, 1]]) # The data is floating point, but the values are small integers, # so assert_array_equal *should* be safe here (rather than, say, # assert_array_almost_equal). assert_array_equal(v, expected)
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