def test_broadcast_equals(self): v1 = Variable((), np.nan) v2 = Variable(('x'), [np.nan, np.nan]) self.assertTrue(v1.broadcast_equals(v2)) self.assertFalse(v1.equals(v2)) self.assertFalse(v1.identical(v2)) v3 = Variable(('x'), [np.nan]) self.assertTrue(v1.broadcast_equals(v3)) self.assertFalse(v1.equals(v3)) self.assertFalse(v1.identical(v3)) self.assertFalse(v1.broadcast_equals(None)) v4 = Variable(('x'), [np.nan] * 3) self.assertFalse(v2.broadcast_equals(v4))
def test_equals_and_identical(self): d = np.random.rand(10, 3) d[0, 0] = np.nan v1 = Variable(('dim1', 'dim2'), data=d, attrs={'att1': 3, 'att2': [1, 2, 3]}) v2 = Variable(('dim1', 'dim2'), data=d, attrs={'att1': 3, 'att2': [1, 2, 3]}) self.assertTrue(v1.equals(v2)) self.assertTrue(v1.identical(v2)) v3 = Variable(('dim1', 'dim3'), data=d) self.assertFalse(v1.equals(v3)) v4 = Variable(('dim1', 'dim2'), data=d) self.assertTrue(v1.equals(v4)) self.assertFalse(v1.identical(v4)) v5 = deepcopy(v1) v5.values[:] = np.random.rand(10, 3) self.assertFalse(v1.equals(v5)) self.assertFalse(v1.equals(None)) self.assertFalse(v1.equals(d)) self.assertFalse(v1.identical(None)) self.assertFalse(v1.identical(d))
def test_equals_and_identical(self): d = np.random.rand(10, 3) d[0, 0] = np.nan v1 = Variable(('dim1', 'dim2'), data=d, attrs={ 'att1': 3, 'att2': [1, 2, 3] }) v2 = Variable(('dim1', 'dim2'), data=d, attrs={ 'att1': 3, 'att2': [1, 2, 3] }) self.assertTrue(v1.equals(v2)) self.assertTrue(v1.identical(v2)) v3 = Variable(('dim1', 'dim3'), data=d) self.assertFalse(v1.equals(v3)) v4 = Variable(('dim1', 'dim2'), data=d) self.assertTrue(v1.equals(v4)) self.assertFalse(v1.identical(v4)) v5 = deepcopy(v1) v5.values[:] = np.random.rand(10, 3) self.assertFalse(v1.equals(v5)) self.assertFalse(v1.equals(None)) self.assertFalse(v1.equals(d)) self.assertFalse(v1.identical(None)) self.assertFalse(v1.identical(d))