import copy import operator as op import pickle import warnings import pytest from pint import DimensionalityError, OffsetUnitCalculusError, UnitStrippedWarning from pint.compat import np from pint.testsuite import helpers from pint.testsuite.test_umath import TestUFuncs @helpers.requires_numpy class TestNumpyMethods: @classmethod def setup_class(cls): from pint import _DEFAULT_REGISTRY cls.ureg = _DEFAULT_REGISTRY cls.Q_ = cls.ureg.Quantity @classmethod def teardown_class(cls): cls.ureg = None cls.Q_ = None @property def q(self): return [[1, 2], [3, 4]] * self.ureg.m @property def q_scalar(self): return np.array(5) * self.ureg.m @property def q_nan(self): return [[1, 2], [3, np.nan]] * self.ureg.m @property def q_zero_or_nan(self): return [[0, 0], [0, np.nan]] * self.ureg.m @property def q_temperature(self): return self.Q_([[1, 2], [3, 4]], self.ureg.degC) def assertNDArrayEqual(self, actual, desired): # Assert that the given arrays are equal, and are not Quantities np.testing.assert_array_equal(actual, desired) assert not isinstance(actual, self.Q_) assert not isinstance(desired, self.Q_) class TestNumpyArrayCreation(TestNumpyMethods): # https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html @helpers.requires_array_function_protocol() def test_ones_like(self): self.assertNDArrayEqual(np.ones_like(self.q), np.array([[1, 1], [1, 1]])) @helpers.requires_array_function_protocol() def test_zeros_like(self): self.assertNDArrayEqual(np.zeros_like(self.q), np.array([[0, 0], [0, 0]])) @helpers.requires_array_function_protocol() def test_empty_like(self): ret = np.empty_like(self.q) assert ret.shape == (2, 2) assert isinstance(ret, np.ndarray) @helpers.requires_array_function_protocol() def test_full_like(self): helpers.assert_quantity_equal( np.full_like(self.q, self.Q_(0, self.ureg.degC)), self.Q_([[0, 0], [0, 0]], self.ureg.degC), ) self.assertNDArrayEqual(np.full_like(self.q, 2), np.array([[2, 2], [2, 2]])) class TestNumpyArrayManipulation(TestNumpyMethods): # TODO # https://www.numpy.org/devdocs/reference/routines.array-manipulation.html # copyto # broadcast , broadcast_arrays # asarray asanyarray asmatrix asfarray asfortranarray ascontiguousarray asarray_chkfinite asscalar require # Changing array shape def test_flatten(self): helpers.assert_quantity_equal(self.q.flatten(), [1, 2, 3, 4] * self.ureg.m) def test_flat(self): for q, v in zip(self.q.flat, [1, 2, 3, 4]): assert q == v * self.ureg.m def test_reshape(self): helpers.assert_quantity_equal( self.q.reshape([1, 4]), [[1, 2, 3, 4]] * self.ureg.m ) def test_ravel(self): helpers.assert_quantity_equal(self.q.ravel(), [1, 2, 3, 4] * self.ureg.m) @helpers.requires_array_function_protocol() def test_ravel_numpy_func(self): helpers.assert_quantity_equal(np.ravel(self.q), [1, 2, 3, 4] * self.ureg.m) # Transpose-like operations @helpers.requires_array_function_protocol() def test_moveaxis(self): helpers.assert_quantity_equal( np.moveaxis(self.q, 1, 0), np.array([[1, 2], [3, 4]]).T * self.ureg.m ) @helpers.requires_array_function_protocol() def test_rollaxis(self): helpers.assert_quantity_equal( np.rollaxis(self.q, 1), np.array([[1, 2], [3, 4]]).T * self.ureg.m ) @helpers.requires_array_function_protocol() def test_swapaxes(self): helpers.assert_quantity_equal( np.swapaxes(self.q, 1, 0), np.array([[1, 2], [3, 4]]).T * self.ureg.m ) def test_transpose(self): helpers.assert_quantity_equal( self.q.transpose(), [[1, 3], [2, 4]] * self.ureg.m ) @helpers.requires_array_function_protocol() def test_transpose_numpy_func(self): helpers.assert_quantity_equal( np.transpose(self.q), [[1, 3], [2, 4]] * self.ureg.m ) @helpers.requires_array_function_protocol() def test_flip_numpy_func(self): helpers.assert_quantity_equal( np.flip(self.q, axis=0), [[3, 4], [1, 2]] * self.ureg.m ) # Changing number of dimensions @helpers.requires_array_function_protocol() def test_atleast_1d(self): actual = np.atleast_1d(self.Q_(0, self.ureg.degC), self.q.flatten()) expected = (self.Q_(np.array([0]), self.ureg.degC), self.q.flatten()) for ind_actual, ind_expected in zip(actual, expected): helpers.assert_quantity_equal(ind_actual, ind_expected) helpers.assert_quantity_equal(np.atleast_1d(self.q), self.q) @helpers.requires_array_function_protocol() def test_atleast_2d(self): actual = np.atleast_2d(self.Q_(0, self.ureg.degC), self.q.flatten()) expected = ( self.Q_(np.array([[0]]), self.ureg.degC), np.array([[1, 2, 3, 4]]) * self.ureg.m, ) for ind_actual, ind_expected in zip(actual, expected): helpers.assert_quantity_equal(ind_actual, ind_expected) helpers.assert_quantity_equal(np.atleast_2d(self.q), self.q) @helpers.requires_array_function_protocol() def test_atleast_3d(self): actual = np.atleast_3d(self.Q_(0, self.ureg.degC), self.q.flatten()) expected = ( self.Q_(np.array([[[0]]]), self.ureg.degC), np.array([[[1], [2], [3], [4]]]) * self.ureg.m, ) for ind_actual, ind_expected in zip(actual, expected): helpers.assert_quantity_equal(ind_actual, ind_expected) helpers.assert_quantity_equal( np.atleast_3d(self.q), np.array([[[1], [2]], [[3], [4]]]) * self.ureg.m ) @helpers.requires_array_function_protocol() def test_broadcast_to(self): helpers.assert_quantity_equal( np.broadcast_to(self.q[:, 1], (2, 2)), np.array([[2, 4], [2, 4]]) * self.ureg.m, ) @helpers.requires_array_function_protocol() def test_expand_dims(self): helpers.assert_quantity_equal( np.expand_dims(self.q, 0), np.array([[[1, 2], [3, 4]]]) * self.ureg.m ) @helpers.requires_array_function_protocol() def test_squeeze(self): helpers.assert_quantity_equal(np.squeeze(self.q), self.q) helpers.assert_quantity_equal( self.q.reshape([1, 4]).squeeze(), [1, 2, 3, 4] * self.ureg.m ) # Changing number of dimensions # Joining arrays @helpers.requires_array_function_protocol() def test_concat_stack(self, subtests): for func in (np.concatenate, np.stack, np.hstack, np.vstack, np.dstack): with subtests.test(func=func): helpers.assert_quantity_equal( func([self.q] * 2), self.Q_(func([self.q.m] * 2), self.ureg.m) ) # One or more of the args is a bare array full of zeros or NaNs helpers.assert_quantity_equal( func([self.q_zero_or_nan.m, self.q]), self.Q_(func([self.q_zero_or_nan.m, self.q.m]), self.ureg.m), ) # One or more of the args is a bare array with at least one non-zero, # non-NaN element nz = self.q_zero_or_nan nz.m[0, 0] = 1 with pytest.raises(DimensionalityError): func([nz.m, self.q]) @helpers.requires_array_function_protocol() def test_block_column_stack(self, subtests): for func in (np.block, np.column_stack): with subtests.test(func=func): helpers.assert_quantity_equal( func([self.q[:, 0], self.q[:, 1]]), self.Q_(func([self.q[:, 0].m, self.q[:, 1].m]), self.ureg.m), ) # One or more of the args is a bare array full of zeros or NaNs helpers.assert_quantity_equal( func( [ self.q_zero_or_nan[:, 0].m, self.q[:, 0], self.q_zero_or_nan[:, 1].m, ] ), self.Q_( func( [ self.q_zero_or_nan[:, 0].m, self.q[:, 0].m, self.q_zero_or_nan[:, 1].m, ] ), self.ureg.m, ), ) # One or more of the args is a bare array with at least one non-zero, # non-NaN element nz = self.q_zero_or_nan nz.m[0, 0] = 1 with pytest.raises(DimensionalityError): func([nz[:, 0].m, self.q[:, 0]]) @helpers.requires_array_function_protocol() def test_append(self): helpers.assert_quantity_equal( np.append(self.q, [[0, 0]] * self.ureg.m, axis=0), [[1, 2], [3, 4], [0, 0]] * self.ureg.m, ) def test_astype(self): actual = self.q.astype(np.float32) expected = self.Q_(np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32), "m") helpers.assert_quantity_equal(actual, expected) assert actual.m.dtype == expected.m.dtype def test_item(self): helpers.assert_quantity_equal(self.Q_([[0]], "m").item(), 0 * self.ureg.m) class TestNumpyMathematicalFunctions(TestNumpyMethods): # https://www.numpy.org/devdocs/reference/routines.math.html # Trigonometric functions @helpers.requires_array_function_protocol() def test_unwrap(self): helpers.assert_quantity_equal( np.unwrap([0, 3 * np.pi] * self.ureg.radians), [0, np.pi] ) helpers.assert_quantity_equal( np.unwrap([0, 540] * self.ureg.deg), [0, 180] * self.ureg.deg ) # Rounding @helpers.requires_array_function_protocol() def test_fix(self): helpers.assert_quantity_equal(np.fix(3.14 * self.ureg.m), 3.0 * self.ureg.m) helpers.assert_quantity_equal(np.fix(3.0 * self.ureg.m), 3.0 * self.ureg.m) helpers.assert_quantity_equal( np.fix([2.1, 2.9, -2.1, -2.9] * self.ureg.m), [2.0, 2.0, -2.0, -2.0] * self.ureg.m, ) # Sums, products, differences @helpers.requires_array_function_protocol() def test_prod(self): axis = 0 where = [[True, False], [True, True]] helpers.assert_quantity_equal(self.q.prod(), 24 * self.ureg.m**4) helpers.assert_quantity_equal(self.q.prod(axis=axis), [3, 8] * self.ureg.m**2) helpers.assert_quantity_equal(self.q.prod(where=where), 12 * self.ureg.m**3) @helpers.requires_array_function_protocol() def test_prod_numpy_func(self): axis = 0 where = [[True, False], [True, True]] helpers.assert_quantity_equal(np.prod(self.q), 24 * self.ureg.m**4) helpers.assert_quantity_equal( np.prod(self.q, axis=axis), [3, 8] * self.ureg.m**2 ) helpers.assert_quantity_equal( np.prod(self.q, where=where), 12 * self.ureg.m**3 ) with pytest.raises(DimensionalityError): np.prod(self.q, axis=axis, where=where) helpers.assert_quantity_equal( np.prod(self.q, axis=axis, where=[[True, False], [False, True]]), [1, 4] * self.ureg.m, ) helpers.assert_quantity_equal( np.prod(self.q, axis=axis, where=[True, False]), [3, 1] * self.ureg.m**2 ) @helpers.requires_array_function_protocol() def test_nanprod_numpy_func(self): helpers.assert_quantity_equal(np.nanprod(self.q_nan), 6 * self.ureg.m**3) helpers.assert_quantity_equal( np.nanprod(self.q_nan, axis=0), [3, 2] * self.ureg.m**2 ) helpers.assert_quantity_equal( np.nanprod(self.q_nan, axis=1), [2, 3] * self.ureg.m**2 ) def test_sum(self): assert self.q.sum() == 10 * self.ureg.m helpers.assert_quantity_equal(self.q.sum(0), [4, 6] * self.ureg.m) helpers.assert_quantity_equal(self.q.sum(1), [3, 7] * self.ureg.m) @helpers.requires_array_function_protocol() def test_sum_numpy_func(self): helpers.assert_quantity_equal(np.sum(self.q, axis=0), [4, 6] * self.ureg.m) with pytest.raises(OffsetUnitCalculusError): np.sum(self.q_temperature) @helpers.requires_array_function_protocol() def test_nansum_numpy_func(self): helpers.assert_quantity_equal( np.nansum(self.q_nan, axis=0), [4, 2] * self.ureg.m ) def test_cumprod(self): with pytest.raises(DimensionalityError): self.q.cumprod() helpers.assert_quantity_equal((self.q / self.ureg.m).cumprod(), [1, 2, 6, 24]) @helpers.requires_array_function_protocol() def test_cumprod_numpy_func(self): with pytest.raises(DimensionalityError): np.cumprod(self.q) with pytest.raises(DimensionalityError): np.cumproduct(self.q) helpers.assert_quantity_equal(np.cumprod(self.q / self.ureg.m), [1, 2, 6, 24]) helpers.assert_quantity_equal( np.cumproduct(self.q / self.ureg.m), [1, 2, 6, 24] ) helpers.assert_quantity_equal( np.cumprod(self.q / self.ureg.m, axis=1), [[1, 2], [3, 12]] ) @helpers.requires_array_function_protocol() def test_nancumprod_numpy_func(self): with pytest.raises(DimensionalityError): np.nancumprod(self.q_nan) helpers.assert_quantity_equal( np.nancumprod(self.q_nan / self.ureg.m), [1, 2, 6, 6] ) @helpers.requires_array_function_protocol() def test_diff(self): helpers.assert_quantity_equal(np.diff(self.q, 1), [[1], [1]] * self.ureg.m) helpers.assert_quantity_equal( np.diff(self.q_temperature, 1), [[1], [1]] * self.ureg.delta_degC ) @helpers.requires_array_function_protocol() def test_ediff1d(self): helpers.assert_quantity_equal(np.ediff1d(self.q), [1, 1, 1] * self.ureg.m) helpers.assert_quantity_equal( np.ediff1d(self.q_temperature), [1, 1, 1] * self.ureg.delta_degC ) @helpers.requires_array_function_protocol() def test_gradient(self): grad = np.gradient([[1, 1], [3, 4]] * self.ureg.m, 1 * self.ureg.J) helpers.assert_quantity_equal( grad[0], [[2.0, 3.0], [2.0, 3.0]] * self.ureg.m / self.ureg.J ) helpers.assert_quantity_equal( grad[1], [[0.0, 0.0], [1.0, 1.0]] * self.ureg.m / self.ureg.J ) grad = np.gradient(self.Q_([[1, 1], [3, 4]], self.ureg.degC), 1 * self.ureg.J) helpers.assert_quantity_equal( grad[0], [[2.0, 3.0], [2.0, 3.0]] * self.ureg.delta_degC / self.ureg.J ) helpers.assert_quantity_equal( grad[1], [[0.0, 0.0], [1.0, 1.0]] * self.ureg.delta_degC / self.ureg.J ) @helpers.requires_array_function_protocol() def test_cross(self): a = [[3, -3, 1]] * self.ureg.kPa b = [[4, 9, 2]] * self.ureg.m**2 helpers.assert_quantity_equal( np.cross(a, b), [[-15, -2, 39]] * self.ureg.kPa * self.ureg.m**2 ) @helpers.requires_array_function_protocol() def test_trapz(self): helpers.assert_quantity_equal( np.trapz([1.0, 2.0, 3.0, 4.0] * self.ureg.J, dx=1 * self.ureg.m), 7.5 * self.ureg.J * self.ureg.m, ) @helpers.requires_array_function_protocol() def test_dot(self): helpers.assert_quantity_equal( self.q.ravel().dot(np.array([1, 0, 0, 1])), 5 * self.ureg.m ) @helpers.requires_array_function_protocol() def test_dot_numpy_func(self): helpers.assert_quantity_equal( np.dot(self.q.ravel(), [0, 0, 1, 0] * self.ureg.dimensionless), 3 * self.ureg.m, ) @helpers.requires_array_function_protocol() def test_einsum(self): a = np.arange(25).reshape(5, 5) * self.ureg.m b = np.arange(5) * self.ureg.m helpers.assert_quantity_equal(np.einsum("ii", a), 60 * self.ureg.m) helpers.assert_quantity_equal( np.einsum("ii->i", a), np.array([0, 6, 12, 18, 24]) * self.ureg.m ) helpers.assert_quantity_equal(np.einsum("i,i", b, b), 30 * self.ureg.m**2) helpers.assert_quantity_equal( np.einsum("ij,j", a, b), np.array([30, 80, 130, 180, 230]) * self.ureg.m**2, ) @helpers.requires_array_function_protocol() def test_solve(self): A = self.q b = [[3], [7]] * self.ureg.s x = np.linalg.solve(A, b) helpers.assert_quantity_almost_equal(x, self.Q_([[1], [1]], "s / m")) helpers.assert_quantity_almost_equal(np.dot(A, x), b) # Arithmetic operations def test_addition_with_scalar(self): a = np.array([0, 1, 2]) b = 10.0 * self.ureg("gram/kilogram") helpers.assert_quantity_almost_equal( a + b, self.Q_([0.01, 1.01, 2.01], self.ureg.dimensionless) ) helpers.assert_quantity_almost_equal( b + a, self.Q_([0.01, 1.01, 2.01], self.ureg.dimensionless) ) def test_addition_with_incompatible_scalar(self): a = np.array([0, 1, 2]) b = 1.0 * self.ureg.m with pytest.raises(DimensionalityError): op.add(a, b) with pytest.raises(DimensionalityError): op.add(b, a) def test_power(self): arr = np.array(range(3), dtype=float) q = self.Q_(arr, "meter") for op_ in [op.pow, op.ipow, np.power]: q_cp = copy.copy(q) with pytest.raises(DimensionalityError): op_(2.0, q_cp) arr_cp = copy.copy(arr) arr_cp = copy.copy(arr) q_cp = copy.copy(q) with pytest.raises(DimensionalityError): op_(q_cp, arr_cp) q_cp = copy.copy(q) q2_cp = copy.copy(q) with pytest.raises(DimensionalityError): op_(q_cp, q2_cp) helpers.assert_quantity_equal( np.power(self.q, self.Q_(2)), self.Q_([[1, 4], [9, 16]], "m**2") ) helpers.assert_quantity_equal( self.q ** self.Q_(2), self.Q_([[1, 4], [9, 16]], "m**2") ) self.assertNDArrayEqual(arr ** self.Q_(2), np.array([0, 1, 4])) def test_sqrt(self): q = self.Q_(100, "m**2") helpers.assert_quantity_equal(np.sqrt(q), self.Q_(10, "m")) def test_cbrt(self): q = self.Q_(1000, "m**3") helpers.assert_quantity_equal(np.cbrt(q), self.Q_(10, "m")) @pytest.mark.xfail @helpers.requires_numpy def test_exponentiation_array_exp_2(self): arr = np.array(range(3), dtype=float) # q = self.Q_(copy.copy(arr), None) q = self.Q_(copy.copy(arr), "meter") arr_cp = copy.copy(arr) q_cp = copy.copy(q) # this fails as expected since numpy 1.8.0 but... with pytest.raises(DimensionalityError): op.pow(arr_cp, q_cp) # ..not for op.ipow ! # q_cp is treated as if it is an array. The units are ignored. # Quantity.__ipow__ is never called arr_cp = copy.copy(arr) q_cp = copy.copy(q) with pytest.raises(DimensionalityError): op.ipow(arr_cp, q_cp) class TestNumpyUnclassified(TestNumpyMethods): def test_tolist(self): with pytest.raises(AttributeError): (5 * self.ureg.m).tolist() assert self.q.tolist() == [ [1 * self.ureg.m, 2 * self.ureg.m], [3 * self.ureg.m, 4 * self.ureg.m], ] def test_fill(self): tmp = self.q tmp.fill(6 * self.ureg.ft) helpers.assert_quantity_equal(tmp, [[6, 6], [6, 6]] * self.ureg.ft) tmp.fill(5 * self.ureg.m) helpers.assert_quantity_equal(tmp, [[5, 5], [5, 5]] * self.ureg.m) def test_take(self): helpers.assert_quantity_equal(self.q.take([0, 1, 2, 3]), self.q.flatten()) def test_put(self): q = [1.0, 2.0, 3.0, 4.0] * self.ureg.m q.put([0, 2], [10.0, 20.0] * self.ureg.m) helpers.assert_quantity_equal(q, [10.0, 2.0, 20.0, 4.0] * self.ureg.m) q = [1.0, 2.0, 3.0, 4.0] * self.ureg.m q.put([0, 2], [1.0, 2.0] * self.ureg.mm) helpers.assert_quantity_equal(q, [0.001, 2.0, 0.002, 4.0] * self.ureg.m) q = [1.0, 2.0, 3.0, 4.0] * self.ureg.m / self.ureg.mm q.put([0, 2], [1.0, 2.0]) helpers.assert_quantity_equal( q, [0.001, 2.0, 0.002, 4.0] * self.ureg.m / self.ureg.mm ) q = [1.0, 2.0, 3.0, 4.0] * self.ureg.m with pytest.raises(DimensionalityError): q.put([0, 2], [4.0, 6.0] * self.ureg.J) with pytest.raises(DimensionalityError): q.put([0, 2], [4.0, 6.0]) def test_repeat(self): helpers.assert_quantity_equal( self.q.repeat(2), [1, 1, 2, 2, 3, 3, 4, 4] * self.ureg.m ) def test_sort(self): q = [4, 5, 2, 3, 1, 6] * self.ureg.m q.sort() helpers.assert_quantity_equal(q, [1, 2, 3, 4, 5, 6] * self.ureg.m) @helpers.requires_array_function_protocol() def test_sort_numpy_func(self): q = [4, 5, 2, 3, 1, 6] * self.ureg.m helpers.assert_quantity_equal(np.sort(q), [1, 2, 3, 4, 5, 6] * self.ureg.m) def test_argsort(self): q = [1, 4, 5, 6, 2, 9] * self.ureg.MeV self.assertNDArrayEqual(q.argsort(), [0, 4, 1, 2, 3, 5]) @helpers.requires_array_function_protocol() def test_argsort_numpy_func(self): self.assertNDArrayEqual(np.argsort(self.q, axis=0), np.array([[0, 0], [1, 1]])) def test_diagonal(self): q = [[1, 2, 3], [1, 2, 3], [1, 2, 3]] * self.ureg.m helpers.assert_quantity_equal(q.diagonal(offset=1), [2, 3] * self.ureg.m) @helpers.requires_array_function_protocol() def test_diagonal_numpy_func(self): q = [[1, 2, 3], [1, 2, 3], [1, 2, 3]] * self.ureg.m helpers.assert_quantity_equal(np.diagonal(q, offset=-1), [1, 2] * self.ureg.m) def test_compress(self): helpers.assert_quantity_equal( self.q.compress([False, True], axis=0), [[3, 4]] * self.ureg.m ) helpers.assert_quantity_equal( self.q.compress([False, True], axis=1), [[2], [4]] * self.ureg.m ) @helpers.requires_array_function_protocol() def test_compress_nep18(self): helpers.assert_quantity_equal( np.compress([False, True], self.q, axis=1), [[2], [4]] * self.ureg.m ) def test_searchsorted(self): q = self.q.flatten() self.assertNDArrayEqual(q.searchsorted([1.5, 2.5] * self.ureg.m), [1, 2]) q = self.q.flatten() with pytest.raises(DimensionalityError): q.searchsorted([1.5, 2.5]) @helpers.requires_array_function_protocol() def test_searchsorted_numpy_func(self): """Test searchsorted as numpy function.""" q = self.q.flatten() self.assertNDArrayEqual(np.searchsorted(q, [1.5, 2.5] * self.ureg.m), [1, 2]) def test_nonzero(self): q = [1, 0, 5, 6, 0, 9] * self.ureg.m self.assertNDArrayEqual(q.nonzero()[0], [0, 2, 3, 5]) @helpers.requires_array_function_protocol() def test_nonzero_numpy_func(self): q = [1, 0, 5, 6, 0, 9] * self.ureg.m self.assertNDArrayEqual(np.nonzero(q)[0], [0, 2, 3, 5]) @helpers.requires_array_function_protocol() def test_any_numpy_func(self): q = [0, 1] * self.ureg.m assert np.any(q) with pytest.raises(ValueError): np.any(self.q_temperature) @helpers.requires_array_function_protocol() def test_all_numpy_func(self): q = [0, 1] * self.ureg.m assert not np.all(q) with pytest.raises(ValueError): np.all(self.q_temperature) @helpers.requires_array_function_protocol() def test_count_nonzero_numpy_func(self): q = [1, 0, 5, 6, 0, 9] * self.ureg.m assert np.count_nonzero(q) == 4 def test_max(self): assert self.q.max() == 4 * self.ureg.m def test_max_numpy_func(self): assert np.max(self.q) == 4 * self.ureg.m @helpers.requires_array_function_protocol() def test_max_with_axis_arg(self): helpers.assert_quantity_equal(np.max(self.q, axis=1), [2, 4] * self.ureg.m) @helpers.requires_array_function_protocol() def test_max_with_initial_arg(self): helpers.assert_quantity_equal( np.max(self.q[..., None], axis=2, initial=3 * self.ureg.m), [[3, 3], [3, 4]] * self.ureg.m, ) @helpers.requires_array_function_protocol() def test_nanmax(self): assert np.nanmax(self.q_nan) == 3 * self.ureg.m def test_argmax(self): assert self.q.argmax() == 3 @helpers.requires_array_function_protocol() def test_argmax_numpy_func(self): self.assertNDArrayEqual(np.argmax(self.q, axis=0), np.array([1, 1])) @helpers.requires_array_function_protocol() def test_nanargmax_numpy_func(self): self.assertNDArrayEqual(np.nanargmax(self.q_nan, axis=0), np.array([1, 0])) def test_maximum(self): helpers.assert_quantity_equal( np.maximum(self.q, self.Q_([0, 5], "m")), self.Q_([[1, 5], [3, 5]], "m") ) def test_min(self): assert self.q.min() == 1 * self.ureg.m @helpers.requires_array_function_protocol() def test_min_numpy_func(self): assert np.min(self.q) == 1 * self.ureg.m @helpers.requires_array_function_protocol() def test_min_with_axis_arg(self): helpers.assert_quantity_equal(np.min(self.q, axis=1), [1, 3] * self.ureg.m) @helpers.requires_array_function_protocol() def test_min_with_initial_arg(self): helpers.assert_quantity_equal( np.min(self.q[..., None], axis=2, initial=3 * self.ureg.m), [[1, 2], [3, 3]] * self.ureg.m, ) @helpers.requires_array_function_protocol() def test_nanmin(self): assert np.nanmin(self.q_nan) == 1 * self.ureg.m def test_argmin(self): assert self.q.argmin() == 0 @helpers.requires_array_function_protocol() def test_argmin_numpy_func(self): self.assertNDArrayEqual(np.argmin(self.q, axis=0), np.array([0, 0])) @helpers.requires_array_function_protocol() def test_nanargmin_numpy_func(self): self.assertNDArrayEqual(np.nanargmin(self.q_nan, axis=0), np.array([0, 0])) def test_minimum(self): helpers.assert_quantity_equal( np.minimum(self.q, self.Q_([0, 5], "m")), self.Q_([[0, 2], [0, 4]], "m") ) def test_ptp(self): assert self.q.ptp() == 3 * self.ureg.m @helpers.requires_array_function_protocol() def test_ptp_numpy_func(self): helpers.assert_quantity_equal(np.ptp(self.q, axis=0), [2, 2] * self.ureg.m) def test_clip(self): helpers.assert_quantity_equal( self.q.clip(max=2 * self.ureg.m), [[1, 2], [2, 2]] * self.ureg.m ) helpers.assert_quantity_equal( self.q.clip(min=3 * self.ureg.m), [[3, 3], [3, 4]] * self.ureg.m ) helpers.assert_quantity_equal( self.q.clip(min=2 * self.ureg.m, max=3 * self.ureg.m), [[2, 2], [3, 3]] * self.ureg.m, ) helpers.assert_quantity_equal( self.q.clip(3 * self.ureg.m, None), [[3, 3], [3, 4]] * self.ureg.m ) helpers.assert_quantity_equal( self.q.clip(3 * self.ureg.m), [[3, 3], [3, 4]] * self.ureg.m ) with pytest.raises(DimensionalityError): self.q.clip(self.ureg.J) with pytest.raises(DimensionalityError): self.q.clip(1) @helpers.requires_array_function_protocol() def test_clip_numpy_func(self): helpers.assert_quantity_equal( np.clip(self.q, 150 * self.ureg.cm, None), [[1.5, 2], [3, 4]] * self.ureg.m ) def test_round(self): q = [1, 1.33, 5.67, 22] * self.ureg.m helpers.assert_quantity_equal(q.round(0), [1, 1, 6, 22] * self.ureg.m) helpers.assert_quantity_equal(q.round(-1), [0, 0, 10, 20] * self.ureg.m) helpers.assert_quantity_equal(q.round(1), [1, 1.3, 5.7, 22] * self.ureg.m) @helpers.requires_array_function_protocol() def test_round_numpy_func(self): helpers.assert_quantity_equal( np.around(1.0275 * self.ureg.m, decimals=2), 1.03 * self.ureg.m ) helpers.assert_quantity_equal( np.round_(1.0275 * self.ureg.m, decimals=2), 1.03 * self.ureg.m ) def test_trace(self): assert self.q.trace() == (1 + 4) * self.ureg.m def test_cumsum(self): helpers.assert_quantity_equal(self.q.cumsum(), [1, 3, 6, 10] * self.ureg.m) @helpers.requires_array_function_protocol() def test_cumsum_numpy_func(self): helpers.assert_quantity_equal( np.cumsum(self.q, axis=0), [[1, 2], [4, 6]] * self.ureg.m ) @helpers.requires_array_function_protocol() def test_nancumsum_numpy_func(self): helpers.assert_quantity_equal( np.nancumsum(self.q_nan, axis=0), [[1, 2], [4, 2]] * self.ureg.m ) def test_mean(self): assert self.q.mean() == 2.5 * self.ureg.m @helpers.requires_array_function_protocol() def test_mean_numpy_func(self): assert np.mean(self.q) == 2.5 * self.ureg.m assert np.mean(self.q_temperature) == self.Q_(2.5, self.ureg.degC) @helpers.requires_array_function_protocol() def test_nanmean_numpy_func(self): assert np.nanmean(self.q_nan) == 2 * self.ureg.m @helpers.requires_array_function_protocol() def test_average_numpy_func(self): helpers.assert_quantity_almost_equal( np.average(self.q, axis=0, weights=[1, 2]), [2.33333, 3.33333] * self.ureg.m, rtol=1e-5, ) @helpers.requires_array_function_protocol() def test_median_numpy_func(self): assert np.median(self.q) == 2.5 * self.ureg.m @helpers.requires_array_function_protocol() def test_nanmedian_numpy_func(self): assert np.nanmedian(self.q_nan) == 2 * self.ureg.m def test_var(self): assert self.q.var() == 1.25 * self.ureg.m**2 @helpers.requires_array_function_protocol() def test_var_numpy_func(self): assert np.var(self.q) == 1.25 * self.ureg.m**2 @helpers.requires_array_function_protocol() def test_nanvar_numpy_func(self): helpers.assert_quantity_almost_equal( np.nanvar(self.q_nan), 0.66667 * self.ureg.m**2, rtol=1e-5 ) def test_std(self): helpers.assert_quantity_almost_equal( self.q.std(), 1.11803 * self.ureg.m, rtol=1e-5 ) @helpers.requires_array_function_protocol() def test_std_numpy_func(self): helpers.assert_quantity_almost_equal( np.std(self.q), 1.11803 * self.ureg.m, rtol=1e-5 ) with pytest.raises(OffsetUnitCalculusError): np.std(self.q_temperature) def test_cumprod(self): with pytest.raises(DimensionalityError): self.q.cumprod() helpers.assert_quantity_equal((self.q / self.ureg.m).cumprod(), [1, 2, 6, 24]) @helpers.requires_array_function_protocol() def test_nanstd_numpy_func(self): helpers.assert_quantity_almost_equal( np.nanstd(self.q_nan), 0.81650 * self.ureg.m, rtol=1e-5 ) def test_conj(self): helpers.assert_quantity_equal((self.q * (1 + 1j)).conj(), self.q * (1 - 1j)) helpers.assert_quantity_equal( (self.q * (1 + 1j)).conjugate(), self.q * (1 - 1j) ) def test_getitem(self): with pytest.raises(IndexError): self.q.__getitem__((0, 10)) helpers.assert_quantity_equal(self.q[0], [1, 2] * self.ureg.m) assert self.q[1, 1] == 4 * self.ureg.m def test_setitem(self): with pytest.raises(TypeError): self.q[0, 0] = 1 with pytest.raises(DimensionalityError): self.q[0, 0] = 1 * self.ureg.J with pytest.raises(DimensionalityError): self.q[0] = 1 with pytest.raises(DimensionalityError): self.q[0] = np.ndarray([1, 2]) with pytest.raises(DimensionalityError): self.q[0] = 1 * self.ureg.J q = self.q.copy() q[0] = 1 * self.ureg.m helpers.assert_quantity_equal(q, [[1, 1], [3, 4]] * self.ureg.m) q = self.q.copy() q[...] = 1 * self.ureg.m helpers.assert_quantity_equal(q, [[1, 1], [1, 1]] * self.ureg.m) q = self.q.copy() q[:] = 1 * self.ureg.m helpers.assert_quantity_equal(q, [[1, 1], [1, 1]] * self.ureg.m) # check and see that dimensionless num bers work correctly q = [0, 1, 2, 3] * self.ureg.dimensionless q[0] = 1 helpers.assert_quantity_equal(q, np.asarray([1, 1, 2, 3])) q[0] = self.ureg.m / self.ureg.mm helpers.assert_quantity_equal(q, np.asarray([1000, 1, 2, 3])) q = [0.0, 1.0, 2.0, 3.0] * self.ureg.m / self.ureg.mm q[0] = 1.0 helpers.assert_quantity_equal(q, [0.001, 1, 2, 3] * self.ureg.m / self.ureg.mm) # Check that this properly masks the first item without warning q = self.ureg.Quantity( np.ma.array([0.0, 1.0, 2.0, 3.0], mask=[False, True, False, False]), "m" ) with warnings.catch_warnings(record=True) as w: q[0] = np.ma.masked # Check for no warnings assert not w assert q.mask[0] def test_iterator(self): for q, v in zip(self.q.flatten(), [1, 2, 3, 4]): assert q == v * self.ureg.m def test_iterable(self): assert np.iterable(self.q) assert not np.iterable(1 * self.ureg.m) def test_reversible_op(self): """ """ x = self.q.magnitude u = self.Q_(np.ones(x.shape)) helpers.assert_quantity_equal(x / self.q, u * x / self.q) helpers.assert_quantity_equal(x * self.q, u * x * self.q) helpers.assert_quantity_equal(x + u, u + x) helpers.assert_quantity_equal(x - u, -(u - x)) def test_pickle(self, subtests): for protocol in range(pickle.HIGHEST_PROTOCOL + 1): with subtests.test(protocol): q1 = [10, 20] * self.ureg.m q2 = pickle.loads(pickle.dumps(q1, protocol)) self.assertNDArrayEqual(q1.magnitude, q2.magnitude) assert q1.units == q2.units def test_equal(self): x = self.q.magnitude u = self.Q_(np.ones(x.shape)) true = np.ones_like(x, dtype=np.bool_) false = np.zeros_like(x, dtype=np.bool_) helpers.assert_quantity_equal(u, u) helpers.assert_quantity_equal(u == u, u.magnitude == u.magnitude) helpers.assert_quantity_equal(u == 1, u.magnitude == 1) v = self.Q_(np.zeros(x.shape), "m") w = self.Q_(np.ones(x.shape), "m") self.assertNDArrayEqual(v == 1, false) self.assertNDArrayEqual( self.Q_(np.zeros_like(x), "m") == self.Q_(np.zeros_like(x), "s"), false, ) self.assertNDArrayEqual(v == v, true) self.assertNDArrayEqual(v == w, false) self.assertNDArrayEqual(v == w.to("mm"), false) self.assertNDArrayEqual(u == v, false) def test_shape(self): u = self.Q_(np.arange(12)) u.shape = 4, 3 assert u.magnitude.shape == (4, 3) @helpers.requires_array_function_protocol() def test_shape_numpy_func(self): assert np.shape(self.q) == (2, 2) @helpers.requires_array_function_protocol() def test_len_numpy_func(self): assert len(self.q) == 2 @helpers.requires_array_function_protocol() def test_ndim_numpy_func(self): assert np.ndim(self.q) == 2 @helpers.requires_array_function_protocol() def test_copy_numpy_func(self): q_copy = np.copy(self.q) helpers.assert_quantity_equal(self.q, q_copy) assert self.q is not q_copy @helpers.requires_array_function_protocol() def test_trim_zeros_numpy_func(self): q = [0, 4, 3, 0, 2, 2, 0, 0, 0] * self.ureg.m helpers.assert_quantity_equal(np.trim_zeros(q), [4, 3, 0, 2, 2] * self.ureg.m) @helpers.requires_array_function_protocol() def test_result_type_numpy_func(self): assert np.result_type(self.q) == np.dtype("int") @helpers.requires_array_function_protocol() def test_nan_to_num_numpy_func(self): helpers.assert_quantity_equal( np.nan_to_num(self.q_nan, nan=-999 * self.ureg.mm), [[1, 2], [3, -0.999]] * self.ureg.m, ) @helpers.requires_array_function_protocol() def test_meshgrid_numpy_func(self): x = [1, 2] * self.ureg.m y = [0, 50, 100] * self.ureg.mm xx, yy = np.meshgrid(x, y) helpers.assert_quantity_equal(xx, [[1, 2], [1, 2], [1, 2]] * self.ureg.m) helpers.assert_quantity_equal(yy, [[0, 0], [50, 50], [100, 100]] * self.ureg.mm) @helpers.requires_array_function_protocol() def test_isclose_numpy_func(self): q2 = [[1000.05, 2000], [3000.00007, 4001]] * self.ureg.mm self.assertNDArrayEqual( np.isclose(self.q, q2), np.array([[False, True], [True, False]]) ) self.assertNDArrayEqual( np.isclose(self.q, q2, atol=1e-5, rtol=1e-7), np.array([[False, True], [True, False]]), ) @helpers.requires_array_function_protocol() def test_interp_numpy_func(self): x = [1, 4] * self.ureg.m xp = np.linspace(0, 3, 5) * self.ureg.m fp = self.Q_([0, 5, 10, 15, 20], self.ureg.degC) helpers.assert_quantity_almost_equal( np.interp(x, xp, fp), self.Q_([6.66667, 20.0], self.ureg.degC), rtol=1e-5 ) x_ = np.array([1, 4]) xp_ = np.linspace(0, 3, 5) fp_ = [0, 5, 10, 15, 20] helpers.assert_quantity_almost_equal( np.interp(x_, xp_, fp), self.Q_([6.6667, 20.0], self.ureg.degC), rtol=1e-5 ) helpers.assert_quantity_almost_equal( np.interp(x, xp, fp_), [6.6667, 20.0], rtol=1e-5 ) def test_comparisons(self): self.assertNDArrayEqual( self.q > 2 * self.ureg.m, np.array([[False, False], [True, True]]) ) self.assertNDArrayEqual( self.q < 2 * self.ureg.m, np.array([[True, False], [False, False]]) ) @helpers.requires_array_function_protocol() def test_where(self): helpers.assert_quantity_equal( np.where(self.q >= 2 * self.ureg.m, self.q, 20 * self.ureg.m), [[20, 2], [3, 4]] * self.ureg.m, ) helpers.assert_quantity_equal( np.where(self.q >= 2 * self.ureg.m, self.q, 0), [[0, 2], [3, 4]] * self.ureg.m, ) helpers.assert_quantity_equal( np.where(self.q >= 2 * self.ureg.m, self.q, np.nan), [[np.nan, 2], [3, 4]] * self.ureg.m, ) helpers.assert_quantity_equal( np.where(self.q >= 3 * self.ureg.m, 0, self.q), [[1, 2], [0, 0]] * self.ureg.m, ) helpers.assert_quantity_equal( np.where(self.q >= 3 * self.ureg.m, np.nan, self.q), [[1, 2], [np.nan, np.nan]] * self.ureg.m, ) helpers.assert_quantity_equal( np.where(self.q >= 2 * self.ureg.m, self.q, np.array(np.nan)), [[np.nan, 2], [3, 4]] * self.ureg.m, ) helpers.assert_quantity_equal( np.where(self.q >= 3 * self.ureg.m, np.array(np.nan), self.q), [[1, 2], [np.nan, np.nan]] * self.ureg.m, ) with pytest.raises(DimensionalityError): np.where( self.q < 2 * self.ureg.m, self.q, 0 * self.ureg.J, ) @helpers.requires_array_function_protocol() def test_fabs(self): helpers.assert_quantity_equal( np.fabs(self.q - 2 * self.ureg.m), self.Q_([[1, 0], [1, 2]], "m") ) @helpers.requires_array_function_protocol() def test_isin(self): self.assertNDArrayEqual( np.isin(self.q, self.Q_([0, 2, 4], "m")), np.array([[False, True], [False, True]]), ) self.assertNDArrayEqual( np.isin(self.q, self.Q_([0, 2, 4], "J")), np.array([[False, False], [False, False]]), ) self.assertNDArrayEqual( np.isin(self.q, [self.Q_(2, "m"), self.Q_(4, "J")]), np.array([[False, True], [False, False]]), ) self.assertNDArrayEqual( np.isin(self.q, self.q.m), np.array([[False, False], [False, False]]) ) self.assertNDArrayEqual( np.isin(self.q / self.ureg.cm, [1, 3]), np.array([[True, False], [True, False]]), ) with pytest.raises(ValueError): np.isin(self.q.m, self.q) @helpers.requires_array_function_protocol() def test_percentile(self): helpers.assert_quantity_equal(np.percentile(self.q, 25), self.Q_(1.75, "m")) @helpers.requires_array_function_protocol() def test_nanpercentile(self): helpers.assert_quantity_equal( np.nanpercentile(self.q_nan, 25), self.Q_(1.5, "m") ) @helpers.requires_array_function_protocol() def test_quantile(self): helpers.assert_quantity_equal(np.quantile(self.q, 0.25), self.Q_(1.75, "m")) @helpers.requires_array_function_protocol() def test_nanquantile(self): helpers.assert_quantity_equal( np.nanquantile(self.q_nan, 0.25), self.Q_(1.5, "m") ) @helpers.requires_array_function_protocol() def test_copyto(self): a = self.q.m q = copy.copy(self.q) np.copyto(q, 2 * q, where=[True, False]) helpers.assert_quantity_equal(q, self.Q_([[2, 2], [6, 4]], "m")) np.copyto(q, 0, where=[[False, False], [True, False]]) helpers.assert_quantity_equal(q, self.Q_([[2, 2], [0, 4]], "m")) np.copyto(a, q) self.assertNDArrayEqual(a, np.array([[2, 2], [0, 4]])) @helpers.requires_array_function_protocol() def test_tile(self): helpers.assert_quantity_equal( np.tile(self.q, 2), np.array([[1, 2, 1, 2], [3, 4, 3, 4]]) * self.ureg.m ) @helpers.requires_numpy_at_least("1.20") @helpers.requires_array_function_protocol() def test_sliding_window_view(self): q = self.Q_([[1, 2, 2, 1], [2, 1, 1, 2], [1, 2, 2, 1]], "m") actual = np.lib.stride_tricks.sliding_window_view(q, window_shape=(3, 3)) expected = self.Q_( [[[[1, 2, 2], [2, 1, 1], [1, 2, 2]], [[2, 2, 1], [1, 1, 2], [2, 2, 1]]]], "m", ) helpers.assert_quantity_equal(actual, expected) @helpers.requires_array_function_protocol() def test_rot90(self): helpers.assert_quantity_equal( np.rot90(self.q), np.array([[2, 4], [1, 3]]) * self.ureg.m ) @helpers.requires_array_function_protocol() def test_insert(self): helpers.assert_quantity_equal( np.insert(self.q, 1, 0 * self.ureg.m, axis=1), np.array([[1, 0, 2], [3, 0, 4]]) * self.ureg.m, ) def test_ndarray_downcast(self): with pytest.warns(UnitStrippedWarning): np.asarray(self.q) def test_ndarray_downcast_with_dtype(self): with pytest.warns(UnitStrippedWarning): qarr = np.asarray(self.q, dtype=np.float64) assert qarr.dtype == np.float64 def test_array_protocol_unavailable(self): for attr in ("__array_struct__", "__array_interface__"): with pytest.raises(AttributeError): getattr(self.q, attr) @helpers.requires_array_function_protocol() def test_resize(self): helpers.assert_quantity_equal( np.resize(self.q, (2, 4)), [[1, 2, 3, 4], [1, 2, 3, 4]] * self.ureg.m ) @helpers.requires_array_function_protocol() def test_pad(self): # Tests reproduced with modification from NumPy documentation a = [1, 2, 3, 4, 5] * self.ureg.m b = self.Q_([4.0, 6.0, 8.0, 9.0, -3.0], "degC") helpers.assert_quantity_equal( np.pad(a, (2, 3), "constant"), [0, 0, 1, 2, 3, 4, 5, 0, 0, 0] * self.ureg.m ) helpers.assert_quantity_equal( np.pad(a, (2, 3), "constant", constant_values=(0, 600 * self.ureg.cm)), [0, 0, 1, 2, 3, 4, 5, 6, 6, 6] * self.ureg.m, ) helpers.assert_quantity_equal( np.pad( b, (2, 1), "constant", constant_values=(np.nan, self.Q_(10, "degC")) ), self.Q_([np.nan, np.nan, 4, 6, 8, 9, -3, 10], "degC"), ) with pytest.raises(DimensionalityError): np.pad(a, (2, 3), "constant", constant_values=4) helpers.assert_quantity_equal( np.pad(a, (2, 3), "edge"), [1, 1, 1, 2, 3, 4, 5, 5, 5, 5] * self.ureg.m ) helpers.assert_quantity_equal( np.pad(a, (2, 3), "linear_ramp"), [0, 0, 1, 2, 3, 4, 5, 3, 1, 0] * self.ureg.m, ) helpers.assert_quantity_equal( np.pad(a, (2, 3), "linear_ramp", end_values=(5, -4) * self.ureg.m), [5, 3, 1, 2, 3, 4, 5, 2, -1, -4] * self.ureg.m, ) helpers.assert_quantity_equal( np.pad(a, (2,), "maximum"), [5, 5, 1, 2, 3, 4, 5, 5, 5] * self.ureg.m ) helpers.assert_quantity_equal( np.pad(a, (2,), "mean"), [3, 3, 1, 2, 3, 4, 5, 3, 3] * self.ureg.m ) helpers.assert_quantity_equal( np.pad(a, (2,), "median"), [3, 3, 1, 2, 3, 4, 5, 3, 3] * self.ureg.m ) helpers.assert_quantity_equal( np.pad(self.q, ((3, 2), (2, 3)), "minimum"), [ [1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1], [3, 3, 3, 4, 3, 3, 3], [1, 1, 1, 2, 1, 1, 1], [1, 1, 1, 2, 1, 1, 1], ] * self.ureg.m, ) helpers.assert_quantity_equal( np.pad(a, (2, 3), "reflect"), [3, 2, 1, 2, 3, 4, 5, 4, 3, 2] * self.ureg.m ) helpers.assert_quantity_equal( np.pad(a, (2, 3), "reflect", reflect_type="odd"), [-1, 0, 1, 2, 3, 4, 5, 6, 7, 8] * self.ureg.m, ) helpers.assert_quantity_equal( np.pad(a, (2, 3), "symmetric"), [2, 1, 1, 2, 3, 4, 5, 5, 4, 3] * self.ureg.m ) helpers.assert_quantity_equal( np.pad(a, (2, 3), "symmetric", reflect_type="odd"), [0, 1, 1, 2, 3, 4, 5, 5, 6, 7] * self.ureg.m, ) helpers.assert_quantity_equal( np.pad(a, (2, 3), "wrap"), [4, 5, 1, 2, 3, 4, 5, 1, 2, 3] * self.ureg.m ) def pad_with(vector, pad_width, iaxis, kwargs): pad_value = kwargs.get("padder", 10) vector[: pad_width[0]] = pad_value vector[-pad_width[1] :] = pad_value b = self.Q_(np.arange(6).reshape((2, 3)), "degC") helpers.assert_quantity_equal( np.pad(b, 2, pad_with), self.Q_( [ [10, 10, 10, 10, 10, 10, 10], [10, 10, 10, 10, 10, 10, 10], [10, 10, 0, 1, 2, 10, 10], [10, 10, 3, 4, 5, 10, 10], [10, 10, 10, 10, 10, 10, 10], [10, 10, 10, 10, 10, 10, 10], ], "degC", ), ) helpers.assert_quantity_equal( np.pad(b, 2, pad_with, padder=100), self.Q_( [ [100, 100, 100, 100, 100, 100, 100], [100, 100, 100, 100, 100, 100, 100], [100, 100, 0, 1, 2, 100, 100], [100, 100, 3, 4, 5, 100, 100], [100, 100, 100, 100, 100, 100, 100], [100, 100, 100, 100, 100, 100, 100], ], "degC", ), ) # Note: Does not support Quantity pad_with vectorized callable use @helpers.requires_array_function_protocol() def test_allclose(self): assert np.allclose([1e10, 1e-8] * self.ureg.m, [1.00001e10, 1e-9] * self.ureg.m) assert not np.allclose( [1e10, 1e-8] * self.ureg.m, [1.00001e10, 1e-9] * self.ureg.mm ) @helpers.requires_array_function_protocol() def test_intersect1d(self): helpers.assert_quantity_equal( np.intersect1d([1, 3, 4, 3] * self.ureg.m, [3, 1, 2, 1] * self.ureg.m), [1, 3] * self.ureg.m, ) @pytest.mark.skip class TestBitTwiddlingUfuncs(TestUFuncs): """Universal functions (ufuncs) > Bittwiddling functions http://docs.scipy.org/doc/numpy/reference/ufuncs.html#bittwiddlingfunctions bitwise_and(x1, x2[, out]) Compute the bitwise AND of two arrays elementwise. bitwise_or(x1, x2[, out]) Compute the bitwise OR of two arrays elementwise. bitwise_xor(x1, x2[, out]) Compute the bitwise XOR of two arrays elementwise. invert(x[, out]) Compute bitwise inversion, or bitwise NOT, elementwise. left_shift(x1, x2[, out]) Shift the bits of an integer to the left. right_shift(x1, x2[, out]) Shift the bits of an integer to the right. Parameters ---------- Returns ------- """ @property def qless(self): return np.asarray([1, 2, 3, 4], dtype=np.uint8) * self.ureg.dimensionless @property def qs(self): return 8 * self.ureg.J @property def q1(self): return np.asarray([1, 2, 3, 4], dtype=np.uint8) * self.ureg.J @property def q2(self): return 2 * self.q1 @property def qm(self): return np.asarray([1, 2, 3, 4], dtype=np.uint8) * self.ureg.m def test_bitwise_and(self): self._test2(np.bitwise_and, self.q1, (self.q2, self.qs), (self.qm,), "same") def test_bitwise_or(self): self._test2( np.bitwise_or, self.q1, (self.q1, self.q2, self.qs), (self.qm,), "same" ) def test_bitwise_xor(self): self._test2( np.bitwise_xor, self.q1, (self.q1, self.q2, self.qs), (self.qm,), "same" ) def test_invert(self): self._test1(np.invert, (self.q1, self.q2, self.qs), (), "same") def test_left_shift(self): self._test2( np.left_shift, self.q1, (self.qless, 2), (self.q1, self.q2, self.qs), "same" ) def test_right_shift(self): self._test2( np.right_shift, self.q1, (self.qless, 2), (self.q1, self.q2, self.qs), "same", )