diff options
Diffstat (limited to 'numpy/lib/tests')
| -rw-r--r-- | numpy/lib/tests/test_function_base.py | 24 | ||||
| -rw-r--r-- | numpy/lib/tests/test_nanfunctions.py | 38 |
2 files changed, 49 insertions, 13 deletions
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index 1bb4c4efa..e38a187d8 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -2973,6 +2973,14 @@ class TestPercentile: o = np.ones((1,)) np.percentile(d, 5, None, o, False, 'linear') + def test_complex(self): + arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G') + assert_raises(TypeError, np.percentile, arr_c, 0.5) + arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D') + assert_raises(TypeError, np.percentile, arr_c, 0.5) + arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F') + assert_raises(TypeError, np.percentile, arr_c, 0.5) + def test_2D(self): x = np.array([[1, 1, 1], [1, 1, 1], @@ -2981,7 +2989,7 @@ class TestPercentile: [1, 1, 1]]) assert_array_equal(np.percentile(x, 50, axis=0), [1, 1, 1]) - @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + @pytest.mark.parametrize("dtype", np.typecodes["Float"]) def test_linear_nan_1D(self, dtype): # METHOD 1 of H&F arr = np.asarray([15.0, np.NAN, 35.0, 40.0, 50.0], dtype=dtype) @@ -2998,9 +3006,6 @@ class TestPercentile: (np.float32, np.float32), (np.float64, np.float64), (np.longdouble, np.longdouble), - (np.complex64, np.complex64), - (np.complex128, np.complex128), - (np.clongdouble, np.clongdouble), (np.dtype("O"), np.float64)] @pytest.mark.parametrize(["input_dtype", "expected_dtype"], H_F_TYPE_CODES) @@ -3040,7 +3045,7 @@ class TestPercentile: np.testing.assert_equal(np.asarray(actual).dtype, np.dtype(expected_dtype)) - TYPE_CODES = np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O" + TYPE_CODES = np.typecodes["AllInteger"] + np.typecodes["Float"] + "O" @pytest.mark.parametrize("dtype", TYPE_CODES) def test_lower_higher(self, dtype): @@ -3517,6 +3522,15 @@ class TestQuantile: x = np.arange(8) assert_equal(np.quantile(x, Fraction(1, 2)), Fraction(7, 2)) + def test_complex(self): + #See gh-22652 + arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G') + assert_raises(TypeError, np.quantile, arr_c, 0.5) + arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D') + assert_raises(TypeError, np.quantile, arr_c, 0.5) + arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F') + assert_raises(TypeError, np.quantile, arr_c, 0.5) + def test_no_p_overwrite(self): # this is worth retesting, because quantile does not make a copy p0 = np.array([0, 0.75, 0.25, 0.5, 1.0]) diff --git a/numpy/lib/tests/test_nanfunctions.py b/numpy/lib/tests/test_nanfunctions.py index 64464edcc..45cacb792 100644 --- a/numpy/lib/tests/test_nanfunctions.py +++ b/numpy/lib/tests/test_nanfunctions.py @@ -404,14 +404,20 @@ class TestNanFunctions_NumberTypes: ) def test_nanfunc_q(self, mat, dtype, nanfunc, func): mat = mat.astype(dtype) - tgt = func(mat, q=1) - out = nanfunc(mat, q=1) + if mat.dtype.kind == "c": + assert_raises(TypeError, func, mat, q=1) + assert_raises(TypeError, nanfunc, mat, q=1) - assert_almost_equal(out, tgt) - if dtype == "O": - assert type(out) is type(tgt) else: - assert out.dtype == tgt.dtype + tgt = func(mat, q=1) + out = nanfunc(mat, q=1) + + assert_almost_equal(out, tgt) + + if dtype == "O": + assert type(out) is type(tgt) + else: + assert out.dtype == tgt.dtype @pytest.mark.parametrize( "nanfunc,func", @@ -1058,6 +1064,14 @@ class TestNanFunctions_Percentile: assert_almost_equal(res, resout) assert_almost_equal(res, tgt) + def test_complex(self): + arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G') + assert_raises(TypeError, np.nanpercentile, arr_c, 0.5) + arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D') + assert_raises(TypeError, np.nanpercentile, arr_c, 0.5) + arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F') + assert_raises(TypeError, np.nanpercentile, arr_c, 0.5) + def test_result_values(self): tgt = [np.percentile(d, 28) for d in _rdat] res = np.nanpercentile(_ndat, 28, axis=1) @@ -1068,7 +1082,7 @@ class TestNanFunctions_Percentile: assert_almost_equal(res, tgt) @pytest.mark.parametrize("axis", [None, 0, 1]) - @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + @pytest.mark.parametrize("dtype", np.typecodes["Float"]) @pytest.mark.parametrize("array", [ np.array(np.nan), np.full((3, 3), np.nan), @@ -1162,6 +1176,14 @@ class TestNanFunctions_Quantile: assert_equal(np.nanquantile(x, 1), 3.5) assert_equal(np.nanquantile(x, 0.5), 1.75) + def test_complex(self): + arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='G') + assert_raises(TypeError, np.nanquantile, arr_c, 0.5) + arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='D') + assert_raises(TypeError, np.nanquantile, arr_c, 0.5) + arr_c = np.array([0.5+3.0j, 2.1+0.5j, 1.6+2.3j], dtype='F') + assert_raises(TypeError, np.nanquantile, arr_c, 0.5) + def test_no_p_overwrite(self): # this is worth retesting, because quantile does not make a copy p0 = np.array([0, 0.75, 0.25, 0.5, 1.0]) @@ -1175,7 +1197,7 @@ class TestNanFunctions_Quantile: assert_array_equal(p, p0) @pytest.mark.parametrize("axis", [None, 0, 1]) - @pytest.mark.parametrize("dtype", np.typecodes["AllFloat"]) + @pytest.mark.parametrize("dtype", np.typecodes["Float"]) @pytest.mark.parametrize("array", [ np.array(np.nan), np.full((3, 3), np.nan), |