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| author | Matteo Raso <33975162+MatteoRaso@users.noreply.github.com> | 2022-12-08 07:01:59 -0500 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2022-12-08 13:01:59 +0100 |
| commit | b3c0960a54c81a26bd07912dda96db9e356b34d1 (patch) | |
| tree | 2eca2dbef14e50ddc3340c20413c60e011c62ff2 /numpy/lib/tests/test_function_base.py | |
| parent | 6f9237e91ef26df62d40161458178f972db7ce26 (diff) | |
| download | numpy-b3c0960a54c81a26bd07912dda96db9e356b34d1.tar.gz | |
BUG: Quantile function on complex number now throws an error (#22652) (#22703)
Since percentile is more or less identical to quantile, I also made it
throw an error if it receives a complex input. I also made nanquantile
and nanpercentile throw errors as well.
* Made the changes recommended by seberg
* Fixed a test for PR 22703
* Fixed tests for quantile
* Shortened some more lines
* Fixup more lines
Co-authored-by: Sebastian Berg <sebastianb@nvidia.com>
Diffstat (limited to 'numpy/lib/tests/test_function_base.py')
| -rw-r--r-- | numpy/lib/tests/test_function_base.py | 24 |
1 files changed, 19 insertions, 5 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]) |