diff options
| -rw-r--r-- | numpy/lib/tests/test_function_base.py | 134 |
1 files changed, 43 insertions, 91 deletions
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index 53254c3e5..22228405a 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -2914,98 +2914,50 @@ class TestPercentile: np.testing.assert_equal(res, np.NAN) np.testing.assert_equal(res.dtype, arr.dtype) - TYPE_CODES = np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O" - - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_inverted_cdf(self, dtype): - # METHOD 1 of H&F - arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) - res = np.percentile( - arr, - 40.0, - interpolation="inverted_cdf") - np.testing.assert_almost_equal(res, 20, 15) - - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_averaged_inverted_cdf(self, dtype): - # METHOD 2 of H&F - arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) - res = np.percentile( - arr, - 40.0, - interpolation="averaged_inverted_cdf") - np.testing.assert_almost_equal(res, 27.5, 15) - - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_closest_observation(self, dtype): - # METHOD 3 of H&F - arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) - res = np.percentile( - arr, - 40.0, - interpolation="closest_observation") - np.testing.assert_almost_equal(res, 20, 15) - - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_interpolated_inverted_cdf(self, dtype): - # METHOD 4 of H&F - arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) - res = np.percentile( - arr, - 40.0, - interpolation="interpolated_inverted_cdf") - np.testing.assert_almost_equal(res, 20, 15) - - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_hazen(self, dtype): - # METHOD 5 of H&F - arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) - res = np.percentile( - arr, - 40.0, - interpolation="hazen") - np.testing.assert_almost_equal(res, 27.5, 15) - - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_weibull(self, dtype): - # METHOD 6 of H&F - arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) - res = np.percentile( - arr, - 40.0, - interpolation="weibull") - np.testing.assert_almost_equal(res, 26, 15) - - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_linear(self, dtype): - # METHOD 7 of H&F - # Test defaults - assert_equal(np.percentile(range(10), 50), 4.5) - # explicit interpolation_method (the default) - res = np.percentile([15.0, 20.0, 35.0, 40.0, 50.0], - 40, - interpolation="linear") - np.testing.assert_almost_equal(res, 29, 15) - - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_median_unbiased(self, dtype): - # METHOD 8 of H&F - arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) - res = np.percentile( - arr, - 40.0, - interpolation="median_unbiased") - np.testing.assert_almost_equal(res, 27, 14) + H_F_TYPE_CODES = [(int_type, np.float64) + for int_type in np.typecodes["AllInteger"] + ] + [(np.float16, np.float64), + (np.float32, np.float64), + (np.float64, np.float64), + (np.float128, np.float128), + (np.complex64, np.complex128), + (np.complex128, np.complex128), + (np.complex256, np.complex256), + (np.dtype("O"), np.float64)] + + @pytest.mark.parametrize(["input_dtype", "expected_dtype"], H_F_TYPE_CODES) + @pytest.mark.parametrize(["interpolation", "expected"], + [("inverted_cdf", 20), + ("averaged_inverted_cdf", 27.5), + ("closest_observation", 20), + ("interpolated_inverted_cdf", 20), + ("hazen", 27.5), + ("weibull", 26), + ("linear", 29), + ("median_unbiased", 27), + ("normal_unbiased", 27.125), + ]) + def test_linear_interpolation(self, + interpolation, + expected, + input_dtype, + expected_dtype): + arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=input_dtype) + actual = np.percentile(arr, 40.0, interpolation=interpolation) + + np.testing.assert_almost_equal(actual, expected, 14) + + if interpolation in ["inverted_cdf", "closest_observation"]: + if input_dtype == "O": + np.testing.assert_equal(np.asarray(actual).dtype, np.float64) + else: + np.testing.assert_equal(np.asarray(actual).dtype, + np.dtype(input_dtype)) + else: + np.testing.assert_equal(np.asarray(actual).dtype, + np.dtype(expected_dtype)) - @pytest.mark.parametrize("dtype", TYPE_CODES) - def test_linear_normal_unbiased(self, dtype): - # METHOD 9 of H&F - arr = np.asarray([15.0, 20.0, 35.0, 40.0, 50.0], dtype=dtype) - res = np.percentile( - arr, - 40.0, - interpolation="normal_unbiased") - np.testing.assert_almost_equal(res, 27.125, 15) + TYPE_CODES = np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O" @pytest.mark.parametrize("dtype", TYPE_CODES) def test_lower_higher(self, dtype): |