numpy.lib.tests.test_function_base.TestPercentile

class documentation

class TestPercentile: (source)

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Undocumented

Method test_2D Undocumented
Method test_api Undocumented
Method test_axis Undocumented
Method test_basic Undocumented
Method test_exception Undocumented
Method test_extended_axis Undocumented
Method test_extended_axis_invalid Undocumented
Method test_fraction Undocumented
Method test_keepdims Undocumented
Method test_keepdims_out Undocumented
Method test_linear_interpolation Undocumented
Method test_linear_interpolation_extrapolation Undocumented
Method test_linear_nan_1D Undocumented
Method test_lower_higher Undocumented
Method test_midpoint Undocumented
Method test_nan_behavior Undocumented
Method test_nan_q Undocumented
Method test_nearest Undocumented
Method test_no_p_overwrite Undocumented
Method test_out Undocumented
Method test_out_nan Undocumented
Method test_percentile_empty_dim Undocumented
Method test_percentile_list Undocumented
Method test_percentile_no_overwrite Undocumented
Method test_percentile_out Undocumented
Method test_percentile_overwrite Undocumented
Method test_scalar_q Undocumented
Method test_sequence Undocumented
Constant H_F_TYPE_CODES Undocumented
Constant TYPE_CODES Undocumented
def test_2D(self): (source)

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def test_api(self): (source)

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def test_axis(self): (source)

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def test_basic(self): (source)

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def test_exception(self): (source)

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def test_extended_axis(self): (source)

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def test_extended_axis_invalid(self): (source)

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def test_fraction(self): (source)

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def test_keepdims(self): (source)

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@pytest.mark.parametrize('q', [7, [1, 7]])
@pytest.mark.parametrize(argnames='axis', argvalues=[None, 1, (1), (0, 1), (-3, -1)])
def test_keepdims_out(self, q, axis): (source)

Undocumented

@pytest.mark.parametrize(['input_dtype', 'expected_dtype'], H_F_TYPE_CODES)
@pytest.mark.parametrize(['method', '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, method, expected, input_dtype, expected_dtype): (source)

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def test_linear_interpolation_extrapolation(self): (source)

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@pytest.mark.parametrize('dtype', np.typecodes['AllFloat'])
def test_linear_nan_1D(self, dtype): (source)

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@pytest.mark.parametrize('dtype', TYPE_CODES)
def test_lower_higher(self, dtype): (source)

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@pytest.mark.parametrize('dtype', TYPE_CODES)
def test_midpoint(self, dtype): (source)

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def test_nan_behavior(self): (source)

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def test_nan_q(self): (source)

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@pytest.mark.parametrize('dtype', TYPE_CODES)
def test_nearest(self, dtype): (source)

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def test_no_p_overwrite(self): (source)

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def test_out(self): (source)

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def test_out_nan(self): (source)

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def test_percentile_empty_dim(self): (source)

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def test_percentile_list(self): (source)

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def test_percentile_no_overwrite(self): (source)

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def test_percentile_out(self): (source)

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def test_percentile_overwrite(self): (source)

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def test_scalar_q(self): (source)

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def test_sequence(self): (source)

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H_F_TYPE_CODES = (source)

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Value
[(int_type, np.float64) for int_type in np.typecodes['AllInteger']]+[(np.float16↵
,
                                                                      np.float16↵
),
                                                                     (np.float32↵
,
                                                                      np.float32↵
...
TYPE_CODES = (source)

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Value
(np.typecodes['AllInteger']+np.typecodes['AllFloat'])+'O'