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import numpy as np
from numpy.testing import assert_equal, assert_allclose
import pytest
from scipy.stats import variation
from scipy._lib._util import AxisError
class TestVariation:
"""
Test class for scipy.stats.variation
"""
def test_ddof(self):
x = np.arange(9.0)
assert_allclose(variation(x, ddof=1), np.sqrt(60/8)/4)
@pytest.mark.parametrize('sgn', [1, -1])
def test_sign(self, sgn):
x = np.array([1, 2, 3, 4, 5])
v = variation(sgn*x)
expected = sgn*np.sqrt(2)/3
assert_allclose(v, expected, rtol=1e-10)
def test_scalar(self):
# A scalar is treated like a 1-d sequence with length 1.
assert_equal(variation(4.0), 0.0)
@pytest.mark.parametrize('nan_policy, expected',
[('propagate', np.nan),
('omit', np.sqrt(20/3)/4)])
def test_variation_nan(self, nan_policy, expected):
x = np.arange(10.)
x[9] = np.nan
assert_allclose(variation(x, nan_policy=nan_policy), expected)
def test_nan_policy_raise(self):
x = np.array([1.0, 2.0, np.nan, 3.0])
with pytest.raises(ValueError, match='input contains nan'):
variation(x, nan_policy='raise')
def test_bad_nan_policy(self):
with pytest.raises(ValueError, match='must be one of'):
variation([1, 2, 3], nan_policy='foobar')
def test_keepdims(self):
x = np.arange(10).reshape(2, 5)
y = variation(x, axis=1, keepdims=True)
expected = np.array([[np.sqrt(2)/2],
[np.sqrt(2)/7]])
assert_allclose(y, expected)
@pytest.mark.parametrize('axis, expected',
[(0, np.empty((1, 0))),
(1, np.full((5, 1), fill_value=np.nan))])
def test_keepdims_size0(self, axis, expected):
x = np.zeros((5, 0))
y = variation(x, axis=axis, keepdims=True)
assert_equal(y, expected)
@pytest.mark.parametrize('incr, expected_fill', [(0, np.inf), (1, np.nan)])
def test_keepdims_and_ddof_eq_len_plus_incr(self, incr, expected_fill):
x = np.array([[1, 1, 2, 2], [1, 2, 3, 3]])
y = variation(x, axis=1, ddof=x.shape[1] + incr, keepdims=True)
assert_equal(y, np.full((2, 1), fill_value=expected_fill))
def test_propagate_nan(self):
# Check that the shape of the result is the same for inputs
# with and without nans, cf gh-5817
a = np.arange(8).reshape(2, -1).astype(float)
a[1, 0] = np.nan
v = variation(a, axis=1, nan_policy="propagate")
assert_allclose(v, [np.sqrt(5/4)/1.5, np.nan], atol=1e-15)
def test_axis_none(self):
# Check that `variation` computes the result on the flattened
# input when axis is None.
y = variation([[0, 1], [2, 3]], axis=None)
assert_allclose(y, np.sqrt(5/4)/1.5)
def test_bad_axis(self):
# Check that an invalid axis raises np.exceptions.AxisError.
x = np.array([[1, 2, 3], [4, 5, 6]])
with pytest.raises(AxisError):
variation(x, axis=10)
def test_mean_zero(self):
# Check that `variation` returns inf for a sequence that is not
# identically zero but whose mean is zero.
x = np.array([10, -3, 1, -4, -4])
y = variation(x)
assert_equal(y, np.inf)
x2 = np.array([x, -10*x])
y2 = variation(x2, axis=1)
assert_equal(y2, [np.inf, np.inf])
@pytest.mark.parametrize('x', [np.zeros(5), [], [1, 2, np.inf, 9]])
def test_return_nan(self, x):
# Test some cases where `variation` returns nan.
y = variation(x)
assert_equal(y, np.nan)
@pytest.mark.parametrize('axis, expected',
[(0, []), (1, [np.nan]*3), (None, np.nan)])
def test_2d_size_zero_with_axis(self, axis, expected):
x = np.empty((3, 0))
y = variation(x, axis=axis)
assert_equal(y, expected)
def test_neg_inf(self):
# Edge case that produces -inf: ddof equals the number of non-nan
# values, the values are not constant, and the mean is negative.
x1 = np.array([-3, -5])
assert_equal(variation(x1, ddof=2), -np.inf)
x2 = np.array([[np.nan, 1, -10, np.nan],
[-20, -3, np.nan, np.nan]])
assert_equal(variation(x2, axis=1, ddof=2, nan_policy='omit'),
[-np.inf, -np.inf])
@pytest.mark.parametrize("nan_policy", ['propagate', 'omit'])
def test_combined_edge_cases(self, nan_policy):
x = np.array([[0, 10, np.nan, 1],
[0, -5, np.nan, 2],
[0, -5, np.nan, 3]])
y = variation(x, axis=0, nan_policy=nan_policy)
assert_allclose(y, [np.nan, np.inf, np.nan, np.sqrt(2/3)/2])
@pytest.mark.parametrize(
'ddof, expected',
[(0, [np.sqrt(1/6), np.sqrt(5/8), np.inf, 0, np.nan, 0.0, np.nan]),
(1, [0.5, np.sqrt(5/6), np.inf, 0, np.nan, 0, np.nan]),
(2, [np.sqrt(0.5), np.sqrt(5/4), np.inf, np.nan, np.nan, 0, np.nan])]
)
def test_more_nan_policy_omit_tests(self, ddof, expected):
# The slightly strange formatting in the follow array is my attempt to
# maintain a clean tabular arrangement of the data while satisfying
# the demands of pycodestyle. Currently, E201 and E241 are not
# disabled by the `# noqa` annotation.
nan = np.nan
x = np.array([[1.0, 2.0, nan, 3.0],
[0.0, 4.0, 3.0, 1.0],
[nan, -.5, 0.5, nan],
[nan, 9.0, 9.0, nan],
[nan, nan, nan, nan],
[3.0, 3.0, 3.0, 3.0],
[0.0, 0.0, 0.0, 0.0]])
v = variation(x, axis=1, ddof=ddof, nan_policy='omit')
assert_allclose(v, expected)
def test_variation_ddof(self):
# test variation with delta degrees of freedom
# regression test for gh-13341
a = np.array([1, 2, 3, 4, 5])
nan_a = np.array([1, 2, 3, np.nan, 4, 5, np.nan])
y = variation(a, ddof=1)
nan_y = variation(nan_a, nan_policy="omit", ddof=1)
assert_allclose(y, np.sqrt(5/2)/3)
assert y == nan_y
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