def test_corners():
# fails for length differences
assert_raises(ValueError, approx, x=[1, 2, 3], y=[1, 2], xout=1.0)
# fails for bad string
assert_raises(ValueError,
approx,
x=table,
y=table,
xout=1.0,
method='bad-string')
# fails for bad length
assert_raises(ValueError, approx, x=[], y=[], xout=[], ties='mean')
# fails for bad length
assert_raises(ValueError, approx, x=[], y=[], xout=[], method='constant')
# fails for linear when < 2 samples
assert_raises(ValueError,
approx,
x=[1],
y=[1],
xout=[],
method='linear',
ties='ordered')
# but *doesn't* fail for constant when < 2 samples
approx(x=[1], y=[1], xout=[], method='constant', ties='ordered')
# fails for bad length
assert_raises(ValueError, approx, x=[], y=[], xout=[], method='constant')
def test_approx_precision():
# Test an example from R vs. Python to compare the expected values and
# make sure we get as close as possible. This is from an ADFTest where k=1
# and x=austres
tableipl = np.array([[-4.0664], [-3.7468], [-3.462], [-3.1572], [-1.2128],
[-0.8928], [-0.6104], [-0.2704]])
_, interpol = approx(tableipl, ADFTest.tablep, xout=-1.337233, rule=2)
assert np.allclose(interpol, 0.84880354) # in R we get 0.8488036
def test_approx_rule2():
# for rule = 2
x, y = approx(table, tablep, stat, rule=2)
assert_array_almost_equal(x, c(1.01))
assert_array_almost_equal(y, c(0.01))
def test_approx_rule1():
# for rule = 1
x, y = approx(table, tablep, stat, rule=1)
assert_array_almost_equal(x, c(1.01))
assert_array_almost_equal(y, c(np.nan))
def test_valid_corner():
# *doesn't* fail for constant when < 2 samples
approx(x=[1], y=[1], xout=[], method='constant', ties='ordered')
def test_corner_errors(kwargs):
with pytest.raises(ValueError):
approx(**kwargs)