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)