def test_tol(self):
"""
Tests that the optimization of OP parameters can be achieved
to different tolerancesself.
Tolerances don't directly translate, so we expand it slightly for
the test (by a factor prop. to e**(len(ps)+1)).
"""
eps = np.finfo(float).eps
hightol_result = orthogonal_polynomial_constants(
self.xs, degree=self.default_degree, tol=10 ** -16
)
for pow in np.linspace(np.log(eps * 1000.0), -5, 3):
tol = np.exp(pow)
with self.subTest(tol=tol):
ret = orthogonal_polynomial_constants(
self.xs, degree=self.default_degree, tol=tol
)
self.assertTrue(not len(ret[0])) # first item is empty
self.assertTrue(len(ret) == self.default_degree)
for ix, ps in enumerate(ret):
if ps:
test_tol = tol * np.exp(len(ps) + 1)
a = np.array(list(ps), dtype=float)
b = np.array(list(hightol_result[ix]), dtype=float)
# print( (abs(a)-abs(b)) / ((abs(a)+abs(b))/2) - test_tol)
self.assertTrue(np.allclose(a, b, atol=test_tol))
def test_xs(self):
"""Tests operation on different x arrays."""
for xs in [self.xs, self.xs[1:], self.xs[2:-2]]:
with self.subTest(xs=xs):
ret = orthogonal_polynomial_constants(xs, degree=self.default_degree)
self.assertTrue(not len(ret[0])) # first item is empty
self.assertTrue(len(ret) == self.default_degree)
def test_degree(self):
"""Tests generation of different degree polynomial parameters."""
max_degree = 4
expected = orthogonal_polynomial_constants(self.xs, degree=max_degree)
for degree in range(1, max_degree):
with self.subTest(degree=degree):
ret = orthogonal_polynomial_constants(self.xs, degree=degree)
self.assertTrue(not len(ret[0])) # first item is empty
self.assertTrue(len(ret) == degree)
# the parameter values should be independent of the degree.
allclose = all([
np.allclose(
np.array(expected[idx], dtype=float),
np.array(tpl, dtype=float),
) for idx, tpl in enumerate(ret)
])
self.assertTrue(allclose)
def test_against_original(self):
"""Check that the constants line up with Hugh's paper."""
ret = orthogonal_polynomial_constants(self.xs, degree=self.default_degree)
print(ret, self.expect)
for out, expect in zip(ret, self.expect):
with self.subTest(out=out, expect=expect):
if out:
self.assertTrue(
np.allclose(
np.array(out, dtype="float"),
np.array(expect, dtype="float"),
)
)
else:
self.assertEqual(out, ()) # first one should be empty tuple
def test_params(self):
# the first three all have the same result - defaulting to ONeill 2016
# the two following use a full REE set for the OP basis function definition
# the last illustrates that custom parameters could be used
degree = self.default_degree
for params in [
None,
"ONeill2016",
"O'Neill (2016)",
"full",
"Full",
orthogonal_polynomial_constants(
get_ionic_radii(REE(), charge=3, coordination=8), # xs
degree=self.default_degree,
),
]:
with self.subTest(params=params):
ret = calc_lambdas(self.df, params=params, degree=degree)
self.assertTrue(ret.columns.size == self.default_degree)
def test_function_params(self):
params = orthogonal_polynomial_constants(self.xs, degree=len(self.lambdas))
ret = get_lambda_poly_func(self.lambdas, params=params)
self.assertTrue(callable(ret))
