def test_exp_factory_with_asympt(test_f: Callable[[float], float],
avoid_log: bool):
"""Test of exponential extrapolator."""
seeded_f = apply_seed_to_func(test_f, SEED)
fac = ExpFactory(X_VALS, asymptote=A, avoid_log=avoid_log)
fac.run_classical(seeded_f)
assert not fac._opt_params
zne_value = fac.reduce()
assert np.isclose(zne_value, seeded_f(0, err=0), atol=CLOSE_TOL)
# There are three parameters in the exponential ansatz
assert len(fac._opt_params) == 3
exp_vals = fac.get_expectation_values()
assert np.isclose(
fac.extrapolate(X_VALS, exp_vals, asymptote=A, avoid_log=avoid_log),
zne_value,
)
assert np.isclose(
fac.extrapolate(
X_VALS,
exp_vals,
asymptote=A,
avoid_log=avoid_log,
full_output=True,
)[0],
zne_value,
)
def test_failing_fit_error():
"""Test error handling for a failing fit."""
fac = ExpFactory(X_VALS, asymptote=None)
fac._instack = [{"scale_factor": x} for x in X_VALS]
fac._outstack = [1.0, 2.0, 1.0, 2.0, 1.0]
with raises(ExtrapolationError,
match=r"The extrapolation fit failed to converge."):
fac.reduce()
# test also the static "extrapolate" method.
with raises(ExtrapolationError,
match=r"The extrapolation fit failed to converge."):
ExpFactory.extrapolate(X_VALS, [1.0, 2.0, 1.0, 2.0, 1.0])
