def test_fit_data_with_user_training_set():
tdir = setup_training_set()
_, name = tempfile.mkstemp(suffix=".cfg", prefix="test_nanite_cli_rate_")
name = pathlib.Path(name)
pf = profile.Profile(path=name)
pf["rating training set"] = tdir
pf["fit param R value"] = 37.28e-6 / 2
pout = tempfile.mkdtemp(prefix="test_nanite_cli_rate_ts")
pout = pathlib.Path(pout)
rating.fit_perform(path=jpkfile2, path_results=pout, profile_path=name)
stats = np.loadtxt(pout / "statistics.tsv", skiprows=1, usecols=(1, 2, 3))
assert np.all(stats[:, 0] == [109, 129, 416])
assert np.all((3.5 < stats[:, 2]) * (stats[:, 2] < 5))
def test_fit_data_with_zef18():
_, name = tempfile.mkstemp(suffix=".cfg", prefix="test_nanite_cli_rate_")
name = pathlib.Path(name)
pf = profile.Profile(path=name)
pf["rating training set"] = "zef18"
pf["weight_cp"] = 2e-6
pf["fit param R value"] = 137.28e-6 / 2
pout = tempfile.mkdtemp(prefix="test_nanite_cli_rate_ts")
pout = pathlib.Path(pout)
rating.fit_perform(path=jpkfile2, path_results=pout, profile_path=name)
stats = np.loadtxt(pout / "statistics.tsv", skiprows=1, usecols=(1, 2, 3))
assert np.all(stats[:, 0] == [109, 129, 416])
assert stats[0, 2] == 9.5
assert stats[1, 2] == 2.4
assert stats[2, 2] == 4.9