def test_table_output(self):
pcs = [1547, 1655]
mlist = [
lmfit.models.VoigtModel(prefix=f"v{i}_") for i in range(len(pcs))
]
model = reduce(lambda x, y: x + y, mlist)
params = model.make_params()
for i, center in enumerate(pcs):
p = f"v{i}_"
dx = 20
params[p + "center"].set(value=center,
min=center - dx,
max=center + dx)
params[p + "sigma"].set(max=50)
params[p + "amplitude"].set(min=0.0001)
out_result = model.fit(self.data.X[0], params, x=getx(self.data))
out_table = fit_peaks(self.data, model, params)
out_row = out_table[0]
self.assertEqual(out_row.x.shape[0],
len(pcs) + len(out_result.var_names) + 1)
attrs = [a.name for a in out_table.domain.attributes[:4]]
self.assertEqual(attrs,
["v0 area", "v0 amplitude", "v0 center", "v0 sigma"])
self.assertNotEqual(0, out_row["v0 area"].value)
self.assertEqual(out_result.best_values["v0_amplitude"],
out_row["v0 amplitude"].value)
self.assertEqual(out_result.best_values["v0_center"],
out_row["v0 center"].value)
self.assertEqual(out_result.best_values["v0_sigma"],
out_row["v0 sigma"].value)
self.assertEqual(out_result.redchi,
out_row["Reduced chi-square"].value)
self.assertEqual(out_row.id, self.data.ids[0])
def test_same_output(self):
model, params = self.matched_models()
out_fit = fit_peaks(self.data, model, params)
self.widget.unconditional_commit()
self.wait_until_finished()
out = self.get_output(self.widget.Outputs.fit_params)
self.assertEqual(out_fit.domain.attributes, out.domain.attributes)
np.testing.assert_array_equal(out_fit.X, out.X)
def test_fit_peaks(self):
model = lmfit.models.VoigtModel(prefix="v1_")
params = model.make_params(center=1655)
out = fit_peaks(self.data, model, params)
assert len(out) == len(self.data)
def test_same_output(self):
out_fit = fit_peaks(self.data, self.model, self.params)
out = self.get_output(self.widget.Outputs.fit_params)
self.assertEqual(out_fit.domain.attributes, out.domain.attributes)
np.testing.assert_array_equal(out_fit.X, out.X)
