initial implementation

python/tests/test_Fit.py

@@ -13,3 +13,41 @@ def test_gaussian_model_evaluates_and_fits_data():
     result = model.fit(x, y)
 
     np.testing.assert_allclose(result["par"], expected, atol=2e-3)
+
+
+def test_minimizer_can_be_selected_and_fumili_fits_data():
+    x = np.linspace(-5.0, 5.0, 51)
+    expected = np.array([20.0, 0.5, 1.2])
+
+    assert aare.Gaussian().minimizer == aare.Minimizer.Migrad
+
+    model = aare.Gaussian(minimizer=aare.Minimizer.Fumili)
+    assert model.minimizer == aare.Minimizer.Fumili
+
+    y = model(x, expected)
+    result = model.fit(x, y)
+    np.testing.assert_allclose(result["par"], expected, atol=2e-3)
+
+    model.minimizer = aare.Minimizer.Migrad
+    assert model.minimizer == aare.Minimizer.Migrad
+
+
+def test_fumili_and_migrad_agree_on_3d_data_with_errors():
+    x = np.linspace(-5.0, 5.0, 51)
+    expected = np.array([20.0, 0.5, 1.2])
+    single = aare.Gaussian()(x, expected)
+    y = np.tile(single, (2, 3, 1))
+    y_err = np.ones_like(y)
+
+    migrad = aare.Gaussian(compute_errors=True)
+    fumili = aare.Gaussian(compute_errors=True, minimizer=aare.Minimizer.Fumili)
+
+    with_migrad = migrad.fit(x, y, y_err, n_threads=2)
+    with_fumili = fumili.fit(x, y, y_err, n_threads=2)
+
+    assert with_fumili["par"].shape == (2, 3, 3)
+    assert with_fumili["par_err"].shape == (2, 3, 3)
+    for result in (with_migrad, with_fumili):
+        np.testing.assert_allclose(result["par"], np.broadcast_to(expected, (2, 3, 3)), atol=2e-3)
+        np.testing.assert_allclose(result["chi2"], 0.0, atol=1e-3)
+    np.testing.assert_allclose(with_fumili["par_err"], with_migrad["par_err"], rtol=2e-2)