fix a test that requires scipy and raise error if invalid value is us…

…ed for use_pdf

src/iminuit/cost.py

@@ -1502,8 +1502,14 @@ def __init__(self, n, xe, model, verbose, grad, use_pdf):
             self._pred_impl = self._pred_approximate
         elif use_pdf == "numerical":
             self._pred_impl = self._pred_numerical
-        else:
+        elif use_pdf == "":
             self._pred_impl = self._pred_cdf
+        else:
+            msg = (
+                f"use_pdf={use_pdf} is not understood, "
+                "allowed values are 'approximate' and 'numerical'"
+            )
+            raise ValueError(msg)
 
         super().__init__(_model_parameters(model), n, xe, verbose)
 

tests/test_cost.py

@@ -753,6 +753,9 @@ def test_BinnedNLL_pickle():
 
 @pytest.mark.parametrize("use_pdf", ["approximate", "numerical"])
 def test_BinnedNLL_with_pdf(use_pdf):
+    if use_pdf == "numerical":
+        pytest.importorskip("scipy")
+
     xe = np.array([0, 0.1, 0.2, 0.3])
     n = [1, 2, 3]
     c = BinnedNLL(n, xe, norm_pdf, use_pdf=use_pdf)
@@ -762,6 +765,11 @@ def test_BinnedNLL_with_pdf(use_pdf):
     assert_allclose(c.prediction(par), ref, rtol=1e-3)
 
 
+def test_BinnedNLL_use_pdf_bad_value():
+    with pytest.raises(ValueError):
+        BinnedNLL([1, 2], [1, 2, 3], norm_pdf, use_pdf="foo")
+
+
 @pytest.mark.parametrize("use_pdf", ["approximate", "numerical"])
 def test_BinnedNLL_with_pdf_3D(use_pdf):
     xe = (np.array([0, 0.1]), np.array([0.1, 0.3, 0.4]), np.array([0, 0.1, 0.2, 0.3]))