Fix LeastSquares visualize on unsorted x-values (#1084)

Closes #1069

Author: HDembinski

.github/workflows/coverage.yml

@@ -3,9 +3,9 @@ name: Coverage
 on:
   pull_request:
     paths:
-      - src
-      - extern
-      - tests
+      - src/**
+      - extern/**
+      - tests/**
       - pyproject.toml
       - noxfile.py
       - CMakeLists.txt
@@ -16,9 +16,9 @@ on:
       - develop
       - beta/*
     paths:
-      - src
-      - extern
-      - tests
+      - src/**
+      - extern/**
+      - tests/**
       - pyproject.toml
       - noxfile.py
       - CMakeLists.txt

.github/workflows/test.yml

@@ -3,9 +3,9 @@ name: Test
 on:
   pull_request:
     paths:
-      - src
-      - extern
-      - tests
+      - src/**
+      - extern/**
+      - tests/**
       - pyproject.toml
       - CMakeLists.txt
       - .github/workflows/test.yml

src/iminuit/cost.py

@@ -2306,17 +2306,21 @@ def visualize(
 
         x, y, ye = self._masked.T
         plt.errorbar(x, y, ye, fmt="ok")
+
+        xmin = np.min(x)
+        xmax = np.max(x)
         if isinstance(model_points, Iterable):
             xm = np.array(model_points)
             ym = self.model(xm, *args)
         elif model_points > 0:
+            # beware, x may not be sorted
             if _detect_log_spacing(x):
-                xm = np.geomspace(x[0], x[-1], model_points)
+                xm = np.geomspace(xmin, xmax, model_points)
             else:
-                xm = np.linspace(x[0], x[-1], model_points)
+                xm = np.linspace(xmin, xmax, model_points)
             ym = self.model(xm, *args)
         else:
-            xm, ym = _smart_sampling(lambda x: self.model(x, *args), x[0], x[-1])
+            xm, ym = _smart_sampling(lambda x: self.model(x, *args), xmin, xmax)
         plt.plot(xm, ym)
         return (x, y, ye), (xm, ym)
 

tests/test_cost.py

@@ -1389,6 +1389,16 @@ def test_LeastSquares_visualize():
     assert_equal(xm, np.linspace(1, 100))
 
 
+def test_LeastSquares_visualize_unsorted():
+    # make sure that xm uses max and min of x, not assuming x is sorted
+    pytest.importorskip("matplotlib")
+
+    c = LeastSquares([1, 0, 3, 2], [2, 1, 4, 3], 0.1, line)
+    xm, _ = c.visualize((1, 1))[1]
+    assert xm[0] == 0
+    assert xm[-1] == 3
+
+
 def test_LeastSquares_visualize_par_array():
     pytest.importorskip("matplotlib")