Avoid zero division

src/ibex_bluesky_core/callbacks/fitting/fitting_utils.py

@@ -332,14 +332,17 @@ def guess(
             # Guessing. gradient of linear-slope part of function
             dy = np.gradient(y)  # Return array of differences in y
             max_dy = np.max(dy)  # Return max y difference, this will always be on the upwards slope
-            dx = x[1] - x[0]  # Find x step
+            dx = abs(x[1] - x[0])  # Find x step
             gradient = max_dy / dx
 
             d2y = np.diff(dy)  # Double differentiate y to find how gradients change
             inflection0 = x[np.argmax(d2y)]  # Where there is positive gradient change
 
             background = min(y)  # The lowest y value is the background
-            inflections_diff = -(background - y[np.argmax(y)]) / gradient
+            if gradient != 0.0:
+                inflections_diff = -(background - y[np.argmax(y)]) / gradient
+            else:
+                inflections_diff = dx  # Fallback case, guess one x step
             # As linear, using y - y1 = m(x - x1) -> x = (y - y1) / gradient - x1
 
             # The highest y value + slightly more to account for further convergence

tests/callbacks/fitting/test_fitting_methods.py

@@ -447,6 +447,13 @@ def test_guess_inflections_diff(self):
 
             assert 3.0 == pytest.approx(outp["inflections_diff"].value, rel=1e-2)
 
+        def test_guess_inflections_diff_with_all_zero_data(self):
+            x = np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0])
+            y = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
+            outp = SlitScan.guess()(x, y)
+
+            assert 1.0 == pytest.approx(outp["inflections_diff"].value, rel=1e-2)
+
         def test_guess_height_above_inflection1(self):
             x = np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0])
             y = np.array([1.0, 1.0, 2.0, 3.0, 4.0, 4.0])