..

tests/benchmark-models/test_petab_benchmark.py

@@ -205,9 +205,9 @@ def test_benchmark_gradient(model, scale, sensitivity_method):
     )
 
     noise_level = 0.1
-
     np.random.seed(0)
 
+    # find a point where the derivative can be computed
     for _ in range(5):
         if scale:
             point = np.asarray(
@@ -244,26 +244,12 @@ def test_benchmark_gradient(model, scale, sensitivity_method):
     )
 
     if debug:
-        df = pd.DataFrame(
-            [
-                {
-                    (
-                        "fd",
-                        r.metadata["size_absolute"],
-                        str(r.method_id),
-                    ): r.value
-                    for c in d.computers
-                    for r in c.results
-                }
-                for d in derivative.directional_derivatives
-            ],
-            index=parameter_ids,
+        write_debug_output(
+            debug_path / f"{model}_scale={scale}.tsv",
+            derivative,
+            expected_derivative,
+            parameter_ids,
         )
-        df[("fd", "full", "")] = derivative.series.values
-        df[("amici", "", "")] = expected_derivative
-
-        file_name = f"{model}_scale={scale}.tsv"
-        df.to_csv(debug_path / file_name, sep="\t")
 
     assert_gradient_check_success(
         derivative,
@@ -272,3 +258,27 @@ def test_benchmark_gradient(model, scale, sensitivity_method):
         rtol=cur_settings.rtol_check,
         atol=cur_settings.atol_check,
     )
+
+
+def write_debug_output(
+    file_name, derivative, expected_derivative, parameter_ids
+):
+    df = pd.DataFrame(
+        [
+            {
+                (
+                    "fd",
+                    r.metadata["size_absolute"],
+                    str(r.method_id),
+                ): r.value
+                for c in d.computers
+                for r in c.results
+            }
+            for d in derivative.directional_derivatives
+        ],
+        index=parameter_ids,
+    )
+    df[("fd", "full", "")] = derivative.series.values
+    df[("amici", "", "")] = expected_derivative
+
+    df.to_csv(file_name, sep="\t")