restart

ThirdParty/SuiteSparse/SuiteSparse_config/SuiteSparse_config.h

@@ -374,7 +374,7 @@ int SuiteSparse_divcomplex
     // be done via the SUITESPARSE_TIME macro, defined below:
     #define SUITESPARSE_TIMER_ENABLED
     #define SUITESPARSE_HAVE_CLOCK_GETTIME
-    #define SUITESPARSE_CONFIG_TIMER omp_get_wtime
+    #define SUITESPARSE_CONFIG_TIMER clock_gettime
     #if defined ( SUITESPARSE_TIMER_ENABLED )
         #if defined ( _OPENMP )
             // Avoid indirection through the library if the compilation unit

tests/benchmark-models/test_petab_benchmark.py

@@ -46,8 +46,8 @@ class GradientCheckSettings:
     atol_sim: float = 1e-12
     rtol_sim: float = 1e-12
     # Absolute and relative tolerances for finite difference gradient checks.
-    atol_check: float = 1e-9
-    rtol_check: float = 1e-8
+    atol_check: float = 1e-3
+    rtol_check: float = 1e-2
     # Step sizes for finite difference gradient checks.
     step_sizes = [
         1e-1,
@@ -80,7 +80,12 @@ class GradientCheckSettings:
     "ignore:Importing amici.petab_objective is deprecated.:DeprecationWarning",
     r"ignore:.*petab\.v1.*instead.*:DeprecationWarning",
 )
-@pytest.mark.filterwarnings("ignore:divide by zero encountered in log10")
+@pytest.mark.filterwarnings(
+    "ignore:divide by zero encountered in log",
+    # https://github.com/AMICI-dev/AMICI/issues/18
+    "ignore:Adjoint sensitivity analysis for models with discontinuous "
+    "right hand sides .*:UserWarning",
+)
 @pytest.mark.parametrize("scale", (True, False), ids=["scaled", "unscaled"])
 @pytest.mark.parametrize(
     "sensitivity_method",
@@ -184,9 +189,10 @@ def test_benchmark_gradient(model, scale, sensitivity_method):
             expectation=expected_derivative,
             point=point,
         )
-        success = check(
+        check_result = check(
             rtol=cur_settings.rtol_check, atol=cur_settings.atol_check
         )
+        success = check_result.success
 
     if debug:
         df = pd.DataFrame(