adding failing test for observables defined by parameter values

Author: sabinala

tests/test_interfaces.py

@@ -826,3 +826,49 @@ def test_intervention_on_constant_param(
             torch.arange(start_time, end_time + logging_step_size, logging_step_size)
         )
         assert processed_result.shape[1] >= 2
+
+
+@pytest.mark.parametrize("sample_method", [sample])
+@pytest.mark.parametrize("model_fixture", MODELS)
+@pytest.mark.parametrize("end_time", END_TIMES)
+@pytest.mark.parametrize("logging_step_size", LOGGING_STEP_SIZES)
+@pytest.mark.parametrize("num_samples", NUM_SAMPLES)
+@pytest.mark.parametrize("start_time", START_TIMES)
+def test_observables_change_with_interventions(
+    sample_method,
+    model_fixture,
+    end_time,
+    logging_step_size,
+    num_samples,
+    start_time,
+):
+    # Assert that sample returns expected result with intervention on constant parameter
+    if "SIR_param" not in model_fixture.url:
+        pytest.skip("Only test 'SIR_param_in_obs' model")
+    else:
+        processed_result = sample_method(
+            model_fixture.url,
+            end_time,
+            logging_step_size,
+            num_samples,
+            start_time=start_time,
+            static_parameter_interventions={
+                torch.tensor(2.0): {"beta": torch.tensor(0.001)}
+            },
+        )["data"]
+
+        print(processed_result["beta_param_observable_state"][0])
+        print(
+            processed_result["beta_param_observable_state"][
+                int(end_time / logging_step_size)
+            ]
+        )
+        print(int(end_time / logging_step_size))
+
+        # The test will fail if values before and after the intervention are the same
+        assert (
+            processed_result["beta_param_observable_state"][0]
+            > processed_result["beta_param_observable_state"][
+                int(end_time / logging_step_size)
+            ]
+        )