Add template for max value of QoI within simulated time

pyciemss/ouu/qoi.py

@@ -16,3 +16,14 @@ def obs_nday_average_qoi(
     dataQoI = samples[contexts[0]].detach().numpy()
 
     return np.mean(dataQoI[:, -ndays:], axis=1)
+
+
+def obs_max_qoi(samples: Dict[str, torch.Tensor], contexts: List) -> np.ndarray:
+    """
+    Return maximum value over simulated time.
+    samples is is the output from a Pyro Predictive object.
+    samples[VARIABLE] is expected to have dimension (nreplicates, ntimepoints)
+    """
+    dataQoI = samples[contexts[0]].detach().numpy()
+
+    return np.max(dataQoI, axis=1)

tests/fixtures.py

@@ -10,7 +10,7 @@
     param_value_objective,
     start_time_objective,
 )
-from pyciemss.ouu.qoi import obs_nday_average_qoi
+from pyciemss.ouu.qoi import obs_max_qoi, obs_nday_average_qoi
 
 T = TypeVar("T")
 
@@ -110,6 +110,19 @@ def __init__(
     "bounds_interventions": [[0.0], [40.0]],
 }
 
+optimize_kwargs_SIRstockflow_param_maxQoI = {
+    "qoi": lambda x: obs_max_qoi(x, ["I_state"]),
+    "risk_bound": 300.0,
+    "static_parameter_interventions": param_value_objective(
+        param_name=["p_cbeta"],
+        param_value=[lambda x: torch.tensor([x])],
+        start_time=[torch.tensor(1.0)],
+    ),
+    "objfun": lambda x: np.abs(0.35 - x),
+    "initial_guess_interventions": 0.15,
+    "bounds_interventions": [[0.1], [0.5]],
+}
+
 OPT_MODELS = [
     ModelFixture(
         os.path.join(MODELS_PATH, "SIR_stockflow.json"),
@@ -121,6 +134,11 @@ def __init__(
         important_parameter="p_cbeta",
         optimize_kwargs=optimize_kwargs_SIRstockflow_time,
     ),
+    ModelFixture(
+        os.path.join(MODELS_PATH, "SIR_stockflow.json"),
+        important_parameter="p_cbeta",
+        optimize_kwargs=optimize_kwargs_SIRstockflow_param_maxQoI,
+    ),
 ]
 
 BAD_AMRS = [

tests/visuals/test_schemas.py

@@ -291,7 +291,6 @@ def test_nested_mark_sources(schema_file):
 
 
 if __name__ == "__main__":
-
     parser = argparse.ArgumentParser("Utility to generate reference images")
     parser.add_argument(
         "schema",