Clean handling of events

src/progpy/predictors/monte_carlo.py

@@ -1,5 +1,6 @@
 # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration.  All Rights Reserved.
 
+from collections import abc
 from copy import deepcopy
 from typing import Callable
 from progpy.sim_result import SimResult, LazySimResult
@@ -99,16 +100,28 @@ def predict(self, state: UncertainData, future_loading_eqn: Callable=None, event
             params['n_samples'] = len(state)  # number of samples is from provided state
 
         if events is None:
-            # Predict to all events
-            # change to list because of limits of jsonify
             if 'events' in params and params['events'] is not None:
-                # Set at a model level
-                events = list(params['events'])
+                # Set at a predictor construction
+                events = params['events']
             else:
                 # Otherwise, all events
-                events = list(self.model.events)
+                events = self.model.events
+
+        if not isinstance(events, (abc.Iterable)) or isinstance(events, (dict, bytes)):
+            # must be string or list-like (list, tuple, set)
+            # using abc.Iterable adds support for custom data structures
+            # that implement that abstract base class
+            raise TypeError(f'`events` must be a single event string or list of events. Was unsupported type {type(events)}.')
         if len(events) == 0 and 'horizon' not in params:
             raise ValueError("If specifying no event (i.e., simulate to time), must specify horizon")
+        if isinstance(events, str):
+            # A single event
+            events = [events]
+        if not all([key in self.model.events for key in events]):
+            raise ValueError("`events` must be event names")
+        if not isinstance(events, list):
+            # Change to list because of the limits of jsonify
+            events = list(events)
 
         if 'events' in params: 
             # Params is provided as a argument in construction

src/progpy/predictors/unscented_transform.py

@@ -1,5 +1,6 @@
 # Copyright © 2021 United States Government as represented by the Administrator of the National Aeronautics and Space Administration.  All Rights Reserved.
 
+from collections import abc
 from copy import deepcopy
 from filterpy import kalman
 from numpy import diag, array, transpose, isnan
@@ -180,11 +181,28 @@ def predict(self, state, future_loading_eqn: Callable = None, events=None, **kwa
             raise ValueError(f"`event_strategy` {params['event_strategy']} not supported. Currently, only 'all' event strategy is supported")
 
         if events is None:
-            # Predict to all events
-            # change to list because of limits of jsonify
-            events = list(self.model.events)
+            if 'events' in params and params['events'] is not None:
+                # Set at a predictor construction
+                events = params['events']
+            else:
+                # Otherwise, all events
+                events = self.model.events
+
+        if not isinstance(events, (abc.Iterable)) or isinstance(events, (dict, bytes)):
+            # must be string or list-like (list, tuple, set)
+            # using abc.Iterable adds support for custom data structures
+            # that implement that abstract base class
+            raise TypeError(f'`events` must be a single event string or list of events. Was unsupported type {type(events)}.')
         if len(events) == 0 and 'horizon' not in params:
             raise ValueError("If specifying no event (i.e., simulate to time), must specify horizon")
+        if isinstance(events, str):
+            # A single event
+            events = [events]
+        if not all([key in self.model.events for key in events]):
+            raise ValueError("`events` must be event names")
+        if not isinstance(events, list):
+            # Change to list because of the limits of jsonify
+            events = list(events)
 
         # Optimizations 
         dt = params['dt']

src/progpy/prognostics_model.py

@@ -871,13 +871,20 @@ def simulate_to_threshold(self, future_loading_eqn: abc.Callable = None, first_o
                 events = kwargs['threshold_keys']
             else:
                 warn('Both `events` and `threshold_keys` were set. `events` will be used.')
-
+
+        if events is None:
+            events = self.events.copy()
+        if not isinstance(events, abc.Iterable):
+            # must be string or list-like
+            raise TypeError(f'`events` must be a single event string or list of events. Was unsupported type {type(events)}.')
         if isinstance(events, str):
-            # A single threshold key
+            # A single event
             events = [events]
-
         if (events is not None) and not all([key in self.events for key in events]):
             raise ValueError("`events` must be event names")
+        if not isinstance(events, list):
+            # Change to list because of the limits of jsonify
+            events = list(events)
 
         # Configure
         config = {  # Defaults

tests/test_predictors.py

@@ -82,6 +82,39 @@ def test_UTP_ThrownObject(self):
         self.assertAlmostEqual(results.time_of_event.mean['falling'], 4.15, 0)
         # self.assertAlmostEqual(mc_results.times[-1], 9, 1)  # Saving every second, last time should be around the 1s after impact event (because one of the sigma points fails afterwards)
 
+        # Setting event manually
+        results = pred.predict(samples, dt=0.01, save_freq=1, events=['falling'])
+        self.assertAlmostEqual(results.time_of_event.mean['falling'], 3.8, 5)
+        self.assertNotIn('impact', results.time_of_event.mean)
+
+        # Setting event in construction
+        pred = UnscentedTransformPredictor(m, events=['falling'])
+        results = pred.predict(samples, dt=0.01, save_freq=1)
+        self.assertAlmostEqual(results.time_of_event.mean['falling'], 3.8, 5)
+        self.assertNotIn('impact', results.time_of_event.mean)
+
+        # Override event set in construction
+        results = pred.predict(samples, dt=0.01, save_freq=1, events=['falling', 'impact'])
+        self.assertAlmostEqual(results.time_of_event.mean['impact'], 8.21, 0)
+        self.assertAlmostEqual(results.time_of_event.mean['falling'], 4.15, 0)
+
+        # String event
+        results = pred.predict(samples, dt=0.01, save_freq=1, events='impact')
+        self.assertAlmostEqual(results.time_of_event.mean['impact'], 7.785, 5)
+        self.assertNotIn('falling', results.time_of_event.mean)
+
+        # Invalid event
+        with self.assertRaises(ValueError):
+            results = pred.predict(samples, dt=0.01, save_freq=1, events='invalid')
+        with self.assertRaises(ValueError):
+            # Mix valid, invalid
+            results = pred.predict(samples, dt=0.01, save_freq=1, events=['falling', 'invalid'])
+        with self.assertRaises(ValueError):
+            # Empty
+            results = pred.predict(samples, dt=0.01, save_freq=1, events=[])
+        with self.assertRaises(TypeError):
+            results = pred.predict(samples, dt=0.01, save_freq=1, events=45)
+
     def test_UTP_ThrownObject_One_Event(self):
         # Test thrown object, similar to test_UKP_ThrownObject, but with only the 'falling' event
         m = ThrownObject()
@@ -168,6 +201,28 @@ def test_MC_ThrownObject(self):
         results = mc.predict(m.initialize(), dt=0.2, num_samples=3, save_freq=1, events=['falling', 'impact'])
         self.assertAlmostEqual(results.time_of_event.mean['falling'], 3.8, 5)
         self.assertAlmostEqual(results.time_of_event.mean['impact'], 8.0, 5)
+
+        # String event
+        results = mc.predict(m.initialize(), dt=0.2, num_samples=3, save_freq=1, events='impact')
+        self.assertAlmostEqual(results.time_of_event.mean['impact'], 8.0, 5)
+        self.assertNotIn('falling', results.time_of_event.mean)
+
+        # Invalid event
+        with self.assertRaises(ValueError):
+            results = mc.predict(m.initialize(), dt=0.2, num_samples=3, save_freq=1, events='invalid')
+        with self.assertRaises(ValueError):
+            # Mix valid, invalid
+            results = mc.predict(m.initialize(), dt=0.2, num_samples=3, save_freq=1, events=['falling', 'invalid'])
+        with self.assertRaises(ValueError):
+            # Empty
+            results = mc.predict(m.initialize(), dt=0.2, num_samples=3, save_freq=1, events=[])
+        with self.assertRaises(TypeError):
+            results = mc.predict(m.initialize(), dt=0.2, num_samples=3, save_freq=1, events=45)
+
+        # Empty with horizon
+        results = mc.predict(m.initialize(), dt=0.2, num_samples=3, save_freq=1, horizon=3, events=[])
+
+        # TODO(CT): Events in other predictor
 
     def test_MC_ThrownObject_First(self):
         # Test thrown object, similar to test_UKP_ThrownObject, but with only the first event (i.e., 'falling')