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')