fix events conflict

src/progpy/predictors/monte_carlo.py

@@ -31,7 +31,7 @@ class MonteCarlo(Predictor):
         'n_samples': None
     }
 
-    def predict(self, state: UncertainData, future_loading_eqn: Callable=None, **kwargs) -> PredictionResults:
+    def predict(self, state: UncertainData, future_loading_eqn: Callable=None, events=None, **kwargs) -> PredictionResults:
         """
         Perform a single prediction
 
@@ -91,7 +91,11 @@ def predict(self, state: UncertainData, future_loading_eqn: Callable=None, **kwa
         elif isinstance(state, UnweightedSamples) and params['n_samples'] is None:
             params['n_samples'] = len(state)  # number of samples is from provided state
 
-        if len(params['events']) == 0 and 'horizon' not in params:
+        if events is None:
+            # Predict to all events
+            # change to list because of limits of jsonify
+            events = list(self.model.events)
+        if len(events) == 0 and 'horizon' not in params:
             raise ValueError("If specifying no event (i.e., simulate to time), must specify horizon")
 
         # Sample from state if n_samples specified or state is not UnweightedSamples (Case 2)
@@ -129,15 +133,15 @@ def predict(self, state: UncertainData, future_loading_eqn: Callable=None, **kwa
             if 'save_freq' in params and not isinstance(params['save_freq'], tuple):
                 params['save_freq'] = (params['t0'], params['save_freq'])
 
-            if len(params['events']) == 0:  # Predict to time
+            if len(events) == 0:  # Predict to time
                 (times, inputs, states, outputs, event_states) = simulate_to_threshold(
                     future_loading_eqn,
                     first_output,
                     events=[],
                     **params
                 )
             else:
-                events_remaining = params['events'].copy()
+                events_remaining = events.copy()
 
                 times = []
                 inputs = SimResult(_copy=False)

src/progpy/predictors/predictor.py

@@ -39,9 +39,6 @@ def __init__(self, model, **kwargs):
         self.model = model
 
         self.parameters = deepcopy(self.default_parameters)
-        # Events to predict to - must be a list
-        # This is because of limitations with jsonify for sets
-        self.parameters['events'] = list(self.model.events.copy())
         self.parameters.update(kwargs)
 
     @abstractmethod

src/progpy/predictors/unscented_transform.py

@@ -123,7 +123,7 @@ def state_transition(x, dt):
         self.filter = kalman.UnscentedKalmanFilter(num_states, num_measurements, self.parameters['dt'], measure, state_transition, self.sigma_points)
         self.filter.Q = self.parameters['Q']
 
-    def predict(self, state, future_loading_eqn: Callable = None, **kwargs) -> PredictionResults:
+    def predict(self, state, future_loading_eqn: Callable = None, events=None, **kwargs) -> PredictionResults:
         """
         Perform a single prediction
 
@@ -175,11 +175,14 @@ def predict(self, state, future_loading_eqn: Callable = None, **kwargs) -> Predi
         params = deepcopy(self.parameters) # copy parameters
         params.update(kwargs) # update for specific run
 
-        if len(params['events']) == 0 and 'horizon' not in params:
+        if events is None:
+            # Predict to all events
+            # change to list because of limits of jsonify
+            events = list(self.model.events)
+        if len(events) == 0 and 'horizon' not in params:
             raise ValueError("If specifying no event (i.e., simulate to time), must specify horizon")
 
         # Optimizations 
-        events_to_predict = params['events']
         dt = params['dt']
         model = self.model
         filt = self.filter
@@ -196,8 +199,8 @@ def predict(self, state, future_loading_eqn: Callable = None, **kwargs) -> Predi
         # Setup first states
         t = params['t0']
         save_pt_index = 0
-        ToE = {key: [float('nan') for i in range(n_points)] for key in events_to_predict}  # Keep track of final ToE values
-        last_state = {key: [None for i in range(n_points)] for key in events_to_predict}  # Keep track of final state values
+        ToE = {key: [float('nan') for i in range(n_points)] for key in events}  # Keep track of final ToE values
+        last_state = {key: [None for i in range(n_points)] for key in events}  # Keep track of final state values
 
         times = []
         inputs = []
@@ -239,7 +242,7 @@ def update_all():
                 t_met = threshold_met(x)
 
                 # Check Thresholds
-                for key in events_to_predict:
+                for key in events:
                     if t_met[key]:
                         if isnan(ToE[key][i]):
                             # First time event has been reached

tests/test_horizon.py

@@ -36,7 +36,7 @@ def future_loading(t, x=None):
         # With this horizon, all samples will reach 'falling', but only some will reach 'impact'
         PREDICTION_HORIZON = 2.127 
         STEP_SIZE = 0.001 
-        mc_results = mc.predict(initial_state, future_loading, dt=STEP_SIZE, horizon = PREDICTION_HORIZON)
+        mc_results = mc.predict(initial_state, future_loading, dt=STEP_SIZE, horizon=PREDICTION_HORIZON)
 
         # 'falling' happens before the horizon is met
         falling_res = [mc_results.time_of_event[iter]['falling'] for iter in range(mc.parameters['n_samples']) if mc_results.time_of_event[iter]['falling'] is not None]