Merge pull request #235 from nasa/dt-changes

src/prog_algs/state_estimators/kalman_filter.py

@@ -30,10 +30,10 @@ class KalmanFilter(state_estimator.StateEstimator):
         alpha (float, optional):
             KF Scaling parameter. An alpha > 1 turns this into a fading memory filter.
         t0 (float, optional):
-            Maximum timestep for prediction in seconds. By default is the difference between new and last t. Some models are unstable at larger dt. Setting a smaller dt will force the model to take smaller steps; resulting in multiple prediction steps for each estimate step. Default is the parameters['dt]
-            e.g., dt = 1e-2
+            Starting time (s)
         dt (float, optional):
-            time step (s)
+            Maximum timestep for prediction in seconds. By default, the timestep dt is the difference between the last and current call of .estimate(). Some models are unstable at larger dt. Setting a smaller dt will force the model to take smaller steps; resulting in multiple prediction steps for each estimate step. Default is the parameters['dt']
+            e.g., dt = 1e-2
         Q (list[list[float]], optional):
             Kalman Process Noise Matrix 
         R (list[list[float]], optional):
@@ -116,7 +116,7 @@ def estimate(self, t: float, u, z, **kwargs):
         Keyword Arguments
         -----------------
         dt : float, optional
-            Maximum timestep for prediction in seconds. By default is the difference between new and last t. Some models are unstable at larger dt. Setting a smaller dt will force the model to take smaller steps; resulting in multiple prediction steps for each estimate step. Default is the parameters['dt]
+            Maximum timestep for prediction in seconds. By default, the timestep dt is the difference between the last and current call of .estimate(). Some models are unstable at larger dt. Setting a smaller dt will force the model to take smaller steps; resulting in multiple prediction steps for each estimate step. Default is the parameters['dt']
             e.g., dt = 1e-2
         """
         assert t > self.t, "New time must be greater than previous"

src/prog_algs/state_estimators/particle_filter.py

@@ -32,7 +32,7 @@ class ParticleFilter(state_estimator.StateEstimator):
         t0 (float, optional):
             Starting time (s)
         dt (float, optional): 
-            Maximum timestep for prediction in seconds. By default is the difference between new and last t. Some models are unstable at larger dt. Setting a smaller dt will force the model to take smaller steps; resulting in multiple prediction steps for each estimate step. Default is the parameters['dt]
+            Maximum timestep for prediction in seconds. By default, the timestep dt is the difference between the last and current call of .estimate(). Some models are unstable at larger dt. Setting a smaller dt will force the model to take smaller steps; resulting in multiple prediction steps for each estimate step. Default is the parameters['dt']
             e.g., dt = 1e-2
         num_particles (int, optional):
             Number of particles in particle filter
@@ -100,7 +100,7 @@ def estimate(self, t : float, u, z, dt = None):
         Keyword Args
         ------------
         dt : float, optional
-            Maximum timestep for prediction in seconds. By default is the difference between new and last t. Some models are unstable at larger dt. Setting a smaller dt will force the model to take smaller steps; resulting in multiple prediction steps for each estimate step. Default is the parameters['dt]
+            Maximum timestep for prediction in seconds. By default, the timestep dt is the difference between the last and current call of .estimate(). Some models are unstable at larger dt. Setting a smaller dt will force the model to take smaller steps; resulting in multiple prediction steps for each estimate step. Default is the parameters['dt']
             e.g., dt = 1e-2
 
         Note
@@ -141,7 +141,7 @@ def estimate(self, t : float, u, z, dt = None):
             # Get particle measurements
             zPredicted = output(self.particles)
         else:
-            # Propogate and calculate weights
+            # Propagate and calculate weights
             for i in range(num_particles):
                 t_i = self.t  # Used to mark time for each particle
                 x = self.model.StateContainer({key: particles[key][i] for key in particles.keys()})

src/prog_algs/state_estimators/state_estimator.py

@@ -10,7 +10,7 @@ class StateEstimator(ABC):
     """
     Interface class for state estimators
 
-    Abstract base class for creating state estimators that perform state estimation. Subclasses must implement this interface. Equivilant to "Observers" in NASA's Matlab Prognostics Algorithm Library
+    Abstract base class for creating state estimators that perform state estimation. Subclasses must implement this interface. Equivalent to "Observers" in NASA's Matlab Prognostics Algorithm Library
 
     Args:
         model (PrognosticsModel):
@@ -20,14 +20,14 @@ class StateEstimator(ABC):
             Initial (starting) state, with keys defined by model.states \n
             e.g., x = ScalarData({'abc': 332.1, 'def': 221.003}) given states = ['abc', 'def']
 
-    Keywork Args:
+    Keyword Args:
         t0 (float):
             Initial time at which prediction begins, e.g., 0
         dt (float):
-            Maximum timestep for prediction in seconds. By default is the difference between new and last t. Some models are unstable at larger dt. Setting a smaller dt will force the model to take smaller steps; resulting in multiple prediction steps for each estimate step. Default is the parameters['dt]
+            Maximum timestep for prediction in seconds. By default, the timestep dt is the difference between the last and current call of .estimate(). Some models are unstable at larger dt. Setting a smaller dt will force the model to take smaller steps; resulting in multiple prediction steps for each estimate step. Default is the parameters['dt']
             e.g., dt = 1e-2
         **kwargs: 
-            See state-estimator specific documentation for speicfic keyword arguments.
+            See state-estimator specific documentation for specific keyword arguments.
     """
 
     default_parameters = {
@@ -91,10 +91,10 @@ def estimate(self, t: float, u, z, **kwargs) -> None:
         Keyword Args
         -------------
             dt : float, optional
-                Maximum timestep for prediction in seconds. By default is the difference between new and last t. Some models are unstable at larger dt. Setting a smaller dt will force the model to take smaller steps; resulting in multiple prediction steps for each estimate step. Default is the parameters['dt]
+                Maximum timestep for prediction in seconds. By default, the timestep dt is the difference between the last and current call of .estimate(). Some models are unstable at larger dt. Setting a smaller dt will force the model to take smaller steps; resulting in multiple prediction steps for each estimate step. Default is the parameters['dt']
                 e.g., dt = 1e-2
             **kwargs: 
-                See state-estimator specific documentation for speicfic keyword arguments.
+                See state-estimator specific documentation for specific keyword arguments.
 
         Note
         ----

src/prog_algs/state_estimators/unscented_kalman_filter.py

@@ -33,7 +33,7 @@ class UnscentedKalmanFilter(state_estimator.StateEstimator):
         t0 (float, optional):
             Starting time (s)
         dt (float, optional):
-            Maximum timestep for prediction in seconds. By default is the difference between new and last t. Some models are unstable at larger dt. Setting a smaller dt will force the model to take smaller steps; resulting in multiple prediction steps for each estimate step. Default is the parameters['dt]
+            Maximum timestep for prediction in seconds. By default, the timestep dt is the difference between the last and current call of .estimate(). Some models are unstable at larger dt. Setting a smaller dt will force the model to take smaller steps; resulting in multiple prediction steps for each estimate step. Default is the parameters['dt']
             e.g., dt = 1e-2
         Q (list[list[float]], optional):
             Process Noise Matrix 
@@ -113,7 +113,7 @@ def estimate(self, t: float, u, z, **kwargs):
         Keyword Args
         ------------
         dt : float, optional
-            Maximum timestep for prediction in seconds. By default is the difference between new and last t. Some models are unstable at larger dt. Setting a smaller dt will force the model to take smaller steps; resulting in multiple prediction steps for each estimate step. Default is the parameters['dt]
+            Maximum timestep for prediction in seconds. By default, the timestep dt is the difference between the last and current call of .estimate(). Some models are unstable at larger dt. Setting a smaller dt will force the model to take smaller steps; resulting in multiple prediction steps for each estimate step. Default is the parameters['dt']
             e.g., dt = 1e-2
         """
         assert t > self.t, "New time must be greater than previous"