fix: documentation

Author: zhenyuen

docs/tutorials/11_LevyTransitionModels_&_MarginalisedParticleFilter.py

@@ -4,11 +4,11 @@
 =============================================================
 11 - Tracking linear Levy transition models with the MPF
 =============================================================
-In line with the tutorial examples of the Kalman and particle 
-filters in Stone Soup, a simplified single-target tracking 
-example without clutter is provided here to demonstrate the 
-use of linear transition models driven by non-Gaussian Levy 
-noise, as well as how to perform inference tasks on this class 
+In line with the tutorial examples of the Kalman and particle
+filters in Stone Soup, a simplified single-target tracking
+example without clutter is provided here to demonstrate the
+use of linear transition models driven by non-Gaussian Levy
+noise, as well as how to perform inference tasks on this class
 of models using the Marginalized Particle Filter (MPF).
 """
 
@@ -22,7 +22,7 @@
 from datetime import datetime, timedelta
 from stonesoup.types.groundtruth import GroundTruthPath, GroundTruthState
 from stonesoup.models.base_driver import NoiseCase
-from stonesoup.models.driver import AlphaStableNSMDriver 
+from stonesoup.models.driver import AlphaStableNSMDriver
 from stonesoup.models.transition.levy_linear import LevyLangevin, CombinedLinearLevyTransitionModel
 
 start_time = datetime.now().replace(microsecond=0)
@@ -60,7 +60,7 @@
 # %%
 
 # Random seem for reproducibility
-seed = 1 
+seed = 1
 
 # Driving process parameters
 mu_W = 0
@@ -103,8 +103,8 @@
 # Note that the animated plotter requires a list of timesteps as an input, and that ``tail_length``
 # is set to 0.3. This means that each data point will be on display for 30% of the total
 # simulation time. The mapping argument is ``[0, 2]`` because those are the :math:`x` and
-# :math:`y` position indices from our state vector.
-# If a static plotter is preferred, the :class:`Plotterly` class can be used instead.
+# :math:`y` position indices from our state vector/Users/chongzhenyuen/Documents/iib-project/stonesoup/stonesoup/models.
+# If a static plotter is preferred, the :class:`Plotterly`` class can be used instead.
 
 # from stonesoup.plotter import AnimatedPlotterly
 # plotter = AnimatedPlotterly(timesteps, tail_length=1.0, width=600, height=600)
@@ -238,8 +238,3 @@
 # .. [#] Lemke, Tatjana, and Simon J. Godsill, 'Inference for models with asymmetric α -stable noise processes', in Siem Jan Koopman, and Neil Shephard (eds), Unobserved Components and Time Series Econometrics (Oxford, 2015; online edn, Oxford Academic, 21 Jan. 2016)
 # 
 # .. [#] S. Godsill, M. Riabiz, and I. Kontoyiannis, “The L ́evy state space model,” in 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019, pp. 487–494.
-
-
-
-
-

stonesoup/models/base.py

@@ -436,8 +436,7 @@ def num_samples(self) -> int:
 
 class LevyModel(Model):
     """
-    Class to be derived from for Levy models.
-    For now, we consider only conditionally Gaussian ones
+    Class to be derived from for conditionally Gaussian Levy models.
     """
 
     driver: Union[ConditionallyGaussianDriver, GaussianDriver] = Property(

stonesoup/models/base_driver.py

@@ -51,7 +51,9 @@ def characteristic_func():
 class ConditionallyGaussianDriver(LevyDriver):
     """Conditional Gaussian Levy noise driver.
 
-    Noise samples are generated according to the Levy State-Space Model by Godsill et al.
+    Noise samples are generated according to the Levy State-Space Model, which are Gaussian
+    when conditioned on latent variables in the form of jump times and sizes. The
+    latent variables may be non-Gaussian.
     """
 
     c: np.double = Property(doc="Truncation parameter, expected no. jumps per unit time.")

stonesoup/models/transition/base.py

@@ -145,7 +145,7 @@ def covar(self, **kwargs):
 class CombinedLevyTransitionModel(TransitionModel, LevyModel):
     r"""Combine multiple models into a single model by stacking them.
 
-    The assumption is that all models are Gaussian.
+    The assumption is that all models are conditionally Gaussian Levy.
     Time Variant, and Time Invariant models can be combined together.
     If any of the models are time variant the keyword argument "time_interval"
     must be supplied to all methods