Base Data Types

Stone Soup base data types.

class stonesoup.types.array.Matrix(*args, **kwargs)

Matrix wrapper for numpy.ndarray

This class returns a view to a numpy.ndarray It’s called same as to numpy.asarray().

class stonesoup.types.array.StateVector(*args, **kwargs)

State vector wrapper for numpy.ndarray

This class returns a view to a numpy.ndarray, but ensures that its initialised as an \(N \times 1\) vector. It’s called same as numpy.asarray(). The StateVector will attempt to convert the data given to a \(N \times 1\) vector if it can easily be done. E.g., StateVector([1., 2., 3.]), StateVector ([[1., 2., 3.,]]), and StateVector([[1.], [2.], [3.]]) will all return the same 3x1 StateVector.

It also overrides the behaviour of indexing such that my_state_vector[1] returns the second element (as int, float etc), rather than a StateVector of size (1, 1) as would be the case without this override. Behaviour of indexing with lists, slices or other indexing is unaffected (as you would expect those to return StateVectors). This override avoids the need for client to specifically index with zero as the second element (my_state_vector[1, 0]) to get a native numeric type. Iterating through the StateVector returns a sequence of numbers, rather than a sequence of 1x1 StateVectors. This makes the class behave as would be expected and avoids ‘gotchas’.

Note that code using the pattern my_state_vector[1, 0] will continue to work.

When slicing would result in return of an invalid shape for a StateVector (i.e. not (n, 1)) then a Matrix view will be returned.

Note

It is not recommended to use a StateVector for indexing another vector. Doing so will lead to unexpected effects. Use a tuple, list or np.ndarray for this.

flatten(order='C')

Return a copy of the array collapsed into one dimension.

Parameters:

order ({'C', 'F', 'A', 'K'}, optional) – ‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’.

Returns:

y – A copy of the input array, flattened to one dimension.

Return type:

ndarray

See also

ravel

Return a flattened array.

flat

A 1-D flat iterator over the array.

Examples

>>> import numpy as np
>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])

ravel([order])

Return a flattened array.

Refer to numpy.ravel for full documentation.

See also

numpy.ravel

equivalent function

ndarray.flat

a flat iterator on the array.

class stonesoup.types.association.Association(objects: set)

Association type

An association between objects.

Parameters:

objects (set) – set of objects being associated.

objects: set

set of objects being associated.

class stonesoup.types.detection.Detection(state_vector: StateVector, timestamp: datetime = None, measurement_model: MeasurementModel = None, metadata: MutableMapping = None)

Detection type

Parameters:

• state_vector (StateVector) – State vector.

• timestamp (datetime.datetime, optional) – Timestamp of the state. Default None.

• measurement_model (MeasurementModel, optional) – The measurement model used to generate the detection (the default is None)

• metadata (collections.abc.MutableMapping, optional) – Dictionary of metadata items for Detections.

measurement_model: MeasurementModel

The measurement model used to generate the detection (the default is None)

metadata: MutableMapping

Dictionary of metadata items for Detections.

class stonesoup.types.groundtruth.GroundTruthState(state_vector: StateVector, timestamp: datetime = None, metadata: MutableMapping = None)

Ground Truth State type

Parameters:

• state_vector (StateVector) – State vector.

• timestamp (datetime.datetime, optional) – Timestamp of the state. Default None.

• metadata (collections.abc.MutableMapping, optional) – Dictionary of metadata items for Detections.

metadata: MutableMapping

Dictionary of metadata items for Detections.

class stonesoup.types.groundtruth.GroundTruthPath(states: MutableSequence[GroundTruthState] = None, id: str = None)

Ground Truth Path type

A StateMutableSequence representing a track.

Parameters:

• states (collections.abc.MutableSequence, optional) – List of groundtruth states to initialise path with. Default None which initialises with an empty list.

• id (str, optional) – The unique path ID. Default None where random UUID is generated.

states: MutableSequence[GroundTruthState]

List of groundtruth states to initialise path with. Default None which initialises with an empty list.

id: str

The unique path ID. Default None where random UUID is generated.

class stonesoup.types.hypothesis.Hypothesis

Hypothesis base type

A Hypothesis has sub-types:

‘SingleHypothesis’, which consists of a prediction for a single Track and a single Measurement that might be associated with it

‘MultipleHypothesis’, which consists of a prediction for a single Track and multiple Measurements of which one might be associated with it

class stonesoup.types.multihypothesis.MultipleHypothesis(single_hypotheses: Sequence[SingleHypothesis] = None, normalise: bool = False, total_weight: float = 1)

Multiple Hypothesis base type

A Multiple Hypothesis is a container to store a collection of hypotheses.

Parameters:

• single_hypotheses (collections.abc.Sequence, optional) – The initial list of SingleHypothesis. Default None which initialises with empty list.

• normalise (bool, optional) – Normalise probabilities of SingleHypothesis. Default is False.

• total_weight (float, optional) – When normalising, weights will sum to this. Default is 1.

single_hypotheses: Sequence[SingleHypothesis]

The initial list of SingleHypothesis. Default None which initialises with empty list.

normalise: bool

Normalise probabilities of SingleHypothesis. Default is False.

total_weight: float

When normalising, weights will sum to this. Default is 1.

class stonesoup.types.metric.Metric(title: str, value: Any, generator: Any)

Metric type

Parameters:

• title (str) – Name of the metric

• value (Any) – Value of the metric

• generator (Any) – Generator used to create the metric

title: str

Name of the metric

value: Any

Value of the metric

generator: Any

Generator used to create the metric

class stonesoup.types.particle.Particle(state_vector: StateVector, weight: float, parent: Particle = None)

Particle type

A particle type which contains a state and weight

Parameters:

• state_vector (StateVector) – State vector

• weight (float) – Weight of particle

• parent (Particle, optional) – Parent particle

weight: float

Weight of particle

state_vector: StateVector

State vector

parent: Particle

Parent particle

class stonesoup.types.prediction.Prediction(transition_model: TransitionModel = None, prior: State = None)

Prediction type

This is the base prediction class.

Parameters:

• transition_model (TransitionModel, optional) – The transition model used to make the prediction

• prior (State, optional)

transition_model: TransitionModel

The transition model used to make the prediction

class stonesoup.types.sensordata.SensorData

Sensor Data type

class stonesoup.types.state.State(state_vector: StateVector, timestamp: datetime = None)

State type.

Most simple state type, which only has time and a state vector.

Parameters:

• state_vector (StateVector) – State vector.

• timestamp (datetime.datetime, optional) – Timestamp of the state. Default None.

timestamp: datetime

Timestamp of the state. Default None.

state_vector: StateVector

State vector.

property ndim

The number of dimensions represented by the state.

static from_state(state: State, *args: Any, target_type: Type | None = None, **kwargs: Any) → State

Class utility function to create a new state (or compatible type) from an existing state. The type and properties of this new state are defined by state except for any explicitly overwritten via args and kwargs.

It acts similarly in feel to a copy constructor, with the optional over-writing of properties.

Parameters:

• state (State) – State to use existing properties from, and identify new state-type from.

• *args (Sequence) – Arguments to pass to newly created state, replacing those with same name in state.

• target_type (Type, optional) – Optional argument specifying the type of object to be created. This need not necessarily be State subclass. Any arguments that match between the input state and the target type will be copied from the old to the new object (except those explicitly specified in args and kwargs).

• **kwargs (Mapping) – New property names and associate value for use in newly created state, replacing those on the state parameter.

class stonesoup.types.track.Track(states: MutableSequence[State] = None, id: str = None, init_metadata: MutableMapping = {})

Track type

A StateMutableSequence representing a track.

Notes:

Any manual modifications to metadata or metadatas will be overwritten if a state is inserted at a point prior to where the modifications are made. For example, inserting a state at the start of states will result in a metadatas update that will update all subsequent metadata values, resulting in manual metadata modifications being lost.

Parameters:

• states (collections.abc.MutableSequence, optional) – The initial states of the track. Default None which initialises with empty list.

• id (str, optional) – The unique track ID

• init_metadata (collections.abc.MutableMapping, optional) – Initial dictionary of metadata items for track. Default None which initialises track metadata as an empty dictionary.

states: MutableSequence[State]

The initial states of the track. Default None which initialises with empty list.

init_metadata: MutableMapping

Initial dictionary of metadata items for track. Default None which initialises track metadata as an empty dictionary.

id: str

The unique track ID

insert(index, value)

Insert value at index of states.

Parameters:

• index (int) – Index of states to insert value at.

• value (State) – A state object to be inserted at the specified index of states.

append(value)

Add value at end of states.

Parameters:

value (State) – A state object to be added at the end of states.

property metadata

Current metadata dictionary of track. If track contains no states, this is the initial metadata dictionary init_metadata.

class stonesoup.types.update.Update(hypothesis: Hypothesis)

Update type

The base update class. Updates are returned by :class:’~.Updater’ objects and contain the information that was used to perform the updating

Parameters:

hypothesis (Hypothesis) – Hypothesis used for updating

hypothesis: Hypothesis

Hypothesis used for updating