Data Types
Base Types
Array Types
 class stonesoup.types.array.Matrix(*args, **kwargs)[source]
Bases:
ndarray
Matrix wrapper for
numpy.ndarray
This class returns a view to a
numpy.ndarray
It’s called same as tonumpy.asarray()
.
 class stonesoup.types.array.StateVector(*args, **kwargs)[source]
Bases:
Matrix
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 asnumpy.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.,]])
, andStateVector([[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 a 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
ornp.ndarray
for this. flatten(order='C')[source]
Return a copy of the array collapsed into one dimension.
 Parameters:
order ({'C', 'F', 'A', 'K'}, optional) – ‘C’ means to flatten in rowmajor (Cstyle) order. ‘F’ means to flatten in columnmajor (Fortran style) order. ‘A’ means to flatten in columnmajor order if a is Fortran contiguous in memory, rowmajor 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 1D flat iterator over the array.
Examples
>>> a = np.array([[1,2], [3,4]]) >>> a.flatten() array([1, 2, 3, 4]) >>> a.flatten('F') array([1, 3, 2, 4])
 ravel([order])[source]
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.array.StateVectors(states, *args, **kwargs)[source]
Bases:
Matrix
Wrapper for
numpy.ndarray for multiple State Vectors
This class returns a view to a
numpy.ndarray
that is in shape (num_dimensions, num_components), customising some numpy functions to ensure custom types are handled correctly. This can be initialised by a sequence type (list, tuple; not array) that containsStateVector
, otherwise it’s called same asnumpy.asarray()
.
 class stonesoup.types.array.CovarianceMatrix(*args, **kwargs)[source]
Bases:
Matrix
Covariance matrix wrapper for
numpy.ndarray
.This class returns a view to a
numpy.ndarray
, but ensures that its initialised at a NxN matrix. It’s called similar tonumpy.asarray()
.
 class stonesoup.types.array.PrecisionMatrix(*args, **kwargs)[source]
Bases:
Matrix
Precision matrix. This is the matrix inverse of a covariance matrix.
This class returns a view to a
numpy.ndarray
, but ensures that its initialised as an NxN matrix. It’s called similar tonumpy.asarray()
.
Angle Types
 class stonesoup.types.angle.Angle(value)[source]
Bases:
Real
Angle class.
Angle handles modulo arithmetic for adding and subtracting angles
 class stonesoup.types.angle.Bearing(value)[source]
Bases:
Angle
Bearing angle class.
Bearing handles modulo arithmetic for adding and subtracting angles. The return type for addition and subtraction is Bearing. Multiplication or division produces a float object rather than Bearing.
 class stonesoup.types.angle.Elevation(value)[source]
Bases:
Angle
Elevation angle class.
Elevation handles modulo arithmetic for adding and subtracting elevation angles. The return type for addition and subtraction is Elevation. Multiplication or division produces a float object rather than Elevation.
 class stonesoup.types.angle.Azimuth(value)[source]
Bases:
Angle
Azimuth angle class.
Azimuth handles modulo arithmetic for adding and subtracting angles. The return type for addition and subtraction is Azimuth. Multiplication or division produces a float object rather than Azimuth.
 class stonesoup.types.angle.Longitude(value)[source]
Bases:
Bearing
Longitude angle class.
Longitude handles modulo arithmetic for adding and subtracting angles. The return type for addition and subtraction is Longitude. Multiplication or division produces a float object rather than Longitude.
 class stonesoup.types.angle.Latitude(value)[source]
Bases:
Elevation
Latitude angle class.
Latitude handles modulo arithmetic for adding and subtracting angles. The return type for addition and subtraction is Latitude. Multiplication or division produces a float object rather than Latitude.
 class stonesoup.types.angle.Inclination(value)[source]
Bases:
Angle
(Orbital) Inclination angle class.
Inclination handles modulo arithmetic for adding and subtracting angles. The return type for addition and subtraction is Inclination. Multiplication or division produces a float object rather than Inclination.
 class stonesoup.types.angle.EclipticLongitude(value)[source]
Bases:
Angle
(Orbital) Ecliptic Longitude angle class.
Ecliptic Longitude handles modulo arithmetic for adding and subtracting angles. The return type for addition and subtraction is Ecliptic Longitude. Multiplication or division produces a float object rather than Ecliptic Longitude.
Association Types
 class stonesoup.types.association.Association(objects: Set)[source]
Bases:
Type
Association type
An association between objects.
 Parameters:
objects (
Set
) – Set of objects being associated.
 class stonesoup.types.association.AssociationPair(objects: Set)[source]
Bases:
Association
AssociationPair type
An
Association
representing the association of two objects. Parameters:
objects (
Set
) – Set of objects being associated.
 class stonesoup.types.association.SingleTimeAssociation(objects: Set, timestamp: datetime = None)[source]
Bases:
Association
SingleTimeAssociation type
An
Association
representing the linking of objects at a single time. Parameters:
objects (
Set
) – Set of objects being associated.timestamp (
datetime.datetime
, optional) – Timestamp of the association. Default is None.
 class stonesoup.types.association.TimeRangeAssociation(objects: Set, time_range: CompoundTimeRange  TimeRange = None)[source]
Bases:
Association
TimeRangeAssociation type
An
AssociationPair
representing the linking of objects over a range of times. Parameters:
objects (
Set
) – Set of objects being associated.time_range (
Union[CompoundTimeRange, TimeRange]
, optional) – Range of times that association exists over. Default is None.
 time_range: CompoundTimeRange  TimeRange
Range of times that association exists over. Default is None.
 class stonesoup.types.association.AssociationSet(associations: Set[Association] = None)[source]
Bases:
Type
AssociationSet type
A set of
Association
type objects representing multiple independent associations. Contains functions for indexing into the associations. Parameters:
associations (
Set[Association]
, optional) – Set of independent associations.
 associations: Set[Association]
Set of independent associations.
 property key_times
Returns all timestamps at which a component starts or ends, or where there is a
SingleTimeAssociation
.
 property overall_time_range
Returns a
CompoundTimeRange
covering all times at which at least one association is active.Note:
SingleTimeAssociation
are not counted.
 property object_set
Returns a set of all objects contained by this instance.
 associations_at_timestamp(timestamp)[source]
Return the associations that exist at a given timestamp.
Method will return a set of all the
Association
type objects which occur at the specified time stamp. Parameters:
timestamp (datetime.datetime) – Timestamp at which associations should be identified.
 Returns:
Associations which occur at specified timestamp.
 Return type:
 associations_including_objects(objects)[source]
Return associations that include all the given objects.
Method will return the set of all the
Association
type objects which contain an association with the provided object. Parameters:
objects (set of objects) – A set of objects to look for in associations.
 Returns:
A set of associations containing every member of objects.
 Return type:
Detection Types
 class stonesoup.types.detection.Detection(state_vector: StateVector, timestamp: datetime = None, measurement_model: MeasurementModel = None, metadata: MutableMapping = None)[source]
Bases:
State
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 isNone
)metadata (
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.detection.GaussianDetection(state_vector: StateVector, covar: CovarianceMatrix, timestamp: datetime = None, measurement_model: MeasurementModel = None, metadata: MutableMapping = None)[source]
Bases:
Detection
,GaussianState
GaussianDetection type
 Parameters:
state_vector (
StateVector
) – State vector.covar (
CovarianceMatrix
) – Covariance matrix of state.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.measurement_model (
MeasurementModel
, optional) – The measurement model used to generate the detection (the default isNone
)metadata (
MutableMapping
, optional) – Dictionary of metadata items for Detections.
 class stonesoup.types.detection.Clutter(state_vector: StateVector, timestamp: datetime = None, measurement_model: MeasurementModel = None, metadata: MutableMapping = None)[source]
Bases:
Detection
Clutter type for detections classed as clutter
This is same as
Detection
, but can be used to identify clutter for metrics and analysis purposes. 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 isNone
)metadata (
MutableMapping
, optional) – Dictionary of metadata items for Detections.
 class stonesoup.types.detection.TrueDetection(state_vector: StateVector, groundtruth_path: GroundTruthPath, timestamp: datetime = None, measurement_model: MeasurementModel = None, metadata: MutableMapping = None)[source]
Bases:
Detection
TrueDetection type for detections that come from ground truth
This is same as
Detection
, but can be used to identify true detections for metrics and analysis purposes. Parameters:
state_vector (
StateVector
) – State vector.groundtruth_path (
GroundTruthPath
) – Ground truth path that this detection came fromtimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.measurement_model (
MeasurementModel
, optional) – The measurement model used to generate the detection (the default isNone
)metadata (
MutableMapping
, optional) – Dictionary of metadata items for Detections.
 groundtruth_path: GroundTruthPath
Ground truth path that this detection came from
 class stonesoup.types.detection.MissedDetection(state_vector: StateVector = None, timestamp: datetime = None, measurement_model: MeasurementModel = None, metadata: MutableMapping = None)[source]
Bases:
Detection
Detection type for a missed detection
This is same as
Detection
, but it is used in MultipleHypothesis to indicate the null hypothesis (no detections are associated with the specified track). Parameters:
state_vector (
StateVector
, optional) – State vector. Default None.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.measurement_model (
MeasurementModel
, optional) – The measurement model used to generate the detection (the default isNone
)metadata (
MutableMapping
, optional) – Dictionary of metadata items for Detections.
 state_vector: StateVector
State vector. Default None.
 class stonesoup.types.detection.CategoricalDetection(state_vector: StateVector, timestamp: datetime = None, categories: Sequence[float] = None, measurement_model: MeasurementModel = None, metadata: MutableMapping = None)[source]
Bases:
Detection
,CategoricalState
Categorical detection type.
 Parameters:
state_vector (
StateVector
) – State vector.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.categories (
Sequence[float]
, optional) – Category names. Defaults to a list of integers.measurement_model (
MeasurementModel
, optional) – The measurement model used to generate the detection (the default isNone
)metadata (
MutableMapping
, optional) – Dictionary of metadata items for Detections.
 class stonesoup.types.detection.TrueCategoricalDetection(state_vector: StateVector, groundtruth_path: GroundTruthPath, timestamp: datetime = None, categories: Sequence[float] = None, measurement_model: MeasurementModel = None, metadata: MutableMapping = None)[source]
Bases:
TrueDetection
,CategoricalDetection
TrueCategoricalDetection type for categorical detections that come from ground truth.
 Parameters:
state_vector (
StateVector
) – State vector.groundtruth_path (
GroundTruthPath
) – Ground truth path that this detection came fromtimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.categories (
Sequence[float]
, optional) – Category names. Defaults to a list of integers.measurement_model (
MeasurementModel
, optional) – The measurement model used to generate the detection (the default isNone
)metadata (
MutableMapping
, optional) – Dictionary of metadata items for Detections.
 class stonesoup.types.detection.CompositeDetection(sub_states: Sequence[Detection], default_timestamp: datetime = None, groundtruth_path: GroundTruthPath = None, mapping: Sequence[int] = None)[source]
Bases:
CompositeState
Composite detection type
Composition of
Detection
. Parameters:
sub_states (
Sequence[Detection]
) – Sequence of subdetections comprising the composite detection. All subdetections must have matching timestamp. Must not be empty.default_timestamp (
datetime.datetime
, optional) – Default timestamp if no substates exist to attain timestamp from. Defaults to None, whereby substates will be required to have timestamps.groundtruth_path (
GroundTruthPath
, optional) – Ground truth path that this detection came from.mapping (
Sequence[int]
, optional) – Mapping of detections to composite state space. Defaults to None, where subdetections map to substate spaces in order.
 sub_states: Sequence[Detection]
Sequence of subdetections comprising the composite detection. All subdetections must have matching timestamp. Must not be empty.
 groundtruth_path: GroundTruthPath
Ground truth path that this detection came from.
 mapping: Sequence[int]
Mapping of detections to composite state space. Defaults to None, where subdetections map to substate spaces in order.
 property metadata
Combined metadata of all subdetections.
Ground Truth Types
 class stonesoup.types.groundtruth.GroundTruthState(state_vector: StateVector, timestamp: datetime = None, metadata: MutableMapping = None)[source]
Bases:
State
Ground Truth State type
 Parameters:
state_vector (
StateVector
) – State vector.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.metadata (
MutableMapping
, optional) – Dictionary of metadata items for Detections.
 metadata: MutableMapping
Dictionary of metadata items for Detections.
 class stonesoup.types.groundtruth.CategoricalGroundTruthState(state_vector: StateVector, timestamp: datetime = None, categories: Sequence[float] = None, metadata: MutableMapping = None)[source]
Bases:
GroundTruthState
,CategoricalState
Categorical Ground Truth State type
 Parameters:
state_vector (
StateVector
) – State vector.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.categories (
Sequence[float]
, optional) – Category names. Defaults to a list of integers.metadata (
MutableMapping
, optional) – Dictionary of metadata items for Detections.
 class stonesoup.types.groundtruth.GroundTruthPath(states: MutableSequence[GroundTruthState] = None, id: str = None)[source]
Bases:
StateMutableSequence
Ground Truth Path type
A
StateMutableSequence
representing a track. Parameters:
states (
MutableSequence[GroundTruthState]
, 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.
 class stonesoup.types.groundtruth.CompositeGroundTruthState(sub_states: Sequence[GroundTruthState], default_timestamp: datetime = None)[source]
Bases:
CompositeState
Composite ground truth state type.
A composition of ordered substates (
GroundTruthState
) existing at the same timestamp, representing a true object with a state for (potentially) multiple, distinct state spaces. Parameters:
sub_states (
Sequence[GroundTruthState]
) – Sequence of substates comprising the composite state. All substates must have matching timestamp and metadata attributes. Must not be empty.default_timestamp (
datetime.datetime
, optional) – Default timestamp if no substates exist to attain timestamp from. Defaults to None, whereby substates will be required to have timestamps.
 sub_states: Sequence[GroundTruthState]
Sequence of substates comprising the composite state. All substates must have matching timestamp and metadata attributes. Must not be empty.
 property metadata
Combined metadata of all subdetections.
Hypothesis Types
 class stonesoup.types.hypothesis.Hypothesis[source]
Bases:
Type
Hypothesis base type
A Hypothesis has subtypes:
‘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.hypothesis.ProbabilityHypothesis(probability: Probability)[source]
Bases:
Hypothesis
 Parameters:
probability (
Probability
) – Probability that detection is true location of prediction
 probability: Probability
Probability that detection is true location of prediction
 class stonesoup.types.hypothesis.SingleHypothesis(prediction: Prediction, measurement: Detection, measurement_prediction: MeasurementPrediction = None)[source]
Bases:
Hypothesis
A hypothesis based on a single measurement.
 Parameters:
prediction (
Prediction
) – Predicted track statemeasurement (
Detection
) – Detection used for hypothesis and updatingmeasurement_prediction (
MeasurementPrediction
, optional) – Optional track prediction in measurement space
 prediction: Prediction
Predicted track state
 measurement_prediction: MeasurementPrediction
Optional track prediction in measurement space
 class stonesoup.types.hypothesis.SingleDistanceHypothesis(prediction: Prediction, measurement: Detection, distance: float, measurement_prediction: MeasurementPrediction = None)[source]
Bases:
SingleHypothesis
Distance scored hypothesis subclass.
Notes
As smaller distance is ‘better’, comparison logic is reversed i.e. smaller distance is a greater likelihood.
 Parameters:
prediction (
Prediction
) – Predicted track statemeasurement (
Detection
) – Detection used for hypothesis and updatingdistance (
float
) – Distance between detection and predictionmeasurement_prediction (
MeasurementPrediction
, optional) – Optional track prediction in measurement space
 class stonesoup.types.hypothesis.SingleProbabilityHypothesis(prediction: Prediction, measurement: Detection, probability: Probability, measurement_prediction: MeasurementPrediction = None)[source]
Bases:
ProbabilityHypothesis
,SingleHypothesis
Single Measurement Probability scored hypothesis subclass.
 Parameters:
prediction (
Prediction
) – Predicted track statemeasurement (
Detection
) – Detection used for hypothesis and updatingprobability (
Probability
) – Probability that detection is true location of predictionmeasurement_prediction (
MeasurementPrediction
, optional) – Optional track prediction in measurement space
 class stonesoup.types.hypothesis.JointHypothesis(hypotheses)[source]

Joint Hypothesis base type
A Joint Hypothesis consists of multiple Hypotheses, each with a single Track and a single Prediction. A Joint Hypothesis can be a ‘ProbabilityJointHypothesis’ or a ‘DistanceJointHypothesis’, with a probability or distance that is a function of the Hypothesis probabilities. Multiple Joint Hypotheses can be compared to see which is most likely to be the “correct” hypothesis.
Note: In reality, the property ‘hypotheses’ is a dictionary where the entries have the form ‘Track: Hypothesis’. However, we cannot define it this way because then Hypothesis imports Track, and Track imports Update, and Update imports Hypothesis, which is a circular import.
 Parameters:
hypotheses (
Hypothesis
) – Association hypotheses
 hypotheses: Hypothesis
Association hypotheses
 class stonesoup.types.hypothesis.ProbabilityJointHypothesis(hypotheses)[source]
Bases:
ProbabilityHypothesis
,JointHypothesis
Probabilityscored Joint Hypothesis subclass.
 Parameters:
hypotheses (
Hypothesis
) – Association hypothesesprobability (
Probability
, optional) – Probability that detection is true location of prediction. Defaults to None, whereby the probability is calculated as being the product of the constituent multiplehypotheses’ probabilities.
 probability: Probability
Probability that detection is true location of prediction. Defaults to None, whereby the probability is calculated as being the product of the constituent multiplehypotheses’ probabilities.
 class stonesoup.types.hypothesis.DistanceJointHypothesis(hypotheses)[source]
Bases:
JointHypothesis
Distance scored Joint Hypothesis subclass.
Notes
As smaller distance is ‘better’, comparison logic is reversed i.e. smaller distance is a greater likelihood.
 Parameters:
hypotheses (
Hypothesis
) – Association hypotheses
 class stonesoup.types.hypothesis.CompositeHypothesis(prediction: CompositePrediction, measurement: CompositeDetection, sub_hypotheses: Sequence[SingleHypothesis], measurement_prediction: CompositeMeasurementPrediction = None)[source]
Bases:
SingleHypothesis
Composite hypothesis type
A composition of
SingleHypothesis
. Parameters:
prediction (
CompositePrediction
) – Predicted track statemeasurement (
CompositeDetection
) – Detection used for hypothesis and updatingsub_hypotheses (
Sequence[SingleHypothesis]
) – Sequence of subhypotheses comprising the composite hypothesis. Must not be empty.measurement_prediction (
CompositeMeasurementPrediction
, optional) – Optional track prediction in measurement space
 sub_hypotheses: Sequence[SingleHypothesis]
Sequence of subhypotheses comprising the composite hypothesis. Must not be empty.
 prediction: CompositePrediction
Predicted track state
 measurement: CompositeDetection
Detection used for hypothesis and updating
 measurement_prediction: CompositeMeasurementPrediction
Optional track prediction in measurement space
 class stonesoup.types.hypothesis.CompositeProbabilityHypothesis(prediction: CompositePrediction, measurement: CompositeDetection, probability: Probability = None, measurement_prediction: CompositeMeasurementPrediction = None, sub_hypotheses: Sequence[SingleProbabilityHypothesis] = None)[source]
Bases:
CompositeHypothesis
,SingleProbabilityHypothesis
Composite probability hypothesis type
A composition of
SingleProbabilityHypothesis
.Calculate hypothesis probability via product of subhypotheses’ probabilities. Probability is 1 if there are no subhypotheses.
 Parameters:
prediction (
CompositePrediction
) – Predicted track statemeasurement (
CompositeDetection
) – Detection used for hypothesis and updatingprobability (
Probability
, optional) – Probability that detection is true location of prediction. Default is None, whereby probability is calculated as the product of subhypotheses’ probabilitiesmeasurement_prediction (
CompositeMeasurementPrediction
, optional) – Optional track prediction in measurement spacesub_hypotheses (
Sequence[SingleProbabilityHypothesis]
, optional) – Sequence of probabilityscored subhypotheses comprising the composite hypothesis.
 sub_hypotheses: Sequence[SingleProbabilityHypothesis]
Sequence of probabilityscored subhypotheses comprising the composite hypothesis.
 probability: Probability
Probability that detection is true location of prediction. Default is None, whereby probability is calculated as the product of subhypotheses’ probabilities
 class stonesoup.types.multihypothesis.MultipleHypothesis(single_hypotheses: Sequence[SingleHypothesis] = None, normalise: bool = False, total_weight: float = 1)[source]

Multiple Hypothesis base type
A Multiple Hypothesis is a container to store a collection of hypotheses.
 Parameters:
single_hypotheses (
Sequence[SingleHypothesis]
, optional) – The initial list ofSingleHypothesis
. Default None which initialises with empty list.normalise (
bool
, optional) – Normalise probabilities ofSingleHypothesis
. 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.
 class stonesoup.types.multihypothesis.MultipleCompositeHypothesis(single_hypotheses: Sequence[CompositeHypothesis] = None, normalise: bool = False, total_weight: float = 1)[source]

Multiple composite hypothesis type
A Multiple Composite Hypothesis is a container to store a collection of composite hypotheses.
Interfaces the same as MultipleHypothesis, but permits different input, hence methods are redefined.
 Parameters:
single_hypotheses (
Sequence[CompositeHypothesis]
, optional) – The initial list ofCompositeHypothesis
. Default None which initialises with empty list.normalise (
bool
, optional) – Normalise probabilities ofCompositeHypothesis
. Default is False.total_weight (
float
, optional) – When normalising, weights will sum to this. Default is 1.
 single_hypotheses: Sequence[CompositeHypothesis]
The initial list of
CompositeHypothesis
. Default None which initialises with empty list.
 normalise: bool
Normalise probabilities of
CompositeHypothesis
. Default is False.
Interval Types
 class stonesoup.types.interval.Interval(start: int  float, end: int  float)[source]
Bases:
Type
Closed continuous interval class.
Represents a continuous, closed interval of real numbers. Represented by a lower and upper bound.
 Parameters:
start (
Union[int, float]
) – Lower bound of intervalend (
Union[int, float]
) – Upper bound of interval
 class stonesoup.types.interval.Intervals(intervals: MutableSequence[Interval] = None)[source]
Bases:
Type
Disjoint closed continuous intervals class.
Represents a set of continuous, closed intervals of real numbers. Represented by a list of
Interval
types. Parameters:
intervals (
MutableSequence[Interval]
, optional) – Container ofInterval
 intervals: MutableSequence[Interval]
Container of
Interval
 static overlap(intervals)[source]
Determine whether a pair of intervals in a list overlap (are not disjoint). Returns a pair of overlapping intervals if there are any, otherwise returns None.
Metric Types
 class stonesoup.types.metric.Metric(title: str, value: Any, generator: Any)[source]
Bases:
Type
Metric type
 Parameters:
title (
str
) – Name of the metricvalue (
Any
) – Value of the metricgenerator (
Any
) – Generator used to create the metric
 class stonesoup.types.metric.PlottingMetric(title: str, value: Any, generator: Any)[source]
Bases:
Metric
Metric which is to be visualised as plot, value should be a pyplot figure
 Parameters:
title (
str
) – Name of the metricvalue (
Any
) – Value of the metricgenerator (
Any
) – Generator used to create the metric
 class stonesoup.types.metric.SingleTimeMetric(title: str, value: Any, generator: Any, timestamp: datetime = None)[source]
Bases:
Metric
Metric for a specific timestamp
 Parameters:
title (
str
) – Name of the metricvalue (
Any
) – Value of the metricgenerator (
Any
) – Generator used to create the metrictimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.
 class stonesoup.types.metric.TimeRangeMetric(title: str, value: Any, generator: Any, time_range: TimeRange = None)[source]
Bases:
Metric
Metric for a range of times (e.g. for example an entire run)
 Parameters:
title (
str
) – Name of the metricvalue (
Any
) – Value of the metricgenerator (
Any
) – Generator used to create the metrictime_range (
TimeRange
, optional) – Time range over which metric assessment will be conducted over. Default is None
 class stonesoup.types.metric.TimeRangePlottingMetric(title: str, value: Any, generator: Any, time_range: TimeRange = None)[source]
Bases:
TimeRangeMetric
,PlottingMetric
Plotting metric covering a period of time
 Parameters:
title (
str
) – Name of the metricvalue (
Any
) – Value of the metricgenerator (
Any
) – Generator used to create the metrictime_range (
TimeRange
, optional) – Time range over which metric assessment will be conducted over. Default is None
 class stonesoup.types.metric.SingleTimePlottingMetric(title: str, value: Any, generator: Any, timestamp: datetime = None)[source]
Bases:
SingleTimeMetric
,PlottingMetric
Plotting metric covering a specific timestamp
 Parameters:
title (
str
) – Name of the metricvalue (
Any
) – Value of the metricgenerator (
Any
) – Generator used to create the metrictimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.
Mixture Types
 class stonesoup.types.mixture.GaussianMixture(components: MutableSequence[WeightedGaussianState] = None)[source]
Bases:
Type
,MutableSequence
Gaussian Mixture type
Represents the target space through a Gaussian Mixture. Individual Gaussian components are contained in a
list
ofWeightedGaussianState
. Parameters:
components (
MutableSequence[WeightedGaussianState]
, optional) – The initial list of
WeightedGaussianState
components. Default None which initialises with empty list.
 The initial list of
 components: MutableSequence[WeightedGaussianState]
The initial list of
WeightedGaussianState
components. Default None which initialises with empty list.
 property timestamp
Timestamp
Numeric Types
 class stonesoup.types.numeric.Probability(value, *, log_value=False)[source]
Bases:
Real
Probability class.
Similar to a float, but value stored as natural log value internally. All operations are attempted with log values where possible, and failing that a float will be returned instead.
 Parameters:
 classmethod from_log(value)[source]
Create Probability type from a log value; same as Probability(value, log_value=True)
 Parameters:
value (float) – Value for probability.
 Return type:
 from_log_ufunc = <ufunc 'from_log (vectorized)'>
Particle Types
 class stonesoup.types.particle.Particle(state_vector: StateVector, weight: float, parent: Particle = None)[source]
Bases:
Type
Particle type
A particle type which contains a state and weight
 Parameters:
state_vector (
StateVector
) – State vectorweight (
float
) – Weight of particleparent (
Particle
, optional) – Parent particle
 state_vector: StateVector
State vector
 class stonesoup.types.particle.MultiModelParticle(state_vector: StateVector, weight: float, dynamic_model: int, parent: MultiModelParticle = None)[source]
Bases:
Particle
Particle type
A MultiModelParticle type which contains a state, weight and the dynamic_model
 Parameters:
state_vector (
StateVector
) – State vectorweight (
float
) – Weight of particledynamic_model (
int
) – Assigned dynamic modelparent (
MultiModelParticle
, optional) – Parent particle
 parent: MultiModelParticle
Parent particle
 class stonesoup.types.particle.RaoBlackwellisedParticle(state_vector: StateVector, weight: float, model_probabilities: Sequence[float], parent: RaoBlackwellisedParticle = None)[source]
Bases:
Particle
Particle type
A RaoBlackwellisedParticle type which contains a state, weight, dynamic_model and associated model probabilities
 Parameters:
state_vector (
StateVector
) – State vectorweight (
float
) – Weight of particlemodel_probabilities (
Sequence[float]
) – The dynamic probabilities of changing modelsparent (
RaoBlackwellisedParticle
, optional) – Parent particle
 parent: RaoBlackwellisedParticle
Parent particle
Prediction Types
 class stonesoup.types.prediction.Prediction(transition_model: TransitionModel = None, prior: State = None)[source]
Bases:
Type
,CreatableFromState
Prediction type
This is the base prediction class.
 Parameters:
transition_model (
TransitionModel
, optional) – The transition model used to make the predictionprior (
State
, optional)
 transition_model: TransitionModel
The transition model used to make the prediction
 class stonesoup.types.prediction.MeasurementPrediction[source]
Bases:
Type
,CreatableFromState
Prediction type
This is the base measurement prediction class.
 class stonesoup.types.prediction.StatePrediction(state_vector: StateVector, timestamp: datetime = None, transition_model: TransitionModel = None, prior: State = None)[source]
Bases:
Prediction
,State
StatePrediction type
Most simple state prediction 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.transition_model (
TransitionModel
, optional) – The transition model used to make the predictionprior (
State
, optional)
 class stonesoup.types.prediction.InformationStatePrediction(state_vector: StateVector, precision: PrecisionMatrix, timestamp: datetime = None, transition_model: TransitionModel = None, prior: State = None)[source]
Bases:
Prediction
,InformationState
InformationStatePrediction type
Information state prediction type: contains state vector, precision matrix and timestamp
 Parameters:
state_vector (
StateVector
) – State vector.precision (
PrecisionMatrix
) – precision matrix of state.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.transition_model (
TransitionModel
, optional) – The transition model used to make the predictionprior (
State
, optional)
 class stonesoup.types.prediction.StateMeasurementPrediction(state_vector: StateVector, timestamp: datetime = None)[source]
Bases:
MeasurementPrediction
,State
MeasurementPrediction type
Most simple measurement prediction 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.
 class stonesoup.types.prediction.GaussianStatePrediction(state_vector: StateVector, covar: CovarianceMatrix, timestamp: datetime = None, transition_model: TransitionModel = None, prior: State = None)[source]
Bases:
Prediction
,GaussianState
GaussianStatePrediction type
This is a simple Gaussian state prediction object, which, as the name suggests, is described by a Gaussian distribution.
 Parameters:
state_vector (
StateVector
) – State vector.covar (
CovarianceMatrix
) – Covariance matrix of state.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.transition_model (
TransitionModel
, optional) – The transition model used to make the predictionprior (
State
, optional)
 class stonesoup.types.prediction.ASDGaussianStatePrediction(multi_state_vector: StateVector, timestamps: Sequence[datetime], max_nstep: int, multi_covar: CovarianceMatrix, act_timestamp: datetime, correlation_matrices: MutableSequence[MutableMapping[str, ndarray]] = None, transition_model: TransitionModel = None, prior: State = None)[source]
Bases:
Prediction
,ASDGaussianState
ASDGaussianStatePrediction type
This is a simple ASDGaussian state prediction object, which, as the name suggests, is described by a Gaussian distribution.
 Parameters:
multi_state_vector (
StateVector
) – State vector of all timestampstimestamps (
Sequence[datetime.datetime]
) – List of all timestamps which have a state in the ASDStatemax_nstep (
int
) – Decides when the state is pruned in a prediction step. If 0 then there is no pruningmulti_covar (
CovarianceMatrix
) – Covariance of all timestepsact_timestamp (
datetime.datetime
) – The timestamp for which the state is predictedcorrelation_matrices (
MutableSequence[MutableMapping[str, numpy.ndarray]]
, optional) – Sequence of Correlation Matrices, consisting of \(P_{ll}\), \(P_{ll+1}\) and \(F_{l+1l}\) built in the Kalman predictor and Kalman updater, aligned totimestamps
transition_model (
TransitionModel
, optional) – The transition model used to make the predictionprior (
State
, optional)
 class stonesoup.types.prediction.SqrtGaussianStatePrediction(state_vector: StateVector, sqrt_covar: CovarianceMatrix, timestamp: datetime = None, transition_model: TransitionModel = None, prior: State = None)[source]
Bases:
Prediction
,SqrtGaussianState
SqrtGaussianStatePrediction type
This is a Gaussian state prediction object, with the covariance held as the square root of the covariance matrix
 Parameters:
state_vector (
StateVector
) – State vector.sqrt_covar (
CovarianceMatrix
) – A square root form of the Gaussian covariance matrix.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.transition_model (
TransitionModel
, optional) – The transition model used to make the predictionprior (
State
, optional)
 class stonesoup.types.prediction.WeightedGaussianStatePrediction(state_vector: StateVector, covar: CovarianceMatrix, timestamp: datetime = None, weight: Probability = 0, transition_model: TransitionModel = None, prior: State = None)[source]
Bases:
Prediction
,WeightedGaussianState
WeightedGaussianStatePrediction type
This is a simple Gaussian state prediction object, which, as the name suggests, is described by a Gaussian distribution with an associated weight.
 Parameters:
state_vector (
StateVector
) – State vector.covar (
CovarianceMatrix
) – Covariance matrix of state.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
Probability
, optional) – Weight of the Gaussian State.transition_model (
TransitionModel
, optional) – The transition model used to make the predictionprior (
State
, optional)
 class stonesoup.types.prediction.TaggedWeightedGaussianStatePrediction(state_vector: StateVector, covar: CovarianceMatrix, timestamp: datetime = None, weight: Probability = 0, tag: str = None, transition_model: TransitionModel = None, prior: State = None)[source]
Bases:
Prediction
,TaggedWeightedGaussianState
TaggedWeightedGaussianStatePrediction type
This is a simple Gaussian state prediction object, which, as the name suggests, is described by a Gaussian distribution, with an associated weight and unique tag.
 Parameters:
state_vector (
StateVector
) – State vector.covar (
CovarianceMatrix
) – Covariance matrix of state.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
Probability
, optional) – Weight of the Gaussian State.tag (
str
, optional) – Unique tag of the Gaussian State.transition_model (
TransitionModel
, optional) – The transition model used to make the predictionprior (
State
, optional)
 class stonesoup.types.prediction.GaussianMeasurementPrediction(state_vector: StateVector, covar: CovarianceMatrix, timestamp: datetime = None, cross_covar: CovarianceMatrix = None)[source]
Bases:
MeasurementPrediction
,GaussianState
GaussianMeasurementPrediction type
This is a simple Gaussian measurement prediction object, which, as the name suggests, is described by a Gaussian distribution.
 Parameters:
state_vector (
StateVector
) – State vector.covar (
CovarianceMatrix
) – Covariance matrix of state.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.cross_covar (
CovarianceMatrix
, optional) – The statemeasurement cross covariance matrix
 cross_covar: CovarianceMatrix
The statemeasurement cross covariance matrix
 class stonesoup.types.prediction.ASDGaussianMeasurementPrediction(multi_state_vector: StateVector, timestamps: Sequence[datetime], max_nstep: int, multi_covar: CovarianceMatrix, correlation_matrices: MutableSequence[MutableMapping[str, ndarray]] = None, cross_covar: CovarianceMatrix = None)[source]
Bases:
MeasurementPrediction
,ASDGaussianState
ASD Gaussian Measurement Prediction
 Parameters:
multi_state_vector (
StateVector
) – State vector of all timestampstimestamps (
Sequence[datetime.datetime]
) – List of all timestamps which have a state in the ASDStatemax_nstep (
int
) – Decides when the state is pruned in a prediction step. If 0 then there is no pruningmulti_covar (
CovarianceMatrix
) – Covariance of all timestepscorrelation_matrices (
MutableSequence[MutableMapping[str, numpy.ndarray]]
, optional) – Sequence of Correlation Matrices, consisting of \(P_{ll}\), \(P_{ll+1}\) and \(F_{l+1l}\) built in the Kalman predictor and Kalman updater, aligned totimestamps
cross_covar (
CovarianceMatrix
, optional) – The statemeasurement cross covariance matrix
 cross_covar: CovarianceMatrix
The statemeasurement cross covariance matrix
 class stonesoup.types.prediction.ParticleStatePrediction(state_vector: StateVectors, timestamp: datetime = None, weight: MutableSequence[Probability] = None, log_weight: ndarray = None, parent: ParticleState = None, particle_list: MutableSequence[Particle] = None, fixed_covar: CovarianceMatrix = None, transition_model: TransitionModel = None, prior: State = None)[source]
Bases:
Prediction
,ParticleState
ParticleStatePrediction type
This is a simple Particle state prediction object.
 Parameters:
state_vector (
StateVectors
) – State vectors.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
MutableSequence[Probability]
, optional) – Weights of particleslog_weight (
numpy.ndarray
, optional) – Log weights of particlesparent (
ParticleState
, optional) – Parent particlesparticle_list (
MutableSequence[Particle]
, optional) – List of Particle objectsfixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.transition_model (
TransitionModel
, optional) – The transition model used to make the predictionprior (
State
, optional)
 class stonesoup.types.prediction.ParticleMeasurementPrediction(state_vector: StateVectors, timestamp: datetime = None, weight: MutableSequence[Probability] = None, log_weight: ndarray = None, parent: ParticleState = None, particle_list: MutableSequence[Particle] = None, fixed_covar: CovarianceMatrix = None)[source]
Bases:
MeasurementPrediction
,ParticleState
MeasurementStatePrediction type
This is a simple Particle measurement prediction object.
 Parameters:
state_vector (
StateVectors
) – State vectors.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
MutableSequence[Probability]
, optional) – Weights of particleslog_weight (
numpy.ndarray
, optional) – Log weights of particlesparent (
ParticleState
, optional) – Parent particlesparticle_list (
MutableSequence[Particle]
, optional) – List of Particle objectsfixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.
 class stonesoup.types.prediction.MultiModelParticleStatePrediction(state_vector: StateVectors, timestamp: datetime = None, weight: MutableSequence[Probability] = None, log_weight: ndarray = None, parent: ParticleState = None, particle_list: MutableSequence[Particle] = None, fixed_covar: CovarianceMatrix = None, dynamic_model: ndarray = None, transition_model: TransitionModel = None, prior: State = None)[source]
Bases:
Prediction
,MultiModelParticleState
MultiModelParticleStatePrediction type
This is a simple multimodel Particle state prediction object.
 Parameters:
state_vector (
StateVectors
) – State vectors.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
MutableSequence[Probability]
, optional) – Weights of particleslog_weight (
numpy.ndarray
, optional) – Log weights of particlesparent (
ParticleState
, optional) – Parent particlesparticle_list (
MutableSequence[Particle]
, optional) – List of Particle objectsfixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.dynamic_model (
numpy.ndarray
, optional) – Array of indices that identify which model is associated with each particle.transition_model (
TransitionModel
, optional) – The transition model used to make the predictionprior (
State
, optional)
 class stonesoup.types.prediction.RaoBlackwellisedParticleStatePrediction(state_vector: StateVectors, timestamp: datetime = None, weight: MutableSequence[Probability] = None, log_weight: ndarray = None, parent: ParticleState = None, particle_list: MutableSequence[Particle] = None, fixed_covar: CovarianceMatrix = None, model_probabilities: ndarray = None, transition_model: TransitionModel = None, prior: State = None)[source]
Bases:
Prediction
,RaoBlackwellisedParticleState
RaoBlackwellisedParticleStatePrediction type
This is a simple Rao Blackwellised Particle state prediction object.
 Parameters:
state_vector (
StateVectors
) – State vectors.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
MutableSequence[Probability]
, optional) – Weights of particleslog_weight (
numpy.ndarray
, optional) – Log weights of particlesparent (
ParticleState
, optional) – Parent particlesparticle_list (
MutableSequence[Particle]
, optional) – List of Particle objectsfixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.model_probabilities (
numpy.ndarray
, optional) – 2d NumPy array containing probability of particle belong to particular model. Shape (nmodels, mparticles).transition_model (
TransitionModel
, optional) – The transition model used to make the predictionprior (
State
, optional)
 class stonesoup.types.prediction.BernoulliParticleStatePrediction(state_vector: StateVectors, timestamp: datetime = None, weight: MutableSequence[Probability] = None, log_weight: ndarray = None, parent: ParticleState = None, particle_list: MutableSequence[Particle] = None, fixed_covar: CovarianceMatrix = None, existence_probability: Probability = None, transition_model: TransitionModel = None, prior: State = None)[source]
Bases:
Prediction
,BernoulliParticleState
BernoulliParticleStatePrediction type
This is a simple Bernoulli Particle state prediction object
 Parameters:
state_vector (
StateVectors
) – State vectors.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
MutableSequence[Probability]
, optional) – Weights of particleslog_weight (
numpy.ndarray
, optional) – Log weights of particlesparent (
ParticleState
, optional) – Parent particlesparticle_list (
MutableSequence[Particle]
, optional) – List of Particle objectsfixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.existence_probability (
Probability
, optional) – Target existence probability estimatetransition_model (
TransitionModel
, optional) – The transition model used to make the predictionprior (
State
, optional)
 class stonesoup.types.prediction.EnsembleStatePrediction(state_vector: StateVectors, timestamp: datetime = None, transition_model: TransitionModel = None, prior: State = None)[source]
Bases:
Prediction
,EnsembleState
EnsembleStatePrediction type
This is a simple Ensemble measurement prediction object.
 Parameters:
state_vector (
StateVectors
) – An ensemble of state vectors which represent the statetimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.transition_model (
TransitionModel
, optional) – The transition model used to make the predictionprior (
State
, optional)
 class stonesoup.types.prediction.EnsembleMeasurementPrediction(state_vector: StateVectors, timestamp: datetime = None)[source]
Bases:
MeasurementPrediction
,EnsembleState
EnsembleMeasurementPrediction type
This is a simple Ensemble measurement prediction object.
 Parameters:
state_vector (
StateVectors
) – An ensemble of state vectors which represent the statetimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.
 class stonesoup.types.prediction.CategoricalStatePrediction(state_vector: StateVector, timestamp: datetime = None, categories: Sequence[float] = None, transition_model: TransitionModel = None, prior: State = None)[source]
Bases:
Prediction
,CategoricalState
Categorical state prediction type
 Parameters:
state_vector (
StateVector
) – State vector.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.categories (
Sequence[float]
, optional) – Category names. Defaults to a list of integers.transition_model (
TransitionModel
, optional) – The transition model used to make the predictionprior (
State
, optional)
 class stonesoup.types.prediction.CategoricalMeasurementPrediction(state_vector: StateVector, timestamp: datetime = None, categories: Sequence[float] = None)[source]
Bases:
MeasurementPrediction
,CategoricalState
Categorical measurement prediction type
 Parameters:
state_vector (
StateVector
) – State vector.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.categories (
Sequence[float]
, optional) – Category names. Defaults to a list of integers.
 class stonesoup.types.prediction.CompositePrediction(sub_states: Sequence[Prediction], default_timestamp: datetime = None, transition_model: TransitionModel = None, prior: State = None)[source]
Bases:
Prediction
,CompositeState
Composite prediction type
Composition of
Prediction
. Parameters:
sub_states (
Sequence[Prediction]
) – Sequence of subpredictions comprising the composite prediction. All subpredictions must have matching timestamp. Must not be empty.default_timestamp (
datetime.datetime
, optional) – Default timestamp if no substates exist to attain timestamp from. Defaults to None, whereby substates will be required to have timestamps.transition_model (
TransitionModel
, optional) – The transition model used to make the predictionprior (
State
, optional)
 sub_states: Sequence[Prediction]
Sequence of subpredictions comprising the composite prediction. All subpredictions must have matching timestamp. Must not be empty.
 class stonesoup.types.prediction.CompositeMeasurementPrediction(sub_states: Sequence[MeasurementPrediction] = None, default_timestamp: datetime = None)[source]
Bases:
MeasurementPrediction
,CompositeState
Composite measurement prediction type
Composition of
MeasurementPrediction
. Parameters:
sub_states (
Sequence[MeasurementPrediction]
, optional) – Sequence of submeasurementpredictions comprising the composite measurement prediction. All submeasurementpredictions must have matching timestamp.default_timestamp (
datetime.datetime
, optional) – Default timestamp if no substates exist to attain timestamp from. Defaults to None, whereby substates will be required to have timestamps.
 sub_states: Sequence[MeasurementPrediction]
Sequence of submeasurementpredictions comprising the composite measurement prediction. All submeasurementpredictions must have matching timestamp.
Sensor Data Types
 class stonesoup.types.sensordata.ImageFrame(pixels: ndarray, timestamp: datetime = None)[source]
Bases:
SensorData
Image Frame type used to represent a simple image/video frame
 Parameters:
pixels (
numpy.ndarray
) – An array of shape (w,h,x) containing the individual pixel values, where w:width, h:height and x may vary depending on the color formattimestamp (
datetime.datetime
, optional) – An optional timestamp
State Types
 class stonesoup.types.state.State(state_vector: StateVector, timestamp: datetime = None)[source]
Bases:
Type
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.
 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 [source]
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 overwriting of properties.
 Parameters:
state (State) –
State
to use existing properties from, and identify new statetype 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 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.state.ASDState(multi_state_vector: StateVector, timestamps: Sequence[datetime], max_nstep: int)[source]
Bases:
Type
ASD State type
For the use of Accumulated State Densities.
 Parameters:
multi_state_vector (
StateVector
) – State vector of all timestampstimestamps (
Sequence[datetime.datetime]
) – List of all timestamps which have a state in the ASDStatemax_nstep (
int
) – Decides when the state is pruned in a prediction step. If 0 then there is no pruning
 multi_state_vector: StateVector
State vector of all timestamps
 max_nstep: int
Decides when the state is pruned in a prediction step. If 0 then there is no pruning
 property state_vector
The State vector of the newest timestamp
 property timestamp
The newest timestamp
 property ndim
Dimension of one State
 property nstep
Number of timesteps which are in the ASDState
 property state
A
State
object representing latest timestampNote
This will be cached until
multi_state_vector
ortimestamps
are replaced.
 property states
Note
This will be cached until
multi_state_vector
ortimestamps
are replaced.
 class stonesoup.types.state.StateMutableSequence(states: MutableSequence[State] = None)[source]
Bases:
Type
,MutableSequence
A mutable sequence for
State
instancesThis sequence acts like a regular list object for States, as well as proxying state attributes to the last state in the sequence. This sequence can also be indexed/sliced by
datetime.datetime
instances.Notes
If shallow copying, similar to a list, it is safe to add/remove states without affecting the original sequence.
Example
>>> t0 = datetime.datetime(2018, 1, 1, 14, 00) >>> t1 = t0 + datetime.timedelta(minutes=1) >>> state0 = State([[0]], t0) >>> sequence = StateMutableSequence([state0]) >>> print(sequence.state_vector, sequence.timestamp) [[0]] 20180101 14:00:00 >>> sequence.append(State([[1]], t1)) >>> for state in sequence[t1:]: ... print(state.state_vector, state.timestamp) [[1]] 20180101 14:01:00
 Parameters:
states (
MutableSequence[State]
, optional) – The initial list of states. Default None which initialises with empty list.
 states: MutableSequence[State]
The initial list of states. Default None which initialises with empty list.
 last_timestamp_generator()[source]
Generator yielding the last state for each timestamp
This provides a method of iterating over a sequence of states, such that when multiple states for the same timestamp exist, only the last state is yielded. This is particularly useful in cases where you may have multiple
Update
states for a single timestamp e.g. multisensor tracking example. Yields:
State – A state for each timestamp present in the sequence.
 append(value)
S.append(value) – append value to the end of the sequence
 clear() None  remove all items from S
 count(value) integer  return number of occurrences of value
 extend(values)
S.extend(iterable) – extend sequence by appending elements from the iterable
 index(value[, start[, stop]]) integer  return first index of value.
Raises ValueError if the value is not present.
Supporting start and stop arguments is optional, but recommended.
 pop([index]) item  remove and return item at index (default last).
Raise IndexError if list is empty or index is out of range.
 remove(value)
S.remove(value) – remove first occurrence of value. Raise ValueError if the value is not present.
 reverse()
S.reverse() – reverse IN PLACE
 class stonesoup.types.state.GaussianState(state_vector: StateVector, covar: CovarianceMatrix, timestamp: datetime = None)[source]
Bases:
State
Gaussian State type
This is a simple Gaussian state object, which, as the name suggests, is described by a Gaussian state distribution.
 Parameters:
state_vector (
StateVector
) – State vector.covar (
CovarianceMatrix
) – Covariance matrix of state.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.
 covar: CovarianceMatrix
Covariance matrix of state.
 property mean
The state mean, equivalent to state vector
 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 overwriting of properties.
 Parameters:
state (State) –
State
to use existing properties from, and identify new statetype 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 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.
 property ndim
The number of dimensions represented by the state.
 state_vector: StateVector
State vector.
 class stonesoup.types.state.SqrtGaussianState(state_vector: StateVector, sqrt_covar: CovarianceMatrix, timestamp: datetime = None)[source]
Bases:
State
A Gaussian State type where the covariance matrix is stored in a form \(W\) such that \(P = WW^T\)
For \(P\) in general, \(W\) is not unique and the user may choose the form to their taste. No checks are undertaken to ensure that a sensible square root form has been chosen.
 Parameters:
state_vector (
StateVector
) – State vector.sqrt_covar (
CovarianceMatrix
) – A square root form of the Gaussian covariance matrix.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.
 sqrt_covar: CovarianceMatrix
A square root form of the Gaussian covariance matrix.
 property mean
The state mean, equivalent to state vector
 property covar
The full covariance matrix.
Note
This will be cached until
sqrt_covar
is replaced.
 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 overwriting of properties.
 Parameters:
state (State) –
State
to use existing properties from, and identify new statetype 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 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.
 property ndim
The number of dimensions represented by the state.
 state_vector: StateVector
State vector.
 class stonesoup.types.state.InformationState(state_vector: StateVector, precision: PrecisionMatrix, timestamp: datetime = None)[source]
Bases:
State
Information State Type
The information state class carries the
state_vector
, \(\mathbf{y}_k = Y_k \mathbf{x}_k\) and the precision or information matrix \(Y_k = P_k^{1}\), where \(\mathbf{x}_k\) and \(P_k\) are the mean and covariance, respectively, of a Gaussian state. Parameters:
state_vector (
StateVector
) – State vector.precision (
PrecisionMatrix
) – precision matrix of state.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.
 precision: PrecisionMatrix
precision matrix of state.
 property gaussian_state
The Gaussian state.
Note
This will be cached until
state_vector
orprecision
are replaced.
 property covar
Covariance matrix, inverse of
precision
matrix.Note
This will be cached until
precision
is replaced.
 property mean
Equivalent Gaussian mean
Note
This will be cached until
state_vector
orprecision
are replaced.
 classmethod from_gaussian_state(gaussian_state, *args, **kwargs)[source]
Returns an InformationState instance based on the gaussian_state.
 Parameters:
gaussian_state (
GaussianState
) – The guassian_state used to create the new WeightedGaussianState.*args (See main
InformationState
) – args are passed toInformationState
__init__()**kwargs (See main
InformationState
) – kwargs are passed toInformationState
__init__()
 Returns:
Instance of InformationState.
 Return type:
 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 overwriting of properties.
 Parameters:
state (State) –
State
to use existing properties from, and identify new statetype 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 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.
 property ndim
The number of dimensions represented by the state.
 state_vector: StateVector
State vector.
 class stonesoup.types.state.ASDGaussianState(multi_state_vector: StateVector, timestamps: Sequence[datetime], max_nstep: int, multi_covar: CovarianceMatrix, correlation_matrices: MutableSequence[MutableMapping[str, ndarray]] = None)[source]
Bases:
ASDState
ASDGaussian State type
This is a simple Accumulated State Density Gaussian state object, which as the name suggests is described by a Gaussian state distribution.
 Parameters:
multi_state_vector (
StateVector
) – State vector of all timestampstimestamps (
Sequence[datetime.datetime]
) – List of all timestamps which have a state in the ASDStatemax_nstep (
int
) – Decides when the state is pruned in a prediction step. If 0 then there is no pruningmulti_covar (
CovarianceMatrix
) – Covariance of all timestepscorrelation_matrices (
MutableSequence[MutableMapping[str, numpy.ndarray]]
, optional) – Sequence of Correlation Matrices, consisting of \(P_{ll}\), \(P_{ll+1}\) and \(F_{l+1l}\) built in the Kalman predictor and Kalman updater, aligned totimestamps
 multi_covar: CovarianceMatrix
Covariance of all timesteps
 correlation_matrices: MutableSequence[MutableMapping[str, ndarray]]
Sequence of Correlation Matrices, consisting of \(P_{ll}\), \(P_{ll+1}\) and \(F_{l+1l}\) built in the Kalman predictor and Kalman updater, aligned to
timestamps
 property mean
The state mean, equivalent to state vector
 property state
A
GaussianState
object representing latest timestampNote
This will be cached until
multi_state_vector
,multi_covar
ortimestamps
are replaced.
 property states
Note
This will be cached until
multi_state_vector
,multi_covar
ortimestamps
are replaced.
 max_nstep: int
Decides when the state is pruned in a prediction step. If 0 then there is no pruning
 multi_state_vector: StateVector
State vector of all timestamps
 property ndim
Dimension of one State
 property nstep
Number of timesteps which are in the ASDState
 property state_vector
The State vector of the newest timestamp
 property timestamp
The newest timestamp
 class stonesoup.types.state.WeightedGaussianState(state_vector: StateVector, covar: CovarianceMatrix, timestamp: datetime = None, weight: Probability = 0)[source]
Bases:
GaussianState
Weighted Gaussian State Type
Gaussian State object with an associated weight. Used as components for a GaussianMixtureState.
 Parameters:
state_vector (
StateVector
) – State vector.covar (
CovarianceMatrix
) – Covariance matrix of state.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
Probability
, optional) – Weight of the Gaussian State.
 weight: Probability
Weight of the Gaussian State.
 property gaussian_state
The Gaussian state.
Note
This will be cached until
state_vector
orcovar
are replaced.
 classmethod from_gaussian_state(gaussian_state, *args, copy=True, **kwargs)[source]
Returns a WeightedGaussianState instance based on the gaussian_state.
 Parameters:
gaussian_state (
GaussianState
) – The guassian_state used to create the new WeightedGaussianState.*args (See main
WeightedGaussianState
) – args are passed toWeightedGaussianState
__init__()copy (Boolean, optional) – If True, the WeightedGaussianState is created with copies of the elements of gaussian_state. The default is True.
**kwargs (See main
WeightedGaussianState
) – kwargs are passed toWeightedGaussianState
__init__()
 Returns:
Instance of WeightedGaussianState.
 Return type:
 covar: CovarianceMatrix
Covariance matrix of 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 overwriting of properties.
 Parameters:
state (State) –
State
to use existing properties from, and identify new statetype 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 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.
 property mean
The state mean, equivalent to state vector
 property ndim
The number of dimensions represented by the state.
 state_vector: StateVector
State vector.
 class stonesoup.types.state.TaggedWeightedGaussianState(state_vector: StateVector, covar: CovarianceMatrix, timestamp: datetime = None, weight: Probability = 0, tag: str = None)[source]
Bases:
WeightedGaussianState
Tagged Weighted Gaussian State Type
Gaussian State object with an associated weight and tag. Used as components for a GaussianMixtureState.
 Parameters:
state_vector (
StateVector
) – State vector.covar (
CovarianceMatrix
) – Covariance matrix of state.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
Probability
, optional) – Weight of the Gaussian State.tag (
str
, optional) – Unique tag of the Gaussian State.
 BIRTH = 'birth'
Tag value used to signify birth component
 covar: CovarianceMatrix
Covariance matrix of state.
 classmethod from_gaussian_state(gaussian_state, *args, copy=True, **kwargs)
Returns a WeightedGaussianState instance based on the gaussian_state.
 Parameters:
gaussian_state (
GaussianState
) – The guassian_state used to create the new WeightedGaussianState.*args (See main
WeightedGaussianState
) – args are passed toWeightedGaussianState
__init__()copy (Boolean, optional) – If True, the WeightedGaussianState is created with copies of the elements of gaussian_state. The default is True.
**kwargs (See main
WeightedGaussianState
) – kwargs are passed toWeightedGaussianState
__init__()
 Returns:
Instance of WeightedGaussianState.
 Return type:
 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 overwriting of properties.
 Parameters:
state (State) –
State
to use existing properties from, and identify new statetype 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 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.
 property gaussian_state
The Gaussian state.
Note
This will be cached until
state_vector
orcovar
are replaced.
 property mean
The state mean, equivalent to state vector
 property ndim
The number of dimensions represented by the state.
 state_vector: StateVector
State vector.
 weight: Probability
Weight of the Gaussian State.
 class stonesoup.types.state.ASDWeightedGaussianState(multi_state_vector: StateVector, timestamps: Sequence[datetime], max_nstep: int, multi_covar: CovarianceMatrix, correlation_matrices: MutableSequence[MutableMapping[str, ndarray]] = None, weight: Probability = 0)[source]
Bases:
ASDGaussianState
ASD Weighted Gaussian State Type
ASD Gaussian State object with an associated weight. Used as components for a GaussianMixtureState.
 Parameters:
multi_state_vector (
StateVector
) – State vector of all timestampstimestamps (
Sequence[datetime.datetime]
) – List of all timestamps which have a state in the ASDStatemax_nstep (
int
) – Decides when the state is pruned in a prediction step. If 0 then there is no pruningmulti_covar (
CovarianceMatrix
) – Covariance of all timestepscorrelation_matrices (
MutableSequence[MutableMapping[str, numpy.ndarray]]
, optional) – Sequence of Correlation Matrices, consisting of \(P_{ll}\), \(P_{ll+1}\) and \(F_{l+1l}\) built in the Kalman predictor and Kalman updater, aligned totimestamps
weight (
Probability
, optional) – Weight of the Gaussian State.
 weight: Probability
Weight of the Gaussian State.
 correlation_matrices: MutableSequence[MutableMapping[str, ndarray]]
Sequence of Correlation Matrices, consisting of \(P_{ll}\), \(P_{ll+1}\) and \(F_{l+1l}\) built in the Kalman predictor and Kalman updater, aligned to
timestamps
 max_nstep: int
Decides when the state is pruned in a prediction step. If 0 then there is no pruning
 property mean
The state mean, equivalent to state vector
 multi_covar: CovarianceMatrix
Covariance of all timesteps
 multi_state_vector: StateVector
State vector of all timestamps
 property ndim
Dimension of one State
 property nstep
Number of timesteps which are in the ASDState
 property state
A
GaussianState
object representing latest timestampNote
This will be cached until
multi_state_vector
,multi_covar
ortimestamps
are replaced.
 property state_vector
The State vector of the newest timestamp
 property states
Note
This will be cached until
multi_state_vector
,multi_covar
ortimestamps
are replaced.
 property timestamp
The newest timestamp
 class stonesoup.types.state.ParticleState(state_vector: StateVectors, timestamp: datetime = None, weight: MutableSequence[Probability] = None, log_weight: ndarray = None, parent: ParticleState = None, particle_list: MutableSequence[Particle] = None, fixed_covar: CovarianceMatrix = None)[source]
Bases:
State
Particle State type
This is a particle state object which describes the state as a distribution of particles
 Parameters:
state_vector (
StateVectors
) – State vectors.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
MutableSequence[Probability]
, optional) – Weights of particleslog_weight (
numpy.ndarray
, optional) – Log weights of particlesparent (
ParticleState
, optional) – Parent particlesparticle_list (
MutableSequence[Particle]
, optional) – List of Particle objectsfixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.
 particle_list: MutableSequence[Particle]
List of Particle objects
 fixed_covar: CovarianceMatrix
Fixed covariance value. Default None, whereweighted sample covariance is then used.
 parent: ParticleState
Parent particles
 state_vector: StateVectors
State vectors.
 classmethod from_state(state: State, *args: Any, target_type: Type  None = None, **kwargs: Any) State [source]
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 overwriting of properties.
 Parameters:
state (State) –
State
to use existing properties from, and identify new statetype 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 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.
 property particles
Sequence of individual
Particle
objects.Note
This will be cached until
state_vector
orlog_weight
are replaced.
 property ndim
The number of dimensions represented by the state.
 property mean
Sample mean for particles
Note
This will be cached until
state_vector
orlog_weight
are replaced.
 property covar
Sample covariance matrix for particles
Note
This will be cached until
state_vector
,log_weight
orfixed_covar
are replaced.
 weight: MutableSequence[Probability]
Weights of particles
 class stonesoup.types.state.MultiModelParticleState(state_vector: StateVectors, timestamp: datetime = None, weight: MutableSequence[Probability] = None, log_weight: ndarray = None, parent: ParticleState = None, particle_list: MutableSequence[Particle] = None, fixed_covar: CovarianceMatrix = None, dynamic_model: ndarray = None)[source]
Bases:
ParticleState
MultiModel Particle State type
This is a particle state object which describes the state as a distribution of particles, where each particle has an associated dynamics model
 Parameters:
state_vector (
StateVectors
) – State vectors.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
MutableSequence[Probability]
, optional) – Weights of particleslog_weight (
numpy.ndarray
, optional) – Log weights of particlesparent (
ParticleState
, optional) – Parent particlesparticle_list (
MutableSequence[Particle]
, optional) – List of Particle objectsfixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.dynamic_model (
numpy.ndarray
, optional) – Array of indices that identify which model is associated with each particle.
 dynamic_model: ndarray
Array of indices that identify which model is associated with each particle.
 property covar
Sample covariance matrix for particles
Note
This will be cached until
state_vector
,log_weight
orfixed_covar
are replaced.
 fixed_covar: CovarianceMatrix
Fixed covariance value. Default None, whereweighted sample covariance is then used.
 classmethod 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 overwriting of properties.
 Parameters:
state (State) –
State
to use existing properties from, and identify new statetype 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 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.
 property mean
Sample mean for particles
Note
This will be cached until
state_vector
orlog_weight
are replaced.
 property ndim
The number of dimensions represented by the state.
 parent: ParticleState
Parent particles
 particle_list: MutableSequence[Particle]
List of Particle objects
 property particles
Sequence of individual
Particle
objects.Note
This will be cached until
state_vector
orlog_weight
are replaced.
 state_vector: StateVectors
State vectors.
 weight: MutableSequence[Probability]
Weights of particles
 class stonesoup.types.state.RaoBlackwellisedParticleState(state_vector: StateVectors, timestamp: datetime = None, weight: MutableSequence[Probability] = None, log_weight: ndarray = None, parent: ParticleState = None, particle_list: MutableSequence[Particle] = None, fixed_covar: CovarianceMatrix = None, model_probabilities: ndarray = None)[source]
Bases:
ParticleState
 Parameters:
state_vector (
StateVectors
) – State vectors.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
MutableSequence[Probability]
, optional) – Weights of particleslog_weight (
numpy.ndarray
, optional) – Log weights of particlesparent (
ParticleState
, optional) – Parent particlesparticle_list (
MutableSequence[Particle]
, optional) – List of Particle objectsfixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.model_probabilities (
numpy.ndarray
, optional) – 2d NumPy array containing probability of particle belong to particular model. Shape (nmodels, mparticles).
 model_probabilities: ndarray
2d NumPy array containing probability of particle belong to particular model. Shape (nmodels, mparticles).
 property covar
Sample covariance matrix for particles
Note
This will be cached until
state_vector
,log_weight
orfixed_covar
are replaced.
 fixed_covar: CovarianceMatrix
Fixed covariance value. Default None, whereweighted sample covariance is then used.
 classmethod 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 overwriting of properties.
 Parameters:
state (State) –
State
to use existing properties from, and identify new statetype 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 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.
 property mean
Sample mean for particles
Note
This will be cached until
state_vector
orlog_weight
are replaced.
 property ndim
The number of dimensions represented by the state.
 parent: ParticleState
Parent particles
 particle_list: MutableSequence[Particle]
List of Particle objects
 property particles
Sequence of individual
Particle
objects.Note
This will be cached until
state_vector
orlog_weight
are replaced.
 state_vector: StateVectors
State vectors.
 weight: MutableSequence[Probability]
Weights of particles
 class stonesoup.types.state.BernoulliParticleState(state_vector: StateVectors, timestamp: datetime = None, weight: MutableSequence[Probability] = None, log_weight: ndarray = None, parent: ParticleState = None, particle_list: MutableSequence[Particle] = None, fixed_covar: CovarianceMatrix = None, existence_probability: Probability = None)[source]
Bases:
ParticleState
Bernoulli Particle State type
This is a particle state object that describes the target as a distribution of particles and an estimated existence probability according to the Bernoulli particle filter [1].
References
 Parameters:
state_vector (
StateVectors
) – State vectors.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
MutableSequence[Probability]
, optional) – Weights of particleslog_weight (
numpy.ndarray
, optional) – Log weights of particlesparent (
ParticleState
, optional) – Parent particlesparticle_list (
MutableSequence[Particle]
, optional) – List of Particle objectsfixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.existence_probability (
Probability
, optional) – Target existence probability estimate
 existence_probability: Probability
Target existence probability estimate
 property covar
Sample covariance matrix for particles
Note
This will be cached until
state_vector
,log_weight
orfixed_covar
are replaced.
 fixed_covar: CovarianceMatrix
Fixed covariance value. Default None, whereweighted sample covariance is then used.
 classmethod 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 overwriting of properties.
 Parameters:
state (State) –
State
to use existing properties from, and identify new statetype 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 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.
 property mean
Sample mean for particles
Note
This will be cached until
state_vector
orlog_weight
are replaced.
 property ndim
The number of dimensions represented by the state.
 parent: ParticleState
Parent particles
 particle_list: MutableSequence[Particle]
List of Particle objects
 property particles
Sequence of individual
Particle
objects.Note
This will be cached until
state_vector
orlog_weight
are replaced.
 state_vector: StateVectors
State vectors.
 weight: MutableSequence[Probability]
Weights of particles
 class stonesoup.types.state.EnsembleState(state_vector: StateVectors, timestamp: datetime = None)[source]
Bases:
State
Ensemble State type
This is an Ensemble state object which describes the system state as a ensemble of state vectors for use in Ensemble based filters.
This approach is functionally identical to the Particle state type except it doesn’t use any weighting for any of the “particles” or ensemble members. All “particles” or state vectors in the ensemble are equally weighted.
\[\mathbf{X} = [x_1, x_2, ..., x_M]\] Parameters:
state_vector (
StateVectors
) – An ensemble of state vectors which represent the statetimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.
 state_vector: StateVectors
An ensemble of state vectors which represent the state
 classmethod from_gaussian_state(gaussian_state, num_vectors, **kwargs)[source]
Returns an EnsembleState instance, from a given GaussianState object.
 Parameters:
gaussian_state (
GaussianState
) – The GaussianState used to create the new EnsembleState.num_vectors (int) – The number of desired column vectors present in the ensemble.
 Returns:
Instance of EnsembleState.
 Return type:
 static generate_ensemble(mean, covar, num_vectors)[source]
Returns a StateVectors wrapped ensemble of state vectors, from a given mean and covariance matrix.
 Parameters:
 Returns:
Instance of EnsembleState.
 Return type:
 property num_vectors
Number of columns in state ensemble
 property mean
The state mean, numerically equivalent to state vector
Note
This will be cached until
state_vector
is replaced.
 property covar
Sample covariance matrix for ensemble
Note
This will be cached until
state_vector
is replaced.
 property sqrt_covar
 sqrt of sample covariance matrix for ensemble, useful for
some EnKF algorithms
Note
This will be cached until
state_vector
is replaced.
 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 overwriting of properties.
 Parameters:
state (State) –
State
to use existing properties from, and identify new statetype 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 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.
 property ndim
The number of dimensions represented by the state.
 class stonesoup.types.state.CategoricalState(state_vector: StateVector, timestamp: datetime = None, categories: Sequence[float] = None)[source]
Bases:
State
CategoricalState type.
State object representing an object in a categorical state space. A state vector \(\mathbf{\alpha}_t^i = P(\phi_t^i)\) defines a categorical distribution over a finite set of discrete categories \(\Phi = \{\phi^mm\in \mathbf{N}, m\le M\}\) for some finite \(M\).
 Parameters:
state_vector (
StateVector
) – State vector.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.categories (
Sequence[float]
, optional) – Category names. Defaults to a list of integers.
 state_vector: StateVector
State vector.
 property category
Return the name of the most likely category.
 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 overwriting of properties.
 Parameters:
state (State) –
State
to use existing properties from, and identify new statetype 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 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.
 property ndim
The number of dimensions represented by the state.
 class stonesoup.types.state.CompositeState(sub_states: Sequence[State], default_timestamp: datetime = None)[source]
Bases:
Type
Composite state type.
A composition of ordered substates (
State
) existing at the same timestamp, representing an object with a state for (potentially) multiple, distinct state spaces. Parameters:
sub_states (
Sequence[State]
) – Sequence of substates comprising the composite state. All substates must have matching timestamp. Must not be empty.default_timestamp (
datetime.datetime
, optional) – Default timestamp if no substates exist to attain timestamp from. Defaults to None, whereby substates will be required to have timestamps.
 sub_states: Sequence[State]
Sequence of substates comprising the composite state. All substates must have matching timestamp. Must not be empty.
 default_timestamp: datetime
Default timestamp if no substates exist to attain timestamp from. Defaults to None, whereby substates will be required to have timestamps.
 property state_vector
A combination of the component states’ state vectors.
OrbitalState Types
Time Types
 class stonesoup.types.time.TimeRange(start: datetime, end: datetime)[source]
Bases:
Interval
TimeRange type
An object representing a time range between two timestamps.
Can be used to check if timestamp is within via in operator
Example
>>> t0 = datetime.datetime(2018, 1, 1, 14, 00) >>> t1 = datetime.datetime(2018, 1, 1, 15, 00) >>> time_range = TimeRange(t0, t1) >>> test_time = datetime.datetime(2018, 1, 1, 14, 30) >>> print(test_time in time_range) True
 Parameters:
start (
datetime.datetime
) – Start of the time rangeend (
datetime.datetime
) – End of the time range
 class stonesoup.types.time.CompoundTimeRange(intervals: MutableSequence[Interval] = None)[source]
Bases:
Intervals
CompoundTimeRange type
A container class representing one or more
TimeRange
objects together Parameters:
intervals (
MutableSequence[Interval]
, optional) – Container ofInterval
 add(time_range)[source]
Add a
TimeRange
orCompoundTimeRange
object to time_ranges.
Track Types
 class stonesoup.types.track.Track(states: MutableSequence[State] = None, id: str = None, init_metadata: MutableMapping = {})[source]
Bases:
StateMutableSequence
Track type
A
StateMutableSequence
representing a track. Notes:
Any manual modifications to
metadata
ormetadatas
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 ofstates
will result in ametadatas
update that will update all subsequent metadata values, resulting in manual metadata modifications being lost.
 Parameters:
states (
MutableSequence[State]
, optional) – The initial states of the track. Default None which initialises with empty list.id (
str
, optional) – The unique track IDinit_metadata (
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.
 property metadata
Current metadata dictionary of track. If track contains no states, this is the initial metadata dictionary
init_metadata
.
Update Types
 class stonesoup.types.update.Update(hypothesis: Hypothesis)[source]
Bases:
Type
,CreatableFromState
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
 class stonesoup.types.update.StateUpdate(state_vector: StateVector, hypothesis: Hypothesis, timestamp: datetime = None)[source]

StateUpdate type
Most simple state update type, where everything only has time and a state vector. Requires a prior state that was updated, and the hypothesis used to update the prior.
 Parameters:
state_vector (
StateVector
) – State vector.hypothesis (
Hypothesis
) – Hypothesis used for updatingtimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.
 class stonesoup.types.update.GaussianStateUpdate(state_vector: StateVector, covar: CovarianceMatrix, hypothesis: Hypothesis, timestamp: datetime = None)[source]
Bases:
Update
,GaussianState
GaussianStateUpdate type
This is a simple Gaussian state update object, which, as the name suggests, is described by a Gaussian distribution.
 Parameters:
state_vector (
StateVector
) – State vector.covar (
CovarianceMatrix
) – Covariance matrix of state.hypothesis (
Hypothesis
) – Hypothesis used for updatingtimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.
 class stonesoup.types.update.SqrtGaussianStateUpdate(state_vector: StateVector, sqrt_covar: CovarianceMatrix, hypothesis: Hypothesis, timestamp: datetime = None)[source]
Bases:
Update
,SqrtGaussianState
SqrtGaussianStateUpdate type
This is equivalent to a Gaussian state update object, but with the covariance of the Gaussian distribution stored in matrix square root form.
 Parameters:
state_vector (
StateVector
) – State vector.sqrt_covar (
CovarianceMatrix
) – A square root form of the Gaussian covariance matrix.hypothesis (
Hypothesis
) – Hypothesis used for updatingtimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.
 class stonesoup.types.update.WeightedGaussianStateUpdate(state_vector: StateVector, covar: CovarianceMatrix, hypothesis: Hypothesis, timestamp: datetime = None, weight: Probability = 0)[source]
Bases:
Update
,WeightedGaussianState
WeightedGaussianStateUpdate type
This is a simple Gaussian state update object, which, as the name suggests, is described by a Gaussian distribution with an associated weight.
 Parameters:
state_vector (
StateVector
) – State vector.covar (
CovarianceMatrix
) – Covariance matrix of state.hypothesis (
Hypothesis
) – Hypothesis used for updatingtimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
Probability
, optional) – Weight of the Gaussian State.
 class stonesoup.types.update.TaggedWeightedGaussianStateUpdate(state_vector: StateVector, covar: CovarianceMatrix, hypothesis: Hypothesis, timestamp: datetime = None, weight: Probability = 0, tag: str = None)[source]
Bases:
Update
,TaggedWeightedGaussianState
TaggedWeightedGaussianStateUpdate type
This is a simple Gaussian state update object, which, as the name suggests, is described by a Gaussian distribution, with an associated weight and unique tag.
 Parameters:
state_vector (
StateVector
) – State vector.covar (
CovarianceMatrix
) – Covariance matrix of state.hypothesis (
Hypothesis
) – Hypothesis used for updatingtimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
Probability
, optional) – Weight of the Gaussian State.tag (
str
, optional) – Unique tag of the Gaussian State.
 class stonesoup.types.update.GaussianMixtureUpdate(hypothesis: Hypothesis, components: MutableSequence[WeightedGaussianState] = None)[source]
Bases:
Update
,GaussianMixture
GaussianMixtureUpdate type
This is a Gaussian mixture update object, which, as the name suggests, is described by a Gaussian mixture.
 Parameters:
hypothesis (
Hypothesis
) – Hypothesis used for updatingcomponents (
MutableSequence[WeightedGaussianState]
, optional) – The initial list of
WeightedGaussianState
components. Default None which initialises with empty list.
 The initial list of
 class stonesoup.types.update.ASDGaussianStateUpdate(multi_state_vector: StateVector, timestamps: Sequence[datetime], max_nstep: int, multi_covar: CovarianceMatrix, hypothesis: Hypothesis, correlation_matrices: MutableSequence[MutableMapping[str, ndarray]] = None)[source]
Bases:
Update
,ASDGaussianState
ASDGaussianStateUpdate type
This is a simple ASD Gaussian state update object, which, as the name suggests, is described by a Gaussian distribution.
 Parameters:
multi_state_vector (
StateVector
) – State vector of all timestampstimestamps (
Sequence[datetime.datetime]
) – List of all timestamps which have a state in the ASDStatemax_nstep (
int
) – Decides when the state is pruned in a prediction step. If 0 then there is no pruningmulti_covar (
CovarianceMatrix
) – Covariance of all timestepshypothesis (
Hypothesis
) – Hypothesis used for updatingcorrelation_matrices (
MutableSequence[MutableMapping[str, numpy.ndarray]]
, optional) – Sequence of Correlation Matrices, consisting of \(P_{ll}\), \(P_{ll+1}\) and \(F_{l+1l}\) built in the Kalman predictor and Kalman updater, aligned totimestamps
 class stonesoup.types.update.ParticleStateUpdate(state_vector: StateVectors, hypothesis: Hypothesis, timestamp: datetime = None, weight: MutableSequence[Probability] = None, log_weight: ndarray = None, parent: ParticleState = None, particle_list: MutableSequence[Particle] = None, fixed_covar: CovarianceMatrix = None)[source]
Bases:
Update
,ParticleState
ParticleStateUpdate type
This is a simple Particle state update object.
 Parameters:
state_vector (
StateVectors
) – State vectors.hypothesis (
Hypothesis
) – Hypothesis used for updatingtimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
MutableSequence[Probability]
, optional) – Weights of particleslog_weight (
numpy.ndarray
, optional) – Log weights of particlesparent (
ParticleState
, optional) – Parent particlesparticle_list (
MutableSequence[Particle]
, optional) – List of Particle objectsfixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.
 class stonesoup.types.update.MultiModelParticleStateUpdate(state_vector: StateVectors, hypothesis: Hypothesis, timestamp: datetime = None, weight: MutableSequence[Probability] = None, log_weight: ndarray = None, parent: ParticleState = None, particle_list: MutableSequence[Particle] = None, fixed_covar: CovarianceMatrix = None, dynamic_model: ndarray = None)[source]
Bases:
Update
,MultiModelParticleState
MultiModelStateUpdate type
This is a simple MultiModel Particle state update object.
 Parameters:
state_vector (
StateVectors
) – State vectors.hypothesis (
Hypothesis
) – Hypothesis used for updatingtimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
MutableSequence[Probability]
, optional) – Weights of particleslog_weight (
numpy.ndarray
, optional) – Log weights of particlesparent (
ParticleState
, optional) – Parent particlesparticle_list (
MutableSequence[Particle]
, optional) – List of Particle objectsfixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.dynamic_model (
numpy.ndarray
, optional) – Array of indices that identify which model is associated with each particle.
 class stonesoup.types.update.RaoBlackwellisedParticleStateUpdate(state_vector: StateVectors, hypothesis: Hypothesis, timestamp: datetime = None, weight: MutableSequence[Probability] = None, log_weight: ndarray = None, parent: ParticleState = None, particle_list: MutableSequence[Particle] = None, fixed_covar: CovarianceMatrix = None, model_probabilities: ndarray = None)[source]
Bases:
Update
,RaoBlackwellisedParticleState
RaoBlackwellisedStateUpdate type
This is a simple Rao Blackwellised Particle state update object.
 Parameters:
state_vector (
StateVectors
) – State vectors.hypothesis (
Hypothesis
) – Hypothesis used for updatingtimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
MutableSequence[Probability]
, optional) – Weights of particleslog_weight (
numpy.ndarray
, optional) – Log weights of particlesparent (
ParticleState
, optional) – Parent particlesparticle_list (
MutableSequence[Particle]
, optional) – List of Particle objectsfixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.model_probabilities (
numpy.ndarray
, optional) – 2d NumPy array containing probability of particle belong to particular model. Shape (nmodels, mparticles).
 class stonesoup.types.update.BernoulliParticleStateUpdate(state_vector: StateVectors, hypothesis: Hypothesis, timestamp: datetime = None, weight: MutableSequence[Probability] = None, log_weight: ndarray = None, parent: ParticleState = None, particle_list: MutableSequence[Particle] = None, fixed_covar: CovarianceMatrix = None, existence_probability: Probability = None)[source]
Bases:
Update
,BernoulliParticleState
BernoulliStateUpdate type
This is a simple Bernoulli Particle state update object.
 Parameters:
state_vector (
StateVectors
) – State vectors.hypothesis (
Hypothesis
) – Hypothesis used for updatingtimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.weight (
MutableSequence[Probability]
, optional) – Weights of particleslog_weight (
numpy.ndarray
, optional) – Log weights of particlesparent (
ParticleState
, optional) – Parent particlesparticle_list (
MutableSequence[Particle]
, optional) – List of Particle objectsfixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.existence_probability (
Probability
, optional) – Target existence probability estimate
 class stonesoup.types.update.EnsembleStateUpdate(state_vector: StateVectors, hypothesis: Hypothesis, timestamp: datetime = None)[source]
Bases:
Update
,EnsembleState
EnsembleStateUpdate type
This is a simple Ensemble state update object.
 Parameters:
state_vector (
StateVectors
) – An ensemble of state vectors which represent the statehypothesis (
Hypothesis
) – Hypothesis used for updatingtimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.
 class stonesoup.types.update.InformationStateUpdate(state_vector: StateVector, precision: PrecisionMatrix, hypothesis: Hypothesis, timestamp: datetime = None)[source]
Bases:
Update
,InformationState
InformationUpdate type
This is a simple Information state update object, which, as the name suggests, is described by a precision matrix and its corresponding state vector.
 Parameters:
state_vector (
StateVector
) – State vector.precision (
PrecisionMatrix
) – precision matrix of state.hypothesis (
Hypothesis
) – Hypothesis used for updatingtimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.
 class stonesoup.types.update.CategoricalStateUpdate(state_vector: StateVector, hypothesis: Hypothesis, timestamp: datetime = None, categories: Sequence[float] = None)[source]
Bases:
Update
,CategoricalState
Categorical state prediction type
 Parameters:
state_vector (
StateVector
) – State vector.hypothesis (
Hypothesis
) – Hypothesis used for updatingtimestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.categories (
Sequence[float]
, optional) – Category names. Defaults to a list of integers.
 class stonesoup.types.update.CompositeUpdate(sub_states: Sequence[Update], hypothesis: CompositeHypothesis, default_timestamp: datetime = None)[source]
Bases:
Update
,CompositeState
Composite update type
Composition of
Update
. Parameters:
sub_states (
Sequence[Update]
) – Sequence of subupdates comprising the composite update. All subupdates must have matching timestamp. Must not be empty.hypothesis (
CompositeHypothesis
) – Hypothesis used for updatingdefault_timestamp (
datetime.datetime
, optional) – Default timestamp if no substates exist to attain timestamp from. Defaults to None, whereby substates will be required to have timestamps.
 sub_states: Sequence[Update]
Sequence of subupdates comprising the composite update. All subupdates must have matching timestamp. Must not be empty.
 hypothesis: CompositeHypothesis
Hypothesis used for updating