Data Types
Base Types
- class stonesoup.types.base.Type[source]
Bases:
stonesoup.base.Base
Base type
Array Types
- class stonesoup.types.array.Matrix(*args, **kwargs)[source]
Bases:
numpy.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:
stonesoup.types.array.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 row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’.
- Returns
y – A copy of the input array, flattened to one dimension.
- Return type
ndarray
See also
ravel
Return a flattened array.
flat
A 1-D flat iterator over the array.
Examples
>>> 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:
stonesoup.types.array.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:
stonesoup.types.array.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:
stonesoup.types.array.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:
numbers.Real
Angle class.
Angle handles modulo arithmetic for adding and subtracting angles
- class stonesoup.types.angle.Bearing(value)[source]
Bases:
stonesoup.types.angle.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:
stonesoup.types.angle.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.Longitude(value)[source]
Bases:
stonesoup.types.angle.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:
stonesoup.types.angle.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:
stonesoup.types.angle.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:
stonesoup.types.angle.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:
stonesoup.types.base.Type
Association type
An association between objects
- Parameters
objects (
Set
) – Set of objects being associated
- objects: Set
Set of objects being associated
- class stonesoup.types.association.AssociationPair(objects: Set)[source]
Bases:
stonesoup.types.association.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.datetime = None)[source]
Bases:
stonesoup.types.association.Association
SingleTimeAssociation type
An
Association
representing the linking of objects at a single time- Parameters
objects (
Set
) – Set of objects being associatedtimestamp (
datetime.datetime
, optional) – Timestamp of the association. Default is None.
- timestamp: datetime.datetime
Timestamp of the association. Default is None.
- class stonesoup.types.association.TimeRangeAssociation(objects: Set, time_range: TimeRange = None)[source]
Bases:
stonesoup.types.association.Association
TimeRangeAssociation type
An
AssociationPair
representing the linking of objects over a range of times- Parameters
objects (
Set
) – Set of objects being associatedtime_range (
TimeRange
, optional) – Range of times that association exists over. Default is None
- time_range: stonesoup.types.time.TimeRange
Range of times that association exists over. Default is None
- class stonesoup.types.association.AssociationSet(associations: Set[Association] = None)[source]
Bases:
stonesoup.types.base.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[stonesoup.types.association.Association]
Set of independent associations
- 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
set of
Association
- 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) – Set of objects to look for in associations
- Returns
A set of objects which have been associated
- Return type
set of
Association
Detection Types
- class stonesoup.types.detection.Detection(state_vector: StateVector, timestamp: datetime.datetime = None, measurement_model: MeasurementModel = None, metadata: MutableMapping = None)[source]
Bases:
stonesoup.types.state.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: stonesoup.models.measurement.base.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.datetime = None, measurement_model: MeasurementModel = None, metadata: MutableMapping = None)[source]
Bases:
stonesoup.types.detection.Detection
,stonesoup.types.state.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.datetime = None, measurement_model: MeasurementModel = None, metadata: MutableMapping = None)[source]
Bases:
stonesoup.types.detection.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.datetime = None, measurement_model: MeasurementModel = None, metadata: MutableMapping = None)[source]
Bases:
stonesoup.types.detection.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: stonesoup.types.groundtruth.GroundTruthPath
Ground truth path that this detection came from
- class stonesoup.types.detection.MissedDetection(state_vector: StateVector = None, timestamp: datetime.datetime = None, measurement_model: MeasurementModel = None, metadata: MutableMapping = None)[source]
Bases:
stonesoup.types.detection.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: stonesoup.types.array.StateVector
State vector. Default None.
- class stonesoup.types.detection.CategoricalDetection(state_vector: StateVector, timestamp: datetime.datetime = None, categories: Sequence[float] = None, measurement_model: MeasurementModel = None, metadata: MutableMapping = None)[source]
Bases:
stonesoup.types.detection.Detection
,stonesoup.types.state.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.datetime = None, categories: Sequence[float] = None, measurement_model: MeasurementModel = None, metadata: MutableMapping = None)[source]
Bases:
stonesoup.types.detection.TrueDetection
,stonesoup.types.detection.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.datetime = None, groundtruth_path: GroundTruthPath = None, mapping: Sequence[int] = None)[source]
Bases:
stonesoup.types.state.CompositeState
Composite detection type
Composition of
Detection
.- Parameters
sub_states (
Sequence[Detection]
) – Sequence of sub-detections comprising the composite detection. All sub-detections must have matching timestamp. Must not be empty.default_timestamp (
datetime.datetime
, optional) – Default timestamp if no sub-states exist to attain timestamp from. Defaults to None, whereby sub-states 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 sub-detections map to sub-state spaces in order.
- sub_states: Sequence[stonesoup.types.detection.Detection]
Sequence of sub-detections comprising the composite detection. All sub-detections must have matching timestamp. Must not be empty.
- groundtruth_path: stonesoup.types.groundtruth.GroundTruthPath
Ground truth path that this detection came from.
- mapping: Sequence[int]
Mapping of detections to composite state space. Defaults to None, where sub-detections map to sub-state spaces in order.
- property metadata
Combined metadata of all sub-detections.
Ground Truth Types
- class stonesoup.types.groundtruth.GroundTruthState(state_vector: StateVector, timestamp: datetime.datetime = None, metadata: MutableMapping = None)[source]
Bases:
stonesoup.types.state.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.datetime = None, categories: Sequence[float] = None, metadata: MutableMapping = None)[source]
Bases:
stonesoup.types.groundtruth.GroundTruthState
,stonesoup.types.state.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:
stonesoup.types.state.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[stonesoup.types.groundtruth.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.datetime = None)[source]
Bases:
stonesoup.types.state.CompositeState
Composite ground truth state type.
A composition of ordered sub-states (
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 sub-states comprising the composite state. All sub-states must have matching timestamp and metadata attributes. Must not be empty.default_timestamp (
datetime.datetime
, optional) – Default timestamp if no sub-states exist to attain timestamp from. Defaults to None, whereby sub-states will be required to have timestamps.
- sub_states: Sequence[stonesoup.types.groundtruth.GroundTruthState]
Sequence of sub-states comprising the composite state. All sub-states must have matching timestamp and metadata attributes. Must not be empty.
- property metadata
Combined metadata of all sub-detections.
Hypothesis Types
- class stonesoup.types.hypothesis.Hypothesis[source]
Bases:
stonesoup.types.base.Type
Hypothesis base type
A Hypothesis has sub-types:
‘SingleHypothesis’, which consists of a prediction for a single Track and a single Measurement that might be associated with it
‘MultipleHypothesis’, which consists of a prediction for a single Track and multiple Measurements of which one might be associated with it
- class stonesoup.types.hypothesis.ProbabilityHypothesis(probability: Probability)[source]
Bases:
stonesoup.types.hypothesis.Hypothesis
- Parameters
probability (
Probability
) – Probability that detection is true location of prediction
- probability: stonesoup.types.numeric.Probability
Probability that detection is true location of prediction
- class stonesoup.types.hypothesis.SingleHypothesis(prediction: Prediction, measurement: Detection, measurement_prediction: MeasurementPrediction = None)[source]
Bases:
stonesoup.types.hypothesis.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: stonesoup.types.prediction.Prediction
Predicted track state
- measurement: stonesoup.types.detection.Detection
Detection used for hypothesis and updating
- measurement_prediction: stonesoup.types.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:
stonesoup.types.hypothesis.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:
stonesoup.types.hypothesis.ProbabilityHypothesis
,stonesoup.types.hypothesis.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]
Bases:
stonesoup.types.base.Type
,collections.UserDict
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: stonesoup.types.hypothesis.Hypothesis
Association hypotheses
- class stonesoup.types.hypothesis.ProbabilityJointHypothesis(hypotheses)[source]
Bases:
stonesoup.types.hypothesis.ProbabilityHypothesis
,stonesoup.types.hypothesis.JointHypothesis
Probability-scored 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 multiple-hypotheses’ probabilities.
- probability: stonesoup.types.numeric.Probability
Probability that detection is true location of prediction. Defaults to None, whereby the probability is calculated as being the product of the constituent multiple-hypotheses’ probabilities.
- class stonesoup.types.hypothesis.DistanceJointHypothesis(hypotheses)[source]
Bases:
stonesoup.types.hypothesis.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:
stonesoup.types.hypothesis.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 sub-hypotheses comprising the composite hypothesis. Must not be empty.measurement_prediction (
CompositeMeasurementPrediction
, optional) – Optional track prediction in measurement space
- sub_hypotheses: Sequence[stonesoup.types.hypothesis.SingleHypothesis]
Sequence of sub-hypotheses comprising the composite hypothesis. Must not be empty.
- prediction: stonesoup.types.prediction.CompositePrediction
Predicted track state
- measurement: stonesoup.types.detection.CompositeDetection
Detection used for hypothesis and updating
- measurement_prediction: stonesoup.types.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:
stonesoup.types.hypothesis.CompositeHypothesis
,stonesoup.types.hypothesis.SingleProbabilityHypothesis
Composite probability hypothesis type
A composition of
SingleProbabilityHypothesis
.Calculate hypothesis probability via product of sub-hypotheses’ probabilities. Probability is 1 if there are no sub-hypotheses.
- 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 sub-hypotheses’ probabilitiesmeasurement_prediction (
CompositeMeasurementPrediction
, optional) – Optional track prediction in measurement spacesub_hypotheses (
Sequence[SingleProbabilityHypothesis]
, optional) – Sequence of probability-scored sub-hypotheses comprising the composite hypothesis.
- sub_hypotheses: Sequence[stonesoup.types.hypothesis.SingleProbabilityHypothesis]
Sequence of probability-scored sub-hypotheses comprising the composite hypothesis.
- probability: stonesoup.types.numeric.Probability
Probability that detection is true location of prediction. Default is None, whereby probability is calculated as the product of sub-hypotheses’ probabilities
- class stonesoup.types.multihypothesis.MultipleHypothesis(single_hypotheses: Sequence[SingleHypothesis] = None, normalise: bool = False, total_weight: float = 1)[source]
Bases:
stonesoup.types.base.Type
,collections.abc.Sized
,collections.abc.Iterable
,collections.abc.Container
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[stonesoup.types.hypothesis.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]
Bases:
stonesoup.types.base.Type
,collections.abc.Sized
,collections.abc.Iterable
,collections.abc.Container
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[stonesoup.types.hypothesis.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(left: Union[int, float], right: Union[int, float])[source]
Bases:
stonesoup.types.base.Type
Closed continuous interval class.
Represents a continuous, closed interval of real numbers. Represented by a lower and upper bound.
- Parameters
left (
Union[int, float]
) – Lower bound of intervalright (
Union[int, float]
) – Upper bound of interval
- class stonesoup.types.interval.Intervals(intervals: MutableSequence[Interval] = None)[source]
Bases:
stonesoup.types.base.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[stonesoup.types.interval.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:
stonesoup.types.base.Type
Metric type
- Parameters
title (
str
) – Name of the metricvalue (
Any
) – Value of the metricgenerator (
Any
) – Generator used to create the metric
- value: Any
Value of the metric
- generator: Any
Generator used to create the metric
- class stonesoup.types.metric.PlottingMetric(title: str, value: Any, generator: Any)[source]
Bases:
stonesoup.types.metric.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.datetime = None)[source]
Bases:
stonesoup.types.metric.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.
- timestamp: datetime.datetime
Timestamp of the state. Default None.
- class stonesoup.types.metric.TimeRangeMetric(title: str, value: Any, generator: Any, time_range: TimeRange = None)[source]
Bases:
stonesoup.types.metric.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
- time_range: stonesoup.types.time.TimeRange
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:
stonesoup.types.metric.TimeRangeMetric
,stonesoup.types.metric.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.datetime = None)[source]
Bases:
stonesoup.types.metric.SingleTimeMetric
,stonesoup.types.metric.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:
stonesoup.types.base.Type
,collections.abc.Sized
,collections.abc.Iterable
,collections.abc.Container
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[stonesoup.types.state.WeightedGaussianState]
The initial list of
WeightedGaussianState
components. Default None which initialises with empty list.
Numeric Types
- class stonesoup.types.numeric.Probability(value, *, log_value=False)[source]
Bases:
numbers.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.
Particle Types
- class stonesoup.types.particle.Particle(state_vector: StateVector, weight: float, parent: Particle = None)[source]
Bases:
stonesoup.types.base.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: stonesoup.types.array.StateVector
State vector
- parent: stonesoup.types.particle.Particle
Parent particle
- class stonesoup.types.particle.Particles(state_vector: StateVectors = None, weight: MutableSequence[Probability] = None, parent: Particles = None, particle_list: MutableSequence[Particle] = None)[source]
Bases:
stonesoup.types.base.Type
Particle type
A collection of particles. Contains a state and weight for each particle
- Parameters
state_vector (
StateVectors
, optional) – State vectors of particlesweight (
MutableSequence[Probability]
, optional) – Weights of particlesparent (
Particles
, optional) – Parent particlesparticle_list (
MutableSequence[Particle]
, optional) – List of Particle objects
- state_vector: stonesoup.types.array.StateVectors
State vectors of particles
- weight: MutableSequence[stonesoup.types.numeric.Probability]
Weights of particles
- parent: stonesoup.types.particle.Particles
Parent particles
- particle_list: MutableSequence[stonesoup.types.particle.Particle]
List of Particle objects
Prediction Types
- class stonesoup.types.prediction.Prediction(transition_model: TransitionModel = None)[source]
Bases:
stonesoup.types.base.Type
,stonesoup.types.state.CreatableFromState
Prediction type
This is the base prediction class.
- Parameters
transition_model (
TransitionModel
, optional) – The transition model used to make the prediction
- transition_model: stonesoup.models.transition.base.TransitionModel
The transition model used to make the prediction
- class stonesoup.types.prediction.MeasurementPrediction[source]
Bases:
stonesoup.types.base.Type
,stonesoup.types.state.CreatableFromState
Prediction type
This is the base measurement prediction class.
- class stonesoup.types.prediction.StatePrediction(state_vector: StateVector, timestamp: datetime.datetime = None, transition_model: TransitionModel = None)[source]
Bases:
stonesoup.types.prediction.Prediction
,stonesoup.types.state.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 prediction
- class stonesoup.types.prediction.InformationStatePrediction(state_vector: StateVector, precision: PrecisionMatrix, timestamp: datetime.datetime = None, transition_model: TransitionModel = None)[source]
Bases:
stonesoup.types.prediction.Prediction
,stonesoup.types.state.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 prediction
- class stonesoup.types.prediction.StateMeasurementPrediction(state_vector: StateVector, timestamp: datetime.datetime = None)[source]
Bases:
stonesoup.types.prediction.MeasurementPrediction
,stonesoup.types.state.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.datetime = None, transition_model: TransitionModel = None)[source]
Bases:
stonesoup.types.prediction.Prediction
,stonesoup.types.state.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 prediction
- class stonesoup.types.prediction.SqrtGaussianStatePrediction(state_vector: StateVector, sqrt_covar: CovarianceMatrix, timestamp: datetime.datetime = None, transition_model: TransitionModel = None)[source]
Bases:
stonesoup.types.prediction.Prediction
,stonesoup.types.state.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 prediction
- class stonesoup.types.prediction.WeightedGaussianStatePrediction(state_vector: StateVector, covar: CovarianceMatrix, timestamp: datetime.datetime = None, weight: Probability = 0, transition_model: TransitionModel = None)[source]
Bases:
stonesoup.types.prediction.Prediction
,stonesoup.types.state.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 prediction
- class stonesoup.types.prediction.TaggedWeightedGaussianStatePrediction(state_vector: StateVector, covar: CovarianceMatrix, timestamp: datetime.datetime = None, weight: Probability = 0, tag: str = None, transition_model: TransitionModel = None)[source]
Bases:
stonesoup.types.prediction.Prediction
,stonesoup.types.state.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 prediction
- class stonesoup.types.prediction.GaussianMeasurementPrediction(state_vector: StateVector, covar: CovarianceMatrix, timestamp: datetime.datetime = None, cross_covar: CovarianceMatrix = None)[source]
Bases:
stonesoup.types.prediction.MeasurementPrediction
,stonesoup.types.state.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 state-measurement cross covariance matrix
- cross_covar: stonesoup.types.array.CovarianceMatrix
The state-measurement cross covariance matrix
- class stonesoup.types.prediction.ParticleStatePrediction(particles: Particles, fixed_covar: CovarianceMatrix = None, timestamp: datetime.datetime = None, transition_model: TransitionModel = None)[source]
Bases:
stonesoup.types.prediction.Prediction
,stonesoup.types.state.ParticleState
ParticleStatePrediction type
This is a simple Particle state prediction object.
- Parameters
particles (
Particles
) – All particles.fixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.transition_model (
TransitionModel
, optional) – The transition model used to make the prediction
- class stonesoup.types.prediction.ParticleMeasurementPrediction(particles: Particles, fixed_covar: CovarianceMatrix = None, timestamp: datetime.datetime = None)[source]
Bases:
stonesoup.types.prediction.MeasurementPrediction
,stonesoup.types.state.ParticleState
MeasurementStatePrediction type
This is a simple Particle measurement prediction object.
- Parameters
particles (
Particles
) – All particles.fixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.
- class stonesoup.types.prediction.CategoricalStatePrediction(state_vector: StateVector, timestamp: datetime.datetime = None, categories: Sequence[float] = None, transition_model: TransitionModel = None)[source]
Bases:
stonesoup.types.prediction.Prediction
,stonesoup.types.state.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 prediction
- class stonesoup.types.prediction.CategoricalMeasurementPrediction(state_vector: StateVector, timestamp: datetime.datetime = None, categories: Sequence[float] = None)[source]
Bases:
stonesoup.types.prediction.MeasurementPrediction
,stonesoup.types.state.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.datetime = None, transition_model: TransitionModel = None)[source]
Bases:
stonesoup.types.prediction.Prediction
,stonesoup.types.state.CompositeState
Composite prediction type
Composition of
Prediction
.- Parameters
sub_states (
Sequence[Prediction]
) – Sequence of sub-predictions comprising the composite prediction. All sub-predictions must have matching timestamp. Must not be empty.default_timestamp (
datetime.datetime
, optional) – Default timestamp if no sub-states exist to attain timestamp from. Defaults to None, whereby sub-states will be required to have timestamps.transition_model (
TransitionModel
, optional) – The transition model used to make the prediction
- sub_states: Sequence[stonesoup.types.prediction.Prediction]
Sequence of sub-predictions comprising the composite prediction. All sub-predictions must have matching timestamp. Must not be empty.
- class stonesoup.types.prediction.CompositeMeasurementPrediction(sub_states: Sequence[MeasurementPrediction] = None, default_timestamp: datetime.datetime = None)[source]
Bases:
stonesoup.types.prediction.MeasurementPrediction
,stonesoup.types.state.CompositeState
Composite measurement prediction type
Composition of
MeasurementPrediction
.- Parameters
sub_states (
Sequence[MeasurementPrediction]
, optional) – Sequence of sub-measurement-predictions comprising the composite measurement prediction. All sub-measurement-predictions must have matching timestamp.default_timestamp (
datetime.datetime
, optional) – Default timestamp if no sub-states exist to attain timestamp from. Defaults to None, whereby sub-states will be required to have timestamps.
- sub_states: Sequence[stonesoup.types.prediction.MeasurementPrediction]
Sequence of sub-measurement-predictions comprising the composite measurement prediction. All sub-measurement-predictions must have matching timestamp.
Sensor Data Types
- class stonesoup.types.sensordata.SensorData[source]
Bases:
stonesoup.types.base.Type
Sensor Data type
- class stonesoup.types.sensordata.ImageFrame(pixels: numpy.ndarray, timestamp: datetime.datetime = None)[source]
Bases:
stonesoup.types.sensordata.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
- 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 format
- timestamp: datetime.datetime
An optional timestamp
State Types
- class stonesoup.types.state.State(state_vector: StateVector, timestamp: datetime.datetime = None)[source]
Bases:
stonesoup.types.base.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.
- timestamp: datetime.datetime
Timestamp of the state. Default None.
- state_vector: stonesoup.types.array.StateVector
State vector.
- property ndim
The number of dimensions represented by the state.
- static from_state(state: State, *args: Any, target_type: Optional[Type] = None, **kwargs: Any) stonesoup.types.state.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 over-writing of properties.
- Parameters
state (State) –
State
to use existing properties from, and identify new state-type from.*args (Sequence) – Arguments to pass to newly created state, replacing those with same name in state.
target_type (Type, optional) – Optional argument specifying the type of 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.StateMutableSequence(states: MutableSequence[State] = None)[source]
Bases:
stonesoup.types.base.Type
,collections.abc.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.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]] 2018-01-01 14:00:00 >>> sequence.append(State([[1]], t1)) >>> for state in sequence[t1:]: ... print(state.state_vector, state.timestamp) [[1]] 2018-01-01 14:01:00
- Parameters
states (
MutableSequence[State]
, optional) – The initial list of states. Default None which initialises with empty list.
- states: MutableSequence[stonesoup.types.state.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. multi-sensor 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.datetime = None)[source]
Bases:
stonesoup.types.state.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: stonesoup.types.array.CovarianceMatrix
Covariance matrix of state.
- property mean
The state mean, equivalent to state vector
- static from_state(state: State, *args: Any, target_type: Optional[Type] = None, **kwargs: Any) stonesoup.types.state.State
Class utility function to create a new state (or compatible type) from an existing state. The type and properties of this new state are defined by state except for any explicitly overwritten via args and kwargs.
It acts similarly in feel to a copy constructor, with the optional over-writing of properties.
- Parameters
state (State) –
State
to use existing properties from, and identify new state-type from.*args (Sequence) – Arguments to pass to newly created state, replacing those with same name in state.
target_type (Type, optional) – Optional argument specifying the type of 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: stonesoup.types.array.StateVector
State vector.
- timestamp: datetime.datetime
Timestamp of the state. Default None.
- class stonesoup.types.state.SqrtGaussianState(state_vector: StateVector, sqrt_covar: CovarianceMatrix, timestamp: datetime.datetime = None)[source]
Bases:
stonesoup.types.state.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: stonesoup.types.array.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.
- Returns
The covariance matrix calculated via \(W W^T\), where \(W\) is a
SqrtCovarianceMatrix
- Return type
- static from_state(state: State, *args: Any, target_type: Optional[Type] = None, **kwargs: Any) stonesoup.types.state.State
Class utility function to create a new state (or compatible type) from an existing state. The type and properties of this new state are defined by state except for any explicitly overwritten via args and kwargs.
It acts similarly in feel to a copy constructor, with the optional over-writing of properties.
- Parameters
state (State) –
State
to use existing properties from, and identify new state-type from.*args (Sequence) – Arguments to pass to newly created state, replacing those with same name in state.
target_type (Type, optional) – Optional argument specifying the type of 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: stonesoup.types.array.StateVector
State vector.
- timestamp: datetime.datetime
Timestamp of the state. Default None.
- class stonesoup.types.state.InformationState(state_vector: StateVector, precision: PrecisionMatrix, timestamp: datetime.datetime = None)[source]
Bases:
stonesoup.types.state.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: stonesoup.types.array.PrecisionMatrix
precision matrix of state.
- static from_state(state: State, *args: Any, target_type: Optional[Type] = None, **kwargs: Any) stonesoup.types.state.State
Class utility function to create a new state (or compatible type) from an existing state. The type and properties of this new state are defined by state except for any explicitly overwritten via args and kwargs.
It acts similarly in feel to a copy constructor, with the optional over-writing of properties.
- Parameters
state (State) –
State
to use existing properties from, and identify new state-type from.*args (Sequence) – Arguments to pass to newly created state, replacing those with same name in state.
target_type (Type, optional) – Optional argument specifying the type of 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: stonesoup.types.array.StateVector
State vector.
- timestamp: datetime.datetime
Timestamp of the state. Default None.
- class stonesoup.types.state.WeightedGaussianState(state_vector: StateVector, covar: CovarianceMatrix, timestamp: datetime.datetime = None, weight: Probability = 0)[source]
Bases:
stonesoup.types.state.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: stonesoup.types.numeric.Probability
Weight of the Gaussian State.
- property gaussian_state
The Gaussian state.
- 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: stonesoup.types.array.CovarianceMatrix
Covariance matrix of state.
- static from_state(state: State, *args: Any, target_type: Optional[Type] = None, **kwargs: Any) stonesoup.types.state.State
Class utility function to create a new state (or compatible type) from an existing state. The type and properties of this new state are defined by state except for any explicitly overwritten via args and kwargs.
It acts similarly in feel to a copy constructor, with the optional over-writing of properties.
- Parameters
state (State) –
State
to use existing properties from, and identify new state-type from.*args (Sequence) – Arguments to pass to newly created state, replacing those with same name in state.
target_type (Type, optional) – Optional argument specifying the type of 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: stonesoup.types.array.StateVector
State vector.
- timestamp: datetime.datetime
Timestamp of the state. Default None.
- class stonesoup.types.state.TaggedWeightedGaussianState(state_vector: StateVector, covar: CovarianceMatrix, timestamp: datetime.datetime = None, weight: Probability = 0, tag: str = None)[source]
Bases:
stonesoup.types.state.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: stonesoup.types.array.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: Optional[Type] = None, **kwargs: Any) stonesoup.types.state.State
Class utility function to create a new state (or compatible type) from an existing state. The type and properties of this new state are defined by state except for any explicitly overwritten via args and kwargs.
It acts similarly in feel to a copy constructor, with the optional over-writing of properties.
- Parameters
state (State) –
State
to use existing properties from, and identify new state-type from.*args (Sequence) – Arguments to pass to newly created state, replacing those with same name in state.
target_type (Type, optional) – Optional argument specifying the type of 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.
- property mean
The state mean, equivalent to state vector
- property ndim
The number of dimensions represented by the state.
- state_vector: stonesoup.types.array.StateVector
State vector.
- timestamp: datetime.datetime
Timestamp of the state. Default None.
- weight: stonesoup.types.numeric.Probability
Weight of the Gaussian State.
- class stonesoup.types.state.ParticleState(particles: Particles, fixed_covar: CovarianceMatrix = None, timestamp: datetime.datetime = None)[source]
Bases:
stonesoup.types.base.Type
Particle State type
This is a particle state object which describes the state as a distribution of particles
- Parameters
particles (
Particles
) – All particles.fixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.
- particles: stonesoup.types.particle.Particles
All particles.
- fixed_covar: stonesoup.types.array.CovarianceMatrix
Fixed covariance value. Default None, whereweighted sample covariance is then used.
- timestamp: datetime.datetime
Timestamp of the state. Default None.
- property mean
The state mean, equivalent to state vector
- property state_vector
The mean value of the particle states
- class stonesoup.types.state.CategoricalState(state_vector: StateVector, timestamp: datetime.datetime = None, categories: Sequence[float] = None)[source]
Bases:
stonesoup.types.state.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 \(\Phi = \{\phi^m|m\in \mathbf{N}, m\le M\}\) 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: stonesoup.types.array.StateVector
State vector.
- property category
Return the name of the most likely category.
- static from_state(state: State, *args: Any, target_type: Optional[Type] = None, **kwargs: Any) stonesoup.types.state.State
Class utility function to create a new state (or compatible type) from an existing state. The type and properties of this new state are defined by state except for any explicitly overwritten via args and kwargs.
It acts similarly in feel to a copy constructor, with the optional over-writing of properties.
- Parameters
state (State) –
State
to use existing properties from, and identify new state-type from.*args (Sequence) – Arguments to pass to newly created state, replacing those with same name in state.
target_type (Type, optional) – Optional argument specifying the type of 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.
- timestamp: datetime.datetime
Timestamp of the state. Default None.
- class stonesoup.types.state.CompositeState(sub_states: Sequence[State], default_timestamp: datetime.datetime = None)[source]
Bases:
stonesoup.types.base.Type
Composite state type.
A composition of ordered sub-states (
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 sub-states comprising the composite state. All sub-states must have matching timestamp. Must not be empty.default_timestamp (
datetime.datetime
, optional) – Default timestamp if no sub-states exist to attain timestamp from. Defaults to None, whereby sub-states will be required to have timestamps.
- sub_states: Sequence[stonesoup.types.state.State]
Sequence of sub-states comprising the composite state. All sub-states must have matching timestamp. Must not be empty.
- default_timestamp: datetime.datetime
Default timestamp if no sub-states exist to attain timestamp from. Defaults to None, whereby sub-states will be required to have timestamps.
- property state_vector
A combination of the component states’ state vectors.
OrbitalState Types
- class stonesoup.types.orbitalstate.CoordinateSystem(value)[source]
Bases:
enum.Enum
Enumerates the allowable coordinate systems. See OrbitalState help for full explanation of what each of the elements does.
- class stonesoup.types.orbitalstate.Orbital(coordinates: CoordinateSystem = CoordinateSystem.CARTESIAN, grav_parameter: float = 398600441800000.0, metadata: Mapping[Any, Any] = None)[source]
Bases:
stonesoup.types.base.Type
The orbital base type. This is the building block of Stone Soup’s orbital inference routines and follows the principle that you shouldn’t have to care which parameterisation you use. The class stores relevant information internally and undertakes whatever conversions are necessary.
The gravitational parameter \(\mu = GM\) can be defined. If left undefined it defaults to that of the Earth, \(3.986004418 \, (\pm \, 0.000000008) \times 10^{14} \mathrm{m}^3 \mathrm{s}^{−2}\)
An orbital state is constructed from the input vector \(X_{t_{0}}\) at epoch
State.timestamp
, \(t_0\). The coordinates of \(X_{t_{0}}\) are Cartesian Earth-Centered Inertial (ECI) [m] by default, but may be selected via the “coordinates” keyword by passing aCoordinateSystem
object, or an appropriate string. Allowable coordinate systems are:Coordinates = “Cartesian”, the input state vector is
\[X_{t_0} = [r_x, r_y, r_z, \dot{r}_x, \dot{r}_y, \dot{r}_z]^{T}\]where \(r_x, r_y, r_z\) are the Cartesian position coordinates in the Primary-Centered Inertial frame and \(\dot{r}_x, \dot{r}_y, \dot{r}_z\) are the corresponding velocity coordinates.
Coordinates = “Keplerian” (Keplarian elements), construct using input state vector:
\[\begin{split}X_{t_0} = [e, a, i, \Omega, \omega, \theta]^{T} \\\end{split}\]where: \(e\) is the orbital eccentricity (unitless), \(a\) the semi-major axis ([length]), \(i\) the inclination (radian), \(\Omega\) is the longitude of the ascending node (radian), \(\omega\) the argument of periapsis (radian), and \(\theta\) the true anomaly (radian).
Coordinates = “TLE” (Two-Line Elements 1), initiates using input vector
\[X_{t_0} = [i, \Omega, e, \omega, M_0, n]^{T}\]where \(i\) the inclination (radian), \(\Omega\) is the longitude of the ascending node (radian), \(e\) is the orbital eccentricity (unitless), \(\omega\) the argument of perigee (radian), \(M_0\) the mean anomaly (radian) and \(n\) the mean motion (radian / [time]).
This can also be constructed by passing state_vector=None and using the metadata. In this instance the metadata must conform to the TLE standard format 2 and be included in the metadata dictionary as ‘line_1’ and ‘line_2’.
Coordinates = “Equinoctial” (equinoctial elements 2),
\[\begin{split}X_{t_0} = [a, h, k, p, q, \lambda]^{T} \\\end{split}\]where \(a\) the semi-major axis ([length]), \(h\) is the horizontal component of the eccentricity (unitless), \(k\) is the vertical component of the eccentricity (unitless), \(q\) is the horizontal component of the inclination (radian), \(k\) is the vertical component of the inclination (radian), \(\lambda\) is the mean longitude (radian).
References
- 1
NASA, Definition of Two-line Element Set Coordinate System, [spaceflight.nasa.gov]( https://spaceflight.nasa.gov/realdata/sightings/SSapplications/Post/JavaSSOP/ SSOP_Help/tle_def.html)
- 2(1,2,3)
Broucke, R. A. & Cefola, P. J. 1972, Celestial Mechanics, Volume 5, Issue 3, pp. 303-310
- Parameters
coordinates (
CoordinateSystem
, optional) – The parameterisation used on initiation. Acceptable values are ‘CARTESIAN’ (default), ‘KEPLERIAN’, ‘TLE’, or ‘EQUINOCTIAL’. All other inputs will return errors. Will accept string inputs.grav_parameter (
float
, optional) – Standard gravitational parameter \(\mu = G M\). The default is \(3.986004418 \times 10^{14} \,\) \(\mathrm{m}^3 \mathrm{s}^{-2}\).metadata (
Mapping[Any, Any]
, optional) – Dictionary containing metadata about orbit.
- coordinates: stonesoup.types.orbitalstate.CoordinateSystem
The parameterisation used on initiation. Acceptable values are ‘CARTESIAN’ (default), ‘KEPLERIAN’, ‘TLE’, or ‘EQUINOCTIAL’. All other inputs will return errors. Will accept string inputs.
- grav_parameter: float
Standard gravitational parameter \(\mu = G M\). The default is \(3.986004418 \times 10^{14} \,\) \(\mathrm{m}^3 \mathrm{s}^{-2}\).
- metadata: Mapping[Any, Any]
Dictionary containing metadata about orbit.
- property specific_angular_momentum
The specific angular momentum, \(\mathbf{h}\).
- property cartesian_state_vector
The state vector \(X_{t_0} = [r_x, r_y, r_z, \dot{r}_x, \dot{r}_y, \dot{r}_z]^{T}\) in ‘Primary-Centred’ Inertial coordinates, equivalent to ECI in the case of the Earth.
- property epoch
The epoch, or state timestamp.
- property range
The distance to object (from gravitational centre of primary).
- property speed
The current instantaneous speed (scalar).
- property eccentricity
The orbital eccentricity, \(e \; (0 \le e \le 1)\).
Note
This version of the calculation uses a form dependent only on scalars.
- property semimajor_axis
The orbital semi-major axis.
- property inclination
Orbital inclination, \(i \; (0 \le i < \pi)\), [rad].
- property longitude_ascending_node
math:Omega ; (0 leq Omega < 2pi).
- Type
The longitude (or right ascension) of ascending node,
- property argument_periapsis
The argument of periapsis, \(\omega \; (0 \le \omega < 2\pi)\) in radians.
- property true_anomaly
The true anomaly, \(\theta \; (0 \le \theta < 2\pi)\) in radians.
- property eccentric_anomaly
The eccentric anomaly, \(E \; (0 \le E < 2\pi)\) in radians.
Note
This computes the quantity exactly via the Keplerian eccentricity and true anomaly rather than via the mean anomaly using an iterative procedure.
- property mean_anomaly
Mean anomaly, \(M \; (0 \le M < 2\pi\)), in radians.
Note
Uses the eccentric anomaly and Kepler’s equation to get mean anomaly from true anomaly and eccentricity.
- property period
Orbital period, \(T\) ([time]).
- property mean_motion
The mean motion, \(\frac{2 \pi}{T}\), where \(T\) is the period, (rad / [time]).
- property mag_specific_angular_momentum
The magnitude of the specific angular momentum, \(h\).
- property specific_orbital_energy
Specific orbital energy (\(\frac{-GM}{2a}\)).
- property equinoctial_h
The horizontal component of the eccentricity in equinoctial coordinates is \(h = e \sin (\omega + \Omega)\).
- property equinoctial_k
The vertical component of the eccentricity in equinoctial coordinates is \(k = e \cos (\omega + \Omega)\).
- property equinoctial_p
The horizontal component of the inclination in equinoctial coordinates is \(p = \tan (i/2) \sin \Omega\).
- property equinoctial_q
The vertical component of the inclination in equinoctial coordinates is \(q = \tan (i/2) \cos \Omega\).
- property mean_longitude
The mean longitude, defined as \(\lambda = M_0 + \omega + \Omega\) (rad).
- property keplerian_elements
The vector of Keplerian elements \(X = [e, a, i, \Omega, \omega, \theta]^{T}\) where \(e\) is the orbital eccentricity (unitless), \(a\) the semi-major axis ([length]), \(i\) the inclination (radian), \(\Omega\) is the longitude of the ascending node (radian), \(\omega\) the argument of periapsis (radian), and \(\theta\) the true anomaly (radian).
- property two_line_element
The Two-Line Element vector \(X = [i, \Omega, e, \omega, M_0, n]^{T}\) where \(i\) the inclination (radian) \(\Omega\) is the longitude of the ascending node (radian), \(e\) is the orbital eccentricity (unitless), \(\omega\) the argument of periapsis (radian), \(M_0\) the mean anomaly (radian) \(n\) the mean motion (rad/[time]). 2
- property equinoctial_elements
The equinoctial elements, \(X = [a, h, k, p, q, \lambda]^{T}\) where \(a\) the semi-major axis ([length]), \(h\) and \(k\) are the horizontal and vertical components of the eccentricity respectively (unitless), \(p\) and \(q\) are the horizontal and vertical components of the inclination respectively (radian) and \(\lambda\) is the mean longitude (radian). 3
- class stonesoup.types.orbitalstate.OrbitalState(state_vector: StateVector, timestamp: datetime.datetime = None, coordinates: CoordinateSystem = CoordinateSystem.CARTESIAN, grav_parameter: float = 398600441800000.0, metadata: Mapping[Any, Any] = None)[source]
Bases:
stonesoup.types.orbitalstate.Orbital
,stonesoup.types.state.State
The orbital state class which inherits from
Orbital
andState
.The
state_vector
is held as \([\mathbf{r}, \dot{\mathbf{r}}]\), the “Orbital State Vector” (as traditionally understood in orbital mechanics), where \(\mathbf{r}\) is the (3D) Cartesian position in the primary-centered inertial frame, while \(\dot{\mathbf{r}}\) is the corresponding velocity vector. All methods provided byOrbital
are available. Formulae for conversions are generally found in, or derived from 3.References
- 3(1,2)
Curtis, H.D. 2010, Orbital Mechanics for Engineering Students (3rd Ed), Elsevier Aerospace Engineering Series
- Parameters
state_vector (
StateVector
) – State vector.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.coordinates (
CoordinateSystem
, optional) – The parameterisation used on initiation. Acceptable values are ‘CARTESIAN’ (default), ‘KEPLERIAN’, ‘TLE’, or ‘EQUINOCTIAL’. All other inputs will return errors. Will accept string inputs.grav_parameter (
float
, optional) – Standard gravitational parameter \(\mu = G M\). The default is \(3.986004418 \times 10^{14} \,\) \(\mathrm{m}^3 \mathrm{s}^{-2}\).metadata (
Mapping[Any, Any]
, optional) – Dictionary containing metadata about orbit.
- class stonesoup.types.orbitalstate.GaussianOrbitalState(state_vector: StateVector, covar: CovarianceMatrix, timestamp: datetime.datetime = None, coordinates: CoordinateSystem = CoordinateSystem.CARTESIAN, grav_parameter: float = 398600441800000.0, metadata: Mapping[Any, Any] = None)[source]
Bases:
stonesoup.types.orbitalstate.Orbital
,stonesoup.types.state.GaussianState
An orbital state for use in Kalman filters (and perhaps elsewhere). Inherits from GaussianState so has a covariance matrix. As no checks on the validity of the covariance matrix are made, care should be exercised in its use. The propagator will generally require a particular coordinate reference which must be understood.
All methods provided by
Orbital
are available.- Parameters
state_vector (
StateVector
) – State vector.covar (
CovarianceMatrix
) – Covariance matrix of state.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.coordinates (
CoordinateSystem
, optional) – The parameterisation used on initiation. Acceptable values are ‘CARTESIAN’ (default), ‘KEPLERIAN’, ‘TLE’, or ‘EQUINOCTIAL’. All other inputs will return errors. Will accept string inputs.grav_parameter (
float
, optional) – Standard gravitational parameter \(\mu = G M\). The default is \(3.986004418 \times 10^{14} \,\) \(\mathrm{m}^3 \mathrm{s}^{-2}\).metadata (
Mapping[Any, Any]
, optional) – Dictionary containing metadata about orbit.
Time Types
- class stonesoup.types.time.TimeRange(start_timestamp: datetime.datetime, end_timestamp: datetime.datetime)[source]
Bases:
stonesoup.types.base.Type
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_timestamp (
datetime.datetime
) – Start of the time rangeend_timestamp (
datetime.datetime
) – End of the time range
- start_timestamp: datetime.datetime
Start of the time range
- end_timestamp: datetime.datetime
End of the time range
- property duration
Duration of the time range
Track Types
- class stonesoup.types.track.Track(states: MutableSequence[State] = None, id: str = None, init_metadata: MutableMapping = {})[source]
Bases:
stonesoup.types.state.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[stonesoup.types.state.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:
stonesoup.types.base.Type
,stonesoup.types.state.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: stonesoup.types.hypothesis.Hypothesis
Hypothesis used for updating
- class stonesoup.types.update.StateUpdate(state_vector: StateVector, hypothesis: Hypothesis, timestamp: datetime.datetime = None)[source]
Bases:
stonesoup.types.update.Update
,stonesoup.types.state.State
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.datetime = None)[source]
Bases:
stonesoup.types.update.Update
,stonesoup.types.state.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.datetime = None)[source]
Bases:
stonesoup.types.update.Update
,stonesoup.types.state.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.GaussianMixtureUpdate(hypothesis: Hypothesis, components: MutableSequence[WeightedGaussianState] = None)[source]
Bases:
stonesoup.types.update.Update
,stonesoup.types.mixture.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.ParticleStateUpdate(particles: Particles, hypothesis: Hypothesis, fixed_covar: CovarianceMatrix = None, timestamp: datetime.datetime = None)[source]
Bases:
stonesoup.types.update.Update
,stonesoup.types.state.ParticleState
ParticleStateUpdate type
This is a simple Particle state update object.
- Parameters
particles (
Particles
) – All particles.hypothesis (
Hypothesis
) – Hypothesis used for updatingfixed_covar (
CovarianceMatrix
, optional) – Fixed covariance value. Default None, whereweighted sample covariance is then used.timestamp (
datetime.datetime
, optional) – Timestamp of the state. Default None.
- class stonesoup.types.update.InformationStateUpdate(state_vector: StateVector, precision: PrecisionMatrix, hypothesis: Hypothesis, timestamp: datetime.datetime = None)[source]
Bases:
stonesoup.types.update.Update
,stonesoup.types.state.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.datetime = None, categories: Sequence[float] = None)[source]
Bases:
stonesoup.types.update.Update
,stonesoup.types.state.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.datetime = None)[source]
Bases:
stonesoup.types.update.Update
,stonesoup.types.state.CompositeState
Composite update type
Composition of
Update
.- Parameters
sub_states (
Sequence[Update]
) – Sequence of sub-updates comprising the composite update. All sub-updates must have matching timestamp. Must not be empty.hypothesis (
CompositeHypothesis
) – Hypothesis used for updatingdefault_timestamp (
datetime.datetime
, optional) – Default timestamp if no sub-states exist to attain timestamp from. Defaults to None, whereby sub-states will be required to have timestamps.
- sub_states: Sequence[stonesoup.types.update.Update]
Sequence of sub-updates comprising the composite update. All sub-updates must have matching timestamp. Must not be empty.
- hypothesis: stonesoup.types.hypothesis.CompositeHypothesis
Hypothesis used for updating